Cigarette data processing result display system

Abstract

The invention discloses a cigarette data processing result display system, which comprises a data layer used for collecting cigarette data according to a data collection standard and constructing a cigarette product authenticity identification database according to the cigarette data, and the cigarette data comprises case-related data of cigarette products, cigarette product information data and cigarette authenticity identification data; the processing layer is used for analyzing cigarette data of multiple dimensions in the cigarette product authenticity identification database according to at least one of a proportion analysis rule, a correlation analysis rule and a sorting analysis rule to obtain an analysis result, and the multiple dimensions comprise a logistics code, a bar-packed steel seal number, a box-packed steel seal number, a packaging machine type and a product specification; and the application layer is used for displaying the analysis result.

Description

Technical Field

[0001] The present application relates to the field of display technology, and in particular to a display system for cigarette data processing results. Background Art

[0002] Counterfeit and shoddy cigarette products sometimes appear in the cigarette market. In response to this situation, relevant agencies will conduct quality inspections on cigarette products sold by various merchants in the market and take corresponding measures to deal with counterfeit and shoddy products discovered during the inspection.

[0003] Currently, the management of these counterfeit and substandard cigarette products mainly relies on manual operations, which is inefficient. Summary of the invention

[0004] To this end, this application discloses the following technical solutions:

[0005] The present application provides a display system for cigarette data processing results, comprising:

[0006] The data layer is used to collect cigarette data according to the data collection standard, and to build a cigarette product authenticity identification database based on the cigarette data, wherein the cigarette data includes case-related data of cigarette products, cigarette product information data and cigarette authenticity identification data;

[0007] A processing layer, for analyzing the cigarette data of multiple dimensions in the cigarette product authenticity identification database according to at least one of a ratio analysis rule, a correlation analysis rule and a sorting analysis rule to obtain an analysis result, wherein the multiple dimensions include logistics code, strip steel stamp number, box steel stamp number, packaging machine model and product specification;

[0008] The application layer is used to display the analysis result.

[0009] Optionally, the processing layer is further used for:

[0010] According to the input target specification, the cigarette samples corresponding to the target specification are retrieved in the cigarette product authenticity identification database, and the case data of the cigarette product associated with the retrieved cigarette samples are displayed.

[0011] Optionally, when collecting cigarette data according to the data collection standard, the data layer is specifically used for:

[0012] Establishing communication connection with data source systems, the data source systems including cigarette production system, cigarette sales system, cigarette licensing system and cigarette logistics system;

[0013] According to the data collection standard, cigarette data is collected from the data source system.

[0014] Optionally, when the data layer constructs a cigarette product authenticity identification database according to the cigarette data, it is specifically used to:

[0015] Performing data validation on the cigarette data to determine outliers and missing values ​​in the cigarette data;

[0016] Correcting the abnormal values ​​and the missing values ​​in the cigarette data;

[0017] Deleting duplicate data, erroneous data and incomplete data in the cigarette data;

[0018] A cigarette product authenticity identification database is constructed based on the remaining cigarette data.

[0019] Optionally, when the processing layer analyzes the cigarette data according to the ratio analysis rule, it is specifically used to:

[0020] Determine the target time period;

[0021] Determine the true and false ratio of cigarettes in multiple regions within the target time period according to the cigarette data recorded in the cigarette product authenticity identification database, or determine the true and false ratio of cigarettes of multiple specifications within the target time period;

[0022] The target region as the analysis result is determined based on the genuine-fake ratio of cigarettes in multiple regions, or the target specification as the analysis result is determined based on the genuine-fake ratio of cigarettes in multiple specifications.

[0023] Optionally, when the processing layer analyzes cigarette data according to the correlation analysis rule, it is specifically used to:

[0024] Determine the target time period;

[0025] According to the cigarette data recorded in the cigarette product authenticity identification database, determine the cigarette products involved in the case with the same logistics code, strip steel stamp number, box steel stamp number, and packaging machine model within the target time period;

[0026] The case-related data associated with the determined cigarette products involved in the case are obtained as analysis results.

