CN119477123B - A food traceability system based on blockchain - Google Patents
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Abstract
The invention relates to the field of food tracing, in particular to a block chain-based food tracing system, which is provided with a feature storage module for storing packaging features of foods to be transported, a packaging analysis module for analyzing damage tendency characterization values of the inclusions in transportation based on the packaging features of the foods to be transported contained in a single inclusion and the volumes of the inclusions, an identification module for adaptively detecting the risks of the damage tendency of the inclusions in transportation according to the risks of the damage tendency, a tracing module for judging whether the foods in the inclusions need to be cracked or not based on data labels stored by the block chain of the inclusions, and a cracking detection module for scanning the food contours in the food package and identifying scattered food fragments to judge whether the foods meet cracking standards.
Description
Technical Field
The invention relates to the field of food tracing, in particular to a block chain-based food tracing system.
Background
Traditional food traceability system has the problems of data storage centering, opaque data and the like, and the real condition of food in the transportation state cannot be guaranteed, and the position of inclusion with risk in the transportation process can be accurately positioned through the block chain technology capable of removing centering, so that the subsequent timely repackaging of the inclusion is facilitated, and the efficiency and reliability of food traceability are improved.
The Chinese patent application publication No. CN109214829A discloses a food safety tracing method and device, wherein the method comprises the steps of obtaining ingredient identification information in the food production, processing and transportation process, wherein the ingredient identification information comprises, but is not limited to, production date, quality guarantee period, storage condition and quality information, comparing the matching degree of the ingredient identification information and corresponding pre-stored food safety standard data, detecting whether food is qualified or not, searching the ingredient identification information in the food production, processing and transportation process when the food is detected to be unqualified, determining a corresponding error link, and generating corresponding quality safety assessment information according to the matching degree and storing the quality safety assessment information into a block chain when the food is detected to be qualified.
There are problems in the prior art that,
In the actual transportation process of food, partial food packages are in an inflatable form, and a plurality of foods are packaged in the package body due to the gas space of the foods and the interaction between the foods and the packages in the package body, so that the foods are easy to crack, and the phenomenon that the foods are easy to crack after transportation due to the fact that the transported package body is monitored and traced back against the phenomenon is not considered in the prior art.
Disclosure of Invention
Therefore, the invention provides a block chain-based food tracing system, which is used for solving the problems that in the actual transportation process of food, part of food packages are in an inflatable form, and a plurality of foods are packaged in an inclusion, so that the food is easy to crack and the tracing means are lacked due to the gas space of the foods and the interaction between the foods and the packages in the inclusion.
To achieve the above object, the present invention provides a blockchain-based food tracing system, comprising:
a feature storage module for storing packaging features of food to be transported, the packaging features including size features of a food package and a gas content ratio inside the food package;
The package analysis module is connected with the characteristic storage module and is used for analyzing the damage tendency characterization value of the inclusion in the transportation process based on the package characteristics of each food to be transported contained in a single inclusion and the volume of the inclusion so as to divide the damage tendency risk category of the inclusion in the transportation process;
The identification module is respectively connected with the characteristic storage module and the packaging analysis module and is used for detecting each inclusion according to the damage tendency risk category, and comprises,
Determining a risk bump time domain segment based on a vibration amplitude of a transport in transit, evaluating whether to generate a data tag for the inclusion based on a total duration of the risk bump time domain segment, and storing the data tag through a blockchain;
Or, evaluating whether there is a jerkiness based on the vibration amplitude of the in-transit vehicle to determine whether a data tag for the inclusion is generated, and storing the data tag through a blockchain;
The traceability module is connected with the identification module and used for judging whether food in each inclusion needs to be subjected to fragmentation detection or not based on the data labels stored in the blockchain of each inclusion;
And the fragmentation detection module is used for scanning the food outline inside the food package, identifying scattered food fragments and judging whether the food meets fragmentation standards or not.
