A blockchain-based food traceability system

Abstract

The present invention relates to the field of food traceability, and particularly to a food traceability system based on blockchain. The present invention is provided with a feature storage module for storing the packaging features of foods to be transported; a packaging analysis module for analyzing the damage tendency characterization value of the package during transportation and classifying the damage tendency risk category of the package during transportation based on the packaging features of each food to be transported contained in a single package body in combination with the volume of the package body; an identification module for adaptively detecting each package body according to the damage tendency risk category; a traceability module for determining whether it is necessary to perform a fragmentation detection on the food in the package body based on the data tags stored in the blockchain for each package body; and a fragmentation detection module for scanning the food contour inside the food packaging, identifying scattered food fragments, and determining whether the food meets the fragmentation standard. The present invention can improve the efficiency of food traceability, timely locate the packages with risks, and ensure the integrity and quality of the food.

Description

Technical Field

[0001] The present invention relates to the field of food traceability, and particularly to a food traceability system based on blockchain. Background Art

[0002] Traditional food traceability systems have problems such as centralized data storage and data opacity, and cannot guarantee the true situation of the state of food during transportation. Through the blockchain technology that can be decentralized, the position of the package with risks during transportation can be accurately located, which is convenient for subsequent timely repackaging of the package, and improves the efficiency and reliability of food traceability.

[0003] Chinese Patent Application Publication No.: CN109214829A, discloses a food safety traceability method and device, the method includes: obtaining ingredient identification information during the food production, processing and transportation process, and the ingredient identification information includes but is not limited to production date, shelf life, storage conditions, quality information; comparing the matching degree of the ingredient identification information with the corresponding pre-stored food safety standard data to detect whether the food is qualified; when it is detected that the food is unqualified, retrieving the ingredient identification information during the food production, processing and transportation process to determine the corresponding error link; when it is detected that the food is qualified, generating corresponding quality and safety evaluation information according to the matching degree and storing it in the blockchain.

[0004] However, the following problems still exist in the prior art.

[0005] During the actual transportation of food, some food packages are in an inflated form. Multiple foods are encapsulated in a package. Due to the gas space of the package itself and the interaction with multiple packages inside the package, the food is easily broken. The prior art does not consider monitoring and tracing the package after transportation for the above phenomenon, resulting in the phenomenon of food breakage easily occurring after transportation. Summary of the Invention

[0006] Therefore, the present invention provides a food traceability system based on blockchain to overcome the problem that during the actual transportation of food, some food packages are in an inflated form, multiple foods are encapsulated in a package, and due to the gas space of the package itself and the interaction with multiple packages inside the package, the food is easily broken and there is a lack of traceability means.

[0007] To achieve the above object, the present invention provides a food traceability system based on blockchain, which includes:

[0008] A feature storage module for storing the packaging features of the food to be transported, and the packaging features include the size features of the food packaging and the gas content ratio inside the food packaging;

[0009] A packaging analysis module, which is connected to the feature storage module, is used to analyze the damage tendency characterization value of the package during transportation based on the packaging features of each food to be transported contained in a single package and the volume of the package, so as to classify the damage tendency risk category of the package during transportation;

[0010] An identification module, which is respectively connected to the feature storage module and the packaging analysis module, is used to detect each of the packages according to the damage tendency risk category, including,

[0011] Determine the risk bump time domain segment based on the vibration amplitude of the transportation vehicle during transportation, evaluate whether to generate a data label for the package based on the total duration of the risk bump time domain segment, and store the data label through the blockchain;

[0012] Or, evaluate whether there is a severe bump based on the vibration amplitude of the transportation vehicle during transportation, determine whether to generate a data label for the package, and store the data label through the blockchain;

[0013] A traceability module, which is connected to the identification module, is used to determine whether it is necessary to perform a fragmentation detection on the food in the package based on the data label stored in the blockchain for each package;

[0014] A fragmentation detection module, which is used to scan the food contour inside the food packaging, identify scattered food fragments, and determine whether the food meets the fragmentation standard.

