Cigarette characteristic data processing method and system

Abstract

The invention discloses a cigarette characteristic data processing method and system, and the method comprises the steps: determining a sample cigarette meeting a target screening condition in a cigarette identification database, and obtaining the cigarette characteristic data related to the sample cigarette meeting the target screening condition in the cigarette identification database, the cigarette characteristic data comprises quality specifications, packaging machine types, packaging steel seal numbers, logistics codes and case numbers; performing data cleaning on the cigarette feature data to obtain cleaned cigarette feature data; statistical processing is conducted on the characteristic data of the cleaned cigarettes from multiple statistical dimensions, statistical results of the characteristic data of the cleaned cigarettes in the multiple statistical dimensions are obtained, and the multiple statistical dimensions comprise the month, the case number, the product specification, the logistics code, the packaging steel seal number and the packaging machine type; and in response to the obtained data analysis instruction, outputting a target statistical result matched with the data analysis instruction and the cleaned cigarette feature data associated with the target statistical result.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a method and system for processing cigarette characteristic data. 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] In order to detect and deal with possible counterfeit and shoddy cigarette products as early as possible, relevant agencies sometimes need to analyze the data of counterfeit and shoddy products discovered in previous inspections to discover the characteristics of these counterfeit and shoddy products, such as which specifications (breeds and specifications) have more counterfeit and shoddy products, which regions have more counterfeit and shoddy products, which logistics channels have more counterfeit and shoddy products, etc., and take countermeasures in advance based on the analysis results.

[0004] At present, the processing method of counterfeit and shoddy product data is relatively simple. It can only query and display the original data of counterfeit and shoddy products in the database that meet the query conditions specified by the user, such as querying the original data of products with specified specifications, and querying the original data of products inspected within a specified time period. Subsequent data analysis work depends on manual operation, and there is a large amount of original data of counterfeit and shoddy products stored in the database. Manual analysis is prone to errors and has low efficiency. Summary of the invention

[0005] To this end, this application discloses the following technical solutions:

[0006] The first aspect of the present application provides a method for processing cigarette characteristic data, comprising:

[0007] Determine sample cigarettes that meet the target screening conditions in the cigarette identification database, and obtain cigarette feature data associated with the sample cigarettes that meet the target screening conditions in the cigarette identification database, wherein the cigarette feature data includes product specifications, packaging machine type, packaging steel stamp number, logistics code and case number;

[0008] Performing data cleaning on the cigarette characteristic data to obtain cleaned cigarette characteristic data;

[0009] Performing statistical processing on the characteristic data of the cleaned cigarettes from multiple statistical dimensions respectively to obtain statistical results of the characteristic data of the cleaned cigarettes in the multiple statistical dimensions, wherein the multiple statistical dimensions include month, case number, product specification, logistics code, packaging steel stamp number and packaging machine model;

[0010] In response to obtaining the data analysis instruction, the target statistical result matching the data analysis instruction and the post-cleaning cigarette characteristic data associated with the target statistical result are output.

[0011] Optionally, the data analysis instructions include key product specification analysis instructions;

[0012] The output of the target statistical result matching the data analysis instruction, and the post-cleaning cigarette characteristic data associated with the target statistical result, includes:

[0013] Determine key product specifications based on the statistical results of product specification dimensions;

[0014] Output the key specifications, the statistical results corresponding to the key specifications, and the growth rate of the key specifications.

[0015] Optionally, the data analysis instruction includes a logistics code analysis instruction;

[0016] The output of the target statistical result matching the data analysis instruction, and the post-cleaning cigarette characteristic data associated with the target statistical result, includes:

[0017] Determine the target logistics code according to the frequency of occurrence of each logistics code;

[0018] Output the target logistics code, the statistical results of the target logistics code, and the product specifications associated with the target logistics code;

[0019] Obtaining case data associated with the target logistics code from a case database according to the case number associated with the target logistics code;

[0020] Output the case data associated with the target logistics code.

[0021] Optionally, the data analysis instruction includes a case association analysis instruction;

[0022] The output of the target statistical result matching the data analysis instruction, and the post-cleaning cigarette characteristic data associated with the target statistical result, includes:

[0023] Determine the target case number corresponding to the case association analysis instruction;

[0024] Searching for an associated case number associated with the target case number, where both the target case number and the associated case number are associated with the first product specification;

[0025] Outputting the statistical results of the target case number under the first product specification and the statistical results of the associated case number under the first product specification;

[0026] Analyze the cleaned cigarette characteristic data of the sample cigarettes of the first specification corresponding to the target case number and the cleaned cigarette characteristic data of the sample cigarettes of the first specification corresponding to the associated case number to obtain associated characteristic information of the target case number and the associated case number;

[0027] The associated feature information is output.

[0028] Optionally, also include:

[0029] In response to a cigarette feature query request from a terminal device, post-cleaning cigarette feature data matching the cigarette feature query request is sent to the terminal device.

[0030] Optionally, also include:

[0031] Detecting new sample cigarettes entered into the cigarette identification database;

[0032] When the input cigarette characteristic data of the new sample cigarette is different from the original cigarette characteristic data in the cigarette identification database, a prompt message is output.