[0027] Optionally, when the processing layer analyzes the cigarette data according to the sorting analysis rule, it is specifically used to:

[0028] According to the cigarette data recorded in the cigarette product authenticity identification database, the cigarette samples submitted for inspection in different regions are summarized to obtain a cigarette data set corresponding to each region;

[0029] For each of the regions, the number of samples submitted for inspection by each inspection agency in the region is counted according to the cigarette data set corresponding to the region;

[0030] The plurality of inspection institutions are sorted according to the number of samples submitted for inspection, so as to determine the target area according to the sorting results.

[0031] Optionally, the application layer is also used for at least one of the following:

[0032] Display cigarette identification data dashboard;

[0033] Display real-time warning information of cigarette data;

[0034] The fake query application interface is displayed, and the query results are displayed on the fake query application interface.

[0035] Optionally, the application layer is further used for:

[0036] The analysis result is shared with at least one shared application through an application integration interface.

[0037] The beneficial effect of this solution is that: the processing layer automatically analyzes cigarette data in multiple dimensions according to proportion analysis rules, correlation analysis rules and sorting analysis rules, and displays the analysis results at the application layer, providing an automated auxiliary means for the management of cigarette products. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0039] Figure 1 is a flow chart of a method for displaying cigarette data processing results provided in an embodiment of the present application;

[0040] Figure 2 This is a schematic diagram of a cigarette identification data display board provided by an embodiment of the present application;

[0041] Figure 3 It is a schematic diagram of another cigarette identification data display board provided in an embodiment of the present application;

[0042] Figure 4 This is a schematic diagram of another cigarette identification data display board provided in an embodiment of the present application;

[0043] Figure 5 This is a schematic diagram of an interface for displaying real-time warning information of cigarette data provided by an embodiment of the present application;

[0044] Figure 6 It is a schematic diagram of a fake query application interface provided by an embodiment of the present application;

[0045] Figure 7 is a schematic diagram of another fake query application interface provided in an embodiment of the present application;

[0046] Figure 8 This is a schematic diagram of another fake query application interface provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0048] This application provides a display system for cigarette data processing results, see Figure 1 , which is a schematic diagram of the system. The system may include a data layer, a processing layer and an application layer.

[0049] The data layer is used to collect cigarette data according to the data collection standard, and to build a cigarette product authenticity identification database based on the cigarette data. The cigarette data includes the case data of cigarette products, cigarette product information data and cigarette authenticity identification data.

[0050] The processing layer is used to analyze the cigarette data of multiple dimensions in the cigarette product authenticity identification database according to at least one of the ratio analysis rule, the correlation analysis rule and the sorting analysis rule to obtain the analysis result, wherein the multiple dimensions include the logistics code, the strip steel stamp number, the box steel stamp number, the packaging machine model and the product specification;

[0051] Application layer, used to display analysis results.

[0052] When the data layer collects cigarette data, it can first establish a communication connection with the data source system. The data source system includes the cigarette production system, cigarette sales system, cigarette licensing system and cigarette logistics system. After establishing the communication connection, the data layer can collect cigarette data from the data source system in real time according to the data collection standards at a certain period.

[0053] The cigarette production system may include the cigarette production quality control of existing industrial enterprises.

[0054] The cigarette licensing system may include an administrative licensing system for authorizing corresponding merchants to sell cigarette products.

[0055] Optionally, after establishing a communication connection with the data source system, the data layer can first identify which of the data provided by these data source systems is useful for identifying the authenticity of cigarettes and which is useless for identifying the authenticity of cigarettes, and collect the former as cigarette data.

[0056] Establishing a communication connection with the above data source system helps to build a stable and reliable data collection channel for collecting cigarette data.

[0057] The data layer can obtain cigarette data from the above-mentioned data source system through a variety of existing data collection technologies. For example, it can directly query the database, or collect cigarette data from the data source system through API interface calls, thereby realizing seamless integration and deep fusion of cross-platform and multi-source data.

[0058] In addition, the data layer can also collect cigarette data from the above data source system through Extract-Transform-Load (ETL) technology.

