CN116911698A - Food and drug detection system - Google Patents

Food and drug detection system Download PDF

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CN116911698A
CN116911698A CN202311183464.6A CN202311183464A CN116911698A CN 116911698 A CN116911698 A CN 116911698A CN 202311183464 A CN202311183464 A CN 202311183464A CN 116911698 A CN116911698 A CN 116911698A
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odor
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CN116911698B (en
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魏文强
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Tongwei County Food And Drug Inspection And Testing Center
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Abstract

The application discloses a food and drug detection system, which belongs to the field of food and drug detection, and specifically comprises a data acquisition port, a data processing port and a data feedback port, wherein the data acquisition port is used for acquiring image, smell and storage time data of a to-be-detected product in real time, the data processing port is used for processing the acquired image, smell and storage time data of the to-be-detected product, further estimating the deterioration time under the influence of the environment, estimating the deterioration time of the to-be-detected product under different environments, effectively improving the grasp of the deterioration time of the to-be-detected product, feeding back the estimated deterioration time, and simultaneously giving an alarm when the estimated deterioration time is smaller than or equal to a preset deterioration time threshold value, and further improving the solving speed of the to-be-deteriorated food and drug.

Description

Food and drug detection system
Technical Field
The application belongs to the technical field of food and drug detection, and particularly relates to a food and drug detection system.
Background
In the process of detecting food and medicine, sampling and testing are usually carried out on the food and medicine, the deterioration time of the food and medicine in different environments cannot be estimated, the quality guarantee period of the existing food and medicine is only the designated quality guarantee period of the designated preservation environment, and when the preservation environment is not in the designated preservation environment or the quality of the food and medicine is changed, the food and medicine is extremely easy to deteriorate in advance or deteriorate in a delayed manner, so that the quality guarantee period of the food and medicine which is not in the designated preservation environment cannot be reasonably estimated in the prior art, and the problems exist in the prior art;
for example, patent application publication number CN116298338A discloses a food and drug quality detection control method, which comprises: the detection control manager controls the detection flow and manages detection tasks implemented by the flow in the detection flow, and sends the detection tasks to corresponding detection personnel; detecting personnel perform detection operation by using detection equipment and generate detection data information; the detection information collector is connected with the detection equipment and collects detection data information through the detection equipment; the detection information processor carries out detection information processing and detection result correction on the detection information and judges whether the data passes detection or not; the detection information finder finds detection information and detection results, and analyzes the found results to generate an analysis chart; the detection information display receives and displays the analysis chart. According to the application, the detection flow is automatically managed according to the requirements of food and medicine quality detection, traceability of detection information is realized, the quality inspection efficiency is improved, and the standardization, standardization and automation degree of quality detection are improved;
for example, chinese patent application publication No. CN114091854a discloses a food and drug detection and quality management system, which includes a detection management subsystem and a quality management subsystem; the detection management subsystem comprises a task manager, a data acquisition unit, a detection instrument, a calculation processor, a query device, a report display device and a system database; the quality management subsystem comprises a personnel manager, an instrument manager, a material manager, a standard manager and an environment manager. According to the application, tracking of the detection flow, the detection personnel, the detection instrument, the standard substance and the detection environment according to the food and drug detection business requirements achieves the requirements of automatic management of the detection flow and traceability of detection data, reduces errors possibly generated in the aspects of manual input, manual calculation and data transmission, greatly improves the working efficiency, and improves the automation level, the standardization and the standardization degree.
The problems proposed in the background art exist in the above patents: in the process of detecting food and medicine, sampling and testing are usually carried out on the food and medicine, the deterioration time of the food and medicine in different environments cannot be estimated, the quality guarantee period of the existing food and medicine is only the designated quality guarantee period in the designated preservation environment, and when the preservation environment is not in the designated preservation environment or the quality of the food and medicine changes, the food and medicine is extremely easy to deteriorate in advance or deteriorate in a delayed manner, so that the quality guarantee period of the food and medicine which is not in the designated preservation environment cannot be reasonably estimated in the prior art, and the food and medicine detection system is designed for solving the problems.
