Logistics cargo right transfer monitoring system based on artificial intelligence

Abstract

The invention discloses a logistics cargo right transfer monitoring system based on artificial intelligence, relates to the technical field of logistics monitoring, and discloses a cargo right transfer node determination module, a cargo right transfer monitoring module, a cargo right transfer analysis module, a cargo right transfer node determination module and a cargo right transfer monitoring module. Each logistics cargo right transfer node existing in the system can be monitored, monitoring requirements of cargos and the cargo right transfer nodes are comprehensively analyzed, different video frame extraction standards are customized for cargo right transfer videos, reasonable monitoring and analysis of the cargo right transfer process are ensured, the monitoring and analysis intensity of the system is reasonably distributed, and the monitoring and analysis efficiency of the system is improved. A cargo right transfer analysis module is arranged, deep analysis is carried out on the transfer process of the cargo right through a neural network model, dynamic analysis is carried out on the transfer process of the cargo right, whether the transfer process of the cargo right is compliant or not is comprehensively analyzed, and corresponding measures are taken for the transfer process of the cargo right.

Description

Technical Field

[0001] The present invention relates to the field of logistics monitoring technology, and more specifically, to a logistics cargo ownership transfer monitoring system based on artificial intelligence. Background Art

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

[0003] With the rapid development of the logistics industry and the continuous advancement of intelligent technology, people have put forward higher requirements for the monitoring and management of the cargo transfer process. Traditional monitoring methods 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 cargo transfer monitoring system.

[0004] However, most existing logistics monitoring systems can only realize simple video monitoring and video playback functions, lacking in-depth analysis and dynamic monitoring of the cargo transfer process. These systems are often unable to accurately determine whether the cargo transfer process is compliant, and are unable to take appropriate measures in a timely manner to deal with possible problems. Summary of the invention

[0005] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a logistics cargo ownership transfer monitoring system based on artificial intelligence.

[0006] To achieve the above object, the present invention provides the following technical solutions: An artificial intelligence-based logistics cargo ownership transfer monitoring system, including 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 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 of the cargo ownership transfer value and the cargo ownership transfer standard value.

[0007] Furthermore, the comprehensive monitoring value of the goods is obtained, and the frame extraction standard of the goods transfer video is adjusted based on the comprehensive monitoring value. Specifically, the comprehensive monitoring value Kpre of the goods is obtained, and the frame extraction standard of the goods transfer video is adjusted based on the comprehensive monitoring value. The video of cargo transfer is processed frame by frame within seconds.

[0008] Furthermore, 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 The comprehensive monitoring value Kpre of the goods is obtained, where wa is the basic coefficient and wb is the type coefficient.

[0009] Furthermore, 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, sort all transfer alarm records in the order of transfer alarm time, calculate the time difference between the transfer alarm times of the two adjacent transfer alarm records after sorting, and 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 transfer interruption time, calculate the time difference between the transfer interruption times of the two adjacent transfer interruption records after sorting, and obtain the transfer interruption interval, sum up all transfer interruption intervals and take the average to obtain 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.

[0010] Furthermore, the type monitoring adjustment value YCY of the cargo type is obtained as follows: obtain all cargo transfer photos of the same cargo type of cargo with a duration of T, obtain the cargo transfer value of the cargo transfer photos, sum up the cargo transfer values ​​of all cargo transfer photos and take the average to obtain the type monitoring adjustment value YCY of the cargo type.

[0011] Furthermore, 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.

[0012] Furthermore, the cargo ownership transfer value of the cargo ownership transfer photo is obtained 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.

[0013] Furthermore, based on the comparison result between the cargo ownership transfer value and the cargo ownership transfer standard value, it is determined whether to interrupt the cargo ownership transfer process of the cargo, specifically: the cargo ownership transfer standard value is set, when the cargo ownership transfer value of the cargo ownership transfer photo is greater than or equal to the cargo ownership transfer standard value, the node transfer process value of the cargo at the cargo ownership transfer node is updated, and the node transfer process threshold is set, when the node transfer process value is greater than or equal to the node transfer process threshold, an alarm message is sent to the cargo ownership transfer personnel in the cargo ownership transfer node, a transfer alarm record is generated synchronously, and the cargo ownership transfer process of the cargo is continued, and when the node transfer process value is less than the node transfer process threshold, the cargo ownership transfer process of the cargo is continued; 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.

