Big data network store assessment method driven by regional operation strategy map

Abstract

The invention relates to the technical field of network store evaluation, in particular to a big data network store evaluation method driven by a regional operation strategy map, which comprises the following steps of: respectively calculating a store flow value index and a real-time value index based on flow data and operation profit data of a target network store; constructing a linear algebraic model to generate a comprehensive value index of the store; evaluating the operation condition of the store based on the comprehensive value index of the store, and generating a regional operation strategy map in combination with a geographic information system technology; and predicting a future operation condition and outputting an operation risk index by using a distributed deep learning model so as to optimize a regional operation strategy map and generate an intelligent operation strategy. According to the method, dynamic monitoring and prospective analysis of the operation state of the network store can be realized, the intelligent level of evaluation and the accuracy of decision making are improved, and the problems of response lag and lack of strategy output in an existing evaluation method are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of online store evaluation, and more specifically, to a big data online store evaluation method driven by a regional business strategy map. Background Art

[0002] With the digital transformation of the retail industry, the number and scale of online stores are growing rapidly. Scientifically and efficiently evaluating the operational performance of these massive stores and formulating precise regional operating strategies has become crucial for companies to enhance their core competitiveness. Traditional store evaluation methods often rely on single, static financial indicators or employ manual sampling analysis. This evaluation model has significant limitations: First, the single-dimensional data fails to fully reflect the "health" of a store. For example, a store with high traffic but low conversion rates may be evaluated as either "good" or "bad" as well as a store with low traffic but high average customer spending, leading to distorted decision-making. Second, the analysis process is static and lagging, often based on retrospective summaries based on historical monthly or quarterly reports. This lacks real-time insight into dynamic changes in store operations, let alone effective predictions of future trends. Third, evaluation results are disconnected from regional operating strategies. Store data is often presented in lists or reports, lacking an intuitive spatial perspective. This makes it difficult for managers to quickly identify regional operational differences, hotspots, and problem areas. This results in a lack of precise geographic targeting for resource allocation and marketing strategy formulation, hindering cross-regional collaborative optimization.

[0003] Furthermore, in the face of a rapidly changing market and increasingly competitive environment, traditional approaches have also exposed their reactive nature in addressing risks. Due to the lack of intelligent predictive models and risk warning mechanisms, managers often intervene passively after operational issues occur, missing the optimal opportunity for intervention. Consequently, existing technologies are unable to achieve a closed-loop management system from data collection, dynamic assessment, intelligent prediction, to strategy generation, and thus fail to meet the urgent need of modern online stores for intelligent, dynamic, and accurate operational analysis. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a big data network store evaluation method driven by a regional business strategy map, which solves the problems raised in the above-mentioned background technology through the following scheme.

[0005] S1: Select designated online stores as target online stores, collect online store traffic data and operating profit data, and realize dynamic data transmission and access; S2: Calculate the store traffic value index based on the online store traffic data; calculate the store real-time value index based on the operating profit data; and further calculate the store comprehensive value index by combining the store traffic value index and the store real-time value index. S3: Evaluate store operations based on comprehensive network store value indicators and generate a regional business strategy map; S4: Real-time monitoring and analysis of comprehensive value indicators of online stores and regional business strategy maps, using distributed deep learning models for predictions, while outputting operational risk indicators. Based on the above predictions and operational risk indicators, the regional business strategy map is updated, and intelligent strategy optimization and operational decision-making plans for online stores are generated.

[0006] Preferably, the network store traffic data in S1 specifically includes: the total flow of people per unit time, the flow of people entering the store per unit time; the operating profit data specifically includes: the total turnover per unit time, the total profit per unit time.

[0007] Preferably, the store traffic value index calculation method described in S2 includes: obtaining the store traffic value index based on the ratio of the total traffic volume to the traffic volume entering the store within a unit time; the store real-time value index calculation method includes: obtaining the store real-time value index based on the ratio of the total turnover to the total profit within a unit time.

[0008] Preferably, the store comprehensive value index described in S2 is based on the store traffic value index and the store real-time value index, and is calculated using linear algebra methods to obtain a comprehensive value index. The specific analysis method is: first, a symmetric covariance matrix is ​​constructed, and then the store comprehensive value index is calculated based on the matrix.