Disclosure of Invention
Aiming at the defects of the prior art, the application provides a food and drug detection system, which is characterized in that the image, smell and storage time data of an object to be detected are collected, the collected image of the object to be detected is guided into an image deterioration influence calculation strategy to calculate the image deterioration value of the object to be detected, the collected smell gas data of the object to be detected is guided into the smell deterioration influence calculation strategy to calculate the smell deterioration value of the object to be detected, the calculated image deterioration value and smell deterioration value are guided into a deterioration time calculation strategy to calculate the deterioration time, the deterioration time of the object to be detected under different environments is estimated, the grasp of the deterioration time of the object to be detected is effectively improved, the estimated deterioration time is fed back, and meanwhile, the alarm is given when the estimated deterioration time is smaller than or equal to a preset deterioration time threshold value, so that the solution speed of the object to be deteriorated is further improved.
In order to achieve the above purpose, the present application provides the following technical solutions:
the food and drug detection system specifically comprises a data acquisition port, a data processing port and a data feedback port, wherein the data acquisition port is used for acquiring image, smell and storage time data of a to-be-detected product in real time, the data processing port is used for processing the acquired image, smell and storage time data of the to-be-detected product so as to estimate the deterioration time under the influence of environment, and the data feedback port is used for feeding back the estimated deterioration time and giving an alarm when the estimated deterioration time is smaller than or equal to a preset deterioration time threshold value;
the data acquisition port comprises an image acquisition module, an odor acquisition module and a storage time acquisition module, wherein the image acquisition module is used for acquiring images of the to-be-detected product in real time, the odor acquisition module is used for acquiring odor gas data emitted by the to-be-detected product in real time, and the storage time acquisition module is used for acquiring stored time data of the to-be-detected product in real time.
Specifically, the data processing port comprises an image deterioration detection module, an odor deterioration detection module and a deterioration time calculation module, wherein the image deterioration detection module is used for guiding an acquired image of the to-be-detected product into an image deterioration influence calculation strategy to calculate an image deterioration value of the to-be-detected product, the odor deterioration detection module is used for guiding acquired odor gas data of the to-be-detected product into the odor deterioration influence calculation strategy to calculate an odor deterioration value of the to-be-detected product, and the deterioration time calculation module is used for guiding the calculated image deterioration value and the odor deterioration value into the deterioration time calculation strategy to calculate deterioration time.
Specifically, the data feedback port includes a spoilage early warning module, a spoilage time feedback module and a time comparison module, wherein the spoilage time feedback module is used for feeding back the calculated spoilage time to a user, the time comparison module is used for comparing the calculated spoilage time with a preset spoilage time threshold, the spoilage early warning module is used for warning that the predicted spoilage time is smaller than or equal to the preset spoilage time threshold, and here, the preset spoilage time threshold is determined according to the selling time/eating frequency of the to-be-measured product by a customer, for example, the lowest selling time/eating frequency of the to-be-measured product is 1 day for 1 time, the preset spoilage time threshold is set to be greater than or equal to 1 day, and the spoilage early warning module carries out early warning to the user in an acousto-optic or short message reminding mode.
Specifically, the image deterioration detection module comprises an image extraction unit, an image processing unit and an image deterioration influence calculation unit, wherein the image extraction unit is used for extracting real-time image data of the sample to be detected, which is acquired by the image acquisition module, the image processing unit is used for substituting the real-time image data into image processing software to perform image sharpening, and the image deterioration influence calculation unit is used for calculating an image deterioration value of the sharpening image.
Specifically, the image deterioration influence calculation unit runs an image deterioration influence calculation strategy including the specific steps of:
s11, extracting an image of the to-be-detected product at the beginning of storage and an image of the to-be-detected product at the detection time, dividing pixels of the two images, extracting pixel values of all the pixels to form a pixel value sequence, wherein the pixel value sequence is expressed as follows:andwherein->The superscript s of (1) is the moment when storage starts, the subscript i is the ith pixel point in the image, i belongs to (1, n), c is the pixel value, wherein +.>The superscript j of (1) is the moment of the detection time;
s12, substituting the two pixel value sequences into an image difference value calculation formula to calculate an image difference value, wherein the image difference value calculation formula is as follows:wherein->A time interval from the start of storage to the detection time;
s13, extracting an image of the sample to be detected during deterioration, and obtaining pixel values of all pixel points of the image to be detected, wherein the pixel values are expressed asWherein->Meaning is the pixel value of the ith pixel point of the image when the to-be-detected sample is deteriorated; the image to be measured when the quality of the product to be measured is changed is taken as an image after the specified preservation environment has a specified shelf life, for example, the image is preserved for 3 weeks at the temperature of 0 ℃ in general milk, and the pixel average value of each corresponding pixel point of 500 groups of images after the image is preserved for 3 weeks at the temperature of 0 ℃ in milk is taken as the pixel value of each pixel point;
s14, substituting the pixel values of all the pixel points of the image of the to-be-detected product when the to-be-detected product is deteriorated, the image difference value and the pixel values of all the pixel points of the image of the to-be-detected product at the detection time into an image deterioration value calculation formula to calculate the image deterioration value, wherein the image deterioration value calculation formula is as follows:wherein->The shelf life of the to-be-measured product in a specified preservation environment is ensured.
