A meal delivery cabinet and meal sorting control method and system thereof

Abstract

The application discloses a meal delivery cabinet and a meal sorting control method and system thereof, and belongs to the technical field of meal delivery cabinets, and comprises the following steps: collecting environment parameters in the meal delivery cabinet in real time, obtaining characteristic parameters of meal products and environment identification factors; calculating a moisture absorption rate representing a moisture absorption risk of the meal products, an oxidation rate representing an oxidation risk of the meal products, and a dew point temperature difference representing a dew risk; dynamically determining weight coefficients according to the environment parameters, the characteristic parameters and the environment identification factors, and performing weighted calculation on the moisture absorption rate, the oxidation rate and the dew point temperature difference by using the weight coefficients to obtain a comprehensive risk score; dividing the meal products into sorting priorities according to the comprehensive risk score, and generating corresponding sorting path instructions and environment parameter adjustment instructions based on the sorting priorities and the weight coefficients. The application improves the safety and energy efficiency of the meal products through dynamic risk assessment and intelligent sorting regulation, and is suitable for the fields of efficient and energy-saving industries, intelligent post-production drying and fine selection of agricultural products and the like.

Description

Technical Field

[0001] This invention belongs to the field of food delivery cabinet technology, and more specifically, relates to a food delivery cabinet and its food sorting and control method and system. Background Technology

[0002] As a crucial link between food delivery and end-users, the storage safety and quality assurance of the food stored in food delivery lockers are of paramount importance. Traditional food delivery locker environmental control strategies are often simplistic and static. For example, they may trigger dehumidification mode solely through a single humidity sensor, neglecting the synergistic effect between food oxidation and condensation risks. Alternatively, when adjusting the internal airflow, they may fail to fully consider the differentiated risks posed by the characteristics of different food packaging materials (such as permeability), leading to an imbalance between system energy consumption and risk control effectiveness, making it difficult to achieve refined and intelligent management of various types of food. In energy-efficient industries and post-harvest processing of agricultural products, such as energy-saving refrigerators, freezers, and intelligent precision sorting equipment for agricultural products, higher demands are placed on dynamic, precise, and energy-efficient control of the storage environment.

[0003] To improve the storage environment, existing technologies have proposed several control schemes based on preset parameters. For example, some schemes determine the required humidity and oxygen concentration ranges for the stored items and control the operation of the modified atmosphere storage system and fan to maintain the humidity and oxygen concentration within the cavity within the statically set ranges. However, the shortcomings of such schemes are that their control logic heavily relies on preset static thresholds, failing to assess dynamic risk indicators such as the actual moisture absorption rate and oxidation rate of the food in real time, and completely ignoring the critical factor of condensation risk. Furthermore, the lack of a mechanism for prioritizing food sorting and the fixed control weights lead to problems in practical applications such as high condensation rates, high system energy consumption, and a high rate of misjudgment of food risk status.

[0004] Therefore, there is an urgent need in this field for a control method and system that can sense the status of the environment and food in real time, dynamically assess multi-dimensional risks, and intelligently optimize sorting priorities and environmental parameters accordingly, so as to comprehensively improve the food safety assurance capabilities and operational efficiency of food delivery cabinets. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a food delivery cabinet and its food sorting and control method and system.

[0006] The present invention adopts the following technical solution.

[0007] A first aspect of the present invention provides a method for controlling the sorting of meals in a food delivery locker, comprising:

[0008] Real-time collection of environmental parameters inside the food delivery locker, acquisition of food characteristic parameters, and environmental identification factors;

[0009] Based on the environmental parameters and the characteristic parameters, calculate the moisture absorption rate, which characterizes the food's moisture absorption risk, the oxidation rate, which characterizes the food's oxidation risk, and the dew point temperature difference, which characterizes the condensation risk.

[0010] The weighting coefficients are dynamically determined based on the environmental parameters, the characteristic parameters, and the environmental identification factors. The weighting coefficients are then used to calculate the moisture absorption rate, the oxidation rate, and the dew point temperature difference to obtain a comprehensive risk score.

[0011] The food items are sorted according to the comprehensive risk score, and corresponding sorting path instructions and environmental parameter adjustment instructions are generated based on the sorting priority and the weight coefficient.

[0012] Optionally, the environmental parameters include current temperature, current humidity, wet-bulb temperature, and dew point temperature;

[0013] The characteristic parameters of the food include packaging permeability, oxidation sensitivity, oxidation sensitivity type identification, and humidity threshold.

[0014] The environmental labeling factors include the rainy season label and the diurnal temperature range label.

[0015] Optionally, the calculation of the moisture absorption rate includes: calculating the difference between the current humidity and the humidity threshold, and combining the packaging permeability to obtain the moisture absorption rate;

[0016] The calculation of the oxidation rate includes: calculating the difference between the wet-bulb temperature and the critical shelf-life temperature, and combining the oxidation sensitivity to obtain the oxidation rate;

[0017] The calculation of the dew point temperature difference includes: calculating the dew point temperature difference by means of the difference between the dew point temperature and the current temperature.