[0059] The above data collection standards can be determined based on key indicators of cigarette production, circulation, supervision, identification, etc.

[0060] In this embodiment, the data collection standard may include the following information.

[0061] On the one hand, the data collection standard may include output requirement definition information, which defines the data collection indicators corresponding to each link of cigarette production, circulation, supervision, identification, etc., and defines the classification, value type, level, etc. for each indicator. Based on the requirement definition information, the data layer can determine the data of the indicators defined in the requirement definition information as cigarette data useful for cigarette authenticity identification, and collect these data. At the same time, the collected raw data can be pre-processed according to the classification, value type, level, etc. corresponding to each indicator, so that the collected data conforms to the defined classification, value type, and level.

[0062] On the other hand, the data collection standard may include data source indication information, which can be used to specify the system that serves as the data source, such as specifying a specific cigarette production system, cigarette sales system, cigarette licensing system, and cigarette logistics system as the data source, and the data source indication information may include an access link to the system that serves as the data source, so that the data layer can access the corresponding system according to the access link and establish a communication connection with the corresponding system.

[0063] On the other hand, the data collection standards may include data security information. The data security information may indicate the data security measures that should be taken for the collected cigarette data, including but not limited to specific encryption measures, access control measures and data monitoring measures, etc. The data security information can ensure the security and privacy of the data and prevent the leakage, tampering or destruction of the data.

[0064] Optionally, when the data layer constructs a cigarette product authenticity identification database based on cigarette data, the database may be constructed as follows:

[0065] Data validation was performed on cigarette data to identify outliers and missing values ​​in cigarette data;

[0066] Correct outliers and missing values ​​in cigarette data;

[0067] Remove duplicate, erroneous and incomplete data from cigarette data;

[0068] Construct a cigarette product authenticity identification database based on the remaining cigarette data.

[0069] Outliers can be defined as values ​​in cigarette data that are outside the range of values.

[0070] The way to correct outliers may be to replace outliers with values ​​of other adjacent data items of the same type. The way to correct missing values ​​may be to fill missing values ​​with values ​​of other adjacent data items of the same type.

[0071] Optionally, the data layer can also check whether the syntax, semantics and format of the collected cigarette data are correct, and treat incorrect data as outliers and make corrections.

[0072] Duplicate data refers to two pieces of cigarette data that have exactly the same content. In this case, one piece of cigarette data can be deleted.

[0073] Incomplete data refers to data that is missing multiple indicators, such as cigarette data that is missing product specifications, carton stamp numbers, and box stamp numbers.

[0074] Error data refers to data in which the values ​​of multiple indicators exceed the appropriate range of values ​​at the same time.

[0075] The data layer can directly delete the identified incomplete data and erroneous data, and build a cigarette product authenticity identification database based on the remaining cigarette data that has not been deleted.

[0076] The method of constructing a database can be found in the relevant prior art and will not be described in detail.

[0077] Optionally, the constructed cigarette product authenticity identification database may include three sub-databases: a cigarette product case-related database, a cigarette product information database, and a cigarette authenticity identification database.

[0078] Optionally, the processing layer analyzes the cigarette data of multiple dimensions in the cigarette product authenticity identification database according to at least one of the ratio analysis rule, the correlation analysis rule and the sorting analysis rule, and before obtaining the analysis result, the cigarette data in the database may be firstly processed by data understanding in the following manner. Then the corresponding analysis rule is applied for analysis.

[0079] Processing method 1: classify multiple cigarette data in the database according to one or more dimensions to determine multiple cigarette data sets with the same characteristics in one or more dimensions. For example, you can classify by dimensions such as cigarette specifications, logistics code, strip steel stamp, box steel stamp, packaging machine model, etc., to obtain several cigarette data sets with the same specifications and several cigarette data sets with the same logistics code.

[0080] Exemplarily, the processing layer may divide a large amount of cigarette data in the database into multiple cigarette data sets according to the cigarette product specifications, and each cigarette data set may include multiple cigarette data corresponding to the same product specification.

