Control method and system for loading and unloading detection of container reach stacker

Abstract

The invention provides a control method and system for loading and unloading detection of a container reach stacker, and relates to the field of container detection.The method comprises the steps that S100, equipment is installed, specifically, a high-resolution camera and a sonic sensor are installed on hoisting equipment; s200, fragile product and explosive product identification is recognized through images, wherein image information of the surface of the container is collected in real time through the camera, and the images are analyzed through a convolutional neural network model. According to the system, by combining various sensor data such as image recognition, vibration analysis, sound wave feedback and swing detection, the loading state and the internal article distribution condition of the container can be judged more comprehensively and accurately, the system can automatically reduce the lifting and moving speed, potential safety hazards caused by misoperation are avoided, and the working efficiency is improved. The damage caused by mutual collision of the articles in the container due to large relative shaking amplitude and rapid movement of the articles in the container is avoided, the error rate is low, and the steps of weighing and manually opening the container to look up the internal condition in the loading and unloading process of the container are reduced.

Description

Technical Field

[0001] The present invention relates to the field of container detection, and in particular to a control method and system for container front crane loading and unloading detection. Background Art

[0002] At present, most of the port containers are loaded and unloaded by people operating cranes and other equipment. Since there are labels on the outside of containers containing fragile or explosive items, it is easy for operators to observe and reduce the speed of container lifting and loading and unloading to avoid excessive speed affecting the items in the container. When lifting and unloading heavy containers, the speed of lifting and loading and unloading should also be reduced to avoid excessive inertia, which will cause excessive swing during movement, causing damage to the items in the container and safety hazards. However, if the container is fully loaded with items, it will move quickly, and the items in the box will have a relatively small swaying amplitude, so it is not easy to be damaged. It should be moved quickly to improve loading and unloading efficiency. If the container is not fully loaded with items, regardless of whether it is heavy or not, the moving speed should be reduced to avoid a relatively large swaying amplitude of the items in the box. Rapid movement will cause the items in the box to collide with each other and be damaged. However, these situations are judged based on the operator's work experience, which is not only prone to errors but also inefficient.

[0003] For example: the Chinese invention patent (publication number: CN119004012A) discloses a container loading and unloading detection control method and system based on artificial intelligence. Its specification discloses: In the prior art, for railway containers, during the weighing and lifting process, multiple weight sensors are used to detect the weight of the container to determine the weight threshold of the railway container, and then realize the lifting operation of the railway container. However, multiple weight sensors will have problems of error accumulation and interference from environmental factors during the measurement process, which will affect the measurement accuracy and thus lead to a decrease in measurement accuracy; the above patent can prove the defects of the prior art.

[0004] Therefore, we made improvements and proposed a control method for container front crane loading and unloading detection. Summary of the invention

[0005] The purpose of the present invention is to address the problem that the current situations are all judged based on the operator's work experience, which is not only prone to errors but also has low efficiency.

[0006] In order to achieve the above-mentioned purpose of the invention, the present invention provides a control method and system for container front crane loading and unloading detection to improve the above-mentioned problem.

[0007] The specific application is as follows:

[0008] The following steps are involved:

[0009] S100, Installation equipment: Install high-resolution cameras and acoustic sensors on the lifting equipment;

[0010] S200, image recognition of fragile and explosive goods labels: the camera collects image information of the container surface in real time, and uses a convolutional neural network model to analyze the image to identify whether there are fragile and explosive goods labels;

[0011] If the mark is detected, the lifting speed and moving speed will be reduced to the preset safety speed V safe and V move_safe ;

[0012] S300, image change detection vibration data: if no fragile or explosive item mark is detected, then this step is entered, and the container is impacted by a vibrator on the crane, and the container is subjected to impact vibration, and vibration data of the container is detected based on the image information;

[0013] S300, detecting vibration data through image changes, specifically includes the following steps:

[0014] S310, continuously collecting an image sequence of the container by the camera;

