A smart variable frequency cold storage and preservation method and system with multi-zone independent temperature control

Abstract

The present invention provides an intelligent variable frequency refrigeration and preservation method and system with independent temperature control in multiple zones, which relates to the technical field of refrigeration and preservation. Through the coordinated work of a zone division module, a temperature control module, an environmental monitoring module, a data analysis module, an intelligent variable frequency control module and a user interface module, refined temperature control management of different zones inside a refrigeration device is realized. The system uses three-dimensional laser scanning and multi-spectral image acquisition technology to automatically divide temperature zones. A quantum-level precision thermistor array and an intelligent fuzzy PID controller ensure precise temperature control, while monitoring humidity and air flow to optimize the storage environment. The data analysis module combines deep learning and association rule mining algorithms to provide decision support for variable frequency control and achieve energy consumption optimization. The user interface module provides an intuitive operating experience. The overall solution improves the intelligence, accuracy and energy efficiency of refrigeration and preservation, and is suitable for a variety of cold chain scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of refrigeration and fresh-keeping, and in particular to an intelligent variable-frequency refrigeration and fresh-keeping method and system with multi-zone independent temperature control. Background Art

[0002] With the development of the global economy and the continuous improvement of people's living standards, the demand for cold chain transportation and refrigerated storage of various perishable foods, such as fresh agricultural products, meat, dairy products, medicines, biological products, etc., is growing. Traditional refrigeration equipment often adopts a unified temperature setting, which is difficult to meet the diverse preservation requirements of different commodities. For example, fruits and vegetables require specific humidity and relatively higher temperatures to maintain freshness and taste, while medicines, seafood, etc. have more stringent requirements on temperature accuracy and stability. In addition, different usage scenarios, such as supermarket refrigerated display cabinets, warehouse cold storage, cold chain transportation vehicles, etc., also need to flexibly adjust the temperature control strategy of the refrigerated area according to factors such as spatial layout and cargo turnover frequency.

[0003] The existing technology has the disadvantages of single and extensive temperature control and lack of intelligent regulation and monitoring. Summary of the invention

[0004] In order to overcome the problems of single and extensive temperature control and lack of intelligent regulation and monitoring in the prior art, the present invention designs an intelligent variable frequency refrigeration and preservation method and system with independent temperature control in multiple zones, which can effectively solve the above technical problems.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] An intelligent variable frequency refrigeration and fresh-keeping system with multi-zone independent temperature control, comprising:

[0007] A zone division module is used to automatically divide the space layout of the refrigeration equipment into multiple independent temperature control zones; the zone division module outputs the zone division information to the temperature control module, the environment monitoring module and the data analysis module;

[0008] A temperature control module, linked with the area division module, is used to independently control the temperature of each temperature control area, feed back the temperature status to the data analysis module, and receive temperature optimization suggestions given by the data analysis module in real time;

[0009] An environmental monitoring module, used to monitor the environmental parameters of each temperature-controlled area, including humidity and air flow rate, and continuously transmit the environmental parameters to the data analysis module;

[0010] The data analysis module integrates and analyzes the received area division results, temperature and environmental parameters, analyzes the characteristics and environmental parameters of the goods stored in each temperature-controlled area, and outputs control instructions to the intelligent frequency conversion control module;

[0011] An intelligent frequency conversion control module dynamically adjusts the operating frequency of the refrigeration equipment according to the control instructions of the data analysis module, and pushes the operating energy consumption and status information of the refrigeration equipment to the user interface module;

[0012] The user interface module is used to display the temperature of each temperature control area and the environmental parameters, allow the user to set the temperature parameters, and transmit the user setting instructions to the temperature control module.

[0013] Preferably, the area division module includes:

[0014] A spatial layout acquisition unit, which uses laser and multi-spectral image fusion technology to acquire spatial layout information of the refrigeration equipment and transmits the acquired spatial layout information to the area division decision unit;

[0015] A regional division decision unit, with a built-in partition strategy model optimized based on genetic algorithm and simulated annealing algorithm, is used to divide the refrigeration equipment into multiple areas according to the spatial layout information, and output a regional division plan that meets the needs of different refrigeration scenarios;

[0016] The spatial layout acquisition unit further includes:

[0017] The 3D laser scanning subunit is equipped with multiple sets of adjustable angle and high-precision laser rangefinders, which are distributed in a matrix at key points in the refrigeration equipment to scan the internal spatial structure characteristics of the refrigeration equipment and generate point cloud data;

[0018] The multispectral image acquisition subunit uses a high-resolution camera with infrared-visible light multi-band imaging capabilities to capture the color and texture information of the internal materials of the refrigeration equipment, and matches it with the point cloud data through an image fusion algorithm, and outputs the complete spatial layout information of the refrigeration equipment to the area division decision unit;

[0019] The area division decision unit comprises:

[0020] The scene adaptation subunit has a built-in cold chain scene case library. It predicts the cold storage scene based on the spatial layout information, the type of goods entering the warehouse, and the turnover frequency, and retrieves the corresponding sub-region template to form a preliminary regional division plan;

[0021] The optimization algorithm execution subunit uses the genetic algorithm crossover, mutation operation and simulated annealing algorithm's cooling search strategy to iteratively optimize the preliminary area division scheme to balance the independence of the temperature control areas, the uniformity of cooling capacity distribution and the convenience of access.

[0022] Preferably, the temperature control module comprises:

[0023] A temperature sensor unit, using a thermistor array with quantum-level precision, is used to measure the temperature of each of the temperature-controlled areas and report the temperature data to the temperature adjustment unit;

[0024] The temperature regulating unit integrates an intelligent fuzzy PID controller and an adaptive variable frequency refrigeration component, and is used to adjust the operating state of the refrigeration equipment according to the measurement result of the temperature sensor unit to maintain the temperature of each temperature control area stable.

