CN111552775A - Food data processing method and system - Google Patents

Food data processing method and system Download PDF

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CN111552775A
CN111552775A CN202010332016.8A CN202010332016A CN111552775A CN 111552775 A CN111552775 A CN 111552775A CN 202010332016 A CN202010332016 A CN 202010332016A CN 111552775 A CN111552775 A CN 111552775A
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CN111552775B (en
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王祉干
赵秀芸
王子生
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Shanghai Mint Health Technology Co ltd
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Abstract

The invention discloses a food data processing method and a system, wherein the method comprises the following steps: collecting food data; judging whether food corresponding to the food data is abnormal or not according to the collected food data, and if so, acquiring abnormal food information; and carrying out routing inspection according to the acquired food abnormal information. According to the technical scheme, manpower and material resources consumed in searching and analyzing food abnormal information are saved, and potential food safety hazards are avoided.

Description

Food data processing method and system
Technical Field
The invention relates to the technical field of data processing, in particular to a food data processing method and system.
Background
Food safety (food safety) always is a social hotspot problem, and means that food is nontoxic and harmless, meets the existing nutritional requirements, and does not cause any acute, subacute or chronic harm to human health. At present, the food is supervised, the searching and analyzing of food abnormal information depend on the manpower paid by people for checking, a large amount of manpower and material resources are consumed, the abnormal analysis of the whole quality of the food cannot be accurately carried out, and the potential safety hazard of the food can be caused.
Disclosure of Invention
The invention provides a food data processing method, which adopts the following technical scheme that the method comprises the following steps:
collecting food data;
judging whether food corresponding to the food data is abnormal or not according to the collected food data, and if so, acquiring abnormal food information;
and carrying out routing inspection according to the obtained food abnormal information.
In one embodiment, the step of collecting food data comprises:
acquiring a data acquisition service requirement;
configuring a data acquisition task rule according to the data acquisition service requirement; the data acquisition task rule corresponds to at least one acquisition characteristic information;
and acquiring data according to the acquisition task rule to obtain the food data.
In one embodiment, the step of collecting food data further comprises:
detecting food keywords in a target website, an application APP or a target scene, wherein the food keywords are common vocabularies in the food industry;
when the food keywords are detected in the target website, the application APP or the target scene, determining the information segment where the detected food keywords are located;
determining the information segment where the detected food key words are located as food characteristic information;
judging whether the food characteristic information is matched with the collected characteristic information in the data collection task rule or not,
and if so, acquiring data in the target website, the application APP or the target scene according to the acquisition task rule to obtain the food data.
In one embodiment, when collecting the food data, further comprising:
acquiring the current time of the food data acquisition, wherein the current time comprises year, month, day, hour, minute and second;
judging whether the food data meet the standard of private information, storing the food data in an encryption mode according to the current time when the food data meet the standard of the private information,
and when the food data does not meet the standard of the private information, storing the food data in a non-encryption mode according to the current time.
In one embodiment, judging whether food corresponding to the food data is abnormal according to the collected food data, and if so, acquiring food abnormal information, including:
converting the collected food data and data in a food data standard library into a data structure in a preset tabular form;
searching an original standard data set which is the same as at least one component of the food corresponding to the food data in the food data standard library;
searching and screening the original standard data set according to the food name in the food data to obtain a current standard data set;
similarity calculation is carried out on the current standard data set according to all the components of the food corresponding to the food data, and the similarity of the food data and any one piece of data in the current standard data set is obtained;
determining the data with the highest similarity with the food data in the current standard data set according to the similarity;
judging whether the data with the highest similarity to the food data in the current standard data set is the same as the food data,
if not, determining that the food corresponding to the food data is abnormal, and acquiring abnormal food information;
wherein the composition comprises ingredient composition and/or nutritional composition.