[0033] A second aspect of the present application provides a cigarette feature data processing system, comprising:

[0034] A determination unit, used to determine sample cigarettes that meet the target screening conditions in the cigarette identification database, and obtain cigarette feature data associated with the sample cigarettes that meet the target screening conditions in the cigarette identification database, wherein the cigarette feature data includes product specifications, packaging machine model, packaging steel stamp number, logistics code and case number;

[0035] A cleaning unit, used for cleaning the cigarette characteristic data to obtain cleaned cigarette characteristic data;

[0036] A statistical unit, used to perform statistical processing on the characteristic data of the cleaned cigarettes from multiple statistical dimensions respectively, to obtain statistical results of the characteristic data of the cleaned cigarettes in the multiple statistical dimensions, wherein the multiple statistical dimensions include month, case number, product specification, logistics code, packaging steel stamp number and packaging machine model;

[0037] The output unit is used for outputting the target statistical result matching the data analysis instruction and the post-cleaning cigarette characteristic data associated with the target statistical result in response to obtaining the data analysis instruction.

[0038] Optionally, the data analysis instructions include key product specification analysis instructions;

[0039] When the output unit outputs the target statistical result matching the data analysis instruction and the characteristic data of the cleaned cigarettes associated with the target statistical result, it is specifically used to:

[0040] Determine key product specifications based on the statistical results of product specification dimensions;

[0041] Output the key specifications, the statistical results corresponding to the key specifications, and the growth rate of the key specifications.

[0042] Optionally, the data analysis instruction includes a logistics code analysis instruction;

[0043] When the output unit outputs the target statistical result matching the data analysis instruction and the characteristic data of the cleaned cigarettes associated with the target statistical result, it is specifically used to:

[0044] Determine the target logistics code according to the frequency of occurrence of each logistics code;

[0045] Output the target logistics code, the statistical results of the target logistics code, and the product specifications associated with the target logistics code;

[0046] Obtaining case data associated with the target logistics code from a case database according to the case number associated with the target logistics code;

[0047] Output the case data associated with the target logistics code.

[0048] Optionally, the data analysis instruction includes a case association analysis instruction;

[0049] When the output unit outputs the target statistical result matching the data analysis instruction and the characteristic data of the cleaned cigarettes associated with the target statistical result, it is specifically used to:

[0050] Determine the target case number corresponding to the case association analysis instruction;

[0051] Searching for an associated case number associated with the target case number, where both the target case number and the associated case number are associated with the first product specification;

[0052] Outputting the statistical results of the target case number under the first product specification and the statistical results of the associated case number under the first product specification;

[0053] Analyze the cleaned cigarette characteristic data of the sample cigarettes of the first specification corresponding to the target case number and the cleaned cigarette characteristic data of the sample cigarettes of the first specification corresponding to the associated case number to obtain associated characteristic information of the target case number and the associated case number;

[0054] The associated feature information is output.

[0055] The beneficial effect of this solution is that it pre-processes the cigarette characteristic data entered into the cigarette characteristic database according to multiple specified statistical dimensions. When the user needs to analyze these cigarette characteristic data, this solution can promptly output the target statistical results that match the user's data analysis instructions, so that the user can perform subsequent analysis work based on the statistical results without directly analyzing a large amount of raw data, thereby achieving the effect of improving the data analysis efficiency of cigarette products. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] 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.

[0057] Figure 1 is a flow chart of a method for processing cigarette characteristic data provided by an embodiment of the present application;

[0058] Figure 2 It is a structural schematic diagram of a cigarette characteristic data processing system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0059] 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.

[0060] This application embodiment provides a method for processing cigarette feature data, see Figure 1 , the method may include the following steps.

[0061] S101, determine sample cigarettes that meet the target screening conditions in the cigarette identification database, and obtain cigarette feature data associated with the sample cigarettes that meet the target screening conditions in the cigarette identification database, the cigarette feature data including product specifications, packaging machine model, packaging steel stamp number, logistics code and case number.

[0062] The execution subject of this embodiment may be a database system for storing cigarette characteristic data of sample cigarettes, for example, a cigarette identification database system (hereinafter referred to as database system).

[0063] The system and its corresponding database can be deployed on multiple servers that are interconnected based on a distributed architecture to improve the system's storage capacity and processing efficiency.

[0064] Sample cigarettes may be cigarette products collected from various merchants selling cigarette products on the market for the purpose of quality inspection. Each cigarette used for quality inspection may be regarded as a sample cigarette.

[0065] After each sample cigarette has been inspected for quality, its corresponding inspection results and cigarette characteristic data will be entered into the cigarette identification database (hereinafter referred to as the database). Thus, each sample cigarette in the database can correspond to a sample record, which includes the sample number of the sample cigarette, such as sample No. 0001, sample No. 0002, etc., the inspection result of the sample cigarette, and the cigarette characteristic data of the sample cigarette.

[0066] Among them, the inspection result of a sample cigarette can be "quality compliance" or "counterfeit registered trademark and inferior cigarette".

[0067] The cigarette characteristic data of a sample cigarette may include, but is not limited to, product specifications, packaging model, packaging stamp number, logistics code and case number.

[0068] The product specification represents the brand and specification of the sample cigarette. For example, the product specification corresponding to a sample cigarette may be Brand A Specification A.

[0069] The packaging machine model refers to the type of packaging machine used to package the sample cigarette into the cigarette box. For example, the packaging machine model corresponding to a sample cigarette may be an X1 packaging machine, indicating that the sample cigarette is packaged into the cigarette box by an X1 packaging machine.

[0070] The packaging stamp number may include the strip stamp number and the box stamp number. The box stamp number refers to the stamp number printed on the independent packaging box that packs the sample cigarette, and the strip stamp number refers to the stamp number printed on the strip packaging box to which the sample cigarette belongs.

