Dredger operation early warning method and device, dredger and storage medium

Abstract

The application discloses a dredger operation early warning method and device, a dredger and a storage medium, and relates to the field of hydraulic engineering.The method comprises the following steps: calculating external load forces of corresponding monitoring points based on real-time working condition data of the dredger at the monitoring points; calculating abnormal energy increments of the corresponding monitoring points based on the external load forces of the monitoring points; performing space-time weighting processing on the abnormal energy increments of the monitoring points to obtain a comprehensive risk index of the dredger; performing deviation fusion processing on real-time working condition data and reference working condition data of the monitoring points to obtain a comprehensive deviation index of the dredger; and performing early warning according to the comprehensive risk index and the comprehensive deviation index.The technical scheme of the application improves the accuracy of dredger operation early warning.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of water conservancy engineering, and particularly to a method, device, dredger and storage medium for early warning of dredging operations. Background Technology

[0002] As the core equipment of dredging projects, dredgers operate in complex and variable environments. They usually need to carry out high-load operations in waters rich in obstacles and with uneven soil conditions. During operation, mechanical failures, work interruptions, and even safety accidents can easily occur due to sudden changes in external loads and deviations in equipment operating conditions. Therefore, accurate early warning of the dredger's operating status is crucial.

[0003] However, existing early warning methods for dredgers mainly rely on subjective judgment based on the operator's experience or on a single indicator (such as drive motor current, shaft torque, ship draft, trim / pitch angle, etc.). The former is highly dependent on personal experience, has strong subjectivity and instability, makes it difficult to maintain accurate judgments, and cannot form objective, quantifiable early warning standards; the latter fails to comprehensively consider multiple influencing factors, resulting in inaccurate and untimely early warning results, and failing to detect potential risks in advance.

[0004] Therefore, there is an urgent need to propose a new method to solve the above problems. Summary of the Invention

[0005] This invention provides a method, device, dredger, and storage medium for early warning of dredging operations, thereby improving the accuracy of early warning for dredging operations.

[0006] In a first aspect, embodiments of the present invention provide an operation early warning method for a dredger, the method comprising:

[0007] The external load force at each monitoring point is calculated based on the real-time operating data of the dredger at each monitoring point.

[0008] The abnormal energy increment of each monitoring point is calculated based on the external load force at each monitoring point.

[0009] The abnormal energy increments at each monitoring point are processed by spatiotemporal weighting to obtain the comprehensive risk index of the dredger.

[0010] The real-time operating data and baseline operating data of each monitoring point are fused to obtain the comprehensive deviation index of the dredger.

[0011] Early warnings are issued based on the comprehensive risk index and the comprehensive deviation index.

[0012] The technical solution of this invention first calculates the external load force at each monitoring point based on the real-time operating data of the dredger at each monitoring point. This allows for the timely capture of abnormal loads, providing a data foundation for subsequent calculations of abnormal energy increments at the monitoring points. Next, based on the external load force at each monitoring point, the abnormal energy increment is calculated, transforming the abnormal forces at the monitoring points into quantifiable energy accumulation indicators, providing data support for subsequent early warnings. Then, the abnormal energy increments at each monitoring point are subjected to spatiotemporal weighting to obtain the dredger's comprehensive risk index. This transforms the scattered abnormal data from each monitoring point into a unified-dimensional comprehensive risk index, achieving a scientific integration from single-point anomalies to overall risk. This not only eliminates the need for maintenance personnel to analyze massive amounts of monitoring point data individually, effectively reducing manual judgment costs, but also improves the accuracy and reliability of subsequent early warnings. Subsequently, the abnormal energy increments at each monitoring point were spatiotemporally weighted to obtain the comprehensive risk index of the dredger. This not only effectively solved the problem of difficulty in summarizing and evaluating dispersed operating data, but also avoided the potential hidden danger of "seemingly normal single points but ignoring the coordinated deviation of multiple parts." This made the overall operating status of the dredger quantifiable and perceptible, thus significantly improving the accuracy and reliability of subsequent early warnings. Finally, early warnings were issued based on the comprehensive risk index and the comprehensive deviation index, achieving dual-dimensional verification of both indices. This not only greatly improved the accuracy of early warnings and effectively reduced false alarms and missed alarms, but also ensured the operational safety and economical maintenance of the dredger, ultimately achieving long-term control over equipment operation risks. Therefore, the technical solution of this invention solves the problem in existing technologies where early warnings rely on personal experience or are based on only a single indicator, making it difficult to form objective and quantifiable early warning standards, and resulting in inaccurate and untimely warning results, thus failing to detect potential risks in advance.

[0013] Secondly, embodiments of the present invention also provide an operation early warning device for a dredger, the device comprising:

[0014] The load calculation module is used to calculate the external load force at each monitoring point based on the real-time operating data of the dredger at each monitoring point.

[0015] The energy calculation module is used to calculate the abnormal energy increment of each monitoring point based on the external load force at each monitoring point.

[0016] The risk index calculation module is used to perform spatiotemporal weighted processing on the abnormal energy increments at each monitoring point to obtain the comprehensive risk index of the dredger.

[0017] The deviation index calculation module is used to perform deviation fusion processing on the real-time operating data and baseline operating data of each monitoring point to obtain the comprehensive deviation index of the dredger.

[0018] The early warning module is used to issue early warnings based on the comprehensive risk index and the comprehensive deviation index.

[0019] Thirdly, embodiments of the present invention also provide a dredger, the dredger comprising:

[0020] At least one processor; and a memory communicatively connected to said at least one processor.

[0021] The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the dredging vessel operation early warning method according to any embodiment of the present invention.

[0022] Fourthly, embodiments of the present invention also provide a storage medium containing computer-executable instructions, which, when executed by a computer processor, implement the dredging vessel operation early warning method described in any embodiment of the present invention.

[0023] It should be noted that the aforementioned computer instructions may be stored, in whole or in part, on a computer-readable storage medium. This computer-readable storage medium may be packaged together with the processor of the dredger's operation early warning device, or it may be packaged separately from the processor of the dredger's operation early warning device; this application does not impose any limitations on this.

[0024] The descriptions of the second, third, and fourth aspects in this application can be referenced to the detailed description of the first aspect; and the beneficial effects described in the second, third, and fourth aspects can be referenced to the analysis of the beneficial effects of the first aspect, which will not be repeated here.

[0025] In this application, the name of the aforementioned dredging vessel operation early warning device does not limit the equipment or functional module itself. In actual implementation, these devices or functional modules may appear under other names. As long as the function of each device or functional module is similar to that of this application, it falls within the scope of the claims of this application and its equivalents.

[0026] These or other aspects of this application will become more readily apparent in the following description. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1A flowchart illustrating an operational early warning method for a dredger provided in an embodiment of the present invention;

[0029] Figure 2 A flowchart illustrating another method for early warning of dredging vessel operations provided in an embodiment of the present invention;

[0030] Figure 3 A schematic diagram of the structure of an early warning device for a dredger provided in an embodiment of the present invention;

[0031] Figure 4 This is a schematic diagram of the structure of a dredger provided in an embodiment of the present invention. Detailed Implementation

[0032] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0033] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0034] The terms "first" and "second," etc., used in the specification and drawings of this application are used to distinguish different objects or to distinguish different treatments of the same object, rather than to describe a specific order of objects.

