Intelligent monitoring and control system based on fruit and vegetable preservation

Abstract

The invention discloses an intelligent monitoring and control system based on fruit and vegetable preservation. The intelligent monitoring and control system comprises an environment monitoring sensor subsystem, a fruit and vegetable physiological state sensor subsystem, an intelligent control module and an automatic fruit and vegetable recognition subsystem. The intelligent control module dynamically regulates and controls environmental parameters based on the states of fruits and vegetables; the fruit and vegetable physiological status sensor subsystem monitors the physiological indexes of fruits and vegetables in a non-intrusive manner and evaluates the storage status of the fruits and vegetables; the improved novel prediction algorithm can predict the optimal storage time according to the fruit and vegetable physiological state and the environmental change trend, and can perform remote monitoring and alarm when the state is abnormal. According to the system, the fruit and vegetable storage environment is monitored in real time with high precision and intelligently regulated and controlled, the stability and the storage quality of the storage environment are guaranteed, the system uses the sensor for non-damage detection, and the efficiency and the accuracy of fruit and vegetable detection are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fruit and vegetable preservation, and in particular to an intelligent monitoring and control system based on fruit and vegetable preservation. Background Art

[0002] With the development of the global food supply chain, the preservation and transportation of fruits and vegetables has become a vital research area. Traditional fruit and vegetable preservation methods often rely on manual monitoring and empirical judgment, which is not only inefficient but also difficult to ensure the quality of fruits and vegetables during storage. Existing fruit and vegetable preservation technologies are mainly faced with challenges such as insufficient gas composition control, limited physiological status monitoring, inaccurate environmental monitoring, insufficient intelligent control, and lack of remote monitoring and alarm functions.

[0003] In the storage of fruits and vegetables, gas composition plays a crucial role in their preservation. Ethylene, a plant hormone, plays a significant role in the ripening and aging processes of fruits and vegetables. Traditional fruit and vegetable preservation methods rely primarily on simple temperature and humidity control, often neglecting the regulation of ethylene gas and lacking real-time monitoring and precise control of the physiological state of fruits and vegetables. Existing gas control systems mostly rely on simple mechanical ventilation or chemical absorbents. These methods are not only inefficient but also unable to precisely control gas composition. These limitations are particularly pronounced when different fruits and vegetables have widely varying gas composition requirements. Furthermore, the sensor-integrated fruit and vegetable monitoring systems currently available on the market often have limited functionality, making it difficult to achieve real-time, non-invasive monitoring. They are unable to comprehensively monitor the physiological state of fruits and vegetables and have limitations in automatic control. Furthermore, existing fruit and vegetable storage systems lack intelligent control modules, making it impossible to automatically adjust the storage environment based on real-time monitoring data to meet the preservation requirements of the fruits and vegetables. Furthermore, existing sensors lack sufficient accuracy to meet the requirements of high-precision monitoring and lack effective algorithms for comprehensive multi-parameter control. Furthermore, existing systems often lack remote monitoring and alarm capabilities, resulting in delayed problem detection and resolution. Summary of the Invention

[0004] In response to the shortcomings of the prior art, the present invention aims to provide an intelligent monitoring and control system for fruit and vegetable preservation, thereby resolving the problems of insufficient gas composition control, limited physiological status monitoring, inaccurate environmental monitoring, insufficient intelligent control, and lack of remote monitoring and alarm functions. To achieve the above-mentioned objectives and other advantages of the present invention, an intelligent monitoring and control system for fruit and vegetable preservation is provided, comprising:

[0005] An environmental monitoring sensor subsystem, which is used to monitor the temperature, humidity, gas composition, and water evaporation of the fruit and vegetable storage environment;

[0006] Fruit and vegetable physiological status sensor subsystem, which is used to calculate the transpiration rate of fruits and vegetables, reflect the changes in moisture content of fruits and vegetables, and monitor the weight changes, respiration intensity, moisture content and other physiological indicators of fruits and vegetables in real time;

[0007] An intelligent control module, which is used to automatically adjust the temperature, humidity and gas composition of the storage environment based on data from the environmental monitoring sensor subsystem;

[0008] The automatic fruit and vegetable identification subsystem is used to obtain the comprehensive image features, gas composition and physiological status monitoring results of fruit and vegetable images, identify the type and current physiological status of fruits and vegetables, and transmit the identified fruit and vegetable type and status information to the user via a wireless network.

