An intelligent monitoring method and system for seabed geological environment

Abstract

The application relates to an intelligent seabed geological environment monitoring method and system, and relates to the field of marine surveying technology.The method comprises the following steps: acquiring a propeller rotating speed, seawater density and multi-source sensing features; inputting the multi-source sensing features into a preset geological classification neural network model to output a seabed geological type, and determining a median particle size corresponding to the seabed geological type according to a preset type matching relationship; calculating the critical starting shear stress according to the median particle size and the seawater density; calculating the dynamic safety height according to the critical starting shear stress, the propeller rotating speed and the seawater density, and determining the feasible monitoring range according to the dynamic safety height and a preset reasonable monitoring range; determining the actual monitoring height in the feasible monitoring range, and controlling the underwater robot to move to the actual monitoring height to work.The application has the function of improving the seabed geological environment monitoring effect.

Description

Technical Field

[0001] This application relates to the field of marine exploration technology, and in particular to an intelligent monitoring method and system for the seabed geological environment. Background Technology

[0002] Currently, sophisticated monitoring of the seabed geological environment typically relies on underwater robotic platforms equipped with various acoustic, optical, and physical sensors. In actual operations, to ensure the validity and resolution of monitoring data, the underwater robot needs to maintain its navigation at a certain height above the seabed. The existing conventional practice is for operators to manually set a fixed height threshold above the seabed before operations, based on historical nautical chart data or estimated seabed flatness. After the underwater robot is launched, its automatic control system uses this threshold as the sole reference, adjusting buoyancy or thruster thrust to maintain the robot at a constant height for cruising and data collection.

[0003] The vast seabed region has complex and varied geological features, with significant differences in seabed sediment types in different operating areas, ranging from soft silty mud and clay to dense sand, gravel, and even bedrock. The dynamic factors of underwater robots during navigation, such as the jet disturbance generated by their propulsion and the flow field of their appendages, are closely related to the characteristics of the seabed sediment and its ability to disturb seabed sediments.

[0004] In areas with soft sediments, even at the usual set altitude, the minute turbulence generated by the thrusters can easily induce sediment resuspension, forming "mud clouds" that severely obstruct the viewing windows of acoustic and optical sensors, leading to a sharp drop in the signal-to-noise ratio. Conversely, above hard or rough seabeds, if the fixed altitude is set too high, the effective detection range of the sensors is insufficient, resulting in a loss of resolution for high-precision topographic and shallow seismic profile data, thus failing to achieve optimal monitoring results. Therefore, current monitoring technologies result in relatively poor monitoring of the seabed geological environment and have room for improvement. Summary of the Invention

[0005] To improve the effectiveness of seabed geological environment monitoring, this application provides an intelligent monitoring method and system for seabed geological environment.

[0006] Firstly, this application provides an intelligent monitoring method for the seabed geological environment, employing the following technical solution: A method for intelligent monitoring of the seabed geological environment includes: Acquire thruster rotation speed, seawater density, and multi-source sensing features; Multi-source sensing features are input into a pre-defined geological classification neural network model to output seabed geological types, and the median particle size corresponding to the seabed geological types is determined according to the pre-defined type matching relationship. The critical starting shear stress is determined by calculation based on the median particle size and seawater density. The dynamic safety height is determined by calculation based on the critical starting shear stress, thruster speed, and seawater density, and the feasible monitoring range is determined based on the dynamic safety height and the preset reasonable monitoring range. Determine the actual monitoring height within the feasible monitoring range, and control the underwater robot to move to the actual monitoring height to carry out operations.

[0007] Optionally, after the dynamic safety height is determined, the intelligent monitoring method for the seabed geological environment also includes: Determine whether the dynamic safety height exceeds the upper limit of the reasonable monitoring range; If the dynamic safety height is not greater than the upper limit of the reasonable monitoring range, then the feasible monitoring range shall be determined based on the dynamic safety height and the reasonable monitoring range. If the dynamic safety height is greater than the upper limit of the reasonable monitoring range, the recommended speed range is determined based on the reasonable monitoring range, and the recommended speed range is output to the preset management terminal.

