Container weighing and overload and unbalanced load detection method and system

Abstract

The invention belongs to the technical field of overload and unbalanced load detection, and particularly relates to a container weighing and overload and unbalanced load detection method and system, and the method comprises the steps: carrying out the curve fitting of the stress data of each corner of a container at all moments in a hoisting detection process, obtaining a peak value region and a valley value region, and obtaining the stress period measurement of each corner of the container; performing self-correlation analysis on the acceleration data of the container in each direction in the hoisting detection process, and determining the acceleration periodical measurement of the container; the influence degree of the wind speed data on the stress data and the acceleration data in the hoisting detection process is analyzed, and the wind power influence coefficient of the container in the hoisting detection process is determined; the average stress data of each angle of the container after compensation is obtained, the unbalance loading value of the container is obtained according to the moment balance principle, and overload and unbalance loading detection is conducted on the container. The invention aims to improve the precision of overload and unbalanced load detection of the container.

Description

Technical Field

[0001] The present application belongs to the technical field of overloading and unbalanced loading detection, and specifically relates to a container weighing and overloading and unbalanced loading detection method and system. Background Art

[0002] Container transport is an important mode of cargo transportation, and the need to ensure the safety and efficiency of container transportation is becoming increasingly urgent. Among the many factors that affect transportation safety, the problem of overloading and uneven loading of containers is particularly prominent. Overloading may reduce the maneuverability of transport vehicles and increase the risk of traffic accidents; while uneven loading may cause the container to become unbalanced during transportation, resulting in damage or loss of cargo. Traditional weighing methods such as static weighing scales are inefficient and difficult to adapt to the rapid turnover needs of modern logistics. Dynamic weighing improves efficiency but lacks accuracy. Hoisting detection has certain advantages in detection efficiency and accuracy, making it a commonly used method for overloading and uneven loading detection.

[0003] However, this method still has certain shortcomings. The lifting inspection requires lifting the container and determining the center of gravity based on the force conditions at the four corners to determine whether there is an overload problem. This method makes the lifting inspection method extremely susceptible to external environmental factors. For example, wind can cause the container to shake during the lifting process, resulting in unstable force conditions at the four corners. This instability directly affects the accuracy of the subsequent calculation of the total weight and center of gravity based on the force data. Although the error can be reduced by taking the average value of multiple samples, this method cannot completely eliminate the source of error due to the presence of environmental interference. At the same time, the need for multiple measurements affects the efficiency of overload 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 lifting detection process and achieve efficient and accurate container overload detection.

[0005] A first aspect of the present application provides a container weighing and overloading detection method, the method comprising: Collect wind speed data, force data at each corner of the container, and acceleration data in all directions of the container during each lifting test; Perform curve fitting on the force data of each corner of the container at all times during the lifting test, and obtain peak areas and valley areas based on the shape characteristics of the curve fitting; analyze the degree of area dispersion of the peak areas and valley areas to obtain the force period measurement of each corner of the container; perform autocorrelation analysis on the acceleration data of the container in all directions during the lifting test to determine the acceleration periodicity measurement of the container; analyze the influence of wind speed data on the force data and acceleration data during the lifting test, and determine the wind influence coefficient of the container during the lifting test based on the fitting results and corresponding force period measurement of each corner of the container, combined with the acceleration periodicity measurement; Based on the integrated area of ​​the fitting curve of each corner of the container during the lifting test, as well as the area difference between the corresponding peak area and valley area, combined with the wind force influence coefficient, the average force data of each corner of the container after compensation is obtained; The torque balance principle is used to calculate the average force-bearing area of ​​each corner of the container to obtain the container's eccentricity value and perform over-eccentricity detection on the container.

[0006] The specific process of obtaining the peak area and the valley area is as follows: Obtain the force fitting curve of each corner of the container, take 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 record the trough area below the dividing line as the valley area.