[0009] Preferably, the fruit and vegetable physiological status monitoring subsystem and intelligent control module can monitor the temperature, humidity, gas composition, and physiological status of the fruit and vegetable storage environment in real time, and automatically adjust the storage environment based on the monitoring data. In particular, the gas regulation module not only monitors the ethylene concentration in the storage environment in real time but also automatically adjusts the gas composition ratio based on the specific needs of the fruit and vegetable to achieve optimal preservation. By precisely controlling the ethylene concentration, the present invention can effectively delay the ripening process of fruits and vegetables, reduce losses, and improve the storage quality of fruits and vegetables. This improves the accuracy of monitoring and the intelligent level of regulation, thus overcoming the shortcomings of existing technologies.

[0010] Preferably, in terms of gas regulation, the system monitors the ethylene concentration in real time and automatically adjusts the gas composition ratio according to the physiological needs of fruits and vegetables to achieve the best preservation effect.

[0011] Preferably, the system also uses multispectral imaging sensor technology, which uses light of different wavelengths to illuminate fruits and vegetables, and monitors the moisture content and internal respiratory metabolism levels of fruits and vegetables in real time based on changes in the reflectivity of the fruit and vegetable surface to light.

[0012] Preferably, the intelligent control module applies a Kalman algorithm to accurately estimate the state of the dynamic system from a series of measurement data in the presence of noise and uncertainty, and predict the optimal storage time for fruits and vegetables. Based on this data, the intelligent control module can adjust the storage environment in advance, optimizing the environmental conditions and preventing adverse conditions from affecting the fruits and vegetables.

[0013] Preferably, the system can collect high-precision data on temperature, humidity, gas composition and physiological status of fruits and vegetables in real time, and adjust the environment and optimize the preservation strategy based on these data through the intelligent control module, thereby achieving precise control of the fruit and vegetable preservation environment.

[0014] Preferably, the system also integrates advanced wireless communication functions, including Wi-Fi, Bluetooth, ZigBee and 5G communication technologies to achieve remote monitoring and alarm functions. Through these wireless communication technologies, the system transmits monitoring data in real time to the cloud platform and the mobile devices of relevant personnel. The Wi-Fi module operates in the 2.4GHz and 5GHz frequency bands for regular data synchronization and status updates; the Bluetooth module operates in the 2.4GHz ISM band, and improves anti-interference capabilities through frequency hopping and spread spectrum, and is used for short-range device pairing and data transmission; ZigBee technology supports a variety of network topologies for communication between multiple sensor nodes; 5G communication technology is suitable for long-distance, large-data transmission scenarios, providing high-speed, low-latency, and large-capacity communication capabilities. When an abnormal situation is detected, the system will automatically send an alarm to notify relevant personnel for timely processing. This function is achieved through an integrated wireless communication module, ensuring that any abnormalities in the fruit and vegetable storage environment can be quickly discovered and handled, thereby minimizing losses.

[0015] Compared with the existing technology, the present invention has the following advantages: high-precision temperature, humidity, gas composition and fruit and vegetable physiological status monitoring sensors are used to collect relevant data in real time, and the intelligent control module then adjusts the environment and optimizes the preservation strategy based on the data. In addition, by integrating wireless communication technology, remote monitoring and alarm functions are realized, thereby achieving precise control of the fruit and vegetable storage environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Schematic diagram of the structure of the intelligent monitoring and control system based on fruit and vegetable preservation according to the present invention;

[0017] Figure 2 for Figure 1 A partial view of

[0018] Figure 3 This is a flow chart of capturing and identifying the status of fruits and vegetables according to the intelligent monitoring and control system based on fruit and vegetable preservation of the present invention;

[0019] Figure 4 This is a flow chart of the multispectral imaging technology based on the intelligent monitoring and control system for fruit and vegetable preservation according to the present invention;

[0020] Figure 5 This is a workflow diagram of the gas regulation module of the intelligent monitoring and control system based on fruit and vegetable preservation according to the present invention;

[0021] Figure 6 A flow chart of fruit and vegetable identification based on the intelligent monitoring and control system for fruit and vegetable preservation according to the present invention;

[0022] Figure 7The figure is a flow chart of the intelligent monitoring and control system based on fruit and vegetable preservation according to the present invention. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0024] Reference Figure 1-2 , an intelligent monitoring and control system based on fruit and vegetable preservation, including:

