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

Abstract

The invention discloses a fresh agricultural product shelf life quality control method, system, medium and equipment, and relates to the technical field of intelligent agriculture. The method comprises the following steps: acquiring current physiological parameters of agricultural products in each region on a shelf, and determining the shelf life of the agricultural products in each region according to the current physiological parameters; dividing the shelf into a first area and a second area based on the shelf life; performing early warning on the first areas according to the shelf lives of the first agricultural products in the first areas; determining target environment parameters corresponding to the second areas according to the types of the second agricultural products in the second areas and the current physiological parameters; and adjusting the intelligent environment control system of each second area according to the target environment parameters, wherein the intelligent environment control system at least comprises a temperature control subsystem, a humidity control subsystem, an illumination control subsystem, a ventilation control subsystem and a disinfection control subsystem. By implementing the technical scheme provided by the invention, the control quality of the shelf life of the fresh agricultural products can be improved.

Description

Technical Field

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

[0002] As consumers pay more and more attention to the freshness and safety of food, the shelf life quality management of fresh agricultural products has become crucial. Since fresh agricultural products are highly perishable and sensitive to environmental conditions, the management of their shelf life is of vital significance for maintaining product freshness, reducing losses and improving economic benefits.

[0003] At present, the shelf life management of fresh agricultural products mainly relies on basic environmental control technologies, such as temperature and humidity regulation and regular manual inspection and evaluation. These methods can extend the shelf life of agricultural products to a certain extent and reduce losses caused by quality degradation. However, different types of agricultural products have different sensitivities to environmental factors. Traditional management methods often use a unified environmental setting, resulting in poor quality control of the shelf life of fresh agricultural products, thus affecting the freshness of agricultural products. Summary of the invention

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

[0005] In a first aspect, the present application provides a method for controlling the quality of fresh agricultural products during shelf life, the method comprising: Acquire current physiological parameters of agricultural products in each area on the shelf, and determine the shelf life of 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; Providing early warning to the first areas according to the shelf life of the first agricultural products in each of the first areas; Determine the target environmental parameters of the corresponding second area according to the type and current physiological parameters of the second agricultural product in each of the second areas; The intelligent environmental control system of each second area is adjusted according to the target environmental parameters, and the intelligent environmental control system includes at least a temperature control subsystem, a humidity control subsystem, a lighting control subsystem, a ventilation control subsystem and a disinfection control subsystem.

[0006] By adopting the above technical solution, the current physiological parameters of agricultural products can be acquired and analyzed in real time, the shelf life status of agricultural products in different areas can be accurately evaluated, and the shelves can be divided into the first area and the second area. The system realizes the classified management of agricultural products with different shelf lives. For agricultural products with a shorter shelf life in the first area, early warning information is issued in a timely manner, which effectively reduces the risk of loss due to shelf life expiration. For agricultural products in the second area, the optimal target environmental parameters are accurately calculated according to their type characteristics and current physiological status, and then the intelligent environmental control system that integrates multiple subsystems such as temperature, humidity, light, ventilation and disinfection is controlled according to the target environmental parameters, which effectively improves the quality of control over the shelf life of fresh agricultural products, thereby significantly improving the preservation effect of fresh agricultural products.

[0007] Optionally, the acquiring current physiological parameters of agricultural products in each area on the shelf and determining the shelf life of agricultural products in each area according to the current physiological parameters includes: Acquire current physiological parameters of agricultural products in each area on the shelf, wherein the current physiological parameters include ethylene release, carbon dioxide release and water content of the agricultural products; Determining a reference shelf life based on the ethylene release amount; Calculating a decay coefficient based on the carbon dioxide release amount; determining a correction factor based on the water content; The shelf life is calculated based on the reference shelf life, the decay coefficient and the correction coefficient.

[0008] By adopting the above technical scheme, by obtaining the current physiological parameters of agricultural products such as ethylene release, carbon dioxide release and water content, the reference shelf life is determined according to the ethylene release, the decay coefficient is calculated according to the carbon dioxide release, and the correction coefficient is determined in combination with the water content. Finally, based on the comprehensive calculation of the reference shelf life, the decay coefficient and the correction coefficient, an accurate assessment of the shelf life of agricultural products is achieved, overcoming the problem of insufficient accuracy caused by traditional methods relying only on a single parameter assessment, thereby significantly improving the accuracy and reliability of the shelf life prediction of fresh agricultural products.

[0009] Optionally, the shelf life is calculated based on the reference shelf life, the decay coefficient and the correction coefficient, including: Taking the product of the reference shelf life and the decay coefficient as a first calculation result; taking the product of the first calculation result and the correction coefficient as the second calculation result; When the second calculation result is greater than the time threshold corresponding to the agricultural product, determining the time threshold 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.

[0010] By adopting the above technical scheme, the reference shelf life is multiplied by the decay coefficient to obtain a first calculation result, and then the first calculation result is multiplied by the correction coefficient to obtain a second calculation result. By comparing the second calculation result with the time threshold corresponding to the agricultural product, when the second calculation result is greater than the time threshold, the time threshold is used as the shelf life, and when the second calculation result is less than or equal to the time threshold, the second calculation result is used as the shelf life. This realizes differentiated calculation and threshold constraints of the shelf life of different agricultural products, avoids the problem that the shelf life calculation results in traditional methods may exceed the actual shelf life of the agricultural product, thereby improving the accuracy and practicality of the shelf life prediction results.

[0011] Optionally, dividing the shelf into a first area and a second area based on the shelf life includes: Obtain the standard shelf life of agricultural products in each region; Calculate the ratio of the shelf life of agricultural products in each region to the standard shelf life; Dividing the area in the shelf where the ratio is less than the ratio threshold as a first area; The area in the shelf where the ratio is greater than or equal to the ratio threshold is divided into a second area.

