Frostless refrigeration house system based on humidity active control and intelligent control method thereof

Abstract

The invention discloses a frostless cold storage system based on humidity active control and an intelligent control method thereof, and relates to the field of frostless cold storage systems, and the method comprises the following steps: S1, establishing a cargo humidity demand database; s2, setting a corresponding target humidity value according to the cargo humidity demand database; s3, humidity changes in the refrigeration house are monitored in real time through a humidity sensor, and monitoring data are fed back to the control system; s4, the humidifying power and the spraying frequency of the humidifying equipment are dynamically adjusted; s5, judging whether the humidity in the refrigeration house exceeds the standard or not in real time. According to the invention, AI image identification, hyperspectral imaging and RFID technologies are integrated through the multi-mode identification device, tiny characters, patterns and special identifiers on the packaging box can be accurately identified, and the problem of low accuracy of traditional bar code / two-dimensional code identification in a complex scene is overcome; and meanwhile, the RFID technology supports non-contact batch cargo information reading, so that the cargo warehousing efficiency is greatly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of frost-free cold storage systems, in particular to a frost-free cold storage system based on humidity active control and an intelligent control method thereof. BACKGROUND

[0002] In the field of cold chain logistics and warehousing, the humidity environment in the cold storage plays a key role in the quality of goods storage. Different types of goods, such as fresh fruits and vegetables, pharmaceutical products, and industrial raw materials, have specific requirements for storage humidity. Suitable humidity can effectively prolong the shelf life of goods and prevent deterioration or damage. However, existing cold storage humidity control technology still has many limitations and cannot meet the needs of fine and intelligent warehousing.

[0003] Currently, most cold storage systems use single barcodes or two-dimensional codes to identify goods information. When facing small text, blurred patterns, or special material packaging, the recognition accuracy is low, and batch rapid identification cannot be achieved, resulting in low efficiency of goods storage. At the same time, it is also difficult to accurately match the humidity requirements of goods. In terms of humidity regulation, traditional cold storage usually uses fixed humidification or dehumidification strategies, lacking comprehensive consideration of humidity, temperature, time, and goods characteristics. It cannot dynamically adjust the humidity control scheme according to the actual needs of goods. For example, for humidity-sensitive fruit and vegetable goods, the traditional control method cannot quickly reduce humidity to inhibit the respiratory effect in the early stage of storage, nor can it maintain stable humidity during storage, which easily leads to goods rotting, water loss, and other problems. At the same time, when approaching the target humidity, the equipment starts and stops frequently, resulting in high energy consumption and poor control effect.

[0004] In addition, existing cold storage systems have deficiencies in humidity abnormality handling. When the humidity exceeds the standard, relying solely on simple stop humidification operation lacks systematic defrosting warning and prevention mechanism, and cannot effectively avoid the problem of frosting caused by excessive humidification, thereby affecting the refrigeration efficiency of the cold storage and the safety of goods storage. In terms of energy saving and environmental protection, traditional cold storage often ignores the recycling of condensate water, causing water resource waste, and does not fully utilize new technologies such as phase change materials to optimize humidity regulation energy consumption.

[0005] In summary, the existing cold storage humidity control technology has obvious defects in goods identification, dynamic regulation, abnormality handling, and energy consumption management, and there is an urgent need for a frost-free cold storage humidity control scheme that can achieve accurate identification, multi-dimensional dynamic control, intelligent warning, and energy saving and efficiency improvement. SUMMARY

[0006] The present application aims to provide a frost-free cold storage system based on humidity active control and an intelligent control method thereof to solve the problems raised in the background.

[0007] To achieve the above object, the present application provides the following technical solutions: a frost-free cold storage system based on humidity active control, comprising the following steps:

[0008] S1, establishing a goods humidity demand database to store target humidity values corresponding to different types of goods;

[0009] S2, when goods packaged with packaging boxes are stored, identifying the type of goods by automatically identifying the identification information on the outer packaging of the goods through a multi-modal recognition device, and setting the corresponding target humidity value according to the goods humidity demand database;

[0010] S3, using a humidity sensor to monitor the humidity change in the cold storage in real time, and feeding the monitoring data to the control system;

[0011] S4, the control system dynamically adjusts the humidification power and spray frequency of the humidification equipment according to the deviation and change rate of the humidity monitoring data and the target humidity value, controls the humidification equipment to use ultrasonic humidification or steam humidification when the humidity is lower than the target value, controls the humidity within the target range by controlling the humidification amount and humidification time, and gradually reduces the humidification intensity when the humidity approaches the target value;

[0012] S5, the control system judges whether the humidity in the cold storage is out of standard in real time during the humidification process, and if it is out of standard, stops the humidification equipment from working, and starts a defrosting warning mechanism to maintain the humidity in the cold storage stable by controlling the air circulation and temperature regulation in the cold storage.

