Cigarette retail store supervision route planning method

Abstract

The invention relates to the technical field of cigarette supervision, in particular to a cigarette retail store supervision route planning method, which solves the problems of data missing and heterogeneity through data cleaning and standardization processing, and provides high-quality basic data for subsequent analysis. Due to the application of the analytic hierarchy process, scientific quantification of multi-dimensional indexes is realized, and the reliability of an evaluation result is improved; the improved ant colony algorithm is combined with pheromone concentration parameter adjustment, so that the routing inspection path planning efficiency and pertinence are optimized; key problems can be quickly positioned by introducing an anomaly detection algorithm, and the timeliness and accuracy of supervision are enhanced; a clustering analysis algorithm reveals a potential rule of a regional problem by grouping abnormal stores and calculating spatial distribution density; the thermodynamic diagram visualization technology presents the distribution of supervision key areas in a visual mode, and provides clear information support for decision makers; dynamic supervision and trend analysis are achieved through interactive function design, and scientificity and flexibility of supervision decision making are further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cigarette supervision, and specifically relates to a method for planning a supervision route for cigarette retail stores. Background Art

[0002] The supervision of cigarette retail stores, as the core link of the tobacco monopoly management system, is directly related to the maintenance of market order and the healthy development of the industry. With the continuous growth of the number of retail outlets and the increasingly complex market environment, the traditional supervision mode faces severe challenges, and the supervision efficiency and accuracy urgently need to be improved.

[0003] Among them, the method for planning a supervision route for cigarette retail stores is a complex technical means, and its purpose is to achieve efficient supervision of retail stores. The core tasks of supervision include comprehensively evaluating the business conditions of stores, optimizing resource allocation, and enhancing the scientific nature of supervision decisions. Through this goal, the market order can be maintained more effectively, the healthy development of the industry can be promoted, and reliable support can be provided for tobacco monopoly management.

[0004] There are many deficiencies in the traditional methods in actual operation. Most solutions only perform simple path optimization based on geographical location, ignoring the differences in the business conditions of stores. Although some methods introduce basic data analysis, the in-depth evaluation mechanism for the rationality of quota allocation is still imperfect, resulting in unbalanced allocation of supervision resources. The existing evaluation systems usually have a single index and are difficult to comprehensively reflect the true operation status of stores, affecting the support ability for scientific decision-making. In addition, in the process of establishing a multi-dimensional index system, it is difficult to accurately grasp the complex correlation relationships between indicators such as sales achievement rate and inventory turnover rate simply relying on traditional statistical methods. For the differentiated characteristics of stores in different regions and types, the traditional methods also have limitations in data processing and analysis capabilities. The effective presentation and application of evaluation results also face challenges, and it is necessary to transform complex analysis results into intuitive and operable decision-making support information. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for planning a supervision route for cigarette retail stores aiming at the above deficiencies in the prior art.

[0006] The purpose of the present invention is achieved through the following technical solutions: A method for planning a supervision route for cigarette retail stores, comprising the following steps:

[0007] S1. Obtain the geographical coordinate information, historical sales data, inventory turnover records, and quota execution status of each cigarette retail store. Through data cleaning and standardization, a store basic data set in a unified format is obtained. If there are missing values in the data, an interpolation algorithm is used to complete them, generating a complete store information matrix. In this step, data cleaning uses mean filling or neighboring point interpolation to handle outliers and missing values to ensure data integrity; standardization processing converts data with different dimensions into a unified range through a normalization formula for subsequent calculation and analysis.

[0008] S2. According to the store information matrix, use the analytic hierarchy process to construct a multi-dimensional evaluation index system including sales achievement rate, inventory turnover rate, and quota execution deviation degree. By calculating the weight coefficients w1, w2, and w3 of each index (where w1 represents the weight of the sales achievement rate, w2 represents the weight of the inventory turnover rate, and w3 represents the weight of the quota execution deviation degree), a formula for calculating the comprehensive evaluation score of the store is obtained. Specifically, the analytic hierarchy process determines the relative importance weights of each index by constructing a judgment matrix and calculating the maximum eigenvalue and its corresponding eigenvector. The calculation process of the weights combines consistency testing to ensure the reliability of the evaluation results.

[0009] S3. Quantitatively evaluate each store through the comprehensive evaluation score formula. If the store evaluation score is lower than the preset threshold T1, it is marked as a key supervision object; if the evaluation score is higher than the preset threshold T2, it is marked as an excellent store. Finally, the store risk level classification result and the corresponding supervision priority ranking are obtained. In this step, the settings of the threshold T1 and the threshold T2 are based on the historical data distribution and business requirements, and a dynamic adjustment mechanism is used to adapt to the specific conditions of different regions.

