Cigarette and deep anti-counterfeiting method and system based on linkage of cigarette and cigarette case

Abstract

The invention relates to a cigarette and a deep anti-counterfeiting method and system based on linkage of the cigarette and a cigarette case, and solves the problem that it is difficult for a consumer to distinguish the authenticity of the cigarette through a single verification mode, and the method comprises the following steps: generating a second code logically associated with a first code according to the first code printed on the cigarette case; wherein the first code uniquely corresponds to the cigarette case and can be scanned and identified; the second code is printed on the cigarette, uniquely corresponds to the cigarette and can be scanned and identified; identifying the first code to obtain first code identification information; identifying the second code to obtain second code identification information; judging whether the first code identification information and the second code identification information have a logic association relationship or not; and if the first code identification information and the second code identification information have a logic association relationship, outputting first verification passing information, otherwise, triggering anti-counterfeiting alarm information. According to the application, multiple anti-counterfeiting is formed in a mode of correlating a plurality of codes, so that the anti-counterfeiting reliability is improved.

Description

Technical Field

[0001] The present application relates to the technical field of cigarette anti-counterfeiting, and particularly relates to a cigarette and a deep anti-counterfeiting method and system based on the linkage between the cigarette and the cigarette case. Background Art

[0002] Cigarette anti-counterfeiting is a complex field involving technology, law, and market supervision. With the upgrading of counterfeit cigarette manufacturing technology and the intensification of global circulation, anti-counterfeiting technology is constantly evolving. The current mainstream anti-counterfeiting technologies include physical anti-counterfeiting technology, digital anti-counterfeiting technology, and so on.

[0003] Among them, physical anti-counterfeiting technology is achieved by means of holograms / laser anti-counterfeiting labels, special printing processes, paper anti-counterfeiting, etc. Holograms / laser anti-counterfeiting labels: The difficulty of counterfeiting is increased through dynamic light-changing patterns (such as three-dimensional effects, color-changing effects), but some high-end counterfeiters have been able to imitate them; Special printing processes: Such as microtext, fluorescent ink, thermochromic ink (color-changing when heated), etc., which require professional tools for detection and are difficult for ordinary consumers to identify; Paper anti-counterfeiting: Using cigarette paper with a security thread or special watermark paper, such as RMB-level anti-counterfeiting materials.

[0004] Among them, digital anti-counterfeiting technology is achieved by means of two-dimensional codes / barcodes, RFID electronic tags, digital watermarks, etc. Two-dimensional codes / barcodes: Implement "one code for one item", combined with blockchain or cloud databases, and consumers can verify the authenticity by scanning; RFID electronic tags: Embedded in cigarette packages for supply chain traceability and retail verification, but the cost is relatively high and is mostly used for high-end products or pilot projects; Digital watermarks: Invisible digital information is embedded in packaging printing and requires special equipment for reading, and it is difficult to be replicated by ordinary scanners.

[0005] The existing cigarette anti-counterfeiting technology mostly uses two-dimensional codes / barcodes in digital anti-counterfeiting technology. This anti-counterfeiting form mostly relies on a single two-dimensional code verification method. For example, an irreplicable two-dimensional code is printed on the cigarette case surface, and consumers can complete the authenticity verification by scanning the two-dimensional code and jumping to the official page (for example, the "one code for one item" traceability system of China National Tobacco Corporation).

[0006] The above-mentioned existing technical solutions have the following defects: Since most purchasers do not consciously verify the cigarettes, the two-dimensional code / barcode is not scanned, so there is no verification record in the official system. Some illegal manufacturers recycle the two-dimensional codes / barcodes on un-verified cigarette cases or use technical means to copy real two-dimensional codes / barcodes and print the forged two-dimensional codes / barcodes on fake cigarette cases, resulting in the failure of the traditional scanning anti-counterfeiting system. This loophole makes it difficult for consumers to distinguish the authenticity through a single verification method and reduces the credibility of anti-counterfeiting technology. Summary of the Invention

[0007] In order to form multiple anti-counterfeiting measures and improve the reliability of anti-counterfeiting, the present application provides a deep anti-counterfeiting method based on the linkage between cigarettes and cigarette cases.

