Fresh food transportation monitoring system

Abstract

The invention relates to the technical field of fresh food logistics, in particular to a fresh food transportation monitoring system which comprises an acquisition module, a deep analysis module, an infrared analysis module, a color analysis module and a monitoring alarm module. The acquisition module is used for acquiring a depth image, an RGB image and an infrared image of the packaging box body; the depth analysis module is used for determining a pixel fluctuation area of the depth image; the infrared analysis module is used for intercepting the infrared image of the pixel fluctuation area as a local infrared image, determining difference parameters of the local infrared image and judging whether the packaging box body is damaged or not according to the difference parameters; the color analysis module comprises a damage verification unit used for verifying the judgment result according to the RGB image of the pixel fluctuation area, and a damage analysis unit used for determining the number of damaged layers of the packaging box; and the monitoring alarm module responds to the color analysis module to verify that the packaging box body is damaged and outputs alarm information. And through multi-dimensional image acquisition and analysis, the real-time performance and accuracy of damage detection are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fresh food logistics, and in particular to a fresh food transportation monitoring system. Background Art

[0002] At present, for the transportation of fresh food, a combination of four packaging materials, namely corrugated paper boxes, foam boxes, ice bags and vacuum plastic bags, is mostly used for packaging, which basically achieves transportation safety guarantee within 3-5 days. However, the structural strength and impact resistance of the four packaging materials are poor, and there is a risk of box damage due to the sharpness of the transported products (such as frozen fish) or vibration during transportation and external impact. For the monitoring of box damage during the transportation of fresh products, a fresh product safety intelligent detection and management system based on big data is disclosed in the prior art, including a product weight detection module, a packaging damage analysis module, an environmental parameter acquisition module, a parameter preprocessing module, an image acquisition and processing module, an image recognition and analysis module, a product parameter database, a freshness analysis module, a management server and a display terminal. It comprehensively considers the weight change, temperature change, humidity change, odor concentration, product color, volume and whether there are scratches and mildew on the surface of the product, and analyzes the damage of the product. The damage is detected by the difference in weight.

[0003] However, the above scheme has poor real-time performance for damage detection and lacks real-time monitoring of the packaging box during transportation, resulting in the inability to timely detect food quality abnormalities caused by damage to the packaging box during the transportation of fresh food. Summary of the invention

[0004] The purpose of the present invention is to provide a fresh food transportation monitoring system to solve the problem that the existing technology has poor real-time detection of damage and lacks real-time monitoring of packaging boxes during transportation, resulting in the inability to timely detect food quality abnormalities caused by damage to packaging boxes during fresh food transportation.

[0005] The present invention provides a fresh food transportation monitoring system, which includes:

[0006] A collection module, used to continuously collect depth images, RGB images, and infrared images of fresh food packaging boxes;

[0007] A depth analysis module, connected to the acquisition module, for determining a pixel fluctuation area of ​​the depth image;

[0008] an infrared analysis module, connected to the acquisition module and the depth analysis module respectively, for intercepting the infrared image of the pixel fluctuation area as a local infrared image, determining difference parameters of the local infrared image, and judging whether the packaging box is damaged according to the difference parameters;

[0009] a color analysis module, connected to the acquisition module and the depth analysis module respectively, comprising a damage verification unit for verifying the determination result according to the RGB image of the pixel fluctuation area, and a damage analysis unit for determining the number of damaged layers of the packaging box;

[0010] The monitoring and alarm module outputs an alarm message in response to the color analysis module verifying that the packaging box is damaged.

[0011] As a preferred technical solution of the fresh food transportation monitoring system, the difference parameter determined by the infrared analysis module is the absolute value of the temperature mean difference between the local infrared image and the rest of the infrared image except the local infrared image.

[0012] As a preferred technical solution of the fresh food transportation monitoring system, the specific process of the infrared analysis module determining whether the packaging box is damaged includes:

[0013] The absolute value of the temperature mean difference is compared with the first threshold. If the absolute value of the temperature mean difference is greater than the first threshold, it is determined that the packaging box is damaged. If the absolute value of the temperature mean difference is less than or equal to the first threshold, it is determined that the packaging box is not damaged.

[0014] As a preferred technical solution of the fresh food transportation monitoring system, the specific process of the damage verification unit verifying the determination result according to the RGB image of the pixel fluctuation area includes:

[0015] Obtain the chi-square difference between the histograms of the first frame RGB image and the last frame RGB image in the corresponding period of the pixel fluctuation area.

