CN111861307B - Fruit multi-element quality space-time distribution sensing method and system - Google Patents
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Abstract
The embodiment of the invention provides a fruit multi-quality space-time distribution sensing method and a system, wherein the method comprises the following steps: determining the change rate of the multiple quality of the fruits under different temperature and humidity conditions through periodic sampling, and establishing a dynamic mathematical model of the multiple quality of the fruits; acquiring the spatial-temporal distribution state of temperature and humidity of the fruits in the storage and transportation process; and acquiring the space-time distribution state of the fruit quality in the cold chain storage and transportation process based on the fruit multi-element quality dynamic mathematical model and the space-time distribution state of the temperature and the humidity. According to the method and the system for sensing the multi-element quality space-time distribution of the fruits, provided by the embodiment of the invention, the non-destructive and non-contact dynamic sensing of the multi-element quality of the fruits is realized on the premise of ensuring the integrity of the fruits by fusing the fruit and environment thermal mass coupling transmission mechanism and the fruit quality change dynamic model, so that the method and the system have important economic values and practical engineering application values for accurately regulating and controlling the quality safety of the fruits, reducing the loss rate of stored and transported fruits and promoting the quality and the sale of the cold chain.
Description
Technical Field
The embodiment of the invention relates to the technical field of intelligent storage of agricultural products, in particular to a fruit multi-element quality space-time distribution sensing method and system.
Background
In the process of fruit cold chain storage and transportation, the temperature and humidity of the cold air in the storage and transportation carrier are fluctuated, the cargo stack, the fruit packaging and other objective conditions influence the spatial and temporal distribution of the temperature and humidity of the fruits is uneven, so that the quality distribution of the fruits in the same batch is different.
The traditional cold chain is mainly used for judging whether the current storage and transportation state meets the storage and transportation requirements of the fruits by monitoring the environmental temperature and humidity at a representative position, and does not directly sense the temperature and humidity of each fruit, particularly the dynamic change of the quality of the fruit, so that the traditional cold chain regulation decision is extremely blind and uncertain, and the traditional cold chain regulation decision is also a main cause of serious loss of the cold chain fruits in China. On the premise of ensuring the integrity of the fruits, the direct acquisition of the quality information of the fruits in cold chain storage and transportation becomes a great demand and urgent need to solve the problem of the fruit industry in China, and is one of the main research hotspots of relevant scholars in recent years.
The heat and mass coupling transmission is an interaction tie between the cold chain storage and transportation environment and the quality of the fruits, and is also a direct cause for influencing the dynamic change of the temperature and humidity of the fruits and the dynamic change of the quality. Therefore, how to realize the accurate coupling sensing and cooperative regulation of the cold chain environment and the fruit quality has important significance and practical engineering application value for reducing the whole-course loss of the cold chain fruit, the energy consumption waste and the like.
Disclosure of Invention
The embodiment of the invention provides a fruit multi-quality space-time distribution sensing method and system, which are used for overcoming or partially solving the defects of low operation efficiency, unreasonable distribution and the like in the fruit multi-quality space-time distribution sensing in the prior art.
In a first aspect, an embodiment of the present invention provides a method for sensing spatial-temporal distribution of fruit multi-element quality, which mainly includes the following steps:
s1, determining the multi-element quality change rate of the fruits under different temperature and humidity conditions through periodic sampling, and establishing a multi-element quality dynamic mathematical model of the fruits;
s2, acquiring the space-time distribution state of the temperature and the humidity of the fruits in the storage and transportation process;
s3, acquiring the spatial-temporal distribution state of the fruit quality in the cold chain storage and transportation process based on the dynamic mathematical model of the fruit multi-element quality and the spatial-temporal distribution state of the temperature and the humidity.
Optionally, in step S1, a dynamic mathematical model of the fruit multiple is built, which mainly includes:
s11, determining index values of the multiple qualities of the fruits under different temperature and humidity conditions through periodic sampling, and establishing a mathematical model between the change rate of the multiple qualities of the fruits and the temperature and humidity;
s12, establishing a dynamic mathematical model of the fruit multi-element quality based on the mathematical model between the change rate of the fruit multi-element quality and the temperature and the humidity.
Optionally, in step S11, a mathematical model between the rate of change of the fruit multi-element quality and the temperature and humidity is established, which mainly includes:
the mathematical expression for determining the mathematical model between the fruit multi-element quality change rate and the temperature and humidity is as follows:
；
wherein,x 1 in order to be able to determine the temperature,x 2 in order to be a degree of humidity,yis the multi-element quality of the fruits,b 0 、b i 、b ii andb ij all are the constant coefficients of the two-dimensional space,iandjis an intermediate variable;
performing group individual coding by using a group intelligent difference algorithm and constant coefficients as variables, determining the coefficients as fitness functions, and assigning values to the constant coefficients through iterative optimization;
the mathematical expression of the fitness function is:
；
wherein,nin order to be able to measure the data quantity,y r as a result of the fact that the value,y p in order to be able to predict the value,y avg as an average value of the values,in order to determine the coefficient of the coefficient,kis an intermediate variable.
Optionally, in step S12, based on the mathematical model between the rate of change of the fruit multi-element quality and the temperature and humidity, the dynamic mathematical model of the fruit multi-element quality is built, which mainly includes:
calculating the change rate of the multiple qualities of the fruits under different humiture according to a mathematical model between the change rate of the multiple qualities of the fruits and the humiture;
respectively constructing a fruit multi-element quality dynamic mathematical model based on a zero-order quality dynamic equation or a first-order quality dynamic equation;
the mathematical expression of the zero-order quality kinetic equation is:
；
the mathematical expression of the first order quality dynamics equation is:
；
wherein,tin order to be able to take time,pis thattThe individual quality index values of the fruit at the moment,p 0 for a single quality index value of the fruit at the initial moment,yis the multiple quality of fruits.