[0015] S320, using image processing technology to compare the two frames in the image sequence, and calculate the displacement change Δx of the container in the front and rear directions in the image. front-back And the displacement change Δx in the left and right directions left-right and rotation change Δθ;

[0016] S400, acoustic feedback detection: detect the sound changes before and after the container vibrates;

[0017] S400, sound wave feedback detection specifically includes the following steps:

[0018] S410, capturing the sound changes in the container through the acoustic wave sensor, and recording the acoustic wave signal S(t) after the impact;

[0019] S500, comprehensive judgment and adjustment of lifting and moving speed: adjusting the lifting and moving speed of the container in combination with vibration data and sound wave data;

[0020] S500, comprehensively judge and adjust the lifting and moving speed, specifically including the following steps:

[0021] S510, when R v (front-back)<1, R v (left-right)<1, R s <1, and is_uneven=False, the container is considered full, fast movement is allowed, and the lifting speed is set to Vfast , and the moving speed is set to V move_fast ;

[0022] S520: When any of the following conditions is met, it is determined that the container is not fully loaded or the items are unevenly distributed:

[0023] R v (front-back)>1 or R v (left-right)>1 or R s >1 or is_uneven=True, regardless of the weight, the lifting speed is reduced to V slow And the movement speed is reduced to V move_slow .

[0024] As a preferred technical solution of the present application, S300 further includes the following steps:

[0025] S330, according to the formula Calculate the vibration displacement coefficient D, where D reflects the vibration amplitude of the container and Δx represents the displacement change Δx in the front-to-back direction front-back and the left-right displacement change Δx left-right , the displacement time series in each direction is converted into a frequency domain signal using the fast Fourier transform (FFT), and the calculated front-to-back direction D front-back (t) and left and right directions D left-right The displacement time series (t) is converted into a frequency domain signal S front-back (f) and S left-right (f).

[0026] As a preferred technical solution of the present application, S300 further includes the following steps:

[0027] S340, according to the formula Calculate the vibration response ratio R in the front-to-back direction and the left-to-right direction respectively v (front-bacl) and R v (left-right), where f0 is the reference frequency, D threshold is the preset displacement threshold, f c Corresponding to the vibration frequencies in the front-to-back direction and the left-to-right direction respectively.

[0028] As a preferred technical solution of the present application, S300 further includes the following steps:

[0029] S350, if | R v (front-back)-R v(left-right)|>∈, that is, if the difference between the vibration response ratios in the front-to-back direction and the left-to-right direction is greater than the preset threshold ∈, it is determined that the items in the container are unevenly distributed, and the flag is_uneven=True is set, otherwise it is is_uneven=False.

[0030] As a preferred technical solution of the present application, S400 further specifically includes the following steps:

[0031] S420, Calculate the sound wave energy where t1 and t2 are the time windows before and after the impact.

[0032] As a preferred technical solution of the present application, S400 further specifically includes the following steps:

[0033] S430, according to the formula Calculate the sound wave energy ratio R s , where E threshold is the preset energy threshold.

[0034] As a preferred technical solution of the present application, S330 specifically includes the following steps:

[0035] S331. Convert the displacement time series in each direction into frequency domain signals respectively:

[0036] Front and rear direction: S front-back (f) = fft(D front-back (t));

[0037] Left and right direction: S left-right (f) = fft(D left-right (t)).

[0038] As a preferred technical solution of the present application, S500 further includes the following steps:

[0039] S530, during the movement, continuously monitor the swing amplitude A(t) of the container and calculate the value according to the formula Calculate the swing response ratio R a , where A threshold is the preset swing threshold (take 15°), if R a >1, then further reduce the moving speed to 0.8V move_safe , to reduce the swing and ensure safety, if R a <1, then increase the moving speed to 1.2V move-safe .