[0035] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0036] Before discussing the exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the figures. The process can correspond to a method, function, procedure, subroutine, subroutine, etc. Moreover, without conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0037] It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0038] In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0039] Figure 1 This is a flowchart illustrating a dredging vessel operation early warning method according to an embodiment of the present invention. This embodiment is applicable to situations requiring early warning of dredging vessel operations. The method can be executed by an operation early warning device on the dredging vessel, which can be implemented using software and / or hardware. For example, the device can be an electronic device within the dredging vessel. (Reference) Figure 1 The dredging vessel operation early warning method in this embodiment specifically includes the following steps:

[0040] Step 110: Calculate the external load force at the corresponding monitoring point based on the real-time operating data of the dredger at each monitoring point.

[0041] Specifically, a dredger refers to an engineering vessel used for dredging and transporting underwater sediment, soil, or rock. A monitoring point refers to a specific location or sensor installation point pre-deployed on the key structures of the dredger (such as the dredging bucket, transport pipelines, hull support parts, rake head, and rake arm) to collect operational data, based on actual conditions or needs. Real-time operational data refers to raw data reflecting the equipment's operating status and work conditions collected in real time by the monitoring points during dredging operations. Examples include pressure, temperature, vibration frequency, rotational speed, torque, displacement, average contact stress on the rake head surface, and wave-related data (such as wave height, wavelength, instantaneous velocity of wave particles, and acceleration). External load forces refer to the forces exerted on the monitoring point location by the external environment or the work object during dredging operations. Examples include the impact force of sediment on the dredging bucket during dredging and the force of water flow on the hull.

[0042] In practice, real-time operating data of each monitoring point during dredging operations can be collected using sensors (such as pressure sensors, torque sensors, vibration sensors, displacement sensors, fiber optic grating sensors, gyroscopes, and wave sensors) deployed at various monitoring points. The collected data is then preprocessed (e.g., outlier removal, unit standardization, and time synchronization) to improve data quality. Subsequently, for each monitoring point, the preprocessed real-time operating data and its attribute information (such as force type and deployment location) are input into a pre-trained load determination model to obtain the external load force at that monitoring point. The pre-trained load determination model refers to a model obtained by training a deep learning model based on historical operating data, attribute information, and corresponding real external load forces at different monitoring points of the dredging vessel.

[0043] In this embodiment, the above steps can capture abnormal loads in a timely manner, providing a data basis for subsequent calculation of abnormal energy increments at monitoring points.

[0044] Step 120: Calculate the abnormal energy increment of the corresponding monitoring point based on the external load force of each monitoring point.

[0045] Specifically, abnormal energy increment refers to a parameter used to quantify the degree of energy exceedance at a monitoring point due to abnormal load.

[0046] In practice, for each monitoring point, the difference between the external load force and the reference load force can be calculated first to obtain the excess load. Then, the product of the square of the excess load and the preset proportional coefficient is calculated to obtain the abnormal energy increment of the monitoring point. The reference load force is a reasonable benchmark value for the monitoring point under normal dredging vessel operation, determined in advance based on actual conditions or needs. For example, the average of the normal external load force data for the monitoring point over the past three months (excluding abnormal values ​​caused by equipment failure or extreme environments) can be selected as the reference load force. The preset proportional coefficient is a quantitative coefficient pre-set based on the structural parameters of the monitoring point (such as material elastic modulus, cross-sectional area under stress, etc.) and the characteristics of the operating scenario. It is used to match the physical relationship between energy and load force. For example, the preset proportional coefficient is 0.5.

[0047] In this embodiment, the above steps can transform the abnormal stress at the monitoring point into a quantifiable energy accumulation index, providing data support for subsequent early warning.

[0048] Step 130: Perform spatiotemporal weighted processing on the abnormal energy increments at each monitoring point to obtain the comprehensive risk index of the dredger.

[0049] Specifically, the comprehensive risk index refers to a quantitative indicator that reflects the overall safety risk level of a dredger under its current operating conditions due to abnormal loads.

[0050] In the specific implementation, based on the deployment location of each monitoring point, the spatial weight of each monitoring point is obtained by querying the correspondence table between location and spatial weight. Then, based on the difference between the current acquisition time and the previous acquisition time of each monitoring point, the temporal weight of each monitoring point is calculated. The specific calculation formula is: Temporal weight of monitoring point = e^[-attenuation parameter × (current acquisition time - previous acquisition time)]. Finally, the product of the abnormal energy increment, spatial weight, and temporal weight of each monitoring point is calculated, and the product results of all monitoring points are summed to obtain the comprehensive risk index of the dredger. The correspondence table between location and spatial weight is a pre-determined table of deployment locations and spatial weights based on actual conditions or needs. For example, when the deployment location of the monitoring point is a bucket or cutterhead, the spatial weight is 0.3; when the deployment location of the monitoring point is a conveying pipeline, the spatial weight is 0.25. The attenuation parameter is a parameter pre-set according to actual conditions or needs to control the attenuation rate of the time weight, such as an attenuation parameter of 0.1.

[0051] In this embodiment, the above steps transform the scattered abnormal data from various monitoring points into a unified comprehensive risk index, achieving a scientific integration from single-point anomalies to overall risk. This process not only eliminates the need for maintenance personnel to analyze massive amounts of monitoring point data one by one, effectively reducing the cost of manual judgment, but also improves the accuracy and reliability of subsequent early warnings.

[0052] Step 140: Perform deviation fusion processing on the real-time operating data and baseline operating data of each monitoring point to obtain the comprehensive deviation index of the dredger.

[0053] Specifically, baseline operating condition data refers to standard operating condition data that is pre-set according to actual conditions or needs and reflects the dredger under normal and stable operating conditions. For example, the baseline operating condition data for a certain monitoring point can be the average of the normal operating condition data of that monitoring point over the past three months. The comprehensive deviation index is a quantitative parameter used to comprehensively reflect the degree of deviation between the operating parameters of various key parts of the dredger and the standard state.

[0054] In practice, the difference between the real-time operating data of each monitoring point and the corresponding baseline operating data can be calculated first to obtain the deviation value of each monitoring point. Then, deviation values ​​with absolute values ​​greater than the preset fluctuation threshold are selected. Finally, all the selected deviation values ​​are summed to obtain the comprehensive deviation index of the dredger. The preset fluctuation threshold is an allowable deviation threshold set in advance according to the actual situation or needs, such as preset fluctuation threshold = baseline operating data - baseline operating data × 0.05.

[0055] In this embodiment, the above steps can integrate the individual deviations of each monitoring point from the benchmark into a unified comprehensive deviation index. This process not only effectively solves the problem of the difficulty in summarizing and evaluating dispersed operating data, but also avoids the potential hidden danger of "seemingly normal individual points but ignoring the coordinated deviations of multiple parts," making the overall operating status of the dredger quantifiable and perceptible, thereby significantly improving the accuracy and reliability of subsequent early warnings.

[0056] Step 150: Issue an early warning based on the comprehensive risk index and the comprehensive deviation index.

