CN117891290B - Fresh food transportation wind-temperature joint debugging system based on data analysis - Google Patents
Fresh food transportation wind-temperature joint debugging system based on data analysis Download PDFInfo
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Abstract
The invention discloses a fresh food transportation wind temperature joint regulation system based on data analysis, which belongs to the technical field of fresh transportation.
Description
Technical Field
The invention relates to the technical field of fresh food transportation, in particular to a fresh food transportation wind-temperature joint debugging system based on data analysis.
Background
Fresh foods such as fruits, vegetables, etc. are susceptible to temperature fluctuations during transportation, resulting in reduced quality or spoilage. Thus, there is a need to precisely control the temperature in the shipping environment to extend the shelf life of the product. Meanwhile, the air speed is needed in the temperature transmission process, and in the precooling process, the proper air speed can improve the cooling efficiency and shorten the cooling time of fresh food. In the prior art, after the current temperature is collected by adopting a temperature sensor, the current temperature is compared with the target temperature, when the temperature is higher than the target temperature, the temperature is lowered, when the temperature is lower than the target temperature, the temperature in the transport cabin is always in a fluctuation state, the accurate control of the temperature is difficult to realize, and meanwhile, the wind speed is lack of regulation and control, the fresh temperature response is slow, so that the temperature control precision is further reduced.
Disclosure of Invention
Aiming at the defects in the prior art, the fresh food transportation wind temperature joint debugging system based on data analysis solves the problem of low fresh temperature control precision in the prior art.
In order to achieve the aim of the invention, the invention adopts the following technical scheme: a fresh food transportation wind-temperature joint debugging system based on data analysis, comprising: the system comprises a temperature acquisition module, a wind speed acquisition module, a temperature loop control module and a wind speed control module;
the temperature acquisition module is used for calculating the transportation real-time temperature of the fresh food according to the temperature sensing data of the temperature sensor;
the wind speed acquisition module is used for calculating the real-time wind speed of the fresh food according to the wind speed sensing data of the wind speed sensor;
The temperature loop control module is used for improving the PI control model through multi-order recursion according to the difference value between the transportation real-time temperature and the target temperature, and calculating the temperature driving quantity;
The wind speed control module is used for adjusting the wind speed driving quantity according to the temperature driving quantity and the real-time wind speed.
The beneficial effects of the invention are as follows: according to the method, the transportation real-time temperature is obtained through the temperature obtaining module, the real-time wind speed to which the fresh food is subjected is obtained through the wind speed obtaining module, the temperature loop control module is adopted, the temperature driving quantity is calculated through the difference value between the transportation real-time temperature and the target temperature, the accuracy of temperature control is improved, the wind speed control module is adopted to adjust the wind speed driving quantity according to the temperature driving quantity and the real-time wind speed, the temperature transfer time is shortened, the temperature response time of the fresh food is increased, and the temperature control accuracy is further improved.
Further, the temperature acquisition module includes: a temperature sensor, a first filtering unit and a temperature acquisition unit;
The temperature sensor is used for collecting temperature sensing data of fresh food;
The first filtering unit is used for filtering the temperature sensing data to obtain temperature filtering data;
the temperature acquisition unit is used for calculating the transportation real-time temperature of the fresh food according to the temperature filtering data.
Further, the wind speed acquisition module includes: the wind speed sensor, the second filtering unit and the wind speed acquisition unit;
The wind speed sensor is used for collecting wind speed sensing data of fresh food;
The second filtering unit is used for filtering the wind speed sensing data to obtain wind speed filtering data;
the wind speed acquisition unit is used for calculating the real-time wind speed suffered by fresh food according to wind speed filtering data.
Further, the first filtering unit and the second filtering unit each include: a filtering proportion calculating subunit and a filter;
The filtering proportion calculating subunit is used for calculating a filtering proportion coefficient according to the sensing data and the historical filtering data;
The filter is used for filtering the sensing data according to the filtering proportionality coefficient.