[0010] S4. Use an improved ant colony algorithm to perform route planning on the supervision priority ranking result. Adjust the pheromone concentration parameter according to the store risk level, set a high pheromone concentration value for key supervision objects, and obtain the optimal inspection path that takes into account geographical distance and supervision importance through iterative optimization. The improved ant colony algorithm introduces an adaptive pheromone update strategy on the basis of the traditional algorithm, combines local search and global search, and improves the efficiency and accuracy of route planning.

[0011] S5. Obtain the real-time business data of each store in the optimal inspection path, identify abnormal situation types such as abnormal sales fluctuations, inventory backlogs, and over-issuance of quotas through an anomaly detection algorithm. If an outlier is detected, trigger an early warning mechanism to generate a list of abnormal stores that need to be focused on. The anomaly detection algorithm uses a box plot method based on statistics or an isolation forest model based on machine learning to quickly locate abnormal points from a large amount of data.

[0012] S6. According to the abnormal store list, use the clustering analysis algorithm to group the abnormal stores according to the types and severity of abnormal situations. By calculating the spatial distribution density of abnormal stores in each group, determine whether there are regional problems, and obtain the analysis results of the spatial aggregation characteristics of abnormal problems. The clustering analysis uses the K-means algorithm or the DBSCAN algorithm, and combines spatial distance and attribute similarity to achieve precise grouping of abnormal stores.

[0013] S7. Through the analysis results of the spatial aggregation characteristics, use the heat map visualization technology to generate a distribution map of key supervision areas. Combine the store risk level and the type of abnormal situation to construct a multi-level visual supervision map including color coding, icon identification, and numerical annotation. The heat map uses a gradient color scale to represent the abnormal intensity, the icon identification is used to distinguish different types of abnormal problems, and the numerical annotation provides quantitative information on specific abnormal indicators.

[0014] S8. Obtain the interaction operation records of the visual supervision map. Through functions such as click query, area screening, or timeline playback, dynamically display the change trend of the store quota execution situation in different time periods. If the trend shows continuous deterioration, automatically generate a supervision recommendation report to form a precise supervision decision support plan. The design of the interaction function combines the front-end framework and the back-end database to ensure real-time data update and efficient response.

[0015] The present invention is further configured to obtain the geographical coordinate information, historical sales data, inventory turnover records, and quota execution situations of each cigarette retail store, and obtain a store basic data set in a unified format through data cleaning and standardization processing. If there are missing values in the data, use the interpolation algorithm to complete them, and generate a complete store information matrix, which specifically includes:

[0016] Based on the geographical coordinate information, historical sales data, inventory turnover records, and quota execution situations of each cigarette retail store, use data cleaning technology to eliminate duplicate, incorrect, and redundant data, and convert the data into a store basic data set in a unified format through standardization processing;

[0017] For the missing values in the data, use the linear interpolation or spline interpolation algorithm to complete them, and at the same time introduce an error correction mechanism to ensure the reliability and accuracy of the completed data;

[0018] Integrate the geographical coordinate information, historical sales data, inventory turnover records, and quota execution situations, construct a multi-dimensional data matrix, and extract key feature variables through principal component analysis to generate a store information matrix;

[0019] Based on the store information matrix, use the spatial index technology to quickly retrieve and match the geographical coordinate information to ensure the efficiency and accuracy in the subsequent analysis process.

[0020] The present invention is further configured such that, based on the store information matrix, the analytic hierarchy process is used to construct a multi-dimensional evaluation index system. By calculating the weight coefficients of each index, the specific formula for calculating the comprehensive evaluation score of the store includes:

[0021] Based on the store information matrix, the analytic hierarchy process is used to construct the framework of the evaluation index system, and the business conditions, risk hazards, and supervision requirements of the store are divided into a multi-dimensional evaluation index system with multiple hierarchical structures;

[0022] The relative importance of each hierarchical structure is determined by the expert scoring method, and the rationality of the weight distribution is verified by the consistency test to generate the initial weight coefficients;

[0023] Combined with the actual business scenario, the initial weight coefficients are dynamically adjusted to ensure that the evaluation system can adapt to the characteristics of stores in different regions and types;

[0024] Based on the adjusted weight coefficients, a formula for calculating the comprehensive evaluation score of the store is constructed.

