An artificial intelligence-based logistics cargo right transfer monitoring system

Abstract

The present invention discloses an artificial intelligence-based logistics goods ownership transfer monitoring system, which relates to the technical field of logistics monitoring. The system discloses a goods ownership transfer node determination module, a goods ownership transfer monitoring module, and a goods ownership transfer analysis module. By setting the goods ownership transfer node determination module and the goods ownership transfer monitoring module, each logistics goods ownership transfer node existing in the system can be monitored, and the monitoring requirements of the goods and the goods ownership transfer nodes are comprehensively analyzed. Different video frame extraction standards are customized for the goods ownership transfer videos to ensure reasonable monitoring and analysis of the goods ownership transfer process, and the monitoring and analysis intensity of the system is reasonably allocated. By setting the goods ownership transfer analysis module, the goods ownership transfer process is deeply analyzed through a neural network model, and the goods ownership transfer process is dynamically analyzed. It is comprehensively analyzed whether the goods ownership transfer process is compliant, and corresponding measures are taken for the goods ownership transfer process.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics monitoring, and more specifically, it relates to a logistics goods right transfer monitoring system based on artificial intelligence. Background Art

[0002] In the logistics industry, the transfer of goods rights is a crucial link, which involves the transfer of the ownership of goods, the division of responsibilities, and subsequent logistics processes. The traditional process of goods rights transfer often relies on manual monitoring and paper records. This method is not only inefficient but also prone to errors, making it difficult to ensure the accuracy and compliance of the goods rights transfer process.

[0003] With the rapid development of the logistics industry and the continuous progress of intelligent technologies, people have put forward higher requirements for the monitoring and management of the goods rights transfer process. Traditional monitoring means can no longer meet the needs of modern logistics for real-time, accuracy, and traceability. Therefore, it is particularly important to develop an efficient and intelligent logistics goods rights transfer monitoring system.

[0004] However, most of the existing logistics monitoring systems can only achieve simple video monitoring and video playback functions, lacking in-depth analysis and dynamic monitoring of the goods rights transfer process. These systems often cannot accurately judge whether the goods rights transfer process is compliant, nor can they take corresponding measures in a timely manner to deal with possible problems. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide a logistics goods rights transfer monitoring system based on artificial intelligence.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A logistics goods rights transfer monitoring system based on artificial intelligence, including a goods rights transfer node determination module, a goods rights transfer monitoring module, and a goods rights transfer analysis module;

[0008] The goods rights transfer node determination module is used to determine the goods rights transfer nodes that the goods will pass through;

[0009] When the goods arrive at each goods rights transfer node and the goods rights transfer starts, the goods rights transfer monitoring module monitors the goods rights transfer process, generates a goods rights transfer video in real time, obtains the comprehensive monitoring value of the goods, and adjusts the frame extraction standard of the goods rights transfer video based on the comprehensive monitoring value;

[0010] Each time a frame is extracted, the goods rights transfer analysis module generates a goods rights transfer photo, obtains the goods rights transfer value of the goods rights transfer photo, and determines whether to interrupt the goods rights transfer process of the goods based on the comparison result between the goods rights transfer value and the goods rights transfer standard value.

[0011] Further, obtain the comprehensive monitoring value of the goods, and adjust the frame extraction standard of the video for transfer of goods ownership based on the comprehensive monitoring value. Specifically: obtain the comprehensive monitoring value Kpre of the goods, and perform frame extraction processing on the video for transfer of goods ownership every second.

[0012] Further, the method for obtaining the comprehensive monitoring value Kpre of the goods is as follows: obtain the basic monitoring adjustment value JCJ of the goods ownership transfer node where the goods are located, obtain the type of the goods, obtain the type monitoring adjustment value YCY of this goods type, and use the formula to obtain the comprehensive monitoring value Kpre of the goods, where wa is the basic coefficient and wb is the type coefficient.