[0018] Optionally, the weighting coefficients include a moisture-proof weighting coefficient, a freshness-preserving weighting coefficient, and a condensation weighting coefficient, wherein:

[0019] The moisture-proof weighting coefficient is calculated based on the deviation of the current humidity from the preset benchmark humidity and the plum rain season indicator, and the maximum value of the moisture-proof weighting coefficient is 1.

[0020] The preservation weight coefficient is calculated based on the current temperature and the oxidation sensitivity type identifier, and the maximum value of the preservation weight coefficient is 1.

[0021] The dew point weighting coefficient is calculated by weighting the dew point temperature difference and the diurnal temperature difference indicator, and the maximum value of the dew point weighting coefficient is 1.

[0022] Optionally, prioritizing the sorting of meals based on the comprehensive risk score includes:

[0023] When the overall risk score is higher than the preset high priority threshold, it is determined to be a high priority meal.

[0024] When the comprehensive risk score is higher than the preset low priority threshold but not higher than the high priority threshold, it is determined to be a medium priority meal.

[0025] When the overall risk score is not higher than the low priority threshold, the food is judged as a low priority food.

[0026] Optionally, the generation of corresponding sorting path instructions and environmental parameter adjustment instructions includes:

[0027] For high-priority meals, generate sorting path instructions to the low-temperature zone, and generate environmental parameter adjustment instructions to reduce wind speed and start dehumidification mode;

[0028] For medium-priority meals, a sorting path instruction is generated to sort the meals to the ambient temperature zone after processing high-priority meals, and a targeted environmental parameter adjustment instruction is generated based on the dominant risk type, wherein the dominant risk type is determined by the weighting coefficient.

[0029] For low-priority meals, generate sorting path instructions for sorting along the normal path, and maintain environmental parameter adjustment instructions for normal wind speed.

[0030] Optionally, the rule for determining the dominant risk type is:

[0031] The risk type corresponding to the highest value among the moisture-proof weight coefficient, the freshness-preserving weight coefficient, and the condensation weight coefficient is selected as the dominant risk type:

[0032] When the moisture-proof weight coefficient is at its maximum value, the dominant risk type is determined to be moisture absorption risk;

[0033] When the preservation weight coefficient is at its maximum value, the dominant risk type is determined to be oxidation risk;

[0034] When the condensation weight coefficient is at its maximum value, the dominant risk type is determined to be condensation risk;

[0035] When two or three weighting coefficients are all at the maximum value, the dominant risk type is determined according to a preset priority order, wherein the priority order is that the condensation risk takes precedence over the moisture absorption risk, and the moisture absorption risk takes precedence over the oxidation risk.

[0036] Optionally, the targeted environmental parameter adjustment instructions include:

[0037] When the dominant risk type is the moisture absorption risk, the wind speed is adjusted to a first preset value, which is lower than the normal wind speed;

[0038] When the dominant risk type is oxidation risk, the wind speed is adjusted to a second preset value, which is higher than the normal wind speed.

[0039] When the dominant risk type is the condensation risk, adjust the wind speed to zero to shut off the air duct circulation.

[0040] A second aspect of the present invention provides a food delivery locker sorting control system for implementing the food delivery locker sorting control method described in the first aspect of the present invention, comprising:

[0041] The system includes an environmental sensor module, a food parameter library, a data interface module, a risk calculation module, a dynamic weight calculation module, a dynamic weighted calculation module, a sorting decision module, and an execution control module, among which:

[0042] The environmental sensor module is used to collect environmental parameters inside the food delivery cabinet in real time.

[0043] The food parameter library is used to obtain the characteristic parameters of the food.

[0044] The data interface module is used to obtain environmental identification factors;

[0045] The risk calculation module calculates the moisture absorption rate, the oxidation rate, and the dew point temperature difference, which characterize the food's moisture absorption risk, based on the environmental parameters and the characteristic parameters.

[0046] The dynamic weight calculation module is used to dynamically determine the weight coefficients based on the environmental parameters, the characteristic parameters, and the environmental identification factors;

[0047] The dynamic weighted calculation module is used to calculate the moisture absorption rate, the oxidation rate and the dew point temperature difference by using the weighting coefficients to obtain a comprehensive risk score;

[0048] The sorting decision module is used to classify the food items into sorting priorities based on the comprehensive risk score, and generate corresponding sorting path instructions and environmental parameter adjustment instructions based on the sorting priorities and the weight coefficients.

[0049] The execution control module is connected to the sorting decision module and is used to receive instructions issued by the module and drive the sorting robotic arm, air duct motor, dehumidifier and temperature control equipment to perform sorting path adjustment and environmental parameter adjustment operations.

[0050] A third aspect of the present invention provides a food delivery cabinet, comprising a food delivery cabinet food sorting control system as described in the second aspect of the present invention.

[0051] Compared with the prior art, the beneficial effects of the present invention include at least the following:

[0052] 1. This invention collects environmental parameters, food characteristic parameters, and environmental identification factors in real time, and calculates moisture absorption rate, oxidation rate, and dew point temperature difference based on these parameters. It dynamically determines the weighting coefficients and calculates the comprehensive risk score, thereby realizing a comprehensive quantitative assessment of food risk and dynamic sorting priority classification. This solves the problem of risk control imbalance caused by relying solely on static thresholds in traditional methods, and improves sorting efficiency and food safety.

