Storage temperature correction method, device and equipment and air conditioner

Abstract

The invention provides a storage temperature correction method, device and equipment and an air conditioner, and the method realizes the optimization of the storage temperature by analyzing the fresh-keeping duration and the temperature fluctuation change. According to the method, the actual temperature data and the preservation duration data of each batch of fruits and vegetables are collected to generate the corresponding temperature fluctuation data, so that the temperature control precision and stability of the storage environment are effectively reflected. And on the basis of comparative analysis of two batches of data, misjudgment caused by random fluctuation or external interference can be avoided, so that the adjustment accuracy is ensured. According to the method, through continuous batch data closed-loop feedback, dynamic optimization of the storage temperature can be achieved, the fresh-keeping time of fruits and vegetables is effectively prolonged, and the operation efficiency and stability of a fruit and vegetable storage system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fruit and vegetable storage, and in particular to a storage temperature correction method, device, equipment and air conditioner. Background Art

[0002] The purpose of fruit and vegetable storage is usually to prolong the shelf life of fruits and vegetables, maintain their nutritional value and commodity quality, and store and manage the harvested fruits and vegetables in a controlled environment. Storage scenarios include cold storage, refrigerators, freezers or freezers, which can inhibit the respiration of fruits and vegetables, reduce water loss, and delay the ripening and corruption process by adjusting the temperature, humidity, gas composition (such as oxygen and carbon dioxide concentration) and other conditions of the environment.

[0003] In the scenario of fruit and vegetable storage, there is a technical problem that the temperature setting value is not accurate enough. Summary of the invention

[0004] The purpose of the present invention is to overcome the above technical deficiencies and provide a method, device, equipment and air conditioner for correcting storage temperature to solve the technical problem of inaccurate temperature setting value in the related art.

[0005] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a method for correcting storage temperature, the method comprising:

[0007] During the storage period of the Nth batch of fruits and vegetables, the preset storage temperature is implemented and the first actual temperature data and the first fruit and vegetable preservation time data are collected; wherein N is a positive integer greater than or equal to 1;

[0008] generating first temperature fluctuation data based on the storage temperature and the first actual temperature data;

[0009] During the storage period of the fruit and vegetable batch (N+1), the same storage temperature is implemented and the second actual temperature data and the second fruit and vegetable preservation time data are collected;

[0010] generating second temperature fluctuation data based on the storage temperature and the second actual temperature data;

[0011] The storage temperature is corrected based on the first temperature fluctuation data, the first fruit and vegetable fresh-keeping time data, the second temperature fluctuation data, and the second fruit and vegetable fresh-keeping time data.

[0012] In a second aspect, the present invention provides a storage temperature correction device, the device comprising:

[0013] A first execution and collection module, which is used to execute a preset storage temperature and collect first actual temperature data and first fruit and vegetable preservation time data during the storage period of the Nth batch of fruits and vegetables; wherein N is a positive integer greater than or equal to 1;

[0014] a first generating module, configured to generate first temperature fluctuation data based on the storage temperature and the first actual temperature data;

[0015] A second execution and collection module, which is used to execute the same storage temperature and collect second actual temperature data and second fruit and vegetable preservation time data during the storage period of the N+1th batch of fruits and vegetables;

[0016] a second generating module, configured to generate second temperature fluctuation data based on the storage temperature and the second actual temperature data;

[0017] A correction module is used to correct the storage temperature based on the first temperature fluctuation data, the first fruit and vegetable preservation time data, the second temperature fluctuation data and the second fruit and vegetable preservation time data.

[0018] In a third aspect, the present invention provides an electronic device comprising: a memory, and one or more processors communicatively connected to the memory; the memory stores instructions executable by the one or more processors, and the instructions are executed by the one or more processors so that the one or more processors implement the above-mentioned method.

[0019] In a fourth aspect, the present invention provides an air conditioner, comprising a controller, wherein the controller is used to implement the above method.

[0020] Beneficial effects:

[0021] The present invention collects actual temperature data and fresh-keeping time data of multiple batches of fruits and vegetables, generates temperature fluctuation data and corrects the storage temperature, overcomes the defects of unreasonable setting values ​​and poor adaptability to external interference of the static temperature control method, and solves the technical problem that the temperature setting value is not accurate enough in the scene of fruit and vegetable storage. The method optimizes the storage temperature by analyzing the fresh-keeping time and temperature fluctuation changes. The method collects the actual temperature data and fresh-keeping time data of each batch of fruits and vegetables, generates corresponding temperature fluctuation data, and effectively reflects the temperature control accuracy and stability of the storage environment. Based on the comparative analysis of two batches of data, misjudgment caused by random fluctuations or external interference can be avoided, thereby ensuring the accuracy of adjustment. The method can realize dynamic optimization of storage temperature through continuous closed-loop feedback of batch data, effectively extend the fresh-keeping time of fruits and vegetables, and improve the operating efficiency and stability of the fruit and vegetable storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of a storage temperature correction method provided by an embodiment of the present invention;

[0023] Figure 2 is a flow chart of a storage temperature correction method provided by an embodiment of the present invention;

[0024] Figure 3 It is a schematic diagram of a temperature control system for a fruit and vegetable preservation cold storage provided by an embodiment of the present invention;

[0025] Figure 4 is a schematic diagram of collecting data provided by an embodiment of the present invention;

[0026] Figure 5 is a schematic diagram of a fruit and vegetable feature acquisition unit provided in an embodiment of the present invention;

[0027] Figure 6 It is a schematic diagram of a fruit and vegetable fresh-keeping storage temperature database provided by an embodiment of the present invention;

[0028] Figure 7 is a block diagram of a device used in an embodiment of the present invention;

[0029] Figure 8 It is a block diagram of an electronic device used in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0031] In the relevant technology, fruit and vegetable storage is an important part of agricultural product preservation, and its core goal is to extend the shelf life of fruits and vegetables, reduce the spoilage rate, and improve economic benefits through precise temperature control environment. In storage environments such as fruit and vegetable cold storage, refrigerators, and freezers, the temperature control system usually needs to set a reasonable storage temperature based on the variety and maturity characteristics of fruits and vegetables to meet the storage needs of different stages. For example, immature fruits and vegetables require higher temperatures to delay maturity, while mature or over-mature fruits and vegetables require lower temperatures to inhibit spoilage. However, in actual operation, the temperature setting value usually depends on a pre-set experience value or a static database.

[0032] The temperature control system can include the following main parts:

[0033] Sensor module: used to collect temperature data in the actual storage environment;

[0034] Controller module: Generates temperature control signals based on the set storage temperature to control the operation of the refrigeration equipment (compressor).

[0035] Execution equipment: such as cold storage refrigeration system, which achieves temperature regulation by adjusting power.

[0036] Data collection and feedback: record actual temperature and provide real-time data of system operation.

[0037] Related hormones or usually based on a fixed storage temperature gradient, set corresponding storage temperatures for fruits and vegetables at different maturity stages. When fruits and vegetables are put into storage, the system sets the storage temperature based on the type and initial maturity of the fruits and vegetables, and then maintains the set value throughout the storage cycle. In order to ensure the stability of the storage environment, the system detects the deviation between the actual temperature and the set value through sensors, and controls the compressor to adjust the power in real time to correct the deviation. This method has certain practicality in standardized operation scenarios, but it has significant limitations in complex storage scenarios.

[0038] 1. The temperature setting value is not accurate enough

[0039] The storage temperature in related technologies is usually set based on fixed empirical values ​​or static databases, which makes it difficult to adapt to the dynamic needs of fruits and vegetables in real time. Especially in the following scenarios:

[0040] In the scenario where different varieties of fruits and vegetables are stored together, the temperature requirements vary greatly, and a single setting value is difficult to meet the storage needs of multiple types of fruits and vegetables.

[0041] In scenarios where different batches of fruits and vegetables have different states, the same batch of fruits and vegetables may be mixed with immature, mature, and over-mature stages, and fixed set values ​​cannot cover the optimal storage requirements of all fruits and vegetables.

[0042] In scenarios where there is environmental interference, interference factors such as external temperature changes and equipment performance fluctuations may cause the actual storage temperature to deviate from the target value, but the existing technology lacks an effective adaptive optimization mechanism.