[0081] Processing method 2: the processing layer can sort the multiple cigarette data according to the corresponding identification and inspection time to form time series data, and the time series data includes the multiple cigarette data sorted according to the identification and inspection time.

[0082] Processing method three: the processing layer can collect statistics on a large amount of cigarette data in the database to determine the key indicators corresponding to these cigarette data. In this embodiment, the determined key indicators may include but are not limited to cigarette specifications, case numbers, regions involved, and base numbers involved.

[0083] The key indicator may be determined by displaying each indicator of the cigarette data on the screen and determining the selected indicator as the key indicator according to the user's selection operation.

[0084] Processing method four: after determining the key indicators, the processing layer can divide the key indicators into dimensions based on these cigarette data to determine the strength of the influence relationship between the key indicators and the dimensions.

[0085] For example, the processing layer can divide the indicator of cigarette specifications according to the dimension of logistics code. The division method is to first determine the multiple cigarette data corresponding to each cigarette specification, and then, for each cigarette specification, divide the multiple cigarette data corresponding to the cigarette specification again according to the corresponding logistics code, thereby obtaining multiple data sets as shown below:

[0086] A product specification, a data set consisting of multiple cigarette data corresponding to logistics code 1; A product specification, a data set consisting of multiple cigarette data corresponding to logistics code 2; A product specification, a data set consisting of multiple cigarette data corresponding to logistics code 3, and so on.

[0087] After this division, the strength of the influence relationship between key indicators and dimensions can be analyzed based on the division results:

[0088] For any key indicator, if most of the cigarette data with the same value on the key indicator also have the same value on a certain dimension, it is considered that there is a strong correlation between the key indicator and the dimension. If most of the cigarette data with the same value on the key indicator have relatively scattered values ​​on a certain dimension, it is considered that there is a weak correlation between the key indicator and the dimension.

[0089] For example, for the key indicator product specification and logistics code dimensions, if most of the cigarette data belonging to product specification A have logistics code 1, and most of the cigarette data belonging to product specification B have logistics code 2, it can be considered that the product specification and the logistics code have a strong correlation.

[0090] Optionally, the processing layer may also simultaneously apply any one or more of the above processing methods 1 to 4 to process the cigarette data. For example, the cigarette data may be sorted according to processing method 1 first, and then the sorted time series data may be classified according to one or more dimensions.

[0091] The specific contents of the proportion analysis rules, correlation analysis rules and sorting analysis rules can be designed based on cigarette identification data indicators and specific business scenarios.

[0092] As an example, the goal of the proportion analysis rule can be to determine the true-fake ratio of cigarettes sent for inspection, and to assess the prevalence of counterfeit cigarettes in the current market through percentage, year-on-year comparison, etc. The content of the proportion analysis rule can be to compare the true-fake ratio of cigarettes in a certain time period, different regions, and different brands, and identify the regions or brands that require focus.

[0093] Optionally, when the processing layer analyzes cigarette data according to the ratio analysis rule, the analysis can be performed as follows:

[0094] A1, determine the target time period;

[0095] A2, determining the ratio of genuine and fake cigarettes in multiple regions within a target time period based on cigarette data recorded in the cigarette product authenticity identification database, or determining the ratio of genuine and fake cigarettes of multiple specifications within a target time period;

[0096] A3, determining a target region as the analysis result based on the genuine / fake ratio of cigarettes in multiple regions, or determining a target specification as the analysis result based on the genuine / fake ratio of cigarettes in multiple specifications.

[0097] In step A1, the processing layer may determine the time period specified by the user as the target time period, or may determine the target time period in combination with the time series data obtained by the second processing method.

[0098] Specifically, the processing layer can determine, based on the time series data obtained in the second processing method, which time period has the largest number of cigarettes submitted for inspection, and determine this time period as the target time period.

