Fruit and vegetable storage environment regulation and control method, device, equipment, medium and product

Abstract

The invention discloses a fruit and vegetable storage environment regulation and control method, device and equipment, a medium and a product. The method comprises the following steps: determining the initial quality of each fruit and vegetable individual according to a quality evaluation index, and determining the initial quality of the category according to the initial quality of all fruit and vegetable individuals in the same category; based on a pre-trained quality prediction model, according to each category, the current environmental parameters, the initial quality of the category and the expected storage period, determining the estimated quality of the category under the current environmental parameters; if the estimated quality does not meet the expectation, current environment parameters are adjusted according to the sequence of the temperature, the humidity and the oxygen volume fraction in a single-parameter limit mode till the estimated quality meets the expectation, and the current environment parameters are the optimal storage environment of the current category; and determining the optimal storage environment of the comprehensive category according to the optimal storage environment of each category, and adjusting the container environment according to the optimal storage environment of the comprehensive category. According to the embodiment of the invention, the quality of fruits and vegetables can be guaranteed in a low-energy-consumption storage environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fruit and vegetable storage, and particularly relates to a fruit and vegetable storage environment regulation method, device, equipment, medium and product. BACKGROUND

[0002] Limited by the marine environment, the fruit and vegetable storage in the marine preservation container is usually of multiple types, large quantity, long storage time and unstable storage environment, and fixed environmental parameter settings are usually adopted. On the one hand, when multiple fruits and vegetables are stored together, the optimal storage environment for each type of fruit and vegetable cannot be met, and the changes in physiological indexes of different fruits and vegetables will affect each other, resulting in a decrease in the quality of the fruits and vegetables and a serious impact on the storage period of the fruits and vegetables. On the other hand, due to the different storage time requirements of fruits and vegetables, if the optimal environmental conditions are maintained all the time, the energy consumption will be inevitably high, although the quality of the fruits and vegetables can be maintained. Therefore, the storage management of fruits and vegetables in the marine container needs to be strengthened. SUMMARY

[0003] The present application provides a fruit and vegetable storage environment regulation method, device, equipment, medium and product to solve the problem that the quality of fruits and vegetables and energy consumption cannot be considered in the storage process of fruits and vegetables in the marine container.

[0004] According to an aspect of the present application, a fruit and vegetable storage environment regulation method is provided, comprising:

[0005] determining the initial quality of each fruit and vegetable individual according to the quality evaluation index, and determining the initial quality of the same type of fruit and vegetable according to the initial quality of all fruit and vegetable individuals of the same type;

[0006] determining the estimated quality of the fruit and vegetable of each type under the current environmental parameters based on the pre-trained quality prediction model, the initial quality of the fruit and vegetable of the type and the expected storage period, wherein the environmental parameters include temperature, humidity and oxygen volume fraction;

[0007] if the estimated quality does not meet the expectation, adjusting the current environmental parameters according to the order of temperature, humidity and oxygen volume fraction, until the estimated quality meets the expectation, and the current environmental parameters are the optimal storage environment for the current type of fruit and vegetable;

[0008] determining the optimal storage environment for the comprehensive type of fruit and vegetable according to the optimal storage environment for each type of fruit and vegetable, and adjusting the container environment according to the optimal storage environment for the comprehensive type of fruit and vegetable.

[0009] According to another aspect of the present application, a fruit and vegetable storage environment regulation device is provided, comprising:

[0010] an initial quality determination module configured to determine the initial quality of each fruit and vegetable individual according to the quality evaluation index, and determine the initial quality of the same type of fruit and vegetable according to the initial quality of all fruit and vegetable individuals of the same type;

[0011] an estimated quality determination module configured to determine, based on a pre-trained quality prediction model, an estimated quality of each category under the current environmental parameters according to the category, the current environmental parameters, the initial quality of the category, and the expected storage period; the environmental parameters include temperature, humidity, and oxygen volume fraction;

[0012] a category storage environment module configured to adjust the current environmental parameters in the order of temperature, humidity, and oxygen volume fraction according to a single parameter limit if the estimated quality does not meet the expectation, until the estimated quality meets the expectation, and the current environmental parameters are the optimal storage environment for the current category;

[0013] a comprehensive storage environment module configured to determine the optimal storage environment for the comprehensive category according to the optimal storage environment for each category, and adjust the container environment according to the optimal storage environment for the comprehensive category.

