Port and wharf sediment monitoring system based on unmanned aerial vehicle technology

Abstract

The invention discloses a port and wharf sediment monitoring system based on an unmanned aerial vehicle technology, relates to the technical field of port sediment monitoring, and solves the technical problems that the process is extensive, the model is single, the scene adaptability is weak, and dynamic sediment monitoring of port refined operation and maintenance is difficult to meet. According to the method, data acquisition and preprocessing processes are dynamically optimized for different monitoring scenes, multiple models are introduced to invert the sediment concentration, the inversion precision is improved, indexes such as sediment concentration periodic characteristics, quantitative trend slope and periodic amplitude are mined in combination with time sequence analysis, richer data support is provided for operation and maintenance decisions, periodic phase and long-period influence are fused, and the accuracy of the operation and maintenance decisions is improved. The method combines collaborative indexes to construct graded early warning, corrects the settling velocity through a dynamic threshold value and a physical model, realizes accurate early warning and scientific response of abnormal increase, systematically analyzes natural and human factors, clarifies the reason of abnormal decrease, perfects full-chain analysis of abnormal change of sediment concentration, and assists a port to formulate an operation and maintenance strategy in a targeted manner.

Description

Technical Field

[0001] This invention relates to the field of port sediment monitoring technology, specifically a port terminal sediment monitoring system based on unmanned aerial vehicle (UAV) technology. Background Technology

[0002] The problem of siltation at ports and wharves has long affected their operational efficiency and safety. Traditional monitoring methods have shortcomings such as limited coverage, poor timeliness, and data accuracy being greatly affected by environmental interference. With the development of drone technology, using drones for silt monitoring has become a trend. However, existing drone-based silt monitoring systems still suffer from problems such as crude processes, simple models, and weak scenario adaptability in terms of precise selection of operating periods, scientific data processing and analysis, and in-depth judgment of anomalies. These issues make it difficult to meet the needs of refined port operation and maintenance for dynamic silt monitoring and intelligent early warning. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a port terminal sediment monitoring system based on UAV technology, which solves the problems of extensive processes, simple models, weak scenario adaptability, and difficulty in meeting the requirements of refined port operation and maintenance for dynamic sediment monitoring.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a port terminal sediment monitoring system based on unmanned aerial vehicle (UAV) technology, comprising: The monitoring area analysis module is used to analyze the monitoring information transmitted by the sediment monitoring information acquisition module. It obtains monitoring area data based on the monitoring information and performs preprocessing to obtain preprocessed data. At the same time, it calculates sediment concentration by combining empirical formulas and judges its changes by combining historical data, generates signals of concentration increase or decrease, and transmits them respectively. The concentration increase analysis module is used to analyze the acquired concentration increase signal, determine the threshold based on historical periodic data, compare the sediment concentration with it to determine whether the sediment concentration is normal, and combine it with the collaborative indicators of the monitoring area to make a comprehensive judgment and generate abnormal increase information. The abnormal increase information is analyzed, the settlement velocity of the monitored area is calculated and matched with the level matching interval to generate early warning level information, and then transmitted to the monitoring information output module. The concentration reduction analysis module judges the current concentration reduction rate, generates an abnormal reduction signal, and determines the specific cause by combining natural and human factors, generating abnormal reduction information and transmitting it to the monitoring information output module.

[0005] As a further embodiment of the present invention, it also includes a sediment monitoring information acquisition module, which is used to determine the monitoring area and monitoring period, generate monitoring information, and transmit it to the monitoring area analysis module. The monitoring information output module is used to display early warning level information and anomaly reduction information to the corresponding operators.

[0006] As a further aspect of the present invention, the specific method by which the sediment monitoring information acquisition module generates monitoring information is as follows: The monitoring area is obtained, and its corresponding tide table is obtained to determine the low tide period within 3 days. Then, the weather conditions of the monitoring area are obtained, and dates with wind force ≤ level 3 and visibility > 8km are selected. At the same time, the ship traffic management system is used to obtain the ship entry and exit plans to confirm the time periods when no large ships are berthed. The monitoring period is determined by combining the above analysis.

