CN108734475B - Traceability anti-counterfeiting method based on big data relevance analysis - Google Patents

Abstract

The invention discloses a traceability anti-counterfeiting method based on big data relevance analysis, which is characterized in that a consumer scans a traceability label on a commodity once by using a traceability terminal and obtains a traceability serial number and a relevance data item at the same time; inquiring whether the traceability serial numbers and the association degree data items exist or not and meet actual preset conditions of the commodities, so as to ensure whether the inquiry data are effective or not; according to the query result, performing big data association analysis on the tracing serial number and the association degree data item set; and comparing whether the traceability serial number and the association degree data item of the query meet the association degree analysis result or not according to the analysis result, thereby ensuring the authenticity of the commodity information, namely achieving the effect of traceability and anti-counterfeiting of the commodity information.

Description

Traceability anti-counterfeiting method based on big data relevance analysis

Technical Field

The invention relates to the technical field of big data analysis, in particular to a traceability anti-counterfeiting method based on big data relevance analysis.

Background

Food safety is significant to daily life of people, and the judgment of authenticity of commodity traceability information is a hotspot of research in the field of food traceability all the time. Although various traceability systems, anti-counterfeit labels and anti-counterfeit technologies are available, for example, pork traceability systems, RFID labels, two-dimensional code inner and outer code anti-counterfeit, random dynamic two-dimensional codes, logistics tracking anti-counterfeit, and the like, the anti-counterfeit labels and the anti-counterfeit technologies have a common feature: the anti-counterfeit label has too high manufacturing cost, too complex operation flow and no continuous anti-counterfeit inquiry and verification function.

Disclosure of Invention

The invention provides a traceability anti-counterfeiting method based on big data association degree analysis, aiming at overcoming at least one defect in the prior art.

In order to solve the technical problems, the technical scheme of the invention is as follows:

a tracing anti-counterfeiting method based on big data relevance analysis comprises the following steps:

s1: obtaining a tracing code, and obtaining a tracing serial number according to the tracing code;

s2: the tracing terminal application acquires data items with correlation degree when commodities circulate or sell;

s3: the tracing terminal sends the obtained tracing serial number and the data item of the correlation degree to a tracing system platform;

s4: a tracing anti-counterfeiting function interface in a tracing system platform receives a tracing serial number and associated degree data sent by a tracing terminal, and performs matching query;

s5: analyzing the association degree of the traceability serial number and the commodity association degree data item;

s6: and the tracing terminal displays the result.

In a preferred embodiment, in step S1, the tracing terminal application is opened, and the tracing code on the commodity packaging box is scanned to obtain the tracing serial number.

In a preferred embodiment, in step S1, the tracing terminal application is opened, and the tracing code query button is clicked to obtain the tracing serial number.

In a preferred embodiment, in step S1, the traceable serial number is generated by encrypting and performing bit operation on a traceable code generated by a traceable coding rule and a random sequence, and the steps are as follows:

s11: the tracing code is converted into a byte sequence through encryption operation;

s12: performing bit operation on the byte sequence and converting the byte sequence into characters;

s13: and splicing the characters converted each time together to generate a tracing serial number.

In a preferred embodiment, the step of querying the matching between the traceability serial number and the commodity association degree data in step S4 is as follows:

s41: the tracing anti-counterfeiting function module firstly searches whether a tracing serial number exists in a database, if the tracing serial number does not exist, the tracing serial number is returned to the tracing terminal to be displayed, and the query is finished;

s42: if the traceability serial number exists, matching whether the traceability serial number relevance data item sent by the traceability terminal meets the preset condition or not according to the relevance data item preset according to the actual condition of the commodity, if not, returning to the traceability terminal for displaying if the traceability serial number relevance data item does not meet the preset condition, prompting the consumer of the preset condition of the traceability serial number relevance data item, and finishing the query;

s43: if the traceability serial number exists and the current traceability serial number association degree data item meets the actual preset conditions of the commodity, the step S5 is performed.