[0009] Preferably, the method described in S3 is to evaluate the store operation status based on the comprehensive value index of the network store and generate a regional operation strategy map, which specifically includes: using the hierarchical analysis method to evaluate the store operation status and combining the geographic information system technology to generate a regional operation strategy map.

[0010] Preferably, the use of the hierarchical analysis method to evaluate the store's operating conditions specifically includes: decomposing the store's comprehensive value indicators according to a hierarchical structure, and constructing an evaluation model to evaluate the store's operating conditions.

[0011] Preferably, the hierarchical structure includes: a target layer, a criterion layer, and a solution layer.

[0012] Preferably, the store operation status is combined with geographic information system technology to generate a regional business strategy map, which specifically includes: the geographic information system is a technical system for collecting, storing, analyzing and displaying geographic data, and the regional business strategy map is generated by combining the store operation status with the geographic information system.

[0013] Preferably, in S4, based on the store comprehensive value data indicators and the regional business strategy map, analysis is performed through a streaming computing framework to identify the dynamic change trend of the store comprehensive value data indicators over time. Subsequently, a distributed deep learning model is used to analyze the store comprehensive value indicators, and the operation status of the network stores in the future is predicted by integrating the historical store comprehensive value data indicators with the current store comprehensive value data indicators, and the operation risk indicators are output at the same time.

[0014] Preferably, based on the predicted network store operation conditions and operation risk indicators, the regional operation strategy map is updated, and the intelligent strategy optimization and operation decision-making plan of the network store is generated according to the updated regional operation strategy map.

[0015] The technical effects and advantages of the present invention are as follows: 1. The present invention constructs store traffic value indicators and store real-time value indicators, and combines them with linear algebra methods to generate store comprehensive value indicators, which comprehensively reflect the market appeal and profitability of stores. It breaks through the limitation of existing technologies that rely only on single-dimensional data for evaluation, and significantly improves the accuracy and credibility of the evaluation.

[0016] 2. This invention introduces geographic information system technology, combines it with the comprehensive value indicators of stores, and constructs a regional business strategy map, which enables visual analysis and spatial distribution of store operations in different regions, providing a scientific basis for regional resource allocation and layout optimization.

[0017] 3. The present invention adopts a streaming computing framework and a dynamic data access mechanism to realize the continuous collection and real-time processing of online store traffic data and operating profit data, overcoming the problem of data update lag in traditional methods, ensuring that the evaluation results always reflect the current operating reality and improving the decision-making response speed.

[0018] 4. Through the distributed deep learning model, the present invention not only realizes the trend prediction of the store's future comprehensive value indicators, but also outputs operational risk indicators to help stores identify potential risks in advance and enhance their forward-looking management capabilities for operating fluctuations.

[0019] 5. Based on the prediction results and risk levels, the present invention combines the dynamic update mechanism of the regional business strategy map to automatically generate intelligent operation strategies for multiple types of stores, thereby realizing the automation, differentiation and refinement of operation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a basic flow chart of the present invention; Figure 2 A flow chart for generating a regional business strategy map for the present invention; Figure 3A flow chart is generated for the decision-making scenario of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0022] like Figure 1-Figure 3 The present invention provides a big data network store evaluation method driven by a regional business strategy map, which specifically includes the following steps: S1: Select designated online stores as target online stores, collect online store traffic data and operating profit data, and realize dynamic data transmission and access; S2: Calculate the store traffic value index based on the online store traffic data; calculate the store real-time value index based on the operating profit data; and further calculate the store comprehensive value index by combining the store traffic value index and the store real-time value index. S3: Evaluate store operations based on comprehensive network store value indicators and generate a regional business strategy map; S4: Real-time monitoring and analysis of comprehensive value indicators of online stores and regional business strategy maps, using distributed deep learning models for predictions, while outputting operational risk indicators. Based on the above predictions and operational risk indicators, the regional business strategy map is updated, and intelligent strategy optimization and operational decision-making plans for online stores are generated.

[0023] In S1, a designated online store is selected as the target online store, and the traffic data and operating profit data of the online store are collected, wherein the traffic data includes the total flow of people per unit time and the flow of people entering the store per unit time, and the operating profit data includes the total turnover per unit time and the total profit per unit time.