[0040] A control system for container front crane loading and unloading detection, including the following modules:

[0041] Vibration module, used to vibrate the container before lifting;

[0042] The image recognition module uses a convolutional neural network model to analyze images and identify logos on the surface of containers;

[0043] The vibration analysis module calculates the vibration frequency of the container through image changes and calculates the vibration response ratio according to the formula;

[0044] The acoustic wave feedback module captures the acoustic wave signal and calculates the acoustic wave energy ratio;

[0045] The comprehensive judgment module combines the results of mark recognition, vibration analysis and sound wave feedback to comprehensively judge the loading status and item distribution of the container and determine the lifting and moving speed.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] In the scheme of this application:

[0048] 1. By combining multiple sensor data such as image recognition, vibration analysis, sound wave feedback and swing detection, the system can more comprehensively and accurately judge the loading status of the container and the distribution of internal items. The system will automatically reduce the lifting and moving speed to avoid safety hazards caused by improper operation, avoid large relative shaking of items in the container, and prevent rapid movement from causing damage to items in the container due to collision. The error rate is low, and the steps of weighing and manually opening the container to check the internal situation during the loading and unloading process of the container are reduced. The detection equipment is installed on the crane, which is more convenient for centralized maintenance and operation, and does not need to be installed for each container, which is low in cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of a control method for container reach crane loading and unloading detection provided in this application;

[0050] Figure 2 A schematic diagram of a control system for container reach crane loading and unloading detection provided in this application. DETAILED DESCRIPTION

[0051] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0052] As described in the background art, these situations are all judged based on the operator's work experience, which is not only prone to errors but also inefficient.

[0053] In order to solve this technical problem, the present invention provides a control method for container front crane loading and unloading detection, which is applied to the field of container detection.

[0054] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings.

[0055] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features and technical solutions in the embodiments may be combined with each other.

[0056] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0057] Example 1, please refer to Figure 1 , a control method for container front crane loading and unloading detection, comprising the following steps:

[0058] S100, Installation equipment: Install high-resolution cameras and acoustic sensors on the lifting equipment;

[0059] S200, image recognition of fragile and explosive items: the camera collects image information of the container surface in real time, uses a convolutional neural network model to analyze the image, and identifies whether there are fragile and explosive items (this is a known technology, so it is not repeated here). By automatically detecting the fragile and explosive items on the container surface, the system can adjust the lifting and moving speed in time, reduce the risk of accidents caused by improper operation, rely on the automation system instead of manual judgment, reduce misjudgment caused by operator inexperience or fatigue, and improve the safety of the overall operation;

[0060] If the mark is detected, the lifting speed and moving speed will be reduced to the preset safety speed V safe and V move-safe ;

[0061] S300, image change detection vibration data: if the fragile and explosive goods labels are not detected, then enter this step, the container is impacted by the vibrator on the crane, the container is subjected to impact vibration, the vibration data of the container is detected according to the image information, and the image is recognized by a high-resolution camera and a convolutional neural network (CNN). The system can quickly and accurately recognize the labels on the surface of the container, thereby improving the detection speed;

[0062] S300, detecting vibration data through image changes, specifically includes the following steps:

[0063] S310, continuously collecting an image sequence of the container by the camera;

[0064] S320, using image processing technology to compare the two frames in the image sequence, and calculate the displacement change Δx of the container in the front and rear directions in the image. front-back And the displacement change Δx in the left and right directions left-right and rotation change Δθ; (this is prior art and will not be described in detail);

[0065] S400, acoustic feedback detection: detect the sound changes before and after the container vibrates;

[0066] S400, sound wave feedback detection specifically includes the following steps:

[0067] S410, capturing the sound changes in the container through the acoustic wave sensor, and recording the acoustic wave signal S(t) after the impact;

[0068] S500, comprehensive judgment and adjustment of lifting and moving speed: adjusting the lifting and moving speed of the container in combination with vibration data and sound wave data;

[0069] S500, comprehensively judge and adjust the lifting and moving speed, specifically including the following steps:

[0070] S510, when R v (front-back)<1, R v (left-right)<1, R s <1, and is_uneven=False, the container is considered full, fast movement is allowed, and the lifting speed is set to V fast , and the moving speed is set to V move-fast , automatically adjust the lifting and moving speed according to the vibration response ratio and the sound wave response ratio, avoiding unnecessary deceleration and improving loading and unloading efficiency;

[0071] S520: When any of the following conditions is met, it is determined that the container is not fully loaded or the items are unevenly distributed:

[0072] R v (front-back)>1 or R v (left-right)>1 or R s >1 or is_uneven=True, regardless of the weight, the lifting speed is reduced to V slow And the movement speed is reduced to V move_slow .