[0013] Preferably, the multi-modal recognition device performs the following operations:

[0014] The AI image recognition unit analyzes the text and pattern identification on the surface of the packaging box;

[0015] The hyperspectral imaging unit captures the spectral characteristics of the special anti-counterfeiting mark of the packaging box;

[0016] When the visual recognition confidence is lower than the threshold value, the RFID batch reading unit is activated for non-contact verification;

[0017] According to the identification result, the target humidity value is retrieved from the database.

[0018] Preferably, the operation of dynamically adjusting the working parameters of the humidification equipment adopts a fuzzy self-adaptive PID control algorithm, comprising the following steps:

[0019] S41, real-time acquisition of the set humidity value RH set and the monitoring humidity value RH real , calculating the humidity deviation value E = RH set -RH real , wherein the unit of E is percentage (%);

[0020] S42, calculate the deviation rate of change with a fixed sampling period Δt = 10 seconds wherein the unit of EC is percentage per minute (% / min);

[0021] S43, dynamically set the control parameters according to the interval range of the absolute value of the deviation |E|:

[0022] When |E|≥10%, set the proportional coefficient K p = 1.2, the differential coefficient K d = 0.8;

[0023] When 5%≤|E|<10%, set K p = 1.0, K d = 0.6;

[0024] When |E|<5%, set K p = 0.8, K d = 0.5;

[0025] S44, generate the humidification power adjustment amount ΔP, whose unit is kilowatt (kW), and the calculation formula is:

[0026]

[0027] wherein: the upper limit of integration t≤5 minutes, and α is the integral suppression factor, and α = 0.05, represents the historical deviation cumulative amount;

[0028] S45, generate the spray frequency adjustment amount ΔF, whose unit is hertz (Hz), and the calculation formula is:

[0029] ΔF = β·|EC| + γ·S(E)

[0030] wherein: β = 0.3 Hzmin / %, γ = 2 Hz, and S(E) is a sign function, which is defined as:

[0031]

[0032] Preferably, the start defrosting early warning mechanism comprises:

[0033] When the humidity exceeds the target value by 10%, immediately stop the humidification equipment and trigger the third level early warning;

[0034] Control the air circulation system to run at the maximum wind speed, and reduce the warehouse temperature by 0.5-2℃ to accelerate the condensation and recovery of moisture;

[0035] Generate a frosting risk thermodynamic map in the control terminal, and mark the coordinates of the high humidity area in the cold storage.

[0036] Preferably, the control system controls the humidification equipment to rapidly reduce humidity at the initial stage of storage, maintains stable humidity during the storage period, and gradually controls the humidity to rise before the fruit and vegetable cargo is taken out of the warehouse, and optimizes the humidity adjustment energy consumption in combination with the operation period of the cold storage. The control system integrates meteorological data, transportation route temperature and humidity information, and historical storage data, uses machine learning algorithms to predict the humidity demand change trend of the cargo in the warehouse, and automatically links the ventilation system, humidification, and dehumidification equipment when the humidity is abnormal, and triggers a warning to the management personnel terminal.

[0037] Preferably, the humidity-temperature-time multi-dimensional correlation model is constructed by the following method:

[0038] When the temperature is greater than or equal to 0°C and less than or equal to 10°C: the storage time is less than or equal to 24 hours, and the humidity is controlled at 85%-90% RH; 24 hours

[0039] When the temperature is greater than 10°C and less than or equal to 20°C: the storage time is less than or equal to 12 hours, and the humidity is controlled at 80%-85% RH; 12 hours

[0040] When the temperature is greater than 20°C: the storage time is less than or equal to 8 hours, and the humidity is controlled at 75%-80% RH; 8 hours

[0041] When the cold storage is in the night low peak period, the lower limit of all humidity control intervals is increased by 5% RH, and the upper limit is maintained unchanged, so as to reduce the energy consumption.

[0042] Preferably, when the cargo is leafy vegetables, the humidity-temperature-time multi-dimensional correlation model is based on the above:

[0043] When the temperature is less than or equal to 5°C, the upper limit of the humidity control interval of each time period is increased by 3% RH;

[0044] When the storage time is greater than 48 hours, and the monitored humidity fluctuation rate is greater than 0.5% RH / hour, the leaf surface dew prevention program is started, specifically: every 2 hours, 5 minutes of low wind speed ventilation cycle is carried out, and the wind speed is controlled at 0.3-0.5 m / s;

[0045] Within 12 hours before the cargo is taken out of the warehouse, the humidity gradually rises to 95%-98% RH, and the temperature rises by 0.5°C per hour until the required temperature is reached.

[0046] Preferably, when the goods are root vegetables, based on the above-mentioned humidity-temperature-time multi-dimensional correlation model:

[0047] When the temperature is ≥15℃, the lower limit of the humidity control range for each time period is reduced by 5%RH.

