Method and system for assessing condition of perishable cargo using digital twin

Abstract

The subject matter of the invention is a method and system for assessing the condition of perishable goods using digital twin. The disclosed embodiments describe systems and methods for assessing the condition of perishable cargo inside a refrigeration container of a vehicle using digital twin. In accordance with an embodiment, a method includes performing a simulation process to simulate at least one refrigerated compartment by using a digital twin model characterizing vehicle parameters, food parameters, and variability of conditions related to at least specific operations of transportation of perishable goods and initial storage conditions. The method periodically evaluates a current quality condition of the perishable article based on a result of the simulation process and a plurality of predetermined evaluation parameters, and compares a result of the evaluation to a threshold associated with the current quality condition of the perishable article. The method predicts an impact on a current quality condition of the perishable item based on a result of the comparison and provides a recommendation.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 509,066, filed on June 20, 2023, which is incorporated herein by reference in its entirety. Technical Field

[0003] The disclosure relates to the field of transportation of perishable goods, and more particularly, to methods and systems for assessing and / or predicting the condition of perishable goods within a refrigerated compartment of a vehicle. Background Art

[0004] Evaluating the condition of perishables in shipment is essential for ensuring the quality and safety of perishables. Conventionally, the condition of such perishables is evaluated based on monitoring the temperature history of air and / or food pulp during transportation, manually measuring the sample pulp temperature upon arrival, and performing additional manual inspections. According to a conventional method, several arbitrary samples of perishable goods are selected to perform a manual evaluation process, because routine samples of a large number (or all) of perishable goods are operationally impractical. Due to consideration of such several arbitrary samples for evaluation, only problems related to such arbitrary samples can be captured. However, unassessed perishable goods samples may have some (major) problems, which may be completely missed due to the fact that such samples are not considered during the evaluation process. In addition, the sampling of several arbitrary samples may provide misleading outputs. In addition, routine sampling of large samples is operationally impractical. Therefore, it is required to perform a more extensive inspection and evaluation process.

[0005] In view of the above, it would be advantageous to provide methods and systems that can automatically assess the condition of perishable goods during shipment of multiple quality attributes. Summary of the invention

[0006] This summary is provided to introduce a selection of concepts in a simplified form that are further described in the detailed description of the disclosure. This summary is neither intended to identify key or essential inventive concepts of the disclosure nor is it intended to be used to determine the scope of the disclosure.

[0007] Disclosed herein is a method for evaluating the condition of perishable goods inside a refrigerated container of a vehicle using a digital twin. The method includes obtaining first information including real-time temperature data and sensor data. The method further includes receiving second information associated with vehicle parameters based on one of user input or acquisition from a remote database, the vehicle parameters including vehicle type and load conditions for one or more shipments planned for a specific transportation trip. Thereafter, the method includes performing a simulation process based on the received first information and the received second information to simulate at least one refrigerated compartment by using a digital twin model, the digital twin model characterizing the initial storage condition of the perishable items and the variability of the vehicle parameters during the specific transportation trip. The method further includes periodically evaluating the current quality condition of the perishable items during the specific transportation trip based on the results of the simulation process and a plurality of predetermined evaluation parameters. The method further includes comparing the results of the periodic evaluation with a threshold value associated with the current quality condition of the perishable items. Thereafter, the method includes predicting an impact on the current quality condition of the perishable items based on the results of the comparison. The method further includes providing recommendations via a user interface based on the predicted impact on the current quality condition of the perishable items.

[0008] In one or more embodiments, the method may further include performing a simulation process. Thus, the simulation process includes generating, using the digital twin model, a virtual configuration of at least one refrigerated compartment based on the first information and the second information. The at least one simulated refrigerated compartment depicts at least a standard operating procedure (SOP) associated with the arrangement of perishable items in the virtual configuration of the refrigerated compartment during an event of a specific transport trip of the vehicle, a virtual configuration of the refrigerated compartment, and a virtual loading configuration of the perishable items in the refrigerated compartment.

[0009] In one or more embodiments, the method may further include using a digital twin model to calculate multiple quality metrics of the perishable items based on the received first information and second information. The multiple quality metrics correspond to temperature values, mass loss, mass loss, shelf life loss, loss of available post-harvest life of the perishable items, the current state of the perishable items relative to the initial state, the risk of quality degradation of the perishable items due to the temperature inside the refrigerated compartment, and the risk of pathogen growth on the surface of the perishable items due to the temperature inside the refrigerated compartment for each perishable item in the refrigerated compartment at different time intervals throughout the transportation. In addition, the state of the perishable items is determined based on multiple measurement parameters related to abnormal temperature inside the refrigerated compartment, condensation inside the refrigerated compartment, moisture loss in the perishable items, and pathogen growth on the surface of the perishable items. In addition, the current quality status of the perishable items is evaluated based on the calculated multiple quality metrics.

[0010] In one or more embodiments, the method may further include providing a performance score to the virtual configuration of the at least one refrigerated compartment based on the calculation of the predetermined evaluation parameters and the plurality of quality metrics. The prediction of the impact on the current condition of the perishables is based on the provided performance score.

[0011] In one or more embodiments, the method further includes executing the simulation process at one or more specific times of a specific transport of the perishable item in response to a user input or a triggering event.

[0012] In one or more embodiments, the simulation process is performed before a specific transport trip, at one or more specific times during a specific transport trip, and after the specific transport trip is completed.

[0013] In one or more embodiments, the real-time sensor data includes data related to air quality inside at least one refrigerated compartment, gas presence inside at least one refrigerated compartment, and geographic location data of the vehicle, wherein the real-time temperature data includes an internal air temperature value of at least one refrigerated compartment. The vehicle parameters related to the load condition include data related to the arrangement pattern of perishable items, the packaging of perishable items, the number / volume of perishable items loaded inside the vehicle, and the loading pattern of perishable items.

[0014] In one or more embodiments, the method may further include identifying a geographic location of a vehicle or a specific area within a refrigerated compartment where a value of a periodically assessed current quality condition of perishable items is below a threshold, and then providing, via user input, a recommendation associated with an inspection of the perishable items at the identified geographic location.

[0015] In one or more embodiments, the vehicle parameters include parameters set by a user of the vehicle prior to a particular transportation trip, at one or more particular times during a particular transportation trip, or after completion of a particular transportation trip.

[0016] In one or more embodiments, the second information associated with the vehicle parameters includes information about one or more shipments planned for the particular transportation trip, including information related to a planned route to be taken for the particular transportation trip, information related to stops to be taken for the particular transportation trip, information related to durations of stops to be taken for the particular transportation trip, the number of times a door is opened and closed by a user, the duration that a door is kept open by a user, and potential routes taken by the user to complete one or more shipments on previous transportation trips.