[0007] The force period measurement of each corner of the container is obtained as follows: The sum of the discrete degrees of the peak area and the discrete degrees of the valley area on each force fitting curve is calculated as the force cycle measurement of each corner of the container.

[0008] The determination of the acceleration periodicity measurement of the container is specifically as follows: The autocorrelation function diagram of the acceleration data of the container in all directions at all times during the lifting inspection process is obtained, and the peaks that exceed the preset confidence interval are marked as significant peaks. The hysteresis values ​​corresponding to all significant peaks are combined into a hysteresis sequence, and the first discrete degree of all elements in the first-order difference sequence of the hysteresis sequence is obtained; the average value of the first discrete degree corresponding to the acceleration data in all directions is used as the acceleration periodicity measurement of the container.

[0009] The process of obtaining the wind influence coefficient of the container during the hoisting test is specifically as follows: Obtain the second discrete degree of all force data at each corner of the container, average the second discrete degrees obtained for all corners, and obtain the average force fluctuation of the container; Based on the correlation between wind speed data and force data at each corner of the container during the lifting test, as well as the correlation between wind speed data and acceleration in all directions of the container, the force correlation measurement of the container is obtained; Calculate the force period measurement corresponding to each corner of the container and the goodness of fit measurement obtained by curve fitting and perform forward fusion. Accumulate the forward fusion results obtained from all corners of the container with the acceleration periodic measurement to obtain the denominator of the wind influence coefficient. Forward fuse the absolute value of the average force fluctuation of the container with the force correlation measurement to obtain the numerator of the wind influence coefficient.

[0010] The obtaining of the force correlation measurement of the container includes: Obtain the correlation between all wind speed data and the acceleration data in each direction during the container lifting and detection process, which is recorded as the first correlation; calculate the correlation between all force data and all wind speed data at each corner of the container, which is recorded as the second correlation; average all first correlations and all second correlations obtained from all wind speed data during the container lifting and detection process to obtain the force correlation measurement of the container.

[0011] The average force data of each corner of the container after compensation is obtained as follows: The wind disturbance area at each corner of the container is determined based on the integrated area of ​​the force fitting curve obtained at each corner of the container and the difference in area distribution between the peak area and the valley area. The difference between the integrated area and the wind disturbance area is calculated and divided by the number of all force data of each corner of the container to obtain the average force data of each corner of the container after compensation.

[0012] The process of determining the wind disturbance area of ​​each corner of the container is as follows: Obtain the integrated area of ​​the force fitting curve for each corner of the container; calculate the difference between the areas of all peak regions and all valley regions corresponding to each corner, and perform forward fusion with the normalized value of the wind influence coefficient to obtain the wind disturbance area of ​​each corner of the container.

[0013] The above-mentioned overloading and uneven loading detection of the container is specifically as follows: When the obtained container overload value exceeds a preset threshold, it is determined that the container is overloaded.

[0014] In a second aspect, an embodiment of the present application also provides a container weighing and overloading detection system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.

[0015] This application has at least the following beneficial effects: In response to the shortcomings of the existing technology, this application first calculates the wind influence coefficient based on the time-series influence characteristics of wind on motion acceleration and the stress conditions at the four corners of the container during the overload detection process, which helps to accurately measure the impact of wind on container lifting detection, thereby enhancing the accuracy of subsequent compensation; and then uses the wind influence coefficient and the periodic peak and trough change characteristics of the container stress conditions to obtain average stress data, compensate for the stress conditions of the container, and help enhance the measurement of the stress conditions at the four corners of the container. Through this method, the stress conditions are measured according to the degree of influence of wind on container lifting detection, which can achieve compensation for wind disturbances to a certain extent, enhance the accuracy of stress condition measurement, and thus realize an efficient and accurate container overload detection method. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of a container weighing and overloading detection method provided in one embodiment of the present application; Figure 2 A schematic diagram of obtaining average force data for each corner of a container provided in one embodiment of the present application. DETAILED DESCRIPTION

[0017] In the description of the embodiments of this application, words such as "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "or," and "for example" is intended to present the relevant concepts in a concrete manner.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the art of this application. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0019] It should also be noted that the terms "first" and "second" in this application and the 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 methods. Without departing from the scope of protection of this application, the order of executing multiple steps can be interchanged with each other, and some steps can also be deleted.