[0012] By adopting the above technical scheme, by obtaining the standard shelf life of agricultural products in each area, calculating the ratio of the current shelf life to the standard shelf life, and using the ratio threshold as the judgment standard, the area where the ratio is less than the ratio threshold is divided into the first area, and the area where the ratio is greater than or equal to the ratio threshold is divided into the second area, thereby realizing intelligent zoning management based on the shelf life of agricultural products, thereby improving the scientificity and rationality of the shelf area division.

[0013] Optionally, the step of issuing an early warning to the first areas according to the shelf life of the first agricultural products in each of the first areas includes: Acquire historical sales data of the first agricultural product in each of the first regions, and calculate an average sales cycle of each of the first agricultural products; When the shelf life of the first agricultural product is less than a preset ratio of the corresponding average sales cycle, a first early warning mode is triggered; When the shelf life of the first agricultural product is greater than or equal to a preset ratio of the corresponding average sales cycle, determining whether the current inventory of the first agricultural product is greater than a target inventory; 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; When the current inventory of the first agricultural product is less than the target inventory, the third early warning mode is triggered.

[0014] 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 shelf life of the first agricultural product is compared with the preset ratio of the average sales cycle. When the shelf life is less than the preset ratio, the first warning mode is triggered. When the shelf life is greater than or equal to the preset ratio, the relationship between the current inventory and the target inventory is further judged. When the current inventory is greater than or equal to the target inventory, the second warning mode is triggered, and when the current inventory is less than the target inventory, the third warning mode is triggered. A multi-level warning mechanism based on shelf life and inventory is realized, overcoming the problem of single warning mechanism and lack of pertinence in traditional methods, thereby improving the accuracy and timeliness of warning, and effectively reducing the loss risk of fresh agricultural products. .

[0015] Optionally, determining the target environmental parameters of the corresponding second areas according to the types and current physiological parameters of the second agricultural products in each of the second areas includes: Acquire historical environmental parameters of the second agricultural product in each of the second regions, and a quality change rate of the second agricultural product under the historical environmental parameters; Determining an adjustment direction of an environmental parameter and an adjustment step size of the corresponding environmental parameter based on the quality change rate; The current environmental parameters are updated according to the adjustment direction and the adjustment step size to obtain the target environmental parameters.

[0016] By adopting the above technical scheme, by obtaining the quality change rate of the second agricultural product under historical environmental parameters, determining the adjustment direction and adjustment step of the environmental parameters based on the quality change rate, and updating the current environmental parameters accordingly to obtain the target environmental parameters, the environmental parameter adaptive optimization based on historical data is achieved, thereby improving the accuracy and scientificity of environmental parameter adjustment and effectively extending the shelf life of agricultural products in the second area.

[0017] Optionally, the method further includes: Detect the uniformity of environmental parameters in each area on the shelf; Determine an abnormal area where the environmental parameter is abnormal based on the uniformity of the environmental parameter; Analyze the control subsystems affected by the abnormal area; Calculating the compensation adjustment amount of the environmental parameter corresponding to the abnormal area; The abnormal area is locally regulated based on the compensation adjustment amount.

[0018] By adopting the above technical scheme, the uniformity of environmental parameters in each area is detected, the abnormal area is determined based on the uniformity of environmental parameters, and the affected control subsystem corresponding to the abnormal area is analyzed, and the compensation adjustment amount of the environmental parameters corresponding to the abnormal area is calculated. Then, based on the compensation adjustment amount, the abnormal area is locally regulated, thereby realizing accurate monitoring and compensatory adjustment of the shelf storage environment, thereby improving the uniformity and stability of the storage environment and ensuring the reliability of the storage conditions of fresh agricultural products.

[0019] In a second aspect of the present application, a fresh agricultural product shelf life quality control system is provided, the system comprising: A shelf life determination module, used to obtain current physiological parameters of agricultural products in each area on the shelf, and determine the shelf life of agricultural products in each area according to the current physiological parameters; A region division module, used for dividing the shelf into a first region and a second region based on the shelf life; An early warning module, configured to issue an early warning to the first areas according to the shelf life of the first agricultural products in each of the first areas; An environmental parameter determination module, used to determine the target environmental parameters of the corresponding second area according to the type and current physiological parameters of the second agricultural product in each of the second areas; A control module is used to adjust the intelligent environmental control system of each second area according to the target environmental parameters, and the intelligent environmental control system includes at least a temperature control subsystem, a humidity control subsystem, a lighting control subsystem, a ventilation control subsystem and a disinfection control subsystem.

[0020] In a third aspect of the present application, a computer storage medium is provided, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the above method steps.

[0021] In a fourth aspect of the present application, an electronic device is provided, comprising: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the above-mentioned method steps.

[0022] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: This application can accurately evaluate the shelf life status of agricultural products in different areas by acquiring and analyzing the current physiological parameters of agricultural products in real time, and divide the shelves into the first area and the second area. The system realizes the classified management of agricultural products with different shelf lives. For agricultural products with a shorter shelf life in the first area, early warning information is issued in a timely manner, which effectively reduces the risk of loss due to shelf life expiration. For agricultural products in the second area, the optimal target environmental parameters are accurately calculated according to their type characteristics and current physiological state, and then the intelligent environmental control system that integrates multiple subsystems such as temperature, humidity, light, ventilation and disinfection is controlled according to the target environmental parameters, which effectively improves the quality of control over the shelf life of fresh agricultural products, thereby significantly improving the preservation effect of fresh agricultural products. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of a method for quality control of shelf life of fresh agricultural products provided in an embodiment of the present application; Figure 2 This is a module diagram of a fresh agricultural product shelf life quality control system provided by an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0024] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0025] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0026] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0027] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0028] The following will provide a clear and complete description of the technical solutions in the embodiments of the present application 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, rather than all of the embodiments.