[0053] 2. This invention achieves refined monitoring of the food storage environment and comprehensive coverage of risk factors by specifically defining environmental parameters including current temperature, current humidity, wet-bulb temperature and dew point temperature, and food characteristic parameters including packaging permeability, oxidation sensitivity, oxidation sensitivity type identification and humidity threshold. Environmental identification factors include rainy season identification and diurnal temperature difference identification. This solves the problem of inaccurate assessment caused by incomplete parameters in traditional methods.

[0054] 3. This invention achieves accurate quantification of the risks of moisture absorption, oxidation, and condensation in food products by specifically calculating the moisture absorption rate, oxidation rate, and dew point temperature difference, combined with packaging permeability and oxidation sensitivity, thus solving the problem of inaccurate risk calculation in traditional methods.

[0055] 4. This invention achieves adaptive adjustment of the weight coefficients by dynamically calculating the moisture-proof weight coefficient, freshness-preserving weight coefficient, and condensation weight coefficient, based on environmental parameters and environmental identification factors. This solves the problem that fixed weights cannot adapt to environmental changes and improves the accuracy of risk scoring.

[0056] 5. This invention classifies food items into high, medium, and low priority categories based on a comprehensive risk score, thereby enabling risk-level-based sorting decisions. This solves the problem of unclear sorting priorities in traditional methods and ensures that high-risk food items are processed first.

[0057] 6. This invention generates specific sorting path instructions and environmental parameter adjustment instructions for meals of different priorities. For example, it sorts high-priority meals to a low-temperature zone and starts a dehumidification mode, thereby achieving targeted risk control, solving the problem of one-size-fits-all environmental adjustment in traditional methods, and optimizing energy consumption and food safety.

[0058] 7. This invention achieves rapid identification and response to major risks by determining the dominant risk type based on the maximum value of the weight coefficient, and determining the type according to priority when multiple weight coefficients are the same, thus solving the problem of decision-making confusion when multiple risks coexist.

[0059] 8. This invention achieves precise adjustment of environmental parameters by adjusting wind speed according to the dominant risk type, such as reducing wind speed for moisture absorption risk, increasing wind speed for oxidation risk, and closing the air duct for condensation risk, thus solving the problem of wind speed adjustment not matching the risk type in traditional methods.

[0060] 9. This invention provides a food sorting control system for food delivery lockers, including an environmental sensor module, a food parameter library, a risk calculation module, etc., which realizes the automated execution of food sorting control in food delivery lockers and solves the problem of low efficiency of manual operation in traditional methods.

[0061] 10. This invention provides a food delivery cabinet that includes the above-mentioned system, which realizes intelligent and efficient food sorting and solves the problem of insufficient risk control in traditional delivery cabinets.

[0062] 11. This invention achieves precise environmental control and energy-saving operation of the food delivery cabinet through dynamic weighting and intelligent sorting decision-making, which is in line with the technological development trend of high-efficiency energy-saving industries and intelligent fine sorting equipment for agricultural products after harvest, and improves the intelligence level and industrial application value of the equipment. Attached Figure Description

[0063] Figure 1 This is a flowchart of a method provided according to an embodiment of the present invention. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0065] In Embodiment 1, this invention provides a method for controlling the sorting of meals in a food delivery locker, such as... Figure 1 As shown, it includes:

[0066] Step 1: Collect environmental parameters inside the food delivery cabinet in real time, obtain the characteristic parameters of the meals, and environmental identification factors.

[0067] Preferably, step 1 includes:

[0068] Step 1.1: Collect environmental parameters inside the delivery locker in real time using environmental sensors, including:

[0069] Current temperature Current humidity (RH) and wet-bulb temperature and dew point temperature ;

[0070] Step 1.2: Obtain the characteristic parameters of the current dish from the dish parameter library, including:

[0071] Packaging permeability, oxidative sensitivity (kox), oxidative sensitivity type identification (Oxidizable Meal), and humidity threshold. ;

[0072] Step 1.3: Obtain environmental identification factors from external interfaces (such as the Weather API), including:

[0073] Rain Season indicator (1 = Yes, 0 = No) and Large Temp Diff indicator (1 when the diurnal temperature difference is >8℃, otherwise 0).

[0074] It should be noted that this invention achieves comprehensive perception of the food storage environment and dynamic capture of risk factors by collecting environmental parameters, obtaining food characteristic parameters and environmental identification factors in real time, thus solving the problem of evaluation bias caused by incomplete data collection in traditional methods.

[0075] Step 2: Based on the environmental parameters and the characteristic parameters, calculate the moisture absorption rate (characterizing the food's moisture absorption risk), the oxidation rate (characterizing the food's oxidation risk), and the dew point temperature difference (characterizing the condensation risk).

[0076] Preferably, step 2 includes:

[0077] Step 2.1: Calculate the moisture absorption rate by measuring the difference between the current humidity and the humidity threshold, and combining this with the packaging permeability.

