A method and system for identifying market saturation of commercial district pharmacies

Abstract

This invention discloses a method and system for identifying the market saturation level of pharmacies in a business district. The method includes: S1, determining business district units and calculating the number of high-potential grids for each business district unit; S2, based on the number of high-potential grids and existing store information, calculating the remaining physical space for each business district unit as a reserve, and calculating the operational quality index of the business district unit based on internal operational data of the brand; S3, combining the reserve and the operational quality index to classify the business district units into different levels, thereby obtaining the densification priority of the business district units to characterize the market saturation level. This invention achieves accurate identification of saturation level from two dimensions: reserve and quality, through spatial gridding analysis and the fusion of internal and external data, effectively avoiding the self-cannibalizing effect caused by blind densification.

Description

Technical Field

[0001] This invention relates to the field of information mining technology, and in particular to a method and system for identifying the market saturation level of pharmacies in a business district. Background Technology

[0002] The development of store layout technology in the retail chain pharmacy industry has mainly gone through two stages. The first stage is the traditional experience-based layout stage, which relies entirely on the regional operating experience of industry practitioners for layout judgments, without any quantitative indicators as support. Layout decisions are highly random, and store survival rates are significantly affected by the individual capabilities of practitioners, making it impossible to achieve large-scale, standardized expansion. The second stage is the preliminary quantitative layout stage, where the industry begins to use the national average number of customers served per pharmacy as the core criterion for judging the remaining space for regional layouts. This represents a shift from experience-based judgments to data-driven decisions, improving the scientific nature of layout to some extent. However, the technical solutions at this stage still have significant limitations and defects. Specifically, the shortcomings of existing quantitative layout technologies are mainly reflected in three aspects: First, it ignores regional differences. There are significant differences in economic level, population quality, population density and business atmosphere in different cities. The maximum number of people that a single pharmacy can serve varies greatly in different regions. The "one-size-fits-all" approach of using the national average as a uniform judgment standard will lead to significant deviations in the calculation of the remaining capacity of pharmacies.

[0003] Secondly, competitor data collection is lagging and incomplete. Traditional methods rely heavily on manual surveys to count the number of other brand pharmacies, which is not only inefficient but also prone to data omissions and untimely updates, failing to accurately reflect the current state of regional competition.

[0004] Third, the response of internal operational quality was not considered. The potential for expansion was judged solely by the number of people served, ignoring the operational efficiency of the brand's existing stores in the business district. This could easily lead to blind expansion, with new and old stores being too close to each other, causing the brand to cannibalize each other, resulting in waste of corporate resources and operating losses.

[0005] The aforementioned deficiencies make it difficult for companies to accurately identify high-value, densely populated areas with sufficient physical space and sustainable operational quality. This can lead to poor accuracy in identifying the market saturation of pharmacies in commercial districts, resulting in resource misallocation and investment losses. Summary of the Invention

[0006] The technical solution of this invention is a method for identifying the market saturation level of pharmacies in a business district, comprising the following steps: S1. Determine the business district unit and calculate the number of high-potential grid cells for the business district unit; S2. Based on the number of high-potential grids and the existing deployment information, calculate the remaining number of stores in the physical space of the business district unit as the margin, and calculate the operating quality index of the business district unit based on the company's internal operating data. S3. Combine the remaining amount with the operating quality index to obtain the encryption priority of the business district unit, which is used to characterize the market saturation of the business district unit.

[0007] Furthermore, step S1 specifically includes: S11: Business districts are defined by administrative subdistricts and towns; S12: Construct a grid network within the town or street, with each area as a unit; S13: Standardize the negative conversion values ​​of its permanent resident population, number of catering service POIs, and number of existing pharmacies respectively; S14: Calculate the potential score for each grid cell by weighted summation, using the following formula:

[0008] in, This represents the potential score of the i-th grid cell. , , These are the standardized values ​​of the grid's basic population indicators, traffic-related indicators, and competition adaptation indicators. , , Weight and satisfy ; S15: Select rasters with a potential score not lower than the lowest score threshold among historically deployed rasters as high-potential rasters, and count the total number of high-potential rasters.

[0009] Furthermore, the competitive fit index Ci is obtained by negatively transforming the number of existing pharmacies within the grid; the more existing pharmacies there are, the lower the index value.

