A test method for verifying the feasibility of a product short-term storage and transportation scheme acquisition tool

Abstract

The application discloses a kind of test methods for verifying the feasibility of product short-term storage and transportation scheme acquisition tool, the test method includes the following steps: setting the same packaging test group, wherein, at least two groups in the same storage and transportation environment and for containing the same sample but with different humidity control materials test group A, at least two groups in the same storage and transportation environment and for no sample but with different humidity control materials test group B;Collect test data of all test groups every day within the preset target period;The test data includes packaging internal temperature and packaging internal humidity, packaging external temperature and packaging external humidity, and is converted into water vapor partial pressure;The prediction data is obtained by inputting the test data converted into water vapor partial pressure into the preset prediction model framework fitting prediction;After comparing prediction data with test data, verify the feasibility of storage and transportation scheme and give suggestions.Can effectively verify the test tool, ensure that short-term storage and transportation scheme generated by it has reliability in practical application.

Description

Technical Field

[0001] This invention relates to the field of product packaging technology, and in particular to a test method for verifying the feasibility of obtaining tools for short-term product storage and transportation solutions. Background Technology

[0002] As the coverage of modern distribution networks continues to expand and the scale of cross-regional commodity trade grows rapidly, the quality assurance requirements for different product categories in the storage and transportation process are constantly upgrading. Among these requirements, humidity control is one of the core indicators for ensuring the quality of most perishable and easily damaged products. Currently, the industry's commonly used product packaging humidity control solutions generally adopt a standardized packaging structure combined with a fixed ratio of humidity-regulating materials, using a generic design logic. This primarily relies on the product's conventional long-term storage parameters and the characteristics of common distribution environments to pre-determine the type, quantity, and packaging method of humidity-regulating materials, without providing targeted adaptation for the differentiated storage and transportation needs of different orders.

[0003] To enable dynamic and targeted design of short-term storage and transportation solutions, a forecasting tool was developed. This tool can predict and optimize for short-term storage and transportation scenarios, and accurately customize packaging solutions based on the product's target shelf life and actual storage environment parameters. However, the feasibility of the forecasting tool and the generated packaging solutions still needs to be verified. Summary of the Invention

[0004] In view of the above-mentioned shortcomings in the current product packaging technology field, the present invention provides a testing method for verifying the feasibility of a tool for obtaining short-term storage and transportation solutions for products. This method can effectively verify the testing tool and ensure that the short-term storage and transportation solutions it generates are reliable and accurate in practical applications.

[0005] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions: A testing method for verifying the feasibility of a product short-term storage and transportation solution acquisition tool, the testing method comprising the following steps: Set up test groups with the same packaging, among which at least two groups are test group A, which is in the same storage and transportation environment and contains the same sample but with different humidity-regulating materials, and at least two groups are test group B, which is in the same storage and transportation environment and does not contain a sample but has different humidity-regulating materials. Collect test data from all test groups daily within the preset target timeframe; The test data includes the internal temperature and humidity of the packaging, the external temperature and humidity of the packaging, and is converted into water vapor partial pressure. Predictive data is obtained by inputting the test data converted into water vapor partial pressure into a preset prediction model framework for fitting and prediction. The feasibility of the storage and transportation plan was verified by comparing the predicted data with the test data, and recommendations were given.

[0006] According to one aspect of the present invention, the conversion to water vapor partial pressure specifically includes: obtaining the saturated water vapor pressure using the Tetens saturated water vapor pressure formula, where the water vapor partial pressure is the ratio of humidity to saturated water vapor pressure.

[0007] According to one aspect of the present invention, the preset prediction model framework is: de_in / dt = k_e(T_bar, a_bar) · (e_out - e_in) - K_m(T_in, a_in) · (e_in - e_set); where, a_in=RH_in / 100, a_out=RH_out / 100, T_bar=(T_in+T_out) / 2, a_bar=(a_in+a_out) / 2, e_in=a_in·e_s(T_in), e_out=a_out·e_s(T_out), e_set(T_in)=a_w·e_s(T_in); T_in is the internal temperature of the product packaging, T_out is the external ambient temperature of the product packaging; e_s(T_out) is the external ambient temperature. Saturated vapor pressure at temperature T_out; e_s(T_in) is the saturated vapor pressure at internal temperature T_in; e_set(T_in) is the set partial pressure of the material at internal temperature T_in at time t; a_w is the water activity of the material; a_in is the water activity inside the product packaging; a_out is the water activity of the external environment of the product packaging; K_m is the equivalent humidity regulation coefficient; k_e is the equivalent exchange coefficient; de_in / dt is the rate of change of the partial pressure of water vapor inside the product packaging with time t; RH_in is the relative humidity inside the product packaging; RH_out is the relative humidity outside the product packaging.