[0025] Preferably, the temperature sensor unit comprises:

[0026] Thermistor array layout subunit adopts a non-uniform density layout strategy based on the volume and shape of each temperature control area, and encrypts the distribution of sensors in temperature-sensitive areas and corners where the cooling volume is prone to fluctuate, ensuring accurate monitoring of the temperature in the entire area;

[0027] A data preprocessing and transmission subunit, integrating a low noise amplifier and an analog-to-digital converter, calibrating the collected data on site, removing abnormal values, and transmitting the temperature data to the temperature regulating unit;

[0028] The temperature regulating unit comprises:

[0029] Intelligent fuzzy PID controller subunit, taking temperature deviation and deviation change rate as input, automatically adjusts PID parameters online, and outputs precise control signals to drive refrigeration components;

[0030] The adaptive variable frequency refrigeration component sub-unit is equipped with a vector variable frequency compressor, an electronic expansion valve and an intelligent air duct adjustment mechanism, which adjusts the cooling capacity, refrigerant flow and cold air distribution in real time according to the control signal.

[0031] Preferably, the environment monitoring module includes:

[0032] A humidity sensor unit, equipped with a highly sensitive sensor based on the hygroscopic properties of graphene, for measuring the humidity data of each of the temperature control areas and correcting humidity data errors in real time;

[0033] The air flow sensor unit uses a MEMS microcomputer electrical flow vector sensing chip to measure the air flow rate in each temperature control area, build a real-time air flow field model in the temperature control area and dynamically update it.

[0034] Preferably, the humidity sensor unit further comprises:

[0035] The graphene moisture absorption sensor subunit uses graphene's super strong adsorption and desorption properties of water vapor molecules to quickly respond to humidity changes;

[0036] The data calibration and compensation subunit automatically calibrates the zero point and range in a set time period, corrects the humidity data in combination with the ambient temperature compensation algorithm, and outputs accurate humidity values ​​to the data analysis module;

[0037] The air flow sensor unit further comprises:

[0038] The MEMS airflow vector chip subunit integrates X, Y, and Z three-axis micro anemometers to capture the airflow velocity and direction vector information in the temperature control area in real time;

[0039] The airflow field modeling and analysis module, based on the collected data, uses the computational fluid dynamics algorithm to reconstruct the airflow field model of the temperature control area, marks the airflow dead corners and vortex areas, and simultaneously pushes the marked airflow dead corners and vortex area data to the data analysis module.

[0040] Preferably, the data analysis module includes:

[0041] The product characteristics analysis unit integrates the big data product knowledge base with near-infrared spectroscopy and dielectric characteristics detection technology to analyze the thermal sensitivity and freshness requirements of stored products and generate product-specific freshness preservation strategies;

[0042] Environmental parameter analysis unit, using deep learning time series prediction model and association rule mining algorithm to analyze the impact of temperature and environmental parameters on product preservation, and predict temperature and environmental change trends and associated risks in advance;

[0043] The environmental parameter analysis unit comprises:

[0044] The deep learning time series prediction subunit mines the time series data patterns of historical temperature, humidity and air velocity based on the long short-term memory network to predict the changing trends of future temperature and environmental parameters;

[0045] The association rule mining subunit, based on the change trend of the temperature and environmental parameters, uses the Apriori algorithm to analyze the association between the temperature and environmental parameters and the product preservation effect, and quantifies the impact of the changes in temperature and environmental parameters on the product quality, and outputs early warning information and regulation suggestions to the intelligent frequency conversion control module.

[0046] Preferably, the intelligent frequency conversion control module includes:

[0047] The frequency conversion algorithm unit is embedded with the deep reinforcement learning and model predictive control algorithm core, which is used to dynamically adjust the operating frequency of the refrigeration equipment according to the results of the data analysis module;

[0048] The energy consumption optimization unit is connected to the IoT smart meter and energy management system to optimize the energy consumption of refrigeration equipment.

[0049] Preferably, the user interface module comprises:

[0050] A display unit, equipped with an ultra-high-definition flexible touch screen and a 3D visualization engine, for displaying the temperature and environmental parameters of each of the temperature control areas in an intuitive and dynamic form;

[0051] The control unit integrates voice recognition, gesture control and smart touch technology to receive temperature settings input by the user and send them to the temperature control module.

[0052] An intelligent variable frequency refrigeration and preservation method with independent temperature control in multiple zones, comprising the following steps:

[0053] S1, using the area division module to automatically divide the refrigeration equipment into a plurality of independent temperature control areas according to the spatial layout of the refrigeration equipment, and outputting the division information of each temperature control area to the temperature control module, the environment monitoring module and the data analysis module;

[0054] S2, using the temperature control module to work in conjunction with the area division module to independently control the temperature of each temperature control area, and feeding back the actual temperature to the data analysis module, while receiving temperature optimization suggestions given by the data analysis module in real time;

[0055] S3, monitoring the environmental parameters of each of the temperature control areas, including humidity and air flow rate, through the environmental monitoring module, and transmitting the monitored environmental parameters to the data analysis module;

[0056] S4, the data analysis module integrates and analyzes the received area division results, temperature and environmental parameters, analyzes the characteristics and environmental parameters of the goods stored in each temperature-controlled area, and outputs control instructions to the intelligent frequency conversion control module;

[0057] S5, the intelligent frequency conversion control module dynamically adjusts the operating frequency of the refrigeration equipment according to the control instructions of the data analysis module, and pushes the operating energy consumption and status information of the refrigeration equipment to the user interface module;

[0058] S6. The user interface module displays the temperature and environmental parameters of each temperature control area, allows the user to set temperature parameters, and transmits the user setting instructions to the temperature control module.

[0059] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention, through the introduction of the area division module, utilizes the three-dimensional laser scanning and multi-spectral image acquisition fusion technology to realize the accurate identification and optimized partitioning of the internal structure of the refrigeration equipment, so that the refrigeration equipment can be set in fine temperature zones according to the storage requirements of different commodities, such as fresh food, medicine, etc., thereby avoiding the problem of improper temperature control in the traditional unified temperature control mode. For example, the fresh food area can be subdivided into temperature zones according to the characteristics of fruits, vegetables, meat, and seafood, and the medicine area can be accurately temperature-controlled according to the storage requirements of medicines. This not only greatly extends the shelf life of the goods, but also, the innovative design of the temperature control module adopts a quantum-level precision thermistor array and a non-uniform density layout strategy, combined with an intelligent fuzzy PID controller and an adaptive variable frequency refrigeration component, to realize the temperature control of each temperature control area. This precise temperature control not only ensures the quality of food, but also significantly improves energy efficiency and reduces energy waste. The humidity sensor unit and air flow sensor unit of the environmental monitoring module use highly sensitive sensors based on the hygroscopic characteristics of graphene and MEMS microcomputer electrical flow vector sensing chips, respectively, to achieve real-time monitoring of humidity and air flow in each temperature-controlled area. These data are mined through deep learning and association rules of the data analysis module, providing a scientific basis for regulation and control for the intelligent frequency conversion control module, further improving the intelligence level of the system. The advanced design of the user interface module allows users to intuitively view the temperature and environmental data of each temperature-controlled area, and set temperature parameters through a variety of interactive methods, thus achieving convenient interaction between users and the system and improving user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived based on the provided drawings without paying any creative work.