[0024] It should be further explained that the reason for collecting traffic data and operating profit data of online stores is that these two types of data together constitute the basic dimensions of store operation evaluation, reflecting the market attractiveness and profitability of the store respectively, and are key variables for evaluating store operations.

[0025] It should be further explained that the traffic data and operating profit data of the network store are collected by automatically collecting data through the sensor devices and cash register systems deployed at the store site, and the remote transmission and access of data are completed with the help of dynamic data interfaces and communication modules, thereby building a dynamic access environment for the traffic data and operating profit data of the network store.

[0026] In S2, the store traffic value index is obtained by calculating the network store traffic data. The specific analysis method is: store traffic value index ,in, It is a store traffic value indicator, I is the store traffic per unit time, and T is the total store traffic per unit time.

[0027] The real-time value index of the store is obtained by calculating the operating profit data. The specific analysis method is: real-time value index of the store ,in, It is a real-time value indicator of the store, P is the total profit per unit time, and R is the total turnover per unit time.

[0028] Combining the store traffic value index and the store real-time value index, the store comprehensive value index is further calculated. The specific analysis method is: first, construct a symmetric covariance matrix ,in, is the square of the store traffic value index, It is the square of the store's real-time value index. Secondly, the store's comprehensive value index is calculated based on the matrix , where a is the upper left element of the matrix M, i.e. ; b is the upper right (lower left) element of the matrix M, that is ; d is the lower right element of the matrix M, that is .

[0029] In S3, the store operation status is evaluated based on the comprehensive value index of the network store, which specifically includes: using the hierarchical analysis method to build an evaluation model and divide the store operation status into multiple levels for analysis, namely the target level, the criterion level, and the solution level.

[0030] It should be further explained that: the target layer is the store's operating conditions, the criterion layer is the store's comprehensive value index, and the solution layer is to give corresponding adjustment suggestions for different operating conditions. The specific analysis method is as follows: at the criterion layer, the store's comprehensive value index is evaluated through the preset threshold interval. The threshold interval is Less than When it is set as an inefficient store; Greater than and less than When it is set as a medium-efficiency store; Greater than or equal to When setting up as an efficient store.

[0031] It needs further explanation: and is the preset threshold of the store’s comprehensive value index. Set to the mean value of historical store comprehensive value indicators minus the standard deviation, Set to the mean of the historical store comprehensive value indicators plus the standard deviation.

[0032] At the target level, store operations are evaluated based on the results from the criteria level. Specifically, these include: Inefficient stores: These stores' operating efficiency is significantly below industry standards, indicating significant problems attracting customers or generating profits; Average stores: These stores' operating efficiency is at the industry average, with neither significant advantages nor serious weaknesses. These stores maintain a relatively stable appeal and profitability; and High-Performance stores: These stores perform exceptionally well, with operating efficiency significantly superior to other stores in the same industry. High-Performance stores have strong market appeal and high profitability, maintaining high traffic and conversion rates.

[0033] At the solution level, based on the results of the target level, corresponding adjustment suggestions are given for different operating situations, including: Inefficient stores: It is recommended to adjust the operating strategy, increase marketing investment, optimize product pricing and increase promotional activities. At the same time, the internal management, service quality and customer experience of the store should be comprehensively improved to enhance its competitiveness and market appeal; Medium-efficiency stores: Further improve the overall operating efficiency of the store by optimizing customer experience, improving employee service quality, and enhancing internal and external marketing activities of the store. In addition, the store can also expand or increase investment in a certain area to increase market share; High-efficiency stores: Further improve operational efficiency through continuous optimization, focusing on innovation and expansion, such as expanding product lines, strengthening cross-channel marketing, and improving user experience. At the same time, you can also consider replicating successful experiences and expanding in other regions to further increase market share.

[0034] Subsequently, the process of generating a regional business strategy map combines the comprehensive value indicators of the stores with the geographic location information and uses geographic information system technology for visualization. Specifically, the process includes: first, assigning different colors to the stores based on the operating conditions of the stores to intuitively display the operating status of the stores.