[0015] Further, the process by which the packaging analysis module analyzes the damage tendency characterization value of the package during transportation includes,

[0016] Based on the size characteristics of the food packaging, determine the total volume of each food packaging, and calculate the ratio of the volume of the package to the total volume as the first tendency feature;

[0017] Determine the average gas content ratio of each food packaging, solve the ratio of the average gas content ratio to the predetermined gas content ratio threshold, and determine the ratio as the second tendency feature;

[0018] Determine the sum of the first tendency feature and the second tendency feature as the damage tendency characterization value.

[0019] Further, the packaging analysis module is used to classify the damage tendency risk category of the package during transportation, including,

[0020] If the damage tendency characterization value is greater than or equal to the damage tendency characterization threshold, determine that the damage tendency risk category of the package during transportation is a strong risk category;

[0021] If the damage tendency characterization value is less than the damage tendency characterization threshold, it is determined that the damage tendency risk category of the inclusion during transportation is the weak risk category.

[0022] Further, the identification module is used to detect each inclusion according to the damage tendency risk category, including,

[0023] If the damage tendency risk category of the inclusion during transportation is the strong risk category, based on the vibration amplitude of the transportation vehicle during transportation, a risk bump time domain segment is determined, and based on the total duration of the risk bump time domain segment, it is evaluated whether to generate a data label for the inclusion, and the data label is stored through the blockchain;

[0024] If the damage tendency risk category of the inclusion during transportation is the weak risk category, based on the vibration amplitude of the transportation vehicle during transportation, it is evaluated whether there is a severe bump to determine whether to generate a data label for the inclusion, and the data label is stored through the blockchain.

[0025] Further, the process by which the identification module determines the risk bump time domain segment based on the vibration amplitude of the transportation vehicle during transportation includes,

[0026] Taking time as the horizontal axis and the vibration amplitude of the transportation vehicle as the vertical axis to construct a rectangular coordinate system;

[0027] Constructing a vibration amplitude time domain curve and dividing the vibration amplitude time domain curve into several curve segments;

[0028] If there is a curve segment whose average vibration amplitude is greater than a predetermined vibration amplitude threshold, it is determined that this curve segment is the risk bump time domain segment.

[0029] Further, the process by which the identification module evaluates whether to generate a data label for the inclusion based on the total duration of the risk bump time domain segment includes,

[0030] Used to determine the risk bump time domain segments existing in the transportation vehicle within a predetermined time period;

[0031] Used to obtain the duration corresponding to each risk bump time domain segment to determine the total duration of each risk bump time domain segment;

[0032] Used to compare the total duration of the risk bump time domain segment with the total duration threshold, where,

[0033] If the total duration of the risk bump time domain segment is greater than or equal to the total duration threshold, a data label for the inclusion is generated;

[0034] The data label includes the number of the inclusion and the damage tendency risk category corresponding to the inclusion.

[0035] Further, the recognition module is used to evaluate whether there is severe jolting based on the vibration amplitude of the transportation vehicle during transportation, including,

[0036] If the vibration amplitude of the transportation vehicle is greater than or equal to the severe jolting threshold, it is determined that there is severe jolting.

[0037] Further, the recognition module is used to determine whether to generate a data label for the package and store the data label through the blockchain, including,

[0038] If there is severe jolting in the transportation vehicle, a data label for the package is generated and stored through the blockchain.

[0039] Further, the traceability module is used to determine whether to perform fragmentation detection on the food in the package based on the data label stored in the blockchain for each package, including,

[0040] If the damage tendency risk category corresponding to the package shown by the stored data label is a strong risk category, fragmentation detection needs to be performed on the food in the package.