[0025] The environmental monitoring sensor subsystem is used to monitor the temperature, humidity, gas composition, and water evaporation of the fruit and vegetable storage environment; the environmental monitoring sensor subsystem includes a digital temperature sensor 2, a temperature and humidity sensor 3, a fluorescent oxygen sensor 4, a carbon dioxide monitoring sensor 5, an ethylene colorimetric sensor 6, and a gas composition sensor. The digital temperature sensor 2 is a DS18B20 digital temperature sensor that provides 9-bit to 12-bit Celsius temperature measurement accuracy and has a user-programmable non-volatile alarm function. The temperature and humidity sensor 3 is a Testo 608-H2 temperature and humidity sensor that can continuously measure air temperature, ambient humidity, and dew point temperature, and can issue an LED alarm when the limit is exceeded. The fluorescent oxygen sensor 4 is a fluorescent oxygen sensor Lox-02 that can be directly connected to the microcontroller without the need for additional equipment. The carbon dioxide monitoring sensor 5 is a MINIR infrared carbon dioxide sensor / CO2 sensor with ultra-low power consumption (3.5mW), a measurement range from 0 to 100%, a supply voltage of 3.3V, a peak current of only 33mA, and a 20mm diameter probe package. The ethylene colorimetric sensor 6 is used to monitor ethylene. Ethylene gas reacts with chemicals on the sensor, causing the sensor to change color. This color change is proportional to the ethylene concentration, and the sensor's color change can be used to indicate the ripeness of fruits and vegetables, providing intuitive results.

[0026] Furthermore, the DS18B20 digital temperature sensor has a temperature detection range of -55°C to +125°C and maintains an accuracy of ±0.5°C outside the -10°C to 85°C range. The DS18B20 has a programmable resolution of 9 to 12 bits, corresponding to temperatures of 0.5°C, 0.25°C, 0.125°C, and 0.0625°C, respectively. At 9-bit resolution, the temperature can be converted to digital form in a maximum of 93.75ms, and at 12-bit resolution, the temperature can be converted to digital form in 750ms. It can be powered directly from the data line, eliminating the need for an external power supply. It is compact, has low hardware overhead, and exhibits strong anti-interference capabilities. The DS18B20 digital temperature sensor includes a temperature sensor, a digital converter, and memory. It obtains temperature information by driving a constant current through a thermistor and measuring its resistance. It includes a constant current source to drive current through the thermistor and a digital converter to convert the sensor's analog signal into a digital signal. The DS18B20 digital temperature sensor uses the 1-Wire bus protocol to communicate with a host controller. The 1-Wire bus requires only a single data line and ground line, with the host controller sending commands and receiving sensor responses. Each temperature measurement requires four steps: initialization, ROM command transmission, RAM command transmission, and data exchange. The memory is used to store information such as the sensor's unique identifier (ROM code) and configuration parameters. During temperature measurement, Counter 1 subtracts the pulse signal generated by the low-temperature-coefficient crystal oscillator. When Counter 1's preset value reaches 0, the temperature register value increments by 1, and Counter 1 restarts counting the pulse signal generated by the low-temperature-coefficient crystal oscillator. This cycle continues until Counter 2 reaches 0, at which point the temperature register value ceases to accumulate. The value in the temperature register at this point represents the measured temperature. A slope accumulator compensates for and corrects for nonlinearities in the temperature measurement process, and its output is used to correct the preset value of Counter 1.

[0027] Furthermore, the testo 608-H2 temperature and humidity sensor uses an NTC temperature sensor for temperature measurement. This sensor has a measurement range of -40 to +70°C, with an accuracy of ±0.5°C at +25°C and a resolution of 0.1°C. Humidity is measured by the capacitance change of the built-in capacitive sensor. The measurement range is 2 to 98% RH, with an accuracy of ±2% RH and a resolution of 0.1% RH. This thermo-hygrometer can calculate the dew point temperature based on the measured temperature and humidity. This is particularly important for monitoring the storage environment of fruits and vegetables, as the dew point temperature is directly related to the evaporation and condensation of water in fruits and vegetables. If the measured temperature or humidity exceeds the user-set limit, the testo 608-H2 will issue a visual alarm via LED, prompting you to take action.

[0028] Furthermore, the fluorescence oxygen sensor 4 uses the principle of fluorescence quenching to measure the ambient oxygen content. By measuring the change in fluorescence intensity, the partial pressure of oxygen (ppO2) can be calculated. The internal air pressure sensor measurement and ppO2 measurement allow the oxygen concentration (O2%) value to be calculated.

[0029] Furthermore, the infrared CO2 sensor / MINIR CO2 sensor determines CO2 concentration by measuring the absorption of infrared light of a specific wavelength by CO2. An infrared light source within the sensor emits infrared light, which passes through the gas being measured in the optical path, through a narrowband filter, and reaches the infrared detector 8. Carbon dioxide has an absorption peak in the 4.26μm infrared region. At this wavelength, gases like oxygen, nitrogen, carbon monoxide, and water vapor have no significant absorption. The detector measures the intensity of the remaining light, and the reduction in this intensity directly reflects the amount of light absorbed by CO2.

