Transportation temperature control collaborative optimization method based on dynamic monitoring of fruit respiration intensity

Abstract

The invention relates to the technical field of agricultural cold-chain logistics, and discloses a transportation temperature control collaborative optimization method based on dynamic monitoring of fruit respiration intensity, and the method comprises the steps: building a fruit body data collection module, a fruit transportation dynamic monitoring module, a data preprocessing module, a data analysis module, a transportation temperature control collaborative module and a management module; the fruit body data acquisition module constructs a fruit initial attribute database, the fruit transportation dynamic monitoring module monitors the real-time change dynamic state of the fruit respiration intensity in real time, the data preprocessing module performs denoising and normalization processing on the acquired and monitored data, and the data analysis module analyzes the data based on the acquired data and the monitored data. A respiration intensity dynamic model is established, a temperature compensation coefficient Txl and a humidity compensation coefficient Wxl are calculated, meanwhile, temperature, humidity and respiration intensity changes are integrated, a corruption early warning index Yz is constructed, a transportation temperature control cooperation module starts an emergency temperature control plan in advance by applying the calculation result, and a management module generates a visual transportation management report.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural cold chain logistics, and specifically to a collaborative optimization method for transport temperature control based on dynamic monitoring of fruit respiration intensity. Background Art

[0002] During the transportation of fruit, traditional temperature control methods have many shortcomings. These methods usually rely on manual operation or fixed temperature settings and cannot be dynamically adjusted according to changes in the respiration intensity of the fruit, resulting in low temperature control accuracy. Since the respiration and metabolism of the fruit cannot be effectively slowed down, the shelf life is short and the quality of the fruit declines rapidly. In addition, traditional methods cannot monitor the changes in the respiration intensity of the fruit in real time, making it difficult to discover and solve problems in a timely manner, which can easily lead to losses. The above shortcomings seriously affect the transportation quality and economic benefits of the fruit. Therefore, there is an urgent need for a method that can dynamically monitor and coordinately optimize transportation temperature control according to the respiration intensity of the fruit to improve the preservation effect and transportation efficiency of the fruit. Summary of the Invention

[0003] (1) Technical problems solved

[0004] In response to the shortcomings of the existing technology, the present invention provides a transportation temperature control collaborative optimization method based on dynamic monitoring of fruit respiration intensity, which has the advantages of precise temperature control, good preservation effect, and real-time monitoring and adjustment, and solves the problems of low precision, short preservation period and inability to monitor in real time of traditional temperature control methods.

[0005] (2) Technical solution

[0006] To achieve the above object, the present invention provides the following technical solution: a method for collaborative optimization of transportation temperature control based on dynamic monitoring of fruit respiration intensity, comprising the following steps:

[0007] Step 1: Establish a fruit body data collection module, a fruit transportation dynamic monitoring module, a data preprocessing module, a data analysis module, a transportation temperature control collaboration module, and a management module;

[0008] Step 2: The fruit body data acquisition module obtains the fruit variety, maturity, initial respiration intensity and moisture content by combining the sensor array with the RFID tag, and constructs the fruit initial attribute database;

[0009] Step 3: The fruit transportation dynamic monitoring module monitors the real-time dynamic changes of the fruit's respiratory intensity in real time through a distributed wireless sensor network;

[0010] Step 4: The data preprocessing module performs denoising and normalization on the collected and monitored data and classifies them into three unit data;

[0011] Step 5: The data analysis module establishes a dynamic model of respiratory intensity based on the collected data and monitoring data, and calculates the temperature compensation coefficient Txl and the humidity compensation coefficient Wxl. At the same time, it integrates the changes in temperature, humidity and respiratory intensity to construct the corruption early warning indicator Yz;

[0012] Step 6: The transport temperature control coordination module uses the above calculation results, combined with the fruit respiration intensity and environmental parameters, to initiate the emergency temperature control plan in advance;

[0013] Step 7: The management module conducts in-depth analysis of the data from each module to generate a visual transportation management report that includes the fruit status change curve during transportation, the operating efficiency of the temperature control equipment, and the early warning event processing record.