[0025] The present invention is further configured such that, through the formula for calculating the comprehensive evaluation score, each store is quantitatively evaluated to obtain the risk level classification result of the store and the corresponding supervision priority ranking, which specifically includes:

[0026] Based on the formula for calculating the comprehensive evaluation score of the store, the business conditions, risk hazards, and supervision requirements of each store are quantitatively evaluated to generate a preliminary evaluation result;

[0027] According to the preliminary evaluation result, the fuzzy clustering method is used to classify the risk levels of the stores, and the stores are divided into three risk levels: high, medium, and low;

[0028] Combined with the risk levels of the stores, the weighted sorting algorithm is used to sort the supervision priorities of the stores to ensure that high-risk stores are included in the supervision scope first;

[0029] Based on the supervision priority ranking result, the risk level classification result of the store and the corresponding supervision priority ranking are generated.

[0030] The present invention is further configured such that the improved ant colony algorithm is used to plan the route for the supervision priority ranking result. The pheromone concentration parameter is adjusted according to the risk level of the store, and a high pheromone concentration value is set for the key supervision objects. The optimal inspection path is obtained through iterative optimization, which specifically includes:

[0031] Based on the supervision priority ranking result, the parameter configuration of the improved ant colony algorithm is initialized, including the pheromone concentration, heuristic factor, and number of iterations;

[0032] The pheromone concentration parameter is dynamically adjusted according to the risk level classification result of the store to ensure that high-risk stores have a higher priority in the path planning;

[0033] By simulating the foraging behavior of ants and combining with improved path selection rules, the inspection path is gradually optimized to reduce the total travel time and resource consumption;

[0034] After multiple rounds of iterative optimization, the optimal inspection path is generated.

[0035] The present invention is further configured to obtain the real-time business data of each store in the optimal inspection path, identify the types of abnormal situations through an anomaly detection algorithm, and if an abnormal value is detected, trigger an early warning mechanism to generate a list of abnormal stores that need to be focused on, specifically including:

[0036] Based on the optimal inspection path, the business data of each store is collected in real time;

[0037] An anomaly detection method based on rules and statistical analysis is adopted to identify the abnormal values in the business data and classify and mark them according to the types of abnormal situations;

[0038] If an abnormal value is detected, trigger an early warning mechanism to generate a list of abnormal stores and mark the types of abnormal situations;

[0039] Based on the list of abnormal stores, combined with historical data and business rules, generate suggestions for anomaly handling.

[0040] The present invention is further configured to, according to the list of abnormal stores, use a clustering analysis algorithm to group the abnormal stores according to the types and severity levels of abnormal situations; by calculating the spatial distribution density of each group of abnormal stores, determine whether there are regional problems, and obtain the analysis result of the spatial aggregation characteristics of abnormal problems, specifically including:

[0041] Based on the list of abnormal stores, use the K-means clustering algorithm to group the abnormal stores according to the types and severity levels of abnormal situations to generate differentiated abnormal groups;

[0042] Through the spatial density clustering algorithm, calculate the spatial distribution density of each abnormal group to identify the hot spots of abnormal stores;

[0043] Combined with the spatial distribution characteristics of the abnormal groups, analyze whether there are regional problems and generate the analysis result of the spatial aggregation characteristics of abnormal problems;

[0044] Based on the analysis result, generate a regional distribution map of abnormal problems.

[0045] The present invention is further configured to, through the analysis result of the spatial aggregation characteristics, use heat map visualization technology to generate a distribution map of key supervision areas, and combine the store risk levels and types of abnormal situations to construct a multi-level visual supervision map, specifically including:

[0046] Based on the results of spatial aggregation feature analysis, a heat map visualization technology is used to generate a distribution map of key supervision areas;

[0047] Combined with the store risk level and the types of abnormal situations, a multi-level visual supervision map is constructed;

[0048] Through the interactive query function, in-depth analysis and dynamic monitoring of specific areas, stores or types of abnormal situations are realized;

[0049] Based on the multi-level visual supervision map, a detailed analysis report of the key supervision areas is generated.

[0050] The present invention is further configured to obtain the interactive operation records of the visual supervision map, dynamically display the change trend of the store quota execution situation in different time periods, and if the trend shows continuous deterioration, an automatic supervision recommendation report is generated, and a precise supervision decision support plan specifically includes:

[0051] Based on the interactive operation records of the visual supervision map, dynamically display the change trend of the store quota execution situation in different time periods;

[0052] If the trend shows that the quota execution situation continues to deteriorate, an automated analysis process is triggered to generate an analysis report on the causes of the problems;

[0053] Combined with the analysis results of the causes of the problems, a supervision recommendation report is generated, including suggestions for optimization measures and predictions of implementation effects;

[0054] Based on the supervision recommendation report, a precise supervision decision support plan is formed.