[0008] In a first aspect, the present application provides a deep anti-counterfeiting method based on the linkage between cigarettes and cigarette cases, including generating a second code that is logically associated with the first code according to the first code printed on the cigarette case; wherein, the first code corresponds uniquely to the cigarette case and can be scanned and recognized; the second code is printed on the cigarette and corresponds uniquely to the cigarette and can be scanned and recognized; identifying the first code to obtain the first code recognition information; identifying the second code to obtain the second code recognition information; judging whether there is a logical association relationship between the first code recognition information and the second code recognition information; If there is a logical association relationship between the first code recognition information and the second code recognition information, output the first verification passed information, otherwise trigger the anti-counterfeiting warning information.

[0009] Preferably, the second code is generated by an encryption algorithm and includes the following information: the production batch number of the cigarette, the geometric position arrangement feature, and the random encryption factor; Obtaining the production batch number B, the geometric position coordinates P of the cigarette, and the random encryption factor R; Generating an initial hash value H0 through a dynamic hashing algorithm, specifically as follows: ; wherein, B is the production batch number, P is the geometric position coordinates of the cigarette, t is the production timestamp, R is the random encryption factor, and ROT(P, t) represents circularly shifting P by t; Based on the key splitting function F k Splitting H0 into several sub-codes C i , and each sub-code satisfies the constraint condition: ; wherein, n is an integer; Assigning the sub-code C i to the corresponding cigarette and printing it as the second code.

[0010] Preferably, the sub-code C i corresponds one-to-one with each cigarette and each cigarette has a separate second code or the sub-code C i is split and printed on at least two cigarettes.

[0011] Preferably, if the anti-counterfeiting warning information is triggered, collect the store warning information corresponding to the cigarette that triggers the anti-counterfeiting warning information, and the store warning information includes the store name and the store address; Store the store warning information and form a warning database; Locate and display the store warning information in the warning database on the map.

[0012] Preferably, divide the area with store warning information according to the preset rules to form a distribution area. If the number of store warning information in the distribution area is greater than or equal to the preset reference value, define this distribution area as a disaster area, and display it with color marking and send a request for investigation information to the law enforcement department.

[0013] Preferably, perform a store risk score on the stores corresponding to the warning database according to the store risk scoring model, and display the store risk score; the specific store risk scoring model is as follows: ; Wherein, Freq is the historical warning frequency, Recency is the time decay factor of the most recent warning, β0 is the intercept term (baseline risk), β1 is the frequency weight coefficient (positive number), and β2 is the time decay weight coefficient (negative number).

[0014] Preferably, generate a third code that is logically associated with the first code according to the first code printed on the cigarette case; Wherein, the third code is printed on the aluminum foil and can be scanned and recognized; the third code represents traceability information.

[0015] Preferably, the cigarette case is printed with the taste information of the corresponding cigarette, and the ink used for printing the sub-code C i is the ink corresponding to the taste information.

[0016] In a second aspect, the present application provides a deep anti-counterfeiting system based on the linkage between cigarettes and cigarette cases, including a memory, a processor, and a program stored on the memory and executable on the processor. When the program is loaded and executed by the processor, it can implement the deep anti-counterfeiting method based on the linkage between cigarettes and cigarette cases.

[0017] In a third aspect, the present application provides a cigarette, including a cigarette filter and a cigarette stick. The cigarette stick is printed with a second code, which is uniquely corresponding to the cigarette stick and can be scanned and recognized; the cigarette case is printed with a first code, and the first code has a logical association with the second code.

[0018] In summary, the present application includes the following beneficial technical effects: 1. By the way of mutual association between the first code and the second code, multiple anti-counterfeiting is achieved; and the first code is printed on the cigarette case, while the second code is printed on the cigarette. After smoking, it is equivalent to the second code being destroyed, so the second code cannot be recycled, and recycling only the first code cannot be counterfeited anymore, improving the anti-counterfeiting reliability; 2. A method of adding flavor anti-counterfeiting to cigarettes; that is, when the cigarette burns to the corresponding position, the user will draw a puff of cigarette with a specific flavor, and this flavor will be indicated on the cigarette case for the convenience of the user to correspond; at the same time, the formula of the printing coating will also be kept strictly confidential and protected in the form of trade secrets; if other manufacturers counterfeit, they will not be able to formulate the coating with exactly the same flavor, thus playing the role of flavor anti-counterfeiting. At the same time, it can be combined with the second coding for anti-counterfeiting, that is, the flavor information of the corresponding cigarette is printed on the cigarette case, and the ink used for printing the sub-coding Ci is the ink corresponding to the flavor information; on the basis of the combination of the first coding and the second coding for anti-counterfeiting, adding a method of flavor anti-counterfeiting; further increasing the difficulty of counterfeiting by manufacturers; 3. If there is no logical correlation between the first coding recognition information and the second coding recognition information, it indicates that the cigarette may be a counterfeit cigarette. Therefore, data collection is carried out on the sales stores of the cigarette, etc., and relevant analysis is formed, not only to remind users and purchasers, but also to facilitate the subsequent investigation and punishment by law enforcement departments in the case of a large quantity of counterfeit cigarettes. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flowchart of a deep anti-counterfeiting method based on the linkage between cigarettes and cigarette cases in one embodiment of the present application Figure One 。

