Food traceability system based on block chain

Abstract

The invention relates to the field of food traceability, in particular to a food traceability system based on a block chain, and the system is provided with a feature storage module which is used for storing the packaging features of to-be-transported food; the package analysis module is used for analyzing the damage tendency characterization value of the inclusion in the transportation process based on the package characteristics of the to-be-transported food contained in the single inclusion in combination with the volume of the inclusion, and dividing the damage tendency risk category of the inclusion in the transportation process; the identification module is used for adaptively detecting each inclusion according to the damage tendency risk category; the tracing module is used for judging whether the food in the inclusion needs to be subjected to fragmentation detection or not based on the data label stored in the block chain for each inclusion; and the fragmentation detection module is used for scanning the food contour in the food package, identifying scattered food fragments and judging whether the food meets the fragmentation standard. The food traceability efficiency can be improved, the inclusion with risks can be positioned in time, and the intact degree and quality of the food are ensured.

Description

Technical Field

[0001] The present invention relates to the field of food traceability, and in particular 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 are unable to guarantee the true status of food during transportation. Decentralized blockchain technology can accurately locate the location of risky packages during transportation, facilitate subsequent timely repackaging of the packages, and improve the efficiency and reliability of food traceability.

[0003] China Patent Application Publication No.: CN109214829A, discloses a food safety traceability method and device, the method includes: obtaining ingredient identification information during food production, processing and transportation, the ingredient identification information including but 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 assessment information according to the matching degree, and storing it in the blockchain.

[0004] However, there are still the following problems in the prior art:

[0005] During the actual transportation of food, some food packages are inflated. Multiple foods are encapsulated in the package, which may easily cause the food to break due to the gas space itself and the interaction with multiple packages in the package. The prior art does not consider monitoring and tracing the packages after transportation in view of the above phenomenon, which leads to the phenomenon that food is easily broken after transportation. Summary of the invention

[0006] To this end, 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 inflated, and multiple foods are encapsulated in the package. Due to the gas space itself and the interaction with multiple packages in the package, the food is easily broken and there is a lack of traceability means.

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

[0008] 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;

[0009] 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;

[0010] 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:

[0011] 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;

[0012] 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;

[0013] 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 tags stored in the blockchain for each inclusion;

[0014] The 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.

[0015] Furthermore, the process of the packaging analysis module analyzing the damage tendency characterization value of the package during transportation includes:

[0016] 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;

[0017] to determine the mean value of the gas content ratio of each food package, calculate the ratio of the mean value of the gas content ratio to a predetermined gas content ratio threshold value, and determine the ratio as a second tendency feature;

[0018] The sum of the first tendency feature and the second tendency feature is determined as a damage tendency characterization value.

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

[0020] 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;

[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 a weak risk category.

[0022] Furthermore, 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 a strong risk category, then determine the risk bumpy time domain segment based on the vibration amplitude of the transportation 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 the blockchain;

[0024] If the risk category of the damage tendency of the inclusion during transportation is a weak risk category, then 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.

[0025] Furthermore, 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:

[0026] A rectangular coordinate system is constructed with time as the horizontal axis and the vibration amplitude of the transport vehicle as the vertical axis;

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

[0028] 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.

[0029] Furthermore, 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:

[0030] To determine the risk of turbulence in a transportation tool within a predetermined time period;

[0031] 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;

[0032] The total duration of the risk turbulence time domain segment is compared with the total duration threshold, wherein:

[0033] 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;

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

[0035] Furthermore, the identification module is used to evaluate whether there is severe turbulence based on the vibration amplitude of the transportation vehicle during transportation, including:

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

[0037] Furthermore, 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, including:

[0038] If there is severe turbulence in the means of transport, a data tag for the inclusion is generated and stored through the blockchain.

[0039] Furthermore, 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:

[0040] 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.

[0041] Furthermore, the process of the fragmentation detection module for determining whether the food meets the fragmentation standard includes:

[0042] Used to obtain food contour scan results inside food packaging and identify scattered food fragments;

[0043] The total area of ​​the contour of the scattered food fragments is extracted and compared with a preset area threshold, including:

[0044] 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;

[0045] 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.

