Method and device for determining damage association of damaged express, equipment and storage medium

Abstract

The invention relates to a damaged express item damage association determination method and device, equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: acquiring a target image collected for a damaged express in a logistics link of the damaged express; acquiring a target video collected for the damaged express in the logistics link; detecting violation items respectively corresponding to each logistics node of the logistics link based on the target image and the target video; obtaining logistics information and claim settlement communication records of the damaged express; and based on the violation items, the logistics information and the claim settlement communication records, determining a damage association result for each logistics node. The method can reduce the labor cost associated with the determination of the damage of the damaged express.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, device, and storage medium for determining the damage association of damaged express shipments. Background Technology

[0002] As the public's demands for delivery quality continue to rise, courier companies are also increasing their efforts to manage damaged packages year by year. In order to determine responsibility for the damage, courier companies need to identify whether there are problems at each logistics node in the damaged package's logistics chain.

[0003] In traditional technology, the main method involves manually tracking and recording express parcels across cameras on a video surveillance platform. The process of manually reviewing the target video to determine if any violations have occurred requires a significant amount of manpower, resulting in high labor costs. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, equipment, and storage medium for determining the damage association of damaged parcels, which can reduce the human cost of determining the damage association of damaged parcels, in order to address the above-mentioned technical problems.

[0005] Firstly, this application provides a method for determining the damage association of damaged express shipments, the method comprising:

[0006] Acquire target images of the damaged express shipment during its logistics chain;

[0007] Acquire target video of the damaged package during the logistics chain;

[0008] Based on the target image and target video, violations corresponding to each logistics node in the logistics chain were detected;

[0009] Obtain the logistics information and claims communication records of the damaged shipment;

[0010] Based on the aforementioned violations, logistics information, and claims communication records, the damage correlation results for each logistics node are determined.

[0011] In one embodiment, acquiring the target video of the damaged package during the logistics chain includes:

[0012] For each logistics node in the logistics chain, the video captured by the video acquisition device corresponding to the logistics node is obtained to obtain the recorded video corresponding to the logistics node.

[0013] Determine the scanning time for the damaged package by the scanning device corresponding to the logistics node;

[0014] By extracting a video segment containing the scanning time from the recorded video, a target video of the damaged package located at the logistics node is obtained.

[0015] In one embodiment, the target image includes images of the inner and outer packaging of the damaged package taken by a barcode scanner during pickup and delivery, six-sided scan images of the damaged package taken by a six-sided scanner at each logistics node and security inspection images taken by a security inspection device, and repackaging images of the damaged package taken when it is repackaged during the logistics process.

[0016] In one embodiment, detecting violations corresponding to each logistics node in the logistics chain based on the target image and target video includes:

[0017] For each logistics node in the logistics chain, the inner and outer packaging images, six-sided scan images, and security inspection images of the logistics node are respectively input into the first visual large model to determine the first violation of the logistics node.

[0018] If the damaged parcel is repackaged during the logistics process, the repackaging images, damage descriptions, and recorded videos of the damaged parcel at each logistics node in the logistics chain are input into the multimodal big data model to determine the second violation corresponding to each logistics node.

[0019] For each logistics node in the logistics chain, the target video of the logistics node is input into a second visual large model to determine the third violation of the logistics node;

[0020] For each logistics node in the logistics chain, the first violation, the second violation, and the third violation of the logistics node are merged to obtain the violation corresponding to the logistics node.

[0021] In one embodiment, determining the damage correlation results for each logistics node based on the violation, the logistics information, and the claims communication records includes:

[0022] The violation, the logistics information, and the claims communication record are input into a large language model to extract the event features of the violation, the information features of the logistics information, and the content features of the claims communication record.

[0023] Based on the event characteristics, information characteristics, and content characteristics, damage association results are determined for each logistics node.

[0024] In one embodiment, determining the damage correlation results for each logistics node based on the violation, the logistics information, and the claims communication records includes:

[0025] Based on the aforementioned event characteristics, information characteristics, and content characteristics, preliminary diagnostic results are determined for each logistics node; wherein, the preliminary diagnostic result for each logistics node includes a responsibility conclusion for the logistics node and the reasons for the responsibility conclusion;

[0026] Based on the initial diagnosis results of each logistics node, the damage correlation results of each logistics node are determined.