[0020] Figure 2 is a flowchart of a deep anti-counterfeiting method based on the linkage between cigarettes and cigarette cases in one embodiment of the present application Figure Two 。

[0021] Figure 3 is a flowchart of a deep anti-counterfeiting method based on the linkage between cigarettes and cigarette cases in one embodiment of the present application Figure Three 。

[0022] Figure 4 is a flowchart of a deep anti-counterfeiting method based on the linkage between cigarettes and cigarette cases in one embodiment of the present application Figure Four 。

[0023] Figure 5 is a flowchart of a deep anti-counterfeiting method based on the linkage between cigarettes and cigarette cases in one embodiment of the present application Figure Five 。 DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] The present application will be further described in detail below with reference to the accompanying drawings.

[0025] This specific embodiment is only an interpretation of the present application and does not limit the present application. After reading this specification, those skilled in the art can make modifications to this embodiment without creative contributions as needed, but as long as it is within the scope of the claims of the present application, it is protected by the patent law.

[0026] The embodiment of the present application provides a deep anti-counterfeiting method based on the linkage between cigarettes and cigarette cases. Through the combination of the first code, the second code, and the third code, the purpose of multiple anti-counterfeiting is achieved; that is, through the correlation between the first code and the second code, after smoking, it is equivalent to the destruction of the second code, so the second code cannot be recycled, and recycling only the first code alone cannot be counterfeited anymore, reducing the risk of cigarette counterfeiting and improving the reliability of anti-counterfeiting; through the correlation between the first code and the third code, the entire sales process of cigarettes can be traced. Once a counterfeiting situation is found, it is convenient for law enforcement departments to quickly trace the location of cigarette counterfeiting behavior; by printing the second code with flavored ink, users can initially judge whether the cigarette is the same as before through the taste. If there is a deviation in the taste, verification can be carried out, which is convenient for users to quickly judge whether it may be a counterfeit product.

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present application.

[0028] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, unless otherwise specified.

[0029] The embodiments of the present application will be further described in detail below with reference to the accompanying drawings of the specification.

[0030] The embodiment of the present application provides a deep anti-counterfeiting method based on the linkage between cigarettes and cigarette cases. The main process of the method is described as follows.

[0031] As Figure 1 shown: Step 1000: Generate a second code that is logically associated with the first code according to the first code printed on the cigarette case.

[0032] Among them, a first code is printed on the cigarette case. The first code corresponds uniquely to the cigarette case and can be recognized by scanning. The first code can be a two-dimensional code, a bar code, or other codes with recognition functions. The verifier can scan it with a smart device. After scanning, the smart device will jump to the corresponding web verification interface, and the corresponding production batch, model, specific graphics, etc. will be displayed on the interface, so as to provide official verification.

[0033] Due to the copyability of the first code, some unqualified manufacturers will recycle the first code and print it on fake cigarettes, so as to achieve the effect of passing off the fake as real. Therefore, a second code is printed on the cigarette and corresponds uniquely to the cigarette and can be recognized by scanning, and there is a logical correlation between the second code and the first code. Through the logical correlation between the first code and the second code, the effect of multiple anti-counterfeiting is achieved.

[0034] There are various ways to print the second code on the cigarette. In one implementation, the complete second code can be printed on each cigarette, so that any cigarette in the cigarette case has the possibility of verification. In one implementation, the second code can be split into at least two parts. For example, if it is split into two parts, it is printed on two cigarettes; if it is split into three parts, it is printed on three cigarettes, and so on.

[0035] The specific method for generating the second code is as follows: The second code is generated by an encryption algorithm and contains the following information: the production batch number of the cigarette, the geometric position arrangement feature, and the random encryption factor.

[0036] Step 1100: Obtain the parameter production batch number B, the cigarette geometric position coordinates P, and the random encryption factor R.

[0037] Among them, the parameter production batch number B, the cigarette geometric position coordinates P, and the random encryption factor R can all be retrieved from the originally preset database and can be adjusted in real time according to the actual situation.