Further, the process by which the package analysis module analyzes the damage propensity characteristic of the inclusion during transit includes,
For determining a total volume of each food package based on the dimensional characteristics of the food package, calculating a ratio of the volume of the inclusion to the total volume as a first trend characteristic;
the method comprises the steps of determining a gas content ratio average value of each food package, solving a ratio of the gas content ratio average value to a preset gas content ratio threshold value, and determining the ratio as a second trend characteristic;
determining a sum of the first trend feature and the second trend feature as a damage trend characterization value.
Further, the package analysis module is configured to categorize damage propensity risk categories for the inclusion during transit, including,
If the damage tendency characterization value is greater than or equal to the damage tendency characterization threshold, determining that the damage tendency risk category of the inclusion in the transportation process is a strong risk category;
if the damage tendency characterization value is less than the damage tendency characterization threshold, determining that the damage tendency risk category of the inclusion in transit is a weak risk category.
Further, the identification module is configured to detect each inclusion according to the damage tendency risk category, including,
If the damage tendency risk category of the inclusion in the transportation process is a strong risk category, determining a risk bump time domain segment based on the vibration amplitude of the transportation means in the transportation process, evaluating whether to generate a data tag for the inclusion based on the total duration of the risk bump time domain segment, and storing the data tag through a blockchain;
If the damage tendency risk category of the inclusion in transit is a weak risk category, evaluating whether there is a severe jolt based on the vibration amplitude of the transit vehicle in transit to determine whether to generate a data tag for the inclusion, and storing the data tag through a blockchain.
Further, the process by which the identification module determines the risk bump time domain segment based on the vibration amplitude of the in-transit vehicle includes,
Constructing a rectangular coordinate system by taking time as a horizontal axis and the vibration amplitude of a transport tool as a vertical axis;
Constructing a vibration amplitude time domain curve, and dividing the vibration amplitude time domain curve into a plurality of curve segments;
and if the average vibration amplitude of the curve segment is larger than the preset vibration amplitude threshold value, determining that the curve segment is a risk bump time domain segment.
Further, the process by which the identification module evaluates whether to generate a data tag for the inclusion based on the total duration of the risk bump time-domain segment includes,
Determining a risk bump time domain segment for the vehicle during a predetermined time period;
the method comprises the steps of obtaining the time length corresponding to each risk bump time domain segment to determine the total time length of each risk bump time domain segment;
To compare the total duration of the risk bump time-domain segment to a total duration threshold, wherein,
If the total time length of the risk jolt time domain segment is greater than or equal to the total time length threshold, generating a data tag for the inclusion;
The data tag comprises the number of the inclusion and the damage tendency risk category corresponding to the inclusion.
Further, the identification module is configured to evaluate whether there is a jerk based on the vibration amplitude of the in-transit vehicle, including,
If the vibration amplitude of the vehicle is greater than or equal to the jounce threshold, then a jounce is determined to be present.
Further, the identification module is configured to determine whether to generate a data tag for the inclusion and store the data tag via a blockchain, including,
If the transportation means has severe jolt, generating a data tag for the inclusion, and storing the data tag through a blockchain.
Further, the traceability module is configured to determine whether to perform fragmentation detection on food in an inclusion based on the data tags stored in the blockchain for each inclusion, including,
If the damage tendency risk category corresponding to the inclusion displayed by the stored data label is a strong risk category, the food in the inclusion needs to be subjected to fragmentation detection.
Further, the process by which the fragmentation detection module determines whether the food product meets fragmentation criteria includes,
The method comprises the steps of acquiring a food profile scanning result inside a food package and identifying scattered food fragments;
The total area of the profile used to extract the scattered food items is compared with a predetermined area threshold, including,
If the total area of the outlines of scattered food fragments is smaller than the area threshold, judging that the food corresponding to the scattered food fragments meets the fragmentation standard;
And if any food fragment profile is smaller than the food fragment profile threshold, determining that the food fragments corresponding to the food fragment profile are scattered food fragments.