[0030] Furthermore, the microcontroller unit converts the received light intensity signal into an electrical signal proportional to the carbon dioxide concentration through circuits and software algorithms, and then converts it into a digital or analog output. In another implementation, a sensor for monitoring the physiological status of fruits and vegetables uses multispectral imaging technology to detect surface moisture and respiratory metabolism levels of fruits and vegetables through spectral reflectance, enabling contactless, real-time monitoring and adaptively adjusting monitoring accuracy based on the type and maturity of the fruit and vegetable.

[0031] Furthermore, a fruit and vegetable physiological status sensor subsystem is provided, which is used to calculate the transpiration rate of fruits and vegetables, reflect changes in the moisture content of fruits and vegetables, and monitor the weight changes, respiration intensity, moisture content and other physiological indicators of fruits and vegetables in real time; the fruit and vegetable physiological status sensor subsystem includes a weighing sensor 9 and a plant wearable stem flow sensor 7, wherein the weighing sensor 9 is used to measure the weight of fruits and vegetables, and can be used to calculate the transpiration rate of fruits and vegetables, reflecting changes in the moisture content of fruits and vegetables. The plant wearable stem flow sensor 7 is only 0.01 mm thick and weighs 0.24 grams. It can be attached to the surface of plant stems for stem flow monitoring, has excellent biocompatibility, and allows sunlight, oxygen, water and carbon dioxide to pass freely. The fruit and vegetable physiological status sensor subsystem adopts multispectral imaging technology, which can detect the moisture and respiratory metabolism levels on the surface of fruits and vegetables through spectral reflectance, realize contactless real-time monitoring, and adaptively adjust the monitoring accuracy according to the different types and maturity of fruits and vegetables. Multispectral imaging technology uses light of different wavelengths to illuminate fruits and vegetables. Because different substances have different absorption and reflection characteristics for different wavelengths of light, the surface of fruits and vegetables produces different reflectances. By detecting changes in spectral reflectance, information about the surface moisture content and internal respiratory and metabolic levels of the fruits and vegetables can be obtained. Multispectral imaging technology can adaptively adjust detection accuracy. Different types of fruits and vegetables have different internal structures and chemical compositions, and the relationship between their physiological state and spectral characteristics varies. For example, apples and bananas have different peel structures and flesh compositions, resulting in different spectral reflectances under the same physiological state. The instrument can adaptively adjust detection accuracy based on the type and maturity of the fruit and vegetable. Maturity also affects the internal structure and chemical composition of fruits and vegetables. As fruits and vegetables mature, chlorophyll content decreases, the relative concentrations of other pigments such as carotenoids change, and moisture and sugar content also change. These changes cause spectral reflectance to vary at different wavelengths. Adaptive adjustment can leverage an existing spectral database of fruits and vegetables of different types and maturity levels to adjust detection algorithm parameters based on the type of fruit and vegetable being tested and the range of possible maturity to improve detection accuracy. The fruit and vegetable physiological status monitoring subsystem monitors physiological indicators such as respiration rate and moisture content. These physiological indicators are closely related to gas composition. The respiration rate of fruits and vegetables affects the concentrations of oxygen and carbon dioxide within the storage space. The gas control module uses data from the fruit and vegetable physiological status monitoring sensors to more accurately adjust the gas composition. As the respiration rate of fruits and vegetables increases, more frequent adjustments to the gas composition are required to maintain an optimal fresh-keeping environment.

[0032] Further, the flowchart of multispectral imaging technology can be seen in the process Figure 3 , its workflow can be divided into the following steps:

[0033] 1. Image acquisition: Use multispectral imaging equipment to image fruits and vegetables and obtain image information of fruits and vegetables.

[0034] 2. Spectral Data Extraction: A light source emits light of varying wavelengths onto the surface of fruits and vegetables. These rays, carrying information about their physiological status, are reflected by the surface. Sensors receive the reflected light, and multispectral imaging equipment analyzes it and calculates its spectral reflectance, enabling contactless, real-time monitoring. From the collected multispectral images, the spectral data corresponding to each pixel is extracted, generating spectral reflectance curves for different locations on the surface of the fruit or vegetable.

[0035] 3. Data preprocessing: Preprocess the extracted spectral data, including removing noise, correcting errors caused by factors such as uneven lighting, etc.