[0043] 2. Temperature control feedback lag

[0044] Although the relevant technology collects the deviation between the actual temperature and the set value in real time for feedback adjustment, it lacks dynamic optimization of the adjustment strategy. For example, if the set value itself is unreasonable, the feedback process of frequent adjustments will not improve the storage effect, but may increase energy consumption.

[0045] Therefore, in the scenario of fruit and vegetable storage, there is a technical problem that the temperature setting value is not accurate enough.

[0046] like Figure 1The present embodiment provides a method for correcting storage temperature. The execution subject of the method may be a controller, and may include:

[0047] Step S10: During the storage period of the Nth batch of fruits and vegetables, a preset storage temperature is implemented and first actual temperature data and first fruit and vegetable preservation time data are collected; wherein N is a positive integer greater than or equal to 1.

[0048] In this embodiment, the fruit and vegetable storage cycle can be expressed as the storage cycle of the fruit and vegetable in the cold storage, refrigerator, freezer or refrigerator. The cycle can be a complete cycle, for example, the fruit and vegetable are placed in the cold storage, refrigerator, freezer or refrigerator for storage when they are not mature, and are taken out after they are mature.

[0049] In this embodiment, the fruit and vegetable storage cycle may also be an incomplete cycle. For example, the fruits and vegetables are placed in the cold storage, refrigerator, freezer or refrigerator for storage when they are not mature, and are taken out before they are over-mature.

[0050] In this embodiment, the fruit and vegetable storage period can also be that some of the fruits and vegetables in the Nth batch are in the unripe stage and some are in the just ripe stage, and they are placed in the storage system (cold storage, refrigerator, freezer or cold storage box) for storage, and taken out before some of them reach the over-mature stage.

[0051] In this embodiment, the fruits and vegetables may be a single type of fruits and vegetables, or may be mixed types of fruits and vegetables.

[0052] In this embodiment, the storage temperature may be a single temperature value. For example, only one storage temperature is used for management during the fruit and vegetable storage cycle. In other words, the storage temperature remains constant during the fruit and vegetable storage cycle and is not adjusted according to changes in the maturity stage of the fruit and vegetable. Specifically, the controller calls a preset single storage temperature value (such as 2°C) and keeps it constant through the temperature control system of the cold storage or refrigerator.

[0053] In this embodiment, the storage temperature may also be a plurality of temperature values, such as a preset temperature gradient. That is, different storage temperatures may be set according to different maturity stages of fruits and vegetables during the storage period. In the immature stage of fruits and vegetables, a relatively high storage temperature may be set, while in the mature and over-mature stages of fruits and vegetables, a relatively low storage temperature may be set. Accordingly, the maturity stage of fruits and vegetables may be determined by collecting maturity features of fruits and vegetables (e.g., image features or odor features of fruits and vegetables, etc.).

[0054] Specifically, the storage temperature can be dynamically adjusted according to the different maturity stages within the fruit and vegetable storage cycle, and each stage corresponds to a different storage temperature. In the immature stage, a relatively high storage temperature (5°C-10°C) can be set to delay the ripening process of fruits and vegetables and inhibit the release of ethylene. In the mature stage, a lower storage temperature (1°C-5°C) can be set to maintain the best quality of fruits and vegetables and extend the shelf life. In the over-mature stage, a lower storage temperature (0°C or lower) can be set to delay corruption and deterioration and extend the storage time as much as possible.

[0055] In a possible and specific embodiment, the preset temperature gradient may be:

[0056] Immature stage: Higher temperature (5°C) is used to delay ripening.

[0057] Ripening stage: Lower temperature (1°C) to maintain optimal quality.

[0058] Over-maturity stage: lower temperature (-1°C) to delay corruption and deterioration.

[0059] In this embodiment, corresponding temperature gradients can be set for different types of fruits and vegetables. In this case, it is understandable that different fruits and vegetables can be stored separately in the cold storage, for example, there are different storage rooms, each storage room has a separate temperature control system, and the temperature gradient can be accurately controlled according to the needs of the target fruits and vegetables. In other words, the storage area in the cold storage (or refrigerator, freezer or refrigerator) can be divided according to the category of fruits and vegetables, and the target temperature of the storage room can be set according to the variety, maturity stage, and sensitivity to temperature control of each fruit and vegetable.

[0060] In a possible and specific embodiment, the cold storage may include:

[0061] Storage room 1: Apples, the temperature gradient is set from 5°C (unripe stage) to 1°C (ripe stage).

[0062] Storage room 2: Bananas, the temperature gradient is set from 8°C (unripe stage) to 2°C (ripe stage).

[0063] Storage room 3: Grapes, temperature gradient set to 0°C (entire storage period).

[0064] In this embodiment, corresponding temperature gradients can also be set based on fruits and vegetables of the same nature but different types. The fruits and vegetables of the same nature but different types can be different types of fruits and vegetables with similar storage conditions and temperature control requirements. For example, bananas, mangoes, pineapples, these tropical fruits are suitable for higher storage temperature gradients (for example, 8°C-12°C). For example, apples, pears, grapes, these temperate fruits are suitable for lower storage temperature gradients (for example, 0°C-5°C). It can be understood that fruits and vegetables of the same nature but different types can be stored in a group, which is not only convenient for unified management, but also reduces the complexity brought about by diversified temperature control requirements.

[0065] In this embodiment, the preset storage temperature can be expressed as follows: when a new batch of fruits and vegetables is placed in the storage system (e.g., a cold storage, refrigerator, freezer, or refrigerator), the controller can control the preset maturity feature acquisition device (image acquisition device, odor sensor) and fruit and vegetable category information acquisition device (image acquisition device + target detection model) to acquire the maturity features of the fruits and vegetables and determine the category information of the fruits and vegetables. Then, the maturity stage and category of the fruits and vegetables stored in the current batch can be determined.

[0066] Specifically, an image acquisition device and an odor sensor may be pre-installed in a storage system, such as a cold storage. The image acquisition device may be a high-definition camera or an industrial camera, which is used to capture image features of fruits and vegetables (such as color, shape, surface texture, etc.). The odor sensor is used to detect odor molecules (ethylene concentration) released by fruits and vegetables to determine maturity. A maturity judgment model and a fruit and vegetable category judgment model may be preset in the controller. The image acquisition device may collect image data of fruits and vegetables and input them into the maturity judgment model and the fruit and vegetable category judgment model for feature extraction respectively, and the odor sensor may input the data of the detected ethylene concentration into the maturity judgment model for judgment. The maturity judgment model may judge the maturity of fruits and vegetables based on the maturity features and ethylene concentration extracted from the image data. The fruit and vegetable category judgment model may judge the category of fruits and vegetables based on the image features extracted from the image data.

[0067] More specifically, the maturity judgment model can be a pre-trained machine learning model or a deep learning model. For example, it can be a random forest and a support vector machine, or it can be a convolutional neural network CNN and a long short-term memory network LSTM. The maturity judgment model can extract maturity features (color change, surface texture) from image data, analyze the ethylene concentration level from the odor sensor data, and combine these two types of data to judge whether the fruits and vegetables are currently in the immature stage, mature stage or over-mature stage based on the feature extraction and classification capabilities of the model. The fruit and vegetable category judgment model can be a machine learning model or a deep learning model. For example, it can be a decision tree and KNN, or it can be a YOLO target detection model and a ResNet classification model. The fruit and vegetable category judgment model can extract the image features (shape contour, color distribution) of fruits and vegetables, and judge the current type of fruits and vegetables (apples, bananas, grapes, etc.) based on the image features.

[0068] After the maturity stage and category of fruits and vegetables are determined, the preset storage temperature can be called from the database. Specifically, the database can store the storage temperatures (i.e., temperature gradient values) corresponding to different types of fruits and vegetables at different maturity stages, and the temperature gradient value of each fruit and vegetable can be stored in a table or tree structure. For example.

[0069] Table 1: Temperature gradient values ​​for fruits and vegetables

[0070]

[0071] The controller can generate a corresponding temperature control signal based on calling the preset storage temperature to control the operation of the corresponding device. The corresponding device can be a compressor. It is understandable that the compressor is a refrigeration device of the storage system, and the compressor receives the temperature control signal sent by the controller to adjust its operating state.