[0041] Further, the process by which the fragmentation detection module determines whether the food meets the fragmentation standard includes,

[0042] Obtaining the food contour scan result inside the food package and identifying scattered food fragments;

[0043] Extracting the total contour area of the scattered food fragments and comparing it with a preset area threshold, including,

[0044] If the total contour area of the scattered food fragments is less than the area threshold, it is determined that the food corresponding to the scattered food fragments meets the fragmentation standard;

[0045] Among them, if there is any food fragment contour smaller than the food fragment contour threshold, it is determined that the food fragment corresponding to the food fragment contour is a scattered food fragment.

[0046] Compared with the prior art, the present invention is provided with a feature storage module for storing the packaging features of the food to be transported; a packaging analysis module, which is connected to the feature storage module, for analyzing the damage tendency characterization value of the package during transportation based on the packaging features of each food to be transported contained in a single package and the volume of the package, so as to classify the damage tendency risk category of the package during transportation; an identification module, which is respectively connected to the feature storage module and the packaging analysis module, for adaptively detecting each package according to the damage tendency risk category; a traceability module, which is connected to the identification module, for determining whether it is necessary to perform fragmentation detection on the food in the package based on the data tag stored in the blockchain for each package; a fragmentation detection module, which is used to scan the food contour inside the food package, identify scattered food fragments, and determine whether the food meets the fragmentation standard. The present invention can improve the efficiency of food traceability, timely locate the risky packages, and ensure the integrity and quality of the food.

[0047] In particular, the present invention analyzes the damage tendency characterization value of the inclusion during transportation by combining the packaging characteristics of the food to be transported with the volume of the inclusion. During the food transportation process, the amount of food contained in the inclusion and the size characteristics of each food package are different, and the proportion of the gas content filled in the food package is also inconsistent. Due to the gaps and gas space, the bumps during transportation cause the food in the food package to be easily broken. For example, when multiple inflatable puffed foods are placed in the same inclusion, the possibility of damage during transportation is high. When the proportion of the gas content filled in the food package to the food itself is too large, the food package is easily damaged and the gas leaks due to the friction and bumps caused by transportation. The damage tendency of the inclusion during actual transportation is determined by combining the above characteristics with the volume of the inclusion, and then, data support is provided for the subsequent classification of the wind direction category of the damage tendency of the inclusion during transportation, and each inclusion is tested. The present invention determines the degree of damage tendency risk of each inclusion before actual transportation, so as to carry out corresponding tests in a targeted manner during the actual transportation process to ensure stable and safe transportation of the inclusion.

[0048] In particular, the present invention classifies the risk categories of the damage tendency of inclusions during transportation. Under the strong risk category of the damage tendency risk category, the risk bump time domain segment is determined according to the vibration amplitude of the transportation vehicle during transportation. Since the bumpiness of the road surface traveled by the transportation vehicle is different, the vibration degrees generated by the reaction of the above road surface on the transportation vehicle during driving are different, which will have a corresponding bumping impact on the driving stability of the transportation vehicle. This application extracts the feature of the vibration amplitude of the transportation vehicle during transportation for evaluation. If the vibration amplitude of the transportation vehicle is too large within a certain period of time, it can be determined that the risk degree of bumping during the above period is relatively high. The time period corresponding to the above situation is determined as the risk bump time domain segment, and further analyze the number of risk bump time domain segments of the transportation vehicle within a predetermined time period and the duration corresponding to each time domain segment to determine the total duration of the risk bump time domain segment. If the total duration of the risk bump time domain segment within the predetermined time period is too long, the inclusions carried on the transportation vehicle are marked, and the data label containing the inclusion number and the corresponding damage tendency risk category of the inclusion is stored through the blockchain. Storing through the blockchain can trace the inclusions with risk bumps more timely, so as to facilitate subsequent detection, avoid food from flowing into the market, and can intercept problem food in time. Under the weak risk category of the damage tendency risk category, the packaging characteristics of the food inside the inclusion present a relatively stable state, and the possibility of being damaged during actual transportation is relatively small. However, if there is a severe bump, it may still affect the smooth operation of the transportation vehicle, and then affect the inclusions carried by the transportation vehicle accordingly. Therefore, in the above situation, when there is a severe bump, the inclusions carried on the transportation vehicle are marked, and the corresponding data label is set and stored through the blockchain. The present invention detects the bump situations that pose risk tendencies to the inclusions specifically, more effectively determines the states of the inclusions under different damage tendency risk categories, and stores the data labels of the inclusions through the blockchain to improve the tracing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 FIG. is a functional module diagram of a food traceability system based on blockchain according to an embodiment of the invention;