[0014] Preferably, the data preprocessing module is classified into basic attribute data units, environmental parameter data units and dynamic breathing data units.

[0015] Preferably, the basic attribute data unit pre-processes basic data on fruit variety, maturity characteristics, initial storage temperature and humidity of the fruit, fruit origin and picking time.

[0016] Preferably, the environmental parameter data unit preprocesses the temperature and humidity change data monitored in real time.

[0017] Preferably, the dynamic respiration data unit preprocesses the ethylene and carbon dioxide concentration change data generated by the real-time respiration of the fruit, and combines it with the temperature and humidity change data preprocessed by the environmental parameter data unit to automatically calculate the real-time respiration intensity value of the fruit through the respiration intensity mathematical model preset in the data preprocessing module, and synchronizes it to each module in minutes.

[0018] Preferably, the data analysis module includes a temperature and humidity control unit and a corruption early warning analysis unit.

[0019] Preferably, the temperature and humidity control unit calculates the temperature compensation coefficient Txl, and the calculation formula is:

[0020]

[0021] In the formula, Txl represents the temperature compensation coefficient, H t represents the real-time respiratory intensity, H0 represents the basic respiratory intensity threshold, K t Indicates the current temperature, K z represents the optimal transportation temperature for the fruit of this variety, a1 and a2 represent the weight coefficients of fruit respiration intensity and transportation temperature during transportation, respectively.

[0022] Preferably, the temperature and humidity control unit calculates the humidity compensation coefficient Wxl, and the calculation formula is:

[0023]

[0024] In the formula, Wxl represents the humidity compensation coefficient, S t represents the real-time water vapor produced by fruit respiration, S0 represents the initial water vapor, W t Indicates the current humidity, W z represents the optimal transport humidity, b1 and b2 represent the weight coefficients of the fruit's respiratory water vapor and transport humidity during transportation, respectively.

[0025] Preferably, the corruption early warning analysis unit calculates the corruption early warning index Yz, and its calculation formula is:

[0026]

[0027] In the formula, Yz represents the corruption early warning indicator, t0 and t t Indicates the time range of the integration, t0 indicates the initial time, t t represents the current moment, μ represents the time process, μ∈[t0, t t ], α1, α2, and α3 represent the weights of respiratory intensity change, temperature and humidity deviation, and energy metabolism in corruption early warning, respectively. t Indicates real-time breathing intensity. Represents the second derivative of respiratory intensity, K t Indicates the current temperature, K z Indicates the optimal transportation temperature for this variety of fruit, W t Indicates the current humidity, W z Indicates the optimal transport humidity, represents the normalized sum of temperature and humidity deviations, F t Indicates the real-time energy metabolism level of the fruit, F l represents the critical threshold of energy metabolism, Indicates the relative value of energy metabolism level, e μv represents the time weighting function, v represents the attenuation coefficient, and d represents the time μ∈[t0, t t ] differential operation.

[0028] Preferably, the transport temperature control collaborative module uses the above calculation results to obtain disaster warning information through the meteorological data interface before a natural disaster occurs, and combines the fruit respiration intensity and environmental parameters to start the emergency temperature control plan in advance. Then, combined with the temperature compensation coefficient Txl and the humidity compensation coefficient Wxl, the PID control algorithm is used to dynamically adjust the refrigeration intensity and humidification / dehumidification amount of the refrigerated truck. At the same time, when the corruption warning is triggered, the emergency mode is started.

[0029] Compared with the existing technology, the present invention provides a collaborative optimization method for transport temperature control based on dynamic monitoring of fruit respiration intensity, which has the following beneficial effects:

[0030] 1. The present invention calculates the temperature compensation coefficient Txl as the core basis for the transport temperature control collaborative module to adjust the temperature, and makes real-time temperature control decisions. When the real-time respiration intensity of the fruit deviates from the basic respiration intensity threshold, or there is a deviation between the current temperature and the optimal transport temperature, the temperature compensation coefficient Tx is promptly added to the PID control algorithm of the transport temperature control collaborative module to adjust the operating power of the refrigeration or heating equipment, ultimately achieving the effect of dynamically compensating for the impact of ambient temperature changes on fruit respiration, so that the transport environment temperature is accurately maintained in the optimal range that is conducive to fruit preservation, and the fruit ripening and decay process is delayed.