[0008] According to one aspect of the present invention, the step of obtaining prediction data by inputting the test data converted into water vapor partial pressure into a preset prediction model framework for fitting and prediction includes: rewriting de_in / dt=k_e·(e_out - e_in) - K_m·(e_in- e_set) as a linear regression. When K_m≈0, that is, when testing group B or the material depletion segment, k_e can be obtained directly by linear regression.

[0009] According to one aspect of the present invention, the step of obtaining prediction data by inputting test data converted into water vapor partial pressure into a preset prediction model framework for fitting and prediction includes: under the given k_e, only using the effective segment data of each test group to back-calculate the corresponding K_m.

[0010] According to one aspect of the present invention, the verification of the predicted data with the test data includes: obtaining discrete predicted values ​​of the relative humidity inside the product packaging based on the back-calculated k_e and K_m.

[0011] According to one aspect of the present invention, the verification of the feasibility of the storage and transportation scheme after reviewing the predicted data and test data includes: comparing the discrete predicted value of the relative humidity inside the product packaging with the corresponding collected test data to determine whether it exceeds the allowable error range.

[0012] According to one aspect of the invention, the error range is within ±15%.

[0013] According to one aspect of the present invention, the step of verifying the feasibility of the storage and transportation scheme and providing suggestions after reviewing the predicted data and test data includes: based on a preset target humidity range, determining whether the relative humidity inside the product packaging in the test data falls within the preset target humidity range and the corresponding time, thereby determining whether it meets the preset multiple failure modes, wherein the multiple failure modes include at least any of the following: initial dryness, suspected material equivalent depletion, forced drying environment, overall dampness, and risk of exceeding the upper limit.

[0014] According to one aspect of the present invention, the step of verifying the feasibility of the storage and transportation scheme and providing recommendations after reviewing the predicted data and test data includes: generating corresponding recommendations based on the failure mode in combination with the target humidity range, product, humidity conditioning materials and external environment.

[0015] The advantages of this invention are as follows: By setting up at least two test groups to collect test data, and then using the predictive model framework based on the test data to obtain predicted data, the predicted data can be verified against the actual collected data. This verifies the accuracy of the predictive model and determines whether the model can be used for prediction. It can accurately identify potential risks before implementation, providing solid data support for the scientific selection of humidity-regulating materials and the optimized design of packaging structures, thereby ensuring the stability and safety of product quality during short-term distribution.

[0016] In addition, this method replaces the traditional empirical parameter determination method by quantifying the equivalent humidity control coefficient, effectively avoiding cost waste caused by excessive humidity control or product deterioration caused by insufficient humidity control, and achieving the economic optimization of the packaging solution while ensuring quality. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of a test method for verifying the feasibility of a product short-term storage and transportation solution acquisition tool as described in this invention; Figure 2 This is a schematic diagram illustrating the longest consecutive days that each test group meets the 50–70%RH requirement as described in Embodiment 3 of the present invention. Figure 3 This is the A1RH curve of the test group described in Embodiment 3 of the present invention; Figure 4 This is the voltage divider trajectory diagram of test group A1 as described in Embodiment 3 of the present invention; Figure 5 This is the B1RH curve of the test group described in Embodiment 3 of the present invention; Figure 6 This is the voltage divider trajectory diagram of test group B1 as described in Embodiment 3 of the present invention; Figure 7 This is the A2RH curve of the test group described in Embodiment 3 of the present invention; Figure 8 This is the voltage divider trajectory diagram of test group A2 as described in Embodiment 3 of the present invention; Figure 9 This is the B2RH curve of the test group described in Embodiment 3 of the present invention; Figure 10 This is the voltage divider trajectory diagram of test group B2 as described in Embodiment 3 of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1

[0021] like Figure 1 As shown, a test method for verifying the feasibility of a product short-term storage and transportation solution acquisition tool is provided, the test method comprising the following steps: Step S1: Set up test groups with the same packaging, wherein at least two groups are in the same storage and transportation environment and contain the same sample but with different humidity-regulating materials (test group A), and at least two groups are in the same storage and transportation environment and do not contain a sample but have different humidity-regulating materials (test group B). Specifically, this involves placing Test Group A and Test Group B in the same simulated storage and transportation environment, specifically simulating environmental temperature and humidity changes under different seasons and logistics routes to cover various climatic scenarios that the product may encounter during actual storage and transportation. In step S1, Test Group A is used to simulate the actual moisture absorption and release dynamics when containing the product, while Test Group B is used to isolate the influence of the product's own moisture exchange, thereby accurately quantifying the independent effectiveness of packaging airtightness and moisture-regulating materials.

[0022] For example, in a simulated summer high temperature and high humidity logistics scenario, test group A used high water activity humidity conditioning materials, while test group B used low water activity humidity conditioning materials to construct a control benchmark with different humidity conditioning intensities.