[0073] Embodiment 2 further optimizes the control method for container front crane loading and unloading detection provided in Embodiment 1. Specifically, Figure 1 As shown, S300 also includes the following steps:

[0074] S330, according to the formula Calculate the vibration displacement coefficient D, where D reflects the vibration amplitude of the container and Δx represents the displacement change Δx in the front-to-back direction front-back and the left-right displacement change Δx left-right , the displacement time series in each direction is converted into a frequency domain signal using the fast Fourier transform (FFT), and the calculated front-to-back direction D front-back (t) and left and right directions D left-right The displacement time series (t) is converted into a frequency domain signal S front-back (f) and S left-right (f).

[0075] S300 also includes the following steps:

[0076] S340, according to the formula Calculate the vibration response ratio R in the front-to-back direction and the left-to-right direction respectively v (front-back) and R v (left-right), where f0 is the reference frequency (50Hz), D threshold is the preset displacement threshold (0.5 mm). This ratio reflects the vibration intensity of the container in different directions relative to the preset standard. c Corresponding to the vibration frequencies in the front-to-back direction and the left-to-right direction respectively, through frequency domain analysis and vibration response ratio calculation, the system can accurately evaluate the vibration conditions of the container, adjust the speed in time, and reduce damage to items caused by excessive vibration.

[0077] S300 also includes the following steps:

[0078] S350, if | R v (front-back)-R v (left-right)|>∈, that is, the difference between the vibration response ratios in the front-to-back direction and the left-to-right direction is greater than the preset threshold ∈ (taken as 0.2), then it is determined that the items in the container are unevenly distributed, and the flag is_uneven=True is set, otherwise is_uneven=False; this indicates that the items in the container are not evenly distributed, which may affect the stability and safety during transportation.

[0079] S330 specifically includes the following steps:

[0080] S331. Convert the displacement time series in each direction into frequency domain signals respectively:

[0081] Front and rear direction: S front-back (f) = fft(D front-back (t));

[0082] Left and right direction: S left-right (f) = fft(D left-right (t));

[0083] 332. Analyze the main frequency components in the spectrum and calculate the vibration frequency f in the front-to-back direction and the left-to-right direction respectively. front-back and f left-right :

[0084] f front-back = arg max(|S front-back (f)|);

[0085] f left-right = arg max(|S left-right (f)|);

[0086] S333. Calculate the amplitude of the spectrum and select the maximum frequency component as the vibration frequency, where |S (f) | represents the amplitude of the spectrum, and the argmax function is used to find the frequency with the largest amplitude in the spectrum.

[0087] Embodiment 3 further optimizes the control method for container front crane loading and unloading detection provided in Embodiment 1 or 2. Specifically, in S400, the following steps are further specifically included:

[0088] S420, Calculate the sound wave energy where t1 and t2 are the time windows before and after the impact.

[0089] S400 further specifically includes the following steps:

[0090] S430, according to the formula Calculate the sound wave energy ratio R s , where E threshold is the preset energy threshold (taken as 1000J).

[0091] S500 further includes the following steps:

[0092] S530, during the movement, continuously monitor the swing amplitude A(t) of the container and calculate the value according to the formula Calculate the swing response ratio R a , where A threshold is the preset swing threshold (take 15°), if R aIf it is >1, further reduce the moving speed to 0.8V move_safe , to reduce the swing amplitude and ensure safety. If R a <1, increase the moving speed to 1.2V move_safe , comprehensively consider the displacement changes, vibration frequencies and acoustic wave energies in the front-back direction and left-right direction, provide multi-dimensional data support, and ensure the accuracy of the operation.