[0017] In one or more embodiments, the second information associated with the vehicle parameters includes a vehicle condition, the vehicle condition being related to information associated with at least one of the vehicle configuration prior to a particular transport trip, the vehicle configuration at one or more particular times during a particular transport trip, the vehicle configuration after completion of a particular transport trip, the volume of the refrigerated compartment, the vehicle geometry, the current condition of the interior walls of the refrigerated compartment, the presence of debris inside the refrigerated compartment, and the current mode of a transport refrigeration unit (TRU) controlling the refrigerated compartment. The vehicle geometry includes data associated with the size of the vehicle, the weight of the vehicle.

[0018] In one or more embodiments, the predetermined evaluation parameters include parameters associated with the quality of the perishable item, the efficiency of the vehicle, and the costs involved in the transportation of the perishable item.

[0019] In one or more embodiments, the method may further include receiving, based on user input or image data of the perishable item, second information related to a food parameter associated with an initial food mass of the perishable item. The method further includes performing a simulation process based on the received first information and the received second information to simulate at least one refrigerated compartment by using a digital twin model that characterizes variability of the food parameter of the perishable item during a particular transport trip.

[0020] Also disclosed is a system for evaluating the condition of perishable items. The system includes a server, the server including at least one processor coupled to at least a memory, a remote database, and a cloud database. The server is further wirelessly coupled to a vehicle including a transport refrigeration unit (TRU) and a human-machine interface (HMI). The at least one processor is configured to obtain first information including real-time temperature data, sensor data. Thereafter, the at least one processor is configured to receive second information associated with vehicle parameters based on one of user input or acquisition from a remote database, the vehicle parameters including vehicle type and load conditions for one or more shipments planned for a specific transport trip. Thereafter, the at least one processor is configured to perform a simulation process based on the received first information and the received second information to simulate at least one refrigerated compartment by using a digital twin model, the digital twin model characterizing the initial storage condition of the perishable items and the variability of the vehicle parameters during the specific transport trip. The at least one processor is further configured to periodically evaluate the current quality condition of the perishable items during the specific transport trip based on the results of the simulation process and a plurality of predetermined evaluation parameters. Thereafter, the at least one processor is further configured to compare the result of the periodic evaluation with a threshold value associated with the current quality condition of the perishable item, and then predict an impact on the current condition of the perishable item based on the result of the comparison. The at least one processor is further configured to provide a recommendation via a user interface based on the predicted impact on the current quality condition of the perishable item.

[0021] In one or more embodiments, the at least one processor is configured to generate a virtual configuration of at least one refrigerated compartment based on the first information and the second information using a digital twin model. The at least one simulated refrigerated compartment depicts at least a standard operating procedure (SOP) associated with the arrangement of perishable items in the virtual configuration of the refrigerated compartment during an event of a specific transportation trip of a vehicle, a virtual configuration of the refrigerated compartment, and a virtual loading configuration of perishable items in the refrigerated compartment.

[0022] In one or more embodiments, the at least one processor is configured to calculate multiple quality metrics for perishable items based on the received first information and second information using a digital twin model. The multiple quality metrics correspond to temperature values, weight loss, mass loss, shelf life loss, loss of available post-harvest life of the perishable items, current state of the perishable items relative to an initial state, risk of quality degradation of the perishable items due to the temperature inside the refrigerated compartment, and risk of pathogen growth on the surface of the perishable items due to the temperature inside the refrigerated compartment for each of the perishable items in the refrigerated compartment at different time intervals throughout transportation. The state of the perishable items is determined based on multiple measurement parameters, which are related to abnormal temperature inside the refrigerated compartment, condensation inside the refrigerated compartment, moisture loss in the perishable items, and pathogen growth on the surface of the perishable items. The current quality state of the perishable items is evaluated based on the calculated multiple quality metrics.

[0023] In one or more embodiments, the at least one processor is configured to provide a performance score to the virtual configuration of at least one refrigerated compartment based on the calculation of the predetermined evaluation parameters and the plurality of quality metrics. The prediction of the impact on the current condition of the perishable items is based on the provided performance score.

[0024] In one or more embodiments, the method further includes executing the simulation process in response to a user input or a triggering event at one or more specific times of a specific shipment of the perishable item.

[0025] In one or more embodiments, the simulation process is performed before a specific transport trip, at one or more specific times during a specific transport trip, and after the specific transport trip is completed.

[0026] In one or more embodiments, the real-time sensor data includes data related to air quality inside at least one refrigerated compartment, gas presence inside at least one refrigerated compartment, and geographic location data of the vehicle, wherein the real-time temperature data includes an internal air temperature value of at least one refrigerated compartment. The vehicle parameters related to the load condition include data related to the arrangement pattern of perishable items, the packaging of perishable items, the number / volume of perishable items loaded inside the vehicle, and the loading pattern of perishable items.

[0027] In one or more embodiments, at least one processor is configured to identify a geographic location of a vehicle or a specific area within a refrigerated compartment where a value of a periodically assessed current quality condition of perishable items is below a threshold, and then provide, via user input, a recommendation associated with an inspection of the perishable items at the identified geographic location.

[0028] In one or more embodiments, the vehicle parameters include parameters set by a user of the vehicle prior to a particular transportation trip, at one or more particular times during a particular transportation trip, or after completion of a particular transportation trip.

[0029] In one or more embodiments, the second information associated with the vehicle parameters includes information about one or more shipments planned for the particular transportation trip, including information related to a planned route to be taken for the particular transportation trip, information related to stops to be taken for the particular transportation trip, information related to durations of stops to be taken for the particular transportation trip, the number of times a door is opened and closed by a user, the duration that a door is kept open by a user, and potential routes taken by the user to complete one or more shipments on previous transportation trips.

[0030] In one or more embodiments, the second information associated with the vehicle parameters includes a vehicle condition, the vehicle condition being related to information associated with at least one of the vehicle configuration prior to a particular transport trip, the vehicle configuration at one or more particular times during a particular transport trip, the vehicle configuration after completion of a particular transport trip, the volume of the refrigerated compartment, the vehicle geometry, the current condition of the interior walls of the refrigerated compartment, the presence of debris inside the refrigerated compartment, and the current mode of a transport refrigeration unit (TRU) controlling the refrigerated compartment. The vehicle geometry includes data associated with the size of the vehicle, the weight of the vehicle.