[0061] Figure 1 It is a structural diagram of an intelligent variable frequency refrigeration and preservation system with independent temperature control in multiple zones;

[0062] Figure 2 The present invention is a step-by-step diagram of an intelligent variable frequency refrigeration and preservation method with independent temperature control in multiple zones. DETAILED DESCRIPTION

[0063] The drawings are for illustrative purposes only and should not be construed as limiting the present patent;

[0064] In order to better illustrate the present embodiment, some parts in the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product;

[0065] It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0066] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0067] Example 1

[0068] An intelligent variable frequency refrigeration and preservation system with independent temperature control in multiple zones, such as Figure 1 As shown, including:

[0069] A zone division module is used to automatically divide the space layout of the refrigeration equipment into multiple independent temperature control zones; the zone division module outputs the zone division information to the temperature control module, the environment monitoring module and the data analysis module;

[0070] A temperature control module, linked with the area division module, is used to independently control the temperature of each temperature control area, feed back the temperature status to the data analysis module, and receive temperature optimization suggestions given by the data analysis module in real time;

[0071] An environmental monitoring module, used to monitor the environmental parameters of each temperature-controlled area, including humidity and air flow rate, and continuously transmit the environmental parameters to the data analysis module;

[0072] The data analysis module integrates and analyzes the received area division results, temperature and environmental parameters, analyzes the characteristics and environmental parameters of the goods stored in each temperature-controlled area, and outputs control instructions to the intelligent frequency conversion control module;

[0073] An intelligent frequency conversion control module dynamically adjusts the operating frequency of the refrigeration equipment according to the control instructions of the data analysis module, and pushes the operating energy consumption and status information of the refrigeration equipment to the user interface module;

[0074] The user interface module is used to display the temperature of each temperature control area and the environmental parameters, allow the user to set the temperature parameters, and transmit the user setting instructions to the temperature control module.

[0075] The area division module comprises:

[0076] A spatial layout acquisition unit, which uses laser and multi-spectral image fusion technology to acquire spatial layout information of the refrigeration equipment and transmits the acquired spatial layout information to the area division decision unit;

[0077] A regional division decision unit, with a built-in partition strategy model optimized based on genetic algorithm and simulated annealing algorithm, is used to divide the refrigeration equipment into multiple areas according to the spatial layout information, and output a regional division plan that meets the needs of different refrigeration scenarios;

[0078] The spatial layout acquisition unit further includes:

[0079] The 3D laser scanning subunit is equipped with multiple sets of adjustable angle and high-precision laser rangefinders, which are distributed in a matrix at key points in the refrigeration equipment to scan the internal spatial structure characteristics of the refrigeration equipment and generate point cloud data;

[0080] The multispectral image acquisition subunit uses a high-resolution camera with infrared-visible light multi-band imaging capabilities to capture the color and texture information of the internal materials of the refrigeration equipment, and matches it with the point cloud data through an image fusion algorithm, and outputs the complete spatial layout information of the refrigeration equipment to the area division decision unit;

[0081] The 3D laser scanning subunit has multiple sets of high-precision laser rangefinders with an accuracy of ±0.05mm and adjustable at multiple angles. They are reasonably distributed on the top, four walls, key support points of the shelves and the positions of the layered partitions in the cold storage. They scan synchronously to quickly obtain detailed contour information inside the cold storage, the layout of the partitions and the spatial characteristics of various supporting structures, and generate massive point cloud data. At the same time, the high-resolution camera in the multispectral image acquisition subunit with a resolution of 4K and infrared-visible light multi-band imaging capabilities begins to capture the color and texture changes of the insulation materials inside the cold storage, accurately identify potential weak insulation areas and details of ventilation structures such as ventilation ducts, air outlets, and return air outlets. Subsequently, through advanced image fusion algorithms, the image data obtained by the camera is accurately matched with the point cloud data obtained by laser scanning to form a complete and accurate cold storage space layout information, which is then passed to the regional division decision unit.

[0082] The area division decision unit comprises:

[0083] The scene adaptation subunit has a built-in cold chain scene case library. It predicts the cold storage scene based on the spatial layout information, the type of goods entering the warehouse, and the turnover frequency, and retrieves the corresponding sub-region template to form a preliminary regional division plan;

[0084] The optimization algorithm execution subunit uses the genetic algorithm crossover, mutation operation and simulated annealing algorithm's cooling search strategy to iteratively optimize the preliminary area division scheme to balance the independence of the temperature control areas, the uniformity of cooling capacity distribution and the convenience of access.

[0085] After receiving the spatial layout information, the regional division decision unit uses the built-in massive cold chain scenario case library, which covers the best practice cases for storing various types of food ingredients and the application experience of cold storages of different sizes, combined with the types of food ingredients purchased daily, the approximate purchase volume of each ingredient, and the turnover frequency. For example, green leafy vegetables are consumed in large quantities every day and have a fast turnover, so they need to be placed in an area that is easy to access. Some imported high-end seafood has a small purchase volume but high preservation requirements, so they need to be separately divided into safe and stable storage areas. The current cold storage scenario is comprehensively predicted, and the corresponding regional templates are retrieved from it to preliminarily divide the storage areas for different ingredients. Then, the optimization algorithm execution subunit runs The crossover and mutation operations of the genetic algorithm and the cooling search strategy of the simulated annealing algorithm are used to carefully optimize the preliminary divided area plan. During the optimization process, the independence of each temperature control area is fully considered to avoid mutual interference between the temperature and humidity environments required by different ingredients; ensure that the cooling capacity is evenly distributed in each area to prevent the situation where the local temperature is too high or too low and affects the freshness of the ingredients; at the same time, take into account the convenience of the staff in storing and accessing the ingredients, and facilitate daily inventory and retrieval operations. After multiple rounds of iterative optimization, the final output is the area division plan that meets the actual needs of the food cold storage, and the area division information is sent to the temperature control module, environmental monitoring module and data analysis module respectively.