[0014] According to another aspect of the present application, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the fruit and vegetable storage environment regulation method according to any one of the embodiments of the present application.

[0015] According to another aspect of the present application, there is provided an electronic device comprising:

[0016] at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the fruit and vegetable storage environment regulation method according to any one of the embodiments of the present application.

[0017] According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for causing a processor to perform the fruit and vegetable storage environment regulation method according to any one of the embodiments of the present application when executed by the processor.

[0018] According to another aspect of the present application, there is provided a computer program product comprising computer program / instructions which, when executed by a processor, implements the fruit and vegetable storage environment regulation method according to any one of the embodiments of the present application.

[0019] The embodiment of the present application determines the initial quality of each fruit and vegetable individual according to the quality evaluation index, and determines the initial quality of the same category according to the initial quality of all fruit and vegetable individuals in the category; based on the pre-trained quality prediction model, the estimated quality of the category under the current environmental parameters is determined according to each category, the current environmental parameters, the initial quality of the category and the expected storage period; if the estimated quality does not meet the expectation, the current environmental parameters are adjusted according to the order of temperature, humidity and oxygen volume fraction single parameter limit until the estimated quality meets the expectation, and the current environmental parameters are the best storage environment of the current category. The best storage environment of the comprehensive category is determined according to the best storage environment of each category, and the container environment is adjusted according to the best storage environment of the comprehensive category. Through multi-level quality evaluation from individual to category, the accuracy of the initial quality is ensured; the pre-trained quality prediction model is used to dynamically predict the quality change under different storage conditions, and scientific decision-making is realized; the single parameter sequential limit adjustment strategy is adopted to efficiently find the optimal storage scheme while ensuring the quality of fruits and vegetables, which avoids the complexity of multi-parameter adjustment and systematically explores the optimization space of each environmental parameter, avoiding excessive energy consumption. Finally, the best storage environment of the comprehensive category is determined under the condition of low energy consumption, which ensures that different categories of fruits and vegetables can achieve the expected quality, and significantly improves the storage quality of each category of fruits and vegetables and prolongs the shelf life under the condition of avoiding excessive energy consumption.

[0020] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating laborious work.

[0022] Figure 1 is a first flowchart of a fruit and vegetable storage environment regulation method provided by the embodiment of the present application;

[0023] Figure 2 is a second flowchart of a fruit and vegetable storage environment regulation method provided by the embodiment of the present application;

[0024] Figure 3 is a structural schematic diagram of a fruit and vegetable storage environment regulation device provided by the embodiment of the present application;

[0025] Figure 4 is a structural schematic diagram of an electronic device for implementing the embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to better understand the technical scheme of the present application, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts should fall within the scope of the present application.

[0027] It should be noted that the terms “first”, “second”, and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms “include” and “have” and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device including a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0028] Figure 1 is a first flowchart of a fruit and vegetable storage environment regulation method provided by an embodiment of the present application. The embodiment can be applicable to the correlation between different storage environment conditions, fruit and vegetable quality, and storage period, to achieve regulation of environmental parameters, so as to obtain a dynamic balance between fruit and vegetable quality and energy consumption. The method can be executed by a fruit and vegetable storage environment regulation device, which can be realized in the form of hardware and / or software, and can be configured in an electronic device with corresponding data processing capability. As shown in the figure, the method comprises: Figure 1

[0029] S110, determining the initial quality of each fruit and vegetable individual according to the quality evaluation index, and determining the initial quality of the same category according to the initial quality of all fruit and vegetable individuals in the same category.

[0030] Celery, Shanghai green, spinach, apples, and pears are selected for mixed storage in a controlled atmosphere refrigerated container. The container has function modules such as precise temperature control, humidification, controlled atmosphere, ethylene removal, ozone sterilization, and ethanol detection. The fruits and vegetables are freshly picked, sorted, pre-cooled, and classified into the warehouse after being packed into frames for mixed storage. The temperature of the container is adjustable in the range of -3℃ to 18℃, and the temperature control accuracy is ±0.5℃; the oxygen volume fraction is adjustable in the range of 3% to 21%, and the oxygen volume fraction control accuracy is ±0.5%. The humidity is adjustable in the range of 60% to 100%, and the humidity control accuracy is ±5%.​

[0031] To realize comprehensive evaluation of the quality of fruits and vegetables, a quality evaluation index is constructed. Optionally, the quality evaluation index includes important physiological indexes such as edible rate, vitamin C content, soluble solids, titratable acid content and sensory evaluation.