Optionally, in step S2, the acquiring the spatial-temporal distribution state of the temperature and humidity of the fruit during the storage and transportation process mainly includes:
s21, constructing a digital twin body structure model of the three-dimensional structure of the storage and transportation carrier based on at least one of the three information of the storage and transportation carrier size, the air outlet position of the air cooler and the fruit stacking mode; performing grid division on the digital twin body structure model;
s22, determining a boundary condition value of a digital twin body structure model based on a fruit temperature and humidity initial value, a fruit multi-element quality initial value, a cold air outlet volume flow and a fruit wet heat transfer coefficient, and constructing a temperature and humidity space-time prediction model of a three-dimensional space inside the storage and transportation carrier;
s23, acquiring the temperature and humidity space-time distribution state of the fruits in the storage and transportation process based on the temperature and humidity space-time prediction model.
Optionally, in step S22, boundary condition values of the digital twin body structure model are determined, and a temperature and humidity space-time prediction model of the three-dimensional space inside the storage and transportation carrier is constructed, which mainly includes:
setting the air outlet position of an air cooler of the digital twin body structure model as a fluid inlet boundary; setting the fan blade rotating surface of the air cooler as a boundary of a fluid outlet; setting the area where the fruit is located as a solid area; setting the non-solid region as a fluid region;
determining turbulence characteristic dimensions, fluid Reynolds numbers, turbulence energy and turbulence specific dissipation rates according to the volume flow of cold air outlet;
based on a computational fluid dynamics numerical simulation method, a temperature and humidity space-time prediction model of a three-dimensional space inside a storage carrier is constructed by combining a fruit temperature and humidity initial value, a fruit multi-element quality initial value and a fruit wet heat transfer coefficient.
Optionally, in step S21, meshing the digital twin structure model may further include: based on Richardson extrapolation, discrete errors of model spaces of different grid numbers are evaluated, and the optimal model grid number is determined.
In a second aspect, an embodiment of the present invention provides a fruit multi-element quality spatiotemporal distribution sensing system, mainly including: the system comprises a mathematical model construction module, a temperature and humidity field simulation module and a quality field simulation module; the mathematical model construction module is mainly used for determining the change rate of the multiple qualities of the fruits under different temperature and humidity conditions through periodic sampling and establishing a dynamic mathematical model of the multiple qualities of the fruits; the temperature and humidity field simulation module is mainly used for acquiring the temperature and humidity space-time distribution state of the fruits in the storage and transportation process; the quality field simulation module is mainly used for acquiring the space-time distribution state of the fruit quality in the cold chain storage and transportation process based on the fruit multi-element quality dynamic mathematical model and the space-time distribution state of the temperature and the humidity.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the fruit multi-quality spatiotemporal distribution sensing method according to any one of the first aspects when the processor executes the program.
In a fourth aspect, embodiments of the present invention provide a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the fruit multi-quality spatiotemporal distribution awareness method of any of the first aspects.
According to the method and the system for sensing the multi-element quality space-time distribution of the fruits, provided by the embodiment of the invention, the non-destructive and non-contact dynamic sensing of the multi-element quality of the fruits is realized on the premise of ensuring the integrity of the fruits by fusing the fruit and environment thermal mass coupling transmission mechanism and the fruit quality change dynamic model, so that the method and the system have important economic values and practical engineering application values for accurately regulating and controlling the quality safety of the fruits, reducing the loss rate of stored and transported fruits and promoting the quality and the sale of the cold chain.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for sensing the spatial and temporal distribution of the multiple qualities of fruits according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a fruit multi-element quality space-time distribution sensing system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of another spatial-temporal distribution sensing system for fruit multi-element quality according to an embodiment of the present invention;
fig. 4 is a physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
For convenience of explanation, in each subsequent embodiment, the fruit cold chain transportation process is taken as a research object, and apples are taken as cargo test materials, so that the method and the system for sensing the multi-element quality space-time distribution of the fruits provided by the embodiment of the invention are specifically explained.
Fig. 1 is a schematic flow chart of a fruit multi-element quality space-time distribution sensing method according to an embodiment of the present invention, as shown in fig. 1, the method includes, but is not limited to, the following steps:
step S1, determining the multi-element quality change rate of the fruits under different temperature and humidity conditions through periodic sampling, and establishing a multi-element quality dynamic mathematical model of the fruits;
s2, acquiring the space-time distribution state of the temperature and the humidity of the fruits in the storage and transportation process;
and step S3, acquiring the spatial-temporal distribution state of the fruit quality in the cold chain storage and transportation process based on the dynamic mathematical model of the fruit multi-element quality and the spatial-temporal distribution state of the temperature and the humidity.
Generally, the same fruit will have different quality change rates under different temperature and humidity conditions. For example, low temperatures can inhibit the physiological metabolism of microorganisms, and thus can inhibit the growth and proliferation of microorganisms. The metabolic rate may decrease 2-3 times for every 10 degrees celsius decrease in temperature. In addition, under different humidity conditions, the method has important influence on various quality of the fruits, the too dry environment can lead to the acceleration of the dehydration rate of the fruits, but the too wet environment can lead to the acceleration of the propagation of microorganisms, and the speed of the spoilage of the fruits is increased.
In step S1 of the embodiment of the present invention, the temperature and humidity in the carriage where the fruit is located in the cold chain transportation process are measured by periodically using the temperature and humidity sensor and other devices, and the index values of the multiple qualities of the fruit under different temperature and humidity conditions are recorded. Further, based on a mathematical statistical method, a mathematical model is established by taking temperature and humidity parameters of the fruits as independent variables and index values of the multiple qualities of the fruits as dependent variables, namely, a dynamic mathematical model of the multiple qualities of the fruits is established.