[0086] The temperature control module comprises:

[0087] A temperature sensor unit, using a thermistor array with quantum-level precision, is used to measure the temperature of each of the temperature-controlled areas and report the temperature data to the temperature adjustment unit;

[0088] The temperature regulating unit integrates an intelligent fuzzy PID controller and an adaptive variable frequency refrigeration component, and is used to adjust the operating state of the refrigeration equipment according to the measurement result of the temperature sensor unit to maintain the temperature of each temperature control area stable.

[0089] The temperature sensor unit comprises:

[0090] Thermistor array layout subunit adopts a non-uniform density layout strategy based on the volume and shape of each temperature control area, and encrypts the distribution of sensors in temperature-sensitive areas and corners where the cooling volume is prone to fluctuate, ensuring accurate monitoring of the temperature in the entire area;

[0091] A data preprocessing and transmission subunit, integrating a low noise amplifier and an analog-to-digital converter, calibrating the collected data on site, removing abnormal values, and transmitting the temperature data to the temperature regulating unit;

[0092] The temperature sensor unit is carefully arranged through thermistor array layout sub-units according to the specific volume and shape characteristics of each temperature control area. In areas such as near the cold storage door and the air outlet of the refrigeration equipment where the temperature is easily affected by external factors and fluctuates greatly, as well as in corners where food is densely stacked and temperature differences are prone to occur, such as the bottom layer of shelves and against walls, these temperature-sensitive areas and corners where the amount of cold is prone to fluctuate, quantum-level precision thermistors are densely arranged. These thermistors can sensitively sense subtle temperature changes and achieve accurate monitoring of the global temperature. The collected temperature data will be transmitted to the data preprocessing and transmission sub-unit, which integrates a low-noise amplifier and an analog-to-digital converter. It first amplifies the collected weak electrical signals, then converts the analog signals into digital signals, and calibrates the data on-site to eliminate abnormal values ​​caused by occasional sensor errors or short-term external interference. Finally, the processed temperature data is reported to the temperature control unit in real time through the stable low-power Bluetooth 4.2 wireless transmission protocol.

[0093] The temperature regulating unit comprises:

[0094] Intelligent fuzzy PID controller subunit, taking temperature deviation and deviation change rate as input, automatically adjusts PID parameters online, and outputs precise control signals to drive refrigeration components;

[0095] The adaptive variable frequency refrigeration component sub-unit is equipped with a vector variable frequency compressor, an electronic expansion valve and an intelligent air duct adjustment mechanism, which adjusts the cooling capacity, refrigerant flow and cold air distribution in real time according to the control signal.

[0096] After the temperature adjustment unit receives the temperature data from the temperature sensor unit, the intelligent fuzzy PID controller subunit will use the temperature deviation and the deviation change rate as key input parameters. For example, when the temperature setting of the area storing leafy greens is 3°C, and the actual measured temperature is 3.2°C, and through continuous monitoring, it is found that the temperature has a continuous upward trend, the intelligent fuzzy PID controller subunit will immediately automatically adjust the PID parameters online according to the built-in intelligent algorithm, output accurate control signals, and drive the adaptive variable frequency refrigeration component subunit to work. The vector variable frequency compressor in the adaptive variable frequency refrigeration component subunit dynamically adjusts the operating frequency according to the control signal. When the temperature difference is large, such as exceeding 0.5°C, the operating frequency is quickly increased, the cooling power is increased, and the temperature is quickly reduced to the set range; when the temperature is close to the set value and the temperature difference is reduced to within 0.2°C, the operating frequency is automatically reduced, the energy-saving operation mode is entered, and the constant temperature state is maintained. At the same time, the electronic expansion valve will adjust the operating frequency according to 0.03 g / The refrigerant flow rate is finely controlled with precision of seconds. The intelligent air duct adjustment mechanism reasonably adjusts the distribution direction and air volume of cold air according to the temperature distribution in each area, ensuring that the temperature in each temperature-controlled area is stably maintained within the range of ±0.2℃ of the set value, providing a suitable and stable temperature environment for different ingredients, and feeding back the real-time temperature situation to the data analysis module.

[0097] The environmental monitoring module comprises:

[0098] A humidity sensor unit, equipped with a highly sensitive sensor based on the hygroscopic properties of graphene, for measuring the humidity data of each of the temperature control areas and correcting humidity data errors in real time;

[0099] The air flow sensor unit uses a MEMS microcomputer electrical flow vector sensing chip to measure the air flow rate in each temperature control area, build a real-time air flow field model in the temperature control area and dynamically update it.

[0100] The humidity sensor unit also includes:

[0101] The graphene moisture absorption sensor subunit uses graphene's super strong adsorption and desorption properties of water vapor molecules to quickly respond to humidity changes;

[0102] The data calibration and compensation subunit automatically calibrates the zero point and range in a set time period, corrects the humidity data in combination with the ambient temperature compensation algorithm, and outputs accurate humidity values ​​to the data analysis module;