[0032] The initial quality of each fruit and vegetable individual is determined according to the quality evaluation index, and the initial quality of the category is determined according to the initial quality of all fruit and vegetable individuals in the same category. Optionally, the initial quality of all fruit and vegetable individuals in the same category is clustered, and the cluster center is taken as the initial quality of the category. The mean or median of the initial quality of all fruit and vegetable individuals in the same category can also be taken as the initial quality of the category.

[0033] S120, based on the pre-trained quality prediction model, determining the estimated quality of the category under the current environmental parameters according to each category, the current environmental parameters, the initial quality of the category and the expected storage period.

[0034] The environmental parameters include temperature, humidity and oxygen volume fraction. A total of five categories of celery, green vegetables, spinach, apples and pears are included, and the categories can be one-hot encoded to numberize the discrete features.

[0035] Each category, the current environmental parameters, the initial quality of the category and the expected storage period to be achieved are input into the pre-trained quality prediction model to obtain the estimated quality of the category under the current environmental parameters.

[0036] S130, if the estimated quality does not meet the expectation, adjusting the current environmental parameters according to the order of temperature, humidity and oxygen volume fraction, until the estimated quality meets the expectation, and the current environmental parameters are the optimal storage environment for the current category.

[0037] The temperature of the marine container can reach the range of -3℃ to 25℃; the humidity can reach the range of 60% to 100%; and the oxygen volume fraction can reach the range of 3% to 21%. According to the use requirements, if the vegetable category, the adjustable ranges of temperature, humidity and oxygen volume fraction can be set in advance: for example, the adjustable range of temperature is 1℃ to 25℃; the adjustable range of humidity is 60% to 95%; and the adjustable range of oxygen volume fraction is 3% to 21%.

[0038] When the estimated quality does not meet the expectation, first adjust the temperature step by step, if the estimated quality meets the expectation, terminate the adjustment, the current environmental parameter is the best storage environment for the current category, if the temperature is adjusted to the lowest temperature and the estimated quality still does not meet the expectation, adjust the humidity step by step, if the estimated quality meets the expectation, terminate the adjustment, the current environmental parameter is the best storage environment for the current category, if the humidity is adjusted to the highest humidity and the estimated quality still does not meet the expectation, adjust the oxygen volume fraction step by step, if the estimated quality meets the expectation, terminate the adjustment, the current environmental parameter is the best storage environment for the current category, if the oxygen volume fraction is adjusted to the lowest oxygen volume fraction and the estimated quality still does not meet the expectation, determine that the expectation cannot be met, that is, the quality of the category after the expected storage period under the current initial quality cannot meet the expectation.

[0039] Optionally, if the estimated quality does not meet the expectation, adjust the current environmental parameter according to the order of temperature, humidity and oxygen volume fraction, until the estimated quality meets the expectation, comprising: if the estimated quality does not meet the expectation, gradually reduce the temperature according to the temperature step, if the estimated quality meets the expectation, terminate the adjustment, and the current environmental parameter is the best storage environment for the current category; if the temperature is adjusted to the lowest temperature and the estimated quality still does not meet the expectation, gradually increase the humidity according to the humidity step, if the estimated quality meets the expectation, terminate the adjustment, and the current environmental parameter is the best storage environment for the current category; if the humidity is adjusted to the highest humidity and the estimated quality still does not meet the expectation, gradually reduce the oxygen volume fraction according to the oxygen step, if the estimated quality meets the expectation, terminate the adjustment, and the current environmental parameter is the best storage environment for the current category. If the oxygen volume fraction is adjusted to the lowest oxygen volume fraction and the estimated quality still does not meet the expectation, determine that the expectation cannot be met.

[0040] Limited by the marine environment, the temperature and humidity conditions of the marine fresh-keeping container are easy to adjust, and the oxygen volume fraction is relatively difficult to adjust. The priority adjustment level of the three environmental parameters is temperature, humidity and oxygen volume fraction in turn. First, start from the most relaxed condition and gradually tighten: for example, the initial temperature is 25℃, the humidity is 60%, and the oxygen volume fraction is 21%. In order to make the estimated quality meet the expectation, first choose to gradually reduce the temperature, when the temperature is gradually reduced to 1℃, if the estimated quality meets the expectation, the current environmental parameter is the best storage environment for the current category, if the expectation is not met, continue to increase the humidity, in the process of gradually increasing the humidity from 60% to 95%, if the estimated quality meets the expectation, the current environmental parameter is the best storage environment for the current category, if the expectation is still not met, continue to reduce the oxygen volume fraction, in the process of gradually reducing the oxygen volume fraction from 21% to 3%, if the expectation is met, the current environmental parameter is the best storage environment for the current category, if the expectation is still not met, determine that the expectation cannot be met.