[0038] Step 1200: Generate the initial hash value H0 through the dynamic hash algorithm, specifically as follows: ; Among them, B is the production batch number, P is the cigarette geometric position coordinates, t is the production timestamp, R is the random encryption factor, and ROT(P, t) represents circularly shifting P by t. By dynamically combining the production batch number (B), the cigarette geometric position coordinates (P), the production timestamp (t), and the random encryption factor (R), the initial hash value H0 is obtained, avoiding the code being guessed or copied. The code for each production batch is unique, and the codes for different cigarettes in the same batch vary due to different positions and times, increasing the difficulty of forgery.

[0039] Step 1300: Based on the key splitting function F k Split H0 into several sub - codes C i , and each sub - code satisfies the constraint conditions: ; where n is an integer. Split the initial hash into multiple sub - codes C i , and reconstruct the hash according to the logical constraint relationship during verification.

[0040] Step 1400: Assign the sub - code C i to the corresponding cigarette and print it as the second code.

[0041] In one implementation, the sub - code C i corresponds to each cigarette one by one and each cigarette has a separate second code. If multiple sub - codes C i are printed on one cigarette, then each cigarette has a verification function, that is, if the code of a certain cigarette is tampered with, the overall checksum does not match and an alarm can be triggered.

[0042] In one implementation, the sub - code C i is split and printed on at least two cigarettes. If multiple sub - codes C i are split and printed on multiple cigarettes, then they are verified by combining multiple cigarettes. If the code is tampered with, the overall checksum does not match and an alarm can be triggered.

[0043] Step 2000: Identify the first code to obtain the first - code identification information; identify the second code to obtain the second - code identification information.

[0044] In one implementation, the identification of the first code and the second code can be completed by an intelligent device, that is, an intelligent device with a photographing and scanning function; during the identification process, directly identify the first code and the second code to obtain the corresponding first - code identification information and second - code identification information.

[0045] In one implementation, the identification of the first code and the second code can be completed by an intelligent device, that is, an intelligent device with a photographing and scanning function; during the identification process, take a photo of the part with the first code and the second code, and identify the first code and the second code in the photo based on the content of the photo through image recognition technology, so as to obtain the corresponding first - code identification information and second - code identification information.

[0046] Step 3000: Determine whether there is a logical association relationship between the first - code identification information and the second - code identification information.

[0047] The logical association relationship includes but is not limited to series relationship and parallel relationship.

[0048] The serial relationship can be expressed as follows: after scanning the first code through a smart device, it jumps to the corresponding web page verification interface. There is an excuse for re-scanning verification on this web page verification interface. Enter the secondary scanning interface through the web page verification interface, and then scan the second code, or the final verification result.

[0049] The parallel relationship can be expressed as follows: after scanning the first code, half of the first verification data can be obtained, such as half of a specific pattern (which can be half of a Chinese zodiac pattern, a marked pattern, etc.). After scanning the second code, the other half of the second verification data is obtained. According to the matching of the first verification data and the second verification data, the verification effect can be completed.

[0050] Step 4000: If there is a logical association relationship between the first code recognition information and the second code recognition information, output the first verification passed information; otherwise, trigger the anti-counterfeiting warning information.

[0051] Among them, if there is a logical association relationship between the first code recognition information and the second code recognition information, then output the first verification passed information, which means that the cigarette is genuine. If there is no logical association relationship, trigger the anti-counterfeiting warning information, indicating that the cigarette may be counterfeited; key attention is required.

[0052] To improve the overall anti-counterfeiting effect, a taste anti-counterfeiting method can be added to the cigarette; print a pattern or other content on the cigarette stick, and add the corresponding taste to the paint used for printing. That is, when the cigarette burns to the corresponding position, the user will draw a puff of cigarette with a specific taste, and this taste will be indicated on the cigarette case for the user to correspond; at the same time, the formula of the printing paint will also be strictly confidential and protected in the form of trade secrets; if other manufacturers counterfeit, they cannot formulate the paint with exactly the same taste, thus playing the role of taste anti-counterfeiting.

[0053] At the same time, it can be combined with the second code for anti-counterfeiting, that is, the taste information of the corresponding cigarette is printed on the cigarette case, and the printed sub-code C i The ink used is the ink corresponding to the taste information; on the basis of the combination of the first code and the second code for anti-counterfeiting, add a taste anti-counterfeiting method; further increase the difficulty of counterfeiting by manufacturers.

