Bulk cargo wharf cargo stack shape state real-time updating method based on Beidou positioning

Abstract

The invention discloses a bulk cargo wharf cargo stack shape state real-time updating method based on Beidou positioning, and belongs to the technical field of Beidou positioning, and the method comprises the steps: installing a plurality of high-precision positioning devices supporting a Beidou satellite system at a bulk cargo wharf, calibrating the devices, arranging a certain number of identification points around each cargo stack, and carrying out the calibration of the identification points; according to the method, the unmanned aerial vehicle performs first scanning and constructs the initial three-dimensional model, so that the modeling speed is greatly improved, and the accuracy of the model is greatly enhanced. Compared with a model which is drawn manually or generated by a low-resolution image, more details can be captured, the model structure is optimized through technologies such as aerial triangulation, the quality of the model is further improved, and long-term maintenance and updating are facilitated.

Description

Technical Field

[0001] The present invention belongs to the field of Beidou positioning technology, and specifically refers to a method for real-time updating of the shape and status of cargo stacks at a bulk cargo terminal based on Beidou positioning. Background Art

[0002] In traditional bulk terminal operations, changes in cargo stacking position and shape often rely on manual recording and measurement, which is not only inefficient but also prone to errors.

[0003] However, the existing real-time update of the shape status of cargo stacks at bulk cargo terminals still has certain defects. The existing real-time update of the shape status of cargo stacks at bulk cargo terminals relies on manual drawing or the use of low-resolution images to generate a three-dimensional model of the cargo stack. This method is not only time-consuming and labor-intensive, but also difficult to capture complex details, resulting in low model accuracy. Direct use of data from sensors without screening and cleaning is easily affected by sensor failures or harsh environmental conditions, resulting in inaccurate or noisy data. Reliance on a single data source cannot provide a comprehensive information perspective, especially when calculating changes in cargo stack shape. This limitation is particularly evident. The difference between Beidou positioning data and actual ground measurements is usually not corrected in a timely manner, resulting in the model parameters not reflecting the real situation, affecting the real-time and accuracy of the model. Therefore, a real-time update method for the shape status of cargo stacks at bulk cargo terminals based on Beidou positioning is proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide a real-time updating method for the shape and status of cargo stacks at a bulk cargo terminal based on Beidou positioning, so as to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solution: a method for real-time updating of the shape and status of cargo stacks at bulk cargo terminals based on Beidou positioning, comprising the following steps:

[0006] S1. Install several high-precision positioning devices supporting the Beidou satellite system at the bulk cargo terminal, calibrate these devices, and arrange a certain number of identification points around each cargo stack;

[0007] S2, Beidou receiver continuously collects the location information of each mark point on the cargo stack and sends the data to the central processing server via wireless network;

[0008] S3. Use drone equipment to scan all cargo stacks for the first time and build an initial 3D model of each cargo stack;

[0009] S4, the central server performs preliminary screening and cleaning of the received data to remove outliers and noise;

[0010] S5. Using BeiDou positioning receivers installed on transportation vehicles, the location information of the operating machinery is collected in real time, as well as data on the movement of cargo stacks during operation.

[0011] S6. Automatically calculate the changes in the shape of the cargo stack during the loading and unloading process based on the collected data;

[0012] S7. Adjust model parameters by comparing the differences between Beidou positioning data and actual ground measurements;

[0013] S8. Monitor the status of all cargo stacks in real time and set safety thresholds.

[0014] Among them, the S1 is to install several high-precision positioning devices supporting the Beidou satellite system at the bulk cargo terminal, calibrate these devices, and arrange a certain number of identification points around each cargo stack; install Beidou receivers and other auxiliary equipment at selected locations, and arrange a certain number of identification points around each cargo stack. The identification points are used as reference points to accurately determine the position and shape of the cargo stack. The identification points should be evenly distributed around and on the top of the cargo stack. The arrangement density of the identification points should be adjusted according to the size and shape of the cargo stack. Each identification point is associated with the corresponding Beidou positioning device, and the high-precision positioning device is connected to the central processing server to initialize the device. After the equipment is installed and calibrated, the system verification is performed.