[0031] 2. The present invention calculates the humidity compensation coefficient Wxl as a quantitative indicator for the transport temperature control collaborative module to adjust the humidity, and performs real-time humidity adjustment operations. When the amount of water vapor generated by fruit respiration changes significantly, or the current humidity deviates from the optimal transport humidity, the humidity compensation coefficient Wxl is promptly added to the humidity control program of the transport temperature control collaborative module to adjust the working state of the humidification or dehumidification equipment, ultimately achieving the effect of dynamically adjusting the humidity of the transport environment according to the changes in moisture during the fruit's respiration process, avoiding fruit mildew due to excessively high humidity, or fruit dehydration and shriveling due to excessively low humidity, and effectively improving the fruit's freshness preservation quality.

[0032] 3. The present invention calculates the corruption warning index Yz. When the corruption warning index Yz exceeds the set threshold, the transportation temperature control collaborative module will trigger the warning mechanism, reminding the operator to take corresponding measures, such as adjusting the temperature and humidity, checking the fruit status and transportation speed, to prevent the fruit from spoiling and ensure the quality of the fruit during transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0035] See also Figure 1 A collaborative optimization method for transport temperature control based on dynamic monitoring of fruit respiration intensity comprises the following steps:

[0036] Step 1: Establish a fruit data collection module, a fruit transportation dynamic monitoring module, a data preprocessing module, a data analysis module, a transportation temperature control collaboration module, and a management module. The management module will integrate the data from each module and generate a visual transportation management report, allowing managers to intuitively grasp the entire transportation process information. It also supports remote configuration of module parameters to achieve intelligent management.

[0037] Step 2: The fruit data acquisition module uses a sensor array and RFID tags to acquire the inherent properties of the fruit, such as variety, maturity, initial respiration intensity, and moisture content. Specifically, a spectral sensor is used to obtain optical characteristics such as fruit variety and maturity; a temperature and humidity sensor records the initial storage temperature and humidity of the fruit; and an RFID tag stores basic information about the fruit's origin and harvest time, thereby constructing a database of initial fruit properties.

[0038] Step 3: The fruit transportation dynamic monitoring module monitors the real-time changes in the fruit's respiration intensity through a distributed wireless sensor network. Micro gas sensors are evenly deployed in the transport compartment to detect the changes in the ethylene and carbon dioxide concentrations produced by the fruit's respiration in real time.

[0039] Step 4: The data preprocessing module performs denoising and normalization on the collected and monitored data and classifies them into three unit data;

[0040] Step 5: The data analysis module establishes a dynamic model of respiratory intensity based on the collected data and monitoring data, and calculates the temperature compensation coefficient Txl and the humidity compensation coefficient Wxl to quantify the impact of temperature and humidity on respiratory intensity. At the same time, it integrates the changes in temperature, humidity and respiratory intensity to construct a corruption warning indicator Yz. When the corruption warning indicator Yz exceeds the threshold, an alarm is triggered;

[0041] Step 6: The transport temperature control coordination module uses the above calculation results, combined with the fruit respiration intensity and environmental parameters, to initiate the emergency temperature control plan in advance;

[0042] Step 7. The management module conducts in-depth analysis of the data from each module to generate a visual transportation management report that includes the fruit status change curve during transportation, the operating efficiency of the temperature control equipment, and the early warning event processing record. At the same time, managers can view the report through mobile devices or PCs, and support remote configuration of each module parameter, such as adjusting the early warning threshold and optimizing the temperature control strategy, thereby realizing intelligent monitoring and management of the entire fruit transportation process.

[0043] The data preprocessing module is classified into basic attribute data units (fruit inherent parameters), environmental parameter data units (temperature, humidity, vibration) and dynamic breathing data units (real-time breathing intensity).

