Fresh agricultural product shelf life quality control method, system, medium and equipment

Abstract

A method, system, medium and device for controlling the shelf life quality of fresh agricultural products, related to the field of intelligent agricultural technology. The method comprises: acquiring the current physiological parameters of the agricultural products in each area on the shelf, and determining the shelf life of the agricultural products in each area according to the current physiological parameters; dividing the shelf into a first area and a second area based on the shelf life; warning the first area according to the shelf life of the first agricultural products in each first area; determining the target environmental parameters of the corresponding second area according to the type and current physiological parameters of the second agricultural products in each second area; adjusting the intelligent environmental control system of each second area according to the target environmental parameters, which at least includes a temperature control subsystem, a humidity control subsystem, a light control subsystem, a ventilation control subsystem and a disinfection control subsystem. The technical solution provided by the present application can improve the control quality of the shelf life of fresh agricultural products.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent agricultural technology, in particular to a method and system for quality control of fresh agricultural products during shelf life, a medium and equipment. BACKGROUND

[0002] With increasing emphasis on food freshness and safety, the shelf life quality management of fresh agricultural products has become crucial. Due to the highly perishable and sensitive nature of fresh agricultural products, the management of their shelf life is of great significance to maintaining product freshness, reducing losses and improving economic efficiency.

[0003] Currently, the shelf life management of fresh agricultural products mainly relies on basic environmental control techniques, such as temperature and humidity adjustment, as well as regular manual inspection and evaluation. These methods can to some extent extend the shelf life of agricultural products and reduce losses due to quality deterioration. However, different types of agricultural products have different sensitivities to environmental factors, and traditional management methods often use uniform environmental settings, resulting in poor quality control of fresh agricultural products during shelf life, which affects the freshness of agricultural products. SUMMARY

[0004] The present application provides a method and system for quality control of fresh agricultural products during shelf life, which can improve the quality of control of fresh agricultural products during shelf life.

[0005] In a first aspect, the present application provides a method for quality control of fresh agricultural products during shelf life, the method comprising:

[0006] obtaining current physiological parameters of agricultural products in each region on the shelf, and determining the shelf life of agricultural products in each region according to the current physiological parameters;

[0007] dividing the shelf into first and second regions based on the shelf life;

[0008] warning the first region according to the shelf life of the first agricultural products in each first region;

[0009] determining target environmental parameters for the corresponding second region according to the type and current physiological parameters of the second agricultural products in each second region;

[0010] adjusting the intelligent environmental control system of each second region according to the target environmental parameters, the intelligent environmental control system comprising at least a temperature control subsystem, a humidity control subsystem, a light control subsystem, a ventilation control subsystem and a disinfection control subsystem.

[0011] By adopting the technical scheme, the current physiological parameters of the agricultural products are acquired and analyzed in real time, the shelf life states of the agricultural products in different regions are accurately evaluated, the shelf is divided into a first region and a second region, and the system realizes classified management of the agricultural products in different shelf life periods. For the agricultural products in the first region with a short shelf life, early warning information is timely sent, and the loss risk caused by the overage of the shelf life is effectively reduced. For the agricultural products in the second region, the optimal target environment parameters are accurately calculated according to the type characteristics and the current physiological state of the agricultural products, and then the intelligent environment control system integrated with multiple subsystems such as temperature, humidity, illumination, ventilation and disinfection is controlled and regulated according to the target environment parameters, the management and control quality of the shelf life of the fresh agricultural products is effectively improved, and the preservation effect of the fresh agricultural products is significantly improved.

[0012] Optionally, the current physiological parameters of the agricultural products in each region on the shelf are acquired, and the shelf life of the agricultural products in each region is determined according to the current physiological parameters, including:

[0013] The current physiological parameters of the agricultural products in each region on the shelf are acquired, and the current physiological parameters include the ethylene release amount, the carbon dioxide release amount and the water content of the agricultural products.

[0014] The reference shelf life is determined according to the ethylene release amount.

[0015] The decay coefficient is calculated according to the carbon dioxide release amount.

[0016] The correction coefficient is determined according to the water content.

[0017] The shelf life is calculated based on the reference shelf life, the decay coefficient and the correction coefficient.

[0018] By adopting the technical scheme, the current physiological parameters of the agricultural products such as the ethylene release amount, the carbon dioxide release amount and the water content are acquired, the reference shelf life is determined according to the ethylene release amount, the decay coefficient is calculated according to the carbon dioxide release amount, and the correction coefficient is determined in combination with the water content. Finally, the shelf life is accurately evaluated based on the comprehensive calculation of the reference shelf life, the decay coefficient and the correction coefficient, the problem of insufficient accuracy caused by the evaluation of a single parameter in the traditional method is overcome, and the accuracy and reliability of the shelf life prediction of the fresh agricultural products are significantly improved.

[0019] Optionally, the shelf life is calculated based on the reference shelf life, the decay coefficient and the correction coefficient, including:

[0020] The product of the reference shelf life and the decay coefficient is taken as a first calculation result.

[0021] The product of the first calculation result and the correction coefficient is taken as a second calculation result.

[0022] determining the time threshold as the shelf life when the second calculation result is greater than the time threshold corresponding to the agricultural product;

[0023] determining the second calculation result as the shelf life when the second calculation result is less than or equal to the time threshold corresponding to the agricultural product.

[0024] By adopting the above technical solutions, the first calculation result is obtained by multiplying the reference shelf life and the decay coefficient, the second calculation result is obtained by multiplying the first calculation result and the correction coefficient, and the second calculation result is compared with the time threshold corresponding to the agricultural product, the time threshold is used as the shelf life when the second calculation result is greater than the time threshold, and the second calculation result is used as the shelf life when the second calculation result is less than or equal to the time threshold, thereby realizing differentiated calculation and threshold constraint of the shelf life of different agricultural products, avoiding the problem that the calculation result of the shelf life may exceed the actual shelf life of the agricultural product in the traditional method, and improving the accuracy and practicability of the prediction result of the shelf life.

[0025] Optionally, the shelf is divided into a first region and a second region based on the shelf life, including:

[0026] obtaining a standard shelf life of the agricultural product in each region;

[0027] calculating a ratio of the shelf life of the agricultural product in each region to the standard shelf life;

[0028] dividing a region in the shelf, in which the ratio is less than a ratio threshold, as the first region;

[0029] dividing a region in the shelf, in which the ratio is greater than or equal to the ratio threshold, as the second region.

[0030] By adopting the above technical solutions, the standard shelf life of the agricultural product in each region is obtained, the ratio of the current shelf life to the standard shelf life is calculated, and the ratio threshold is used as a judgment standard, the region in which the ratio is less than the ratio threshold is divided as the first region, and the region in which the ratio is greater than or equal to the ratio threshold is divided as the second region, thereby realizing intelligent zoning management based on the shelf life of the agricultural product, and improving the scientificity and rationality of the division of the shelf region.

