A method for inland waterway navigability test and evaluation based on multi-source sensing and space-time matching

Abstract

The application discloses a kind of multi-source sensing and space-time matching inland waterway navigability test and evaluation method, belong to waterway engineering and ship test evaluation technical field.Pass through the layout shore base monitoring unit and integrated shipboard perception unit, unified space-time datum is constructed;Based on unified space-time datum, space-time matching fusion is carried out to ship dynamic response data and waterway environment data, forms ship response-environment excitation coupling data set, constructs three-layer navigability performance evaluation system, uses improved fuzzy analytic hierarchy process to determine index weight in combination with three scale method and optimal transfer matrix, uses cloud model to complete index quantization and comprehensive evaluation value calculation, finally determines waterway navigability effect grade according to comprehensive expectation value.The application realizes the quantitative evaluation of multi-source data accurate fusion and subjective and objective fusion, forms standardization test and evaluation process, truly reflects the coupling navigability characteristics of waterway and ship, provides technical support for inland waterway engineering acceptance, navigation safety management and waterway optimization design.

Description

Technical Field

[0001] This invention relates to the fields of waterway engineering and ship testing and evaluation technology, specifically to a method for testing and evaluating the seaworthiness of inland waterways using multi-source sensing and spatiotemporal matching. Background Technology

[0002] According to statistics from the Ministry of Transport, the navigable mileage of inland waterways in my country has exceeded 120,000 kilometers, of which approximately 16,000 kilometers are high-grade waterways. The navigation density of major channels such as the Yangtze River, the Xijiang River, and the Beijing-Hangzhou Grand Canal has been increasing year by year. Inland waterways are characterized by shallow water depth, narrow channels, numerous bends, significant changes in water flow, and complex navigation environments. In recent years, with the rapid advancement of inland waterway infrastructure construction, the scale of waterway improvement projects has been continuously expanding. Although relevant mathematical or physical model studies are generally conducted during the design phase of waterway improvement projects, it is difficult to fully reproduce the unique and complex boundary conditions of inland waterways, such as irregular shorelines, sudden changes in local water depth, and bend circulation, leading to discrepancies between experimental results and actual ship navigation performance.

[0003] Navigability effectiveness refers to the degree to which the navigation conditions of the engineering section of the river meet the requirements for safe and efficient passage of ships. Ship trials, as the core technical means to verify the navigation effectiveness of a waterway, play an irreplaceable role in ensuring navigation safety and optimizing waterway design. Ship trials involve selecting a representative design vessel type and conducting upstream and downstream navigation tests in the actual engineering waters to obtain firsthand data reflecting the navigation performance of the engineering waters. The test vessel navigates along the designed route, and real-time measurements are taken of the water depth, topography, surface current velocity and direction, and water level of the test section, as well as the vessel's navigation trajectory, speed on the opposite bank, rudder angle, drift angle, safe distance between the vessel and the riverbank, bridge piers, and during encounters. These navigation indicators and states are analyzed and evaluated in conjunction with navigation regulations and standards to assess the rationality of the technical parameters of the improved waterway. Compared with physical model tests or numerical simulations, ship trial data originates from objective reality and can truly reflect the movement process and state of ships passing through the engineering section, providing a clearer and more reliable explanation of the relationship between the engineering project and ship navigation.

[0004] Currently, there are no specific, targeted technical standards for conducting actual ship tests of waterway seaworthiness. Standards related to ship navigation performance testing, such as the "Ship Test Method for Navigation Technical Performance of Yangtze River Vessels (Fleets)" (JT / T352-2004), mainly focus on testing the ship's own performance, rather than verifying waterway seaworthiness. Regarding seaworthiness evaluation, some scholars have proposed a ship seaworthiness assessment method based on fuzzy comprehensive evaluation. This method determines the weights of evaluation indicators through expert surveys and uses fuzzy set theory to establish membership functions for a comprehensive evaluation of ship seaworthiness. This method transforms qualitative evaluation into quantitative evaluation, which is a significant improvement. However, its indicator weights rely on subjective expert judgment and lack the integration and utilization of real-time monitoring data. Recent studies have combined fuzzy hierarchical analysis (FAHP) with cloud models to assess inland waterway navigation safety risks. This method can handle the fuzziness and randomness in the assessment process and generate a visualized risk cloud map. However, this research focuses on the risk assessment of the navigation environment, rather than the experimental assessment of waterway seaworthiness performance. Summary of the Invention

[0005] Based on the aforementioned technical problems, this application discloses a method for navigation testing and evaluation of inland waterways using multi-source sensing and spatiotemporal matching, specifically including:

[0006] S1: Deploy shore-based monitoring units on the selected inland waterway test section and integrate shipborne sensing units on the test vessel; establish a unified spatiotemporal reference covering the shore-based monitoring units and shipborne sensing units;

[0007] S2: Control the test vessel to navigate along the preset route in the test section, and use the shipborne sensing unit to collect the dynamic response data of the vessel in real time, while using the shore-based monitoring unit to collect the environmental data of the test section in real time.