[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 damage tendency risk categories of inclusions during transportation. When the damage tendency risk category is a strong risk category, the risk bumpy time domain segment is determined according to the vibration amplitude of the transportation vehicle during transportation. Since the bumpiness of the road surface on which the transportation vehicle travels is different, the vibration degree generated by the reaction of the above-mentioned road surface to the transportation vehicle during driving is different, which will have a corresponding bumpy impact on the driving stability of the transportation vehicle. The present application extracts 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 time period, it can be determined that the risk of bumpy occurrence in the above time period is high. The time period corresponding to the above situation is determined as the risk bumpy time domain segment, and the number of risk bumpy time domain segments of the transportation vehicle within the predetermined time period and the duration corresponding to each time domain segment are further analyzed to determine the total duration of the risk bumpy time domain segments. If the total duration of the risk bumpy time domain segments within the predetermined time period is too long, the inclusions carried on the transportation vehicle are marked, and the number of inclusions is marked with the inclusion number. The data tags of the damage tendency risk categories corresponding to the inclusions are stored through the blockchain. The storage through the blockchain can trace the inclusions with risk bumps more timely, so as to facilitate subsequent detection, avoid the food from entering the market, and intercept the problematic food in time. When the damage tendency risk category is a weak risk category, the packaging characteristics of the food inside the inclusions are relatively stable, and the possibility of being damaged during actual transportation is small. However, if severe bumps occur, it may still affect the smooth operation of the transportation vehicle, and then affect the inclusions carried by the transportation vehicle. Therefore, in the above case, when there is severe bumps, the inclusions carried on the transportation vehicle are marked, and corresponding data tags are set, and stored through the blockchain. The present invention detects targetedly through different bumps that have risk tendencies for the inclusions, more effectively determines the status of the inclusions under different damage tendency risk categories, and stores the data tags of the inclusions through the blockchain to improve the efficiency of traceability. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0050] Figure 2 A logical decision diagram for classifying the risk categories of damage tendency of inclusions during transportation for an embodiment of the invention;

[0051] Figure 3 A logical decision diagram for determining a risk turbulence time domain segment for an embodiment of the invention;

[0052] Figure 4 A logical decision diagram for evaluating whether severe turbulence is present for an embodiment of the invention. DETAILED DESCRIPTION

[0053] In order to make the objects and advantages of the present invention more clearly understood, the present invention is 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 are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to 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] See also Figures 1 to 4 As shown, Figure 1 This is a functional module diagram of a food traceability system based on blockchain according to an embodiment of the present invention. Figure 2 A logical decision diagram for classifying the risk categories of damage tendency of inclusions during transportation according to an embodiment of the present invention, Figure 3 A logical decision diagram for determining a risk turbulence time domain segment according to an embodiment of the present invention, Figure 4 To provide a logical decision diagram for evaluating whether there is severe turbulence in the embodiment of the invention, the food traceability system based on blockchain in the embodiment of the invention includes:

[0057] 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;

[0058] 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;

[0059] 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:

[0060] 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;

[0061] 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;

[0062] 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 tags stored in the blockchain for each inclusion;

[0063] The 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.

[0064] Specifically, there is no specific limitation on the packaging characteristics of food to be transported. The packaging characteristics can be pre-determined. The size characteristics include the length, width and volume of the packaging bag. The gas content ratio inside the food packaging is the ratio of the gas volume in the food packaging to the volume of the packaging bag.

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

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

[0067] Specifically, the process of the packaging analysis module for analyzing the damage tendency characterization value of the package during transportation includes:

[0068] 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;

[0069] to determine the mean value of the gas content ratio of each food package, calculate the ratio of the mean value of the gas content ratio to a predetermined gas content ratio threshold value, and determine the ratio as a second tendency feature;

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

[0071] The gas content ratio threshold is preset, and packaging characteristics of several food packages are obtained in advance, the gas content ratio is obtained, the mean of the gas content ratio is solved, and the gas content ratio threshold is set to 0.8 times the mean of the 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, then determine the risk bumpy time domain segment based on the vibration amplitude of the transportation 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 the blockchain;

[0079] If the risk category of the damage tendency of the inclusion during transportation is a weak risk category, then 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 of the identification module determining the risk bumpy time domain segment based on the vibration amplitude of the transportation tool during transportation includes:

[0081] 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:

[0082] A rectangular coordinate system is constructed with time as the horizontal axis and the vibration amplitude of the transport vehicle as the vertical axis;

[0083] Constructing a vibration amplitude time domain curve, and dividing the vibration amplitude time domain curve into a plurality of curve segments;

[0084] 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.

[0085] It can be understood that the vibration amplitude threshold is pre-set, and the average vibration amplitude of the transportation tool during several transportation processes is obtained. In order to characterize the bumps, the vibration amplitude threshold is set to 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 matlab related fitting software, which will not be described in detail.

[0087] Specifically, 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:

[0088] To determine the risk of turbulence in a transportation tool within a predetermined time period;

[0089] 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;

[0090] The total duration of the risk turbulence time domain segment is compared with the total duration threshold, wherein:

[0091] 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;

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

[0093] Specifically, the predetermined time period can be determined based on the time required for the transportation tool to travel between the origin and destination of the transportation, which will not be elaborated herein.