[0031] Optionally, the first region is warned according to the shelf life of the first agricultural product in each first region, including:

[0032] obtaining historical sales data of the first agricultural product in each first region, and calculating an average sales cycle of each first agricultural product;

[0033] triggering a first warning mode when the shelf life of the first agricultural product is less than a preset proportion of the corresponding average sales cycle;

[0034] determining whether the current inventory quantity of the first agricultural product is greater than or equal to the target inventory quantity when the shelf life of the first agricultural product is greater than or equal to a preset proportion of the corresponding average sales cycle;

[0035] triggering a second early warning mode when the current inventory quantity of the first agricultural product is greater than or equal to the target inventory quantity;

[0036] triggering a third early warning mode when the current inventory quantity of the first agricultural product is less than the target inventory quantity.

[0037] By adopting the above technical solution, the average sales cycle is calculated based on the historical sales data of the first agricultural product, and the preset proportion of the average sales cycle is compared with the shelf life of the first agricultural product. When the shelf life is less than the preset proportion, the first early warning mode is triggered. When the shelf life is greater than or equal to the preset proportion, the relationship between the current inventory quantity and the target inventory quantity is further determined. When the current inventory quantity is greater than or equal to the target inventory quantity, the second early warning mode is triggered. When the current inventory quantity is less than the target inventory quantity, the third early warning mode is triggered. A multi-level early warning mechanism based on the shelf life and the inventory quantity is realized. The problem of single early warning mechanism and lack of pertinence in the traditional method is overcome. The accuracy and timeliness of the early warning are improved. The loss risk of fresh agricultural products is effectively reduced.

[0038] Optionally, the target environment parameter of the corresponding second area is determined according to the type of the second agricultural product in each second area and the current physiological parameter, comprising:

[0039] obtaining the historical environment parameter of the second agricultural product in each second area and the quality change rate of the second agricultural product under the historical environment parameter;

[0040] determining the adjustment direction of the environment parameter and the adjustment step of the corresponding environment parameter based on the quality change rate;

[0041] updating the current environment parameter according to the adjustment direction and the adjustment step to obtain the target environment parameter.

[0042] By adopting the above technical solution, the quality change rate of the second agricultural product under the historical environment parameter is obtained. The adjustment direction of the environment parameter and the adjustment step are determined based on the quality change rate. The current environment parameter is updated to obtain the target environment parameter based on the historical data. The adaptive optimization of the environment parameter is realized. The accuracy and scientificity of the adjustment of the environment parameter are improved. The shelf life of the agricultural product in the second area is effectively prolonged.

[0043] Optionally, the method further comprises:

[0044] detecting the uniformity of the environment parameter of each area on the shelf;

[0045] determine an abnormal area of an environment parameter abnormality based on the environment parameter uniformity;

[0046] analyze a control subsystem affected by the abnormal area;

[0047] calculate a compensation adjustment amount of the environment parameter corresponding to the abnormal area;

[0048] perform local regulation on the abnormal area based on the compensation adjustment amount.

[0049] By adopting the above technical solution, by detecting the environment parameter uniformity of each area, determining the abnormal area based on the environment parameter uniformity, analyzing the control subsystem affected by the abnormal area, calculating the compensation adjustment amount of the environment parameter corresponding to the abnormal area, and then performing local regulation on the abnormal area based on the compensation adjustment amount, precise monitoring and compensation adjustment of the shelf storage environment are realized, thereby improving the uniformity and stability of the storage environment and ensuring the reliability of the fresh agricultural product storage conditions.

[0050] In a second aspect of the present application, a fresh agricultural product shelf life quality control system is provided, which comprises:

[0051] a shelf life determination module configured to acquire current physiological parameters of agricultural products in each area on a shelf and determine shelf life of the agricultural products in each area according to the current physiological parameters;

[0052] a region division module configured to divide the shelf into a first region and a second region based on the shelf life;

[0053] a warning module configured to warn the first region according to the shelf life of first agricultural products in each of the first regions;

[0054] an environment parameter determination module configured to determine target environment parameters of each of the second regions according to the types and current physiological parameters of second agricultural products in each of the second regions;

[0055] a control module configured to adjust intelligent environment control systems of each of the second regions according to the target environment parameters, wherein the intelligent environment control systems at least include a temperature control subsystem, a humidity control subsystem, an illumination control subsystem, a ventilation control subsystem, and a sterilization control subsystem.

[0056] In a third aspect of the present application, a computer storage medium is provided, which stores a plurality of instructions suitable for being loaded and executed by a processor to perform the above method steps.

[0057] In a fourth aspect of the present application, an electronic device is provided, which comprises a processor and a memory; wherein the memory stores a computer program suitable for being loaded and executed by the processor to perform the above method steps.

[0058] To sum up, the one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0059] By acquiring and analyzing the current physiological parameters of the agricultural products in real time, the embodiments of the present application can accurately evaluate the shelf life state of the agricultural products in different areas, divide the shelf into a first area and a second area, and realize the classified management of the agricultural products with different shelf life. For the agricultural products with short shelf life in the first area, early warning information is timely sent, and the loss risk caused by the overage of the shelf life is effectively reduced. For the agricultural products in the second area, the optimal target environment parameters are accurately calculated according to the type characteristics and the current physiological state of the agricultural products, and then the intelligent environment control system integrated with multiple subsystems such as temperature, humidity, illumination, ventilation and disinfection is controlled according to the target environment parameters, so as to effectively improve the management and control quality of the shelf life of the fresh agricultural products, thereby significantly improving the preservation effect of the fresh agricultural products. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 is a flowchart of a fresh agricultural product shelf life quality management and control method provided by the embodiments of the present application;

[0061] Figure 2 is a module schematic diagram of a fresh agricultural product shelf life quality management and control system provided by the embodiments of the present application;

[0062] Figure 3 is a structural schematic diagram of an electronic device provided by the embodiments of the present application.

[0063] Explanation of reference signs: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0064] In order for those skilled in the art to better understand the technical solutions in the specification, the technical solutions in the embodiments of the specification will be clearly and completely described below in conjunction with the drawings in the embodiments of the specification. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments.

[0065] In the description of the embodiments of the present application, the words such as "for example" or "for instance" are used to represent an example, illustration or description. Any embodiment or design scheme described as "for example" or "for instance" in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the words such as "for example" or "for instance" are intended to present the relevant concept in a specific manner.

[0066] In the description of the embodiments of the present application, the term "a plurality of" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. In addition, the terms "first", "second", "third", etc. are used only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the technical features indicated. Therefore, the features defined with "first", "second", etc. can be explicitly or implicitly included one or more of the features. The terms "include", "contain", "have" and their variants mean "include but not limited to", unless otherwise specifically emphasized.