In a preferred scheme, in step S5, querying a traceability code before encryption and bit operation corresponding to a database traceability serial number to obtain a batch number of the traceability code; according to the tracing batch number, writing the tracing serial number and the association degree data items into a scanned tracing serial number and association degree data item set and carrying out big data association degree analysis, wherein the steps are as follows:

s51: reading a traceability serial number and an association degree data item set, and distributing the traceability serial number and the association degree data item set to each computing node of the big data platform according to the traceability batch number;

s52: counting the occurrence frequency of each batch of traceability code relevance data items by each computing node of the big data platform, and converting the occurrence frequency into relevance indexes;

s53: comparing whether the query tracing serial number and the association degree data item meet the association degree index of big data association degree analysis or not, if not, returning to the condition that the tracing serial number and the association degree data item do not meet the association degree index of big data association degree analysis, prompting a consumer that the correlation degree relation between the tracing serial number and the association degree data item should be met, and finishing the query; otherwise, if the tracing sequence number is satisfied, the tracing related information of the tracing sequence number is returned.

Compared with the prior art, the technical scheme of the invention has the beneficial effects that: a traceability anti-counterfeiting method based on big data relevance analysis is characterized in that a consumer scans a traceability label on a commodity once by using a traceability terminal to obtain a traceability serial number and relevance data items at the same time; inquiring whether the traceability serial numbers and the association degree data items exist or not and meet actual preset conditions of the commodities, so as to ensure whether the inquiry data are effective or not; according to the query result, performing big data association analysis on the source tracing serial number and the association degree data item set; according to the analysis result, whether the traceability serial number and the association degree data item of the inquiry meet the association degree analysis result or not is compared, so that the authenticity of the commodity information is ensured, namely the traceability and anti-counterfeiting effect of the commodity information is achieved.

Drawings

FIG. 1 is a block diagram of the structure of the correlation analysis traceability anti-counterfeiting method of the present invention.

FIG. 2 is a flow chart of the relevance analysis traceability anti-counterfeiting method of the present invention.

Fig. 3 is a flowchart of decoding and decoding two-dimensional codes according to embodiment 2.

Fig. 4 is a block diagram of an association degree analysis processing structure of the big data platform in embodiment 2.

FIG. 5 is a block diagram of the structure of the big data relevancy analysis traceability anti-counterfeiting system in embodiment 2.

Detailed Description

The drawings are for illustrative purposes only and are not to be construed as limiting the patent;

for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;

it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.

The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.

Example 1

As shown in fig. 1-2, a tracing anti-counterfeiting method based on big data association degree analysis includes the following steps:

s1: obtaining a tracing code, and obtaining a tracing serial number according to the tracing code;

s2: the tracing terminal application acquires data items with correlation degree when commodities circulate or sell;

s3: the tracing terminal sends the obtained tracing serial number and the data item of the correlation degree to a tracing system platform;

s4: a tracing anti-counterfeiting function interface in a tracing system platform receives a tracing serial number and associated degree data sent by a tracing terminal, and performs matching query;

s5: analyzing the association degree of the traceability serial number and the commodity association degree data item;

s6: and the tracing terminal displays the result.

In a specific implementation process, in step S1, the tracing terminal application is opened, and the tracing code on the commodity packaging box is scanned to obtain the tracing serial number.

In a specific implementation process, in step S1, the tracing terminal application is opened, and the tracing code query button is clicked to obtain the tracing serial number.

In a specific implementation process, in step S1, the tracing sequence number is generated by encrypting and bit-operating a tracing code generated by a tracing encoding rule and a random sequence, and the steps are as follows:

s11: the tracing code is converted into a byte sequence through encryption operation;

s12: performing bit operation on the byte sequence and converting the byte sequence into characters;

s13: and splicing the characters converted each time together to generate a tracing serial number.

In a specific implementation process, the matching query step of the traceability serial number and the commodity association degree data in step S4 is as follows:

s41: the tracing anti-counterfeiting function module firstly searches whether a tracing serial number exists in a database, if the tracing serial number does not exist, the tracing serial number is returned to the tracing terminal to be displayed, and the query is finished;

s42: if the traceability serial number exists, matching whether the traceability serial number relevance data item sent by the traceability terminal meets the preset condition or not according to the relevance data item preset according to the actual condition of the commodity, if not, returning to the traceability terminal for displaying if the traceability serial number relevance data item does not meet the preset condition, prompting the consumer of the preset condition of the traceability serial number relevance data item, and finishing the query;

s43: if the traceability serial number exists and the current traceability serial number association degree data item meets the actual preset conditions of the commodity, the step S5 is performed.

