A vegetable refrigeration system integrating AI and IoT technologies

Abstract

The present invention relates to the technical field of refrigerator equipment. The present invention provides a vegetable refrigeration system integrating AI and Internet of Things technology, including a refrigerator, a vegetable verification module, a monitoring module, an AI evaluation module, an environmental control module, and an early warning module. The vegetable verification module verifies the vegetables stored in the refrigerator to obtain the verification data of the vegetables and generate corresponding adaptive RFID tags. The monitoring module collects the storage environment in the refrigerator. The AI ​​evaluation module evaluates the storage state of the vegetables according to the verification data collected by the vegetable verification module and the storage environment collected by the monitoring module to form an evaluation result. The environmental control module triggers the adjustment of the storage environment in the refrigerator according to the evaluation result, and the early warning module gives an early warning prompt to the user according to the evaluation result. The present invention ensures that the entire system has the advantages of high intelligence, strong management level, good refrigeration effect, and high refinement ability through the cooperation of the monitoring module and the AI ​​evaluation module.

Description

Technical Field

[0001] The present invention relates to the technical field of refrigerator equipment, and in particular to a vegetable refrigeration system integrating AI and Internet of Things technologies. Background Art

[0002] At present, most vegetable refrigeration systems rely on manual setting and monitoring of environmental parameters, such as temperature and humidity. This method not only requires a lot of manpower costs, but also makes it difficult to ensure the timeliness and accuracy of parameter adjustment, which often results in the refrigeration environment not being able to maintain the best fresh-keeping state for vegetables, thus shortening the shelf life of vegetables.

[0003] For example, a vegetable refrigeration device disclosed in Chinese patent CN107372798B solves the problem of spraying water to prevent moisture loss in vegetables, but ignores humidity monitoring, which can easily lead to bacterial growth caused by excessive humidity and fails to achieve a balance between humidity and moisture.

[0004] At the same time, the existing technology still has the following problems:

[0005] 1. The data processing capacity of traditional refrigeration systems is relatively limited, and it is impossible to effectively analyze and utilize the data during the refrigeration process. Due to the lack of intelligent analysis, the system cannot perform personalized management based on the characteristics of different vegetables, resulting in poor refrigeration effects. Some high-value vegetables may be lost due to improper refrigeration parameters.

[0006] 2. The existing refrigeration management system is not intelligent enough and often cannot achieve fine control, resulting in waste of resources. For example, improper temperature or humidity control not only affects the preservation of vegetables, but may also cause energy waste and increase operating costs.

[0007] 3. Many traditional refrigeration systems lack remote monitoring and alarm functions, making it impossible for users to grasp the changes in the refrigeration environment in real time. Once abnormal environmental parameters or equipment failure occur, it is often difficult to detect and deal with them in time, which can easily cause vegetable losses.

[0008] The present invention is made to solve the common problems in the field such as the inability to achieve refined management, poor refrigeration effect, poor interaction ability, low intelligence and poor energy consumption management level. Summary of the invention

[0009] The purpose of the present invention is to propose a vegetable refrigeration system that integrates AI and Internet of Things technologies to address the current deficiencies.

[0010] In order to overcome the shortcomings of the prior art, the present invention adopts the following technical solutions:

[0011] A vegetable refrigeration system integrating AI and Internet of Things technologies, the vegetable refrigeration system comprising a server and a refrigerator, the vegetable refrigeration system further comprising a vegetable verification module, a monitoring module, an AI evaluation module, an environmental control module, and an early warning module, the server being connected to the vegetable verification module, the monitoring module, the AI ​​evaluation module, the environmental control module, and the early warning module respectively;

[0012] The vegetable verification module, monitoring module, AI evaluation module, environment control module, and early warning module are all arranged in the refrigerator. The vegetable verification module verifies the vegetables stored in the refrigerator to obtain the verification data of the vegetables and generate corresponding adaptive RFID tags. The monitoring module collects the data of the storage environment in the refrigerator. The AI ​​evaluation module evaluates the storage status of the vegetables according to the verification data collected by the vegetable verification module and the storage environment collected by the monitoring module to form an evaluation result. The environment control module triggers the adjustment of the storage environment in the refrigerator according to the evaluation result. The early warning module gives an early warning prompt to the user according to the evaluation result.

[0013] Among them, the vegetable inspection module includes an inspection unit, a label supply unit, and a status analysis unit. The inspection unit inspects refrigerated vegetables to obtain inspection data of the vegetables. The status analysis unit analyzes the inspection data collected by the inspection unit to form initial status data of the vegetables. The label supply unit supplies RFID tags and binds the RFID tags to the initial status data.

[0014] Optionally, the calibration unit includes a calibration area, a storage component and a calibration component, the storage component is arranged in the calibration area, the storage component is used to place the vegetables, and the calibration component calibrates the vegetables placed in the calibration area to obtain calibration data of the vegetables.

[0015] Optionally, the storage component includes a steering seat, a steering gear, and a steering drive mechanism, the placement plate is provided with an articulated cavity for placing the steering seat, one side end face of the steering seat is hinged to the bottom wall of the articulated cavity to form a hinged portion, the steering gear is nested in the outer periphery of the steering seat, the steering drive mechanism is engaged with the steering gear and drives the steering seat to rotate along the axis of the articulated portion, so that the steering seat rotates along its own axis.

[0016] Optionally, the verification component includes a γ height adjustment rod, a γ adjustment drive mechanism, a β height adjustment rod, a β adjustment drive mechanism, a laser, and a fluorescence detector, the laser emitting laser light toward the stored vegetables, the fluorescence detector being used to capture the fluorescence signals of the vegetables and convert the fluorescence signals into electrical signals, the γ adjustment drive mechanism being drivingly connected to one end of the γ height adjustment rod to form a γ adjustment portion, the other end of the γ height adjustment rod being connected to the laser, the β adjustment drive mechanism being drivingly connected to one end of the β height adjustment rod to form a β adjustment portion, the other end of the β height adjustment rod being connected to the fluorescence detector;

[0017] The γ height adjustment unit adjusts the position of the laser, and the β height adjustment unit adjusts the position of the fluorescence detector.