[0015] Among them, in S2, the Beidou receiver continuously collects the location information of each identification point on the cargo stack and sends the data to the central processing server through the wireless network; starts the Beidou receiver to enable it to start continuously collecting the location information of the identification points, configures the wireless network connection parameters of the Beidou receiver, and sets the data transmission protocol, format and encryption and security measures for data transmission. During the data transmission process, the received data is verified, a data transmission confirmation mechanism is set, and the data is sent to the central processing server in real time.

[0016] Among them, the S3 is to perform an initial scan of all cargo stacks using drone equipment to construct an initial three-dimensional model of each cargo stack; according to the distribution and size of the cargo stacks, plan the flight path of the drone, set the flight altitude and speed, start the drone, scan the cargo stacks according to the planned path, use laser radar to obtain three-dimensional point cloud data of the cargo stacks, and use a camera to collect high-resolution images. The collected point cloud data and image data are transmitted in real time to a ground station or a central processing server via a wireless network, and the collected point cloud data and image data are preliminarily processed.

[0017] Among them, the S3 processes the data through 3D modeling software based on the preprocessed data, generates a 3D model of the cargo stack, submits it for aerial triangulation calculation, optimizes the geometric structure of the model, adjusts the range and resolution of the model, and after completing the initial version of the 3D model, compares and verifies it with the actual situation on site to confirm the accuracy of the model, further optimizes the model according to actual needs, stores the generated 3D model in a central database, and updates the model regularly.

[0018] Among them, the S4, the central server performs preliminary screening and cleaning of the received data to remove outliers and noise; obtains data from Beidou receivers and drone equipment, and stores it in the database, integrates data from different sources according to timestamps, checks whether the data format meets the predefined standards, marks data that does not meet the format requirements, verifies the integrity of the data, marks missing data, and supplements it as needed, detects outliers in the data through the interquartile range, and marks data points that are out of a reasonable range as outliers, analyzes the volatility of the data, identifies random noise, removes noise through filtering algorithms, processes data points marked as outliers or noise, and stores the cleaned data in the central database.

[0019] Among them, the S5 collects the location information of the operating machinery in real time through the Beidou positioning receiver installed on the transportation tool and the operating machinery, and collects data on the changes in the cargo stack during the operation; installs the Beidou positioning receiver on the transportation tool and the operating machinery, initializes the data acquisition module through the Beidou receiver, sets the sampling frequency and data format, and the Beidou receiver continuously collects the location information of the transportation tool and the operating machinery, and records the data on the changes in the cargo stack during the operation, obtains the working status and operating parameters of the operating machinery through the sensor, and transmits the collected location information and cargo stack change data to the central server in real time through the wireless network. In the central server, the location information of the transportation tool and the operating machinery is associated with the identification point data of the cargo stack, and the cargo stack change data recorded during the operation is integrated to form a complete operation data set.

[0020] The S6 automatically calculates the changes in the shape of the cargo stack during the loading and unloading process based on the collected data; pre-processes the collected Beidou positioning data, drone scanning data and operating data of the operating machinery, analyzes the position data of the identification points collected by the Beidou positioning equipment, calculates the position changes of the identification points during the loading and unloading process, and deduces the shape change trend of the cargo stack through the changes in the three-dimensional coordinates, combines the three-dimensional model scanned by the drone with the position change data of the identification points, updates the three-dimensional model of the cargo stack, adjusts the model through the three-dimensional modeling software, calculates the volume and surface area changes of the cargo stack, calculates the changes in volume and surface area by comparing the initial three-dimensional model with the updated three-dimensional model, and analyzes the changes in the center of gravity position of the cargo stack. The moving trajectory of the center of gravity is derived through the three-dimensional coordinate changes of the identification points, and the key features of the shape changes of the cargo stack are extracted. The image processing and machine learning algorithms identify the shape change pattern of the cargo stack, compare the updated model with the previous baseline model, identify the shape differences caused by the addition or removal of cargo, and adjust the three-dimensional model of the cargo stack according to the analysis results.