In the specific implementation process, in step S5, the database tracing serial number and the tracing code before encryption and bit operation are queried to obtain the batch number of the tracing code; according to the tracing batch number, writing the tracing serial number and the association degree data items into a scanned tracing serial number and association degree data item set and carrying out big data association degree analysis, wherein the steps are as follows:

s51: reading a traceability serial number and an association degree data item set, and distributing the traceability serial number and the association degree data item set to each computing node of the big data platform according to the traceability batch number;

s52: counting the occurrence frequency of each batch of traceability code relevance data items by each computing node of the big data platform, and converting the occurrence frequency into relevance indexes;

s53: comparing whether the query tracing serial number and the association degree data item meet the association degree index of big data association degree analysis or not, if not, returning to the condition that the tracing serial number and the association degree data item do not meet the association degree index of big data association degree analysis, prompting a consumer that the correlation degree relation between the tracing serial number and the association degree data item should be met, and finishing the query; otherwise, if the tracing sequence number is satisfied, the tracing related information of the tracing sequence number is returned.

A traceability anti-counterfeiting method based on big data relevance analysis is characterized in that a consumer scans a traceability label on a commodity once by using a traceability terminal to obtain a traceability serial number and relevance data items at the same time; inquiring whether the traceability serial numbers and the association degree data items exist or not and meet actual preset conditions of the commodities, so as to ensure whether the inquiry data are effective or not; according to the query result, performing big data association analysis on the source tracing serial number and the association degree data item set; and comparing whether the traceability serial number and the association degree data item of the query meet the association degree analysis result or not according to the analysis result, thereby ensuring the authenticity of the commodity information, namely achieving the effect of traceability and anti-counterfeiting of the commodity information.

Example 2

The tracing code on the commodity label is scanned through the tracing terminal, and the tracing serial number in the tracing code is obtained through analysis and decoding, and fig. 3 is a two-dimensional code decoding flow chart, for example, the analyzed tracing serial number is as follows:

“faeded7e66b906cad1075b96c84a67b0”；

the tracing serial number is generated by encrypting and bit operation according to a tracing coding rule and a tracing code generated by a random sequence; for example, the traceability code generated according to the encoding rule is "daxueching 09105n90j 833", and the traceability serial number after md5 encryption and bit operation is: "faded 7e66b906cad1075b96c84a67b 0", the traceability serial number is used as the consumer traceability anti-counterfeiting query entry, thus ensuring the irreversibility of the traceability code to the traceability serial number, and leading a counterfeiter not to be capable of obtaining the traceability code coding rule through the traceability sequence decoding, thereby leading the counterfeiter not to generate the traceability serial number in advance; counterfeiters can only obtain limited traceability serial numbers scanned and taken in a market at present, so that a premise is provided for the association analysis and anti-counterfeiting of big data in the following;

the method comprises the steps of obtaining relevant data items of commodities, such as time information, geographical location information and the like, wherein the time information and the geographical location information are selected in the embodiment, and different commodities can be selected according to different requirements;

the tracing terminal sends a tracing serial number and related data items to a tracing system tracing anti-counterfeiting function interface;

the tracing anti-counterfeiting function interface inquires a database, judges whether the sent tracing serial number exists or not, and if not, returns information that the tracing code does not exist and the commodity is false to the tracing terminal;

otherwise, whether the associated data item related to the commodity meets the actual preset condition of the commodity is judged, for example, whether the query term corresponding to the time information compared with the traceability serial number in the database is due or not is judged, and if not, the 'the commodity related data item does not meet the actual preset condition of the commodity' is returned to the traceability terminal.

It should be noted that the preset conditions of the traceability serial numbers and the associated data items can be set according to the actual conditions of the commodities; for example, the expiration time of the traceable serial number can be set according to the shelf life time of the commodity, and in this way, the consumer can automatically judge whether the commodity is a counterfeit or not according to the current time of scanning the commodity.

