Cigarette-related monopoly law enforcement data analysis management method and platform

Abstract

The invention discloses a cigarette-related monopoly law enforcement data analysis management method and platform, and relates to the technical field of data analysis. According to the method, law enforcement data and retail data in cigarette-related monopoly law enforcement data are analyzed, and the cigarette-related safety level of each sub-region is analyzed; then, according to complaint data in the cigarette-related monopoly law enforcement data, law enforcement optimization is performed on each target sub-region with insufficient cigarette-related safety level, a law enforcement scheme of each target sub-region is set, and different types of data in the cigarette-related monopoly law enforcement data are analyzed respectively, so that the law enforcement scheme of each target sub-region is set; according to the method, meanings and values represented by different types of data can be intuitively understood, market dynamics and law enforcement key points can be quickly grasped, a proper optimization method can be selected according to data characteristics, the application value of the data is improved, and targeted decision support is provided for law enforcement officers, so that the law enforcement efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to a data analysis management method and platform for tobacco monopoly law enforcement. Background Art

[0002] By analyzing tobacco monopoly law enforcement data, we can quickly discover anomalies in the market, help law enforcement personnel accurately locate problem areas and key regulatory targets, and carry out targeted law enforcement actions, thereby improving law enforcement efficiency and saving law enforcement resources.

[0003] Existing technology, such as a method for constructing an ER model for tobacco monopoly market supervision big data, disclosed in application publication number CN111858946A, includes the following steps: Step 1: Taking historical tobacco-related data from the tobacco monopoly market as the object, construct a tobacco-related data sample set for clustering tobacco-related data elements; Step 2: Using information entropy based on tobacco-related keywords, extract and generate tobacco-related data elements from the sample data set; Step 3: Substitute tobacco-related semantics into the data elements through the content structure view of the tobacco monopoly field; Step 4: Construct a tobacco monopoly market supervision big data ER model through tobacco-related data elements; Step 5: Use the big data ER model to guide and generate a corresponding tobacco-related big data data management system to provide data support for market supervision and information-based analysis of tobacco-related cases. Through this invention, data can be provided for tobacco market supervision and data analysis of tobacco-related crimes by public security departments, as well as information-based supervision by tobacco monopoly administrative law enforcement departments at all levels.

[0004] The above scheme has at least the following shortcomings: the data used for tobacco monopoly law enforcement includes several different types of data, and different data represent different aspects of cigarette supervision. However, when constructing the tobacco-related data model in the above scheme, the data in the tobacco-related data was not classified, and then further analysis and application were carried out on different types of data. This makes it difficult to intuitively understand the meaning and value represented by the data from a business perspective, which is not conducive to quickly grasping market dynamics and law enforcement priorities. Without data classification and analysis, it is impossible to select appropriate optimization methods based on data characteristics, which affects the application value of the data and makes it difficult to provide targeted decision-making support. Summary of the Invention

[0005] In response to the above-mentioned technical deficiencies, the purpose of the present invention is to provide a data analysis and management method and platform for tobacco monopoly law enforcement.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions: First, the present invention provides a data analysis and management method based on tobacco monopoly law enforcement, including the following steps: S1, divide the area into grids to obtain sub-areas, assign law enforcement teams to each sub-area, obtain historical law enforcement records and historical retail data in each sub-area, and analyze the tobacco safety level of each sub-area.

[0007] S2. Each sub-area whose tobacco safety level is lower than the qualified safety level is recorded as a target sub-area, and the law enforcement team and consumer complaint data of each target sub-area are obtained. The law enforcement optimization direction of each target sub-area is analyzed, and the law enforcement plan for each target sub-area is set.

[0008] S3. Provide corresponding feedback on the law enforcement plan for each target sub-area.

[0009] In the first aspect, the present invention provides a data analysis and management platform for tobacco monopoly law enforcement, including: a tobacco safety analysis module for dividing the area into grids to obtain sub-areas, assigning law enforcement teams to each sub-area, obtaining historical law enforcement records and historical retail data in each sub-area, and analyzing the tobacco safety level of each sub-area.

[0010] The law enforcement plan setting module is used to record each sub-area whose tobacco safety level is lower than the qualified safety level as a target sub-area, obtain the law enforcement team and consumer complaint data of each target sub-area, analyze the law enforcement optimization direction of each target sub-area, and set the law enforcement plan for each target sub-area.

[0011] The law enforcement plan feedback module is used to provide corresponding feedback on the law enforcement plan of each target sub-area.