Specifically, the odor deterioration detection module includes an odor extraction unit for extracting a gas type and a gas concentration generated during storage of the sample, and an odor deterioration influence calculation unit for introducing the collected gas type and gas concentration into an odor deterioration influence calculation strategy to calculate an odor deterioration value of the sample, where it is to be noted that the gas type is a gas generated by deterioration of the sample, and the odor deterioration detection module mainly includes:
1. volatile Organic Compounds (VOCs): this is a class of organic compounds that are generated during the spoilage of food and which can emit specific odors, for example, acetic acid, ethyl acetate and acetone are common VOCs that can cause the food to develop odors of acetic acid, fruit esters or solvents;
2. sulfur compound: certain microorganisms metabolize sulfur compounds in foods, possibly producing hydrogen sulfide (sulfur odor) or other sulfur compounds, which are often described as rotting, rotten eggs or sulfur odors;
3. nitrogen compound: such as amines, may be released when the food is spoiled, resulting in ammonia odor or other unpleasant odors;
4. ketone compounds: ketone compounds such as acetone, pentanone, etc. may produce fruit taste or sweet taste, and may also emit smell when food is spoiled;
5. phenols and alcohols: these compounds may cause bitter, sweet or other off-note notes to be produced after deterioration of the food product;
6. acids and esters: acids and esters compounds are often associated with sour and fruity flavors, which can produce unusual odors in foods.
Specifically, the odor deterioration influence calculation unit runs an odor deterioration influence calculation strategy comprising the specific steps of:
s21, extracting the type of the metamorphic gas and the variation of the concentration of the metamorphic gas at the detection time t;
s22, substituting the variation of the concentration of the metamorphic gas and the detection time t into a metamorphic gas generation speed calculation formula to calculate the generation speed of the metamorphic gas, wherein the metamorphic gas generation speed calculation formula is as follows:wherein m is the species of metamorphic gas, < >>The variation of j-type metamorphic gas concentration within the detection time t;
s23, extracting the generation concentration of the metamorphic gas of the metamorphic product, and introducing the generation concentration of the metamorphic gas of the metamorphic product and the calculated generation speed of the metamorphic gas into an odor metamorphic value calculation formula to calculate the odor metamorphic value, wherein the odor metamorphic value calculation formula is as follows:wherein->For the variation of the concentration of the metamorphic gas of the j species during metamorphic process, here +.>The calculation mode of (a) is as follows: 500 groups of the specified shelf life are extracted, and the extracted 500 groups of the specified shelf life are placed in the to-be-detected products in the specified preservation environment, and the concentration of each metamorphic gas generated by the to-be-detected products is measured and averaged.
Specifically, the spoilage time calculation module runs a spoilage time calculation strategy, which comprises the following specific contents: the smell deterioration value and the image deterioration value obtained by extraction are led into a deterioration time calculation formula to calculate estimated deterioration time, wherein the expression of the deterioration time calculation formula is as follows:wherein->For the image deterioration value duty factor, +.>For the smell deterioration value duty factor, +.>。
Compared with the prior art, the application has the beneficial effects that:
according to the application, the image, smell and storage time data of the to-be-measured product are acquired, the acquired image of the to-be-measured product is imported into an image deterioration influence calculation strategy to calculate the image deterioration value of the to-be-measured product, the acquired smell gas data of the to-be-measured product is imported into a smell deterioration influence calculation strategy to calculate the smell deterioration value of the to-be-measured product, the calculated image deterioration value and smell deterioration value are imported into a deterioration time calculation strategy to calculate deterioration time, the deterioration time of the to-be-measured product under different environments is estimated, the grasp of the deterioration time of the to-be-measured product is effectively improved, the estimated deterioration time is fed back, and meanwhile, the alarm is given when the estimated deterioration time is smaller than or equal to a preset deterioration time threshold value, so that the solution speed of the to-be-deteriorated product is further improved.