[0055] The beneficial effects of the present invention: The present invention solves the problems of data loss and heterogeneity through data cleaning and standardization processing, providing high-quality basic data for subsequent analysis; the application of the analytic hierarchy process realizes the scientific quantification of multi-dimensional indicators such as sales achievement rate, inventory turnover rate, and quota execution deviation degree, improving the reliability of the evaluation results; the improved ant colony algorithm combined with the adjustment of the pheromone concentration parameter optimizes the efficiency and pertinence of the inspection path planning; the introduction of the anomaly detection algorithm can quickly locate key problems, enhancing the timeliness and precision of supervision; the clustering analysis algorithm reveals the potential laws of regional problems by grouping abnormal stores and calculating the spatial distribution density; the heat map visualization technology presents the distribution of key supervision areas in an intuitive way, providing clear information support for decision-makers; the interactive function design realizes dynamic supervision and trend analysis, further improving the scientificity and flexibility of supervision decisions. Description of the Drawings

[0056] The invention will be further described with reference to the accompanying drawings. However, the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, other drawings can be obtained based on the following drawings without creative efforts.

[0057] Figure 1 is the method flow chart of the present invention. Detailed implementation manners

[0058] The present invention will be further described in conjunction with the following embodiments.

[0059] As Figure 1 can be seen, a method for planning the supervision route of cigarette retail stores in this embodiment is based on a system for planning the supervision route of cigarette retail stores. The system includes a data acquisition module, a multi-dimensional evaluation index system, a comprehensive evaluation score calculation module, an improved ant colony algorithm path planning module, an anomaly detection module, a clustering analysis module, a heat map visualization module, and an interactive supervision map.

[0060] First, in the data acquisition module, the geographical coordinate information, historical sales data, inventory turnover records, and quota execution status of each cigarette retail store are obtained through the tobacco monopoly management system or relevant database interfaces. These data come from multiple channels, such as the store reporting system, the logistics distribution record platform, and market research data. To ensure the integrity and consistency of the data, the original data needs to be cleaned and standardized. In actual operation, if missing values are found in the data, they are filled in by means of mean filling or neighboring point interpolation. For example, for the missing monthly records in the historical sales data of a certain store, they can be filled in with the average value of adjacent months. At the same time, since the dimensions of different data items may be inconsistent, for example, the sales amount is in yuan while the inventory turnover rate is expressed as a percentage, it is necessary to convert them to a unified range, such as the [0,1] interval, through a normalization formula. This process ensures that the subsequent modules can perform analysis and calculations based on high-quality data. The output result of the data acquisition module is a complete store information matrix, which provides basic data support for the subsequent modules.

[0061] Next, the multi-dimensional evaluation index system is constructed based on the store information matrix generated by the data acquisition module. The core of this module lies in determining the weight coefficients w1, w2, and w3 of three key indicators, namely sales achievement rate, inventory turnover rate, and quota execution deviation degree, through the analytic hierarchy process. Specifically, first, a judgment matrix is constructed according to expert experience and business requirements, where the matrix elements represent the relative importance between two indicators. For example, if the sales achievement rate is considered more important than the inventory turnover rate, the corresponding matrix element is assigned a value of 3; otherwise, it is assigned a value of 1 / 3. Subsequently, by solving the maximum eigenvalue and its corresponding eigenvector of the judgment matrix, the weight coefficients of each indicator are obtained. To ensure the reliability of the evaluation results, a consistency test is also required, that is, calculating the consistency ratio CR and ensuring that it is less than 0.1. Finally, the weight coefficients w1, w2, and w3 are used in the formula construction of the comprehensive evaluation score calculation module. A connection relationship is established between the multi-dimensional evaluation index system and the data acquisition module through the store information matrix to ensure that the construction of evaluation indicators is based on comprehensive and accurate basic data.

[0062] The comprehensive evaluation score calculation module receives the weight coefficients from the multi-dimensional evaluation index system and combines the specific data in the store information matrix to calculate the comprehensive evaluation score for each store. The specific calculation formula is: Comprehensive evaluation score = w1 × sales achievement rate + w2 × inventory turnover rate - w3 × quota execution deviation degree. In practical applications, the sales achievement rate reflects the ratio of the actual sales amount of the store to the planned sales amount, the inventory turnover rate reflects the inventory management efficiency, and the quota execution deviation degree measures the difference between the actual quota usage of the store and the allocation plan. Through the above formula, the operation status of each store can be quantified. Further, the stores are classified according to the preset threshold T1 and threshold T2. If the comprehensive evaluation score is lower than the threshold T1, it is marked as a key supervision object; if it is higher than the threshold T2, it is marked as an excellent store; the remaining stores belong to the general supervision objects. The setting of the thresholds T1 and T2 is based on the historical data distribution and business requirements, and can be adjusted dynamically to adapt to the specific situations of different regions. The output results of the comprehensive evaluation score calculation module are the classification of store risk levels and the ranking of supervision priorities, and these results are directly transmitted to the improved ant colony algorithm path planning module.