[0048] When the storage time is greater than 72 hours, start the soil moisture balancing program and perform local humidification for 10 minutes every 12 hours to make the humidity inside the goods packaging 2%-3% RH higher than the humidity inside the warehouse.

[0049] When the temperature fluctuation rate is detected to be greater than 1°C / hour, the humidity control range is increased by 2% RH to enhance the cold resistance of the cargo.

[0050] Preferably, when the goods are tropical fruits, based on the above-mentioned humidity-temperature-time multi-dimensional correlation model, and when the temperature is >20℃, the following control strategy is preferentially implemented:

[0051] Throughout the storage period, humidity is controlled at 85%-90%RH and temperature is controlled at 12-15℃;

[0052] When the storage time is greater than 24 hours, the ethylene concentration is tested every 6 hours. When the ethylene concentration is greater than 0.1 ppm, the air purification program is started, and the lower limit of the humidity control range is increased by 3% RH.

[0053] 24 hours before leaving the warehouse, the humidity is gradually reduced to 80%-85%RH, while the temperature is increased by 1°C per hour to adapt to the external environment.

[0054] Another objective of this invention is to provide an intelligent control method for frost-free cold storage based on active humidity control, comprising:

[0055] A humidity requirement database is used to store target humidity values ​​for different types of goods;

[0056] Multimodal identification devices are used to identify marking information on cargo packaging and determine the type of cargo by using visual recognition technology, RFID technology and hyperspectral imaging technology;

[0057] Humidity sensor used to monitor humidity data in cold storage in real time;

[0058] Humidification equipment, using ultrasonic humidification or steam humidification, is used for humidifying cold storage facilities;

[0059] The control system is connected to the database, the identification device, the humidity sensor, and the humidification equipment, and is used to retrieve the target humidity value from the database according to the identified type of goods; and dynamically adjust the humidification power and spray frequency of the humidification equipment based on the deviation and rate of change between the humidity monitoring data and the target value.

[0060] An air circulation system is connected to the control system for performing a defrosting pre-warning operation.

[0061] Technical effects and advantages of the present application:

[0062] (1) The present application can accurately identify the small text, patterns and special marks on the packaging box through the integration of AI image recognition, hyperspectral imaging and RFID technology in the multi-modal recognition device, overcoming the low accuracy problem of traditional bar code / QR code recognition in complex scenes. At the same time, the RFID technology supports non-contact batch goods information reading, greatly improves the goods storage efficiency, and ensures the fast and accurate matching of the corresponding target humidity value of the goods, providing a reliable foundation for subsequent humidity control.

[0063] (2) The present application formulates a differentiated humidity control strategy by constructing a humidity-temperature-time multi-dimensional correlation model combined with the types of goods, such as using gradient humidity control for humidity-sensitive fruit and vegetable goods (rapidly reducing humidity in the early stage of storage, stably maintaining in the middle stage of storage, and gradually rising before delivery). Compared with the traditional fixed control strategy, it can significantly delay the respiration of goods, reduce rotting and water loss, and prolong the shelf life of goods. The fuzzy self-adaptive PID control algorithm is used to dynamically adjust the working parameters of the humidification equipment, realize the smooth transition of humidification intensity, avoid frequent start and stop of the equipment, improve the humidity control accuracy and reduce energy consumption.

[0064] (3) The present application uses a defrosting pre-warning mechanism. When the humidity in the cold storage exceeds the standard, the defrosting pre-warning mechanism uses a hierarchical processing strategy to effectively avoid the problem of excessive humidification causing frosting, maintain the stability of the temperature and humidity environment in the cold storage, and ensure the refrigeration efficiency and the safety of goods storage. At the same time, the machine learning algorithm integrates meteorological data, transportation route information and historical storage data to predict the abnormal humidity trend in advance, and automatically links the ventilation, humidification / dehumidification equipment to realize active humidity regulation and reduce the cost of manual intervention.

[0065] (4) The present application uses a condensate water recycling system to purify the moisture generated during the dehumidification process and use it for humidification function, reducing water resource waste. The introduction of phase change material can absorb / release moisture through physical phase change when the humidity fluctuates, assisting the equipment to adjust the humidity, reducing the equipment operation frequency, significantly reducing the energy consumption, and realizing the green energy-saving goal of cold storage humidity control. BRIEF DESCRIPTION OF DRAWINGS

[0066] Figure 1 It is a no-frost cold storage method framework schematic diagram.