[0016] As a preferred technical solution of the fresh food transportation monitoring system, the specific process of the damage verification unit verifying the determination result according to the RGB image of the pixel fluctuation area also includes:

[0017] The chi-square difference is compared with a second threshold value. If the chi-square difference is less than the second threshold value, the verification result is correct and the packaging box is damaged.

[0018] As a preferred technical solution for the fresh food transportation monitoring system, the damage analysis unit pre-stores the RGB data intervals of each layer of material in the packaging box, and determines the number of damaged layers of the packaging box by the number of matches between the RGB image and the RGB data intervals.

[0019] As a preferred technical solution for the fresh food transportation monitoring system, the fresh food transportation monitoring system also includes a pattern determination module, which only collects RGB images for packaging boxes with the number of different RGB data intervals in each layer greater than 2, and directly determines the number of damaged layers through the damage analysis unit. In response to the number of damaged layers being 0, it is determined that the packaging box is not damaged, and in response to the number of damaged layers being greater than 0, it is determined that the packaging box is damaged.

[0020] As a preferred technical solution for the fresh food transportation monitoring system, the monitoring alarm module outputs alarm signals in different alarm modes for different numbers of damaged layers of the packaging box.

[0021] The beneficial effects of the present invention are:

[0022] Through the acquisition and mutual verification of depth images, infrared images and RGB images, it is possible to accurately determine whether the packaging box is damaged. For packaging boxes in cold chain transportation, considering the temperature difference between the inside and outside due to the insulation effect of the foam box, the image depth will inevitably change at the first time of damage. If the depth image changes, there are two situations: foreign matter attachment and damage. The acquisition of infrared images can effectively distinguish the two. If it is foreign matter attachment, since both it and the packaging box are exposed to a low temperature environment, the temperature of the two should be roughly the same. Therefore, if there is a pixel fluctuation area in the depth image that is consistent with the surrounding environment, There is a large temperature difference between the environments, that is, the temperature difference inside the packaging box is released outward, and there is damage. Taking into account the accidental factors of this situation, the present invention also sets a last verification, that is, the change of the RGB image within the time period of pixel fluctuation. If the change is large, it is verified as damage, and it can cover the acquisition of each image. It can be performed on multiple packaging boxes at the same time, and it can realize the damage detection of multiple packaging boxes in a single transportation. The detection of damage has better real-time performance, and the packaging box can be monitored in real time during transportation, so as to avoid the failure to timely discover food quality abnormalities caused by damaged packaging boxes during the transportation of fresh food.

[0023] Furthermore, the present invention can accurately distinguish between damage and foreign matter attachment through the combined analysis of three types of images (depth, RGB and infrared). For example, after the initial area of ​​damage is determined by the depth image, the infrared image further accurately locates the damage through temperature differences, and the RGB image is used for final verification, thereby significantly reducing the misjudgment rate.

[0024] Furthermore, the fresh food transportation monitoring system of the present invention supports simultaneous monitoring of multiple packaging boxes, without the need to separately arrange complex detection equipment for each packaging box. The design of the image acquisition module enables it to adapt to the monitoring needs of large quantities of fresh food in a single transportation, thereby improving the application efficiency and practicality of the system.

[0025] Furthermore, the color analysis module of the present invention not only verifies the damage, but also accurately determines the number of packaging layers involved in the damage based on the matching degree between the RGB image and the pre-stored data. For example, the damage information of different layers of materials such as foam boxes and corrugated paper boxes can provide more specific data support for quality traceability during transportation.

[0026] Furthermore, the monitoring alarm module of the present invention outputs a variety of alarm modes (such as sound and light alarms, electronic prompts in the transportation system, etc.) according to the number of damaged layers, which is convenient for management personnel to quickly identify and respond to problems of different levels.

[0027] Furthermore, in cold chain transportation, due to the insulation effect of the foam box, this system further optimizes the damage identification mechanism through infrared temperature difference. It can not only identify the damage itself, but also monitor the potential impact of the resulting temperature abnormality on food quality, providing reliable support for food safety assurance in cold chain logistics.

