High-efficiency logistics line

Abstract

The invention relates to the technical field of machine vision, in particular to a high-efficiency logistics line. The high-efficiency logistics line comprises the following parts: an information acquisition module used for acquiring source information of logistics package piles sorted by a current logistics transfer station and acquiring weather conditions of a transportation line from the source to the current logistics transfer station in a current transportation process; the transfer learning module is used for carrying out transfer learning on a pre-trained logistics express sheet identification basic model by utilizing express sheet data collected from the transportation line according to the weather condition from the source to the current logistics transfer station so as to generate a logistics express sheet identification model adapted to corresponding damage types of the transportation line under different weather conditions; and the identification module is used for selecting the express sheet data collected in the corresponding weather to train the obtained logistics express sheet identification model according to the weather condition from the source to the current logistics transfer station so as to identify the logistics express sheet. According to the invention, the express sheet can be identified more accurately.

Description

Technical Field

[0001] The present application relates to the field of machine vision technology, and in particular to a high-efficiency logistics line. Background Art

[0002] With the rapid development of e-commerce and logistics industries, the number of express parcels has exploded. In order to meet the needs of efficient and accurate logistics, many logistics companies have introduced automated parcel sorting and identification systems. These systems usually rely on scanning and identifying parcel labels to obtain key data such as destination and recipient information, thereby realizing automated classification and transfer of parcels.

[0003] However, in the actual logistics and transportation process, express delivery labels are often damaged by various factors. For example, in rainy and snowy weather conditions, the package may be attacked by moisture or rain, causing the information on the label to be blurred or blurred; during the transportation process, the package may be damaged, worn or contaminated due to physical effects such as friction and collision. These damages will have an adverse effect on the recognition accuracy and efficiency of the package identification system, and may cause the package to be unable to be correctly identified and delayed delivery.

[0004] Current parcel identification systems usually use fixed image processing and character recognition algorithms to identify various types of shipping labels. However, these algorithms have varying recognition effects when faced with shipping labels with different types of damage, and are unable to adapt to various complex damage situations in a timely manner. Summary of the invention

[0005] In order to solve the above technical problems or at least partially solve the above technical problems, the present application provides a high-efficiency logistics line that can more accurately identify express delivery bills.

[0006] The present application provides a high-efficiency logistics line, which includes the following parts:

[0007] An information acquisition module is used to obtain the source information of the logistics package pile sorted by the current logistics transfer station, and thereby obtain the weather conditions of the transportation route from the source to the current logistics transfer station during the current transportation process;

[0008] A transfer learning module is used to perform transfer learning on the pre-trained logistics waybill recognition basic model according to the weather conditions from the source to the current logistics transfer station, using the waybill data collected from the transportation route, so as to generate a logistics waybill recognition model that is suitable for the corresponding damage type of the transportation route under different weather conditions;

[0009] The recognition module selects a logistics waybill recognition model trained with the waybill data collected during the corresponding weather conditions according to the weather conditions from the source to the current logistics transfer station, so as to recognize the logistics waybill.

[0010] Optionally, the high-efficiency logistics line also includes the following parts:

[0011] A high wear identification module identifies logistics waybills with a wear degree above a preset wear threshold as waybills to be processed;

[0012] A pre-worn bill transformation module is used to train a bill transformation model based on bill wear image data in the transportation route from the current logistics transfer station to the next logistics node, transform the bill to be processed through the bill transformation model, and generate a pre-worn bill image of the current bill to be processed;

[0013] A data transmission module, used for sending the pre-worn bill image and corresponding bill information to the next logistics node;

[0014] The classification module further performs the following steps:

[0015] It is used to compare the current package’s waybill image with all pre-worn waybill images at the next logistics node when the waybill cannot be recognized normally, so as to identify and obtain the waybill information of the closest pre-worn waybill image as the waybill information of the currently unrecognizable logistics waybill.