[0072] In this embodiment, the first actual temperature data may represent the temperature data actually collected during the fruit and vegetable storage period. It may be a time series data. In other words, the first actual temperature data may be a complete temperature time series that changes over time, recording the timestamp of each sampling point and the corresponding actual temperature value.

[0073] For example, at least one actual temperature sampling value may be sampled at different maturity stages. The actual temperature sampling value may be sampled at a preset time interval. For example, an actual temperature value is sampled every 10 minutes. The actual temperature sampling value may also be dynamically sampled based on the maturity stage. For example, the sampling frequency may be increased in the early stage of storage (the temperature control system startup stage) and the late stage of storage (the temperature may fluctuate greatly). The sampling frequency may be reduced in mid-term storage (when the temperature tends to stabilize).

[0074] The actual temperature sampling value can also be uniformly distributed sampling. During the storage period, the sampling is evenly distributed according to different maturity stages to ensure that there is enough temperature data for each stage. For example, 3 temperature values ​​are collected in the immature stage, 3 temperature values ​​are collected in the mature stage, and 3 temperature values ​​are collected in the over-mature stage.

[0075] In this embodiment, the first fruit and vegetable preservation time data can be the length of time that the fruit and vegetable are in the maturity stage. It is understandable that the maturity stage is the stage with the best quality of fruits and vegetables. This data directly reflects the impact of storage temperature on the preservation effect of fruits and vegetables, and is also an important basis for subsequent storage temperature adjustment. It is understandable that based on the maturity judgment model, the controller can monitor the changes in the maturity of fruits and vegetables in real time, and record the following time points: the time when fruits and vegetables enter the mature stage from the unripe stage and the time when fruits and vegetables enter the over-mature stage from the mature stage.

[0076] Then, the first fruit and vegetable fresh-keeping time data may be = the time for the fruit and vegetable to enter the over-mature stage from the mature stage - the time for the fruit and vegetable to enter the mature stage from the unmature stage.

[0077] In one possible and specific embodiment, the current batch of fruits and vegetables is bananas.

[0078] Bananas will enter maturity at 12:00 on November 30, 2023;

[0079] Bananas will enter the over-maturity period at 18:00 on December 2, 2023;

[0080] Then, the first fruit and vegetable freshness-keeping time data = 18:00 on December 2, 2023 - 12:00 on November 30, 2023 = 2 days and 6 hours.

[0081] It is understandable that the longer the fruit and vegetable shelf life data is, the closer the storage temperature is to the optimal requirements of the fruits and vegetables.

[0082] In some cases, when different individuals in a batch of fruits and vegetables are at different stages of maturity (for example, some fruits and vegetables have entered the mature stage, some are still in the immature stage, and some have entered the over-mature stage), the maturity stage corresponding to the most mature fruits and vegetables can be used as the benchmark. For example, if some fruits and vegetables have entered the mature stage, while some are still in the immature stage, the storage temperature of the mature stage can be used as the target.

[0083] Step S12: generating first temperature fluctuation data based on the storage temperature and the first actual temperature data.

[0084] In this embodiment, as described above, the storage temperature may be a fixed temperature value or a temperature gradient value that varies with the maturity stage of fruits and vegetables.

[0085] In this embodiment, as described above, the first actual temperature data is a complete temperature time series that changes with time, recording the timestamp of each sampling point and the corresponding actual temperature value.

[0086] In this embodiment, the first temperature fluctuation data may be represented as data for describing the deviation between the storage temperature and the actual temperature during the storage period of the Nth batch of fruits and vegetables.

[0087] In this embodiment, a difference between the storage temperature and the first actual temperature data may be calculated, and the difference may be used as the first temperature fluctuation data.

[0088] In this embodiment, when the first actual temperature data has a plurality of actual temperature sampling values, the first temperature fluctuation data may be a maximum value of a difference between the storage temperature and the first actual temperature data.

[0089] In this embodiment, when the first actual temperature data has a plurality of actual temperature sampling values, the first temperature fluctuation data may be an average value of the difference between the storage temperature and the first actual temperature data.

[0090] In this embodiment, when the first actual temperature data has a plurality of actual temperature sampling values, the first temperature fluctuation data may be an integral value of a difference between the storage temperature and the first actual temperature data.

[0091] In this embodiment, when the first actual temperature data has a plurality of actual temperature sampling values, the first temperature fluctuation data may also be the temperature fluctuation frequency, that is, the number of times the actual temperature sampling value exceeds the storage temperature.

[0092] In this embodiment, when the first actual temperature data has a plurality of actual temperature sampling values, the first temperature fluctuation data may also be the accumulated time during which the actual temperature sampling values ​​deviate from the storage temperature.

[0093] In this implementation, when the first actual temperature data has a plurality of actual temperature sampling values, the first temperature fluctuation data may also be a temperature fluctuation distribution.

[0094] In a possible and specific embodiment, the first temperature fluctuation data can be calculated by the following method:

[0095] First, calculate the deviation between the storage temperature and the actual temperature at each sampling point:

[0096] ΔT i =T actual,i -T target

[0097] In the formula, ΔT i Expressed as the temperature difference of the i-th sampling point, T actual,i It is represented as the actual temperature of the i-th sampling point, T target Indicated as storage temperature.

[0098] Then, calculate the integral of the difference:

[0099]

[0100] In the formula, I ΔT It is represented as a difference integral value, n represents the total number of sampling points, and Δt represents the time interval between adjacent sampling points (for example, it may be 10 minutes).

[0101] It can be understood that the differential integral value can reflect the temperature control effect during the entire storage period, indicating the cumulative degree of deviation of the actual temperature from the target temperature.

[0102] Step S14: During the storage period of the N+1th batch of fruits and vegetables, the same storage temperature is implemented and the second actual temperature data and the second fruit and vegetable preservation time data are collected;

[0103] In this embodiment, during the storage period of the N+1th batch of fruits and vegetables, the main purpose is to evaluate the suitability of the current storage temperature by comparing the data of the two batches (the second actual temperature data and the second fruit and vegetable preservation time data) with the data of the first batch, and then optimize the temperature control strategy.

[0104] Therefore, in this embodiment, the second actual temperature data can be represented as the temperature data actually collected during the storage period of the fruit and vegetable of the N+1th batch. It can also be a time series data. In other words, the second actual temperature data can be a complete temperature time series that changes over time, recording the timestamp of each sampling point and the corresponding actual temperature value.

[0105] In this embodiment, the second fruit and vegetable fresh-keeping time data may be the length of time that the fruits and vegetables are in the ripening period during the storage period of the N+1th batch of fruits and vegetables.

[0106] Step S16: generating second temperature fluctuation data based on the storage temperature and the second actual temperature data;

[0107] In this embodiment, the second temperature fluctuation data may be represented as data for describing the deviation between the storage temperature and the actual temperature during the storage period of the N+1th batch of fruits and vegetables.

[0108] In this embodiment, a difference between the storage temperature and the second actual temperature data may be calculated, and the difference may be used as the second temperature fluctuation data.

[0109] In this embodiment, when the second actual temperature data has a plurality of actual temperature sampling values, the second temperature fluctuation data may be a maximum value of a difference between the storage temperature and the second actual temperature data.

[0110] In this embodiment, when the second actual temperature data has a plurality of actual temperature sampling values, the second temperature fluctuation data may be an average value of a difference between the storage temperature and the second actual temperature data.

[0111] In this embodiment, when the second actual temperature data has a plurality of actual temperature sampling values, the second temperature fluctuation data may be an integral value of a difference between the storage temperature and the second actual temperature data.

[0112] In this embodiment, when the second actual temperature data has a plurality of actual temperature sampling values, the second temperature fluctuation data may also be the temperature fluctuation frequency, that is, the number of times the actual temperature sampling value exceeds the storage temperature.

[0113] In this embodiment, when the second actual temperature data has a plurality of actual temperature sampling values, the second temperature fluctuation data may also be the accumulated time during which the actual temperature sampling values ​​deviate from the storage temperature.

[0114] In this implementation, when the second actual temperature data has a plurality of actual temperature sampling values, the second temperature fluctuation data may also be a temperature fluctuation distribution.