[0063] The improved ant colony algorithm path planning module conducts route planning based on the regulatory priority ranking results generated by the comprehensive evaluation score calculation module. The core of this module lies in introducing an adaptive pheromone update strategy, combining local search and global search to improve the efficiency and accuracy of path planning. Specifically, a higher pheromone concentration value is set for key regulatory objects, while a lower pheromone concentration value is set for high-quality stores. This differential setting makes the algorithm more inclined to prioritize visiting key regulatory objects during the iteration process, thus taking into account both geographical distance and regulatory importance. In actual operation, first, the path pheromone concentration between all stores is initialized, and the initial number of ants and the number of iterations are set. Subsequently, each ant selects the next visit target based on the current path pheromone concentration and distance until all stores have been visited. After each iteration, the pheromone concentration is updated according to the total length and visit order of the path passed by the ant. Through multiple iterations, an optimal inspection path is finally obtained. A connection relationship is established between the improved ant colony algorithm path planning module and the comprehensive evaluation score calculation module through the regulatory priority ranking results to ensure the pertinence and scientific nature of path planning.

[0064] The anomaly detection module receives the optimal inspection path generated by the improved ant colony algorithm path planning module and conducts anomaly situation type detection based on the real-time business data of each store on the path. Specifically, by collecting the real-time sales data, inventory data, and quota execution data of the store, anomaly detection algorithms are used to identify potential problems. For example, when using the box plot method based on statistics, by calculating the quartiles and outlier bounds of the sales data, stores with abnormal sales fluctuations can be quickly located; while when using the isolation forest model based on machine learning, the model is trained to distinguish normal data from abnormal data, thus realizing automated detection. Once an outlier is detected, such as the inventory turnover rate of a store being significantly lower than the average level or the quota execution deviation being too large, the warning mechanism is triggered and a list of abnormal stores is generated. A connection relationship is established between the anomaly detection module and the improved ant colony algorithm path planning module through the optimal inspection path to ensure that anomaly detection covers all stores in the planned path.

[0065] The clustering analysis module groups the abnormal stores using the K-means algorithm or DBSCAN algorithm based on the list of abnormal stores generated by the anomaly detection module. Specifically, first, the spatial distribution density of each abnormal store is calculated, and clustering analysis is carried out in combination with attribute similarity. For example, stores located in the same area and having similar types of abnormal situations are grouped together and the spatial distribution characteristics of the group are calculated. Through this process, the potential laws of regional problems can be revealed. For example, if multiple stores in a certain area simultaneously show inventory backlogs, it may indicate a decline in market demand or problems in the supply chain in that area. A connection relationship is established between the clustering analysis module and the anomaly detection module through the list of abnormal stores to ensure that the grouping results are based on comprehensive abnormal data.

[0066] The heat map visualization module generates a distribution map of key supervision areas based on the results of the clustering analysis module. This module uses a gradient color scale to represent the anomaly intensity, icon labels to distinguish different types of anomaly problems, and numerical annotations to provide quantitative information on specific anomaly indicators. For example, the red area represents the area with a higher anomaly intensity, and the blue area represents the area with a lower anomaly intensity; the circular icon represents abnormal sales fluctuations, and the triangular icon represents inventory backlog problems. In addition, through the combination of color coding and icon labels, the spatial aggregation characteristics of anomaly problems can be intuitively displayed. The heat map visualization module and the clustering analysis module establish a connection relationship through the anomaly grouping results to ensure that the visualized content is based on accurate analysis results.

[0067] The interactive supervision map is based on the distribution map of key supervision areas generated by the heat map visualization module and provides dynamic display and interactive functions. Specifically, users can view the change trends of the implementation of store quotas in different time periods through functions such as click query, area filtering, and timeline playback. For example, when the user selects a certain time period and clicks on a certain area, the system will display the detailed anomaly information and its change trends of all stores in that area. If the trend shows continuous deterioration, a supervision recommendation report will be automatically generated to form a precise supervision decision support plan. The interactive supervision map and the heat map visualization module establish a connection relationship through the distribution map of key supervision areas to ensure that the interactive functions are based on intuitive visualized content.

[0068] The specific implementation manners of the present invention are described above. From the data acquisition module to the interactive supervision map, each module is closely connected through data streams and logical relationships, jointly realizing the whole process of the supervision route planning for cigarette retail stores.