If the tracing serial number exists and the related data item of the commodity meets the actual preset condition of the commodity, inquiring an original tracing code corresponding to the tracing serial number, obtaining a commodity batch number corresponding to the tracing serial number from the original tracing code, obtaining the batch number, scanning and verifying the existing tracing serial number and the related data item data set, and calling a big data platform to perform correlation analysis on the tracing serial number and the data item set, wherein the steps mainly comprise:

and acquiring a traceability serial number and an associated data item set, distributing data to the big data platform computing node according to the traceability batch number, and distributing the read data to the next computing node by the big data platform computing node according to actual requirements. For example, the Storm flow computing big data platform is selected in the implementation of the embodiment, and other big data platforms can be selected according to needs. FIG. 4 is a block diagram of a processing structure using Storm big data association analysis, wherein the graph only reflects a processing framework model in an embodiment, and the specific detail how many nodes are deployed is planned according to the size of data volume; the node Spout acquires a related data item set according to the batch number, and distributes the preprocessed related data item to the next processing node Bolt; the processing node Bolt counts the occurrence frequency of the associated data items of the traceability serial numbers of each batch, and finally forms an association analysis result, for example, the association relationship between the traceability serial numbers and the geographic positions;

and inquiring whether the traceability serial number and the associated data item meet the association analysis result. For example, the query has a source sequence number of "faded 7e66b906cad1075b96c84a67b 0", and the associated data items are: "20170215, Guangzhou railway station", after big data association analysis, the commodity association geographical position is found to be: "Guangzhou south station" and "Guangzhou north station". The result shows that the consumer can know whether the commodity purchased by the consumer is purchased in the place above the market geographical position when the commodity meets the actual preset condition, so that the authenticity of the commodity is judged.

As shown in fig. 5, the two-dimensional code scanning module mainly uses the foregoing description as a standard, the commodity associated data item, for example, the geographic location, may be scanned and located by using the traceability terminal LBS location unit, the traceability anti-counterfeit module sequentially performs the query unit, the determination unit, the query counting unit, the association degree analysis unit, and the association result comparison unit, and finally returns the traceability anti-counterfeit result.

The same or similar reference numerals correspond to the same or similar parts;

the terms describing positional relationships in the drawings are for illustrative purposes only and are not to be construed as limiting the patent;

it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (4)