In one embodiment, the step of performing routing inspection according to the acquired food abnormal information comprises:
acquiring the food abnormal information;
acquiring a polling instruction;
based on the inspection instruction, performing inspection according to the food abnormal information to obtain an inspection result;
sending out a prompt according to the inspection result;
and performing label processing or extraction processing on the food corresponding to the food abnormal information according to the prompt.
In one embodiment, the food data includes any one or more of a food name, a food production date, a food shelf life, a composition, a place of manufacture, and a production license number.
In one embodiment, further comprising:
and carrying out pollution-free recovery or destruction on the food corresponding to the food abnormal information.
In one embodiment, the determining whether the food corresponding to the food data is abnormal according to the collected food data specifically includes:
A. preliminarily judging the food corresponding to the food data according to the food data;
Figure BDA0002465279640000031
wherein, the preliminary judgment value x of the food corresponding to the food dataiIs the ith data value of the food data, n is the number of data included in the food data, [ m [ ]i,ni]The normal value range of the ith data of the food data is obtained;
and when the food data is larger than the preset judgment value, the food corresponding to the food data is suspected to be abnormal, further judgment is carried out on the food corresponding to the food data suspected to be abnormal according to the judgment result B, otherwise, the food corresponding to the food data is normal, and the judgment result B is not required. .
B. Further judging the food corresponding to the food data suspected to be abnormal;
calculating judgment values of chemical attribute data and physical attribute data of the food corresponding to the food data;
Figure BDA0002465279640000041
Figure BDA0002465279640000042
in the above formula, kiα is the judgment value of the ith data in the chemical attributes of the food corresponding to the food dataiIs the ith data value, a in the chemical attribute of the food corresponding to the food dataiThe lower limit of the normal range of the ith data in the chemical attributes of the food corresponding to the food data, biThe upper limit, g, of the normal range of the ith data in the chemical attributes of the food corresponding to the food dataiβ is the judgment value of the ith data in the physical attribute of the food corresponding to the food dataiIs the ith data, z, in the physical attributes of the food corresponding to the food dataiAnd the ith data standard value in the physical attribute of the food corresponding to the food data is exp is an exponential function, lg is a logarithmic function, and e is a natural number.
Obtaining a further judgment result;
Figure BDA0002465279640000043
wherein ,
Figure BDA0002465279640000044
a further determination result, k, for the food corresponding to the food dataiIs the judgment value of the ith data in the chemical attributes of the food corresponding to the food data, giIs the judgment value of the ith data in the physical attributes of the food corresponding to the food data, AiPresetting a confidence interval, p, of the ith data in the chemical attributes of the food corresponding to the food dataiPresetting the physics of the food corresponding to the food dataAnd the threshold value of the ith data in the attribute is 1, which represents that the food corresponding to the food data is abnormal, and 0, which represents that the food corresponding to the food data is normal.
The present invention provides a food data processing system, comprising:
the data acquisition end is connected with the server end and used for acquiring food data and sending the food data to the server end;
the server end is respectively connected with the data acquisition end and the inspection end and is used for receiving the food data sent by the data acquisition end, judging whether food corresponding to the food data is abnormal or not according to the food data, and if the food corresponding to the food data is abnormal, acquiring abnormal food information and sending the abnormal food information to the inspection end;
and the inspection end is used for receiving the food abnormal information sent by the server end so as to perform inspection according to the food abnormal information.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
firstly, collecting food data; then, judging whether the food corresponding to the food data is abnormal or not according to the food data, and if so, acquiring abnormal food information; and finally, carrying out inspection according to the food abnormal information. According to the technical scheme, food data do not need to be checked and analyzed manually, a large amount of manpower and material resources are saved, the overall quality of food can be analyzed accurately, the abnormal condition of the food can be accurately obtained according to the food data, and the potential safety hazard of the food is avoided.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of a food data processing method according to an embodiment of the present invention;
FIG. 2 is a flow chart of another food data processing method according to an embodiment of the present invention;
FIG. 3 is a flow chart of another food data processing method according to an embodiment of the present invention;
FIG. 4 is a flow chart of another food data processing method according to an embodiment of the present invention;
FIG. 5 is a block diagram of a food data processing system in accordance with an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
The invention provides a food data processing method, as shown in fig. 1, comprising:
step 1: collecting food data;
step 2: judging whether food corresponding to the food data is abnormal or not according to the collected food data, and if so, acquiring abnormal food information;
and step 3: and carrying out routing inspection according to the obtained food abnormal information.
