CN111426806A - Automatic monitoring and early warning method for freshness degree of aquatic product cold-chain logistics based on means of Internet of things - Google Patents
Automatic monitoring and early warning method for freshness degree of aquatic product cold-chain logistics based on means of Internet of things Download PDFInfo
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Abstract
The invention belongs to the field of aquatic product quality control, and particularly relates to an automatic monitoring and early warning method for the freshness degree of aquatic product cold-chain logistics based on the means of Internet of things, which comprises the following steps: (1) arranging a gas sensor in the monitoring device in the cold-chain logistics storage chamber of the aquatic products; (2) collecting environmental data of aquatic products in a storage room through a gas sensor; (3) simulating a group of circularly superposed random numbers as aquatic product decay reference data; (4) carrying out image simulation on the obtained aquatic product decay reference data; (5) carrying out polynomial fitting on the corruption reference data by using a polynomial fitting method to form a simulated corruption function; (6) and comparing the obtained environmental data with the simulated corruption function value, and starting an alarm to give an alarm if the difference reaches a set threshold value. The invention has the advantages of high detection speed and high sensitivity, can accurately identify the rotting degree of the aquatic products, and avoids the careless omission which is easily caused by long-time uninterrupted fresh-keeping monitoring work.
Description
Technical Field
The invention belongs to the field of aquatic product quality control, and particularly relates to an automatic monitoring and early warning method for the freshness degree of aquatic product cold-chain logistics based on an Internet of things means.
Technical Field
Cold chain logistics generally refers to a system project of producing, storing, transporting and selling refrigerated and frozen foods, and keeping the refrigerated and frozen foods in a specified low-temperature environment in all links before consumption so as to ensure the quality of the foods and reduce the loss of the foods. It is established with the progress of science and technology and the development of refrigeration technology, and is a low-temperature logistics process based on refrigeration technology and by means of refrigeration technology. The rapid development of the cold-chain logistics industry of agricultural products in China must make and implement scientific and effective macro-policy as early as possible. The requirements of cold-chain logistics are higher, and the corresponding management and capital investment are larger than that of ordinary normal-temperature logistics.
With the development of economy and the improvement of living standard of people, people begin to put the center on the balance of nutrition as a big agricultural country, and the three agricultural industries are also the basis of the development of national economy and society. The timeliness of the aquatic products requires that each link of the cold chain has higher tissue harmony, so the operation of the aquatic product cold chain is always related to the energy consumption cost, and the effective control of the operation cost is closely related to the development of the aquatic product cold chain. But the aquatic products have the characteristics of easy deterioration and easy damage, the preservation technology is seriously lagged, and the product quality is unstable, so the cold chain logistics of the aquatic products have great effect on food safety, the cold chain logistics run through the processes of fishing, production, sale and the like of the aquatic products, and the operation condition of the cold chain logistics influences the development of the industry and the benefit of enterprises. The technology of improving cold chain logistics can ensure the product quality and the safety of consumers. But in the current aquatic product cold chain logistics, the fresh-keeping is mainly carried out aiming at the aquatic products, and few alarms are carried out aiming at emergencies in the transportation process. In addition, at present, the domestic aquatic product cold chain logistics lack a unified operation standard in management, and lack standards in the aspects of operation of all links of transportation, storage, distribution and sale, so that most of the domestic aquatic product sale environments cannot be controlled below the cold chain temperature. The cold-chain logistics need to implement the whole-course temperature control management and must rely on advanced information technology as a support. In order to increase the competitiveness of agriculture in China, the development and utilization of the aquatic product refrigeration and preservation technology are reluctant.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides the automatic monitoring and early warning method for the freshness degree of the aquatic product cold chain logistics based on the Internet of things, which can accurately identify the putrefaction degree of the aquatic product, can monitor the putrefaction degree of the aquatic product in real time, has high detection speed, good repeatability and high sensitivity, and avoids the omission easily caused by long-time uninterrupted freshness monitoring work.