[0020] Unless defined otherwise, 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.

[0021] The following describes in detail a container weighing and overload detection method and system provided by the present application with reference to the accompanying drawings.

[0022] See also Figure 1 , which shows a flowchart of a container weighing and overloading detection method provided by an embodiment of the present application, the method comprising the following steps: The first step: Collect wind speed data, force data at each corner of the container, and acceleration data in all directions of the container during each lifting inspection.

[0023] The container weighing and detection system of the present application uses a container sling to perform container lifting detection. The container sling is equipped with four weighing sensors, which are respectively located at the four rotary lock positions of the container sling to measure the force conditions of the four corners of the container during the lifting detection process; and a three-axis acceleration sensor is installed on the sling to measure the motion acceleration in various directions during the container lifting detection process; the anemometer in the detection system is used to collect the wind speed changes during the detection process. All types of data use the same acquisition frequency, and the signals collected by each sensor and instrument are converted into electrical signals. Then, they are processed by the data acquisition instrument, amplified and A / D converted, and the data sequence of each type of data is obtained, including the wind speed sequence, the acceleration sequence in the x, y, and z directions, and the force sequence of each corner of the container.

[0024] The second step: perform curve fitting on the force data of each corner of the container at all times during the lifting and testing process, and obtain the peak area and valley area based on the shape characteristics of the curve fitting; analyze the area discreteness of the peak area and the valley area to obtain the force period measurement of each corner of the container; perform autocorrelation analysis on the acceleration data of the container in various directions during the lifting and testing process to determine the acceleration periodicity measurement of the container; analyze the influence of wind speed data on the force data of each corner of the container during the lifting and testing process, and the influence of wind speed data on the acceleration of the container in various directions; based on the fitting results of each corner of the container and the corresponding force period measurement, combined with the acceleration periodicity measurement, determine the wind influence coefficient of the container during the lifting and testing process.

[0025] Container hoisting detection is a common method for detecting overloads and uneven loads. However, during the lifting process, environmental factors can cause errors in detection. The most significant environmental factor is wind, which can cause the lifted container to shake or vibrate. This can lead to unstable and inaccurate data collected by the four-corner load cells. This, in turn, can lead to errors in the subsequent calculation of the center of gravity based on the applied force, compromising the accuracy of overload and uneven load detection. Therefore, it is necessary to compensate for wind disturbances to minimize their impact.

[0026] Since the disturbance caused by wind to container overload detection varies in size, different adjustment methods are required for different wind disturbances to achieve better compensation effects. Therefore, it is necessary to first distinguish the impact caused by wind disturbances.

[0027] First, in strong winds, due to the effects of wind and gravity, the suspended container will experience a certain degree of lateral movement or swing, resulting in large fluctuations in the forces on the four corners of the container. At the same time, this swing will also cause the peak and valley values ​​of the force changes on the four corners to fluctuate periodically. Secondly, due to the changes in the direction of the wind and the direction of the wind during the swing, the acceleration of the container in all directions will show a large fluctuation of alternating fast and slow periodic changes. Finally, since these changes are all caused by the influence of wind, the fluctuations in the forces and acceleration of the container at the four corners show a strong correlation with the wind.

[0028] However, if the wind is weak or not obvious, the force on the container is mainly affected by gravity. At this time, the effect of wind is relatively smaller, so that the force fluctuations at the four corners of the container are relatively small. Even if it may be affected by a certain wind force, the periodicity of the force changes at the four corners will be relatively weak; secondly, in this case, the container is relatively stable, so the fluctuations of the movement acceleration in all directions are small, and the periodicity of the acceleration is not obvious; finally, since the effect of wind on the container is relatively small, the correlation between the fluctuations of the force and movement acceleration at the four corners of the container and the wind changes is also weak.