[0012] The beneficial effects of the present invention are as follows: this application analyzes the tobacco safety level of each sub-region by analyzing the law enforcement data and retail data in the tobacco monopoly law enforcement data, and then optimizes law enforcement for each target sub-region with insufficient tobacco safety level based on the complaint data in the tobacco monopoly law enforcement data, and sets up an enforcement plan for each target sub-region. By separately analyzing different types of data in the tobacco monopoly law enforcement data, the meaning and value represented by different types of data can be intuitively understood, which is conducive to quickly grasping market dynamics and law enforcement priorities, and helps to select appropriate optimization methods according to data characteristics, improve the application value of data, and provide targeted decision-making support for law enforcement personnel, thereby improving law enforcement efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0014] Figure 1 The figure is a schematic flow chart of the steps for implementing the method of the present invention.

[0015] Figure 2 This is a schematic diagram of the platform structure connection of the present invention. DETAILED DESCRIPTION

[0016] 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.

[0017] Example 1:

[0018] See also Figure 1 As shown, the data analysis and management method for tobacco monopoly law enforcement includes the following steps: S1, dividing the area into grids to obtain sub-areas, assigning law enforcement teams to each sub-area, obtaining historical law enforcement records and historical retail data in each sub-area, and analyzing the tobacco safety level of each sub-area.

[0019] It should be noted that enforcement management personnel assign enforcement teams to each sub-region. Historical enforcement records and retail data for each sub-region are obtained from the tobacco monopoly enforcement data stored in the data center. The tobacco monopoly enforcement data contains relevant data from the enforcement teams in each sub-region during their enforcement process.

[0020] In a specific embodiment, the specific process of analyzing the tobacco safety level of each sub-region is: S1-1, obtaining the number of abnormal retailers in each enforcement, the abnormal retail type set of each abnormal retailer and the associated sub-region set of abnormal retail from the historical law enforcement records in each sub-region, and analyzing the law enforcement effect characteristic value of each sub-region.

[0021] It should be noted that abnormal retail sales include bundled sales, excessively low prices, and purchasing cigarettes from informal channels. When retailers in a sub-area engage in abnormal transactions with consumers or retailers in other sub-areas, the other sub-areas will be recorded as associated sub-areas of abnormal retail sales. Abnormal transactions include purchasing cigarettes at prices far below market value and selling counterfeit cigarettes.

[0022] Bundling is the practice of bundling cigarettes with other products. For example, purchasing a pack of cigarettes requires a bottle of a designated beverage. This infringes on consumers' right to choose independently and may force them to purchase unnecessary cigarettes or other goods, increasing their financial burden. This practice also violates relevant laws and regulations and hinders fair competition in the market. By analyzing unusual retail situations in different regions and formulating enforcement plans, we can maintain market order and consumer rights. We can also guide businesses to comply with laws and regulations, strengthen industry self-discipline, establish sound business practices, and achieve profits through legal and honest operations. Furthermore, we can educate consumers about the law so that they can promptly file complaints with relevant authorities when encountering unusual sales practices and use legal tools to protect their rights.

[0023] Preferably, the calculation process of the law enforcement effect characteristic value of each sub-area is: using the number of abnormal retailers in each law enforcement, the abnormal retail type set of each abnormal retailer and the associated sub-area set of abnormal retail, the number of retailers with abnormalities for two or more consecutive times, the number of retailers with the same abnormal retail type set for two or more consecutive times, and the number of associated sub-areas with the same abnormal retail for two or more times in each sub-area are counted, and recorded as the number of Class I abnormal retailers, the number of Class II abnormal retailers and the number of Class I associated sub-areas respectively.

[0024] The number of Category I abnormal retailers, the number of Category II abnormal retailers, and the number of Category I related sub-regions in each sub-region are input into the law enforcement effectiveness analysis model, and the characteristic value of the law enforcement effect of each sub-region is output.

[0025] In the above, the number of abnormal retailers of type 1, the number of abnormal retailers of type 2, and the number of related sub-regions of type 1 are recorded as 、 and , f represents the number of each sub-area, f is a positive integer, and the expression of the law enforcement effect analysis model is: , where represents the characteristic value of law enforcement effect in the f-th sub-region, 、 and They represent the number threshold of the first category of abnormal retailers, the number threshold of the second category of abnormal retailers, and the number threshold of the first category of associated sub-regions in the f-th sub-region, respectively. and They respectively represent the weight factor of the number of type one abnormal retailers and the weight factor of the number of type two abnormal retailers.