[0016] Optionally, the waybill transformation model is trained based on the waybill wear image data in the transportation route from the current logistics transfer station to the next logistics node, including the following steps:

[0017] Collect the images of waybills before and after the actual transportation from the current logistics transfer station to the next logistics node under different weather conditions to construct the first training data set;

[0018] A generative adversarial network is used as the model framework of the face order transformation model. The weather information during actual transportation and the face order image before actual transportation in the first training data set are used as inputs of the model framework of the face order transformation model. The face order image after actual transportation is used as the label of the model framework of the face order transformation model. The model framework of the face order transformation model is trained to obtain the face order transformation model.

[0019] Optionally, the logistics bill recognition basic model is obtained by the following steps:

[0020] Collect logistics waybill data under different weather conditions, including normal state, rainy day soaking, friction wear, collision damage, etc., as well as waybill information recorded in the waybill images, to construct a second training data set;

[0021] The convolutional neural network is used as the network framework of the basic model for logistics waybill recognition. The logistics waybill image data in the second training data machine is used as the input of the basic model for logistics waybill recognition. The corresponding waybill information is used as the label of the basic model for logistics waybill recognition. The basic model for logistics waybill recognition is obtained by training.

[0022] Optionally, using the bill data collected from the transportation process, transfer learning is performed on the pre-trained logistics bill recognition basic model to generate a logistics bill recognition model that is adapted to the corresponding damage type of the transportation route under different weather conditions, including the following steps:

[0023] Collecting the waybill data generated during the transportation process under different weather conditions of the transportation route;

[0024] A preset number of fully connected layers are added before the output layer at the end of the basic model for logistics waybill recognition, and the weights of the added fully connected layers are initialized, so as to obtain a training model for logistics waybill recognition to be transferred;

[0025] The logistics waybill recognition training model to be transferred is trained using the waybill data generated during the transportation process under different weather conditions, so as to obtain a logistics waybill recognition model that is suitable for the corresponding damage types of the transportation route under different weather conditions.

[0026] Optionally, the different weathers include rainy days and sunny days.

[0027] Compared with the prior art, the technical solution provided by this application has the following advantages:

[0028] One of its beneficial effects is that the damage pattern will be different in different weather conditions. And because the porters or the handling environment are usually fixed, the damage pattern caused by the corresponding weather will form a fixed pattern. If the general recognition model is directly used to recognize the delivery bill data, this fixed pattern cannot be utilized, and it is difficult to more accurately recognize the logistics information recorded on the damaged logistics delivery bill.

[0029] In this application, the system first uses the transfer learning module to perform targeted transfer learning on the pre-trained recognition model for the corresponding damage caused under different weather conditions on the transportation route, thereby obtaining a logistics waybill recognition model that is adapted to the corresponding damage types of the transportation route under different weather conditions.

[0030] In this way, the present application realizes the ability to dynamically adjust the recognition direction of the face sheet recognition model according to actual environmental conditions and damage characteristics, thereby improving the recognition accuracy and efficiency of damaged face sheets.

[0031] The second beneficial effect is that it should be noted that if we only select different recognition models to identify and read the waybill information according to different conditions (such as weather conditions, environmental factors), this method is usually more suitable for dealing with the problem of identification after the first damage occurs during sorting at the transfer station.

[0032] However, during the initial transportation stage, the package label may be dampened by rain, causing the paper to soften and the ink to smudge; during the subsequent handling process, the label may be torn and worn due to friction and collision. In this way, different types of damage are superimposed on each other, making the damage to the label more serious, presenting a complex and unpredictable damage pattern. This complex damage often exceeds the processing capacity of the recognition model, and the aforementioned method is difficult to continue to work. Therefore, simply using the corresponding recognition model under different conditions cannot effectively deal with the difficulty of label recognition caused by the superposition of wear during multiple transportation processes.

[0033] In this application, at the current logistics transfer station, the system identifies the waybills whose degree of wear exceeds a preset threshold, and estimates that these waybills may be further damaged in the next transportation stage due to the large degree of wear and tear, resulting in failure to be properly identified at the next logistics node.