[0018] Optionally, the monitoring module includes a humidity monitoring unit, a temperature monitoring unit, an odor detection unit and a position adjustment unit, the temperature monitoring unit collects temperature data in the refrigerator, the humidity monitoring unit collects humidity data in the refrigerator, the odor detection unit collects odor concentration value data in the refrigerator, and the position adjustment unit adjusts the positions of the humidity monitoring unit, the temperature monitoring unit and the odor detection unit.

[0019] Optionally, the AI ​​evaluation module obtains the processed verification data collected by the vegetable verification module and the storage state index SSI of the storage environment for vegetables collected by the monitoring module:

[0020] ;

[0021] In the formula, F Chl is the chlorophyll fluorescence intensity, F Car is the fluorescence intensity of carotenoids, H is the storage environment humidity, W is the moisture content index, T is the storage environment temperature, and G is the odor concentration value;

[0022] If the storage status index SSI does not fall within the monitoring threshold range [fresh1, fresh2] set by the system, the environment control module is triggered to adjust the storage environment.

[0023] Optionally, the monitoring module is arranged on the periphery of the storage area of ​​the refrigerator, and collects storage environment data at different heights of the storage area.

[0024] Optionally, the early warning module includes an early warning unit and an interactive unit, wherein the early warning unit triggers different early warning prompts according to the relationship between the storage status index SSI and the set monitoring threshold range [fresh1, fresh2], and the interactive unit interactively prompts the user with the early warning prompt issued by the early warning unit.

[0025] Optionally, the vegetable identification module further includes an inspection unit, which unbinds the vegetables and RFID tags that have established a binding relationship.

[0026] Optionally, the disinfection inspection unit is arranged on one side of the refrigerator, and is used by the user to perform disinfection inspection on the edible vegetables.

[0027] The beneficial effects achieved by the present invention are:

[0028] 1. Through the cooperation between the monitoring module and the AI ​​evaluation module, the refrigeration status of the vegetables can be accurately evaluated, ensuring that the entire system has the advantages of high intelligence, strong management level, good refrigeration effect and high refinement ability;

[0029] 2. Through the cooperation between the control module and the AI ​​evaluation module, the refrigeration of the vegetables is managed more finely, ensuring that the entire system has the advantages of good refrigeration effect, good interaction ability, high intelligence and strong management level;

[0030] 3. Through the cooperation between the steering seat and the steering drive mechanism, the stored vegetables can be inspected from multiple angles, improving the accuracy of inspection at each position and ensuring that the entire system can accurately control the state of the vegetables;

[0031] 4. Through the mutual cooperation between the AI ​​evaluation module and the early warning module, the refrigeration status of the vegetables can be prompted, ensuring that the entire system has the advantages of good prompt effect, strong early warning interaction capability, high intelligence and good interactive comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The present invention can be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily drawn to scale, but the emphasis is placed on illustrating the principles of the embodiments. In different views, the same reference numerals designate the same parts.

[0033] Figure 1 It is an overall block diagram of the present invention.

[0034] Figure 2 Schematic diagram of the evaluation process of the AI ​​evaluation module of the present invention.

[0035] Figure 3 It is a structural schematic diagram of the vegetable identification module and the decision-making module of the present invention.

[0036] Figure 4 It is a front view schematic diagram of the freezer of the present invention.

[0037] Figure 5 for Figure 4 Schematic cross-sectional view at AA in the middle.

[0038] Figure 6 It is a right side schematic diagram of the freezer of the present invention.

[0039] Figure 7 for Figure 6 Schematic cross-sectional view at the middle BB.

[0040] Figure 8 for Figure 7 Enlarged schematic diagram of point C in the middle.

[0041] Explanation of the reference numerals: 1. Freezer; 2. Prompt screen; 3. RFID reader; 4. Storage rack; 5. Placement seat; 6. Lifting rod; 7. Hidden slot; 8. Moving seat; 9. Moving track; 10. Laser; 11. γ adjustment part; 12. Fluorescence detector; 13. β adjustment part; 14. Vegetables; 15. Placement plate; 16. Steering seat; 17. Articulated part; 18. Steering drive mechanism; 19. Steering gear. DETAILED DESCRIPTION

[0042] The following is an explanation of the embodiments of the present invention through specific embodiments. Those skilled in the art can understand the advantages and effects of the present invention from the contents disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and the details in this specification can also be modified and changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. In addition, the drawings of the present invention are only simple schematic illustrations and are not depicted according to actual sizes. It is stated in advance. The following embodiments will further explain the relevant technical contents of the present invention in detail, but the disclosed contents are not intended to limit the scope of protection of the present invention.

[0043] Embodiment 1: According to Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 As shown, this embodiment provides a vegetable refrigeration system integrating AI and Internet of Things technologies, the vegetable refrigeration system includes a server and a refrigerator, the vegetable refrigeration system also includes a vegetable inspection module, a monitoring module, an AI evaluation module, an environmental control module, and an early warning module, the server is respectively connected to the vegetable inspection module, the monitoring module, the AI ​​evaluation module, the environmental control module, and the early warning module, and the intermediate data and process data of the vegetable inspection module, the monitoring module, the AI ​​evaluation module, the environmental control module, and the early warning module are stored in the database of the server;

[0044] The vegetable verification module, monitoring module, AI evaluation module, environmental control module, and early warning module are all arranged in the refrigerator. The vegetable verification module verifies the vegetables 14 stored in the refrigerator to obtain verification data of the vegetables 14 and generate corresponding adaptive RFID tags. The monitoring module collects the storage environment in the refrigerator. The AI ​​evaluation module evaluates the storage status of the vegetables 14 according to the verification data collected by the vegetable verification module and the storage environment collected by the monitoring module to form an evaluation result. The environmental control module triggers the adjustment of the storage environment in the refrigerator according to the evaluation result. The early warning module gives an early warning prompt to the user according to the evaluation result.