[0010] Furthermore, in step S2, the method for calculating the remaining number of shops in the physical space of the street / town as a reserve includes:

[0011] in, This represents the remaining number of grid cells that can be deployed. For high-potential raster count, For the number of high-potential grid cells already deployed, The number of high-potential grid cells already deployed in the surrounding area, This refers to the number of high-potential grid cells that have already been deployed in the surrounding area but have high capacity. Calculate the per capita carrying capacity of the remaining deployable grid cells; Based on this carrying capacity, the remaining number of available grid cells is proportionally calibrated to obtain the remaining number of stores in the physical space.

[0012] Furthermore, in step S2, the method for calculating the operational quality index includes: Select internal operational metrics and standardize them, including sales profit metrics, net profit metrics, membership growth metrics, retention and stability metrics, and new store quality metrics; The operating quality index is calculated by weighted summation, and the formula is as follows:

[0013] Where Q represents the operational quality index. Let be the weights of the k-th category of indicators, and satisfy the condition that the sum of the weights is 1. is the standardized value of the k-th category index.

[0014] Furthermore, the sales profit indicator is obtained by multiplying the year-on-year sales growth rate and the ratio of the average monthly sales per store to the city average, respectively.

[0015] Furthermore, the net profit indicator is obtained by multiplying the year-on-year growth rate of net profit by the absolute value of net profit in the current period by the city average.

[0016] Furthermore, the method for combining and grading the remaining balance with the operating quality index in S3 includes: The remaining margin is divided into three levels: high, medium, and low, based on the quantiles. The operational quality index is divided into two levels, high and low, based on quantiles. Based on the combination of margin and operational quality index levels, the encryption priority is determined to be four levels: S, A, B, and C.

[0017] This application also provides a system for identifying the market saturation level of pharmacies in a business district, and a method for implementing the method for identifying the market saturation level of pharmacies in a business district, comprising a grid potential calculation unit, a surplus and quality calculation unit, and an output characterization unit, wherein: The grid potential calculation unit is used to determine a business district unit and calculate the number of high-potential grids in the business district unit. The margin and quality calculation unit is used to calculate the remaining number of stores in the physical space of the business district unit as the margin based on the number of high-potential grids and the existing deployment information, and to calculate the operating quality index of the business district unit based on the company's internal operating data. The output characterization unit is used to combine the remaining amount with the operating quality index to classify and obtain the encryption priority of the business district unit, which is used to characterize the market saturation of the business district unit.

[0018] This application also provides an electronic device, including: a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the method for identifying the market saturation of pharmacies in a business district as described above.

[0019] In practical applications, the modules described in the methods and systems disclosed in this invention can be deployed on a single target server, or each module can be deployed independently on different target servers. In particular, as needed, to provide more powerful computing capabilities, the modules can also be deployed on a cluster of target servers.

[0020] Therefore, the method and system for identifying the market saturation of pharmacies in a business district according to the present invention form a complete, standardized, and practical pharmacy location analysis system, from business district division, grid analysis, indicator calculation to hierarchical decision-making, which effectively improves the accuracy of identifying the market saturation of pharmacies in a business district.

[0021] To provide a clearer and more comprehensive understanding of the present invention, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0023] Figure 1 This is a schematic diagram of the method for identifying the market saturation level of pharmacies in a business district according to an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram of the combination of margin and mass in an embodiment of the present invention. Detailed Implementation

[0025] This invention provides a method for identifying the market saturation of pharmacies in a business district that takes into account regional characteristics, competitive status, and internal operational quality. This method effectively improves the accuracy of identification and enables multi-dimensional and refined identification of the market saturation of pharmacies in a business district for pharmacy location. It provides a scientific basis for pharmacy location decisions and avoids self-destruction and resource waste caused by blind expansion.

[0026] Please see Figure 1 To address the shortcomings of existing technologies, this invention proposes a method for identifying the market saturation level of pharmacies in a commercial district, comprising the following steps: S1. Determine the business district unit and calculate the number of high-potential grid cells for the business district unit; S2. Based on the number of high-potential grids and the existing deployment information, calculate the remaining number of stores in the physical space of the business district unit as the margin, and calculate the operating quality index of the business district unit based on the company's internal operating data. S3. Combine the remaining amount with the operating quality index to obtain the encryption priority of the business district unit, which is used to characterize the market saturation of the business district unit.

[0027] The technical solution of this application will be described below with reference to various preferred embodiments and implementation methods.

[0028] S1. Determine the business district unit and calculate the number of high-potential grid cells for the business district unit.