[0057] In practice, if the comprehensive risk index is greater than the risk threshold and the comprehensive deviation index is greater than the deviation threshold, an emergency warning is triggered (e.g., an audible and visual alarm is triggered in the cab, or a warning detail is pushed to the operation and maintenance platform via a pop-up window). If the comprehensive risk index is not greater than the risk threshold but the comprehensive deviation index is greater than the deviation threshold, a high deviation warning is triggered (e.g., a deviation warning sound is emitted from the cab). If the comprehensive risk index is greater than the risk threshold but the comprehensive deviation index is not greater than the deviation threshold, a high-risk warning is triggered (e.g., a risk warning sound is emitted from the cab). If the comprehensive risk index is not greater than the risk threshold and the comprehensive deviation index is not greater than the deviation threshold, the dredger is determined to be in normal operation. In this case, no warning is activated, and real-time operating data of each monitoring point can be reacquired. The external load force of the corresponding monitoring point is then calculated based on the new real-time operating data, and the judgment is repeated cyclically according to the above rules. The risk threshold is a pre-set risk threshold based on actual conditions or requirements. The deviation threshold is a pre-set allowable deviation value based on actual conditions or requirements.

[0058] In this embodiment, the above steps achieve dual-dimensional verification of the comprehensive risk index and the comprehensive deviation index, which not only greatly improves the accuracy of early warning and effectively reduces the false alarm and false alarm rates, but also effectively ensures the operational safety and maintenance economy of dredgers, and ultimately achieves long-term control over equipment operation risks.

[0059] The dredging vessel operation early warning method provided in this invention first calculates the external load force at each monitoring point based on the real-time operating data of the dredging vessel at each monitoring point. This allows for timely detection of abnormal loads, providing a data foundation for subsequent calculations of abnormal energy increments at the monitoring points. Next, based on the external load force at each monitoring point, the abnormal energy increment at that monitoring point is calculated, transforming the abnormal force at the monitoring point into a quantifiable energy accumulation index, providing data support for subsequent early warnings. Then, the abnormal energy increments at each monitoring point are subjected to spatiotemporal weighting to obtain the dredging vessel's comprehensive risk index. This transforms the scattered abnormal data from each monitoring point into a unified-dimensional comprehensive risk index, achieving a scientific integration from single-point anomalies to overall risk. This not only eliminates the need for maintenance personnel to analyze massive amounts of monitoring point data individually, effectively reducing manual judgment costs, but also improves the accuracy and reliability of subsequent early warnings. Subsequently, the abnormal energy increments at each monitoring point were spatiotemporally weighted to obtain the comprehensive risk index of the dredger. This not only effectively solved the problem of difficulty in summarizing and evaluating dispersed operating data, but also avoided the potential hidden danger of "seemingly normal single points but ignoring the coordinated deviation of multiple parts." This made the overall operating status of the dredger quantifiable and perceptible, thus significantly improving the accuracy and reliability of subsequent early warnings. Finally, early warnings were issued based on the comprehensive risk index and the comprehensive deviation index, achieving dual-dimensional verification of both indices. This not only greatly improved the accuracy of early warnings and effectively reduced false alarms and missed alarms, but also ensured the operational safety and economical maintenance of the dredger, ultimately achieving long-term control over equipment operation risks. Therefore, the technical solution of this invention solves the problem in existing technologies where early warnings rely on personal experience or are based on only a single indicator, making it difficult to form objective and quantifiable early warning standards, and resulting in inaccurate and untimely warning results, thus failing to detect potential risks in advance.

[0060] Figure 2 This is a flowchart illustrating another dredging vessel operation early warning method provided by an embodiment of the present invention. This embodiment is a specific implementation based on the above embodiment. In this embodiment, the method may further include:

[0061] Step 210: Calculate the external load force at the corresponding monitoring point based on the real-time operating data of the dredger at each monitoring point.

[0062] Optional, real-time operating data includes lateral deformation and deformation rate.

[0063] Further, step 210 may specifically include: for the current monitoring point, determining whether the current monitoring point is a flow-induced force monitoring point; if the current monitoring point is a flow-induced force monitoring point, determining the elastic deformation flow-induced force of the current monitoring point based on the product of the stiffness coefficient and the transverse deformation of the current monitoring point; determining the damped energy-dissipating flow-induced force of the current monitoring point based on the product of the damping coefficient and the deformation rate of the current monitoring point; calculating the sum of the elastic deformation flow-induced force and the damped energy-dissipating flow-induced force of the current monitoring point to obtain the external load force of the current monitoring point.

[0064] Specifically, flow-induced force monitoring points are specific monitoring points designed to monitor flow-induced forces. They are typically placed on underwater structural parts of a dredger that are susceptible to water flow (such as the boom and bucket). Flow-induced force is the dynamic force exerted on the surface of a structure when there is relative motion between the water flow (including mud flow during dredging operations and ambient water flow during navigation) and the underwater structure of the dredger. The stiffness coefficient is a physical parameter characterizing the structure's resistance to deformation at the monitoring point. Lateral deformation refers to the deformation of the structure at the monitoring point in the direction of the water flow (i.e., perpendicular to the structural axis or the main flow direction). Elastic deformation flow-induced force refers to the reverse force experienced by the structure due to its elastic recovery tendency when the structure undergoes elastic deformation caused by the water flow. The damping coefficient is a physical parameter characterizing the rate at which the structure at the monitoring point resists deformation. The deformation rate refers to the rate of deformation change of the structure at the monitoring point during lateral deformation. Damping energy-dissipating flow-induced force refers to the energy-dissipating force (i.e., the damping reaction force induced by the flow-induced force) generated by factors such as internal friction of materials, friction of structural connections, and medium damping when a structure undergoes deformation.

[0065] In practice, for the current monitoring point, the system first queries the database storing monitoring point configuration information based on the current monitoring point's serial number to obtain the type of the current monitoring point, and then determines whether the current monitoring point is a flow-induced force monitoring point. If the current monitoring point is a flow-induced force monitoring point, the system first queries the correspondence table between stiffness coefficients and attribute information based on the current monitoring point's attribute information (such as model) to obtain the stiffness coefficient of the current monitoring point; then, based on the product of the current monitoring point's stiffness coefficient and lateral deformation, the elastic deformation flow-induced force of the current monitoring point is determined. The specific calculation formula is as follows: Where F1 is the elastic deformation flow excitation force, k(i) is the stiffness coefficient of the i-th monitoring point; ω is the wave circular frequency, ω=2π / T, T is the wave period; δ(t,i) is the lateral deformation of the rake arm at the i-th monitoring point at time t (i.e., the current time). Simultaneously, the damping coefficient of the current monitoring point can be obtained by first querying the correspondence table between the damping coefficient and attribute information based on the attribute information of the current monitoring point; then, the damping energy dissipation flow excitation force of the current monitoring point is determined based on the product of the damping coefficient and the deformation rate. The specific calculation formula is as follows: Where F2 is the damping energy dissipation flow excitation force, and c(i) is the damping coefficient of the rake arm at the i-th monitoring point; Let t be the deformation rate of the rake arm at the i-th monitoring point at time t (i.e., the current time). Finally, calculate the sum of the elastic deformation flow excitation force and the damping energy dissipation flow excitation force at the current monitoring point to obtain the external load force at the current monitoring point. The specific calculation formula is as follows: ;in, This refers to the external load force. In summary, if the current monitoring point is a flow-induced force monitoring point, then the formula for calculating the external load force at the current monitoring point can be expressed as: Among them, the stiffness coefficient and attribute information correspondence table and the damping coefficient and attribute information correspondence table are standard reference tables that are pre-formulated based on actual scenario requirements (such as experimental measurement data, engineering application experience, etc.) to clarify the mapping relationship between monitoring point attributes and corresponding coefficients.

