An intelligent cooling system for dynamic monitoring, prediction and optimization of storage conditions in real time

Abstract

An intelligent cooling system for dynamic monitoring, prediction and optimization of storage conditions in real time, consisting of: a refrigerator with heat-insulated walls; a sensor array in the refrigerator comprising temperature sensors, humidity sensors, pressure sensors, and pathogen detection biosensors, the sensor array being configured to continuously monitor internal environmental parameters in the refrigerator and external environmental conditions; a control dashboard configured to display real-time environmental data and enable remote monitoring and manual adjustment of the environment in the refrigerator; an Internet of Things (IoT) communication module configured to transmit the monitored internal and external environmental data to the control dashboard; a proportional-integral-derivative (PID) controller operatively connected to the IoT communication module and configured to analyze the monitored data and calculate correction factors to maintain optimal environmental conditions; a plurality of actuators within the refrigerator, the plurality of actuators comprising a temperature actuator, a humidity actuator, and a pressure actuator, the actuators being configured to independently adjust temperature, humidity, and pressure within the refrigerator based on the control instructions; a processor configured to execute machine learning algorithms to analyze historical and real-time sensor data, predict optimal environmental conditions based on the analyzed data, and generate control instructions for the actuators based on the predicted optimal environmental conditions and the calculated correction factors; and a solar power supply configured to provide energy for operating the system.

Description

FIELD OF THE INVENTION

[0001] The present disclosure relates to an intelligent cooling system with multiple sensors and actuators for controlling the internal environment, in particular to an intelligent cooling system for dynamically monitoring, predicting and optimizing storage conditions in real time. BACKGROUND OF THE INVENTION

[0002] Conventional refrigeration systems face critical limitations in maintaining ideal storage conditions due to fluctuations in external environmental factors. These limitations include increased waste generation, lower security levels, and inefficient energy consumption. Most households own a refrigerator (an average of 0.9 per household), and it has become common to own more than one television (an average of 1.3 per household). Most existing systems cannot dynamically adjust critical parameters such as temperature, humidity, and pressure in real time. Furthermore, they do not mitigate the risks posed by pathogens detected by sensor gases such as ammonia and hydrogen sulfide. Current solutions rely on static configurations or manual intervention and are therefore unsuitable for modern cold chain logistics.

[0003] Given the foregoing discussion, it is clear that there is a need for a refrigeration system that solves the problem of pathogen development and ensures optimal storage conditions due to various external environmental factors. The present invention provides an intelligent refrigeration system for dynamic monitoring, prediction, and optimization of storage conditions in real time. Summary of the invention

[0004] The present disclosure relates to an intelligent refrigeration system for dynamically monitoring, predicting, and optimizing storage conditions in real time. The system is configured for intelligent refrigeration and dynamically monitors, predicts, and optimizes storage conditions in real time. It includes functions such as pathogenic gas control. Biosensors detect and neutralize harmful gases in the refrigeration system. IoT and machine learning technology are integrated into the system. Real-time data acquisition and analysis enable dynamic adjustment of environmental parameters to external and internal conditions. The system features independent actuation mechanisms. Special actuators are used for more precise monitoring of temperature, humidity, and pressure. The system is configured for predictive optimization. The analysis of historical data and the use of adaptive algorithms enable the prediction and optimization of storage conditions.The system is energy-efficient and solar-powered. The proposed system is a smart refrigeration solution that addresses the urgent need for a smart, adaptable, and energy-efficient refrigeration system, ensuring the safe transport of goods in a sustainable and efficient manner.

[0005] The present disclosure aims to provide an intelligent refrigeration system that can dynamically monitor, predict, and optimize storage conditions in real time. The system includes: a refrigerator with thermally insulated walls; a sensor array within the refrigerator including temperature sensors, humidity sensors, pressure sensors, and pathogen detection biosensors, configured to continuously monitor the internal environmental parameters within the refrigerator and the external environmental conditions; a control panel for displaying real-time environmental data and for remotely monitoring and manually adjusting the environment within the refrigerator; an Internet of Things (IoT) communication module that transmits the monitored internal and external environmental data to the control panel;a PID (proportional-integral-derivative) controller operatively connected to the IoT communication module that analyzes the monitored data and calculates correction factors to maintain optimal environmental conditions; a plurality of actuators in the refrigerator, including a temperature actuator, a humidity actuator, and a pressure actuator, configured to independently control temperature, humidity, and pressure in the refrigerator based on the control instructions; a processor configured to execute machine learning algorithms to analyze historical and real-time sensor data, predict optimal environmental conditions based on the analyzed data, and generate control instructions for the actuators based on the predicted optimal environmental conditions and the calculated correction factors;and a solar power supply configured to provide energy to operate the system.;

[0006] One objective of this disclosure is to provide an intelligent cooling system that can dynamically monitor, predict, and optimize storage conditions in real time.