[0026] Among the above, , where F represents the number of sub-regions, .

[0027] is 0.2 times the number of retailers in the f-th sub-region, is 0.1 times the number of retailers in the f-th sub-region, The product of the ratio of the number of retailers in the fth sub-region to the total number of retailers in all sub-regions and the number of sub-regions, and then multiplied by 0.5.

[0028] It should be noted that 、 and If the result is not an integer, it is rounded up.

[0029] S1-3. Based on the historical retail data in each sub-region, each cigarette type is classified into a best-selling cigarette type and a slow-selling cigarette type, and then the characteristic values of cigarette retail standards in each sub-region are analyzed.

[0030] Preferably, the calculation process of the cigarette retail standard characteristic value in each sub-region is: obtaining the purchase quantity, inventory quantity and sales quantity corresponding to each cigarette type in each retail household from the historical retail data in each sub-region, and then classifying each cigarette type into best-selling cigarette types and slow-selling cigarette types, and then obtaining each cigarette type of the best-selling cigarette type and each cigarette type of the slow-selling cigarette type in each retail household in each sub-region.

[0031] In the above, the purchase volume, inventory volume and sales volume corresponding to each cigarette type in each retailer are accumulated respectively to obtain the purchase volume, inventory volume and sales volume of each cigarette type, and then the purchase volume of each cigarette type is divided by the total purchase volume of each cigarette type to obtain the purchase rate of each cigarette type, and the inventory rate and sales rate of each cigarette type are analyzed according to the analysis method of the purchase rate.

[0032] The purchase rate, inventory rate and sales rate of each cigarette type are compared with the preset purchase rate range, inventory rate range and sales rate range of the best-selling cigarette types. If the purchase rate, inventory rate and sales rate of a cigarette type are within the purchase rate range, inventory rate range and sales rate range of the best-selling cigarette types and the slow-selling cigarette types respectively, then the cigarette type is a best-selling cigarette type; otherwise, it is a slow-selling cigarette type.

[0033] It should be noted that the preset purchase rate range, inventory rate range and sales rate range of best-selling cigarette types are set and modified by law enforcement management personnel based on regional market demand, and no specific numerical restrictions are imposed here.

[0034] The location of each retailer in each sub-area is obtained from the data center, and each retailer in each sub-area is grouped into two groups. The distance between two retailers in each retail group in each sub-area is obtained, and the difference factor of each retail group in each sub-area is set.

[0035] In the above, the process of setting the difference factor of each retail group in each sub-area is: obtain the retail difference factor per unit length from the data center, and then multiply the distance between two retailers in each retail group in each sub-area by the retail difference factor per unit length. The result is the difference factor of each retail group in each sub-area.

[0036] The difference factors of each retail group in each sub-region represent the differences in consumption preferences at different distances. The retail difference factors per unit length are set and modified by law enforcement management personnel based on the region's consumption habits, economy, and tobacco sales policies. No specific numerical restrictions are imposed here.

[0037] Consumers in different locations have different consumption preferences, which leads to certain differences in the types of best-selling and slow-selling cigarettes in different locations. Therefore, a difference factor is introduced for calculation to ensure the reliability of the calculation results.

[0038] Using the best-selling cigarette types and slow-selling cigarette types of the two retailers in each retail group in each sub-region, the difference number of best-selling cigarette types, the difference number of slow-selling cigarette types and the difference type of slow-selling cigarette types in each retail group in each sub-region were counted.

[0039] Comparing the different cigarette types of the best-selling cigarette types in each retail group in each subregion, counting the number of occurrences of each different cigarette type in the best-selling cigarette types in each subregion, and defining the different cigarette types with a number of occurrences greater than a preset number as abnormal cigarette types, thereby obtaining the number of abnormal cigarette types of the best-selling cigarette types in each subregion, and obtaining the number of abnormal cigarette types of the slow-selling cigarette types in each subregion;

[0040] The difference numbers of best-selling cigarette types, the difference numbers of slow-selling cigarette types in each retail group in each sub-region, the abnormal numbers of best-selling cigarette types and the abnormal numbers of slow-selling cigarette types in each sub-region are input into the retail specification analysis model, and the characteristic values of cigarette retail specifications in each sub-region are output.