[0066] If the current monitoring point is not a flow-induced force monitoring point, the real-time operating data of the current monitoring point and the attribute information of the monitoring point (such as force type, deployment location, etc.) are input into the pre-trained load force determination model to obtain the external load force of the monitoring point.

[0067] In this embodiment, precise screening is first achieved by determining the type of monitoring point, and the dynamic stress characteristics of the dedicated flow-induced force monitoring point are adapted to enhance the targeting of data calculation. At the same time, by splitting the core components of the flow-induced force and combining the calculation logic of the inherent parameters of the structure with the real-time data, the accuracy of the external load force calculation is further improved.

[0068] Optional, real-time operating data also includes the average contact stress of the rake head and the soil type.

[0069] Furthermore, after determining whether the current monitoring point is a flow-induced force monitoring point, the process also includes: if the current monitoring point is a contact force monitoring point, then querying the soil type and soil correction coefficient correspondence table based on the soil type of the current monitoring point to obtain the soil correction coefficient of the current monitoring point; and calculating the product of the average contact stress of the rake head, the soil correction coefficient, and the contact area of ​​the rake head at the current monitoring point to obtain the external load force of the current monitoring point.

[0070] Specifically, contact force monitoring points refer to specific monitoring points used to monitor the contact force between the rake head and the underwater soil layer. These are typically placed on the cutting teeth, contact plates, and other parts of the rake head that directly contact the soil layer. Soil type refers to the soil category of the underwater soil layer in the dredging area, such as clay, sand, and gravelly soil. The soil type and soil correction coefficient correspondence table is a standardized table pre-compiled according to actual conditions or needs, establishing a one-to-one correspondence between soil type and soil correction coefficient. For example, when the soil type is clay, the soil correction coefficient is 1.8; when the soil type is sand, the soil correction coefficient is 1.2; and when the soil type is silt, the soil correction coefficient is 0.6. The soil correction coefficient is a parameter used to quantify the influence of different soil types on the rake head contact force. The average contact stress of the rake head is the average force per unit area within the contact region between the rake head and the soil layer, reflecting the stress concentration degree when the rake head contacts the soil layer. The rake head contact area is the effective area of ​​actual contact between the rake head and the soil layer.

[0071] In practice, after determining whether the current monitoring point is a flow-induced force monitoring point, it can also be determined whether it is a contact force monitoring point. If it is a contact force monitoring point, the soil type and soil correction coefficient correspondence table is consulted to obtain the soil correction coefficient for the current monitoring point. Then, the product of the average contact stress of the rake head, the soil correction coefficient, and the contact area of ​​the rake head is calculated to obtain the external load force of the current monitoring point. The specific calculation formula is as follows: ;in, σ(t,i) is the average contact stress on the surface of the rake head at the i-th monitoring point at time t; S is the contact area of ​​the rake head; f(i) is the soil correction coefficient for the i-th monitoring point. If the current monitoring point is not a contact force monitoring point, the real-time working condition data of the current monitoring point and the attribute information of the monitoring point are input into the pre-trained load determination model to obtain the external load force of the monitoring point.

[0072] In this embodiment, the accuracy of external load force calculation is improved through the above steps.

[0073] Optionally, the soil correction coefficient can also be identified and determined using the K-means++ clustering algorithm.

[0074] Optionally, before calculating the external load force at each monitoring point based on the real-time operating data of the dredger at each monitoring point, the real-time operating data at each monitoring point can be denoised using the Sage-Husa adaptive Kalman filter denoising formula to improve data quality and thus improve the accuracy of subsequent calculations. The Sage-Husa adaptive Kalman filter denoising formula includes: (1) State equation: (2) Process noise covariance update: (3) Update of observation noise covariance: ;in, Φ represents the state estimate at time k (including operating condition data); Φ is the state transition matrix. Kalman gain; Let be the observation value at time k; H is the observation matrix; Let k be the process noise covariance matrix at time k; Let J be the prior error covariance matrix at time j; Let be the observation noise covariance matrix at time k.

[0075] Step 211: Calculate the abnormal energy increment of the corresponding monitoring point based on the external load force of each monitoring point.

[0076] Further, step 211 may specifically include: for the current monitoring point, calculating the reference energy of the current monitoring point based on the reference working condition data within the preset period of the current monitoring point; if the current monitoring point is a flow-induced force monitoring point, calculating the real-time energy of the current monitoring point based on the external load force, stiffness coefficient and real-time wave circular frequency of the current monitoring point; if the current monitoring point is a contact force monitoring point, integrating the external load force of the current monitoring point based on the real-time movement speed of the rake head to obtain the real-time energy of the current monitoring point; calculating the difference between the real-time energy of the current monitoring point and the reference energy to obtain the abnormal energy increment of the current monitoring point.

[0077] Specifically, the preset period refers to a pre-set data acquisition time window for baseline operating conditions based on actual conditions or needs, such as a preset period of 30 seconds. In this embodiment, the baseline operating condition data is the continuous operating condition data collected at the current monitoring point within the preset period when the dredger is in a stable operating state (such as uniform excavation or normal water flow environment). The baseline energy is the average energy level or stable energy value of the current monitoring point under normal operating conditions, calculated based on the baseline operating condition data. The real-time wave circular frequency is a physical parameter characterizing the dynamic characteristics of waves / water flow in the current operating water area, calculated using the formula ω=2π / T, directly reflecting the frequency of water flow fluctuations. The real-time movement speed of the rake head is the instantaneous movement speed of the rake head during the excavation operation.

[0078] In practice, for the current monitoring point, the baseline energy of the current monitoring point can be calculated based on the baseline operating condition data within a preset period. Specifically, if the current monitoring point is a flow-driven monitoring point, the formula for calculating the baseline energy is: ;in, Let i be the reference energy of the i-th monitoring point. for The external load force at the i-th monitoring point at time t (calculated from the baseline working condition data within the preset period, using the same method as described above); The wave circular frequency is set within a preset period, and Time is the preset period. For example, if the preset period is 30 seconds, Time = 30. If the current monitoring point is a contact force monitoring point, the base energy calculation formula is: ;in, The speed of the rake head at time τ within a preset period.

[0079] Next, if the current monitoring point is a flow-induced force monitoring point, then the real-time energy of the current monitoring point is calculated based on the external load force, stiffness coefficient, and real-time wave circular frequency. The specific calculation formula is as follows: ,in, The real-time energy of the i-th monitoring point at the current moment; Let be the external load force at the i-th monitoring point at the current moment. If the current monitoring point is a contact force monitoring point, then the real-time energy of the current monitoring point is obtained by integrating the external load force at the current monitoring point based on the real-time movement speed of the rake head. The specific calculation formula is as follows: Where v(t) is the real-time velocity of the rake head at time t. The integration start time is determined based on actual needs (obtained by subtracting the preset time interval difference from t).

