A method, system, device, and medium for multi-beam measurement scheme risk assessment

Abstract

The present application relates to the technical field of multi-beam measurement scheme evaluation in digital data processing, in particular to a multi-beam measurement scheme risk evaluation method, system, device and medium; the method of the present application firstly combines the ship performance parameters to finely calculate the effective detection time, the total turning time and the non-navigation auxiliary time, and obtains the full-cycle operation time; then, based on the full-cycle operation time, the oil consumption rate is calculated respectively by dividing the navigation section and the working condition, and the time integration is performed on the navigation section, and the finely calculated fuel cost is obtained after accumulation; then, the continuous days meeting the operation safety limit value are extracted from the weather forecast data to form the available window, the daily effective operation time is modeled as a truncated normal distribution random variable, the variance reduction Monte Carlo simulation is performed on each window, the completion probability estimation and the corresponding confidence interval are obtained, and finally the full-factor comprehensive economic cost is summarized to obtain the scheme evaluation result, which provides a reliability scale for voyage planning evaluation.

Description

Technical Field

[0001] This invention relates to the field of multibeam measurement scheme evaluation technology in electronic digital data processing, specifically to a method, system, device, and medium for risk assessment of multibeam measurement schemes. Background Technology

[0002] Invention patent CN118171586A discloses a method, system, equipment, and storage medium for multibeam survey line layout, and invention patent CN119783551A discloses a method, system, equipment, and storage medium for optimizing multibeam survey line layout. Both provide automatic design of multibeam survey line schemes, with the core optimization objective being "minimum total survey line length." However, in actual marine surveying engineering bidding, scheme review, and cruise planning, decision-makers need to consider far more dimensions than just the total survey line length.

[0003] First, the total length of the survey line only reflects the effective detection range and fails to fully reflect the actual operational cycle. In actual operations, survey vessels need to perform non-detection operations such as turning around between adjacent survey lines, traveling back and forth between work areas, and equipment calibration. Especially in rectangular work areas with densely distributed survey lines, the time consumed by turning around can account for a considerable proportion of the total operation time. Due to the different survey line layout methods of different schemes, the number of turns and the length of the turning paths vary, which may lead to misleading evaluation of schemes based solely on the detection range. Second, the fuel consumption rate of ships varies significantly under different speeds and maneuvering conditions, but existing scheme evaluation systems usually use the method of multiplying the range length by a fixed fuel consumption rate for rough estimation, which is difficult to reflect the distribution of fuel consumption in actual operations. Third, multibeam bathymetry operations at sea usually have specific requirements for sea state. When the expected operation time exceeds the available weather window, the scheme will face the risk of not being completed on schedule, and the invested ship time and costs may be lost. However, existing scheme evaluation methods lack quantification tools for weather uncertainties during the window period, making it difficult to transform meteorological risks into completion probability indicators with confidence guarantees. This results in the feasibility assessment of schemes still relying heavily on experience-based judgments. Furthermore, decision-making methods such as TOPSIS are commonly used when comparing multiple schemes, treating each evaluation indicator as a deterministic value. However, when evaluation indicators include estimated values ​​obtained through simulation or prediction, they inherently contain sampling errors or prediction biases. Ignoring this uncertainty may lead to unreliable scheme ranking results. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, device, and medium for risk assessment of multi-beam measurement schemes.

[0005] The technical solution of this invention is as follows: A risk assessment method for multi-beam measurement schemes includes the following operations: S1. Obtain the multibeam survey line scheme to be evaluated. The multibeam survey line scheme includes the number of survey lines and the coordinates of the ordered track point sequence for each survey line. S2. Based on the multibeam survey scheme and corresponding ship performance parameters, obtain the effective detection time, total turning time and non-navigation auxiliary time, and calculate the full cycle operation time. S3. Based on the full-cycle operation time, corresponding ship performance parameters, and economic parameters, the comprehensive economic cost of all factors is obtained. The comprehensive economic cost of all factors includes direct operation cost, refined fuel cost, and carbon emission cost. In the process of obtaining the refined fuel cost, all segments in the multibeam survey scheme are traversed, and the segments are divided into straight detection segments and turning segments. The instantaneous fuel consumption rate of the straight detection segments and turning segments is calculated separately. The segment fuel consumption is obtained by time integration of the segments. The refined fuel cost is obtained by summing the fuel consumption of all segments and multiplying it by the fuel unit price. From the meteorological forecast data corresponding to the multibeam survey scheme, the number of consecutive days that meet the operation safety limit is extracted to form an available window. The daily effective operation time is modeled as a truncated normal distribution random variable. A variance-reduced Monte Carlo simulation is performed on each available window to obtain the completion probability estimate and the corresponding confidence interval. S4. Summarize the comprehensive economic cost, completion probability estimate, and corresponding confidence interval of the multibeam survey scheme to obtain the scheme risk assessment results.