[0014] Furthermore, 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.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The cargo ownership transfer node determination module and cargo ownership transfer monitoring module are set up to monitor each logistics cargo ownership transfer node in the system, and comprehensively analyze the monitoring requirements of cargo and cargo ownership transfer nodes, customize different video frame extraction standards for cargo ownership transfer videos, ensure reasonable monitoring and analysis of the cargo ownership transfer process, and reasonably allocate the monitoring and analysis intensity of the system; A cargo ownership transfer analysis module is set up to conduct an in-depth analysis of the cargo ownership transfer process through a neural network model, and to dynamically analyze the cargo ownership transfer process, to comprehensively analyze whether the cargo ownership transfer process is compliant, and to take corresponding measures for the cargo ownership transfer process. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a system flow chart of a logistics cargo ownership transfer monitoring system based on artificial intelligence; Figure 2 A flow chart for determining whether to interrupt the transfer of ownership of goods; Figure 3 This is a system module diagram of an artificial intelligence-based logistics cargo transfer monitoring system. DETAILED DESCRIPTION

[0017] Reference Figure 1-Figure 3 , a logistics cargo ownership transfer monitoring system based on artificial intelligence, including a cargo ownership transfer node determination module, a cargo ownership transfer monitoring module, and a cargo ownership transfer analysis module.

[0018] Cargo ownership transfer node determination module: determine the cargo ownership transfer nodes that the goods will pass through (the starting point of logistics cargo transportation is the shipper, and the end point of transportation is the consignee. Multiple cargo ownership transfers are required between the shipper and the consignee. The transfer location is called the cargo ownership transfer node. The cargo ownership transfer node is commonly known as the logistics transit point. The goods need to transfer the goods and cargo ownership at this location. Before the logistics transportation, all the cargo ownership transfer nodes that the goods need to pass through need to be determined).

[0019] Cargo transfer monitoring module: Determine the cargo type of the cargo. When the cargo arrives at a cargo transfer node and starts the cargo transfer, monitor the cargo transfer process, generate a cargo transfer video in real time, and obtain the comprehensive monitoring value Kpre of the cargo. The video of cargo transfer is processed frame by frame within seconds.

[0020] 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 The comprehensive monitoring value Kpre of the goods is obtained, 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.

[0021] The basic monitoring adjustment value JCJ of the cargo transfer node where the goods are 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, sort all transfer alarm records in the order of transfer alarm time, calculate the time difference between the transfer alarm times of the two adjacent transfer alarm records after sorting, and 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 transfer interruption time, calculate the time difference between the transfer interruption times of the two adjacent transfer interruption records after sorting, and obtain the transfer interruption interval, sum up all transfer interruption intervals and take the average to obtain 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 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.

[0022] The method for obtaining the type monitoring adjustment value YCY of the cargo type is as follows: obtain all cargo ownership transfer photos of the same cargo type for a period of 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.

[0023] By setting up a cargo ownership transfer node determination module and a cargo ownership transfer monitoring module, each logistics cargo ownership transfer node in the system can be monitored, and the monitoring requirements of the goods and cargo ownership transfer nodes can be comprehensively analyzed. Different video frame extraction standards can be customized for the cargo ownership transfer video to ensure reasonable monitoring and analysis of the cargo ownership transfer process and reasonably allocate the monitoring and analysis intensity of the system.

[0024] Cargo transfer analysis module: generate a cargo transfer photo every time a frame is extracted, obtain the cargo transfer value of the cargo transfer photo, set the cargo transfer standard value (the cargo transfer standard value is a custom preset value in the system, and the size of the preset value is adjusted according to demand), when the cargo transfer value of the cargo transfer photo is greater than or equal to the cargo transfer standard value, update the node transfer process value of the cargo at the cargo transfer node, set the node transfer process threshold (the node transfer process threshold is a custom preset value in the system, and the size of the preset value is adjusted according to demand), when the node transfer process value is greater than or equal to the node transfer process threshold, send an alarm message to the cargo transfer personnel in the cargo transfer node, and simultaneously generate a transfer alarm record, the transfer alarm record includes the cargo transfer node number (each cargo transfer node corresponds to an independent number), the transfer alarm time, and continue the cargo transfer process of the cargo; when the node transfer process value is less than the node transfer process threshold, continue the cargo transfer process of the cargo.