[0044] The basic attribute data unit preprocesses the basic data of fruit variety, maturity characteristics, initial storage temperature and humidity of the fruit, fruit origin and picking time.

[0045] The environmental parameter data unit preprocesses the temperature and humidity change data monitored in real time.

[0046] The dynamic respiration data unit preprocesses the ethylene and carbon dioxide concentration change data generated by the real-time respiration of the fruit, and combines it with the temperature and humidity change data monitored in real time preprocessed by the environmental parameter data unit. Together, the real-time respiration intensity value of the fruit is automatically calculated through the respiration intensity mathematical model preset within the data preprocessing module, and synchronized to each module in minutes.

[0047] The data analysis module includes a temperature and humidity control unit and a corruption early warning analysis unit.

[0048] The temperature and humidity control unit calculates the temperature compensation coefficient Txl, and the calculation formula is:

[0049]

[0050] In the formula, Txl represents the temperature compensation coefficient, H t represents the real-time respiratory intensity, H0 represents the basic respiratory intensity threshold, K t Indicates the current temperature, K z represents the optimal transportation temperature for the fruit of this variety, a1 and a2 represent the weight coefficients of fruit respiration intensity and transportation temperature during transportation, respectively.

[0051] The advantages are: by calculating the temperature compensation coefficient Txl, which serves as the core basis for the transportation temperature control collaborative module to adjust the temperature, real-time temperature control decisions are made. When the real-time respiration intensity of the fruit deviates from the basic respiration intensity threshold, or there is a deviation between the current temperature and the optimal transportation temperature, the temperature compensation coefficient Tx is promptly added to the PID control algorithm of the transportation temperature control collaborative module to adjust the operating power of the refrigeration or heating equipment, ultimately achieving the effect of dynamically compensating for the impact of ambient temperature changes on fruit respiration, so that the transportation environment temperature is accurately maintained in the optimal range that is conducive to fruit preservation, and the fruit ripening and decay process is delayed.

[0052] The temperature and humidity control unit calculates the humidity compensation coefficient Wxl, and the calculation formula is:

[0053]

[0054] In the formula, Wxl represents the humidity compensation coefficient, S t represents the real-time water vapor produced by fruit respiration, S0 represents the initial water vapor, W t Indicates the current humidity, W zrepresents the optimal transport humidity, b1 and b2 represent the weight coefficients of the fruit's respiratory water vapor and transport humidity during transportation, respectively.

[0055] The advantages are: by calculating the humidity compensation coefficient Wxl, which is used as a quantitative indicator for the transport temperature control collaborative module to adjust the humidity, real-time humidity adjustment operations are performed. When the amount of water vapor generated by fruit respiration changes significantly, or the current humidity deviates from the optimal transport humidity, the humidity compensation coefficient Wxl is promptly added to the humidity control program of the transport temperature control collaborative module to adjust the working status of the humidification or dehumidification equipment, ultimately achieving the effect of dynamically adjusting the humidity of the transport environment according to the changes in moisture during the fruit's respiration process, avoiding fruit mildew due to excessively high humidity, or fruit dehydration and shriveling due to excessively low humidity, and effectively improving the fruit's freshness preservation quality.

[0056] The corruption early warning analysis unit calculates the corruption early warning index Yz, and its calculation formula is:

[0057]

