A remote intelligent management system for cold chain transportation

Abstract

The present invention discloses a remote intelligent management system for cold chain transportation, which relates to the technical field of cold chain transportation management systems. It includes a monitoring unit, and the monitoring unit includes a temperature monitoring module and an image acquisition module. The temperature monitoring module is used to collect temperature data of the ventilation path of the stacked items inside the cold chain transportation carriage in real time, and the image acquisition module is used to collect images of the stacked items inside the cold chain transportation carriage in real time; a temperature response analysis unit, which is used to retrieve the temperature data collected by the temperature monitoring module and perform correlation analysis and calculation on the obtained temperature data to obtain a temperature response change coefficient. The present invention realizes the integrated evaluation of the attenuation change of the ventilation effect of the ventilation path between the stacked items from two perspectives: the temperature response change of the ventilation path and the path offset change, so as to avoid the reduction of the ventilation path between the goods due to movement during cold chain transportation, which reduces the overall ventilation effect and affects the freshness preservation effect of the goods.

Description

Technical Field

[0001] The present invention relates to the technical field of cold chain transportation management systems, and specifically to a remote intelligent management system for cold chain transportation. Background Art

[0002] As is well known, cold chain transportation refers to a transportation method that maintains a certain temperature condition throughout the transportation process to ensure the quality, freshness, and safety of goods. Cold chain transportation management refers to a series of activities for the whole-process temperature control, quality monitoring, and safety management of products (such as food, medicine, etc.) transported in a specific low-temperature environment. The purpose of cold chain transportation is to ensure that the product maintains its original quality and safety during transportation and prevent the quality of the item from deteriorating due to temperature fluctuations or unevenness.

[0003] For example, the authorized publication number is CN116502980B, the authorized publication date is September 8, 2023, and the name is "A Management System for Cold Chain Transportation". It includes a drug management unit, which includes a production management module for controlling the production rate of the factory and a drug warehousing management module for storing drugs; a transportation equipment supervision unit, which includes a time monitoring module for controlling the time limit for loading and unloading goods of the transportation equipment, a temperature monitoring module for recording and adjusting the temperature inside the transportation equipment, and a positioning module for recording the position and speed of the transportation equipment; a drug transportation management unit, which includes a transportation process management module for calculating the evaluation index of the cold chain transportation monitoring intensity, a backup module for storing management plans, an administrator client for manually intervening in the cold chain transportation process and determining the range of the cold chain transportation monitoring intensity evaluation index according to the transportation method selected by the drug merchant.

[0004] When cold-chain transporting items, the items need to be stacked in the cold-chain carriage with a certain stacking interval to ensure the free flow of cold air between the goods, so that the temperature inside the stacked items is evenly maintained. Similar to the above application, although the prior art collects the temperature of the carriage through a temperature sensor, it cannot comprehensively monitor and analyze the temperature inside the stacked items. During cold-chain transportation, due to the influence of bumps, braking, or acceleration inertia, the positions of some goods may move, resulting in a smaller ventilation path between the goods, making it difficult for the cold air in the carriage to flow freely between the goods, thereby affecting the overall ventilation effect. At this time, if it is not detected and warned in time, it will affect the preservation effect of the goods and reduce the quality of the goods, resulting in the deterioration of the goods. Summary of the Invention

[0005] The purpose of the present invention is to provide a remote intelligent management system for cold chain transportation to solve the above-mentioned deficiencies in the prior art.