[0093] Example 4 further optimizes a control method for container reachstacker loading and unloading detection provided in Example 1. Specifically, four weighing sensors are further included in S100, which are installed on the spreader suspension locks and used to measure the weights of the four corners of the container. The four weighing sensors collect the weights F1, F2, F3, and F4 of the four corners of the container in real time. The total weight W = F1 + F2 + F3 + F4, and the accuracy error ≤ 3%. The calculation of the center of gravity offset is as follows:

[0094] It is configured with an FPGA chip and an ARM Cortex-A9 dual-core processor, and executes the following algorithms:

[0095] Δx = (F1 + F4 - F2 - F3) / W × Lx, where Lx is the lateral span;

[0096] Δy = (F1 + F2 - F3 - F4) / W × Ly, where Lx is the lateral span;

[0097] Vibration spectrum analysis algorithm: FFT(front) = ΣΔx(t)e^(-j2πft);

[0098] Multi-modal fusion decision tree: IF (W < W_min) & (E_ratio > 1.2) THEN v = 0.6v_max";

[0099] Control logic: If an empty container (W < Wmin) or the center of gravity offset exceeds the threshold (Δx) 2 +(Δy) 2 > D th , where D th is the displacement threshold, specifically 0.5 mm, trigger a voice alarm and lock the vehicle. The data is uploaded to the cloud through a wireless module, supporting historical query and printing. The sensor adopts a wide-temperature compensation technology (-20°C to 45°C), combined with the reverse connection / overvoltage protection of the IO module, to ensure stability in harsh environments. The weight and center of gravity offset of the container are accurately measured through the weighing sensor, realizing static load analysis, empty / heavy container identification and vehicle locking control.

[0100] Example 5, a control system for container reachstacker loading and unloading detection, includes the following modules:

[0101] Vibration module, used to generate vibrations before lifting the container;

[0102] The image recognition module uses a convolutional neural network model to analyze images and identify logos on the surface of containers;

[0103] The vibration analysis module calculates the vibration frequency of the container through image changes and calculates the vibration response ratio according to the formula;

[0104] The weighing module consists of four weighing sensors, which are used to measure the weight of the four corners of the container;

[0105] The acoustic wave feedback module captures the acoustic wave signal and calculates the acoustic wave energy ratio;

[0106] The comprehensive judgment module combines the results of mark recognition, vibration analysis and sound wave feedback to comprehensively judge the loading status and item distribution of the container and determine the lifting and moving speed.

[0107] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral one; it can be a mechanical connection, an electrical connection, or communication with each other; it can be a direct connection, or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0108] Obviously, the embodiments described above are only some embodiments of the present invention, rather than all embodiments. The preferred embodiments of the present invention are given in the accompanying drawings, but they do not limit the patent scope of the present invention. The present invention can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive. Although the present invention has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific embodiments, or to perform equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of the present invention, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present invention.

Claims

1. A control method for container front crane loading and unloading detection, characterized in that: The following steps are involved: S100, Install equipment: Install vibrators, high-resolution cameras and acoustic sensors on the lifting equipment; S200, image recognition of fragile and explosive goods labels: the camera collects image information of the container surface in real time, and uses a convolutional neural network model to analyze the image to identify whether there are fragile and explosive goods labels; If the mark is detected, the lifting speed and moving speed will be reduced to the preset safety speed V safe and V move_safe ; S300, image change detection vibration data: if no fragile or explosive item mark is detected, then this step is entered, and the container is impacted by a vibrator on the crane, and the container is subjected to impact vibration, and vibration data of the container is detected based on the image information; S300, detecting vibration data through image changes, specifically includes the following steps: S310, continuously collecting an image sequence of the container by the camera; S320, using image processing technology to compare the two frames in the image sequence, and calculate the displacement change Δx of the container in the front and rear directions in the image. front-back And the displacement change Δx in the left and right directions left-right and the rotation change Δθ S400, acoustic feedback detection: detect the sound changes before and after the container vibrates; S500, comprehensive judgment and adjustment of lifting and moving speed: adjusting the lifting and moving speed of the container in combination with the vibration data and the sound wave data.