Compared with the prior art, the food package inspection system comprises a feature storage module, a package analysis module, an identification module, a tracing module and a fragmentation detection module, wherein the feature storage module is used for storing package features of foods to be transported, the package analysis module is connected with the feature storage module and used for analyzing damage tendency characterization values of the packages in the transportation process based on the package features of the foods to be transported contained in a single package body and the volumes of the packages, so as to divide damage tendency risk categories of the packages in the transportation process, the identification module is respectively connected with the feature storage module and the package analysis module and used for adaptively detecting the packages according to the damage tendency risk categories, the tracing module is connected with the identification module and used for judging whether fragmentation detection is needed for the foods in the packages based on data labels stored in block chains of the packages, and the fragmentation detection module is used for scanning the food outlines in the foods packages and identifying scattered in the foods fragments and judging whether the foods accord with fragmentation standards.
In particular, the method analyzes the characteristic value of the damage tendency of the inclusion in the transportation process by combining the packaging characteristics of the food to be transported with the volume of the inclusion, the quantity of the food contained in the inclusion and the size characteristics of each food package are different in the food transportation process, meanwhile, the proportion of the gas content filled in the food package is inconsistent, and due to gaps and gas spaces, the food in the food package is easy to crack due to bumping in the transportation process, for example, a plurality of inflatable puffed foods are placed in the same inclusion, so that the possibility of damage in the transportation process is higher, when the proportion of the gas content filled in the food package to the food is too large, the situation of damage and gas leakage of the food package is easy to occur due to friction, jolt and the like caused by transportation is determined by combining the characteristics with the volume of the inclusion, and further, the data support is provided for the damage tendency classification of the follow-up tendency of the inclusion in the transportation process, the damage risk of each inclusion is determined before the actual transportation process, and the corresponding package is detected, so that the real transportation safety and stability of the inclusion in the transportation process are ensured.
In particular, the application divides the damage tendency risk category of the package in the transportation process, under the condition that the damage tendency risk category is a strong risk category, determines a risk bumping time domain according to the vibration amplitude of the transportation tool in the transportation process, and determines the total time length of the risk bumping time domain according to the number of times of the risk bumping time domain and the time length corresponding to each time of the risk bumping time domain of the transportation tool in the preset time period because the vibration degree of the road pavement is different to the vibration degree generated by the reaction of the transportation tool in the transportation process, marks the corresponding bumping influence on the driving stability of the transportation tool, extracts the characteristic of the vibration amplitude of the transportation tool in the transportation process, evaluates the characteristic of the vibration amplitude of the transportation tool in the transportation process, can determine that the risk degree of bumping occurs in the time domain is higher if the vibration amplitude of the transportation tool is overlarge in a certain time period, determines the time domain corresponding to the risk bumping time domain and further analyzes the total time length of the risk bumping time domain corresponding to the time domain in the transportation tool in the preset time period, can conveniently and can detect the damage tendency of the package in the food in the time domain by detecting the data of the package in the time domain, can prevent the package from being damaged in the food from being more stable in time, can prevent the food from damaging the food from being damaged in the food package and the food from the time chain, however, if severe jolt occurs, the smooth running of the transportation means may still be affected, and the inclusion carried by the transportation means is further affected to generate corresponding influence, so in the above case, when severe jolt exists, the inclusion carried on the transportation means is marked, and corresponding data labels are set, and stored through the blockchain.
Drawings
FIG. 1 is a functional block diagram of a blockchain-based food traceability system according to an embodiment of the invention;
FIG. 2 is a logic decision diagram of an embodiment of the invention for classifying risk categories of damage propensity of inclusions during transportation;
FIG. 3 is a logic decision diagram of determining a risk bump time domain segment according to an embodiment of the invention;
FIG. 4 is a logic decision diagram of an embodiment of the invention to evaluate if there is a jerky.
Detailed Description
The invention will be further described with reference to examples for the purpose of making the objects and advantages of the invention more apparent, it being understood that the specific examples described herein are given by way of illustration only and are not intended to be limiting.