[0029] Please refer to Figure 1 , a flowchart of a method for quality control of shelf life of fresh agricultural products is proposed. The method can be implemented by a computer program, can be implemented by a single-chip microcomputer, or can be run on a fresh agricultural product shelf life quality control system. The computer program can be integrated into a computer device or can be run as an independent tool application. Specifically, the method includes steps 10 to 40, and the above steps are as follows: 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 based on the current physiological parameters.

[0030] The current physiological parameters in this embodiment of the application refer to key indicators that reflect the current freshness and physiological state of agricultural products, such as but not limited to ethylene release, carbon dioxide release, and water content.

[0031] In the present application, shelf life refers to the longest time that agricultural products retain their commercial value under specific storage conditions.

[0032] Specifically, in order to accurately evaluate the freshness of agricultural products and predict their saleable time, it is necessary to obtain the current physiological parameters of agricultural products in various areas on the shelf. This embodiment uses multiple sensor arrays distributed in various areas of the shelf to collect physiological parameters such as ethylene release, carbon dioxide release and water content of agricultural products in real time. Among them, the ethylene sensor is used to detect the concentration of ethylene gas released by agricultural products, the carbon dioxide sensor is used to monitor the respiratory intensity, and the moisture sensor is used to measure the moisture content. These sensors transmit the collected data to the control system through the data acquisition module for processing and analysis.

[0033] After obtaining these physiological parameters, the preset reference shelf life database is first queried based on the ethylene release to determine the initial reference shelf life. Since the ethylene release of different agricultural products at different maturity stages has a strong correspondence with their freshness, it can be used as an important basis for determining the reference shelf life. Next, the system calculates the decay coefficient based on the carbon dioxide release. The carbon dioxide release reflects the respiration intensity of the agricultural product. The stronger the respiration, the faster the metabolism and the corresponding decay rate. At the same time, the system will also determine the correction coefficient based on the water content, because changes in 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 shelf life calculation method based on multi-dimensional physiological parameters, the actual shelf life of agricultural products can be predicted more accurately, avoiding the prediction bias caused by traditional single parameters.

[0034] 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 may also include the following steps: Step 101: obtaining current physiological parameters of agricultural products in various areas on the shelf, the current physiological parameters including ethylene release, carbon dioxide release and water content of the agricultural products.

[0035] Specifically, ethylene gas sensors, carbon dioxide gas sensors and moisture sensors are arranged in various areas of the shelves. These sensors are arranged in an array to ensure the representativeness of the sampling. Among them, the ethylene gas sensor uses a metal oxide semiconductor sensor to monitor the ethylene release of agricultural products by detecting the concentration changes of ethylene gas in the environment. As an important plant hormone, the release of ethylene can reflect the maturity and aging of agricultural products; the carbon dioxide gas sensor uses an infrared absorption 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 moisture content by measuring the relative humidity of the surface of agricultural products and the surrounding environment. Changes in moisture content will affect the texture and appearance quality of agricultural products.

[0036] Among them, these sensors collect data in real time through the data acquisition module. The sampling frequency can be adjusted according to the characteristics of different agricultural products, and is generally set to sample once every 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 raw 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.

[0037] Step 102: Determine a reference shelf life according to the ethylene release amount.

[0038] Specifically, when determining the reference shelf life, first obtain the currently detected ethylene emission value, and query the pre-established database of the correspondence between ethylene emission and shelf life. The database stores the standard shelf life data corresponding to different types of agricultural products at different ethylene emission levels, which are obtained through a large number of experimental statistics. Through interpolation calculation, the initial reference shelf life is determined based on the currently detected ethylene emission. Since ethylene is a key hormone in the ripening and aging process of agricultural products, its release is closely related to the physiological maturity of agricultural products, so it has a strong scientific basis to use it as the basis for determining the reference shelf life.

[0039] For example, when the ethylene release of apples is 0.1 μL / kg·h, the corresponding standard shelf life is 15 days, and when the ethylene release is 0.2 μL / kg·h, the corresponding standard shelf life is 12 days. The initial reference shelf life is calculated based on the currently detected ethylene release using linear interpolation. 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 closest to the current ethylene release in the database, C is the currently detected ethylene release, and C1 and C2 are the standard ethylene releases corresponding to T1 and T2, respectively.

[0040] Step 103: Calculate the decay coefficient according to the carbon dioxide release amount.

[0041] Specifically, when calculating the decay coefficient, the carbon dioxide emission data of agricultural products are collected and compared with the preset benchmark carbon dioxide emission. Through the preset mathematical model, the ratio of the current carbon dioxide emission to the benchmark value is substituted into the decay coefficient calculation formula to obtain the decay coefficient reflecting the current metabolic state of the agricultural product. When the carbon dioxide emission is higher than the benchmark value, it indicates that the metabolism of the agricultural product is accelerated and the decay coefficient decreases accordingly; conversely, when the carbon dioxide emission is close to or lower than the benchmark value, the decay coefficient increases accordingly. This calculation method accurately reflects the impact of the physiological activity of agricultural products on their shelf life.

[0042] Exemplarily, the decay coefficient is calculated as: K=e^(-α×(R-R0) / R0), where K is the decay coefficient, R is the current carbon dioxide emission, R0 is the baseline carbon dioxide emission, and α is the decay constant, which ranges from 0.1 to 0.5 and can be adjusted according to different types of agricultural products. For example, when the baseline carbon dioxide emission of a certain fruit is 20 mg / kg·h, 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 can be substituted into the formula, indicating that the shelf life is shortened due to increased respiration intensity.