[0021] Among them, the S7 adjusts the model parameters by comparing the differences between the Beidou positioning data and the actual ground measurement values; the real-time position data of the cargo stack identification points provided by the Beidou positioning equipment are compared with the Beidou positioning data and the ground measurement values, the difference between the two is calculated, the distribution of the differences is analyzed, and the relevant parameters in the three-dimensional model are adjusted according to the results of the comparative analysis.

[0022] Among them, the S8 monitors the status of all cargo stacks in real time and sets a safety threshold; obtains the real-time position, volume, and center of gravity status data of the cargo stack from Beidou positioning equipment, sensors, and drone equipment in real time, sets a safety threshold based on the physical characteristics of the cargo stack and the operating environment, compares the real-time collected data with the set safety threshold, and evaluates whether the current status of the cargo stack exceeds the safety range. When the status of the cargo stack exceeds the safety threshold, an early warning signal is issued.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] 1. This invention uses drones to perform the initial scan and construct the initial 3D model, significantly increasing modeling speed and accuracy. Compared to models drawn by hand or generated from low-resolution images, it can capture more details. Using techniques such as aerial triangulation to optimize the model structure, it further improves model quality and facilitates long-term maintenance and updates.

[0025] 2. This invention performs preliminary screening and cleaning of data acquired from different sources, removing outliers and noise, thereby ensuring the authenticity and validity of the data and effectively resolving the problem of data inaccuracy caused by sensor failure or environmental interference. The cleaned, high-quality data set provides a reliable basis for subsequent analysis, helping to make better decisions and reduce the risk of misjudgment.

[0026] 3. This invention automatically calculates changes in the shape of cargo stacks by combining data from multiple sources, improving both accuracy and processing speed. This overcomes the limitations of relying on a single data source and provides a more comprehensive perspective. By accurately calculating changes in volume, surface area, and center of gravity, this helps managers better understand the dynamics of cargo stacks, enabling them to take appropriate management measures and improve safety.

[0027] 4. The present invention adjusts model parameters by comparing the differences between Beidou positioning data and actual ground measurements, ensuring the real-time and accuracy of the model, effectively correcting deviations caused by signal interference or other factors, maintaining the high fidelity of the model, and continuous parameter adjustment keeps the model up to date. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 The operating process of the method for real-time updating of the shape and status of bulk cargo stacks at bulk cargo terminals based on Beidou positioning of the present invention Figure 1 ;

[0029] Figure 2 The operating process of the method for real-time updating of the shape and status of bulk cargo stacks at bulk cargo terminals based on Beidou positioning of the present invention Figure 2 ;

[0030] Figure 3 The operating process of the method for real-time updating of the shape and status of bulk cargo stacks at bulk cargo terminals based on Beidou positioning of the present invention Figure 3 . DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0032] Example

[0033] See also Figure 1-3 As shown, the present invention provides a technical solution: comprising the following steps:

[0034] S1. Install several high-precision positioning devices supporting the Beidou satellite system at the bulk cargo terminal, calibrate these devices, and arrange a certain number of identification points around each cargo stack;

[0035] S2, Beidou receiver continuously collects the location information of each mark point on the cargo stack and sends the data to the central processing server via wireless network;

[0036] S3. Use drone equipment to scan all cargo stacks for the first time and build an initial 3D model of each cargo stack;

[0037] S4, the central server performs preliminary screening and cleaning of the received data to remove outliers and noise;

[0038] S5. Using BeiDou positioning receivers installed on transportation vehicles, the location information of the operating machinery is collected in real time, as well as data on the movement of cargo stacks during operation.