[0071] The logistics code is the logistics code printed on the strip packaging box to which the sample cigarette belongs, and the logistics code corresponds to the logistics channel information for transporting the sample cigarette.

[0072] The case number refers to the number of the case related to the sample cigarette. For example, if a sample cigarette is collected by the relevant agency when handling a case numbered 20210010001, then the case number corresponding to the sample cigarette can be 20210010001.

[0073] In step S101, the target screening condition may be that the inspection result is "counterfeit registered trademark and inferior cigarettes". Therefore, in step S101, the database system may identify each sample record whose inspection result is "counterfeit registered trademark and inferior cigarettes" in the database, and extract the cigarette characteristic data contained in these sample records as the cigarette characteristic data associated with the sample cigarettes that meet the target screening condition, wherein each sample cigarette corresponds to one piece of cigarette characteristic data, for example, one sample cigarette may correspond to the following piece of cigarette characteristic data:

[0074] Product specifications: Brand A, specification A; Packaging machine model: X1 packaging machine; Packaging stamp number: box stamp number, 100012, strip stamp number, 54127; Logistics code 1254638; Case number: 20210010001.

[0075] S102, cleaning the cigarette characteristic data to obtain cleaned cigarette characteristic data.

[0076] In step S102, the database system may perform data cleaning on each cigarette characteristic data of the sample cigarettes that meets the target screening condition obtained in step S101. The data cleaning process may be performed one by one according to each dimension of the cigarette characteristic data.

[0077] Specifically, for each piece of cigarette characteristic data, the database system can analyze one by one whether the cigarette characteristic data contains information of each dimension such as product specification, packaging model, packaging steel stamp number, logistics code and case number, and whether the information contained is correct. If the information of any one or more dimensions is missing, or the information of any one or more dimensions is incorrect, it can be determined that this cigarette characteristic data is incomplete cigarette characteristic data or incorrect cigarette characteristic data, and the cigarette characteristic data is deleted and not used for subsequent statistical processing; if the information of each dimension is included and is correct information, it can be determined that the cigarette characteristic data contains complete and correct information, and the cigarette characteristic data is determined to be retained after cleaning for subsequent statistical processing.

[0078] Among them, the way to determine whether the information of a certain dimension is correct can be to compare the format of the information of this dimension in the cigarette characteristic data with the standard format of this dimension. If the comparison finds that the information format of this dimension is consistent with the standard format, then the information of this dimension is determined to be correct; if the comparison is inconsistent, then the information of this dimension is determined to be incorrect.

[0079] S103, statistically processing the characteristic data of the cleaned cigarettes from multiple statistical dimensions to obtain statistical results of the characteristic data of the cleaned cigarettes in multiple statistical dimensions, wherein the multiple statistical dimensions include month, case number, product specification, logistics code, packaging steel stamp number and packaging machine model.

[0080] In step S103, the database system may perform statistics on the plurality of post-cleaning cigarette characteristic data from a plurality of statistical dimensions to obtain one or more statistical results corresponding to each statistical dimension.

[0081] The statistical dimensions may include the dimensions of the cigarette characteristic data itself, such as case number, product specification, logistics code, packaging stamp number and packaging machine model, and may also include other dimensions, such as time dimensions, such as monthly, quarterly, annual, etc.

[0082] The obtained statistical results can be stored in the database by the database system and read from the database when needed.

[0083] Optionally, the above statistical results may include statistical results of a single dimension, or may include statistical results of a combination of multiple dimensions. In some embodiments, the database system may determine several commonly used single statistical dimensions, and several commonly used combinations of multiple statistical dimensions, based on the user's settings and previous usage requirements, and then perform statistical processing on these commonly used single statistical dimensions and statistical dimension combinations in step S103 to obtain corresponding statistical results.

[0084] The statistical result may be the number of characteristic data of cleaned cigarettes under the corresponding dimension (or combination of dimensions). Since each characteristic data of cleaned cigarettes corresponds to a sample cigarette, the statistical result may also be considered as the number of sample cigarettes under the corresponding dimension (or combination of dimensions).

[0085] As an example, the database system can perform statistical processing by product specification dimension, and count the total number of characteristic data of cleaned cigarettes of each product specification in the database. For example, after statistical processing, it can be obtained that the number of samples of brand A specification is 10,000, the number of samples of brand B specification is 5,000, and the number of samples of brand B specification is 8,000.

[0086] Among them, the statistical result that the number of samples of Brand A Specification A is 10,000 can be understood as that in the cigarette identification database, there are 10,000 counterfeit registered trademark and inferior cigarettes belonging to Brand A Specification A registered.

[0087] As another example, the database system can perform joint statistics by product specification dimension and case number dimension, and count the sample cigarette data of each product specification involved in each case number. For example, the number of samples of Brand B Specification C involved in case number 20210010001 is 300, and the number of samples of Brand A Specification A involved in case number 20230130054 is 200.

[0088] S104, in response to obtaining the data analysis instruction, outputting the target statistical result matching the data analysis instruction and the post-cleaning cigarette characteristic data associated with the target statistical result.

[0089] In step S104, the database system can be connected to the terminal device of the relevant organization for communication. The user can select the appropriate data analysis instruction on the terminal device according to the specific analysis requirements. The terminal device then sends the selected data analysis instruction to the database system. The database system responds to the data analysis instruction and outputs the matching target statistical results and the characteristic data of the cleaned cigarettes associated with the target statistical results, thereby providing all-round and multi-level data support for the relevant organizations.