[0099] In step A2, the processing layer can classify the cigarettes submitted for inspection within the target time period according to the inspection region based on the cigarette data, for example, into a data set of cigarettes submitted for inspection in city A and a data set of cigarettes submitted for inspection in city B. Then, for each data set, based on the authenticity identification results recorded in the cigarette data, determine how many compliant cigarette products (i.e., real cigarettes) and how many counterfeit, fake and inferior cigarette products (fake cigarettes) are in the data set, and determine the ratio of the two quantities as the authenticity ratio of cigarettes in the region corresponding to the data set.

[0100] When determining the true-fake ratio of cigarettes of each specification, the processing layer can use the classification results obtained by the aforementioned processing method 1 to obtain the cigarette data set corresponding to each specification, and then determine the cigarette data corresponding to the samples sent for inspection within the target time period in the cigarette data set corresponding to each specification, and count the number of true and false cigarettes in these data, thereby obtaining the true-fake ratio of cigarettes of the specification within the target time period.

[0101] In step A3, the processing layer may determine the region with the highest proportion of fake cigarettes in the genuine-fake ratio as the target region, or determine the cigarette specification with the highest proportion of fake cigarettes in the genuine-fake ratio as the target specification.

[0102] Optionally, after determining the target area, the processing layer can count indicators such as the number of genuine cigarettes, the number of counterfeit cigarettes, the number of samples submitted for inspection, the number of high-imitation machine-packaged counterfeit cigarettes, etc. in the samples submitted for inspection in the target area within the target time period, and determine these indicators and the target area as the analysis results.

[0103] After determining the target product specifications, the processing layer can count indicators such as the number of genuine cigarettes, the number of counterfeit cigarettes, the number of samples submitted for inspection, and the number of high-imitation counterfeit cigarettes in machine-packaged products among the samples of the target product specifications submitted within the target time period, and determine these indicators and the target product specifications as the analysis results.

[0104] The goal of the correlation analysis rules can be to match the related case information with the same characteristics (region, number, and seizure link) by analyzing the cigarettes involved in the case and their related data, so as to track the possible source or distribution network. The content of the correlation analysis rules can include matching the counterfeit cigarettes involved in the case with the same characteristics such as the box stamp, logistics code, and packaging machine model within a certain period of time, associating the counterfeit cigarettes' data, and matching a series of related cases.

[0105] Optionally, when the processing layer analyzes cigarette data according to the correlation analysis rule, it is specifically used to:

[0106] B1, determine the target time period;

[0107] B2, based on the cigarette data recorded in the cigarette product authenticity identification database, determine the cigarette products involved in the case with the same logistics code, strip steel stamp number, box steel stamp number, and packaging machine model within the target time period;

[0108] B3, obtaining the case-related data associated with the determined cigarette products involved in the case as the analysis result.

[0109] The method of determining the target time period in B1 can refer to step A1 and will not be described in detail.

[0110] In step B2, the processing layer can, as needed, filter out all the cigarette products involved in the case that are sent for inspection within the target time period and have the same logistics code recorded in the cigarette data, and obtain a data set consisting of the cigarette data corresponding to these products; or, it can filter out all the cigarette products involved in the case that are sent for inspection within the target time period and have the same packing stamp number recorded in the cigarette data, and obtain a data set consisting of the cigarette data corresponding to these products; or, it can filter out all the cigarette products involved in the case that are sent for inspection within the target time period and have the same values ​​in other dimensions recorded in the cigarette data, and obtain a data set consisting of the cigarette data corresponding to these products.

[0111] Cigarette products involved in the case refer to the cigarette products that are related to the cases handled by the relevant agencies. Whether the cigarette products are involved in the case can be determined based on the corresponding cigarette data. If the cigarette data corresponding to a certain cigarette product contains the case number, then the cigarette product is a cigarette product involved in the case. The case number indicates the number of the case related to the cigarette product.

[0112] In step B3, for the data set filtered out in step B2, each case number appearing in the data set can be counted, and the cases related to the cigarette products involved in the data set recorded in the data set can be determined based on the case number, and then the case data corresponding to the case can be obtained from the relevant case database, and these case data can be determined as analysis results.