[0023] Step S2: Collect test data from all test groups daily within the preset target timeframe; First, a preset target period is set. This period can be set according to product characteristics and simulation scenarios, typically 7 to 15 days, to cover the complete temperature and humidity fluctuation cycle and ensure the sufficiency of data samples. Then, high-precision sensors are used to record the temperature and humidity changes inside and outside each group of packages in real time, ensuring the continuity and accuracy of the data and providing reliable basic data support for subsequent model fitting.

[0024] Step S3: The test data includes the internal temperature and humidity of the packaging, the external temperature and humidity of the packaging, and is converted into water vapor partial pressure; The test data collected in step S2 mainly includes the internal temperature and humidity of the packaging, as well as the external temperature and humidity. Specifically, the saturated vapor pressure at the corresponding temperature is calculated using the Tetens formula, and then the partial pressure of water vapor is calculated in combination with the relative humidity. The internal water vapor partial pressure is ein = ain·es(Tin), the external water vapor partial pressure is eout = aout·es(Tout), and the material-defined partial pressure is eset = aw·es(Tin). This conversion process transforms the nonlinear temperature and humidity data into water vapor partial pressures with linear superposition characteristics, eliminating the direct interference of temperature fluctuations on humidity readings and providing a unified physical dimension basis for the subsequent construction of a dynamic prediction model based on the law of conservation of mass.

[0025] Step S4: Obtain the predicted data by inputting the test data converted into water vapor partial pressure into the preset prediction model framework for fitting and prediction; First, a prediction model framework is constructed. The preset prediction model framework is: de_in / dt = k_e(T_bar,a_bar) · (e_out - e_in) - K_m(T_in, a_in) · (e_in - e_set); where a_in=RH_in / 100, a_out=RH_out / 100, T_bar=(T_in+T_out) / 2, a_bar=(a_in+a_out) / 2, e_in=a_in·e_s(T_in), e_out=a_out·e_s(T_out), e_set(T_in)=a_w·e_s(T_in); T_in is the internal temperature of the product packaging, T_out is the external ambient temperature of the product packaging, and e_s(T_out) is the external ambient temperature. Saturated vapor pressure at temperature T_out; e_s(T_in) is the saturated vapor pressure at internal temperature T_in; e_set(T_in) is the set partial pressure of the material at internal temperature T_in at time t; a_w is the water activity of the material; a_in is the water activity inside the product packaging; a_out is the water activity of the external environment of the product packaging; K_m is the equivalent humidity regulation coefficient; k_e is the equivalent exchange coefficient; de_in / dt is the rate of change of the partial pressure of water vapor inside the product packaging with time t; RH_in is the relative humidity inside the product packaging; RH_out is the relative humidity outside the product packaging.

[0026] The specific construction method of the dynamic prediction model is as follows: First, it is assumed that the volume V of the product packaging is fixed, and the influence of changes in the total air volume inside the product packaging on the partial pressure of water vapor is negligible; the airtightness is represented by an equivalent exchange coefficient (including the combined effects of permeation, micro-leakage, and breathing effect); the humidity-regulating material can be equivalently regarded as a "buffer" that pulls the partial pressure inside the product packaging back to its set partial pressure e_set, and the equivalent humidity-regulating intensity is represented by the coefficient K_m. In the short term (approximately one week), the performance of the product packaging and materials should not undergo irreversible degradation; if degradation occurs, a retest and correction can be performed in the second week. The selection of the humidity-regulating material should meet the following requirements: the material should be in a reversible moisture absorption and release operating range within the target shelf life, and should not approach its capacity limit within the shelf life. Furthermore, the packaging structure or sealing state of the product packaging should remain stable within the target shelf life to ensure the stability of the equivalent exchange coefficient and the humidity-regulating coefficient, thereby ensuring the accuracy of the prediction model.

[0027] In practical applications, the process of obtaining predicted data by inputting test data converted to water vapor partial pressure into a preset prediction model framework includes: rewriting de_in / dt=k_e·(e_out - e_in) - K_m·(e_in - e_set) as a linear regression. When K_m≈0, i.e., when testing group B or the material depletion phase, k_e can be directly obtained through linear regression. Based on the known k_e, K_m is calculated using the least squares method with full-cycle data, thereby completing the quantitative evaluation of the humidity-regulating material performance and the calibration of model parameters.

[0028] Step S5: Verify the feasibility of the storage and transportation plan by reviewing the predicted data and test data, and provide recommendations.

[0029] The step of verifying the predicted data against the test data includes: obtaining discrete predicted values ​​of the relative humidity inside the product packaging based on the back-calculated k_e and K_m. The step of verifying the feasibility of the storage and transportation plan after verifying the predicted data against the test data includes: comparing the obtained discrete predicted values ​​of the relative humidity inside the product packaging with the corresponding collected test data to determine whether they exceed the allowable error range.