[0006] To achieve the above purpose, the present invention provides the following technical solution: A remote intelligent management system for cold chain transportation, comprising:

[0007] A monitoring unit, the monitoring unit includes a temperature monitoring module and an image acquisition module. The temperature monitoring module is used to collect the temperature data of the ventilation path of the stacked items inside the cold chain transportation carriage in real time, and the image acquisition module is used to collect the images of the stacked items inside the cold chain transportation carriage in real time;

[0008] A temperature response analysis unit, which is used to retrieve the temperature data collected by the temperature monitoring module and perform correlation analysis and calculation on the obtained temperature data to obtain the temperature response change coefficient K T ;

[0009] A ventilation interference analysis unit, which is used to retrieve the images of the items inside the cold chain transportation carriage collected by the image detection module and integrate and analyze the offset interference coefficient K of the stacked items inside the carriage relative to the ventilation path based on the item images L ;

[0010] A comprehensive analysis platform, which correlates the temperature response change coefficient and the offset interference coefficient to comprehensively evaluate the overall ventilation effect attenuation coefficient of the current state of the stacked items inside the cold chain operation carriage, sets a ventilation effect attenuation threshold, and compares and warns and analyzes the ventilation effect attenuation coefficient with the ventilation effect attenuation threshold.

[0011] As a further description of the above technical solution: The specific operation of the temperature monitoring module for collecting the temperature data of the ventilation path of the stacked items inside the cold chain transportation carriage in real time is as follows:

[0012] The temperature monitoring module includes a temperature acquisition module. Each temperature acquisition module is arranged in each ventilation path of the goods stacked inside the cold chain transportation carriage, and each temperature acquisition module is used to collect the temperature data of the corresponding detection area in real time.

[0013] As a further description of the above technical solution: The specific operation of performing correlation analysis and calculation on the obtained temperature data to obtain the temperature response change coefficient is as follows:

[0014] Calculate the temperature response coefficient of each ventilation path at the initial regulated temperature, marked as Se m ;

[0015] Calculate the temperature response coefficient of each ventilation path at the current regulated temperature, marked as Sc m ;

[0016] Integrate the temperature response coefficients of each ventilation path at the initial regulated temperature and compare them with the temperature response coefficients of each ventilation path at the current regulated temperature to calculate the temperature response change coefficient K T ;

[0017] The temperature response change coefficient K T The calculation formula is:

[0018]

[0019] Among them, Se m represents the temperature response coefficient of the initial m-th ventilation path, and Sc m represents the temperature response coefficient of the m-th ventilation path when the current regulated temperature is applied;

[0020] The temperature response coefficients of each ventilation path when the initial regulated temperature is applied and the temperature response coefficients of each ventilation path when the current regulated temperature is applied are calculated on the same basis.

[0021] As a further description of the above technical solution: The specific calculation method of the temperature response coefficient is as follows:

[0022] Collect the current temperature data of each ventilation path and the corresponding time data when the temperature is regulated;

[0023] Preset a temperature value, and collect the time data when each ventilation path reaches the preset temperature value when the temperature is regulated;

[0024] Integrate the data collected in the two temperature acquisitions, and divide the temperature change difference by the corresponding temperature regulation time of each ventilation path to obtain the temperature response coefficient of each ventilation path.

[0025] As a further description of the above technical solution: Retrieve the images of the items inside the cold chain transportation vehicle collected by the image acquisition module, and the specific method for integrating and analyzing the offset interference coefficient of the stacked items inside the vehicle relative to the ventilation path based on the item images is as follows:

[0026] Retrieve the image of the items inside the cold chain transportation vehicle collected by the image acquisition module at the initial time, mark it as the first item image, identify and calculate the center points of each cargo on the first item image, calculate the distance between the two cargo center points relative to the center line trajectory of the ventilation path, and record the standard spacing Le i ;

[0027] Retrieve the image of the items inside the cold chain transportation vehicle collected by the image acquisition module when the current regulated temperature is applied, mark it as the second item image, identify and calculate the center points of each cargo on the second item image, calculate the distance between the two cargo center points relative to the center line trajectory of the ventilation path, record it as the detection spacing, and integrate to obtain the detection spacing data set U i , where U i ∈(L1, L2, L3... L i );

[0028] Associate the standard spacing and the detection spacing to calculate the offset interference coefficient K L :

[0029]

[0030] Among them, Le iIt represents the distance between the centers of two goods of the i-th group on the first article image with respect to the trajectory of the ventilation path center line, L i It represents the distance between the centers of two goods of the i-th group on the second article image with respect to the trajectory of the ventilation path center line, and d represents the distance from the center of the goods to the edge of the goods.