[0054] If there is no logical association relationship between the first code recognition information and the second code recognition information, it means that the cigarette may be a counterfeit cigarette. Therefore, data collection is carried out on the sales store of the cigarette, etc., and relevant analysis is formed, not only to remind users and purchasers, but also to facilitate the subsequent investigation and handling by law enforcement departments in the case of a large quantity of counterfeit cigarettes; specifically as follows: Step 5100: If an anti-counterfeiting warning message is triggered, collect the store warning information of the cigarette corresponding to the triggered anti-counterfeiting warning message. The store warning information includes the store name and the store address.

[0055] Among them, various forms can be adopted in the process of collecting store warning information. On the one hand, it can be in the form of self-reporting by the verifier. For example, after completing the first code recognition and / or the second code recognition, it jumps to the corresponding web verification interface, and there will be an input port in this web verification interface, that is, it can be directly input through an intelligent device; on the other hand, it can be through the relevance between the cigarette and the sales store. For example, the cigarettes sold in advance are associated with the sales store, and the associated data is uploaded to the cloud database, and this cloud database can also be used as one of the data links for subsequent traceability; when the anti-counterfeiting warning message is triggered, the corresponding data in the cloud database is automatically called, that is, the data of the sales store that sells this cigarette, and this data is collected as the store warning information.

[0056] Step 5200: Store the store warning information and form a warning database.

[0057] Among them, all the collected store warning information is stored, and finally a warning database is formed. The construction of this warning database can be built by the official or by other service providers, and it can be an entity database or a cloud database, which can be set according to the actual situation.

[0058] Step 5300: Locate and display the store warning information in the warning database on the map.

[0059] Among them, since the store warning information includes the store name and the store address, with the help of existing relevant map tools, the store warning information in the warning database can be located and displayed, so as to facilitate the verifier to view; for example, after scanning the first code, it directly jumps to the web verification interface, and there is an entrance to view the store warning information in the warning database in the web verification interface. After entering, the store warning information located and displayed on the map can be directly viewed.

[0060] Step 5400: Divide the area with store warning information according to the preset rules to form a distribution area.

[0061] Among them, the preset rules can borrow existing division schemes such as cities, regions, or streets that have been completed, or can be divided independently according to actual needs. For example, the area to be divided is divided in an equal division form, and the equal division form can adopt forms such as matrix division. Through area division, it is convenient for the verifier to quickly understand the situation of stores that may have the risk of fake cigarettes in the current area, reducing the risk of purchasing fake cigarettes; at the same time, it is also convenient for law enforcement departments to conduct investigations.

[0062] Step 5500: If the number of store warning messages in the distribution area is greater than or equal to the preset reference value, define this distribution area as a disaster area, display it with color marking, and send a request for investigation and punishment information to the law enforcement department.

[0063] Among them, the preset reference value can be set according to the actual situation. Since the number of stores in different regions is inconsistent, the present application is not limited to the specific numerical value of the reference value. In one implementation, the reference value can be set according to one-tenth of the number of stores in the affiliated region. Monitor the number of store warning messages in the distribution area. At the same time, when the number of store warning messages reaches a certain value, it indicates that this area belongs to the disaster area, and color marking is performed at the same time; remind the law enforcement department to investigate and punish this area to avoid the situation of fake cigarettes running wild.

[0064] In the process of storing store warning messages and forming a warning database, at the same time, perform a store risk score for the stores, that is, it is necessary to distinguish high-risk stores (such as repeatedly selling fakes) and occasional violation stores among the stores in the same area (such as a certain block). Traditional pure counting ("quantity ≥ reference value") is likely to confuse these two situations. Therefore, it is necessary to establish a quantitative risk scoring model. Thus, it is convenient to have a preliminary basis in the subsequent investigation and punishment process by the law enforcement department, improve the efficiency of investigation and punishment, and can start the investigation and punishment for stores with a relatively high store risk score in the early stage. Specifically as follows: Perform a store risk score for the stores corresponding to in the warning database according to the store risk scoring model, and display the store risk score; convert the data in the warning database into 0-1 probability values (indicating the confidence that the store is a "high-risk store"); the specific store risk scoring model is as follows: ; Among them, Freq is the historical warning frequency, that is, the cumulative number of times the store is marked. The more times, the higher the risk (such as a certain store with 10 warnings vs 2 warnings). This data can be directly counted from the warning database; Recency is the time decay factor of the most recent warning, and the calculation formula is: 1 / (current warning time - most recent warning time + 1). The closer this time is, the higher the risk (such as a store warned yesterday vs a store warned half a year ago); β0 is the intercept term (baseline risk), representing the default baseline risk probability of the system even without any warning records (usually close to 0); β1 is the frequency weight coefficient (positive number), and for each additional warning, the increase in the risk probability (such as β1 = 0.5 → for each additional warning, log-odds increases by 0.5); β2 is the time decay weight coefficient (negative number), and the longer the time interval, the faster the risk probability decays (such as β2 = -0.3 → for each increase in the time interval, log-odds decreases by 0.3); e is the base of the natural logarithm (≈2.718).