[0029] The force sequence of each corner of the container is taken as input, and the quadratic B-spline curve fitting method is used to output the force fitting curve of each corner. The goodness of fit measure of each corner, that is, the coefficient of determination, is obtained. The goodness of fit measure of the i-th corner is recorded as The quadratic B-spline curve fitting method is a well-known technology and will not be described in detail.

[0030] On each force fitting curve, the average of the maximum and minimum values ​​of the ordinate is used as the dividing line. Each continuous peak area above the dividing line constitutes a peak area, and each continuous trough area below the dividing line constitutes a valley area. The cumulative sum of the discrete degrees of the peak area and the discrete degrees of the valley area on each force fitting curve is calculated as the force period measurement of each corner of the container, which is recorded as In this embodiment, the degree of dispersion is calculated using variance.

[0031] The acceleration sequence in each direction is used as input, and its autocorrelation function graph is obtained. The confidence level is set to 95%, and the peaks outside the confidence interval are marked as significant peaks. The hysteresis corresponding to all significant peaks is constructed into a hysteresis sequence in order of size, and the first discrete degree of all elements in the first-order difference sequence of the hysteresis sequence is obtained. The average value of the first discrete degree corresponding to the acceleration sequence in all directions is used as the acceleration periodicity measure of the container, which is recorded as , wherein the autocorrelation function is a well-known technology and will not be described in detail.

[0032] Based on the above analysis, the wind influence coefficient is calculated to measure the impact of wind on container overload detection. Specifically, the second discrete degree of the force sequence of each corner of the container is obtained, and the second discrete degrees obtained for all corners are averaged to obtain the average force fluctuation of the container, which is recorded as In this embodiment, the degree of 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, which is recorded as the first correlation; the correlation between the force sequence of each corner of the container and the wind speed sequence is calculated, which is recorded as the second correlation; all the first correlations and all the second correlations obtained from the container wind speed sequence are averaged to obtain the force correlation measure of the container, which is recorded as In this embodiment, the correlation between sequences is calculated using the Pearson correlation coefficient; the force period measurement corresponding to each corner of the container is forward fused with the goodness of fit measurement, and the forward fusion results obtained for all corners of the container are accumulated with the acceleration periodic measurement to obtain the denominator of the wind 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 influence coefficient.

[0033] In this embodiment, the forward fusion between variables is first performed by multiplication calculation method, and the wind force influence coefficient is recorded as A. The formula is: .

[0034] It should be understood that when wind has a significant impact on a container, the forces acting on each corner of the container will fluctuate significantly, and this will cause the force changes to exhibit periodic variations. Secondly, the acceleration of motion in each direction will also exhibit periodic variations. Finally, the changes in force and acceleration are primarily due to wind fluctuations, and therefore exhibit a strong correlation with wind variations, resulting in a larger wind influence coefficient. Conversely, when wind has a smaller impact on the container, the wind influence coefficient is also smaller.

[0035] The third step: Based on the integral area of ​​the fitting curve of each corner of the container during the lifting test, as well as the area difference between the corresponding peak area and valley area, combined with the wind influence coefficient, the average force data of each corner of the container after compensation is obtained.

[0036] The wind impact coefficient measures the extent of wind's impact on container overload detection. Based on this, wind disturbances need to be compensated for based on the degree of wind influence, thereby improving the accuracy of container overload detection. Therefore, it is first necessary to analyze the impact of wind on container lifting detection. Wind can cause a certain amount of oscillation in the container after it is lifted. During this oscillation, the forces acting on the container's four corners change, resulting in errors in overload detection. However, due to the periodicity of the oscillation, the forces acting on the container also exhibit periodic fluctuations. These periodic changes in the forces manifest as peaks and troughs in the force data. Secondly, the greater the wind's impact on container overload detection, the greater the frequency and amplitude of the container's oscillation. This change, while causing peaks and troughs in the force data, also increases the container's vibration. This vibration increases the instability of overload detection, and this instability is compounded by the peaks and troughs.