[0013] Further, the method for obtaining the basic monitoring adjustment value JCJ of the goods ownership transfer node where the goods are located is as follows: obtain all transfer alarm records and all transfer interruption records generated by the goods ownership transfer node within the duration T, mark the total number of transfer alarm records as NumbGJ, mark the total number of transfer interruption records as NumbZD, sort all transfer alarm records in the order of the transfer alarm time, calculate the time difference between the transfer alarm times of two adjacent transfer alarm records after sorting to obtain the transfer alarm interval, sum up all transfer alarm intervals and take the average value to obtain the average alarm interval AVJGJ, sort all transfer interruption records in the order of the transfer interruption time, calculate the time difference between the transfer interruption times of two adjacent transfer interruption records after sorting to obtain the transfer interruption interval, sum up all transfer interruption intervals and take the average value to obtain the average interruption interval AVJZD, and use the formula to obtain the basic monitoring adjustment value JCJ of the goods ownership transfer node, where R1 is the first alarm coefficient, R2 is the first interruption coefficient, R3 is the second alarm coefficient, and R4 is the second interruption coefficient.

[0014] Further, the method for obtaining the type monitoring adjustment value YCY of the goods type is as follows: obtain all photos of goods ownership transfer of goods of the same goods type within the duration T, obtain the goods ownership transfer value of the photos of goods ownership transfer, sum up the goods ownership transfer values of all photos of goods ownership transfer and take the average value to obtain the type monitoring adjustment value YCY of this goods type.

[0015] Further, the transfer alarm record includes the goods ownership transfer node number and the transfer alarm time, and the transfer interruption record includes the goods ownership transfer node number and the transfer interruption time.

[0016] Further, the method for obtaining the goods right transfer value of the goods right transfer photo is as follows: extract the features of the goods right transfer photo, organize the extracted features into a goods right transfer feature set, obtain the goods type of the goods, obtain the goods right transfer feature model of the goods type, use the goods right transfer feature set as the input data of the goods right transfer feature model, and the goods right transfer feature model outputs the goods right transfer value of the goods right transfer photo.

[0017] Further, based on the comparison result between the goods right transfer value and the goods right transfer standard value, determine whether to interrupt the goods right transfer process of the goods. Specifically: set the goods right transfer standard value. When the goods right transfer value of the goods right transfer photo is greater than or equal to the goods right transfer standard value, update the node transfer process value of the goods at this goods right transfer node. Set the node transfer process threshold. When the node transfer process value is greater than or equal to the node transfer process threshold, send an alarm message to the goods right transfer personnel in this goods right transfer node, synchronously generate a transfer alarm record, and continue the goods right transfer process of the goods. When the node transfer process value is less than the node transfer process threshold, continue the goods right transfer process of the goods;

[0018] When the goods right transfer value of the goods right transfer photo is less than the goods right transfer standard value, interrupt the goods right transfer process of the goods and synchronously generate a transfer interruption record.

[0019] Further, the update method of the node transfer process value of the goods at this goods right transfer node is as follows: obtain all the goods right transfer photos generated when the goods right of the goods moves at this goods right transfer node, obtain the goods right transfer value of the goods right transfer photo, sort all the goods right transfer values in the order of generation of the goods right transfer photo, calculate the difference between two adjacent goods right transfer values after sorting and take the absolute value to obtain the process compliance fluctuation value, sum up all the process compliance fluctuation values and take the average value to obtain the process compliance fluctuation average value Comtpy. Sum up two adjacent goods right transfer values after sorting to obtain the stage transfer value. Set the stage transfer limit value. When the stage transfer value is greater than or equal to the stage transfer limit value, no further processing is performed. When the stage transfer value is less than the stage transfer limit value, increase the number of transfer fuzzy stages by one, mark the number of transfer fuzzy stages as Vagus, and use the formula to obtain the node transfer process value of the goods at this goods right transfer node.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] Set up a goods right transfer node determination module and a goods right transfer monitoring module, which can monitor each logistics goods right transfer node existing in the system, comprehensively analyze the monitoring requirements of the goods and the goods right transfer node, customize different video frame extraction standards for the goods right transfer video, ensure reasonable monitoring and analysis of the goods right transfer process, and reasonably allocate the monitoring and analysis intensity of the system;