[0041] In the above, the difference number of best-selling cigarette types, the difference number of slow-selling cigarette types, the abnormal number of best-selling cigarette types and the abnormal number of slow-selling cigarette types in each retail group in each sub-region are recorded as 、 、 and , the expression of the retail specification analysis model is: , where represents the characteristic value of cigarette retail specifications in the f-th sub-region, g represents the number of each retail group, g is a positive integer, and G represents the number of retail groups. represents the difference factor of the g-th retail group in the f-th sub-region, 、 、 、 They represent the difference cigarette quantity threshold of the f-th best-selling cigarette type, the difference cigarette quantity threshold of the slow-selling cigarette type, the abnormal cigarette type quantity threshold of the best-selling cigarette type, and the abnormal cigarette type quantity threshold of the slow-selling cigarette type, respectively. 、 、 、 They respectively represent the weight factors of the difference cigarette quantity of the best-selling cigarette type, the weight factors of the difference cigarette quantity of the slow-selling cigarette type, the weight factors of the abnormal cigarette quantity of the best-selling cigarette type, and the weight factors of the abnormal cigarette quantity of the slow-selling cigarette type.

[0042] It should be noted that 、 、 、 The calculation process and and The calculation process is the same and will not be repeated here. The value is 0.2 times the total number of cigarette types in the fth best-selling cigarette type. The value is 0.2 times the total number of cigarette types in the f-th unsaleable cigarette type.

[0043] and The values of are 0.1 times the total number of cigarette types in the fth best-selling cigarette type and the fth slow-selling cigarette type respectively.

[0044] If the above values are not integers, they are rounded up.

[0045] S1-4. Analyze the tobacco safety level of each sub-region based on the characteristic values of law enforcement effectiveness and cigarette retail regulations in each sub-region.

[0046] In the above, the analysis process of the tobacco safety level of each sub-area is as follows: The formula of tobacco safety level is: , where represents the smoking safety level of the f-th sub-area, 、 are the weight factors of the preset law enforcement effect characteristic value and the cigarette retail specification characteristic value, respectively. 、 、 、 They are respectively the upper limit of the smoking safety characteristic value of the preset level 1 smoking safety level, the upper limit of the smoking safety characteristic value of the n-1 smoking safety level, the upper limit of the smoking safety characteristic value of the n-1 smoking safety level, and the upper limit of the smoking safety characteristic value of the N-1 smoking safety level, where n represents the smoking safety level and N represents the maximum smoking safety level, and both n and N are positive integers. , N>1, f represents the number of each sub-region, and f is a positive integer.

[0047] It should be noted that 、 The calculation process and and The calculation process is the same and will not be repeated here. 、 、 、 They are all set and modified by law enforcement management personnel based on regional needs, and no specific numerical restrictions are imposed here.

[0048] S2. Each sub-area whose tobacco safety level is lower than the qualified safety level is recorded as a target sub-area, and the law enforcement team and consumer complaint data of each target sub-area are obtained. The law enforcement optimization direction of each target sub-area is analyzed, and the law enforcement plan for each target sub-area is set.

[0049] It should be noted that the qualified safety level is set and modified by law enforcement management personnel based on regional needs, and no specific numerical limit is imposed here. Consumer complaint data for each target sub-region is obtained from the tobacco monopoly law enforcement data in the data center.

[0050] In a specific embodiment, the specific steps of S2 are: S2-1. Obtain the retailer, abnormal retail type set, complaint channel and complainant information of each complaint content within a preset period from the consumer complaint data of each target sub-area, integrate the same complainant information to obtain the number of complainants, and at the same time count the retailers, abnormal retail type number, and complaint channel of each complaint of each complainant, and calculate the complaint rich feature value of each target sub-area.

[0051] In the above, the number of complainants, the number of retailers with total complaints, the number of abnormal retail types with total complaints, and the number of complaint channels in each target sub-area are counted and then normalized and recorded as 、 、 and , using the calculation formula: , get the complaint rich feature value of each target sub-area , where 、 、 、 They are the weight factors of the number of complainants, the weight factors of the number of retailers with total complaints, the weight factors of the number of abnormal retail types with total complaints, and the weight factors of the number of complaint channels. x represents the number of each target sub-area and is a positive integer.

[0052] It should be noted that 、 、 、 The calculation process and and The calculation process is the same and will not be repeated here.