[0043] Step 104: Determine a correction factor according to the water content.

[0044] Specifically, when determining the correction coefficient, the system compares the detected moisture content with the standard moisture content range of this type of agricultural product. When the moisture content is in the optimal range, the correction coefficient is 1; when the moisture content deviates from the optimal range, the system calculates the corresponding correction coefficient through the preset correction model. Too high or too low moisture content will cause the correction coefficient to be less than 1, thereby reducing the final shelf life prediction value. This correction mechanism takes into account the characteristics of agricultural product quality changing with moisture content, making the shelf life prediction more in line with actual conditions.

[0045] Exemplarily, the correction coefficient is calculated using a piecewise function model: when the moisture content W is in the optimal range [W1, W2], the correction coefficient M=1; when the moisture content is lower than W1, M=(W / W1)^β; when the moisture 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 sensitivity of agricultural products to moisture. For example, the optimal moisture content of a certain vegetable is in the range of 85%-90%, and the current moisture content is 80%. 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 moisture content.

[0046] Step 105: Calculate the shelf life based on the reference shelf life, the decay coefficient and the correction coefficient.

[0047] 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. Taking into account that different agricultural products have their inherent maximum shelf life limits, the system pre-sets time thresholds corresponding to each type of agricultural product. 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.

[0048] 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 may further include the following steps: Step 1051: taking the product of the reference shelf life and the decay coefficient as the first calculation result.

[0049] Specifically, the product of the reference shelf life and the decay coefficient is used as the first calculation result. For example, 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 calculation step reflects the impact of the current metabolic state of agricultural products on the basic shelf life. When the carbon dioxide release increases and the decay coefficient decreases, the first calculation result will decrease accordingly, reflecting the negative impact of increased respiration intensity on the shelf life.

[0050] Step 1052: taking the product of the first calculation result and the correction coefficient as the second calculation result.

[0051] Specifically, the product of the first calculation result and the correction coefficient is used as the second calculation result. For example, 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 calculation step further considers the correction effect of moisture content changes on shelf life. When the moisture content deviates from the optimal range, the correction coefficient is less than 1, which will cause the second calculation result to decrease, reflecting the impact of abnormal moisture status on shelf life.

[0052] Step 1053: 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.

[0053] Specifically, first, the time threshold corresponding to the current agricultural product is retrieved from the preset agricultural product information database. The time threshold is determined based on a large amount of experimental data and practical experience, and reflects the longest shelf life of a specific agricultural product under optimal conditions. For example, the time threshold for strawberries is set to 7 days, the time threshold for apples is set to 15 days, and the time threshold for tomatoes is set to 10 days. The system compares the obtained time threshold with the second calculation result, and determines the final shelf life based on the comparison result.

[0054] When the second calculation result is greater than the time threshold 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 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 of 15 days. The system will determine the final shelf life as 15 days. This processing method avoids the occurrence of unrealistic and excessively long shelf life predictions and ensures the reliability of the prediction results.

[0055] Step 1054: 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.

[0056] 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. At this time, 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 its corresponding time threshold of 10 days. The system determines the final shelf life as 8 days. In this case, retaining the second calculation result reflects the impact of the current physiological state of the agricultural product on its actual shelf life.

[0057] Step 20: Divide the shelf into a first area and a second area based on the shelf life.

[0058] 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 divides the shelf space into a first area and a second area accordingly. When the shelf life of the agricultural product is less than the time threshold, its 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, and its storage area is divided into the first area. When the shelf life of the agricultural product is greater than or equal to the first time threshold, its 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, and its storage area is divided into the second area.

[0059] Based on the above embodiment, as an optional embodiment, the step of dividing the shelf into the first area and the second area based on the shelf life may further include the following steps: Step 201: Obtain the standard shelf life of agricultural products in each region.

[0060] Specifically, the standard shelf life of all agricultural products in each region 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 region is 5 days, and the standard shelf life of a batch of apples in the second region is 15 days.

[0061] Step 202: Calculate the ratio of the shelf life of agricultural products in each region to the standard shelf life.

[0062] Specifically, the actual shelf life of agricultural products in each region is divided by its corresponding standard shelf life to obtain the shelf life ratio. For example, when the actual shelf life of a batch of strawberries in the first region is 4 days and its 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 its standard shelf life is 15 days, the calculated ratio is 12 / 15=0.8. This ratio intuitively reflects the degree of change in the current shelf life of agricultural products relative to the normal level. The lower the ratio, the more significantly the actual shelf life of agricultural products is lower than the standard level.

[0063] Step 203: Divide the area in the shelf where the ratio is less than the ratio threshold into the first area.

[0064] Step 204: Divide the area in the shelf where the ratio is greater than or equal to the ratio threshold as a second area.

[0065] Specifically, first set a preset ratio threshold, which is determined based on the agricultural product quality management experience and actual operational needs, for example, it can be set to 0.85. The system compares the calculated shelf life ratio with the ratio threshold, and dynamically divides the shelf space accordingly. When the shelf life ratio of agricultural products in a certain area is less than the ratio threshold, the area is divided into the first area. For example, the shelf life ratio of a batch of agricultural products is 0.8, which is less than the set ratio threshold of 0.85, and the area where it is located is divided into the first area. When the shelf life ratio of agricultural products in a certain area is greater than or equal to the ratio threshold, the area is divided into the second area.

[0066] Step 30: issuing an early warning to the first area according to the shelf life of the first agricultural product in each first area.

