Multi-bulk-cargo automatic sampling and identification system based on artificial intelligence

Abstract

The invention discloses a multi-bulk-cargo automatic sampling and recognition system based on artificial intelligence, and belongs to the technical field of logistics transportation. The multi-bulk-cargo automatic sampling and recognition system comprises an automatic sampling device used for carrying out mechanical sampling operation on bulk cargos; the image acquisition equipment is used for acquiring image data of the taken sample; the control and recognition unit is in communication connection with the automatic sampling equipment and the image acquisition equipment, and the automatic sampling equipment is responsible for replacing manual work to complete a high-intensity physical sampling task; the image acquisition equipment acquires a high-quality image of the sample in real time along with the sampling action; the images are analyzed in real time through a built-in deep learning model, and automatic judgment of the types of the bulk cargos is achieved; the full-process automation and intellectualization of bulk cargo inspection are realized, the problems of low manual inspection efficiency, high labor intensity, high-altitude operation danger, strong recognition subjectivity and the like are fundamentally solved, and the efficiency and safety of logistics inspection in scenes such as ports, wharfs and the like are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of logistics transportation, and particularly relates to a multi-variety bulk cargo automatic sampling and identification system based on artificial intelligence. BACKGROUND

[0002] Currently, in the daily inspection and examination work of key nodes such as port, logistics park and large cargo yard, traditional manual operation mode is generally relied on, which exposes many technical defects and operation bottlenecks to be solved in practical application.

[0003] Firstly, manual examination has systematic short boards of incomplete coverage and low efficiency. The inspection personnel are limited by their physical energy and field of vision, and the inspection of goods in the truck compartment can only be carried out in a sampling way, and cannot achieve full scanning. Especially for the piled bulk cargo, only the surface can be roughly observed, and the comprehensiveness and representativeness of the inspection are seriously insufficient. The whole process from climbing, sampling to identification and judgment is time-consuming, which seriously restricts the passing efficiency of the gate, and easily forms a congestion bottleneck at the peak of traffic flow, directly affecting the running speed of the whole logistics chain. Secondly, the operation mode has significant safety risks and high labor costs. In order to ensure the inspection effect, the workers often need to climb to the top of the truck for high-altitude operation. This not only has high labor intensity, but also has the risk of falling from a high altitude. At the same time, in order to maintain such inspection work, the enterprise needs to configure a large number of skilled workers and continuously invest in safety training and protective equipment, which constitutes long-term and high human resource and management costs. This mode relying on human resources is contrary to the development direction of automation and intelligence pursued by modern logistics. Thirdly, manual inspection has physical limitations and is difficult to cope with complex cargo conditions. First, "insufficient strength": in the face of frozen goods, lumped mineral powder or tightly packed goods, manual use of simple tools often cannot effectively break and sample the deep layer, resulting in insufficient inspection strength and inability to obtain representative core samples. Second, "insufficient depth and breadth": manual inspection cannot effectively touch and inspect the bottom goods of the truck compartment. This blind area provides the possibility for illegal behavior, and there is a risk of monitoring loopholes such as "loading good goods on the top and loading inferior goods at the bottom" or even illegal goods for cargo reloading and port exit, which poses potential economic and safety threats to cargo owners and port safety management.

[0004] Therefore, a multi-variety bulk cargo automatic sampling and identification system based on artificial intelligence is needed to solve the problems raised in the background. SUMMARY

[0005] The purpose of the present application is to provide a multi-variety bulk cargo automatic sampling and identification system based on artificial intelligence to solve the problems raised in the background.

[0006] In order to achieve the above object, the present application provides the following technical scheme: a kind of multiple bulk cargo automatic sampling and identification system based on artificial intelligence, comprising: Automatic sampling equipment for performing mechanical sampling operation on bulk cargo; Image acquisition device, fixedly installed on the automatic sampling equipment, synchronously moves with it, for collecting the image data of the sample; Control and identification unit, respectively with the automatic sampling equipment and the image acquisition device communication connection, for controlling sampling action and identifying the type of bulk cargo based on the image data through deep learning algorithm; Terminal alarm module, connected with the control and identification unit, for issuing an alarm when the identification result is abnormal.