[0090] In the above steps, the database system can determine the target dimension corresponding to the data analysis instruction based on the data analysis instruction, and then use one or more statistical results obtained based on this target dimension as the target statistical results, and output these target statistical results and the associated post-cleaning cigarette characteristic data to the terminal device that sends the data analysis instruction, so that the terminal device can display the target statistical results and the associated data.

[0091] The beneficial effects of this embodiment are:

[0092] This solution pre-processes the cigarette characteristic data entered into the cigarette characteristic database according to multiple specified statistical dimensions. When the user needs to analyze these cigarette characteristic data, this solution can promptly output the target statistical results that match the user's data analysis instructions, so that the user can perform subsequent analysis work based on the statistical results without directly analyzing a large amount of raw data, thereby achieving the effect of improving the data analysis efficiency of cigarette products.

[0093] In some optional embodiments, the database system may also perform sorting based on statistical results.

[0094] For example, with respect to the statistical results of multiple specifications, these specifications are sorted according to the number of sample cigarettes corresponding to the specifications, with specifications with smaller numbers being at the end and specifications with larger numbers being at the front.

[0095] For another example, the database system may sort different logistics codes based on the quantity of sample cigarettes corresponding to the logistics codes, with logistics codes corresponding to smaller quantities being placed at the end and logistics codes corresponding to larger quantities being placed at the front.

[0096] The sorted results can be sent to the terminal devices of the relevant institutions for display.

[0097] In some optional embodiments, the data analysis instruction includes a key specification analysis instruction. The user can select the analysis instruction when it is necessary to perform a key specification analysis of counterfeit cigarettes.

[0098] After receiving the key product specification analysis instruction, the database system can output the statistical results and related data in the following ways:

[0099] Determine key product specifications based on the statistical results of product specification dimensions;

[0100] Output key specifications, corresponding statistical results of key specifications and growth rates of key specifications.

[0101] The database system may first obtain the statistical results corresponding to each specification in the current statistical period, that is, the number of sample cigarettes corresponding to each specification.

[0102] The statistical period can be set as needed, for example, to any one of annual, quarterly and monthly. Taking the statistical period as monthly as an example, the database system can obtain the number of sample cigarettes corresponding to each specification in the current month, and then sort these specifications according to the number of corresponding sample cigarettes. The more the number, the higher the ranking. Finally, the top N specifications are determined as key specifications. N is an integer set as needed, for example, it can be set to 3.

[0103] After determining the key specifications, the database system may compare the number of sample cigarettes corresponding to the key specifications in the current statistical period with the number of sample cigarettes of the key specifications in previous statistical periods to determine the growth rate of the key specifications.

[0104] Taking the monthly statistical period as an example, the number of sample cigarettes corresponding to the key specifications of the current month can be compared with the number of sample cigarettes corresponding to the key specifications of the same month last year to obtain the year-on-year growth rate of the key specifications; the number of sample cigarettes corresponding to the key specifications of the current month can be compared with the number of sample cigarettes corresponding to the key specifications of the previous month to obtain the month-on-month growth rate of the key specifications.

[0105] The year-on-year growth rate can reflect the growth or decline trend of the corresponding specifications of cigarettes that meet the target screening conditions over a longer period of time. A high growth rate usually indicates that the market activity of the specifications is increasing; while the month-on-month growth rate helps to monitor short-term fluctuations. Large fluctuations may indicate that the manufacturing or sales activities of cigarettes that meet the target screening conditions of the specifications have appeared in the market. By outputting the statistical results and growth rates corresponding to key specifications, it can help identify hot spots or key specifications in the corresponding statistical period, and then take targeted measures for these key specifications of cigarette products.

[0106] As an example, the key specifications, statistical results corresponding to the key specifications, and growth rates of the key specifications in the current statistical period (e.g., X year Y month) output in step S104 may be shown in the following Table 1. In Table 1, the first column is recorded as key specifications, the second column is recorded as target statistical results corresponding to the key specifications, and the third and fourth columns are recorded as corresponding growth rates.

[0107] Table 1

[0108]

[0109] From Table 1, we can see that the number of sample cigarettes corresponding to Brand A Specification in the current statistical period is the highest, and both the year-on-year growth rate and the month-on-month growth rate are relatively high, indicating that this specification is a hot specification in the market and requires special attention.

[0110] Although the number of sample cigarettes corresponding to Brand A Specification B in the current statistical period is not low, the month-on-month growth rate has declined, which may mean that the manufacturing or sales activities of cigarette products of this specification have decreased.

[0111] Brand A’s C specification had the highest month-on-month growth rate, indicating that the market activity for this specification is increasing rapidly.

[0112] In some optional embodiments, the data analysis instruction includes a logistics code analysis instruction. The user can select the logistics code analysis instruction when it is necessary to analyze the cigarette products that meet the target screening conditions and are circulated in various logistics channels.

[0113] When receiving the logistics code analysis instruction, the database system can output the target statistical results and related data as follows:

[0114] A1, determine the target logistics code according to the occurrence frequency of each logistics code;

[0115] A2, outputs the target logistics code, the statistical results of the target logistics code, and the product specifications associated with the target logistics code;

[0116] A3, obtaining case data associated with the target logistics code from the case database according to the case number associated with the target logistics code;

[0117] A4, outputs the case data associated with the target logistics code.

[0118] In step A1, the database system can first count the occurrence frequency of each logistics code, sort the logistics codes from most to least according to the occurrence frequency, and select the first M logistics codes with the highest occurrence frequency as the target logistics codes. M can be set as needed, for example, M can be set to 2.