[0006] If there are multiple multibeam survey schemes to be evaluated, obtain the comprehensive economic cost, completion probability estimate and corresponding standard error of each multibeam survey scheme, calculate their relative proximity, and sort the multiple multibeam survey schemes from high to low according to their relative proximity to obtain the scheme evaluation ranking results.

[0007] The method for obtaining relative proximity is as follows: using the ratio of the completion probability estimate to the corresponding standard error as the signal-to-noise ratio, construct positive and negative ideal solutions, and calculate the distance from the scheme to the positive and negative ideal solutions based on the preset cost weight and signal-to-noise ratio weight to generate relative proximity.

[0008] When calculating the instantaneous fuel consumption rate for the straight detection section and the U-turn section, for the straight detection section, the zero steering angle is substituted into the fuel consumption model to calculate the instantaneous fuel consumption rate; for the U-turn section, the arc curvature is converted into an equivalent steering angle and substituted into the fuel consumption model to calculate the instantaneous fuel consumption rate.

[0009] The fuel consumption model is as follows: , , , , For fuel consumption rate, Shaft power, , , These are the first main engine fuel consumption characteristic coefficient, the second main engine fuel consumption characteristic coefficient, and the third main engine fuel consumption characteristic coefficient, respectively. For hydrostatic resistance, Added drag to the rudder. For speed, For the steering angle, , , These are the first propulsion efficiency coefficient, the second propulsion efficiency coefficient, and the third propulsion efficiency coefficient, respectively. , These are the first hydrostatic resistance coefficient and the second hydrostatic resistance coefficient, respectively. The density of seawater, For rudder area, This is the additional resistance term caused by the change in flow around the ship's hull. The instantaneous curvature of the path, As for the rudder aspect ratio, The rudder lift coefficient, It is the drift angle.

[0010] In S3, if the multi-beam surveying scheme contains a safe pause point, the entire cycle operation time is divided into multiple stages. The probability of the multi-beam surveying scheme completing at least any stage within the available window is calculated recursively through dynamic programming. The segmented completion probability curve is output and combined with the comprehensive economic cost of all factors to form the scheme risk assessment result.

[0011] In dynamic programming recursion: if the current stage is not completed, i.e. , If the current stage has been completed, that is , , To make the remaining available windows n Days, current stage accumulated working time t Under the given conditions, the probability of completing the current stage and all subsequent stages is... For the first j The required time for each stage of the work. Let be the probability density function of the daily effective working time. To make the remaining available windows n Days, current stage accumulated working time Under certain conditions, the probability of completing the current stage and all subsequent stages.

[0012] A multi-beam measurement scheme risk assessment system, used to implement the above-mentioned multi-beam measurement scheme risk assessment method, includes: The multibeam survey line scheme acquisition module is used to acquire the multibeam survey line scheme to be evaluated. The multibeam survey line scheme includes the number of survey lines and the coordinates of the ordered track point sequence of each survey line. The full-cycle operation time generation module is used to obtain the effective detection time, total turning time and non-navigation auxiliary time based on the multi-beam survey line scheme and corresponding ship performance parameters, and to calculate the full-cycle operation time. The completion probability estimation and corresponding confidence interval generation module is used to obtain the comprehensive economic cost of all factors based on the full-cycle operation time, corresponding ship performance parameters, and economic parameters. The comprehensive economic cost of all factors includes direct operation cost, refined fuel cost, and carbon emission cost. In the process of obtaining refined fuel cost, all segments in the multibeam survey scheme are traversed, and the segments are divided into straight detection segments and turning segments. The instantaneous fuel consumption rate of the straight detection segments and turning segments is calculated separately. The segment fuel consumption is obtained by time integration of the segments. The refined fuel cost is obtained by summing the fuel consumption of all segments and multiplying it by the fuel unit price. From the meteorological forecast data corresponding to the multibeam survey scheme, the number of consecutive days that meet the operation safety limit is extracted to form an available window. The daily effective operation time is modeled as a truncated normal distribution random variable. A variance-reduced Monte Carlo simulation is performed on each available window to obtain the completion probability estimate and the corresponding confidence interval. The module for generating risk assessment results is used to summarize the comprehensive economic cost, completion probability estimate, and corresponding confidence interval of the multibeam survey scheme to obtain the risk assessment results.

[0013] A risk assessment device for a multi-beam measurement scheme includes a processor and a memory, wherein the processor executes a computer program stored in the memory to implement the risk assessment method for the multi-beam measurement scheme as described above.