[0065] With the overall output of this model being more intuitive, the result falls within 0 to 1 and can be directly understood as the "risk probability" (for example, 0.8 means an 80% probability of a high-risk store); it has a relatively high interpretability, and each parameter (β1, β2) represents the contribution weight of the feature to the risk, facilitating business analysis; overall, it is suitable for scenarios with high real-time requirements (such as processing 1000 store ratings per second).

[0066] For example: Suppose a store has Freq = 5, Recency = 0.25, and the β coefficients are β0 = -2.1, β1 = 0.6, β2 = -1.3 respectively; that is, the linear part = -2.1 + 0.6 * 5 + (-1.3) * 0.25 = 0.95; ; The model believes that this store has a 72% high-risk probability.

[0067] Among them, β0, β1, and β2 are obtained through model training, and the specific method is as follows: Step 6100: Label the training data existing in the existing training database.

[0068] Among them, the training data in the training database includes but is not limited to data that has been inspected and confirmed as "high-risk stores" in the past, stores that have been inspected due to alarm records but have no actual problems, etc.; define positive samples as cases that have been inspected and confirmed as "high-risk stores" in the past (labeled as 1); define negative samples as stores that only have alarm records but no actual problems (labeled as 0).

[0069] For example: Store 001: Historical alarm frequency (Freq) is 5, the time difference from the most recent alarm (days) is 3, the time decay factor of the most recent alarm (Recency) is 1 / (3 + 1) = 0.25, and whether it is high-risk (y) is 1; Store 002: Historical alarm frequency (Freq) is 2, the time difference from the most recent alarm (days) is 100, the time decay factor of the most recent alarm (Recency) is 1 / (100 + 1) = 0.01, and whether it is high-risk (y) is 0; Store 003: Historical alarm frequency (Freq) is 8, the time difference from the most recent alarm (days) is 7, the time decay factor of the most recent alarm (Recency) is 1 / (3 + 1) = 1 / (7 + 1) = 0.125, and whether it is high-risk (y) is 0.

[0070] Step 6200: Train the maximum likelihood estimation model with the labeled training data, specifically as follows: Measure the matching degree between the model prediction result and the true result (y = 0 / 1) according to each labeled training data. The objective function for maximum likelihood estimation is as follows: ; where, is the likelihood function, and the goal is to maximize this value; is the product symbol (the product of the correct prediction probabilities of all samples); P(yi = 1) is the probability that the model predicts the i-th sample as high risk; y i is the true result (0 or 1) of the i-th sample.

[0071] For example: For 1 positive sample (y1 = 1), if P = 0.8, then its contribution is 0.8^1 * (1 - 0.8)^0 = 0.8; For 1 negative sample (y2 = 0), if P = 0.3, then its contribution is 0.3^0 * (1 - 0.3)^1 = 0.7; The objective function of this maximum likelihood estimation is to maximize the product of the joint probabilities of the two.

[0072] Convert the likelihood function to the log-likelihood function; specifically as follows: ; where, if directly optimizing the product is impossible because when the sample size is large, the product will cause numerical underflow (close to 0) or overflow (extremely large), resulting in unstable calculations; after taking the natural logarithm (a monotonically increasing function), the product becomes a sum, but the maximization goal remains unchanged, thereby simplifying the calculation and maintaining monotonicity.

[0073] To adapt to the minimization optimization framework (such as gradient descent), take the negative of the log-likelihood function. At this time: maximizing is equivalent to minimizing ; specifically as follows: ; where, during the optimization process, when the prediction is accurate P(y i = 1) ≈ y i ; if y i = 1, then -logP(y i = 1) approaches 0, and P approaches 1; if y i = 0, then -log(1 - P(y i = 1)) approaches 0, and P approaches 0; When the model prediction is incorrect, the loss value increases significantly. The negative log-likelihood is equivalent to minimizing the cross-entropy between the true label distribution and the model prediction distribution. The smaller the cross-entropy, the more the model prediction probability matches the true label.