[0069] In order to better enable relevant personnel in the technical field to fully understand and implement the present invention, the specific implementation principles of the present invention are further supplemented below in combination with a specific application scenario.

[0070] In practical applications, a local tobacco monopoly administrative department needs to plan the supervision routes for 100 cigarette retail stores in its jurisdiction. First, through the data acquisition module, the geographical coordinate information, historical sales data, inventory turnover records, and quota implementation situations of each store are extracted from the tobacco monopoly administrative system and the logistics distribution record platform. For example, the historical sales data of store A shows that the sales amount in a certain month is missing, and the system automatically fills it with the average value of adjacent months. At the same time, the data with different dimensions is converted to the [0,1] interval using the normalization formula to ensure data consistency. After cleaning and standardization processing, a complete store information matrix is generated as the basic data support for subsequent modules.

[0071] Next, in the multi-dimensional evaluation index system, a judgment matrix is constructed based on expert experience and business requirements to determine the weight coefficients w1, w2, and w3 of the sales achievement rate, inventory turnover rate, and quota execution deviation degree. For example, if the sales achievement rate is considered more important than the inventory turnover rate, the corresponding matrix element is assigned a value of 3; otherwise, it is assigned a value of 1 / 3. By solving the maximum eigenvalue and its corresponding eigenvector of the judgment matrix, the weight coefficients of each index are obtained, and a consistency test is performed to ensure the reliability of the evaluation results. Finally, the weight coefficients are used to construct the formula in the comprehensive evaluation score calculation module to ensure that the evaluation index system is based on comprehensive and accurate basic data.

[0072] In the comprehensive evaluation score calculation module, the comprehensive evaluation score of each store is calculated by combining the weight coefficients generated by the multi-dimensional evaluation index system and the specific data in the store information matrix. For example, the sales achievement rate of store B is 85%, the inventory turnover rate is 70%, and the quota execution deviation degree is 15%. Its comprehensive evaluation score is 68.5 obtained through the formula "Comprehensive evaluation score = w1×Sales achievement rate + w2×Inventory turnover rate - w3×Quota execution deviation degree". According to the preset thresholds T1 and T2, if the comprehensive evaluation score is lower than the threshold T1, it is marked as a key supervision object; if it is higher than the threshold T2, it is marked as an excellent store; the remaining stores belong to general supervision objects. Finally, the store risk level classification and supervision priority ranking results are output.

[0073] The improved ant colony algorithm path planning module performs route planning based on the supervision priority ranking results generated by the comprehensive evaluation score calculation module. For example, the pheromone concentration value of store C, which is a key supervision object, is set at a relatively high level, while the pheromone concentration value of store D, which is an excellent store, is set at a relatively low level. Initialize the pheromone concentration of the path information between all stores, and set the initial number of ants to 20 and the number of iterations to 50. Each ant selects the next visit target based on the current path pheromone concentration and distance until all stores have been visited. After each iteration, update the pheromone concentration according to the total length and visit order of the path passed by the ants. After multiple iterations, an optimal inspection path that takes into account both geographical distance and supervision importance is finally obtained.

[0074] The anomaly detection module receives the optimal inspection path generated by the improved ant colony algorithm path planning module and performs anomaly detection based on the real-time business data of each store on the path. For example, for store E, collect its real-time sales data, inventory data, and quota execution data, and use the box plot method to calculate the quartiles and outlier bounds of the sales data to quickly locate the store with abnormal sales fluctuations; or use the isolation forest model to train to distinguish normal data from abnormal data, so as to achieve automated detection. If it is detected that the inventory turnover rate of store F is significantly lower than the average level or the quota execution deviation degree is too large, the early warning mechanism is triggered and a list of abnormal stores is generated.

[0075] Based on the list of abnormal stores generated by the anomaly detection module, the clustering analysis module groups the abnormal stores using the K-means algorithm or the DBSCAN algorithm. For example, for stores G and H located in the same area and having similar types of anomalies, they are grouped together and the spatial distribution characteristics of this group are calculated. Through this process, the potential patterns of regional problems can be revealed. For example, if inventory backlogs occur simultaneously in multiple stores within a certain area, it may indicate a decline in market demand or problems in the supply chain in that area.

[0076] The heat map visualization module generates a distribution map of key supervision areas based on the results of the clustering analysis module. For example, the red area represents an area with a higher anomaly intensity, and the blue area represents an area with a lower anomaly intensity; circular icons represent abnormal fluctuations in sales, and triangular icons represent inventory backlog problems. Through the combination of color coding and icon identification, the spatial aggregation characteristics of abnormal problems are visually displayed. A connection relationship is established between the heat map visualization module and the clustering analysis module through the anomaly grouping results to ensure that the visualized content is based on accurate analysis results.