[0078] Specifically, the moisture absorption rate, which characterizes the risk of moisture absorption in food, is calculated:

[0079]

[0080] in, This is an empirical coefficient derived from historical data statistics.

[0081] Step 2.2: The oxidation rate is calculated by calculating the difference between the wet-bulb temperature and the critical shelf-life temperature, and in combination with the oxidation sensitivity.

[0082] Specifically, the oxidation rate, which characterizes the risk of food oxidation, is calculated:

[0083]

[0084] in, These are empirical coefficients derived from historical data. This refers to the critical temperature for the shelf life of food products.

[0085] Step 2.3: Calculate the dew point temperature difference using the difference between the dew point temperature and the current temperature.

[0086] Specifically, the dew point temperature difference characterizing the risk of condensation is calculated:

[0087]

[0088] It should be noted that this invention achieves precise quantification of multiple risks of food products by calculating the moisture absorption rate, oxidation rate, and dew point temperature difference, and combining them with the characteristics of the food products, thus solving the problem of single and static risk indicators in traditional methods.

[0089] Step 3: Dynamically determine the weighting coefficients based on the environmental parameters, the characteristic parameters, and the environmental identification factors, and use the weighting coefficients to perform a weighted calculation on the moisture absorption rate, the oxidation rate, and the dew point temperature difference to obtain a comprehensive risk score.

[0090] It should be noted that this invention solves the problem that fixed weights cannot adapt to complex environments such as sudden weather changes by introducing environmental factors such as the plum rain season indicator and the diurnal temperature difference indicator to dynamically calculate the three major weight coefficients of moisture prevention, freshness preservation and condensation. This improves the environmental adaptability and decision-making intelligence of the risk assessment model.

[0091] Preferably, step 3 includes:

[0092] Step 3.1: Based on the environmental parameters, characteristic parameters and environmental identification factors obtained in Step 1, dynamically calculate the weight coefficients of each risk dimension, including moisture-proof weight coefficient, freshness-preserving weight coefficient and condensation weight coefficient.

[0093] More preferably, step 3.1 includes:

[0094] The moisture-proof weighting coefficient is calculated based on the deviation of the current humidity from the preset benchmark humidity and the plum rain season indicator, and the maximum value of the moisture-proof weighting coefficient is 1.

[0095] Specifically, calculate the moisture-proof weighting coefficient:

[0096]

[0097] Of these, 70% is the preset baseline humidity.

[0098] The preservation weight coefficient is calculated based on the current temperature and the oxidation sensitivity type identifier, and the maximum value of the preservation weight coefficient is 1.

[0099] Specifically, calculate the preservation weighting coefficient:

[0100]

[0101] Among them, OxidizableMeal is an oxidation sensitivity type identifier obtained from the meal parameter library, 1=easily oxidizable meals such as nuts / dairy products, 0=other.

[0102] The dew point weighting coefficient is calculated by weighting the dew point temperature difference and the diurnal temperature difference indicator, and the maximum value of the dew point weighting coefficient is 1.

[0103] Specifically, calculate the condensation weighting coefficient:

[0104]

[0105] Step 3.2, and using the weighting coefficients, a weighted calculation is performed on the moisture absorption rate, the oxidation rate, and the dew point temperature difference to obtain a comprehensive risk score:

[0106]

[0107] It should be noted that this invention achieves adaptive optimization of risk assessment by dynamically determining weight coefficients and calculating comprehensive risk scores, solving the problem that fixed weights cannot respond to environmental changes, and improving the accuracy and real-time performance of the scores.

[0108] Step 4: Based on the comprehensive risk score, classify the food items into sorting priorities, and based on the sorting priorities and the weight coefficients, generate corresponding sorting path instructions and environmental parameter adjustment instructions.

[0109] Preferably, step 4 includes:

[0110] Step 4.1: When the comprehensive risk score is higher than the preset high priority threshold, it is determined to be a high priority meal.

[0111] When the comprehensive risk score is higher than the preset low priority threshold but not higher than the high priority threshold, it is determined to be a medium priority meal.

[0112] When the overall risk score is not higher than the low priority threshold, the food is judged as a low priority food.

[0113] Specifically, the calculated comprehensive risk score is compared with the system's preset threshold to determine sorting priorities:

[0114] like If so, it is determined to be of high priority;

[0115] like If so, it is determined to be of medium priority;

[0116] like If so, it is judged as low priority;

[0117] in, To preset a high-priority threshold, This is a preset low-priority threshold.

[0118] It should be noted that this invention automatically classifies high, medium, and low sorting priorities by comparing the comprehensive risk score with preset high and low priority thresholds. This solves the problem of the traditional first-come-first-served sorting method that ignores the real-time risk status of the food, and ensures that high-risk food items are disposed of first.

[0119] Step 4.2: Based on the sorting priority and the weight coefficient, generate corresponding sorting path instructions and environmental parameter adjustment instructions, including:

[0120] For high-priority meals, generate sorting path instructions to the low-temperature zone, and generate environmental parameter adjustment instructions to reduce wind speed and start dehumidification mode;

[0121] For medium-priority meals, a sorting path instruction is generated to sort the meals to the ambient temperature zone after processing high-priority meals, and a targeted environmental parameter adjustment instruction is generated based on the dominant risk type, wherein the dominant risk type is determined by the weighting coefficient.