[0074] The comparison legend is as follows:

[0075] However, the more accurate the prediction, the smaller the corresponding loss value.

[0076] Through the processes of maximum likelihood estimation, log-likelihood, and negative log-likelihood, numerical calculation problems (multiplication → addition) are solved, the maximization problem is transformed into minimization, the optimization algorithm is adapted, and the difference between the model prediction and the true label is quantified; the finally obtained coefficients β0, β1, and β2 can maximize the probability of the data occurrence, making the model prediction as consistent as possible with the true label.

[0077] In order to facilitate law enforcement by law enforcement departments, all cigarettes are traced, which is convenient for understanding the production manufacturers, production situations, logistics situations, sales store situations, etc. of this pack of cigarettes. The entire process from production to sales is recorded, and real-name systems are implemented for each link to facilitate subsequent investigations; specifically as follows: Step 7100: Generate a third code that is logically associated with the first code according to the first code printed on the cigarette case.

[0078] Among them, the third code is printed on the aluminum foil paper and can be scanned and recognized; the third code is characterized as traceability information. The traceability information includes but is not limited to the real-name information of the production manufacturer, the real-name information of logistics transportation, real-time transportation information, the real-name information of the sales store, etc. The entire traceability process can be obtained by scanning the third code.

[0079] Step 7200: If the anti-counterfeiting warning information is triggered, a reminder message is fed back, and the reminder message is used to prompt the recognition of the third code.

[0080] Step 7300: Obtain the traceability information corresponding to the third code and upload it to the analysis database.

[0081] Among them, due to the huge number of cigarettes, the traceability information is screened and obtained, that is, only when the anti-counterfeiting warning information is triggered, the user is reminded to scan and recognize the third code, and the traceability information corresponding to the third code can be recognized and collected. After collection, it is automatically uploaded to the analysis database for subsequent analysis.

[0082] Step 7400: Perform hierarchical early warning on the traceability information uploaded to the analysis database. The hierarchical early warning includes low risk, medium risk, and high risk.

[0083] Among them, low risk means there are minor anomalies, that is, within the preset time period, no more than 3 pieces of the same traceability information are recorded; medium risk means there are certain anomalies, that is, within the preset time period, more than 3 but no more than 10 pieces of the same traceability information are recorded; high risk means there are relatively large anomalies, that is, within the preset time period, more than 10 pieces of the same traceability information are recorded.

[0084] Step 7500: Feed back the high-risk traceability information to the law enforcement department.

[0085] Through the law enforcement department's investigation of the traceability information, the corresponding lawbreakers can be finally traced.

[0086] Based on the same inventive concept, the embodiment of the present application provides a deep anti-counterfeiting system based on the linkage between cigarettes and cigarette cases, including a memory, a processor, and a program stored on the memory and executable on the processor. When the program is loaded and executed by the processor, it can implement the deep anti-counterfeiting method based on the linkage between cigarettes and cigarette cases in the process.

[0087] Based on the same inventive concept, the present application provides a cigarette, including a cigarette filter and a cigarette stick. A second code is printed on the cigarette stick, and the second code corresponds uniquely to the cigarette stick and can be scanned and recognized; a first code is printed on the cigarette case, and there is a logical association between the first code and the second code. The anti-counterfeiting can be verified through the logical association relationship between the first code and the second code, improving the authenticity of anti-counterfeiting and also increasing the difficulty of counterfeiting.

[0088] In one embodiment, the cigarette includes a tin foil, and a third code is printed on the tin foil. Through the third code, the traceability information of the cigarette can be obtained, and through the analysis of the traceability information, it is further convenient for the law enforcement department to investigate and punish the corresponding illegal acts and clean up the overall market.

[0089] In one embodiment, the cigarette includes a cigarette case, and corresponding taste descriptions can be printed on the cigarette case; a pattern corresponding to the taste is printed on the cigarette, and the pattern can be any pattern. In this embodiment, it is preferably to print the second code as the pattern. That is, after the second code is printed, once the user uses the cigarette stick, the second code will disappear, so that it cannot be recovered and verified, preventing lawbreakers from taking advantage of loopholes. At the same time, through the setting of the taste, it is convenient for the user to form a taste verification and further form an anti-counterfeiting solution.

[0090] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the division of the above functional modules is used as an example. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.

[0091] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical functional division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0092] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0093] In addition, in each embodiment of the present application, the functional units can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0094] If the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories, random access memories, magnetic disks, or optical discs that can store program codes.