[0067] Figure 2 It is a no-frost cold storage system framework schematic diagram. DETAILED DESCRIPTION

[0068] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] This invention provides, for example Figure 1 The intelligent control method for frost-free cold storage based on active humidity control, as shown, includes the following steps: S1, establishing a cargo humidity requirement database to store target humidity values ​​corresponding to different types of cargo; S2, when cargo packaged in boxes enters the warehouse, a multimodal recognition device automatically identifies the marking information on the outer packaging to determine the cargo type, and sets the corresponding target humidity value according to the cargo humidity requirement database; S3, using humidity sensors to monitor humidity changes in the cold storage in real time and feeding the monitoring data back to the control system; S4, the control system dynamically adjusts the humidification power and spray frequency of the humidification equipment based on the magnitude and rate of deviation between the humidity monitoring data and the target humidity value, and controls the humidification equipment to switch to automatic humidification when the humidity is lower than the target value. Humidification is achieved using ultrasonic or steam humidification methods. By controlling the humidification rate and time, the humidity is kept within the target range. As the humidity approaches the target value, the humidification intensity is gradually reduced. During humidification, the control system continuously monitors the humidity level in the cold storage. If the humidity exceeds the target, the humidification equipment stops operating, and a defrost warning mechanism is activated. The system also maintains stable humidity by controlling air circulation and temperature regulation within the cold storage. By establishing a database of goods humidity requirements, using a multimodal recognition device to determine the type of goods and setting target humidity values, and combining real-time monitoring by humidity sensors, dynamic adjustment of the humidification equipment by the control system, and the defrost warning mechanism to maintain stable humidity, a complete and closed-loop intelligent humidity control system for cold storage is formed. This solution enables automated and precise humidity management throughout the entire process from goods entering the warehouse to storage, avoiding errors caused by human intervention and significantly improving the reliability and stability of cold storage humidity control. It is suitable for the storage needs of various types of goods.

[0070] Specifically, the multi-modal recognition device performs the following operations: analyzing the text and pattern identification on the surface of the packaging box through the AI image recognition unit; capturing the spectral characteristics of the special anti-counterfeit mark of the packaging box through the hyperspectral imaging unit; when the visual recognition confidence is lower than the threshold value, activating the RFID batch reading unit for non-contact verification; according to the recognition result, the target humidity value is retrieved from the database, and the multi-modal recognition device cooperates with AI image recognition, hyperspectral imaging and RFID technology to break through the limitations of traditional single recognition mode. AI image recognition and hyperspectral imaging can accurately analyze the tiny text, patterns and special anti-counterfeit marks on the packaging box, solving the problem of difficult identification of complex packaging; RFID technology realizes non-contact batch reading, which greatly shortens the goods warehousing time compared with the traditional scanning mode, and improves the goods type identification accuracy to more than 99%, ensuring the rapid and accurate setting of the target humidity value, the threshold value: based on the historical recognition accuracy, the initial value is set to 0.85; when the light intensity is <500 lux, it is automatically reduced by 0.05, and when the goods stacking density is >80%, it is reduced by 0.03; the Bayesian optimization algorithm is used, and the threshold value parameter is updated once every 1000 goods processed.

[0071] Specifically, the operation of dynamically adjusting the working parameters of the humidifying equipment adopts a fuzzy self-adaptive PID control algorithm, including the following steps: S41, real-time acquisition of the set humidity value RH set and the monitored humidity value RH real , calculation of the humidity deviation value E = RH set - RH real , wherein the unit of E is percentage (%); S42, calculation of the deviation change rate EC with a fixed sampling period Δt = 10 seconds, wherein the unit of EC is percentage per minute (% / min); S43, dynamic setting of the control parameters according to the interval range of the absolute value |E|: when |E|≥10%, set the proportional coefficient K p =1.2, the differential coefficient K d =0.8; when 5%≤|E|<10%, set K p =1.0, K d =0.6; when |E|<5%, set K p =0.8, K d =0.5; S44, generation of the humidification power adjustment amount ΔP, whose unit is kilowatt (kW), and the calculation formula is:

[0072]

[0073] wherein: the integral upper limit t≤5 minutes, α is the integral suppression factor, and α=0.05, represents the historical deviation cumulative amount; S45, generation of the spray frequency adjustment amount ΔF, whose unit is hertz (Hz), and the calculation formula is:

[0074] ΔF = β · |EC| + γ · S(E)

[0075] Where: β = 0.3 Hzmin / %, γ = 2 Hz, S(E) is a sign function, defined as: The application of fuzzy adaptive PID control algorithm enables the control system to dynamically adjust the working parameters of the humidification equipment in real time according to the humidity deviation and the change rate. Compared with the traditional fixed parameter control mode, this algorithm can reduce the overshoot and the adjustment time in the humidity regulation process, for example, it can quickly respond when the humidity fluctuates greatly, and realize smooth transition when approaching the target humidity, effectively avoiding the frequent start and stop of the humidification equipment, reducing the equipment loss, and at the same time, improving the humidity control accuracy to ±1%RH, significantly improving the fine degree of humidity control.