In order to solve the technical problem, the invention is realized as follows:
the method for automatically monitoring and early warning the freshness degree of the aquatic product cold-chain logistics based on the means of the Internet of things is implemented according to the following steps:
(1) arranging a gas sensor in the monitoring device in the cold-chain logistics storage chamber of the aquatic products;
(2) collecting environmental data of aquatic products in a storage room through a gas sensor;
(3) simulating a group of circularly superposed random numbers as aquatic product decay reference data;
(4) performing image simulation on the aquatic product decay reference data obtained in the step (3);
(5) carrying out polynomial fitting on the corruption reference data by using a polynomial fitting method to form a simulated corruption function;
(6) and (3) comparing the environmental data obtained in the step (2) with the simulated corruption function value, and if the difference reaches a set threshold value, starting an alarm to give an alarm.
As a preferable scheme, in step (3) of the present invention, random.uniform (x, y) function is used to select the random number, where x is the minimum value of the random number, inclusive, and y is the maximum value of the random number, exclusive, inclusive.
Further, the random number generation of the present invention increases in the range of 0 to 0.5.
Furthermore, in the step (5), a ployfit curve simulation function in python is adopted to perform polynomial simulation; p is ployfit (x, y, n), where p is the returned modeled polynomial, x is the independent variable to be fitted, y is the dependent variable, and n is the number of fits.
Furthermore, the fitting times of the method are more than or equal to 3 and less than or equal to 5.
Further, in the step (5), the parameters of the simulated decay equation are as follows:
[5.29848429e-06 -1.32651365e-03 2.25170824e-01 7.70966386e-01]；
the simulated decay function is: 4.603e-06x3-0.0002391x2+0.08701x+1.122。
Furthermore, in the step (6) of the present invention, when the set threshold is greater than or equal to 5, a moderate alarm is performed, and when the set threshold is greater than or equal to 20, a high alarm is performed.
The invention has the advantages of high detection speed, good repeatability and high sensitivity, can accurately identify the decay degree of aquatic products, and avoids the careless omission which is easily caused by long-time uninterrupted fresh-keeping monitoring work. The invention calculates the data measured by the sensor in the logistics vehicle to prevent the situation that the logistics vehicle suddenly appears, or the aquatic products suddenly appear large-scale rot due to temperature, and the like to give an alarm.
Drawings
The invention is further described with reference to the following figures and detailed description. The scope of the invention is not limited to the following expressions.
FIG. 1 is a first stage data point of the present invention;
FIG. 2 is a diagram of data points and simulated function images at a first stage of the present invention;
FIG. 3 is a screenshot of a first stage console of the present invention;
FIG. 4 is a new data point diagram for the second stage of the present invention;
FIG. 5 is a diagram illustrating the fitting of new data points to the corruption function at the second stage of the present invention;
FIG. 6 is a second stage console alarm scenario of the present invention;
FIG. 7 is a schematic block diagram of the control circuit of the system of the present invention.
Detailed Description
As shown in the figure, the automatic monitoring and early warning method for the freshness degree of the aquatic product cold-chain logistics is implemented according to the following steps:
(1) arranging a gas sensor in the monitoring device in the cold-chain logistics storage chamber of the aquatic products;
(2) collecting environmental data of aquatic products in a storage room through a gas sensor;
(3) simulating a group of circularly superposed random numbers as aquatic product decay reference data;
the random number selected in the present invention uses random. The growing range of the random number generation is between 0 and 0.5, the data set quantifies 100 numerical values, each numerical value is accurate to 3 bits after a decimal point, the specific use method is as follows, random. uniform (x, y), wherein x is the minimum value of the random number and contains the numerical value, y is the maximum value of the random number and does not contain the numerical value, the initial variable of the invention is 1, the random number is increased once to form a random number group, and the random number group is used as the reference data of the decay of the simulated aquatic products;
(4) performing image simulation on the aquatic product decay reference data obtained in the step (3);
(5) carrying out polynomial fitting on the corruption reference data by using a polynomial fitting method to form a simulated corruption function;
(6) and (3) comparing the environmental data obtained in the step (2) with the simulated corruption function value, and if the difference reaches a set threshold value, starting an alarm to give an alarm.
In step (3), random.uniform (x, y) function is used to select random numbers, where x is the minimum value of the random numbers, including the value, and y is the maximum value of the random numbers, not including the value.
The random number generation of the present invention increases in the range of 0 to 0.5.
In the step (5), a ployfit curve simulation function in python is adopted to carry out polynomial simulation; the ployfit function is used as follows: p is ployfit (x, y, n), where p is the returned modeled polynomial, x is the independent variable to be fitted, y is the dependent variable, and n is the number of fits.