[0067] Specifically, first obtain the actual shelf life data of each batch of the first agricultural product in the first area, and set the corresponding warning time nodes. 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 warning time points of different levels. The system monitors the changes in the shelf life of the first agricultural product in the first area in real time. 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 its storage time reaches 2 days (50%), a primary warning is triggered, when it reaches 3 days (75%), a mid-level warning is triggered, and when it reaches 3.6 days (90%), a high-level warning is triggered.

[0068] Among them, the triggering of the early warning signal adopts a multi-level early warning mechanism, and different levels of early warning correspond to different treatment measures. When the primary early warning is triggered, the system sends an early warning reminder to the management personnel through the monitoring terminal, suggesting that the batch of agricultural products should be paid special attention. When the intermediate early warning is triggered, the system automatically generates treatment suggestions, prompting the management personnel to consider adjusting the storage conditions or formulating promotion plans. When the advanced early warning is triggered, the system sends an emergency processing signal, requiring the management personnel to take immediate measures, such as adjusting the sales price or transferring the storage location.

[0069] Based on the above embodiment, as an optional embodiment, the step of issuing an early warning to the first area according to the shelf life of the first agricultural product in each first area may further include the following steps: Step 301: Obtain historical sales data of first agricultural products in each first region, and calculate the average sales period of each first agricultural product.

[0070] Specifically, the historical sales data of each batch of the first agricultural product in the first region is obtained from the sales management database, including but not limited to information such as sales date, sales quantity, inventory changes, etc. For example, the system extracts the daily sales records of a certain first agricultural product in the past three months, including key time nodes such as storage time and sales completion time. Based on these historical data, the system calculates the time required for each batch of agricultural products from storage to sold out, and obtains specific sales cycle data. Then, the system performs arithmetic averaging on the collected multiple batches of sales cycle data to obtain the average sales cycle of the first agricultural product. For example, the sales cycles of the last 10 batches of a certain leafy first agricultural product were 2.5 days, 2.8 days, 2.3 days, etc., and the average sales cycle was calculated to be 2.5 days.

[0071] 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.

[0072] Specifically, first set a preset ratio value, which is determined based on actual operating experience and safety inventory management requirements, for example, it can be set to 1.2. This preset ratio means 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 ratio. When the shelf life of a batch of first agricultural products is less than the result of its average sales cycle multiplied by the preset ratio, the system triggers the first early warning mode. For example, the average sales cycle of a batch of leafy first agricultural products is 2.5 days, and the preset ratio is 1.2. When its shelf life is less than 3 days (2.5×1.2), the system will trigger the first early warning mode.

[0073] Among them, the first early warning mode adopts a three-level early warning mechanism, including reminder warning, key warning and emergency warning. When the shelf life of the first agricultural product is between 85% and 100% of the preset proportion of the average sales cycle, the system triggers a reminder warning and sends a warning reminder message to relevant personnel through the management terminal, suggesting that they pay close attention to the sales of this batch of agricultural products. When the shelf life drops to between 70% and 85% of the preset proportion of the average sales cycle, the system triggers a key warning. In addition to sending a warning message, it also automatically generates a price adjustment suggestion, prompting to consider taking promotional measures. When the shelf life is lower than 70% of the preset proportion of the average sales cycle, the system triggers an emergency warning, immediately pushes an emergency processing notification to the management personnel, and automatically generates an emergency processing plan including a substantial price reduction, special promotions, etc.

[0074] Step 303: When the shelf life of the first agricultural product is greater than or equal to a preset ratio of the corresponding average sales cycle, it is determined whether the current inventory of the first agricultural product is greater than the target inventory.

[0075] Step 304: When the current inventory of the first agricultural product is greater than or equal to the target inventory, the second early warning mode is triggered.

[0076] Specifically, the shelf life status of the first agricultural product is confirmed. When the shelf life is greater than or equal to a preset ratio of the average sales cycle (such as 1.2 times), the system enters the inventory comparison phase. In the inventory comparison, the system obtains the current inventory data of the first agricultural product in real time and compares it with the preset target inventory. The target inventory is determined based on historical sales data, seasonal characteristics and safety stock requirements, for example, it can be set to 3 times the average daily sales volume. When the system detects that the current inventory exceeds the target inventory, the second warning mode is triggered.

[0077] The second early warning mode also adopts a graded early warning mechanism, including three levels: mild early warning, moderate early warning and severe early warning. When the current inventory level exceeds 100%-120% of the target inventory level, a mild early warning is triggered, and the system issues an inventory early warning prompt through the management terminal, suggesting that replenishment be suspended and sales monitoring be strengthened. When the inventory level reaches 120%-150% of the target inventory level, a moderate early warning is triggered. In addition to sending early warning information, the system also automatically generates inventory adjustment suggestions, including adjusting display strategies, launching promotional activities and other measures. When the inventory level exceeds 150% of the target inventory level, a severe early warning is triggered, and the system immediately pushes an emergency processing notification and generates emergency plans including cross-store deployment and special promotions.

[0078] Step 305: When the current inventory of the first agricultural product is less than the target inventory, the third early warning mode is triggered.

[0079] Specifically, when the current inventory of the first agricultural product is less than the target inventory, the third warning mode is triggered. This warning mode can also adopt a three-level warning mechanism, including primary replenishment warning, expedited replenishment warning and emergency replenishment warning. When the current inventory is between 70%-100% of the target inventory, the primary replenishment warning is triggered, and the system sends a regular replenishment reminder to the purchasing staff. When the inventory drops to between 40%-70% of the target inventory, the expedited replenishment warning is triggered. In addition to sending the warning information, the system also automatically generates expedited procurement suggestions. When the inventory is less than 40% of the target inventory, the emergency replenishment warning is triggered, and the system immediately pushes an emergency processing notification and starts the emergency procurement process.

