Container weighing and overload detection method and system

Abstract

The application belongs to the technical field of super unbalanced load detection, and particularly relates to a container weighing and super unbalanced load detection method and system, which comprises the following steps: performing curve fitting on stress data of each corner of a container at all times during hoisting detection to obtain a peak value region and a valley value region and obtain stress periodicity measurement of each corner of the container; performing autocorrelation analysis on acceleration data of the container in each direction during the hoisting detection to determine acceleration periodicity measurement of the container; analyzing an influence degree of wind speed data on the stress data and the acceleration data during the hoisting detection to determine a wind force influence coefficient of the container during the hoisting detection; obtaining average stress data of each corner of the container after compensation, obtaining an unbalanced load value of the container by adopting a moment balance principle, and performing super unbalanced load detection on the container. The application aims to improve the precision of super unbalanced load detection of the container.

Description

Technical Field

[0001] This application belongs to the field of overload detection technology, specifically a container weighing and overload detection method and system. Background Technology

[0002] Container shipping is a crucial mode of cargo transportation, and ensuring its safety and efficiency is becoming increasingly urgent. Among the many factors affecting transportation safety, the problem of container overloading and off-center loading is particularly prominent. Overloading can reduce the maneuverability of transport vehicles, increasing the risk of traffic accidents; while off-center loading can cause containers to become unbalanced during handling, leading to damage or loss of goods. Traditional weighing methods, such as static weighbridges, are inefficient and difficult to adapt to the demands of modern fast-turnaround logistics; dynamic weighing improves efficiency but lacks accuracy; while hoisting inspection has advantages in both efficiency and accuracy, thus becoming a commonly used method for detecting overloading and off-center loading.

[0003] However, this method still has certain shortcomings. Lifting inspection requires hoisting the container and determining the center of gravity based on the forces at the four corners to identify any overloading or off-center loading issues. This method is highly susceptible to external environmental factors, such as wind, which can cause the container to sway during lifting, leading to instability in the forces at the four corners. This instability directly affects the accuracy of subsequent calculations of the total weight and center of gravity position based on the force data. Although errors can be reduced by averaging multiple samples, this method cannot completely eliminate error sources due to environmental interference. Furthermore, the need for multiple measurements affects the efficiency of overloading and off-center loading detection. Summary of the Invention

[0004] In view of the above, it is necessary to provide a container weighing and overload detection method and system to overcome the influence of environmental factors during the hoisting and inspection process and achieve efficient and accurate container overload detection.

[0005] The first aspect of this application provides a method for weighing and detecting overloading / off-center loading of a container, the method comprising:

[0006] Collect wind speed data, force data at each corner of the container, and acceleration data in each direction of the container during each hoisting and inspection process;

[0007] Curve fitting was performed on the force data of each corner of the container at all times during the lifting and inspection process. Based on the shape characteristics of the curve fitting, peak and valley regions were obtained. The area dispersion of the peak and valley regions was analyzed to obtain the force period measurement of each corner of the container. Autocorrelation analysis was performed on the acceleration data of the container in various directions during the lifting and inspection process to determine the acceleration periodicity measurement of the container. The influence of wind speed data on force and acceleration data during the lifting and inspection process was analyzed. Based on the fitting results of each corner of the container and the corresponding force period measurement, combined with the acceleration periodicity measurement, the wind force influence coefficient of the container during the lifting and inspection process was determined.

[0008] Based on the integral area of ​​the fitted curve of each corner of the container during the hoisting and inspection process, as well as the area difference between the corresponding peak and valley regions, and combined with the wind force influence coefficient, the average force data of each corner of the container after compensation is obtained.

[0009] The principle of torque balance is used to obtain the off-center load value of the container by calculating the average force area of ​​each corner, and the container is then tested for over-off-center load.

[0010] The specific process for obtaining the peak region and the valley region is as follows:

[0011] Obtain the force fitting curve for each corner of the container. Use the average of the maximum and minimum values ​​of the force fitting curve on the vertical axis as the dividing line. Record the peak area above the dividing line as the peak area and the trough area below the dividing line as the trough area.