Drawings
FIG. 1 is a schematic diagram of a food and drug detection system framework of the present application;
FIG. 2 is a schematic diagram of an image deterioration detecting module of the food and drug detecting system according to the present application;
FIG. 3 is a schematic diagram of an odor deterioration detection module of the food and drug detection system of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments.
Example 1
Referring to fig. 1-2, an embodiment of the present application is provided: the food and drug detection system specifically comprises a data acquisition port, a data processing port and a data feedback port, wherein the data acquisition port is used for acquiring image, smell and storage time data of a to-be-detected product in real time, the data processing port is used for processing the acquired image, smell and storage time data of the to-be-detected product, further estimating the deterioration time under the influence of environment, and the data feedback port is used for feeding back the estimated deterioration time and alarming when the estimated deterioration time is less than or equal to a preset deterioration time threshold value;
the data acquisition port comprises an image acquisition module, an odor acquisition module and a storage time acquisition module, wherein the image acquisition module is used for acquiring images of the to-be-detected product in real time, the odor acquisition module is used for acquiring odor gas data emitted by the to-be-detected product in real time, and the storage time acquisition module is used for acquiring stored time data of the to-be-detected product in real time;
in this embodiment, the data processing port includes an image modification detection module, an odor modification detection module, and a modification time calculation module, where the image modification detection module is configured to introduce the collected image of the to-be-measured product into an image modification influence calculation policy to calculate an image modification value of the to-be-measured product, the odor modification detection module is configured to introduce the collected odor gas data of the to-be-measured product into the odor modification influence calculation policy to calculate an odor modification value of the to-be-measured product, and the modification time calculation module is configured to introduce the calculated image modification value and odor modification value into the modification time calculation policy to calculate modification time.
In this embodiment, the data feedback port includes a spoilage early warning module, a spoilage time feedback module and a time comparison module, the spoilage time feedback module is used for feeding back the calculated spoilage time to the user, the time comparison module is used for comparing the calculated spoilage time with a preset spoilage time threshold, the spoilage early warning module is used for warning that the predicted spoilage time is less than or equal to the preset spoilage time threshold, and here, the preset spoilage time threshold is determined according to the selling time/eating frequency of the to-be-measured product by the client, for example, the lowest selling time/eating frequency of the to-be-measured product is 1 day for 1 time, the preset spoilage time threshold is set to be greater than or equal to 1 day, and the spoilage early warning module carries out early warning to the user in an acousto-optic or short message reminding mode.
In this embodiment, the image modification detection module includes an image extraction unit, an image processing unit, and an image modification influence calculation unit, where the image extraction unit is configured to extract real-time image data of the sample to be detected collected by the image collection module, the image processing unit is configured to substitute the real-time image data into the image processing software to perform image sharpening, and the image modification influence calculation unit is configured to calculate an image modification value of the sharpened image.
In this embodiment, the image deterioration influence calculation unit runs an image deterioration influence calculation strategy including the specific steps of:
s11, extracting an image of the to-be-detected product at the beginning of storage and an image of the to-be-detected product at the detection time, dividing pixels of the two images, extracting pixel values of all the pixels to form a pixel value sequence, wherein the pixel value sequence is expressed as follows:andwherein->The superscript s of (1) is the moment when storage starts, the subscript i is the ith pixel point in the image, i belongs to (1, n), c is the pixel value, wherein +.>The superscript j of (1) is the moment of the detection time;
s12, substituting the two pixel value sequences into an image difference value calculation formula to calculate an image difference value, wherein the image difference value calculation formula is as follows:wherein->A time interval from the start of storage to the detection time;
s13, extracting an image of the sample to be detected during deterioration, and obtaining pixel values of all pixel points of the image to be detected, wherein the pixel values are expressed asWherein->Meaning is the pixel value of the ith pixel point of the image when the to-be-detected sample is deteriorated; the image to be measured when the quality of the product to be measured is changed is taken as an image after the specified preservation environment has a specified shelf life, for example, the image is preserved for 3 weeks at the temperature of 0 ℃ in general milk, and the pixel average value of each corresponding pixel point of 500 groups of images after the image is preserved for 3 weeks at the temperature of 0 ℃ in milk is taken as the pixel value of each pixel point;
s14, substituting the pixel values of all the pixel points of the image of the to-be-detected product when the to-be-detected product is deteriorated, the image difference value and the pixel values of all the pixel points of the image of the to-be-detected product at the detection time into an image deterioration value calculation formula to calculate the image deterioration value, wherein the image deterioration value calculation formula is as follows:wherein->The shelf life of the to-be-detected product in a specified preservation environment is ensured;
specifically, the spoilage time calculation module runs a spoilage time calculation strategy that includes the following specific contents: the extracted image deterioration value is imported into a deterioration time calculation formula to calculate estimated deterioration time, wherein the expression of the deterioration time calculation formula is as follows:。
it should be noted that, in this embodiment, the deterioration time of the to-be-measured product under different environments is estimated only by the image.