[0115] Step S18: Based on the first temperature fluctuation data, the first fruit and vegetable fresh-keeping time data, and the second temperature fluctuation data and the second fruit and vegetable fresh-keeping time data, the storage temperature is corrected.

[0116] In this embodiment, the storage temperature may be corrected based on the first temperature fluctuation data, the first fruit and vegetable preservation time data, the second temperature fluctuation data, and the second fruit and vegetable preservation time data in the following manner:

[0117] Based on the first temperature fluctuation data, the first fruit and vegetable fresh-keeping time data, and the second temperature fluctuation data and the second fruit and vegetable fresh-keeping time data, determine whether the first condition is met; wherein the first condition is represented by the second fruit and vegetable fresh-keeping time data being greater than the first fruit and vegetable fresh-keeping time data, and the difference between the second temperature fluctuation data and the first temperature fluctuation data being greater than a preset threshold; or, based on the first temperature fluctuation data, the first fruit and vegetable fresh-keeping time data, and the second temperature fluctuation data and the second fruit and vegetable fresh-keeping time data, determine whether the second condition is met; wherein the second condition is represented by the second fruit and vegetable fresh-keeping time data being less than the first fruit and vegetable fresh-keeping time data, and the difference between the second temperature fluctuation data and the first temperature fluctuation data being greater than a preset threshold.

[0118] When the first condition is met, the preset threshold is increased based on the preset storage temperature to correct the storage temperature; when the second condition is met, the preset threshold is reduced based on the preset storage temperature to correct the storage temperature.

[0119] In this embodiment, the storage temperature can also be corrected based on the first temperature fluctuation data, the first fruit and vegetable preservation time data and the second temperature fluctuation data and the second fruit and vegetable preservation time data in the following manner: the storage temperature is dynamically adjusted by analyzing the difference ratio of the preservation time of two batches of fruits and vegetables and the difference ratio of the temperature difference integral.

[0120] First, define the freshness time ratio:

[0121]

[0122] In the formula, t n+1 It is represented by the fresh-keeping time data of the n+1th batch of fruits and vegetables (the second fresh-keeping time data of fruits and vegetables), t n It is represented as the fresh-keeping time data of the nth batch of fruits and vegetables (the first fresh-keeping time data of fruits and vegetables).

[0123] Define the temperature difference integral ratio:

[0124]

[0125] In the formula, I n+1 It is expressed as the integral value of the difference of the n+1th batch (the second temperature fluctuation data), I n It is expressed as the integrated value of the difference of the nth batch (first temperature fluctuation data).

[0126] Then, execute the judgment:

[0127] If R tn >0, and R In>Threshold, indicating that there is room for optimization of temperature control and the temperature can be increased.

[0128] If R tn <0, and R In >Threshold, indicating that the current temperature is not suitable and the temperature can be reduced.

[0129] Finally, perform the adjustment:

[0130] T new =T current +α·R n -β·R I

[0131] In the formula, T current is the current storage temperature (i.e., the storage temperature of the n+1th batch), α and β are adjustment coefficients used to control the adjustment range, T new Expressed as adjusted storage temperature.

[0132] In this embodiment, the storage temperature can also be corrected based on the first temperature fluctuation data, the first fruit and vegetable preservation time data, the second temperature fluctuation data, and the second fruit and vegetable preservation time data in the following manner: weights are assigned to the preservation time changes and the temperature difference integral changes of the two batches of data, and the temperature adjustment direction is determined according to the weighted results. Specifically, first, the preservation time change (second fruit and vegetable preservation time data-first fruit and vegetable preservation time data) is defined, and the temperature difference integral change (second temperature fluctuation data-first temperature fluctuation data) is defined. Then, weights are assigned to the preservation time change and the temperature difference integral change for weighted fusion. Finally, a judgment is performed. If the weighted fusion value is greater than the first threshold, it means that the storage effect has improved, but the temperature control still has room for optimization, and the temperature can be increased. If the weighted fusion value is less than the second threshold, it means that the storage effect has deteriorated and the temperature needs to be reduced. Correspondingly, if the weighted fusion value is greater than the second threshold and less than the first threshold, the current temperature is maintained.

[0133] This embodiment collects the actual temperature data and fresh-keeping time data of multiple batches of fruits and vegetables, generates temperature fluctuation data and corrects the storage temperature, overcomes the defects of unreasonable setting values ​​and poor adaptability to external interference in the static temperature control method, and solves the technical problem that the temperature setting value is not accurate enough in the scene of fruit and vegetable storage. The method optimizes the storage temperature by analyzing the fresh-keeping time and temperature fluctuation changes. The method collects the actual temperature data and fresh-keeping time data of each batch of fruits and vegetables, generates corresponding temperature fluctuation data, and effectively reflects the temperature control accuracy and stability of the storage environment. Based on the comparative analysis of two batches of data, misjudgment caused by random fluctuations or external interference can be avoided, thereby ensuring the accuracy of adjustment. The method can realize dynamic optimization of storage temperature through continuous closed-loop feedback of batch data, effectively extend the fresh-keeping time of fruits and vegetables, and improve the operating efficiency and stability of the fruit and vegetable storage system.

[0134] like Figure 2 As shown, in some embodiments, the step of correcting the storage temperature based on the first temperature fluctuation data, the first fruit and vegetable preservation time data and the second temperature fluctuation data and the second fruit and vegetable preservation time data includes:

[0135] Step S180: Based on the first temperature fluctuation data, the first fruit and vegetable freshness-keeping time data, and the second temperature fluctuation data and the second fruit and vegetable freshness-keeping time data, determine whether a first condition is met; wherein the first condition is represented by the second fruit and vegetable freshness-keeping time data being greater than the first fruit and vegetable freshness-keeping time data, and the difference between the second temperature fluctuation data and the first temperature fluctuation data being greater than a preset threshold.

[0136] In this embodiment, the first condition determines whether the necessity of optimizing the storage temperature is met by comparing the second fruit and vegetable preservation time data with the first fruit and vegetable preservation time data and combining the difference between the second temperature fluctuation data and the first temperature fluctuation data.

[0137] The first condition may be:

[0138] t n+1 -t n >0 and |I n+1 -I n |>M

[0139] In the formula, t n+1 It is represented by the fresh-keeping time data of the n+1th batch of fruits and vegetables (the second fresh-keeping time data of fruits and vegetables), t n It is represented as the fresh-keeping time data of the nth batch of fruits and vegetables (the first fresh-keeping time data of fruits and vegetables), I n+1 It is expressed as the integral value of the difference of the n+1th batch (the second temperature fluctuation data), I nIt is expressed as the integrated value of the difference of the nth batch (the first temperature fluctuation data), and M is a preset threshold.

[0140] The fresh-keeping time data of the n+1th batch of fruits and vegetables (the second fresh-keeping time data of fruits and vegetables) is greater than the fresh-keeping time data of the nth batch of fruits and vegetables (the first fresh-keeping time data of fruits and vegetables), indicating that the current storage temperature has a better storage effect on the second batch of fruits and vegetables, extending the maturity period or the best quality period of fruits and vegetables. The increase in fresh-keeping time means that the storage temperature is closer to the optimal requirements of fruits and vegetables.

[0141] The difference between the integral value of the difference of the n+1th batch (the second temperature fluctuation data) and the integral value of the difference of the nth batch (the first temperature fluctuation data) exceeds the preset threshold value M, indicating that the temperature control of the storage environment still has certain fluctuations, and the storage effect needs to be improved by further optimizing the storage temperature. A large integral difference of the temperature difference indicates that the current temperature control has not been completely stabilized within the target range.

[0142] Therefore, the first condition can indicate that in the process of optimizing the storage temperature, the current storage temperature has achieved good results (increased freshness preservation time), but there is still room for further optimization (temperature control fluctuation exceeds the threshold). At this time, appropriately adjusting the temperature to further meet the storage needs of fruits and vegetables may continue to extend the freshness preservation time or improve the storage effect.

[0143] Step S182: Alternatively, based on the first temperature fluctuation data, the first fruit and vegetable freshness-keeping time data and the second temperature fluctuation data and the second fruit and vegetable freshness-keeping time data, determine whether the second condition is met; wherein the second condition is represented by the second fruit and vegetable freshness-keeping time data being less than the first fruit and vegetable freshness-keeping time data, and the difference between the second temperature fluctuation data and the first temperature fluctuation data being greater than a preset threshold.