[0007] As a further aspect of the present invention, the specific method by which the monitoring area analysis module generates a concentration increase or decrease signal is as follows: Data is collected from the monitored area, and preprocessed data is obtained through data cleaning and image correction, combined with empirical formulas. Calculate the sediment concentration C in the monitored area, where a and b are empirical coefficients, and R... 700 R refers to the spectral reflectance near the red band in a multispectral image. 550 It is the spectral reflectance near the green light band; The system acquires historical sediment concentrations in the monitored area and sorts them according to time series. It also analyzes the changes in sediment concentration in the monitored area based on the sorting order, generating concentration increase signals or concentration decrease signals. Then, the concentration increase signals are transmitted to the concentration increase analysis module, and the concentration decrease signals are transmitted to the concentration decrease analysis module.

[0008] As a further aspect of the present invention, the specific method by which the concentration increase analysis module analyzes the acquired concentration increase signal is as follows: Historical periodic data of the monitored area is acquired, and a complete period is obtained. This period is then broken down into multiple phases, denoted as p. The impact of long periods on sediment concentration is then considered. Based on the historical periodic data, the concentration percentile threshold for each period phase is calculated, and the threshold is determined according to the formula. p，t =Percentiles (C p，t (95%), where p represents phase, t represents season, and C p，t This provides sediment concentration data for all corresponding phases p and seasons t in the past. At the same time, the sediment concentration C in the current monitoring area is compared with the threshold. If the sediment concentration C is greater than the threshold, the sediment concentration is marked as abnormal; otherwise, if the sediment concentration C is less than the threshold, the sediment concentration is marked as normal.

[0009] As a further aspect of the present invention, the specific method by which the concentration increase analysis module generates abnormal increase information is as follows: Acquire the coordination indicators of the monitoring area, which include the flow velocity, topography, and weather conditions of the monitoring area. Judge the status of each coordination indicator. If all are normal, the coordination indicator is normal; otherwise, the coordination indicator is abnormal. At the same time, make a comprehensive judgment based on the sediment concentration. If both sediment concentration and synergistic indicators are normal, the increase in concentration is considered a normal periodic fluctuation, and normal increase information is generated without further processing. Conversely, if any set of abnormalities exists in sediment concentration or synergistic indicators, the increase in concentration is considered an abnormal increase, and abnormal increase information is generated.

[0010] As a further aspect of the present invention, the specific method by which the concentration increase analysis module analyzes the abnormal increase information is as follows: According to the formula The settlement velocity of the monitored area was calculated. , where d 50 The median particle size obtained from the hyperspectral inversion by the UAV is represented by f, which is the flocculation coefficient, and the attenuation factor is also represented by f. Settlement velocity Make corrections according to the formula. The correction speed was calculated. And v is the flow velocity, exp(·) is the natural exponential function, which will correct the velocity. Match with the level range, if ≥ A red alert will be generated if... ≤ < If an orange alert is generated, then an orange alert will be issued. < If the warning is triggered, a yellow alert will be generated, along with alert level information, and among them... and The specific values ​​are set by the operator.

[0011] As a further aspect of the present invention, the specific method by which the concentration reduction analysis module generates abnormal reduction information is as follows: Acquire sediment data for the same period phase in historical periodic data and process them in groups. Calculate the standard deviation corresponding to the historical concentration distribution. If the current concentration decrease is within the historical periodic fluctuation range, it is judged as a normal periodic fluctuation and no processing is performed. Conversely, if the decrease exceeds the historical fluctuation, it is marked as an abnormal decrease, an abnormal decrease signal is generated, and it is analyzed. The specific causes are analyzed from both natural and human factors, and the reasons for the abnormal reduction are determined, generating information on the abnormal reduction.