[0039] S6. Automatically calculate the changes in the shape of the cargo stack during the loading and unloading process based on the collected data;

[0040] S7. Adjust model parameters by comparing the differences between Beidou positioning data and actual ground measurements;

[0041] S8. Monitor the status of all cargo stacks in real time and set safety thresholds.

[0042] Among them, the S1 is to install several high-precision positioning devices supporting the Beidou satellite system at the bulk cargo terminal, calibrate these devices, and arrange a certain number of identification points around each cargo stack; install Beidou receivers and other auxiliary equipment at selected locations, and arrange a certain number of identification points around each cargo stack. The identification points are used as reference points to accurately determine the position and shape of the cargo stack. The identification points should be evenly distributed around and on the top of the cargo stack. The arrangement density of the identification points should be adjusted according to the size and shape of the cargo stack. Each identification point is associated with the corresponding Beidou positioning device, and the high-precision positioning device is connected to the central processing server to initialize the device. After the equipment is installed and calibrated, the system verification is performed.

[0043] Among them, in S2, the Beidou receiver continuously collects the location information of each identification point on the cargo stack and sends the data to the central processing server through the wireless network; starts the Beidou receiver to enable it to start continuously collecting the location information of the identification points, configures the wireless network connection parameters of the Beidou receiver, and sets the data transmission protocol, format and encryption and security measures for data transmission. During the data transmission process, the received data is verified, a data transmission confirmation mechanism is set, and the data is sent to the central processing server in real time.

[0044] Among them, the S3 is to perform an initial scan of all cargo stacks using drone equipment to construct an initial three-dimensional model of each cargo stack; according to the distribution and size of the cargo stacks, plan the flight path of the drone, set the flight altitude and speed, start the drone, scan the cargo stacks according to the planned path, use laser radar to obtain three-dimensional point cloud data of the cargo stacks, and use a camera to collect high-resolution images. The collected point cloud data and image data are transmitted in real time to a ground station or a central processing server via a wireless network, and the collected point cloud data and image data are preliminarily processed.

[0045] Among them, the S3 processes the data through 3D modeling software based on the preprocessed data, generates a 3D model of the cargo stack, submits it for aerial triangulation calculation, optimizes the geometric structure of the model, adjusts the range and resolution of the model, and after completing the initial version of the 3D model, compares and verifies it with the actual situation on site to confirm the accuracy of the model, further optimizes the model according to actual needs, stores the generated 3D model in a central database, and updates the model regularly.

[0046] Among them, the S4, the central server performs preliminary screening and cleaning of the received data to remove outliers and noise; obtains data from Beidou receivers and drone equipment, and stores it in the database, integrates data from different sources according to timestamps, checks whether the data format meets the predefined standards, marks data that does not meet the format requirements, verifies the integrity of the data, marks missing data, and supplements it as needed, detects outliers in the data through the interquartile range, and marks data points that are out of a reasonable range as outliers, analyzes the volatility of the data, identifies random noise, removes noise through filtering algorithms, processes data points marked as outliers or noise, and stores the cleaned data in the central database.

[0047] Among them, the S5 collects the location information of the operating machinery in real time through the Beidou positioning receiver installed on the transportation tool and the operating machinery, and collects data on the changes in the cargo stack during the operation; installs the Beidou positioning receiver on the transportation tool and the operating machinery, initializes the data acquisition module through the Beidou receiver, sets the sampling frequency and data format, and the Beidou receiver continuously collects the location information of the transportation tool and the operating machinery, and records the data on the changes in the cargo stack during the operation, obtains the working status and operating parameters of the operating machinery through the sensor, and transmits the collected location information and cargo stack change data to the central server in real time through the wireless network. In the central server, the location information of the transportation tool and the operating machinery is associated with the identification point data of the cargo stack, and the cargo stack change data recorded during the operation is integrated to form a complete operation data set.