[0014] A computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the above-described risk assessment method for multi-beam measurement schemes.

[0015] The beneficial effects of this invention are as follows: This invention provides a risk assessment method for multibeam survey schemes. First, by acquiring the number of survey lines and the coordinate sequence of track points for a multibeam survey scheme, and combining this with ship performance parameters, the effective detection time, total turning time, and non-navigation auxiliary time are calculated in detail to obtain the full-cycle operation time. This method overcomes the shortcomings of traditional methods that only estimate the construction period based on the total length of the survey lines while ignoring non-detection times such as turning and travel to and from the work area, allowing for objective differentiation between schemes with significant differences in the number of turning times and path lengths. Then, based on the full-cycle operation time, the instantaneous fuel consumption rate is calculated separately for each segment and working condition, and the time is integrated over the segment. The accumulated results yield a refined fuel cost, revealing for the first time that even with the same path length, different turning strategies can have varying fuel costs due to differences in steering angle timing. The resulting fuel consumption differences necessitate a shift from extensive to intensive fuel estimation. Next, the number of consecutive days meeting operational safety limits is extracted from weather forecast data to form a usable window. Daily effective operating time is modeled as a truncated normally distributed random variable, and a variance-reduced Monte Carlo simulation is performed on each window. This upgrades the probability of completing the weather window from an empirical qualitative judgment to a quantitative indicator with physical mechanism support and confidence boundaries, effectively distinguishing between schemes with high probability but unstable estimates and those with moderate probability but reliable estimates. Finally, the comprehensive economic cost of all factors, the estimated probability of completion, and their confidence intervals are summarized, allowing quantitative information on the two core dimensions of cost and risk to jointly constitute the scheme evaluation results, providing a reliability benchmark for voyage planning and evaluation. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the exemplary embodiments of this application clearer, the technical solutions in the exemplary embodiments of this application are described clearly and completely below. Obviously, the described exemplary embodiments are only some embodiments of this application, and not all embodiments.

[0017] This embodiment provides a risk assessment method for multi-beam measurement schemes, including the following operations: S1. Obtain the multibeam survey line scheme to be evaluated. The multibeam survey line scheme includes the number of survey lines and the coordinates of the ordered track point sequence for each survey line. S2. Based on the multibeam survey scheme and corresponding ship performance parameters, obtain the effective detection time, total turning time and non-navigation auxiliary time, and calculate the full cycle operation time. S3. Based on the full-cycle operation time, corresponding ship performance parameters, and economic parameters, the comprehensive economic cost of all factors is obtained. The comprehensive economic cost of all factors includes direct operation cost, refined fuel cost, and carbon emission cost. In the process of obtaining the refined fuel cost, all segments in the multibeam survey scheme are traversed, and the segments are divided into straight detection segments and turning segments. The instantaneous fuel consumption rate of the straight detection segments and turning segments is calculated separately. The segment fuel consumption is obtained by time integration of the segments. The refined fuel cost is obtained by summing the fuel consumption of all segments and multiplying it by the fuel unit price. From the meteorological forecast data corresponding to the multibeam survey scheme, the number of consecutive days that meet the operation safety limit is extracted to form an available window. The daily effective operation time is modeled as a truncated normal distribution random variable. A variance-reduced Monte Carlo simulation is performed on each available window to obtain the completion probability estimate and the corresponding confidence interval. S4. Summarize the comprehensive economic cost, completion probability estimate, and corresponding confidence interval of the multibeam survey scheme to obtain the scheme risk assessment results.

[0018] The specific steps are detailed below.

[0019] S1. Obtain the multibeam survey line scheme to be evaluated. The multibeam survey line scheme includes the number of survey lines and the coordinates of the ordered track point sequence of each survey line.

[0020] S2. Based on the multibeam survey scheme and corresponding ship performance parameters, obtain the effective detection time, total turning time and non-navigation auxiliary time, and calculate the full cycle operation time.

[0021] Ship performance parameters include ship motion parameters, ship hydrodynamic parameters, ship economic parameters, and ship fuel consumption rate function. Ship motion parameters include cruising speed, turning speed, and minimum turning radius. Ship hydrodynamic parameters include ship length, turning index, first still water drag coefficient, second still water drag coefficient, rudder area, rudder aspect ratio, first propulsion efficiency coefficient, second propulsion efficiency coefficient, and third propulsion efficiency coefficient. Ship economic parameters include fleet daily charter rate, light fuel oil unit price, and carbon emission trading unit price. The ship fuel consumption rate function is established based on the ship's still water drag characteristics and propulsion efficiency curve; the input is the ship's speed. and steering angle The output is the fuel consumption per unit time. The formula for calculating the ship's fuel consumption rate function is: ,in For fuel consumption rate, Shaft power, , , These are the first main engine fuel consumption characteristic coefficient, the second main engine fuel consumption characteristic coefficient, and the third main engine fuel consumption characteristic coefficient, respectively.