[0080] Step 40: Determine the target environmental parameters corresponding to the second areas according to the types and current physiological parameters of the second agricultural products in each second area.

[0081] Specifically, first obtain the type information and current physiological parameters of the second agricultural product in each second area. Physiological parameters include key indicators such as respiration intensity, water content, and maturity. Based on the information obtained, the system queries the preset agricultural product environmental parameter database, which stores the optimal environmental parameter configuration schemes for different types of agricultural products under different physiological states. Through data matching and intelligent algorithm analysis, the system determines the corresponding target environmental parameters for each second area, including temperature, humidity, gas composition, etc. The determination of the target environmental parameters adopts a dynamic optimization mechanism. The system will adjust the environmental parameter settings in real time according to the changing trend of the physiological parameters of the second agricultural product. When a significant change in the physiological parameters is detected, such as a sudden increase in respiration intensity, the system will automatically calculate the new target environmental parameters and make corresponding adjustments through the environmental control equipment.

[0082] Based on the above embodiment, as an optional embodiment, the step of determining the target environmental parameters of the corresponding second area according to the type of the second agricultural product and the current physiological parameters in each second area may further include the following steps: Step 401: Acquire historical environmental parameters of the second agricultural product in each second area, and the quality change rate of the second agricultural product under the historical environmental parameters.

[0083] Specifically, the environmental parameter data of each second area is continuously collected and recorded through the environmental monitoring module, including the historical change data of key indicators such as temperature, humidity, and gas composition. At the same time, the quality indicators of the second agricultural products, such as hardness, color, water content, soluble solid content, etc., are regularly collected through quality testing equipment to calculate the quality change rate under different environmental parameter combinations. The system stores these data in a special database and establishes the corresponding relationship between environmental parameters and quality change rate.

[0084] Step 402: Determine the adjustment direction of the environmental parameter and the adjustment step size of the corresponding environmental parameter based on the quality change rate.

[0085] Specifically, a mapping model between the quality change rate and the environmental parameters is first 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 weight of the impact of each environmental parameter on the quality change through mathematical model analysis, and then determines the environmental parameters that need to be adjusted first and their adjustment direction. At the same time, the system calculates the corresponding environmental parameter adjustment step according to the degree of deviation of the quality change rate and historical adjustment experience.

[0086] For example, for the second area where tomatoes are stored, when the softening rate of tomatoes is detected to be 0.8kg / cm²·day, which exceeds the preset safety threshold of 0.5kg / cm²·day, the system analysis finds that temperature and humidity are the main factors affecting the softening rate, of which the influence weight of temperature is 0.7 and the influence weight of humidity is 0.3. Based on this analysis result, the system gives priority to adjusting the temperature parameters. By querying historical data, it is determined that under current conditions, the softening rate can be reduced by 0.2kg / cm²·day for every 1°C decrease in temperature. Therefore, the system calculates that the temperature adjustment step is 1.5°C, that is, the storage temperature is adjusted from the original 8°C to 6.5°C. Step 403: Update the current environmental parameters according to the adjustment direction and the adjustment step size to obtain the target environmental parameters.

[0087] Specifically, a progressive parameter update 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. During the update process, the system sets a safe interval for parameter changes to ensure that the updated target environmental parameters do not exceed the physiological range suitable for agricultural products. At the same time, the system also considers the mutual influence between environmental parameters, and adopts a collaborative optimization algorithm to update the parameters to obtain the target environmental parameters.

[0088] Step 50: Adjust the intelligent environmental control system of each second area according to the target environmental parameters, the intelligent environmental control system including at least a temperature control subsystem, a humidity control subsystem, a lighting control subsystem, a ventilation control subsystem and a disinfection control subsystem.

[0089] Specifically, the embodiment of the present application provides a comprehensive intelligent environmental control system adjustment solution. Through the coordinated cooperation of each subsystem, the storage environment can be fully regulated to create the best storage conditions for the second agricultural product, thereby delaying the shelf life. The target environmental parameters are decomposed into control instructions for each subsystem. The temperature control subsystem includes a refrigeration unit, a heating device and a temperature sensor, which automatically switches the refrigeration or heating mode according to the target temperature value. For example, when the target temperature is 4°C, the refrigeration unit operates according to the preset refrigeration curve, and precise temperature control is achieved through frequency conversion regulation, with a control accuracy of ±0.2°C. When it is detected that the temperature deviates from the target value, the system will automatically adjust the cooling capacity or heating capacity to ensure temperature stability.

[0090] The humidity control subsystem consists of a humidifier, a dehumidifier and a humidity sensor, and can be adjusted bidirectionally according to the target humidity value. When the target relative humidity is set to 90%, the system calculates the required humidification or dehumidification through an intelligent algorithm. Humidification uses ultrasonic atomization technology to produce water mist with a particle size of 2-4 microns to ensure uniform humidification; dehumidification uses condensation dehumidification to achieve the best balance between energy efficiency and dehumidification effect.

[0091] The lighting control subsystem is equipped with a dimmable LED light source and a light intensity sensor, which can simulate the natural lighting rhythm. The system automatically adjusts the light intensity and lighting time according to the lighting requirements of different agricultural products. For example, for potatoes that are prone to greening, the system will control the light intensity to below 5lux and use intermittent lighting to effectively inhibit the occurrence of greening.