[0113] Exemplarily, the processing layer can collect data on cigarette products involved in the case that are sent for inspection within the target time period and have logistics code 1 to obtain a data set, and then obtain the data involved in the case associated with the cigarette products involved in the case that are sent for inspection within the target time period and have logistics code 1 as analysis results in the above manner.

[0114] The data involved in the case may include the handling unit of the corresponding case, the handling time, the quantity of cigarette products involved, the amount, etc.

[0115] Optionally, the processing layer can also generate target case fake cigarette features and target case fake cigarette warnings based on the obtained case data. The target case can be any case specified by the relevant user.

[0116] Specifically, the processing layer can identify whether there is a target case in the case-related data. If so, it can extract the case-related data corresponding to the target case, find the related cigarette products involved in the case based on the case-related data corresponding to the target case, extract the cigarette data of these cigarette products involved in the case from the database, and use the extracted cigarette data as the counterfeit cigarette features of the target case.

[0117] The processing layer can also analyze whether each case involves the same cigarette products as the target case based on the case data involved. If so, a counterfeit cigarette warning for the target case is output. For example, if a case X and the target case both involve cigarette products of specification A, if the processing layer finds in step B3 that the obtained case data includes the data of case X, a counterfeit cigarette warning for the target case is output.

[0118] The goal of the sorting analysis rules is to analyze which areas need to be focused on by comparing the data of different regions in terms of the time of submission, number of samples submitted, and units submitted for inspection. The content of the sorting analysis rules can include summarizing the submitted cigarettes by region and comparing the differences in the time of submission, number of samples submitted, and units submitted for inspection in different regions. Find out those units with a large number of samples submitted for inspection and sort them by the region involved. Those ranked higher may be more likely to become the focus of counterfeit cigarettes.

[0119] Optionally, when the processing layer analyzes cigarette data according to the sorting analysis rules, it can be used to:

[0120] C1, based on the cigarette data recorded in the cigarette product authenticity identification database, the cigarette samples sent for inspection in different regions are summarized to obtain the cigarette data set corresponding to each region;

[0121] C2, for each region, count the number of samples sent for inspection by each inspection agency in the region based on the cigarette data set corresponding to the region;

[0122] C3, sort multiple inspection agencies according to the number of samples submitted for inspection, and determine the target area based on the sorting results.

[0123] In step C1, the processing layer may aggregate the cigarette data corresponding to the cigarette samples sent for inspection in each region to form a cigarette data set corresponding to the region.

[0124] In step C2, for each region, the processing layer classifies and counts the cigarette data contained in the data set of the region according to the corresponding inspection agencies, so as to obtain the number of inspection samples of each inspection agency in the region.

[0125] In step C3, the processing layer may sort all the inspection agencies in all regions from most to least according to the corresponding number of samples submitted for inspection, and then determine the regions to which the top N inspection agencies belong as target regions, where N may be set as required, for example, to 5.

[0126] Optionally, the processing layer can also count the number of samples sent for inspection that are counterfeit cigarettes corresponding to each specification based on the cigarette data, count the top N specifications with the largest number, and output these N specifications as analysis results.

[0127] Alternatively, the processing layer can also count the number of samples sent for inspection that belong to counterfeit cigarettes corresponding to each carton stamp number based on the cigarette data, count the top N carton stamp numbers with the largest number, and output these N carton stamp numbers as analysis results.

[0128] Alternatively, the processing layer can also count the number of samples sent for inspection that belong to counterfeit cigarettes corresponding to each box stamp number based on the cigarette data, count the top N box stamp numbers with the largest number, and output these N box stamp numbers as analysis results.

[0129] Alternatively, the processing layer may also count the top N logistics codes with the highest frequency of occurrence according to the frequency of occurrence of each logistics code in the cigarette data, and output these N logistics codes as analysis results.

[0130] Optionally, the processing layer is also used to:

[0131] According to the input target specifications, the cigarette samples submitted for inspection corresponding to the target specifications are retrieved in the cigarette product authenticity identification database, and the case data of the cigarette products associated with the retrieved cigarette samples submitted for inspection are displayed.