[0034] In the current logistics transfer station, the system of this application uses a high wear recognition module to identify logistics waybills with a degree of wear exceeding a preset threshold and mark them as to-be-processed waybills. For these to-be-processed waybills, the pre-worn waybills transformation module uses the collected waybills wear image data to train a waybills transformation model in advance based on the transportation route and weather conditions from the current logistics transfer station to the next logistics node. This model can simulate the further damage that may occur to the waybills in the next transportation stage. The model is used to transform the waybills to be processed, and further pre-worn waybills images are generated based on the wear that has affected the information, and these pre-worn waybills images and corresponding waybills information are sent to the next logistics node through the data transmission module. When the package arrives at the next logistics node, if the waybills cannot be recognized normally due to severe wear, the classification module will use the damaged waybills image of the current package to compare with the pre-transmitted pre-worn waybills image, and obtain the waybills information of the closest pre-worn waybills image by matching, so as to achieve effective recognition of the unrecognizable waybills.

[0035] The principle of this method does not rely on the restoration of damaged information. Even for highly damaged and unrecognizable waybill information, this method can be used to screen out the closest waybill information. It only needs to be screened out and then manually confirmed, which is still helpful in determining the waybill information of the damaged waybill. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic flow chart of a method performed by a high-efficiency logistics line provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] The technical solution in this application will be described below in conjunction with the accompanying drawings.

[0038] In the following description, many specific details are set forth to facilitate a full understanding of the present application, but the present application can also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only part of the embodiments of the present application, not all of the embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0039] The present application provides a high-efficiency logistics line, which includes the following parts:

[0040] S101: An information acquisition module is used to obtain the source information of the logistics package pile sorted by the current logistics transfer station, and thereby obtain the weather conditions of the transportation route from the source to the current logistics transfer station in the current transportation process.

[0041] Specifically, the high-efficiency logistics line of the present application is used in the logistics sorting line of the logistics transfer station, and the logistics package piles sent to the transfer station need to be sorted and then sent to the next transfer station. In the embodiment of the present application, when the logistics package pile starts to be transferred on the package sorting conveyor belt, the packaging and sending place of the current logistics package pile is identified by manually entering or identifying the identification code, which is used as the source information of the logistics package pile.

[0042] The weather conditions of the transport route include at least the weather of the packaging batch at the packaging and sending location, which is at least divided into sunny days or rainy days. The weather of the packaging and sending location at the packaging and sending location can be obtained through the API interface of a third-party weather data provider.

[0043] S102: A transfer learning module is used to perform transfer learning on the pre-trained logistics waybill recognition basic model according to the weather conditions from the source to the current logistics transfer station, using the waybill data collected from the transportation route, so as to generate a logistics waybill recognition model that is suitable for the corresponding damage types of the transportation route under different weather conditions.

[0044] Specifically, the logistics bill recognition basic model is obtained through the following steps:

[0045] Collect logistics waybill data under different weather conditions, wherein the logistics waybill data includes waybill images in normal state, wet in rainy days, friction and wear, collision damage, etc., and waybill information recorded in the waybill images, to construct a second training data set;

[0046] The convolutional neural network is used as the network framework of the basic model for logistics waybill recognition. The logistics waybill image data in the second training data machine is used as the input of the basic model for logistics waybill recognition. The corresponding waybill information is used as the label of the basic model for logistics waybill recognition. The basic model for logistics waybill recognition is obtained by training.

[0047] Specifically, using the waybill data collected from the transportation process, transfer learning is performed on the pre-trained logistics waybill recognition basic model to generate a logistics waybill recognition model that is adapted to the corresponding damage type of the transportation route under different weather conditions, including the following steps:

[0048] Collect the logistics bill data generated during the transportation process under different weather conditions of the transportation route.

[0049] Specifically, in the embodiment of the present application, the logistics waybill image and the corresponding waybill information after the transportation process are collected at least under sunny and rainy conditions.

[0050] A preset number of fully connected layers are added before the output layer at the end of the basic model for logistics waybill recognition, and the weights of the added fully connected layers are initialized, so as to obtain a training model for logistics waybill recognition to be transferred;

[0051] Specifically, the preset number here is generally set to 3-5 layers.

[0052] The weight initialization operation can generally set the weight of the newly added fully connected layer to 0, or other autonomously set values.