The beneficial effects of the above further scheme are: according to the temperature sensor, after the temperature sensing data are acquired, the wind speed sensor filters the wind speed sensing data through the filtering unit, so that the influence of environmental noise is reduced, the accuracy of acquiring temperature and wind speed is improved, and the temperature and wind speed control accuracy is improved.
Further, the expression of the filtering proportion calculating subunit is:
，
，
Wherein r is a filtering proportionality coefficient, d is a distance coefficient, x n is nth sensing data, g n-1 is nth-1 filtering data, x n-i is nth-I sensing data, I is the number of sensing data, I and n are positive integers, and I is an absolute value.
Further, the expression of the filter is:
，
where g n is the nth filtered data.
The beneficial effects of the above further scheme are: according to the invention, the filtering proportionality coefficient increases along with the increase of the absolute value of the distance coefficient, so that when the sensing data is influenced by noise, the n-1 filtering data g n-1 can be considered to filter the noise influence.
Further, the expression of the temperature loop control module is:
，
，
Wherein T k is the temperature driving amount at the k time, T k-m is the temperature driving amount at the k-m time, b j is the j-th order weighting coefficient, a m is the m-th order recursion coefficient, f k-j is the non-improved temperature driving amount at the k-j time, f k is the non-improved temperature driving amount at the k-1 time, f k-1 is the non-improved temperature driving amount at the k-1 time, w p is the proportionality coefficient, w i is the integral coefficient, ϵ k is the difference between the transportation real-time temperature at the k time and the target temperature, k, j and m are positive integers, and N is the recursion network order.
The beneficial effects of the above further scheme are: in the conventional PID (proportional-integral-derivative) algorithm, a driving amount proportional to a deviation is generated by a proportional coefficient, the driving amount is corrected by an integral coefficient according to a cumulative amount of the deviation, and the driving amount is further corrected by controlling a change rate of the deviation by a differential coefficient. When the differential coefficient of the technology is set better, the adjusting time can be reduced, the loop response is accelerated, but because the PID is a fuzzy control algorithm, the actual physical relationship between the acquired deviation and the output driving quantity is difficult to determine, so that the differential coefficient is difficult to control, and the unstable factor of the system is often increased. According to the invention, only PI control is used, an incremental PI output mode is set, iteration is carried out from the driving quantity at the previous moment, the initial value of the driving quantity can be set arbitrarily, the accumulated multiplication operation of a hardware carrier is avoided, and the operation load is reduced; in order to make the system easier to converge, the N-order past recursion and new-quantity weighted update recursion network transmission is used for optimizing and improving the PI output result, so that the temperature loop control module has better robustness and stability than the PID algorithm, and the response speed and convergence time are not weaker than those of the existing PID algorithm.
Further, the expression of the wind speed control module is:
，
Wherein h is the wind speed driving quantity, w s,t is the real-time wind speed, a is the proportionality coefficient, w th is the wind speed threshold value, A is the gain coefficient, pi is the circumference rate, e is the natural constant, c is the perturbation coefficient, sigma is the perturbation damping parameter, θ is the perturbation frequency, T is the imaginary part mark, T g is the target temperature, and arctan is the arctan function.
The beneficial effects of the above further scheme are: when the real-time wind speed w s,t is larger than or equal to the wind speed threshold value w th, the wind speed driving quantity h does not increase, the fresh water is prevented from evaporating too fast, when the real-time wind speed w s,t is smaller than the wind speed threshold value w th, the wind speed driving quantity h changes along with the difference value ϵ k and the temperature driving quantity T k, and when the difference value ϵ k and the temperature driving quantity T k are larger, the wind speed is faster, the temperature transmission is fast, and the temperature transmission time is shortened. In the wind speed control module, a micro disturbance of damping oscillation is set through the perturbation coefficient, the perturbation damping parameter and the perturbation frequency, so that the wind speed driving quantity and the temperature driving quantity show a damping linkage relation, and the overshoot of the system is avoided.