[0067] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments.

[0068] Please refer to Figure 1 , a flowchart of a method for controlling the shelf life quality of fresh agricultural products is proposed. The method can be implemented by relying on a computer program, can be implemented by relying on a single-chip microcomputer, and can also run on a shelf life quality control system for fresh agricultural products. The computer program can be integrated in a computer device or run as an independent tool application. Specifically, the method includes steps 10 to 40, which are as follows:

[0069] Step 10: Obtain the current physiological parameters of the agricultural products in each area on the shelf, and determine the shelf life of the agricultural products in each area according to the current physiological parameters.

[0070] The current physiological parameters in the embodiments of the present application refer to key indicators reflecting the current freshness and physiological state of the agricultural products, such as but not limited to ethylene release, carbon dioxide release, and water content, etc.

[0071] The shelf life in the embodiments of the present application refers to the longest time that the agricultural products can maintain their commodity value under certain storage conditions.

[0072] Specifically, in order to accurately evaluate the freshness of the agricultural products and predict their saleable time, the current physiological parameters of the agricultural products in each area on the shelf need to be obtained. In this embodiment, a plurality of sensor arrays are distributed in each area of the shelf to collect the physiological parameters of the agricultural products such as ethylene release, carbon dioxide release, and water content in real time. Among them, the ethylene sensor is used to detect the ethylene gas concentration released by the agricultural products, the carbon dioxide sensor is used to monitor the respiration intensity, and the water sensor is used to measure the water content. These sensors transmit the collected data to the control system for processing and analysis through the data acquisition module.

[0073] After obtaining these physiological parameters, first, the initial reference shelf life is determined based on the ethylene release amount querying the preset reference shelf life database. Because the ethylene release amount of different agricultural products at different maturity stages has a strong corresponding relationship with the freshness degree, it can be used as an important basis for determining the reference shelf life. Then, the system calculates the decay coefficient according to the carbon dioxide release amount, which reflects the respiration intensity of agricultural products. The stronger the respiration, the faster the metabolism, and the faster the corresponding decay rate. At the same time, the system will also determine the correction coefficient according to the water content, because the change of water content will directly affect the taste and appearance quality of agricultural products. After obtaining the reference shelf life, decay coefficient and correction coefficient, the final shelf life can be calculated. Through this multi-dimensional physiological parameter-based shelf life calculation method, the actual shelf life of agricultural products can be more accurately predicted, and the prediction deviation caused by traditional single parameter can be avoided.

[0074] Based on the above embodiment, as an optional embodiment, the step of obtaining the current physiological parameters of the agricultural products in each area on the shelf and determining the shelf life of the agricultural products in each area according to the current physiological parameters can further include the following steps:

[0075] Step 101: Obtain the current physiological parameters of the agricultural products in each area on the shelf, including the ethylene release amount, carbon dioxide release amount and water content of the agricultural products.

[0076] Specifically, ethylene gas sensors, carbon dioxide gas sensors and moisture sensors are arranged in each area of the shelf. These sensors are arranged in an array to ensure representative sampling. The ethylene gas sensor uses a metal oxide semiconductor sensor to monitor the ethylene release amount of agricultural products by detecting the concentration change of ethylene gas in the environment. Ethylene, as an important plant hormone, its release amount can reflect the maturity and aging degree of agricultural products. The carbon dioxide gas sensor uses an infrared absorption type sensor to determine the respiration intensity of agricultural products by measuring the carbon dioxide concentration. The respiration intensity directly reflects the metabolic state of agricultural products. The moisture sensor uses a capacitive sensor to evaluate the water content by measuring the relative humidity of the surface and surrounding environment of agricultural products. The change of water content will affect the texture and appearance quality of agricultural products.

[0077] Among them, these sensors collect data in real time through a data acquisition module. The sampling frequency can be adjusted according to the characteristics of different agricultural products, and is generally set to sample once an hour. After signal conditioning and analog-to-digital conversion, the collected data is transmitted to the control system for processing. The control system first filters and calibrates the original data to eliminate the influence of environmental noise and sensor drift, and then converts the processed data into standardized physiological parameter values according to the preset algorithm.

[0078] Step 102: Determine the reference shelf life according to the ethylene release amount.

[0079] Specifically, in determining the reference shelf life, firstly, the current detected ethylene release amount is obtained, and a pre-established ethylene release amount and shelf life corresponding relationship database is queried. The database stores the standard shelf life data corresponding to different types of agricultural products under different ethylene release amount levels, which are obtained through a large number of experiments. Through interpolation calculation, the initial reference shelf life is determined according to the current detected ethylene release amount. Since ethylene is a key hormone in the ripening and aging process of agricultural products, its release amount is closely related to the physiological maturity of agricultural products, so it has strong scientific basis to use it as the basis for determining the reference shelf life.

[0080] For example, when the ethylene release amount of apples is 0.1 μL / kg·h, the corresponding standard shelf life is 15 days, and when the ethylene release amount is 0.2 μL / kg·h, the corresponding standard shelf life is 12 days. Using linear interpolation, the initial reference shelf life is calculated according to the current detected ethylene release amount. The specific calculation formula is: reference shelf life = T1 + (T2-T1) × (C-C1) / (C2-C1), where T1 and T2 are the two standard shelf lives in the database closest to the current ethylene release amount, C is the current detected ethylene release amount, and C1 and C2 are the standard ethylene release amounts corresponding to T1 and T2, respectively.

[0081] Step 103: Calculate the decay coefficient according to the carbon dioxide release amount.

[0082] Specifically, in calculating the decay coefficient, the carbon dioxide release amount data of the agricultural products is collected and compared with the pre-set reference carbon dioxide release amount. Through the pre-set mathematical model, the ratio of the current carbon dioxide release amount to the reference value is substituted into the decay coefficient calculation formula to obtain the decay coefficient reflecting the current metabolic state of the agricultural products. When the carbon dioxide release amount is higher than the reference value, it indicates that the metabolism of the agricultural products is accelerated, and the decay coefficient is correspondingly reduced; on the contrary, when the carbon dioxide release amount is close to or lower than the reference value, the decay coefficient is correspondingly increased. This calculation method accurately reflects the influence of the physiological activity of the agricultural products on the shelf life.

[0083] For example, the calculation formula of the decay coefficient is: K = e^(-α×(R-R0) / R0), where K is the decay coefficient, R is the current carbon dioxide release amount, R0 is the reference carbon dioxide release amount, and α is the attenuation constant, which has a value range of 0.1-0.5 and can be adjusted according to different types of agricultural products. For example, when the reference carbon dioxide release amount of a certain fruit is 20 mg / kg·h and the current detection value is 30 mg / kg·h, and α = 0.2 is taken, the decay coefficient K = e^(-0.2×(30-20) / 20) ≈ 0.82 is obtained by substituting the formula, indicating that the shelf life is shortened due to the increase of respiration intensity.