[0041] S140, determine the optimal storage environment of the comprehensive category according to the optimal storage environment of each category, and adjust the container environment according to the optimal storage environment of the comprehensive category.

[0042] Optionally, the estimated quality of the comprehensive category under the optimal storage environment of each category is determined, and the storage environment corresponding to the highest estimated quality is selected as the optimal storage environment of the comprehensive category. Optionally, the optimal storage environment of any category with the most stringent environmental conditions is taken as the optimal storage environment of the comprehensive category. Optionally, the average of the optimal storage environments of all categories is taken as the optimal storage environment of the comprehensive category. The specific way of determining the optimal storage environment of the comprehensive category according to the optimal storage environment of each category is not limited herein.

[0043] By integrating the optimal storage environment parameters of each category, the optimal comprehensive storage condition of the container is intelligently determined, the global optimization of the environmental parameters during mixed storage of multiple categories is realized, and the best balance point is found under the premise of meeting the basic storage needs of each category; by dynamically adjusting the overall environment of the container, the quality loss rate during mixed storage of multiple categories is greatly reduced; by using a data-driven environmental control method, the blindness of traditional experience methods is avoided, and the storage efficiency is improved; while ensuring the storage quality of each category, the space utilization rate and energy use efficiency of the container are maximized, and the comprehensive operation cost is reduced

[0044] In the embodiment of the application, the initial quality of each fruit and vegetable individual is determined according to the quality evaluation index, and the initial quality of the category is determined according to the initial quality of all fruit and vegetable individuals in the same category. Based on the pre-trained quality prediction model, the estimated quality of the category under the current environmental parameters is determined according to each category, the current environmental parameters, the initial quality of the category and the expected storage period. If the estimated quality does not meet the expectation, the current environmental parameters are adjusted in the order of temperature, humidity and oxygen volume fraction single parameter limit until the estimated quality meets the expectation, and the current environmental parameters are the optimal storage environment of the current category. The optimal storage environment of the comprehensive category is determined according to the optimal storage environment of each category, and the container environment is adjusted according to the optimal storage environment of the comprehensive category. Through multi-level quality evaluation from individual to category, the accuracy of the initial quality is ensured. The pre-trained quality prediction model is used to dynamically predict the quality change under different storage conditions, and scientific decision-making is realized. The single parameter sequential limit adjustment strategy is adopted to efficiently find the optimal storage scheme while ensuring the quality of fruits and vegetables, which avoids the complexity of multi-parameter adjustment and systematically explores the optimization space of each environmental parameter, avoiding excessive energy consumption. Finally, the optimal storage environment of the comprehensive category is determined under the condition of low energy consumption, which ensures that different categories of fruits and vegetables can achieve the expected quality, and significantly improves the storage quality of each category of fruits and vegetables and prolongs the shelf life under the condition of avoiding excessive energy consumption.

[0045] Figure 2is a second flowchart of a fruit and vegetable storage environment regulation method provided by an embodiment of the present application, and the present embodiment is an optimized improvement on the basis of the above-mentioned embodiment. The quality evaluation indexes include edible rate, vitamin C content, soluble solids, titratable acid content and sensory evaluation, as shown in Figure 2 The method comprises the following steps:

[0046] In S210, for any category, the principal components of the quality evaluation indexes of the category and the eigenvalues and eigenvectors of the principal components are determined according to the quality evaluation index values of all fruit and vegetable individuals under the category.

[0047] In S220, the principal component quality values of the fruit and vegetable individuals are determined according to the eigenvectors of the principal components and the quality evaluation index values of the fruit and vegetable individuals.

[0048] In S230, the weights of the principal components are determined according to the eigenvalues of the principal components.

[0049] In S240, the quality of the fruit and vegetable individuals is determined according to the principal component weights and the principal component quality values of the fruit and vegetable individuals.