The fitting times of the method are larger, the error is smaller, but the noise degree can be increased, so that the value of n is often between 3 and 5 by combining the error and the noise degree, namely: the fitting times are more than or equal to 3 and less than or equal to 5. The method selects x as time, namely a group of integer sets (x is 1,2, 3.., 100) consisting of 1 to 100, y is the aquatic product decay reference data in the first step, and n is selected to be 3.
In the step (5), the parameters of the simulated corrosion equation are as follows:
[5.29848429e-06-1.32651365e-03 2.25170824e-01 7.70966386e-01]；
the simulated decay function is: 4.603e-06x3-0.0002391x2+0.08701x+1.122。
The y value corresponding to the fixed value time x is an ideal corruption value.
The method is specifically implemented by randomly generating a group of data with the growth range of 0 to 0.5 by utilizing python, wherein the data is subjected to augmentation processing at several points to simulate problem data of a logistics vehicle in the transportation process, and new data is used as a real-time data set y1。
Substituting time x (1,2, 3.., 100) into the corruption equation
y＝4.603e-06x3-0.0002391x2+0.08701x+1.122
Obtaining an ideal decay value y of a constant time x, and comparing y with y1And comparing, setting a threshold value according to the difference between the real-time data and the ideal data, performing moderate alarm when the threshold value is more than or equal to 5, and performing height alarm when the threshold value is more than or equal to 20.
In one aspect, the present invention provides a simulation based on gas release data during transportation of existing aquatic products, comprising: after data measured by a probe in the existing aquatic product cold chain car is collected, the data is used as a dependent variable, the collection time is used as an independent variable, curve simulation is carried out by combining data points, the simulated curve is recorded as a simulated corrosion curve, a computer is used for simulating the simulated corrosion curve by a polynomial, and the simulated polynomial is recorded as a simulated corrosion equation.
On the other hand, the gas sensor in the monitoring device is additionally arranged on the aquatic product cold chain logistics vehicle, the gas generated by the corrosion of the aquatic products is monitored in real time through the environmental data of the aquatic products in the storage vehicle collected by the gas sensor, and the aquatic products are monitored and early warned in real time through the monitored data and the simulated aquatic product corrosion equation.
According to the invention, image simulation is carried out on the corruption reference data of aquatic products, polynomial fitting is carried out on the corruption data by using a polynomial fitting method to form a simulated corruption function, real-time monitored environmental data and the simulated corruption function value are compared, and if the difference reaches a set threshold value, an alarm is started to give an alarm.
Example (b):
the invention provides a method for monitoring and early warning during aquatic product transportation, which analyzes the existing data to obtain a decay curve and simulate a decay equation, can monitor the real-time data according to the simulated decay equation during the real-time monitoring process, alarms under abnormal conditions, and alarms when the decay point is approached quickly.
According to the detection of the aquatic product decay process, the aquatic product releases ammonia gas in the decay process, the greater the decay degree is, the more the ammonia gas release amount is, and the external conditions such as temperature, transport vehicle and the like can also influence the decay of the aquatic product, so the invention is mainly divided into two parts, namely a data preprocessing part and a real-time monitoring part, which will be specifically described below by combining with the accompanying drawings.
The specific method of the invention mainly comprises the following steps:
A. the first stage is as follows:
(1) simulating a group of random numbers which are increased in an indefinite amount through research on aquatic product decay, and using the random numbers as reference data of aquatic product decay;
(2) carrying out image simulation on the decay reference data of the aquatic product by using the technology of the Internet of things;
(3) performing polynomial fitting on the corruption reference data by using a polynomial fitting method, wherein the polynomial fitting is performed by using a cubic polynomial;
according to the monitoring of the freshness of aquatic products, the more the aquatic products are decayed, the more gas is released. Therefore, the method adopts a group of data sets which are increased in circulation as the reference data for the decay of the aquatic products. Wherein the data set is a set of random arrays that grow in the range of 0 to 0.5. The data set is substituted into the system of the invention. Because the gas sensor device on the logistics vehicle measures and monitors at regular time, the time is used as an independent variable x by using a ployfit curve fitting function in python, a dependent variable y is carried out on a data set, the fitting times n are selected to be 3 times for carrying out polynomial fitting, and the result of the fitting is as follows:
simulation of decay equation parameters:
[5.29848429e-06-1.32651365e-03 2.25170824e-01 7.70966386e-01]；
simulating a decay function:
y＝4.603e-06x3-0.0002391x2+0.08701x+1.122。
FIG. 1 is a first stage data point; FIG. 2 is a diagram of data points and simulated function images at a first stage; where the square points are data points (i.e., points with time x and data set y), and the connecting lines are modeled functional images. FIG. 3 is a screenshot of the console, data printed by the console, and a simulation function curve equation.