[0012] Specifically, the measurement of the force period at each corner of the container is obtained as follows:

[0013] Calculate the sum of the dispersion of the peak area and the dispersion of the valley area on each force fitting curve, and use it as a measure of the force cycle at each corner of the container.

[0014] Specifically, determining the periodicity of the container's acceleration is as follows:

[0015] The autocorrelation function graph of the acceleration data of the container in all directions at all times during the hoisting and inspection process is obtained. Peak values ​​that exceed the preset confidence interval are marked as significant peak values. The hysteresis quantities corresponding to all significant peak values ​​are combined into a hysteresis quantity sequence, and the first degree of dispersion of all elements in the first difference sequence of the hysteresis quantity sequence is obtained. The average value of the first degree of dispersion corresponding to the acceleration data in all directions is used as the periodicity measure of the container's acceleration.

[0016] The process of obtaining the wind force influence coefficient of the container during the hoisting and inspection is as follows:

[0017] The second degree of dispersion of all force data at each corner of the container is obtained, and the average of the second degree of dispersion of all corners is calculated to obtain the average force fluctuation of the container.

[0018] Based on the correlation between wind speed data and the force data of each corner of the container during the hoisting and inspection process, as well as the correlation between wind speed data and the acceleration of the container in each direction, the force correlation metric of the container is obtained.

[0019] The result of forward fusion of the force period measurement corresponding to each corner of the container and the goodness of fit measurement obtained by curve fitting is calculated. The forward fusion result obtained from all corners of the container is accumulated with the acceleration periodicity measurement to obtain the denominator of the wind force influence coefficient. The absolute value of the average force fluctuation of the container is forward fused with the force correlation measurement to obtain the numerator of the wind force influence coefficient.

[0020] The method for obtaining the force-related measure of the container includes:

[0021] The correlation between all wind speed data and acceleration data in each direction during container hoisting and inspection is obtained and denoted as the first correlation. The correlation between all force data at each corner of the container and all wind speed data is calculated and denoted as the second correlation. The average of all first correlations and all second correlations obtained from all wind speed data during container hoisting and inspection is used to obtain the force correlation measure of the container.

[0022] Specifically, obtaining the average force data for each corner of the container after compensation is as follows:

[0023] Based on the integral area of ​​the force fitting curve obtained at each corner of the container, and combined with the area distribution differences of the peak and valley regions, the wind disturbance area at each corner of the container is determined.

[0024] The difference between the integral area and the wind disturbance area is calculated and divided by the number of force data points at each corner of the container to obtain the average force data at each corner of the container after compensation.

[0025] The process of determining the wind disturbance area at each corner of the container is as follows:

[0026] Obtain the integral area of ​​the force fitting curve for each corner of the container; calculate the difference between the area of ​​all peak regions and the area of ​​all valley regions corresponding to each corner, and perform positive fusion with the normalized value of the wind force influence coefficient to obtain the wind disturbance area of ​​each corner of the container.

[0027] Specifically, the overload and off-center load detection of the container includes:

[0028] If the off-center load value of the container exceeds the preset threshold, it is determined that the container is overloaded.

[0029] Secondly, embodiments of this application also provide a container weighing and overload detection system, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0030] This application has at least the following beneficial effects:

[0031] To address the shortcomings of existing technologies, this application first calculates a wind force influence coefficient based on the temporal influence characteristics of wind on the acceleration of motion and the stress on the four corners of the container during overload detection. This helps to accurately measure the impact of wind on container lifting detection, thereby enhancing the accuracy of subsequent compensation. Then, using the wind force influence coefficient and the periodic peak-and-trough variation characteristics of the container's stress, average stress data is obtained to compensate for the container's stress, further enhancing the measurement of the stress on the four corners of the container. Through this method, the stress is measured based on the degree of wind influence on container lifting detection, achieving compensation for wind disturbances to a certain extent, enhancing the accuracy of stress measurement, and thus realizing a highly efficient and accurate method for detecting overloaded containers. Attached Figure Description

[0032] Figure 1 A flowchart illustrating the steps of a container weighing and overload detection method according to an embodiment of this application;