[0007] It is pointed out in the scheme that the automatic sampling equipment includes a support platform, a sampling mechanism arranged on the support platform, and a driving device for driving the sampling mechanism to move.

[0008] Further, it is worth mentioning that the sampling mechanism includes a sampling gun, which is driven by a motor and can move up and down.

[0009] Further, it is pointed out that the driving device includes a positioning driving part for controlling the horizontal movement of the sampling gun and a lifting driving part for controlling the vertical movement of the sampling gun.

[0010] As a preferred embodiment, the end of the sampling gun is provided with a spiral drill bit.

[0011] As a preferred embodiment, the image acquisition device is an AI industrial camera with a built-in light source, fixedly installed on the support platform and facing the sampling outlet area of the sampling gun.

[0012] As a preferred embodiment, the image acquisition device further includes a waterproof and dustproof shell.

[0013] As a preferred embodiment, the control and identification unit includes a communication module for receiving image data from the image acquisition device, a processing module for running a pre-trained deep learning model to extract features and classify the received image, and a control module for sending instructions to the terminal alarm module based on the identification result and a preset logic.

[0014] Compared with the prior art, the multiple bulk cargo automatic sampling and identification system based on artificial intelligence provided by the present application has at least the following beneficial effects: The high-intensity physical sampling task is completed by the automatic sampling device instead of manually; the image acquisition device follows the sampling action to acquire high-quality images of the sample in real time; the control and recognition unit serves as the 'brain' of the system, not only coordinating the device action, but also using the built-in deep learning model to analyze the images in real time to realize automatic identification of the bulk cargo type; the terminal alarm module serves as the final output, timely informing the staff of abnormal conditions such as cargo replacement, and realizing the full-process automation and intelligentization of the bulk cargo inspection, fundamentally solving the problems of low efficiency, high labor intensity, high-altitude operation danger and strong subjectivity of the recognition in the manual inspection in the background technology, and significantly improving the logistics inspection efficiency and safety of the port, wharf and other scenes. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 is a schematic diagram of the overall structure of the present application Figure 1 ; Figure 2 is a schematic diagram of the overall structure of the present application Figure 2 ; Figure 3 is a schematic diagram of the overall structure of the present application

[0016] In the figure: 1, automatic sampling device; 11, support platform; 12, sampling mechanism; 13, driving device; 2, image acquisition device; 3, control and recognition unit. DETAILED DESCRIPTION

[0017] The present application will be further described below in conjunction with examples.

[0018] Please refer to Figures 1-3The application provides a kind of multiple bulk cargo automatic sampling and identification system based on artificial intelligence, including automatic sampling equipment 1, for carrying out the mechanized sampling operation to bulk cargo;Image acquisition device 2 is fixedly installed on automatic sampling equipment 1, moves synchronously with it, for collecting the image data of sample;Control and identification unit 3 is respectively connected with automatic sampling equipment 1 and image acquisition device 2, for controlling sampling action and identifying the type of bulk cargo based on image data through deep learning algorithm;Terminal alarm module is connected with control and identification unit 3, for issuing alarm when the identification result is abnormal;Automatic sampling equipment 1 is responsible for replacing manual high-intensity physical sampling task;Image acquisition device 2 follows sampling action, and real-time high-quality image of sample is obtained;Control and identification unit 3 as the "brain" of the system, not only coordinates equipment action, but also uses built-in deep learning model to analyze image in real time, realizes the automatic identification of bulk cargo type;Terminal alarm module 4 is the final output, and timely informs the staff of abnormal conditions such as cargo reloading, realizes the full-process automation and intelligentization of bulk cargo inspection, fundamentally solves the problems of low efficiency, high labor intensity, high-altitude operation danger and strong subjective identification in the background technology, and significantly improves the logistics inspection efficiency and safety of port, wharf and other scenes.