[0053] S2-2. Set the complaint characteristic value threshold for each target sub-area based on the tobacco-related safety level of each target sub-area. Based on the tobacco-related safety level, complaint richness characteristic value and complaint characteristic value threshold of each target sub-area, determine the law enforcement optimization direction of each target sub-area, including optimization of popular legal education for the public and optimization of routine inspections. Classify each target sub-area into Class I optimization areas, Class II optimization areas and Class III optimization areas.

[0054] It should be noted that the complaint characteristic value threshold of each target sub-area = [1-(qualified safety level-smoking safety level of each target sub-area)] ÷ qualified safety level × maximum complaint characteristic value.

[0055] The maximum complaint characteristic value is the maximum value among the complaint characteristic values of each target sub-region. A lower tobacco safety level indicates less standardized cigarette retail in that region, and consumers in that region have a higher level of legal awareness, which strengthens their understanding of and resistance to tobacco-related illegal activities. Therefore, the complaint characteristic value threshold for that region needs to be higher.

[0056] Preferably, the specific process of S2-2 is: 0.7 times the qualified safety level is the lower limit of the tobacco-related safety level, the tobacco-related safety level of each target sub-area is compared with the lower limit of the tobacco-related safety level, and the complaint rich feature value of each target sub-area is compared with the complaint feature value threshold.

[0057] If the smoking safety level of a target sub-area is greater than or equal to the lower limit of the smoking safety level, and the complaint rich feature value of the target sub-area is less than the complaint feature value threshold, then the law enforcement optimization direction of the target sub-area is the popularization of law optimization, and the target sub-area is classified as a first-class optimization area; if the smoking safety level of a target sub-area is less than the lower limit of the smoking safety level, and the complaint rich feature value of the target sub-area is greater than or equal to the complaint feature value threshold, then the law enforcement optimization direction of the target sub-area is routine patrol optimization, and the target sub-area is classified as a second-class optimization area; if the smoking safety level of a target sub-area is less than the lower limit of the smoking safety level, and the complaint rich feature value of the target sub-area is less than the complaint feature value threshold, then the law enforcement optimization direction of the target sub-area is the popularization of law optimization and routine patrol optimization, and the target sub-area is classified as a third-class optimization area. In this way, the law enforcement optimization direction of each target sub-area is analyzed and classified.

[0058] S2-3. Confirm the scope of legal education in each first-class optimized area as the law enforcement plan for each first-class optimized area, set the order of routine inspections in each second-class optimized area as the law enforcement plan for each second-class optimized area, set the scope of legal education and the order of routine inspections in each third-class optimized area as the law enforcement plan for each third-class optimized area.

[0059] Preferably, the scope of legal education in each first-class optimized area is confirmed, and the specific process is as follows: based on the historical law enforcement records in each first-class optimized area, the number of abnormalities and the number of abnormal retail types of each retail business in each first-class optimized area are counted; based on the historical retail data in each first-class optimized area, the best-selling cigarette types and slow-selling cigarette types of each retail business in each first-class optimized area are counted; based on the consumer complaint data in each first-class optimized area, the number of complaints received by each retail business in each first-class optimized area, the number of complainants and the number of complaint types are counted.

[0060] The legal education demand value of each retailer in each first-class optimization area is analyzed by using the number of abnormalities, the number of abnormal retail types, the types of best-selling cigarettes, the types of slow-selling cigarettes, the number of complaints received, the number of complainants and the number of complaint types of each retailer in each first-class optimization area.

[0061] In the above, each type of best-selling cigarettes of each retailer in each first-class optimization area is compared with each type of best-selling cigarettes of each first-class optimization area, and the number of different types of best-selling cigarettes of the retailers in each first-class optimization area and the best-selling cigarettes in each first-class optimization area is counted as the difference number of best-selling cigarettes of each retailer in each first-class optimization area, and the difference number of slow-selling cigarettes of each retailer in each first-class optimization area is obtained according to this method.

[0062] The number of abnormal times, number of abnormal retail types, number of differences in best-selling cigarette types and number of differences in slow-selling cigarette types of each retailer in each optimized area are calculated according to The calculation method is used to calculate the abnormal retail characteristic value of each retailer in each optimization area. , q represents the number of each optimized area, q is a positive integer. The number of complaints received by each retailer in each optimized area, the number of complainants and the number of complaint types are calculated according to The calculation method is used to calculate the complaint characteristic value of each retailer in each optimization area. , then the legal education demand value of each retailer in each type of optimization area is: ,in, 、 They represent the weight factors of abnormal feature values and the weight factors of complained feature values respectively.