B. And a second stage:
(1) collecting data in real time;
(2) substituting the real-time collected data into the decay function simulated in the first stage;
(3) and comparing the data acquired in real time with the simulated corruption function value, and giving an alarm if the difference is large.
Reusing a new set of circularly increased data set, wherein the data set is increased by 0 to 0.5, and amplifying and modifying part of the data to enable part of the data to have large jump in the area, wherein the data is used as an abnormal point in the real-time monitoring data.
In the second stage, the time is substituted into the corruption function stored in the first stage to obtain an ideal simulated corruption value, the difference value between the ideal value y and the new data set measured value y1 is compared to set a threshold value, and the table 1 is an alarm reference level table.
TABLE 1
FIG. 4 is a new data point diagram; FIG. 5 shows the fitting of new data points to the decay function; FIG. 6 is a console alarm condition; wherein figures 3 and 6 contain the data sets employed by the present subject matter.
In the step 1 of the stage A, a group of random numbers which are not increased quantitatively can be selected, random numbers which are selected in the invention use random. uniform function to generate a group of increased numerical values with the increase range of 0 to 0.5, the data set quantifies 100 numerical values, each numerical value is accurate to 3 bits after a decimal point, the specific use method is as follows, random. uniform (x, y), wherein x is the minimum value of the random numbers, the value is included, y is the maximum value of the random numbers, the value is not included, the initial variable of the invention is 1, the random numbers are increased once to form a random number group, and the random number group is used as the reference data for the decay of the simulated aquatic products.
In the step 2, a ployfit curve simulation function in python is used for polynomial simulation, the use method of the ployfit function is as follows, p is ployfit (x, y, n), wherein p is a returned simulated polynomial, x is an independent variable to be fitted, y is a dependent variable, and n is the fitting times, generally speaking, the larger the fitting times are, the smaller the error is, but the floating degree is increased, so that the value of the error and the floating degree are combined, the value of n is often 3-5, x selected by the invention is time, namely a group of integer sets (x is 1,2,3, 100) consisting of 1-100, y is aquatic product decay reference data in the step one, and n is 3.
The parameters of the decay equation simulated by the experiment are as follows:
[5.29848429e-06 -1.32651365e-03 2.25170824e-01 7.70966386e-01]
the decay function is:
y＝4.603e-06x3-0.0002391x2+0.08701x+1.122
in the process of the stage one, the y value corresponding to the fixed value time x is an ideal corruption value.
The phase B is realized by randomly generating a group of data with the growth range of 0 to 0.5 by utilizing python, wherein the growth processing is carried out at several points, problem data which are problematic in the transportation process of the logistics vehicle are simulated, and new data are used as a real-time data set y1。
Substituting time x (1,2, 3.., 100) into the decay equation
y＝4.603e-06x3-0.0002391x2+0.08701x+1.122
Obtaining an ideal decay value y of a constant time x, and comparing y with y1Comparing, and defining at y due to the difference between real-time data and ideal data1And carrying out a moderate early warning when the difference value between the y and the y is more than 5, and carrying out a high early warning when the difference value is more than 20.
The first stage of the method aims to carry out polynomial fitting through existing data, and the second stage aims to compare real-time data with data simulated by a corruption equation to set a threshold value and alarm for abnormity. Compared with the traditional cold-chain logistics, the system analyzes the environmental data measured by the gas sensor in the logistics car so as to prevent the situations that the logistics car suddenly appears, or aquatic products suddenly appear large-scale rot due to temperature, and the like to give an alarm.