[0053] The logistics waybill recognition training model to be transferred is trained using the waybill data generated during the transportation process under different weather conditions, so as to obtain a logistics waybill recognition model that is suitable for the corresponding damage types of the transportation route under different weather conditions.

[0054] Specifically, after the above transfer learning operation, at least the logistics waybill recognition model for the transportation route on sunny days and the logistics waybill recognition model for rainy days will be learned.

[0055] S103: an identification module, according to the weather conditions from the source to the current logistics transfer station, selects a logistics waybill identification model trained with the waybill data collected during the corresponding weather conditions to identify the logistics waybill.

[0056] S104: A high wear identification module identifies logistics waybills with a wear degree above a preset wear threshold as waybills to be processed.

[0057] Specifically, the preset wear threshold is a manually set value.

[0058] The degree of wear of different logistics labels is evaluated by the following formula:

[0059]

[0060] Among them, the confidence score is the confidence level of the logistics waybill recognition model in its output results, which is generally the probability of the output result in the softmax layer.

[0061] The average image recognition time is the average processing time for the system to identify the shipping label, and can also be a manually set benchmark value.

[0062] The current image recognition time is the processing time for the logistics waybill.

[0063] α and β are weight coefficients, both of which are artificially set values.

[0064] S105: A pre-worn waybill transformation module is used to obtain a waybill transformation model based on the waybill wear image data in the transportation route from the current logistics transfer station to the next logistics node, and transform the waybill to be processed through the waybill transformation model to generate a pre-worn waybill image of the current waybill to be processed.

[0065] Specifically, the transport bill transformation model is trained based on the wear image data of the transport bill in the transportation route from the current logistics transfer station to the next logistics node, including the following steps:

[0066] Collect the images of waybills before and after the actual transportation from the current logistics transfer station to the next logistics node under different weather conditions to construct the first training data set;

[0067] A generative adversarial network is used as the model framework of the face order transformation model. The weather information during actual transportation and the face order image before actual transportation in the first training data set are used as inputs of the model framework of the face order transformation model. The face order image after actual transportation is used as the label of the model framework of the face order transformation model. The model framework of the face order transformation model is trained to obtain the face order transformation model.

[0068] Specifically, a pre-worn waybill image is generated based on the images with a wear degree above a preset wear threshold and a waybill transformation model corresponding to the transportation route.

[0069] S106: A data transmission module, used to send the pre-worn waybill image and corresponding waybill information to the next logistics node.

[0070] S107: At the next logistics node, the classification module of the next logistics node further performs the following steps:

[0071] It is used to compare the current package’s waybill image with all pre-worn waybill images at the next logistics node when the waybill cannot be recognized normally, so as to identify and obtain the waybill information of the pre-worn waybill image that is closest to the unrecognizable waybill image, as the waybill information of the current unrecognizable logistics waybill.

[0072] Then, the unrecognizable waybills and the matched waybill information are manually reviewed.

[0073] In summary, the beneficial effects of the high-efficiency logistics line provided by the embodiments of the present application are discussed as follows:

[0074] One of the beneficial effects is that the damage pattern will be different in different weather conditions. And because the porters or the handling environment are usually fixed, the damage pattern caused by the corresponding weather will form a fixed pattern. If the general recognition model is directly used to recognize the delivery bill data, this fixed pattern cannot be utilized, and it is difficult to more accurately recognize the logistics information recorded on the damaged logistics delivery bill.

[0075] In this application, the system first uses the transfer learning module to perform targeted transfer learning on the pre-trained recognition model for the corresponding damage caused under different weather conditions on the transportation route, thereby obtaining a logistics waybill recognition model that is adapted to the corresponding damage types of the transportation route under different weather conditions.

[0076] In this way, the present application realizes the ability to dynamically adjust the recognition direction of the face sheet recognition model according to actual environmental conditions and damage characteristics, thereby improving the recognition accuracy and efficiency of damaged face sheets.

[0077] The second beneficial effect is that it should be noted that if we only select different recognition models to identify and read the waybill information according to different conditions (such as weather conditions, environmental factors), this method is usually more suitable for dealing with the problem of identification after the first damage occurs during sorting at the transfer station.