[0022] The total operation time is the sum of effective detection time, total turning time, and non-navigational assistance time.

[0023] The effective detection time is the ratio of the sum of the lengths of all straight detection segments of the survey line track to the cruise speed.

[0024] In obtaining the total turning time, the turning path between adjacent survey lines is calculated using a Dubins curve that satisfies the minimum turning radius constraint, under kinematic constraints. , , Below, the minimum range path must be piecewise constant curvature control ( ), , These represent the horizontal and vertical velocity components of the ship, respectively. For the ship's forward speed, For the ship's heading angle, The rate of change of heading angle, The minimum turning radius of the ship. To control the input, three discrete states are considered: 00 represents the maximum right turn, and 00 represents straight-line navigation. To achieve the maximum left turn, the connection path between adjacent survey lines is modeled as the shortest path of arc-straight line-arc. The total turning time is obtained by summing all the turning distances and dividing by the turning speed.

[0025] Non-navigational assistance time is calculated using the following formula: , Non-navigational assistance time, For effective detection time, Total time for turning around. As an auxiliary time percentage, it is used to estimate the proportion of non-navigation time such as departure, calibration, and avoidance.

[0026] In this embodiment, S2 obtains the effective detection time, total turning time, and non-navigation auxiliary time based on the multi-beam survey scheme and ship performance parameters, and calculates the full-cycle operation time. This overcomes the shortcomings of traditional methods that only estimate the construction period based on the total length of the survey line while ignoring non-detection times such as turning, round trips to the work area, and equipment calibration. This allows for the objective differentiation between schemes with significant differences in the number of turning times and the length of the turning path. Furthermore, this full-cycle operation time constitutes a common time benchmark for subsequent refined fuel cost estimation and meteorological window completion probability assessment. Without this benchmark, the time integration of fuel consumption is meaningless, and the judgment on whether the cumulative operation time within the meteorological window meets the standard is also lost. Finally, the difference in the full-cycle operation time is directly transmitted to the comprehensive economic cost of all factors and the completion probability estimation, driving the multi-scheme comparison to truly reflect the differences in the construction period, cost, and risk of different survey schemes in actual operation, and avoiding decision-making bias caused by time estimation distortion.

[0027] S3. Based on the full-cycle operation time, corresponding ship performance parameters and economic parameters, obtain the comprehensive economic cost of all factors; extract the number of consecutive days that meet the operation safety limit from the meteorological forecast data corresponding to the multibeam survey line scheme to form an available window, model the daily effective operation time as a truncated normal distribution random variable, and perform a variance-reduced Monte Carlo simulation on each available window to obtain the completion probability estimate and the corresponding confidence interval.

[0028] Based on the full-cycle operation time, corresponding vessel performance parameters, and economic parameters, the comprehensive economic cost is obtained. The comprehensive economic cost includes direct operating costs, refined fuel costs, carbon emission costs, and other variable costs. Other variable costs include fresh water, consumables, and communication costs, which can be entered by the user as fixed values ​​or at daily rates. The comprehensive economic cost is the sum of direct operating costs, refined fuel costs, carbon emission costs, and other variable costs. If the multi-beam survey scheme to be evaluated involves multiple survey vessels operating in parallel, the total comprehensive economic cost is the sum of the comprehensive economic costs of each vessel.

[0029] Direct operating costs are the product of the fleet's daily charter rate and the number of operating days required for the entire cycle. The number of operating days required for the entire cycle is the ratio of the total operating time for the entire cycle to the base value of the average daily effective operating time.

[0030] In obtaining refined fuel cost, all segments of the multibeam survey scheme are traversed, and each segment is divided into a straight detection segment and a turning segment. The instantaneous fuel consumption rate of each segment is calculated separately, and the segment fuel consumption is obtained by time integration. The fuel consumption of all segments is then summed and multiplied by the fuel price per unit to obtain the refined fuel cost. By dividing the segment into straight detection segments and turning segments, and calculating the instantaneous fuel consumption rate at different control angles and integrating the segment over time, the fuel consumption difference between straight cruising and turning can be accurately quantified. This reveals the fuel consumption difference caused by different turning strategies even with the same path length, due to different control angle timings, effectively making up for the blind spots of traditional methods in this technical detail.