[0084] Step 104: determining the correction coefficient according to the water content.

[0085] Specifically, in the determination of the correction coefficient, the system compares the detected water content with the standard water content range of the agricultural product. When the water content is in the optimal range, the correction coefficient is 1; when the water content deviates from the optimal range, the system calculates the corresponding correction coefficient through the preset correction model. Both excessive or insufficient water content will result in a correction coefficient less than 1, thereby reducing the final shelf life prediction value. This correction mechanism takes into account the characteristics of the quality of agricultural products changing with water content, making the shelf life prediction more in line with the actual situation.

[0086] Illustratively, the calculation of the correction coefficient uses a piecewise function model: when the water content W is in the optimal range [W1, W2], the correction coefficient M = 1; when the water content is lower than W1, M = (W / W1)^β; when the water content is higher than W2, M = (W2 / W)^β, where β is a weight coefficient, with a value range of 0.5-2, which can be adjusted according to the water sensitivity of agricultural products. For example, the optimal water content range of a certain vegetable is 85%-90%, the current water content is 80%, and when β = 1, the correction coefficient M = (80 / 85)^1 ≈ 0.94, indicating that the shelf life needs to be appropriately shortened due to the low water content.

[0087] Step 105: calculating the shelf life based on the reference shelf life, the decay coefficient, and the correction coefficient.

[0088] Specifically, the product of the reference shelf life and the decay coefficient is taken as the first calculation result, and the product of the first calculation result and the correction coefficient is taken as the second calculation result. Considering that different agricultural products have their inherent maximum shelf life limits, the system pre-sets time thresholds corresponding to various agricultural products. When the second calculation result is greater than the time threshold corresponding to the agricultural product, the time threshold is determined as the shelf life; when the second calculation result is less than or equal to the time threshold corresponding to the agricultural product, the second calculation result is determined as the shelf life.

[0089] Based on the above embodiment, as an optional embodiment, the step of calculating the shelf life based on the reference shelf life, the decay coefficient, and the correction coefficient can further include the following steps:

[0090] Step 1051: taking the product of the reference shelf life and the decay coefficient as the first calculation result.

[0091] Specifically, the product of the reference shelf life and the decay coefficient is taken as the first calculation result, such as when the reference shelf life is 15 days and the decay coefficient is 0.82, the first calculation result is 15*0.82=12.3 days. This step of calculation reflects the influence of the current metabolic state of the agricultural product on the basic shelf life. When the amount of carbon dioxide released increases, causing the decay coefficient to decrease, the first calculation result will also decrease accordingly, reflecting the negative impact of increased respiration intensity on the shelf life.

[0092] Step 1052: Take the product of the first calculation result and the correction coefficient as the second calculation result.

[0093] Specifically, the product of the first calculation result and the correction coefficient is taken as the second calculation result, such as when the first calculation result is 12.3 days and the correction coefficient is 0.94, the second calculation result is 12.3*0.94=11.56 days. This step of calculation further considers the correction effect of moisture content change on the shelf life. When the moisture content deviates from the optimal range, the correction coefficient less than 1 will cause the second calculation result to decrease, reflecting the impact of abnormal water state on the shelf life.

[0094] Step 1053: When the second calculation result is greater than the time threshold value corresponding to the agricultural product, determine the time threshold value as the shelf life.

[0095] Specifically, first, the time threshold value corresponding to the current agricultural product is retrieved from the pre-set agricultural product information database. This time threshold value is determined based on a large amount of experimental data and practical experience, reflecting the longest shelf life of a specific agricultural product under optimal conditions. For example, the time threshold value of strawberries is set to 7 days, the time threshold value of apples is set to 15 days, and the time threshold value of tomatoes is set to 10 days. The system compares the obtained time threshold value with the second calculation result, and determines the final shelf life according to the comparison result.

[0096] When the second calculation result is greater than the time threshold value corresponding to the agricultural product, it means that the theoretically calculated shelf life exceeds the physiological limit of the agricultural product. At this time, the system will directly determine the time threshold value as the final shelf life. For example, the second calculation result of a batch of apples is 17 days, which is greater than its corresponding time threshold value of 15 days, so the system determines the final shelf life as 15 days. This processing method avoids the prediction of an excessively long shelf life that does not conform to reality, ensuring the reliability of the prediction result.

[0097] Step 1054: When the second calculation result is less than or equal to the time threshold value corresponding to the agricultural product, determine the second calculation result as the shelf life.

[0098] Specifically, when the second calculation result is less than or equal to the time threshold corresponding to the agricultural product, it indicates that the calculated shelf life is within a reasonable range, and the system directly determines the second calculation result as the final shelf life. For example, the second calculation result of a batch of tomatoes is 8 days, which is less than the corresponding time threshold of 10 days, and the system determines the final shelf life as 8 days. In this case, the second calculation result is retained, reflecting the influence of the current physiological state of the agricultural product on its actual shelf life.

[0099] Step 20: dividing the shelf into a first area and a second area based on the shelf life.

[0100] Specifically, the system can set a preset time threshold, which is determined based on the general shelf life characteristics and actual sales cycle of each agricultural product, for example, it can be set to 5 days. The system compares the calculated shelf life with the corresponding time threshold, and accordingly divides the shelf space into a first area and a second area. When the shelf life of an agricultural product is less than the time threshold, the storage location is divided into the first area. For example, the calculated shelf life of a batch of strawberries is 4 days, which is less than the set first time threshold of 5 days, so the storage area is divided into the first area. When the shelf life of an agricultural product is greater than or equal to the first time threshold, the storage location is divided into the second area. For example, the calculated shelf life of a batch of apples is 12 days, which is greater than the set first time threshold of 5 days, so the storage area is divided into the second area.

[0101] Based on the above embodiment, as an optional embodiment, the step of dividing the shelf into a first area and a second area based on the shelf life can further include the following steps:

[0102] Step 201: obtaining the standard shelf life of agricultural products in each area.

[0103] Specifically, the standard shelf life corresponding to all agricultural products in each area is obtained from the preset agricultural product information database. These standard shelf lives are obtained based on a large amount of historical data statistics, reflecting the typical shelf life of a specific type of agricultural product under normal storage conditions. For example, the standard shelf life of a batch of strawberries in the first area is 5 days, and the standard shelf life of a batch of apples in the second area is 15 days.

[0104] Step 202: calculating the ratio of the shelf life of agricultural products in each area to the standard shelf life.

[0105] Specifically, the actual shelf life of each region of agricultural products is divided by the corresponding standard shelf life to obtain a shelf life ratio. For example, when the actual shelf life of a batch of strawberries in the first region is 4 days and the standard shelf life is 5 days, the calculated ratio is 4 / 5 = 0.8; when the actual shelf life of a batch of apples in the second region is 12 days and the standard shelf life is 15 days, the calculated ratio is 12 / 15 = 0.8. This ratio directly reflects the degree of change in the current quality retention capability of agricultural products relative to the normal level, and the lower the ratio, the more significantly the actual shelf life of the agricultural products is lower than the standard level.