[0031] As a further description of the above technical solution: Identifying and calculating the center points of each good on the article image and calculating the distance between the centers of two goods with respect to the trajectory of the ventilation path center line specifically are as follows:

[0032] Establish a plane coordinate system on the article image, perform feature recognition on the article image to determine the center points of each good on the article image and mark them, and obtain the coordinate data of each center point of the goods;

[0033] Mark the trajectory of the ventilation path center line on the article image, identify the center points of each good on the article image, associate and integrate the two center points symmetric with respect to the center line trajectory, and calculate the distance between the centers of two goods with respect to the trajectory of the ventilation path center line.

[0034] As a further description of the above technical solution: Associating the temperature response change coefficient K T And the offset interference coefficient K L Integrating and evaluating the ventilation effect attenuation coefficient K of the current state of the stacked articles inside the cold chain operation carriage s The specific calculation method is:

[0035] Where α1 and α2 are preset weight coefficients.

[0036] As a further description of the above technical solution: Setting the ventilation effect attenuation threshold and comparing and warning and analyzing the ventilation effect attenuation coefficient with the ventilation effect attenuation threshold specifically are as follows:

[0037] Set the ventilation effect attenuation threshold and record it as K e ;

[0038] When the ventilation effect attenuation coefficient K s ≥K e , the comprehensive analysis platform generates a warning prompt and synchronously displays the current ventilation effect attenuation coefficient;

[0039] When the ventilation effect attenuation coefficient K s <K e , the comprehensive analysis platform only displays the current ventilation effect attenuation coefficient.

[0040] As a further description of the above technical solution: It also includes a dead angle analysis unit;

[0041] The dead angle analysis unit is used to retrieve the detection spacing data set Ui ;

[0042] Analyze each detection spacing in the detection spacing dataset U i , retrieve the central point coordinates of two associated items with a detection spacing equal to 2d, and record them as A(X a , Y a ), B(X b , Y b );

[0043] Calculate the blockage point coordinates based on the central point coordinates of the two items, and based on the blockage point coordinates, conduct an associated analysis of the dead zone area information in each ventilation path of the goods stacked inside the cold chain transport carriage.

[0044] As a further description of the above technical solution: Recording the blockage point coordinates based on the central point coordinates of the two items, and the specific content of the associated analysis of the dead zone area information in each ventilation path of the goods stacked inside the cold chain transport carriage based on the blockage point coordinates is as follows:

[0045] Calculate the blockage point coordinates D(X d , Y d ), where the abscissa X d , and the ordinate Y d of the blockage point coordinates D(X d , Y d ) are calculated as follows:

[0046]

[0047] Mark the center line trajectories of each ventilation path on the item image, and mark each blockage point on the center line trajectory of the ventilation path based on the blockage point coordinate data;

[0048] Connect adjacent blockage points based on the center line trajectory of the ventilation path, and mark the line segment between adjacent blockage points as a restricted area;

[0049] Identify and associate the restricted area on the item image with the center line of the ventilation path. When there is only one center line of the ventilation path in the restricted area, mark the restricted area as a dead zone area, and obtain the position data of the dead zone area;

[0050] Transmit the position data of the dead zone area to the comprehensive analysis platform, and the comprehensive analysis platform generates a warning prompt and synchronously displays the position data of the dead zone area.