[0031] When calculating the instantaneous fuel consumption rate for the straight detection section and the U-turn section, for the straight detection section, zero steering angle is substituted into the fuel consumption model to calculate the instantaneous fuel consumption rate; for the U-turn section, the arc curvature is converted into an equivalent steering angle, and then substituted into the fuel consumption model to calculate the instantaneous fuel consumption rate. The conversion of arc curvature into an equivalent steering angle is achieved through the following formula: , As captain, The instantaneous curvature of the path, The cyclicity index, For speed. This innovative method demonstrates the difference in fuel consumption caused by differences in steering angle timing, even when different turning strategies have the same path length.

[0032] The fuel consumption model is as follows: , , , , For fuel consumption rate, Shaft power, , , These are the first main engine fuel consumption characteristic coefficient, the second main engine fuel consumption characteristic coefficient, and the third main engine fuel consumption characteristic coefficient, respectively. For hydrostatic resistance, Added drag to the rudder. For speed, For the steering angle, , , These are the first propulsion efficiency coefficient, the second propulsion efficiency coefficient, and the third propulsion efficiency coefficient, respectively. , These are the first hydrostatic resistance coefficient and the second hydrostatic resistance coefficient, respectively. The density of seawater, For rudder area, This is the additional resistance term caused by the change in flow around the ship's hull. The instantaneous curvature of the path, As for the rudder aspect ratio, The rudder lift coefficient, It is the drift angle.

[0033] Carbon emission cost is the sum of fuel consumption for all flight segments, multiplied by the carbon emission trading unit price and the carbon emission conversion factor.

[0034] Meanwhile, the number of consecutive days that meet the operational safety limits are extracted from the meteorological forecast data corresponding to the multibeam survey line scheme to form an available window. The daily effective operation time is modeled as a truncated normal distribution random variable. A Monte Carlo simulation with variance reduction is performed on each available window to obtain the completion probability estimate and the corresponding confidence interval. The specific steps are detailed below.

[0035] Step 1: Extract the number of consecutive days that meet the operational safety limits from the meteorological forecast data of the target operation area of ​​the multibeam survey scheme, forming several usable windows. Each window includes the start time and the number of days it lasts.

[0036] Step 2: Treat the significant wave height as a stationary narrow-band Gaussian process. The cumulative duration below the maximum wave height within a day follows a normal distribution. In this embodiment, it is explicitly modeled as a truncated normally distributed random variable. The domain is mean The standard deviation is the base value of the average daily effective working time. , , These are the zeroth and second moments of the wave spectrum, respectively, replacing the traditional empirical settings.

[0037] Step 3: For each available window, execute... In each simulation, the effective work time for each day within the available window is randomly sampled, and the effective work time for all days is accumulated to obtain the total available work time for that simulation. If the total available work time is not less than the full cycle work time, it is marked as completed.

[0038] To improve simulation efficiency and accuracy, in this embodiment of the variance-reduced Monte Carlo simulation, let the first... The total available time for this simulation is The number of days in the window is On Independence Day The theoretical distribution is and cut off at Using the expectation of this analytic distribution As a control variable, calculate the probability estimate of completion. , For the first k The probability that all operations can be completed within a weather window. The total number of simulations for Monte Carlo. For indicator functions, when the first i Cumulative available job time for each simulation Not less than the full cycle operation time The value is 1 if the condition is met, and 0 otherwise. The coefficients are the optimal control variable coefficients, and the coefficients are the coefficients that minimize the corrected estimate variance. To optimize the control variable coefficients, this invention requires performing numerous Monte Carlo simulations for each meteorological window when assessing the probability of completion of the meteorological window. Direct statistical methods have a slow convergence speed and require a large number of simulations to obtain a stable probability estimate.

[0039] This embodiment introduces the control variable method and uses the theoretical analysis results of the daily effective work time distribution to construct a corrected completion probability estimate. Under the same number of simulations, it can significantly compress the estimation variance, making the completion probability estimate more stable and accurate, while shortening the calculation time.

[0040] The confidence interval for the probability of completion is , This represents the two-sided quantile of the standard normal distribution.

[0041] If the estimated completion probability of a multibeam survey scheme is lower than the preset completion probability estimate threshold in all available windows, a high-risk warning will be issued, indicating to decision-makers that the schedule of the scheme is unacceptable, and suggesting that the scheme be optimized or additional operational resources be added.

[0042] If the multibeam survey scheme contains J When there is a safety pause point, the entire operation time is divided into multiple stages, namely... J+ One stage, the required work time for each stage is recorded as follows: By using dynamic programming to recursively calculate the probability that a multibeam survey scheme can complete at least any stage within the available window, that is, the probability that the current stage and all subsequent stages can be completed given the accumulated work time in the current stage, the segmented completion probability curve is output, and together with the comprehensive economic cost of all factors, the scheme risk assessment result is formed.