[0058] In the formula, Yz represents the corruption warning index, which is calculated by the integral formula. The larger its value is, the higher the risk of fruit corruption. t Indicates the time range of the integration, t0 indicates the initial time, t t represents the current moment, the integral variable, μ represents the time process (μ∈[t0, t t ]), α1, α2, and α3 represent the weights of respiratory intensity change, temperature and humidity deviation, and energy metabolism in corruption early warning, respectively. t Indicates real-time breathing intensity. It represents the second-order derivative of respiratory intensity and reflects the changing trend of respiratory rate. Indicates accelerated breathing (possibly due to environmental stress or early stages of decay). Indicates slowed respiration (possibly due to low temperature inhibition or late maturity), K t Indicates the current temperature, K z Indicates the optimal transportation temperature for this variety of fruit (e.g. the base temperature for blueberries is 0-1°C), W t Indicates the current humidity, W z Indicates the optimal transport humidity (e.g. the base humidity for strawberries is 85% to 95% RH). Represents the normalized sum of temperature and humidity deviations, quantifying the degree to which the environment deviates from the baseline, F t Indicates the real-time energy metabolism level of the fruit, F l Indicates the critical threshold of energy metabolism, exceeding which indicates that the fruit enters a rapid decay stage. It represents the relative value of energy metabolism level, reflecting the relationship between physiological state and spoilage risk, e μvrepresents the time weighting function, which is used to emphasize the influence of recent data, v represents the attenuation coefficient (when <0, the weight of historical data decreases over time; when >0, the weight of historical data increases), d represents the time μ∈[t0, t t ] differential operation;

[0059] The advantage is that when the corruption warning index Yz calculated by the above formula exceeds the set threshold, the transportation temperature control collaborative module will trigger the early warning mechanism to remind the operator to take corresponding measures, such as adjusting the temperature and humidity, checking the fruit status and transportation speed, to prevent the fruit from spoiling and ensure the quality of the fruit during transportation.

[0060] The transport temperature control collaborative module uses the above calculation results to obtain disaster warning information through the meteorological data interface before a natural disaster occurs. Combined with the fruit respiration intensity and environmental parameters, it initiates the emergency temperature control plan in advance, such as adjusting the power of the cooling and heating equipment and strengthening the sealing of the compartment. At the same time, combined with the temperature compensation coefficient Txl and the humidity compensation coefficient Wxl, the PID control algorithm is used to dynamically adjust the refrigeration intensity and humidification / dehumidification amount of the refrigerated truck. At the same time, when the corruption warning is triggered, the emergency mode (such as rapid cooling or modified atmosphere packaging) is activated.

[0061] The advantages are: the temperature compensation coefficient Txl and humidity compensation coefficient Wxl calculated by the transportation temperature control collaborative module are used to adjust the temperature and humidity of the transportation environment in real time. At the same time, according to the corruption warning indicator Yz, the module can take measures in advance to deal with possible natural disasters or environmental changes, thereby ensuring the freshness and quality of the fruit during transportation.

[0062] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A collaborative optimization method for transport temperature control based on dynamic monitoring of fruit respiration intensity, characterized in that: The following steps are involved: Step 1: Establish a fruit body data collection module, a fruit transportation dynamic monitoring module, a data preprocessing module, a data analysis module, a transportation temperature control collaboration module, and a management module; Step 2: The fruit body data acquisition module obtains the fruit variety, maturity, initial respiration intensity and moisture content by combining the sensor array with the RFID tag, and constructs the fruit initial attribute database; Step 3: The fruit transportation dynamic monitoring module monitors the real-time dynamic changes of the fruit's respiratory intensity in real time through a distributed wireless sensor network; Step 4: The data preprocessing module performs denoising and normalization on the collected and monitored data and classifies them into three unit data; Step 5: The data analysis module establishes a dynamic model of respiratory intensity based on the collected data and monitoring data, and calculates the temperature compensation coefficient Txl and the humidity compensation coefficient Wxl. At the same time, it integrates the changes in temperature, humidity and respiratory intensity to construct the corruption early warning indicator Yz; Step 6: The transport temperature control coordination module uses the above calculation results, combined with the fruit respiration intensity and environmental parameters, to initiate the emergency temperature control plan in advance; Step 7: The management module conducts in-depth analysis of the data from each module to generate a visual transportation management report that includes the fruit status change curve during transportation, the operating efficiency of the temperature control equipment, and the early warning event processing record.

2. A collaborative optimization method for transport temperature control based on dynamic monitoring of fruit respiration intensity according to claim 1, characterized in that: The data preprocessing module is classified into basic attribute data units, environmental parameter data units and dynamic breathing data units.