Food data includes, but is not limited to, food name, food production date, food shelf life, ingredients, place of production, and production license number.
The food abnormal information includes but is not limited to food which is already out of date and is not produced by production license, bad taste caused by food composition not meeting the standard, food composition not meeting the standard and the like.
Firstly, collecting food data; then, judging whether the food corresponding to the food data is abnormal or not according to the food data, and if so, acquiring abnormal food information; and finally, carrying out routing inspection according to the food abnormal information. According to the technical scheme, food data do not need to be checked and analyzed manually, a large amount of manpower and material resources are saved, the overall quality of food can be analyzed accurately, the abnormal condition of the food can be accurately obtained according to the food data, and the potential safety hazard of the food is avoided.
In one embodiment, as shown in fig. 2, the step of collecting food data comprises:
step 11: acquiring a data acquisition service requirement;
step 12: configuring a data acquisition task rule according to the data acquisition service requirement; the data acquisition task rule corresponds to at least one acquisition characteristic information;
step 13: and acquiring data according to the acquisition task rule to obtain the food data.
The collection business requirement refers to the variety of food data to be collected by a user (because the variety of food is many, such as beverage and snacks, and the brand of the food can be further precise, such as coca-cola, etc.), the quantity of the collected food data, and the like, and the data collection task rule is the concretization of the data collection business requirement, and the collected characteristic information can be the specific collected quantity, the specific collected type, and the like.
In the embodiment, the data acquisition service requirement is acquired; then, configuring a data acquisition task rule according to the data acquisition service requirement; furthermore, data acquisition is carried out according to the acquisition task rule, food data can be obtained, and the food data can be acquired in a targeted and purposeful manner by configuring the data acquisition task rule, so that the efficiency is improved.
In one embodiment, as shown in fig. 3, the step of collecting food data further comprises:
step 111: detecting food keywords in a target website, an application APP or a target scene, wherein the food keywords are common vocabularies in the food industry;
step 112: when the food keywords are detected in the target website, the application APP or the target scene, determining the information segment where the detected food keywords are located;
step 113: determining the information segment where the detected food key words are located as food characteristic information;
step 114: judging whether the food characteristic information is matched with the collected characteristic information in the data collection task rule or not,
step 115: and if so, acquiring data in the target website, the application APP or the target scene according to the acquisition task rule to obtain the food data.
In this embodiment, the target website or APP may be a website or APP on which a user sells goods, the purpose of acquiring data of the website or APP is to prevent data logged in the website or APP from being incorrect or being out of date to have adverse effects on a customer, the target scene may be a place, a supermarket, a shopping mall, etc. of food production, and the food keyword may be a food name, a food advertising word, a food manufacturer, a food component, etc.
The information segment of the food keyword refers to a sentence or paragraph of the food keyword, for example, the information segment of the food keyword is "real fruit grain is a first global new-generation milk beverage containing chewable fruit grain and is released by the mongolian cattle group, and a large number of customers are attracted by the bold innovation. As the fruit granule milk drink with the most abundant taste at present, the real fruit granules have five flavors of strawberry, kiwi fruit, coconut, yellow peach and aloe. The requirements of consumers with different preferences at different time, on different occasions and even under different moods are met, and the food keywords are contained in the food keywords, such as: real fruit granules, Mongolian cow group, milk beverage and the like.
For example, the information segment where the food keyword is "real fruit grain is the first global generation milk beverage containing chewable fruit grain from mengku group, the bold innovation attracts a group of customers", if the food characteristic information is "real fruit grain", the two are directly matched, and if the food characteristic information is "cola", the two are not matched.