[0043] The specific operation of dynamic programming recursion is as follows: Define the state variable as the number of days remaining in the current available window. n and the cumulative working time at the current stage t The boundary condition is that the probability is 1 when all stages are completed; if the current stage is not completed, i.e. , If the current phase has been completed, , , To make the remaining available windows n Days, current stage accumulated working time t Under the given conditions, the probability of completing the current stage and all subsequent stages is... For the first j The required time for each stage of the work. Let be the probability density function of the daily effective working time. To make the remaining available windows n Days, current stage accumulated working time Under certain conditions, the probability of completing the current stage and all subsequent stages can be calculated. By using dynamic programming recursion, the probability of the scheme completing at least any stage within the window can be directly calculated without nested simulations within the Monte Carlo framework. The piecewise completion probability curve output by this recursive algorithm can provide management flexibility for large-scale multi-day measurement projects that cannot be covered by traditional window period assessments.

[0044] S4. Summarize the comprehensive economic cost, completion probability estimate, and corresponding confidence interval of the multibeam survey scheme to obtain the scheme risk assessment results.

[0045] If there are multiple multibeam survey schemes to be evaluated, obtain the comprehensive economic cost, completion probability estimate and corresponding standard error of each multibeam survey scheme, calculate their relative proximity, and sort the multiple multibeam survey schemes from high to low according to their relative proximity to obtain the scheme evaluation ranking results.

[0046] The method for obtaining the relative closeness is as follows: using the ratio of the completion probability estimate to the corresponding standard error as the signal-to-noise ratio, constructing the positive and negative ideal solutions for the cost signal-to-noise ratio, and calculating the distance of each scheme to the positive and negative ideal solutions according to the preset cost weight and signal-to-noise ratio weight, thus obtaining the relative closeness.

[0047] The formula for calculating relative closeness is as follows: , , , For the plan i The relative proximity is a factor; a larger value indicates a better solution. , The respective schemes i The weighted distance to the positive ideal solution and the negative ideal solution, the scheme i The smaller the weighted distance to the ideal solution, the closer the solution is to the optimal solution. i A larger weighted distance value to the negative ideal solution indicates that the solution is further away from the worst solution. For the plan i The normalized value of total factor comprehensive economic cost. For the plan i The estimated probability of completion point. The signal-to-noise ratio (SNR) for the completion probability of a positive ideal solution is the maximum SNR among all alternative solutions, representing a benchmark for optimal reliability. The signal-to-noise ratio (SNR) represents the probability of achieving the positive and negative ideal solutions. The minimum SNR among all alternative solutions is taken as the benchmark for the worst reliability. , These are the signal-to-noise ratios of the positive ideal solution and the negative ideal solution, respectively. For the plan i The standard error of the probability of completion estimate, For the plan i The signal-to-noise ratio is used to measure the scheme. i The stability and reliability of the probability of completion estimate; the larger the ratio, the more accurate and reliable the probability of completion estimate. The weighting coefficients for cost indicators, The weighting coefficients for the probability signal-to-noise ratio index are determined by the probability. A higher value indicates that reliability is more important in the selection process.

[0048] Furthermore, cost-risk scatter plots are generated for the total economic cost and completion probability estimates of all multibeam survey schemes. The horizontal axis represents the total economic cost, and the vertical axis represents the completion probability estimate. Bootstrap resampling is performed to construct a joint confidence ellipse, and statistically non-dominated solutions are extracted to form the efficiency frontier. Statistically non-dominated solutions are those that are not significantly dominated by other schemes within their confidence regions, and have statistical significance. This clearly demonstrates the robust and superior combination of schemes even after considering estimation uncertainties.

[0049] This embodiment also provides a multi-beam measurement scheme risk assessment system for implementing the above-described multi-beam measurement scheme risk assessment method, including: The multibeam survey line scheme acquisition module is used to acquire the multibeam survey line scheme to be evaluated. The multibeam survey line scheme includes the number of survey lines and the coordinates of the ordered track point sequence of each survey line. The full-cycle operation time generation module is used to obtain the effective detection time, total turning time and non-navigation auxiliary time based on the multi-beam survey line scheme and corresponding ship performance parameters, and to calculate the full-cycle operation time. The completion probability estimation and corresponding confidence interval generation module is used to obtain the comprehensive economic cost of all factors based on the full-cycle operation time, corresponding ship performance parameters, and economic parameters. The comprehensive economic cost of all factors includes direct operation cost, refined fuel cost, and carbon emission cost. In the process of obtaining refined fuel cost, all segments in the multibeam survey scheme are traversed, and the segments are divided into straight detection segments and turning segments. The instantaneous fuel consumption rate of the straight detection segments and turning segments is calculated separately. The segment fuel consumption is obtained by time integration of the segments. The refined fuel cost is obtained by summing the fuel consumption of all segments and multiplying it by the fuel unit price. From the meteorological forecast data corresponding to the multibeam survey scheme, the number of consecutive days that meet the operation safety limit is extracted to form an available window. The daily effective operation time is modeled as a truncated normal distribution random variable. A variance-reduced Monte Carlo simulation is performed on each available window to obtain the completion probability estimate and the corresponding confidence interval. The module for generating risk assessment results is used to summarize the comprehensive economic cost, completion probability estimate, and corresponding confidence interval of the multibeam survey scheme to obtain the risk assessment results.