[0027] Secondly, this application provides a device for determining the damage association of damaged express shipments, the device comprising:

[0028] The acquisition module is used to acquire target images of the damaged express shipment during the logistics chain; and to acquire target videos of the damaged express shipment during the logistics chain.

[0029] The detection module is used to detect violations corresponding to each logistics node in the logistics chain based on the target image and target video.

[0030] The acquisition module is also used to acquire the logistics information of the damaged package, as well as the claims communication records between the user and customer service during the claims process for the damaged package;

[0031] The determination module is used to determine the damage association results for each logistics node based on the violation, the logistics information, and the claims communication records.

[0032] Thirdly, this application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the various method embodiments of this application.

[0033] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the various method embodiments of this application.

[0034] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps in the various method embodiments of this application.

[0035] The aforementioned method, apparatus, equipment, and storage medium for determining the damage association of damaged express shipments involve: acquiring target images of the damaged express shipments collected along the logistics chain; acquiring target videos of the damaged express shipments collected along the logistics chain; detecting violations corresponding to each logistics node based on the target images and videos; acquiring logistics information and claims communication records of the damaged express shipments; and determining the damage association results for each logistics node based on the violations, logistics information, and claims communication records. The damage association results refer to the attribution of responsibility for the damage to the express shipment. Compared to the traditional method of determining damage association results for each logistics node through manual collection and review of evidence data, this application combines automated acquisition of image and video evidence and automated detection of violations. Then, based on the violations at each logistics node, the logistics information of the damaged express shipment, and the claims communication records, the damage association results for each logistics node are determined. The entire process of determining damage association results is highly automated, reducing the labor costs associated with determining the damage association of damaged express shipments. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is an application environment diagram of a method for determining the damage association of damaged express shipments in one embodiment;

[0038] Figure 2 This is a flowchart illustrating a method for determining the damage association of a damaged package in one embodiment;

[0039] Figure 3 This is a schematic diagram illustrating the logistics information of a damaged package in one embodiment;

[0040] Figure 4 This is a schematic diagram of the inner and outer packaging images of a damaged package in one embodiment;

[0041] Figure 5 This is a schematic diagram of a video showing the unloading, sorting, and loading of damaged parcels in one embodiment;

[0042] Figure 6 This is a schematic diagram illustrating the corrupted association results in one embodiment;

[0043] Figure 7 This is a structural block diagram of a device for determining the damage association of a damaged express shipment in one embodiment;

[0044] Figure 8 This is an internal structural diagram of a computer device in one embodiment;

[0045] Figure 9 This is a diagram of the internal structure of a computer device in another embodiment. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0047] The method for determining the damage association of damaged express shipments provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. The data storage system can be set up independently and can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed on the cloud or other servers. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, cloud security, host security and other network security services, CDN, and big data and artificial intelligence platforms. Terminal 102 and server 104 can be directly or indirectly connected via wired or wireless communication, which is not limited herein.

[0048] Server 104 can obtain target images and videos of damaged parcels collected in the logistics chain from terminal 102. Based on the target images and videos, server 104 can detect violations corresponding to each logistics node in the logistics chain; obtain logistics information and claims communication records of the damaged parcels; and determine the damage correlation results for each logistics node based on the violations, logistics information, and claims communication records.

[0049] It is understood that this embodiment does not limit this aspect. Figure 1 The application scenarios shown are for illustrative purposes only and are not limited to these.

[0050] In one embodiment, such as Figure 2 As shown, a method for determining the damage association of damaged express shipments is provided. This method can be applied to computer equipment, which can be a terminal or a server. That is, the method can be executed by the terminal or the server alone, or it can be implemented through interaction between the terminal and the server. This embodiment illustrates the application of this method to a computer device as an example, including the following steps:

[0051] Step 202: Obtain the target image of the damaged express shipment in the logistics chain.

[0052] Damaged parcels refer to express delivery packages that are damaged. Target images are images collected by each logistics node in the logistics chain for damaged parcels.

[0053] Specifically, the logistics chain of damaged express shipments includes multiple logistics nodes. Each logistics node can acquire images of the damaged express shipment using image acquisition equipment, obtaining a target image of the damaged express shipment within that node. Computer equipment can then acquire these target images of the damaged express shipment at each logistics node.

[0054] Step 204: Obtain the target video collected for damaged parcels in the logistics chain.