[0008] Optionally, the steps for determining the actual monitoring height within the feasible monitoring range include: Obtain the type confidence of seabed geological types; Determine whether the type confidence level is greater than the preset baseline confidence level; If the type confidence level is not greater than the baseline confidence level, then output a confidence missing signal; If the type confidence level is greater than the baseline confidence level, the current cruise altitude is obtained, and the required altitude adjustment is determined based on the current cruise altitude and the values ​​within the feasible monitoring range. The value within the feasible monitoring range corresponding to the minimum required adjustment height is determined as the actual monitoring height.

[0009] Optionally, after the output of a confidence-deficient signal, the intelligent monitoring method for the seabed geological environment also includes: Get the matching confidence scores for other types; The difference is calculated based on the matching confidence and the type confidence to determine the deviation confidence, and the types with a deviation confidence less than the preset close confidence are defined as candidate existence types; The candidate monitoring range is determined by calculation and analysis based on the candidate monitoring range and the feasible monitoring range, and the actual monitoring height is determined based on the intersection monitoring range.

[0010] Optionally, the steps for determining the actual monitoring height based on the intersection monitoring range include: Construct a historical monitoring window with a width equal to the preset historical monitoring duration, and determine historical similarities within the historical monitoring window based on the current candidate existence types and seabed geological types; Obtain the type of subsequent changes and the duration of subsequent changes based on historical similarities; The type prediction value is determined by calculating based on the time interval between each subsequent change type under a single subsequent change type; Determine whether there are subsequent change types whose predicted type value is greater than the preset baseline predicted value; If there is no subsequent change type whose type forecast value is greater than the baseline forecast value, then determine the demand adjustment height and determine the actual monitoring height based on the demand adjustment height within the intersection monitoring range; If there is a subsequent change type whose predicted value is greater than the baseline predicted value, the subsequent monitoring range is determined based on the subsequent change type, and the precise monitoring range is determined based on the subsequent monitoring range and the intersection monitoring range. The actual monitoring height is then determined within the precise monitoring range.

[0011] Optionally, the step of determining historical similarities based on current candidate types and seabed geological types within a historical monitoring window includes: The main deviation is determined by calculating the difference based on the confidence level of the seabed geological type. The minor deviation is determined by calculating the difference based on the type confidence of the candidate existing types; The primary similarity value is determined by calculating based on the preset primary weight parameters and primary deviation, and the secondary similarity value is determined by calculating based on the preset secondary weight parameters and secondary deviation. The comparison similarity value is determined by calculating the primary similarity value and all secondary similarity values, and the time point when the comparison similarity value is greater than the preset required similarity value is defined as the historical similarity point.

[0012] Optionally, after determining historical similarities, intelligent monitoring methods for the seabed geological environment may also include: The number of historical similarities is determined by counting historical similarities. Determine whether the number of historical similarities exceeds the preset number of similarity requirements; If the number of historical similarities is not greater than the number of similar demands, then the currently identified historical similarities will be retained. If the number of historical similarities is greater than the number of similar demands, then the historical similarities of the similar demands are determined by comparing similarity values ​​from largest to smallest, and the remaining historical similarities are removed.

[0013] Secondly, this application provides an intelligent monitoring system for the seabed geological environment, which adopts the following technical solution: An intelligent monitoring system for the seabed geological environment includes: The acquisition module is used to acquire thruster rotation speed, seawater density, and multi-source sensing features; The processing module, connected to the acquisition module, is used for information storage and processing; The processing module inputs multi-source sensing features into a preset geological classification neural network model to output seabed geological types, and determines the median particle size corresponding to the seabed geological types based on preset type matching relationships; The processing module calculates and determines the critical initiation shear stress based on the median particle size and seawater density; The processing module calculates the dynamic safety height based on the critical starting shear stress, thruster speed, and seawater density, and determines the feasible monitoring range based on the dynamic safety height and the preset reasonable monitoring range. The processing module determines the actual monitoring height within the feasible monitoring range and controls the underwater robot to move to the actual monitoring height to perform operations.

[0014] In summary, this application includes at least one of the following beneficial technical effects: During the process of underwater robots monitoring the seabed geological environment, the seabed geological types can be analyzed to determine a suitable operating height that does not disturb sediments, so as to control the underwater robot to complete the monitoring operation better and improve the monitoring effect of the seabed geological environment. In cases where the underwater robot's propulsion speed is inappropriate, a suitable range can be generated for management personnel to refer to, facilitating timely intervention by staff. Attached Figure Description

[0015] Figure 1 This is a flowchart of an intelligent monitoring method for the seabed geological environment.