[0033] Figure 2 This is a schematic diagram illustrating the acquisition of average force data for each corner of a container according to an embodiment of this application. Detailed Implementation

[0034] In the description of the embodiments in this application, the words "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary," "or," and "for example" is intended to present the relevant concepts in a specific manner.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this application's specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0036] It should also be noted that the terms "first" and "second" in this application and its accompanying drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. The methods disclosed in the embodiments of this application or the methods shown in the flowcharts include one or more steps for implementing the method. Without departing from the scope of protection of this application, the execution order of multiple steps can be interchanged, and some steps can also be deleted.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0038] The following description, in conjunction with the accompanying drawings, details the specific scheme of the container weighing and overload / off-center load detection method and system provided in this application.

[0039] Please see Figure 1 The diagram illustrates a flowchart of a container weighing and overload detection method according to an embodiment of this application. The method includes the following steps:

[0040] The first step is to collect wind speed data, force data at each corner of the container, and acceleration data in each direction of the container during each hoisting and inspection process.

[0041] The container weighing and inspection system of this application uses a container spreader for container lifting and inspection. The spreader is equipped with four weighing sensors located at the four pivot points of the spreader to measure the forces acting on the four corners of the container during the lifting and inspection process. A triaxial accelerometer is also installed on the spreader to measure the acceleration in each direction during the lifting and inspection process. An anemometer in the system collects wind speed changes during the inspection process. All data are collected at the same frequency. The signals collected by the sensors and instruments are converted into electrical signals, then processed by a data acquisition unit for signal amplification and A / D conversion to obtain data sequences for each type of data, including wind speed sequences, acceleration sequences in the x, y, and z directions, and force sequences at each corner of the container.

[0042] The second step involves curve fitting of the force data at each corner of the container at all times during the lifting and inspection process. Based on the shape characteristics of the curve fitting, peak and valley regions are obtained. The area dispersion of the peak and valley regions is analyzed to obtain the force period measurement of each corner of the container. Autocorrelation analysis is performed on the acceleration data of the container in various directions during the lifting and inspection process to determine the acceleration periodicity measurement of the container. The influence of wind speed data on the force data at each corner of the container and the influence of wind speed data on the acceleration in various directions of the container during the lifting and inspection process are analyzed. Based on the fitting results of each corner of the container and the corresponding force period measurement, combined with the acceleration periodicity measurement, the wind force influence coefficient of the container during the lifting and inspection process is determined.

[0043] Container lifting inspection is one of the commonly used methods for detecting container overload and off-center loading. However, during the lifting process, environmental factors can introduce errors into the detection, with wind being the most significant. Wind causes the lifted container to sway or vibrate, leading to unstable and inaccurate data from the four corner load cells. This instability, in turn, causes calculation errors when determining the center of gravity based on the stress, affecting the accuracy of the overload and off-center loading detection. Therefore, it is necessary to compensate for wind disturbances to minimize their impact.

[0044] Since the disturbance caused by wind to container overload detection varies in magnitude, and different adjustment methods are required for different wind disturbances in order to achieve a better compensation effect, it is necessary to first distinguish the impact caused by wind disturbance.

[0045] First, in strong winds, the lifting container will experience lateral movement or swaying due to the combined effects of wind and gravity, resulting in significant fluctuations in the forces acting on its four corners. This swaying also causes periodic fluctuations in the peak and trough values ​​of the forces acting on the corners. Second, as the swaying occurs with the changing wind direction, the container's acceleration in each direction exhibits a periodic, alternating pattern of rapid and slow changes. Finally, since all these changes are caused by wind, the fluctuations in the forces acting on the container's four corners and its acceleration show a strong correlation with wind force.

[0046] However, when the wind is weak or insignificant, the container is mainly subjected to gravity, and the effect of the wind is relatively small. As a result, the force fluctuation at the four corners of the container is relatively small. Even if it may be affected by the wind, the periodicity of the force changes at the four corners will be relatively weak. Secondly, the container is relatively stable in this situation, so the acceleration fluctuation in each direction is small, and the periodicity of the acceleration is not obvious. Finally, since the wind has little effect on the container, the correlation between the fluctuation of the force at the four corners of the container and the acceleration is also weak.