[0080] Finally, the difference between the real-time energy at the current monitoring point and the baseline energy is calculated to obtain the abnormal energy increment at the current monitoring point. The specific calculation formula is as follows: ;in, This represents the abnormal energy increment at the i-th monitoring point at the current moment.

[0081] In this embodiment, the accuracy of the determined abnormal energy increment is improved through the above steps.

[0082] Step 212: Calculate the product of the abnormal energy increment at each monitoring point and the distance from the corresponding monitoring point to the preset core component to obtain the first energy parameter of each monitoring point.

[0083] Specifically, the preset core components are key parts of the dredger that are pre-determined based on actual conditions or needs, such as the rake head, rake arm universal joint, etc. The first energy parameter is an intermediate parameter after distance weighting correction for abnormal energy increments at a single monitoring point.

[0084] In practice, for the current monitoring point, the distance from the current monitoring point to the preset core component can be determined first based on the deployment location of the current monitoring point and the location information of the preset core component. Then, the product of the abnormal energy increment and the distance is calculated to obtain the first energy parameter. The specific calculation formula is as follows: ;in, Let be the first energy parameter of the i-th monitoring point. Let be the distance from the i-th monitoring point to the preset core component.

[0085] Optionally, to improve the accuracy of the calculation of the first energy parameter, a corresponding core component can be set for each monitoring point.

[0086] In this embodiment, the above steps quantify and integrate location weights into risk assessment, thereby improving the accuracy of subsequent comprehensive risk index calculation.

[0087] Step 213: Calculate the ratio of the first energy parameter of each monitoring point to the preset response time to obtain the second energy parameter of each monitoring point.

[0088] Specifically, the preset response time is the longest safe response period for the dredging vessel operation and maintenance system to abnormal energy at monitoring points, pre-set according to actual conditions or needs, such as 0.5 seconds. The second energy parameter refers to the intermediate parameter after time correction of the first energy parameter for a single monitoring point.

[0089] In practice, for the current monitoring point, the ratio of the first energy parameter of the current monitoring point to the preset response time is calculated to obtain the second energy parameter of the current monitoring point. The specific calculation formula is as follows: ;in, Let i be the second energy parameter of the i-th monitoring point. This is the preset response time.

[0090] In this embodiment, through the above steps, a time weight is superimposed on the first energy parameter, upgrading the risk assessment from the degree of spatial impact to the spatiotemporal collaborative risk intensity, thereby further improving the reliability and accuracy of the obtained comprehensive risk index.

[0091] Step 214: Sum the second energy parameters of each monitoring point to obtain the comprehensive risk index of the dredger.

[0092] In practice, after obtaining the second energy parameters of each monitoring point, they can be summed to obtain the comprehensive risk index. The specific calculation formula is as follows: ;in, The comprehensive risk index is defined as n, where n is the total number of monitoring points. Furthermore, steps 212-214 can be represented by the following formula: .in, As the first parameter, This is the second parameter.

[0093] In this embodiment, the above steps improve the comprehensiveness and accuracy of the obtained comprehensive risk index, thereby improving the accuracy of subsequent early warnings.

[0094] Step 215: Calculate the mean value of the baseline working condition data of each monitoring point within the preset period to obtain the target baseline data of each monitoring point.

[0095] Specifically, the target baseline data is the value obtained by calculating the average of the baseline operating condition data within a preset period for a single monitoring point.

[0096] In practice, the mean value of the baseline operating condition data within a preset period for each monitoring point can be calculated first to obtain the target baseline data for each monitoring point. This operation can effectively eliminate data fluctuation interference and anchor the stable baseline of normal operating conditions, thereby laying a reliable data foundation for the subsequent calculation of the comprehensive deviation index.

[0097] Step 216: Calculate the difference between the real-time operating data of each monitoring point and the target benchmark data of the corresponding monitoring point to obtain the operating deviation value of each monitoring point.

[0098] Specifically, the operating condition deviation value is the difference between the real-time operating condition data of a single monitoring point and the target baseline data.

[0099] In practice, the operating condition deviation value of the current monitoring point = the real-time operating condition data of the current monitoring point - the target benchmark data of the current monitoring point.

[0100] In this embodiment, the above steps quantify the degree of deviation of the working condition of a single monitoring point, providing a data basis for the subsequent calculation of the total sum of squared deviations.

[0101] Step 217: Sum the squares of the operating condition deviations at each monitoring point to obtain the total sum of squares of deviations.

[0102] Specifically, the total deviation sum of squares is the value obtained by summing the squares of the operating condition deviation values ​​of all monitoring points. It is a parameter that quantifies the degree of abnormality in the overall machine operating condition from a global perspective.

[0103] In practice, the sum of the squares of the operating condition deviations at each monitoring point can be calculated to obtain the total sum of squares of deviations. The specific calculation formula is as follows: Where C is the total sum of squares of deviations. This represents the operating condition deviation value at the monitoring point. Optional, n≥3.

[0104] In this embodiment, global anomaly information is integrated through the above steps.

[0105] Step 218: Determine the comprehensive deviation index of the dredger based on the deviation weights and the total sum of squared deviations.

[0106] Specifically, the deviation weight is a coefficient used to adjust the degree of influence of the total sum of squared deviations on the comprehensive deviation index.

[0107] In practice, the deviation weights can first be determined based on the real-time wave height information of the dredger's environment. For example, when the real-time wave height is less than 1m, the deviation weight is 1.0; when 1m ≤ real-time wave height < 2m, the deviation weight is 1.5; and when the real-time wave height is ≥ 2m, the deviation weight is 2.0. Then, based on the deviation weights and the sum of squared total deviations, the comprehensive deviation index of the dredger is determined. The specific calculation formula is as follows: ;in, γ is the overall deviation index; γ is the deviation weight.

[0108] In this embodiment, the accuracy of the determined comprehensive deviation index is improved through the above steps.

[0109] Step 219: Issue an early warning based on the comprehensive risk index and the comprehensive deviation index.

[0110] Further, step 219 may specifically include: determining a Level 1 warning when the comprehensive risk index is not less than the first risk index and less than the second risk index, and the comprehensive deviation index is not less than the first deviation index and less than the second deviation index; determining a Level 2 warning when the comprehensive risk index is not less than the second risk index and less than the third risk index, and the comprehensive deviation index is not less than the second deviation index and less than the third deviation index; and determining a Level 3 warning when the comprehensive risk index is not less than the third risk index or the comprehensive deviation index is not less than the third deviation index.

[0111] Specifically, the first, second, and third risk indices are all pre-set comprehensive risk index thresholds used to classify risk levels based on actual conditions or needs. The first risk index (e.g., 0.8) < the second risk index (e.g., 1.2) < the third risk index (e.g., 1.6). Similarly, the first, second, and third deviation indices are all pre-set comprehensive deviation index thresholds used to classify deviation levels based on actual conditions or needs. The first deviation index (e.g., 0.6) < the second deviation index (e.g., 0.9) < the third deviation index (e.g., 1.2). Level 1, Level 2, and Level 3 warnings are warning levels determined based on the combined thresholds of the comprehensive risk index and the comprehensive deviation index. Higher levels indicate more severe anomalies and require more urgent measures. For example, a Level 1 warning indicates a minor anomaly requiring close monitoring of the operational status; a Level 2 warning indicates a moderate anomaly requiring adjustments to operational parameters (e.g., reducing digging speed); and a Level 3 warning indicates a severe anomaly requiring emergency shutdown and repair to prevent equipment damage.