[0063] It should be noted that 、 The calculation process and and The calculation process is the same and will not be repeated here.

[0064] The cluster analysis algorithm is applied to the legal education demand value and location of each retailer in each first-class optimization area to obtain the cluster areas of each first-class optimization area as the legal education scope, and the legal education demand value of the center point of each legal education area is obtained. The legal education demand value is sorted in descending order, and the result is the legal education order of each legal education area.

[0065] Preferably, the order of routine inspections of each Class II optimization area is set, and the specific process is: obtaining the number of abnormalities, the number of abnormal retail types, the number of cigarette types of best-selling cigarette types, the number of cigarette types of slow-selling cigarette types, the number of complaints, the number of complainants and the number of complaint types of each retailer in each Class II optimization area, analyzing the abnormal severity value of each retailer in each Class II optimization area, and using a cluster analysis algorithm to obtain each cluster area as each inspection area, obtaining the abnormal severity value of the center point and each retailer in each inspection area, and the descending order of the abnormal severity value of the center point in each inspection area is the inspection order of each inspection area, and the descending order of the abnormal severity value of each retailer in each inspection area is the inspection order of each retailer in each inspection area.

[0066] In the above, the calculation process of the abnormal severity value of each retailer in each second-class optimization area is as follows: and The analysis process is to analyze the abnormal retail characteristic value and the complaint characteristic value of each retailer in each second-class optimization area, which are recorded as and , p is the number of each second-class optimization area, p is a positive integer, and the abnormal severity value of each retailer in each second-class optimization area is: .

[0067] It should be noted that the optimization of popularization of law for the masses and routine inspections in each Class-I optimized area will be carried out by confirming the scope of popularization of law in each Class-I optimized area and conducting sequential analysis of routine inspections in each Class-II optimized area.

[0068] S3. Provide corresponding feedback on the law enforcement plan for each target sub-area.

[0069] The law enforcement plan for each target sub-area is sent to the receiving terminal of the law enforcement team in each target sub-area.

[0070] Example 2:

[0071] See Figure 2 As shown, a data analysis and management platform for tobacco monopoly law enforcement includes: a tobacco safety analysis module for dividing the area into grids to obtain sub-areas, assigning law enforcement teams to each sub-area, obtaining historical law enforcement records and historical retail data in each sub-area, and analyzing the tobacco safety level of each sub-area.

[0072] The law enforcement plan setting module is used to record each sub-area whose tobacco safety level is lower than the qualified safety level as a target sub-area, obtain the law enforcement team and consumer complaint data of each target sub-area, analyze the law enforcement optimization direction of each target sub-area, and set the law enforcement plan for each target sub-area.

[0073] The law enforcement plan feedback module is used to provide corresponding feedback on the law enforcement plan of each target sub-area.

[0074] The above content is merely an example and explanation of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the scope of protection of the present invention.

Claims

1. A data analysis and management method for tobacco monopoly law enforcement, characterized by: The steps include: S1. Divide the area into grids to obtain sub-areas, assign enforcement teams to each sub-area, obtain historical enforcement records and historical retail data for each sub-area, and analyze the tobacco safety level of each sub-area; S2. Mark each sub-area with a tobacco safety level lower than the qualified safety level as a target sub-area, obtain the law enforcement team and consumer complaint data for each target sub-area, analyze the law enforcement optimization direction for each target sub-area, and establish an enforcement plan for each target sub-area; S3. Provide corresponding feedback on the law enforcement plan for each target sub-area.

2. The data analysis and management method for tobacco monopoly law enforcement according to claim 1 is characterized in that: The specific process of analyzing the tobacco safety level of each sub-area is as follows: S1-1. Obtain the number of abnormal retailers in each enforcement event, the set of abnormal retail types for each abnormal retailer, and the set of sub-regions associated with abnormal retail from the historical enforcement records in each sub-region, and analyze the enforcement effect characteristic value of each sub-region; S1-3. Based on the historical retail data in each sub-region, each cigarette type is classified into a best-selling cigarette type and a slow-selling cigarette type, and then the characteristic values of cigarette retail standards in each sub-region are analyzed; S1-4. Analyze the tobacco safety level of each sub-region based on the characteristic values of law enforcement effectiveness and cigarette retail regulations in each sub-region.