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 merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
In addition, it should be noted that, in the description of the present invention, unless explicitly stated and limited otherwise, the term "connected" should be interpreted broadly, and for example, it may be a fixed connection, a detachable connection, or an integral connection, and it may be a mechanical connection or an electrical connection. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1 to 4, fig. 1 is a functional block diagram of a blockchain-based food tracing system according to an embodiment of the present invention, fig. 2 is a logic decision diagram for classifying risk categories of damage tendency of inclusions during transportation according to an embodiment of the present invention, fig. 3 is a logic decision diagram for determining risk bump time domain segments according to an embodiment of the present invention, fig. 4 is a logic decision diagram for evaluating whether there is a severe bump according to an embodiment of the present invention, and the blockchain-based food tracing system according to an embodiment of the present invention includes:
a feature storage module for storing packaging features of food to be transported, the packaging features including size features of a food package and a gas content ratio inside the food package;
The package analysis module is connected with the characteristic storage module and is used for analyzing the damage tendency characterization value of the inclusion in the transportation process based on the package characteristics of each food to be transported contained in a single inclusion and the volume of the inclusion so as to divide the damage tendency risk category of the inclusion in the transportation process;
The identification module is respectively connected with the characteristic storage module and the packaging analysis module and is used for detecting each inclusion according to the damage tendency risk category, and comprises,
Determining a risk bump time domain segment based on a vibration amplitude of a transport in transit, evaluating whether to generate a data tag for the inclusion based on a total duration of the risk bump time domain segment, and storing the data tag through a blockchain;
Or, evaluating whether there is a jerkiness based on the vibration amplitude of the in-transit vehicle to determine whether a data tag for the inclusion is generated, and storing the data tag through a blockchain;
The traceability module is connected with the identification module and used for judging whether food in each inclusion needs to be subjected to fragmentation detection or not based on the data labels stored in the blockchain of each inclusion;
And the fragmentation detection module is used for scanning the food outline inside the food package, identifying scattered food fragments and judging whether the food meets fragmentation standards or not.
In particular, the manner of storing the packaging characteristics of the food to be transported is not particularly limited, and the packaging characteristics may be predetermined, and the size characteristics include the length, width and volume of the packaging bag, and the gas content ratio inside the food package is the ratio of the gas volume inside the food package to the packaging bag volume.
In particular, the specific structures of the feature storage module, the package analysis module, the identification module and the trace back module are not limited, and the feature storage module, the package analysis module, the identification module and the trace back module can be formed by logic components or combinations of logic components, wherein the logic components comprise a field programmable processor, a computer or a microprocessor in the computer
Specifically, the method for identifying scattered food fragments by scanning the food contours inside the food package is not particularly limited, and the food contours inside the food package can be scanned by the X-ray scanning technique, and the scattered food fragments can be identified by the edge detection algorithm.
In particular, the process by which the package analysis module analyzes the damage propensity characteristic of the inclusion during transit includes,
For determining a total volume of each food package based on the dimensional characteristics of the food package, calculating a ratio of the volume of the inclusion to the total volume as a first trend characteristic;
the method comprises the steps of determining a gas content ratio average value of each food package, solving a ratio of the gas content ratio average value to a preset gas content ratio threshold value, and determining the ratio as a second trend characteristic;
determining a sum of the first trend feature and the second trend feature as a damage trend characterization value.
The gas content ratio threshold is preset, the packaging characteristics of a plurality of food packages are obtained in advance, the gas content ratio is obtained, the gas content ratio average value is solved, and the gas content ratio threshold is set to be 0.8 times of the gas content ratio average value.
In particular, the package analysis module is configured to classify damage propensity risk categories for the inclusion during transit, including,
If the damage tendency characterization value is greater than or equal to the damage tendency characterization threshold, determining that the damage tendency risk category of the inclusion in the transportation process is a strong risk category;
if the damage tendency characterization value is less than the damage tendency characterization threshold, determining that the damage tendency risk category of the inclusion in transit is a weak risk category.
The damage propensity characterizes a threshold value, selected within interval [2.45,2.65 ].
According to the method, the characteristic value of the damage tendency of the inclusion in the transportation process is analyzed by combining the package characteristics of the food to be transported with the volume of the inclusion, the quantity of the food contained in the inclusion and the size characteristics of each food package are different in the food transportation process, meanwhile, the proportion of the gas content filled in the food package is inconsistent, and due to gaps and gas spaces, the food in the food package is easy to crack due to bumping in the transportation process, for example, when a plurality of inflatable puffed foods are placed in the same inclusion, the possibility of damage in the transportation process is higher, when the proportion of the gas content filled in the food package is too large for the food, the situation of damage and gas leakage of the food package is easy to occur due to friction, jolt and the like caused by transportation is determined by combining the characteristics with the volume of the inclusion, and further, data support is provided for the damage tendency of the inclusion in the actual transportation process for the follow-up division of the inclusion, the damage tendency degree of each inclusion is determined before the actual transportation process, and the damage risk of each inclusion in the transportation process is correspondingly detected, and the safety and stability of the inclusion in the actual transportation process are ensured.