[0078] However, during the initial transportation stage, the package label may be dampened by rain, causing the paper to soften and the ink to smudge; during the subsequent handling process, the label may be torn and worn due to friction and collision. In this way, different types of damage are superimposed on each other, making the damage to the label more serious, presenting a complex and unpredictable damage pattern. This complex damage often exceeds the processing capacity of the recognition model, and the aforementioned method is difficult to continue to work. Therefore, simply using the corresponding recognition model under different conditions cannot effectively deal with the difficulty of label recognition caused by the superposition of wear during multiple transportation processes.

[0079] In this application, at the current logistics transfer station, the system identifies the waybills whose degree of wear exceeds a preset threshold, and estimates that these waybills may be further damaged in the next transportation stage due to the large degree of wear and tear, resulting in failure to be properly identified at the next logistics node.

[0080] In the current logistics transfer station, the system of this application uses a high wear recognition module to identify logistics waybills with a degree of wear exceeding a preset threshold and mark them as to-be-processed waybills. For these to-be-processed waybills, the pre-worn waybills transformation module uses the collected waybills wear image data to train a waybills transformation model in advance based on the transportation route and weather conditions from the current logistics transfer station to the next logistics node. This model can simulate the further damage that may occur to the waybills in the next transportation stage. The model is used to transform the waybills to be processed, and further pre-worn waybills images are generated based on the wear that has affected the information, and these pre-worn waybills images and corresponding waybills information are sent to the next logistics node through the data transmission module. When the package arrives at the next logistics node, if the waybills cannot be recognized normally due to severe wear, the classification module will use the damaged waybills image of the current package to compare with the pre-transmitted pre-worn waybills image, and obtain the waybills information of the closest pre-worn waybills image by matching, so as to achieve effective recognition of the unrecognizable waybills.

[0081] The principle of this method is that it does not require the restoration of damaged information. Even for highly damaged and unrecognizable waybill information, this method can be used to screen out the closest waybill information. It only needs to be screened out and then manually confirmed, which is still helpful in determining the waybill information of the damaged waybill.

[0082] In summary, the logistics waybill identification process generally occurs in three stages: collection, transit and distribution.

[0083] During the process from collection to the transfer station: Since the waybill recognition problem encountered at the initial transfer station is a problem of recognition after the first damage occurs, the solution proposed in this application can dynamically adjust the recognition direction of the recognition model, and perform more efficient and accurate recognition of the logistics waybill based on the weather and common damage conditions at the source.

[0084] During the process of distributing the waybill to the delivery personnel at the transfer station: When the waybill has been worn out due to multiple transportations and cannot be recognized normally in the end, a pre-worn waybill image can be generated in advance while it can still be accurately recognized during transfer. During distribution and delivery, the pre-worn waybill image can be used to match the unrecognizable waybill, thereby obtaining the damaged and difficult-to-recognize waybill information more accurately and quickly, thereby achieving faster logistics delivery.

[0085] It should be noted that the embodiment of the present application utilizes the advantages of both methods and combines them organically.

[0086] When performing the first recognition, it is not appropriate to use the method of generating a pre-worn face sheet image in advance, because it is impossible to determine the location, shape and size of the contamination that causes the unrecognizable face sheet image at the beginning, and it is impossible to speculate on the subsequent morphological changes of the contamination, and naturally it is impossible to effectively speculate on the contamination of the face sheet. Therefore, this application takes advantage of the single type of contamination from the beginning during the collection to the transfer recognition, and adopts the method of dynamically adjusting the recognition direction of the recognition model to achieve accurate and efficient recognition.

[0087] Subsequently, we also took into account the problem of severely damaged logistics waybills, which could be unrecognizable due to the accumulation of contamination after multiple transfers. We adopted the method of generating pre-worn waybill images in advance. During distribution and delivery, we could use the pre-worn waybill images to match the unrecognizable waybills, thereby obtaining the damaged and difficult-to-recognize waybill information more accurately and quickly.

[0088] Therefore, the high-efficiency logistics line provided in the embodiment of the present application can more accurately identify logistics waybills.