[0007] Another object of the present disclosure is to provide a refrigeration system that enables real-time monitoring and control by continuously tracking and adjusting temperature, humidity, and pressure using IoT and machine learning to ensure optimal storage conditions.

[0008] Another object of the present disclosure is to provide a refrigeration system for detecting and eliminating harmful gases with integrated biosensors to improve food safety and shelf life.

[0009] Another object of the present disclosure is to provide a refrigeration system with predictive optimization, wherein the system uses historical data and adaptive algorithms to predict and proactively optimize refrigeration performance.

[0010] Another objective of this disclosure is to ensure energy efficiency and sustainability by operating on solar energy to reduce energy consumption and provide an environmentally friendly cooling solution.

[0011] To further clarify the advantages and features of the present disclosure, the invention will be explained in more detail with reference to specific embodiments illustrated in the accompanying drawings. These drawings illustrate only typical embodiments of the invention and are therefore not to be considered as limiting its scope. The invention will be described and explained in more detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE CHARACTERS

[0012] These and other features, aspects, and advantages of the present disclosure will become more fully understood when the following detailed description is read with reference to the accompanying drawings, in which like characters represent like parts throughout. Fig. 1 shows a block diagram of an intelligent cooling system for dynamically monitoring, predicting, and optimizing storage conditions in real time according to an embodiment of the present disclosure. Fig. 2 shows a block diagram illustrating the architecture of the proposed intelligent cooling system according to an embodiment of the present disclosure.

[0013] Those skilled in the art will also appreciate that the elements in the drawings are shown for convenience and are not necessarily to scale. For example, the flowcharts illustrate the method by key steps to enhance understanding of aspects of the present disclosure. Furthermore, with respect to device construction, one or more components of the device may be represented in the drawings by conventional symbols. The drawings may show only the specific details relevant to understanding embodiments of the present disclosure in order not to clutter the drawings with details that would be readily apparent to those skilled in the art from the present description. DETAILED DESCRIPTION:

[0014] To facilitate an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and a clear description thereof. However, the scope of the invention is not limited thereby. Changes and further modifications to the illustrated system, as well as further applications of the principles of the invention, are possible, as would normally occur to one skilled in the art to which the invention pertains.

[0015] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting thereof.

[0016] References in this specification to "one aspect," "another aspect," or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, the language "in one embodiment," "in another embodiment," and similar language throughout this specification may or may not refer to the same embodiment.

[0017] The terms "comprises," "comprising," or other variations thereof are intended to cover non-exclusive inclusion, such that a process or method comprising a list of steps may include not only those steps, but also additional steps not expressly listed or inherent in that process or method. Likewise, the statement "comprises" for one or more devices, subsystems, elements, structures, or components does not exclude, without further limitation, the existence of other devices, subsystems, elements, structures, components, or additional devices, subsystems, elements, structures, or components.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains. The systems, methods, and examples provided herein are for illustrative purposes only and should not be considered limiting.

[0019] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0020] The functional units described in this specification are referred to as devices. A device may be implemented in programmable hardware devices such as processors, digital signal processors, central processing units, field-programmable gate arrays, programmable array logic systems, programmable logic devices, cloud processing systems, or the like. The devices may also be implemented in software for execution by various types of processors. An identified device may contain executable code and may consist, for example, of one or more physical or logical blocks of computer instructions, which may be organized, for example, as an object, procedure, function, or other construct.However, the executable file of an identified device does not have to be physically stored in the same location, but may consist of different instructions stored in different locations which, logically linked, form the device and fulfill its purpose.

[0021] The executable code of a device or module may consist of one or more instructions and may even be distributed across multiple code segments, different applications, and multiple storage devices. Similarly, operational data may be identified and represented within the device and presented in any form and data structure. The operational data may be captured as a single data set or distributed across different storage devices and may be present, at least in part, as electronic signals in a system or network.

[0022] References in this specification to "a selected embodiment," "an embodiment," or "an embodiment" mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosed subject matter. Therefore, the phrases "a selected embodiment," "in an embodiment," or "in an embodiment" in various places in this specification do not necessarily refer to the same embodiment.