Further, in step S2, information that the storage and transportation carrier size, the initial value of the fruit temperature and humidity, the initial value of the fruit multi-element quality, the air outlet position of the air cooler, the air outlet volume flow of the cold air, the fruit stacking mode, the fruit wet heat transfer coefficient and the like affect the multi-element quality change rate of the fruit is determined. And then, acquiring the temperature and humidity space-time distribution state in the fruit storage and transportation process according to at least one of the plurality of influencing information.
For example, the space-time temperature distribution state at the air outlet of the air cooler is necessarily greatly different from the space-time temperature distribution state at the position far away from the air outlet of the air cooler, the closer the air outlet of the air cooler is, the larger the volume flow of the air outlet of the air cooler is, the more scattered the fruit stacks are, and the lower the multi-element quality change rate of the fruits can be theoretically; the larger the size of the storage and transportation carrier is, the smaller the change rate of the multi-element quality is correspondingly; the smaller the initial value of the temperature and humidity of the fruit is, the smaller the coefficient of the heat and humidity transfer of the fruit is, and the change rate of the multi-element quality of the fruit is correspondingly slowed down.
Further, in step S3, according to the dynamic mathematical model reflecting the multiple quality time-varying rule and the temperature and humidity of the fruits constructed in step S1, the spatial-temporal distribution state of the quality of the fruits in the storage and transportation process obtained in step S2 is combined, so as to obtain the spatial-temporal distribution situation of the quality of the fruits in the cold chain storage and transportation process.
The above-mentioned fruit quality space-time distribution condition can be understood as the prediction of the change condition of the multiple qualities of the fruit in time and space, according to the obtained fruit quality space-time distribution state, the quality field distribution condition after a certain time in the transportation process can be predicted, and according to the quality field distribution and the property and the preset quality field distribution, the quality field distribution is compared with the property threshold value X, or a certain quality index is compared with the preset standard index threshold value Y of the quality, when the quality field distribution and the property are lower than the threshold value X and/or a certain quality index is lower than the corresponding threshold value Y, the external equipment such as cold chain storage and transportation carrier air cooler is dynamically regulated, and the change rate of the multiple qualities of the fruit is artificially interfered.
Alternatively, the fruit multi-element quality information mainly includes: fruit senses (including hardness, mouthfeel, smell, etc.), nutritional ingredients (vitamins, soluble proteins, soluble solids, ascorbic acid content, etc.). Wherein, fruit damp heat transfer coefficient mainly includes: the fruit moisture transfer coefficient, the fruit heat transfer coefficient, the fruit volume density, the fruit volume specific heat capacity and the like.
According to the fruit multi-element quality space-time distribution sensing method provided by the embodiment of the invention, through fusing a fruit and environment thermal mass coupling transmission mechanism and a fruit quality change dynamics model, on the premise of ensuring the integrity of the fruit, the non-destructive and non-contact dynamic sensing of the fruit multi-element quality is realized, and the method has important economic value and practical engineering application value in accurately regulating and controlling the safety of the fruit quality, reducing the loss rate of stored and transported fruits and promoting the quality and sales of a cold chain.
Based on the foregoing embodiment, as an alternative embodiment, in step S1, the step of establishing a dynamic mathematical model of the fruit multiple may include, but is not limited to, the following steps:
s11, determining index values of the multiple qualities of the fruits under different temperature and humidity conditions through periodic sampling, and establishing a mathematical model between the change rate of the multiple qualities of the fruits and the temperature and humidity;
s12, establishing a dynamic mathematical model of the fruit multi-element quality based on the mathematical model between the change rate of the fruit multi-element quality and the temperature and the humidity.
Specifically, in step S11 of the embodiment of the present invention, the temperature and humidity sensors preset inside the storage carrier are utilized to periodically measure the temperature and humidity at different positions; and recording index values of the fruit multi-element quality at different positions while measuring each time. For example, one of the pieces of record data is: the interior of the carrier a is at 22: the temperature at 00 is m DEG C, the humidity is 68%, and the quality k corresponding to the temperature is the same 1 Is of rate of change of h 1 Quality k 2 Is of rate of change of h 2 …。
Further, according to index values of the fruit multi-element quality under different temperature and humidity conditions recorded by detection and the corresponding change rate of the fruit multi-element quality, a mathematical model between the change rate of the fruit multi-element quality and the temperature and humidity is constructed.
In step S12, the mathematical model constructed in step S11 is processed and converted into a dynamic mathematical model of fruit multi-element quality for characterizing the time-varying law of fruit multi-element quality.
Optionally, in the embodiment of the present invention, in step S11, establishing a mathematical model between the rate of change of the fruit multi-element quality and the temperature and humidity may specifically include:
the mathematical expression for determining the mathematical model between the fruit multi-element quality change rate and the temperature and humidity is as follows:
；
wherein,x 1 in order to be able to determine the temperature,x 2 in order to be a degree of humidity,yis the multi-element quality of the fruits,b 0 、b i 、b ii andb ij all are the constant coefficients of the two-dimensional space,iandjis an intermediate variable;
and (3) performing group individual coding by using a group intelligent differential algorithm and using constant coefficients as variables, determining the coefficients as fitness functions, and assigning values to the constant coefficients through iterative optimization.
Wherein, the mathematical expression of fitness function is:
；
wherein,nin order to be able to measure the data quantity,y r as a result of the fact that the value,y p in order to be able to predict the value,y avg as an average value of the values,in order to determine the coefficient of the coefficient,kis an intermediate variable.