[0092] The ventilation control subsystem includes variable frequency fans, gas sensors and vent control devices, which are responsible for regulating air circulation and gas composition. The system calculates the required ventilation frequency and ventilation mode based on the target gas composition parameters through intelligent algorithms. For example, when the ethylene concentration is detected to be too high, the system will increase the ventilation frequency and adjust the vent opening to ensure uniform gas exchange.

[0093] The disinfection control subsystem uses a composite disinfection technology, including ultraviolet disinfection lamps, ozone generators and plasma disinfection devices. The system automatically selects the most suitable disinfection method and disinfection intensity according to the sanitation 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 without affecting the quality of the agricultural products.

[0094] Based on the above embodiment, as an optional embodiment, a method for controlling the quality of fresh agricultural products during shelf life may also include the following process: Specifically, the spatial distribution of environmental parameters is collected at a high density through a sensor network distributed at different locations on the shelves. The system divides the shelves into several detection units, each of which is equipped with sensors such as temperature, humidity, and gas concentration, and the collection frequency is once every 5 minutes. The uniformity of environmental parameters is evaluated by calculating the deviation value between each detection unit and the target parameter and the parameter gradient between adjacent units. For example, when the temperature difference between a detection unit and the surrounding units exceeds 3°C, or the relative humidity difference exceeds 5%, the system determines the unit as an abnormal area.

[0095] For the identified abnormal areas, the system analyzes the parameter deviation characteristics to determine the main control subsystem causing the abnormality. 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 airflow dead corner causing poor temperature and humidity control. The system will further analyze the positional relationship between the abnormal area and the adjacent air supply and return outlets, as well as the characteristics of the local airflow organization, to accurately locate the source of the problem.

[0096] Based on the analysis of the abnormal cause, the system calculates the required compensation adjustment amount. The compensation adjustment adopts a multi-parameter collaborative compensation strategy, which not only considers the deviation value of the target parameter, but also the coupling effect between parameters. For example, for an abnormal area where the temperature is 3°C higher, the system not only calculates the cooling compensation amount, but also considers the impact on humidity during the cooling process, and comprehensively determines the compensation adjustment amount of temperature and humidity. The calculation of the compensation amount adopts an adaptive algorithm, which can dynamically adjust the compensation strategy according to the historical adjustment effect.

[0097] See also Figure 3 , is a module diagram of a fresh agricultural product shelf life quality control system provided in an embodiment of the present application, the fresh agricultural product shelf life quality control system may include: a shelf life determination module, a region division module, an early warning module, an environmental parameter determination module and a control module, wherein: A shelf life determination module, used to obtain current physiological parameters of agricultural products in each area on the shelf, and determine the shelf life of agricultural products in each area according to the current physiological parameters; A region division module, used for dividing the shelf into a first region and a second region based on the shelf life; An early warning module, configured to issue an early warning to the first areas according to the shelf life of the first agricultural products in each of the first areas; An environmental parameter determination module, used to determine the target environmental parameters of the corresponding second area according to the type and current physiological parameters of the second agricultural product in each of the second areas; A control module is used to adjust the intelligent environmental control system of each second area according to the target environmental parameters, and the intelligent environmental control system includes at least a temperature control subsystem, a humidity control subsystem, a lighting control subsystem, a ventilation control subsystem and a disinfection control subsystem.

[0098] Optionally, the shelf life determination module is also used to obtain current physiological parameters of agricultural products in each area on the shelf, the current physiological parameters including ethylene release, carbon dioxide release and water content of the agricultural products; determine a reference shelf life based on the ethylene release; calculate a decay coefficient based on the carbon dioxide release; determine a correction coefficient based on the water content; and calculate the shelf life based on the reference shelf life, the decay coefficient and the correction coefficient.

[0099] Optionally, the shelf life determination module is further used to take the product of the reference shelf life and the decay coefficient as the first calculation result; take the product of the first calculation result and the correction coefficient as the second calculation result; when the second calculation result is greater than the time threshold corresponding to the agricultural product, determine the time threshold as the shelf life; when the second calculation result is less than or equal to the time threshold corresponding to the agricultural product, determine the second calculation result as the shelf life.

[0100] Optionally, the area division module is also used to obtain the standard shelf life of agricultural products in each area; calculate the ratio of the shelf life of agricultural products in each area to the standard shelf life; divide the area in the shelf where the ratio is less than a ratio threshold into a first area; and divide the area in the shelf where the ratio is greater than or equal to the ratio threshold into a second area.

[0101] Optionally, the early warning module is also used to obtain historical sales data of the first agricultural product in each of the first areas, and calculate the average sales cycle of each of the first agricultural products; when the shelf life of the first agricultural product is less than a preset proportion of the corresponding average sales cycle, triggering a first early warning mode; when the shelf life of the first agricultural product is greater than or equal to a preset proportion of the corresponding average sales cycle, determining 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, triggering a second early warning mode; when the current inventory of the first agricultural product is less than the target inventory, triggering a third early warning mode.

[0102] Optionally, the environmental parameter determination module is also used to obtain historical environmental parameters of the second agricultural product in each of the second areas, and the quality change rate of the second agricultural product under the historical environmental parameters; determine the adjustment direction of the environmental parameters and the adjustment step of the corresponding environmental parameters based on the quality change rate; update the current environmental parameters according to the adjustment direction and the adjustment step to obtain the target environmental parameters.

[0103] Optionally, the environmental parameter determination module is further used to detect the uniformity of environmental parameters in various areas on the shelf; determine abnormal areas with abnormal environmental parameters based on the uniformity of the environmental parameters; analyze the affected control subsystems corresponding to the abnormal areas; and calculate the compensation adjustment amount of the environmental parameters corresponding to the abnormal areas; The abnormal area is locally regulated based on the compensation adjustment amount.