[0119] The frequency of occurrence of a logistics code can be the number of case numbers associated with the logistics code. If a logistics code and a case number appear in the same cigarette feature data, it can be considered that the logistics code is associated with the case number. If a logistics code is associated with 4 different case numbers in the current statistical period, the frequency of occurrence of the logistics code is 4. If a logistics code is associated with 3 different case numbers, the frequency of occurrence of the logistics code is 3.

[0120] The product specifications associated with the target logistics code refer to the product specifications to which the sample cigarettes with the target logistics code belong. For example, based on the characteristic data of the cigarettes after cleaning, it can be determined that a part of the sample cigarettes of Brand A Specification have the logistics code whyc420111622566. Then it can be determined that the product specifications associated with the logistics code whyc420111622566 include Brand A Specification. Similarly, a part of the sample cigarettes of Brand A Specification also have the logistics code. Then it can be determined that the product specifications associated with the logistics code whyc420111622566 include Brand A Specification.

[0121] Optionally, in step A2, the occurrence frequency corresponding to the target logistics code may also be output.

[0122] Optionally, in step A2, regional information related to the target logistics code can also be output. The database system can determine the corresponding logistics channel according to the target logistics code, and determine the main operating area corresponding to the logistics channel as the regional information related to the target logistics code. The regional information can be specific to the county.

[0123] As an example, assuming that M is equal to 2, the database system can output the target logistics code, the statistical results of the target logistics code, and the product specifications associated with the target logistics code as shown in Table 2 below in step A2.

[0124] Table 2

[0125]

[0126] The case database can be used to store case data of cases that have been handled and are related to cigarette products. The case data may include the number of sample cigarettes collected in the relevant cases that meet the target screening conditions (can be in strips as units), the unit handling the case, and the specifications of the sample cigarettes collected in the case.

[0127] In steps A3 and A4, each case number associated with the target logistics code can be determined, and then the case data corresponding to these case numbers can be found from the case database based on the case number. Finally, the data associated with the target logistics code can be filtered out from these case data, and this data can be output on the corresponding terminal device as the case data associated with the target logistics code.

[0128] For example, assume that a case number associated with the target logistics code is 4207110202410020. In the case data corresponding to the case number, the specifications of the collected sample cigarettes include Brand A Specification A and Brand C Specification F. The number of sample cigarettes corresponding to the former is 3, and the number of sample cigarettes corresponding to the latter is 2. After obtaining these case data, the database system determines that Brand A Specification A and its corresponding number of sample cigarettes are associated with the target logistics code, while Brand C Specification F and its corresponding number of sample cigarettes are not associated with the target logistics code.

[0129] The database system then determines the Brand A specification corresponding to the case number and the corresponding number of sample cigarettes as the case data associated with the target logistics code, and outputs this part of the data.

[0130] Optionally, in order to facilitate traceability, the unit handling the case corresponding to the case number associated with the target logistics code may also be output in step A4.

[0131] As an example, for the two target logistics codes shown in Table 2, the database system can output the information shown in the following Table 3 through step A4.

[0132] Table 3

[0133]

[0134] In some optional embodiments, the data analysis instructions may include case association analysis instructions;

[0135] When receiving the case correlation analysis instruction, the database system can output the statistical results and related data as follows:

[0136] B1, determine the target case number corresponding to the case association analysis instruction;

[0137] B2, search for the associated case number related to the target case number. Both the target case number and the associated case number are associated with the first product specification;

[0138] B3, outputs the statistical results of the target case number under the first product specification, and the statistical results of the associated case number under the first product specification;

[0139] B4, analyzing the cleaned cigarette characteristic data of the sample cigarettes of the first specification corresponding to the target case number, and the cleaned cigarette characteristic data of the sample cigarettes of the first specification corresponding to the associated case number, to obtain associated characteristic information of the target case number and the associated case number;

[0140] B5, outputs the associated feature information.

[0141] In step B1, the target case number corresponding to the case association analysis instruction can be directly specified by the case association analysis instruction, that is, the case association analysis instruction can contain a case number input by the user. After receiving the instruction, the database system determines the case number contained therein as the target case number.

[0142] Alternatively, the case association analysis instruction may specify a filtering condition, and the database system determines the case number that meets the filtering condition from the plurality of post-cleaning cigarette feature data as the target case number.

[0143] The filtering condition can be set as needed, for example, the filtering condition can be the largest amount of money involved, or the largest number of cigarettes involved. Based on the filtering condition, the database system can first determine all case numbers that have appeared in multiple pieces of cleaned cigarette feature data, and then for each case number, search for data related to the case number in the case database, such as the number of cigarettes involved in the case number or the amount of money involved, and finally determine the case number that meets the filtering condition as the target case number.

[0144] In step B2, the database system may first determine the first product specification associated with the target case number.

[0145] The method for determining the first product specification is as follows:

[0146] First, determine the specifications associated with the target case number. For any case number, if the case number and a specification appear in the same post-cleaning cigarette feature data, it can be determined that the case number and the specification are associated. For example, if Brand A Specification and Case Number 4207110202410020 appear in the same post-cleaning cigarette feature data, it can be considered that Brand A Specification and Case Number 4207110202410020 are associated.