[0031] In one or more embodiments, the predetermined evaluation parameters include parameters associated with the quality of the perishable item, the efficiency of the vehicle, and the costs involved in the transportation of the perishable item.

[0032] In one or more embodiments, at least one processor is configured to receive second information related to a food parameter associated with an initial food mass of the perishable item based on user input or image data of the perishable item. The at least one processor is configured to perform a simulation process based on the received first information and the received second information to simulate at least one refrigerated compartment using a digital twin model that characterizes variability of the food parameter of the perishable item during a particular transport trip.

[0033] In order to further clarify the advantages and features of the methods, systems and devices, a more specific description of the methods, systems and devices will be presented by referring to specific embodiments thereof illustrated in the accompanying drawings. It is understood that these drawings depict only typical embodiments of the disclosure and are therefore not to be considered as limiting the scope thereof. The present disclosure will be described and explained using the accompanying drawings, using additional specificity and details. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] These and other features, aspects and advantages will become better understood when the following detailed description is read with reference to the accompanying drawings, wherein like reference numerals refer to like parts throughout the drawings, and wherein:

[0035] Figure 1A Description of a vehicle having a refrigerated compartment;

[0036] Figure 1B Illustrate examples of refrigeration compartments;

[0037] Figure 2 A block diagram illustrating the server;

[0038] Figure 3 Illustrate a flow chart for assessing the condition of perishable items inside a refrigerated compartment;

[0039] Figure 4A Examples of digital twin models illustrating simulations;

[0040] Figure 4B Various examples illustrating simulated virtual configurations of refrigeration compartments;

[0041] Figure 5 An example illustrating virtual modeling of a chute configuration; and

[0042] Figure 6 An exemplary hardware configuration of an electronic control unit (ECU) is described.

[0043] Furthermore, those skilled in the art will appreciate that the elements in the drawings are illustrated for simplicity and may not necessarily have been drawn to scale. For example, a flow chart illustrates a method in terms of the most significant steps involved to help promote an understanding of aspects of the invention. Furthermore, with respect to the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details relevant to understanding embodiments of the invention so as not to obscure the drawings with details that would be readily apparent to one of ordinary skill in the art having the benefit of the description herein. DETAILED DESCRIPTION

[0044] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to various embodiments, and specific language will be used to describe the embodiments. It will be understood, however, that no limitation of the scope of the disclosure is intended thereby, and such alterations and further modifications in the described systems and such further applications of the principles of the disclosure as illustrated therein are contemplated as would normally occur to one skilled in the art to which the disclosure relates.

[0045] It will be understood by those skilled in the art that both the foregoing general description and the following detailed description are illustrative of the disclosure and are not intended to limit the disclosure.

[0046] Reference throughout this specification to "aspect," "another aspect," or similar language means that a particular feature, structure, or characteristic described in connection with an embodiment is included in at least one embodiment disclosed. Thus, appearances of the phrases "in an embodiment," "in another embodiment," "some embodiments," "one or more embodiments," and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0047] The term "comprises, comprising" or any other variation thereof is intended to cover a non-exclusive inclusion, such that a process or method that includes a series of steps does not include only those steps, but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or subsystems or elements or structures or components beginning with "comprises ..." do not exclude the presence of other devices or other subsystems or other elements or other structures or other components or additional devices or additional subsystems or additional elements or additional structures or additional components without more constraints.

[0048] The disclosed embodiments will be described in detail below with reference to the accompanying drawings.

[0049] Figure 1A A vehicle 100 having a refrigerated compartment in accordance with a disclosed embodiment is illustrated. In non-limiting examples, the vehicle 100 may be a gasoline vehicle, a diesel vehicle, a compressed natural gas (CNG) vehicle, an electric vehicle, or the like.

[0050] The vehicle 100 includes a transport refrigeration unit (TRU) 101 coupled to a refrigerated compartment 103. The vehicle 100 further includes an engine 108 and an electronic control unit (ECU) 106 coupled to each other. In a non-limiting example, the vehicle 100 includes one or more refrigerated compartments shown as 103a-103h in Figure 1 of the accompanying drawings. For ease of explanation, one or more refrigerated compartments 103a-103h are collectively and interchangeably referred to as 103. One or more perishable items to be transported are stored inside the refrigerated compartment 103. Each of the refrigerated compartments 103a-103h has a corresponding door 105. Each door 105 of the refrigerated compartment 103 includes a latch 107, which can be used by a user to open and close the door 105. Figure 1B An example of a refrigeration compartment is illustrated in .

[0051] According to an embodiment, the TRU 101 includes a compressor 101-1 and a power supply system 101-2. The TRU 101 is configured to cool the refrigeration compartment 103. The compressor 101-1 may correspond to a cooling compressor. In addition to the compressor 101-1, the TRU 101 may further include a cooling fan for cooling the refrigeration compartment 103, and a heating device for thawing and heating the refrigeration compartment 103. The heating device may correspond to at least one of a heating compressor or an electric heater. As an example, the type of the compressor 101-1 depends on the TRU 101, and may vary depending on the type of the container of the TRU 101.

[0052] During the driving mode of the vehicle 100, the engine 108 is configured to provide power to the TRU 101 through the power supply system 101-2 so that the TRU 101 can maintain the temperature of the refrigerated compartment 103. Since the items are perishable, the TRU 101 maintains an appropriate temperature inside the refrigerated compartment 103. In one embodiment, the temperature can be automatically set by the TRU 101 before a specific transportation trip of perishable items. In another embodiment, the temperature of the refrigerated compartment 103 can also be manually set by the user of the vehicle before the start of a specific transportation trip of perishable items or during the transportation trip.

[0053] According to an embodiment, the ECU 106 of the vehicle 100 may include one or more controllers (not shown in FIG. 1 ) configured to control the overall operating mode of the vehicle 100. In addition, the ECU 106 is configured to communicate wirelessly with the server 113 and the cloud database 109. According to an embodiment, the ECU 106 is configured to obtain real-time temperature data and real-time sensor data from the TRU 101. According to an embodiment, the server 113 is configured to receive such temperature and sensor data. Without departing from the scope of the invention, throughout the disclosure, the aforementioned information may alternatively be referred to as the first information.