[0029] This step involves identifying business district units, constructing a grid network, extracting and standardizing indicators, calculating a weighted potential score, and then filtering high-potential grids using a threshold. Finally, it outputs the total number of high-potential grids for each administrative subdistrict, providing foundational data for subsequent residual calculations. As a preferred implementation method, it specifically includes: S11 defines the business district units: Using administrative subdistricts and towns as business district units, this invention uses administrative subdistricts and towns as business district units. These administrative subdistricts and towns have complete population statistics boundaries, commercial management functions, and pharmacy supervision unit attributes, which facilitates data acquisition and policy alignment.

[0030] S12 constructs a raster network: As a preferred implementation method, a grid network with a certain area is constructed within the town or street. Within the town or street, a 300m×300m grid network is constructed as the smallest spatial analysis unit. The geographical boundary of the town or street is divided into non-overlapping, fully covered grids to form a standardized grid network. The scale of this smallest spatial analysis unit is based on the lower limit of the typical service radius of a pharmacy (5-8 minutes walking distance, about 300-500 meters). This can capture micro-population clustering characteristics while avoiding computational redundancy caused by excessively dense grids.

[0031] As a better implementation method, irregular geographical areas without commercial value, such as rivers, mountains, and parks within towns and streets, will have their corresponding grids marked as invalid grids and directly removed, and will not participate in subsequent indicator extraction and scoring calculations, thereby improving the effectiveness of the analysis data.

[0032] S13 Indicator Extraction and Standardization: Based on the negative conversion values ​​of its permanent resident population, the number of catering service POIs (Points of Interest), and the number of existing pharmacies, standardization was performed separately.

[0033] From historical data of existing locations, external raster population data, and POI data, we extract the basic population indicator P (number of permanent residents), the traffic correlation indicator F (number of catering service POIs), and the competition fit indicator C (negative conversion value of the number of existing pharmacies) for each raster. We then standardize these three types of indicators. The basic population indicator (P) directly uses the number of permanent residents within a grid, which is then standardized to the [0,1] interval using Min-Max standardization to reflect the basic passenger flow threshold. For example, if a grid has a population of 850, and the minimum and maximum population values ​​for grids within a street or town are 200 and 2000 respectively, then the standardized value is: P_i=(850-200) / (2000-200)=0.361 The traffic association index (F) selects the number of POIs in the catering service category (including fast food, full-service restaurants, coffee and tea drinks, etc.) because it has a significant customer flow synergy effect with pharmacies (purchase of medicine after meals, association with healthy eating, etc.), and is also standardized.

[0034] The competitive fit metric (C) is negatively converted based on the total number of existing pharmacies within the grid (including this brand and competitors).

[0035] S14 weighted potential score: The potential score for each grid cell is calculated by weighted summation, using the following formula:

[0036] in, This represents the potential score of the i-th grid cell. , , These are the standardized values ​​of the grid's basic population indicators, traffic-related indicators, and competition adaptation indicators. , , Weight and satisfy ; S15 threshold screening: Grids with potential scores no lower than the lowest score threshold among historically deployed grids are selected as high-potential grids, and the total number of high-potential grids is counted. For example, using the lowest potential score among all grids in which a brand has had stores in the town for more than one year as the threshold T, grids that satisfy Pgrid,i≥T are selected as high-potential grids, and the total number of high-potential grids in the town is counted.

[0037] For example, after grid division, 200 effective grids are obtained. After extracting and standardizing indicators, weights are determined by combining the analytic hierarchy process (AHP) with historical data. , , The potential score of each grid is calculated, and the lowest potential score of 0.6 of the historically deployed grids is selected as the threshold T. Finally, 80 high-potential grids are selected, and the total number of high-potential grids in the town is calculated to be Htotal=80.

[0038] Therefore, in this embodiment, the 300m×300m rasterization process enables refined analysis of the region. Compared with traditional overall regional analysis, it can accurately locate small areas with high potential for pharmacy distribution, thus solving the problem of coarse analysis in traditional methods.

[0039] Furthermore, the removal of invalid rasters avoids data analysis interference from worthless areas, ensuring the accuracy of high-potential raster screening.

[0040] Standardized indicators and dynamic weight adjustments make potential scoring more aligned with the actual characteristics of different cities and business districts, avoiding a "one-size-fits-all" approach to indicator setting. Calibration of historical site survival rate data makes the weights more practically valuable, and the selected high-potential grids can truly reflect the actual site requirements of pharmacies, laying a precise data foundation for subsequent reserve calculations.

[0041] S2. Based on the number of high-potential grids and the existing deployment information, calculate the remaining number of stores in the physical space of the business district unit as a margin, and calculate the operating quality index of the business district unit based on the company's internal operating data.