[0050] Figure 2 FIG. is a logical decision diagram for classifying the risk categories of the damage tendency of inclusions during transportation according to an embodiment of the invention;

[0051] Figure 3 FIG. is a logical decision diagram for determining the risk bump time domain segment according to an embodiment of the invention;

[0052] Figure 4 FIG. is a logical decision diagram for evaluating whether there is a severe bump according to an embodiment of the invention. Detailed implementation mode

[0053] In order to make the objectives and advantages of the present invention clearer, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0054] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0055] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the term "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0056] Please refer to Figures 1 to 4 as shown Figure 1 which is a functional module diagram of the blockchain-based food traceability system according to an embodiment of the present invention, Figure 2 which is a logical decision diagram for classifying the risk categories of damage tendency of the inclusion during transportation according to an embodiment of the present invention, Figure 3 which is a logical decision diagram for determining the risk bump time domain section according to an embodiment of the present invention, Figure 4 which is a logical decision diagram for evaluating whether there is a violent bump according to an embodiment of the invention. The blockchain-based food traceability system according to an embodiment of the present invention includes:

[0057] A feature storage module for storing the packaging features of the food to be transported, where the packaging features include the size features of the food packaging and the gas content ratio inside the food packaging;

[0058] A packaging analysis module, which is connected to the feature storage module, for analyzing the damage tendency characterization value of the package during transportation based on the packaging features of each piece of food to be transported contained in a single package in combination with the volume of the package, so as to classify the risk categories of the damage tendency of the package during transportation;

[0059] An identification module, which is respectively connected to the feature storage module and the packaging analysis module, for detecting each of the packages according to the risk categories of the damage tendency, including,

[0060] determining a risk bump time domain section based on the vibration amplitude of the transportation vehicle during transportation, evaluating whether to generate a data label for the package based on the total duration of the risk bump time domain section, and storing the data label through the blockchain;

[0061] Alternatively, based on the vibration amplitude of the transportation vehicle during transportation, it is evaluated whether there is severe jolting to determine whether to generate a data tag for the package, and the data tag is stored through the blockchain;

[0062] A traceability module, which is connected to the identification module, is used to determine whether it is necessary to perform a fragmentation detection on the food in the package based on the data tag stored in the blockchain for each package;

[0063] A fragmentation detection module, which is used to scan the food contour inside the food package, identify scattered food fragments, and determine whether the food meets the fragmentation standard.

[0064] Specifically, there is no specific limitation on the way of storing the packaging characteristics of the food to be transported. The packaging characteristics can be pre-determined. For the dimensional characteristics, including the length, width and volume of the packaging bag, the gas content ratio inside the food package is the ratio of the gas volume inside the food package to the volume of the packaging bag.

[0065] Specifically, there is no limitation on the specific structure of the feature storage module, the packaging analysis module, the identification module and the traceability module. It itself or each unit therein can be composed of logical components or a combination of logical components. The logical components include a field programmable processor, a computer or a microprocessor in the computer.

[0066] Specifically, there is no specific limitation on the way of scanning the food contour inside the food package and identifying scattered food fragments. The food contour inside the food package can be scanned by X-ray scanning technology, and the scattered food fragments can be identified by an edge detection algorithm. Of course, other forms can also be adopted, which will not be elaborated here.