[0012] This invention provides a port terminal sediment monitoring system based on unmanned aerial vehicle (UAV) technology. Compared with existing technologies, it has the following advantages: This invention integrates tide tables, meteorological data, and port VTS vessel dynamics, subdivides routine and emergency monitoring scenarios, and dynamically adjusts UAV flight paths and payload strategies to achieve scientific selection of monitoring periods and ensure the quality of monitoring data.

[0013] This invention dynamically optimizes the data acquisition and preprocessing process for different monitoring scenarios, introduces multi-model inversion of sediment concentration to improve inversion accuracy, and combines time series analysis to mine the periodic characteristics of sediment concentration, quantifies indicators such as trend slope and periodic amplitude, accurately identifies normal fluctuations and abnormal changes in concentration, and provides richer data support for operation and maintenance decisions.

[0014] In the concentration increase analysis of this invention, periodic phase and long-period effects are incorporated, and a graded early warning system is constructed by combining synergistic indicators. The sedimentation rate is corrected by dynamic thresholds and physical models to achieve accurate early warning and scientific response to abnormal increases. In the concentration reduction analysis, the system analyzes natural and human factors, clarifies the causes of abnormal reduction, improves the whole-chain analysis of abnormal changes in sediment concentration, helps ports to formulate targeted operation and maintenance strategies, enhances the intelligence and precision of port sediment monitoring, and effectively ensures the safety and efficiency of port operations. Attached Figure Description

[0015] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1 Please see Figure 1 This application provides a port terminal sediment monitoring system based on UAV technology, including a sediment monitoring information acquisition module, a monitoring area analysis module, a concentration increase analysis module, a concentration decrease analysis module, and a monitoring information output module, and in combination with... Figure 1 It can be seen that the above functional modules are connected electrically in one direction.

[0018] The sediment monitoring information acquisition module is used to determine the monitoring area of ​​the port terminal, determine the corresponding monitoring time period based on the monitoring area, generate monitoring information, and transmit it to the monitoring area analysis module. The specific method for generating monitoring information is as follows: The monitoring area is obtained, and the corresponding tide table is obtained to determine the low tide period within 3 days (e.g., 10:30-11:30 on June 18, low tide at 14:00). Then, the weather conditions of the monitoring area are obtained, and dates with wind force ≤ level 3 and visibility > 8km are selected. At the same time, the port's VTS (Vessel Traffic Management System) is used to obtain the port entry and exit plans to confirm the time periods when no large ships are berthed. The monitoring period is determined by combining the above analysis.

[0019] The monitoring area analysis module is used to analyze the sediment conditions of the monitored area based on the acquired monitoring information. The specific analysis method is as follows: Select the appropriate drone and use professional software (such as Pix4Dcapture, MissionPlanner) to plan a "grid" or "strip" route. Then the drone will fly automatically at the preset waypoints, maintain a fixed altitude, collect data of the monitoring area, and perform data preprocessing on the obtained monitoring area data. The data preprocessing includes data cleaning and image correction to obtain preprocessed data. Specifically, distinguish between routine monitoring (monthly / quarterly) and emergency monitoring (post-typhoon / oil spill events) scenarios, and dynamically adjust flight routes and payload strategies accordingly: Routine monitoring: grid flight path (50m spacing) + multispectral + LiDAR (balancing efficiency and accuracy); Emergency monitoring: Strip-shaped route (along the waterway / pollution zone) + hyperspectral + ADCP (focusing on anomaly areas and increasing water flow data).

[0020] Real-time monitoring of quality indicators during flight: Image quality: Real-time calculation of image sharpness using Python opencv-python (Laplacian variance > 100 is considered sharp); LiDAR quality: Point cloud density fluctuation <15% (compensated for flight jitter via IMU data).

[0021] If the resolution is less than 80, automatically trigger the flight path rescan (repeatedly photograph the blurred area); if the point cloud density is abnormal, adjust the LiDAR transmission frequency (from 100kHz to 150kHz).