[0036] 4. Preliminary judgment of type and maturity: Based on the existing database of fruit and vegetable types and maturity spectral characteristics, a preliminary analysis of the currently collected spectral data is performed to determine the type of fruit and vegetable and the approximate maturity range.

[0037] 5. Adaptive adjustment: Based on the initial judgment of the type and maturity, the corresponding optimization parameters are retrieved from the database to adjust the accuracy settings of the detection algorithm.

[0038] 6. Physiological status analysis: Use the adjusted algorithm to analyze the spectral reflectance data to calculate physiological status indicators such as the moisture content and respiratory metabolism level of the fruit and vegetable surface.

[0039] 7. Result output: The obtained fruit and vegetable physiological status analysis results are output in a visual or numerical form for users to view and further analyze.

[0040] Furthermore, the gas composition sensor includes a gas control module, which is used to adjust the gas composition ratio of the storage space and create an environment that is more conducive to the preservation of fruits and vegetables by changing the concentrations of gases such as oxygen, carbon dioxide and ethylene. When the gas composition sensor detects the ethylene concentration, the gas control module will automatically adjust the gas composition ratio of the storage space according to the received information and create an environment that is more conducive to the preservation of fruits and vegetables by changing the concentrations of gases such as oxygen, carbon dioxide and ethylene, so as to slow down the ripening process of fruits and vegetables and extend the shelf life. The gas control module is closely connected to the gas composition sensor. When the gas composition sensor detects the ethylene concentration, the gas control module will act according to the received information. Its main purpose is to adjust the gas composition ratio of the storage space and create an environment that is more conducive to the preservation of fruits and vegetables by changing the concentrations of gases such as oxygen, carbon dioxide and ethylene.

[0041] Further, the flow chart of the gas control module can be seen in the attached Figure 4 , its workflow can be divided into the following steps:

[0042] 1. Monitoring Phase: Gas composition sensors continuously monitor the gases within the storage space, focusing on ethylene concentrations. For example, in a fruit and vegetable storage warehouse, sensors continuously collect air samples and analyze them for ethylene content.

[0043] 2. Data transmission: Once the ethylene concentration data is detected, the gas composition sensor transmits the data to the gas control module in real time. This transmission process is achieved through internal communication lines or wireless communication methods within the system to ensure timely and accurate data.

[0044] 3. Control Phase: After receiving ethylene concentration data, the gas control module performs calculations based on a pre-set algorithm. The core of this algorithm is to determine how to adjust the concentrations of other gases based on ethylene concentration. If the ethylene concentration is too high, the algorithm will increase the carbon dioxide concentration and decrease the oxygen concentration to inhibit the ripening process of fruits and vegetables. Furthermore, the gas control module will activate the corresponding gas regulating devices to change the gas composition ratio within the storage space.

[0045] The intelligent control module 10 is used to automatically adjust the temperature, humidity and gas composition of the storage environment based on the data of the environmental monitoring sensor subsystem; the algorithm of the intelligent control module 10 is as follows:

[0046]

[0047] The gas regulation module is closely related to the intelligent control module and the fruit and vegetable physiological status monitoring module. The relationship between the two will be further described below. The algorithm of the gas regulation module is as follows:

[0048]

[0049] The gas control module works in conjunction with the intelligent control module, which is the core control unit of the entire fruit and vegetable preservation intelligent monitoring system. The intelligent control module receives status information from the gas control module, including current gas regulation operations and adjusted gas composition ratios. The intelligent control module also uses data from other sensors to comprehensively determine whether adjustments to the gas control module's operations are necessary. If temperature increases, which affect the sensitivity of fruits and vegetables to gas, the intelligent control module will instruct the gas control module to perform more precise gas adjustments.

[0050] Furthermore, the intelligent control module, which includes a Kalman algorithm, can accurately estimate the state of the dynamic system from a series of measurement data in the presence of noise and uncertainty, and predict the optimal storage time of fruits and vegetables based on their physiological status data and environmental change trends; based on this data, the intelligent control module can adjust the storage environment in advance to avoid the impact of adverse conditions on fruits and vegetables.

[0051] Preferably, for ease of understanding, before describing the intelligent control module of the present invention in detail, the Kalman algorithm of the prior art is first exemplarily described.

[0052] Firstly, a fast distributed Kalman consistency filtering algorithm with local feedback regulation is proposed for filtering problems in wireless sensor networks.

[0053] It should be understood that the Kalman algorithm is a recursive algorithm that can update its state estimate based on previous estimates and current measurements. It is suitable for processing large amounts of fruit and vegetable data streams, can predict the optimal storage time for fruits and vegetables, and adjust the storage environment in advance to avoid the impact of adverse conditions on fruits and vegetables.