[0076] Specifically, starting the defrosting early warning mechanism includes: when the humidity exceeds the target value of 10%, immediately stop the humidifying equipment and trigger the third level warning; control the air circulation system to run at the maximum wind speed, and reduce the warehouse temperature by 0.5-2℃ to accelerate the condensation of moisture; generate a frost risk thermal map in the control terminal, mark the high humidity area coordinates in the cold storage, and trigger the first level warning when the humidity in the cold storage exceeds the target value of 5%-10%. At this time, the control system only issues a yellow warning signal in the control terminal, and generates preliminary information on humidity abnormalities, including the current humidity value, the over-standard proportion, and the preliminary positioning of abnormal areas (based on the distribution of humidity sensors). The system starts mild intervention measures, increases the air circulation system wind speed to 1.2 times the normal running speed, accelerates the diffusion of moisture, and delays the further rise of humidity; the second level warning is triggered when the humidity exceeds the target value of 10%-15%. The control terminal issues an orange warning signal, and sends detailed warning notifications to the management personnel through short message, APP push and other ways, including real-time humidity data, historical humidity change curve and possible over-standard reason analysis. In addition to maintaining high wind speed of the air circulation system, the control system starts the temperature regulation device to reduce the warehouse temperature by 0.5-1℃ to accelerate the condensation of moisture. At the same time, the emergency processing mode of the condensate water recycling system is activated to improve the condensate water collection efficiency and avoid the accumulation of moisture in the cold storage; the third level warning is triggered when the humidity exceeds the target value of 15% and above. The control terminal alarms with a red warning signal and a high decibel buzzer, and sends an emergency warning to all relevant management personnel, requiring immediate handling. The control system quickly stops all humidifying equipment, adjusts the air circulation system to the maximum wind speed, and significantly reduces the warehouse temperature by 1-2℃ to force the rapid condensation and recovery of moisture. At this time, the system generates a detailed frost risk thermal map, which marks the accurate coordinates, humidity value and risk level of the high humidity area in the cold storage through a three-dimensional visualization interface, providing accurate disposal basis for the management personnel. In addition, the system automatically cuts off the power supply of unnecessary equipment to avoid circuit failure caused by high humidity, and maximizes the safety of the cold storage operation. The hierarchical processing strategy of the defrosting early warning mechanism provides a systematic solution for the over-standard humidity of the cold storage. When the humidity exceeds the target value of 10%, the humidification is immediately stopped and the third level warning is triggered, and measures such as accelerating air circulation and reducing warehouse temperature are taken to accelerate the condensation and recovery of moisture, combined with the frost risk thermal map generated by the control terminal to directly locate the high humidity area and achieve accurate processing. The mechanism reduces the frost probability caused by excessive humidification by more than 80%, effectively ensuring the refrigeration efficiency of the cold storage and the safety of the stored goods.

[0077] Specifically, the control system, according to the constructed humidity-temperature-time multi-dimensional correlation model, for humidity-sensitive fruit and vegetable goods, controls the humidification equipment to rapidly reduce humidity in the early stage of storage, maintains stable humidity in the middle stage of storage, and gradually controls the humidity to rise before the goods are taken out of the cold storage, and combines the humidity adjustment energy consumption optimization during the cold storage operation period. The control system integrates meteorological data, transportation route temperature and humidity information and historical storage data, uses machine learning algorithms to predict the humidity demand trend of the goods in the cold storage, and when the humidity is abnormal, automatically links the ventilation system, humidification and dehumidification equipment, and triggers an early warning to the management personnel terminal. Based on the humidity-temperature-time multi-dimensional correlation model and machine learning algorithms, the control system can comprehensively consider meteorological data, transportation route temperature and humidity and historical storage data to predict the humidity demand trend of the goods in the cold storage in advance. For humidity-sensitive fruit and vegetable goods, the humidity adjustment strategy can be dynamically adjusted at different storage stages, and the energy consumption can be optimized in combination with the cold storage operation period. Compared with the traditional fixed strategy, the preservation period of fruit and vegetable goods is extended by 30%-50%, and the energy consumption is reduced by 20%-30%, realizing the dual goals of precise control and energy saving and efficiency improvement.

[0078] Specifically, the humidity-temperature-time multi-dimensional correlation model is constructed in the following way: when the temperature is ≥0℃ and ≤10℃: the storage time is ≤24 hours, the humidity is controlled at 85%-90%RH; 24 hours<storage time≤72 hours, the humidity is controlled at 90%-95%RH; storage time>72 hours, the humidity is controlled at 95%-98%RH; when the temperature is >10℃ and ≤20℃: the storage time is ≤12 hours, the humidity is controlled at 80%-85%RH; 12 hours<storage time≤48 hours, the humidity is controlled at 85%-90%RH; storage time>48 hours, the humidity is controlled at 90%-95%RH; when the temperature is >20℃: the storage time is ≤8 hours, the humidity is controlled at 75%-80%RH; 8 hours<storage time≤36 hours, the humidity is controlled at 80%-85%RH; storage time>36 hours, the humidity is controlled at 85%-90%RH; when the cold storage is in the night low peak period, the lower limit of all humidity control intervals is increased by 5%RH, and the upper limit remains unchanged, to reduce energy consumption. The constructed humidity-temperature-time multi-dimensional correlation model divides the humidity control interval according to different temperature ranges and storage times, and combines the cold storage night low peak period optimization strategy to provide a scientific quantitative standard for cold storage humidity control. For example, under different temperatures and storage times, the humidity control range is explicitly set, making the cold storage humidity adjustment more targeted and avoiding resource waste caused by blind adjustment. According to actual verification, the overall humidity control compliance rate of the cold storage can be increased to more than 95% by using the model, and the night energy consumption can be reduced by 15%-20%.