[0023] Furthermore, the described features, structures, or characteristics may be combined in any manner in one or more embodiments. The following description contains numerous specific details in order to provide a thorough understanding of embodiments of the disclosed subject matter. However, those skilled in the art will recognize that the disclosed subject matter may be practiced without one or more of the specific details, or with different methods, components, materials, etc. In other instances, well-known structures, materials, or operations are not shown or described in detail in order not to obscure aspects of the disclosed subject matter.

[0024] According to the exemplary embodiments, the disclosed computer programs or modules may be executed in a variety of ways, for example, as an application in the memory of a device or as a hosted application on a server that communicates with the device application or browser using various standard protocols such as TCP / IP, HTTP, XML, SOAP, REST, JSON, and other suitable protocols. The disclosed computer programs may be written in exemplary programming languages ​​that execute from the memory of the device or from a hosted server, such as BASIC, COBOL, C, C++, Java, Pascal, or scripting languages ​​such as JavaScript, Python, Ruby, PHP, Perl, or other suitable programming languages.

[0025] Some of the disclosed embodiments involve or otherwise involve the transmission of data over a network, for example, the delivery of various inputs or files over the network. The network may include, for example, the Internet, wide area networks (WANs), local area networks (LANs), analog or digital wired and wireless telephone networks (e.g., PSTN, Integrated Services Digital Network (ISDN), cellular networks, and Digital Subscriber Line (xDSL)), radio, television, cable, satellite, and / or other transmission or tunneling mechanisms for transmitting data. The network may include multiple networks or subnetworks, each including, for example, a wired or wireless data path. The network may include a circuit-switched voice network, a packet-switched data network, or another network for transmitting electronic communications.For example, the network may include Internet Protocol (IP) or Asynchronous Transfer Mode (ATM) networks supporting voice, such as VoIP, Voice over ATM, or other comparable protocols for voice data communication. In one implementation, the network includes a cellular network configured for the exchange of text or SMS messages.

[0026] Examples of the network include a Personal Area Network (PAN), a Storage Area Network (SAN), a Home Area Network (HAN), a Campus Area Network (CAN), a Local Area Network (LAN), a Wide Area Network (WAN), a Metropolitan Area Network (MAN), a Virtual Private Network (VPN), an Enterprise Private Network (EPN), the Internet, a Global Area Network (GAN), etc.

[0027] Fig. 1 shows a block diagram of an intelligent cooling system (100) for dynamically monitoring, predicting and optimizing storage conditions in real time according to an embodiment of the present disclosure.

[0028] According to Fig. 1, the intelligent cooling system (100) comprises: a refrigerator (102) with thermally insulated walls; a sensor array (104) in the refrigerator (102) comprising temperature sensors (104a), humidity sensors (104b), pressure sensors (104c), and biosensors (104d) for pathogen detection, wherein the sensor array (104) is configured to continuously monitor internal environmental parameters in the refrigerator (102) and external environmental conditions; a control panel (106) configured to display environmental data in real time and to enable remote monitoring and manual adjustments to the environment in the refrigerator (102); an Internet of Things (IoT) communication module (108) configured to transmit the monitored internal and external environmental data to the control panel (106);a proportional-integral-derivative (PID) controller (110) operatively connected to the IoT communication module (108) that analyzes the monitored data and calculates correction factors to maintain optimal environmental conditions; a plurality of actuators (112) in the refrigerator (102), including a temperature actuator (112a), a humidity actuator (112b), and a pressure actuator (112c), that independently control temperature, humidity, and pressure in the refrigerator (102) based on the control instructions; a processor (114) executing machine learning algorithms to analyze historical and real-time sensor data, predict optimal environmental conditions based on the analyzed data, and generate control instructions for the actuators (112) based on the predicted optimal environmental conditions and the calculated correction factors;and a solar power supply (116) that provides energy for operating the system (100);

[0029] In one embodiment, the pathogen detection biosensors (104d) are configured to detect and identify certain pathogenic gases, including ammonia, ethylene, and hydrogen sulfide, in the refrigerator.

[0030] In one embodiment, the system (100) further comprises a pathogen neutralization mechanism (104e) configured to be automatically activated upon detection of pathogenic gases above a predetermined threshold.

[0031] In one embodiment, the machine learning algorithms configure the processor (114) to analyze patterns in historical environmental data, correlate external environmental conditions with internal environmental changes, identify optimal environmental parameters for particular types of perishable goods, and continuously improve prediction accuracy through feedback learning.