[0047] Taking the force sequence at each corner of the container as input, a quadratic B-spline curve fitting method is used to output the force fitting curve for each corner, and the goodness-of-fit metric (coefficient of determination) for each corner is obtained. The goodness-of-fit metric for the i-th corner is denoted as... The method for fitting quadratic B-spline curves is a well-known technique and will not be elaborated further.

[0048] On each force-fit curve, the mean of the maximum and minimum values ​​of the ordinate is used as the dividing line. Each consecutive peak region above the dividing line constitutes a peak region, and each consecutive trough region below the dividing line constitutes a trough region. The sum of the dispersion of the peak region area and the dispersion of the trough region area on each force-fit curve is calculated and used as a measure of the force period at each corner of the container, denoted as . In this embodiment, the degree of dispersion is calculated using variance.

[0049] The acceleration sequences in each direction are used as input to obtain their autocorrelation function plots. With a confidence level of 95%, peaks exceeding the confidence interval are marked as significant peaks. The hysteresis values ​​corresponding to all significant peaks are arranged in order of magnitude to form a hysteresis sequence. The first degree of dispersion of all elements in the first difference sequence of the hysteresis sequence is then obtained. The average of the first degree of dispersion corresponding to the acceleration sequences in all directions is used as a measure of the periodicity of the container's acceleration, denoted as […]. The autocorrelation function is a well-known technique and will not be elaborated further.

[0050] Based on the above analysis, a wind force influence coefficient is calculated to measure the degree of influence of wind force on container overload detection. Specifically, the second degree of dispersion of the force sequence at each corner of the container is obtained, and the average of the second degree of dispersion obtained from all corners is calculated to obtain the average force fluctuation of the container, denoted as . In this embodiment, the dispersion between sequence elements is calculated using variance; the correlation between the wind speed sequence of the container and the acceleration sequence in each direction is obtained, denoted as the first correlation; the correlation between the force sequence at each corner of the container and the wind speed sequence is calculated, denoted as the second correlation; the average of all first correlations and all second correlations obtained from the container's wind speed sequence is used to obtain the force correlation metric of the container, denoted as... In this embodiment, the correlation between sequences is calculated using the Pearson correlation coefficient; the result of positively fusing the force period measurement and goodness-of-fit measurement corresponding to each corner of the container is calculated, and the positive fusion result obtained from all corners of the container is accumulated with the acceleration periodicity measurement to obtain the denominator of the wind force influence coefficient; the absolute value of the average force fluctuation of the container is positively fused with the force correlation measurement to obtain the numerator of the wind force influence coefficient.

[0051] In this embodiment, the positive fusion of variables is first performed using a multiplication calculation method. The wind force influence coefficient is denoted as A, and its formula is as follows: .

[0052] It should be understood that when wind has a significant impact on containers, it causes large fluctuations in the forces acting on the containers at various corners, resulting in periodic changes in these forces. Secondly, the acceleration in each direction also exhibits periodic variations. Finally, these changes in force and acceleration are primarily due to wind fluctuations, thus showing a strong correlation with wind changes, leading to a large wind influence coefficient. Conversely, when wind has a smaller impact on containers, the wind influence coefficient is also smaller.

[0053] The third step: Based on the integral area of ​​the fitted curve of each corner of the container during the hoisting and inspection process, and the area difference between the corresponding peak and valley areas, combined with the wind influence coefficient, the average force data of each corner of the container after compensation is obtained.

[0054] The wind force influence coefficient measures the degree of wind's impact on container overload detection. Based on this, compensation for wind disturbances needs to be made according to the degree of wind influence to improve the accuracy of container overload detection. Therefore, it is first necessary to analyze the impact of wind on container lifting detection. Wind causes a certain degree of swaying after the container is lifted, and the force on the four corners of the container changes accordingly during this swaying, thus causing errors in overload detection. However, due to the periodicity of the swaying, the force changes also exhibit periodic fluctuations, which are manifested as peaks and troughs in the force data. Secondly, the greater the wind disturbance on container overload detection, the higher the frequency and amplitude of the container's swaying. This change, while causing peaks and troughs in the force data, also increases the container's vibration. This vibration increases the instability during overload detection, and this instability will superimpose with the peaks and troughs.