[0022] Set up a cargo ownership transfer analysis module to deeply analyze the process of cargo ownership transfer through a neural network model, dynamically analyze the process of cargo ownership transfer, comprehensively analyze whether the process of cargo ownership transfer is compliant, and take corresponding measures for the process of cargo ownership transfer. Brief Description of the Drawings

[0023] Figure 1 It is a system flow chart of a logistics cargo ownership transfer monitoring system based on artificial intelligence;

[0024] Figure 2 It is a decision flow chart for determining whether to interrupt the process of cargo ownership transfer;

[0025] Figure 3 It is a system module diagram of a logistics cargo ownership transfer monitoring system based on artificial intelligence. Detailed Implementation Manner

[0026] Refer to Figures 1-3 A logistics cargo ownership transfer monitoring system based on artificial intelligence includes a cargo ownership transfer node determination module, a cargo ownership transfer monitoring module, and a cargo ownership transfer analysis module.

[0027] Cargo ownership transfer node determination module: Determine the cargo ownership transfer nodes that the goods need to pass through (the starting point of the logistics goods transportation is the shipper, and the ending point is the consignee. There are multiple cargo ownership transfers between the shipper and the consignee, and the transfer location is called the cargo ownership transfer node. The cargo ownership transfer node is commonly known as the logistics transfer point. The goods need to transfer the goods and the cargo ownership at this location. Before the logistics transportation of the goods, all the cargo ownership transfer nodes that need to be passed through later need to be determined).

[0028] Cargo ownership transfer monitoring module: Determine the type of the goods. When the goods reach each cargo ownership transfer node and start the cargo ownership transfer, monitor the process of cargo ownership transfer, generate a cargo ownership transfer video in real time, obtain the comprehensive monitoring value Kpre of the goods, and perform frame extraction on the cargo ownership transfer video every second.

[0029] The method for obtaining the comprehensive monitoring value Kpre of the goods is as follows: Obtain the basic monitoring adjustment value JCJ of the cargo ownership transfer node where the goods are located, obtain the type of the goods, obtain the type monitoring adjustment value YCY of this type of goods, and use the formula to obtain the comprehensive monitoring value Kpre of the goods, where wa is the basic coefficient, wb is the type coefficient, the value of wa is 1.21, and the value of wb is 0.87.

[0030] The method for obtaining the basic monitoring adjustment value JCJ of the goods ownership transfer node where the goods are located is as follows: Obtain all transfer alarm records and all transfer interruption records generated within the T time period at the goods ownership transfer node. Mark the total number of transfer alarm records as NumbGJ, and mark the total number of transfer interruption records as NumbZD. Sort all transfer alarm records in the order of the transfer alarm time. Calculate the time difference between the transfer alarm times of two adjacent transfer alarm records after sorting to obtain the transfer alarm interval. Sum up all transfer alarm intervals and take the average to obtain the average alarm interval AVJGJ. Sort all transfer interruption records in the order of the transfer interruption time. Calculate the time difference between the transfer interruption times of two adjacent transfer interruption records after sorting to obtain the transfer interruption interval. Sum up all transfer interruption intervals and take the average to obtain the average interruption interval AVJZD. Use the formula to obtain the basic monitoring adjustment value JCJ of this goods ownership transfer node, where R1 is the first alarm coefficient, R2 is the first interruption coefficient, R3 is the second alarm coefficient, and R4 is the second interruption coefficient. The value of R1 is 0.45, the value of R2 is 0.68, the value of R3 is 0.37, and the value of R4 is 0.52.

[0031] The method for obtaining the type monitoring adjustment value YCY of the goods type is as follows: Obtain all goods ownership transfer photos of goods of the same goods type within the T time period. Obtain the goods ownership transfer values of the goods ownership transfer photos. Sum up the goods ownership transfer values of all goods ownership transfer photos and take the average to obtain the type monitoring adjustment value YCY of this goods type.