[0106] Step 203: dividing the regions in the shelf with a ratio less than the ratio threshold value into the first region.

[0107] Step 204: dividing the regions in the shelf with a ratio greater than or equal to the ratio threshold value into the second region.

[0108] Specifically, a preset ratio threshold value is first set, which is determined based on the quality management experience of agricultural products and actual operation needs, for example, it can be set to 0.85. The system compares the calculated shelf life ratio with the ratio threshold value, and dynamically divides the shelf space accordingly. When the shelf life ratio of agricultural products in a region is less than the ratio threshold value, the region is divided into the first region. For example, the shelf life ratio of a batch of agricultural products is 0.8, which is less than the set ratio threshold value 0.85, so the region is divided into the first region. When the shelf life ratio of agricultural products in a region is greater than or equal to the ratio threshold value, the region is divided into the second region.

[0109] Step 30: warning the first region according to the shelf life of the first agricultural products in each first region.

[0110] Specifically, the actual shelf life data of each batch of first agricultural products in the first region is first obtained, and the corresponding warning time nodes are set. These warning nodes are usually set based on the remaining time of the shelf life, for example, 50%, 75% and 90% of the shelf life can be set as different levels of warning time points. The system monitors the shelf life change of the first agricultural products in the first region in real time, and when the storage time of a batch of agricultural products reaches the preset warning time point, the corresponding level of warning signal is triggered. For example, the shelf life of a batch of first agricultural products is 4 days, when the storage time reaches 2 days (50%), a primary warning is triggered, when it reaches 3 days (75%), a middle-level warning is triggered, and when it reaches 3.6 days (90%), a high-level warning is triggered.

[0111] The triggering of the early warning signal adopts a multi-level early warning mechanism, and different levels of early warning correspond to different processing measures. When a primary early warning is triggered, the system sends an early warning reminder to the manager through the monitoring terminal, suggesting that the batch of agricultural products be given special attention. When a secondary early warning is triggered, the system automatically generates a processing suggestion, prompting the manager to consider adjusting the storage conditions or developing a promotion plan. When a high-level early warning is triggered, the system issues an emergency processing signal, requiring the manager to take immediate action, such as adjusting the sales price or moving the storage location.

[0112] On the basis of the above-mentioned embodiments, as an optional embodiment, the step of early warning the first region according to the shelf life of the first agricultural products in the first region can further include the following steps:

[0113] Step 301: Obtain the historical sales data of the first agricultural products in each first region, and calculate the average sales cycle of each first agricultural product.

[0114] Specifically, the historical sales data of each batch of first agricultural products in the first region is obtained from the sales management database, including but not limited to sales date, sales quantity, inventory change, etc. For example, the system extracts the daily sales records of a certain first agricultural product in the last 3 months, including the time of entering the warehouse, the time of completing the sales, etc. Based on these historical data, the system calculates the time required for each batch of agricultural products from entering the warehouse to being sold out, and obtains the specific sales cycle data. Then, the system performs arithmetic mean on the collected multiple batches of sales cycle data to obtain the average sales cycle of the first agricultural product. For example, the sales cycle of the last 10 batches of leafy vegetable first agricultural products is 2.5 days, 2.8 days, 2.3 days, etc., and the average sales cycle is calculated to be 2.5 days.

[0115] Step 302: When the shelf life of the first agricultural product is less than a preset proportion of the corresponding average sales cycle, a first early warning mode is triggered.

[0116] Specifically, a preset proportion value is first set, which is determined based on actual operation experience and safety inventory management requirements, for example, it can be set to 1.2. This preset proportion indicates that the shelf life should be at least 1.2 times the average sales cycle to ensure sufficient sales time margin. The system monitors the shelf life of the first agricultural product in real time and compares it with the average sales cycle converted by the preset proportion. When the shelf life of a batch of first agricultural products is less than the result of multiplying the average sales cycle by the preset proportion, the system triggers the first early warning mode. For example, the average sales cycle of a batch of leafy vegetable first agricultural products is 2.5 days, and the preset proportion is 1.2, so when its shelf life is less than 3 days (2.5x1.2), the system will trigger the first early warning mode.

[0117] The first early warning mode adopts a three-level early warning mechanism, including a reminder early warning, a key early warning, and an emergency early warning. When the shelf life of the first agricultural product is between 85% and 100% of a preset proportion of the average sales cycle, the system triggers a reminder early warning, sends an early warning reminder information to relevant personnel through a management terminal, and suggests paying close attention to the sales of the batch of agricultural products. When the shelf life decreases to between 70% and 85% of the preset proportion of the average sales cycle, the system triggers a key early warning, and in addition to sending the early warning information, also automatically generates a price adjustment suggestion, prompting to consider taking promotion measures. When the shelf life is less than 70% of the preset proportion of the average sales cycle, the system triggers an emergency early warning, immediately pushes an emergency handling notification to the manager, and automatically generates an emergency handling scheme including a substantial price reduction, a special promotion, and the like.

[0118] Step 303: When the shelf life of the first agricultural product is greater than or equal to a preset proportion of the corresponding average sales cycle, it is determined whether the current inventory of the first agricultural product is greater than or equal to the target inventory.

[0119] Step 304: When the current inventory of the first agricultural product is greater than or equal to the target inventory, a second early warning mode is triggered.

[0120] Specifically, the shelf life state of the first agricultural product is confirmed. When the shelf life is greater than or equal to a preset proportion (such as 1.2 times) of the average sales cycle, the system enters the inventory comparison link. In the inventory comparison, the system obtains the current inventory data of the first agricultural product in real time, and compares it with the target inventory set in advance. The target inventory is determined based on historical sales data, seasonal characteristics, and safety stock requirements, for example, can be set to 3 times the daily sales. When the system detects that the current inventory exceeds the target inventory, the second early warning mode is triggered.

[0121] The second early warning mode also adopts a hierarchical early warning mechanism, including three levels of mild early warning, moderate early warning, and severe early warning. When the current inventory exceeds 100%-120% of the target inventory, the mild early warning is triggered, and the system sends an inventory early warning prompt through the management terminal, suggesting to suspend restocking and strengthen sales monitoring. When the inventory reaches 120%-150% of the target inventory, the moderate early warning is triggered, and in addition to sending the early warning information, the system also automatically generates an inventory adjustment suggestion, including adjusting the display strategy, carrying out promotion activities, and the like. When the inventory exceeds 150% of the target inventory, the severe early warning is triggered, and the system immediately pushes an emergency handling notification, and generates an emergency scheme including cross-store allocation, special promotion, and the like.