[0122] For low-priority meals, generate sorting path instructions for sorting along the normal path, and maintain environmental parameter adjustment instructions for normal wind speed.

[0123] More preferably, the rule for determining the dominant risk type is:

[0124] The risk type corresponding to the highest value among the moisture-proof weight coefficient, the freshness-preserving weight coefficient, and the condensation weight coefficient is selected as the dominant risk type:

[0125] When the moisture-proof weight coefficient is at its maximum value, the dominant risk type is determined to be moisture absorption risk;

[0126] When the preservation weight coefficient is at its maximum value, the dominant risk type is determined to be oxidation risk;

[0127] When the condensation weight coefficient is at its maximum value, the dominant risk type is determined to be condensation risk;

[0128] When two or three weighting coefficients are all at the maximum value, the dominant risk type is determined according to a preset priority order, wherein the priority order is that the condensation risk takes precedence over the moisture absorption risk, and the moisture absorption risk takes precedence over the oxidation risk.

[0129] More preferably, the targeted environmental parameter adjustment command includes:

[0130] When the dominant risk type is the moisture absorption risk, the wind speed is adjusted to a first preset value, which is lower than the normal wind speed;

[0131] When the dominant risk type is oxidation risk, the wind speed is adjusted to a second preset value, which is higher than the normal wind speed.

[0132] When the dominant risk type is the condensation risk, adjust the wind speed to zero to shut off the air duct circulation.

[0133] It should be noted that this invention achieves intelligent sorting and environmental control based on risk levels by dividing sorting priorities according to comprehensive risk scores and generating sorting paths and environmental adjustment instructions. This solves the problem of the disconnect between sorting strategies and environmental parameters in traditional methods, and significantly improves food safety and system energy efficiency.

[0134] In Embodiment 2, this invention provides a food delivery locker sorting control system for implementing the food delivery locker sorting control system described in Embodiment 1, comprising:

[0135] The system includes an environmental sensor module, a food parameter library, a data interface module, a risk calculation module, a dynamic weight calculation module, a dynamic weighted calculation module, a sorting decision module, and an execution control module, among which:

[0136] The environmental sensor module is used to collect environmental parameters inside the food delivery cabinet in real time.

[0137] The food parameter library is used to obtain the characteristic parameters of the food.

[0138] The data interface module is used to obtain environmental identification factors;

[0139] The risk calculation module calculates the moisture absorption rate, the oxidation rate, and the dew point temperature difference, which characterize the food's moisture absorption risk, based on the environmental parameters and the characteristic parameters.

[0140] The dynamic weight calculation module is used to dynamically determine the weight coefficients based on the environmental parameters, the characteristic parameters, and the environmental identification factors;

[0141] The dynamic weighted calculation module is used to calculate the moisture absorption rate, the oxidation rate and the dew point temperature difference by using the weighting coefficients to obtain a comprehensive risk score;

[0142] The sorting decision module is used to classify the food items into sorting priorities based on the comprehensive risk score, and generate corresponding sorting path instructions and environmental parameter adjustment instructions based on the sorting priorities and the weight coefficients.

[0143] The execution control module is connected to the sorting decision module and is used to receive instructions issued by the module and drive the sorting robotic arm, air duct motor, dehumidifier and temperature control equipment to perform sorting path adjustment and environmental parameter adjustment operations.

[0144] It should be noted that this invention provides a food sorting control system for a food delivery locker, which integrates environmental sensors, a food parameter library, and a risk calculation and decision-making module. This achieves automation and intelligence throughout the entire food sorting process, solving the problems of traditional delivery lockers that rely on manual intervention, are inefficient, and have high errors.

[0145] In Embodiment 3, the present invention provides a food delivery cabinet, which includes the food delivery cabinet food sorting control system described in Embodiment 2.

[0146] It should be noted that by integrating the sorting control system into the food delivery cabinet, the present invention achieves integrated intelligent management of food storage and sorting, solving the problems of single function and insufficient risk control capability of traditional delivery cabinets.

[0147] This invention provides an application example of a food delivery locker sorting control method in Embodiment 4, based on the food delivery locker sorting control method described in Embodiment 1 and the food delivery locker sorting control system described in Embodiment 2, comprising:

[0148] Step 1: Collect environmental parameters inside the delivery locker in real time using an environmental sensor module, including the current temperature. Current humidity (RH) and wet-bulb temperature and dew point temperature ;

[0149] Retrieve the characteristic parameters of the current dish from the dish parameter database, including:

[0150] Packaging permeability, oxidative sensitivity (kox), oxidative sensitivity type identification (Oxidizable Meal), and humidity threshold. ;

[0151] Environmental identification factors are obtained through the data interface module, including:

[0152] Rain Season indicator (1 = Yes, 0 = No) and Large Temp Diff indicator (1 when the diurnal temperature difference is >8℃, otherwise 0).