3. The data analysis and management method for tobacco monopoly law enforcement according to claim 2 is characterized in that: The calculation process of the law enforcement effect characteristic value of each sub-area is as follows: Using the number of abnormal retailers in each law enforcement, the set of abnormal retail types of each abnormal retailer, and the set of associated sub-areas of abnormal retail, count the number of retailers in each sub-area that have been abnormal for two or more consecutive times, the number of retailers with the same set of abnormal retail types for two or more consecutive times, and the number of associated sub-areas with the same abnormal retail for two or more times, and record them as the number of Category 1 abnormal retailers, the number of Category 2 abnormal retailers, and the number of Category 1 associated sub-areas, respectively; The number of Category I abnormal retailers, the number of Category II abnormal retailers, and the number of Category I related sub-regions in each sub-region are input into the law enforcement effectiveness analysis model, and the characteristic value of the law enforcement effect of each sub-region is output.

4. The data analysis and management method for tobacco monopoly law enforcement according to claim 2 is characterized in that: The calculation process of the cigarette retail standard characteristic value in each sub-area is as follows: Obtain the purchase quantity, inventory quantity, and sales quantity of each cigarette type for each retailer from the historical retail data in each sub-region, then classify each cigarette type into best-selling cigarette types and slow-selling cigarette types, and finally obtain the cigarette types of each best-selling cigarette type and each cigarette type of each slow-selling cigarette type for each retailer in each sub-region; Obtain the location of each retailer in each sub-area from the data center, group the retailers in each sub-area into pairs, obtain the distance between two retailers in each retail group in each sub-area, and set the difference factor for each retail group in each sub-area; Using the best-selling cigarette types and slow-selling cigarette types of the two retailers in each retail group in each sub-region, count the difference number of best-selling cigarette types, the difference number of slow-selling cigarette types, and the difference number of slow-selling cigarette types in each retail group in each sub-region; Comparing the different cigarette types of the best-selling cigarette types in each retail group in each subregion, counting the number of occurrences of each different cigarette type in the best-selling cigarette types in each subregion, and defining the different cigarette types with a number of occurrences greater than a preset number as abnormal cigarette types, thereby obtaining the number of abnormal cigarette types of the best-selling cigarette types in each subregion, and obtaining the number of abnormal cigarette types of the slow-selling cigarette types in each subregion; The difference numbers of best-selling cigarette types, the difference numbers of slow-selling cigarette types in each retail group in each sub-region, the abnormal numbers of best-selling cigarette types and the abnormal numbers of slow-selling cigarette types in each sub-region are input into the retail specification analysis model, and the characteristic values of cigarette retail specifications in each sub-region are output.

5. The data analysis and management method for tobacco monopoly law enforcement according to claim 1 is characterized in that: The analysis process of the tobacco safety level of each sub-area is as follows: The law enforcement effect characteristic value of each sub-region and the cigarette retail standard characteristic value of each sub-region are respectively denoted as and , the formula for the tobacco safety level is: , where represents the smoking safety level of the f-th sub-area, 、 are the weight factors of the preset law enforcement effect characteristic value and the cigarette retail specification characteristic value, respectively. 、 、 、 They are respectively the upper limit of the smoking safety characteristic value of the preset level 1 smoking safety level, the upper limit of the smoking safety characteristic value of the n-1 smoking safety level, the upper limit of the smoking safety characteristic value of the n-1 smoking safety level, and the upper limit of the smoking safety characteristic value of the N-1 smoking safety level, where n represents the smoking safety level and N represents the maximum smoking safety level, and both n and N are positive integers. , N>1, f represents the number of each sub-region, and f is a positive integer.

6. The data analysis and management method for tobacco monopoly law enforcement according to claim 1 is characterized in that: The specific steps of S2 are: S2-1. From the consumer complaint data of each target sub-region, obtain information on the retailers, unusual retail types, complaint channels, and complainants for each complaint within a preset period. Aggregate the same complainant information to obtain the number of complainants. At the same time, count the retailers, unusual retail types, and complaint channels involved in each complaint by each complainant, and calculate the complaint richness feature value for each target sub-region. S2-2. Set a complaint characteristic value threshold for each target sub-area based on the tobacco safety level. Based on the tobacco safety level, complaint abundance characteristic value, and complaint characteristic value threshold, determine the law enforcement optimization direction for each target sub-area, including optimization for popularizing the law and optimizing routine inspections. Each target sub-area is then classified into Category I, Category II, and Category III optimization areas. S2-3. Confirm the scope of legal education in each first-class optimized area as the law enforcement plan for each first-class optimized area, set the order of routine inspections in each second-class optimized area as the law enforcement plan for each second-class optimized area, set the scope of legal education and the order of routine inspections in each third-class optimized area as the law enforcement plan for each third-class optimized area.