[0089] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. In addition, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or device including the elements. Moreover, in the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can represent A or B; "and / or" in this article is only a kind of association relationship describing the associated objects, indicating that there can be three relationships, for example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. Furthermore, in the description of the embodiments of the present application, “plurality” refers to two or more than two.

[0090] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. High efficiency logistics line, characterized by: The high-efficiency logistics line includes the following parts: An information acquisition module is used to obtain the source information of the logistics package pile sorted by the current logistics transfer station, and thereby obtain the weather conditions of the transportation route from the source to the current logistics transfer station during the current transportation process; A transfer learning module is used to perform transfer learning on the pre-trained logistics waybill recognition basic model according to the weather conditions from the source to the current logistics transfer station, using the waybill data collected from the transportation route, so as to generate a logistics waybill recognition model that is suitable for the corresponding damage type of the transportation route under different weather conditions; The recognition module selects a logistics waybill recognition model trained with the waybill data collected during the corresponding weather conditions according to the weather conditions from the source to the current logistics transfer station, so as to recognize the logistics waybill.

2. The high-efficiency logistics line according to claim 1, characterized in that: The high-efficiency logistics line also includes the following parts: A high wear identification module identifies logistics waybills with a wear degree above a preset wear threshold as waybills to be processed; A pre-worn bill transformation module is used to train a bill transformation model based on bill wear image data in the transportation route from the current logistics transfer station to the next logistics node, transform the bill to be processed through the bill transformation model, and generate a pre-worn bill image of the current bill to be processed; A data transmission module, used for sending the pre-worn bill image and corresponding bill information to the next logistics node; The classification module further performs the following steps: It is used to compare the current package’s waybill image with all pre-worn waybill images at the next logistics node when the waybill cannot be recognized normally, so as to identify and obtain the waybill information of the closest pre-worn waybill image as the waybill information of the currently unrecognizable logistics waybill.

3. The high-efficiency logistics line according to claim 2, characterized in that: The transport bill transformation model is trained based on the wear image data of the transport bill in the transportation route from the current logistics transfer station to the next logistics node, including the following steps: Collect the images of waybills before and after the actual transportation from the current logistics transfer station to the next logistics node under different weather conditions to construct the first training data set; A generative adversarial network is used as the model framework of the face order transformation model. The weather information during actual transportation and the face order image before actual transportation in the first training data set are used as inputs of the model framework of the face order transformation model. The face order image after actual transportation is used as the label of the model framework of the face order transformation model. The model framework of the face order transformation model is trained to obtain the face order transformation model.

4. The high-efficiency logistics line according to claim 1, characterized in that: The logistics bill recognition basic model is obtained by the following steps: Collect logistics waybill data under different weather conditions, wherein the logistics waybill data includes waybill images in normal state, wet in rainy days, friction and wear, collision damage, etc., and waybill information recorded in the waybill images, to construct a second training data set; The convolutional neural network is used as the network framework of the basic model for logistics waybill recognition. The logistics waybill image data in the second training data machine is used as the input of the basic model for logistics waybill recognition. The corresponding waybill information is used as the label of the basic model for logistics waybill recognition. The basic model for logistics waybill recognition is obtained by training.

5. The high-efficiency logistics line according to claim 1, characterized in that: Using the waybill data collected from the transportation process, transfer learning is performed on the pre-trained logistics waybill recognition basic model to generate a logistics waybill recognition model that is adapted to the corresponding damage type of the transportation route under different weather conditions, including the following steps: Collecting the waybill data generated during the transportation process under different weather conditions of the transportation route; A preset number of fully connected layers are added before the output layer at the end of the basic model for logistics waybill recognition, and the weights of the added fully connected layers are initialized, so as to obtain a training model for logistics waybill recognition to be transferred; The logistics waybill recognition training model to be transferred is trained using the waybill data generated during the transportation process under different weather conditions, so as to obtain a logistics waybill recognition model that is suitable for the corresponding damage types of the transportation route under different weather conditions.

6. The high-efficiency logistics line according to claim 1, characterized in that: The different weathers include rainy days and sunny days.

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