[0048] The S6 automatically calculates the changes in the shape of the cargo stack during the loading and unloading process based on the collected data; pre-processes the collected Beidou positioning data, drone scanning data and operating data of the operating machinery, analyzes the position data of the identification points collected by the Beidou positioning equipment, calculates the position changes of the identification points during the loading and unloading process, and deduces the shape change trend of the cargo stack through the changes in the three-dimensional coordinates, combines the three-dimensional model scanned by the drone with the position change data of the identification points, updates the three-dimensional model of the cargo stack, adjusts the model through the three-dimensional modeling software, calculates the volume and surface area changes of the cargo stack, calculates the changes in volume and surface area by comparing the initial three-dimensional model with the updated three-dimensional model, and analyzes the changes in the center of gravity position of the cargo stack. The moving trajectory of the center of gravity is derived through the three-dimensional coordinate changes of the identification points, and the key features of the shape changes of the cargo stack are extracted. The image processing and machine learning algorithms identify the shape change pattern of the cargo stack, compare the updated model with the previous baseline model, identify the shape differences caused by the addition or removal of cargo, and adjust the three-dimensional model of the cargo stack according to the analysis results.

[0049] Among them, the S7 adjusts the model parameters by comparing the differences between the Beidou positioning data and the actual ground measurement values; the real-time position data of the cargo stack identification points provided by the Beidou positioning equipment are compared with the Beidou positioning data and the ground measurement values, the difference between the two is calculated, the distribution of the differences is analyzed, and the relevant parameters in the three-dimensional model are adjusted according to the results of the comparative analysis.

[0050] Among them, the S8 monitors the status of all cargo stacks in real time and sets a safety threshold; obtains the real-time position, volume, and center of gravity status data of the cargo stack from Beidou positioning equipment, sensors, and drone equipment in real time, sets a safety threshold based on the physical characteristics of the cargo stack and the operating environment, compares the real-time collected data with the set safety threshold, and evaluates whether the current status of the cargo stack exceeds the safety range. When the status of the cargo stack exceeds the safety threshold, an early warning signal is issued.

[0051] Working principle: First, multiple high-precision positioning devices supporting the Beidou satellite system are installed at the bulk cargo terminal and calibrated to ensure that the equipment can accurately receive Beidou satellite signals and provide accurate location information. Marking points are arranged around the cargo stack as reference points to accurately determine the position and shape of the cargo stack. The density of the marking points is adjusted according to the size and shape of the cargo stack. The Beidou receiver continuously collects the location information of each marking point on the cargo stack and sends the data to the central processing server in real time through the wireless network. During the data transmission process, the received data is verified to ensure the integrity and accuracy of the data, and a confirmation mechanism for data transmission is set to ensure reliable data transmission. The cargo stack is scanned for the first time by the drone equipment to obtain the three-dimensional point cloud data and high-resolution images of the cargo stack. The collected data is processed by three-dimensional modeling software to generate an initial three-dimensional model of the cargo stack, and compared with the actual situation on site for verification. The central server preliminarily screens and cleans the received Beidou positioning data and drone scanning data to remove outliers and noise, and stores the cleaned data in the central database. Beidou positioning is installed on transportation vehicles and operating machinery The system automatically calculates the shape of the cargo stack during loading and unloading based on the collected Beidou positioning data, drone scanning data, and the operating data of the cargo stack. It derives the shape change trend of the cargo stack by analyzing the position changes of the landmarks, updates the three-dimensional model of the cargo stack, and calculates the changes in the volume, surface area, and center of gravity of the cargo stack. By comparing the differences between the Beidou positioning data and the actual ground measurements, it adjusts the relevant parameters in the three-dimensional model to improve the model's accuracy and reliability. It analyzes the distribution of the differences and identifies factors that may cause errors. Based on the results of the comparative analysis, it optimizes the model parameters. The system obtains the real-time position, volume, center of gravity, and other status data of the cargo stack from the Beidou positioning equipment, sensors, and drone equipment, and compares them with the set safety threshold. When the status of the cargo stack exceeds the safety threshold, the system automatically issues a warning signal, reminding the operator to take timely measures to ensure operational safety.