[0042] Step S2 calculates the remaining capacity based on the number of high-potential grids and the information on existing locations, and calculates the operational quality index based on internal operational data. The two calculation results are independent of each other and provide a basis for subsequent combination and classification.

[0043] The method for calculating the remaining number of shops in the physical space of the street / town as a reserve includes:

[0044] in, This represents the remaining number of grid cells that can be deployed. For high-potential raster count, For the number of high-potential grid cells already deployed, This refers to the number of high-potential grid cells already deployed in the surrounding area (e.g., grid cells within a 300-meter radius of existing grid cells or adjacent grid cells). This refers to the number of high-potential grid cells that have already been deployed in the surrounding area but have high capacity.

[0045] In this application embodiment, "high capacity" refers to those spatial grids in the surrounding area of ​​an existing pharmacy (usually referring to adjacent or close-range grids) that have the potential to support the coexistence and profitability of multiple pharmacies due to their high population density and extremely active commerce.

[0046] Calculate the per capita carrying capacity of the remaining available grid cells, which is the ratio of the total number of permanent residents in the remaining available grid cells to the number of remaining available grid cells, i.e., the average population per grid cell, reflecting the population carrying capacity of the business district for new stores.

[0047] Based on this carrying capacity, the remaining number of available grid cells is proportionally calibrated to obtain the remaining number of stores in the physical space. That is, based on the average service benchmark value per pharmacy in the city, the number of pharmacies that each grid cell can support is calculated, and then multiplied by the remaining number of available grid cells to obtain the final number of stores remaining in the physical space (reserve).

[0048] Therefore, it can be seen that the method for calculating the remaining amount in the embodiments of this application adopts a dual mechanism of "elimination + compensation": First, using each existing grid cell as the center, its neighboring grid cells are marked as the radiation influence zone and removed to prevent competition between two stores within 300 meters. Then, grid cells in the removed areas with extremely high population density (e.g., more than 5,000 people / square kilometer) and extremely active commerce (more than 8 restaurant POIs / square kilometer) are reviewed. If historical data shows successful cases of two stores in similar areas (such as subway hubs or large community centers), these are re-included in the deployment range to avoid missing high-quality locations due to mechanical removal.

[0049] After completing the grid screening, the average population carrying capacity of a single grid (e.g., 1200 people / grid) is calculated and then compared with the average service population of a single pharmacy in the city (e.g., 2500 people) to obtain the carrying capacity ratio coefficient (1200 / 2500=0.48). Finally, the remaining number of physical spaces to open stores = the remaining number of grids that can be deployed × the carrying capacity ratio coefficient, thus realizing a reasonable transformation from "deployable space" to "number of stores that can be opened".

[0050] Methods for calculating the operational quality index include: Internal operational metrics were selected and standardized, including sales profit, net profit, membership growth, customer retention stability, and new store quality. As an example, the calculations for each metric are as follows: Sales profit indicator: (Year-on-year sales growth rate / city average) × (average monthly sales per store / city average) reflects the combined performance of growth and economies of scale.

[0051] Net profit indicator: (Year-on-year growth rate of net profit / city average) × absolute value of net profit in this period, emphasizing the quality of profitability and growth momentum.

[0052] Membership growth metrics: (Average annual increase in regular member stores / median city value) × (Average annual increase in new member stores / median city value) represents customer loyalty and customer acquisition capabilities.

[0053] Indicators of continued stability: 1 − (Annual store closure rate / city average), the higher the value after negative conversion, the more stable it is.

[0054] New store quality indicators: (New store vs. old store performance evaluation / city median) × (average number of new store members developed / city median) × (break-even sales achievement rate / city median) to comprehensively evaluate the health of new store incubation.

[0055] The operating quality index is calculated by weighted summation, and the formula is as follows:

[0056] Where Q represents the operational quality index. Let be the weights of the k-th category of indicators, and satisfy the condition that the sum of the weights is 1. The standardized value of the k-th category indicator is the standardized value of all the above categories of indicators.

[0057] S3. Combine the remaining amount with the operating quality index to obtain the encryption priority of the business district unit, which is used to characterize the market saturation of the business district unit. The high and low priority levels (S, A, B, C) directly and correspondingly reflect the different saturation states of the market from "huge potential, unsaturated" to "highly saturated, need to be temporarily suspended".