[0045] The vegetable refrigeration system also includes a central processor, which is respectively connected to the vegetable verification module, the monitoring module, the AI ​​evaluation module, the environmental control module, and the early warning module, and centrally controls the vegetable verification module, the monitoring module, the AI ​​evaluation module, the environmental control module, and the early warning module based on the central processor, and stores the control data in the database of the server to enhance the refined control of the whole system on the refrigeration of vegetables 14;

[0046] The vegetable verification module includes a verification unit, a label supply unit, and a state analysis unit. The verification unit verifies the refrigerated vegetables 14 to obtain verification data of the vegetables 14. The state analysis unit analyzes the verification data collected by the verification unit to form initial state data of the vegetables 14. The label supply unit supplies RFID labels and binds the RFID labels to the initial state data.

[0047] In this embodiment, the supplied RFID tags are pasted or attached to the vegetables 14 to achieve labeling of the vegetables 14;

[0048] The tag contains information such as the variety, initial state data, and verification time of the vegetable 14; at the same time, the generated RFID tag is bound to the corresponding vegetable 14 to facilitate subsequent tracking and management;

[0049] The freezer 1 is provided with a storage rack 4 for placing vegetables 14;

[0050] The label supply unit includes an RFID label paper and an RFID label printer, and the RFID label printer prints an RFID tag on the RFID label paper to form an RFID label;

[0051] When the RFID tag is supplied, the user attaches the RFID tag to the identified vegetable 14;

[0052] Specific:

[0053] 1) Label supply and printing:

[0054] Prepare RFID label paper and load it into the RFID label printer;

[0055] Connect the RFID tag printer to the data management system and configure the printer settings;

[0056] The vegetable verification module generates initial status data, and the data management system records the data and generates a unique ID;

[0057] RFID label printer prints visual mark and encodes RFID chip to form RFID label;

[0058] 2) Label sticking: manually peeling the RFID label from the label paper and sticking it onto the identified vegetable 14;

[0059] 3) Tag binding: Use the RFID reader 3 to read the attached RFID tag, and map and bind the RFID tag with the initial state data of the vegetable 14, that is, pair the number of the RFID tag with the initial state data corresponding to the vegetable;

[0060] The central processor records and binds the RFID tag and the initial state data of the vegetable 14;

[0061] Update RFID tag information in the tracking system;

[0062] Optionally, the verification unit includes a verification area, a storage component and a verification component, wherein the storage component is arranged in the verification area, the storage component is used for the vegetables 14 to be placed, and the verification component verifies the vegetables 14 placed in the verification area to obtain verification data of the vegetables 14;

[0063] The test data include but are not limited to the following examples: chlorophyll fluorescence intensity, carotenoid fluorescence intensity;

[0064] In addition, in this embodiment, the storage member is arranged on the outer wall of the cabinet body of the refrigerator; specifically,

[0065] like Figure 8 As shown, in the embodiment, the placement plate 15 is arranged on the uppermost layer of the storage rack 4 (such as Figure 8 As shown) or the lowest layer, wherein the storage member is arranged on the placement plate 15;

[0066] The user can place the vegetables 14 to be stored on the storage member, and use the verification member to verify the stored vegetables 14;

[0067] Optionally, the storage member includes a steering seat 16, a steering gear 19, and a steering drive mechanism 18, the placement plate 15 is provided with an articulated cavity for placing the steering seat 16, one side end surface of the steering seat 16 is hinged with the bottom wall of the articulated cavity to form an articulated portion 17, the steering gear 19 is nested in the outer periphery of the steering seat 16, the steering drive mechanism 18 is engaged with the steering gear 19, and drives the steering seat 16 to rotate along the axis of the articulated portion 17, so that the steering seat 16 rotates along its own axis;

[0068] Through the mutual cooperation between the steering seat 16 and the steering drive mechanism 18, the stored vegetables 14 can be inspected from multiple angles, thereby improving the accuracy of inspection at each position and ensuring that the entire system can accurately control the state of the vegetables 14;

[0069] Optionally, the verification component includes a γ height adjustment rod, a γ adjustment drive mechanism, a β height adjustment rod, a β adjustment drive mechanism, a laser 10, and a fluorescence detector 12, wherein the laser 10 emits laser light to the stored vegetables 14, the fluorescence detector 12 is used to capture the fluorescence signal of the vegetables 14 and convert the fluorescence signal into an electrical signal, the γ adjustment drive mechanism is drivingly connected to one end of the γ height adjustment rod to form a γ adjustment portion 11, the other end of the γ height adjustment rod is connected to the laser 10, the β adjustment drive mechanism is drivingly connected to one end of the β height adjustment rod to form a β adjustment portion 13, and the other end of the β height adjustment rod is connected to the fluorescence detector 12;

[0070] The γ height adjustment unit adjusts the position of the laser 10 , and the β height adjustment unit adjusts the position of the fluorescence detector 12 ;

[0071] In this embodiment, the laser 10 is arranged above the placement plate 15, and under the adjustment of the γ adjustment part 11, it can stably irradiate the surface of the vegetable 14; the angle of the laser 10 is adjusted so that the laser beam emitted by it is between 30° and 60° with the surface of the vegetable 14, preferably, at a 45-degree angle with the plane of the detection area;

[0072] Fix the fluorescence detector 12 on the side wall of the freezer 1 so that it can stably receive the fluorescence signal on the surface of the vegetables 14; adjust the angle of the fluorescence detector 12 so that its receiving direction is at a 90-degree angle to the laser beam to avoid interference from the laser reflected light;

[0073] The angle between the laser 10 and the fluorescence detector 12 can be adaptively adjusted according to actual needs and the type, shape, and size of the vegetables 14 to be stored, so they will not be described in detail in this embodiment.