[0144] In this embodiment, the second condition determines whether the storage temperature needs to be adjusted to correct the unfavorable storage effect by comparing the second fruit and vegetable preservation time data with the first fruit and vegetable preservation time data and combining the difference between the second temperature fluctuation data and the first temperature fluctuation data.

[0145] The second condition may be:

[0146] t n+1 -t n <0 and |I n+1 -I n |>M

[0147] The fruit and vegetable preservation time data of the n+1th batch (the second fruit and vegetable preservation time data) is less than the fruit and vegetable preservation time data of the nth batch (the first fruit and vegetable preservation time data), indicating that the current storage temperature is not ideal for the fruit and vegetable storage effect, resulting in a shortened maturity period or optimal quality period of the fruit and vegetable. The difference between the integral value of the difference of the n+1th batch (the second temperature fluctuation data) and the integral value of the difference of the nth batch (the first temperature fluctuation data) exceeds the preset threshold value M, indicating that the current storage temperature control fluctuates significantly, which may further aggravate the unsatisfactory state of the fruit and vegetable storage effect.

[0148] Step S184: When the first condition or the second condition is met, the storage temperature is corrected.

[0149] In this embodiment, when the first condition is met, the preservation time increases, indicating that the storage temperature is close to the optimal requirement of fruits and vegetables, and the temperature control fluctuation data exceeds the threshold, indicating that the stability of the storage environment can still be optimized. Therefore, the storage temperature can be appropriately increased to further improve the preservation effect of fruits and vegetables, while reducing temperature control fluctuations.

[0150] In this embodiment, when the second condition is met, the preservation time is reduced, indicating that the current storage temperature deviates from the optimal storage requirement of fruits and vegetables, and the temperature control fluctuation data exceeds the threshold, indicating that the storage environment is less stable, which may aggravate the deterioration of the preservation effect. Therefore, the storage temperature can be appropriately lowered, the storage environment can be corrected, and the preservation time of fruits and vegetables can be extended.

[0151] In this embodiment, the amplitude of increasing the storage temperature or decreasing the storage temperature may be directly the preset threshold M.

[0152] In this embodiment, the range of increasing the storage temperature or decreasing the storage temperature may also be |I n+1 -I n |(difference between the second temperature fluctuation data and the first temperature fluctuation data).

[0153] In this embodiment, the range of increasing the storage temperature or decreasing the storage temperature can also be based on |I n+1 -I n | is determined dynamically. For example, n+1 -I n |Assign a magnification or reduction factor.

[0154] In this embodiment, the amplitude of increasing or decreasing the storage temperature can also be comprehensively determined based on the difference between the second fruit and vegetable preservation time data and the first fruit and vegetable preservation time data, and the difference between the second temperature fluctuation data and the first temperature fluctuation data. For example, it can be a weighted fusion of the two differences.

[0155] This embodiment corrects the storage temperature based on the comparative analysis of the first temperature fluctuation data, the first fruit and vegetable preservation time data, and the second temperature fluctuation data, the second fruit and vegetable preservation time data, and determines whether the first condition or the second condition is met, thereby achieving the effect of dynamically optimizing the storage temperature control strategy. Specifically, through the judgment logic of the two conditions, it is ensured that the temperature control effect can be further optimized when the storage temperature is appropriate (meeting the first condition), and timely correction is made when the storage temperature is not suitable (meeting the second condition), thereby avoiding the reduction of fruit and vegetable preservation effect or increase in energy consumption due to the blindness of temperature adjustment. This method uses the fruit and vegetable preservation time and temperature control fluctuation data as key indicators, achieves the accuracy and adaptability of storage temperature correction, and improves the preservation effect of fruit and vegetable storage and the stability of system operation.

[0156] In some embodiments, the step of correcting the storage temperature when the first condition or the second condition is met includes:

[0157] Step S1840: When the first condition is met, the preset threshold is increased on the basis of the preset storage temperature to correct the storage temperature.

[0158] In this embodiment, when the first condition is met, it indicates that the current storage temperature is close to the optimal storage requirement for fruits and vegetables, but by comparing the preservation time and temperature fluctuation data, it is found that there is still potential for further optimization of the storage effect. Therefore, the satisfaction of the first condition means that although the current storage temperature has a good effect, it may be slightly lower than the optimal temperature, thereby limiting the optimal response of the storage environment to the preservation of fruits and vegetables.

[0159] In this case, by directly increasing the preset threshold value based on the current preset storage temperature, the temperature of the storage environment is made closer to the requirements of fruits and vegetables.

[0160] For example, if the current storage temperature is 3°C and the preset threshold is 0.5°C, the adjusted temperature is: 3.5°C.

[0161] Directly increasing the threshold can quickly adjust the temperature without complex calculations, improving the storage temperature control system's ability to respond to dynamic demands. In this way, the shelf life of fruits and vegetables can be further extended, ensuring maximum storage effect while reducing temperature fluctuations.

[0162] Step S1842: When the second condition is satisfied, the preset threshold is reduced based on the preset storage temperature to correct the storage temperature.

[0163] In this embodiment, when the second condition is met, it indicates that the current storage temperature deviates from the optimal storage requirements of fruits and vegetables, resulting in a reduction in the preservation time of fruits and vegetables, and the temperature control fluctuates greatly, and the storage environment needs to be adjusted in time. The satisfaction of the second condition means that the current storage temperature may be too high or fails to provide a sufficiently stable refrigerated environment, so the temperature needs to be lowered to re-match the storage requirements of fruits and vegetables.

[0164] In this case, the storage temperature is lowered to improve the environmental conditions by directly reducing the preset threshold value based on the current preset storage temperature.

[0165] For example, if the current storage temperature is 3°C and the preset threshold is 0.5°C, the adjusted temperature is: 2.5°C.

[0166] This embodiment has a significant beneficial effect by directly increasing the preset threshold value on the basis of the preset storage temperature when the first condition is met, or directly reducing the preset threshold value when the second condition is met, so as to correct the storage temperature. Specifically, directly increasing or reducing the preset threshold value simplifies the adjustment strategy, does not require complex calculations, and can respond quickly after the system detects abnormalities in the preservation effect and temperature control fluctuations, thereby achieving instant optimization of the storage temperature. This not only avoids delays in the adjustment process and ensures that the storage environment can adapt to the dynamic changes in fruit and vegetable demand in a timely manner, but also avoids the problem of excessive or insufficient adjustment through fixed threshold adjustment, thereby improving the accuracy of temperature control adjustment and the stability of system operation. In addition, this correction method is reasonably designed, easy to achieve the coordination of hardware and algorithms, and is suitable for a variety of storage conditions and types of fruits and vegetables, effectively improving the intelligence level and practicality of the storage system.

[0167] In some embodiments, the preset storage temperature is expressed as a preset storage temperature gradient, and the preset storage temperature gradient includes a plurality of storage temperature values, and the plurality of storage temperature values ​​correspond to different maturity stages within a fruit and vegetable storage cycle;

[0168] The step of implementing a preset storage temperature during the storage period of the Nth batch of fruits and vegetables includes:

[0169] Step S100: collecting the maturity characteristics of the fruits and vegetables and determining the category information of the fruits and vegetables;

[0170] In this embodiment, the controller can control the preset image acquisition device and the odor sensor to collect the maturity characteristics of fruits and vegetables. Specifically, the controller can issue instructions to the image acquisition device and the odor sensor so that the two devices collect image data and odor data of fruits and vegetables. The controller is preset with a maturity feature extraction model or a maturity judgment model directly (it can be understood that the maturity feature extraction model can extract maturity features based on image data and odor data, and the maturity judgment model can not only extract maturity features based on image data and odor data, but also can further judge the maturity stage of fruits and vegetables based on the maturity features extracted by itself). The maturity judgment model can extract the maturity characteristics of fruits and vegetables based on image data, or can extract the maturity characteristics of fruits and vegetables based on odor data. The maturity judgment model can be an algorithm based on machine learning or deep learning, and the input data is analyzed by the trained model to extract the maturity characteristics of fruits and vegetables. For example, the maturity characteristics are extracted by color changes (such as changes from green to yellow). The maturity is further judged by shape changes (such as skin wrinkling). The maturity judgment model can judge whether fruits and vegetables have entered the maturity period or over-maturity period by detecting volatile gas characteristics such as ethylene concentration.