[0103] The graphene hygroscopic sensor element subunit in the humidity sensor unit can quickly respond to small changes in humidity in the cold storage by virtue of graphene's super strong adsorption and desorption characteristics of water vapor molecules. Even if the humidity changes by only 0.3%, it can be detected immediately. To ensure the measurement accuracy, the matching constant temperature control circuit stabilizes the sensor working environment temperature within an appropriate range, so that the measurement accuracy of the entire humidity sensor unit can reach ±0.6% RH, the data calibration and compensation subunit automatically calibrates the zero point and range of the sensor at an interval of 1 hour, and at the same time, combines the ambient temperature compensation algorithm to correct the collected humidity data. For example, when the temperature in the cold storage fluctuates due to the short-term start and stop of the refrigeration equipment, the humidity reading is accurately adjusted according to the relationship between temperature and the saturated vapor pressure of water vapor to ensure the output of accurate humidity values. Because when relative humidity (RH) is used as a measure of humidity, relative humidity refers to the ratio of the current water vapor content in the air to the maximum water vapor content that the air can accommodate at that temperature. Therefore, when the temperature changes, even if the water vapor content in the air does not change, the relative humidity reading will change. The temperature change will affect the saturated vapor pressure of water vapor, and then affect the relative humidity measurement results. The data calibration and compensation subunit will correct the collected humidity data according to the temperature change and the ambient temperature compensation algorithm to ensure that even in the case of temperature fluctuations, it can provide accurate humidity readings, and transmit these accurate humidity values ​​to the data analysis module in real time, so that each food storage area can maintain a humidity environment that meets the requirements, such as the green leafy vegetable area always maintains a suitable relative humidity range of 90% - 95%.

[0104] The air flow sensor unit further comprises:

[0105] The MEMS airflow vector chip subunit integrates X, Y, and Z three-axis micro anemometers to capture the airflow velocity and direction vector information in the temperature control area in real time;

[0106] The airflow field modeling and analysis module, based on the collected data, uses the computational fluid dynamics algorithm to reconstruct the airflow field model of the temperature control area, marks the airflow dead corners and vortex areas, and simultaneously pushes the marked airflow dead corners and vortex area data to the data analysis module.

[0107] The MEMS airflow vector chip sub-unit of the air flow sensor unit integrates X, Y, and Z three-axis micro anemometers. These anemometers can capture the speed and direction vector information of the airflow in each temperature-controlled area in real time and accurately, and can detect the smallest weak airflow change of 0.003m / s. The airflow field modeling and analysis module reconstructs the real-time airflow field model in each temperature-controlled area based on the collected airflow data using computational fluid dynamics algorithms, and clearly marks the airflow dead corners, such as the back of the shelves, the corners of the cold storage, and other places where air is prone to poor circulation, as well as vortex areas that may cause local temperature and humidity abnormalities. These real-time updated airflow field data are synchronously pushed to the data analysis module, providing strong data support for judging the air circulation conditions of the environment in which the food is located and for subsequent ventilation optimization and adjustment.

[0108] The data analysis module includes:

[0109] The product characteristics analysis unit integrates the big data product knowledge base with near-infrared spectroscopy and dielectric characteristics detection technology to analyze the thermal sensitivity and freshness requirements of stored products and generate product-specific freshness preservation strategies;

[0110] Whenever a new batch of ingredients enters the warehouse, the product characteristic analysis unit starts working immediately. It integrates a huge big data product knowledge base that covers the physical and chemical properties, biological properties, tolerance range of temperature and humidity, and optimal preservation conditions of various commonly used ingredients in hotels, as well as near-infrared spectroscopy and dielectric property detection technology to conduct a comprehensive analysis of the ingredients. For example, for a newly purchased batch of beef, near-infrared spectroscopy technology is used to detect indicators such as the moisture content and fat content of its muscle fibers. Combined with the knowledge base that beef is more sensitive to temperature and can be better preserved in a low temperature and well-ventilated environment, the thermal sensitivity and specific preservation requirements of this batch of beef are analyzed, and then an exclusive preservation strategy for beef is generated. For example, it is recommended to control the temperature at 2-3℃, maintain the humidity at 75%-80%, and store it separately from ingredients with stronger odors. The product characteristic information obtained from these analyses is then passed to the environmental parameter analysis unit.

[0111] Environmental parameter analysis unit, using deep learning time series prediction model and association rule mining algorithm to analyze the impact of temperature and environmental parameters on product preservation, and predict temperature and environmental change trends and associated risks in advance;

[0112] The environmental parameter analysis unit comprises:

[0113] The deep learning time series prediction subunit mines the time series data patterns of historical temperature, humidity and air velocity based on the long short-term memory network to predict the changing trends of future temperature and environmental parameters;

[0114] The association rule mining subunit, based on the change trend of the temperature and environmental parameters, uses the Apriori algorithm to analyze the association between the temperature and environmental parameters and the product preservation effect, and quantifies the impact of the changes in temperature and environmental parameters on the product quality, and outputs early warning information and regulation suggestions to the intelligent frequency conversion control module.

[0115] The deep learning time series prediction subunit in the environmental parameter analysis unit mines the historical temperature, humidity and airflow data patterns based on the long short-term memory network (LSTM), and combines the current seasonal factors, such as high temperature and high humidity in summer, which increases the refrigeration and dehumidification pressure of the cold storage; the low outside temperature in winter requires reasonable regulation to avoid frostbite of food ingredients, as well as the regional environmental factors. The year-round climate characteristics of the area where the insurance site is located, such as high humidity in coastal areas and large temperature differences between day and night in inland areas, accurately predict the environmental trends of each temperature-controlled area 24 hours in advance. For example, if it is predicted that high temperature weather is about to arrive, the temperature in each area of ​​the cold storage will tend to rise, and the refrigeration strategy needs to be adjusted in advance. At the same time, the association rule mining subunit uses Apriori The algorithm analyzes the relationship between environmental parameters and the preservation effect of food ingredients, and quantifies the impact of environmental changes on food quality. For example, for every 5% increase in humidity, the rate of decay of leafy vegetables may be accelerated, and the number of days the shelf life of dairy products will be shortened for a 1°C increase in temperature. Based on these in-depth analysis results, the environmental parameter analysis unit will output early warning information and regulation suggestions covering temperature, humidity, air flow, etc., and send them to the intelligent frequency conversion control module to provide a decision-making basis for the intelligent regulation of the entire cold storage.