[0030] In practical applications, the error range is within ±15%.

[0031] In practical applications, the process of verifying the feasibility of the storage and transportation plan and providing suggestions after reviewing the predicted data and test data includes: based on the preset target humidity range, determining whether the relative humidity inside the product packaging in the test data falls within the preset target humidity range and the corresponding time, thereby determining whether it meets the preset multiple failure modes. The multiple failure modes include at least any of the following: initial dryness, suspected material equivalent depletion, forced drying environment, overall dampness, and risk of exceeding the upper limit.

[0032] In practical applications, the process of verifying the feasibility of the storage and transportation plan and providing recommendations after reviewing the predicted data and test data includes generating corresponding recommendations based on the failure mode, target humidity range, product, humidity conditioning materials, and external environment.

[0033] The specific recommendation generation logic is as follows: For failure modes such as "initial dryness" and "suspected material equivalent depletion," specific adjustment suggestions are given (such as increasing the amount of moisture-regulating material, replacing with high-permeability packaging materials, and optimizing sealing) to form a complete closed-loop solution. For example, if it is determined to be "overall dampness," it is recommended to reduce the equilibrium relative humidity of the material or increase the dosage; if there is a "risk of exceeding the upper limit," priority is given to optimizing the packaging sealing or reducing the permeable area, thereby achieving precise control and risk avoidance of the storage and transportation environment. In addition, for dynamic anomalies such as "slow response" or "local condensation," it is recommended to improve the uniformity of heat and moisture transfer by increasing material dispersion, optimizing the airflow organization inside the packaging, or increasing multi-point temperature and humidity monitoring to ensure environmental stability throughout the entire cycle.

[0034] The advantages of implementing this invention are as follows: By establishing a dynamic prediction model and failure mode diagnosis mechanism, a precise quantitative assessment of the internal microenvironment of the packaging is achieved. This method can not only optimize material selection and dosage during the design phase, but also quickly identify key issues such as sealing, capacity, or moisture permeability during the verification phase, significantly reducing the risk of product loss due to environmental fluctuations. Simultaneously, it provides data support and decision-making basis for subsequent large-scale production, ensuring product quality consistency in complex logistics environments.

[0035] Example 2

[0036] like Figure 1 , Figure 2 , Figure 3 , Figure 4 and Figure 5 As shown, a test method for verifying the feasibility of a product short-term storage and transportation solution acquisition tool is provided, the test method comprising the following steps: Step S1: Set up test groups with the same packaging, wherein at least two groups are in the same storage and transportation environment and contain the same sample but with different humidity-regulating materials (test group A), and at least two groups are in the same storage and transportation environment and do not contain a sample but have different humidity-regulating materials (test group B). Specifically, this involves placing Test Group A and Test Group B in the same simulated storage and transportation environment, specifically simulating environmental temperature and humidity changes under different seasons and logistics routes to cover various climatic scenarios that the product may encounter during actual storage and transportation. In step S1, Test Group A is used to simulate the actual moisture absorption and release dynamics when containing the product, while Test Group B is used to isolate the influence of the product's own moisture exchange, thereby accurately quantifying the independent effectiveness of packaging airtightness and moisture-regulating materials.

[0037] For example, in a simulated summer high temperature and high humidity logistics scenario, test group A used high water activity humidity conditioning materials, while test group B used low water activity humidity conditioning materials to construct a control benchmark with different humidity conditioning intensities.

[0038] Step S2: Collect test data for all test groups daily within the preset target period. The total weight of the packaging can be collected in real time by a weighing sensor at the same or synchronized sampling frequency as the temperature and humidity sensor, to replace or supplement the daily weighing. First, a preset target period is set. This period can be set according to product characteristics and simulation scenarios, typically 7 to 15 days, to cover the complete temperature and humidity fluctuation cycle and ensure the sufficiency of data samples. Then, high-precision sensors are used to record the temperature and humidity changes inside and outside each group of packages in real time, ensuring the continuity and accuracy of the data and providing reliable basic data support for subsequent model fitting.

[0039] In this embodiment, the total weight of the packaging can be collected in real time by a weighing sensor at the same or synchronized sampling frequency as the temperature and humidity sensor, thus replacing or supplementing daily weighing. This high-frequency synchronous acquisition method can capture transient moisture migration processes, eliminate data deviations caused by asynchronous sampling, and thus more accurately reflect the true response characteristics of the packaging system in dynamic environments.

[0040] Step S3: The test data includes the internal temperature and humidity of the packaging, the external temperature and humidity of the packaging, and is converted into water vapor partial pressure; The test data also includes the total weight W(t) of the packaging collected by the weighing sensor, and the net weight change ΔW_net(t) obtained from the weight difference of different test groups.