[0153] Step 2: Based on the environmental parameters and the characteristic parameters, the risk calculation module calculates the moisture absorption rate (characterizing the food's moisture absorption risk), the oxidation rate (characterizing the food's oxidation risk), and the dew point temperature difference (characterizing the condensation risk).

[0154] Calculate the moisture absorption rate by considering the packaging permeability of the food products. :

[0155]

[0156] in, This is an empirical coefficient. The acceptable humidity threshold for food products. and Automatic matching based on the food parameter database, determined by the food type. This indicates the effect of humidity on moisture absorption. The acceptable humidity threshold for food items, such as nuts. The value is 0.05, as nuts are easily hygroscopic foods. High, The acceptable humidity level for nuts is 60%. Packaging Permeability is determined by the food packaging material, such as aluminum foil composite film, which has a permeability of 0.4 g / (m²·day). RH represents the current ambient humidity.

[0157] The oxidation rate (Rox) is calculated based on wet-bulb temperature and the food's oxidation sensitivity (kox):

[0158]

[0159] in, This is an empirical coefficient. The critical temperature for the shelf life of food products. , Kox automatically matches data using its menu parameter database, for example:

[0160] The amount depends on the type of meal; for nuts, it's 0.05 (nuts are high in fat). Higher (the intensity of the effect of temperature on oxidation).

[0161] The shelf life is determined by the characteristics of the food, such as nuts at 25°C (the temperature at which food begins to spoil).

[0162] The kox value is determined by the oxidation sensitivity of a food item, and it ranges from 0 to 1. A higher value indicates a higher oxidation sensitivity of the food item. For example, the kox value for highly sensitive food items such as nuts and dairy products is 0.9.

[0163] The dew point temperature difference, which characterizes the risk of condensation, is calculated based on the difference between the dew point temperature and the current ambient temperature.

[0164]

[0165] Among them, if >0: The air is saturated, and there is a risk of condensation. This risk will be assessed using a weighting factor. Incorporate the overall risk score (RiskScore).

[0166] like ≤0: Air is unsaturated, risk of condensation is low; this risk will be assessed using a weighting factor. Lower the overall risk score.

[0167] Step 3: The dynamic weight calculation module dynamically determines the weight coefficients based on the environmental parameters, the characteristic parameters, and the environmental identification factors, including the moisture-proof weight coefficient, the freshness-preserving weight coefficient, and the condensation weight coefficient.

[0168] Calculate the moisture-proof weighting factor:

[0169]

[0170] Of this, 70% is the preset baseline humidity;

[0171] 0.3 is The baseline value, derived from historical data statistics, shows that even with normal humidity (70%), the risk of moisture still contributes a baseline of 0.3, which is dimensionless (percentage).

[0172] 0.5 is the humidity coefficient, derived from historical data statistics;

[0173] 0.2 is the plum rain season coefficient. Based on historical analysis of the plum rain season data of the past 5 years, the weight of moisture prevention during the plum rain season needs to be increased by an additional 20%. It is dimensionless.

[0174] Calculate the preservation weighting coefficient:

[0175]

[0176] Where, 0.2 is The baseline value, at normal temperatures (e.g., 20°C), still contributes 0.2 to the preservation risk (based on historical data, the preservation risk at 20°C accounts for 20% of the total risk), and is dimensionless.

[0177] 0.4 is the temperature coefficient, which is dimensionless;

[0178] 0.3 is the oxidation sensitivity coefficient, which is dimensionless.

[0179] Calculate the condensation weighting factor:

[0180]

[0181] Where 0.1 is The base value;

[0182] 0.6 is the condensation coefficient, which is dimensionless;

[0183] 0.3 is the diurnal temperature range coefficient, which is dimensionless.

[0184] The dynamic weighted calculation module uses the weighting coefficients to perform a weighted calculation on the moisture absorption rate, the oxidation rate, and the dew point temperature difference to obtain a comprehensive risk score:

[0185]

[0186] Step 4: Based on the comprehensive risk score, classify the food items into sorting priorities, and based on the sorting priorities and the weight coefficients, generate corresponding sorting path instructions and environmental parameter adjustment instructions.

[0187] Prioritizing sorting includes:

[0188] The system presets a high priority threshold. and low priority threshold (For example: =0.8, =0.3);

[0189] when When this happens, it is determined to be of high priority;

[0190] when When that happens, it is classified as medium priority;

[0191] when When that happens, it is judged as low priority.

[0192] The sorting order and route are dynamically adjusted based on priority to ensure that high-risk food items are processed first.

[0193] High-priority food items are immediately sorted to avoid prolonged exposure to high-risk environments. They are directly allocated to low-temperature zones (such as refrigerated or temperature-controlled areas), with fan speeds reduced to minimize moisture evaporation and prevent further moisture absorption. Dehumidification mode is also activated to lower humidity through condensation or desiccants, reducing the risk of moisture absorption, oxidation, and condensation.

[0194] Medium-priority meals are processed after high-priority meals have been sorted. They are allocated to the ambient temperature zone (not a constant temperature zone, but with lower environmental risk), and specific risks are controlled simultaneously: High risk of moisture absorption: reduce airflow to decrease moisture absorption. High risk of oxidation: increase airflow to accelerate oxygen diffusion. High risk of condensation: shut off airflow circulation (v=0) to prevent condensation.