[0077] Based on the distribution map of key supervision areas generated by the heat map visualization module, the interactive supervision map provides dynamic display and interaction functions. For example, users can view the changing trends of the implementation of store quotas over different time periods through functions such as click query, area filtering, and timeline playback. When the user selects a certain time period and clicks on a certain area, the system will display the detailed anomaly information and its changing trends of all stores within that area. If the trend shows continuous deterioration, a supervision recommendation report will be automatically generated to form a precise supervision decision support plan.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than limiting the protection scope of the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. A method for planning the supervision route of cigarette retail stores, characterized in that: It includes the following steps: S1. Obtain the geographical coordinate information, historical sales data, inventory turnover records and quota execution status of each cigarette retail store, and through data cleaning and standardization processing, obtain a store basic data set in a unified format. If there are missing values in the data, interpolation algorithms are used to complete them, and a complete store information matrix is generated; S2. According to the store information matrix, use the analytic hierarchy process to construct a multi-dimensional evaluation index system, and by calculating the weight coefficients of each index, obtain the calculation formula for the comprehensive evaluation score of the store; S3. Quantitatively evaluate each store through the comprehensive evaluation score calculation formula to obtain the store risk level classification results and the corresponding regulatory priority rankings; S4. Use the improved ant colony algorithm to plan the route for the regulatory priority ranking results, adjust the pheromone concentration parameter according to the store risk level, set a high pheromone concentration value for key supervision objects, and obtain the optimal inspection route through iterative optimization; S5. Obtain the real-time business data of each store in the optimal inspection route, identify the types of abnormal situations through anomaly detection algorithms. If abnormal values are detected, trigger the early warning mechanism and generate a list of abnormal stores that need to be focused on; S6. According to the list of abnormal stores, use the clustering analysis algorithm to group the abnormal stores according to the types and severity of abnormal situations; by calculating the spatial distribution density of each group of abnormal stores, judge whether there are regional problems, and obtain the analysis results of the spatial aggregation characteristics of abnormal problems; S7. Through the analysis results of the spatial aggregation characteristics, use heat map visualization technology to generate a distribution map of key supervision areas, and combine the store risk level and the types of abnormal situations to construct a multi-level visual supervision map; S8. Obtain the interaction operation records of the visual supervision map, dynamically display the change trend of the quota execution status of the store in different time periods. If the trend shows continuous deterioration, automatically generate a supervision suggestion report to form a precise supervision decision support plan.

2. The method for planning a supervision route for a cigarette retail store according to claim 1, wherein: The obtaining of the geographical coordinate information, historical sales data, inventory turnover records and quota execution status of each cigarette retail store, and through data cleaning and standardization processing to obtain a store basic data set in a unified format. If there are missing values in the data, interpolation algorithms are used to complete them, and the generation of a complete store information matrix specifically includes: Based on the geographical coordinate information, historical sales data, inventory turnover records and quota execution status of each cigarette retail store, use data cleaning technology to eliminate duplicate, incorrect and redundant data, and through standardization processing, convert the data into a store basic data set in a unified format; For the missing values in the data, use linear interpolation or spline interpolation algorithms to complete them, and at the same time introduce an error correction mechanism to ensure the reliability and accuracy of the completed data; Integrate the geographical coordinate information, historical sales data, inventory turnover records and quota execution status, construct a multi-dimensional data matrix, and extract key feature variables through principal component analysis to generate a store information matrix; Based on the store information matrix, use spatial indexing technology to quickly retrieve and match the geographical coordinate information to ensure the efficiency and accuracy in the subsequent analysis process.

3. A method for planning a supervision route for cigarette retail stores according to claim 1, characterized in that: According to the store information matrix, the analytic hierarchy process is used to construct a multi-dimensional evaluation index system. By calculating the weight coefficients of each index, the specific formula for calculating the comprehensive evaluation score of the store includes: Based on the store information matrix, the analytic hierarchy process is used to construct the framework of the evaluation index system, and the business conditions, risk hazards and supervision requirements of the store are divided into a multi-dimensional evaluation index system with multiple hierarchical structures; The relative importance of each hierarchical structure is determined by the expert scoring method, and the rationality of the weight distribution is verified by the consistency test to generate the initial weight coefficients; Combined with the actual business scenario, the initial weight coefficients are dynamically adjusted to ensure that the evaluation system can adapt to the characteristics of stores in different regions and types; Based on the adjusted weight coefficients, a formula for calculating the comprehensive evaluation score of the store is constructed.