[0112] In practice, a Level 1 warning is issued when the comprehensive risk index is not less than the first risk index and less than the second risk index, and the comprehensive deviation index is not less than the first deviation index and less than the second deviation index. At this time, an audible and visual alarm can be triggered, and the risk area (such as the current operating area) can be displayed simultaneously to prompt the operator to pay close attention. A Level 2 warning is issued when the comprehensive risk index is not less than the second risk index and less than the third risk index, and the comprehensive deviation index is not less than the second deviation index and less than the third deviation index. At this time, equipment parameter adjustment suggestions can be output (these suggestions are generated by a deep learning model trained with historical operating data and corresponding adjustment parameters). A Level 3 warning is issued when the comprehensive risk index is not less than the third risk index or the comprehensive deviation index is not less than the third deviation index. At this time, the rake head drive power can be cut off immediately, and the rake arm emergency lifting program can be started to avoid equipment damage or risk expansion.

[0113] In this embodiment, a clear and quantifiable hierarchical early warning system was constructed through the above steps. By employing tiered risk identification and differentiated handling, an optimal balance between safety and efficiency was achieved. Simultaneously, multi-dimensional comprehensive criteria effectively avoided misjudgments based on a single dimension, significantly improving the accuracy of early warnings. Furthermore, the explicit judgment rules lowered the operational threshold, providing strong support for standardized operation and maintenance.

[0114] Optionally, after issuing an early warning based on the comprehensive risk index and comprehensive deviation index, the equipment adjustment parameters of the dredger can be determined based on the warning results. Specifically, if the warning result is normal, no equipment parameter adjustment is required, and operation can continue according to the current parameters; if the warning result is abnormal, real-time operating data, target coupled load, target scraper head depth, and target scraper arm angle are input into the adjustment model, and the optimization objective of the adjustment model is... ;in, This refers to the rake arm angle deviation (the difference between the current rake arm angle and the target rake arm angle). This represents the rake head depth deviation (the difference between the current rake head depth and the target rake head depth). This represents the coupling load deviation (the difference between the current coupling load and the target coupling load). =0.4, =0.3, =0.3. Then, a genetic algorithm is used to solve the optimization objective, obtaining the equipment adjustment parameters such as the rake arm angle and rake head depth that minimize J. Wherein, the coupled load = the average external load force at each monitoring point + the wave force of the dredger, and the formula for calculating the wave force (F4) of the dredger is: Where ρ is the density of seawater, is the drag force coefficient; Y is the projected area of ​​the rake arm perpendicular to the wave propagation direction; The instantaneous velocity of the wave particle at the current moment; V represents the inertial force coefficient; V is the volume of the rake arm subjected to wave action. η represents the wave particle acceleration; η is the wave nonlinearity correction coefficient, η = 1 + 0.3 × wave height / wavelength. The target coupling load, target rake head depth, and target rake arm angle are all pre-set target values ​​based on actual conditions or requirements.

[0115] Optionally, in order to facilitate users to intuitively grasp the equipment operating status, trace the root cause of abnormalities and optimize the operation strategy, after each acquisition of real-time operating data of each monitoring point of the dredger, the real-time data can be verified for reliability first, and then an integrated digital twin of "ship-equipment-environment" can be constructed based on the verified data to realize the visualization monitoring and dynamic optimization of the entire operation process. Specifically: (1) Data reliability verification: Calculate the reliability index of the real-time data. The formula for calculating the index is: Reliability index = 0.6 × signal-to-noise ratio + 0.4 × multi-source data matching degree; the twin construction process is only started when the reliability index is greater than the preset threshold (such as 0.7), so as to ensure the reliability of the input data and avoid invalid modeling. Among them, the signal-to-noise ratio and the multi-source data matching degree can be determined by existing technologies, which will not be elaborated here. For example, the signal-to-noise ratio can be calculated by using the frequency domain estimation method or the statistical model method, and the multi-source data matching degree can be calculated by using the Pearson correlation coefficient or the cross-correlation coefficient. (2) Twin modeling and synchronization: Based on the Unity3D skeleton system, the ship hull (such as main dimensions and cabin layout), rake tools (such as upper rake tube, lower rake tube, universal joint, rake head) and operating environment (such as water depth, terrain, waves) can be accurately modeled according to the actual size and structural parameters of the real ship and equipment. At the same time, the physical entity and the twin's state are synchronized in real time through a lightweight message transmission protocol. The synchronization frequency is set to 20 Hz to ensure that the deviation of key data such as attitude and load of the twin from the physical entity is ≤1%, thus ensuring the consistency between the twin and the real ship.

[0116] In addition, multi-dimensional simulation scenarios can be preset in this digital twin, such as covering typical wave conditions with wave height of 0.5-3 meters and wave period of 3-15 seconds. Different soil types such as silt, clay and sand can be matched to verify the accuracy of the early warning judgment logic and the adaptability of equipment control parameters (such as wave compensator parameters, rake arm attitude adjustment parameters, etc.) through simulation, identify potential adaptation risks in advance, and significantly reduce the time cost and equipment wear and tear of actual ship testing.

[0117] The dredging vessel operation early warning method provided in this invention first calculates the external load force at each monitoring point based on real-time operating data of the dredging vessel at each monitoring point. This allows for timely detection of abnormal loads, providing a data foundation for subsequent calculation of abnormal energy increments at the monitoring points. Next, the abnormal energy increment at each monitoring point is calculated based on the external load force, transforming the abnormal force at the monitoring point into a quantifiable energy accumulation index, providing data support for subsequent early warning. Then, the product of the abnormal energy increment at each monitoring point and the distance from the corresponding monitoring point to a preset core component is calculated to obtain the first energy parameter for each monitoring point. This quantifies the location weight and integrates it into the risk assessment, thereby improving the accuracy of the subsequent comprehensive risk index calculation. The ratio of the first energy parameter at each monitoring point to a preset response time is calculated to obtain the second energy parameter for each monitoring point. This adds a time weight to the first energy parameter, upgrading the risk assessment from spatial impact to spatiotemporal coordinated risk intensity, further improving the reliability and accuracy of the obtained comprehensive risk index. Finally, the second energy parameters of each monitoring point are summed to obtain the comprehensive risk index of the dredging vessel, improving the comprehensiveness and accuracy of the obtained comprehensive risk index, and thus improving the accuracy of subsequent early warnings. Subsequently, the mean value of the baseline operating condition data within a preset period for each monitoring point is calculated to obtain the target baseline data for each monitoring point. This effectively eliminates data fluctuation interference and anchors the stable baseline of normal operating conditions, thus laying a reliable data foundation for the subsequent calculation of the comprehensive deviation index. The difference between the real-time operating condition data of each monitoring point and the target baseline data of the corresponding monitoring point is calculated to obtain the operating condition deviation value of each monitoring point, quantifying the degree of operating condition deviation of a single monitoring point and providing a data basis for the subsequent calculation of the total deviation sum of squares. The squares of the operating condition deviation values ​​of each monitoring point are summed to obtain the total deviation sum of squares, integrating global anomaly information. Based on the deviation weights and the total deviation sum of squares, the comprehensive deviation index of the dredger is determined, improving the accuracy of the determined comprehensive deviation index. Finally, early warning is issued based on the comprehensive risk index and the comprehensive deviation index, realizing dual-dimensional verification of the comprehensive risk index and the comprehensive deviation index. This not only significantly improves the accuracy of early warning and effectively reduces the false alarm and false negative rates, but also effectively ensures the operational safety and maintenance economy of the dredger, ultimately achieving long-term control over equipment operation risks. Therefore, the technical solution of the present invention solves the problem that in the prior art, relying on personal experience or based on only a single indicator for early warning makes it difficult to form objective and quantitative early warning standards, and the early warning results are inaccurate and untimely, thus failing to detect potential risks in advance.