[0025] 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 synchronously. The transfer interruption record includes the cargo ownership transfer node number (each cargo ownership transfer node corresponds to an independent number) and the transfer interruption time.

[0026] 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 (the features in the cargo ownership transfer feature set include personnel action features, cargo status features, environmental features, etc.), obtain the cargo type of the cargo, obtain the cargo ownership transfer feature model of 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.

[0027] Because different types of goods have different standards for the transfer of goods ownership, different types of goods have different characteristic models for the transfer of goods ownership. For example, electronic products and aquatic products each correspond to a characteristic model for the transfer of goods ownership. The difference between different characteristic models for the transfer of goods ownership is only the replacement of training data. This embodiment takes electronic products as an example, and the method for constructing a public characteristic model for the transfer of goods ownership is as follows: collect j photos of the transfer of goods ownership of electronic products (if it is a characteristic model for the transfer of goods ownership of aquatic products, collect j photos of the transfer of goods ownership of aquatic products), extract features from each photo of the transfer of goods ownership, and then generate a characteristic set of the transfer of goods ownership for each photo of the transfer of goods ownership, build a neural network model, and convert the goods ownership transfer feature set into a single model. The title transfer feature set is used as the training data of the neural network model. A title transfer value is assigned to each training data. The value range of the title transfer value is (0.1~2.9). The closer the title transfer value is to 2.9, the more compliant the title transfer process of the electronic product in the title transfer photo is. The closer the title transfer value is to 0.1, the more non-compliant the title transfer process of the electronic product in the title transfer photo is. All training data are divided into training set, validation set and test set according to the set ratio of 3:2:2. The neural network is iteratively trained on the training set, validation set and test set. After the training is completed, the title transfer feature model of electronic products is constructed.

[0028] 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 (the stage transfer limit is a custom preset value in the system, and the size of the preset value is adjusted according to demand), 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, the number of transfer fuzzy stages is increased by one, and the number of transfer fuzzy stages is marked as Vagus, and the formula is used. Get the node transfer process value of the goods at the node where the goods ownership is transferred.

[0029] A cargo ownership transfer analysis module is set up to conduct an in-depth analysis of the cargo ownership transfer process through a neural network model, and to dynamically analyze the cargo ownership transfer process, to comprehensively analyze whether the cargo ownership transfer process is compliant, and to take corresponding measures for the cargo ownership transfer process.

[0030] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0031] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by 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 process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0032] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0033] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0034] 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 aforementioned method embodiments and will not be repeated here.

[0035] In the 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 only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0036] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0037] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on 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 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 of the cargo ownership transfer value and the cargo ownership transfer standard value.

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 claim 2, a logistics cargo ownership transfer monitoring system based on artificial intelligence is characterized in that: 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 The comprehensive monitoring value Kpre of the goods is obtained, where wa is the basic coefficient and wb is the type coefficient.

4. According to claim 3, a logistics cargo ownership transfer monitoring system based on artificial intelligence is characterized in that: The basic monitoring adjustment value JCJ of the cargo transfer node where the goods are 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, sort all transfer alarm records in the order of transfer alarm time, calculate the time difference between the transfer alarm times of the two adjacent transfer alarm records after sorting, and 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 transfer interruption time, calculate the time difference between the transfer interruption times of the two adjacent transfer interruption records after sorting, and obtain the transfer interruption interval, sum up all transfer interruption intervals and take the average to obtain 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.

5. According to claim 3, the artificial intelligence-based logistics cargo transfer monitoring system is characterized in that: The method for obtaining the type monitoring adjustment value YCY of the cargo type is as follows: obtain all cargo ownership transfer photos of the same cargo type for a period of 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.

6. According to the artificial intelligence-based logistics cargo transfer monitoring system of claim 4, 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.

7. According to the artificial intelligence-based logistics cargo transfer monitoring system of claim 1, it is characterized in that: 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.

8. The artificial intelligence-based logistics cargo transfer monitoring system according to claim 1 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 synchronously.

9. The artificial intelligence-based logistics cargo transfer monitoring system according to claim 8 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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