[0171] The controller can control the preset fruit and vegetable category information collection device to collect the category information of the fruits and vegetables. The fruit and vegetable category information collection device can also be the image collection device, which collects image data of fruits and vegetables. The controller is preset with a fruit and vegetable category judgment model, which can judge the category information of fruits and vegetables based on the image data of fruits and vegetables. For example, it can judge what category the fruits and vegetables are.

[0172] The fruit and vegetable category judgment model can be based on deep learning training. After inputting image data, the fruit and vegetable categories can be classified through feature extraction (color histogram, shape contour analysis).

[0173] Step S102: determining the target maturity stage of the fruits and vegetables based on the maturity characteristics and the category information.

[0174] In this embodiment, the target maturity stage of the fruits and vegetables can be determined by the maturity judgment model.

[0175] In this embodiment, only one maturity judgment model can be configured, and the maturity judgment model is trained by "category information + maturity features + labels (maturity stage)". In other words, the maturity judgment model can identify the maturity stages of multiple fruits and vegetables. Therefore, the maturity judgment model can comprehensively judge the target maturity stage of the fruits and vegetables based on the category information output by the fruit and vegetable category judgment model and the maturity features extracted by itself.

[0176] In this embodiment, only a plurality of maturity judgment models may be configured, and each maturity judgment model can identify the maturity stage of a fruit or vegetable. Specifically, the controller may first match a preset maturity judgment model based on the category information. Then, the matched maturity judgment model is used to receive the maturity features extracted in the previous stage (extracted by the maturity feature extraction model) for judgment, thereby obtaining the target maturity stage of the fruit or vegetable.

[0177] Step S104: Based on the target maturity stage, determine a target storage temperature value from the preset storage temperature gradient.

[0178] In this embodiment, the storage temperature gradient can be a set of storage temperature values ​​preset according to different maturity stages of fruits and vegetables, ensuring that fruits and vegetables can obtain the best storage effect at different stages. The controller first matches the corresponding storage temperature gradient value according to the target maturity stage (such as the immature stage). Each stage can correspond to one or more storage temperature values.

[0179] Step S106: Execute the target storage temperature value.

[0180] The controller first converts the target storage temperature value into a temperature control signal (such as a set temperature value), which is used to instruct the temperature control device (such as a compressor) to adjust the storage temperature. The controller then sends the generated temperature control signal to the temperature control device in the storage system, such as a compressor or a refrigeration system. The temperature control device makes adjustments based on the received target storage temperature value. For example, the compressor increases the operating power to reduce the temperature.

[0181] This embodiment realizes the effect of dynamically adjusting the storage temperature according to the different maturity stages of fruits and vegetables by executing the preset storage temperature gradient within the storage cycle of the Nth batch of fruits and vegetables. Specifically, the system determines the current target maturity stage (immature stage, mature stage or over-mature stage) of fruits and vegetables by collecting the maturity characteristics (color, shape, smell, etc.) of fruits and vegetables and combining the category information of fruits and vegetables. Based on the target maturity stage, the target storage temperature value corresponding to the stage is accurately extracted from the preset storage temperature gradient, and the temperature value is executed to optimize the storage environment. The advantage of this embodiment is that by associating the maturity characteristics of fruits and vegetables with the storage temperature gradient, the storage temperature can accurately match the real-time needs of fruits and vegetables, avoiding the decrease in preservation effect or increase in energy consumption due to improper temperature. At the same time, this method can adapt to the storage needs of various fruits and vegetables, improve the intelligence level and storage effect of the storage system, and extend the optimal quality period of fruits and vegetables.

[0182] In some embodiments, the step of collecting the maturity characteristics of the fruits and vegetables and determining the category information of the fruits and vegetables includes:

[0183] The image data of the fruits and vegetables are obtained.

[0184] In this embodiment, image data of fruits and vegetables can be obtained through a high-definition camera or an industrial camera, and the appearance characteristics of fruits and vegetables (such as color, shape, surface texture, etc.) can be quickly captured.

[0185] Based on the image data, the maturity characteristics of the fruits and vegetables are extracted and based on the image data, the category information of the fruits and vegetables is determined.

[0186] In this embodiment, a maturity feature extraction model or a maturity judgment model is preset in the controller (it can be understood that the maturity feature extraction model can extract maturity features based on image data and odor data, and the maturity judgment model can not only extract maturity features based on image data and odor data, but also can further judge the maturity stage of fruits and vegetables based on the maturity features extracted by itself).

[0187] In this embodiment, a fruit and vegetable category determination model is preset in the controller, and the fruit and vegetable category determination model can determine the category information of the fruit and vegetables based on the image data.

[0188] In this embodiment, the maturity feature extraction model can be an algorithm model specifically used to extract features related to maturity from input data of fruits and vegetables. The maturity feature extraction model can process multimodal data, such as image data and odor data, and extract key features of the maturity of fruits and vegetables, such as color change, surface texture, shape, and ethylene concentration and volatile gas components in the odor.

[0189] In this embodiment, the maturity judgment model is a more comprehensive model that can not only extract maturity features, but also directly judge the maturity stage of fruits and vegetables (such as immature stage, mature stage or over-mature stage) based on the extracted features.

[0190] In this embodiment, the fruit and vegetable category judgment model is a classification model specifically used to identify specific categories of fruits and vegetables (apples, bananas, strawberries).

[0191] In this embodiment, the maturity feature extraction model may be a support vector machine (SVM), a random forest or a principal component analysis (PCA) model, etc. The maturity feature extraction model may also be a convolutional neural network (CNN) and an autoencoder.

[0192] In this embodiment, the maturity judgment model can be a decision tree model, a convolutional neural network (CNN) + fully connected layer model, or a long short-term memory network (LSTM) + CNN model, etc.

[0193] In this embodiment, the fruit and vegetable category judgment model can be a support vector machine (SVM), a convolutional neural network (CNN), or a YOLO model, etc.

[0194] This embodiment collects image data of fruits and vegetables, extracts maturity characteristics of fruits and vegetables and determines their category information, thereby realizing intelligent management and precise regulation during the storage of fruits and vegetables.

[0195] In some embodiments, the step of determining the target maturity stage of the fruit and vegetable based on the maturity characteristics and the category information includes:

[0196] Based on the maturity characteristics and the category information, the maturity stage of each of the fruits and vegetables is determined.

[0197] When there are multiple different maturity stages in the fruits and vegetables, the maturity stage corresponding to the fruits and vegetables with the highest maturity is taken as the target maturity stage.

[0198] In this embodiment, when the judgment result shows that there are multiple different maturity stages of fruits and vegetables in the storage environment, the controller can select a unified target maturity stage. The order of maturity from low to high can be defined in the controller: immature stage < mature stage < over-mature stage. Compare the maturity stages of all fruits and vegetables, and select the maturity stage corresponding to the fruits and vegetables with the highest maturity as the target maturity stage. For example, if some fruits and vegetables are in the immature stage and some are in the mature stage, the target stage is "mature stage". If some fruits and vegetables are in the mature stage and some are over-mature stage, the target stage is "over-mature stage".

[0199] This embodiment determines the maturity stage of each fruit and vegetable based on maturity characteristics and category information, and when there are multiple different maturity stages of fruits and vegetables, the maturity stage corresponding to the fruit and vegetable with the highest maturity is used as the target maturity stage, which has significant beneficial effects. Specifically, this method can accurately identify the maturity stages of different fruits and vegetables in storage scenarios where the types and maturity states of fruits and vegetables are mixed, and by using the fruits and vegetables with the highest maturity as a reference, ensure that the storage temperature setting can give priority to meeting the storage needs of the most sensitive fruits and vegetables, and avoid over-ripening or corruption of sensitive fruits and vegetables due to improper temperature control. In addition, this method can take into account the overall efficiency of the storage system, ensuring that most fruits and vegetables are under suitable storage conditions, thereby maximizing the overall preservation effect.