In particular, the identification module is configured to detect each inclusion according to the damage tendency risk category, including,
If the damage tendency risk category of the inclusion in the transportation process is a strong risk category, determining a risk bump time domain segment based on the vibration amplitude of the transportation means in the transportation process, evaluating whether to generate a data tag for the inclusion based on the total duration of the risk bump time domain segment, and storing the data tag through a blockchain;
If the damage tendency risk category of the inclusion in transit is a weak risk category, evaluating whether there is a severe jolt based on the vibration amplitude of the transit vehicle in transit to determine whether to generate a data tag for the inclusion, and storing the data tag through a blockchain.
In particular, the process by which the identification module determines a risk bump time domain segment based on the vibration amplitude of the in-transit vehicle includes,
The process by which the identification module determines the risk bump time domain segment based on the vibration amplitude of the in-transit vehicle includes,
Constructing a rectangular coordinate system by taking time as a horizontal axis and the vibration amplitude of a transport tool as a vertical axis;
Constructing a vibration amplitude time domain curve, and dividing the vibration amplitude time domain curve into a plurality of curve segments;
and if the average vibration amplitude of the curve segment is larger than the preset vibration amplitude threshold value, determining that the curve segment is a risk bump time domain segment.
It is understood that, for the vibration amplitude threshold value obtained by presetting, the average vibration amplitude of the transportation means during several transportation processes is obtained, and for the characterization of jolts, the vibration amplitude threshold value is set to be between 1.25 times and 1.5 times the average vibration amplitude.
Specifically, the manner of constructing the vibration amplitude time domain curve is not limited, for example, the time domain curve may be fitted by mat l ab correlation fitting software, which is not described herein.
In particular, the process by which the identification module evaluates whether to generate a data tag for the inclusion based on the total duration of the risk bump time-domain segment includes,
Determining a risk bump time domain segment for the vehicle during a predetermined time period;
the method comprises the steps of obtaining the time length corresponding to each risk bump time domain segment to determine the total time length of each risk bump time domain segment;
To compare the total duration of the risk bump time-domain segment to a total duration threshold, wherein,
If the total time length of the risk jolt time domain segment is greater than or equal to the total time length threshold, generating a data tag for the inclusion;
The data tag comprises the number of the inclusion and the damage tendency risk category corresponding to the inclusion.
In particular, the determination of the predetermined time period may be determined based on a required length of the transportation means from the origin and destination of the transportation, which will not be described in detail.
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.
The data tag comprises the number of the inclusion and the damage tendency risk category corresponding to the inclusion.
In particular, the identification module is configured to evaluate whether there is a jerk based on the vibration amplitude of the in-transit vehicle, including,
If the vibration amplitude of the transport is greater than or equal to the sharp bump threshold, determining that a sharp bump exists;
If the vibration amplitude of the vehicle is less than the jerk threshold, it is determined that jerk is not present.
In this embodiment, the jerk threshold is 1.35 times the vibration amplitude threshold.
In particular, the identification module is configured to determine whether to generate a data tag for the inclusion and store the data tag via a blockchain, including,
If the transportation means has severe jolt, generating a data tag for the inclusion, and storing the data tag through a blockchain.