[0041] The test data collected in step S2 mainly includes the internal temperature and humidity of the packaging, as well as the external temperature and humidity. Specifically, the saturated vapor pressure at the corresponding temperature is calculated using the Tetens formula, and then the partial pressure of water vapor is calculated in combination with the relative humidity. The internal water vapor partial pressure is ein = ain·es(Tin), the external water vapor partial pressure is eout = aout·es(Tout), and the material-defined partial pressure is eset = aw·es(Tin). This conversion process transforms the nonlinear temperature and humidity data into water vapor partial pressures with linear superposition characteristics, eliminating the direct interference of temperature fluctuations on humidity readings and providing a unified physical dimension basis for the subsequent construction of a dynamic prediction model based on the law of conservation of mass.

[0042] The test data also includes the total package weight W(t) collected by the weighing sensor, and the net weight change ΔW_net(t) obtained from the weight difference of different test groups.

[0043] Specifically, the real-time net water mass flux J_net(t) is calculated based on ΔW_net(t), and the time, velocity, and peak flux J_peak of water entering the packaging are identified based on J_net(t).

[0044] The cumulative moisture load is calculated based on q(t)=∫J_net(t)dt or q_abs(t)=∫max[J_net(t),0]dt, and the capacity consumption ratio of the humidity-regulating material is determined based on q(t) / C_effective or q_abs(t) / C_effective.

[0045] If the relative humidity inside the product packaging is within the target range but the capacity consumption ratio exceeds the preset threshold, the test group is deemed to have insufficient capacity margin.

[0046] By analyzing the weight differences between the sample test group, the no-sample test group, the blank control group, and the test groups with different humidity-conditioning materials, it can be determined that the moisture entering the packaging system is caused by one or more of the following: humidity-conditioning materials, product samples, packaging materials, condensation on the inner wall, or external seepage.

[0047] Step S4: Obtain the predicted data by inputting the test data converted into water vapor partial pressure into the preset prediction model framework for fitting and prediction; First, a prediction model framework is constructed. The preset prediction model framework is: de_in / dt = k_e(T_bar,a_bar) · (e_out - e_in) - K_m(T_in, a_in) · (e_in - e_set); where a_in=RH_in / 100, a_out=RH_out / 100, T_bar=(T_in+T_out) / 2, a_bar=(a_in+a_out) / 2, e_in=a_in·e_s(T_in), e_out=a_out·e_s(T_out), e_set(T_in)=a_w·e_s(T_in); T_in is the internal temperature of the product packaging, T_out is the external ambient temperature of the product packaging, and e_s(T_out) is the external ambient temperature. Saturated vapor pressure at temperature T_out; e_s(T_in) is the saturated vapor pressure at internal temperature T_in; e_set(T_in) is the set partial pressure of the material at internal temperature T_in at time t; a_w is the water activity of the material; a_in is the water activity inside the product packaging; a_out is the water activity of the external environment of the product packaging; K_m is the equivalent humidity regulation coefficient; k_e is the equivalent exchange coefficient; de_in / dt is the rate of change of the partial pressure of water vapor inside the product packaging with time t; RH_in is the relative humidity inside the product packaging; RH_out is the relative humidity outside the product packaging.

[0048] The specific construction method of the dynamic prediction model is as follows: First, it is assumed that the volume V of the product packaging is fixed, and the influence of changes in the total air volume inside the product packaging on the partial pressure of water vapor is negligible; the airtightness is represented by an equivalent exchange coefficient (including the combined effects of permeation, micro-leakage, and breathing effect); the humidity-regulating material can be equivalently regarded as a "buffer" that pulls the partial pressure inside the product packaging back to its set partial pressure e_set, and the equivalent humidity-regulating intensity is represented by the coefficient K_m. In the short term (approximately one week), the performance of the product packaging and materials should not undergo irreversible degradation; if degradation occurs, a retest and correction can be performed in the second week. The selection of the humidity-regulating material should meet the following requirements: the material should be in a reversible moisture absorption and release operating range within the target shelf life, and should not approach its capacity limit within the shelf life. Furthermore, the packaging structure or sealing state of the product packaging should remain stable within the target shelf life to ensure the stability of the equivalent exchange coefficient and the humidity-regulating coefficient, thereby ensuring the accuracy of the prediction model.

[0049] In practical applications, the process of obtaining predicted data by inputting test data converted to water vapor partial pressure into a preset prediction model framework includes: rewriting de_in / dt=k_e·(e_out - e_in) - K_m·(e_in - e_set) as a linear regression. When K_m≈0, i.e., when testing group B or the material depletion phase, k_e can be directly obtained through linear regression. Based on the known k_e, K_m is calculated using the least squares method with full-cycle data, thereby completing the quantitative evaluation of the humidity-regulating material performance and the calibration of model parameters.

[0050] Step S5: Verify the feasibility of the storage and transportation plan by reviewing the predicted data and test data, and provide recommendations.