[0022] The specific data cleaning involves removing abnormal data caused by flight jitter and sudden changes in light (such as blurred images and abrupt LiDAR points), and synchronizing GPS timestamps with sensor data to ensure spatiotemporal consistency. Image correction involves geometric correction (eliminating UAV attitude deviations) and radiometric correction (unifying lighting conditions) of optical images, and using software such as ENVI and ArcGIS to remove the effects of water surface reflection (solar flare). Based on the obtained preprocessed data and empirical formulas Calculate the sediment concentration C in the monitored area, where a and b are empirical coefficients that need to be determined by fitting measured data, and R... 700 This refers to the spectral reflectance at a center wavelength of approximately 700 nm (near the red band) in multispectral images. Sediment has a strong scattering effect on light in this band, and the reflectance varies with sediment concentration. R 550 It represents the spectral reflectance at a center wavelength of approximately 550nm (near the green light band). The scattering effect of sediment on it is relatively weaker than that on the red light band. By comparing the two, the interference from light and water background can be reduced to a certain extent, highlighting the influence of sediment concentration. Next, the historical sediment concentration of the monitored area is obtained and sorted according to the time series. At the same time, the changes in sediment concentration in the monitored area are analyzed according to the sorting order, and a concentration increase signal or a concentration decrease signal is generated. Then, the concentration increase signal is transmitted to the concentration increase analysis module, and the concentration decrease signal is transmitted to the concentration decrease analysis module.

[0023] For example, analyze the changes in sediment concentration in Port A from March to May 2024 to determine whether the dredging plan needs to be adjusted.

[0024] Raw data: Monitoring once a week from March to May 2020 to 2023, totaling 160 data points, including 3 missing values ​​and 2 outliers; After preprocessing: interpolation fills in missing values, corrects outliers (e.g., outlier on April 12, 2022, 500 mg / L → 350 mg / L), and generates time series with 1-hour intervals.

[0025] Short-period (tidal, 12.4h), medium-period (lunar phase, 28.3d), and long-period (seasonal, 362d) cycles were identified, and then feature calculations were performed: In April 2024, the periodic amplitude A was 220 mg / L (the historical average for the same period was 180 mg / L, representing an increase of 22%). The trend slope k = 0.6 mg / L·d (the historical average for the same period was 0.3 mg / L·d, representing a 100% increase).