The application divides the damage tendency risk category of the inclusion in the transportation process, determines a risk bumping time domain according to the vibration amplitude of the transportation tool in the transportation process under the condition that the damage tendency risk category is a strong risk category, and determines that the bumping degree of the transportation tool in the period of time is higher if the vibration amplitude of the transportation tool in the period of time is overlarge because the bumping degree of the road surface on which the transportation tool is driven is different, so that the vibration degree generated by the reaction of the road surface on the transportation tool in the driving process is different, the corresponding bumping influence is generated on the driving stability degree of the transportation tool, determining the time period corresponding to the situation as a risk bump time period, further analyzing the number of times of the risk bump time period of the transportation tool in the preset time period and the time period corresponding to each time of the time period to determine the total time of the risk bump time period, if the total time of the risk bump time period in the preset time period is overlong, marking the inclusion carried on the transportation tool, storing the data tag containing the inclusion number and the damage tendency risk category corresponding to the inclusion through a deblocking chain, enabling the inclusion with risk bump to be traced more timely through the blocking chain so as to facilitate subsequent detection, avoiding food from flowing into the market, intercepting problem food in time, displaying relatively stable state of the package characteristics of the food inside the inclusion under the weak risk category of damage tendency, having lower possibility of being damaged tendency in the actual transportation, however, if severe jolt occurs, the smooth running of the transportation means may still be affected, and the inclusion carried by the transportation means is further affected to generate corresponding influence, so in the above case, when severe jolt exists, the inclusion carried on the transportation means is marked, and corresponding data labels are set, and stored through the blockchain.
Specifically, the traceability module is configured to determine whether to perform fragmentation detection on food in an inclusion based on data tags stored in the blockchain for each inclusion, including,
If the damage tendency risk category corresponding to the inclusion displayed by the stored data label is a strong risk category, carrying out fragmentation detection on food in the inclusion;
If the damage tendency risk category corresponding to the inclusion displayed by the stored data label is a weak risk category, the food in the inclusion does not need to be subjected to fragmentation detection.
According to the method and the device, whether the food in the inclusion needs to be subjected to fragmentation detection or not is determined according to the inclusion content corresponding to the data tag stored in the blockchain, and the inclusion with the risk of food fragmentation is determined more rapidly and accurately so as to carry out subsequent fragmentation detection.
In particular, the process by which the fragmentation detection module determines whether the food product meets fragmentation criteria includes,
The method comprises the steps of acquiring a food profile scanning result inside a food package and identifying scattered food fragments;
The total area of the profile used to extract the scattered food items is compared with a predetermined area threshold, including,
If the total area of the outlines of scattered food fragments is smaller than the area threshold, judging that the food corresponding to the scattered food fragments meets the fragmentation standard;
And if any food fragment profile is smaller than the food fragment profile threshold, determining that the food fragments corresponding to the food fragment profile are scattered food fragments.
Therefore, in this embodiment, the food fragment profile threshold is obtained by predetermined setting, the food fragment profile data existing in the food package during several times of fragmentation detection is extracted, the food fragment profile average is solved, and the food fragment profile threshold is set to be 1.12 to 1.24 times of the food profile average.
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.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
Claims (8)
1. A blockchain-based food traceability system, comprising:
a feature storage module for storing packaging features of food to be transported, the packaging features including size features of a food package and a gas content ratio inside the food package;
The package analysis module is connected with the characteristic storage module and is used for analyzing the damage tendency characterization value of the inclusion in the transportation process based on the package characteristics of each food to be transported contained in a single inclusion and the volume of the inclusion so as to divide the damage tendency risk category of the inclusion in the transportation process;
The identification module is respectively connected with the characteristic storage module and the packaging analysis module and is used for detecting each inclusion according to the damage tendency risk category, and comprises,
Determining a risk bump time domain segment based on a vibration amplitude of a transport in transit, evaluating whether to generate a data tag for the inclusion based on a total duration of the risk bump time domain segment, and storing the data tag through a blockchain;
Or, evaluating whether there is a jerkiness based on the vibration amplitude of the in-transit vehicle to determine whether a data tag for the inclusion is generated, and storing the data tag through a blockchain;
The traceability module is connected with the identification module and used for judging whether food in each inclusion needs to be subjected to fragmentation detection or not based on the data labels stored in the blockchain of each inclusion;
The fragmentation detection module is used for scanning the food outline inside the food package, identifying scattered food fragments and judging whether the food accords with fragmentation standards or not;
The process by which the package analysis module analyzes the damage propensity characteristic of the inclusion during transit includes,
For determining a total volume of each food package based on the dimensional characteristics of the food package, calculating a ratio of the volume of the inclusion to the total volume as a first trend characteristic;
the gas content ratio average value of each food package is determined, the ratio of the gas content ratio average value to a preset gas content ratio threshold value is solved, and the ratio is determined to be a second trend characteristic;
determining a sum of the first trend feature and the second trend feature as a damage trend characterization value;
The identification module is used for detecting each inclusion according to the damage tendency risk category, and comprises,
If the damage tendency risk category of the inclusion in the transportation process is a strong risk category, determining a risk bump time domain segment based on the vibration amplitude of the transportation means in the transportation process, evaluating whether to generate a data tag for the inclusion based on the total duration of the risk bump time domain segment, and storing the data tag through a blockchain;
If the damage tendency risk category of the inclusion in transit is a weak risk category, evaluating whether there is a severe jolt based on the vibration amplitude of the transit vehicle in transit to determine whether to generate a data tag for the inclusion, and storing the data tag through a blockchain.