[0055] Among them, the target video is the video collected by each logistics node in the logistics chain for damaged express packages.

[0056] Specifically, each logistics node in the damaged express delivery chain can use video capture equipment to collect video of the damaged express delivery at each logistics node, obtaining recorded video of the damaged express delivery at each logistics node. Computer equipment can acquire the recorded video of the damaged express delivery at each logistics node and obtain the target video of the damaged express delivery at each logistics node based on the recorded video.

[0057] In one embodiment, for each logistics node in the damaged express delivery logistics chain, the computer device can directly use the recorded video of the damaged express delivery at that logistics node as the target video of the damaged express delivery at that logistics node.

[0058] Step 206: Based on the target image and target video, detect the violations corresponding to each logistics node in the logistics chain.

[0059] Among them, the violations are those that occur at logistics nodes during the delivery process.

[0060] In one embodiment, the target image and target video are input into a deep learning model to detect violations corresponding to each logistics node in the logistics chain.

[0061] Step 208: Obtain the logistics information and claims communication records of the damaged package.

[0062] The logistics information includes the appearance, weight, type of item, and information about each logistics node the damaged package passed through.

[0063] Specifically, the computer equipment can obtain the tracking number of a damaged package and query its logistics information based on that number. The tracking number is a unique identifier used throughout the package's logistics process. After receiving a damaged package, the user can contact the courier company's customer service for compensation, and the computer equipment can access the communication records between the user and customer service during the compensation process.

[0064] Step 210: Based on the violations, logistics information, and claims communication records, determine the damage correlation results for each logistics node.

[0065] Among them, the damage-related result refers to the result of attributing responsibility for the damage to the express shipment.

[0066] In one embodiment, the computer device can input violations, logistics information, and claims communication records into a deep learning model to determine the damage correlation results for each logistics node based on the violations, logistics information, and claims communication records.

[0067] The aforementioned method for determining the damage association of damaged express shipments involves: acquiring target images of the damaged shipment along its logistics chain; acquiring target videos of the damaged shipment along its logistics chain; detecting violations at each logistics node based on the target images and videos; acquiring logistics information and claims communication records for the damaged shipment; and determining the damage association results for each logistics node based on the violations, logistics information, and claims communication records. The damage association results refer to the attribution of responsibility for the damage to the shipment. Compared to the traditional method of manually collecting and reviewing evidence data to determine the damage association results for each logistics node, this application combines automated acquisition of image and video evidence with automated detection of violations. Then, based on the violations at each logistics node, the logistics information of the damaged shipment, and the claims communication records, the damage association results for each logistics node are determined. The entire process of determining the damage association results is highly automated, reducing the labor costs associated with determining the damage association of damaged express shipments.

[0068] Furthermore, compared to relying solely on images to determine damage association results, this application combines images and videos to determine damage association results for logistics nodes through which damaged packages pass, thereby improving the accuracy of damage association determination for damaged packages.

[0069] In one embodiment, obtaining the target video of the damaged package in the logistics chain includes: for each logistics node in the logistics chain, obtaining the video captured by the video capture device corresponding to the logistics node to obtain the recorded video corresponding to the logistics node; determining the scanning time of the scanning device corresponding to the logistics node scanning the damaged package; and extracting a video segment containing the scanning time from the recorded video to obtain the target video of the damaged package located at the logistics node.

[0070] The video file used to record the video is larger than the video file used to record the target video.

[0071] In one embodiment, the computer device can extract a video segment of preset duration before and after the scan time from the recorded video, and use the extracted video segment as the target video of the damaged package at the logistics node. For example, the computer device can extract a one-minute video segment before and after the scan time from the recorded video, and use these two minutes of extracted video as the target video of the damaged package at the logistics node.

[0072] In one embodiment, the computer device may also randomly extract video segments containing the scan time from the recorded video and use the extracted video segments as the target video of the damaged package at the logistics node.

[0073] In the above embodiments, by scanning the scan time of the damaged package using the scanning device corresponding to the logistics node, the arrival time of the damaged package at the logistics node can be quickly determined. By extracting a video segment containing the scan time from the entire recorded video, the target video containing the damaged package can be quickly obtained. Furthermore, since the amount of data in the extracted target video is relatively small, the detection speed of subsequent violations can be improved, thereby increasing the efficiency of assigning responsibility for damaged packages.