Example 2
As shown in fig. 1-3, a food and drug detection system specifically includes a data acquisition port, a data processing port and a data feedback port, where the data acquisition port is used to acquire image, smell and storage time data of a to-be-detected product in real time, the data processing port is used to process the acquired image, smell and storage time data of the to-be-detected product, further predict deterioration time under environmental influence, and the data feedback port is used to feed back the predicted deterioration time and alarm when the predicted deterioration time is less than or equal to a preset deterioration time threshold;
the data acquisition port comprises an image acquisition module, an odor acquisition module and a storage time acquisition module, wherein the image acquisition module is used for acquiring images of the to-be-detected product in real time, the odor acquisition module is used for acquiring odor gas data emitted by the to-be-detected product in real time, and the storage time acquisition module is used for acquiring stored time data of the to-be-detected product in real time;
in this embodiment, the data processing port includes an image modification detection module, an odor modification detection module and a modification time calculation module, the image modification detection module is used for introducing the collected image of the to-be-detected product into an image modification influence calculation strategy to calculate an image modification value of the to-be-detected product, the odor modification detection module is used for introducing the collected odor gas data of the to-be-detected product into the odor modification influence calculation strategy to calculate an odor modification value of the to-be-detected product, and the modification time calculation module is used for introducing the calculated image modification value and odor modification value into the modification time calculation strategy to calculate modification time;
specifically, the data feedback port includes a spoilage early warning module, a spoilage time feedback module and a time comparison module, the spoilage time feedback module is used for feeding back the calculated spoilage time to a user, the time comparison module is used for comparing the calculated spoilage time with a preset spoilage time threshold, the spoilage early warning module is used for alarming the condition that the estimated spoilage time is smaller than or equal to the preset spoilage time threshold, and the preset spoilage time threshold is determined according to the selling time/eating frequency of the to-be-measured product by a customer, for example, the lowest selling time/eating frequency of the to-be-measured product is 1 day for 1 time, and the preset spoilage time threshold is set to be larger than or equal to 1 day;
in this embodiment, the image modification detection module includes an image extraction unit, an image processing unit, and an image modification influence calculation unit, where the image extraction unit is used to extract real-time image data of the sample to be detected collected by the image collection module, the image processing unit is used to substitute the real-time image data into the image processing software to perform image sharpening, and the image modification influence calculation unit is used to calculate an image modification value of the sharpening image;
in this embodiment, the image deterioration influence calculation unit runs an image deterioration influence calculation strategy including the specific steps of:
s11, extracting an image of the to-be-detected product at the beginning of storage and an image of the to-be-detected product at the detection time, dividing pixels of the two images, extracting pixel values of all the pixels to form a pixel value sequence, wherein the pixel value sequence is expressed as follows:andwherein->The superscript s of (1) is the moment when storage starts, the subscript i is the ith pixel point in the image, i belongs to (1, n), c is the pixel value, wherein +.>The superscript j of (1) is the moment of the detection time;
the following is an example of a code for extracting and processing a sequence of pixel values of two images of a test article written using Python:
```python
import cv2
import numpy as np
image of sample to be measured at the beginning of# reading and storing
img_start=cv2.imread ('image_start.jpg', 0) # read as a grayscale image
Image of sample to be measured for # reading detection time
img_detect=cv2.imread ('image_detect.jpg', 0) # read as a grayscale image
# acquisition of image size information
height, width = img_start.shape
Sequence of pixel values at the beginning of# fetch store
pixels_start = img_start.reshape((height * width,)).tolist()
Sequence of pixel values for# extraction detection time
pixels_detect = img_detect.reshape((height * width,)).tolist()
Sequence of pixel values at the beginning of the# output store
print (' sequence of pixel values at the beginning of storage: ")
print(pixels_start)
Sequence of pixel values for# output detection time
print (' sequence of pixel values for detection time: ")
print(pixels_detect)
Note that 'image_start.jpg' and 'image_detect.jpg' in the above codes should be replaced with actual image file paths, the codes read images using OpenCV library and convert them into grayscale images, and then convert the images into pixel value sequences by reshape function;
s12, substituting the two pixel value sequences into an image difference value calculation formula to calculate an image difference value, wherein the image difference value calculation formula is as follows:wherein->A time interval from the start of storage to the detection time;