In the embodiment, the food keywords are detected in a target website, an application APP or a target scene; when the food keywords are detected in the target website, the application APP or the target scene, determining the information segment where the detected food keywords are located; secondly, determining the information segment where the detected food key words are located as food characteristic information; and finally, judging whether the food characteristic information is matched with the acquisition characteristic information in the data acquisition task rule, if so, acquiring data in the target website, the application APP or the target scene according to the acquisition task rule to obtain the food data. By adding a matching mechanism between the food characteristic information and the collected characteristic information, the food data meeting the requirements of a target website, an application APP or a target scene can be acquired more quickly and accurately.
In one embodiment, when collecting the food data, further comprising:
acquiring the current time of the food data acquisition, wherein the current time comprises year, month, day, hour, minute and second;
judging whether the food data meet the standard of private information, storing the food data in an encryption mode according to the current time when the food data meet the standard of the private information,
and when the food data does not meet the standard of the private information, storing the food data in a non-encryption mode according to the current time.
In this embodiment, the current time is the time of the meeting where the food data is collected, for example, 10 months, 10 days, 16 o' clock, 10 minutes, 30 seconds in 2020.
In this embodiment, the current time when the food data is collected is obtained; and judging whether the food data meet the standard of private information, storing the food data in an encryption mode at the current moment when the food data meet the standard of the private information, and storing the food data in a non-encryption mode at the current moment when the food data do not meet the standard of the private information. The food information is classified by adding an encryption mode or a non-encryption mode, so that the storage pressure caused by storage in a single mode can be avoided, and the leakage of the food data meeting the private information can be prevented by the storage in the encryption mode.
In one embodiment, as shown in fig. 4, determining whether food corresponding to the food data is abnormal according to the collected food data, and if the food corresponding to the food data is abnormal, the step of obtaining food abnormal information includes:
step 41: converting the collected food data and data in a food data standard library into a data structure in a preset tabular form;
step 42: searching an original standard data set which is the same as at least one component of the food corresponding to the food data in the food data standard library;
step 43: searching and screening the original standard data set according to the food name in the food data to obtain a current standard data set;
step 44: similarity calculation is carried out on the current standard data set according to all the components of the food corresponding to the food data, and the similarity of the food data and any one piece of data in the current standard data set is obtained;
step 45: determining the data with the highest similarity with the food data in the current standard data set according to the similarity;
step 46: judging whether the data with the highest similarity to the food data in the current standard data set is the same as the food data,
if not, determining that the food corresponding to the food data is abnormal, and acquiring abnormal food information;
wherein the composition comprises ingredient composition and/or nutritional composition.
In this embodiment, the preset tabular form may be data in an Excel tabular form, for example, the first column is a food name, the second column is food production date type information, and the preset tabular form may be set according to specific requirements of a user, and information such as related information (food name, food production date, food shelf life, composition, production place, and production license number) of food, food safety standard, and the like in the food data standard library.
For example, if the food corresponding to the food data is searched in the food data standard with the ingredient composition sorbitol and the nutrient composition fat 2.5g, a plurality of pieces of food data meeting the characteristic will appear, and the original standard data set is composed of the plurality of pieces of food data.
In the embodiment, the collected food data and the data in the food data standard library are converted into a data structure in a preset tabular form; searching an original standard data set which is the same as at least one component of the food corresponding to the food data in the food data standard library; secondly, searching and screening the original standard data set according to the food name in the food data to obtain a current standard data set; then, similarity calculation is carried out on the current standard data set according to all the components of the food corresponding to the food data, and the similarity of the food data and any one piece of data in the current standard data set is obtained; and determining the data with the highest similarity with the food data in the current standard data set according to the similarity. Judging whether the data with the highest similarity to the food data in the current standard data set is the same as the food data or not, if not, determining that the food corresponding to the food data is abnormal, and acquiring food abnormal information; by carrying out a series of operations and calculations according to the food data and the data in the food data standard library, the food abnormal information corresponding to the food data can be clearly obtained, and the specific abnormal position can be obtained.