[0200] In some embodiments, the preset storage temperature is expressed as a preset storage temperature gradient, the preset storage temperature gradient includes a plurality of storage temperature values, and the plurality of storage temperature values ​​correspond to different maturity stages in the fruit and vegetable storage cycle; the first actual temperature data includes a plurality of actual temperature sampling values, and each maturity stage in the fruit and vegetable storage cycle has at least one actual temperature sampling value; the step of generating the first temperature fluctuation data based on the storage temperature and the first actual temperature data includes:

[0201] Step S120: Calculating a temperature difference integral value based on a plurality of actual temperature sampling values ​​and a plurality of corresponding storage temperature values ​​to generate first temperature fluctuation data.

[0202] In this embodiment, it is understandable that during the storage period of fruits and vegetables, different maturity stages require different storage temperatures (i.e., storage temperature gradients), such as higher temperatures for immature stages, medium temperatures for mature stages, and lower temperatures for over-mature stages. However, during the actual storage process, the actual temperature may deviate from the preset storage temperature due to external interference or equipment adjustment performance. In order to quantify the temperature control effect of the storage environment, this step generates first temperature fluctuation data reflecting the overall temperature control fluctuation by calculating the integral value of the difference between the actual temperature and the preset storage temperature.

[0203] In this embodiment, it can be understood that the multiple actual temperature sampling values ​​can be actual temperature values ​​collected at a certain time point in the storage cycle (collected once every 5 minutes). The multiple actual temperature sampling values ​​can form a time series. Accordingly, according to different stages of the fruit and vegetable storage cycle, a set of storage temperature values ​​are pre-set in the controller. Therefore, the actual temperature sampling values ​​can be assigned to the storage temperature values ​​of the corresponding stage.

[0204] For example, the target temperature in the immature stage is 8°C, matching the actual temperature sampling values ​​[t1, t2, ..., tk] in this stage. The target temperature in the mature stage is 4°C, matching the actual temperature sampling values ​​[tk+1, tk+2, ..., tm] in this stage.

[0205] For each time point, the controller can calculate the difference between the actual temperature and the target storage temperature:

[0206] ΔT i =T actual,i -T set,i

[0207] In the formula, ΔT i Expressed as the temperature difference at the i-th time point, T actual,i It is represented as the actual temperature sampling value at the i-th time point, T set,i Represented as the storage temperature at the i-th time point.

[0208] For each storage stage (or the entire storage period), calculate the absolute value integral of all temperature differences:

[0209]

[0210] In the formula, t n represents the end time point, and t0 represents the initial time point.

[0211] In the case of discrete sampling:

[0212]

[0213] In the formula, |ΔT i | represents the absolute value of the temperature difference at the i-th time point, and Δt represents the sampling time interval (such as 5 minutes).

[0214] The controller uses the temperature difference integral value I as the first temperature fluctuation data of the current storage cycle. The first temperature fluctuation data can reflect the overall deviation between the storage temperature and the target temperature. The smaller the value, the better the temperature control effect.

[0215] This embodiment has a significant beneficial effect by comparing multiple actual temperature sampling values ​​with multiple storage temperature values ​​in a preset storage temperature gradient during the fruit and vegetable storage cycle, and calculating the temperature difference integral value based on this to generate the first temperature fluctuation data. Specifically, through the staged design of the storage temperature gradient (such as different storage temperature values ​​corresponding to the immature stage, mature stage and over-mature stage), the storage requirements of fruits and vegetables at different maturity stages can be accurately matched; at the same time, by collecting multiple actual temperature sampling values ​​in each stage and quantifying the deviation between the actual temperature and the ideal temperature in an integral manner, the temperature control fluctuations in the entire storage cycle can be fully reflected. This method can dynamically evaluate the applicability of the storage temperature and provide high-quality data support for subsequent temperature control optimization. In addition, the use of the temperature difference integral value avoids the limitations of single-point sampling data, reflects the temperature control effect more objectively and comprehensively, and helps to reduce the impact of temperature control deviation on the preservation effect of fruits and vegetables, thereby improving the intelligence level of the storage system, extending the preservation time of fruits and vegetables, and reducing energy consumption and loss during storage.

[0216] like Figure 3 As shown, in a possible and specific embodiment, a temperature control system for a fruit and vegetable preservation cold storage is provided, comprising:

[0217] A cold storage temperature gradient reset unit is a control device with computing storage and communication functions; a cold storage device has a fruit and vegetable refrigeration system with real-time adjustable set temperature; a fruit and vegetable maturity acquisition unit is an image acquisition and processing system with communication feedback information function; a cold storage temperature acquisition unit can be various sensors and devices for acquiring temperature.

[0218] The cold storage temperature gradient reset unit acts as a host and is connected to the temperature controller, maturity acquisition unit, and temperature acquisition unit of the cold storage equipment. The maturity acquisition unit feeds back the maturity characteristic values ​​of fruits and vegetables into the controller through timed sampling. The time interval can be 5 to 10 minutes. The temperature unit feeds back the actual refrigeration temperature value of fruits and vegetables in the cold storage. The cold storage equipment receives the data from the reset unit to adjust the temperature of the cold storage in real time. The system is a closed-loop feedback system based on freshness control and self-optimization.

[0219] The data obtained by the control unit is as follows: Figure 4 shown.

[0220] Maturity acquisition unit Figure 5 As shown, the present invention adopts a method and device for obtaining the maturity of fruits and vegetables based on images, which collects images of fruits and vegetables through a high-resolution camera, extracts color feature values ​​and shape line features, and automatically evaluates the maturity and variety of fruits and vegetables in combination with a preset maturity model, and transmits the evaluated information to the warehouse temperature reset unit through a communication peripheral.

[0221] When fruits and vegetables enter the cold storage, when some fruits and vegetables enter the mature stage from the unripe stage, the storage temperature is adjusted to the corresponding maturity range. Since the temperature control of the mature range is more stringent than that of the immature range, and the mature range is a sub-range of the immature range, the fruits and vegetables in the immature period can also obtain a better preservation effect. When some fruits and vegetables enter the over-ripe stage from the mature stage, low temperature control of the over-ripe stage of fruits and vegetables is adopted to prevent the corruption and deterioration of fruits and vegetables as much as possible.

[0222] In one possible and specific implementation, a method for controlling the temperature of a fruit and vegetable cold storage is provided, which specifically includes:

[0223] Step 1: Establish a relationship model between the output data of the maturity acquisition unit and the maturity of fruits and vegetables. The unit is used to obtain the maturity characteristic information of the stored fruits and vegetables, and determine the maturity of the fruits and vegetables. The maturity is unripe, mature, and overripe.

[0224] Step 2: According to the fruit and vegetable category information, retrieve the initial set temperature gradient ck (k is a positive integer, k=1) of the cold storage corresponding to the fruits and vegetables.

[0225] Step 3: Set the cold storage temperature gradient to ck, regularly sample the maturity characteristic information of fruits and vegetables during the entire cold storage period, and the actual temperature sequence dk in the cold storage, obtain the temperature difference integral value Ik and the preservation time value tk during the maturity period of k batches of fruits and vegetables, and feed the parameters back to the temperature gradient reset unit. The temperature difference integral is Ik=dk-ck.

[0226] Step 4: Let ck+1=ck, keep the set temperature the same, and obtain the temperature difference integral value I k+1 and the preservation time value tk+1 of the k+1 batch of fruits and vegetables during the ripening period.

[0227] Step 5: Compare the fresh-keeping time values ​​of the k and k+1 batches of fruits and vegetables. If the fresh-keeping time value of the K+1 batch increases, that is, tk+1-tk>0, and the absolute value of the integral value of the temperature difference during this time period is greater than the threshold, that is, |I k+1-I k|>M, then the set temperature of the cold storage is increased by the threshold. If the fresh-keeping time of the K+1 batch decreases, that is, tk+1-tk<0, and the absolute value of the integral value of the temperature difference is greater than the threshold, that is, |I k+1-I k|>M, then the set temperature of the cold storage is subtracted from the threshold to obtain the optimized set temperature gradient ck+2 of the cold storage during the fruit and vegetable maturity period (the set temperature value of the k batch of fruits and vegetables).

[0228] Step 6: Let k=k+2 and repeat steps 3 to 5.