[0032] Set up a goods ownership transfer node determination module and a goods ownership transfer monitoring module, which can monitor each logistics goods ownership transfer node existing in the system, comprehensively analyze the monitoring requirements of the goods and the goods ownership transfer node, customize different video frame extraction standards for the goods ownership transfer video, ensure reasonable monitoring and analysis of the goods ownership transfer process, and reasonably allocate the monitoring and analysis intensity of the system.

[0033] Goods ownership transfer analysis module: Each time a frame is extracted, a photo of goods ownership transfer is generated, and the goods ownership transfer value of this photo is obtained. A standard value for goods ownership transfer is set (the standard value for goods ownership transfer is a preset value customized in the system, and the size of the preset value is adjusted according to requirements). When the goods ownership transfer value of the goods ownership transfer photo is greater than or equal to the standard value for goods ownership transfer, the node transfer process value of the goods at this goods ownership transfer node is updated. A threshold for the node transfer process is set (the threshold for the node transfer process is a preset value customized in the system, and the size of the preset value is adjusted according to requirements). When the node transfer process value is greater than or equal to the threshold for the node transfer process, an alarm message is sent to the personnel responsible for goods ownership transfer in this goods ownership transfer node, and a transfer alarm record is generated synchronously. The transfer alarm record includes the goods ownership transfer node number (each goods ownership transfer node corresponds to an independent number) and the alarm time of transfer, and the goods ownership transfer process of the goods continues. When the node transfer process value is less than the threshold for the node transfer process, the goods ownership transfer process of the goods continues.

[0034] When the goods ownership transfer value of the goods ownership transfer photo is less than the standard value for goods ownership transfer, the goods ownership transfer process of this goods is interrupted, and a transfer interruption record is generated synchronously. The transfer interruption record includes the goods ownership transfer node number (each goods ownership transfer node corresponds to an independent number) and the interruption time of transfer.

[0035] The method for obtaining the goods ownership transfer value of the goods ownership transfer photo is as follows: Feature extraction is performed on the goods ownership transfer photo, and the extracted features are organized into a goods ownership transfer feature set (the features in the goods ownership transfer feature set include personnel action features, goods status features, environmental features, etc.). The goods type of this goods is obtained, and the goods ownership transfer feature model of this goods type is obtained. The goods ownership transfer feature set is used as the input data of the goods ownership transfer feature model, and the goods ownership transfer feature model outputs the goods ownership transfer value of this goods ownership transfer photo.

[0036] Since different types of goods have different standards for the transfer of goods rights, different types of goods have different characteristic models for the transfer of goods rights. For example, electronic products and aquatic products each correspond to a characteristic model for the transfer of goods rights. The difference between different characteristic models for the transfer of goods rights lies only in the replacement of training data. In this embodiment, taking electronic products as an example, the construction method of the characteristic model for the transfer of goods rights is as follows: Collect j photos of the transfer of goods rights for electronic products (if it is a characteristic model for the transfer of goods rights of aquatic products, collect j photos of the transfer of goods rights for aquatic products). Extract features from each photo of the transfer of goods rights, and then generate a set of features for the transfer of goods rights for each photo of the transfer of goods rights. Build a neural network model, use the set of features for the transfer of goods rights as the training data of the neural network model, and assign a value for the transfer of goods rights to each training data. The value range of the value for the transfer of goods rights is (0.1~2.9). The closer the value for the transfer of goods rights is to 2.9, the more compliant the process of the transfer of goods rights of the electronic products in the photo of the transfer of goods rights is. The closer the value for the transfer of goods rights is to 0.1, the more non-compliant the process of the transfer of goods rights of the electronic products in the photo of the transfer of goods rights is. Divide all the training data into a training set, a validation set, and a test set according to the set ratio of 3:2:2. Perform neural network iterative training on the training set, the validation set, and the test set. After the training is completed, the characteristic model for the transfer of goods rights of electronic products is constructed.