[0050] This embodiment also provides a multi-beam measurement scheme risk assessment device, including a processor and a memory, wherein the processor executes a computer program stored in the memory to implement the multi-beam measurement scheme risk assessment method as described above.

[0051] This embodiment also provides a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the above-described risk assessment method for multi-beam measurement schemes.

[0052] This embodiment provides a risk assessment method for multibeam surveying schemes. First, by acquiring the number of survey lines and the coordinate sequence of track points for the multibeam surveying scheme, and combining this with ship performance parameters, the effective detection time, total turning time, and non-navigation auxiliary time are calculated in detail to obtain the full-cycle operation time. This method overcomes the shortcomings of traditional methods that only estimate the construction period based on the total length of the survey lines while ignoring non-detection times such as turning and traveling to and from the work area, allowing for objective differentiation between schemes with significant differences in the number of turning times and path lengths. Then, based on the full-cycle operation time, the instantaneous fuel consumption rate is calculated separately for each segment and working condition, and the time is integrated over the segment. The accumulated results yield a refined fuel cost, revealing for the first time that even with the same path length, different turning strategies can have different fuel costs due to differences in steering angle timing. The resulting fuel consumption differences necessitate a shift from extensive to intensive fuel estimation. Next, the number of consecutive days meeting operational safety limits is extracted from weather forecast data to form a usable window. Daily effective operating time is modeled as a truncated normally distributed random variable, and a variance-reduced Monte Carlo simulation is performed on each window. This upgrades the probability of completing the weather window from an empirical qualitative judgment to a quantitative indicator with physical mechanism support and confidence boundaries, effectively distinguishing between schemes with high probability but unstable estimates and those with moderate probability but reliable estimates. Finally, the comprehensive economic cost of all factors, the estimated probability of completion, and their confidence intervals are summarized, allowing quantitative information on the two core dimensions of cost and risk to jointly constitute the scheme evaluation results, providing a reliability benchmark for voyage planning and evaluation.

[0053] While exemplary embodiments of the invention have been described herein, many other variations or modifications conforming to the principles of the invention can be directly determined or derived from the disclosure of this invention without departing from its spirit and scope. Therefore, the scope of the invention should be understood and recognized to cover all such other variations or modifications.

Claims

1. A risk assessment method for a multi-beam measurement scheme, characterized in that, This includes the following operations: S1. Obtain the multibeam survey line scheme to be evaluated. The multibeam survey line scheme includes the number of survey lines and the coordinates of the ordered track point sequence for each survey line. S2. Based on the multibeam survey scheme and corresponding ship performance parameters, obtain the effective detection time, total turning time and non-navigation auxiliary time, and calculate the full cycle operation time. S3. Based on the full-cycle operation time, corresponding ship performance parameters, and economic parameters, the comprehensive economic cost of all factors is obtained. The comprehensive economic cost of all factors includes direct operation cost, refined fuel cost, and carbon emission cost. In the process of obtaining the refined fuel cost, all segments in the multi-beam survey scheme are traversed, and the segments are divided into straight detection segments and turning segments. The instantaneous fuel consumption rate of the straight detection segments and turning segments is calculated separately. The segment fuel consumption is obtained by time integration. The refined fuel cost is obtained by summing the fuel consumption of all segments and multiplying it by the fuel unit price. When calculating the instantaneous fuel consumption rate for the straight detection section and the U-turn section, for the straight detection section, zero steering angle is substituted into the fuel consumption model to calculate the instantaneous fuel consumption rate; for the U-turn section, the arc curvature is converted into an equivalent steering angle and substituted into the fuel consumption model to calculate the instantaneous fuel consumption rate; the calculation formula of the fuel consumption model is: ， ， ， ， For fuel consumption rate, Shaft power, , , These are the first main engine fuel consumption characteristic coefficient, the second main engine fuel consumption characteristic coefficient, and the third main engine fuel consumption characteristic coefficient, respectively. For hydrostatic resistance, Added drag to the rudder. For speed, For the steering angle, , , These are the first propulsion efficiency coefficient, the second propulsion efficiency coefficient, and the third propulsion efficiency coefficient, respectively. , These are the first hydrostatic resistance coefficient and the second hydrostatic resistance coefficient, respectively. The density of seawater, For rudder area, The rudder lift coefficient, The square of the rudder lift coefficient. For drift angle, As for the rudder aspect ratio, This represents the change in the additional resistance term caused by the change in the flow around the ship. The instantaneous curvature of the path; From the meteorological forecast data corresponding to the multibeam survey line scheme, the number of consecutive days that meet the operational safety limits is extracted to form an available window. The daily effective operation time is modeled as a truncated normal distribution random variable. A Monte Carlo simulation with reduced variance is performed on each available window to obtain the completion probability estimate and the corresponding confidence interval. S4. Summarize the comprehensive economic cost, completion probability estimate, and corresponding confidence interval of the multibeam survey scheme to obtain the scheme risk assessment results.