[0032] In one embodiment, the PID controller (110) is configured to: calculate the difference between actual and desired environmental parameters, calculate correction factors based on proportional, integral, and derivative components of the calculated difference, and minimize fluctuations in the internal environmental conditions by dynamically adjusting the control parameters.

[0033] In one embodiment, the temperature actuator (112a) comprises: a cooling mechanism, a heating mechanism, and a temperature control controller configured to selectively activate either the cooling mechanism or the heating mechanism based on the control instructions.

[0034] In one embodiment, the humidity actuator (112b) comprises: a nichrome wire-based dehumidification system; a humidification system; and a humidity control controller configured to selectively activate either the dehumidification system or the humidification system based on the control instructions.

[0035] In one embodiment, the pressure actuator (112c) comprises: an air pressure regulating mechanism and a pressure regulator configured to adjust the internal air pressure to minimize damage or spoilage of perishable goods.

[0036] In one embodiment, the control dashboard (106) comprises: a user interface (106a) that displays environmental parameters in real time; warning indicators for conditions outside predetermined optimal ranges; tools for visualizing historical data; and manual override controls for remotely adjusting system parameters.

[0037] In one embodiment, the system (100) is configured to implement a continuous feedback loop by: monitoring the effects of actuator adjustments on the internal environmental parameters; communicating updated environmental data to the processor (114); refining the machine learning predictions based on the effectiveness of previous adjustments; and dynamically changing control instructions to optimize the environmental conditions.

[0038] The present invention relates to an advanced refrigeration system for dynamic monitoring, prediction, and optimization of storage conditions in real time. It integrates IoT and machine learning for precise environmental control and uses real-time data acquisition and analysis to adjust temperature, humidity, and pressure to internal and external factors. The system features pathogen gas control through biosensors that detect and neutralize harmful gases, thus increasing food safety. Independent actuation mechanisms enable precise environmental adjustments, while predictive optimization utilizes historical data and adaptive algorithms to anticipate and optimize storage conditions. Designed for energy efficiency, the system is powered by solar energy, thus minimizing dependence on conventional energy sources.This intelligent, adaptive and sustainable cooling solution meets the growing need for efficient transport of perishable goods in modern logistics.

[0039] Fig. 2 shows a block diagram illustrating the architecture of the proposed intelligent cooling system according to an embodiment of the present disclosure.

[0040] With reference to Fig. 2, the operation of the proposed system is described as follows: Environmental monitoring with sensors: The system uses sensors to continuously monitor various environmental parameters within the refrigerator, including: a temperature sensor to ensure the temperature remains optimal for the product, a humidity sensor to maintain the correct humidity to prevent spoilage, a pressure sensor to control air pressure to minimize damage or spoilage, and a pathogen detection sensor to detect harmful bacteria or mold that could affect the product. The sensors also detect external environmental conditions and report them to the internal system.

[0041] Data communication via IoT: All sensor data is transmitted to an IoT dashboard accessible to both the transport manager and the driver. This dashboard provides real-time insights into vehicle conditions and enables remote monitoring and adjustment.

[0042] PID controller for state control: The acquired data is fed into a PID (proportional-integral-derivative) controller developed with Simulink. The PID controller analyzes the data and adjusts the cooling system to maintain optimal conditions by minimizing the difference between actual and desired environmental parameters. For example, it lowers or increases the temperature, adjusts the humidity, or regulates the pressure to create optimal storage conditions.

[0043] Separate mechanisms, actuators, and machine learning for environmental control: Once the PID controller optimally implements the environmental conditions in real time, separate actuators for temperature, humidity, and pressure are used to achieve these optimal conditions. The temperature actuator controls the cooling and heating systems to maintain the desired temperature; the humidity actuator regulates the humidity, for example, using nichrome wiring for dehumidification or humidification; and the pressure actuator adjusts the air pressure to prevent damage or deterioration. The machine learning algorithms are implemented using a processor that instructs the actuators in real time, enabling automatic adjustments to dynamically maintain the desired conditions.

[0044] Feedback loop for continuous optimization: The controlled environmental parameters are continuously monitored and fed back to the controller via the same IoT technology. The feedback loop enables the system to adjust in real time, ensuring that conditions remain optimal throughout the entire transport process.

[0045] Power supply: Solar modules are used to power the cooling system, contributing to energy efficiency and sustainability. The solar power system is intended exclusively for the cooling system and not for the transport vehicle itself.