[0147] Then, for multiple product specifications associated with the target case number, determine whether the statistical results corresponding to these product specifications are greater than a preset statistical threshold, and then determine each product specification whose statistical result is greater than the statistical threshold as the first product specification;

[0148] After determining the first specification, the case numbers that appear in the same characteristic data of cleaned cigarettes as the first specification, except for the target case number, can be searched based on the characteristic data of cleaned cigarettes, and the found case numbers can be determined as associated case numbers.

[0149] As an example, assuming that the target case number is 202400082421, the statistical results (by number) corresponding to Brand A Specification A associated with this case number reach 300, which is greater than the statistical threshold of 40. Therefore, it is determined that Brand A Specification A belongs to the first specification. Then, other case numbers associated with Brand A Specification A are searched, and it is determined that case numbers 202400013892 and 5002051 are both associated with Brand A Specification A. Therefore, it is determined that the associated case numbers include 202400013892 and 5002051.

[0150] In step B3, the statistical results corresponding to the dimensional combination of the target case number and the first specification (equivalent to the statistical results of the target case number under the first specification) can be output, and the statistical results corresponding to the dimensional combination of the associated case number and the first specification (equivalent to the statistical results of the associated case number under the first specification) can be output.

[0151] Among them, the statistical result corresponding to the dimensional combination of the target case number and the first specification can be understood as the number of sample cigarettes related to the case corresponding to the target case number and belonging to the first specification. The number can be expressed in bars, for example, it can be 300 bars; similarly, the statistical result corresponding to the dimensional combination of the associated case number and the first specification can be understood as the number of sample cigarettes related to the case corresponding to the associated case number and belonging to the first specification.

[0152] As an example, the statistical results output in step B3 can be represented by Table 4.

[0153] Table 4

[0154]

[0155] The first column of Table 4 represents the first specification.

[0156] The number of cases involved after the case number is the corresponding statistical result. For example, "the number of cases involved is 300" means that the statistical result corresponding to the combination of the target case number and the first product specification is 300. "The number of cases involved is 20" means that the statistical result corresponding to the combination of the associated case number 202400013892 and the first product specification is 20.

[0157] Optionally, in step B3, the database system can also search the case database to obtain the case data corresponding to each case number, obtain the geographical information corresponding to the case number, and output the geographical information together, such as "A City B District", "C City D County", etc. in Table 4.

[0158] Furthermore, the database system may analyze the characteristic data of the cleaned sample cigarettes of the first specification corresponding to the target case number and the characteristic data of the cleaned sample cigarettes of the first specification corresponding to the associated case number to obtain the associated characteristic information of the target case number and the associated case number.

[0159] The associated feature information here may be the same part between the two.

[0160] Combined with the example in Table 4, the case corresponding to 202400082421 is recorded as Case No. 1, the case corresponding to 202400013892 is recorded as Case No. 2, and the case corresponding to 5002051 is recorded as Case No. 3. The database system can analyze the characteristic data of the cleaned sample cigarettes of Brand A and Specification A in Case No. 1, the characteristic data of the cleaned sample cigarettes of Brand A and Specification A in Case No. 2, and the characteristic data of the cleaned sample cigarettes of Brand A and Specification A in Case No. 3. After analysis, it is found that the sample cigarettes of Brand A and Specification A corresponding to these three cases have the same packaging machine model and the same packaging steel stamp number. For example, the packaging machine model is X1 packaging machine, and the packaging steel stamp number is 111171113 / 7J90830 (strip steel stamp number / box steel stamp number). Therefore, the associated characteristic information of the target case number and the associated case number can be determined as follows:

[0161] The packaging machine models are all X1 packaging machines, and the packaging stamp numbers are all 111171113 / 7J90830 (strip stamp number / box stamp number).

[0162] Then, the database system may output the above associated feature information together with the statistical results in step B3, for example, in the form of the third column of Table 4.

[0163] By outputting the statistical results and related feature information of multiple related cases, data support can be provided for relevant institutions and personnel in the process of investigating various types of cigarette products.

[0164] In some optional embodiments, the database system may also directly send the corresponding post-cleaning cigarette characteristic data to the terminal device in the following manner:

[0165] In response to the cigarette feature query request of the terminal device, the cleaned cigarette feature data matching the cigarette feature query request is sent to the terminal device.

[0166] The cigarette feature query request may carry feature data input by the user in one or more dimensions, such as a specific packaging stamp number, such as 111171113 / 7J90830, or a specific product specification, such as Brand A Specification.

[0167] After receiving the cigarette feature query request, the database system searches for the cleaned cigarette feature data that contains the data carried in the request among the multiple cleaned cigarette feature data, and feeds the searched data back to the terminal device that issued the request as the query result.

[0168] Exemplarily, a cigarette feature query request may carry a packaging stamp number: 111171113 / 7J90830. After receiving the request, the database system searches for the cleaned cigarette feature data containing the packaging stamp number among multiple pieces of cleaned cigarette feature data in the database, and feeds the found cleaned cigarette feature data as a query result to the terminal device.

[0169] Through the above-mentioned query function, relevant personnel can be assisted in comparing the cigarette products seen on site with the queried characteristic data during the process of screening various types of cigarette products, so as to quickly determine whether the various types of cigarette products screened meet the target screening conditions, that is, to determine whether the various types of cigarette products screened are counterfeit registered trademarks and fake cigarettes.

[0170] Optionally, the database system can also provide an early warning reminder function for new counterfeit cigarettes. Specifically, the database system can perform the following steps:

[0171] Testing new sample cigarettes entered into the cigarette identification database;

[0172] When the cigarette characteristic data of the entered new sample cigarette is different from the original cigarette characteristic data in the cigarette identification database, a prompt message is output.