[0118] Figure 3 This is a schematic diagram of the structure of a dredger operation early warning device provided in an embodiment of the present invention. This device belongs to the same inventive concept as the dredger operation early warning method in the above embodiments. Details not described in detail in the embodiments of the dredger operation early warning device can be referred to the embodiments of the dredger operation early warning method described above. Figure 3 As shown, the device includes:

[0119] like Figure 3 As shown, the device includes:

[0120] The load calculation module 310 is used to calculate the external load force at the corresponding monitoring point based on the real-time operating data of the dredger at each monitoring point.

[0121] The energy calculation module 320 is used to calculate the abnormal energy increment of the corresponding monitoring point based on the external load force of each monitoring point.

[0122] The risk index calculation module 330 is used to perform spatiotemporal weighted processing on the abnormal energy increments at each monitoring point to obtain the comprehensive risk index of the dredger.

[0123] The deviation index calculation module 340 is used to perform deviation fusion processing on the real-time operating data and baseline operating data of each monitoring point to obtain the comprehensive deviation index of the dredger.

[0124] The early warning module 350 is used to issue early warnings based on the comprehensive risk index and the comprehensive deviation index.

[0125] Based on the above embodiments, the real-time operating data includes lateral deformation and deformation rate. The load calculation module 310 is specifically used for:

[0126] For the current monitoring point, determine whether the current monitoring point is a flow-induced force monitoring point; if the current monitoring point is a flow-induced force monitoring point, determine the elastic deformation flow-induced force of the current monitoring point based on the product of the stiffness coefficient and the lateral deformation of the current monitoring point; determine the damped energy-dissipating flow-induced force of the current monitoring point based on the product of the damping coefficient and the deformation rate of the current monitoring point; calculate the sum of the elastic deformation flow-induced force and the damped energy-dissipating flow-induced force of the current monitoring point to obtain the external load force of the current monitoring point.

[0127] Based on the above embodiments, the real-time working condition data also includes the average contact stress of the harrow head and the soil type, and the device further includes:

[0128] The contact force monitoring point external load module is used to determine whether the current monitoring point is a flow-induced force monitoring point. If the current monitoring point is a contact force monitoring point, it queries the soil type and soil correction coefficient correspondence table based on the soil type of the current monitoring point to obtain the soil correction coefficient of the current monitoring point; and calculates the product of the average contact stress of the rake head, the soil correction coefficient and the contact area of ​​the rake head of the current monitoring point to obtain the external load force of the current monitoring point.

[0129] Based on the above embodiments, the energy calculation module 320 is specifically used for:

[0130] For the current monitoring point, the baseline energy of the current monitoring point is calculated based on the baseline working condition data within the preset period of the current monitoring point; if the current monitoring point is a flow-induced force monitoring point, the real-time energy of the current monitoring point is calculated based on the external load force, stiffness coefficient and real-time wave circular frequency of the current monitoring point; if the current monitoring point is a contact force monitoring point, the real-time energy of the current monitoring point is obtained by integrating the external load force of the current monitoring point based on the real-time movement speed of the rake head; the difference between the real-time energy of the current monitoring point and the baseline energy is calculated to obtain the abnormal energy increment of the current monitoring point.

[0131] Based on the above embodiments, the risk index calculation module 330 is specifically used for:

[0132] The first energy parameter of each monitoring point is obtained by multiplying the abnormal energy increment of each monitoring point by the distance from the corresponding monitoring point to the preset core component. The second energy parameter of each monitoring point is obtained by calculating the ratio of the first energy parameter of each monitoring point to the preset response time. The comprehensive risk index of the dredger is obtained by summing the second energy parameters of each monitoring point.

[0133] Based on the above embodiments, the deviation index calculation module 340 is specifically used for:

[0134] Calculate the mean value of the baseline operating condition data of each monitoring point within a preset period to obtain the target baseline data of each monitoring point; calculate the difference between the real-time operating condition data of each monitoring point and the target baseline data of the corresponding monitoring point to obtain the operating condition deviation value of each monitoring point; sum the squares of the operating condition deviation values ​​of each monitoring point to obtain the total deviation sum of squares; determine the comprehensive deviation index of the dredger based on the deviation weight and the total deviation sum of squares.

[0135] Based on the above embodiments, the early warning module 350 is specifically used for:

[0136] If the comprehensive risk index is not less than the first risk index and less than the second risk index, and the comprehensive deviation index is not less than the first deviation index and less than the second deviation index, a Level 1 warning is issued; if the comprehensive risk index is not less than the second risk index and less than the third risk index, and the comprehensive deviation index is not less than the second deviation index and less than the third deviation index, a Level 2 warning is issued; if the comprehensive risk index is not less than the third risk index or the comprehensive deviation index is not less than the third deviation index, a Level 3 warning is issued.

[0137] The dredging vessel operation early warning device provided in the embodiments of the present invention can execute the dredging vessel operation early warning method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0138] It is worth noting that in the embodiments of the above-mentioned dredging vessel operation early warning device, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0139] Figure 4 This is a schematic diagram of the structure of a dredger provided in an embodiment of the present invention. Figure 4 A block diagram of an exemplary dredger 4 suitable for implementing embodiments of the present invention is shown. Figure 4 The dredger 4 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0140] like Figure 4 As shown, the dredger 4 is represented in the form of a general-purpose computing electronic device. The components of the dredger 4 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and a bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0141] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0142] Dredger 4 typically includes a variety of computer-readable media. These media can be any available media that can be accessed by dredger 4, including volatile and non-volatile media, removable and non-removable media.

[0143] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Dredger 4 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 4 Not shown; usually referred to as a "hard drive"). Although Figure 4Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0144] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.

[0145] The dredger 4 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with the dredger 4, and / or with any device that enables the dredger 4 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through the input / output (I / O) interface 22. Furthermore, the dredger 4 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via the network adapter 20. Figure 4 As shown, network adapter 20 communicates with other modules of the dredger 4 via bus 18. It should be understood that, although... Figure 4 As not shown in the diagram, other hardware and / or software modules can be used in conjunction with the dredger 4, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0146] Processing unit 16 executes various functional applications and page displays by running programs stored in system memory 28, such as implementing the dredging vessel operation early warning method provided in this embodiment of the invention, the method including:

[0147] The external load force at each monitoring point is calculated based on the real-time operating data of the dredger at each monitoring point; the abnormal energy increment at each monitoring point is calculated based on the external load force at each monitoring point; the abnormal energy increment at each monitoring point is subjected to spatiotemporal weighting to obtain the comprehensive risk index of the dredger; the real-time operating data and the baseline operating data at each monitoring point are subjected to deviation fusion processing to obtain the comprehensive deviation index of the dredger; and an early warning is issued based on the comprehensive risk index and the comprehensive deviation index.