[0122] Step 305: When the current inventory of the first agricultural product is less than the target inventory, a third early warning mode is triggered.

[0123] Specifically, when the current inventory of the first agricultural product is less than the target inventory, a third early warning mode is triggered. This early warning mode can also adopt a three-level early warning mechanism, including a primary restocking early warning, an urgent restocking early warning, and an emergency restocking early warning. When the current inventory is between 70%-100% of the target inventory, the primary restocking early warning is triggered, and the system sends a regular restocking reminder to the procurement personnel. When the inventory falls to between 40%-70% of the target inventory, the urgent restocking early warning is triggered, and the system generates an urgent procurement suggestion in addition to sending the early warning information. When the inventory is less than 40% of the target inventory, the emergency restocking early warning is triggered, and the system immediately pushes an emergency handling notification and starts the emergency procurement process.

[0124] Step 40: Determine the target environmental parameters for each second region based on the types and current physiological parameters of the second agricultural products in the second regions.

[0125] Specifically, first, the types and current physiological parameters of the second agricultural products in each second region are obtained. The physiological parameters include key indicators such as respiration intensity, water content, and maturity. Based on the obtained information, the system queries a pre-set agricultural product environmental parameter database that stores optimal environmental parameter configuration schemes for different types of agricultural products in different physiological states. Through data matching and intelligent algorithm analysis, the system determines the corresponding target environmental parameters for each second region, including temperature, humidity, gas composition, etc. The determination of target environmental parameters adopts a dynamic optimization mechanism. The system will adjust the environmental parameter settings in real time according to the change trend of the physiological parameters of the second agricultural products. When a significant change in the physiological parameters is detected, such as a sudden increase in respiration intensity, the system will automatically calculate new target environmental parameters and make corresponding adjustments through environmental control equipment.

[0126] Based on the above embodiment, as an optional embodiment, the step of determining the target environmental parameters for each second region based on the types and current physiological parameters of the second agricultural products in the second regions can further include the following steps:

[0127] Step 401: Obtain the historical environmental parameters of the second agricultural products in each second region and the quality change rate of the second agricultural products under the historical environmental parameters.

[0128] Specifically, the environmental monitoring module continuously collects and records the environmental parameter data of each second region, including the historical change data of key indicators such as temperature, humidity, and gas composition. At the same time, the quality detection equipment regularly collects quality indicators of the second agricultural products, such as hardness, color, water content, and soluble solids content, and calculates the quality change rate under different environmental parameter combinations. The system stores these data in a special database and establishes a corresponding relationship between environmental parameters and quality change rates.

[0129] Step 402: Determine the adjustment direction of the environmental parameter and the adjustment step of the corresponding environmental parameter based on the quality change rate.

[0130] Specifically, first, a mapping model of the quality change rate and the environmental parameter is established. For the detected quality change rate, the system compares it with the preset safety threshold. When the quality change rate exceeds the safety threshold, the system determines the influence weight of each environmental parameter on the quality change through mathematical model analysis, and then determines the environmental parameter that needs to be adjusted preferentially and its adjustment direction. At the same time, the system calculates the corresponding environmental parameter adjustment step according to the deviation degree of the quality change rate and combines historical adjustment experience.

[0131] For example, for a second area storing tomatoes, when it is detected that the softening rate of the tomatoes is 0.8 kg / cm²·day, which exceeds the preset safety threshold of 0.5 kg / cm²·day, the system analyzes and finds that temperature and humidity are the main factors affecting the softening rate, and the influence weight of temperature is 0.7 and the influence weight of humidity is 0.3. Based on this analysis result, the system preferentially adjusts the temperature parameter. By querying historical data, it is determined that under the current conditions, the softening rate can be reduced by 0.2 kg / cm²·day for each 1℃ reduction in temperature. Therefore, the system calculates the adjustment step of the temperature as 1.5℃, i.e. adjusts the storage temperature from the original 8℃ to 6.5℃

[0132] Step 403: Update the current environmental parameter according to the adjustment direction and the adjustment step to obtain the target environmental parameter.

[0133] Specifically, a progressive parameter updating mechanism is adopted. First, the system reads the current environmental parameter value as a reference point, and calculates the theoretical value of the target environmental parameter according to the predetermined adjustment direction and adjustment step. In the updating process, the system sets a safety interval for parameter change to ensure that the updated target environmental parameter will not exceed the physiological range suitable for agricultural products. At the same time, the system also considers the mutual influence between environmental parameters, and uses a collaborative optimization algorithm to update the parameters to obtain the target environmental parameter.

[0134] Step 50: Adjust the intelligent environmental control system of each second area according to the target environmental parameter, and the intelligent environmental control system at least includes a temperature control subsystem, a humidity control subsystem, a light control subsystem, a ventilation control subsystem and a disinfection control subsystem.

[0135] Specifically, the embodiment of the present application provides a comprehensive intelligent environment control system adjustment scheme. Through the cooperation of each subsystem, the storage environment can be comprehensively regulated and controlled, and the optimal storage conditions are created for the second agricultural products, so as to delay the shelf life. The target environment parameters are decomposed into control instructions of each subsystem. The temperature control subsystem includes a refrigeration unit, a heating device and a temperature sensor, and automatically switches the refrigeration or heating mode according to the target temperature value. For example, when the target temperature is 4℃, the refrigeration unit operates according to the preset refrigeration curve, and precise temperature control is realized through frequency adjustment, and the control accuracy reaches ±0.2℃. When the detected temperature deviates from the target value, the system automatically adjusts the refrigeration or heating capacity to ensure the stability of the temperature.

[0136] The humidity control subsystem is composed of a humidifier, a dehumidifier and a humidity sensor, and can be bidirectionally adjusted according to the target humidity value. When the target relative humidity is set to 90%, the system calculates the required humidification or dehumidification amount through an intelligent algorithm. The humidification adopts ultrasonic atomization technology, which can generate water mist with a particle size of 2-4 microns, ensuring uniform humidification; the dehumidification adopts a condensation dehumidification mode, achieving the best balance between energy utilization efficiency and dehumidification effect.

[0137] The light control subsystem is equipped with adjustable light LED light source and illuminance sensor, and can simulate natural light rhythm. The system automatically adjusts the light intensity and light time according to the light demand of different agricultural products. For example, for potatoes prone to greening, the system controls the light intensity below 5lux and adopts intermittent lighting mode, effectively inhibiting the occurrence of greening phenomenon.

[0138] The ventilation control subsystem includes a variable frequency fan, a gas sensor and a ventilation opening control device, and is responsible for adjusting air circulation and gas composition. The system calculates the required air exchange frequency and ventilation mode through an intelligent algorithm according to the target gas composition parameters. For example, when the ethylene concentration is too high, the system will increase the air exchange frequency and adjust the opening degree of the ventilation opening to ensure uniform gas exchange.