[0067] Specifically, the process by which the packaging analysis module analyzes the damage tendency characterization value of the package during transportation includes,

[0068] Based on the dimensional characteristics of the food package, the total volume of each food package is determined, and the ratio of the volume of the package to the total volume is calculated as the first tendency feature;

[0069] To determine the average gas content ratio of each food package, solve the ratio of the average gas content ratio to the predetermined gas content ratio threshold, and determine the ratio as the second tendency feature;

[0070] The sum of the first tendency feature and the second tendency feature is determined as the damage tendency characterization value.

[0071] The gas content ratio threshold is obtained by pre-setting. Several food packaging characteristics are obtained in advance, the gas content ratio is obtained, the average gas content ratio is solved, and the gas content ratio threshold is set to 0.8 times the average gas content ratio.

[0072] Specifically, the packaging analysis module is used to classify the risk categories of damage tendency of the package during transportation, including:

[0073] If the damage tendency characterization value is greater than or equal to the damage tendency characterization threshold, the damage tendency risk category of the inclusion during transportation is determined to be a strong risk category;

[0074] If the damage tendency characterization value is less than the damage tendency characterization threshold, it is determined that the damage tendency risk category of the inclusion during transportation is a weak risk category.

[0075] The damage tendency characterization threshold is selected in the interval [2.45,2.65].

[0076] The present invention analyzes the damage tendency characterization value of the inclusion during transportation by combining the packaging characteristics of the food to be transported with the volume of the inclusion. During the food transportation process, the amount of food contained in the inclusion and the size characteristics of each food package are different, and the proportion of the gas content filled in the food package is also inconsistent. Due to the gap and gas space, the bumps during transportation cause the food in the food package to be easily broken. For example, if multiple inflatable puffed foods are placed in the same inclusion, the possibility of damage during transportation is high. When the proportion of the gas content filled in the food package to the food itself is too large, the food package is easily damaged and the gas leaks due to the friction and bumps caused by transportation. The damage tendency of the inclusion during actual transportation is determined by combining the above characteristics with the volume of the inclusion, and then, data support is provided for the subsequent classification of the wind direction category of the damage tendency of the inclusion during transportation, and each inclusion is tested. The present invention determines the degree of damage tendency risk of each inclusion before actual transportation, so as to carry out corresponding tests in a targeted manner during actual transportation to ensure stable and safe transportation of the inclusion.

[0077] Specifically, the identification module is used to detect each inclusion according to the damage tendency risk category, including:

[0078] If the damage tendency risk category of the inclusion during transportation is a strong risk category, a risk bumpy time domain segment is determined based on the vibration amplitude of the transportation vehicle during transportation, and whether to generate a data tag for the inclusion is evaluated based on the total duration of the risk bumpy time domain segment, and the data tag is stored through the blockchain;

[0079] If the risk category of the damage tendency of the inclusion during transportation is a weak risk category, whether there is severe turbulence is assessed based on the vibration amplitude of the transportation vehicle during transportation to determine whether a data tag for the inclusion is generated and the data tag is stored through the blockchain.

[0080] Specifically, the process by which the recognition module determines the risk bump time domain segment based on the vibration amplitude of the transportation vehicle during transportation includes,

[0081] The process by which the recognition module determines the risk bump time domain segment based on the vibration amplitude of the transportation vehicle during transportation includes,

[0082] Construct a rectangular coordinate system with time as the horizontal axis and the vibration amplitude of the transportation vehicle as the vertical axis;

[0083] Construct a vibration amplitude time domain curve and divide the vibration amplitude time domain curve into several curve segments;

[0084] If the average vibration amplitude of a curve segment is greater than a predetermined vibration amplitude threshold, then determine that this curve segment is a risk bump time domain segment.

[0085] It can be understood that the vibration amplitude threshold is obtained by presetting. The average vibration amplitude during several transportation processes of the transportation vehicle is obtained. To characterize bumps, the vibration amplitude threshold is set to be between 1.25 times and 1.5 times the average vibration amplitude.