[0054] The power supply system 101-2 is configured to supply power to the compressor 101-1. The power supply system 101-2 includes a controller (not shown) configured to control the operation of the power supply system 101-2 to provide power to the compressor 101-1 and the cooling fan 101-1. The controller is configured to operate the power supply system 101-2 to continuously draw power from the engine 108, thereby operating the compressor 101-1 and the cooling fan 101-1 to maintain the temperature environment in the refrigeration compartment 103. The controller is configured to communicate with the ECU 106 via a controller area network (CAN) bus (not shown in FIG. 1). The CAN bus is generally used in connection with a vehicle system, and advantageously, the controller of the power supply system 101-2 can be connected using a pre-existing bus of the vehicle 100. According to some embodiments, the ECU 106 can be connected to a data logger (not shown) for obtaining various data of the refrigeration compartment 103. According to an embodiment, the data logger can be an Internet of Things (IoT) device placed inside the refrigeration compartment 103. According to some embodiments, the data logger may include sensors for obtaining various data related to real-time sensor data, temperature data, humidity data, location data, etc.

[0055] The TRU 101 may also include a plurality of sensors (not shown) for measuring the temperature and pressure inside the refrigerated compartment 103. According to some embodiments, the plurality of sensors may be mounted external to the TRU 101 but internal to the container. The plurality of sensors may include, but are not limited to, compressor discharge and suction pressure measurement sensors, temperature sensors, evaporator exit temperature sensors, and supply, return and ambient air temperature sensors, gas sensors, ethylene sensors, etc. The ECU 106 may further include a transceiver (e.g., such as Figure 6 The transceiver may provide various real-time data related to the load condition to the server 113 or upload to the cloud database 109 or a remote server (not shown). According to some embodiments, a data logger, TRU or sensor installed outside the TRU 101 may be configured to measure the temperature and pressure inside the refrigeration compartment 103.

[0056] In a non-limiting example, real-time sensor data can be obtained from a data logger, a TRU, or a sensor installed outside the TRU 101. Real-time sensor data includes data related to the indoor air quality (air quality inside the refrigeration compartment) of at least one refrigeration compartment 103, the internal air temperature value of at least one refrigeration compartment 103, the presence of gas inside the at least one refrigeration compartment, and / or the geographic location data of the vehicle 100. As an example, the indoor air quality of at least one refrigeration compartment 103 may be related to the air humidity content inside the refrigeration compartment 103. A gas sensor may be configured to detect the presence of various gases inside the at least one refrigeration compartment 103. The gas may include carbon dioxide, nitrogen, etc. In addition, an ethylene sensor may be configured to detect the presence of ethylene gas inside the at least one refrigeration compartment. According to an embodiment, the detection of the gas inside the at least one refrigeration compartment 103 can be used as an input to analyze the quality status of perishable items. For example, detecting the presence of various gases can provide information about the maturity of perishable items, which can further help analyze the quality status of perishable items.

[0057] In a non-limiting example, the indoor air quality indicates the parts per million (ppm) level of air pollutants inside the refrigerated compartment 103. In another non-limiting example, the interior air temperature value of the refrigerated compartment 103 indicates the air temperature inside the refrigerated compartment 103, and the geographic location data of the vehicle 100 indicates the current, past, or next upcoming location of the vehicle 100.

[0058] In another non-limiting example, the real-time temperature data may include an internal air temperature value of at least one refrigeration compartment 103 , which may be obtained from a data logger, the TRU, or a sensor mounted external to the TRU 101 .

[0059] In a non-limiting example, the information related to the load conditions includes data related to the arrangement pattern of perishable items inside the refrigerated compartment 103, the packaging of the perishable items, the number / volume of perishable items loaded inside the vehicle, and / or the loading pattern of the perishable items.

[0060] In a non-limiting example, the arrangement mode of perishable items mentioned herein indicates how perishable items are arranged for a specific transport journey. In a non-limiting example, during a specific transport journey of food items including vegetables, leafy vegetables may be arranged at the top of the refrigerated compartment 103, and solid vegetables (e.g., potatoes) may be arranged in the bottom of the refrigerated compartment 103 to avoid any damage and quality issues during shipment. In another non-limiting example, the type of vehicle 100 mentioned herein may correspond to one of an electric vehicle, a hybrid vehicle, a CNG-based vehicle, a gasoline vehicle, a diesel vehicle, etc. In yet another non-limiting example, the size of the vehicle 100 herein corresponds to a light commercial vehicle (LCV), a light rigid vehicle, a heavy commercial vehicle with a large trailer, a vehicle with a semi-trailer (articulated truck), etc. In addition, in a non-limiting example, the volume of perishable items loaded in the vehicle 100 mentioned herein may indicate the number of perishable items loaded in the vehicle 100.

[0061] According to an embodiment, server 113 may correspond to any conventional server. Various monitoring systems or (one or more) computer systems may access server 113 via the Internet or through any wireless communication medium. Server 113 here refers to any physical server or virtual server that interacts with one or more computer systems having one or more processors. Such an arrangement may also be referred to as a cloud that may be implemented in an extensible manner. As another example, a processor may be implemented virtually in server 113. Server 113 may be configured to communicate wirelessly with cloud database 109.

[0062] Figure 2 A block diagram of a server according to the disclosed embodiments is shown. The server 113 may include one or more processors 201 coupled to a memory 202. In addition, the server 113 may be communicatively coupled to a cloud database 109.

[0063] In an example, processor 201 may be a single processing unit or multiple units, all of which may include multiple computing units. Processor 201 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic processors, virtual processors, state machines, logic circuit systems, and / or any device that manipulates signals based on operating instructions. Among other capabilities, processor 201 is also configured to retrieve and execute computer-readable instructions and data stored in memory 202.

[0064] As another example, the cloud database 109 can be implemented as a combination of hardware and software. Among other things, the cloud database 109 is also used as a repository for storing data processed, received and generated by one or more processors in the processor 201 and / or the vehicle 100 or any other remote server or remote database.

[0065] Memory 202 may include any non-transitory computer-readable medium known in the art, including, for example, volatile memory (such as static random access memory (SRAM) and dynamic random access memory (DRAM)) and / or non-volatile memory (such as read-only memory (ROM), erasable programmable ROM, flash memory, hard disks, optical disks, and tapes).

[0066] Referring back to FIG. 1 , the vehicle 100 may further include a human-machine interface (HMI) 111 configured to display messages or notifications about the current status of perishable items. In addition, the ECU 106 is configured to obtain information from a remote database / cloud database 109 or receive information based on user input via the HMI 111 when the user interacts with the HMI 111. According to an example embodiment, information associated with vehicle parameters may be input to the HMI by the user as user input. According to an example scenario, the vehicle parameters may be received directly via the user input or estimated based on the user input. For example, if the user provides user input as a vehicle type, the vehicle parameters may be estimated based on the vehicle type. For example, considering that the user has provided input that the vehicle type is a truck, the vehicle parameters may be estimated based on a standard truck configuration.