[0195] Low-priority meals: Processed after medium-priority meals have been sorted. Sorted along the standard route (normal area without special temperature and humidity control). No special adjustments required. Maintain normal fan speed to save energy. No special dehumidification / cooling measures required: only basic storage conditions need to be met.

[0196] For example, this system manages the interior of the delivery locker by dividing it into three temperature zones, with the specific definitions and configurations as follows:

[0197] Low-temperature zone: The temperature is maintained at 0–8℃. This zone is a cold storage area, employing constant temperature control and equipped with an active dehumidification module to ensure ambient humidity RH ≤ 65%. This temperature range is the core food safety range for inhibiting microbial growth and controlling the rate of oxidation (Rox). Simultaneously, the dehumidification module ensures a safe dew point temperature difference. ≤0, thus fundamentally eliminating the risk of condensation.

[0198] Normal temperature zone: The temperature is maintained at 15–25℃. This zone is a safe transition area, where risk control is achieved through adjustable air ducts and dynamic wind speed adjustment (switching between 1.0m / s, 3.0m / s, and 0m / s). No active dehumidification is required, and the ambient humidity RH ≤ 75%. This design avoids the high energy consumption of the low-temperature zone while effectively controlling the quality of medium-risk dishes, and its energy consumption is significantly reduced compared to the low-temperature zone.

[0199] Normal area: Temperature range of 25–30℃. This area does not have special active temperature and humidity control, relying on natural environmental conditions, and only maintaining a normal wind speed of 1.5m / s to ensure that the ambient humidity RH ≤ 70%. This temperature range is the upper limit of the safe room temperature that ordinary food (such as vacuum-packed food) can withstand. Because it does not require additional energy consumption, the overall energy-saving goal of the system can be achieved.

[0200] Specifically, the environmental parameter adjustment instructions generated by the system for medium-priority meals include wind speed adjustments relative to a unified benchmark—the standard wind speed. This standard wind speed is the default operating parameter set for low-priority meals, with an example value of 1.5 m / s. Based on this benchmark, the specific wind speed adjustment rules are defined as follows:

[0201] Reduce wind speed: This refers to adjusting the wind speed to 1.0 m / s, which is about 2 / 3 of the normal wind speed, in order to reduce moisture evaporation and absorption.

[0202] Increase wind speed: This refers to adjusting the wind speed to 3.0 m / s, which is about twice the normal wind speed, in order to accelerate oxygen diffusion and delay oxidation.

[0203] This invention achieves a comprehensive quantitative assessment of food safety risks by comprehensively calculating moisture absorption rate, oxidation rate, and condensation risk. Based on the real-time calculation results of risk indicators, high / medium / low priority sorting tasks are dynamically assigned, and corresponding sorting paths and environmental parameters are matched. According to the characteristics of the food and the type of risk, the temperature, humidity, airflow, and dehumidification mode within the delivery cabinet are dynamically adjusted to improve sorting efficiency and food safety.

[0204] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for controlling the sorting of meals in a food delivery locker, characterized in that, The method comprises: real-time acquisition of environmental parameters in the meal delivery cabinet, obtaining characteristic parameters of the meal, and environmental identification factors, wherein the environmental parameters include current temperature, current humidity, wet-bulb temperature and dew point temperature; the characteristic parameters of the meal include packaging permeability, oxidation sensitivity, oxidation sensitivity type identification and humidity threshold; the environmental identification factors include Meiyu season identification and day-night temperature difference identification; based on the environmental parameters and the characteristic parameters, calculating the moisture absorption rate representing the moisture absorption risk of the meal, the oxidation rate representing the oxidation risk of the meal, and the dew point temperature difference representing the condensation risk, wherein: the calculation of the moisture absorption rate includes: calculating the difference between the current humidity and the humidity threshold, and combining the packaging permeability to obtain the moisture absorption rate; the calculation of the oxidation rate includes: calculating the difference between the wet-bulb temperature and the shelf life critical temperature, and combining the oxidation sensitivity to obtain the oxidation rate; the calculation of the dew point temperature difference includes: calculating the difference between the dew point temperature and the current temperature to obtain the dew point temperature difference; dynamically determining a weight coefficient according to the environmental parameters, the characteristic parameters and the environmental identification factors, and performing weighted calculation on the moisture absorption rate, the oxidation rate and the dew point temperature difference using the weight coefficient to obtain a comprehensive risk score; according to the comprehensive risk score, dividing the meal into a sorting priority, and generating corresponding sorting path instructions and environmental parameter adjustment instructions based on the sorting priority and the weight coefficient.

2. The meal delivery cabinet meal sorting control method according to claim 1, wherein: the weight coefficient includes a moisture-proof weight coefficient, a preservation weight coefficient and a condensation weight coefficient, wherein: the moisture-proof weight coefficient is weighted calculated based on the deviation of the current humidity relative to the preset reference humidity and the Meiyu season identification, and the maximum value of the moisture-proof weight coefficient is 1; the preservation weight coefficient is weighted calculated based on the current temperature and the oxidation sensitivity type identification, and the maximum value of the preservation weight coefficient is 1; the condensation weight coefficient is weighted calculated based on the dew point temperature difference and the day-night temperature difference identification, and the maximum value of the condensation weight coefficient is 1.