2. A control method for container front crane loading and unloading detection according to claim 1, characterized in that: S300 also includes the following steps: S330, according to the formula Calculate the vibration displacement coefficient D, where D reflects the vibration amplitude of the container and Δx represents the displacement change Δx in the front-to-back direction front-back and the left-right displacement change Δx left-right , the displacement time series in each direction is converted into a frequency domain signal using the fast Fourier transform (FFT), and the calculated front-to-back direction D front-back (t) and left and right directions D left-right The displacement time series (t) is converted into a frequency domain signal S front-back (f) and S left-right (f).

3. A control method for container front crane loading and unloading detection according to claim 2, characterized in that: S300 also includes the following steps: S340, according to the formula Calculate the vibration response ratio R in the front-to-back direction and the left-to-right direction respectively v (front-back) and R v (left-right), where f0 is the reference frequency, D threshold is the preset displacement threshold, f c Corresponding to the vibration frequencies in the front-to-back direction and the left-to-right direction respectively.

4. A control method for container front crane loading and unloading detection according to claim 3, characterized in that: S300 also includes the following steps: S350, if | R v (front-back)-R v (left-right)|>∈, that is, if the difference between the vibration response ratios in the front-to-back direction and the left-to-right direction is greater than the preset threshold ∈, it is determined that the items in the container are unevenly distributed, and the flag is_uneven=True is set, otherwise it is is_uneven=False.

5. A control method for container front crane loading and unloading detection according to claim 4, characterized in that: In S400, the acoustic wave feedback detection specifically includes the following steps: S410, capturing the sound changes in the container through the acoustic wave sensor, and recording the acoustic wave signal S(t) after the impact; S420, Calculate the sound wave energy where t1 and t2 are the time windows before and after the impact.

6. A control method for container front crane loading and unloading detection according to claim 5, characterized in that: S400 further specifically includes the following steps: S430, according to the formula Calculate the sound wave energy ratio R s , where E threshold is the preset energy threshold.

7. A control method for container front crane loading and unloading detection according to claim 2, characterized in that: S330 specifically includes the following steps: S331. Convert the displacement time series in each direction into frequency domain signals respectively: Front and rear direction: S front-back (f) = fft(D front-back (t)); Left and right direction: S left-right (f) = fft(D left-right (t)).

8. A control method for container front crane loading and unloading detection according to claim 7, characterized in that: In S500, the lifting and moving speeds are comprehensively judged and adjusted, specifically including the following steps: S510, when R v (front-back)<1, R v (left-right)<1, R s <1, and is_uneven=False, the container is considered full, fast movement is allowed, and the lifting speed is set to V fast , and the moving speed is set to V move_fast ; S520: When any of the following conditions is met, it is determined that the container is not fully loaded or the items are unevenly distributed: R v (front-back)>1 or R v (left-right)>1 or R s >1 or is_uneven=True, regardless of the weight, the lifting speed is reduced to V slow And the movement speed is reduced to V move_slow ; S530, during the movement, continuously monitor the swing amplitude A(t) of the container and calculate the value according to the formula Calculate the swing response ratio R a , where A threshold is the preset swing threshold. If R a >1, then further reduce the moving speed to 0.8V move_safe , if R a <1, then increase the moving speed to 1.2V move_safe .

9. A control system for detecting loading and unloading of a container reach crane, applied to the control method for detecting loading and unloading of a container reach crane as claimed in claim 8, characterized in that: Includes the following modules: Vibration module, used to vibrate the container before lifting; The image recognition module uses a convolutional neural network model to analyze images and identify logos on the surface of containers; The vibration analysis module calculates the vibration frequency of the container through image changes and calculates the vibration response ratio according to the formula; The acoustic wave feedback module captures the acoustic wave signal and calculates the acoustic wave energy ratio; The comprehensive judgment module combines the results of mark recognition, vibration analysis and sound wave feedback to comprehensively judge the loading status and item distribution of the container and determine the lifting and moving speed.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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