[0054] The Kalman algorithm estimates the state of a dynamic system from a series of measurement data in the presence of noise and uncertainty. Compared to classical filtering algorithms, this algorithm converges faster under the same conditions and exhibits excellent robustness, effectively handling packet loss. Furthermore, the Kalman filter requires less computation and memory, resulting in higher efficiency.

[0055] Kalman filtering consists of two main steps: prediction and update. In the prediction step, the estimated value and uncertainty of the next state are predicted based on the dynamic model of the system.

[0056] In the update step, when new measurement data arrives, the algorithm combines the predicted value and the new measurement value to update the state estimate by weighted averaging, where the weight of the measurement value depends on its uncertainty.

[0057] In the prior art, a single module is used as an example. The numerical example monitoring area is 30×30, covered by n=50 randomly deployed sensors. Here, the radius is set to R=8 so that all nodes form an undirected fully connected network.

[0058]

[0059] The following is a detailed description of the working steps of the Kalman algorithm:

[0060] Step 1: Initialize and set the initial estimation error covariance matrix of each sensor node.

[0061]

[0062] Step 2: Data collection phase, each sensor node obtains the local measurement value Z with covariance Ti(k) i (k);

[0063] Step 3: Information fusion and update stage, by calculating and fusing information vectors and matrices, calculating Kalman gain, optimizing state estimation, and adapting to different sensor characteristics.

[0064] ①q i (k)=(E i (k)) T (R i (k)) -1 zi(k)

[0065] ②y i (k)=(E i (k)) T (R i (k)) -1 zi(k)

[0066] ③Q i (k)=(E i (k)) T (R i (k)) -1 E i (k)

[0067] ④

[0068] q in the formula i (k) corresponds to the updated value of each sensor node at time k; y i (k) represents the relationship between all neighbor nodes j and q at time k i The weight of (k); Q i (k) represents a matrix at node i; S i (k) represents the matrix Q of all neighbor nodes j to node i at time k i The weight of (k); E i (k) represents the observation matrix, which is used to transform the variables in the state space model into the observation space.

[0069] Step 4: State estimation update phase;

[0070] M i (k)=((P i (k)) -1 +S i (k)) -1

[0071] P i (k+1)=AM i (k)A T +BQ i (k)B T

[0072] Step 5: Calculate the Kalman consistency estimate;

[0073]

[0074] Step 6: Iterative update, repeat steps 2 to 5. Improve the optimization algorithm and enter the iterative process, looping until the maximum number of iterations is reached.

[0075] The local feedback information consisting of the latest estimated value and its neighbors is fed into the conventional consistency algorithm to build a dual-gain adjustment mechanism, which significantly improves the consistency sensitivity speed and has excellent robustness performance.

[0076] Compared with the classical filtering algorithm, the proposed algorithm has a faster convergence speed under the same conditions, good robustness, and can effectively handle packet loss.

[0077] Local information feedback is introduced to construct a dual-gain adjustment mechanism to improve control accuracy and convergence speed.

[0078] Furthermore, the automated fruit and vegetable identification subsystem is designed to obtain comprehensive image features, gas composition, and physiological status monitoring results of fruit and vegetable images, identify the type and current physiological status of the fruit and vegetable, and transmit the identified type and status information to the user via a wireless network. The system identifies the type and current physiological status of fruits and vegetables and transmits the identified type and status information to the user via a wireless network, aiming to improve the efficiency and accuracy of fruit and vegetable classification and status assessment.

[0079] Further, the flowchart of the automatic fruit and vegetable identification technology can be seen in the attached Figure 5 , its workflow can be divided into the following steps:

[0080] 1. Start: System initialization or startup.

[0081] 2. Start the camera: After the system starts running, first start the camera to prepare to capture images of fruits and vegetables.

[0082] 3. Acquire fruit and vegetable images: The camera is pointed at the fruits and vegetables to capture their images. This step is the foundation of the entire process because subsequent recognition work relies on these images.

[0083] 4. Image preprocessing: The acquired image may contain noise or unclear parts. The preprocessing steps include adjusting brightness, contrast, denoising, etc. to improve image quality and facilitate subsequent processing.

[0084] 5. Noise reduction: Based on image preprocessing, further remove noise from the image to make the characteristics of fruits and vegetables more obvious;

[0085] Enhance contrast: By enhancing the contrast of the image, the outlines and details of fruits and vegetables are made clearer, which helps with feature extraction.

[0086] 6. Extract image features: After image preprocessing and enhancement, the system extracts key features from the image, such as color, shape, texture, etc. These features will be used to compare with standard fruit and vegetable features in the database.