3. A collaborative optimization method for transport temperature control based on dynamic monitoring of fruit respiration intensity according to claim 2, characterized in that: The basic attribute data unit pre-processes basic data on fruit variety, maturity characteristics, initial storage temperature and humidity of the fruit, fruit origin and picking time.

4. A collaborative optimization method for transport temperature control based on dynamic monitoring of fruit respiration intensity according to claim 2, characterized in that: The environmental parameter data unit preprocesses the temperature and humidity change data monitored in real time.

5. A collaborative optimization method for transport temperature control based on dynamic monitoring of fruit respiration intensity according to claim 2, characterized in that: The dynamic respiration data unit preprocesses the ethylene and carbon dioxide concentration change data generated by the real-time respiration of the fruit, and combines it with the temperature and humidity change data preprocessed by the environmental parameter data unit to automatically calculate the real-time respiration intensity value of the fruit through the respiration intensity mathematical model preset within the data preprocessing module, and synchronizes it to each module in minutes.

6. A collaborative optimization method for transport temperature control based on dynamic monitoring of fruit respiration intensity according to claim 1, characterized in that: The data analysis module includes a temperature and humidity control unit and a corruption early warning analysis unit.

7. A collaborative optimization method for transport temperature control based on dynamic monitoring of fruit respiration intensity according to claim 6, characterized in that: The temperature and humidity control unit calculates the temperature compensation coefficient Txl, and the calculation formula is: In the formula, Txl represents the temperature compensation coefficient, H t represents the real-time respiratory intensity, H0 represents the basic respiratory intensity threshold, K t Indicates the current temperature, K z represents the optimal transportation temperature for the fruit of this variety, a1 and a2 represent the weight coefficients of fruit respiration intensity and transportation temperature during transportation, respectively.

8. The method for collaborative optimization of transport temperature control based on dynamic monitoring of fruit respiration intensity according to claim 6, characterized in that: The temperature and humidity control unit calculates the humidity compensation coefficient Wxl, and the calculation formula is: In the formula, Wxl represents the humidity compensation coefficient, S t represents the real-time water vapor produced by fruit respiration, S0 represents the initial water vapor, W t Indicates the current humidity, W z represents the optimal transport humidity, b1 and b2 represent the weight coefficients of the fruit's respiratory water vapor and transport humidity during transportation, respectively.

9. The method for collaborative optimization of transport temperature control based on dynamic monitoring of fruit respiration intensity according to claim 6, characterized in that: The corruption early warning analysis unit calculates the corruption early warning index Yz, and its calculation formula is: In the formula, Yz represents the corruption early warning indicator, t0 and t t Indicates the time range of the integration, t0 indicates the initial time, t t represents the current moment, μ represents the time process, μ∈[t0, t t ], α1, α2, and α3 represent the weights of respiratory intensity change, temperature and humidity deviation, and energy metabolism in corruption early warning, respectively. t Indicates real-time breathing intensity. Represents the second derivative of respiratory intensity, K t Indicates the current temperature, K z Indicates the optimal transportation temperature for this variety of fruit, W t Indicates the current humidity, W z Indicates the optimal transport humidity, represents the normalized sum of temperature and humidity deviations, F t Indicates the real-time energy metabolism level of the fruit, F l represents the critical threshold of energy metabolism, Indicates the relative value of energy metabolism level, e μv represents the time weighting function, v represents the attenuation coefficient, and d represents the time μ∈[t0, t t ] differential operation.

10. The method for collaborative optimization of transportation temperature control based on dynamic monitoring of fruit respiration intensity according to claim 1, characterized in that: The transport temperature control collaborative module uses the above calculation results to obtain disaster warning information through the meteorological data interface before a natural disaster occurs, and combines the fruit respiration intensity and environmental parameters to start the emergency temperature control plan in advance. Then, combined with the temperature compensation coefficient Txl and the humidity compensation coefficient Wxl, the PID control algorithm is used to dynamically adjust the refrigeration intensity and humidification / dehumidification amount of the refrigerated truck. At the same time, when the corruption warning is triggered, the emergency mode is started.

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