[0074] In addition, in this embodiment, the laser 10 and the fluorescence detector 12 need to be calibrated so that the laser 10 and the fluorescence detector 12 can accurately detect the vegetables 14;

[0075] The specific calibration process includes:

[0076] 1) Laser 10 debugging

[0077] Start the laser 10, adjust its position and angle, so that the laser beam is evenly irradiated to the surface of the vegetable 14;

[0078] Confirm that the laser beam can cover the entire detection area of ​​the vegetable 14;

[0079] 2) Fluorescence detector 12 calibration

[0080] Start the fluorescence detector 12 and adjust its position and angle so that it can effectively receive the fluorescence signal on the surface of the vegetable 14;

[0081] Calibration is performed using standard fluorescent substances to ensure the sensitivity and accuracy of the fluorescence detector 12;

[0082] In the process of testing the vegetables 14, it is necessary to ensure that the positions of the laser 10 and the fluorescence detector 12 are tested before testing the vegetables 14, so as to improve the accuracy and reliability of the testing of the vegetables 14;

[0083] At the same time, when adjusting the positions of the laser 10 and the fluorescence detector 12, the positions of the laser 10 and the fluorescence detector 12 can be adjusted respectively by adjusting the γ adjustment part 11 through the γ adjustment driving mechanism and adjusting the β adjustment part 13 through the β adjustment driving mechanism;

[0084] Optionally, the monitoring module includes a humidity monitoring unit, a temperature monitoring unit, an odor detection unit and a position adjustment unit, the temperature monitoring unit collects temperature data in the refrigerator, the humidity monitoring unit collects humidity data in the refrigerator, the odor detection unit collects odor concentration value data in the refrigerator, and the position adjustment unit adjusts the positions of the humidity monitoring unit, the temperature monitoring unit and the odor detection unit;

[0085] The humidity monitoring unit includes a humidity sensor and a humidity data buffer, wherein the temperature sensor collects humidity data in the refrigerator, and the humidity data buffer stores the humidity data collected by the humidity sensor;

[0086] The temperature monitoring unit includes a temperature sensor and a memory, wherein the temperature sensor collects the temperature in the refrigerator to determine the real-time temperature data in the refrigerator, and the memory stores the temperature data collected by the temperature sensor;

[0087] The odor detection unit includes a volatile organic compound (VOC) sensor and a data recorder, wherein the volatile organic compound (VOC) sensor detects gas components in the refrigerated environment, and the data recorder stores the gas components collected by the volatile organic compound (VOC) sensor;

[0088] The position adjustment unit includes a hidden slot 7, a placement seat 5, a moving track 9, a transverse moving member and a longitudinal lifting member. The placement seat 5 is used to place the humidity monitoring unit, the temperature monitoring unit, and the odor detection unit. The longitudinal lifting member lifts and adjusts the position of the placement seat 5. The transverse moving member adjusts the position of the longitudinal lifting member. The transverse moving member is arranged on the moving track 9 and is slidably connected to the moving track 9.

[0089] When the lateral moving member is adjusting the longitudinal lifting member, the placement seat 5 also moves together;

[0090] Wherein, the humidity monitoring unit, the temperature monitoring unit, and the odor detection unit are all arranged on the placement seat 5;

[0091] The transverse moving member includes a moving seat 8, a moving driving mechanism, an identification probe, and at least two position markings. The moving seat 8 is slidably connected to the moving track 9, and the moving driving mechanism is arranged on the moving seat 8 and drives the moving seat 8 to slide along the length direction of the moving track 9. At least two position markings are distributed at equal intervals along the length direction of the moving track 9. The identification probe is arranged on the moving seat 8 and moves with the movement of the moving seat 8.

[0092] The longitudinal lifting member includes a lifting rod 6, a lifting drive mechanism and a height detection member, one end of the lifting rod 6 is drivingly connected to the lifting drive mechanism to form a lifting portion, the lifting portion is arranged on the moving seat 8, the other end of the lifting rod 6 is connected to the placement seat 5, and the height detection member detects the extension and contraction amount of the lifting rod 6, thereby obtaining the lifting amount of the lifting rod 6;

[0093] In this embodiment, when it is necessary to lift to a set height and collect the temperature, humidity and odor of the height layer, when the set height does not match the lifting amount, the central processor is triggered to drive the lifting drive mechanism, so that the lifting drive mechanism drives the lifting rod 6 to perform a telescopic action, thereby improving the accuracy of the lifting rod 6;

[0094] In this embodiment, the humidity monitoring unit, the temperature monitoring unit, and the odor detection unit cooperate with each other with the lateral moving member and the longitudinal lifting member to collect the storage environment at different height layers and different positions in the freezer 1;

[0095] Wherein, the odor detection unit is hidden in the hidden slot 7 in the non-detection state, and the position of the hidden slot 7 is as follows: Figure 5 As shown (i.e., located at one inner wall of the refrigerator storage rack 4);

[0096] The AI ​​evaluation module obtains the verification data collected by the vegetable verification module and the storage environment data collected by the monitoring module, and the processing process includes:

[0097] The AI ​​evaluation module obtains the verification data collected by the verification module and processes the verification data according to the following steps:

[0098] S11. Calculate chlorophyll fluorescence intensity F Chl :

[0099] Obtain the calibration data collected by the calibration module and calculate the chlorophyll fluorescence intensity F according to the following formula: Chl :

[0100] ;

[0101] Among them, the fluorescence emission of chlorophyll is mainly concentrated in two wavelength ranges: 660~680 nm and 700~740 nm, and S(λ) is the fluorescence spectrum intensity at wavelength λ;

[0102] S12, obtaining the test data collected by the test module, and calculating the carotenoid fluorescence intensity F according to the following formula: Car :