[0058] This step quantifies and grades the remaining amount obtained in step S2 and the operational quality index, ultimately outputting four encryption priority levels: S, A, B, and C. Different priorities directly correspond to the market saturation status of the business district and the corresponding distribution strategies, as detailed below: Quantile Classification: The quantile method was used to classify the surplus and operational quality index of all towns and subdistricts in the study area. The surplus was divided into three levels: high (≥75% quantile), middle (25%-75% quantile), and low (≤25% quantile) based on the upper quartile (75%) and lower quartile (25%). The operational quality index was divided into two levels: high (≥50% median) and low (≤50% median) based on the median (50%). Gear Combination: Based on the gear combination of remaining capacity and operating quality index, the encryption priority is determined according to preset rules, such as... Figure 2 As shown.

[0059] Characterizing saturation level: The encryption priority is mapped one-to-one with the market saturation state. S-level is high potential and unsaturated, A-level is good potential and slightly saturated, B-level is average potential and moderately saturated, and C-level is highly saturated and needs to be temporarily suspended. At the same time, a clear deployment strategy is matched for each priority.

[0060] This approach differs from the traditional linear thinking of simply encrypting any available space. It establishes a "space-quality" coupled decision-making model, solving the problem of blind expansion caused by "only looking at the potential of the location while ignoring operational quality" in traditional methods. It also avoids missed opportunities caused by "only looking at operational quality while ignoring the potential of the location," effectively improving the accuracy of identification. S-level areas represent "high potential + high return," suitable for rapid acquisition; A-level areas, although lacking in quality or remaining capacity in individual aspects, still have overall development value and require dynamic monitoring; B-level areas require careful evaluation, prioritizing the release of existing value through operational optimization; C-level areas clearly indicate a highly saturated market, avoiding blind investment.

[0061] Therefore, the technical effects achieved by this application are as follows: 1. Achieved refined and multi-dimensional identification of business district saturation: By selecting appropriate unit area for grid-based analysis, the business district is broken down into standardized small areas, enabling precise positioning of distribution potential; combined with the dual-dimensional analysis of distribution potential (surplus) and operational efficiency (operational quality index), the one-sidedness of a single dimension is avoided, making the identification results of market saturation more comprehensive and accurate.

[0062] 2. By adjusting the weights of localized grid indicators and calibrating per capita carrying capacity, the problem of ignoring regional differences was solved; by acquiring competitive and business information in real time through POI data, the problem of lagging and incomplete competitor data collection was solved; by constructing a multi-dimensional operational quality index, the problem of blind expansion was solved by combining the potential of site deployment with internal operational quality.

[0063] 3. Effectively prevents brand cannibalization and reduces trial-and-error costs for enterprises: The refined judgment of surrounding existing grids in the margin calculation can effectively prevent brand cannibalization caused by new and old stores being too close together; the encryption priority classification method allows enterprises to carry out differentiated deployment in different business districts, avoiding ineffective investment in saturated business districts and significantly reducing the trial-and-error costs of pharmacy deployment.

[0064] 4. Improved the efficiency and scientific nature of pharmacy location decisions: The analysis methods were solidified into systems and computer programs, realizing the automation and standardization of data processing and decision output, which greatly improved the efficiency of location decisions; the encrypted priority classification results directly correspond to the location strategy, allowing the analysis results to be quickly transformed into actual location actions, realizing a seamless connection between "data analysis → decision output → implementation".

[0065] 5. Adapting to different enterprise distribution strategies enhances the flexibility of the technical solution: Parameters such as grid index weights and quantile thresholds can be flexibly adjusted according to the enterprise's operating characteristics and distribution strategies, allowing the method of this invention to be adapted to retail chain pharmacies of different sizes and development stages, and has broad application prospects.

[0066] Based on the above embodiments, the present invention also provides a system for identifying the market saturation level of pharmacies in a business district, including a grid potential calculation unit, a surplus and quality calculation unit, and an output characterization unit, wherein: The grid potential calculation unit is used to determine a business district unit and calculate the number of high-potential grids in the business district unit. The margin and quality calculation unit is used to calculate the remaining number of stores in the physical space of the business district unit as the margin based on the number of high-potential grids and the existing deployment information, and to calculate the operating quality index of the business district unit based on the company's internal operating data. The output characterization unit is used to combine the remaining amount with the operating quality index to classify and obtain the encryption priority of the business district unit, which is used to characterize the market saturation of the business district unit.

[0067] This invention also provides an electronic device, including: a processor, a storage medium, and a bus. The storage medium stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the method for identifying the market saturation of pharmacies in a business district as described above.

[0068] It should be noted that those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, which may include, but is not limited to, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.