[0051] In the above technical solution, a remote intelligent management system for cold chain transportation provided by the present invention has the following beneficial effects:

[0052] The cold chain transportation remote intelligent management system collects the temperature data information of the ventilation paths of the stacked items inside the cold chain transportation carriage and the images of the stacked items, and based on the obtained temperature data, performs correlation analysis and calculation to obtain the temperature response change coefficient and the offset interference coefficient of the stacked items inside the carriage relative to the ventilation paths, and integrates and analyzes to comprehensively evaluate the ventilation effect attenuation coefficient of the ventilation paths between the stacked items inside the cold chain transportation carriage. When the ventilation effect attenuation coefficient exceeds the attenuation threshold, a warning is issued to avoid the situation that during cold chain transportation, due to the influence of bumps, braking or acceleration inertia, the positions of some goods move, resulting in a smaller ventilation path between the goods, thereby reducing the overall ventilation effect and affecting the freshness preservation effect of the goods. At the same time, it realizes the identification and analysis of fixed dead corner areas, avoids the occurrence of ventilation dead corner areas leading to the deterioration of goods, and is convenient for the staff to determine the position of the dead corner area and quickly adjust the stacked goods in the carriage. Brief Description of the Drawings

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0054] Figure 1 Schematic diagram of a cold chain transportation remote intelligent management system provided by an embodiment of the present invention. Detailed Embodiments

[0055] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the following will further introduce the present invention in detail in conjunction with the drawings.

[0056] Embodiment 1:

[0057] Please refer to Figure 1 , an embodiment of the present invention provides a technical solution: a cold chain transportation remote intelligent management system, including:

[0058] A monitoring unit, the monitoring unit includes a temperature monitoring module and an image acquisition module. The temperature monitoring module is used to collect the temperature data of the ventilation paths of the stacked items inside the cold chain transportation carriage in real time. Specifically, the temperature monitoring module includes a temperature acquisition module, and each temperature acquisition module is arranged in each ventilation path of the stacked goods inside the cold chain transportation carriage. Each temperature acquisition module is used to collect the temperature data of the corresponding detection area in real time. The image acquisition module is used to collect the images of the stacked items inside the cold chain transportation carriage in real time; the monitoring unit is communicatively connected to the temperature response analysis unit and the ventilation interference analysis unit, and the detection unit is used to transmit the detected and collected data information to the temperature response analysis unit and the ventilation interference analysis unit respectively;

[0059] A temperature response analysis unit, which is used to retrieve the temperature data collected by the temperature monitoring module, and perform correlation analysis and calculation on the obtained temperature data to obtain the temperature response change coefficient K T ;

[0060] A ventilation interference analysis unit, which is used to retrieve the images of the items inside the cold chain transportation carriage collected by the image detection module, and based on the item images, perform integrated analysis to obtain the offset interference coefficient K of the stacked items inside the carriage relative to the ventilation path L ;

[0061] A comprehensive analysis platform, the input end of the comprehensive analysis platform is communicatively connected to the output ends of the temperature response analysis unit and the ventilation interference analysis unit. The comprehensive analysis platform realizes the integrated evaluation of the current state of the stacked items inside the cold chain operation carriage and the overall ventilation effect attenuation coefficient by correlating the temperature response change coefficient and the offset interference coefficient, and sets a ventilation effect attenuation threshold, and compares the ventilation effect attenuation coefficient with the ventilation effect attenuation threshold for early warning analysis. Specifically, set the ventilation effect attenuation threshold and record it as K e , when the ventilation effect attenuation coefficient K s ≥K e , the comprehensive analysis platform generates a warning prompt and synchronously displays the current ventilation effect attenuation coefficient; when the ventilation effect attenuation coefficient K s <K e , the comprehensive analysis platform only displays the current ventilation effect attenuation coefficient.