[0037] Therefore, when compensating for wind disturbances, it is first necessary to 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. Although it has a certain indirect relationship with wind force, it is not directly caused by wind disturbances, and the error caused by this mechanical vibration does not have the same periodicity as the wind disturbance. Therefore, if this part is also directly compensated through the peaks and troughs, over-compensation will occur. Therefore, it is necessary to make certain adjustments to the compensation of peaks and troughs according to the degree of wind disturbance, so as to improve the accuracy of wind disturbance compensation.

[0038] Based on the above analysis, a compensation function is constructed to compensate for wind disturbances. Specifically: the integral area of ​​the force fitting curve of each corner of the container is obtained; the difference between the area of ​​all peak areas and the area of ​​all valley areas corresponding to each corner is calculated, and forward 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; multiple variables are forward fused using the multiplication method; and the normalization method uses the maximum and minimum value normalization method. Among them, the schematic diagram of obtaining the average force data of each corner of the container is shown as follows. Figure 2 shown.

[0039] It is understandable that when the impact of wind disturbances is smaller, the degree of container swing under the action of wind is smaller, and thus the degree of mechanical vibration of the entire device is also smaller. At this time, based on the periodic peaks and troughs caused by the changes in the force on the container due to wind, these peaks and troughs can be compensated, thereby reducing the impact of wind disturbances on overload detection and improving the accuracy of overload detection. When the impact of wind disturbances is greater, the mechanical vibration caused by wind is also greater. At this time, when performing peak and trough compensation, it is necessary to appropriately reduce the compensation amplitude to avoid over-correcting the errors caused by mechanical vibration.

[0040] The fourth step: Use the moment balance principle to calculate the average force-bearing area of ​​each corner of the container, obtain the container's eccentricity value, and perform over-eccentricity detection on the container.

[0041] The average force data of each corner of the container is used as input. According to the moment balance principle, the eccentricity of the container is calculated by the force conditions of the four corners. The calculation of the eccentricity of the container by the force conditions of the four corners is a well-known technology in the field of container weighing. The specific details are not repeated here. When the eccentricity of the container exceeds the threshold When it is determined that there is an overload condition, in this embodiment Take the empirical value of 100mm.

[0042] Based on the same inventive concept as the above method, an embodiment of the present application also provides a container weighing and overloading 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, the steps of any one of the above-mentioned container weighing and overloading detection methods are implemented.

[0043] The flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to the embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the part of the module, program segment or code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified function or action, or may be implemented by a combination of dedicated hardware and computer instructions.

[0044] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the basic features of the present application. Therefore, from any point of view, the above embodiments of the present application should be regarded as exemplary and non-restrictive; modifications to the technical solutions described in the above embodiments, or equivalent replacement of some of the technical features therein, do not deviate from the essence of the corresponding technical solutions within the scope of the technical solutions of the embodiments of the present application, and should be included in the scope of protection of the present application.

Claims

1. A container weighing and overloading detection method, characterized in that: The method comprises the following steps: Collect wind speed data, force data at each corner of the container, and acceleration data in all directions of the container during each lifting test; Perform curve fitting on the force data of each corner of the container at all times during the lifting test, and obtain peak areas and valley areas based on the shape characteristics of the curve fitting; analyze the degree of area dispersion of the peak areas and valley areas to obtain the force period measurement of each corner of the container; perform autocorrelation analysis on the acceleration data of the container in all directions during the lifting test to determine the acceleration periodicity measurement of the container; analyze the influence of wind speed data on the force data and acceleration data during the lifting test, and determine the wind influence coefficient of the container during the lifting test based on the fitting results and corresponding force period measurement of each corner of the container, combined with the acceleration periodicity measurement; Based on the integrated area of ​​the fitting curve of each corner of the container during the lifting test, as well as the area difference between the corresponding peak area and valley area, combined with the wind force influence coefficient, the average force data of each corner of the container after compensation is obtained; The torque balance principle is used to calculate the average force-bearing area of ​​each corner of the container to obtain the container's eccentricity value and perform over-eccentricity detection on the container.