[0067] According to an embodiment, the server 113 is configured to receive this information.Without departing from the scope of the invention, throughout the disclosure, the aforementioned information may alternatively be referred to as second information.

[0068] In a non-limiting example, the second information associated with the vehicle parameters is related to the parameters set by the user of the vehicle before the specific transport journey. In addition, the vehicle parameters are related to one or more specific moments during the specific transport journey or after the specific transport is completed.

[0069] In a non-limiting example, the second information associated with the vehicle parameters includes the number of times the door 105 is opened and closed by the user, the duration of time the door 105 is held open by the user, and potential routes taken by the user to complete one or more shipments in a previous transportation trip, etc. In an example scenario, the ECU 106 can provide the server 113 with potential routes driven by the vehicle 100 for a particular transportation trip. In addition, the number of times the door 105 is opened and closed by the user before, during, and after the completion of the particular transportation trip.

[0070] In another non-limiting example, the second information associated with the vehicle parameters includes information about one or more shipments planned for a particular transport trip. The information about the one or more shipments includes information about the planned route to be taken for the particular transport trip, information about the stops to be taken for the particular transport trip, information about the duration of the stops to be taken for the particular transport trip, and information about the loading pattern of perishable items for the particular transport trip. In addition, the user can provide various data about the one or more shipments planned for the particular transport trip. The data may include the time and date of the shipment.

[0071] In another non-limiting example, the second information associated with the vehicle parameters includes information about the vehicle condition, such as the vehicle configuration before a particular transport trip, at one or more specific instances during a particular transport trip, or after completion of a particular transport trip, the volume of the refrigerated compartment, the current condition of the interior walls of the refrigerated compartment, the presence of debris inside the refrigerated compartment, the current mode of the TRU 101 controlling the refrigerated compartment, and the like.

[0072] In another non-limiting example, the second information associated with vehicle parameters includes information related to vehicle geometry, such as the size of the vehicle, the weight of the vehicle, and the like.

[0073] According to some embodiments, based on user input or image data of the perishable items, the ECU 106 may be configured to receive second information related to food parameters associated with the initial food quality of the perishable items. According to an example embodiment, image data of the perishable items loaded in the vehicle may be captured before the transportation journey. Therefore, the captured images may be analyzed to obtain the initial food quality of the perishable items before shipment. As an example, the food parameters may include information related to different attributes of the perishable items. The different attributes of the perishable items may include, but are not limited to, the hardness of the perishable items, the sugar content of the perishable items, the degree of damage caused by cooling, the freshness of the perishable items, the degree of deviation in the color and size of the perishable items, etc.

[0074] According to an embodiment, the digital representation of the refrigeration compartment 103 is used to simulate the internal environment of the refrigeration compartment 103. The digital twin model is used to simulate the internal environment of the refrigeration compartment 103. According to an embodiment, the digital twin model is a physics-based model that can predict the condition of perishable products anywhere in a closed storage area and characterize the variability of transportation parameters, food parameters and initial storage conditions throughout the shipment. The digital twin model is applied as a virtual representation of temperature-sensitive products. According to an embodiment, the digital twin model is implemented in the server 113 to predict the condition of perishable products and simulate the internal environment of the refrigeration compartment 103. The digital twin simulation of the refrigeration compartment 103 is about the equipment configuration, loading configuration, standard operating procedure (SOP) and tripping events to evaluate performance and potential problems, enabling optimization of loading configuration and unit operation to maximize quality / reduce losses. The detailed working of the invention will be explained in the following paragraphs.

[0075] Figure 3 A flow chart depicting a method 300 for assessing the condition of perishable items inside a refrigerated compartment according to an embodiment of the present disclosure is illustrated. According to an embodiment, the method 300 is implemented in the server 113. In addition, it should be understood that the method 300 is implemented using a processor 201 and a memory 202 in communication with one or more components of the vehicle 100 and the cloud database 109 according to the present disclosure.

[0076] At step 301 of method 300, processor 201 of server 113 is configured to obtain first information including real-time temperature data and sensor data from TRU 101. Examples of the first information are explained in detail above, and therefore, for the sake of brevity, the same content has been omitted here. Thereafter, at step 303 of method 300, processor 201 of server 113 is configured to receive second information based on one of user input or acquisition from a remote database, wherein the second information includes information associated with vehicle parameters, such as vehicle type, load conditions of one or more shipments planned for a particular transportation trip. Examples of the second information are explained in detail above, and therefore, for the sake of brevity, the same content has been omitted here.

[0077] After receiving the first information and the second information, at step 305 of the method 300, the processor 201 of the server 113 is configured to perform a simulation process based on the received first information and the received second information to simulate at least one refrigerated compartment 103 by using a digital twin model. The simulated digital twin model characterizes the initial storage condition of perishable items and the variability of the vehicle parameters during a specific transport journey. The various parameters included in the vehicle parameters are explained in detail above, and therefore, for the sake of brevity, the same content has been omitted here. The storage condition may include, but is not limited to, various storage configurations, such as the arrangement of perishable items, the temperature at which the perishable items are stored, the initial quality condition of the perishable items, the current quality condition of the perishable items at any instance during a specific transport journey, etc.

[0078] According to some embodiments, the simulated digital twin model characterizes the variability of food parameters of perishable items during a specific transportation trip.

[0079] According to an embodiment, the simulation process comprises generating a virtual configuration of the at least one refrigeration compartment based on the first information and the second information by using a digital twin model.

[0080] Figure 4A An example of a simulated digital twin model according to a disclosed embodiment is illustrated. FIG4 shows a simulated refrigerated compartment in blocks 401 and 405. According to an embodiment, the simulated refrigerated compartment in blocks 401 and 405 depicts standard operating procedures (SOPs) associated with the arrangement of perishable items in a virtual configuration of the refrigerated compartment, a virtual configuration of the refrigerated compartment, and a virtual loading configuration of perishable items in the refrigerated compartment during an event of a specific transportation trip of a vehicle.