[0074] In one embodiment, the target images include images of the inner and outer packaging of the damaged package taken by a barcode scanner during pickup and delivery, six-sided scan images of the damaged package taken by a six-sided scanner at each logistics node, security inspection images taken by a security inspection device, and repackaging images of the damaged package taken when it is repackaged during the logistics process.

[0075] The six-sided scan image includes multiple images collected from six sides of the damaged package.

[0076] In one embodiment, the inner and outer packaging images include images of the inner packaging, outer packaging, and consignment items of the damaged shipment.

[0077] In the above embodiments, by collecting different types of target images at each logistics node in the damaged express delivery logistics chain, the richness of the target images can be improved, thereby improving the accuracy of determining responsibility for damaged express delivery.

[0078] In one embodiment, based on target images and target videos, violations corresponding to each logistics node in the logistics chain are detected, including: for each logistics node in the logistics chain, inputting the inner and outer packaging images, six-sided scan images, and security inspection images of the logistics node into a first visual large model to determine the first violation of the logistics node; if the damaged package is repackaged during the logistics process, inputting the repackaging images, damage descriptions, and recorded videos of the damaged package in each logistics node in the logistics chain into a multimodal large model to determine the second violation corresponding to each logistics node; for each logistics node in the logistics chain, inputting the target video of the logistics node into a second visual large model to determine the third violation of the logistics node; and for each logistics node in the logistics chain, merging the first, second, and third violations of the logistics node to obtain the violation corresponding to the logistics node.

[0079] The first violation was determined based on images of the damaged package's inner and outer packaging, six-sided scan images, and security inspection images. The second violation was determined based on repackaging images. The third violation was determined based on the target video.

[0080] In the above embodiments, a large visual model with powerful image processing capabilities is used to detect violations at logistics nodes based on images of inner and outer packaging, six-sided scan images, security inspection images, and target videos. A large multimodal model that supports processing multimodal data is used to detect violations at logistics nodes based on images of repacking, damage descriptions, and recorded videos. This integrates the violations at each logistics node to obtain the final violation information for each logistics node, thereby improving the accuracy of determining violations at logistics nodes.

[0081] In one embodiment, based on violations, logistics information, and claims communication records, the damage correlation results for each logistics node are determined, including: inputting violations, logistics information, and claims communication records into a large language model to extract the event characteristics of violations, the information characteristics of logistics information, and the content characteristics of claims communication records through the large language model; and determining the damage correlation results for each logistics node based on the event characteristics, information characteristics, and content characteristics.

[0082] Specifically, computer equipment can input violations, logistics information, and claims communication records into a large language model, and then extract the event features of violations, the information features of logistics information, and the content features of claims communication records through the large language model. The event features, information features, and content features are then fused to obtain fused features, and the damage correlation results for each logistics node are predicted based on the fused features.

[0083] In the above embodiments, by using a large language model with powerful language processing capabilities based on violations, logistics information, and claims communication records, the damage correlation results for each logistics node can be determined, which can improve the accuracy of liability allocation for damaged express deliveries.

[0084] In one embodiment, determining the damage association results for each logistics node based on event characteristics, information characteristics, and content characteristics includes: determining the preliminary diagnosis results for each logistics node based on event characteristics, information characteristics, and content characteristics; wherein, the preliminary diagnosis results for each logistics node include the responsibility conclusion for the logistics node and the reasons for the responsibility conclusion; and determining the damage association results for each logistics node based on the preliminary diagnosis results for each logistics node.

[0085] In one embodiment, the computer device can determine the preliminary diagnosis results for each logistics node based on event characteristics, information characteristics, and content characteristics, and then output and display the preliminary diagnosis results. Based on the preliminary diagnosis results for each logistics node, the computer device can determine the damage correlation results for each logistics node and output and display the damage correlation results.

[0086] In the above embodiments, the initial diagnosis results of logistics nodes are first determined by the characteristics of the event, information, and content. Then, the damage association results of logistics nodes are determined based on the initial diagnosis results, which can improve the accuracy of liability allocation for damaged express deliveries.