[0173] Each time the relevant agency completes the quality inspection of a batch of newly collected sample cigarettes and enters them into the database, the database system can compare the cigarette characteristic data of these newly collected sample cigarettes with the original cigarette characteristic data, and if the specifications of the former are different from all the specifications of the latter, a prompt message is output. The prompt message can be output to the relevant departments that manage the sales of cigarette products.

[0174] For example, assuming that the sample cigarettes registered in the database only include brand A specification, brand A specification, brand B specification, brand B specification, and brand B specification. If the newly entered sample cigarette belongs to brand C specification, which is different from the original four specifications, the database system will output a prompt message.

[0175] The prompt information may include various characteristic data of these newly entered sample cigarettes whose specifications are different from the original specifications, including specifications, packaging model, packaging stamp number, logistics code and case number, etc.

[0176] In practical applications, the method provided in this embodiment can be widely used in various aspects of tobacco monopoly supervision.

[0177] Through the above application, the method and system of this embodiment achieve the following effects:

[0178] On the one hand, it improves the accuracy of supervision, realizes accurate identification and location of counterfeit cigarette characteristics through data analysis, and provides clear goals;

[0179] On the other hand, we should enhance cross-departmental collaboration capabilities, promote cross-regional and cross-departmental information sharing and collaboration, break down information silos, and improve overall regulatory effectiveness;

[0180] On the other hand, we will improve early warning and prevention capabilities, timely discover and warn of newly emerging counterfeit cigarette specifications and their characteristics, and provide regulatory authorities with preemptive prevention and control measures.

[0181] The present application also provides a cigarette feature data processing system, see Figure 2 The system may include the following units.

[0182] The determination unit 201 is used to determine the sample cigarettes that meet the target screening conditions in the cigarette identification database, and obtain the cigarette characteristic data associated with the sample cigarettes that meet the target screening conditions in the cigarette identification database, the cigarette characteristic data including product specifications, packaging machine type, packaging steel stamp number, logistics code and case number;

[0183] A cleaning unit 202, used for cleaning the cigarette characteristic data to obtain cleaned cigarette characteristic data;

[0184] The statistical unit 203 is used to perform statistical processing on the characteristic data of the cleaned cigarettes from multiple statistical dimensions to obtain statistical results of the characteristic data of the cleaned cigarettes in multiple statistical dimensions, wherein the multiple statistical dimensions include month, case number, product specification, logistics code, packaging steel stamp number and packaging machine model;

[0185] The output unit 204 is used to output the target statistical result matching the data analysis instruction and the post-cleaning cigarette characteristic data associated with the target statistical result in response to obtaining the data analysis instruction.

[0186] Optionally, the data analysis instructions include key product specification analysis instructions;

[0187] When the output unit 204 outputs the target statistical result matching the data analysis instruction and the characteristic data of the cleaned cigarettes associated with the target statistical result, it is specifically used to:

[0188] Determine key product specifications based on the statistical results of product specification dimensions;

[0189] Output key specifications, corresponding statistical results of key specifications and growth rates of key specifications.

[0190] Optionally, the data analysis instruction includes a logistics code analysis instruction;

[0191] When the output unit 204 outputs the target statistical result matching the data analysis instruction and the characteristic data of the cleaned cigarettes associated with the target statistical result, it is specifically used to:

[0192] Determine the target logistics code according to the frequency of occurrence of each logistics code;

[0193] Output the target logistics code, the statistical results of the target logistics code, and the product specifications associated with the target logistics code;

[0194] Obtaining case data associated with the target logistics code from a case database according to the case number associated with the target logistics code;

[0195] Output the case data associated with the target logistics code.

[0196] Optionally, the data analysis instructions include case association analysis instructions;

[0197] When the output unit 204 outputs the target statistical result matching the data analysis instruction and the characteristic data of the cleaned cigarettes associated with the target statistical result, it is specifically used to:

[0198] Determine the target case number corresponding to the case association analysis instruction;

[0199] Find the associated case number related to the target case number. Both the target case number and the associated case number are associated with the first product specification.

[0200] Output the statistical results of the target case number under the first product specification, and the statistical results of the associated case number under the first product specification;

[0201] Analyze the cleaned cigarette characteristic data of the sample cigarettes of the first specification corresponding to the target case number, and the cleaned cigarette characteristic data of the sample cigarettes of the first specification corresponding to the associated case number, to obtain associated characteristic information of the target case number and the associated case number;

[0202] Outputs the associated feature information.

[0203] Optionally, the output unit 204 is further configured to:

[0204] In response to the cigarette feature query request of the terminal device, the cleaned cigarette feature data matching the cigarette feature query request is sent to the terminal device.

[0205] Optionally, the system further includes a prompting unit 205, which is used to:

[0206] Testing new sample cigarettes entered into the cigarette identification database;

[0207] When the cigarette characteristic data of the entered new sample cigarette is different from the original cigarette characteristic data in the cigarette identification database, a prompt message is output.

[0208] The working principle of the cigarette characteristic data processing system provided in this embodiment can refer to the relevant steps of the cigarette characteristic data processing method provided in any embodiment of the present application, and will not be repeated here.

[0209] 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.

[0210] For the convenience of description, the above system or system is described by dividing the functions into various modules or units. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0211] 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., including 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.

[0212] 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.