In one embodiment, the composition comprises an ingredient composition and/or a nutritional composition.
For example, a food named as Qingzui prebiotics fruity buccal tablet candy comprises the following ingredients of sorbitol, fructo-oligosaccharide (five percent of addition), inulin (five percent of addition), citric acid, magnesium stearate, food essence, DL malic acid, aspartame (containing phenylalanine), lemon yellow and allure red. The nutritional components of the food are 1742kj (eleven percent), 0g (zero percent), 2.5g (four percent), 97.0g (thirty-two percent) and 80mg (four percent) of sodium per hundred grams.
In one embodiment, the step of performing routing inspection according to the acquired food abnormal information comprises:
acquiring the food abnormal information;
acquiring a polling instruction;
based on the inspection instruction, performing inspection according to the food abnormal information to obtain an inspection result;
sending out a prompt according to the inspection result;
and performing label processing or extraction processing on the food corresponding to the food abnormal information according to the prompt.
In this embodiment, the patrol inspection instruction may be initiated by the user, and the purpose is to trigger the work of the training module, that is, the patrol inspection module may work only when receiving the patrol inspection instruction, and may not work when not receiving the patrol inspection instruction.
The inspection result can be the position of the food corresponding to the food abnormal information.
And performing label processing or extraction processing on the food corresponding to the food abnormal information according to the prompt, wherein the label processing refers to labeling or marking the position of the food corresponding to the food abnormal information, and the extraction processing refers to extracting the food from a target scene or putting the food on or off the shelf from a target website APP.
In this embodiment, food abnormality information sent by the server is received; acquiring a polling instruction; secondly, based on the inspection instruction, inspection is carried out according to the food abnormal information, and an inspection result can be obtained; then, sending out a prompt according to the inspection result; and finally, performing label processing or extraction processing on the food corresponding to the food abnormal information according to the prompt. The sent reminding can be used for carrying out label processing or extraction processing on the food corresponding to the food abnormal information in time.
In one embodiment, the food data includes any one or more of a food name, a food production date, a food shelf life, a composition, a place of manufacture, and a production license number.
In the embodiment, different food data are acquired to analyze food abnormal information, so that more data can be used, and the analysis result is accurate.
In one embodiment, the method further comprises the step of carrying out pollution-free recovery or destruction on the food corresponding to the food abnormal information.
In one embodiment, the food corresponding to the food abnormal information is recovered or destroyed without pollution, so that the harm to customers or the pollution to the environment can be avoided.
In one embodiment, the determining whether the food corresponding to the food data is abnormal according to the collected food data specifically includes:
A. preliminarily judging the food corresponding to the food data according to the food data;
Figure BDA0002465279640000121
wherein, the preliminary judgment value x of the food corresponding to the food dataiIs the ith data value of the food data, n is the number of data included in the food data, [ m [ ]i,ni]The normal value range of the ith data of the food data is obtained;
and when the food data is larger than the preset judgment value, the food corresponding to the food data is suspected to be abnormal, further judgment is carried out on the food corresponding to the food data suspected to be abnormal according to the judgment result B, otherwise, the food corresponding to the food data is normal, and the judgment result B is not required. .
B. Further judging the food corresponding to the food data suspected to be abnormal;
calculating judgment values of chemical attribute data and physical attribute data of the food corresponding to the food data;
Figure BDA0002465279640000122
Figure BDA0002465279640000123
in the above formula, kiα is the judgment value of the ith data in the chemical attributes of the food corresponding to the food dataiIs the ith data value, a in the chemical attribute of the food corresponding to the food dataiThe lower limit of the normal range of the ith data in the chemical attributes of the food corresponding to the food data, biThe upper limit, g, of the normal range of the ith data in the chemical attributes of the food corresponding to the food dataiβ is the judgment value of the ith data in the physical attribute of the food corresponding to the food dataiIs the ith data, z, in the physical attributes of the food corresponding to the food dataiAnd the ith data standard value in the physical attribute of the food corresponding to the food data is exp is an exponential function, lg is a logarithmic function, and e is a natural number.