[0028] Furthermore, the present invention reduces hardware loss and significantly increases the service life of the system due to the use of non-contact detection means. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a structural block diagram of a fresh food transportation monitoring system in an embodiment of the present invention;

[0030] Figure 2 is a structural block diagram of a color analysis module in an embodiment of the present invention;

[0031] Figure 3 4 is a flowchart of the fresh food transportation monitoring system in an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] In the description of the present invention, it should be noted that the orientations or positional relationships indicated by the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc. are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. Among them, the terms "first position" and "second position" are two different positions, and the first feature "above", "above" and "above" the second feature include the first feature being directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. The first feature "below", "below" and "below" the second feature include the first feature being directly below and obliquely below the second feature, or simply indicates that the first feature is lower in level than the second feature.

[0034] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0035] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0036] like Figure 1 and Figure 2 As shown, this embodiment provides a fresh food transportation monitoring system, including: a collection module, a depth analysis module, an infrared analysis module, a color analysis module and a monitoring alarm module;

[0037] Among them, the acquisition module is used to continuously collect depth images, RGB images and infrared images of the packaging box of fresh food;

[0038] The depth analysis module is connected to the acquisition module and is used to determine the pixel fluctuation area of ​​the depth image;

[0039] The infrared analysis module is connected to the acquisition module and the depth analysis module respectively, and is used to intercept the infrared image of the pixel fluctuation area as a local infrared image, determine the difference parameters of the local infrared image, and determine whether the packaging box is damaged according to the difference parameters;

[0040] The color analysis module is connected to the acquisition module and the depth analysis module respectively, and includes a damage verification unit for verifying the determination result according to the RGB image of the pixel fluctuation area, and a damage analysis unit for determining the number of damaged layers of the packaging box;

[0041] The monitoring and alarm module outputs an alarm message in response to the color analysis module verifying that the packaging box is damaged.

[0042] In the above embodiment, by collecting and mutually verifying the depth image, infrared image and RGB image, it is possible to accurately determine whether the packaging box is damaged. For the packaging box in cold chain transportation, considering the temperature difference between the inside and outside due to the insulation effect of the foam box, the image depth will inevitably change at the first time of damage. If the depth image changes, there are two situations: foreign matter attachment and damage. By collecting the infrared image, the two can be effectively distinguished. If it is foreign matter attachment, since both it and the packaging box are exposed to a low temperature environment, the temperature of the two should be roughly the same. Therefore, if there is a pixel fluctuation area in the depth image, There is a large temperature difference between the packaging box and the surrounding environment, that is, the temperature difference inside the packaging box is released outward, and there is damage. Taking into account the accidental factors of this situation, the present invention also sets a last verification, that is, the change of the RGB image within the time period of pixel fluctuation. If the change is large, it is verified as damage, and the acquisition of each image can be covered. It can be performed on multiple packaging boxes at the same time, and multiple packaging boxes in a single transportation can be detected for damage. The detection of damage has better real-time performance, and the packaging box can be monitored in real time during transportation, so as to avoid the failure to timely discover food quality abnormalities due to damage of the packaging box during the transportation of fresh food.

[0043] Specifically, the difference parameter determined by the infrared analysis module is the absolute value of the temperature mean difference between the local infrared image and the rest of the infrared image except the local infrared image. In detail, the process of determining the temperature mean difference includes:

[0044] The acquisition module acquires the infrared image of the packaging box in real time, partitions it, and records the temperature distribution information of the packaging box surface and surrounding areas. Combined with the pixel fluctuation area output by the depth analysis module, the area is located in the infrared image and the corresponding infrared sub-image is extracted, which is defined as a local infrared image. The local infrared image is used to focus on analyzing possible damaged areas. First, the temperature mean of all pixels in the local infrared image is calculated as the local temperature mean. Then, the temperature mean of the remaining pixels in the infrared image except the local infrared image is calculated as the overall temperature mean. By comparing the difference between the local temperature mean and the overall temperature mean, the absolute difference is calculated to obtain the difference parameter. The difference parameter reflects the degree of temperature difference between the local area and the overall area.

[0045] Specifically, the specific process of the infrared analysis module determining whether the packaging box is damaged includes:

[0046] The absolute value of the temperature mean difference is compared with the first threshold. If the absolute value of the temperature mean difference is greater than the first threshold, the packaging box is determined to be damaged. If the absolute value of the temperature mean difference is less than or equal to the first threshold, the packaging box is determined to be not damaged. In detail, the first threshold is an important parameter for determining whether the packaging box is damaged. The specific value will vary depending on the actual transportation scenario and the characteristics of the packaging material. The following are examples of values ​​in some common scenarios:

[0047] For cold chain transportation (-20℃ to 0℃),

[0048] The selection range of the first threshold is: 3°C to 5°C.