[0086] Specifically, there is no limitation on the method of constructing the vibration amplitude time domain curve. For example, the time domain curve can be fitted by relevant fitting software such as mat l ab, which will not be elaborated here.

[0087] Specifically, the process by which the recognition module evaluates whether to generate a data label for the package based on the total duration of the risk bump time domain segment includes,

[0088] To determine the risk bump time domain segments existing in the transportation vehicle within a predetermined time period;

[0089] To obtain the duration corresponding to each of the risk bump time domain segments to determine the total duration of each risk bump time domain segment;

[0090] To compare the total duration of the risk bump time domain segment with a total duration threshold, where,

[0091] If the total duration of the risk bump time domain segment is greater than or equal to the total duration threshold, then generate a data label for the package;

[0092] The data label includes the number of the package and the risk category of damage tendency corresponding to the package.

[0093] Specifically, for the determination of the predetermined time period, it can be determined according to the required duration between the origin and destination of the transportation vehicle, which will not be elaborated here.

[0094] The total duration threshold of the risk bump time domain segment is determined based on the duration of the predetermined time period and is set to be selected between 0.45 times and 0.75 times the predetermined time period.

[0095] The data label includes the number of the inclusion and the risk category of damage tendency corresponding to the inclusion.

[0096] Specifically, the identification module is used to evaluate whether there is severe jolting based on the vibration amplitude of the means of transportation during transportation, including,

[0097] If the vibration amplitude of the means of transportation is greater than or equal to the severe jolting threshold, it is determined that there is severe jolting;

[0098] If the vibration amplitude of the means of transportation is less than the severe jolting threshold, it is determined that there is no severe jolting.

[0099] In this embodiment, the severe jolting threshold is 1.35 times the vibration amplitude threshold.

[0100] Specifically, the identification module is used to determine whether to generate a data label for the inclusion and store the data label through the blockchain, including,

[0101] If there is severe jolting in the means of transportation, a data label for the inclusion is generated and the data label is stored through the blockchain.

[0102] The present invention classifies the risk categories of the damage tendency of inclusions during transportation. Under the strong risk category of the damage tendency risk category, the risk bump time domain segment is determined according to the vibration amplitude of the transportation vehicle during transportation. Since the bump degree of the road surface traveled by the transportation vehicle is different, therefore, the vibration degree generated by the reaction of the above road surface on the transportation vehicle during driving is different, which will have a corresponding bump impact on the driving stability of the transportation vehicle. This application extracts the vibration amplitude of the transportation vehicle during transportation as a feature for evaluation. If the vibration amplitude of the transportation vehicle is too large within a certain period of time, it can be determined that the risk degree of bumping during the above period is relatively high. The time period corresponding to the above situation is determined as the risk bump time domain segment, and the number of risk bump time domain segments of the transportation vehicle within a predetermined time period and the duration corresponding to each time domain segment are further analyzed to determine the total duration of the risk bump time domain segment. If the total duration of the risk bump time domain segment within the predetermined time period is too long, the inclusions carried on the transportation vehicle are marked, and the data label containing the inclusion number and the corresponding damage tendency risk category of the inclusion is stored through the blockchain. Storing through the blockchain can trace the inclusions with risk bumps more timely, so as to facilitate subsequent detection, avoid food from flowing into the market, and intercept problem food in time. Under the weak risk category of the damage tendency risk category, the packaging characteristics of the food inside the inclusion present a relatively stable state, and the possibility of being damaged during actual transportation is relatively small. However, if there is a severe bump, it may still affect the stable operation of the transportation vehicle, and then affect the inclusions carried by the transportation vehicle accordingly. Therefore, in the above situation, when there is a severe bump, the inclusions carried on the transportation vehicle are marked, and corresponding data labels are set and stored through the blockchain. The present invention detects the bump situations that pose risk tendencies to the inclusions specifically, more effectively determines the states of the inclusions under different damage tendency risk categories, and stores the data labels of the inclusions through the blockchain to improve the tracing efficiency.