[0019] Further as Figure 1 And Figure 2 Described in detail, the automatic sampling equipment 1 includes a support platform 11, a sampling mechanism 12 arranged on the support platform 11, and a driving device 13 for driving the sampling mechanism 12 to move;The support platform 11 provides a stable basic structure for the whole sampling device. The driving device 13, such as the combination of servo motor and screw, can accurately control the positioning of the sampling mechanism 12 in three-dimensional space, so that it can accurately move to different positions of the truck compartment. The system has high flexibility and adaptability, can be compatible with bulk cargo transport vehicles of different sizes and heights, ensures comprehensive coverage of sampling points, including the bottom area of the truck compartment that is difficult to reach by traditional manual operation, thereby effectively preventing the risk of cargo reloading at the bottom.

[0020] Further as Figure 1 And Figure 2 Described in detail, the sampling mechanism 12 includes a sampling gun, which is driven by a motor and can move up and down;The motor provides stable and powerful torque to drive the sampling gun to move vertically downward, so that it can overcome the resistance of the cargo and penetrate into the interior of the bulk cargo. This mechanical force far exceeds the pressing and digging force of human power, so that the system can meet the sampling needs of hard and high-density cargo, and solve the pain point of "insufficient force" in manual inspection. The accurate control of the lifting movement ensures the representativeness of the sample and the reliability of the test results.

[0021] Further asFigure 1 and Figure 2 As shown, it is worth noting that the driving device 13 includes a positioning driving part for controlling the horizontal movement of the sampling gun and a lifting driving part for controlling the vertical movement thereof; the positioning driving part such as a linear module is responsible for driving the sampling gun to move in the horizontal plane and accurately position to the preset sampling point; the lifting driving part is specially used for controlling the penetration and lifting of the sampling gun. The separated driving design realizes the accurate programming and control of the sampling path, and the system can plan the optimal multiple sampling points according to the size of the carriage and the inspection requirements, realize the grid sampling and sampling without dead angle, greatly improve the scientificity and comprehensiveness of the sampling, and avoid the randomness and blind area of manual sampling.

[0022] Further as shown in Figure 1 and Figure 2 It is worth noting that the end of the sampling gun is provided with a spiral drill bit; when the sampling gun rotates and presses down, the spiral drill bit plays a role of breaking and drilling, and easily enters the tight goods.

[0023] Further as shown in Figure 1 and Figure 2 It is worth noting that the image acquisition device 2 is an AI industrial camera with a built-in light source, which is fixedly installed on the support platform 11 and faces the sampling outlet area of the sampling gun; the use of the AI industrial camera ensures the high resolution and high frame rate of the image, and meets the accuracy requirements of subsequent algorithm analysis. The built-in light source is a key design, which ensures that under different environmental light conditions such as night and shadow, stable and uniform illumination can be provided for the sample, so that high-quality and clear-feature images are obtained, and the interference of environmental light on the recognition accuracy is eliminated. It is fixedly installed on the support platform 11 and aligned with the sampling outlet, so that when the sample is extracted to the surface, the camera can automatically complete image acquisition at the best viewing angle and distance, realizing the automatic seamless connection of sampling and imaging.

[0024] Further as shown in Figure 1 and Figure 2 It is worth noting that the image acquisition device 2 further includes a waterproof and dustproof shell; the working environment of the port, wharf and the like is usually dusty and humid, and may even be subjected to rainwater. The waterproof and dustproof shell provides a physical barrier for the precise AI camera, effectively preventing water vapor, dust and impurities from entering the inside of the device, and protecting the long-term stable operation and service life of the core imaging elements under harsh working conditions. This design significantly improves the environmental adaptability and reliability of the whole system, reduces the frequency of downtime maintenance due to equipment failure, and ensures the continuity of the inspection operation.