[0116] The intelligent frequency conversion control module comprises:

[0117] The frequency conversion algorithm unit is embedded with the deep reinforcement learning and model predictive control algorithm core, which is used to dynamically adjust the operating frequency of the refrigeration equipment according to the results of the data analysis module;

[0118] The frequency conversion algorithm unit is embedded with the core of deep reinforcement learning and model predictive control algorithm. It constructs the equipment operation status received from the data analysis module, the current temperature of each area, the working status of the refrigeration components, the temperature, humidity, airflow changes predicted by the environmental data, and the preservation of ingredients, and the preservation strategy required for each ingredient into a complete state space, and sets the control action as the adjustment strategy for the operation frequency of the refrigeration equipment. The intelligent agent continuously interacts with the environment to learn and iteratively optimize the frequency adjustment strategy to maximize the energy efficiency of equipment operation. For example, according to data analysis, when the hotel kitchen uses less ingredients at night, the metabolism of ingredients slows down, and the electricity price is at a low point, the intelligent agent will decide to appropriately reduce the operation frequency of the refrigeration equipment and enter the energy-saving operation mode; and before the hotel dining peak during the day, in order to ensure the best preservation effect of the ingredients, the operation frequency is timely increased to enhance the refrigeration effect, to ensure that the ingredients are always in an ideal preservation environment, and the operation frequency of the refrigeration equipment is dynamically adjusted in real time based on the data analysis results.

[0119] The energy consumption optimization unit is connected to the IoT smart meter and energy management system to optimize the energy consumption of refrigeration equipment.

[0120] The energy consumption optimization unit is connected to the IoT smart meter and energy management system of the hotel, and captures the electricity price fluctuation information in real time, grasps the distribution of peak and valley electricity prices, and uses dynamic programming algorithms to combine the current actual operating conditions of the equipment, the refrigeration demand of each area, the load of the compressor, and the environmental forecast to formulate a scientific and reasonable time-sharing energy-saving strategy. For example, during the low-valley electricity price period at night, the refrigeration capacity is increased in advance for pre-cooling and cold storage, and the cold storage capacity of the cold storage is used to store sufficient cold capacity; during the peak electricity consumption period during the day, the operating frequency of the refrigeration equipment is intelligently reduced, and energy consumption is reduced as much as possible while maintaining the temperature of each area within the acceptable fluctuation range of the food. At the same time, with the waste heat recovery device, the waste heat generated during the refrigeration process is collected and used for some auxiliary heating needs in the cold storage, such as defrosting operations, and slightly raising the temperature in winter to prevent food from freezing. Through these comprehensive energy consumption optimization measures, the energy consumption cost of the hotel food cold storage can be effectively reduced by more than 35%, and the energy consumption and status information of the refrigeration equipment operation can be pushed to the user interface module, so that hotel managers can grasp the operation of the cold storage at any time.

[0121] The user interface module comprises:

[0122] The display unit is equipped with an ultra-high-definition flexible touch screen and a 3D visualization engine, which is used to display the temperature and environmental parameters of each temperature control area in an intuitive and dynamic form;

[0123] The display unit is equipped with an ultra-high-definition flexible touch screen and a 3D visualization engine. It presents the temperature gradient of each temperature-controlled area in an intuitive and dynamic manner, and uses different colors to clearly show the temperature difference in each area, so that people can see the temperature distribution at a glance. The humidity cloud map shows the distribution of humidity in different areas, similar to the display effect of a meteorological cloud map, which is convenient for intuitively judging whether the humidity is appropriate. The airflow trajectory is indicated by dynamic arrows, clearly showing the flow direction and speed of air in each area. Managers can easily call up the temperature and humidity curves and air flow change curves of the past 7 days through the touch screen with one click, and comprehensively view the historical changes in the food preservation environment in each area, which is convenient for analyzing the food preservation effect and the stability of equipment operation. Once an area has abnormal temperature, excessive humidity or equipment failure, the display unit will immediately issue early warning information in a timely manner through a striking pop-up window prompt, a pop-up warning box accompanied by a sound alarm, flashing the corresponding area icon, etc., to fully display the real-time status of the hotel's food cold storage and assist managers in making quick response decisions.

[0124] The control unit integrates voice recognition, gesture control and smart touch technology to receive temperature settings input by the user and send them to the temperature control module.

[0125] The control unit integrates advanced voice recognition, gesture control and intelligent touch technology to provide users with convenient and diverse operation methods. The voice recognition function is equipped with a high-precision microphone array and an advanced voice recognition engine, with a recognition accuracy of more than 99%, and can adapt to multiple languages ​​and dialects in different regions. Managers only need to say voice commands such as "adjust the temperature of the green leafy vegetable area to 2°C", and the system can quickly and accurately recognize and convert it into the corresponding control signal, and send it to the temperature control module to perform the corresponding operation. The gesture control function uses infrared sensing and cameras to capture user gestures. For example, a simple wave of the hand can switch the display interface of different food areas, and the setting values ​​of parameters such as temperature and humidity can be adjusted by sliding the finger. In terms of intelligent touch interaction, the interface design of the touch screen is simple and clear, and standard preservation parameter templates such as temperature control and humidity control for common hotel ingredients such as green leafy vegetables, meat, seafood, dairy products, etc. are preset. Managers only need to click on the template of the corresponding ingredient with one click to quickly apply the corresponding parameter settings, and quickly and conveniently complete the adjustment of environmental parameters in each area. At the same time, the system has set up a strict authority management function. The hotel executive chef or senior management can remotely control the temperature and humidity through the mobile phone APP Or the computer can comprehensively control the equipment parameters of the entire cold storage, while the front-line kitchen staff can only operate the relevant settings of the local food storage and access area for which they are responsible, which ensures the convenience and safety of operation and ensures that every instruction of the user can be transmitted to the temperature control module in time, so as to realize real-time and precise regulation of the hotel's food preservation environment.