Drawings
FIG. 1 is a system block diagram of a fresh food transportation wind temperature joint debugging system based on data analysis.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
As shown in fig. 1, a fresh food transportation wind-temperature joint regulation system based on data analysis includes: the system comprises a temperature acquisition module, a wind speed acquisition module, a temperature loop control module and a wind speed control module;
the temperature acquisition module is used for calculating the transportation real-time temperature of the fresh food according to the temperature sensing data of the temperature sensor;
the wind speed acquisition module is used for calculating the real-time wind speed of the fresh food according to the wind speed sensing data of the wind speed sensor;
The temperature loop control module is used for improving the PI control model through multi-order recursion according to the difference value between the transportation real-time temperature and the target temperature, and calculating the temperature driving quantity;
The wind speed control module is used for adjusting the wind speed driving quantity according to the temperature driving quantity and the real-time wind speed.
The temperature acquisition module includes: a temperature sensor, a first filtering unit and a temperature acquisition unit;
The temperature sensor is used for collecting temperature sensing data of fresh food;
The first filtering unit is used for filtering the temperature sensing data to obtain temperature filtering data;
the temperature acquisition unit is used for calculating the transportation real-time temperature of the fresh food according to the temperature filtering data.
In this embodiment, the manner in which the temperature acquisition unit calculates the transportation real-time temperature is the same as in the prior art.
The wind speed acquisition module includes: the wind speed sensor, the second filtering unit and the wind speed acquisition unit;
The wind speed sensor is used for collecting wind speed sensing data of fresh food;
The second filtering unit is used for filtering the wind speed sensing data to obtain wind speed filtering data;
the wind speed acquisition unit is used for calculating the real-time wind speed suffered by fresh food according to wind speed filtering data.
In the present embodiment, the manner in which the wind speed acquisition unit calculates the real-time wind speed is the same as in the prior art.
The first filtering unit and the second filtering unit each include: a filtering proportion calculating subunit and a filter;
The filtering proportion calculating subunit is used for calculating a filtering proportion coefficient according to the sensing data and the historical filtering data;
The filter is used for filtering the sensing data according to the filtering proportionality coefficient.
According to the temperature sensor, after the temperature sensing data are acquired, the wind speed sensor filters the wind speed sensing data through the filtering unit, so that the influence of environmental noise is reduced, the accuracy of acquiring temperature and wind speed is improved, and the temperature and wind speed control accuracy is improved.
The expression of the filtering proportion calculating subunit is as follows:
，
，
Wherein r is a filtering proportionality coefficient, d is a distance coefficient, x n is nth sensing data, g n-1 is nth-1 filtering data, x n-i is nth-I sensing data, I is the number of sensing data, I and n are positive integers, and I is an absolute value.
The expression of the filter is:
，
where g n is the nth filtered data.
According to the invention, the filtering proportionality coefficient increases along with the increase of the absolute value of the distance coefficient, so that when the sensing data is influenced by noise, the n-1 filtering data g n-1 can be considered to filter the noise influence.
The expression of the temperature loop control module is as follows:
，
，
Wherein T k is the temperature driving amount at the k time, T k-m is the temperature driving amount at the k-m time, b j is the j-th order weighting coefficient, a m is the m-th order recursion coefficient, f k-j is the non-improved temperature driving amount at the k-j time, f k is the non-improved temperature driving amount at the k-1 time, f k-1 is the non-improved temperature driving amount at the k-1 time, w p is the proportionality coefficient, w i is the integral coefficient, ϵ k is the difference between the transportation real-time temperature at the k time and the target temperature, k, j and m are positive integers, and N is the recursion network order.