[0016] Figure 2 This is a flowchart of the modules for intelligent monitoring methods of the seabed geological environment. Detailed Implementation

[0017] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figure 1-2 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0018] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0019] This application discloses an intelligent monitoring method for the seabed geological environment, referring to... Figure 1 The method and process of intelligent monitoring of the seabed geological environment includes the following steps: Step S100: Acquire thruster rotation speed, seawater density, and multi-source sensing features.

[0020] The thruster rotation speed is the real-time rotation speed of the underwater robot's thruster, which can be directly read and calculated from the motor pulse frequency by the motor Hall sensor or encoder of the robot's electric drive system. This data is refreshed at a millisecond frequency via CAN bus or RS-485 bus. The seawater density is the real-time seawater density of the current seabed, which is measured in real time by the onboard CTD profiler at the current water depth. The seawater density of the current layer is calculated online by combining the international seawater state equation. Multi-source sensing features include seabed backscatter intensity data and visual features of seabed surface texture and particle size. The seabed backscatter intensity data is acquired by the multi-beam acoustic subsystem and used to construct the seabed acoustic hardness map. The visual features of seabed surface texture and particle size are acquired by the optical imaging subsystem. The optical imaging subsystem acquires visual images of the seabed surface and then extracts the gray-level co-occurrence matrix from the images, thereby calculating the roughness and texture features of the seabed particles.

[0021] Step S101: Input the multi-source sensing features into the preset geological classification neural network model to output the seabed geological type, and determine the median particle size corresponding to the seabed geological type according to the preset type matching relationship.

[0022] The geological classification neural network model is a pre-trained model for distinguishing seabed geological types based on multi-source sensing features. It can adopt a lightweight one-dimensional convolutional neural network structure, including an input layer, two convolutional layers with kernel sizes of 5 and 3 respectively, and channel numbers of 32 and 64 respectively, a max-pooling layer, a fully connected layer, and a softmax output layer. This network is first trained to convergence using a large amount of field-sampled calibration data. When multi-source sensing features are input into the geological classification neural network model, it will output the probability distribution of each geological type. The geological type with the highest probability is the seabed geological type of the current area. The geological types include at least extremely soft mud, soft mud, sandy mud, muddy sand, sand, gravelly, and bedrock. The median grain size is the median grain size of the sediment. Different seabed geological types correspond to different sediment conditions, and therefore different median grain sizes. The type matching relationship between the two is pre-constructed by the staff, or the median grain size can be determined by inversion calculation.

[0023] Step S102: Calculate the critical starting shear stress based on the median particle size and seawater density.

[0024] The critical initiation shear stress is the force that is considered to cause disturbance to the current geological sediments. The calculation formula is as follows: ,in This is the critical starting shear stress. The density of seawater, The acceleration due to gravity is taken as 9.8 m / s². 2 , The median particle size, The sediment density can be obtained through inversion calculation using the backscattering intensity from the seabed as described above, but generally, this value is taken as 2650 kg / m³ based on experience. 3 .

[0025] Step S103: Calculate the dynamic safety height based on the critical starting shear stress, thruster speed and seawater density, and determine the feasible monitoring range based on the dynamic safety height and the preset reasonable monitoring range.

[0026] Dynamic safety height refers to the minimum distance between the underwater robot and the seabed that must be maintained to avoid disturbing sediment parameters during underwater robot operations. The calculation formula is as follows: In the formula, For dynamic safety height, This refers to the diameter of the underwater robot's thruster, which can be directly obtained based on the underwater robot's model. For the thruster rotation speed, Preset baseline values ​​for staff to prevent the influence of errors. and These are two empirical coefficients, determined in advance by the staff; The reasonable monitoring range is the distance range that the current underwater robot needs to maintain with the bottom surface to perform the monitoring task well. The dynamic safety height is the lower limit and the upper limit of the reasonable monitoring range is the upper limit. The feasible monitoring range that meets the operation requirements can be constructed. That is, the height of the underwater robot only needs to be within the feasible monitoring range to meet the operation requirements without disturbing the sediment.

[0027] Step S104: Determine the actual monitoring height within the feasible monitoring range, and control the underwater robot to move to the actual monitoring height to perform operations.

[0028] The actual monitoring height is a value within the feasible monitoring range. It can be randomly selected or determined by the method in steps S300-S5032. At this time, controlling the underwater robot to move to the actual monitoring height will not disturb the sediment, so that the underwater robot can better monitor the seabed geological environment.