[0079] Specifically, when the cargo is leafy vegetables, on the basis of the above humidity-temperature-time multi-dimensional correlation model: when the temperature is ≤5℃, the upper limit of the humidity control interval of each time period is increased by 3%RH; when the storage time is >48 hours and the humidity fluctuation rate is >0.5%RH / hour, the leaf surface dew prevention program is started, specifically: every 2 hours, a low wind speed ventilation cycle of 5 minutes is carried out, and the wind speed is controlled at 0.3-0.5m / s; within 12 hours before delivery, the humidity gradually rises to 95%-98%RH, and the temperature increases by 0.5℃ per hour until the delivery requirement is met. The special humidity control strategy developed for leafy vegetables further optimizes the basic multi-dimensional model. When the temperature is ≤5℃, the upper limit of the humidity is increased, which can effectively prevent the wilting of leafy vegetables due to water loss; when the storage time is >48 hours and the humidity fluctuation rate is >0.5%RH / hour, the leaf surface dew prevention program is started, and low wind speed ventilation cycle is used to avoid rotting caused by dew; gradually increasing the humidity and adjusting the temperature before delivery helps leafy vegetables to adapt to the external environment. This strategy reduces the loss rate of leafy vegetables by 40%-50%, ensuring the quality and economic benefits of agricultural products.

[0080] Specifically, when the cargo is root vegetables, on the basis of the above humidity-temperature-time multi-dimensional correlation model: when the temperature is ≥15℃, the lower limit of the humidity control interval of each time period is decreased by 5%RH; when the storage time is >72 hours, the soil humidity balancing program is started, and every 12 hours, local humidification of 10 minutes is carried out, so that the humidity in the cargo packaging is 2%-3%RH higher than the humidity in the warehouse; when the temperature fluctuation rate is >1℃ / hour, the humidity control interval is increased by 2%RH as a whole to enhance the cold resistance of the cargo. Special storage requirements. When the temperature is ≥15℃, the lower limit of the humidity interval is decreased, which can inhibit the germination and mold of root vegetables; when the storage time is >72 hours, the soil humidity balancing program is started, and local humidification is used to maintain stable humidity in the packaging and prevent the vegetables from wilting due to water loss; when the temperature fluctuation rate is >1℃ / hour, the humidity control interval is increased to enhance the cold resistance of the cargo. After adopting this strategy, the storage quality of root vegetables is significantly improved, and the loss rate is reduced by 30%-40%.

[0081] Specifically, when the goods are tropical fruits, based on the above humidity-temperature-time multi-dimensional correlation model, and when the temperature > 20℃, the following control strategy is preferentially executed: during the entire storage period, the humidity is controlled at 85%-90%RH, and the temperature is controlled at 12-15℃; when the storage time > 24 hours, the ethylene concentration is detected every 6 hours, and when the ethylene concentration > 0.1ppm, the air purification program is started, and at the same time, the lower limit of the humidity control interval is increased by 3%RH; 24 hours before delivery, the humidity is gradually reduced to 80%-85%RH, and at the same time, the temperature is increased by 1℃ per hour to adapt to the external environment. The exclusive humidity-temperature control scheme for tropical fruits meets the characteristics of their sensitivity to temperature and humidity. The fixed humidity and temperature range during the entire storage period ensures the normal physiological activity of tropical fruits; the ethylene concentration is detected regularly after the storage time > 24 hours, and the humidity is adjusted according to the detection results to effectively delay fruit ripening and rot; the temperature and humidity are gradually adjusted before delivery to make the fruits adapt to the external environment. This scheme prolongs the shelf life of tropical fruits by 20%-30%, reducing the loss of fruits during storage and transportation.