[0049] This is because in cold chain transportation, the temperature difference between the inside and outside of the packaging box is more obvious after it is damaged, so a higher threshold is appropriately set to tolerate normal temperature fluctuations.

[0050] For low temperature transportation (1℃ to 10℃),

[0051] The selection range of the first threshold is: 2°C to 4°C.

[0052] This is because in a low-temperature environment, the temperature difference is small and the temperature anomaly caused by damage is significant, so the threshold can be slightly lower to enhance sensitivity.

[0053] For normal temperature transportation (11℃ to 25℃),

[0054] The selection range of the first threshold is: 1℃ to 3℃

[0055] This is because under normal temperature conditions, the temperature difference between the external environment and the inside of the package is small, and the temperature change caused by damage is not easy to detect. Therefore, the threshold is set lower to ensure detection accuracy.

[0056] Specifically, the specific process of the damage verification unit verifying the determination result according to the RGB image of the pixel fluctuation area includes:

[0057] Obtain the chi-square difference between the histograms of the first frame RGB image and the last frame RGB image in the corresponding period of the pixel fluctuation area. In detail, the specific process of the damage verification unit verifying the judgment result of the damage of the packaging box is as follows: First, the pixel fluctuation area determined by the depth analysis module is extracted in the corresponding period of time. The pixel distribution characteristics of the two frames are compared by using the histogram analysis method to generate the statistical data of the histogram. Then, by calculating the chi-square difference between the first frame and the last frame histogram, the change in the color distribution of the two frames is analyzed. The result of the chi-square difference is used to quantify the degree of color change in the pixel fluctuation area. If the value of the chi-square difference is high, it indicates that the color distribution of the pixel fluctuation area has changed significantly, thereby verifying the possibility of damage to the packaging box; conversely, if the chi-square difference is low, it can be further verified that the packaging box is not damaged. This verification process combines the detailed features of the depth analysis and RGB images to ensure the accuracy and reliability of the damage judgment result, and is particularly suitable for identifying pseudo-damage caused by the external environment of the packaging box.

[0058] Specifically, the specific process of the damage verification unit verifying the determination result according to the RGB image of the pixel fluctuation area also includes:

[0059] The chi-square difference is compared with the second threshold. If the chi-square difference is less than the second threshold, the verification judgment result is correct and the packaging box is damaged. In detail, in the verification process of the color damage verification unit, after calculating the chi-square difference of the RGB image histogram of the first frame and the last frame in the corresponding period of the pixel fluctuation area, the chi-square difference value is compared with the preset second threshold to further confirm the damage state of the packaging box. Specifically, if the chi-square difference value is lower than the second threshold, the previous judgment result is verified to be correct, that is, the change in the pixel fluctuation area is indeed caused by the damage of the packaging box; at this time, the system outputs the final damage confirmation signal and hands it over to the monitoring alarm module for processing. Through this threshold-based verification mechanism, it is possible to effectively avoid erroneous judgments caused by external factors such as changes in ambient light or transportation vibration, thereby enhancing the reliability and accuracy of the judgment results and providing a stronger technical guarantee for real-time monitoring during transportation. For the second threshold, its determination process needs to be comprehensively analyzed in combination with experimental data, actual application scenarios and system sensitivity requirements. For example, in a simulated transportation environment, typical packaging materials (such as corrugated boxes, foam boxes, etc.) are selected, and RGB image data is collected through multiple experiments. The chi-square difference values ​​of the first frame and the last frame RGB image histograms when the packaging box is damaged and undamaged are recorded respectively. The experimental data are statistically analyzed to extract the chi-square difference value range in the damaged case and the typical value range in the undamaged case. The second threshold is usually set to the middle value between the maximum value of the undamaged chi-square difference value and the minimum value in the damaged case to effectively distinguish the two states. The second threshold is adjusted according to the characteristics of different transportation scenarios (such as cold chain and normal temperature environment). For example, in a cold chain environment, the light reflection caused by low temperature may have a slight effect on the histogram of the RGB image, and the second threshold can be appropriately increased; while in a normal temperature environment, the impact of external light is small, and the threshold can be set lower to increase sensitivity. The color characteristics and optical reflectivity of different packaging materials will affect the histogram of the RGB image. For example, for foam boxes with uniform color, the fluctuation of the chi-square difference value is small, and the second threshold can be set lower; while for corrugated boxes with uneven color, the fluctuation is large, and the second threshold needs to be set higher accordingly to avoid misjudgment.