[0052] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

[0053] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. A method for real-time updating of the shape and status of cargo stacks at bulk cargo terminals based on Beidou positioning, characterized in that: The following steps are involved: S1. Install several high-precision positioning devices supporting the Beidou satellite system at the bulk cargo terminal, calibrate these devices, and arrange a certain number of identification points around each cargo stack; S2, Beidou receiver continuously collects the location information of each mark point on the cargo stack and sends the data to the central processing server via wireless network; S3. Use drone equipment to scan all cargo stacks for the first time and build an initial 3D model of each cargo stack; S4, the central server performs preliminary screening and cleaning of the received data to remove outliers and noise; S5. Using BeiDou positioning receivers installed on transportation vehicles, the location information of the operating machinery is collected in real time, as well as data on the movement of cargo stacks during operation. S6. Automatically calculate the changes in the shape of the cargo stack during the loading and unloading process based on the collected data; S7. Adjust model parameters by comparing the differences between Beidou positioning data and actual ground measurements; S8. Monitor the status of all cargo stacks in real time and set safety thresholds.

2. The method for real-time updating of the shape and status of cargo stacks at bulk cargo terminals based on Beidou positioning according to claim 1 is characterized in that: Said S1 is to install several high-precision positioning devices supporting the Beidou satellite system at the bulk cargo terminal, calibrate these devices, and arrange a certain number of identification points around each cargo stack; install Beidou receivers and other auxiliary equipment at selected locations, and arrange a certain number of identification points around each cargo stack. The identification points are used as reference points to accurately determine the position and shape of the cargo stack. The identification points should be evenly distributed around and on the top of the cargo stack. The arrangement density of the identification points should be adjusted according to the size and shape of the cargo stack. Each identification point is associated with the corresponding Beidou positioning device, and the high-precision positioning device is connected to the central processing server to initialize the device. After the equipment is installed and calibrated, the system verification is performed.

3. The method for real-time updating of the shape and status of cargo stacks at bulk cargo terminals based on Beidou positioning according to claim 1 is characterized by: In step S2, the Beidou receiver continuously collects the location information of each identification point on the cargo stack and sends the data to the central processing server via the wireless network; Start the Beidou receiver to enable it to continuously collect the location information of the identification points, configure the wireless network connection parameters of the Beidou receiver, set the data transmission protocol, format, and configure the encryption and security measures for data transmission. During the data transmission process, verify the received data, set the data transmission confirmation mechanism, and send the data to the central processing server in real time.

4. The method for real-time updating of the shape and status of cargo stacks at bulk cargo terminals based on Beidou positioning according to claim 1, characterized in that: The S3 is to perform an initial scan of all cargo stacks using drone equipment to construct an initial three-dimensional model of each cargo stack; plan the drone's flight path based on the distribution and size of the cargo stacks, set the flight altitude and speed, start the drone, scan the cargo stacks according to the planned path, use lidar to obtain three-dimensional point cloud data of the cargo stacks, and use a camera to collect high-resolution images. The collected point cloud data and image data are transmitted in real time to a ground station or a central processing server via a wireless network, and the collected point cloud data and image data are preliminarily processed.

5. The method for real-time updating of the shape and status of cargo stacks at bulk cargo terminals based on Beidou positioning according to claim 4, characterized in that: The S3 processes the preprocessed data using 3D modeling software to generate a 3D model of the cargo stack, submits it for aerial triangulation calculation, optimizes the model's geometric structure, adjusts the model's range and resolution, and after completing the initial 3D model, compares and verifies it with the actual situation on site to confirm the accuracy of the model. The model is further optimized according to actual needs, and the generated 3D model is stored in a central database and updated regularly.