[0029] Once the dynamic safety height is determined, intelligent monitoring methods for the seabed geological environment also include: Step S200: Determine whether the dynamic safety height is greater than the upper limit of the reasonable monitoring range.

[0030] The purpose of the assessment is to determine whether the currently determined dynamic safety height meets the requirements of the mission.

[0031] Step S2001: If the dynamic safety height is not greater than the upper limit of the reasonable monitoring range, then determine the feasible monitoring range based on the dynamic safety height and the reasonable monitoring range.

[0032] When the dynamic safety height is not greater than the upper limit of the reasonable monitoring range, it means that the current dynamic operating height meets the task requirements, and the feasible monitoring range can be determined normally.

[0033] Step S2002: If the dynamic safety height is greater than the upper limit of the reasonable monitoring range, the recommended speed range is determined based on the reasonable monitoring range, and the recommended speed range is output to the preset management terminal.

[0034] When the dynamic safety height exceeds the upper limit of the reasonable monitoring range, it indicates that the current operation will definitely disturb the sediment, and therefore the thruster speed needs to be adjusted. The recommended speed range is the feasible speed range of the thruster that meets the requirements of not disturbing the sediment and conforms to the mission requirements of the underwater robot. It is obtained by reverse calculation using the aforementioned calculation formula. The recommended speed range is then output to the management terminal so that the management personnel can deal with the situation in a timely manner and provide a reference for the speed adjustment that the management personnel need to make.

[0035] The steps for determining the actual monitoring height within the feasible monitoring range include: Step S300: Obtain the type confidence of the seabed geological type.

[0036] Type confidence is the confidence value for identifying seabed geological types, where the probability of confirming a type is the corresponding confidence value.

[0037] Step S301: Determine whether the type confidence level is greater than the preset baseline confidence level.

[0038] The baseline confidence level is the minimum type confidence level that needs to be achieved when the identification type set by the staff is relatively reliable. The purpose of the judgment is to determine whether the reliability of the currently identified seabed geological type is relatively high.

[0039] Step S3011: If the type confidence is not greater than the baseline confidence, output a confidence missing signal.

[0040] When the type confidence level is not greater than the baseline confidence level, it indicates that the data reliability is not high. Therefore, a confidence missing signal is output to identify and distinguish this situation, which will facilitate further analysis.

[0041] Step S3012: If the type confidence level is greater than the baseline confidence level, obtain the current cruise altitude and determine the required altitude adjustment based on the current cruise altitude and the values ​​within the feasible monitoring range.

[0042] When the type confidence level is greater than the baseline confidence level, it indicates that the data is highly reliable. At this time, the height can be adjusted according to the determined geological type. The current cruising height is the current operating position height of the underwater robot, and the required adjustment height is the height change value required for the underwater robot to adjust its height from the current height to the height within the feasible monitoring range, which is the difference between the two data.

[0043] Step S302: Determine the value within the feasible monitoring range corresponding to the minimum required adjustment height as the actual monitoring height.

[0044] The minimum required adjustment height corresponds to the height value that is most convenient for the underwater robot to complete the height adjustment, so it can be defined as the actual monitoring height.

[0045] Following the output of confidence-deficient signals, intelligent monitoring methods for the seabed geological environment also include: Step S400: Obtain the matching confidence scores for the remaining types.

[0046] Match confidence is the confidence score of other types when the seabed geological type is determined.

[0047] Step S401: Calculate the difference based on the matching confidence and type confidence to determine the deviation confidence, and define the type with a deviation confidence less than the preset similar confidence as the candidate existence type.

[0048] The deviation confidence level is the value of the type confidence level of the seabed geological type minus the matching confidence level of other types. The similarity confidence level is the maximum difference between two confidence levels that are allowed when the confidence level values ​​set by the staff are relatively close. When the deviation confidence level is less than the similarity confidence level, it indicates that the other types corresponding to the deviation confidence level may exist as the current actual seabed geological type. Therefore, it is defined as a candidate type for identification and differentiation, which is convenient for subsequent analysis.

[0049] Step S402: Calculate and analyze the alternative monitoring range based on the alternative types of existence to determine the alternative monitoring range, and determine the intersection monitoring range based on the alternative monitoring range and the feasible monitoring range, and determine the actual monitoring height based on the intersection monitoring range.