2. The blockchain-based food traceability system according to claim 1, wherein said package analysis module is configured to categorize damage-prone risk categories of said inclusions during transportation, including,
If the damage tendency characterization value is greater than or equal to the damage tendency characterization threshold, determining that the damage tendency risk category of the inclusion in the transportation process is a strong risk category;
if the damage tendency characterization value is less than the damage tendency characterization threshold, determining that the damage tendency risk category of the inclusion in transit is a weak risk category.
3. The blockchain-based food traceability system according to claim 1, wherein said identification module is configured to determine risk bump time domain segments based on vibration amplitude of the in-transit vehicle comprising,
Constructing a rectangular coordinate system by taking time as a horizontal axis and the vibration amplitude of a transport tool as a vertical axis;
Constructing a vibration amplitude time domain curve, and dividing the vibration amplitude time domain curve into a plurality of curve segments;
and if the average vibration amplitude of the curve segment is larger than the preset vibration amplitude threshold value, determining that the curve segment is a risk bump time domain segment.
4. The blockchain-based food traceability system of claim 1, wherein the process by which the identification module evaluates whether to generate a data tag for the inclusion based on a total length of a risk bump time domain segment comprises,
Determining a risk bump time domain segment for the vehicle during a predetermined time period;
the method comprises the steps of obtaining the time length corresponding to each risk bump time domain segment to determine the total time length of each risk bump time domain segment;
To compare the total duration of the risk bump time-domain segment to a total duration threshold, wherein,
If the total time length of the risk jolt time domain segment is greater than or equal to the total time length threshold, generating a data tag for the inclusion;
The data tag comprises 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, wherein said identification module is configured to evaluate whether there is a jerky based on the vibration amplitude of the transport in transit, comprising,
If the vibration amplitude of the vehicle is greater than or equal to the jounce threshold, then a jounce is determined to be present.
6. The blockchain-based food traceability system of claim 1, wherein said identification module to determine whether to generate a data tag for said inclusion and store said data tag via a blockchain comprises,
If the transportation tool is judged to have severe jolt, generating a data tag for the inclusion, and storing the data tag through a blockchain.
7. The blockchain-based food traceability system of claim 1, wherein the traceability module is configured to determine whether or not to perform fragmentation detection on food items within an inclusion based on data tags stored by the blockchain for each inclusion, comprising,
If the damage tendency risk category corresponding to the inclusion displayed by the stored data label is a strong risk category, the food in the inclusion needs to be subjected to fragmentation detection.
8. The blockchain-based food traceability system according to claim 1, wherein said process by which said fragmentation detection module determines whether said food product meets fragmentation criteria comprises,
The method comprises the steps of acquiring a food profile scanning result inside a food package and identifying scattered food fragments;
The total area of the profile used to extract the scattered food items is compared with a predetermined area threshold, including,
If the total area of the outlines of scattered food fragments is smaller than the area threshold, judging that the food corresponding to the scattered food fragments meets the fragmentation standard;
And if any food fragment profile is smaller than the food fragment profile threshold, determining that the food fragments corresponding to the food fragment profile are scattered food fragments.
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