6. The method for real-time updating of the shape and status of cargo stacks at bulk cargo terminals based on Beidou positioning according to claim 1, characterized in that: In S4, the central server performs preliminary screening and cleaning of the received data to remove outliers and noise; obtains data from Beidou receivers and drone equipment, and stores it in a database, integrates data from different sources according to timestamps, checks whether the format of the data meets predefined standards, marks data that does not meet format requirements, verifies the integrity of the data, marks missing data, and supplements it as needed, detects outliers in the data through the interquartile range, marks data points that are out of a reasonable range as outliers, analyzes data volatility, identifies random noise, removes noise through filtering algorithms, processes data points marked as outliers or noise, and stores the cleaned data in the central database.

7. The method for real-time updating of the shape and status of cargo stacks at bulk cargo terminals based on Beidou positioning according to claim 1, characterized in that: Said S5 collects the position information of the operating machinery in real time through the Beidou positioning receiver installed on the transportation tool and the operating machinery, and collects the data on the changes of the cargo stack during the operation; installs the Beidou positioning receiver on the transportation tool and the operating machinery, initializes the data acquisition module through the Beidou receiver, sets the sampling frequency and data format, and the Beidou receiver continuously collects the position information of the transportation tool and the operating machinery, and records the data on the changes of the cargo stack during the operation, obtains the working status and operating parameters of the operating machinery through the sensor, and transmits the collected position information and cargo stack change data to the central server in real time through the wireless network. In the central server, the position information of the transportation tool and the operating machinery is associated with the identification point data of the cargo stack, and the cargo stack change data recorded during the operation is integrated to form a complete operation data set.

8. The method for real-time updating of the shape and status of cargo stacks at bulk cargo terminals based on Beidou positioning according to claim 1, characterized in that: The S6 automatically calculates the changes in the shape of the cargo stack during the loading and unloading process based on the collected data; pre-processes the collected Beidou positioning data, drone scanning data and operating data of the operating machinery, analyzes the position data of the identification points collected by the Beidou positioning equipment, calculates the position changes of the identification points during the loading and unloading process, and deduces the shape change trend of the cargo stack through the changes in the three-dimensional coordinates, combines the three-dimensional model scanned by the drone with the position change data of the identification points, updates the three-dimensional model of the cargo stack, adjusts the model through the three-dimensional modeling software, calculates the volume and surface area changes of the cargo stack, calculates the changes in volume and surface area by comparing the initial three-dimensional model with the updated three-dimensional model, and analyzes the changes in the center of gravity position of the cargo stack. The moving trajectory of the center of gravity is derived through the three-dimensional coordinate changes of the identification points, and the key features of the shape changes of the cargo stack are extracted. The image processing and machine learning algorithms identify the shape change pattern of the cargo stack, compare the updated model with the previous baseline model, identify the shape differences caused by the addition or removal of cargo, and adjust the three-dimensional model of the cargo stack according to the analysis results.

9. The method for real-time updating of the shape and status of cargo stacks at bulk cargo terminals based on Beidou positioning according to claim 1, characterized in that: The S7 adjusts the model parameters by comparing the differences between the Beidou positioning data and the actual ground measurement values; the real-time position data of the cargo stack identification points provided by the Beidou positioning equipment are compared with the Beidou positioning data and the ground measurement values, the difference between the two is calculated, the distribution of the differences is analyzed, and the relevant parameters in the three-dimensional model are adjusted according to the results of the comparative analysis.

10. The method for real-time updating of the shape and status of cargo stacks at bulk cargo terminals based on Beidou positioning according to claim 9, characterized in that: S8, real-time monitoring of the status of all cargo stacks and setting safety thresholds; The real-time location, volume, and center of gravity status data of the cargo stack are obtained from Beidou positioning equipment, sensors, and drone equipment. According to the physical characteristics of the cargo stack and the operating environment, a safety threshold is set. The real-time collected data is compared with the set safety threshold to evaluate whether the current status of the cargo stack exceeds the safety range. When the status of the cargo stack exceeds the safety threshold, an early warning signal is issued.

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