Obtaining a further judgment result;
Figure BDA0002465279640000131
wherein ,
Figure BDA0002465279640000132
a further determination result, k, for the food corresponding to the food dataiIs the judgment value of the ith data in the chemical attributes of the food corresponding to the food data, giIs the judgment value of the ith data in the physical attributes of the food corresponding to the food data, AiPresetting a confidence interval, p, of the ith data in the chemical attributes of the food corresponding to the food dataiPresetting a limit value of ith data in physical attributes of food corresponding to the food data, wherein 1 represents that the food corresponding to the food data is abnormal, and 0 represents that the food corresponding to the food data is normal.
The beneficial effects of this embodiment: according to the technical scheme, whether the food corresponding to the food data is abnormal is judged, whether the food corresponding to the food data is suspected to be abnormal is judged firstly, then the food suspected to be abnormal is further judged, the secondary judgment can effectively avoid the phenomenon of misjudgment, the judgment can be more accurate according to the physical attribute and the chemical attribute of the food corresponding to the food data during the further judgment, and the whole judgment can be realized through a computer, so that the method is fast and convenient.
The present invention provides a food data processing system, as shown in fig. 5, comprising:
the data acquisition terminal 11 is connected with the server terminal 12 and used for acquiring food data and sending the food data to the server terminal 12;
the server end 12 is respectively connected with the data acquisition end 11 and the inspection end 13, and is used for receiving the food data sent by the data acquisition end 11, judging whether food corresponding to the food data is abnormal according to the food data, and if so, acquiring abnormal food information and sending the abnormal food information to the inspection end;
and the inspection terminal 13 is configured to receive the food abnormal information sent by the server terminal 12, so as to perform inspection according to the food abnormal information.
The beneficial effects of the embodiment are as follows: the food data are not required to be artificially checked and analyzed, a large amount of manpower and material resources are saved, the overall quality of the food can be accurately analyzed, the abnormal condition of the food can be accurately obtained according to the food data, and the potential safety hazard of the food is avoided.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (10)

1. A food data processing method, comprising:
collecting food data;
judging whether food corresponding to the food data is abnormal or not according to the collected food data, and if so, acquiring abnormal food information;
and carrying out routing inspection according to the obtained food abnormal information.
2. The method of claim 1, wherein the step of collecting food data comprises:
acquiring a data acquisition service requirement;
configuring a data acquisition task rule according to the data acquisition service requirement; the data acquisition task rule corresponds to at least one acquisition characteristic information;
and acquiring data according to the acquisition task rule to obtain the food data.
3. The method of claim 2, wherein the step of collecting food data further comprises:
detecting food keywords in a target website, an application APP or a target scene, wherein the food keywords are common vocabularies in the food industry;
when the food keywords are detected in the target website, the application APP or the target scene, determining the information segment where the detected food keywords are located;
determining the information segment where the detected food key words are located as food characteristic information;
judging whether the food characteristic information is matched with the collected characteristic information in the data collection task rule or not,
and if so, acquiring data in the target website, the application APP or the target scene according to the acquisition task rule to obtain the food data.
4. The method of claim 1, wherein in collecting food data, further comprising:
acquiring the current time of the food data acquisition, wherein the current time comprises year, month, day, hour, minute and second;
judging whether the food data meet the standard of private information, storing the food data in an encryption mode according to the current time when the food data meet the standard of the private information,
and when the food data does not meet the standard of the private information, storing the food data in a non-encryption mode according to the current time.