[0103] Specifically, the tracing module is used to determine whether it is necessary to perform fragmentation detection on the food in the inclusion based on the data label stored in the blockchain for each inclusion, including,

[0104] If the damage tendency risk category corresponding to the inclusion shown by the stored data label is a strong risk category, it is necessary to perform fragmentation detection on the food in the inclusion;

[0105] If the damage tendency risk category corresponding to the inclusion shown by the stored data label is a weak risk category, it is not necessary to perform fragmentation detection on the food in the inclusion.

[0106] The present invention determines whether it is necessary to perform fragmentation detection on the food inside the package according to the inclusion content corresponding to the data tag stored in the blockchain, and more quickly and accurately determines the packages with the risk of food fragmentation for subsequent fragmentation detection.

[0107] Specifically, the process by which the fragmentation detection module determines whether the food meets the fragmentation standard includes

[0108] obtaining the food contour scanning result inside the food package and identifying scattered food fragments;

[0109] extracting the total contour area of the scattered food fragments and comparing it with a preset area threshold, including

[0110] if the total contour area of the scattered food fragments is less than the area threshold, it is determined that the food corresponding to the scattered food fragments meets the fragmentation standard;

[0111] wherein, if there is any food fragment contour less than the food fragment contour threshold, it is determined that the food fragment corresponding to the food fragment contour is a scattered food fragment.

[0112] Therefore, in this embodiment, the food fragment contour threshold is obtained by pre-determined setting. The food fragment contour data existing inside the food package during several fragmentation detection processes are extracted, and the average value of the food fragment contours is solved. It is set that the food contour threshold is between 1.12 and 1.24 times the average value of the food contours.

[0113] The preset area threshold is determined based on the total contour area of the food main body and is set to be between 0.15 times and 0.3 times the total contour area of the food main body.

[0114] So far, the technical solution of the present invention has been described in combination with the preferred embodiments shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present invention.

Claims

1. A food traceability system based on blockchain, characterized in that: include: A feature storage module, used to store packaging features of the food to be transported, wherein the packaging features include size features of the food packaging and a gas content ratio inside the food packaging; a packaging analysis module connected to the feature storage module, for analyzing the damage tendency characterization value of the package during transportation based on the packaging characteristics of each food to be transported contained in a single package and the volume of the package, so as to classify the damage tendency risk category of the package during transportation; An identification module, which is connected to the feature storage module and the packaging analysis module respectively, is used to detect each of the inclusions according to the damage tendency risk category, including: Determine a risk bumpy time domain segment based on the vibration amplitude of the transport vehicle during transportation, evaluate whether to generate a data tag for the inclusion based on the total duration of the risk bumpy time domain segment, and store the data tag through blockchain; Or, based on the vibration amplitude of the transportation vehicle during transportation, assess whether there is severe turbulence to determine whether to generate a data tag for the inclusion, and store the data tag through the blockchain; A traceability module connected to the identification module, for determining whether a fragmentation detection is required for the food in the inclusion based on the data tag stored in the blockchain for each inclusion; A fragmentation detection module is used to scan the food contour inside the food package, identify scattered food fragments, and determine whether the food meets the fragmentation standard; The process of the packaging analysis module for analyzing the damage tendency characterization value of the package during transportation includes: for determining the total volume of each food package based on the size characteristics of the food package, and calculating the ratio of the volume of the package to the total volume as the first tendency characteristic; Determine the average value of the gas content ratio of each food package, calculate the ratio of the average value of the gas content ratio to a predetermined gas content ratio threshold, and determine the ratio as a second tendency feature; determining a sum of the first tendency feature and the second tendency feature as a damage tendency characterization value; The identification module is used to detect each inclusion according to the damage tendency risk category, including: If the damage tendency risk category of the inclusion during transportation is a strong risk category, a risk bumpy time domain segment is determined based on the vibration amplitude of the transportation vehicle during transportation, and whether to generate a data tag for the inclusion is evaluated based on the total duration of the risk bumpy time domain segment, and the data tag is stored through the blockchain; If the risk category of the damage tendency of the inclusion during transportation is a weak risk category, whether there is severe turbulence is assessed based on the vibration amplitude of the transportation vehicle during transportation to determine whether a data tag for the inclusion is generated and the data tag is stored through the blockchain.