[0050] The alternative monitoring range is the range of underwater robot operating height obtained by performing steps S101-S103 based on the alternative existence type. At this time, there is an intersection range between each alternative monitoring range and the feasible monitoring range, which is called the intersection monitoring range. The actual monitoring height can be determined from the intersection monitoring range, which can meet the monitoring operation requirements even if the classification is inaccurate. The method for determining the actual monitoring height from the intersection monitoring range can be random determination or determination through the method of steps S500-S5032.

[0051] The steps for determining the actual monitoring height based on the intersection monitoring range include: Step S500: Construct a historical monitoring window with a width equal to the preset historical monitoring duration, and determine historical similarities within the historical monitoring window based on the current candidate existence types and seabed geological types.

[0052] The historical monitoring duration is a fixed duration set by the staff. The historical monitoring duration is a time period with the current time point as the end point and the width of the historical monitoring duration. By constructing a historical monitoring time window, the data of seabed monitoring within the historical monitoring duration can be acquired and analyzed. The historical similarity point is the time point within the historical monitoring time window when the geological type analysis determines that the type is the current seabed geological type and at the same time the current alternative type exists. For the specific determination method, please refer to steps S600-S603.

[0053] Step S501: Obtain the subsequent change type and subsequent interval duration from historical similarity points.

[0054] The subsequent change type is the first geological type that appears when the geological type changes during the underwater robot's mobile operation at a historical similar point, and the subsequent interval is the time interval between the historical similar point and the time point when the geological type changes.

[0055] Step S502: Under a single subsequent change type, calculate the type prediction value based on the time interval between each subsequent change.

[0056] The type prediction value is obtained by dividing the subsequent time interval by the preset calculation weight and then summing all the values.

[0057] Step S503: Determine whether there is a subsequent change type whose type prediction value is greater than the preset baseline prediction value.

[0058] The baseline prediction value is the minimum type prediction value that needs to be reached when the geological type is about to change, as set by the staff. The purpose of the judgment is to determine whether the geological type will change due to the movement of the underwater robot under the current circumstances.

[0059] Step S5031: If there is no subsequent change type whose type prediction value is greater than the baseline prediction value, then determine the demand adjustment height and determine the actual monitoring height within the intersection monitoring range based on the demand adjustment height.

[0060] When there is no subsequent change type whose predicted value is greater than the baseline predicted value, it indicates that the geological type of the current underwater robot operation area is relatively uniform, so normal analysis can be performed.

[0061] Step S5032: If there is a subsequent change type whose type prediction value is greater than the baseline prediction value, then determine the subsequent monitoring range based on the subsequent change type, and determine the precise monitoring range based on the subsequent monitoring range and the intersection monitoring range, and determine the actual monitoring height within the precise monitoring range.

[0062] When there is a subsequent change type whose predicted value is greater than the baseline predicted value, it indicates that the geological type may change due to regional changes during the current movement and operation of the underwater robot. Therefore, the subsequent monitoring range that matches the underwater robot's operating height can be determined based on the corresponding subsequent change type. At this time, the precise monitoring range can be determined by the intersection of the subsequent monitoring range and the intersection monitoring range. Determining the actual monitoring height from the precise monitoring range can avoid subsequent readjustments of the underwater robot and reduce the frequency of adjustments.

[0063] The steps for determining historical similarities within a historical monitoring window based on current candidate types and seabed geological types include: Step S600: Calculate the difference based on the confidence level of the seabed geological type to determine the main deviation.

[0064] The main deviation is the difference between the current type confidence level and the type confidence level at the time point when the same seafloor geological type was determined; this difference is an absolute value.

[0065] Step S601: Calculate the difference based on the type confidence of the candidate existing types to determine the minor deviation.

[0066] Similarly, the minor deviation value is the difference in type confidence between the candidate existence types.

[0067] Step S602: Calculate the primary similarity value based on the preset primary weight parameters and primary deviation, and calculate the secondary similarity value based on the preset secondary weight parameters and secondary deviation.

[0068] The primary and secondary weight parameters are pre-set values ​​used for calculation. The primary weight parameter is greater than the secondary weight parameter. The primary similarity value can be determined by dividing the primary weight parameter by the primary deviation. Similarly, the secondary similarity value can be determined by dividing the secondary weight parameter by the secondary deviation.