s13, extracting an image of the sample to be detected during deterioration, and obtaining pixel values of all pixel points of the image to be detected, wherein the pixel values are expressed asWherein->Meaning is the pixel value of the ith pixel point of the image when the to-be-detected sample is deteriorated; the image to be measured is stored for 3 weeks at the temperature of 0 ℃ in the normal milk shelf life, wherein the average value of the pixels corresponding to 500 groups of images stored for 3 weeks at the temperature of 0 ℃ in milk is taken as the pixel value of each pixel;
s14, substituting the pixel values of all the pixel points of the image of the to-be-detected product when the to-be-detected product is deteriorated, the image difference value and the pixel values of all the pixel points of the image of the to-be-detected product at the detection time into an image deterioration value calculation formula to calculate the image deterioration value, wherein the image deterioration value calculation formula is as follows:wherein->The shelf life of the to-be-detected product in a specified preservation environment is ensured;
in this embodiment, the smell deterioration detecting module includes a smell extracting unit for extracting a gas species and a gas concentration generated during storage of the sample, and a smell deterioration influence calculating unit for introducing the collected gas species and gas concentration into a smell deterioration influence calculating strategy to calculate a smell deterioration value of the sample, where it is to be noted that the gas species is a gas generated by deterioration of the sample, and mainly includes:
1. volatile Organic Compounds (VOCs): this is a class of organic compounds that are generated during the spoilage of food and which can emit specific odors, for example, acetic acid, ethyl acetate and acetone are common VOCs that can cause the food to develop odors of acetic acid, fruit esters or solvents;
2. sulfur compound: certain microorganisms metabolize sulfur compounds in foods, possibly producing hydrogen sulfide (sulfur odor) or other sulfur compounds, which are often described as rotting, rotten eggs or sulfur odors;
3. nitrogen compound: such as amines, may be released when the food is spoiled, resulting in ammonia odor or other unpleasant odors;
4. ketone compounds: ketone compounds such as acetone, pentanone, etc. may produce fruit taste or sweet taste, and may also emit smell when food is spoiled;
5. phenols and alcohols: these compounds may cause bitter, sweet or other off-note notes to be produced after deterioration of the food product;
6. acids and esters: acids and esters are often associated with sour and fruity flavors, which can produce unusual odors in foods;
in this embodiment, the smell spoilage influence calculation unit runs a smell spoilage influence calculation strategy including the specific steps of:
s21, extracting the type of the metamorphic gas and the variation of the concentration of the metamorphic gas at the detection time t;
s22, substituting the variation of the concentration of the metamorphic gas and the detection time t into a metamorphic gas generation speed calculation formula to calculate the metamorphic gasThe generation speed, wherein the calculation formula of the metamorphic gas generation speed is as follows:wherein m is the species of metamorphic gas, < >>The variation of j-type metamorphic gas concentration within the detection time t;
s23, extracting the generation concentration of the metamorphic gas of the metamorphic product, and introducing the generation concentration of the metamorphic gas of the metamorphic product and the calculated generation speed of the metamorphic gas into an odor metamorphic value calculation formula to calculate the odor metamorphic value, wherein the odor metamorphic value calculation formula is as follows:wherein->For the variation of the concentration of the metamorphic gas of the j species during metamorphic process, here +.>The calculation mode of (a) is as follows: extracting 500 groups of specified shelf life, placing the 500 groups of specified shelf life in to-be-detected products in a specified preservation environment, measuring the concentration of each metamorphic gas generated by the to-be-detected products, and taking an average value;
in this embodiment, the modification time calculation module runs a modification time calculation policy, which includes the following specific contents: the smell deterioration value and the image deterioration value obtained by extraction are led into a deterioration time calculation formula to calculate estimated deterioration time, wherein the expression of the deterioration time calculation formula is as follows:wherein->For the image deterioration value duty factor, +.>For the smell deterioration value duty factor, +.>；
The method is characterized in that the image, smell and storage time data of the to-be-detected product are collected, the collected image of the to-be-detected product is led into an image deterioration influence calculation strategy to calculate the image deterioration value of the to-be-detected product, the collected smell gas data of the to-be-detected product is led into the smell deterioration influence calculation strategy to calculate the smell deterioration value of the to-be-detected product, the calculated image deterioration value and smell deterioration value are led into a deterioration time calculation strategy to calculate the deterioration time, the deterioration time of the to-be-detected product under different environments is estimated, the grasp of the deterioration time of the to-be-detected product is effectively improved, the estimated deterioration time is fed back, and meanwhile, the alarm is given to the condition that the estimated deterioration time is smaller than or equal to a preset deterioration time threshold value, so that the solution speed of the to-be-deteriorated product is further improved.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