[0051] The step of verifying the predicted data against the test data includes: obtaining discrete predicted values ​​of the relative humidity inside the product packaging based on the back-calculated k_e and K_m. The step of verifying the feasibility of the storage and transportation plan after verifying the predicted data against the test data includes: comparing the obtained discrete predicted values ​​of the relative humidity inside the product packaging with the corresponding collected test data to determine whether they exceed the allowable error range.

[0052] In practical applications, the error range is within ±15%.

[0053] In practical applications, the process of verifying the feasibility of the storage and transportation plan and providing suggestions after reviewing the predicted data and test data includes: based on the preset target humidity range, determining whether the relative humidity inside the product packaging in the test data falls within the preset target humidity range and the corresponding time, thereby determining whether it meets the preset multiple failure modes. The multiple failure modes include at least any of the following: initial dryness, suspected material equivalent depletion, forced drying environment, overall dampness, and risk of exceeding the upper limit.

[0054] In practical applications, the model-predicted moisture flux J_model(t) is compared with the J_net(t) obtained by the weighing sensor, and when the residual between the two exceeds the preset range, at least one of the following diagnostic results is output: packaging micro-leakage, weighing drift, sensor abnormality, sample moisture release or condensation abnormality.

[0055] Introducing the mass conservation equation for real-time weighing: ΔW_net(t)=[W_Test(t)-W_Test(0)]-[W_blank(t)-W_blank(0)], J_net(t)=dΔW_net(t) / dt, where W_Test(t) is the total weight of the test package containing the sample and / or conditioning material, W_blank(t) is the total weight of the blank control package, and J_net(t) is the net water mass flux after deducting blank drift.

[0056] When J_net(t) and e_out(t)-e_in(t) change synchronously in time, it can be used to enhance the identification of the equivalent exchange coefficient k_e on the packaging side; when RH_in(t) remains stable while ΔW_net(t) continues to increase, it indicates that external moisture is entering the packaging and being absorbed by the humidity conditioning material or product, and the capacity consumption rate of the humidity conditioning material can be further calculated.

[0057] For a 7-day short-term verification window, q_7d=∫_0^7d J_net(t)dt or q_abs,7d=∫_0^7d max[J_net(t),0]dt can be calculated, and q_7d / C_effective or q_abs,7d / C_effective can be used as the effective capacity consumption rate of the material.

[0058] Wherein, C_effective is the effective moisture absorption or release capacity of the humidity-regulating material within the product's allowable humidity window, rather than the material's fully saturated capacity; when the capacity consumption rate exceeds the preset threshold, even if RH_in(t) is still within the target range, it should be determined that the packaging scheme has a risk of insufficient capacity margin.

[0059] Simultaneously read the outputs of W_Test(t) and W_blank(t) from the weighing sensor, and calculate ΔW_net(t), J_net(t), q_7d, and q_7d / C_effective to determine the actual water load and capacity consumption within the target time window; Residual analysis is performed on the moisture flux obtained from the prediction model and J_net(t) obtained from real-time weighing. If the deviation between the two exceeds the preset range, it indicates that there may be sensor drift, micro-leakage in packaging, condensation on the outer or inner wall, moisture release from the sample, or weighing disturbance. When RH_in(t) is qualified but q_7d / C_effective is high, the output should suggest increasing the amount of conditioning material, increasing the effective moisture permeability area of ​​the material, reducing the water activity of the material, or improving the airtightness of the packaging.

[0060] In practical applications, the process of verifying the feasibility of the storage and transportation plan and providing recommendations after reviewing the predicted data and test data includes generating corresponding recommendations based on the failure mode, target humidity range, product, humidity conditioning materials, and external environment.

[0061] The specific recommendation generation logic is as follows: For failure modes such as "initial dryness" and "suspected material equivalent depletion," specific adjustment suggestions are given (such as increasing the amount of moisture-regulating material, replacing with high-permeability packaging materials, and optimizing sealing) to form a complete closed-loop solution. For example, if it is determined to be "overall dampness," it is recommended to reduce the equilibrium relative humidity of the material or increase the dosage; if there is a "risk of exceeding the upper limit," priority is given to optimizing the packaging sealing or reducing the permeable area, thereby achieving precise control and risk avoidance of the storage and transportation environment. In addition, for dynamic anomalies such as "slow response" or "local condensation," it is recommended to improve the uniformity of heat and moisture transfer by increasing material dispersion, optimizing the airflow organization inside the packaging, or increasing multi-point temperature and humidity monitoring to ensure environmental stability throughout the entire cycle.