[0008] S3: Based on the unified spatiotemporal benchmark, the collected ship dynamic response data and environmental data are spatiotemporally matched and fused to construct a ship response-environmental excitation coupled dataset containing ship motion parameters and corresponding location environmental parameters.

[0009] S4: Based on the coupled dataset, construct an airworthiness performance evaluation system including a target layer, a criterion layer, and an indicator layer; use an improved fuzzy hierarchical analysis method to determine the weight of each level of indicator, and combine cloud model theory to quantify the indicators, calculate the comprehensive airworthiness performance evaluation value, and determine the airworthiness effect level of the waterway accordingly.

[0010] Preferably, in step S1, establishing a unified spatiotemporal reference covering both the shore-based monitoring unit and the shipborne sensing unit specifically includes:

[0011] By using a high-precision clock synchronization server and employing a precise time protocol or GPS timing method, the system time of the shore-based monitoring unit and the shipborne sensing unit is synchronized to the same standard time to eliminate time synchronization errors between the sensors.

[0012] All raw data coordinates collected by sensors were uniformly converted to the national geodetic coordinate system, and elevation data were uniformly converted to the national elevation datum to achieve spatial uniformity.

[0013] Preferably, in step S2, the test vessel is controlled to navigate along a preset route on the test section, and the shipborne sensing unit collects the vessel's dynamic response data in real time. Simultaneously, the shore-based monitoring unit collects environmental data for the test section in real time. Specifically:

[0014] The ship dynamic response data includes at least the ship's real-time position coordinates, ground speed, heading angle, roll angle, pitch angle, yaw rate, rudder angle, and propeller speed.

[0015] The environmental data includes at least the instantaneous water depth distribution, three-dimensional current velocity distribution, water level, wind speed, and wind direction of the test segment;

[0016] The shipborne sensing unit and the shore-based monitoring unit record data synchronously according to a preset sampling frequency.

[0017] Preferably, in step S3, the spatiotemporal matching and fusion of the collected ship dynamic response data and environmental data specifically includes:

[0018] Using the timestamp of the data collected by the shipborne sensing unit as the reference time point, an interpolation algorithm is used to process the environmental data to obtain the environmental parameter values ​​corresponding to the reference time point;

[0019] Based on the real-time position coordinates of the ship at the reference time point, the water depth and current velocity vector at that coordinate position are extracted from the environmental data.

[0020] Noise filtering based on Kalman filtering, and abnormal data detection and repair;

[0021] The matched time, ship position, ship motion parameters, and corresponding water depth and current velocity vectors are integrated into a single fused data record to generate the coupled dataset.

[0022] Preferably, in step S4, the constructed airworthiness performance evaluation system includes:

[0023] The target layer is the comprehensive index of seaworthiness performance of vessels in inland waterways;

[0024] The criteria layer includes basic airworthiness indicators, heading stability indicators, roll comfort indicators, shallow water adaptability indicators, and risk control indicators;

[0025] The indicator layer includes several underlying quantitative indicators belonging to each criterion layer. These underlying quantitative indicators include channel depth margin, maximum drift angle, root mean square of roll angle, and minimum ship-to-shore distance.

[0026] Preferably, in step S4, after constructing the evaluation system, the method further includes a step of calculating the ship dynamic response parameters in the index layer based on the coupled dataset, wherein the drift angle formula is:

[0027]

[0028] in, Indicates the drift angle. and These represent the components of the ship's speed relative to the ground along the x-axis and y-axis of the coordinate system, respectively. Indicates the bow angle of the ship.

[0029] Preferably, the step of determining the weights of each level of indicators using an improved fuzzy hierarchical analysis method specifically includes:

[0030] When constructing the judgment matrix, the three-scale method is used instead of the traditional nine-scale method, and the relative importance of each indicator is compared and assigned a value of -1, 0 or 1.

[0031] The optimal transfer matrix algorithm is used to transform the three-scale matrix into a judgment matrix that meets the consistency requirements, and the weight vector of each index is calculated to reduce the bias caused by subjective judgment.

[0032] Preferably, the quantitative processing of indicators based on cloud model theory specifically includes:

[0033] For each underlying quantitative indicator, based on the distribution characteristics of the measured data, a reverse cloud generator is used to generate the cloud digital features of that indicator. These cloud digital features include the expected value. ,entropy and hyperentropy ;

[0034] Among them, expected value The central tendency of the evaluation value of the characterization index, entropy Uncertainty range of the characterization index, hyperentropy Characterizes the degree of dispersion of entropy.