[0046] The present invention relates to an intelligent refrigeration system that overcomes the shortcomings of conventional refrigeration systems through active monitoring, forecasting, and dynamic optimization of storage conditions in real time. The system combines state-of-the-art technologies for improved efficiency, reliability, and sustainability, thus ensuring the safety of perishable goods during transport and storage. The system's key innovations include: Pathogenic gas control: To prevent hazards caused by pathogenic gas emissions during the storage of perishable goods, the system integrates state-of-the-art biosensors. These detect and neutralize hazardous gases such as ethylene or ammonia, which can accelerate spoilage or indicate bacterial contamination if overproduction occurs. Continuous monitoring of the gas composition in the cold room by these biosensors ensures optimal air quality to extend the shelf life of stored products and prevent losses due to contamination.

[0047] IoT and machine learning integration: The system includes a comprehensive IoT framework for real-time data acquisition from numerous environmental sensors. The sensors, which measure critical parameters such as temperature, humidity, gas composition, and pressure, are continuously monitored. The machine learning algorithms implemented in the system processor process and analyze this data to dynamically adapt storage conditions to the internal and external environmental conditions. This allows the system to proactively respond to changes in storage conditions, thus ensuring long-term quality while minimizing waste.

[0048] Independent Actuator Mechanism: Unlike the traditional centralized control mechanisms of conventional refrigeration systems, this invention utilizes independent actuators to precisely control storage conditions. Special actuators calibrate temperature, humidity, and pressure independently, enabling regional control that meets the real-time requirements of the stored goods. Machine learning further refines the control by anticipating necessary changes before adverse conditions occur, thus ensuring optimal storage conditions.

[0049] Predictive optimization: By using an adaptive optimization algorithm and combining it with historical data, the system can estimate future storage needs and make adjustments early on. Future problems can be predicted through trend analyses of temperature fluctuations, humidity changes, and gas emissions, allowing appropriate measures to be taken. This predictive technique ensures that the risk of spoilage is minimized and optimal operation is achieved with the required amount of energy at any given time, avoiding unnecessary parallel cooling. The proposed system is energy-efficient and offers sustainable operation because it uses solar energy.

[0050] The present invention presents a portable system for controlling pathogenic gases such as ammonia, ethylene, and hydrogen sulfide, which simultaneously monitors temperature and humidity. Using IoT and machine learning algorithms, the system dynamically adjusts temperature and humidity based on external environmental conditions and real-time data analytics, thus ensuring optimal storage conditions for perishable goods.

[0051] In addition, the invention features a cooling system that dynamically regulates temperature, humidity, and airflow within the container. This control is achieved through real-time analysis of external environmental factors such as temperature, pressure, and humidity, as well as internal parameters such as perishability and predicted risk of spoilage. By continuously optimizing these factors, the system ensures effective preservation with minimal energy consumption.

[0052] A key innovation of the system is the implementation of a PID controller that calculates a dynamic correction factor based on real-time measurements of internal and external environmental parameters. This correction factor optimizes actuator performance and enables precise control of storage conditions to ensure the quality and shelf life of perishable goods during transport.

[0053] In addition, the refrigeration system uses adaptive machine learning models to predict and proactively adjust internal conditions. By leveraging real-time data and historical patterns, the system improves the preservation of perishable goods. It also integrates advanced biosensors that can detect specific pathogens in the cold storage. Upon detection, automated mitigation measures are initiated to neutralize these threats, ensuring the safety and integrity of stored goods.

[0054] The energy-efficient cooling system is powered by solar energy, reducing dependence on conventional energy sources. Separate actuators for temperature, humidity, and pressure control operate in real time based on machine learning insights, enabling precise environmental adjustments while maintaining sustainability. The integration of adaptive machine learning algorithms further enhances predictive adjustments, continuously analyzing real-time data and historical trends to dynamically optimize storage conditions.

[0055] In one embodiment, the system integrates real-time monitoring with predictive optimization and energy-efficient operation, thus representing an innovative solution in cold storage technology. The system addresses modern logistics requirements by enabling the safe and efficient transport of perishable goods, ensuring sustainability while minimizing operating costs. The intelligent integration of IoT, machine learning, and independent drive mechanisms is enabling a new generation of refrigeration systems that will become an indispensable option for temperature-sensitive storage industries.

[0056] The drawings and the foregoing description illustrate examples of embodiments. Those skilled in the art will recognize that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be separated into multiple functional elements. Elements of one embodiment may be added to another embodiment. For example, the order of the processes described herein may be changed and is not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the order shown; nor do all actions need to be performed. Also, actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and use of materials, are possible. The scope of the embodiments is at least as broad as indicated in the following claims.