[0095] As described above, the above embodiments are only used to introduce the technical solutions of the present application in detail. However, the description of the above embodiments is only for helping to understand the method and its core idea of the present application, and should not be construed as a limitation of the present application. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present application should be covered within the protection scope of the present application.

Claims

1. A deep anti-counterfeiting method based on the linkage between cigarettes and cigarette cases, characterized in that: including generating a second code that is logically associated with the first code according to the first code printed on the cigarette case; wherein, the first code corresponds uniquely to the cigarette case and can be recognized by scanning the code; the second code is printed on the cigarette and corresponds uniquely to the cigarette and can be recognized by scanning the code; identifying the first code to obtain first code identification information; identifying the second code to obtain second code identification information; judging whether there is a logical association relationship between the first code identification information and the second code identification information; if there is a logical association relationship between the first code identification information and the second code identification information, outputting a first verification passed message, otherwise triggering an anti-counterfeiting warning message.

2. The depth anti-counterfeiting method based on the linkage between a cigarette and a cigarette case according to claim 1, wherein: The second code is generated by an encryption algorithm and includes the following information: the production batch number of the cigarette, the geometric position arrangement feature, and the random encryption factor; obtaining the production batch number B, the geometric position coordinates P of the cigarette, and the random encryption factor R; generating an initial hash value H0 through a dynamic hashing algorithm, specifically as follows: ； wherein, B is the production batch number, P is the geometric position coordinates of the cigarette, t is the production timestamp, R is the random encryption factor, and ROT(P, t) represents circularly shifting P by t; Based on the key splitting function F k Split H0 into several sub-codes C i , and each sub-code satisfies the constraint condition: ； wherein, n is an integer; Assign the sub-code C i to the corresponding cigarette and print it as the second code.

3. The deep anti-counterfeiting method based on the linkage between a cigarette and a cigarette case according to claim 2, characterized in that: The sub-code C i corresponds to each cigarette one by one, and each cigarette has a separate second code or the sub-code C i is split and printed on at least two cigarettes.

4. The depth anti-counterfeiting method based on the linkage between a cigarette and a cigarette case according to claim 1, characterized in that: if the anti-counterfeiting warning message is triggered, collecting the store warning information corresponding to the cigarette for which the anti-counterfeiting warning message is triggered, and the store warning information includes the store name and the store address; storing the store warning information and forming a warning database; locating and displaying the store warning information in the warning database on a map.

5. The deep anti-counterfeiting method based on the linkage between cigarettes and cigarette cases according to claim 4, characterized in that: dividing the area with store warning information according to the preset rules to form a distribution area. If the number of store warning information in the distribution area is greater than or equal to the preset reference value, defining the distribution area as a disaster area, and displaying it by color marking and sending a request for investigation information to the law enforcement department.

6. The depth anti-counterfeiting method based on the linkage between a cigarette and a cigarette case according to claim 4, wherein: performing a store risk score on the stores corresponding to in the warning database according to a store risk scoring model, and displaying the store risk score; the specific store risk scoring model is as follows: ； wherein, Freq is the historical warning frequency, Recency is the time decay factor of the most recent warning, β0 is the intercept term (baseline risk), β1 is the frequency weight coefficient (positive number), and β2 is the time decay weight coefficient (negative number).

7. The deep anti-counterfeiting method based on the linkage between cigarette and cigarette case according to claim 1 or 4 or 5 or 6, characterized in that: generating a third code that is logically associated with the first code according to the first code printed on the cigarette case; wherein, the third code is printed on the aluminum foil paper and can be recognized by scanning the code; the third code represents traceability information.

8. The depth anti-counterfeiting method based on the linkage between a cigarette and a cigarette case according to claim 3, characterized in that: The flavor information corresponding to the cigarettes is printed on the cigarette case, and the sub-code C is printed. i The ink used is the ink corresponding to the flavor information.

9. A deep anti-counterfeiting system based on the linkage between cigarettes and cigarette cases, characterized in that, including a memory, a processor, and a program stored on the memory and executable on the processor, and when the program is loaded and executed by the processor, it can implement the deep anti-counterfeiting method based on the linkage between cigarette and cigarette case according to any one of claims 1 to 8.

10. A cigarette, characterized in that: including a cigarette filter and a cigarette stick, and the second code is printed on the cigarette stick, and the second code corresponds uniquely to the cigarette stick and can be recognized by scanning the code; the first code is printed on the cigarette case, and the first code has a logical association with the second code.

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