Further, in step S12, based on the mathematical model between the rate of change of the fruit multi-element quality and the temperature and humidity, establishing the dynamic mathematical model of the fruit multi-element quality may include: and calculating the change rate of the multi-element quality of the fruits under different humiture according to a mathematical model between the change rate of the multi-element quality of the fruits and the humiture. And respectively constructing a fruit multi-element quality dynamic mathematical model based on the zero-order quality dynamic equation or the first-order quality dynamic equation.
Wherein, the mathematical expression of the zero-order quality dynamics equation can be:
；
the mathematical expression of the first order quality dynamics equation may be:
；
wherein,tin order to be able to take time,pis thattThe individual quality index values of the fruit at the moment,p 0 for a single quality index value of the fruit at the initial moment,yis the multiple quality of fruits.
In the embodiment of the invention, a crowd-sourcing differential algorithm is utilized to iteratively optimize constant coefficients in a mathematical expression of a mathematical model. The differential evolution algorithm mainly optimizes searching through cooperation and competition among group individuals; the intelligent swarm algorithm may be ant swarm algorithm or particle swarm algorithm. Optionally, in the embodiment of the invention, the group intelligent algorithm and the differential evolution algorithm are combined to form a group intelligent differential algorithm, that is, the result obtained after the group intelligent algorithm is subjected to simple and reasonable random disturbance, so that epsilon-differential privacy can be satisfied, the clustering efficiency and convergence are effectively improved, and the optimal assignment of the constant coefficient can be obtained.
According to the fruit multi-element quality space-time distribution sensing method provided by the embodiment of the invention, the change rate of the fruit multi-element quality under different humiture is characterized by utilizing a quality dynamics equation, a fruit multi-element quality dynamic mathematical model is constructed, a fruit and environment thermal mass coupling transmission mechanism and a fruit quality change dynamics model are fused, on the premise of ensuring the integrity of the fruit, the non-destructive and non-contact dynamic sensing of the fruit multi-element quality is realized, and the method has important economic value and practical engineering application value for accurately regulating and controlling the safety of the fruit quality, reducing the loss rate of stored and transported fruits and promoting the quality and the sale of a cold chain.
Based on the foregoing embodiments, as an optional embodiment, in step S2, the acquiring the spatial-temporal distribution state of the temperature and humidity of the fruit during the storage and transportation process may include:
s21, constructing a digital twin body structure model of the three-dimensional structure of the storage and transportation carrier based on at least one of the three information of the storage and transportation carrier size, the air outlet position of the air cooler and the fruit stacking mode; performing grid division on the digital twin body structure model;
s22, determining a boundary condition value of a digital twin body structure model based on a fruit temperature and humidity initial value, a fruit multi-element quality initial value, a cold air outlet volume flow and a fruit wet heat transfer coefficient, and constructing a temperature and humidity space-time prediction model of a three-dimensional space inside the storage and transportation carrier;
s23, acquiring the temperature and humidity space-time distribution state of the fruits in the storage and transportation process based on the temperature and humidity space-time prediction model.
Specifically, in the embodiment of the present invention, step S2 may include:
firstly, constructing a three-dimensional structure digital twin body structure model of a storage and transportation carrier based on at least one of the size of the storage and transportation carrier, the position of an air outlet of an air cooler and information of a fruit stacking mode, and carrying out grid division on the model;
then, determining boundary condition values of a three-dimensional structure digital twin body structure model of the storage and transportation carrier based on the initial value of the temperature and humidity of the fruit, the initial value of the multi-element quality of the fruit, the volume flow of the cold air outlet and the wet heat transfer coefficient of the fruit, constructing a three-dimensional space temperature and humidity space time prediction model inside the storage and transportation carrier,
finally, the spatial-temporal distribution state of the quality of the fruits in the cold chain storage and transportation process is obtained.
Based on the foregoing embodiments, optionally, in step S22, determining a boundary condition value of the digital twin body structural model, and constructing a temperature and humidity space-time prediction model of the three-dimensional space inside the storage and transportation carrier includes:
setting the air outlet position of an air cooler of the digital twin body structure model as a fluid inlet boundary; setting the fan blade rotating surface of the air cooler as a boundary of a fluid outlet; setting the area where the fruit is located as a solid area; setting the non-solid region as a fluid region; determining turbulence characteristic dimensions, fluid Reynolds numbers, turbulence energy and turbulence specific dissipation rates according to the volume flow of cold air outlet; based on a computational fluid dynamics numerical simulation method, combining the initial value of the temperature and the humidity of the fruits, the initial value of the multi-element quality of the fruits and the wet heat transfer coefficient of the fruits, and constructing a temperature and humidity space-time prediction model of the three-dimensional space inside the storage and transportation carrier.
Specifically, step S22 may include: setting an air cooler air outlet of the storage and transportation carrier three-dimensional structure digital twin structure model as a fluid inlet boundary, setting an air cooler fan blade rotating surface of the storage and transportation carrier three-dimensional structure digital twin structure model as a fluid outlet boundary, setting stacked and packed fruits of the storage and transportation carrier three-dimensional structure digital twin structure model as a solid area, and setting a non-solid area of the storage and transportation carrier three-dimensional structure digital twin structure model as a fluid area; and determining turbulence characteristic dimensions, fluid Reynolds numbers, turbulence energy and turbulence specific dissipation rate based on the cold air outlet volume flow of the storage and transportation carrier, and establishing a three-dimensional space temperature and humidity space-time prediction model inside the storage and transportation carrier by using a computational fluid dynamics numerical simulation method to obtain the fruit quality space-time distribution condition in the cold chain storage and transportation process.