[0229] In one possible and specific implementation, a control method for optimizing the temperature of fruit and vegetable storage is provided, which specifically includes:

[0230] From the upper limit x1 of the mature period warehouse temperature, different warehouse temperature values ​​are selected in descending order according to the fixed value n to know the lower limit x2. Different warehouse temperatures are used to control the mature period of (x1-x2+1) batches of fruits and vegetables respectively. The maturity information of fruits and vegetables is sampled, and the shelf life of all batches during the mature period is compared. The warehouse temperature with the longest shelf life is set as the optimal temperature, and the warehouse temperature database is corrected.

[0231] The actual temperature sequence dk of the cold storage is a discrete temperature sequence value sampled regularly during the entire cold storage period of fruits and vegetables.

[0232] The fresh-keeping time value tk is the time value consumed during the entire ripening period of k batches of fruits and vegetables during the cold storage period.

[0233] An implementation example of the fruit and vegetable database is as follows: Figure 6 As shown, the initial storage temperature is set to the average of the upper and lower limits.

[0234] According to an embodiment of the present invention, a storage temperature correction device 100 is provided. Figure 7 , the device comprises:

[0235] The first execution and collection module 101 is used to execute the preset storage temperature and collect the first actual temperature data and the first fruit and vegetable preservation time data during the storage period of the Nth batch of fruits and vegetables; wherein N is a positive integer greater than or equal to 1;

[0236] A first generating module 102, configured to generate first temperature fluctuation data based on the storage temperature and the first actual temperature data;

[0237] The second execution and collection module 103 is used to execute the same storage temperature and collect the second actual temperature data and the second fruit and vegetable preservation time data during the storage period of the N+1th batch of fruits and vegetables;

[0238] A second generating module 104, configured to generate second temperature fluctuation data based on the storage temperature and the second actual temperature data;

[0239] The correction module 105 is used to correct the storage temperature based on the first temperature fluctuation data, the first fruit and vegetable preservation time data, and the second temperature fluctuation data and the second fruit and vegetable preservation time data.

[0240] According to an embodiment of the present invention, an electronic device is provided. Figure 8 The electronic device in this embodiment may include one or more of the following components: a processor, a network interface, a memory, a non-volatile memory, and one or more applications, wherein the one or more applications may be stored in the non-volatile memory and configured to be executed by one or more processors, and the one or more programs are configured to execute the method described in the aforementioned method embodiment.

[0241] According to an embodiment of the present invention, an air conditioner is provided. The air conditioner includes a controller. The controller is used to implement the above method.

[0242] According to an embodiment of the present invention, a computer program product including instructions is also provided. When the instructions are executed by a computer, the computer executes a method in any one of the above embodiments.

[0243] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, 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 "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0244] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment will not be described in detail here.

[0245] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0246] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0247] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for correcting storage temperature, characterized in that: The method comprises: During the storage period of the Nth batch of fruits and vegetables, the preset storage temperature is implemented and the first actual temperature data and the first fruit and vegetable preservation time data are collected; wherein N is a positive integer greater than or equal to 1; generating first temperature fluctuation data based on the storage temperature and the first actual temperature data; During the storage period of the fruit and vegetable batch (N+1), the same storage temperature is implemented and the second actual temperature data and the second fruit and vegetable preservation time data are collected; generating second temperature fluctuation data based on the storage temperature and the second actual temperature data; The storage temperature is corrected based on the first temperature fluctuation data, the first fruit and vegetable fresh-keeping time data, the second temperature fluctuation data, and the second fruit and vegetable fresh-keeping time data.

2. The method according to claim 1, characterized in that The step of correcting the storage temperature based on the first temperature fluctuation data, the first fruit and vegetable preservation time data, and the second temperature fluctuation data and the second fruit and vegetable preservation time data comprises: Based on the first temperature fluctuation data, the first fruit and vegetable fresh-keeping time data, and the second temperature fluctuation data and the second fruit and vegetable fresh-keeping time data, determine whether a first condition is met; wherein the first condition is represented by the second fruit and vegetable fresh-keeping time data being greater than the first fruit and vegetable fresh-keeping time data, and the difference between the second temperature fluctuation data and the first temperature fluctuation data being greater than a preset threshold; or, Based on the first temperature fluctuation data, the first fruit and vegetable fresh-keeping time data, and the second temperature fluctuation data and the second fruit and vegetable fresh-keeping time data, it is judged whether a second condition is met; wherein the second condition is represented by the second fruit and vegetable fresh-keeping time data being less than the first fruit and vegetable fresh-keeping time data, and the difference between the second temperature fluctuation data and the first temperature fluctuation data being greater than a preset threshold; When the first condition or the second condition is satisfied, the storage temperature is corrected.

3. The method according to claim 2, characterized in that The step of correcting the storage temperature when the first condition or the second condition is met includes: When the first condition is met, the preset threshold is increased based on the preset storage temperature to correct the storage temperature; When the second condition is met, the preset threshold is reduced based on the preset storage temperature to correct the storage temperature.

4. The method according to claim 1, characterized in that: The preset storage temperature is expressed as a preset storage temperature gradient, and the preset storage temperature gradient includes a plurality of storage temperature values, and the plurality of storage temperature values ​​correspond to different maturity stages within the fruit and vegetable storage cycle; The step of implementing a preset storage temperature during the storage period of the Nth batch of fruits and vegetables includes: Collecting maturity characteristics of the fruits and vegetables and determining category information of the fruits and vegetables; Based on the maturity characteristics and the category information, determining the target maturity stage of the fruits and vegetables; Based on the target maturity stage, determining a target storage temperature value from the preset storage temperature gradient; Implement the target storage temperature value.

5. The method according to claim 4, characterized in that The step of collecting the maturity characteristics of the fruits and vegetables and determining the category information of the fruits and vegetables comprises: Acquiring image data of the fruits and vegetables; Based on the image data, the maturity characteristics of the fruits and vegetables are extracted and based on the image data, the category information of the fruits and vegetables is determined.

6. The method according to claim 5, characterized in that The step of determining the target maturity stage of the fruits and vegetables based on the maturity characteristics and the category information comprises: Determining the maturity stage of each of the fruits and vegetables based on the maturity characteristics and the category information; When there are multiple different maturity stages in the fruits and vegetables, the maturity stage corresponding to the fruits and vegetables with the highest maturity is taken as the target maturity stage.

7. The method according to claim 1, characterized in that The preset storage temperature is expressed as a preset storage temperature gradient, and the preset storage temperature gradient includes a plurality of storage temperature values, and the plurality of storage temperature values ​​correspond to different maturity stages in the fruit and vegetable storage cycle; the first actual temperature data includes a plurality of actual temperature sampling values, and each maturity stage in the fruit and vegetable storage cycle has at least one actual temperature sampling value; The step of generating first temperature fluctuation data based on the storage temperature and the first actual temperature data comprises: Based on a plurality of actual temperature sampling values ​​and a plurality of corresponding storage temperature values, a temperature difference integral value is calculated to generate first temperature fluctuation data.

8. A storage temperature correction device, characterized in that: The device comprises: A first execution and collection module, which is used to execute a preset storage temperature and collect first actual temperature data and first fruit and vegetable preservation time data during the storage period of the Nth batch of fruits and vegetables; wherein N is a positive integer greater than or equal to 1; a first generating module, configured to generate first temperature fluctuation data based on the storage temperature and the first actual temperature data; A second execution and collection module, which is used to execute the same storage temperature and collect second actual temperature data and second fruit and vegetable preservation time data during the storage period of the N+1th batch of fruits and vegetables; a second generating module, configured to generate second temperature fluctuation data based on the storage temperature and the second actual temperature data; A correction module is used to correct the storage temperature based on the first temperature fluctuation data, the first fruit and vegetable preservation time data, the second temperature fluctuation data and the second fruit and vegetable preservation time data.

9. An electronic device, characterized in that: include: a memory, and one or more processors communicatively coupled to the memory; The memory stores instructions that can be executed by the one or more processors. The instructions are executed by the one or more processors to enable the one or more processors to implement the method according to any one of claims 1 to 7.

10. An air conditioner, characterized in that: The air conditioner comprises a controller for implementing the method according to any one of claims 1 to 7.

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