Translated fromChinese

1.一种基于大数据关联度分析的溯源防伪方法，其特征在于，包括以下步骤：1. a traceability anti-counterfeiting method based on big data correlation analysis, is characterized in that, comprises the following steps:S1：获取溯源码，根据溯源码得到溯源序列号；S1: Obtain the traceability source code, and obtain the traceability serial number according to the traceability source code;S2：溯源终端应用获取商品流通或售卖时具有关联度的数据项；S2: The traceability terminal application obtains data items with a degree of relevance when the commodity is circulated or sold;S3：溯源终端发送获取的溯源序列号以及相关联度的数据项到溯源系统平台；S3: The traceability terminal sends the acquired traceability serial number and the data item of the correlation degree to the traceability system platform;S4：溯源系统平台中溯源防伪功能接口接受溯源终端发送的溯源序列号和相关联度数据，进行匹配查询；S4: The traceability anti-counterfeiting function interface in the traceability system platform accepts the traceability serial number and correlation data sent by the traceability terminal, and performs matching query;S5：对溯源序列号和商品关联度数据项进行关联度分析；S5: Perform correlation analysis on the traceability serial number and the commodity correlation data item;S6：溯源终端显示结果；S6: The traceability terminal displays the result;在步骤S1中，所述的溯源序列号由溯源编码规则和随机序列生成的溯源码经过加密、位运算生成，步骤如下：In step S1, the traceability serial number is generated by the traceability coding rule and the traceability source code generated by the random sequence through encryption and bit operation, and the steps are as follows:S11：溯源码经过加密运算转化为字节序列；S11: The traceable source code is converted into a byte sequence through encryption operation;S12：针对该字节序列进行位运算，转化为字符；S12: Perform a bit operation on the byte sequence and convert it into a character;S13：将每次转化的字符拼接在一起，生成溯源序列号；S13: splicing together the characters converted each time to generate a traceability serial number;在步骤S5中,查询数据库溯源序列号与之对应加密、位运算前的溯源码，得到该溯源码的批次号；根据溯源批次号，把溯源序列号和关联度数据项写入已扫描的溯源序列号和关联度数据项集合中并进行大数据关联度度分析，步骤如下：In step S5, query the database traceability serial number and its corresponding traceability source code before encryption and bit operation to obtain the batch number of the traceability source code; according to the traceability batch number, write the traceability serial number and the correlation data item into the scanned data item. The traceability serial number and the correlation degree data item collection of the data item and the big data correlation degree analysis are carried out, and the steps are as follows:S51：读取溯源序列号和关联度数据项集合，根据溯源批次号把溯源序列号和关联度数据项集合分发至大数据平台各计算节点中；S51: Read the traceability serial number and the set of relevance data items, and distribute the traceability serial number and the set of relevance data items to each computing node of the big data platform according to the traceability batch number;S52：大数据平台各计算节点统计各批次溯源码关联度数据项的出现频次，并转化为关联度指标；S52: Each computing node of the big data platform counts the occurrence frequency of each batch of traceability source correlation data items, and converts them into correlation indicators;S53：比较本次查询溯源序列号和关联度数据项是否满足大数据关联度分析的关联度指标，若不满足，则返回“溯源序列号和关联度数据项不满足大数据关联度分析的关联度指标”，提示消费者该溯源序列号和关联度数据项应满足的关联度关系，查询结束；反之，若满足，则返回溯源序列号的溯源相关信息。S53: Compare whether the traceability serial number and the correlation data item of the current query satisfy the correlation index of the big data correlation analysis, if not, return "The traceability serial number and the correlation data item do not meet the correlation of the big data correlation analysis. "degree index", which reminds consumers of the relationship between the traceability serial number and the relatedness data item, and the query ends; on the contrary, if it is satisfied, the traceability related information of the traceability serial number is returned.2.根据权利要求1所述基于大数据关联度分析的溯源防伪方法，其特征在于，步骤S1中，打开溯源终端应用，扫描商品包装盒上溯源码，得到溯源序列号。2. The traceability anti-counterfeiting method based on big data correlation analysis according to claim 1, characterized in that, in step S1, the traceability terminal application is opened, and the traceability source code is scanned on the commodity packaging box to obtain the traceability serial number.3.根据权利要求1所述基于大数据关联度分析的溯源防伪方法，其特征在于，步骤S1中，打开溯源终端应用，点击溯源码查询按钮，得到溯源序列号。3. The traceability anti-counterfeiting method based on big data correlation analysis according to claim 1, wherein in step S1, open the traceability terminal application, click the traceability source query button, and obtain the traceability serial number.4.根据权利要求1所述基于大数据关联度分析的溯源防伪方法，其特征在于，步骤S4中所述溯源序列号和商品关联度数据的匹配查询步骤如下：4. The traceability anti-counterfeiting method based on big data correlation degree analysis according to claim 1, is characterized in that, the matching query steps of traceability serial number and commodity correlation degree data described in step S4 are as follows:S41：溯源防伪功能模块首先在数据库查找溯源序列号是否存在，若溯源序列号不存在，则返回“溯源序列号不存在，商品信息为假”到溯源终端显示，查询结束；S41: The traceability and anti-counterfeiting function module first searches the database for the existence of the traceability serial number. If the traceability serial number does not exist, it returns "the traceability serial number does not exist, and the product information is false" to be displayed on the traceability terminal, and the query ends;S42：若溯源序列号存在，则根据商品实际情况预设的关联度数据项，匹配查询溯源终端发送的溯源序列号关联度数据项是否满足预设条件，若不满足预设条件，则返回“查询溯源序列号关联度数据项不满足预设条件”到溯源终端显示，提示消费者溯源序列号关联度数据项的预设条件，查询结束；S42: If the traceability serial number exists, according to the preset correlation data item based on the actual situation of the product, match and query whether the traceability serial number correlation data item sent by the traceability terminal satisfies the preset condition, and if it does not meet the preset condition, return " Query the traceability serial number correlation degree data item does not meet the preset conditions" will be displayed on the traceability terminal, prompting the consumer to the preset conditions of the traceability serial number correlation degree data item, and the query ends;S43：若溯源序列号存在且当前溯源序列号关联度数据项满足商品实际预设条件，进行步骤S5。S43: If the traceability serial number exists and the current traceability serial number correlation degree data item satisfies the actual preset condition of the product, go to step S5.

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