[0055] Therefore, when compensating for wind disturbances, it is necessary to first compensate for the peaks and troughs in the force data. However, if only the peaks and troughs are compensated, the detected instability may also be compensated as part of the peaks and troughs. This instability is caused by mechanical vibration, which, although indirectly related to wind, is not directly caused by wind disturbances. Moreover, the error caused by this mechanical vibration does not have the same periodicity as wind disturbances. Therefore, if this part is also directly compensated through peaks and troughs, it will lead to overcompensation. Thus, it is necessary to adjust the compensation of peaks and troughs according to the degree of wind disturbance to improve the accuracy of wind disturbance compensation.

[0056] Based on the above analysis, a compensation function is constructed to compensate for wind disturbance. Specifically: the integral area of ​​the force fitting curve for each corner of the container is obtained; the difference between the area of ​​all peak regions and the area of ​​all valley regions corresponding to each corner is calculated and positively fused with the normalized value of the wind influence coefficient to obtain the wind disturbance area of ​​each corner of the container; the difference between the integral area and the wind disturbance area is calculated and divided by the number of elements in the force sequence of each corner of the container to obtain the average force data of each corner of the container after compensation. In this embodiment, the difference between variables is calculated using the difference method; the positive fusion of multiple variables is calculated using the multiplication method; the normalization method uses the maximum-minimum normalization method. A schematic diagram of obtaining the average force data of each corner of the container is shown below. Figure 2 As shown.

[0057] Understandably, the smaller the impact of wind disturbance, the less the container sways under wind force, resulting in less mechanical vibration of the entire device. In this case, compensation can be made based on the periodic peaks and troughs of the force changes on the container caused by wind, thereby reducing the impact of wind disturbance on overload detection and improving its accuracy. Conversely, the greater the impact of wind disturbance, the greater the mechanical vibration caused by wind. In this case, the compensation amplitude needs to be appropriately reduced when performing peak and trough compensation to avoid overcorrecting errors caused by mechanical vibration.

[0058] The fourth step: Using the principle of torque balance, the average force-bearing area of ​​each corner of the container is used to obtain the off-center load value of the container, and the container is then subjected to over-off-center load detection.

[0059] Using the average force data at each corner of the container as input, and based on the principle of torque balance, the off-center load value of the container is calculated by analyzing the force conditions at the four corners. This calculation of off-center load value based on the force conditions at the four corners is a well-known technique in the field of container weighing, and its specific details will not be elaborated further. When the off-center load value of the container exceeds a threshold... In this embodiment, it is determined that there is an overload situation. Take the empirical value of 100mm.

[0060] Based on the same inventive concept as the above methods, this application also provides a container weighing and overload detection system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described container weighing and overload detection methods.

[0061] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description; sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0062] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from its essential characteristics. Therefore, the embodiments described above should be considered exemplary and non-limiting in all respects; modifications to the technical solutions described in the foregoing embodiments, or equivalent substitutions of some technical features, without causing the essence of the corresponding technical solutions to deviate from the scope of the technical solutions in the embodiments of this application, should all be included within the protection scope of this application.