In the conventional PID (proportional-integral-derivative) algorithm, a driving amount proportional to a deviation is generated by a proportional coefficient, the driving amount is corrected by an integral coefficient according to a cumulative amount of the deviation, and the driving amount is further corrected by controlling a change rate of the deviation by a differential coefficient. When the differential coefficient of the technology is set better, the adjusting time can be reduced, the loop response is accelerated, but because the PID is a fuzzy control algorithm, the actual physical relationship between the acquired deviation and the output driving quantity is difficult to determine, so that the differential coefficient is difficult to control, and the unstable factor of the system is often increased. According to the invention, only PI control is used, an incremental PI output mode is set, iteration is carried out from the driving quantity at the previous moment, the initial value of the driving quantity can be set arbitrarily, the accumulated multiplication operation of a hardware carrier is avoided, and the operation load is reduced; in order to make the system easier to converge, the N-order past recursion and new-quantity weighted update recursion network transmission is used for optimizing and improving the PI output result, so that the temperature loop control module has better robustness and stability than the PID algorithm, and the response speed and convergence time are not weaker than those of the existing PID algorithm.
The expression of the wind speed control module is as follows:
，
Wherein h is the wind speed driving quantity, w s,t is the real-time wind speed, a is the proportionality coefficient, w th is the wind speed threshold value, A is the gain coefficient, pi is the circumference rate, e is the natural constant, c is the perturbation coefficient, sigma is the perturbation damping parameter, θ is the perturbation frequency, T is the imaginary part mark, T g is the target temperature, and arctan is the arctan function.
When the real-time wind speed w s,t is larger than or equal to the wind speed threshold value w th, the wind speed driving quantity h does not increase, the fresh water is prevented from evaporating too fast, when the real-time wind speed w s,t is smaller than the wind speed threshold value w th, the wind speed driving quantity h changes along with the difference value ϵ k and the temperature driving quantity T k, and when the difference value ϵ k and the temperature driving quantity T k are larger, the wind speed is faster, the temperature transmission is fast, and the temperature transmission time is shortened. In the wind speed control module, a micro disturbance of damping oscillation is set through the perturbation coefficient, the perturbation damping parameter and the perturbation frequency, so that the wind speed driving quantity and the temperature driving quantity show a damping linkage relation, and the overshoot of the system is avoided.
In the invention, in order to properly increase the wind speed in the wind speed control process, specific values of the perturbation coefficient, the perturbation damping parameter and the perturbation frequency can be adjusted, and the wind speed driving quantity is slowed down or increased.
According to the method, the transportation real-time temperature is obtained through the temperature obtaining module, the real-time wind speed to which the fresh food is subjected is obtained through the wind speed obtaining module, the temperature loop control module is adopted, the temperature driving quantity is calculated through the difference value between the transportation real-time temperature and the target temperature, the accuracy of temperature control is improved, the wind speed control module is adopted to adjust the wind speed driving quantity according to the temperature driving quantity and the real-time wind speed, the temperature transfer time is shortened, the temperature response time of the fresh food is increased, and the temperature control accuracy is further improved.
After the wind speed is increased, the temperature transmission is faster, so that the difference ϵ k in the temperature loop control module is more effective, and the temperature control precision is further improved.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. Fresh food transportation wind temperature joint debugging system based on data analysis, characterized by comprising: the system comprises a temperature acquisition module, a wind speed acquisition module, a temperature loop control module and a wind speed control module;
the temperature acquisition module is used for calculating the transportation real-time temperature of the fresh food according to the temperature sensing data of the temperature sensor;
the wind speed acquisition module is used for calculating the real-time wind speed of the fresh food according to the wind speed sensing data of the wind speed sensor;
The temperature loop control module is used for improving the PI control model through multi-order recursion according to the difference value between the transportation real-time temperature and the target temperature, and calculating the temperature driving quantity;
The wind speed control module is used for adjusting the wind speed driving quantity according to the temperature driving quantity and the real-time wind speed;
The expression of the temperature loop control module is as follows:
，
，
wherein T k is the temperature driving amount at the k time, T k-m is the temperature driving amount at the k-m time, b j is the j-th order weighting coefficient, a m is the m-th order recursion coefficient, f k-j is the non-improved temperature driving amount at the k-j time, f k is the non-improved temperature driving amount at the k-1 time, f k-1 is the non-improved temperature driving amount at the k-1 time, w p is the proportionality coefficient, w i is the integral coefficient, ϵ k is the difference value between the transportation real-time temperature at the k time and the target temperature, k, j and m are positive integers, and N is the recursion network order;
The expression of the wind speed control module is as follows:
，
Wherein h is the wind speed driving quantity, w s,t is the real-time wind speed, a is the proportionality coefficient, w th is the wind speed threshold value, A is the gain coefficient, pi is the circumference rate, e is the natural constant, c is the perturbation coefficient, sigma is the perturbation damping parameter, θ is the perturbation frequency, T is the imaginary part mark, T g is the target temperature, and arctan is the arctan function.