[0081] In a non-limiting example, the virtual configuration of the refrigerated compartment 103 may depict a virtual configuration or virtual modeling of interior walls, chutes, perishable items, etc. In yet another non-limiting example, the virtual loading configuration of the perishable items in the refrigerated compartment depicts a virtual configuration or virtual modeling of the arrangement of the perishable items. Figure 4B Various examples of simulated virtual configurations of refrigerated compartments are shown in FIG. Boxes 406, 407, and 408 illustrate a virtual configuration of the inside of the refrigerated compartment, a virtual configuration of the outside of the refrigerated compartment, and a virtual loading configuration of perishable items in the refrigerated compartment, respectively. This helps determine how perishable items are placed inside the refrigerated container. In yet another non-limiting example, the virtual configuration or virtual modeling can depict one or more standard operating procedures (SOPs) associated with the placement of perishable items. Return to Reference Figure 4B, block 408 illustrates a virtual configuration of SOPs associated with the placement of perishable items. As an example, SOPs may relate to, but are not limited to, pre-cooling, packaging, palletizing, loading of perishable items. SOPs may relate to, but are not limited to, the condition and maintenance of the equipment of the vehicle, the temperature maintained during transportation, etc.

[0082] Therefore, if Figure 4A and Figure 4B It can be seen that the simulated refrigerated compartments in blocks 401 to 408 depict variability in storage scenarios with respect to vehicle parameters and perishable items. In a non-limiting example, Figure 4B Various pallet configurations in a simulated refrigeration compartment are depicted in box 408. The results of the simulation further depict a highly permeable pallet, a very permeable pallet, a permeable pallet, an impermeable pallet.

[0083] Figure 5 Another example of virtual modeling of a chute configuration according to the disclosed embodiments is described. In a non-limiting example, Figure 5 5 , the virtual modeling of air chute 501, spacer 503 and rivet 505 is depicted. Based on the virtual modeling of, for example, the chute configuration, the vehicle condition during transportation can be evaluated. Thus, the condition of perishable items can be determined.

[0084] According to an embodiment, the simulation process is executed in response to a user input or a triggering event at any (one or more) specific moments of a specific transport journey of perishable items. For example, a user can manually provide a command to start the simulation process so that he / she can view the virtual configuration of (one or more) perishable items at any specific moment of the specific transport journey, or before the specific transport journey, or after the specific transport journey. In addition, for example, a user can preset the simulation process to trigger the simulation process to be executed at regular time intervals during the specific transport journey, before the specific transport journey, or after the specific transport journey. Therefore, the simulation process is executed before the specific transport journey, at one or more specific moments during the specific transport journey, and after the specific transport journey is completed.

[0085] Return to reference Figure 3 At step 307 of method 300 , the processor 201 of the server 113 is configured to periodically evaluate the current quality status of the perishable items based on the results of the simulation process and a plurality of predetermined evaluation parameters.

[0086] As an example, the evaluation of the quality condition may be related to different attributes of the perishable item relative to the initial storage condition. The different attributes of the perishable item may include, but are not limited to, the hardness of the perishable item, the sugar content of the perishable item, the degree of damage due to cooling, the freshness of the perishable item, the size of the perishable item, the degree of color deviation, etc. The different attributes of the perishable item may be evaluated relative to the attributes during the initial storage condition.

[0087] As an example, the predetermined evaluation parameters include parameters associated with the quality of perishables, the efficiency of transportation vehicles, and the costs involved in the transportation of perishables. According to an embodiment, in order to evaluate the condition of perishables and how various factors affect the shipment performance in terms of product quality / safety / efficiency / cost attributes, the results of the simulation process are evaluated based on the predetermined evaluation parameters. As an example, the predetermined evaluation parameters can be set by the user so that the quality / safety / efficiency / cost attributes are evaluated on this basis. Therefore, the evaluation of the simulation process helps to optimize each transportation trip.

[0088] According to some embodiments, the processor 201 of the server 113 is configured to identify a geographic location of the vehicle or a specific area within the refrigerated compartment where the value of the periodically evaluated current condition of the perishable items is below a threshold.

[0089] According to an embodiment, for the evaluation, the processor 201 of the server 113 is configured to calculate a plurality of quality metrics of the perishable items using the digital twin model based on the received first information and the second information. In a non-limiting example, the plurality of quality metrics correspond to temperature values, weight loss, mass loss, shelf life loss, loss of usable post-harvest life of the perishable items at different time intervals throughout the transportation for each of the perishable items in the refrigerated compartment, the current state of the perishable items relative to the initial state, the risk of quality degradation of the perishable items due to the temperature inside the refrigerated compartment, and the risk of pathogen growth on the surface of the perishable items due to the temperature inside the refrigerated compartment.

[0090] In another non-limiting example, the status of the perishable item is determined based on multiple measurement parameters related to abnormal temperature inside the refrigerated compartment, condensation inside the refrigerated compartment, moisture loss in the perishable item, and pathogen growth on the surface of the perishable item.

[0091] In addition, at step 309 of method 300, processor 201 of server 113 is configured to compare the result of the evaluation with a threshold value associated with the current quality status of the perishable item. Thus, the current quality status of the perishable item is evaluated based on the calculated multiple quality metrics.

[0092] After the calculation of the plurality of quality metrics, the processor 201 of the server 113 is configured to provide a performance score for the virtual configuration of the at least one refrigeration compartment 103 based on the predetermined evaluation parameters and the calculation of the plurality of quality metrics.

[0093] At step 311 of method 300, processor 201 of server 113 is configured to predict the impact on the current quality condition of the perishable item based on the comparison result. As an example, the prediction of the impact on the condition of the perishable item is based on the provided performance score.

[0094] At step 313 of method 300, the processor 201 of the server 113 is configured to provide recommendations via a user interface based on the predicted impact on the current condition of the perishable items. As an example, the recommendations may be provided for a quick check by the user, or an alert may be generated to notify of a degraded condition of the perishable items. Additionally, recommendations may be provided regarding changing the configuration / arrangement of the perishable items or changing the temperature value of the refrigerated compartment 103, etc. In non-limiting examples, the user interface may include a cloud-based user interface or a human-machine interface (HMI).

[0095] According to an example embodiment, a recommendation of perishable items may be provided at the identified geographic location based on the evaluation at step 307. Thus, a notification is disclosed to be provided at the identified geographic location or a specific area within the refrigerated compartment 103 where perishable products may have a lower quality metric. Thus, the notification ensures that the user's immediate attention is required.

[0096] Thus, the disclosure allows users to automatically assess the condition of temperature-sensitive products in a shipment across multiple quality attributes. Performing such assessments in a fast, efficient, and targeted manner reduces costs by reducing manual inspections when performed and making those inspections more meaningful and efficient. Additionally, the use of digital twins allows for comprehensive virtual inspections to be performed to create a digital fingerprint that characterizes the variability of a shipment's condition.