Claims

1. A method for weighing and detecting overloading / off-center loading of containers, characterized in that, The method includes the following steps: Collect wind speed data, force data at each corner of the container, and acceleration data in each direction of the container during each hoisting and inspection process; Curve fitting was performed on the force data of each corner of the container at all times during the lifting and inspection process. Based on the shape characteristics of the curve fitting, peak and valley regions were obtained. The area dispersion of the peak and valley regions was analyzed to obtain the force period measurement of each corner of the container. Autocorrelation analysis was performed on the acceleration data of the container in various directions during the lifting and inspection process to determine the acceleration periodicity measurement of the container. The influence of wind speed data on force and acceleration data during the lifting and inspection process was analyzed. Based on the fitting results of each corner of the container and the corresponding force period measurement, combined with the acceleration periodicity measurement, the wind force influence coefficient of the container during the lifting and inspection process was determined. Based on the integral area of ​​the fitted curve of each corner of the container during the hoisting and inspection process, as well as the area difference between the corresponding peak and valley regions, and combined with the wind force influence coefficient, the average force data of each corner of the container after compensation is obtained. The principle of torque balance is used to obtain the off-center load value of the container by calculating the average force area of ​​each corner, and the container is then tested for over-off-center load. The second degree of dispersion of all force data at each corner of the container is obtained, and the average of the second degree of dispersion of all corners is calculated to obtain the average force fluctuation of the container. Based on the correlation between wind speed data and the force data of each corner of the container during the hoisting and inspection process, as well as the correlation between wind speed data and the acceleration of the container in each direction, the force correlation metric of the container is obtained. The result of forward fusion of the force period measurement corresponding to each corner of the container and the goodness of fit measurement obtained by curve fitting is calculated. The forward fusion result obtained from all corners of the container is accumulated with the acceleration periodicity measurement to obtain the denominator of the wind force influence coefficient. The absolute value of the average force fluctuation of the container is forward fused with the force correlation measurement to obtain the numerator of the wind force influence coefficient.

2. The container weighing and overload / off-center loading detection method as described in claim 1, characterized in that, The specific process for obtaining the peak and valley regions is as follows: Obtain the force fitting curve for each corner of the container. Use the average of the maximum and minimum values ​​of the force fitting curve on the vertical axis as the dividing line. Record the peak area above the dividing line as the peak area and the trough area below the dividing line as the trough area.

3. The container weighing and overload / off-center loading detection method as described in claim 2, characterized in that, The force period measurement for each corner of the container is obtained as follows: Calculate the sum of the dispersion of the peak area and the dispersion of the valley area on each force fitting curve, and use it as a measure of the force cycle at each corner of the container.

4. The container weighing and overload / off-center loading detection method as described in claim 1, characterized in that, The determination of the periodicity of the container's acceleration is specifically as follows: The autocorrelation function graph of the acceleration data of the container in all directions at all times during the hoisting and inspection process is obtained. Peak values ​​that exceed the preset confidence interval are marked as significant peak values. The hysteresis quantities corresponding to all significant peak values ​​are combined into a hysteresis quantity sequence, and the first degree of dispersion of all elements in the first difference sequence of the hysteresis quantity sequence is obtained. The average value of the first degree of dispersion corresponding to the acceleration data in all directions is used as the periodicity measure of the container's acceleration.

5. The container weighing and overload / off-center loading detection method as described in claim 1, characterized in that, The obtained stress correlation metric for the container includes: The correlation between all wind speed data and acceleration data in each direction during container hoisting and inspection is obtained and denoted as the first correlation. The correlation between all force data at each corner of the container and all wind speed data is calculated and denoted as the second correlation. The average of all first correlations and all second correlations obtained from all wind speed data during container hoisting and inspection is used to obtain the force correlation measure of the container.

6. The container weighing and overload / off-center loading detection method as described in claim 2, characterized in that, The average force data obtained after compensation for each corner of the container is specifically as follows: Based on the integral area of ​​the force fitting curve obtained at each corner of the container, and combined with the area distribution differences of the peak and valley regions, the wind disturbance area at each corner of the container is determined. The difference between the integral area and the wind disturbance area is calculated and divided by the number of force data points at each corner of the container to obtain the average force data at each corner of the container after compensation.

7. The container weighing and overload / off-center loading detection method as described in claim 6, characterized in that, The process of determining the wind disturbance area at each corner of the container is as follows: Obtain the integral area of ​​the force fitting curve for each corner of the container; calculate the difference between the area of ​​all peak regions and the area of ​​all valley regions corresponding to each corner, and perform positive fusion with the normalized value of the wind force influence coefficient to obtain the wind disturbance area of ​​each corner of the container.

8. The container weighing and overload / off-center loading detection method as described in claim 1, characterized in that, The aforementioned overload and off-center load detection of containers specifically includes: If the off-center load value of the container exceeds the preset threshold, it is determined that the container is overloaded.

9. A container weighing and overload / off-center loading detection system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-8.

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