[0126] Example 2

[0127] An intelligent variable frequency refrigeration and preservation method with independent temperature control in multiple zones, such as Figure 2 As shown, the following steps are included:

[0128] S1, using the area division module to automatically divide the refrigeration equipment into a plurality of independent temperature control areas according to the spatial layout of the refrigeration equipment, and outputting the division information of each temperature control area to the temperature control module, the environment monitoring module and the data analysis module;

[0129] S2, using the temperature control module to work in conjunction with the area division module to independently control the temperature of each temperature control area, and feeding back the actual temperature to the data analysis module, while receiving temperature optimization suggestions given by the data analysis module in real time;

[0130] S3, monitoring the environmental parameters of each of the temperature control areas, including humidity and air flow rate, through the environmental monitoring module, and transmitting the monitored environmental parameters to the data analysis module;

[0131] S4, the data analysis module integrates and analyzes the received area division results, temperature and environmental parameters, analyzes the characteristics and environmental parameters of the goods stored in each temperature-controlled area, and outputs control instructions to the intelligent frequency conversion control module;

[0132] S5, the intelligent frequency conversion control module dynamically adjusts the operating frequency of the refrigeration equipment according to the control instructions of the data analysis module, and pushes the operating energy consumption and status information of the refrigeration equipment to the user interface module;

[0133] S6. The user interface module displays the temperature and environmental parameters of each temperature control area, allows the user to set temperature parameters, and transmits the user setting instructions to the temperature control module.

[0134] First, we conducted a spatial layout analysis of the refrigeration equipment. Through 3D laser scanning and multispectral image acquisition technology, the interior of the refrigeration equipment was automatically divided into four independent temperature-controlled areas: fruit area, vegetable area, meat area and seafood area. The division information of each area was output to S2, S3 and S4. S2 independently controlled the temperature of each area according to the area division information. For example, the fruit area was set to 2-4°C, the vegetable area was set to 0-2°C, the meat area was set to -2°C, and the seafood area was set to -4°C. The temperature sensor monitored and fed back the temperature data in real time, and received temperature optimization suggestions from S4. S3 installed humidity sensors and air flow sensors in each temperature-controlled area to continuously monitor environmental parameters. For example, the humidity in the fruit area was monitored to be 85% and the air flow speed was 0.5 m / s. These data were transmitted to S 4. After receiving the area division, temperature and environmental data, S4 analyzes it and finds that the humidity in the fruit area is slightly high, which may affect the preservation of some fruits. Therefore, it outputs a control instruction and recommends reducing the humidity in this area. S5 dynamically adjusts the operating frequency of the refrigeration equipment according to the control instructions of S4 data analysis. For example, it reduces the fan speed near the fruit area to reduce air flow, and increases the cooling power to maintain a constant temperature. At the same time, it pushes the operating energy consumption and status information of the refrigeration equipment to the management personnel through S6. S6 displays the temperature and environmental parameters of each temperature control area. The management personnel view the status of each area through the touch screen and decide whether to adjust the temperature setting. For example, the management personnel finds that the temperature in the seafood area is lower than expected, and adjusts the temperature setting to -3°C through the user interface. The instruction is immediately transmitted to the temperature control module.

[0135] The same or similar reference numerals correspond to the same or similar components;

[0136] The terms used in the drawings to describe positional relationships are only used for illustrative purposes and should not be construed as limiting this patent;

[0137] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made on the basis of the above description. It is not necessary and impossible to list all the implementation methods here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. An intelligent variable frequency refrigeration and preservation system with independent temperature control in multiple zones, characterized in that: include: A zone division module is used to automatically divide the space layout of the refrigeration equipment into multiple independent temperature control zones; the zone division module outputs the zone division information to the temperature control module, the environment monitoring module and the data analysis module; A temperature control module, linked with the area division module, is used to independently control the temperature of each temperature control area, feed back the temperature status to the data analysis module, and receive temperature optimization suggestions given by the data analysis module in real time; An environmental monitoring module, used to monitor the environmental parameters of each temperature-controlled area, including humidity and air flow rate, and continuously transmit the environmental parameters to the data analysis module; The data analysis module integrates and analyzes the received area division results, temperature and environmental parameters, analyzes the characteristics and environmental parameters of the goods stored in each temperature-controlled area, and outputs control instructions to the intelligent frequency conversion control module; An intelligent frequency conversion control module dynamically adjusts the operating frequency of the refrigeration equipment according to the control instructions of the data analysis module, and pushes the operating energy consumption and status information of the refrigeration equipment to the user interface module; A user interface module, used to display the temperature of each temperature control area and the environmental parameters, and allow the user to set the temperature parameters, and transmit the user setting instructions to the temperature control module; The area division module comprises: A spatial layout acquisition unit, which uses laser and multi-spectral image fusion technology to acquire spatial layout information of the refrigeration equipment and transmits the acquired spatial layout information to the area division decision unit; A regional division decision unit, with a built-in partition strategy model optimized based on genetic algorithm and simulated annealing algorithm, is used to divide the refrigeration equipment into multiple areas according to the spatial layout information, and output a regional division plan that meets the needs of different refrigeration scenarios; The spatial layout acquisition unit further includes: The 3D laser scanning subunit is equipped with multiple sets of adjustable angle and high-precision laser rangefinders, which are distributed in a matrix at key points in the refrigeration equipment to scan the internal spatial structure characteristics of the refrigeration equipment and generate point cloud data; The multispectral image acquisition subunit uses a high-resolution camera with infrared-visible light multi-band imaging capabilities to capture the color and texture information of the internal materials of the refrigeration equipment, and matches it with the point cloud data through an image fusion algorithm, and outputs the complete spatial layout information of the refrigeration equipment to the area division decision unit.

2. According to claim 1, a multi-zone independent temperature control intelligent variable frequency refrigeration and preservation system is characterized in that: The area division decision unit comprises: The scene adaptation subunit has a built-in cold chain scene case library. It predicts the cold storage scene based on the spatial layout information, the type of goods entering the warehouse, and the turnover frequency, and retrieves the corresponding sub-region template to form a preliminary regional division plan; The optimization algorithm execution subunit uses the genetic algorithm crossover, mutation operation and simulated annealing algorithm's cooling search strategy to iteratively optimize the preliminary area division scheme to balance the independence of the temperature control areas, the uniformity of cooling capacity distribution and the convenience of access.

3. According to claim 1, a multi-zone independent temperature control intelligent variable frequency refrigeration and preservation system is characterized in that: The temperature control module comprises: A temperature sensor unit, using a thermistor array with quantum-level precision, is used to measure the temperature of each of the temperature-controlled areas and report the temperature data to the temperature adjustment unit; The temperature regulating unit integrates an intelligent fuzzy PID controller and an adaptive variable frequency refrigeration component, and is used to adjust the operating state of the refrigeration equipment according to the measurement result of the temperature sensor unit to maintain the temperature of each temperature control area stable.