2. The risk assessment method for a multi-beam measurement scheme according to claim 1, characterized in that, If there are multiple multibeam survey schemes to be evaluated, obtain the comprehensive economic cost, completion probability estimate and corresponding standard error of each multibeam survey scheme, calculate their relative proximity, and sort the multiple multibeam survey schemes from high to low according to their relative proximity to obtain the scheme evaluation ranking results.

3. The risk assessment method for a multi-beam measurement scheme according to claim 2, characterized in that, The method for obtaining relative proximity is as follows: using the ratio of the completion probability estimate to the corresponding standard error as the signal-to-noise ratio, construct positive and negative ideal solutions, and calculate the distance from the scheme to the positive and negative ideal solutions based on the preset cost weight and signal-to-noise ratio weight to generate relative proximity.

4. The risk assessment method for a multi-beam measurement scheme according to claim 1, characterized in that, In S3, if the multi-beam surveying scheme contains a safe pause point, the entire cycle operation time is divided into multiple stages. The probability of the multi-beam surveying scheme completing at least any stage within the available window is calculated recursively through dynamic programming. The segmented completion probability curve is output and combined with the comprehensive economic cost of all factors to form the scheme risk assessment result.

5. The risk assessment method for a multi-beam measurement scheme according to claim 4, characterized in that, In dynamic programming recursion: if the current stage is not completed, i.e. , If the current stage has been completed, that is , , To make the remaining available windows n Days, current stage accumulated working time t Under the given conditions, the probability of completing the current stage and all subsequent stages is... For the first j The required time for each stage of the work. Let be the probability density function of the daily effective working time. To make the remaining available windows n Days, current stage accumulated working time Under certain conditions, the probability of completing the current stage and all subsequent stages.

6. A risk assessment system for multi-beam measurement schemes, used to implement the risk assessment method for multi-beam measurement schemes as described in claim 1, characterized in that, include: The multibeam survey line scheme acquisition module is used to acquire the multibeam survey line scheme to be evaluated. The multibeam survey line scheme includes the number of survey lines and the coordinates of the ordered track point sequence of each survey line. The full-cycle operation time generation module is used to obtain the effective detection time, total turning time and non-navigation auxiliary time based on the multi-beam survey line scheme and corresponding ship performance parameters, and to calculate the full-cycle operation time. The completion probability estimation and corresponding confidence interval generation module is used to obtain the comprehensive economic cost of all factors based on the full-cycle operation time, corresponding ship performance parameters, and economic parameters. The comprehensive economic cost of all factors includes direct operation cost, refined fuel cost, and carbon emission cost. In the process of obtaining refined fuel cost, all segments in the multibeam survey scheme are traversed, and the segments are divided into straight detection segments and turning segments. The instantaneous fuel consumption rate of the straight detection segments and turning segments is calculated separately. The segment fuel consumption is obtained by time integration of the segments. The refined fuel cost is obtained by summing the fuel consumption of all segments and multiplying it by the fuel unit price. From the meteorological forecast data corresponding to the multibeam survey scheme, the number of consecutive days that meet the operation safety limit is extracted to form an available window. The daily effective operation time is modeled as a truncated normal distribution random variable. A variance-reduced Monte Carlo simulation is performed on each available window to obtain the completion probability estimate and the corresponding confidence interval. The module for generating risk assessment results is used to summarize the comprehensive economic cost, completion probability estimate, and corresponding confidence interval of the multibeam survey scheme to obtain the risk assessment results.

7. A risk assessment device for a multi-beam measurement scheme, characterized in that, It includes a processor and a memory, wherein the processor executes a computer program stored in the memory to implement the risk assessment method for a multibeam measurement scheme as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, Used to store a computer program, wherein the computer program, when executed by a processor, implements the risk assessment method for a multi-beam measurement scheme as described in any one of claims 1-5.

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