[0087] In one embodiment, this application also provides a system for determining the damage association of damaged parcels, the system including a computer device for executing the method for determining the damage association of damaged parcels of this application. Figure 3 As shown, the system for determining the damage association of damaged parcels operates as follows: The user first enters the tracking number of the damaged parcel. The system will then provide the parcel's logistics information, such as its appearance, weight, type of item, and information about each logistics node it passed through. For example... Figure 4 As shown, when a user selects a receiving location, they can see images of the inner and outer packaging of the damaged package. Figure 5 As shown, when a user selects another logistics node, the system automatically retrieves key videos, i.e., target videos, and simultaneously assesses each target video for any violations. Finally, all violations from logistics nodes, logistics information for damaged packages, and records of claims communication between the user and customer service are aggregated into the large language model and output. Figure 6 The initial diagnosis results and the final damage correlation results are shown.

[0088] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially, these steps are not necessarily executed in that order. Unless otherwise expressly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the above embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0089] Based on the same inventive concept, this application also provides a device for determining the damage association of damaged parcels to implement the aforementioned method for determining the damage association of damaged parcels. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the device for determining the damage association of damaged parcels provided below can be found in the limitations of the method for determining the damage association of damaged parcels described above, and will not be repeated here.

[0090] In one embodiment, such as Figure 7 As shown, a device 700 for determining the damage association of damaged express shipments is provided. This device specifically includes:

[0091] The acquisition module 702 is used to acquire target images of damaged express shipments in the logistics chain; and to acquire target videos of damaged express shipments in the logistics chain.

[0092] The detection module 704 is used to detect violations corresponding to each logistics node in the logistics chain based on the target image and target video.

[0093] The acquisition module 702 is also used to acquire logistics information and claims communication records of damaged express shipments;

[0094] Module 706 is used to determine the damage correlation results for each logistics node based on the violation, logistics information, and claims communication records.

[0095] In one embodiment, the acquisition module 702 is further configured to acquire, for each logistics node in the logistics chain, the video captured by the video acquisition device corresponding to the logistics node, and obtain the recorded video corresponding to the logistics node; determine the scanning time of the scanning device corresponding to the logistics node scanning the damaged package; and extract the video segment containing the scanning time from the recorded video to obtain the target video of the damaged package located at the logistics node.

[0096] In one embodiment, the target images include images of the inner and outer packaging of the damaged package taken by a barcode scanner during pickup and delivery, six-sided scan images of the damaged package taken by a six-sided scanner at each logistics node, security inspection images taken by a security inspection device, and repackaging images of the damaged package taken when it is repackaged during the logistics process.

[0097] In one embodiment, the detection module 704 is further configured to input the inner and outer packaging images, six-sided scan images, and security inspection images of each logistics node in the logistics chain into a first visual large model to determine the first violation of each logistics node; if the damaged parcel is repackaged during the logistics process, the repackaging images, damage descriptions, and recorded videos of the damaged parcels in each logistics node of the logistics chain are input into a multimodal large model to determine the second violation corresponding to each logistics node; for each logistics node in the logistics chain, the target video of the logistics node is input into the second visual large model to determine the third violation of the logistics node; for each logistics node in the logistics chain, the first, second, and third violations of the logistics node are merged to obtain the violation corresponding to the logistics node.

[0098] In one embodiment, the determining module 706 is further configured to input the violation, logistics information, and claims communication records into the big language model, so as to extract the event characteristics of the violation, the information characteristics of the logistics information, and the content characteristics of the claims communication records through the big language model; and determine the damage association results for each logistics node based on the event characteristics, information characteristics, and content characteristics.

[0099] In one embodiment, the determining module 706 is further configured to determine the preliminary diagnosis results for each logistics node based on event characteristics, information characteristics, and content characteristics; wherein, the preliminary diagnosis results for each logistics node include the responsibility conclusion for the logistics node and the reasons for the responsibility conclusion; and determine the damage association results for each logistics node based on the preliminary diagnosis results for each logistics node.

[0100] The aforementioned device for determining the damage association of damaged express shipments acquires target images of the damaged shipment along its logistics chain; acquires target videos of the damaged shipment along the logistics chain; detects violations corresponding to each logistics node based on the target images and videos; acquires logistics information and claims communication records of the damaged shipment; and determines the damage association results for each logistics node based on the violations, logistics information, and claims communication records. The damage association results refer to the attribution of responsibility for the damage to the shipment. Compared to the traditional method of manually collecting and reviewing evidence data to determine the damage association results for each logistics node, this application combines automated acquisition of image and video evidence with automated detection of violations. Then, based on the violations at each logistics node, the logistics information of the damaged shipment, and the claims communication records, the damage association results for each logistics node are determined. The entire process of determining the damage association results is highly automated, reducing the labor costs associated with determining the damage association of damaged express shipments.