4. A method for planning a supervision route of a cigarette retail store according to claim 1, characterized in that: The specific process of quantitatively evaluating each store through the comprehensive evaluation score formula to obtain the risk level classification results of the stores and the corresponding supervision priority rankings includes: Based on the formula for calculating the comprehensive evaluation score of the store, the business conditions, risk hazards and supervision requirements of each store are quantitatively evaluated to generate preliminary evaluation results; According to the preliminary evaluation results, the fuzzy clustering method is used to classify the risk levels of the stores, and the stores are divided into three risk levels: high, medium and low; Combined with the store risk levels, the weighted sorting algorithm is used to sort the supervision priorities of the stores to ensure that high-risk stores are included in the supervision scope first; Based on the supervision priority ranking results, the risk level classification results of the stores and the corresponding supervision priority rankings are generated.

5. A method for planning a supervision route of a cigarette retail store according to claim 1, characterized in that: The specific process of using the improved ant colony algorithm to plan the route for the supervision priority ranking results, adjusting the pheromone concentration parameter according to the store risk level, setting a high pheromone concentration value for key supervision objects, and obtaining the optimal inspection route through iterative optimization includes: Based on the supervision priority ranking results, the parameter configuration of the improved ant colony algorithm is initialized, including the pheromone concentration, heuristic factor and number of iterations; The pheromone concentration parameter is dynamically adjusted according to the store risk level classification results to ensure that high-risk stores have a higher priority in route planning; By simulating the foraging behavior of ants and combining the improved path selection rules, the inspection route is gradually optimized to reduce the total travel time and resource consumption; After multiple rounds of iterative optimization, the optimal inspection route is generated.

6. The regulatory route planning method for a cigarette retail store according to claim 1, characterized in that: The specific process of obtaining the real-time business data of each store in the optimal inspection route, identifying the types of abnormal situations through the anomaly detection algorithm, and triggering the early warning mechanism to generate a list of abnormal stores that need to be focused on includes: Based on the optimal inspection route, the business data of each store is collected in real time; An anomaly detection method based on rules and statistical analysis is used to identify the abnormal values in the business data and classify and mark them according to the types of abnormal situations; If abnormal values are detected, the early warning mechanism is triggered to generate a list of abnormal stores and mark the types of abnormal situations; Based on the list of abnormal stores, combined with historical data and business rules, suggestions for anomaly handling are generated.

7. A method for planning a supervision route of a cigarette retail store according to claim 1, characterized in that: According to the abnormal store list, the clustering analysis algorithm is used to group the abnormal stores according to the types and severity levels of abnormal situations; by calculating the spatial distribution density of each group of abnormal stores, it is judged whether there are regional problems, and the analysis results of the spatial aggregation characteristics of abnormal problems specifically include: Based on the abnormal store list, the K-means clustering algorithm is used to group the abnormal stores according to the types and severity levels of abnormal situations, and generate differentiated abnormal groups; Through the spatial density clustering algorithm, calculate the spatial distribution density of each abnormal group to identify the hot spots of abnormal stores; Combined with the spatial distribution characteristics of the abnormal groups, analyze whether there are regional problems and generate the analysis results of the spatial aggregation characteristics of abnormal problems; Based on the analysis results, generate the regional distribution map of abnormal problems.

8. A method for planning a supervision route for a cigarette retail store according to claim 1, characterized in that: Through the analysis results of spatial aggregation characteristics, the heat map visualization technology is used to generate the distribution map of key supervision areas, and combined with the store risk level and the types of abnormal situations, a multi-level visualization supervision map is constructed, which specifically includes: Based on the analysis results of spatial aggregation characteristics, the heat map visualization technology is used to generate the distribution map of key supervision areas; Combined with the store risk level and the types of abnormal situations, construct a multi-level visualization supervision map; Through the interactive query function, realize in-depth analysis and dynamic monitoring of specific regions, stores or types of abnormal situations; Based on the multi-level visualization supervision map, generate a detailed analysis report of key supervision areas.

9. A method for planning a supervision route of a cigarette retail store according to claim 1, characterized in that: Obtain the interactive operation records of the visualization supervision map, dynamically display the change trend of the store quota execution situation in different time periods. If the trend shows continuous deterioration, automatically generate a supervision recommendation report to form a precise supervision decision support plan, which specifically includes: Based on the interactive operation records of the visualization supervision map, dynamically display the change trend of the store quota execution situation in different time periods; If the trend shows that the quota execution situation continues to deteriorate, trigger an automated analysis process and generate an analysis report on the causes of the problem; Combined with the analysis results of the causes of the problem, generate a supervision recommendation report, including suggestions for optimization measures and predictions of implementation effects; Based on the supervision recommendation report, form a precise supervision decision support plan.

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