[0062] The advantages of implementing this invention are as follows: By establishing a dynamic prediction model and failure mode diagnosis mechanism, a precise quantitative assessment of the internal microenvironment of the packaging is achieved. This method can not only optimize material selection and dosage during the design phase, but also quickly identify key issues such as sealing, capacity, or moisture permeability during the verification phase, significantly reducing the risk of product loss due to environmental fluctuations. Simultaneously, it provides data support and decision-making basis for subsequent large-scale production, ensuring product quality consistency in complex logistics environments.

[0063] Example 3

[0064] The test method for maintaining the product transportation environment for one week includes the following experimental procedures: S01: Prepare test group A and test group B; Test Group A: Box: 65L storage box, sealing method as described above; The contents consist of several combinations of water activity and dosage of the sample and humidification tablets, as shown in Table 1.

[0065] Test Group B: The enclosure is the same model, volume, and sealing method as Test Group A; the contents and environment are shown in Table 1 below.

[0066] Table 1:

[0067] Sensors: Two temperature and humidity loggers are placed in each box (capable of sampling every 5–10 minutes); This data was collected over a 24-hour period. The target humidity range is 50-70%.

[0068] The following data was obtained through experiments: Table 2:

[0069] The experimental data are input into the predictive model framework to back-calculate the results ke and Km.

[0070] The equivalent processing is performed using the framework's recommended approach of "ke first, then Km": The latter part of the 69% group and the 75% (testing room) group (t_mid>3 days) is regarded as the "segment where the effective Km of the material is ≈0", and the data of this segment is used to regress and obtain ke.

[0071] Given ke, the corresponding Km is calculated by using only the "effective segments" (69% and 75% (testing room) with t_mid ≤ 3 days) of each group.

[0072] 62% of the out-of-box data were missing in the 0–4 day range, so the KmA1 fit only used the continuous interval of day 5–13 (which is the main reason for the wide confidence interval of KmA1).

[0073] The estimated parameter values ​​(unit: 1 / day; 1 / h and time constant are also given) are shown in Table 3 below: Table 3:

[0074] A1: aw=0.62 (62% film, archive / no air conditioning, including customer samples) Except for the initial point (47.2%RH), the subsequent 1–13 days all fall within 50–70%RH; the maximum continuous satisfaction is 13 days.

[0075] B1: aw=0.69 (69% film, testing room / air-conditioned) can maintain 50-70%RH for 1–6 days, and drop below 50%RH from 7 days onwards; maximum continuous satisfaction for 6 days.

[0076] A2: aw=0.75 (75% film, archive / no air conditioning, including customer samples) If the humidity is consistently higher than 70%RH (70.9–73.1%RH) for 1 day or more, there is a risk of "excessive humidity / exceeding the upper limit".

[0077] B2: aw=0.75 (75% film, testing room / air-conditioned) can maintain 50-70%RH for 1–6 days, and drop below 50%RH from 7 days onwards; maximum continuous satisfaction for 6 days.

[0078] Equivalent parameter back-calculation results (unit: 1 / day): ke≈0.206 (95% CI 0.063–0.349); Km differences with material aw and environmental performance: Km62≈0.240; Km69 (valid for the first 3 days)≈0.401; Km75-archive room≈1.489; Km75-testing room (valid for the first 3 days)≈0.896.

[0079] The predicted data was verified against the test data, and the specific results are as follows: Figures 2 to 10 As shown. Figure 3 The model prediction only starts from day 5 because the external temperature and humidity of the chamber were missing for days 0–4 of this experiment.

[0080] The following results and suggestions can be obtained:

[0081] Configuration recommendations for a target humidity range of 50–70%RH (within the scope of existing data support): If the transportation / storage environment is similar to an "archive room without air conditioning" (temperature approximately 13–18°C, ambient RH approximately 35–56%), it is recommended to prioritize aw=0.62 (62% of the tablets) as the baseline configuration. To avoid initial dryness, it is recommended to pre-equilibrate for at least 12–24 hours after packing before shipping, or increase the initial RH of the packing (e.g., pre-place conditioning tablets in a sealed environment).

[0082] An aw of 0.75 (75% flakes) will push the humidity inside the container to above 70% RH in this type of environment. If the customer is sensitive to the upper limit (risk of mold / moisture absorption), it is not recommended to use this configuration directly. Consider reducing the aw (e.g., 0.62–0.69) or reducing the dosage (reducing km).

[0083] If the transportation process involves an environment with air conditioning / constantly dry conditions (the outdoor RH measured in this report is approximately 23–44%): Under existing non-airtight enclosures, both the 69% and 75% configurations can only maintain an RH level of 50–70% for about 6 days. To meet the 10–15 day window, it is recommended to prioritize optimizing the combination of two approaches: reducing ke (improving airtightness, such as adding sealing strips / sealing tape) and increasing Km (increasing the dosage / increasing the effective permeable area). This optimization should be confirmed through subsequent calibration tests.