2. The blockchain-based food traceability system according to claim 1 is characterized in that: The packaging analysis module is used to classify the risk categories of damage tendency of the package during transportation, including: If the damage tendency characterization value is greater than or equal to the damage tendency characterization threshold, the damage tendency risk category of the inclusion during transportation is determined to be a strong risk category; If the damage tendency characterization value is less than the damage tendency characterization threshold, it is determined that the damage tendency risk category of the inclusion during transportation is a weak risk category.

3. The blockchain-based food traceability system according to claim 1, characterized in that: The process of the identification module determining the risk bumpy time domain segment based on the vibration amplitude of the transportation tool during transportation includes: A rectangular coordinate system is constructed with time as the horizontal axis and the vibration amplitude of the transport vehicle as the vertical axis; Constructing a vibration amplitude time domain curve, and dividing the vibration amplitude time domain curve into a plurality of curve segments; If there is a curve segment whose average vibration amplitude is greater than a predetermined vibration amplitude threshold, the curve segment is determined to be a risky turbulence time domain segment.

4. The blockchain-based food traceability system according to claim 1, characterized in that: The process of the identification module evaluating whether to generate a data label for the inclusion based on the total duration of the risk turbulence time domain segment includes: To determine the risk of turbulence in a transportation tool within a predetermined time period; To obtain the duration corresponding to each of the risk turbulence time domain segments to determine the total duration of each of the risk turbulence time domain segments; The total duration of the risk turbulence time domain segment is compared with the total duration threshold, wherein: If the total duration of the risk turbulence time domain segment is greater than or equal to the total duration threshold, a data label for the inclusion is generated; The data tag includes the number of the inclusion and the damage tendency risk category corresponding to the inclusion.

5. The blockchain-based food traceability system according to claim 1, characterized in that: The recognition module is used to evaluate whether there is severe turbulence based on the vibration amplitude of the transportation vehicle during transportation. include, If the vibration amplitude of the vehicle is greater than or equal to the severe turbulence threshold, it is determined that severe turbulence exists.

6. The blockchain-based food traceability system according to claim 1, characterized in that: The identification module is used to determine whether to generate a data tag for the inclusion body and store the data tag through the blockchain. include, If it is determined that there is severe turbulence in the means of transport, a data tag for the inclusion is generated and stored through the blockchain.

7. The blockchain-based food traceability system according to claim 1, characterized in that: The traceability module is used to determine whether it is necessary to perform a fragmentation detection on the food in the package based on the data tags stored in the blockchain for each package, including: If the damage tendency risk category corresponding to the inclusion shown by the stored data label is a strong risk category, the food in the inclusion needs to be tested for breakage.

8. The blockchain-based food traceability system according to claim 1, characterized in that: The process of the fragmentation detection module for determining whether the food meets the fragmentation standard includes: Used to obtain food contour scan results inside food packaging and identify scattered food fragments; The total area of ​​the contour of the scattered food fragments is extracted and compared with a preset area threshold, including: If the total area of ​​the outlines of the scattered food fragments is less than the area threshold, it is determined that the food corresponding to the scattered food fragments meets the fragmentation standard; If there is any food fragment contour smaller than the food fragment contour threshold, the food fragments corresponding to the food fragment contour are determined to be scattered food fragments.

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