[0035] It should be further explained that: inefficient stores are represented in red, highlighting their operational problems; medium-efficiency stores are represented in yellow, indicating that their operations are at a medium level; and efficient stores are represented in green, indicating that their operations are excellent.

[0036] Then, through geographic information system technology, the specific geographical location information of the store is matched with its operating conditions, and the area is assigned a corresponding color. This not only helps to understand the performance of a single store, but also can quickly identify the operational differences of stores in different areas through the map. Through color changes and markings, it can be clearly seen which areas of stores perform poorly and which areas of stores operate well, further regional strategy adjustments can be made, resource allocation and marketing strategies can be optimized, and overall operational results can be improved.

[0037] In S4, the comprehensive value indicators of network stores and regional business strategy maps are monitored and analyzed in real time, and distributed deep learning models are used for prediction. At the same time, operational risk indicators are output. Specifically, the comprehensive value indicators of stores are monitored in real time through a streaming computing framework to ensure their continuous updating and feedback. In this process, the system obtains the latest comprehensive value indicators from each store in real time, compares them with the previous store comprehensive value indicators, and analyzes their dynamic change trends.

[0038] Next, a distributed deep learning model is used to conduct a deeper predictive analysis of the store's comprehensive value indicators. Specifically, the deep learning model identifies potential patterns and rules in store operations by training historical data, and predicts the direction of store operations in the future. The prediction result is the store's comprehensive value indicator at a certain moment in the future.

[0039] It is necessary to further explain that: collect and construct a series of comprehensive value indicators of stores in the past N consecutive time periods, recorded as: ,in, It represents the comprehensive value index of the store in the t-th time period. After training the sequence Z using the distributed deep learning model, the comprehensive value index of the future N+1-th time period is predicted to be , where f represents the trained distributed deep learning model.

[0040] Secondly, based on the prediction results, the model further calculates and outputs operational risk indicators, including: operational risk indicators , where the average value of comprehensive value indicators in the historical N periods is , Represents the predicted future comprehensive value indicator, Represents the comprehensive value index in the tth time period.

[0041] It should be further explained that the threshold interval is set based on the operational risk index and different risk levels are assigned: low risk, medium risk and high risk. Specifically, the calculated risk index R is less than , it is judged as low risk, indicating that the store's future operating conditions are close to the historical average level and are generally stable; when R is between and If R is greater than or equal to , it is considered high risk, indicating that the store may face greater challenges in future operations.

[0042] It needs further explanation: and is the preset threshold of the store’s comprehensive value index. Set to the mean of historical operational risk indicators minus the standard deviation, Set to the mean of the historical operational risk indicator plus the standard deviation.

[0043] Then, using geographic information system technology, we dynamically map the predicted comprehensive value indicators and risk levels of the stores and their regions, visually reflecting the store's operating status and potential risks. Finally, intelligent strategy optimization and operational decision-making plans for online stores are generated based on different operational risk levels. Specifically, for stores with low risk levels, their future operating conditions are close to historical levels, showing a high degree of stability. Such stores usually have a mature customer base, highly standardized operational processes, and low volatility. Based on this, a "growth enhancement" strategy plan is generated for them; for stores with medium risk levels, their operating indicators have fluctuated and require key optimization to prevent the expansion of risks. For them, a "structural adjustment" strategy plan is generated; for stores with high risk levels, their future operations face significant uncertainty or a downward trend. For them, a "risk control" strategy plan is generated. It should be further explained that the "growth enhancement" strategy plan specifically includes: increasing the budget for traffic introduction, launching a new product trial mechanism, introducing a member loyalty incentive mechanism, and expanding the circle of high-potential users to expand operating benefits on a stable basis; the "structural adjustment" strategy plan specifically includes: redesigning operating processes, adjusting product category structure, improving service experience, and optimizing online and offline integration paths to ensure operational optimization based on fluctuation control; the "risk control" strategy plan specifically includes: compressing marketing budgets, suspending new investment, temporarily reducing staffing, adjusting operating rhythm, and recommending the establishment of a rapid response plan.

[0044] These measures include, but are not limited to, increasing the budget for traffic introduction, launching new product trial mechanisms, introducing member loyalty incentive mechanisms, expanding high-potential user circles, and other measures to expand operating benefits on a stable basis. At the same time, through the optimal strategy matching module in the historical operating effect database, it intelligently recommends growth methods that have been more effective in similar situations in the past, and formulates refined schedules and effect prediction models to ensure execution efficiency.