[0025] Further as shown in Figure 1 and Figure 2 Figure 1 Figure 2As shown, it is worth noting that the control and recognition unit 3 includes: a communication module for receiving image data from the image acquisition device 2; a processing module running a pre-trained deep learning model for feature extraction and classification recognition of the received image; a control module sending instructions to the terminal alarm module based on the recognition result and the preset logic; the communication module is responsible for stable reception of data, and the processing module is the intelligent core, which runs the deep learning model trained by a large number of sample data, can learn the visual features such as color, texture and particle morphology of different bulk cargoes such as coal, grain and mineral sand, and thus accurately classify the newly collected sample images; the control module makes decisions according to the recognition result. The whole working process is efficient and intelligent: after the image data is transmitted, the system can complete the whole process of "feature extraction-comparison analysis-output result" in a very short time, and the "normal" or "abnormal" conclusion will trigger the subsequent action through the control module. This AI-based identification method overcomes the shortcomings of manual inspection such as fatigue and empiricism, and has high recognition accuracy and good stability.

[0026] In summary: the automatic sampling device 1 is responsible for replacing manual work to complete the high-intensity physical sampling task; the image acquisition device 2 follows the sampling action to obtain high-quality images of the sample in real time; the control and recognition unit 3 as the "brain" of the system not only coordinates the device action, but also uses the built-in deep learning model to analyze the image in real time to realize the automatic identification of bulk cargo types; the terminal alarm module 4 as the final output executes the output in time to inform the staff of the abnormal situation such as cargo replacement, realizes the full-process automation and intelligentization of bulk cargo inspection, fundamentally solves the problems of low efficiency, high labor intensity, high-altitude operation danger and strong subjectivity of identification in the background technology, and significantly improves the logistics inspection efficiency and safety of port, wharf and other scenes.

Claims

1. An automated sampling and identification system for various bulk goods based on artificial intelligence, characterized in that, include: Automatic sampling equipment (1) is used to perform mechanized sampling operations on bulk materials; Image acquisition device (2) is fixedly installed on the automatic sampling device (1) and moves synchronously with it to acquire image data of the sample taken; The control and identification unit (3) is connected to the automatic sampling device (1) and the image acquisition device (2) respectively, and is used to control the sampling action and identify the type of bulk cargo based on the image data using a deep learning algorithm; The terminal alarm module is connected to the control and identification unit (3) and is used to issue an alarm when the identification result is abnormal.

2. The AI-based automatic sampling and identification system for various bulk cargoes according to claim 1, characterized in that, The automatic sampling device (1) includes a support platform (11), a sampling mechanism (12) disposed on the support platform (11), and a driving device (13) for driving the sampling mechanism (12) to move.

3. The AI-based automatic sampling and identification system for various bulk cargoes according to claim 2, characterized in that, The sampling mechanism (12) includes a sampling gun, which is driven by a motor and can move up and down.

4. The AI-based automatic sampling and identification system for various bulk cargoes according to claim 3, characterized in that, The drive unit (13) includes a positioning drive unit for controlling the horizontal movement of the sampling gun and a lifting drive unit for controlling its vertical movement.

5. The AI-based automatic sampling and identification system for various bulk cargoes according to claim 3, characterized in that, The sampling gun is equipped with a spiral drill bit at its end.

6. The AI-based automatic sampling and identification system for various bulk cargoes according to claim 5, characterized in that, The image acquisition device (2) is an AI industrial camera with a built-in light source, which is fixedly installed on the support platform (11) and faces the sampling outlet area of ​​the sampling gun.

7. The AI-based automatic sampling and identification system for various bulk cargoes according to claim 6, characterized in that, The image acquisition device (2) also includes a waterproof and dustproof housing.

8. The AI-based automatic sampling and identification system for various bulk cargoes according to claim 1, characterized in that, The control and identification unit (3) includes: A communication module is used to receive image data from the image acquisition device (2); The processing module runs a pre-trained deep learning model to extract features and classify the received images. The control module sends instructions to the terminal alarm module based on the identification results and preset logic.

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