[0094] The total duration threshold of the risk turbulence 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 of the predetermined time period.

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

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

[0097] If the vibration amplitude of the transport vehicle is greater than or equal to the severe turbulence threshold, it is determined that severe turbulence exists;

[0098] If the vibration amplitude of the vehicle is less than the severe turbulence threshold, it is determined that no severe turbulence exists.

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

[0100] Specifically, 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, including:

[0101] If there is severe turbulence in the means of transport, a data tag for the inclusion is generated and stored through the blockchain.

[0102] The present invention classifies the damage tendency risk categories of inclusions during transportation. When the damage tendency risk category is a strong risk category, the risk bumpy time domain segment is determined according to the vibration amplitude of the transportation vehicle during transportation. Since the bumpiness of the road surface on which the transportation vehicle travels is different, the vibration degree generated by the reaction of the above-mentioned road surface to the transportation vehicle during driving is different, which will have a corresponding bumpy impact on the driving stability of the transportation vehicle. The present application extracts 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 time period, it can be determined that the risk of bumpy occurrence in the above time period is high. The time period corresponding to the above situation is determined as the risk bumpy time domain segment, and the number of risk bumpy time domain segments of the transportation vehicle in the predetermined time period and the duration corresponding to each time domain segment are further analyzed to determine the total duration of the risk bumpy time domain segments. If the total duration of the risk bumpy time domain segments within the predetermined time period is too long, the inclusions carried on the transportation vehicle are marked, and the number of inclusions and the package number are included. The data label of the damage tendency risk category corresponding to the package is stored through the blockchain. The storage through the blockchain can trace the package with risk bumps more timely, so as to facilitate subsequent detection, avoid food from entering the market, and intercept problematic food in time. When the damage tendency risk category is a weak risk category, the packaging characteristics of the food inside the package are relatively stable, and the possibility of being damaged during actual transportation is small. However, if severe bumps occur, it may still affect the smooth operation of the transportation vehicle, and then affect the packages carried by the transportation vehicle. Therefore, in the above case, when there is severe bumps, the packages carried on the transportation vehicle are marked, and corresponding data labels are set, and stored through the blockchain. The present invention detects targetedly through different bumps that have risk tendencies for the packages, more effectively determines the status of the packages under different damage tendency risk categories, and stores the data labels of the packages through the blockchain to improve the efficiency of traceability.

[0103] Specifically, 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:

[0104] If the damage tendency risk category corresponding to the inclusion shown in the stored data label is a strong risk category, the food in the inclusion needs to be tested for breakage;

[0105] If the damage tendency risk category corresponding to the inclusion shown by the stored data label is a weak risk category, there is no need to perform a breakage 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 inclusion according to the inclusion content corresponding to the data tag stored in the blockchain, and more quickly and accurately determines the inclusions with the risk of food fragmentation for subsequent fragmentation detection.

[0107] Specifically, the process of the fragmentation detection module for determining whether the food meets the fragmentation standard includes:

[0108] Used to obtain food contour scan results inside food packaging and identify scattered food fragments;

[0109] The total area of ​​the contour of the scattered food fragments is extracted and compared with a preset area threshold, including:

[0110] 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;

[0111] 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.

[0112] Therefore, in this embodiment, the food fragment contour threshold is obtained by a predetermined setting, and the food fragment contour data present in the food package during several fragmentation detection processes are extracted to solve the mean value of the food fragment contour, and the food contour threshold is set to be between 1.12 and 1.24 times the food contour mean value.

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

[0114] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying 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 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 tags stored in the blockchain for each inclusion; The 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.

2. The blockchain-based food traceability system according to claim 1 is characterized in that: The process of the packaging analysis module 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; to determine the mean value of the gas content ratio of each food package, calculate the ratio of the mean value of the gas content ratio to a predetermined gas content ratio threshold value, and determine the ratio as a second tendency feature; The sum of the first tendency feature and the second tendency feature is determined as a damage tendency characterization value.

3. The blockchain-based food traceability system according to claim 1, 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.

4. The blockchain-based food traceability system according to claim 1, characterized in that: 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, then determine the risk bumpy time domain segment based on the vibration amplitude of the transportation 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 the blockchain; If the risk category of the damage tendency of the inclusion during transportation is a weak risk category, then 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.

5. The blockchain-based food traceability system according to claim 1 is 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.

6. 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.

7. 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.

8. 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.

9. 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.

10. 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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