[0026] Next, the obtained characteristics are compared with the preset rules. The current concentration of 400 mg / L is greater than the 95th percentile of the same period in history (380 mg / L), and k=0.6>0.5 → the preliminary judgment is "concentration increase". The concentration increase analysis module analyzes acquired concentration increase signals, obtains historical periodic data of the monitored area, and captures a complete period, breaking it down into multiple phases denoted as p (e.g., a tidal cycle has 8 phases: early high tide, middle high tide, etc.). It then considers the impact of long-term cycles (seasons, months) on sediment concentration. Specifically, because different seasons (e.g., rainy / dry season), different months (e.g., flood season / dry season), sediment sources (runoff, wind and wave sediment), and hydrodynamic conditions differ, the "normal fluctuation range" of concentration also varies. Based on historical periodic data, it calculates the concentration percentile threshold for each period phase (e.g., the 95th percentile is the warning value, and the 99th percentile is the early warning value), and applies the threshold according to the formula. p，t =Percentiles (C p，t (95%), where p represents phase, t represents season or month, and C p，t It refers to "the sediment concentration data corresponding to all past phases p and seasons t"; At the same time, the sediment concentration C in the current monitoring area is compared with the threshold. If the sediment concentration C is greater than the threshold, the sediment concentration is marked as abnormal; otherwise, if the sediment concentration C is less than the threshold, the sediment concentration is marked as normal. Next, the coordinated indicators of the monitoring area are obtained. These indicators include the flow velocity, topography, and weather conditions of the monitoring area. The coordinated indicators are judged separately. If all are normal, the coordinated indicators are normal; otherwise, they are abnormal. Abnormality here means that there is one or more abnormal groups, which can be one, two, or three groups. At the same time, the sediment concentration is considered for comprehensive judgment. If both sediment concentration and synergistic indicators are normal, it indicates that the increase in concentration is a normal periodic fluctuation, and normal increase information is generated without processing it. Conversely, if any set of abnormalities exists in sediment concentration and synergistic indicators, it indicates that the increase in concentration is abnormal, and abnormal increase information is generated. Next, the generated abnormal increase information is analyzed, and the settlement velocity of the monitored area is calculated according to the formula. The settlement velocity of the monitored area was calculated. , where d 50 The median particle size obtained from the UAV hyperspectral inversion is represented by f, which is the flocculation coefficient calibrated through laboratory flocculation tests. The specific value range is 1-2.5. Specifically, when the sediment concentration is >300 mg / L, flocculation leads to an increase in the equivalent particle size; therefore, the flocculation coefficient is introduced for correction. A drag coefficient C is also introduced. d And based on the drag coefficient, the settlement velocity Make corrections, specific calculation formula The correction speed was calculated. And v is the flow velocity, and the settlement velocity is calculated accordingly. To determine the early warning level, the settlement rate is... The system matches the warning level range to generate warning level information and transmits it to the monitoring information output module. like ≥ If so, a red alert will be generated; like ≤ < If so, an orange alert will be generated; like < If so, a yellow alert will be generated; in and The specific values ​​are set by the operator. The specific judgment criteria are as follows: For example, an abnormally high sediment concentration (450 mg / L, exceeding the historical 99th percentile) was detected in Channel A (design depth -12.5 m), requiring calculation of the settling velocity and issuance of an early warning. Inputting the reflectance of 6 bands, the BP neural network inverts to obtain d 50 =0.03mm, then according to the formula f=1+0.002C, f=1.9 is obtained, and the corrected equivalent particle size d 50 =0.03×1.9=0.057mm, then substitute it into the formula to calculate the settlement velocity. =0.012×(0.057) 1.8 ×exp(-0.05×0.6)=0.028m / s, and then match it with the judgment criteria to determine that an orange warning level is generated.

[0027] The monitoring information output module is used to display the acquired early warning level information to the corresponding management personnel.

[0028] Example 2 As a second embodiment of the present invention, it is implemented based on the first embodiment, and the difference from the first embodiment is as follows: The concentration reduction analysis module analyzes the acquired concentration reduction signals and acquires sediment data for the same period in historical periodic data. It then groups the data in the same way as the concentration increase signals and calculates the historical concentration distribution, determined by the standard deviation. If the current concentration reduction is within the historical periodic fluctuation range (e.g., historical standard deviation ±20%, current reduction 15%), it is considered a normal periodic fluctuation and no further processing is performed (e.g., during neap tides, weak water flow leads to sediment settling, naturally reducing the concentration). Conversely, if the reduction exceeds historical fluctuations (e.g., historical minimum concentration 200 mg / L, current 150 mg / L), it is marked as an abnormal reduction, generating an abnormal reduction signal for analysis. The generated abnormal reduction signal is analyzed by conducting specific cause analysis from both natural and human factors, determining the cause of the abnormal reduction, generating abnormal reduction information, and transmitting it to the monitoring information output module.

[0029] The specific analysis of natural factors will focus on the following aspects: Hydrodynamic changes: Tides: Compared with the historical tidal range for the same period, if the tidal range decreases (e.g., the tidal range during spring tides changes from 3m to 2.5m), the intensity of the rising and falling tides decreases, the sediment suspension capacity weakens, and the concentration is more likely to decrease.

[0030] Runoff: Data from the watershed hydrological stations shows that if runoff decreases during the dry season (e.g., river flow decreases from 500 m³ / s to 300 m³ / s), the supply of sediment to the sea is insufficient, and the concentration near the port decreases.