[0050] In S250, the quality of all fruit and vegetable individuals under the category is normalized to obtain the initial quality of the fruit and vegetable individuals.

[0051] The quality evaluation indexes of the fruit and vegetable include important physiological indexes such as edible rate, vitamin C content, soluble solids, titratable acid content and sensory evaluation. During the storage of the fruit and vegetable, in order to realize the comprehensive evaluation of the quality of the fruit and vegetable, based on the storage environment conditions and the storage period, the changes of each quality evaluation index of each fruit and vegetable individual are tested regularly. Each fruit and vegetable individual is divided into its corresponding category, for any category, the principal components of the quality evaluation indexes of the category and the eigenvalues and eigenvectors of the principal components are determined according to the quality evaluation index values of all fruit and vegetable individuals under the category. According to the principal components of the quality evaluation indexes of the category and the eigenvalues and eigenvectors of the principal components, and the quality evaluation index values of each fruit and vegetable individual under the category, the initial quality of each fruit and vegetable individual under the category is determined.

[0052] Before the principal components of the quality evaluation indexes of the category and the eigenvalues and eigenvectors of the principal components are determined according to the quality evaluation index values of all fruit and vegetable individuals under the category, the quality evaluation index values of all fruit and vegetable individuals under the category are standardized.

[0053] The initial quality determination process of the fruit and vegetable individuals in any category is specifically shown in the following formulas (1)-(5).

[0054] The quality evaluation index values of the fruit and vegetable individuals under the category are subjected to Z-score standardization processing.

[0055]

[0056] wherein μ is the mean of the quality evaluation index data of the fruit and vegetable individuals under the category, σ is the standard deviation; and x is the quality evaluation index value of the fruit and vegetable individual.

[0057] Based on the principal component analysis method, the principal components of the quality evaluation index of the fruit and vegetable under the category and the eigenvalues and eigenvectors of the principal components are obtained. Specifically, the principal components with a variance cumulative contribution rate greater than 85% are extracted. As shown in the following formula (2), the weight of each principal component is determined according to the eigenvalue of the principal component. As shown in the following formula (3), the principal component quality value of the fruit and vegetable individual is determined according to the eigenvector of the principal component and the quality evaluation index value of the fruit and vegetable individual under the category. As shown in the following formula (4), the quality of the fruit and vegetable individual is determined according to the principal component weight and the principal component quality value of the fruit and vegetable individual under the category. As shown in the following formula (5), the quality of all fruit and vegetable individuals under the category is normalized to obtain the initial quality of the fruit and vegetable individual.

[0058]

[0059] PC j = X std × v j (3)

[0060]

[0061] wherein w j is the weight of the jth principal component, λ j is the eigenvalue of the jth principal component, m is the number of selected principal components, X std is the quality evaluation index value of the fruit and vegetable individual under the category after standardization, PC j is the principal component quality value of the jth principal component of the fruit and vegetable individual under the category, v j is the eigenvector of the jth principal component, and Scores is the quality of the fruit and vegetable individual under the category.

[0062] To facilitate evaluation, the quality of the fruit and vegetable individual is linearly scaled to 0-100 according to formula (5). The initial quality is 100 by default, and the higher the score, the higher the freshness. A score lower than 0 indicates that the fruit and vegetable individual is inedible. The initial quality and the estimated quality are limited to the range of 0-100.

[0063] Optionally, the freshness division standard is determined according to the quality distribution of the fruit and vegetable individual, and the freshness of the fruit and vegetable is determined according to the quality of the fruit and vegetable individual and the division standard.

[0064] S260, the initial quality of the category is determined according to the initial quality of all fruit and vegetable individuals under the same category.

[0065] S270, determining the estimated quality of each category under the current environmental parameters based on the pre-trained quality prediction model, the current environmental parameters, the initial quality of the category and the expected storage period; the environmental parameters include temperature, humidity and oxygen volume fraction.

[0066] S280, if the estimated quality does not meet the expectation, adjusting the current environmental parameters according to the order of temperature, humidity and oxygen volume fraction, until the estimated quality meets the expectation and the current environmental parameters are the optimal storage environment for the current category.

[0067] S290, determining the optimal storage environment for the comprehensive category based on the optimal storage environment of each category, and adjusting the container environment based on the optimal storage environment of the comprehensive category.