5. The method according to claim 1, wherein whether the food corresponding to the food data is abnormal is judged according to the collected food data, and if the food corresponding to the food data is abnormal, the step of obtaining food abnormal information comprises the following steps:
converting the collected food data and data in a food data standard library into a data structure in a preset tabular form;
searching an original standard data set which is the same as at least one component of the food corresponding to the food data in the food data standard library;
searching and screening the original standard data set according to the food name in the food data to obtain a current standard data set;
similarity calculation is carried out on the current standard data set according to all the components of the food corresponding to the food data, and the similarity of the food data and any one piece of data in the current standard data set is obtained;
determining the data with the highest similarity with the food data in the current standard data set according to the similarity;
judging whether the data with the highest similarity to the food data in the current standard data set is the same as the food data,
if not, determining that the food corresponding to the food data is abnormal, and acquiring abnormal food information;
wherein the composition comprises ingredient composition and/or nutritional composition.
6. The method of claim 1, wherein the step of performing inspection according to the acquired food abnormality information comprises:
acquiring the food abnormal information;
acquiring a polling instruction;
based on the inspection instruction, performing inspection according to the food abnormal information to obtain an inspection result;
sending out a prompt according to the inspection result;
and performing label processing or extraction processing on the food corresponding to the food abnormal information according to the prompt.
7. The method of any one of claims 1 to 5, wherein the food data comprises any one or more of a food name, a food production date, a food shelf life, a composition, a place of manufacture, and a production license number.
8. The method of claim 1, further comprising:
and carrying out pollution-free recovery or destruction on the food corresponding to the food abnormal information.
9. The method according to claim 1, wherein judging whether the food corresponding to the food data is abnormal according to the collected food data specifically comprises:
A. preliminarily judging the food corresponding to the food data according to the food data;
Figure FDA0002465279630000031
wherein, the preliminary judgment value x of the food corresponding to the food dataiIs the ith data value of the food data, n is the number of data included in the food data, [ m [ ]i,ni]The normal value range of the ith data of the food data is obtained;
and when the food data is larger than the preset judgment value, the food corresponding to the food data is suspected to be abnormal, further judgment is carried out on the food corresponding to the food data suspected to be abnormal according to the judgment result B, otherwise, the food corresponding to the food data is normal, and the judgment result B is not required. .
B. Further judging the food corresponding to the food data suspected to be abnormal;
calculating judgment values of chemical attribute data and physical attribute data of the food corresponding to the food data;
Figure FDA0002465279630000032
Figure FDA0002465279630000041
in the above formula, kiα is the judgment value of the ith data in the chemical attributes of the food corresponding to the food dataiIs the ith data value, a in the chemical attribute of the food corresponding to the food dataiThe lower limit of the normal range of the ith data in the chemical attributes of the food corresponding to the food data, biThe upper limit, g, of the normal range of the ith data in the chemical attributes of the food corresponding to the food dataiβ is the judgment value of the ith data in the physical attribute of the food corresponding to the food dataiIs the ith data, z, in the physical attributes of the food corresponding to the food dataiAnd the ith data standard value in the physical attribute of the food corresponding to the food data is exp is an exponential function, lg is a logarithmic function, and e is a natural number.
Obtaining a further judgment result;
Figure FDA0002465279630000042
wherein ,
Figure FDA0002465279630000043
a further determination result, k, for the food corresponding to the food dataiIs the judgment value of the ith data in the chemical attributes of the food corresponding to the food data, giIs the foodJudgment value of ith data in physical attribute of food corresponding to data, AiPresetting a confidence interval, p, of the ith data in the chemical attributes of the food corresponding to the food dataiPresetting a limit value of ith data in physical attributes of food corresponding to the food data, wherein 1 represents that the food corresponding to the food data is abnormal, and 0 represents that the food corresponding to the food data is normal.
10. A food data processing system, comprising:
the data acquisition end is connected with the server end and used for acquiring food data and sending the food data to the server end;
the server end is respectively connected with the data acquisition end and the inspection end and is used for receiving the food data sent by the data acquisition end, judging whether food corresponding to the food data is abnormal or not according to the food data, and if the food corresponding to the food data is abnormal, acquiring abnormal food information and sending the abnormal food information to the inspection end;
and the inspection end is used for receiving the food abnormal information sent by the server end so as to perform inspection according to the food abnormal information.
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