[0097] In addition, the disclosure provides improved quality / safety / cost performance for individual shipments and overall refrigerated transport performance. The disclosure also provides temperature information on perishable items and provides insights into various factors that affect shipment performance in terms of product quality attributes / safety / efficiency / cost, thereby optimizing shipment performance. Therefore, users should receive performance predictions before travel, in transit, and / or post-shipment recommendations.

[0098] Although shown and described in a particular order Figure 3 The above steps are described, but according to various disclosed embodiments, the steps may occur in variations of the described order.

[0099] Figure 6An exemplary hardware configuration of the ECU is illustrated. ECU 600 is similar in functionality to the functionality of ECU 106 of FIG. 1 .

[0100] The ECU 600 includes a control unit (CPU 601) that controls various system components, including a memory 603, a read-only memory (ROM) 605, and a random access memory (RAM) 607. The ECU 600 further includes one or more sensors 609 configured to measure the temperature inside the vehicle 100, the pressure of the tires of the vehicle 100, the speed of the vehicle 100, etc. The one or more sensors 609 may include, but are not limited to, a temperature sensor, an acceleration sensor, a motion sensor, a gyroscope sensor, an oxygen sensor, an air index measurement sensor, an intake temperature sensor, an engine coolant temperature sensor, etc.

[0101] ECU 600 may also include a cache of high-speed memory that is directly connected to, adjacent to, or integrated as part of CPU 601. CPU 601 copies data from memory 603 to the cache for fast access. CPU 601 may essentially be a completely independent computing system, including multiple cores or processors, buses, memory controllers, caches, etc. (such as a system on a chip). Multi-core processors may be symmetric or asymmetric.

[0102] ECU 600 may also include a system bus, which may be any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. ECU 600 may further include a storage device as memory 603, such as a hard disk drive, a disk drive, an optical drive, a tape drive, a solid-state drive, etc. The storage device may include a software module that can be controlled and executed by CPU 601. The storage device may be connected to ECU 600 via a drive interface. The drive and associated computer-readable storage medium provide non-volatile storage of computer-readable instructions, data structures, program modules, and other data for ECU 600. In one aspect, the hardware module that performs a specific function includes a software component stored in a tangible computer-readable storage medium, which is combined with necessary hardware components (such as CPU 601, input device 611, output device 613, etc.) to perform the operation of the vehicle 100. Depending on the type of vehicle, basic components and appropriate changes are envisioned.

[0103] The ECU 600 may also include a communication interface 615. The communication interface 615 may be connected to a network such as a wireless network. The wireless network may be a cellular telephone network, IEEE 802.11, 802.16, 802.20, 802.1Q, or Worldwide Interoperability for Microwave Access. Network. Additionally, the network may be a public network such as the Internet, a private network such as an intranet, or a combination thereof, and may utilize various networking protocols now available or later developed, including but not necessarily limited to TCP / IP-based network protocols.

[0104] The communication interface 615 may include a transceiver further including a transmitter and a receiver for receiving data from or transmitting data to a cloud network used in V2V communications.

[0105] Although the embodiments discussed above relate to evaluating the condition of perishable goods placed in (one or more) refrigerated containers of a vehicle, it will be apparent to those skilled in the art that the embodiments should not be limited to only evaluating the condition of perishable goods placed in (one or more) refrigerated containers of a vehicle without departing from the scope of the present disclosure.

[0106] Although specific language has been used to describe the subject matter, it is not intended that any limitation will be generated thereby. As will be apparent to those skilled in the art, various work modifications may be made to the method to implement the inventive concept as taught herein. The accompanying drawings and the foregoing description provide examples of embodiments. Those skilled in the art will appreciate that one or more of the elements described may be well combined into a single functional element. Alternatively, some elements may be segmented into multiple functional elements. Elements from one embodiment may be added to another embodiment.

Claims

1. A method for assessing the condition of perishable items within a refrigerated compartment of a vehicle, the method comprising: obtaining first information including real-time temperature data and sensor data; receiving, based on one of user input or acquisition from a remote database, second information associated with vehicle parameters including vehicle type and load conditions for one or more shipments planned for a particular transportation trip; Based on the received first information and the received second information, performing a simulation process to simulate at least one refrigerated compartment by using a digital twin model that characterizes an initial storage condition of the perishable items and a variability of the vehicle parameters during the specific transportation trip; periodically evaluating the current quality status of the perishable item during the specific transportation trip based on the results of the simulation process and a plurality of predetermined evaluation parameters; comparing the result of the periodic evaluation to a threshold value associated with the current quality condition of the perishable item; predicting the impact on the current quality status of the perishable item based on the comparison result; as well as A recommendation is provided via a user interface based on the predicted impact on the current quality condition of the perishable item.

2. The method according to claim 1, wherein: Executing the simulation process includes: using the digital twin model, generating a virtual configuration of the at least one refrigeration compartment based on the first information and the second information, Wherein the at least one simulated refrigerated compartment depicts at least standard operating procedures (SOPs) associated with the arrangement of the perishable items in the virtual configuration of the refrigerated compartment, the virtual configuration of the refrigerated compartment, and a virtual loading configuration of the perishable items in the refrigerated compartment during an event of the specific transportation trip of the vehicle.

3. The method according to claim 2, further comprising: using the digital twin model, calculating a plurality of quality metrics for the perishable item based on the received first information and the second information, wherein the plurality of quality metrics correspond to temperature values, weight loss, mass loss, shelf life loss, loss of useful post-harvest life of the perishable items, a current state of the perishable items relative to an initial state, a risk of quality degradation of the perishable items due to the temperature inside the refrigerated compartment, and a risk of pathogen growth on a surface of the perishable items due to the temperature inside the refrigerated compartment for each of the perishable items in the refrigerated compartment at different time intervals throughout the transport, wherein the state of the perishable item is determined based on a plurality of measured parameters related to abnormal temperature inside the refrigerated compartment, condensation inside the refrigerated compartment, moisture loss in the perishable item, and growth of the pathogen on the surface of the perishable item, and The current quality status of the perishable item is evaluated based on the calculated plurality of quality metrics.

4. The method according to claim 3, further comprising: providing a performance score to said virtual configuration of said at least one refrigeration compartment based on said predetermined evaluation parameters and said calculation of said plurality of quality metrics, Wherein the prediction of the impact on the current quality condition of the perishable item is based on the provided performance score.

5. The method of claim 1, further comprising executing the simulation process in response to the user input or triggering event at one or more specific times during the specific transportation of the perishable item.

6. The method according to claim 1, wherein: The simulation process is performed before the specific transport trip, at one or more specific times during the specific transport trip, and after the specific transport trip is completed.