[0037] The update method of the node transfer process value of the goods at this node for the transfer of goods rights is as follows: Obtain all the photos of the transfer of goods rights generated when the goods move their rights at this node for the transfer of goods rights, obtain the value for the transfer of goods rights of the photos of the transfer of goods rights, sort all the values for the transfer of goods rights in the order of generation of the photos of the transfer of goods rights, calculate the difference between two adjacent values for the transfer of goods rights after sorting and take the absolute value to obtain the process compliance fluctuation value, sum up all the process compliance fluctuation values and take the average value to obtain the process compliance fluctuation average value Comtpy, sum up two adjacent values for the transfer of goods rights after sorting to obtain the stage transfer value, set the stage transfer limit value (the stage transfer limit value is a preset value defined in the system, and the size of the preset value is adjusted according to requirements). When the stage transfer value is greater than or equal to the stage transfer limit value, no further processing is performed. When the stage transfer value is less than the stage transfer limit value, increase the number of transfer fuzzy stages by one, mark the number of transfer fuzzy stages as Vagus, and use the formula to obtain the node transfer process value of the goods at this node for the transfer of goods rights.

[0038] Set up a module for analyzing the transfer of goods rights, deeply analyze the process of the transfer of goods rights through a neural network model, and dynamically analyze the process of the transfer of goods rights, comprehensively analyze whether the process of the transfer of goods rights is compliant, and take corresponding measures for the process of the transfer of goods rights.

[0039] The above formulas are all dimensionless and only take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0040] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0041] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution, and the order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0042] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0043] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0044] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be in electrical, mechanical, or other forms.

[0045] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs, and other various media that can store program codes.

[0046] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A logistics cargo transfer monitoring system based on artificial intelligence, characterized in that: It includes a cargo ownership transfer node determination module, a cargo ownership transfer monitoring module, and a cargo ownership transfer analysis module; The cargo ownership transfer node determination module is used to determine the cargo ownership transfer node that the cargo will pass through; The cargo ownership transfer monitoring module monitors the cargo ownership transfer process each time the cargo arrives at a cargo ownership transfer node and starts the cargo ownership transfer, monitors and generates a cargo ownership transfer video in real time, obtains a comprehensive monitoring value of the cargo, and adjusts the frame extraction standard of the cargo ownership transfer video based on the comprehensive monitoring value; The comprehensive monitoring value Kpre of the goods is obtained as follows: obtain the basic monitoring adjustment value JCJ of the cargo transfer node where the goods are located, obtain the cargo type of the goods, obtain the type monitoring adjustment value YCY of the cargo type, and use the formula Get the comprehensive monitoring value Kpre of the goods, where wa is the basic coefficient and wb is the type coefficient; The basic monitoring adjustment value JCJ of the cargo transfer node where the cargo is located is obtained as follows: obtain all transfer alarm records and all transfer interruption records generated by the cargo transfer node within T time, mark the total number of transfer alarm records as NumbGJ, mark the total number of transfer interruption records as NumbZD, obtain the average alarm interval AVJGJ and the average interruption interval AVJZD, and use the formula The basic monitoring adjustment value JCJ of the cargo ownership transfer node is obtained, where R1 is the first alarm coefficient, R2 is the first interruption coefficient, R3 is the second alarm coefficient, and R4 is the second interruption coefficient; The type monitoring adjustment value YCY of the cargo type is obtained as follows: obtain all cargo ownership transfer photos of the cargo type with the same cargo ownership transfer duration T, obtain the cargo ownership transfer value of the cargo ownership transfer photos, sum up the cargo ownership transfer values ​​of all cargo ownership transfer photos and take the average to obtain the type monitoring adjustment value YCY of the cargo type; The cargo ownership transfer analysis module generates a cargo ownership transfer photo every time it extracts a frame, obtains the cargo ownership transfer value of the cargo ownership transfer photo, and determines whether to interrupt the cargo ownership transfer process based on the comparison result between the cargo ownership transfer value and the cargo ownership transfer standard value; The method for obtaining the cargo ownership transfer value of the cargo ownership transfer photo is as follows: perform feature extraction on the cargo ownership transfer photo, organize the extracted features into a cargo ownership transfer feature set, obtain the cargo type of the goods, obtain a cargo ownership transfer feature model for the cargo type, use the cargo ownership transfer feature set as input data of the cargo ownership transfer feature model, and the cargo ownership transfer feature model outputs the cargo ownership transfer value of the cargo ownership transfer photo.