[0132] In some optional embodiments, the processing layer can also determine the cigarette data corresponding to the counterfeit cigarettes in the database, and then perform statistical processing on these cigarette data from multiple dimensions such as product specifications, logistics codes, strip steel stamp numbers, box steel stamp numbers and packaging machine models to obtain corresponding statistical results. When the user inputs a data analysis instruction, the processing layer can respond to the data analysis instruction and output the target statistical results that match the data analysis instruction, as well as the cigarette data associated with the target statistical results to the application layer, so that the application layer can display the target statistical results and the associated cigarette data.

[0133] Optionally, in addition to the aforementioned analysis methods, the processing layer may also analyze the cigarette data in the database according to analysis rules corresponding to other multiple indicators to obtain corresponding analysis results.

[0134] As an example, the processing layer may analyze the cigarette data according to the analysis rule corresponding to any indicator shown in Table 1 below to obtain the corresponding analysis result.

[0135] Table 1

[0136]

[0137]

[0138]

[0139]

[0140]

[0141] Optionally, the application layer is also used for at least one of the following:

[0142] Display cigarette identification data dashboard;

[0143] Display real-time warning information of cigarette data;

[0144] Display the fake query application interface and display the query results on the fake query application interface.

[0145] The cigarette identification data dashboard is used to display the authenticity of cigarette samples sent for inspection within a period of time. As an example, the application layer can display the following: Figure 2 , Figure 3 and Figure 4 A cigarette identification data dashboard as shown in any one of the above.

[0146] The application layer can be Figure 5 The display interface shown displays real-time data warning information.

[0147] The application layer can be displayed as Figure 6 , Figure 7 and Figure 8 Any of the fake query application interfaces shown in the Figure 6 (1), Figure 7 (1) and Figure 8 (1) are all interfaces for inputting search keywords (such as stamp number, QR code, product specification) in counterfeit search applications. Figure 6 (2), Figure 7 (2) and (3), Figure 8 (2) and (3) are the display interfaces of the query results.

[0148] Optionally, the application layer may display one or more obtained analysis results on a cigarette identification data dashboard, or a display interface for real-time warning information of cigarette data;

[0149] Optionally, the application layer may share the analysis result with at least one shared application through an application integration interface.

[0150] The beneficial effect of this solution is that it can automatically analyze cigarette data in multiple dimensions according to the proportion analysis rules, correlation analysis rules and sorting analysis rules through the processing layer, and display the analysis results at the application layer, thus providing an automated auxiliary means for the management of cigarette products.

[0151] Furthermore, on the first aspect, this solution realizes the comprehensive convergence of information required for cigarette identification by connecting multiple data source systems. In this process, the adopted data collection standards and advanced technologies ensure the stability and reliability of the data, build a stable and reliable data collection channel, and realize the seamless integration and deep fusion of cross-platform and multi-source data, laying a solid foundation for subsequent analysis.

[0152] Secondly, this solution ensures the accuracy and completeness of the data through a strict verification and cleaning process in the data processing link. The processing of missing values ​​and outliers, as well as the deep cleaning of duplicate, erroneous and incomplete records, effectively improve the data quality. The constructed cigarette product database, market supervision case database and cigarette identification database provide strong support for the analysis work and ensure the accuracy and reliability of the analysis results.

[0153] Thirdly, this solution deeply analyzes the time series data of the entire chain related to cigarette production and sales, determines key business indicators through classification and quantitative processing, and conducts multi-dimensional division and relationship analysis. Using analysis methods such as proportion analysis, correlation analysis, and sorting analysis, it conducts precise analysis of different scenarios (such as cigarette identification, steel stamp characteristics, logistics code characteristics, machine-packaged counterfeit cigarettes, and serious counterfeit cigarette characteristics), providing a scientific basis and strong support for cigarette identification.

[0154] Fourthly, in the data application link, this solution presents the analysis results in a visual form through intuitive display methods such as data large screens and data reports, which is convenient for users to quickly understand and make decisions. At the same time, according to different business needs, personalized data service products are output, such as cigarette identification analysis reports, special analysis reports on major cases, special sample early warning monitoring reports, etc., to meet the needs of different user groups. In addition, the shared integration of analysis data and identification data further enhances the application value of data and promotes the coordinated development of industrial and commercial related applications.