[0084] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A test method for verifying the feasibility of a tool for obtaining short-term product storage and transportation solutions, characterized in that, The testing method includes the following steps: Set up test groups with the same packaging, among which at least two groups are test group A, which is in the same storage and transportation environment and contains the same sample but with different humidity-regulating materials, and at least two groups are test group B, which is in the same storage and transportation environment and does not contain a sample but has different humidity-regulating materials. Collect test data from all test groups daily within the preset target timeframe; The test data includes the internal temperature and humidity of the packaging, the external temperature and humidity of the packaging, and is converted into water vapor partial pressure. Predictive data is obtained by inputting the test data converted into water vapor partial pressure into a preset prediction model framework for fitting and prediction. The feasibility of the storage and transportation plan was verified by comparing the predicted data with the test data, and recommendations were given.

2. The test method for verifying the feasibility of a short-term storage and transportation solution acquisition tool according to claim 1, characterized in that, The conversion to water vapor partial pressure specifically includes: obtaining the saturated water vapor pressure using the Tetens saturated water vapor pressure formula, where the water vapor partial pressure is the ratio of humidity to saturated water vapor pressure.

3. The test method for verifying the feasibility of a short-term storage and transportation solution acquisition tool according to claim 1, characterized in that, The preset prediction model framework type is: de_in / dt=k_e(T_bar,a_bar)·(e_out-e_in)-K_m(T_in,a_in)·(e_in-e_set); where, a_in=RH_in / 100, a_out=RH_out / 100, T _bar=(T_in+T_out) / 2, a_bar=(a_in+a_out) / 2, e_in=a_in·e_s(T_in), e_out=a_out·e_s(T_out), e_set(T_in)=a_w·e_s(T_in); T_in is the product The internal temperature of the packaging is T_out, which is the external ambient temperature of the product packaging; e_s(T_out) is the saturated vapor pressure at the external temperature T_out, and e_s(T_in) is the saturated vapor pressure at the internal temperature T_in; e_set(T_in) is the set partial pressure of the material at the internal temperature T_in at time t; a_w is the water activity of the material, a_in is the water activity inside the product packaging, and a_out is the water activity of the external environment of the product packaging; K_m is the equivalent humidity regulation coefficient; k_e is the equivalent exchange coefficient; de_in / dt is the rate of change of the partial pressure of water vapor inside the product packaging with time t; RH_in is the relative humidity inside the product packaging, and RH_out is the relative humidity outside the product packaging.

4. The test method for verifying the feasibility of a product short-term storage and transportation scheme acquisition tool according to claim 3, characterized in that, The method of obtaining prediction data by inputting the test data converted into water vapor partial pressure into a preset prediction model framework includes: rewriting de_in / dt=k_e·(e_out-e_in)-K_m·(e_in-e_set) as a linear regression. When K_m≈0, that is, when testing group B or the material depletion segment, k_e can be obtained directly by linear regression.

5. The test method for verifying the feasibility of a product short-term storage and transportation scheme acquisition tool according to claim 4, characterized in that, The method of obtaining prediction data by inputting the test data converted into water vapor partial pressure into a preset prediction model framework for fitting and prediction includes: under the given k_e, only using the effective segment data of each test group to back-calculate the corresponding K_m.

6. The test method for verifying the feasibility of a short-term storage and transportation solution acquisition tool according to claim 5, characterized in that, The process of verifying the predicted data against the test data includes obtaining discrete predicted values ​​of the relative humidity inside the product packaging based on the back-calculated k_e and K_m.

7. The test method for verifying the feasibility of a product short-term storage and transportation scheme acquisition tool according to claim 6, characterized in that, The process of verifying the feasibility of the storage and transportation plan by comparing the predicted data with the test data includes: comparing the discrete predicted values ​​of the relative humidity inside the product packaging with the corresponding collected test data to determine whether they exceed the allowable error range.

8. The test method for verifying the feasibility of a short-term storage and transportation solution acquisition tool according to claim 7, characterized in that, The error range is within ±15%.

9. The test method for verifying the feasibility of a product short-term storage and transportation scheme acquisition tool according to claim 8, characterized in that, The process of verifying the feasibility of the storage and transportation plan and providing suggestions after reviewing the predicted data and test data includes: based on the preset target humidity range, determining whether the relative humidity inside the product packaging in the test data falls within the preset target humidity range and the corresponding time, thereby determining whether it meets the preset multiple failure modes. The multiple failure modes include at least any of the following: initial dryness, suspected material equivalent depletion, forced drying environment, overall dampness, and risk of exceeding the upper limit.

10. The test method for verifying the feasibility of a product short-term storage and transportation scheme acquisition tool according to claim 9, characterized in that, The process of verifying the feasibility of the storage and transportation plan and providing recommendations after reviewing the predicted data and test data includes generating corresponding recommendations based on the failure mode, target humidity range, product, humidity conditioning materials, and external environment.

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