[0139] The disinfection control subsystem adopts a composite disinfection technology, including ultraviolet disinfection lamp, ozone generator and plasma disinfection device. The system automatically selects the most suitable disinfection mode and disinfection intensity according to the hygiene requirements of the storage environment. For example, after the agricultural products enter the warehouse, the system will start the low-concentration ozone disinfection program, and the concentration is controlled below 0.1ppm, which can ensure the disinfection effect and will not affect the quality of the agricultural products.

[0140] On the basis of the above embodiment, as an optional embodiment, a fresh agricultural product shelf life quality control method can further include the following process:

[0141] Specifically, the spatial distribution of environmental parameters is collected at high density by a sensor network distributed at different positions of the shelf. The system divides the shelf into several detection units, each equipped with temperature, humidity, gas concentration, etc. sensors, with a collection frequency of every 5 minutes. By calculating the deviation of each detection unit from the target parameter and the parameter gradient between adjacent units, the uniformity of the environmental parameter is evaluated. For example, when the temperature difference between a certain detection unit and the surrounding units exceeds 3℃, or the relative humidity difference exceeds 5%, the system will determine that the unit is an abnormal area.

[0142] For the identified abnormal area, the system determines the main control subsystem causing the abnormality through parameter deviation feature analysis. For example, if the abnormal area is located in the corner of the shelf and is mainly characterized by high temperature and low humidity, the system will determine that this may be due to the poor control effect of temperature and humidity caused by the airflow dead angle. The system will further analyze the positional relationship between the abnormal area and the nearby air supply outlets, return air inlets, and local airflow organization characteristics to accurately locate the problem source.

[0143] Based on the analysis of the abnormal causes, the system calculates the required compensation adjustment. The compensation adjustment adopts a multi-parameter collaborative compensation strategy, considering not only the deviation of the target parameter, but also the coupling effect between parameters. For example, for an abnormal area with a temperature deviation of 3℃, the system not only calculates the cooling compensation amount, but also considers the impact on humidity during the cooling process, and determines the compensation adjustment amount of temperature and humidity comprehensively. The calculation of compensation amount adopts an adaptive algorithm, which can dynamically adjust the compensation strategy according to the historical adjustment effect.

[0144] See Figure 3 A module schematic diagram of a fresh agricultural product shelf-life quality control system provided by the embodiment of the present application can include: a shelf-life determination module, a region division module, a warning module, an environmental parameter determination module, and a control module, wherein:

[0145] The shelf-life determination module is configured to obtain the current physiological parameters of the agricultural products in each region of the shelf, and determine the shelf life of the agricultural products in each region according to the current physiological parameters.

[0146] The region division module is configured to divide the shelf into a first region and a second region based on the shelf life.

[0147] The warning module is configured to warn the first region according to the shelf life of the first agricultural products in each first region.

[0148] The environmental parameter determination module is configured to determine the target environmental parameters of the corresponding second regions according to the types and current physiological parameters of the second agricultural products in each second region.

[0149] A control module is configured to adjust an intelligent environment control system of each second region according to the target environment parameter, wherein the intelligent environment control system comprises at least a temperature control subsystem, a humidity control subsystem, an illumination control subsystem, a ventilation control subsystem and a sterilization control subsystem.

[0150] Optionally, the shelf life determining module is further configured to acquire current physiological parameters of the agricultural products in each region on the shelf, wherein the current physiological parameters comprise ethylene release amount, carbon dioxide release amount and water content of the agricultural products; determine a reference shelf life according to the ethylene release amount; calculate a decay coefficient according to the carbon dioxide release amount; determine a correction coefficient according to the water content; and calculate the shelf life based on the reference shelf life, the decay coefficient and the correction coefficient.

[0151] Optionally, the shelf life determining module is further configured to multiply the reference shelf life by the decay coefficient to obtain a first calculation result; multiply the first calculation result by the correction coefficient to obtain a second calculation result; when the second calculation result is greater than a time threshold corresponding to the agricultural products, determine the time threshold as the shelf life; and when the second calculation result is less than or equal to the time threshold corresponding to the agricultural products, determine the second calculation result as the shelf life.

[0152] Optionally, the region dividing module is further configured to acquire standard shelf lives of the agricultural products in each region; calculate a ratio of the shelf life to the standard shelf life of the agricultural products in each region; divide a region in which the ratio is less than a ratio threshold in the shelf into a first region; and divide a region in which the ratio is greater than or equal to the ratio threshold in the shelf into a second region.

[0153] Optionally, the early warning module is further configured to acquire historical sales data of the first agricultural products in each first region and calculate average sales cycles of the first agricultural products; when the shelf life of the first agricultural products is less than a preset proportion of the corresponding average sales cycle, trigger a first early warning mode; when the shelf life of the first agricultural products is greater than or equal to the preset proportion of the corresponding average sales cycle, determine whether a current inventory of the first agricultural products is greater than a target inventory; when the current inventory of the first agricultural products is greater than or equal to the target inventory, trigger a second early warning mode; and when the current inventory of the first agricultural products is less than the target inventory, trigger a third early warning mode.

[0154] Optionally, the environment parameter determining module is further configured to acquire historical environment parameters of the second agricultural products in each second region and a quality change rate of the second agricultural products under the historical environment parameters; determine an adjustment direction of the environment parameter and an adjustment step of the corresponding environment parameter based on the quality change rate; and update the current environment parameter according to the adjustment direction and the adjustment step to obtain the target environment parameter.

[0155] Optionally, the environment parameter determination module is further configured to detect uniformity of environment parameters of each region on the shelf; determine an abnormal region with abnormal environment parameters based on the uniformity of environment parameters; analyze a control subsystem affected by the abnormal region; and calculate a compensation adjustment amount of the environment parameters of the abnormal region.

[0156] The abnormal region is locally regulated based on the compensation adjustment amount.

[0157] It should be noted that the system provided in the above embodiments, when implementing its functions, is only exemplified by the above division of functional modules. In actual applications, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above-described functions. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process is described in the method embodiments, which will not be repeated here.

[0158] The computer storage medium provided in the embodiments of the present application can store a plurality of instructions. The instructions are suitable for being loaded by a processor and executing the above-mentioned fresh agricultural product shelf life quality control method. The specific execution process can be referred to the specific description of the above-mentioned embodiments, which will not be repeated here.

[0159] Please refer to Figure 3 The present application also discloses an electronic device. Figure 3 is a structural schematic diagram of an electronic device disclosed in the embodiments of the present application. The electronic device 300 can include at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0160] The communication bus 302 is used to realize the connection and communication between the components.

[0161] The user interface 303 can include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 can also include a standard wired interface and a wireless interface.