[0213] 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 method for processing cigarette characteristic data, characterized in that: include: Determine sample cigarettes that meet the target screening conditions in the cigarette identification database, and obtain cigarette feature data associated with the sample cigarettes that meet the target screening conditions in the cigarette identification database, wherein the cigarette feature data includes product specifications, packaging machine type, packaging steel stamp number, logistics code and case number; Performing data cleaning on the cigarette characteristic data to obtain cleaned cigarette characteristic data; Performing statistical processing on the characteristic data of the cleaned cigarettes from multiple statistical dimensions respectively to obtain statistical results of the characteristic data of the cleaned cigarettes in the multiple statistical dimensions, wherein the multiple statistical dimensions include month, case number, product specification, logistics code, packaging steel stamp number and packaging machine model; In response to obtaining the data analysis instruction, the target statistical result matching the data analysis instruction and the post-cleaning cigarette characteristic data associated with the target statistical result are output.

2. The method according to claim 1, characterized in that The data analysis instructions include key product specification analysis instructions; The output of the target statistical result matching the data analysis instruction, and the post-cleaning cigarette characteristic data associated with the target statistical result, includes: Determine key product specifications based on the statistical results of product specification dimensions; Output the key specifications, the statistical results corresponding to the key specifications, and the growth rate of the key specifications.

3. The method according to claim 1, characterized in that The data analysis instructions include logistics code analysis instructions; The output of the target statistical result matching the data analysis instruction, and the post-cleaning cigarette characteristic data associated with the target statistical result, includes: Determine the target logistics code according to the frequency of occurrence of each logistics code; Output the target logistics code, the statistical results of the target logistics code, and the product specifications associated with the target logistics code; Obtaining case data associated with the target logistics code from a case database according to the case number associated with the target logistics code; Output the case data associated with the target logistics code.

4. The method according to claim 1, characterized in that: The data analysis instructions include case association analysis instructions; The output of the target statistical result matching the data analysis instruction, and the post-cleaning cigarette characteristic data associated with the target statistical result, includes: Determine the target case number corresponding to the case association analysis instruction; Searching for an associated case number associated with the target case number, where both the target case number and the associated case number are associated with the first product specification; Outputting the statistical results of the target case number under the first product specification and the statistical results of the associated case number under the first product specification; Analyze the cleaned cigarette characteristic data of the sample cigarettes of the first specification corresponding to the target case number and the cleaned cigarette characteristic data of the sample cigarettes of the first specification corresponding to the associated case number to obtain associated characteristic information of the target case number and the associated case number; The associated feature information is output.

5. The method according to claim 1, characterized in that: Also includes: In response to a cigarette feature query request from a terminal device, post-cleaning cigarette feature data matching the cigarette feature query request is sent to the terminal device.

6. The method according to claim 1, characterized in that Also includes: Detecting new sample cigarettes entered into the cigarette identification database; When the input cigarette characteristic data of the new sample cigarette is different from the original cigarette characteristic data in the cigarette identification database, a prompt message is output.

7. A cigarette characteristic data processing system, characterized in that: include: A determination unit, used to determine sample cigarettes that meet the target screening conditions in the cigarette identification database, and obtain cigarette feature data associated with the sample cigarettes that meet the target screening conditions in the cigarette identification database, wherein the cigarette feature data includes product specifications, packaging machine model, packaging steel stamp number, logistics code and case number; A cleaning unit, used for cleaning the cigarette characteristic data to obtain cleaned cigarette characteristic data; A statistical unit, used to perform statistical processing on the characteristic data of the cleaned cigarettes from multiple statistical dimensions respectively, to obtain statistical results of the characteristic data of the cleaned cigarettes in the multiple statistical dimensions, wherein the multiple statistical dimensions include month, case number, product specification, logistics code, packaging steel stamp number and packaging machine model; The output unit is used for outputting the target statistical result matching the data analysis instruction and the post-cleaning cigarette characteristic data associated with the target statistical result in response to obtaining the data analysis instruction.

8. The system according to claim 7, characterized in that The data analysis instructions include key product specification analysis instructions; When the output unit outputs the target statistical result matching the data analysis instruction and the characteristic data of the cleaned cigarettes associated with the target statistical result, it is specifically used to: Determine key product specifications based on the statistical results of product specification dimensions; Output the key specifications, the statistical results corresponding to the key specifications, and the growth rate of the key specifications.

9. The system according to claim 7, characterized in that The data analysis instructions include logistics code analysis instructions; When the output unit outputs the target statistical result matching the data analysis instruction and the characteristic data of the cleaned cigarettes associated with the target statistical result, it is specifically used to: Determine the target logistics code according to the frequency of occurrence of each logistics code; Output the target logistics code, the statistical results of the target logistics code, and the product specifications associated with the target logistics code; Obtaining case data associated with the target logistics code from a case database according to the case number associated with the target logistics code; Output the case data associated with the target logistics code.

10. The system according to claim 7, characterized in that The data analysis instructions include case association analysis instructions; When the output unit outputs the target statistical result matching the data analysis instruction and the characteristic data of the cleaned cigarettes associated with the target statistical result, it is specifically used to: Determine the target case number corresponding to the case association analysis instruction; Searching for an associated case number associated with the target case number, where both the target case number and the associated case number are associated with the first product specification; Outputting the statistical results of the target case number under the first product specification and the statistical results of the associated case number under the first product specification; Analyze the cleaned cigarette characteristic data of the sample cigarettes of the first specification corresponding to the target case number and the cleaned cigarette characteristic data of the sample cigarettes of the first specification corresponding to the associated case number to obtain associated characteristic information of the target case number and the associated case number; The associated feature information is output.

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