[0045] Secondly: The drawings of the embodiments disclosed in the present invention only involve structures related to the embodiments disclosed in the present invention. Other structures may refer to conventional designs. The same embodiment and different embodiments of the present invention may be combined with each other without conflict. Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A big data network store evaluation method driven by a regional business strategy map, characterized by: The following steps are involved: S1: Select designated online stores as target online stores, collect online store traffic data and operating profit data, and realize dynamic data transmission and access; S2: Calculate the store traffic value index based on the online store traffic data; calculate the store real-time value index based on the operating profit data; and further calculate the store comprehensive value index by combining the store traffic value index and the store real-time value index. S3: Evaluate store operations based on comprehensive network store value indicators and generate a regional business strategy map; S4: Real-time monitoring and analysis of comprehensive value indicators of online stores and regional business strategy maps, using distributed deep learning models for predictions, while outputting operational risk indicators. Based on the above predictions and operational risk indicators, the regional business strategy map is updated, and intelligent strategy optimization and operational decision-making plans for online stores are generated.

2. The big data network store evaluation method driven by a regional business strategy map according to claim 1 is characterized in that: The online store traffic data in S1 specifically includes: the total flow of people per unit time, the flow of people entering the store per unit time; the operating profit data specifically includes: the total turnover per unit time, the total profit per unit time.

3. The big data network store evaluation method driven by a regional business strategy map according to claim 2 is characterized in that: The method for calculating the store traffic value index described in S2 includes: obtaining the store traffic value index based on the ratio of the total traffic volume to the traffic volume entering the store within a unit time; the method for calculating the store real-time value index includes: obtaining the store real-time value index based on the ratio of the total turnover to the total profit within a unit time.

4. The big data network store evaluation method driven by a regional business strategy map according to claim 3 is characterized in that: The store comprehensive value index described in S2 is based on the store traffic value index and the store real-time value index, and is calculated using linear algebra methods to obtain a comprehensive value index. The specific analysis method is: first, a symmetric covariance matrix is ​​constructed, and then the store comprehensive value index is calculated based on the matrix.

5. The big data network store evaluation method driven by a regional business strategy map according to claim 1 is characterized in that: S3 describes evaluating store operations based on comprehensive online store value indicators and generating a regional business strategy map, which specifically includes: using a hierarchical analysis method to evaluate store operations and combining geographic information system technology to generate a regional business strategy map.

6. The big data network store evaluation method driven by a regional business strategy map according to claim 5 is characterized in that: The use of the hierarchical analysis method to evaluate the store's operating conditions specifically includes: decomposing the store's comprehensive value indicators according to a hierarchical structure, and constructing an evaluation model to evaluate the store's operating conditions.

7. The big data network store evaluation method driven by a regional business strategy map according to claim 6 is characterized in that: The hierarchical structure includes: a target layer, a criterion layer, and a solution layer.

8. The big data network store evaluation method driven by a regional business strategy map according to claim 6 is characterized in that: The store operation status is combined with geographic information system technology to generate a regional operation strategy map, which specifically includes: the geographic information system is a technical system for collecting, storing, analyzing and displaying geographic data. By combining the store operation status with the geographic information system, a regional operation strategy map is generated.

9. The big data network store evaluation method driven by a regional business strategy map according to claim 1 is characterized in that: In S4, based on the store comprehensive value data indicators and regional business strategy map, analysis is performed through a streaming computing framework to identify the dynamic change trend of the store comprehensive value data indicators over time. Subsequently, a distributed deep learning model is used to analyze the store comprehensive value indicators, and the operation status of the network stores in the future is predicted by integrating the historical store comprehensive value data indicators with the current store comprehensive value data indicators, and the operation risk indicators are output at the same time.

10. The big data network store evaluation method driven by a regional business strategy map according to claim 9 is characterized in that: Based on the predicted network store operation conditions and operation risk indicators, the regional operation strategy map is updated, and the intelligent strategy optimization and operation decision-making plan of the network store is generated according to the updated regional operation strategy map.

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