[0069] Step S603: Calculate and determine the comparison similarity value based on the primary similarity value and all secondary similarity values, and define the time point where the comparison similarity value is greater than the preset required similarity value as the historical similarity point.

[0070] The comparison similarity value is the sum of the primary similarity value and all secondary similarity values. The required similarity value is the minimum comparison similarity value that needs to be achieved when the geological type analysis results are relatively similar, as set by the staff. Therefore, historical similarity points can be determined.

[0071] Once historical similarities are identified, intelligent monitoring methods for the seabed geological environment also include: Step S700: Count the historical similarities to determine the number of historical similarities.

[0072] The number of historical similarities is the total number of historical similarities identified.

[0073] Step S701: Determine whether the number of historical similarities is greater than the preset number of similarity requirements.

[0074] The number of similar requirements is the maximum number of historical similarities allowed when performing type analysis. The purpose of this determination is to determine whether there are too many historical similarities currently identified, so as to determine whether there will be a situation where the amount of data analysis is too large.

[0075] Step S7011: If the number of historical similarities is not greater than the number of similarity requirements, then retain the currently determined historical similarities.

[0076] When the number of historical similarities is not greater than the number of similarities required, it means that there will not be a situation where the amount of data analysis is too large. In this case, the identified historical similarities can be retained for analysis.

[0077] Step S7012: If the number of historical similarities is greater than the number of similar demands, then determine the historical similarities of the similar demands based on the comparison similarity values ​​from largest to smallest, and remove the remaining historical similarities.

[0078] When the number of historical similarities exceeds the number of similar demands, it indicates that there are many historical similarities. In order to reduce the amount of data analysis, only the historical similarities with the largest number of similar demands are retained in descending order of similarity value to facilitate data analysis.

[0079] Reference Figure 2 Based on the same inventive concept, embodiments of the present invention provide an intelligent monitoring system for the seabed geological environment, comprising: The acquisition module is used to acquire thruster rotation speed, seawater density, and multi-source sensing features; The processing module, connected to the acquisition module, is used for information storage and processing; The processing module inputs multi-source sensing features into a preset geological classification neural network model to output seabed geological types, and determines the median particle size corresponding to the seabed geological types based on preset type matching relationships; The processing module calculates and determines the critical initiation shear stress based on the median particle size and seawater density; The processing module calculates the dynamic safety height based on the critical starting shear stress, thruster speed, and seawater density, and determines the feasible monitoring range based on the dynamic safety height and the preset reasonable monitoring range. The processing module determines the actual monitoring height within the feasible monitoring range and controls the underwater robot to move to the actual monitoring height to perform operations; The recommended speed range output module is used to process the output of the recommended speed range; The actual monitoring height determination module is used to determine the actual monitoring height from the feasible monitoring range; The intersection monitoring range determination module is used to determine the intersection monitoring range; The precise monitoring range determination module is used to determine the precise monitoring range; The historical similarity point determination module is used to determine historical similarities within the historical monitoring time window; The historical similarity filtering module filters historical similarities when there are a large number of such similarities.

[0080] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

Claims

1. A method for intelligent monitoring of the seabed geological environment, characterized in that, include: Acquire thruster rotation speed, seawater density, and multi-source sensing features; Multi-source sensing features are input into a pre-defined geological classification neural network model to output seabed geological types, and the median particle size corresponding to the seabed geological types is determined according to the pre-defined type matching relationship. The critical starting shear stress is determined by calculation based on the median particle size and seawater density. The dynamic safety height is determined by calculation based on the critical starting shear stress, thruster speed, and seawater density, and the feasible monitoring range is determined based on the dynamic safety height and the preset reasonable monitoring range. Determine the actual monitoring height within the feasible monitoring range, and control the underwater robot to move to the actual monitoring height to carry out operations.

2. The intelligent monitoring method for the seabed geological environment according to claim 1, characterized in that, Once the dynamic safety height is determined, intelligent monitoring methods for the seabed geological environment also include: Determine whether the dynamic safety height exceeds the upper limit of the reasonable monitoring range; If the dynamic safety height is not greater than the upper limit of the reasonable monitoring range, then the feasible monitoring range shall be determined based on the dynamic safety height and the reasonable monitoring range. If the dynamic safety height is greater than the upper limit of the reasonable monitoring range, the recommended speed range is determined based on the reasonable monitoring range, and the recommended speed range is output to the preset management terminal.