[0062] Specifically, this embodiment provides a remote intelligent management system for cold chain transportation. By collecting the temperature data information of the ventilation paths of the stacked items inside the cold chain transportation carriage and the images of the stacked items, based on the obtained temperature data, perform correlation analysis and calculation to obtain the temperature response change coefficient and the offset interference coefficient of the stacked items inside the carriage relative to the ventilation path, and perform integrated analysis to comprehensively evaluate the ventilation effect attenuation coefficient of the ventilation paths between the stacked items inside the cold chain transportation carriage. When the ventilation effect attenuation coefficient exceeds the attenuation threshold, an early warning is issued to avoid the ventilation path between the goods becoming smaller due to the influence of bumps, braking or accelerating inertia during cold chain transportation, which may cause the position of some goods to move, thereby reducing the overall ventilation effect and affecting the freshness preservation effect of the goods.

[0063] The specific process of performing correlation analysis and calculation on the obtained temperature data to obtain the temperature response change coefficient is as follows:

[0064] Calculate the temperature response coefficient of each ventilation path at the initial regulated temperature, and mark it as Se m ; It should be noted that the initial regulated temperature is the temperature response coefficient of each ventilation path calculated by regulating the temperature inside the cold chain transportation carriage after stacking the goods inside the cold chain transportation carriage but before transportation. That is, at this time, the ventilation effect of the ventilation paths of the stacked goods inside the cold chain transportation carriage is the optimal ventilation effect;

[0065] Calculate the temperature response coefficient of each ventilation path when calculating the current regulated temperature, denoted as Sc m ; Obviously, when the cold chain transport vehicle moves, due to the influence of bumps, braking or accelerating inertia, the positions of some goods move, resulting in a smaller ventilation path between the goods. At this time, the temperature response coefficients of each ventilation path will change;

[0066] Integrate the temperature response coefficients of each ventilation path at the initial regulated temperature and compare them with the temperature response coefficients of each ventilation path at the current regulated temperature to calculate the temperature response change coefficient K T ;

[0067] Temperature response change coefficient K T The calculation formula is:

[0068]

[0069] Where Se m represents the temperature response coefficient of the m-th ventilation path at the initial stage, and Sc m represents the temperature response coefficient of the m-th ventilation path at the current regulated temperature;

[0070] The temperature response coefficients of each ventilation path at the initial regulated temperature and the temperature response coefficients of each ventilation path at the current regulated temperature are calculated while maintaining the same benchmark. Specifically, maintaining the same benchmark for calculation means that the temperature change intervals at the initial regulated temperature and the current regulated temperature are the same, and the cold air speed and temperature conveyed by the regulated temperature of the cold chain transport vehicle compartment are the same.

[0071] Furthermore, the specific calculation method of the temperature response coefficient is as follows:

[0072] Collect the current temperature data and corresponding time data of each ventilation path when regulating the temperature;

[0073] Preset a temperature value, and collect the time data of each ventilation path reaching the preset temperature value when regulating the temperature;

[0074] Integrate the data collected from the two temperature acquisitions, and divide the temperature change difference by the corresponding temperature regulation time of each ventilation path to obtain the temperature response coefficient of each ventilation path.

[0075] Retrieve the image of the items inside the cold chain transport vehicle compartment collected by the image acquisition module, and based on the item image, integrate and analyze the offset interference coefficient of the stacked items inside the compartment relative to the ventilation path specifically as follows:

[0076] Retrieve the image of the items inside the cold chain transport vehicle compartment collected by the image acquisition module at the initial stage, denoted as the first item image, identify and calculate the center points of each cargo on the first item image, and calculate the distance between the two cargo center points relative to the center line trajectory of the ventilation path and record the standard spacing Lei Specifically, multiple cargo center points are associated and integrated into multiple groups of cargo center points. Each group of cargo center points consists of two, and the two cargo center points are mirror-symmetrical about the center line trajectory of the ventilation path.