[0069] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for identifying the market saturation level of pharmacies in a business district, characterized in that, Includes the following steps: S1. Determine the business district unit and calculate the number of high-potential grid cells for the business district unit; S2. Based on the number of high-potential grids and the existing deployment information, calculate the remaining number of stores in the physical space of the business district unit as the margin, and calculate the operating quality index of the business district unit based on the company's internal operating data. S3. Combine the remaining amount with the operating quality index to obtain the encryption priority of the business district unit, which is used to characterize the market saturation of the business district unit.

2. The method for identifying the market saturation level of pharmacies in a business district as described in claim 1, characterized in that, Step S1 specifically includes: S11: Business districts are defined by administrative subdistricts and towns; S12: Construct a grid network within the town or street, with each area as a unit; S13: Standardize the negative conversion values ​​of its permanent resident population, number of catering service POIs, and number of existing pharmacies respectively; S14: Calculate the potential score for each grid cell by weighted summation, using the following formula: ; in, This represents the potential score of the i-th grid cell. , , These are the standardized values ​​of the grid's basic population indicators, traffic-related indicators, and competition adaptation indicators. , , Weight and satisfy ; S15: Select rasters with a potential score not lower than the lowest score threshold among historically deployed rasters as high-potential rasters, and count the total number of high-potential rasters.

3. The method for identifying the market saturation level of pharmacies in a business district as described in claim 2, characterized in that, The competitive fit index Ci is obtained by negatively transforming the number of existing pharmacies within the grid; the more existing pharmacies there are, the lower the index value.

4. The method for identifying the market saturation level of pharmacies in a business district as described in claim 3, characterized in that, In step S2, the method for calculating the remaining number of shops in the physical space of the street / town as the reserve includes: ； in, This represents the remaining number of grid cells that can be deployed. For high-potential raster count, For the number of high-potential grid cells already deployed, The number of high-potential grid cells already deployed in the surrounding area, This refers to the number of high-potential grid cells that have already been deployed in the surrounding area but have high capacity. Calculate the per capita carrying capacity of the remaining deployable grid cells; Based on this carrying capacity, the remaining number of available grid cells is proportionally calibrated to obtain the remaining number of stores in the physical space.

5. The method for identifying the market saturation level of pharmacies in a business district as described in claim 4, characterized in that, In step S2, the method for calculating the operational quality index includes: Select internal operational metrics and standardize them, including sales profit metrics, net profit metrics, membership growth metrics, retention and stability metrics, and new store quality metrics; The operating quality index is calculated by weighted summation, and the formula is as follows: ； Where Q represents the operational quality index. Let be the weights of the k-th category of indicators, and satisfy the condition that the sum of the weights is 1. is the standardized value of the k-th category index.

6. The method for identifying the market saturation level of pharmacies in a business district as described in claim 5, characterized in that, The sales profit indicator is obtained by multiplying the year-on-year sales growth rate and the ratio of the average monthly sales per store to the city average.

7. The method for identifying the market saturation level of pharmacies in a business district as described in claim 5, characterized in that, The net profit indicator is obtained by multiplying the year-on-year growth rate of net profit by the absolute value of net profit in the current period and the ratio of the city average.

8. The method for identifying the market saturation level of pharmacies in a business district as described in claim 1, characterized in that, The method for combining and grading the remaining balance with the operating quality index in S3 includes: The remaining margin is divided into three levels: high, medium, and low, based on the quantiles. The operational quality index is divided into two levels, high and low, based on quantiles. Based on the combination of margin and operational quality index levels, the encryption priority is determined to be four levels: S, A, B, and C.

9. A system for identifying the market saturation level of pharmacies in a business district, used to implement the method for identifying the market saturation level of pharmacies in a business district as described in any one of claims 1-8, characterized in that, It includes a grid potential calculation unit, a margin and quality calculation unit, and an output characterization unit, wherein: The grid potential calculation unit is used to determine a business district unit and calculate the number of high-potential grids for that business district unit. The margin and quality calculation unit is used to calculate the remaining number of stores in the physical space of the business district unit as the margin based on the number of high-potential grids and the existing deployment information, and to calculate the operating quality index of the business district unit based on the company's internal operating data. The output characterization unit is used to combine the remaining amount with the operating quality index to classify and obtain the encryption priority of the business district unit, which is used to characterize the market saturation of the business district unit.

10. An electronic device, comprising: The device includes a processor, a storage medium, and a bus. The storage medium stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform a method for identifying the market saturation of pharmacies in a business district, as described above.

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