3. The intelligent monitoring method for the seabed geological environment according to claim 1, characterized in that, The steps for determining the actual monitoring height within the feasible monitoring range include: Obtain the type confidence of seabed geological types; Determine whether the type confidence level is greater than the preset baseline confidence level; If the type confidence level is not greater than the baseline confidence level, then output a confidence missing signal; If the type confidence level is greater than the baseline confidence level, the current cruise altitude is obtained, and the required altitude adjustment is determined based on the current cruise altitude and the values ​​within the feasible monitoring range. The value within the feasible monitoring range corresponding to the minimum required adjustment height is determined as the actual monitoring height.

4. The intelligent monitoring method for the seabed geological environment according to claim 3, characterized in that, Following the output of confidence-deficient signals, intelligent monitoring methods for the seabed geological environment also include: Get the matching confidence scores for other types; The difference is calculated based on the matching confidence and the type confidence to determine the deviation confidence, and the types with a deviation confidence less than the preset close confidence are defined as candidate existence types; The candidate monitoring range is determined by calculation and analysis based on the candidate monitoring range and the feasible monitoring range, and the actual monitoring height is determined based on the intersection monitoring range.

5. The intelligent monitoring method for the seabed geological environment according to claim 4, characterized in that, The steps for determining the actual monitoring height based on the intersection monitoring range include: Construct a historical monitoring window with a width equal to the preset historical monitoring duration, and determine historical similarities within the historical monitoring window based on the current candidate existence types and seabed geological types; Obtain the type of subsequent changes and the duration of subsequent changes based on historical similarities; The type prediction value is determined by calculating based on the time interval between each subsequent change type under a single subsequent change type; Determine whether there are subsequent change types whose predicted type value is greater than the preset baseline predicted value; If there is no subsequent change type whose type forecast value is greater than the baseline forecast value, then determine the demand adjustment height and determine the actual monitoring height based on the demand adjustment height within the intersection monitoring range; If there is a subsequent change type whose predicted value is greater than the baseline predicted value, the subsequent monitoring range is determined based on the subsequent change type, and the precise monitoring range is determined based on the subsequent monitoring range and the intersection monitoring range. The actual monitoring height is then determined within the precise monitoring range.

6. The intelligent monitoring method for the seabed geological environment according to claim 5, characterized in that, The steps for determining historical similarities based on current candidate types and seabed geological types within a historical monitoring window include: The main deviation is determined by calculating the difference based on the confidence level of the seabed geological type. The minor deviation is determined by calculating the difference based on the type confidence of the candidate existing types; The primary similarity value is determined by calculating based on the preset primary weight parameters and primary deviation, and the secondary similarity value is determined by calculating based on the preset secondary weight parameters and secondary deviation. The comparison similarity value is determined by calculating the primary similarity value and all secondary similarity values, and the time point when the comparison similarity value is greater than the preset required similarity value is defined as the historical similarity point.

7. The intelligent monitoring method for the seabed geological environment according to claim 6, characterized in that, Once historical similarities are identified, intelligent monitoring methods for the seabed geological environment also include: The number of historical similarities is determined by counting historical similarities. Determine whether the number of historical similarities exceeds the preset number of similarity requirements; If the number of historical similarities is not greater than the number of similar demands, then the currently identified historical similarities will be retained. If the number of historical similarities is greater than the number of similar demands, then the historical similarities of the similar demands are determined by comparing similarity values ​​from largest to smallest, and the remaining historical similarities are removed.

8. An intelligent monitoring system for the seabed geological environment, used to implement the intelligent monitoring method for the seabed geological environment as described in any one of claims 1-7, characterized in that, include: The acquisition module is used to acquire thruster rotation speed, seawater density, and multi-source sensing features; The processing module, connected to the acquisition module, is used for information storage and processing; The processing module inputs multi-source sensing features into a preset geological classification neural network model to output seabed geological types, and determines the median particle size corresponding to the seabed geological types based on preset type matching relationships; The processing module calculates and determines the critical initiation shear stress based on the median particle size and seawater density; The processing module calculates the dynamic safety height based on the critical starting shear stress, thruster speed, and seawater density, and determines the feasible monitoring range based on the dynamic safety height and the preset reasonable monitoring range. The processing module determines the actual monitoring height within the feasible monitoring range and controls the underwater robot to move to the actual monitoring height to perform operations.

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