3. The meal delivery cabinet meal sorting control method according to claim 2, wherein: the division of the meal into a sorting priority according to the comprehensive risk score includes: when the comprehensive risk score is higher than a preset high priority threshold, it is determined as a high priority meal; when the comprehensive risk score is higher than a preset low priority threshold and not higher than the preset high priority threshold, it is determined as a medium priority meal; when the comprehensive risk score is not higher than the preset low priority threshold, it is determined as a low priority meal.

4. The meal delivery cabinet meal sorting control method according to claim 3, wherein: the generation of corresponding sorting path instructions and environmental parameter adjustment instructions includes: for high priority meals, generate sorting path instructions to sort into a low temperature zone, and generate environmental parameter adjustment instructions to reduce wind speed and start dehumidification mode. For medium-priority dishes, generate sorting path instructions for sorting to the normal-temperature zone after high-priority dish processing, and generate targeted environmental parameter adjustment instructions based on the dominant risk type determined by the weight coefficient; For low-priority dishes, generate sorting path instructions for sorting by the regular path, and maintain environmental parameter adjustment instructions for the regular wind speed.

5. The meal delivery cabinet dish sorting control method according to claim 4, characterized in that: The determination rule of the dominant risk type is: Select the risk type corresponding to the highest value among the moisture-proof weight coefficient, the preservation weight coefficient, and the dew weight coefficient as the dominant risk type: When the moisture-proof weight coefficient is the maximum value, determine the dominant risk type as the moisture absorption risk; When the preservation weight coefficient is the maximum value, determine the dominant risk type as the oxidation risk; When the dew weight coefficient is the maximum value, determine the dominant risk type as the dew risk; When two or three weight coefficients are the maximum value, then determine the dominant risk type according to the preset priority order, wherein the priority order is that the dew risk is prior to the moisture absorption risk, and the moisture absorption risk is prior to the oxidation risk.

6. The meal delivery cabinet dish sorting control method according to claim 5, characterized in that: The targeted environmental parameter adjustment instructions include: When the dominant risk type is the moisture absorption risk, adjust the wind speed to a first preset value, which is lower than the regular wind speed; When the dominant risk type is the oxidation risk, adjust the wind speed to a second preset value, which is higher than the regular wind speed; When the dominant risk type is the dew risk, adjust the wind speed to zero to close the air duct circulation.

7. A meal delivery cabinet meal item sorting control system for implementing a meal delivery cabinet meal item sorting control method according to any one of claims 1-6, characterized by, Comprise: An environmental sensor module, a dish parameter library, a data interface module, a risk calculation module, a dynamic weight calculation module, a dynamic weighting calculation module, a sorting decision module, and an execution control module, wherein: The environmental sensor module is used to collect environmental parameters in the meal delivery cabinet in real time, and the environmental parameters include the current temperature, the current humidity, the wet bulb temperature, and the dew point temperature; The dish parameter library is used to obtain characteristic parameters of dishes, and the characteristic parameters of dishes include the packaging permeability, the oxidation sensitivity, the oxidation sensitivity type identifier, and the humidity threshold; The data interface module is used to obtain environmental identification factors, and the environmental identification factors include the Meiyu season identifier and the day and night temperature difference identifier; The risk calculation module calculates the moisture absorption rate representing the moisture absorption risk of dishes, the oxidation rate representing the oxidation risk of dishes, and the dew point temperature difference representing the dew risk based on the environmental parameters and the characteristic parameters, wherein: The calculation of the moisture absorption rate includes: calculating the difference between the current humidity and the humidity threshold, and combining the packaging permeability to obtain the moisture absorption rate; The calculation of the oxidation rate includes: calculating the difference between the wet bulb temperature and the shelf life critical temperature, and combining the oxidation sensitivity to obtain the oxidation rate; The dew point temperature difference is calculated by a difference between the dew point temperature and the current temperature; The dynamic weight calculation module is configured to dynamically determine a weight coefficient according to the environmental parameter, the characteristic parameter, and the environmental identification factor; The dynamic weight calculation module is configured to perform weighted calculation on the hygroscopic rate, the oxidation rate, and the dew point temperature difference by using the weight coefficient to obtain a comprehensive risk score; The sorting decision module is configured to divide a sorting priority of the meal product according to the comprehensive risk score, and generate corresponding sorting path instructions and environmental parameter adjustment instructions based on the sorting priority and the weight coefficient; The execution control module is connected with the sorting decision module, configured to receive the instructions issued by the sorting decision module, and drive a sorting mechanical arm, an air duct motor, a dehumidifier, and a temperature control device to perform sorting path adjustment and environmental parameter adjustment operations.

8. A meal delivery cabinet, characterized by, A meal delivery cabinet meal product sorting control system comprising the meal delivery cabinet meal product sorting control system according to claim 7.

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