[0101] Furthermore, compared to relying solely on images to determine damage association results, this application combines images and videos to determine damage association results for logistics nodes through which damaged packages pass, thereby improving the accuracy of damage association determination for damaged packages.

[0102] Each module in the aforementioned device for determining damage association of damaged parcels can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can invoke and execute the corresponding operations of each module.

[0103] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for determining the damage association of damaged express shipments.

[0104] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 9 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a method for determining the damage association of damaged express shipments. The display unit of the computer device is used to form a visually visible image. It can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0105] Those skilled in the art will understand that Figure 8 and Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0106] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0107] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0108] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0109] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0110] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0111] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0112] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for determining the damage association of damaged express shipments, characterized in that, The method includes: Acquire target images of the damaged express shipment during its logistics chain; Acquire target video of the damaged package during the logistics chain; Based on the target image and target video, violations corresponding to each logistics node in the logistics chain were detected; Obtain the logistics information and claims communication records of the damaged shipment; Based on the aforementioned violations, logistics information, and claims communication records, the damage correlation results for each logistics node are determined.

2. The method according to claim 1, characterized in that, The acquisition of the target video collected during the logistics chain for the damaged package includes: For each logistics node in the logistics chain, the video captured by the video acquisition device corresponding to the logistics node is obtained to obtain the recorded video corresponding to the logistics node. Determine the scanning time for the damaged package by the scanning device corresponding to the logistics node; By extracting a video segment containing the scanning time from the recorded video, a target video of the damaged package located at the logistics node is obtained.

3. The method according to claim 1, characterized in that, The target images include images of the inner and outer packaging of the damaged parcel taken by a barcode scanner during pickup and delivery, six-sided scan images of the damaged parcel taken by a six-sided scanner at each logistics node, security inspection images taken by a security inspection device, and repackaging images of the damaged parcel taken when it is repackaged during the logistics process.

4. The method according to claim 3, characterized in that, The detection of violations corresponding to each logistics node in the logistics chain based on the target image and target video includes: For each logistics node in the logistics chain, the inner and outer packaging images, six-sided scan images, and security inspection images of the logistics node are respectively input into the first visual large model to determine the first violation of the logistics node. If the damaged parcel is repackaged during the logistics process, the repackaging images, damage descriptions, and recorded videos of the damaged parcel at each logistics node in the logistics chain are input into the multimodal big data model to determine the second violation corresponding to each logistics node. For each logistics node in the logistics chain, the target video of the logistics node is input into a second visual large model to determine the third violation of the logistics node; For each logistics node in the logistics chain, the first violation, the second violation, and the third violation of the logistics node are merged to obtain the violation corresponding to the logistics node.

5. The method according to claim 1, characterized in that, The determination of damage correlation results for each logistics node based on the violation, the logistics information, and the claims communication records includes: The violation, the logistics information, and the claims communication record are input into a large language model to extract the event features of the violation, the information features of the logistics information, and the content features of the claims communication record. Based on the event characteristics, information characteristics, and content characteristics, damage association results are determined for each logistics node.

6. The method according to claim 5, characterized in that, The determination of damage association results for each logistics node based on the event characteristics, information characteristics, and content characteristics includes: Based on the characteristics of the matter, the characteristics of the information, and the characteristics of the content, the preliminary diagnosis results for each logistics node are determined; wherein, the preliminary diagnosis results for each logistics node include the responsibility conclusion for the logistics node and the reasons for the responsibility conclusion; Based on the initial diagnosis results of each logistics node, the damage correlation results of each logistics node are determined.

7. A device for determining the damage association of damaged express shipments, characterized in that, The device includes: The acquisition module is used to acquire target images of the damaged express shipment during the logistics chain; and to acquire target videos of the damaged express shipment during the logistics chain. The detection module is used to detect violations corresponding to each logistics node in the logistics chain based on the target image and target video. The acquisition module is also used to acquire the logistics information and claims communication records of the damaged express shipment; The determination module is used to determine the damage association results for each logistics node based on the violation, the logistics information, and the claims communication records.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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