2. According to the artificial intelligence-based logistics cargo transfer monitoring system of claim 1, it is characterized in that: Get the comprehensive monitoring value of the goods, and adjust the frame extraction standard of the goods transfer video based on the comprehensive monitoring value. Specifically, get the comprehensive monitoring value Kpre of the goods, and The video of cargo transfer is processed frame by frame within seconds.

3. According to the artificial intelligence-based logistics cargo transfer monitoring system of claim 1, it is characterized in that: The average alarm interval AVJGJ and the average interruption interval AVJZD are obtained as follows: sort all handover alarm records in the order of handover alarm time, calculate the time difference between the handover alarm times of two adjacent handover alarm records after sorting, and obtain the handover alarm interval, sum up all handover alarm intervals and take the average value to obtain the average alarm interval AVJGJ, sort all handover interruption records in the order of handover interruption time, calculate the time difference between the handover interruption times of two adjacent handover interruption records after sorting, and obtain the handover interruption interval, sum up all handover interruption intervals and take the average value to obtain the average interruption interval AVJZD.

4. According to the artificial intelligence-based logistics cargo transfer monitoring system of claim 1, it is characterized in that: The transfer alarm record includes the cargo ownership transfer node number and the transfer alarm time, and the transfer interruption record includes the cargo ownership transfer node number and the transfer interruption time.

5. According to the artificial intelligence-based logistics cargo transfer monitoring system of claim 1, it is characterized in that: Based on the comparison result between the cargo ownership transfer value and the cargo ownership transfer standard value, determine whether to interrupt the cargo ownership transfer process of the cargo, specifically: set the cargo ownership transfer standard value, when the cargo ownership transfer value of the cargo ownership transfer photo is greater than or equal to the cargo ownership transfer standard value, update the node transfer process value of the cargo at the cargo ownership transfer node, set the node transfer process threshold, when the node transfer process value is greater than or equal to the node transfer process threshold, send an alarm message to the cargo ownership transfer personnel in the cargo ownership transfer node, synchronously generate a transfer alarm record, and continue the cargo ownership transfer process of the cargo, when the node transfer process value is less than the node transfer process threshold, continue the cargo ownership transfer process of the cargo; When the cargo ownership transfer value of the cargo ownership transfer photo is less than the cargo ownership transfer standard value, the cargo ownership transfer process of the goods is interrupted and a transfer interruption record is generated simultaneously.

6. The artificial intelligence-based logistics cargo transfer monitoring system according to claim 5 is characterized in that: The updating method of the node transfer process value of the goods at the cargo transfer node is as follows: obtain all cargo transfer photos generated when the cargo transfer is carried out at the cargo transfer node, obtain the cargo transfer value of the cargo transfer photos, sort all cargo transfer values ​​in the order of generation of the cargo transfer photos, calculate the difference between the two adjacent cargo transfer values ​​after sorting and take the absolute value to obtain the process compliance fluctuation value, sum up all the process compliance fluctuation values ​​and take the average to obtain the process compliance fluctuation mean Comtpy, sum up the two adjacent cargo transfer values ​​after sorting to obtain the stage transfer value, set the stage transfer limit, when the stage transfer value is greater than or equal to the stage transfer limit, no further processing is performed, when the stage transfer value is less than the stage transfer limit, increase the number of transfer fuzzy stages by one, mark the number of transfer fuzzy stages as Vagus, and use the formula Get the node transfer process value of the goods at the node where the goods ownership is transferred.

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