2. The fresh food transportation wind-temperature joint adjustment system based on data analysis of claim 1, wherein the temperature acquisition module comprises: a temperature sensor, a first filtering unit and a temperature acquisition unit;
The temperature sensor is used for collecting temperature sensing data of fresh food;
The first filtering unit is used for filtering the temperature sensing data to obtain temperature filtering data;
the temperature acquisition unit is used for calculating the transportation real-time temperature of the fresh food according to the temperature filtering data.
3. The fresh food transportation wind temperature joint adjustment system based on data analysis of claim 2, wherein the wind speed acquisition module comprises: the wind speed sensor, the second filtering unit and the wind speed acquisition unit;
The wind speed sensor is used for collecting wind speed sensing data of fresh food;
The second filtering unit is used for filtering the wind speed sensing data to obtain wind speed filtering data;
the wind speed acquisition unit is used for calculating the real-time wind speed suffered by fresh food according to wind speed filtering data.
4. The fresh food transportation wind temperature joint debugging system based on data analysis according to claim 3, wherein the first filtering unit and the second filtering unit each comprise: a filtering proportion calculating subunit and a filter;
The filtering proportion calculating subunit is used for calculating a filtering proportion coefficient according to the sensing data and the historical filtering data;
The filter is used for filtering the sensing data according to the filtering proportionality coefficient.
5. The system for simultaneously adjusting the temperature of fresh food transportation wind based on data analysis according to claim 4, wherein the expression of the filtering proportion calculating subunit is:
，
，
Wherein r is a filtering proportionality coefficient, d is a distance coefficient, x n is nth sensing data, g n-1 is nth-1 filtering data, x n-i is nth-I sensing data, I is the number of sensing data, I and n are positive integers, and I is an absolute value.
6. The fresh food transportation wind temperature joint debugging system based on data analysis of claim 5, wherein the expression of the filter is:
，
where g n is the nth filtered data.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5564625A (en) * | 1993-09-14 | 1996-10-15 | Mercedes-Benz Ag | Method for controlling motor vehicle interior temperature |
| CN111130123A (en) * | 2019-12-30 | 2020-05-08 | 华中科技大学 | Self-adaptive control method of parallel active power filter |
| CN113988262A (en) * | 2021-10-28 | 2022-01-28 | 重庆大学 | Cold-chain logistics temperature prediction method and temperature regulation and control method |
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- 2024-03-14 CN CN202410288415.7A patent/CN117891290B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5564625A (en) * | 1993-09-14 | 1996-10-15 | Mercedes-Benz Ag | Method for controlling motor vehicle interior temperature |
| CN111130123A (en) * | 2019-12-30 | 2020-05-08 | 华中科技大学 | Self-adaptive control method of parallel active power filter |
| CN113988262A (en) * | 2021-10-28 | 2022-01-28 | 重庆大学 | Cold-chain logistics temperature prediction method and temperature regulation and control method |
Non-Patent Citations (4)
| Title |
|---|
| 基于分数阶微积分的线控转向系统的控制研究;田杰;高翔;陈宁;;机械科学与技术;20100915(09);全文 * |
| 基于改进遗传算法的无刷直流电动机递归模糊神经网络控制;乔维德;;微特电机;20080528(05);全文 * |
| 基于蚁群模糊神经网络的无刷直流电机仿真控制研究;乔维德;;职教与经济研究(娄底职业技术学院学报);20090630(02);全文 * |
| 永磁同步电机的改进对角递归神经网络PI控制策略;彭熙伟;高瀚林;;电机与控制学报;20190415(04);全文 * |
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