[0068] The embodiment of the application effectively solves the information redundancy and collinearity problem between multiple quality indicators by using principal component analysis method to reduce the dimension and extract features of the multi-dimensional quality evaluation indicators of all fruit and vegetable individuals under the category, and extracts the most representative principal component features; the objective weight of each principal component is automatically determined by the eigenvalue, avoiding the subjectivity of artificial weighting; the individual quality value calculated by combining the principal component score and the weight can more comprehensively and scientifically reflect the real quality level of fruits and vegetables; the normalization processing ensures the comparability of quality evaluation between different categories, and provides a standardized data basis for subsequent quality prediction and storage optimization. The embodiment of the application is particularly suitable for processing complex data features of multiple indicators and high dimension in fruit and vegetable quality evaluation, and significantly improves the accuracy and reliability of quality evaluation.

[0069] Optionally, the quality prediction model adopts a neural network model with a double-hidden-layer decreasing structure. The specific configuration is: input layer: node number self-adaption (one-hot encoding of category, three continuous environmental parameters, initial quality, expected storage period), first hidden layer containing 20 neurons using Sigmoid activation function, second hidden layer containing 15 neurons using Sigmoid activation function, output layer containing 1 neuron using linear activation function. The first layer of wider network (20 nodes) captures the interaction features of environmental parameters and categories, the second layer (15 nodes) compresses and extracts key features, and the final output layer realizes regression prediction. The key training parameters mainly include: maximum training rounds are 500 times; the threshold of verification failure times is 25 times, the initial damping coefficient is 1x10 -5The μ decay rate was 0.05, and the L2 regularization coefficient was 0.1. The Mersenne Twister pseudorandom algorithm (seed value 66) was used to partition the data to ensure experimental reproducibility: a 70% training set, a 15% validation set, and a 15% test set. The model was evaluated by calculating the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Calculations were performed according to formulas (6-9).

[0070]

[0071] y i is the true value of the i-th sample (the true quality when stored to the expected storage period); y i ' is the predicted value (estimated quality) of the i-th sample; y i ” is the average value of the true value; n is the sample size of the test set.

[0072] In an optional embodiment, the method further includes: determining, based on a pre-trained quality prediction model, a curve showing the estimated quality of any category changing with storage period under specific environmental conditions; and determining, based on the change curve and quality expectations, the longest storage period of any category under specific environmental conditions.

[0073] Based on a pre-trained quality prediction model, the model inputs the fruit and vegetable category, along with the specific storage temperature, humidity, oxygen volume fraction, initial quality, and different expected storage periods. The model then generates an estimated quality curve for each category, showing how the quality changes over storage period. Based on this curve and quality expectations, the model then determines the maximum storage period for each category under these specific environmental conditions.

[0074] The pre-trained quality prediction model can accurately predict the changing trends of fruit and vegetable quality over time under different storage conditions. By establishing a quantitative relationship between multidimensional environmental parameters and quality decline, scientific predictions of storage periods can be achieved, avoiding the blindness of traditional empirical methods. The quality change curve generated by the neural network model can intuitively determine the longest storage period that meets quality requirements under specific environmental conditions, providing a decision-making basis for optimizing storage plans. It supports dynamic evaluation of different storage period targets, which can be used for both short-term preservation plan formulation and long-term storage environmental parameter optimization. It significantly reduces the cost and time of storage tests, replaces a large number of physical tests with virtual predictions, and improves the efficiency of storage plan development. It enables modern cold chain logistics and warehousing management systems to accurately control the quality and storage cycle of fruits and vegetables.

[0075] Figure 3 It is a structural schematic diagram of a fruit and vegetable storage environment control device provided by an embodiment of the present invention.

[0076] likeFigure 3 The device comprises:

[0077] An initial quality determination module 310 is configured to determine the initial quality of each fruit and vegetable individual according to the quality evaluation index, and determine the initial quality of the same category according to the initial quality of all fruit and vegetable individuals in the same category;

[0078] A predicted quality determination module 320 is configured to determine the predicted quality of each category under the current environmental parameters based on the pre-trained quality prediction model, the initial quality of the category, and the expected storage period, wherein the environmental parameters include temperature, humidity, and oxygen volume fraction.

[0079] A category storage environment module 330 is configured to adjust the current environmental parameters according to the order of temperature, humidity, and oxygen volume fraction if the predicted quality does not meet the expectation, until the predicted quality meets the expectation, and the current environmental parameters are the optimal storage environment for the current category.