[0087] 7. Comparison with standard fruit and vegetable features in the database: Compare the extracted features with the standard fruit and vegetable features stored in the database to determine the type of fruit and vegetable.

[0088] 8. Combined with sensors: Use gas sensors to detect ethylene and other gas components around fruits and vegetables, and use fruit and vegetable physiological status monitoring sensors to monitor the physiological status of fruits and vegetables such as moisture content and hardness.

[0089] 9. Identify the type and status of fruits and vegetables: Based on the results of image features, gas composition and physiological status monitoring, the system identifies the type of fruits and vegetables and their current physiological status.

[0090] 10. Output recognition results: The recognized fruit and vegetable types and status information are transmitted to the user via the wireless network.

[0091] 11. End: The system has completed a recognition task.

[0092] The automated fruit and vegetable recognition system, which makes comprehensive use of image processing and sensor technologies, aims to improve the efficiency and accuracy of fruit and vegetable classification and status assessment.

[0093] Furthermore, the system includes an integrated wireless communication module that supports multiple wireless communication technologies, including Wi-Fi, Bluetooth, ZigBee, and 5G. This integrated design exemplifies the system's ability to flexibly select the most appropriate communication method based on specific application scenarios and requirements, achieving efficient and stable data transmission. This wireless communication module design is not the only implementation, but rather serves to illustrate the technical features and objectives of the present invention. The system's internal intelligent control module dynamically selects the most appropriate wireless communication technology based on data priority, size, transmission distance, and energy consumption requirements. Furthermore, the system is capable of seamlessly switching between different communication technologies to adapt to changing network conditions and application requirements. This dynamic selection and seamless switching capability exemplifies the adaptability and flexibility of the present invention in diverse application environments. When the system detects abnormal environmental indicators or the status of fruit and vegetables, the intelligent control module immediately sends an alert via the integrated wireless communication module. This mechanism exemplifies that in any abnormal situation, relevant personnel are promptly notified and can take appropriate measures. This instant alert function is not limited to a specific communication technology but serves to illustrate the effectiveness of the present invention in ensuring the safe storage of fruit and vegetables.

[0094] In summary, the advantages of the present invention are:

[0095] (1) High-precision monitoring and automatic adjustment: The system uses high-precision sensors that can accurately measure and control the temperature, humidity, gas composition and physiological state of the fruit and vegetable storage environment, ensuring that fruits and vegetables are stored under optimal conditions and reducing losses.

[0096] (2) Real-time monitoring and dynamic adjustment: By real-time monitoring of the physiological state of fruits and vegetables and changes in the storage environment, the system can dynamically adjust environmental parameters to maintain optimal storage conditions and improve the storage quality of fruits and vegetables.

[0097] (3) Application of multispectral imaging technology: Using multispectral imaging technology, the system can monitor the surface moisture and respiratory metabolism levels of fruits and vegetables in real time without contact, and achieve accurate assessment of the physiological status of fruits and vegetables.

[0098] (4) Automatic adjustment of gas composition: The system can automatically adjust the gas composition ratio in the storage space according to the monitored ethylene concentration, effectively delaying the ripening process of fruits and vegetables and extending the shelf life.

[0099] (5) Predictive algorithms and intelligent control: The intelligent control module includes the Kalman algorithm, which is a recursive algorithm that can update its state estimate based on previous estimates and current measurements. It is very suitable for processing large amounts of fruit and vegetable data streams. It can predict the optimal storage time for fruits and vegetables and adjust the storage environment in advance to avoid the impact of adverse conditions on fruits and vegetables. This algorithm can more accurately estimate the state of the dynamic system in the presence of noise and uncertainty.

[0100] Compared to classic filtering algorithms, this algorithm has a faster convergence speed under the same conditions, good robustness, and can effectively handle packet loss. At the same time, Kalman filtering has a small amount of computation, takes up less memory, and is more efficient.

[0101] (6) Remote monitoring and alarm function: The system is equipped with remote monitoring and alarm function, which can transmit fruit and vegetable storage environment and status data to mobile devices or cloud platforms in real time, and automatically send alarms when environmental indicators or fruit and vegetable status are abnormal, thereby improving response speed and processing efficiency.

[0102] (7) Integration of wireless communication network: The system realizes real-time data transmission through wireless communication network, which improves the flexibility and scalability of the system and reduces wiring cost and complexity.

[0103] (8) Integration and functions of cloud platform: The system is integrated with the cloud platform to achieve centralized management and analysis of data, improve data processing capabilities, and facilitate large-scale monitoring and management.