7. The method according to claim 1, wherein: the real-time sensor data comprises data related to air quality inside the at least one refrigerated compartment, gas presence inside the at least one refrigerated compartment, and geographic location data of the vehicle, wherein the real-time temperature data comprises an internal air temperature value of the at least one refrigerated compartment, The vehicle parameters related to the load condition include data related to the arrangement pattern of the perishable items, the packaging of the perishable items, the quantity / volume of the perishable items loaded inside the vehicle, and the loading pattern of the perishable items.

8. The method according to claim 1, further comprising: identifying a geographic location of the vehicle or a specific area within the refrigerated compartment where a value of the periodically evaluated current quality condition of the perishable item is below the threshold; as well as Recommendations associated with inspection of the perishable item at the identified geographic location are provided via the user interface.

9. The method according to claim 1, wherein: The transport parameters include parameters set by a user of the transport before the specific transport trip, at one or more specific times during the specific transport trip, or after the specific transport trip is completed.

10. The method according to claim 1, wherein: The second information associated with vehicle parameters includes information about one or more shipments planned for the particular transportation trip, including information related to a planned route to be taken for the particular transportation trip, information related to stops to be taken for the particular transportation trip, information related to durations of stops to be taken for the particular transportation trip, the number of times a door is opened and closed by the user, the duration that the door is kept open by the user, and potential routes taken by the user to complete the one or more shipments in previous transportation trips.

11. The method according to claim 1, wherein: the second information associated with a vehicle parameter includes a vehicle condition, the vehicle condition relating to information associated with at least one of a vehicle configuration prior to the particular transport trip, the vehicle configuration at one or more particular times during the particular transport trip, the vehicle configuration after completion of the particular transport trip, a volume of the refrigerated compartment, a vehicle geometry, a current condition of an interior wall of the refrigerated compartment, a presence of debris inside the refrigerated compartment, and a current mode of a transport refrigeration unit (TRU) controlling the refrigerated compartment, The vehicle geometry includes data related to the size of the vehicle and the weight of the vehicle.

12. The method according to claim 1, wherein: The predetermined evaluation parameters include parameters associated with the quality of the perishable item, the efficiency of the transportation vehicle, and the costs involved in the transportation of the perishable item.

13. The method according to claim 1, further comprising: receiving, based on the user input or image data of the perishable item, the second information related to a food parameter associated with an initial food quality of the perishable item; as well as Based on the received first information and the received second information, the simulation process is performed to simulate at least one refrigerated compartment by using the digital twin model, the digital twin model characterizing the variability of the food parameters of the perishable items during the specific transportation trip.

14. A system for assessing the condition of a perishable item, the system comprising: a server comprising at least one processor coupled to at least a memory, a remote database, and a cloud database, wherein the server is further wirelessly coupled to a vehicle comprising a transport refrigeration unit (TRU) and a human machine interface (HMI), wherein the at least one processor is configured to: obtaining first information including real-time temperature data and sensor data; receiving, based on one of user input or acquisition from a remote database, second information associated with vehicle parameters including vehicle type and load conditions for one or more shipments planned for a particular transportation trip; Based on the received first information and the received second information, performing a simulation process to simulate at least one refrigerated compartment by using a digital twin model that characterizes an initial storage condition of the perishable items and a variability of the vehicle parameters during the specific transportation trip; periodically evaluating the current quality status of the perishable item during the specific transportation trip based on the results of the simulation process and a plurality of predetermined evaluation parameters; comparing the result of the periodic evaluation to a threshold value associated with the current quality condition of the perishable item; predicting the impact on the current quality status of the perishable item based on the comparison result; as well as A recommendation is provided via a user interface based on the predicted impact on the current quality condition of the perishable item.

15. The system of claim 14, wherein: The at least one processor is further configured to: using the digital twin model, generating a virtual configuration of the at least one refrigeration compartment based on the first information and the second information, Wherein the at least one simulated refrigerated compartment depicts at least standard operating procedures (SOPs) associated with the arrangement of the perishable items in the virtual configuration of the refrigerated compartment, the virtual configuration of the refrigerated compartment, and a virtual loading configuration of the perishable items in the refrigerated compartment during an event of the specific transportation trip of the vehicle.

16. The system of claim 15, wherein: The at least one processor is further configured to: using the digital twin model, calculating a plurality of quality metrics for the perishable item based on the received first information and the second information, wherein the plurality of quality metrics correspond to temperature values, weight loss, mass loss, shelf life loss, loss of useful post-harvest life of the perishable items, a current state of the perishable items relative to an initial state, a risk of quality degradation of the perishable items due to the temperature inside the refrigerated compartment, and a risk of pathogen growth on a surface of the perishable items due to the temperature inside the refrigerated compartment for each of the perishable items in the refrigerated compartment at different time intervals throughout the transport, wherein the state of the perishable item is determined based on a plurality of measured parameters related to abnormal temperature inside the refrigerated compartment, condensation inside the refrigerated compartment, moisture loss in the perishable item, and growth of the pathogen on the surface of the perishable item, and The current quality status of the perishable item is evaluated based on the calculated plurality of quality metrics.

17. The system of claim 16, wherein: The at least one processor is further configured to provide a performance score for the virtual configuration of the at least one refrigeration compartment based on the predetermined evaluation parameter and the calculation of the plurality of quality metrics, Wherein the prediction of the impact on the current quality condition of the perishable item is based on the provided performance score.

18. The system of claim 14, wherein: the real-time sensor data comprises data related to air quality inside the at least one refrigerated compartment, gas presence inside the at least one refrigerated compartment, and geographic location data of the vehicle, wherein the real-time temperature data comprises an internal air temperature value of the at least one refrigerated compartment, The vehicle parameters related to the load condition include data related to the arrangement pattern of the perishable items, the packaging of the perishable items, the quantity / volume of the perishable items loaded inside the vehicle, and the loading pattern of the perishable items.

19. The system of claim 14, wherein: The at least one processor is further configured to: identifying a geographic location of the vehicle or a specific area within the refrigerated compartment where a value of the periodically evaluated current quality condition of the perishable item is below the threshold; as well as Recommendations associated with inspection of the perishable item at the identified geographic location are provided via the user interface.

20. The system of claim 14, wherein: The at least one processor is further configured to: receiving, based on the user input or image data of the perishable item, the second information related to a food parameter associated with an initial food quality of the perishable item; as well as Based on the received first information and the received second information, the simulation process is performed to simulate at least one refrigerated compartment by using the digital twin model, the digital twin model characterizing the variability of the food parameters of the perishable items during the specific transportation trip.

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