[0077] Retrieve the image of the items inside the cold chain transportation carriage collected by the image acquisition module at the current regulated temperature, mark it as the second item image, identify and calculate the center points of each cargo on the second item image, calculate the distance between the two cargo center points relative to the center line trajectory of the ventilation path and record it as the detection distance, and integrate to obtain the detection distance dataset U i , where U i ∈(L1, L2, L3...L i );

[0078] Associate the standard distance and the detection distance to calculate the offset interference coefficient K L :

[0079]

[0080] where Le i represents the distance between the two cargo center points relative to the center line trajectory of the i-th group on the first item image, and L i represents the distance between the two cargo center points relative to the center line trajectory of the i-th group on the second item image, and d represents the distance from the cargo center to the cargo edge.

[0081] The specific method for identifying and calculating the center points of each cargo on the item image and calculating the distance between the two cargo center points relative to the center line trajectory of the ventilation path is as follows:

[0082] Establish a plane coordinate system on the item image, perform feature recognition on the item image to determine and mark the center points of each cargo on the item image, and obtain the coordinate data of each cargo center point;

[0083] Mark the center line trajectory of the ventilation path on the item image, identify the center points of each cargo on the item image, associate and integrate the two center points symmetrical about the center line trajectory, and calculate the distance between the two cargo center points relative to the center line trajectory of the ventilation path.

[0084] Associate the temperature response change coefficient K T and the offset interference coefficient K L Integrate and evaluate the ventilation effect attenuation coefficient K of the current state of the stacked items inside the cold chain operation carriage s The specific calculation method is:

[0085]

[0086] It should be noted that α1 and α2 are preset weight coefficients, where α1 represents the associated influence weight of the temperature response change coefficient on the ventilation effect change, and α2 represents the associated influence weight of the offset interference coefficient on the ventilation effect change. Moreover, α1>0 and α2>0. Optionally, α1 = 0.75 and α2 = 0.45.

[0087] This application realizes the integrated evaluation of the ventilation effect attenuation of the ventilation paths between stacked items from two perspectives: the temperature response change of the ventilation path and the path offset change, significantly improving the accuracy of evaluating the overall ventilation effect attenuation of the current state of the stacked items inside the cold chain operation carriage, reducing the probability of misjudgment, and enhancing the practicality of the cold chain transportation remote intelligent management system.

[0088] Embodiment 2:

[0089] It further includes a dead angle analysis unit, and the dead angle analysis unit is used to retrieve the detection spacing data set U i ;

[0090] Analyze each detection spacing in the detection spacing data set U i , retrieve the central point coordinates of two associated items with a detection spacing equal to 2d and record them as A(X a , Y a ), B(X b , Y b );

[0091] Calculate the blocking point coordinates based on the central point coordinates of the two items, and analyze the information of the dead angle areas that appear in each ventilation path of the goods stacked inside the cold chain transportation carriage based on the blocking point coordinates.

[0092] Recording the blocking point coordinates based on the central point coordinates of the two items and analyzing the information of the dead angle areas that appear in each ventilation path of the goods stacked inside the cold chain transportation carriage based on the blocking point coordinates specifically includes:

[0093] Calculate the blocking point coordinates D(X d , Y d ), where the abscissa X d and the ordinate Y d of the blocking point coordinates D(X d , Y d ) are calculated as follows:

[0094]

[0095] Mark the center line trajectories of each ventilation path on the item image, and mark each blocking point on the center line trajectory of the ventilation path based on the blocking point coordinate data;

[0096] Connect adjacent blocking points based on the center line trajectory of the ventilation path, and mark the line segment between two adjacent blocking points as a restricted area;

[0097] Identify and associate the center line of the ventilation path with the restricted area on the item image. When there is only one center line of the ventilation path in the restricted area, mark the restricted area as a dead corner area and obtain the position data of the dead corner area;

[0098] Transmit the position data of the dead corner area to the comprehensive analysis platform. The comprehensive analysis platform generates a warning prompt and synchronously displays the position data of the dead corner area.

[0099] On the basis of realizing the evaluation of the attenuation change of the overall ventilation effect of the stacked goods in the cold chain transportation carriage, the identification and analysis of the fixed-point dead corner area are realized at the same time, so as to avoid the deterioration of the goods caused by the ventilation dead corner area, and at the same time facilitate the staff to determine the position of the dead corner area and quickly adjust the stacked goods in the carriage.