[0080] A comprehensive storage environment module 340 is configured to determine the optimal storage environment of the comprehensive category according to the optimal storage environment of each category, and adjust the container environment according to the optimal storage environment of the comprehensive category.

[0081] The fruit and vegetable storage environment regulation device provided in the embodiments of the present application can execute the fruit and vegetable storage environment regulation method provided in any embodiment of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0082] Optionally, the category storage environment module is specifically configured to gradually reduce the temperature according to a temperature step if the predicted quality does not meet the expectation, and terminate the adjustment if the predicted quality meets the expectation; if the temperature is adjusted to the lowest temperature and the predicted quality still does not meet the expectation, gradually increase the humidity according to a humidity step, and terminate the adjustment if the predicted quality meets the expectation; if the humidity is adjusted to the highest humidity and the predicted quality still does not meet the expectation, gradually reduce the oxygen volume fraction according to an oxygen step, and terminate the adjustment if the predicted quality meets the expectation.

[0083] Optionally, the quality evaluation index includes the edible rate, vitamin C content, soluble solids, titratable acid content, and sensory evaluation.

[0084] Optionally, the initial quality determination module is specifically configured to: for any category, determine principal components of a quality evaluation index of the category and eigenvalues and eigenvectors of the principal components according to quality evaluation index values of all fruit and vegetable individuals under the category; determine principal component quality values of the fruit and vegetable individuals according to the eigenvectors of the principal components and the quality evaluation index values of the fruit and vegetable individuals; determine weights of the principal components according to the eigenvalues of the principal components; determine qualities of the fruit and vegetable individuals according to the weights of the principal components and the principal component quality values of the fruit and vegetable individuals; and normalize the qualities of all the fruit and vegetable individuals under the category to obtain initial qualities of the fruit and vegetable individuals.

[0085] Optionally, the device further comprises a storage period determination module specifically configured to determine a change curve of a predicted quality of any category with a storage period under specific environmental conditions according to the pre-trained quality prediction model; and determine a longest storage period of any category under specific environmental conditions according to the change curve and a quality expectation.

[0086] Optionally, the comprehensive storage environment module comprises a first comprehensive environment unit configured to determine predicted qualities of a comprehensive category under optimal storage environments of each category, and select a storage environment corresponding to the highest predicted quality as the optimal storage environment of the comprehensive category.

[0087] Optionally, the quality prediction model adopts a neural network model with a double-hidden-layer decreasing structure.

[0088] The fruit and vegetable storage environment regulation device further described can also execute the fruit and vegetable storage environment regulation method provided by any embodiment of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0089] According to the embodiments of the present application, the present application further provides an electronic device, a readable storage medium and a computer program product.

[0090] Figure 4 A structural schematic diagram of an electronic device 40 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit implementations of the present application described and / or claimed in this document.

[0091] As Figure 4As shown, the electronic device 40 includes at least one processor 41, and a memory, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., connected to the at least one processor 41 in communication. The memory stores computer programs executable by the at least one processor 41, and the processor 41 can perform various appropriate actions and processes according to the computer programs stored in the read-only memory (ROM) 42 or loaded from the storage unit 48 into the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0092] Various components in the electronic device 40 are connected to the I / O interface 45, including an input unit 46, such as a keyboard, a mouse, etc., an output unit 47, such as various types of displays, a speaker, etc., a storage unit 48, such as a magnetic disk, an optical disk, etc., and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0093] The processor 41 can be various general and / or special-purpose processing components having processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 41 performs various methods and processes described above, such as the fruit and vegetable storage environment regulation method.

[0094] In some embodiments, the fruit and vegetable storage environment regulation method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the fruit and vegetable storage environment regulation method described above can be performed. Alternatively, in other embodiments, the processor 41 can be configured to perform the fruit and vegetable storage environment regulation method by any other appropriate means, such as by means of firmware.

[0095] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a load programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0096] Computer programs used to implement the processes of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program, when executed, can cause instructions defined in the flow charts and / or block diagrams to be implemented. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a standalone software package and partially on a remote machine or entirely on a remote machine or server.