[0031] Meteorological conditions: Wind speed: Reanalyzing the data using ERA5, if the wind speed is consistently below level 3, the effect of wind and waves lifting sand is weak, making it difficult for sediment to suspend at the bottom of the water, and the concentration tends to stabilize or decrease.

[0032] Rainfall: Rainfall is less during the rainy season (e.g., monthly rainfall decreases from 200mm to 100mm), resulting in reduced sediment load in surface runoff and insufficient sediment inflow from the sea near the port.

[0033] The analysis of human factors will focus on the following aspects: Watershed engineering: Reservoir scheduling: If a new reservoir is built upstream (e.g., with a sediment retention capacity of 1 million tons / year), the sediment content of the downstream water flow will decrease from 3 kg / m³ to 1 kg / m³, resulting in a long-term reduction in sediment concentration at the port.

[0034] River dredging: Large-scale dredging of river channels in the basin (such as dredging 500,000 m³ per year) reduces the replenishment of riverbed sediment and affects port siltation.

[0035] Port engineering: Channel dredging: The recent increase in dredging volume (e.g., from 500,000 m³ / year to 800,000 m³ / year) has directly removed sediment from the channel, resulting in a decrease in the monitored concentration.

[0036] Breakwater renovation: The construction of new breakwaters alters the water flow path (such as weakening the coastal flow), obstructing sediment transport channels and reducing local concentrations.

[0037] Example 3 As a third embodiment of the present invention, the focus is on combining the implementation processes of the first and second embodiments.

[0038] The data in the above formulas are all calculated using numerical values, without substituting the units of the parameters. In addition, the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0039] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A port terminal sediment monitoring system based on unmanned aerial vehicle (UAV) technology, characterized in that, include: The monitoring area analysis module is used to analyze the monitoring information transmitted by the sediment monitoring information acquisition module. It obtains monitoring area data based on the monitoring information and performs preprocessing to obtain preprocessed data. At the same time, it calculates sediment concentration by combining empirical formulas and judges its changes by combining historical data, generates signals of concentration increase or decrease, and transmits them respectively. The concentration increase analysis module is used to analyze the acquired concentration increase signal, determine the threshold based on historical periodic data, compare the sediment concentration with it to determine whether the sediment concentration is normal, and combine it with the collaborative indicators of the monitoring area to make a comprehensive judgment and generate abnormal increase information. The abnormal increase information is analyzed, the settlement velocity of the monitored area is calculated and matched with the level matching interval to generate early warning level information, and then transmitted to the monitoring information output module. The concentration reduction analysis module judges the current concentration reduction rate, generates an abnormal reduction signal, and determines the specific cause by combining natural and human factors, generating abnormal reduction information and transmitting it to the monitoring information output module.

2. The port terminal sediment monitoring system based on UAV technology according to claim 1, characterized in that, It also includes a sediment monitoring information acquisition module, which is used to determine the monitoring area and monitoring period, generate monitoring information, and transmit it to the monitoring area analysis module; The monitoring information output module is used to display early warning level information and anomaly reduction information to the corresponding operators.

3. A port terminal sediment monitoring system based on unmanned aerial vehicle (UAV) technology according to claim 2, characterized in that, The specific method by which the sediment monitoring information acquisition module generates monitoring information is as follows: The monitoring area is obtained, and its corresponding tide table is obtained to determine the low tide period within 3 days. Then, the weather conditions of the monitoring area are obtained, and dates with wind force ≤ level 3 and visibility > 8km are selected. At the same time, the ship traffic management system is used to obtain the ship entry and exit plans to confirm the time periods when no large ships are berthed. The monitoring period is determined by combining the above analysis.