7. The data analysis and management method for tobacco monopoly law enforcement according to claim 6 is characterized in that: The specific process of S2-2 is as follows: 0.7 times the qualified safety level is the lower limit of the tobacco-related safety level. The tobacco-related safety level of each target sub-area is compared with the lower limit of the tobacco-related safety level. The complaint-rich feature value of each target sub-area is compared with the complaint feature value threshold. If the tobacco safety level of a target sub-area is greater than or equal to the lower limit of the tobacco safety level, and the complaint richness feature value of the target sub-area is less than the complaint feature value threshold, then the law enforcement optimization direction of the target sub-area is the optimization of popularizing law among the masses, and the target sub-area is classified as a first-class optimization area; If the smoking safety level of a target sub-area is lower than the lower limit of the smoking safety level, and the complaint richness feature value of the target sub-area is greater than or equal to the complaint feature value threshold, the law enforcement optimization direction of the target sub-area is conventional patrol optimization, and the target sub-area is classified as a second-class optimization area; If the tobacco-related safety level of a target sub-area is lower than the lower limit of the tobacco-related safety level, and the complaint-rich characteristic value of the target sub-area is lower than the complaint characteristic value threshold, then the law enforcement optimization direction of the target sub-area is the optimization of popular legal education for the masses and the optimization of routine inspections, and the target sub-area is classified into three types of optimization areas. In this way, the law enforcement optimization direction of each target sub-area is analyzed and classified.

8. The data analysis and management method for tobacco monopoly law enforcement according to claim 6 is characterized in that: The specific process of confirming the scope of legal education for each type of optimized area is as follows: Based on the historical law enforcement records in each first-class optimized area, the number of abnormal times and the number of abnormal retail types of each retailer in each first-class optimized area are counted; based on the historical retail data in each first-class optimized area, the best-selling cigarette types and slow-selling cigarette types of each retailer in each first-class optimized area are counted; Based on the consumer complaint data of each first-class optimized area, statistics are collected on the number of complaints received by each retailer in each first-class optimized area, the number of complainants, and the number of complaint types; The legal education needs of each retailer in each first-class optimization area are analyzed by using the number of abnormal sales, the number of abnormal retail types, the types of best-selling cigarettes, the types of slow-selling cigarettes, the number of complaints received, the number of complainants, and the number of complaint types of each retailer in each first-class optimization area. The cluster analysis algorithm is applied to the legal education demand value and location of each retailer in each first-class optimization area to obtain the cluster areas of each first-class optimization area as the legal education scope, and the legal education demand value of the center point of each legal education area is obtained. The legal education demand value is sorted in descending order, and the result is the legal education order of each legal education area.

9. The data analysis and management method for tobacco monopoly law enforcement according to claim 8 is characterized in that: The order of routine inspection of each type II optimized area is set as follows: The number of abnormalities, the number of abnormal retail types, the number of best-selling cigarette types, the number of slow-selling cigarette types, the number of complaints, the number of complainants and the number of complaint types of each retailer in each second-category optimization area are obtained, the abnormal severity value of each retailer in each second-category optimization area is analyzed, and the cluster analysis algorithm is used to obtain each cluster area as each inspection area, and the abnormal severity value of the center point and each retailer in each inspection area is obtained. The descending order of the abnormal severity value of the center point in each inspection area is the inspection order of each inspection area, and the descending order of the abnormal severity value of each retailer in each inspection area is the inspection order of each inspection area.

10. A data analysis and management platform for tobacco monopoly law enforcement that implements the data analysis and management method for tobacco monopoly law enforcement according to any one of claims 1 to 9, characterized in that: include: The tobacco safety analysis module is used to divide the area into grids, obtain sub-areas, assign enforcement teams to each sub-area, obtain historical enforcement records and historical retail data in each sub-area, and analyze the tobacco safety level of each sub-area; The enforcement plan setting module is used to record sub-areas with tobacco safety levels lower than the qualified safety level as target sub-areas, obtain the enforcement team and consumer complaint data of each target sub-area, analyze the enforcement optimization direction of each target sub-area, and set enforcement plans for each target sub-area; The law enforcement plan feedback module is used to provide corresponding feedback on the law enforcement plan of each target sub-area.

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