4. According to claim 3, a multi-zone independent temperature control intelligent variable frequency refrigeration and preservation system is characterized in that: The temperature sensor unit comprises: Thermistor array layout subunit adopts a non-uniform density layout strategy based on the volume and shape of each temperature control area, and encrypts the distribution of sensors in temperature-sensitive areas and corners where the cooling volume is prone to fluctuate, ensuring accurate monitoring of the temperature in the entire area; A data preprocessing and transmission subunit, integrating a low noise amplifier and an analog-to-digital converter, calibrating the collected data on site, removing abnormal values, and transmitting the temperature data to the temperature regulating unit; The temperature regulating unit comprises: Intelligent fuzzy PID controller subunit, taking temperature deviation and deviation change rate as input, automatically adjusts PID parameters online, and outputs precise control signals to drive refrigeration components; The adaptive variable frequency refrigeration component sub-unit is equipped with a vector variable frequency compressor, an electronic expansion valve and an intelligent air duct adjustment mechanism, which adjusts the cooling capacity, refrigerant flow and cold air distribution in real time according to the control signal.

5. According to claim 1, a multi-zone independent temperature control intelligent variable frequency refrigeration and preservation system is characterized in that: The environmental monitoring module comprises: A humidity sensor unit, equipped with a highly sensitive sensor based on the hygroscopic properties of graphene, for measuring the humidity data of each of the temperature control areas and correcting humidity data errors in real time; The air flow sensor unit uses a MEMS microcomputer electrical flow vector sensing chip to measure the air flow rate in each temperature control area, build a real-time air flow field model in the temperature control area and dynamically update it.

6. The intelligent variable frequency refrigeration and fresh-keeping system with independent temperature control in multiple zones according to claim 5 is characterized in that: The humidity sensor unit also includes: The graphene moisture absorption sensor subunit uses graphene's super strong adsorption and desorption properties of water vapor molecules to quickly respond to humidity changes; The data calibration and compensation subunit automatically calibrates the zero point and range in a set time period, corrects the humidity data in combination with the ambient temperature compensation algorithm, and outputs accurate humidity values ​​to the data analysis module; The air flow sensor unit further comprises: The MEMS airflow vector chip subunit integrates X, Y, and Z three-axis micro anemometers to capture the airflow velocity and direction vector information in the temperature control area in real time; The airflow field modeling and analysis module, based on the collected data, uses the computational fluid dynamics algorithm to reconstruct the airflow field model of the temperature control area, marks the airflow dead corners and vortex areas, and simultaneously pushes the marked airflow dead corners and vortex area data to the data analysis module.

7. The intelligent variable frequency refrigeration and fresh-keeping system with independent temperature control in multiple zones according to claim 1 is characterized in that: The data analysis module includes: The product characteristics analysis unit integrates the big data product knowledge base with near-infrared spectroscopy and dielectric characteristics detection technology to analyze the thermal sensitivity and freshness requirements of stored products and generate product-specific freshness preservation strategies; Environmental parameter analysis unit, using deep learning time series prediction model and association rule mining algorithm to analyze the impact of temperature and environmental parameters on product preservation, and predict temperature and environmental change trends and associated risks in advance; The environmental parameter analysis unit comprises: The deep learning time series prediction subunit mines the time series data patterns of historical temperature, humidity and air velocity based on the long short-term memory network to predict the changing trends of future temperature and environmental parameters; The association rule mining subunit, based on the change trend of the temperature and environmental parameters, uses the Apriori algorithm to analyze the association between the temperature and environmental parameters and the product preservation effect, and quantifies the impact of the changes in temperature and environmental parameters on the product quality, and outputs early warning information and regulation suggestions to the intelligent frequency conversion control module.

8. The intelligent variable frequency refrigeration and fresh-keeping system with independent temperature control in multiple zones according to claim 1 is characterized in that: The intelligent frequency conversion control module comprises: The frequency conversion algorithm unit is embedded with the deep reinforcement learning and model predictive control algorithm core, which is used to dynamically adjust the operating frequency of the refrigeration equipment according to the results of the data analysis module; The energy consumption optimization unit is connected to the IoT smart meter and energy management system to optimize the energy consumption of refrigeration equipment.

9. The intelligent variable frequency refrigeration and fresh-keeping system with independent temperature control in multiple zones according to claim 1, characterized in that: The user interface module comprises: A display unit, equipped with an ultra-high-definition flexible touch screen and a 3D visualization engine, for displaying the temperature and environmental parameters of each of the temperature control areas in an intuitive and dynamic form; The control unit integrates voice recognition, gesture control and smart touch technology to receive temperature settings input by the user and send them to the temperature control module.

10. An intelligent variable frequency refrigeration and preservation method with independent temperature control in multiple zones, used to implement an intelligent variable frequency refrigeration and preservation system with independent temperature control in multiple zones as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: S1, using the area division module to automatically divide the refrigeration equipment into a plurality of independent temperature control areas according to the spatial layout of the refrigeration equipment, and outputting the division information of each temperature control area to the temperature control module, the environment monitoring module and the data analysis module; S2, using the temperature control module to work in conjunction with the area division module to independently control the temperature of each temperature control area, and feeding back the actual temperature to the data analysis module, while receiving temperature optimization suggestions given by the data analysis module in real time; S3, monitoring the environmental parameters of each of the temperature control areas, including humidity and air flow rate, through the environmental monitoring module, and transmitting the monitored environmental parameters to the data analysis module; S4, the data analysis module integrates and analyzes the received area division results, temperature and environmental parameters, analyzes the characteristics and environmental parameters of the goods stored in each temperature-controlled area, and outputs control instructions to the intelligent frequency conversion control module; S5, the intelligent frequency conversion control module dynamically adjusts the operating frequency of the refrigeration equipment according to the control instructions of the data analysis module, and pushes the operating energy consumption and status information of the refrigeration equipment to the user interface module; S6. The user interface module displays the temperature and environmental parameters of each temperature control area, allows the user to set temperature parameters, and transmits the user setting instructions to the temperature control module.

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