[0082] The application provides a frostless cold storage system based on humidity active control, as shown in the formula (I): Figure 2 The application provides a frostless cold storage system based on humidity active control, as shown in the formula (I): The frostless cold storage system based on humidity active control comprises a humidity demand database for storing target humidity values corresponding to different goods categories; a multi-modal recognition device for recognizing identification information on goods packaging through visual recognition technology, RFID technology and hyperspectral imaging technology to determine the goods category; a humidity sensor for monitoring humidity data in the cold storage in real time; a humidifying device adopting ultrasonic humidifying or steam humidifying for humidifying the cold storage; a control system connected with the database, the recognition device, the humidity sensor and the humidifying device, and the control system is used for calling the target humidity value from the database according to the recognized goods category; based on the deviation size and the change rate of the humidity monitoring data and the target value, the humidifying power and the spraying frequency of the humidifying device are dynamically adjusted; and an air circulation system connected with the control system and used for executing defrosting warning operation. The frostless cold storage system based on humidity active control realizes full-process automation of humidity control through the collaborative work of various hardware components (the humidity demand database, the multi-modal recognition device, the humidity sensor, the humidifying device, the control system and the like). The system architecture is clear, the functions of the components are clear and the components cooperate with each other, thereby providing a reliable hardware foundation for intelligent control of the humidity of the cold storage. Compared with the traditional cold storage system, the scheme has higher integration, faster response speed, can adapt to diversified humidity control requirements of different goods, and has wide application prospect and market competitiveness.

[0083] Finally, it should be noted that the above is only the preferred embodiment of the present application, and is not intended to limit the present application, although the foregoing embodiments of the present application are described in detail, for those skilled in the art, it still can be modified to the technical solution recorded in the foregoing embodiments, or equivalent replacement of some technical features, any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application, should be included in the protection scope of the present application.

Claims

1. A frost-free cold storage intelligent control method based on humidity active control, characterized in that, The method comprises the following steps: S1, establishing a cargo humidity requirement database to store target humidity values corresponding to different types of cargo; S2, when the cargo packaged with the packaging box is stored, identifying the type of cargo by automatically recognizing the identification information on the outer packaging of the cargo through a multi-modal recognition device, and setting the corresponding target humidity value according to the cargo humidity requirement database; S3, monitoring the humidity change in the cold storage in real time by using a humidity sensor, and feeding back the monitoring data to a control system; S4, dynamically adjusting the humidification power and spray frequency of the humidification equipment according to the deviation and change rate of the humidity monitoring data and the target humidity value, when the humidity is lower than the target value, controlling the humidification equipment to adopt ultrasonic humidification or steam humidification method for humidification, controlling the humidification amount and humidification time to control the humidity within the target range, and gradually reducing the humidification intensity when the humidity approaches the target value; S5, the control system judges whether the humidity in the cold storage is out of standard in real time during the humidification process, if it is out of standard, the humidification equipment stops working, and a defrosting early warning mechanism is started, and the humidity in the cold storage is maintained stably by controlling the air circulation and temperature regulation in the cold storage.

2. The intelligent control method of the frost-free cold storage based on humidity active control according to claim 1, characterized in that, The multi-modal recognition device performs the following operations: Analyzing the text and pattern identification on the surface of the packaging box through an AI image recognition unit; Capturing the spectral characteristics of the special anti-counterfeiting mark of the packaging box through a hyperspectral imaging unit; When the visual recognition confidence is lower than the threshold value, activating the RFID batch reading unit for non-contact verification; According to the identification result, the target humidity value is retrieved from the database.

3. The intelligent control method of the frost-free cold storage based on humidity active control according to claim 1, characterized in that, The operation of dynamically adjusting the working parameters of the humidification equipment adopts a fuzzy self-adaptive PID control algorithm, comprising the following steps: S41, real-time acquisition of the set humidity value RH set With the monitored humidity value RH real , calculate the humidity deviation value E = RH set -RH real ; S42, calculate the deviation change rate with a fixed sampling period Δt = 10 seconds S43, dynamically setting the control parameters according to the interval range of the absolute value |E| of the deviation: When |E| ≥ 10%, set the proportionality coefficient K p = 1.2, the differential coefficient K d = 0.8; When 5% < |E| < 10%, set K = 1.0, K p = 1.0, K d = 0.6; When |E| < 5%, set K p = 0.8, K d = 0.5; S44, generating a humidification power adjustment amount ΔP, the calculation formula is: wherein: the upper limit of integration t < 5 minutes, and a is the integration suppression factor, and a = 0.05, represents the history deviation cumulative amount; S45, generating a spray frequency adjustment amount ΔF, the calculation formula is: ΔF = β·|EC| + γ·S(E) Where: β = 0.3 Hzmin / %, γ = 2 Hz, S((E) is a sign function, defined as:

4. The intelligent control method of the frost-free cold storage based on humidity active control according to claim 1, characterized in that, The start of the defrosting early warning mechanism includes: When the humidity exceeds the target value by 10%, the humidification equipment is immediately stopped and a three-level warning is triggered; Controlling the air circulation system to run at the maximum wind speed, and reducing the warehouse temperature by 0.5-2℃ to accelerate the condensation and recovery of water; Generating a frost risk thermodynamic map in the control terminal, and marking the coordinates of the high humidity area in the cold storage.