It should be understood that determining B from a does not mean determining B from a alone, but can also determine B from a and/or other information.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by way of wired or/and wireless networks from one website site, computer, server, or data center to another. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc. that contain one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely one, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the application disclosed above are intended only to assist in the explanation of the application. The preferred embodiments are not intended to be exhaustive or to limit the application to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the application and the practical application, to thereby enable others skilled in the art to best understand and utilize the application. The application is limited only by the claims and the full scope and equivalents thereof.

Claims (9)

1. The food and drug detection system is characterized by comprising a data acquisition port, a data processing port and a data feedback port, wherein the data acquisition port is used for acquiring image, smell and storage time data of a to-be-detected product in real time, the data processing port is used for processing the acquired image, smell and storage time data of the to-be-detected product so as to estimate the deterioration time under the influence of environment, and the data feedback port is used for feeding back the estimated deterioration time and alarming when the estimated deterioration time is smaller than or equal to a preset deterioration time threshold value; the data acquisition port comprises an image acquisition module, an odor acquisition module and a storage time acquisition module, wherein the image acquisition module is used for acquiring images of the to-be-detected product in real time, the odor acquisition module is used for acquiring odor gas data emitted by the to-be-detected product in real time, and the storage time acquisition module is used for acquiring stored time data of the to-be-detected product in real time; the data processing port comprises an image deterioration detection module, an odor deterioration detection module and a deterioration time calculation module, wherein the image deterioration detection module is used for guiding an acquired image of a to-be-detected article into an image deterioration influence calculation strategy to calculate an image deterioration value of the to-be-detected article, the odor deterioration detection module is used for guiding acquired odor gas data of the to-be-detected article into the odor deterioration influence calculation strategy to calculate an odor deterioration value of the to-be-detected article, and the deterioration time calculation module is used for guiding the calculated image deterioration value and the odor deterioration value into the deterioration time calculation strategy to calculate deterioration time.
2. The food and drug detection system according to claim 1, wherein the data feedback port comprises a spoilage early warning module, a spoilage time feedback module and a time comparison module, the spoilage time feedback module is used for feeding back the calculated spoilage time to a user, the time comparison module is used for comparing the calculated spoilage time with a preset spoilage time threshold, and the spoilage early warning module is used for warning that the estimated spoilage time is smaller than or equal to the preset spoilage time threshold.
3. The food and drug inspection system according to claim 2, wherein the image deterioration detection module comprises an image extraction unit for extracting real-time image data of the object to be inspected collected by the image collection module, an image processing unit for substituting the real-time image data into image processing software for image sharpness processing, and an image deterioration influence calculation unit for calculating an image deterioration value of the sharpness processed image.
4. A food and drug detection system as in claim 3 wherein the image spoilage effect calculation unit operates an image spoilage effect calculation strategy comprising the specific steps of:
s11, extracting an image of the to-be-detected product at the beginning of storage and an image of the to-be-detected product at the detection time, dividing pixels of the two images, extracting pixel values of all the pixels to form a pixel value sequence, wherein the pixel value sequence is expressed as follows:andwherein->The superscript s of (1) is the moment when storage starts, the subscript i is the ith pixel point in the image, i belongs to (1, n), c is the pixel value, wherein +.>The superscript j of (1) is the moment of the detection time;
s12, substituting the two pixel value sequences into an image difference value calculation formula to calculate an image difference value, wherein the image difference value calculation formula is as follows:wherein->For the time interval from the start of storage to the detection time.