[0103] ;

[0104] Among them, the fluorescence emission of carotenoids is mainly concentrated in the range of 450-550 nm, and S(λ) is the fluorescence spectrum intensity at wavelength λ;

[0105] In this embodiment, the fluorescence spectrum intensity S(λ) at wavelength λ can be determined according to specific experiments, that is:

[0106] 1) Laser-induced fluorescence (LIF) technology

[0107] The laser is used to emit light of a specific wavelength to illuminate the surface of vegetables, thereby stimulating fluorescent substances (such as chlorophyll and carotenoids) in the vegetables to emit fluorescence; the fluorescence detector captures and records the fluorescence spectrum;

[0108] 2) Data Collection

[0109] Use a spectrometer to record the intensity S(λ) of the fluorescence spectrum as it changes with wavelength; the S(λ) obtained in each measurement reflects the current real-time status of the vegetables;

[0110] At the same time, in this embodiment, examples of fluorescence spectrum intensity data of typical vegetables are also provided, as shown in (Table 1-Table 4):

[0111] Table 1 Typical data of fluorescence intensity of stem and leaf vegetables (such as lettuce, spinach, cabbage) in the range of 660-680 nm and 700-740 nm

[0112]

[0113] Table 2 Typical data of fluorescence intensity of stem and leaf vegetables (such as lettuce, spinach, cabbage) in the range of 450-550 nm

[0114]

[0115] Table 3 Fluorescence spectral intensity S(λ) data of tuber vegetables (such as carrots, potatoes, and beets) in the range of 660-680 nm and 700-740 nm

[0116]

[0117] Table 4 Typical data of fluorescence intensity of tuber vegetables (such as carrots, potatoes, and beets) in the range of 450-550 nm

[0118]

[0119] The data of the fluorescence intensity needs to be obtained according to the specific position detection of the actual laser and the fluorescence detector. The above example is only used as an exemplary description, and needs to be determined according to the parameters set at the actual factory. This is a technical means well known to those skilled in the art. Those skilled in the art can refer to the relevant technical manuals to learn about the technology, so it will not be described one by one in this embodiment.

[0120] In addition, the specific setting value needs to be preset in the central processor before leaving the factory, and after selecting the stored vegetable type by using the selection, the specific fluorescence intensity is called in the central processor and participates in the above calculation, thereby calculating the chlorophyll fluorescence intensity F of the refrigerated vegetables. Chl , and carotenoid fluorescence intensity F Car ;

[0121] S12, obtaining the calibration data collected by the calibration module, and determining the moisture content index W by the fluorescence intensity in the wet state and the dry state, specifically:

[0122] ;

[0123] In the formula, I 湿 is the fluorescence intensity in wet state, I 干 is the fluorescence intensity in the dry state; the wet state and dry state are both the states of the vegetables to be tested, the wet state means that the surface of the vegetables to be tested contains water, and the dry state means that the surface does not contain water. Usually, the state of vegetables just picked or in a humid environment is the wet state. The state of vegetables stored for a period of time or in a dry environment is the dry state.

[0124] Among them, the fluorescence intensity of the wet state I 湿 Calculate according to the following formula:

[0125] ;

[0126] In the formula, S 湿 (λ) is the fluorescence spectrum intensity at wavelength λ in the wet state, λ1 and λ2 are the start and end wavelengths of the integration range;

[0127] Fluorescence intensity in dry state I 干 Calculate according to the following formula:

[0128] ;

[0129] In the formula, S 干 (λ) is the fluorescence spectrum intensity at wavelength λ in the dry state, λ1 and λ2 are the start and end wavelengths of the integration range;

[0130] For the measurement of chlorophyll fluorescence intensity: the wavelength range used is: 660~680 nm and 700~740 nm;

[0131] For the measurement of chlorophyll fluorescence intensity: the wavelength range used is: 660~680 nm and 700~740 nm; if the wavelength range is: 660~680 nm, then: λ1=660nm, 𝜆λ2=680nm;

[0132] If the wavelength range is 700~740 nm, then: 𝜆λ1=700nm, 𝜆λ2=740nm;

[0133] For the fluorescence intensity measurement of carotenoids, the applicable wavelength range is 450-550 nm;

[0134] Meanwhile, in this embodiment, typical examples of fluorescence spectrum intensity in wet state and fluorescence spectrum intensity in dry state are also provided, as shown in Table 5 to Table 8:

[0135] Table 5: Dry state fluorescence spectrum intensity S in the range of 660-680 nm and 700-740 nm 干 (λ)

[0136] In this embodiment, different wavelength ranges are applicable to the fluorescence intensity measurement of chlorophyll and carotenoids. The fluorescence emission peak of chlorophyll is concentrated in the 660-680 nm and 700-740 nm bands, while the fluorescence emission peak of carotenoids is concentrated in the 450-550 nm band. By integrating and calculating the fluorescence spectrum intensity within each wavelength range, the fluorescence intensity in the wet state and the dry state can be obtained, thereby calculating the moisture content index W;

[0137]

[0138] Table 6: Fluorescence spectrum intensity S in the wet state in the range of 660-680 nm and 700-740 nm 湿 (λ)

[0139]

[0140] Table 7 Dry state fluorescence spectrum intensity S in the range of 450-550 nm 干 (λ)

[0141]

[0142] Table 8 Fluorescence spectrum intensity S in wet state in the range of 450-550 nm 湿 (λ)

[0143]

[0144] The data of the fluorescence intensity needs to be obtained according to the specific position detection of the actual laser and the fluorescence detector. The above example is only used as an exemplary description, and needs to be determined according to the parameters set at the actual factory. This is a technical means well known to those skilled in the art. Those skilled in the art can refer to the relevant technical manuals to learn about the technology, so it will not be described one by one in this embodiment.