[0100] Only some exemplary embodiments of the present invention have been described above by way of illustration. Without doubt, for those of ordinary skill in the art, various different ways can be used to modify the described embodiments without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the protection scope of the claims of the present invention.

Claims

1. A remote intelligent management system for cold chain transportation, characterized in that: include: A monitoring unit, the monitoring unit includes a temperature monitoring module and an image acquisition module, the temperature monitoring module is used to collect temperature data of the ventilation path of the stacked items in the cold chain transport compartment in real time, and the image acquisition module is used to collect images of the stacked items in the cold chain transport compartment in real time; The temperature response analysis unit is used to retrieve the temperature data collected by the temperature monitoring module, and to calculate the temperature response change coefficient K by correlation analysis of the acquired temperature data. T ; The temperature response variation coefficient obtained by correlation analysis of the obtained temperature data is as follows: Calculate the temperature response coefficient of each ventilation path when the initial temperature is controlled, marked as Se m ; Calculate the temperature response coefficient of each ventilation path when the current temperature is controlled, marked as Sc m ; The calculation method of the temperature response coefficient is as follows: Collect the current temperature data and corresponding time data of each ventilation path when adjusting the temperature; Preset a temperature value and collect the time data when each ventilation path reaches the preset temperature value when adjusting the temperature; Integrate the data of the two temperature collections, divide the temperature change difference by the temperature control time corresponding to each ventilation path to obtain the temperature response coefficient of each ventilation path; The temperature response coefficient K is calculated by integrating the temperature response coefficient of each ventilation path when the initial temperature is controlled and comparing it with the temperature response coefficient of each ventilation path when the current temperature is controlled. T ; Temperature response variation coefficient K T The calculation formula is: Among them, Se m represents the temperature response coefficient of the initial m-th ventilation path, Sc m Indicates the temperature response coefficient of the mth ventilation path when the current temperature is controlled; The temperature response coefficient of each ventilation path when calculating the initial temperature control and the temperature response coefficient of each ventilation path when calculating the current temperature control are calculated and obtained on the same basis; The ventilation interference analysis unit is used to retrieve the image of the items inside the cold chain transport compartment collected by the image detection module, and integrate and analyze the offset interference coefficient K of the stacked items inside the compartment relative to the ventilation path based on the item image. L ; The image of the items inside the cold chain transport compartment collected by the image acquisition module is retrieved, and the offset interference coefficient of the stacked items inside the compartment relative to the ventilation path is analyzed based on the image integration of the items. Specifically: Retrieve the image of the items inside the cold chain transport compartment collected by the image acquisition module at the beginning, mark it as the first item image, identify and calculate the center points of each item on the first item image, and calculate the distance between the center line trajectory of the ventilation path relative to the center points of the two items and record it as the standard spacing Le i ; Retrieve the image of the items inside the cold chain transport compartment collected by the image acquisition module during the current temperature control, mark it as the second item image, identify and calculate the center points of each item on the second item image, and calculate the distance between the center line trajectory of the ventilation path and the two center points of the items, record it as the detection distance, and integrate it to obtain the detection distance dataset U i , where U i ∈(L1, L2, L3...L i ); Calculate the offset interference coefficient K by associating the standard spacing and the detection spacing L : Among themLe i represents the distance between the centerline trajectory of the ventilation path and the center points of the two goods in the i-th group on the first object image, L i represents the distance between the centerline trajectory of the ventilation path of the i-th group on the second object image and the two center points of the goods, and d represents the distance from the center of the goods to the edge of the goods; The comprehensive analysis platform correlates the temperature response variation coefficient and the offset interference coefficient to integrate and evaluate the overall ventilation effect attenuation coefficient of the stacked items inside the cold chain carriage in the current state, sets the ventilation effect attenuation threshold, and compares the ventilation effect attenuation coefficient with the ventilation effect attenuation threshold for early warning analysis.