5. The food and drug product detection system of claim 4 wherein the image spoilage effect calculation strategy further comprises the specific steps of:
s13, extracting an image of the sample to be detected during deterioration, and obtaining pixel values of all pixel points of the image to be detected, wherein the pixel values are expressed asWhereinMeaning is the pixel value of the ith pixel point of the image when the to-be-detected sample is deteriorated;
s14, substituting the pixel values of all the pixel points of the image of the to-be-detected product when the to-be-detected product is deteriorated, the image difference value and the pixel values of all the pixel points of the image of the to-be-detected product at the detection time into an image deterioration value calculation formula to calculate the image deterioration value, wherein the image deterioration value calculation formula is as follows:wherein->The shelf life of the to-be-measured product in a specified preservation environment is ensured.
6. The food and drug inspection system according to claim 5, wherein the odor deterioration detection module includes an odor extraction unit for extracting a gas species and a gas concentration generated during storage of the test article, and an odor deterioration influence calculation unit for introducing the collected gas species and gas concentration into an odor deterioration influence calculation strategy to calculate an odor deterioration value of the test article.
7. The food and drug product detection system of claim 6, wherein the odor deterioration influence calculation unit operates an odor deterioration influence calculation strategy comprising the specific steps of:
s21, extracting the type of the metamorphic gas and the variation of the concentration of the metamorphic gas at the detection time t;
s22, substituting the variation of the concentration of the metamorphic gas and the detection time t into a metamorphic gas generation speed calculation formula to calculate the generation speed of the metamorphic gas, wherein the metamorphic gas generation speed calculation formula is as follows:wherein m is the species of metamorphic gas, < >>The variation of the j-type modified gas concentration at the detection time t.
8. The food and drug product detection system of claim 7, wherein the odor deterioration effect calculation strategy further comprises the specific steps of: s23, extracting the generation concentration of the metamorphic gas of the metamorphic product, and introducing the generation concentration of the metamorphic gas of the metamorphic product and the calculated generation speed of the metamorphic gas into an odor metamorphic value calculation formula to calculate the odor metamorphic value, wherein the odor metamorphic value calculation formula is as follows:wherein->Is the variation of j-type metamorphic gas concentration in the metamorphic process.
9. The food and drug product detection system of claim 8 wherein the spoilage time calculation module operates a spoilage time calculation strategy comprising the following specific contents: the smell deterioration value and the image deterioration value obtained by extraction are led into a deterioration time calculation formula to calculate estimated deterioration time, wherein the expression of the deterioration time calculation formula is as follows:wherein->For the image deterioration value duty factor, +.>For the smell deterioration value duty factor, +.>。
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107389575A (en) * 2017-08-01 2017-11-24 华南农业大学 A kind of rotten automatic testing method of reagent and system
CN108982782A (en) * 2018-06-26 2018-12-11 广州视源电子科技股份有限公司 Food spoilage detection method, device, mobile terminal and storage medium
CN111027479A (en) * 2019-12-10 2020-04-17 珠海格力电器股份有限公司 Food material processing method and device and refrigerator
CN114544658A (en) * 2022-02-10 2022-05-27 青岛海尔科技有限公司 Method and device for determining deteriorated food, storage medium and electronic device
WO2022142080A1 (en) * 2020-12-30 2022-07-07 稿定（厦门）科技有限公司 Image comparison method and apparatus

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107389575A (en) * 2017-08-01 2017-11-24 华南农业大学 A kind of rotten automatic testing method of reagent and system
CN108982782A (en) * 2018-06-26 2018-12-11 广州视源电子科技股份有限公司 Food spoilage detection method, device, mobile terminal and storage medium
CN111027479A (en) * 2019-12-10 2020-04-17 珠海格力电器股份有限公司 Food material processing method and device and refrigerator
WO2022142080A1 (en) * 2020-12-30 2022-07-07 稿定（厦门）科技有限公司 Image comparison method and apparatus
CN114544658A (en) * 2022-02-10 2022-05-27 青岛海尔科技有限公司 Method and device for determining deteriorated food, storage medium and electronic device

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