[0148] Of course, those skilled in the art will understand that the processor can also implement the technical solution of the dredging vessel operation early warning method provided in any embodiment of the present invention.

[0149] This invention provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the program implements, for example, the dredging vessel operation early warning method provided in this invention embodiment. The method includes:

[0150] The external load force at each monitoring point is calculated based on the real-time operating data of the dredger at each monitoring point; the abnormal energy increment at each monitoring point is calculated based on the external load force at each monitoring point; the abnormal energy increment at each monitoring point is subjected to spatiotemporal weighting to obtain the comprehensive risk index of the dredger; the real-time operating data and the baseline operating data at each monitoring point are subjected to deviation fusion processing to obtain the comprehensive deviation index of the dredger; and an early warning is issued based on the comprehensive risk index and the comprehensive deviation index.

[0151] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0152] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0153] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0154] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0155] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computing device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0156] Furthermore, the acquisition, storage, use, and processing of data in the technical solution of this invention all comply with relevant laws and regulations.

[0157] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for early warning of dredging operations, characterized in that, The method includes: Calculate the external load force at each monitoring point based on the real-time operating data of the dredger at each monitoring point; Calculate the abnormal energy increment at each monitoring point based on the external load force at each monitoring point; The abnormal energy increments at each monitoring point are subjected to spatiotemporal weighting to obtain the comprehensive risk index of the dredger; The real-time operating data and baseline operating data of each monitoring point are fused to obtain the comprehensive deviation index of the dredger. Early warnings are issued based on the comprehensive risk index and the comprehensive deviation index; The real-time operating data includes lateral deformation and deformation rate. Based on the real-time operating data of the dredger at each monitoring point, the external load force at the corresponding monitoring point is calculated, including: For the current monitoring point, determine whether the current monitoring point is a flow-induced force monitoring point; if the current monitoring point is a flow-induced force monitoring point, determine the elastic deformation flow-induced force of the current monitoring point based on the product of the stiffness coefficient and the lateral deformation of the current monitoring point; determine the damped energy-dissipating flow-induced force of the current monitoring point based on the product of the damping coefficient and the deformation rate of the current monitoring point; calculate the sum of the elastic deformation flow-induced force and the damped energy-dissipating flow-induced force of the current monitoring point to obtain the external load force of the current monitoring point.

2. The dredging vessel operation early warning method according to claim 1, characterized in that, The real-time operating data also includes the average contact stress of the rake head and the soil type. After determining whether the current monitoring point is a flow-induced stress monitoring point, it also includes: If the current monitoring point is a contact force monitoring point, then the soil correction coefficient of the current monitoring point is obtained by querying the correspondence table between soil type and soil correction coefficient based on the soil type of the current monitoring point. The external load force at the current monitoring point is obtained by multiplying the average contact stress of the rake head, the soil correction coefficient, and the contact area of ​​the rake head.

3. The dredging vessel operation early warning method according to claim 1, characterized in that, The abnormal energy increment at each monitoring point is calculated based on the external load force at each monitoring point, including: For the current monitoring point, the baseline energy of the current monitoring point is calculated based on the baseline operating condition data within the preset period of the current monitoring point. If the current monitoring point is a flow-induced force monitoring point, the real-time energy of the current monitoring point is calculated based on the external load force, stiffness coefficient, and real-time wave circular frequency of the current monitoring point; if the current monitoring point is a contact force monitoring point, the real-time energy of the current monitoring point is obtained by integrating the external load force of the current monitoring point based on the real-time movement speed of the rake head. Calculate the difference between the real-time energy and the baseline energy at the current monitoring point to obtain the abnormal energy increment at the current monitoring point.

4. The dredging vessel operation early warning method according to claim 1, characterized in that, The spatiotemporal weighted processing of abnormal energy increments at each monitoring point yields a comprehensive risk index for dredgers, including: The first energy parameter of each monitoring point is obtained by multiplying the abnormal energy increment of each monitoring point by the distance from the corresponding monitoring point to the preset core component. Calculate the ratio of the first energy parameter of each monitoring point to the preset response time to obtain the second energy parameter of each monitoring point; The comprehensive risk index of the dredger is obtained by summing the second energy parameters at each monitoring point.

5. The dredging vessel operation early warning method according to claim 1, characterized in that, The real-time operating data and baseline operating data from each monitoring point are fused to obtain the comprehensive deviation index of the dredger, including: Calculate the mean value of the baseline working condition data of each monitoring point within a preset period to obtain the target baseline data of each monitoring point; Calculate the difference between the real-time operating data of each monitoring point and the target benchmark data of the corresponding monitoring point to obtain the operating deviation value of each monitoring point; The sum of the squares of the operating condition deviations at each monitoring point is obtained to obtain the total sum of squares of deviations. The comprehensive deviation index of the dredger is determined based on the deviation weights and the sum of squares of the total deviations.

6. The dredging vessel operation early warning method according to claim 1, characterized in that, Early warnings are issued based on the comprehensive risk index and the comprehensive deviation index, including: If the comprehensive risk index is not less than the first risk index and less than the second risk index, and the comprehensive deviation index is not less than the first deviation index and less than the second deviation index, it is determined to be a Level 1 warning. If the comprehensive risk index is not less than the second risk index and less than the third risk index, and the comprehensive deviation index is not less than the second deviation index and less than the third deviation index, it is determined to be a level two warning. If the comprehensive risk index is not less than the third risk index or the comprehensive deviation index is not less than the third deviation index, it is determined to be a level three warning.

7. An early warning device for dredging operations, characterized in that, The device includes: The load calculation module is used to calculate the external load force at the corresponding monitoring point based on the real-time operating data of the dredger at each monitoring point. The energy calculation module is used to calculate the abnormal energy increment of each monitoring point based on the external load force at each monitoring point. The risk index calculation module is used to perform spatiotemporal weighted processing on the abnormal energy increments at each monitoring point to obtain the comprehensive risk index of the dredger. The deviation index calculation module is used to perform deviation fusion processing on the real-time operating condition data and the baseline operating condition data of each monitoring point to obtain the comprehensive deviation index of the dredger. The early warning module is used to issue early warnings based on the comprehensive risk index and the comprehensive deviation index; The real-time operating data includes lateral deformation and deformation rate. The load calculation module is specifically used for: for the current monitoring point, determining whether the current monitoring point is a flow-induced force monitoring point; if the current monitoring point is a flow-induced force monitoring point, determining the elastic deformation flow-induced force of the current monitoring point based on the product of the stiffness coefficient and the lateral deformation; determining the damped energy-dissipating flow-induced force of the current monitoring point based on the product of the damping coefficient and the deformation rate; and calculating the sum of the elastic deformation flow-induced force and the damped energy-dissipating flow-induced force of the current monitoring point to obtain the external load force of the current monitoring point.

8. A dredger, characterized in that, The dredger includes: At least one processor; and a memory communicatively connected to said at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the dredging vessel operation early warning method according to any one of claims 1-6.

9. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the operation early warning method for a dredger as described in any one of claims 1-6.

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