Further, in step S3, based on the constructed dynamic mathematical model of the multiple quality time-varying law of the fruits and the spatial-temporal distribution situation of the quality of the fruits in the cold chain storage and transportation process, the spatial-temporal distribution state of the quality field of the fruits in the cold chain storage and transportation process can be obtained.
Optionally, in step S21, meshing the digital twin structure model may further include: based on Richardson extrapolation, discrete errors of model spaces of different grid numbers are evaluated, and the optimal model grid number is determined.
In general, extrapolation is an effective approximation calculation method, and a high-precision approximation can be obtained efficiently by performing the simplest four-way operation for the obtained low-precision approximation. The Richardson extrapolation is an algorithm that uses a low-order equation to generate a high-precision convergence effect, thereby improving the sequence convergence efficiency. In the field of numerical analysis, the Richardson extrapolation method has many practical applications, such as Long Beige integration method, which is derived by applying the Richardson extrapolation method on the basis of a trapezoidal formula; there is also the brirsch-Stoer algorithm for solving ordinary differential equations. In the embodiment of the invention, the spatial discrete errors of the models with different grid numbers are evaluated by using the Richardson extrapolation method so as to determine the optimal model grid numbers and improve the prediction precision and convergence speed of the temperature and humidity field simulation module. Meanwhile, the mathematical model construction precision between the fruit multi-element quality change rate and the temperature and humidity is optimized by utilizing a crowd difference algorithm, and the accuracy and the robustness of the dynamic mathematical model of the model construction module fruit multi-element quality time-varying law are further improved.
Fig. 2 is a schematic diagram of a fruit multi-element quality space-time distribution sensing system according to an embodiment of the present invention, as shown in fig. 2, including but not limited to: mathematical model construction module 1, humiture field simulation module 2 and quality field simulation module 3, wherein: the mathematical model construction module 1 is mainly used for determining the change rate of the multiple quality of the fruits under different temperature and humidity conditions through periodic sampling and establishing a dynamic mathematical model of the multiple quality of the fruits; the temperature and humidity field simulation module 2 is mainly used for acquiring the temperature and humidity space-time distribution state of the fruits in the storage and transportation process; the quality field simulation module 3 is mainly used for acquiring the space-time distribution state of the quality of the fruits in the cold chain storage and transportation process based on the multi-element quality dynamic mathematical model and the space-time distribution state of the temperature and the humidity of the fruits.
Specifically, as an alternative, the mathematical model construction module 1 is mainly used for constructing a dynamic mathematical model of a fruit multi-element quality time-varying law and a three-dimensional structure digital twin body structure model of a storage carrier. The storage and transportation carrier three-dimensional structure digital twin body construction information can comprise at least one of storage and transportation carrier size, air cooler air outlet position and fruit stacking mode information;
the temperature and humidity field simulation module 2 is mainly used for simulating the space-time distribution condition of the temperature and humidity field of the environment and the fruit area in the three-dimensional space model according to the initial value of the temperature and humidity of the fruit, the initial value of the multi-element quality of the fruit, the volume flow of cold air outlet and the heat and humidity transfer coefficient of the fruit;
the quality field simulation module 3 is mainly used for modeling the spatial-temporal distribution condition of the quality field of the fruit area in the three-dimensional space model according to the dynamic mathematical model of the multi-element quality time-varying rule of the fruit and the spatial-temporal distribution condition of the temperature and humidity field of the fruit area;
the multi-element quality space-time distribution sensing system provided by the embodiment of the invention realizes nondestructive and non-contact dynamic sensing of the multi-element quality of the fruits on the premise of ensuring the integrity of the fruits by fusing a fruit and environment thermal mass coupling transmission mechanism and a fruit quality change dynamic model, and has important economic value and practical engineering application value in accurately regulating and controlling the quality safety of the fruits, reducing the loss rate of stored and transported fruits and promoting the quality and sales of a cold chain.
Further, as shown in fig. 3, the spatial-temporal distribution sensing system for multi-quality of fruits provided by the embodiment of the invention can be also applied to a system for adjusting spatial-temporal distribution of multi-quality of fruits so as to predict the spatial-temporal distribution sensing state of multi-quality of fruits in real time by using the sensing system.
The fruit multi-quality space-time distribution adjusting system can be used for constructing a fruit multi-quality space-time distribution sensing system of a fruit multi-quality time-varying rule, and can also comprise: a calculation module 4 and a control module 5.
The calculating module 4 is mainly used for calculating the time-space distribution uniformity of the quality field or the time-sequence variation value of the quality variation of the fruit multiple elements; the control module 5 is mainly used for feeding back the spatial and temporal distribution condition of the temperature and the humidity of the fruits, and dynamically regulating and controlling the cold chain storage and transportation carrier air cooler according to the environment and the spatial and temporal distribution condition of the temperature and the humidity of the fruits, particularly according to the spatial and temporal distribution condition of the quality field of the fruits. If the quality field distribution condition after a certain time in the transportation process is predicted according to the obtained quality space-time distribution state of the fruits, and the quality field distribution is compared with the preset quality field distribution and the property threshold X according to the quality field distribution and the property, or a certain quality index is compared with the preset standard index threshold Y of the quality, when the quality field distribution and the property are lower than the threshold X and/or a certain quality index is lower than the corresponding threshold Y, external equipment such as cold chain storage and transportation carrier air coolers are dynamically regulated and controlled to automatically pre-predict the change rate of the multiple qualities of the fruits.
In the multi-quality space-time distribution adjustment system provided by the embodiment of the invention, the space-time distribution state of the fruit quality in the cold chain storage and transportation process can be constructed according to periodic acquisition and adoption so as to predict the space-time distribution of the fruit quality in real time; and external equipment such as cold chain storage and transportation carrier air coolers can be dynamically controlled in real time according to the spatial and temporal distribution state of the quality of the fruits, so that the change rate of the multiple qualities of the fruits is automatically pre-dried.