Referring to fig. 7, the automatic monitoring and early warning control platform for the freshness degree of aquatic product cold-chain logistics comprises a central processing unit, a wireless network module, a gas sensor, a ZigBee module, a display module and an alarm; and signal transmission ports of the wireless network module, the gas sensor, the ZigBee module and the display module are respectively connected with a signal transmission port of the central processing unit. The automatic monitoring and early warning control platform for the freshness degree of the aquatic product cold-chain logistics can be arranged by adopting a mobile phone, the mobile phone is used for inquiring measurement data, and the ZigBee module is in wireless short-distance connection with the mobile phone end. The wireless network module can realize the remote communication of the platform. The gas sensor can detect and monitor the decay index of the aquatic product and send the collected data to the central processing unit for processing. The display module can display the current aquatic product decay information in real time. The automatic monitoring and early warning control platform for the freshness degree of the aquatic product cold-chain logistics is in wireless connection with the mobile phone end through the ZigBee module, and sets platform parameters by the mobile terminal, so that various indexes of the platform are set in time.
The invention provides a cold-chain logistics detection technology based on the technology of the Internet of things. And in the second stage, real-time monitoring and comparison are carried out on the data generated by real-time monitoring through real-time substitution polynomial, and abnormal data are monitored and early warned.
The invention relates to a technology for detecting the Internet of things by combining the Internet of things technology and cold-chain logistics. The first stage aims to carry out polynomial fitting through the existing data, and the second stage aims to compare the real-time data with the data simulated by the decay equation and alarm the abnormity. Compared with the traditional cold-chain logistics, the system and the method have the advantages that the data measured by the sensors in the logistics vehicles are calculated, so that the condition that the logistics vehicles suddenly appear, or the aquatic products suddenly appear large-scale rot due to temperature is prevented, and the like, so that the alarm is given.
It should be understood that the detailed description of the present invention is only for illustrating the present invention and is not limited by the technical solutions described in the embodiments of the present invention, and those skilled in the art should understand that the present invention can be modified or substituted equally to achieve the same technical effects; as long as the use requirements are met, the method is within the protection scope of the invention.
Claims (7)
1. An automatic monitoring and early warning method for the freshness degree of aquatic product cold-chain logistics based on the means of Internet of things is characterized by comprising the following steps:
(1) arranging a gas sensor in the monitoring device in the cold-chain logistics storage chamber of the aquatic products;
(2) collecting environmental data of aquatic products in a storage room through a gas sensor;
(3) simulating a group of circularly superposed random numbers as aquatic product decay reference data;
(4) performing image simulation on the aquatic product decay reference data obtained in the step (3);
(5) carrying out polynomial fitting on the corruption reference data by using a polynomial fitting method to form a simulated corruption function;
(6) and (3) comparing the environmental data obtained in the step (2) with the simulated corruption function value, and if the difference reaches a set threshold value, starting an alarm to give an alarm.
2. The method for automatically monitoring and early warning the freshness degree of aquatic product cold-chain logistics based on the means of the internet of things according to claim 1, characterized by comprising the following steps: in the step (3), a random. uniform (x, y) function is used to select the random number, where x is the minimum value of the random number and includes the value, and y is the maximum value of the random number and does not include the value.
3. The method for automatically monitoring and early warning the freshness degree of aquatic product cold-chain logistics based on the means of the internet of things according to claim 2, characterized by comprising the following steps: random number generation increases in the range of 0 to 0.5.
4. The method for automatically monitoring and early warning the freshness degree of aquatic product cold-chain logistics based on the means of the internet of things according to claim 3, characterized by comprising the following steps: in the step (5), a ployfit curve simulation function in python is adopted to carry out polynomial simulation; p is ployfit (x, y, n), where p is the returned modeled polynomial, x is the independent variable to be fitted, y is the dependent variable, and n is the number of fits.
5. The method for automatically monitoring and early warning the freshness degree of aquatic product cold-chain logistics based on the means of the Internet of things according to claim 4, characterized by comprising the following steps: and n is more than or equal to 3 and less than or equal to 5.
6. The method for automatically monitoring and early warning the freshness degree of aquatic product cold-chain logistics based on the means of the Internet of things according to claim 5, characterized by comprising the following steps: in the step (5), the simulation corruption equation parameters are as follows:
[5.29848429e-06-1.32651365e-03 2.25170824e-01 7.70966386e-01]；
the simulated decay function is: 4.603e-06x3-0.0002391x2+0.08701x+1.122。
7. The method for automatically monitoring and early warning the freshness degree of aquatic product cold-chain logistics based on the means of the internet of things according to claim 6, characterized by comprising the following steps: in the step (6), when the set threshold is larger than or equal to 5, a moderate alarm is performed, and when the set threshold is larger than or equal to 20, a height alarm is performed.
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