2. A remote intelligent management system for cold chain transportation according to claim 1, characterized in that: The temperature monitoring module is used to collect real-time temperature data of the ventilation path of stacked items in the cold chain transport compartment: The temperature monitoring module includes a temperature acquisition module, each of which is arranged in each ventilation path of the goods stacked inside the cold chain transport compartment. Each temperature acquisition module is used to collect temperature data of the corresponding detection area in real time.

3. According to claim 1, a remote intelligent management system for cold chain transportation is characterized in that: Identify and calculate the center points of each cargo on the object image and calculate the distance between the center line trajectory of the ventilation path and the center points of the two cargoes: Establish a plane coordinate system on the item image, perform feature recognition on the item image, determine the center point of each item in the item image and mark it, and obtain the coordinate data of each center point of the item; The centerline trajectory of the ventilation path is marked on the object image, the center point of each cargo on the object image is identified, two center points that are symmetrical about the centerline trajectory are associated and integrated, and the distance between the centerline trajectory of the ventilation path and the two cargo center points is calculated.

4. A remote intelligent management system for cold chain transportation according to claim 3, characterized in that: Correlation temperature response variation coefficient K T And the offset interference coefficient K L Integrated evaluation of the ventilation effect attenuation coefficient K of the current state of stacked items in the cold chain running compartment s The specific calculation method is: Among them, α1 and α2 are preset weight coefficients.

5. The cold chain transportation remote intelligent management system according to claim 1 is characterized in that: The ventilation effect attenuation threshold is set, and the ventilation effect attenuation coefficient is compared with the ventilation effect attenuation threshold for early warning analysis as follows: Set the ventilation effect attenuation threshold and record it as K e ; When the ventilation effect attenuation coefficient K s ≥K e When the ventilation is turned on, the comprehensive analysis platform generates an early warning prompt and simultaneously displays the current ventilation effect attenuation coefficient; When the ventilation effect attenuation coefficient K s <K e When the ventilation is turned on, the comprehensive analysis platform only displays the current ventilation effect attenuation coefficient.

6. A remote intelligent management system for cold chain transportation according to claim 5, characterized in that: It also includes a blind spot analysis unit; The blind spot analysis unit is used to retrieve the detection distance data set U i ; For the detection distance dataset U i Analyze each detection interval in the detection interval, retrieve the coordinates of the two associated object center points with a detection interval equal to 2d and record them as A(X a ,Y a )、B(X b ,Y b ); The coordinates of the blocking point are calculated based on the coordinates of the center points of the two objects, and the blind spot area information appearing in each ventilation path of the goods stacked inside the cold chain transport compartment is analyzed based on the association analysis of the blocking point coordinates.

7. A remote intelligent management system for cold chain transportation according to claim 6, characterized in that: The coordinates of the blocking points are recorded based on the coordinates of the center points of the two items, and the dead angle area information in each ventilation path of the goods stacked inside the cold chain transport compartment is analyzed based on the correlation analysis of the blocking point coordinates. Specifically: Calculate the coordinates of the blocking point D(X d , Y d ), where the blocking point coordinates D(X d , Y d ) d , vertical coordinate Y d The calculation method is: Marking the centerline trajectory of each ventilation path on the object image, and marking each blocking point on the centerline trajectory of the ventilation path based on the blocking point coordinate data; Connect adjacent blocking points based on the centerline trajectory of the ventilation path, and mark the line segment between two adjacent blocking points as a restricted area; Identify the restricted area on the object image and associate it with the center line of the ventilation path. When there is only one center line of the ventilation path in the restricted area, mark the restricted area as a blind spot area, and obtain the position data of the blind spot area. The location data of the blind spot area is transmitted to the comprehensive analysis platform, which generates early warning prompts and simultaneously displays the location data of the blind spot area.

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