Further, the system for adjusting the multi-quality space-time distribution provided by the embodiment of the invention can also comprise an optimization module 6 and a man-machine interaction module 7.
The optimizing module 6 can be used for evaluating the space discrete errors of the models with different grid numbers by using a Richardson extrapolation method, determining the grid numbers of the optimal models and improving the prediction precision and the convergence speed of the temperature and humidity field simulation module; the method is used for optimizing the mathematical model construction precision between the fruit multi-element quality change rate and the temperature and humidity by utilizing a crowd difference algorithm, and improving the accuracy and the robustness of the dynamic mathematical model of the model construction module fruit multi-element quality time-varying law.
The man-machine interaction module 7 is mainly used for sending information input by a user to a corresponding module, wherein the information input by the user comprises a fruit multi-element quality change rate, storage and transportation carrier three-dimensional structure digital twin body construction information, a fruit temperature and humidity initial value, a fruit multi-element quality initial value, cold air outlet volume flow, a fruit wet and heat transfer coefficient and a quality regulation threshold (at least one of a threshold X and/or a threshold Y).
According to the fruit multi-element quality space-time distribution adjustment system provided by the embodiment of the invention, the spatial discrete errors of different grid quantity models are evaluated by using the Richardson extrapolation method through additionally arranging the optimization module 6, so that the optimal model grid quantity is determined, and the prediction precision and the convergence speed of the temperature and humidity field simulation module are improved. Meanwhile, the mathematical model construction precision between the fruit multi-element quality change rate and the temperature and humidity is optimized by utilizing a crowd difference algorithm, and the accuracy and the robustness of the dynamic mathematical model of the model construction module fruit multi-element quality time-varying law are further improved.
Meanwhile, in the adjusting system provided by the embodiment, the man-machine interaction module 7 is arranged to realize the functions of parameter setting, data reading, real-time state display and the like according to actual conditions, so that the possibility is provided for intelligently improving the maintenance of the quality of fruits in the process of conveying the fruits.
It should be noted that, the fruit multi-quality space-time distribution sensing system provided by the embodiment of the present invention may be used to execute the fruit multi-quality space-time distribution sensing method described in any one of the above embodiments during specific operation, and will not be described in detail herein.
Fig. 4 illustrates a physical schematic diagram of an electronic device, as shown in fig. 4, which may include: processor (processor) 310, communication interface (Communications Interface) 320, memory (memory) 430, and communication bus 440, wherein processor 410, communication interface 420, and memory 430 communicate with each other via communication bus 440. The processor 410 may call logic instructions in the memory 430 to perform the following method:
s1, determining the multi-element quality change rate of the fruits under different temperature and humidity conditions through periodic sampling, and establishing a multi-element quality dynamic mathematical model of the fruits;
s2, acquiring the space-time distribution state of the temperature and the humidity of the fruits in the storage and transportation process;
s3, acquiring the spatial-temporal distribution state of the fruit quality in the cold chain storage and transportation process based on the dynamic mathematical model of the fruit multi-element quality and the spatial-temporal distribution state of the temperature and the humidity.
Further, the logic instructions in the memory 430 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, an embodiment of the present invention further provides a non-transitory computer readable storage medium, on which a computer program is stored, where the computer program is implemented when executed by a processor to perform the fruit multi-quality spatiotemporal distribution sensing method provided in the foregoing embodiments, for example, including:
s1, determining the multi-element quality change rate of the fruits under different temperature and humidity conditions through periodic sampling, and establishing a multi-element quality dynamic mathematical model of the fruits;
s2, acquiring the space-time distribution state of the temperature and the humidity of the fruits in the storage and transportation process;
s3, acquiring the spatial-temporal distribution state of the fruit quality in the cold chain storage and transportation process based on the dynamic mathematical model of the fruit multi-element quality and the spatial-temporal distribution state of the temperature and the humidity.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. The fruit multi-element quality space-time distribution sensing method is characterized by comprising the following steps of:
s1, determining the multi-element quality change rate of the fruits under different temperature and humidity conditions through periodic sampling, and establishing a multi-element quality dynamic mathematical model of the fruits;
s2, acquiring the space-time distribution state of the temperature and the humidity of the fruits in the storage and transportation process;
s3, acquiring the spatial-temporal distribution state of the fruit quality in the cold chain storage and transportation process based on the dynamic mathematical model of the fruit multi-element quality and the spatial-temporal distribution state of the temperature and the humidity;
in step S1, the establishing a dynamic mathematical model of the fruit multiple product includes:
s11, determining index values of the multiple qualities of the fruits under different temperature and humidity conditions through periodic sampling, and establishing a mathematical model between the change rate of the multiple qualities of the fruits and the temperature and humidity;
s12, establishing a dynamic mathematical model of the fruit multi-element quality based on the mathematical model between the change rate of the fruit multi-element quality and the temperature and the humidity;
in step S11, the establishing a mathematical model between the multi-element quality change rate of the fruit and the temperature and humidity includes:
the mathematical expression of the mathematical model between the fruit multi-element quality change rate and the temperature and humidity is determined as follows:
；
wherein,x 1 in order to be able to determine the temperature,x 2 in order to be a degree of humidity,yis the multi-element quality of the fruits,b 0 、b i 、b ii andb ij all are the constant coefficients of the two-dimensional space,iandjis an intermediate variable;
performing group individual coding by using the constant coefficient as a variable through a group intelligent difference algorithm, and performing assignment on the constant coefficient through iterative optimization by using a decision coefficient as an fitness function;
the mathematical expression of the fitness function is as follows:
；
wherein,nin order to be able to measure the data quantity,y r as a result of the fact that the value,y p in order to be able to predict the value,y avg as an average value of the values,in order to determine the coefficient of the coefficient,kis an intermediate variable;
in step S12, the establishing the dynamic mathematical model of the fruit multi-element quality based on the mathematical model between the fruit multi-element quality change rate and the temperature and humidity includes:
calculating the change rate of the multiple qualities of the fruits under different humiture according to the mathematical model between the change rate of the multiple qualities of the fruits and the humiture;
respectively constructing the fruit multi-element quality dynamic mathematical model based on a zero-order quality dynamic equation or a first-order quality dynamic equation;
the mathematical expression of the zero-order quality kinetic equation is as follows:
；
the mathematical expression of the first-order quality dynamics equation is as follows:
；
wherein,tin order to be able to take time,pis thattThe individual quality index values of the fruit at the moment,p 0 for a single quality index value of the fruit at the initial moment,yis the multiple quality of fruits.