5. The intelligent control method of frost-free cold storage based on humidity active control according to claim 1, characterized in that, According to the constructed humidity-temperature-time multi-dimensional correlation model, for humidity-sensitive fruit and vegetable cargo, the control system controls the humidification equipment to rapidly reduce humidity in the early storage period, maintains stable humidity in the middle storage period, gradually controls the humidity to rise before the cargo is taken out, and optimizes the humidity regulation energy consumption in combination with the running period of the cold storage. The control system integrates meteorological data, transportation route temperature and humidity information and historical storage data, uses a machine learning algorithm to predict the humidity requirement change trend of the cargo in the warehouse, and when the humidity is abnormal, automatically links the ventilation system, humidification and dehumidification equipment, and triggers a warning to be pushed to the management personnel terminal.

6. The intelligent control method of the frost-free cold storage based on humidity active control according to claim 5, characterized in that, The humidity-temperature-time multi-dimensional correlation model is constructed by the following methods: When the temperature is ≥0℃ and ≤10℃: the storage time is ≤24 hours, the humidity is controlled at 85%-90%RH; 24 hours<storage time≤72 hours, the humidity is controlled at 90%-95%RH; storage time>72 hours, the humidity is controlled at 95%-98%RH; When the temperature is >10℃ and ≤20℃: the storage time is ≤12 hours, the humidity is controlled at 80%-85%RH; 12 hours<storage time≤48 hours, the humidity is controlled at 85%-90%RH; storage time>48 hours, the humidity is controlled at 90%-95%RH; When the temperature is >20℃: the storage time is ≤8 hours, the humidity is controlled at 75%-80%RH; 8 hours<storage time≤36 hours, the humidity is controlled at 80%-85%RH; storage time>36 hours, the humidity is controlled at 85%-90%RH; When the refrigeration warehouse is in the night off-peak period, the lower limit of all humidity control intervals is increased by 5%RH, and the upper limit remains unchanged, so as to reduce energy consumption.

7. The intelligent control method of frost-free cold storage based on humidity active control according to claim 1 or 6, characterized in that, When the goods are leafy vegetables, on the basis of the above humidity-temperature-time multi-dimensional correlation model: When the temperature is ≤5℃, the upper limit of the humidity control interval of each time period is increased by 3%RH; When the storage time is >48 hours, and the monitored humidity fluctuation rate is >0.5%RH / hour, the leaf surface dew prevention program is started, specifically: every 2 hours, a low wind speed ventilation cycle of 5 minutes is carried out, and the wind speed is controlled at 0.3-0.5m / s; Within 12 hours before delivery, the humidity gradually rises to 95%-98%RH, and the temperature increases by 0.5℃ per hour until the delivery requirement is met.

8. The intelligent control method of frost-free cold storage based on humidity active control according to claim 1 or 6, characterized in that, When the goods are root vegetables, on the basis of the above humidity-temperature-time multi-dimensional correlation model: When the temperature is ≥15℃, the lower limit of the humidity control interval of each time period is decreased by 5%RH; When the storage time is >72 hours, the soil humidity balancing program is started, and every 12 hours, local humidification of 10 minutes is carried out, so that the humidity in the goods packaging is 2%-3%RH higher than the humidity in the warehouse; When the monitored temperature fluctuation rate is >1℃ / hour, the humidity control interval is increased by 2%RH as a whole, so as to enhance the cold resistance of the goods.

9. The intelligent control method of frost-free cold storage based on humidity active control according to claim 1 or 6, characterized in that, When the goods are tropical fruits, on the basis of the above humidity-temperature-time multi-dimensional correlation model, and when the temperature is >20℃, the following control strategies are preferentially executed: During the whole storage period, the humidity is controlled at 85%-90%RH, and the temperature is controlled at 12-15℃; When the storage time is >24 hours, ethylene concentration detection is carried out every 6 hours, and when the ethylene concentration is >0.1ppm, the air purification program is started, and at the same time, the lower limit of the humidity control interval is increased by 3%RH; 24 hours before delivery, the humidity gradually decreases to 80%-85%RH, and the temperature increases by 1℃ per hour to adapt to the external environment.

10. The frost-free intelligent control method of cold storage based on humidity active control, characterized in that, It includes: a humidity demand database for storing target humidity values corresponding to different goods categories; a multi-modal recognition device for identifying identification information on the goods packaging through visual recognition technology, RFID technology and hyperspectral imaging technology to determine the goods category; a humidity sensor for monitoring the humidity data in the refrigeration warehouse in real time; The humidifying device is used for humidifying the cold storage by ultrasonic humidification or steam humidification; The control system is connected with the database, the identification device, the humidity sensor and the humidifying device respectively, and is used for calling the target humidity value from the database according to the identified goods category; the humidifying power and the spraying frequency of the humidifying device are dynamically adjusted based on the deviation size and the change rate of the target value of the humidity monitoring data; The air circulation system is connected with the control system and is used for executing the defrosting early warning operation.

Images