[0097] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store computer programs for use by or in connection with an instruction execution system, apparatus, or device. Computer-readable storage media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0098] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0099] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0100] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within a cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0101] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0102] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for regulating the storage environment of fruits and vegetables, characterized in that: The method comprises: Determine the initial quality of each individual fruit and vegetable based on the quality evaluation index, and determine the initial quality of the category based on the initial quality of all individual fruits and vegetables in the same category; Based on the pre-trained quality prediction model, the estimated quality of each product category under the current environmental parameters, including temperature, humidity, and oxygen volume fraction, is determined based on the product category, current environmental parameters, the product category's initial quality, and the expected storage period. If the estimated quality does not meet expectations, the current environmental parameters are adjusted according to the single parameter limits in the order of temperature, humidity, and oxygen volume fraction until the estimated quality meets expectations and the current environmental parameters are the optimal storage environment for the current category; Determine the optimal storage environment for the comprehensive category based on the optimal storage environment for each category, and adjust the container environment based on the optimal storage environment for the comprehensive category.

2. The method according to claim 1, characterized in that If the estimated quality does not meet expectations, the current environmental parameters are adjusted according to the single parameter limits in the order of temperature, humidity, and oxygen volume fraction until the estimated quality meets expectations, including: If the estimated quality does not meet expectations, the temperature is gradually lowered according to the temperature step length. If the estimated quality meets expectations, the adjustment is terminated. If the temperature is adjusted to the lowest temperature and the estimated quality still does not meet expectations, the humidity is gradually increased according to the humidity step length. If the estimated quality meets expectations, the adjustment is terminated. If the humidity is adjusted to the highest humidity and the estimated quality still does not meet expectations, the oxygen volume fraction is gradually lowered according to the oxygen step length. If the estimated quality meets expectations, the adjustment is terminated.

3. The method according to claim 1, characterized in that The quality evaluation indicators include edible rate, vitamin C content, soluble solids, titratable acid content and sensory evaluation. The initial quality of each individual fruit and vegetable is determined based on the quality evaluation indicators, including: For any category, based on the quality evaluation index values ​​of all fruits and vegetables in the category, determine the principal components of the quality evaluation index of the category and the eigenvalues ​​and eigenvectors of the principal components; Determine the principal component quality value of individual fruits and vegetables based on the eigenvector of the principal component and the quality evaluation index value of individual fruits and vegetables; Determine the weight of each principal component according to the eigenvalue of the principal component; The quality of individual fruits and vegetables is determined based on the principal component weights and the principal component quality values ​​of individual fruits and vegetables; The quality of all individual fruits and vegetables in this category is normalized to obtain the initial quality of the individual fruits and vegetables.

4. The method according to claim 1, wherein The method further comprises: Based on the pre-trained quality prediction model, determine the estimated quality curve of any category as it changes with storage period under specific environmental conditions; Based on the change curve and quality expectations, determine the maximum storage period of any category under specific environmental conditions.

5. The method according to claim 1, wherein Determining the optimal storage environment for the comprehensive category based on the optimal storage environment for each category includes: Determine the estimated quality of the comprehensive category under the optimal storage environment of each category, and select the storage environment corresponding to the highest estimated quality as the optimal storage environment for the comprehensive category.

6. The method according to claim 1, characterized in that The quality prediction model adopts a neural network model with a double hidden layer decreasing structure.

7. A device for regulating and controlling the storage environment of fruits and vegetables, characterized in that: The device comprises: The initial quality determination module is used to determine the initial quality of each individual fruit and vegetable based on the quality evaluation index, and to determine the initial quality of the category based on the initial quality of all individual fruits and vegetables in the same category; An estimated quality determination module is configured to determine the estimated quality of each product category under current environmental parameters based on a pre-trained quality prediction model, current environmental parameters, the initial quality of the product category, and the expected storage period; the environmental parameters include temperature, humidity, and oxygen volume fraction; A category storage environment module is configured to adjust the current environmental parameters according to the single parameter limits of temperature, humidity, and oxygen volume fraction in the order of temperature, humidity, and oxygen volume fraction if the estimated quality does not meet expectations, until the estimated quality meets expectations and the current environmental parameters are the optimal storage environment for the current category; The comprehensive storage environment module is used to determine the optimal storage environment of the comprehensive category based on the optimal storage environment of each category, and adjust the container environment based on the optimal storage environment of the comprehensive category.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the fruit and vegetable storage environment control method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the fruit and vegetable storage environment control method according to any one of claims 1 to 6 when executed.

10. A computer program product, comprising a computer program, wherein when executed by a processor, the computer program implements the method for regulating and controlling a fruit and vegetable storage environment according to any one of claims 1 to 6.

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