2. A container weighing and overloading detection method according to claim 1, characterized in that: The specific process of obtaining the peak area and the valley area is as follows: Obtain the force fitting curve of each corner of the container, take 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 record the trough area below the dividing line as the valley area.

3. A container weighing and overloading detection method according to claim 2, characterized in that: The force cycle measurement of each corner of the container is obtained as follows: The sum of the discrete degrees of the peak area and the discrete degrees of the valley area on each force fitting curve is calculated as the force cycle measurement of each corner of the container.

4. A container weighing and overloading detection method according to claim 1, characterized in that: The determination of the periodic measurement of the acceleration of the container is specifically as follows: The autocorrelation function diagram of the acceleration data of the container in all directions at all times during the lifting inspection process is obtained, and the peaks that exceed the preset confidence interval are marked as significant peaks. The hysteresis values ​​corresponding to all significant peaks are combined into a hysteresis sequence, and the first discrete degree of all elements in the first-order difference sequence of the hysteresis sequence is obtained; the average value of the first discrete degree corresponding to the acceleration data in all directions is used as the acceleration periodicity measurement of the container.

5. A container weighing and overloading detection method according to claim 1, characterized in that: The specific process of obtaining the wind influence coefficient of the container during the hoisting test is as follows: Obtain the second discrete degree of all force data at each corner of the container, average the second discrete degrees obtained for all corners, and obtain the average force fluctuation of the container; Based on the correlation between wind speed data and force data at each corner of the container during the lifting test, as well as the correlation between wind speed data and acceleration in all directions of the container, the force correlation measurement of the container is obtained; Calculate the force period measurement corresponding to each corner of the container and the goodness of fit measurement obtained by curve fitting and perform forward fusion. Accumulate the forward fusion results obtained from all corners of the container with the acceleration periodic measurement to obtain the denominator of the wind influence coefficient. Forward fuse the absolute value of the average force fluctuation of the container with the force correlation measurement to obtain the numerator of the wind influence coefficient.

6. A container weighing and overloading detection method according to claim 5, characterized in that: Obtaining the force correlation measurement of the container includes: Obtain the correlation between all wind speed data and the acceleration data in each direction during the container lifting and detection process, which is recorded as the first correlation; calculate the correlation between all force data and all wind speed data at each corner of the container, which is recorded as the second correlation; average all first correlations and all second correlations obtained from all wind speed data during the container lifting and detection process to obtain the force correlation measurement of the container.

7. A container weighing and overloading detection method according to claim 2, characterized in that: The average force data of each corner of the container after compensation is obtained as follows: The wind disturbance area at each corner of the container is determined based on the integrated area of ​​the force fitting curve obtained at each corner of the container and the difference in area distribution between the peak area and the valley area. The difference between the integrated area and the wind disturbance area is calculated and divided by the number of all force data of each corner of the container to obtain the average force data of each corner of the container after compensation.

8. A container weighing and overloading detection method according to claim 7, characterized in that: The process of determining the wind disturbance area of ​​each corner of the container is as follows: Obtain the integrated area of ​​the force fitting curve for each corner of the container; calculate the difference between the areas of all peak regions and all valley regions corresponding to each corner, and perform forward fusion with the normalized value of the wind influence coefficient to obtain the wind disturbance area of ​​each corner of the container.

9. A container weighing and overloading detection method according to claim 1, characterized in that: The overload and uneven load detection of the container is specifically as follows: When the obtained container overload value exceeds a preset threshold, it is determined that the container is overloaded.

10. A container weighing and overloading 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, the steps of the method according to any one of claims 1 to 9 are implemented.

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