[0104] (9) Improving the storage quality of fruits and vegetables: By precisely controlling the ethylene concentration and storage environment, the present invention can effectively improve the storage quality of fruits and vegetables, reduce losses, and extend the shelf life of fruits and vegetables.

[0105] (10) Make up for the deficiencies of existing technologies: The present invention makes up for the deficiencies of existing technologies in gas composition control, physiological status monitoring, environmental monitoring and intelligent control by integrating high-precision sensors and intelligent control modules.

[0106] The number of devices and processing scales described herein are intended to simplify the description of the present invention, and the application, modification, and variation of the present invention will be apparent to those skilled in the art. Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiment. They can be applied to various fields suitable for the present invention. For those skilled in the art, additional modifications can be easily implemented. Therefore, the present invention is not limited to the specific details and illustrations shown and described herein without departing from the general concept defined by the claims and their equivalents.

Claims

1. An intelligent monitoring and control system based on fruit and vegetable preservation, characterized in that: include: An environmental monitoring sensor subsystem, which is used to monitor the temperature, humidity, gas composition, and water evaporation of the fruit and vegetable storage environment; Fruit and vegetable physiological status sensor subsystem, which is used to calculate the transpiration rate of fruits and vegetables, reflect the changes in moisture content of fruits and vegetables, and monitor the weight changes, respiration intensity, moisture content and other physiological indicators of fruits and vegetables in real time; An intelligent control module (10), the intelligent control module (10) being used to automatically adjust the temperature, humidity and gas composition of the storage environment according to data from the environmental monitoring sensor subsystem; The automatic fruit and vegetable identification subsystem is used to obtain the comprehensive image features, gas composition and physiological status monitoring results of fruit and vegetable images, identify the type and current physiological status of fruits and vegetables, and transmit the identified fruit and vegetable type and status information to the user via a wireless network.

2. The intelligent monitoring and control system based on fruit and vegetable preservation according to claim 1, characterized in that: The environmental monitoring sensor subsystem comprises a digital temperature sensor (2), a temperature and humidity sensor (3), a fluorescent oxygen sensor (4), a carbon dioxide monitoring sensor (5), an ethylene colorimetric sensor (6), and a gas composition sensor.

3. The intelligent monitoring and control system based on fruit and vegetable preservation according to claim 2, characterized in that: The gas composition sensor includes a gas control module, which is used to adjust the gas composition ratio in the storage space and create an environment that is more conducive to preserving fruits and vegetables by changing the concentration of gases such as oxygen, carbon dioxide and ethylene.

4. The intelligent monitoring and control system based on fruit and vegetable preservation according to claim 3, characterized in that: The specific working process of the gas control module is as follows: Monitoring phase: Gas composition sensors continuously monitor the gas in the storage space, focusing on ethylene concentration; Data transmission stage: When the ethylene concentration data is monitored, the gas composition sensor will transmit the data to the gas control module in real time; Control stage: After receiving the ethylene concentration data, the gas control module will calculate according to the preset algorithm, and the gas control module will start the corresponding gas regulating equipment to change the gas composition ratio in the storage space.

5. The intelligent monitoring and control system based on fruit and vegetable preservation according to claim 1, characterized in that: The specific working process of the automatic fruit and vegetable identification subsystem is as follows: Start the camera: After the system starts running, first start the camera to prepare to capture images of fruits and vegetables; Acquire images of fruits and vegetables: Aim the camera at the fruits and vegetables to capture their images; Image preprocessing: The acquired image may contain noise or unclear parts. The preprocessing steps include adjusting brightness, contrast, and denoising; Noise reduction: Based on image preprocessing, further remove the noise in the image to make the features of fruits and vegetables more obvious; Enhance contrast: By enhancing the contrast of the image, the outlines and details of fruits and vegetables are clearer; Extracting image features: After image preprocessing and enhancement, the system extracts key features from the image, including color, shape, and texture, and compares these features with standard fruit and vegetable features in a database. Comparison with standard fruit and vegetable features in the database: Compare the extracted features with the standard fruit and vegetable features stored in the database to determine the type of fruit and vegetable; Combined with sensors: Use gas sensors to detect ethylene and other gas components around fruits and vegetables, and use fruit and vegetable physiological status monitoring sensors to monitor the physiological status of fruits and vegetables; Identify the type and status of fruits and vegetables: Based on the results of image features, gas composition and physiological status monitoring, the system identifies the type and current physiological status of fruits and vegetables; Output recognition results: The recognized fruit and vegetable types and status information are transmitted to the user via the wireless network.

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