4. A port terminal sediment monitoring system based on unmanned aerial vehicle (UAV) technology according to claim 1, characterized in that, The specific method by which the monitoring area analysis module generates concentration increase or decrease signals is as follows: Data is collected from the monitored area, and preprocessed data is obtained through data cleaning and image correction, combined with empirical formulas. Calculate the sediment concentration C in the monitored area, where a and b are empirical coefficients, and R... 700 R refers to the spectral reflectance near the red band in a multispectral image. 550 It is the spectral reflectance near the green light band; The system acquires historical sediment concentrations in the monitored area and sorts them according to time series. It also analyzes the changes in sediment concentration in the monitored area based on the sorting order, generating concentration increase signals or concentration decrease signals. Then, the concentration increase signals are transmitted to the concentration increase analysis module, and the concentration decrease signals are transmitted to the concentration decrease analysis module.

5. A port terminal sediment monitoring system based on unmanned aerial vehicle (UAV) technology according to claim 1, characterized in that, The specific method by which the concentration increase analysis module analyzes the acquired concentration increase signal is as follows: Historical periodic data of the monitored area is acquired, and a complete period is obtained. This period is then broken down into multiple phases, denoted as p. The impact of long periods on sediment concentration is then considered. Based on the historical periodic data, the concentration percentile threshold for each period phase is calculated, and the threshold is determined according to the formula. p，t =Percentiles (C p，t (95%), where p represents phase, t represents season, and C p，t This provides sediment concentration data for all corresponding phases p and seasons t in the past. At the same time, the sediment concentration C in the current monitoring area is compared with the threshold. If the sediment concentration C is greater than the threshold, the sediment concentration is marked as abnormal; otherwise, if the sediment concentration C is less than the threshold, the sediment concentration is marked as normal.

6. A port terminal sediment monitoring system based on unmanned aerial vehicle (UAV) technology according to claim 1, characterized in that, The specific method by which the concentration increase analysis module generates abnormal increase information is as follows: Acquire the coordination indicators of the monitoring area, which include the flow velocity, topography, and weather conditions of the monitoring area. Judge the status of each coordination indicator. If all are normal, the coordination indicator is normal; otherwise, the coordination indicator is abnormal. At the same time, make a comprehensive judgment based on the sediment concentration. If both sediment concentration and synergistic indicators are normal, the increase in concentration is considered a normal periodic fluctuation, and normal increase information is generated without further processing. Conversely, if any set of abnormalities exists in sediment concentration or synergistic indicators, the increase in concentration is considered an abnormal increase, and abnormal increase information is generated.

7. A port terminal sediment monitoring system based on unmanned aerial vehicle (UAV) technology according to claim 1, characterized in that, The specific method by which the concentration increase analysis module analyzes abnormal increase information is as follows: According to the formula The settlement velocity of the monitored area was calculated. , where d 50 The median particle size obtained from the hyperspectral inversion by the UAV is represented by f, where f is the flocculation coefficient and the attenuation factor is also present. Settlement velocity Make corrections according to the formula. The correction speed was calculated. And v is the flow velocity, exp(·) is the natural exponential function, which will correct the velocity. Match with the level range, if ≥ If a red alert is generated, then a red alert will be issued. ≤ < If an orange alert is generated, then an orange alert will be issued. < If so, a yellow alert will be generated, along with alert level information, and among them... and The specific values ​​are set by the operator.

8. A port terminal sediment monitoring system based on unmanned aerial vehicle (UAV) technology according to claim 7, characterized in that, The specific method by which the concentration reduction analysis module generates abnormal reduction information is as follows: Acquire sediment data for the same period phase in historical periodic data and process them in groups. Calculate the standard deviation corresponding to the historical concentration distribution. If the current concentration decrease is within the historical periodic fluctuation range, it is judged as a normal periodic fluctuation and no processing is performed. Conversely, if the decrease exceeds the historical fluctuation, it is marked as an abnormal decrease, an abnormal decrease signal is generated, and it is analyzed. The specific causes are analyzed from both natural and human factors, and the reasons for the abnormal reduction are determined, generating information on the abnormal reduction.

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