[0060] Specifically, the damage analysis unit pre-stores the RGB data intervals of each layer of the packaging box material, and determines the number of damaged layers of the packaging box by the number of matches between the RGB image and the RGB data interval. The specific working process of the damage analysis unit is as follows: the system first pre-stores the RGB data intervals of each layer of the packaging box material. These data intervals are calibrated according to the color characteristics and optical reflectivity of the packaging box material, including the standard RGB value range of common packaging materials such as corrugated paper boxes, foam boxes and vacuum plastic bags. In actual monitoring, when the packaging box body is damaged, the RGB image obtained by the acquisition module will contain the color information of each layer of material after exposure. The damage analysis unit performs a pixel-by-pixel color matching operation on the RGB image of the pixel fluctuation area, compares the collected RGB value with the pre-stored RGB data intervals of each layer of material, records the number of matched material layers, and determines the number of damaged layers of the packaging box according to the number of matches. For example, if it is detected that the RGB data intervals of two layers of material are matched, it is determined to be a single-layer damage. This layer-by-layer matching and statistical method can not only accurately identify the damage depth of the packaging box, but also provide hierarchical and detailed information for the status monitoring of the packaging box during transportation, providing a scientific basis for subsequent packaging improvements and transportation safety assurance.

[0061] Specifically, the fresh food transportation monitoring system also includes a mode determination module, which only collects RGB images for packaging boxes with different RGB data intervals of each layer greater than 2, directly determines the number of damaged layers through the damage analysis unit, and determines that the packaging box is not damaged in response to the number of damaged layers being 0, and determines that the packaging box is damaged in response to the number of damaged layers being greater than 0. It can be understood that the collection of RGB images has limitations and contingency, and is only adapted when the packaging layer is rich in color. For situations where the color difference is not large, combining the depth image and the infrared image to jointly determine can reduce the computing power and improve the accuracy. In detail, the mode determination module included in the fresh food transportation monitoring system is intended to optimize the operating efficiency and resource utilization of the system. Specifically, the module determines the complexity of the packaging box by analyzing the number of RGB data intervals of each layer of material exposed by the packaging box. When it is detected that the number of RGB data intervals corresponding to each layer of material is greater than 2, the mode determination module will only collect the RGB image of the packaging box, and directly determine the number of damaged layers through the damage analysis unit, without additionally calling the depth analysis module or the infrared analysis module, so as to reduce the amount of data processing and speed up the determination. In this process, if the damage analysis unit determines that the number of damaged layers is 0, the mode determination module outputs the conclusion that the packaging box is not damaged; if the number of damaged layers is greater than 0, the packaging box is directly determined to be damaged, and the monitoring alarm module is triggered to output the corresponding damage alarm signal. This design not only improves the detection efficiency of multi-layer complex packaging boxes, but also ensures the adaptability of the system to different types of packaging boxes, so that it can flexibly adjust the detection mode according to the complexity of the packaging in actual transportation scenarios, and realize efficient and accurate damage monitoring.

[0062] Specifically, the monitoring and alarm module outputs alarm signals in different alarm modes for different damaged layers of the packaging box. For example, when the number of damaged layers of the packaging box reaches a preset threshold, the monitoring and alarm module can issue a sound alarm through a buzzer or a voice reminder function. The frequency and intensity of the sound can be adjusted according to the severity of the damage, such as a low-frequency alarm for minor damage and a high-frequency alarm for major damage. Or, warning lights or icons of different colors are displayed on the display interface. For example, when the number of damaged layers of the packaging box is 0, a green icon is displayed to indicate normal; when the number of damaged layers is 1, a yellow icon is displayed to indicate minor damage; when the number of damaged layers is greater than 1, a red icon is displayed to indicate serious damage. Detailed information such as the damaged location, number of layers, and temperature difference can also be displayed on the system interface. When the number of damaged layers exceeds a certain threshold, the system can automatically send text messages or emails to relevant personnel to remind the transportation personnel or monitoring personnel to take immediate measures to ensure that the quality of fresh food is not damaged.