2. The method according to claim 1, wherein in step S2, the step of obtaining the spatial-temporal distribution state of the temperature and humidity of the fruit during the storage and transportation process comprises:
s21, constructing a digital twin body structure model of a three-dimensional structure of the storage and transportation carrier based on at least one of the three information of the storage and transportation carrier size, the air outlet position of the air cooler and the fruit stacking mode; performing grid division on the digital twin body structure model;
s22, determining boundary condition values of the digital twin body structure model based on initial values of fruit temperature and humidity, initial values of fruit multi-element quality, volume flow of cold air outlet and fruit wet heat transfer coefficients, and constructing a temperature and humidity space-time prediction model of a three-dimensional space inside the storage and transportation carrier;
s23, acquiring the temperature and humidity space-time distribution state of the fruits in the storage and transportation process based on the temperature and humidity space-time prediction model.
3. The method according to claim 2, wherein in step S22, the determining the boundary condition values of the digital twin body structure model, and constructing a temperature and humidity space-time prediction model of the three-dimensional space inside the storage and transportation carrier, comprises:
setting the air outlet position of the air cooler of the digital twin body structure model as a fluid inlet boundary; setting the fan blade rotating surface of the air cooler as a boundary of a fluid outlet; setting the area where the fruit is located as a solid area; setting the non-solid region as a fluid region;
determining turbulence characteristic dimensions, fluid Reynolds numbers, turbulence energy and turbulence specific dissipation rates according to the cold air outlet volume flow;
based on a computational fluid dynamics numerical simulation method, combining the initial value of the temperature and the humidity of the fruits, the initial value of the multi-element quality of the fruits and the wet heat transfer coefficient of the fruits, and constructing a temperature and humidity space-time prediction model of the three-dimensional space inside the storage and transportation carrier.
4. A fruit multiple quality spatiotemporal distribution sensing method according to claim 3, characterized in that in step S21, said meshing of said digital twin body structure model further comprises:
based on Richardson extrapolation, discrete errors of model spaces of different grid numbers are evaluated, and the optimal model grid number is determined.
5. A fruit multiple quality spatiotemporal distribution sensing system, comprising: the system comprises a mathematical model construction module, a temperature and humidity field simulation module and a quality field simulation module;
the mathematical model construction module is used for determining the change rate of the multiple quality of the fruits under different temperature and humidity conditions through periodic sampling, and establishing a dynamic mathematical model of the multiple quality of the fruits, and comprises the following steps:
s11, determining index values of the multiple qualities of the fruits under different temperature and humidity conditions through periodic sampling, and establishing a mathematical model between the change rate of the multiple qualities of the fruits and the temperature and humidity;
s12, establishing a dynamic mathematical model of the fruit multi-element quality based on the mathematical model between the change rate of the fruit multi-element quality and the temperature and the humidity;
in step S11, the establishing a mathematical model between the multi-element quality change rate of the fruit and the temperature and humidity includes:
the mathematical expression of the mathematical model between the fruit multi-element quality change rate and the temperature and humidity is determined as follows:
；
wherein,x 1 in order to be able to determine the temperature,x 2 in order to be a degree of humidity,yis the multi-element quality of the fruits,b 0 、b i 、b ii andb ij all are the constant coefficients of the two-dimensional space,iandjis an intermediate variable;
performing group individual coding by using the constant coefficient as a variable through a group intelligent difference algorithm, and performing assignment on the constant coefficient through iterative optimization by using a decision coefficient as an fitness function;
the mathematical expression of the fitness function is as follows:
；
wherein,nin order to be able to measure the data quantity,y r as a result of the fact that the value,y p in order to be able to predict the value,y avg as an average value of the values,in order to determine the coefficient of the coefficient,kis an intermediate variable;
in step S12, the establishing the dynamic mathematical model of the fruit multi-element quality based on the mathematical model between the fruit multi-element quality change rate and the temperature and humidity includes:
calculating the change rate of the multiple qualities of the fruits under different humiture according to the mathematical model between the change rate of the multiple qualities of the fruits and the humiture;
respectively constructing the fruit multi-element quality dynamic mathematical model based on a zero-order quality dynamic equation or a first-order quality dynamic equation;
the mathematical expression of the zero-order quality kinetic equation is as follows:
；
the mathematical expression of the first-order quality dynamics equation is as follows:
；
wherein,tin order to be able to take time,pis thattThe individual quality index values of the fruit at the moment,p 0 for a single quality index value of the fruit at the initial moment,ythe quality of the fruits is multiple;
the temperature and humidity field simulation module is used for acquiring the temperature and humidity space-time distribution state of the fruits in the storage and transportation process;
the quality field simulation module is used for acquiring the fruit quality space-time distribution state in the cold chain storage and transportation process based on the fruit multi-element quality dynamic mathematical model and the temperature and humidity space-time distribution state.
6. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor performs the steps of the fruit multiplex quality spatiotemporal distribution awareness method of any of claims 1 to 4.
7. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the fruit multiplex quality spatiotemporal distribution perception method according to any of claims 1 to 4.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004020032A (en) * | 2002-06-14 | 2004-01-22 | Yamatake Corp | Temperature/humidity control system and temperature/humidity control method |
| CN1900956A (en) * | 2006-07-11 | 2007-01-24 | 南京大学 | Design method for improved mixed genetic algorithm optimizing water quality model parameter |
| WO2007148299A2 (en) * | 2006-06-20 | 2007-12-27 | Concept Technologies Limited | Environmental monitor and power management system |
| CN101172580A (en) * | 2007-10-22 | 2008-05-07 | 山东农业大学 | Automatic control method and controller for universal ozone generator for fresh-keeping of fruits and vegetables |
| CN103543703A (en) * | 2013-07-17 | 2014-01-29 | 中国海洋石油总公司 | Real-time food freshness monitor and monitoring method thereof |
| JP2015043715A (en) * | 2013-08-28 | 2015-03-12 | パナソニック株式会社 | Plant raising apparatus |
| CN104732328A (en) * | 2015-02-03 | 2015-06-24 | 天津市农村工作委员会信息中心 | Agricultural Internet of Things platform system |
| CN105844360A (en) * | 2016-03-29 | 2016-08-10 | 中国农业大学 | Agricultural product cold chain storage transport fine control modeling and optimizing method |
| CN108564212A (en) * | 2018-04-09 | 2018-09-21 | 中国农业大学 | A kind of regulation and control method and system of the influence factor of fruit and vegetable growth |
| CN110132355A (en) * | 2019-05-22 | 2019-08-16 | 北京农业信息技术研究中心 | A kind of fruit Temperature Humidity Sensor and fruit cold chain compartment humiture observation system |
| CN111263922A (en) * | 2017-10-23 | 2020-06-09 | 栗田工业株式会社 | Water temperature control method and system |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9367825B2 (en) * | 2009-10-23 | 2016-06-14 | Viridity Energy, Inc. | Facilitating revenue generation from wholesale electricity markets based on a self-tuning energy asset model |
| US9460633B2 (en) * | 2012-04-16 | 2016-10-04 | Eugenio Minvielle | Conditioner with sensors for nutritional substances |
| NL2020337B1 (en) * | 2017-01-30 | 2019-02-25 | John Bean Technologies Corp | Commercial scale sous-vide system and method |
-
2020
- 2020-06-16 CN CN202010548404.XA patent/CN111861307B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004020032A (en) * | 2002-06-14 | 2004-01-22 | Yamatake Corp | Temperature/humidity control system and temperature/humidity control method |
| WO2007148299A2 (en) * | 2006-06-20 | 2007-12-27 | Concept Technologies Limited | Environmental monitor and power management system |
| CN1900956A (en) * | 2006-07-11 | 2007-01-24 | 南京大学 | Design method for improved mixed genetic algorithm optimizing water quality model parameter |
| CN101172580A (en) * | 2007-10-22 | 2008-05-07 | 山东农业大学 | Automatic control method and controller for universal ozone generator for fresh-keeping of fruits and vegetables |
| CN103543703A (en) * | 2013-07-17 | 2014-01-29 | 中国海洋石油总公司 | Real-time food freshness monitor and monitoring method thereof |
| JP2015043715A (en) * | 2013-08-28 | 2015-03-12 | パナソニック株式会社 | Plant raising apparatus |
| CN104732328A (en) * | 2015-02-03 | 2015-06-24 | 天津市农村工作委员会信息中心 | Agricultural Internet of Things platform system |
| CN105844360A (en) * | 2016-03-29 | 2016-08-10 | 中国农业大学 | Agricultural product cold chain storage transport fine control modeling and optimizing method |
| CN111263922A (en) * | 2017-10-23 | 2020-06-09 | 栗田工业株式会社 | Water temperature control method and system |
| CN108564212A (en) * | 2018-04-09 | 2018-09-21 | 中国农业大学 | A kind of regulation and control method and system of the influence factor of fruit and vegetable growth |
| CN110132355A (en) * | 2019-05-22 | 2019-08-16 | 北京农业信息技术研究中心 | A kind of fruit Temperature Humidity Sensor and fruit cold chain compartment humiture observation system |
Non-Patent Citations (7)
| Title |
|---|
| 《Air temperature, relative humidity, and enthalpy phase space analysis: an innovative proposal for failures diagnosis in a cold chain》;Correa, E. C.;《Acta Horticulturae》;第1057-1063页 * |
| 《Compressive sensing approach in the table grape cold chain logistics》;Draganic, A;《2017 6th Mediterranean Conference on Embedded Computing (MECO)》;全文 * |
| 《The Remote Monitoring System of Vegetable Greenhouse》;Bai, QC;《2017 10TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID)》;第64-67页 * |
| 《Zigbee-based information collection system for the environment of cold-chain logistics of fruits and vegetables》;yang xinting;《Transactions of the Chinese Society of Agricultural Engineering》;第208-213页 * |
| 《不同构造冷藏车厢体的冷却性能模拟与对比》;张翔;《制冷学报》;第89-98页 * |
| 《不同温湿度条件下青花菜失水率预测模型的建立》;孙婧;《食品工业科技》;第69-71页 * |
| 《冷链物流生鲜品感知仪系统设计》;程如岐;《保鲜与加工》;第136-140页 * |
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