[0145] In this embodiment, the specific setting value needs to be preset in the central processing unit before leaving the factory, and is called by the central processing unit according to the actual situation;

[0146] Optionally, the AI ​​evaluation module obtains the processed verification data collected by the vegetable verification module and the storage environment data collected by the monitoring module for the storage state index SSI of the vegetables:

[0147] ;

[0148] In the formula, F Chl is the chlorophyll fluorescence intensity, F Car is the fluorescence intensity of carotenoids, H is the storage environment humidity, and its value is obtained by detecting the humidity sensor, W is the moisture content index, T is the storage environment temperature, and its value is obtained by collecting the temperature sensor, and G is the odor concentration value, and its value is obtained by collecting the volatile organic compound (VOC) gas concentration by the VOC sensor;

[0149] If the storage status index SSI does not fall within the monitoring threshold range [fresh1, fresh2] set by the system, the environment control module is triggered to adjust the storage environment;

[0150] If the storage status index SSI falls into the monitoring threshold range [fresh1, fresh2] set by the system, it means that the vegetables are in good refrigeration condition;

[0151] The monitoring threshold range [fresh1, fresh2] set by the system is obtained by the system or the administrator according to the actual situation or a limited number of experiments, and is input from the human-computer interaction interface. This is a technical means well known to those skilled in the art. Those skilled in the art can refer to relevant technical manuals to learn about the technology, so it will not be described in detail in this embodiment.

[0152] Through the cooperation between the monitoring module and the AI ​​evaluation module, the refrigeration status of the vegetables can be accurately evaluated, ensuring that the entire system has the advantages of high intelligence, strong management level, good refrigeration effect and high refinement ability;

[0153] Optionally, the monitoring module is arranged on the periphery of the storage area of ​​the refrigerator, and collects storage environment data at different heights of the storage area;

[0154] Optionally, the early warning module includes an early warning unit and an interactive unit, wherein the early warning unit triggers different early warning prompts according to the relationship between the storage state index SSI and the set monitoring threshold range [fresh1, fresh2], and the interactive unit interactively prompts the user with the early warning prompt issued by the early warning unit;

[0155] Optionally, the vegetable verification module further includes an inspection unit, which unbinds the vegetables and RFID tags that have established a binding relationship;

[0156] Optionally, the disinfection inspection unit is arranged on one side of the refrigerator, and allows the user to perform disinfection inspection on the edible vegetables 14;

[0157] The inspection unit includes an RFID reader 3, an unbinding device, a user interface, an operation button, and a log recorder. The RFID reader 3 is used to read the RFID tag information bound to the vegetable 14 to confirm the binding relationship between the vegetable 14 and the tag. The unbinding device receives the RFID tag information transmitted by the identification unit and compares it with the background database to confirm the identity of the vegetable 14, performs the unbinding operation, and deletes the information of the vegetable 14 and the RFID tag from the database or marks it as inspected. The user interface is used to display relevant information of the vegetable 14, such as type, storage time, quality status, etc., to help the user confirm the inspection operation. The operation button is used for the user to perform the inspection confirmation operation, wherein the operation button can be a physical button or a touch screen interface. The log recorder records the detailed information of each inspection operation, including the inspection time, the operator, the type of vegetable 14, etc., to facilitate subsequent traceability and management;

[0158] The inspection steps include:

[0159] S21, identification: the user places the vegetables in the identification area of ​​the inspection unit, and the RFID reader reads the RFID tag information;

[0160] S22, confirmation: the display screen displays vegetable information, and the user confirms the vegetable type and quality status;

[0161] S23, unbinding: the user presses the inspection confirmation button, and the unbinding device performs the unbinding operation, deleting the information of the vegetables and RFID tags from the database or marking them as inspected;

[0162] S24, record: the log recorder records the detailed information of the inspection operation for subsequent tracing;

[0163] S25, update: the central processor uploads the unbinding operation result to the backend server and updates the database information in real time;

[0164] The environmental control module obtains the evaluation result (i.e., the storage state index SSI) and the monitoring threshold range [fresh1, fresh2] set by the system, and determines the specific temperature, humidity, and power according to the following steps:

[0165] S31, determining the vegetables with the largest proportion: determining the vegetables with the largest proportion according to the proportions of different vegetables in the refrigerator;

[0166] S32, using the storage requirements of vegetables with the largest proportion as the target range;

[0167] S33, dynamically adjust temperature, humidity and power according to SSI value, specifically:

[0168] ;

[0169] Where, T new is the new temperature of the refrigerator, T max is the maximum storage temperature required for vegetables with the largest proportion, T min is the minimum storage temperature required for vegetables with the largest proportion, T current is the real-time temperature value of the refrigerator, SSI is the storage status index, fresh1 is the lower limit of the monitoring threshold range set by the system, and fresh2 is the upper limit of the monitoring threshold range set by the system;

[0170] ;

[0171] In the formula, H new is the new humidity of the refrigerator, H max is the maximum humidity value required for the storage of vegetables with the largest proportion, T min is the minimum humidity value required for the storage of vegetables with the largest proportion, H current is the real-time humidity value of the refrigerator, SSI is the storage status index, fresh1 is the lower limit of the monitoring threshold range set by the system, and fresh2 is the upper limit of the monitoring threshold range set by the system;

[0172] ;

[0173] Where P new is the new power of the refrigerator, P max is the maximum power required for storing the vegetables with the largest proportion, T min is the minimum power required for the storage of vegetables with the largest proportion, P current is the real-time power value of the refrigerator, SSI is the storage status index, fresh1 is the lower limit of the monitoring threshold range set by the system, and fresh2 is the upper limit of the monitoring threshold range set by the system;

[0174] In this embodiment, for example, assuming that lettuce accounts for the largest proportion according to the actual proportion, the storage requirement for lettuce is:

[0175] Temperature range: 1°C ~ 5°C

[0176] Humidity range: 90% ~ 95%

[0177] Power range: 50 ~100

[0178] Then substitute the maximum and minimum values ​​of the temperature range, the maximum and minimum values ​​of the humidity range, and the maximum and minimum values ​​of the power range into the above formula, and determine the new temperature, humidity, and power;

[0179] In this embodiment, the vegetables with the largest proportion are determined according to the following steps:

[0180] S41. Calculate the proportion of each vegetable

[0181] Assume there are n kinds of vegetables in the refrigerator, and use RFID tags to count the number of each vegetable N i , the total number of vegetables is N total ;

[0182] in, ;

[0183] The proportion of each vegetable i Calculate according to the following formula:

[0184] ;

[0185] Where N i is the quantity of each vegetable, N total is the total amount of vegetables;

[0186] S42. Identify vegetables with the largest proportion

[0187] Determine the largest proportion of vegetables i max ,satisfy:

[0188]

[0189] S43. Use the storage requirements of the largest proportion of vegetables as the target range

[0190] Vegetables with the largest share max The storage requirements are:

[0191] Temperature range: T min and T max ;

[0192] Humidity range: H min and H max ;

[0193] Power range: P min and P max ;

[0194] Substitute the maximum and minimum values ​​of the temperature range, the maximum and minimum values ​​of the humidity range, and the maximum and minimum values ​​of the power range into the above formula, and determine the new temperature, humidity, and power;

[0195] Through the mutual cooperation of the regulation module and the AI evaluation module, the refrigeration of the vegetables is more refined, ensuring that the whole system has the advantages of good refrigeration effect, good interaction ability, high intelligence level and strong management level;

[0196] The warning module includes a prompt unit and a warning unit. The warning unit obtains the evaluation result of the AI evaluation module and generates a corresponding warning message, and the prompt unit interactively prompts the warning message to the user;

[0197] Among them, the warning unit generates a warning prompt according to the following warning steps:

[0198] S51. Obtain the evaluation result:

[0199] Obtain the storage state index (SSI) of the vegetables from the AI evaluation module;

[0200] Judge whether the SSI falls within the set monitoring threshold range [fresh1, fresh2];

[0201] S52. Generate a warning prompt:

[0202] Condition 1: When SSI < fresh1, generate a warning prompt of "The storage environment is too cold";

[0203] Condition 2: When SSI > fresh2, generate a warning prompt of "The storage environment is too hot";

[0204] Condition 3: When the SSI is within the range of [fresh1, fresh2], generate a prompt of "The storage environment is normal" and no warning is required;

[0205] S53. Format the warning information:

[0206] Format the warning information into a standardized information structure for the prompt unit to perform interactive prompts;

[0207] The prompt unit includes a prompt screen and an executable program. The executable program converts the warning prompt into standard display data and displays the display data on the prompt screen. The prompt screen is for the user to view to dynamically display the warning prompt of the freezer;

[0208] The prompt steps of the prompt unit are as follows:

[0209] S61. Receive the warning information:

[0210] Receive the generated warning information from the warning unit;

[0211] S62. Information display:

[0212] Condition 1: If the warning message is "Storage environment is too cold", the prompt unit displays "Warning: Storage environment is too cold, please adjust the temperature!" on the display screen;

[0213] Condition 2: If the warning message is "Storage environment overheated", the prompt unit displays "Warning: Storage environment overheated, please adjust the temperature!" on the display screen;

[0214] Condition 3: If the warning message is "storage environment is normal", the prompt unit displays "storage environment is normal, everything is good" on the display screen;

[0215] S63. Record log:

[0216] Record each warning information in the system log to facilitate subsequent query and analysis, and also allow the user to view it;

[0217] Through the mutual cooperation between the AI ​​evaluation module and the early warning module, the refrigeration status of the vegetables can be prompted, ensuring that the entire system has the advantages of good prompt effect, strong early warning interaction capability, high intelligence and good interactive comfort.

[0218] Embodiment 2: This embodiment should be understood to include all the features of any of the above embodiments, and further improve upon them. Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 As shown, the vegetable refrigeration system also includes a decision-making module, which analyzes the vegetable edible priority index PI according to the evaluation result of the AI ​​evaluation module, and warns the user of the evaluation result through the early warning module;

[0219] Wherein, the decision module includes a decision analysis unit and a decision prompt unit, the decision analysis unit analyzes the edible priority index PI according to the evaluation result, and the decision prompt unit transmits the analyzed edible priority index PI to the early warning module and gives an early warning prompt to the user;

[0220] The decision analysis unit obtains the evaluation result of the AI ​​evaluation module and calculates the consumption priority index PI according to the following formula:

[0221] ;

[0222] Where, t storageis the total refrigeration time, SSI is the storage status index, △SSI is the range of storage status in historical data, satisfying: △SSI=SSI max -SSI min , among which, SSI max The maximum value of the storage status index in the historical data, SSI min The minimum value of the storage status index in the historical data, SSI ref The storage status index is the reference value, which is calculated according to the following formula:

[0223] ;

[0224] In the formula, fresh1 is the lower limit of the monitoring threshold range set by the system, and fresh2 is the upper limit of the monitoring threshold range set by the system;

[0225] For the total refrigeration time t storage Calculate according to the following formula:

[0226] ;

[0227] Where, T start is the time when the vegetables are put into the refrigerator, and its value is equal to the time of verification (where, is set to be equal to the time of verification), T current is the current moment, T ref The optimal usage time set for the system, usually for a fixed period of time (e.g. 1 hour, 3 days, 5 days, 7 days, 30 days);

[0228] Sort the vegetables stored in the refrigerator according to their edible priority index PI to form an edible decision list;

[0229] Among them, the larger the PI value, the more priority the user needs to consume;

[0230] When the usage decision list is formed, the consumption decision list is displayed on the display screen of the early warning module to prompt the user the order of consumption of the vegetables in the freezer to improve the user's comfort;

[0231] In this embodiment, through the mutual cooperation between the AI ​​evaluation module and the decision-making module, the order of eating the vegetables can be decided, ensuring that the entire system has the advantages of strong decision-making ability, high intelligence and good refined management capabilities.