[0155] It should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0156] For the convenience of description, the above system or device is described by dividing it into various modules or units according to its functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0157] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the relevant technology in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present application or certain parts of the embodiments.

[0158] Finally, it should be noted that, in this article, relational terms such as first, second, third and fourth are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0159] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A display system for cigarette data processing results, characterized in that: include: The data layer is used to collect cigarette data according to the data collection standard, and to build a cigarette product authenticity identification database based on the cigarette data, wherein the cigarette data includes case-related data of cigarette products, cigarette product information data and cigarette authenticity identification data; A processing layer, for analyzing the cigarette data of multiple dimensions in the cigarette product authenticity identification database according to at least one of a ratio analysis rule, a correlation analysis rule and a sorting analysis rule to obtain an analysis result, wherein the multiple dimensions include logistics code, strip steel stamp number, box steel stamp number, packaging machine model and product specification; The application layer is used to display the analysis result.

2. The system according to claim 1, characterized in that The processing layer is also used to: According to the input target specification, the cigarette samples corresponding to the target specification are retrieved in the cigarette product authenticity identification database, and the case data of the cigarette product associated with the retrieved cigarette samples are displayed.

3. The system according to claim 1, characterized in that The data layer is specifically used to collect cigarette data according to the data collection standard: Establishing communication connection with data source systems, the data source systems including cigarette production system, cigarette sales system, cigarette licensing system and cigarette logistics system; According to the data collection standard, cigarette data is collected from the data source system.

4. The system according to claim 1, characterized in that When the data layer constructs a database for identifying the authenticity of cigarette products according to the cigarette data, it is specifically used to: Performing data validation on the cigarette data to determine outliers and missing values ​​in the cigarette data; Correcting the abnormal values ​​and the missing values ​​in the cigarette data; Deleting duplicate data, erroneous data and incomplete data in the cigarette data; A cigarette product authenticity identification database is constructed based on the remaining cigarette data.

5. The system according to claim 1, characterized in that When the processing layer analyzes cigarette data according to the ratio analysis rule, it is specifically used to: Determine the target time period; Determine the true and false ratio of cigarettes in multiple regions within the target time period according to the cigarette data recorded in the cigarette product authenticity identification database, or determine the true and false ratio of cigarettes of multiple specifications within the target time period; The target region as the analysis result is determined based on the genuine / fake ratio of cigarettes in multiple regions, or the target specification as the analysis result is determined based on the genuine / fake ratio of cigarettes in multiple specifications.

6. The system according to claim 1, characterized in that When the processing layer analyzes cigarette data according to the correlation analysis rules, it is specifically used to: Determine the target time period; According to the cigarette data recorded in the cigarette product authenticity identification database, determine the cigarette products involved in the case with the same logistics code, strip steel stamp number, box steel stamp number, and packaging machine model within the target time period; The case-related data associated with the determined cigarette products involved in the case are obtained as analysis results.

7. The system according to claim 1, characterized in that When the processing layer analyzes cigarette data according to the sorting analysis rules, it is specifically used to: According to the cigarette data recorded in the cigarette product authenticity identification database, the cigarette samples submitted for inspection in different regions are summarized to obtain a cigarette data set corresponding to each region; For each of the regions, the number of samples submitted for inspection by each inspection agency in the region is counted according to the cigarette data set corresponding to the region; The plurality of inspection institutions are sorted according to the number of samples submitted for inspection, so as to determine the target area according to the sorting results.

8. The system according to claim 1, characterized in that The application layer is also used for at least one of the following: Display cigarette identification data dashboard; Display real-time warning information of cigarette data; The fake query application interface is displayed, and the query results are displayed on the fake query application interface.

9. The system according to claim 1, characterized in that The application layer is also used for: The analysis result is shared with at least one shared application through an application integration interface.

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