[0063] See also Figure 3 As shown, when the fresh food transportation monitoring system in this embodiment is working, its working process is as follows:

[0064] Step S1, collecting a depth image, an RGB image, and an infrared image of a packaging box of fresh food;

[0065] Step S2, determining the pixel fluctuation area of ​​the depth image;

[0066] Step S3, intercepting the infrared image of the pixel fluctuation area as a local infrared image, and determining a difference parameter of the local infrared image;

[0067] Step S4, determining whether the packaging box is damaged according to the difference parameters;

[0068] Step S5, verifying the determination result according to the RGB image of the pixel fluctuation area;

[0069] Step S6, determining the number of damaged layers of the packaging box;

[0070] Step S7, outputting warning information according to the number of damaged layers.

[0071] Except for the acquisition module (which is a combination of corresponding acquisition cameras or a camera with multiple image acquisition functions), the modules described in the embodiments of the present application can be implemented by software or hardware. The described modules can also be set in a processor. For example, it can be described as: a processor includes a depth analysis module, an infrared analysis module, a color analysis module, and a monitoring alarm module. Among them, the names of these modules do not constitute a limitation on the module itself in some cases.

[0072] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the devices, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based device that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0073] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A fresh food transportation monitoring system, characterized in that: include: A collection module, used to continuously collect depth images, RGB images, and infrared images of fresh food packaging boxes; A depth analysis module, connected to the acquisition module, for determining a pixel fluctuation area of ​​the depth image; an infrared analysis module, connected to the acquisition module and the depth analysis module respectively, for intercepting the infrared image of the pixel fluctuation area as a local infrared image, determining difference parameters of the local infrared image, and judging whether the packaging box is damaged according to the difference parameters; a color analysis module, connected to the acquisition module and the depth analysis module respectively, comprising a damage verification unit for verifying the determination result according to the RGB image of the pixel fluctuation area, and a damage analysis unit for determining the number of damaged layers of the packaging box; The monitoring and alarm module outputs an alarm message in response to the color analysis module verifying that the packaging box is damaged.

2. The fresh food transportation monitoring system according to claim 1, characterized in that: The difference parameter determined by the infrared analysis module is the absolute value of the temperature mean difference between the local infrared image and the rest of the infrared image except the local infrared image.

3. The fresh food transportation monitoring system according to claim 2, characterized in that: The specific process of the infrared analysis module determining whether the packaging box is damaged includes: The absolute value of the temperature mean difference is compared with the first threshold. If the absolute value of the temperature mean difference is greater than the first threshold, it is determined that the packaging box is damaged. If the absolute value of the temperature mean difference is less than or equal to the first threshold, it is determined that the packaging box is not damaged.

4. The fresh food transportation monitoring system according to claim 3, characterized in that: The specific process of the damage verification unit verifying the determination result according to the RGB image of the pixel fluctuation area includes: Obtain the chi-square difference between the histograms of the first frame RGB image and the last frame RGB image in the corresponding period of the pixel fluctuation area.

5. The fresh food transportation monitoring system according to claim 4, characterized in that: The specific process of the damage verification unit verifying the determination result according to the RGB image of the pixel fluctuation area also includes: The chi-square difference is compared with a second threshold value. If the chi-square difference is less than the second threshold value, the verification result is correct and the packaging box is damaged.

6. The fresh food transportation monitoring system according to claim 5, characterized in that: The damage analysis unit pre-stores the RGB data intervals of each layer of material of the packaging box, and determines the number of damaged layers of the packaging box according to the number of matches between the RGB image and the RGB data intervals.

7. The fresh food transportation monitoring system according to claim 6, characterized in that: The fresh food transportation monitoring system also includes a pattern determination module, which only collects RGB images for packaging boxes with the number of different RGB data intervals in each layer greater than 2, and directly determines the number of damaged layers through the damage analysis unit. In response to the number of damaged layers being 0, it is determined that the packaging box is not damaged, and in response to the number of damaged layers being greater than 0, it is determined that the packaging box is damaged.

8. The fresh food transportation monitoring system according to claim 7, characterized in that: The monitoring alarm module outputs alarm signals in different alarm modes according to different numbers of damaged layers of the packaging box.

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