[0232] The contents disclosed above are only preferred feasible embodiments of the present invention, and do not limit the protection scope of the present invention. Therefore, all equivalent technical changes made using the contents of the present invention specification and drawings are included in the protection scope of the present invention. In addition, the elements therein can be updated as technology develops.

Claims

1. A vegetable refrigeration system integrating AI and Internet of Things technologies, the vegetable refrigeration system comprising a server and a refrigerator, characterized in that: The vegetable refrigeration system further includes a vegetable verification module, a monitoring module, an AI evaluation module, an environmental control module, and an early warning module, and the server is connected to the vegetable verification module, the monitoring module, the AI ​​evaluation module, the environmental control module, and the early warning module respectively; The vegetable verification module, monitoring module, AI evaluation module, environment control module, and early warning module are all arranged in the refrigerator. The vegetable verification module verifies the vegetables stored in the refrigerator to obtain the verification data of the vegetables and generate corresponding adaptive RFID tags. The monitoring module collects the data of the storage environment in the refrigerator. The AI ​​evaluation module evaluates the storage status of the vegetables according to the verification data collected by the vegetable verification module and the storage environment collected by the monitoring module to form an evaluation result. The environment control module triggers the adjustment of the storage environment in the refrigerator according to the evaluation result. The early warning module gives an early warning prompt to the user according to the evaluation result. The vegetable verification module includes a verification unit, a label supply unit, and a state analysis unit. The verification unit verifies the refrigerated vegetables to obtain verification data of the vegetables. The state analysis unit analyzes the verification data collected by the verification unit to form initial state data of the vegetables. The label supply unit supplies RFID labels and binds the RFID labels to the initial state data. The verification unit comprises a verification area, a storage component and a verification component. The storage component is arranged in the verification area. The storage component is used to store the vegetables. The verification component verifies the vegetables placed in the verification area to obtain verification data of the vegetables. The verification component comprises a laser and a fluorescence detector. The laser emits laser light to the stored vegetables. The fluorescence detector is used to capture the fluorescence signal of the vegetables and convert the fluorescence signal into an electrical signal. The monitoring module includes a humidity monitoring unit, a temperature monitoring unit, an odor detection unit and a position adjustment unit, wherein the temperature monitoring unit collects temperature data in the refrigerator, the humidity monitoring unit collects humidity data in the refrigerator, the odor detection unit collects odor concentration value data in the refrigerator, and the position adjustment unit adjusts the positions of the humidity monitoring unit, the temperature monitoring unit and the odor detection unit; The AI ​​evaluation module obtains the processed verification data collected by the vegetable verification module and the storage state index SSI of the storage environment for vegetables collected by the monitoring module: ； In the formula, F Chl is the chlorophyll fluorescence intensity, F Car is the fluorescence intensity of carotenoids, H is the storage environment humidity, T is the storage environment temperature, G is the odor concentration value, and W is the moisture content index, whose value is determined by the fluorescence intensity in the wet state and the dry state. ； In the formula, I 湿 is the fluorescence intensity in wet state, I 干 is the fluorescence intensity in the dry state; the wet state and the dry state are both the states of the vegetables to be tested, the wet state means that the surface of the vegetables to be tested contains moisture, and the dry state means that the surface does not contain moisture; if the storage state index SSI does not fall into the monitoring threshold range [fresh1, fresh2] set by the system, the environmental control module is triggered to adjust the storage environment.

2. The vegetable refrigeration system integrating AI and Internet of Things technologies according to claim 1 is characterized in that: The storage component includes a steering seat, a steering gear, and a steering drive mechanism. An articulation cavity for placing the steering seat is provided on a placement plate. One side end surface of the steering seat is hinged to the bottom wall of the articulation cavity to form an articulation portion. The steering gear is nested in the outer periphery of the steering seat. The steering drive mechanism is engaged with the steering gear and drives the steering seat to rotate along the axis of the articulation portion so that the steering seat rotates along its own axis.

3. The vegetable refrigeration system integrating AI and Internet of Things technologies according to claim 2 is characterized in that: The verification component also includes a γ height adjustment rod, a γ adjustment driving mechanism, a β height adjustment rod, and a β adjustment driving mechanism, wherein the γ adjustment driving mechanism is drivingly connected to one end of the γ height adjustment rod to form a γ adjustment portion, and the other end of the γ height adjustment rod is connected to the laser, and the β adjustment driving mechanism is drivingly connected to one end of the β height adjustment rod to form a β adjustment portion, and the other end of the β height adjustment rod is connected to the fluorescence detector; The γ height adjustment unit adjusts the position of the laser, and the β height adjustment unit adjusts the position of the fluorescence detector.

4. The vegetable refrigeration system integrating AI and Internet of Things technologies according to claim 3 is characterized in that: The monitoring module is arranged on the periphery of the storage area of ​​the refrigerator and collects storage environment data at different heights of the storage area.

5. The vegetable refrigeration system integrating AI and Internet of Things technologies according to claim 4 is characterized in that: The early warning module includes an early warning unit and an interactive unit. The early warning unit triggers different early warning prompts according to the relationship between the storage status index SSI and the set monitoring threshold range [fresh1, fresh2]. The interactive unit interactively prompts the early warning prompts issued by the early warning unit to the user.

6. The vegetable refrigeration system integrating AI and Internet of Things technologies according to claim 5 is characterized in that: The vegetable identification module also includes an inspection unit, which unbinds the vegetables and RFID tags that have established a binding relationship.

7. The vegetable refrigeration system integrating AI and IoT technologies according to claim 6 is characterized in that: The disinfection inspection unit is arranged on one side of the refrigerator and is used by the user to perform disinfection inspection on edible vegetables.

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