[0162] The network interface 304 can optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0163] The processor 301 can include one or more processing cores. The processor 301 connects various parts within the server through various interfaces and lines, performs various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Alternatively, the processor 301 can be implemented in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 301 can integrate a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes operating systems, user interfaces, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 301, but can be realized by a separate chip.

[0164] The memory 305 can include a random access memory (RAM) and a read-only memory (ROM). Alternatively, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area can store data involved in the above-mentioned various method embodiments, etc. The memory 305 can alternatively be at least one storage device located away from the aforementioned processor 301. Referring to Figure 3 The memory 305 as a computer storage medium can include an operating system, a network communication module, a user interface module, and an application program of a fresh agricultural product shelf life quality control method.

[0165] In Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an interface for the user to input, and obtain data input by the user; and the processor 301 can be used to invoke an application program of a fresh agricultural product shelf life quality control method stored in the memory 305, and when executed by one or more processors 301, the electronic device 300 performs the method described in one or more of the above embodiments. It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all described as a combination of a series of actions, but those skilled in the art should know that the present application is not limited to the order of the actions described, because according to the present application, certain steps can be performed in other order or at the same time. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0166] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0167] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic. The division of the units is only a logical function division. There can be another division manner for actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical or other forms.

[0168] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0169] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0170] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable memory. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a memory and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned memory includes: a U disk, a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0171] The above-described are only exemplary embodiments of the present disclosure, and cannot limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon considering the specification and practicing the true principles of the present disclosure.

[0172] The present application is intended to cover any variations, uses, or adaptive changes of the present disclosure that follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and examples are only considered as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for controlling the shelf-life quality of fresh agricultural products, characterized by, The method comprises: acquiring current physiological parameters of agricultural products in each region of the shelf, the current physiological parameters including ethylene release amount, carbon dioxide release amount and water content of the agricultural products; determining a reference shelf life according to the ethylene release amount; the reference shelf life = T1 + (T2-T1) × (C-C1) / (C2-C1), wherein T1 and T2 are respectively two standard shelf lives closest to the current ethylene release amount in the database, C is the current detected ethylene release amount, C1 and C2 are respectively standard ethylene release amounts corresponding to T1 and T2; calculating a decay coefficient according to the carbon dioxide release amount; the calculation formula of the decay coefficient is K = e^(-α×(R-R0) / R0), wherein K is the decay coefficient, R is the current carbon dioxide release amount, R0 is a reference carbon dioxide release amount, and α is an attenuation constant; determining a correction coefficient according to the water content; the correction coefficient is calculated using a piecewise function model: when the water content W is in an optimal range [W1, W2], the correction coefficient M = 1; when the water content is lower than W1, M = (W / W1)^β; when the water content is higher than W2, M = (W2 / W)^β, wherein β is a weight coefficient and the value range is 0.5-2; calculating a shelf life based on the reference shelf life, the decay coefficient and the correction coefficient; acquiring standard shelf lives of the agricultural products in each region; calculating a ratio of the shelf life to the standard shelf life of the agricultural products in each region; dividing a region with a ratio less than a ratio threshold value in the shelf into a first region; dividing a region with a ratio greater than or equal to the ratio threshold value in the shelf into a second region; warning the first region according to the shelf life of a first agricultural product in each first region; wherein the warning the first region according to the shelf life of the first agricultural product in each first region comprises: acquiring historical sales data of the first agricultural product in each first region and calculating an average sales cycle of each first agricultural product; triggering a first warning mode when the shelf life of the first agricultural product is less than a preset proportion of the corresponding average sales cycle; acquiring historical environmental parameters of a second agricultural product in each second region and a quality change rate of the second agricultural product under the historical environmental parameters; determining an adjustment direction of the environmental parameters and an adjustment step of the corresponding environmental parameters based on the quality change rate; updating the current environmental parameters according to the adjustment direction and the adjustment step to obtain target environmental parameters; adjusting an intelligent environmental control system of each second region according to the target environmental parameters, the intelligent environmental control system at least including a temperature control subsystem, a humidity control subsystem, a light control subsystem, a ventilation control subsystem and a disinfection control subsystem; The method further comprises: dividing the shelf into a plurality of detection units, evaluating the uniformity of the environmental parameters by calculating the deviation of each detection unit from the target environmental parameters and the parameter gradient between adjacent units; determining an abnormal region of environmental parameter abnormality based on the uniformity of the environmental parameters; The abnormal area is analyzed to correspond to an affected control subsystem; based on the abnormal reason analysis, the required compensation adjustment amount is calculated; the compensation adjustment adopts a multi-parameter collaborative compensation strategy, which not only considers the deviation value of the target parameter, but also considers the coupling effect between parameters; and the abnormal area is locally regulated based on the compensation adjustment amount.

2. The fresh produce shelf-life quality management method according to claim 1, wherein, The shelf life is calculated based on the reference shelf life, the decay coefficient and the correction coefficient, including: The product of the reference shelf life and the decay coefficient is taken as a first calculation result; The product of the first calculation result and the correction coefficient is taken as a second calculation result; When the second calculation result is greater than the time threshold value corresponding to the agricultural product, the time threshold value is determined as the shelf life; When the second calculation result is less than or equal to the time threshold value corresponding to the agricultural product, the second calculation result is determined as the shelf life.

3. The method of claim 1, wherein the method further comprises: The first region is prewarned according to the shelf life of the first agricultural product in each first region, including: When the shelf life of the first agricultural product is greater than or equal to a preset proportion of the corresponding average sales cycle, it is determined whether the current inventory of the first agricultural product is greater than the target inventory; When the current inventory of the first agricultural product is greater than or equal to the target inventory, a second prewarning mode is triggered; When the current inventory of the first agricultural product is less than the target inventory, a third prewarning mode is triggered.

4. A system for managing the quality of fresh produce during shelf life, characterized in that, The system for performing the fresh agricultural product shelf life quality control method of claim 1, comprising: A shelf life determination module for obtaining current physiological parameters of agricultural products in each region on the shelf and determining the shelf life of agricultural products in each region according to the current physiological parameters; A region division module for dividing the shelf into a first region and a second region based on the shelf life; A prewarning module for prewarning the first region according to the shelf life of the first agricultural product in each first region; An environmental parameter determination module for determining target environmental parameters of the corresponding second region according to the type and current physiological parameters of the second agricultural product in each second region; A control module for adjusting intelligent environmental control systems of each second region according to the target environmental parameters, wherein the intelligent environmental control systems at least include a temperature control subsystem, a humidity control subsystem, a light control subsystem, a ventilation control subsystem and a disinfection control subsystem.

5. A computer readable storage medium, characterized in that, The computer readable storage medium stores a plurality of instructions, which are suitable for being loaded and executed by the processor to perform the method of any one of claims 1-3.

6. An electronic device, comprising: The electronic device includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to make the electronic device perform the method of any one of claims 1-3.

Images