[0104] It should be noted that: when the system provided in the above embodiment realizes its functions, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0105] An embodiment of the present application also provides a computer storage medium, which can store multiple instructions. The instructions are suitable for being loaded by a processor and executing a fresh agricultural product shelf life quality control method of the above embodiment. The specific execution process can be found in the specific description of the above embodiment, which will not be repeated here.

[0106] Please refer to Figure 3 The application also discloses an electronic device. Figure 3 The electronic device 300 may 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 .

[0107] The communication bus 302 is used to realize the connection and communication between these components.

[0108] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0109] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0110] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes 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. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0111] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, 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 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a method for controlling the shelf life quality of fresh agricultural products.

[0112] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program stored in the memory 305 for a method for quality control of the shelf life of fresh agricultural products. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0113] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0114] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0115] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0116] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0117] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0118] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0119] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for controlling the quality of fresh agricultural products during shelf life, characterized in that: The method comprises: Acquire current physiological parameters of agricultural products in each area on the shelf, and determine the shelf life of 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; Providing early warning to the first areas according to the shelf life of the first agricultural products in each of the first areas; Determine the target environmental parameters of the corresponding second area according to the type and current physiological parameters of the second agricultural product in each of the second areas; The intelligent environmental control system of each second area is adjusted according to the target environmental parameters, and the intelligent environmental control system includes at least a temperature control subsystem, a humidity control subsystem, a lighting control subsystem, a ventilation control subsystem and a disinfection control subsystem.

2. The method for controlling the quality of fresh agricultural products during shelf life according to claim 1, characterized in that: The obtaining of current physiological parameters of agricultural products in each area on the shelf and determining the shelf life of agricultural products in each area according to the current physiological parameters includes: Acquire current physiological parameters of agricultural products in each area on the shelf, wherein the current physiological parameters include ethylene release, carbon dioxide release and water content of the agricultural products; Determining a reference shelf life based on the ethylene release amount; Calculating a decay coefficient based on the carbon dioxide release amount; determining a correction factor based on the water content; The shelf life is calculated based on the reference shelf life, the decay coefficient and the correction coefficient.

3. The method for controlling the quality of fresh agricultural products during shelf life according to claim 2, characterized in that: The calculating the shelf life based on the reference shelf life, the decay coefficient and the correction coefficient comprises: Taking the product of the reference shelf life and the decay coefficient as a first calculation result; taking the product of the first calculation result and the correction coefficient as the second calculation result; When the second calculation result is greater than the time threshold corresponding to the agricultural product, determining the time threshold 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.

4. The method for controlling the quality of fresh agricultural products during shelf life according to claim 1, characterized in that: The step of dividing the shelf into a first area and a second area based on the shelf life comprises: Obtain the standard shelf life of agricultural products in each region; Calculate the ratio of the shelf life of agricultural products in each region to the standard shelf life; Dividing the area in the shelf where the ratio is less than the ratio threshold as a first area; The area in the shelf where the ratio is greater than or equal to the ratio threshold is divided into a second area.

5. The method for controlling the quality of fresh agricultural products during shelf life according to claim 1, characterized in that: The step of providing an early warning to the first areas according to the shelf life of the first agricultural products in each of the first areas comprises: Acquire historical sales data of the first agricultural product in each of the first regions, and calculate an average sales cycle of each of the first agricultural products; When the shelf life of the first agricultural product is less than a preset ratio of the corresponding average sales cycle, a first early warning mode is triggered; When the shelf life of the first agricultural product is greater than or equal to a preset ratio of the corresponding average sales cycle, determining whether the current inventory of the first agricultural product is greater than a target inventory; 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; When the current inventory of the first agricultural product is less than the target inventory, the third early warning mode is triggered.

6. The method for controlling the quality of fresh agricultural products during shelf life according to claim 1, characterized in that: The step of determining the target environmental parameters of the corresponding second areas according to the types of the second agricultural products in the second areas and the current physiological parameters includes: Acquire historical environmental parameters of the second agricultural product in each of the second regions, and a quality change rate of the second agricultural product under the historical environmental parameters; Determining an adjustment direction of an environmental parameter and an adjustment step size of the corresponding environmental parameter based on the quality change rate; The current environmental parameters are updated according to the adjustment direction and the adjustment step size to obtain the target environmental parameters.

7. The method for controlling the quality of fresh agricultural products during shelf life according to claim 1, characterized in that: The method further comprises: Detect the uniformity of environmental parameters in each area on the shelf; Determine an abnormal area where the environmental parameter is abnormal based on the uniformity of the environmental parameter; Analyze the control subsystems affected by the abnormal area; Calculating the compensation adjustment amount of the environmental parameter corresponding to the abnormal area; The abnormal area is locally regulated based on the compensation adjustment amount.

8. A fresh agricultural product shelf life quality control system, characterized in that: The system comprises: A shelf life determination module, used to obtain current physiological parameters of agricultural products in each area on the shelf, and determine the shelf life of agricultural products in each area according to the current physiological parameters; A region division module, used for dividing the shelf into a first region and a second region based on the shelf life; An early warning module, configured to issue an early warning to the first areas according to the shelf life of the first agricultural products in each of the first areas; An environmental parameter determination module, used to determine the target environmental parameters of the corresponding second area according to the type and current physiological parameters of the second agricultural product in each of the second areas; A control module is used to adjust the intelligent environmental control system of each second area according to the target environmental parameters, and the intelligent environmental control system includes at least a temperature control subsystem, a humidity control subsystem, a lighting control subsystem, a ventilation control subsystem and a disinfection control subsystem.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It 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 so that the electronic device executes the method as described in any one of claims 1-7.

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