[0057] Advantages, further benefits, and solutions to problems have been described above with reference to specific embodiments. However, the advantages, advantages, solutions to problems, and any components that may result in or enhance an advantage, advantage, or solution are not to be construed as critical, required, or essential features or components of any or all of the claims. REFERENCES 100 An intelligent cooling system for dynamic monitoring, prediction and optimization of storage conditions in real time. 102 refrigerators 104 sensor array 104a Temperature sensors 104b Humidity sensors 104c pressure sensors 104d Biosensors for pathogen detection 104e Mechanism for neutralizing pathogens 106 Control panel 108 Internet of Things (IoT) communication module 110 PID controllers 112 variety of actuators 112a Temperature actuator 112b Humidity actuator 112c pressure actuator 114 processor 116 Solar power supply 202 Sensor-based environmental monitoring 204 IoT data communication 206 PID controller for state control 208 Conditional prerequisite test 210 Actuator and Processor for Machine Learning 212 Feedback optimization 214 Solar power supply

Claims

[1] An intelligent cooling system for dynamic monitoring, prediction and optimization of storage conditions in real time, consisting of: a refrigerator with heat-insulated walls; a sensor array in the refrigerator comprising temperature sensors, humidity sensors, pressure sensors, and pathogen detection biosensors, the sensor array being configured to continuously monitor internal environmental parameters in the refrigerator and external environmental conditions; a control dashboard configured to display real-time environmental data and enable remote monitoring and manual adjustment of the environment in the refrigerator; an Internet of Things (IoT) communication module configured to transmit the monitored internal and external environmental data to the control dashboard; a proportional-integral-derivative (PID) controller operatively connected to the IoT communication module and configured to analyze the monitored data and calculate correction factors to maintain optimal environmental conditions; a plurality of actuators within the refrigerator, the plurality of actuators comprising a temperature actuator, a humidity actuator, and a pressure actuator, the actuators being configured to independently adjust temperature, humidity, and pressure within the refrigerator based on the control instructions; a processor configured to execute machine learning algorithms to analyze historical and real-time sensor data, predict optimal environmental conditions based on the analyzed data, and generate control instructions for the actuators based on the predicted optimal environmental conditions and the calculated correction factors; and a solar power supply configured to provide energy for operating the system. [2] The system of claim 1, wherein the pathogen detection biosensors are configured to detect and identify certain pathogenic gases, including ammonia, ethylene, and hydrogen sulfide, in the refrigerator. [3] The system of claims 1 and 2, further comprising a pathogen gas neutralisation mechanism configured to be automatically activated upon detection of pathogen gases above a predetermined threshold. [4] The system of claim 1, wherein the machine learning algorithms configure the processor to: analyze patterns in historical environmental data, correlate external environmental conditions with internal environmental changes, determine optimal environmental parameters for specific types of perishable goods, and continuously improve prediction accuracy through feedback learning. [5] The system of claim 1, wherein the PID controller is configured to: calculate the difference between actual and desired environmental parameters, calculate correction factors based on the proportional, integral and derivative components of the calculated difference, and minimize fluctuations in the internal environmental conditions by dynamically adjusting the control parameters. [6] The system of claim 1, wherein the temperature actuator comprises: a cooling mechanism; a heating mechanism; and a temperature control controller configured to selectively activate either the cooling mechanism or the heating mechanism based on the control instructions. [7] The system of claim 1, wherein the humidity actuator comprises: a nichrome wire-based dehumidification system; a humidification system; and a humidity control controller configured to selectively activate either the dehumidification system or the humidification system based on the control instructions. [8] The system of claim 1, wherein the pressure actuator comprises: an air pressure regulating mechanism and a pressure regulator configured to adjust the internal air pressure to minimize damage or spoilage of perishable goods. [9] The system of claim 1, wherein the control dashboard comprises: a user interface that displays environmental parameters in real time; warning indicators for conditions outside predetermined optimal ranges; tools for visualizing historical data; and manual override controls for remotely adjusting system parameters. [10] The system of claim 1, wherein the system is configured to implement a continuous feedback loop by: monitoring the effects of actuator adjustments on the internal environmental parameters; communicating updated environmental data to the processor; refining the machine learning predictions based on the effectiveness of previous adjustments; and dynamically changing control instructions to optimize the environmental conditions.

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