[0035] Preferably, the calculation of the comprehensive airworthiness performance evaluation value is performed by using a weighted comprehensive cloud model algorithm to integrate the cloud digital features of each underlying quantitative indicator upwards to calculate the comprehensive cloud digital features of the target layer. The formula is as follows:

[0036]

[0037]

[0038]

[0039] in, For the first The global weight of each indicator, , , The first Cloud digital characteristics of each indicator This represents the total number of indicators involved in the calculation.

[0040] Preferably, determining the navigational effectiveness level of the waterway specifically includes:

[0041] Based on the expected value in the comprehensive cloud digital characteristics of the target layer obtained by calculation, it is compared with the preset airworthiness effect classification standard.

[0042] The preset airworthiness rating standard includes multiple non-overlapping numerical ranges, each of which corresponds to a specific airworthiness rating.

[0043] If the expected value falls into any of the multiple non-overlapping numerical intervals, then the specific airworthiness level corresponding to that interval is determined as the waterway airworthiness level.

[0044] Compared with the prior art, the technical solution of this application has the following technical effects:

[0045] This invention constructs a multi-source sensor network that coordinates shore-based and shipboard sensors, enabling the complete acquisition of dynamic environmental field information of inland waterways and full-dimensional navigation response data of ships. It achieves continuous and stable acquisition of various key parameters during the experiment, and the establishment of a unified spatiotemporal benchmark ensures that all monitoring equipment works in the same time and space system, providing basic support for accurate data fusion. This allows the ship's navigation status and waterway environmental excitations to be fully recorded, providing a true and reliable source of raw data for subsequent analysis and evaluation.

[0046] This invention performs standardized spatiotemporal matching and fusion of multi-source heterogeneous data, which can automatically generate a well-structured coupled dataset of ship response and environmental excitation, simplifying the data processing process and improving data utilization efficiency. The time interpolation and spatial extraction operations based on a unified spatiotemporal benchmark ensure that each set of ship motion parameters corresponds to precise on-site environmental conditions, fully restoring the ship's actual navigation process in the waterway, and ensuring that the data foundation on which subsequent evaluation and analysis are based has high consistency and high credibility.

[0047] The three-tiered seaworthiness performance evaluation system established by this invention can comprehensively characterize the seaworthiness level of a waterway from multiple dimensions such as basic seaworthiness, course stability, roll comfort, shallow water adaptability, and risk control. It covers core contents such as the inherent attributes of the waterway, the dynamic response of the ship, and the safety margin of navigation. Each underlying indicator directly corresponds to the actual ship test monitoring data, realizing the seamless connection between the evaluation dimensions and the test data, fully reflecting the coupling and adaptation characteristics between the ship and the waterway, and improving the systematicness and comprehensiveness of the evaluation system.

[0048] This invention employs an evaluation method combining an improved fuzzy hierarchical analysis (AHP) with a cloud model. This method objectively determines the weights of each indicator and transforms qualitative concepts into quantitative values. Through weighted comprehensive calculation, it obtains a comprehensive index of waterway airworthiness performance and determines the level, outputting standardized and quantifiable evaluation conclusions. The entire methodology is clear, highly operable, and can form a standardized airworthiness testing and evaluation process, providing stable and reliable technical support for waterway engineering acceptance, navigation management, and optimized design.

[0049] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the preferred embodiments of this application are described in detail below with reference to the accompanying drawings.

[0050] The above and other objects, advantages and features of this application will become more apparent to those skilled in the art from the following detailed description of specific embodiments in conjunction with the accompanying drawings. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In all drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0052] Based on the description of the figures and their corresponding technical content in the document, the titles of the figures are as follows:

[0053] Figure 1 A schematic diagram of the overall process steps for inland waterway navigation test and evaluation methods;

[0054] Figure 2 A schematic diagram of the architecture of a multi-source sensor network deployment and data acquisition system;

[0055] Figure 3A schematic diagram of the test section of the inland waterway improvement project and the location of the sensor deployment;

[0056] Figure 4 Radar chart showing the distribution of evaluation results for each criterion level of waterway seaworthiness performance. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. In the following description, specific details such as specific configurations and components are provided merely to help fully understand the embodiments of this application. Therefore, those skilled in the art should understand that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. In addition, for clarity and brevity, descriptions of known functions and structures are omitted in the embodiments.

[0058] It should be understood that the phrase "an embodiment" or "this embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "an embodiment" or "this embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.

[0059] Furthermore, reference numerals and / or letters may be repeated in different examples within this application. Such repetition is for the purpose of simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or settings discussed.

[0060] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" in this article describes another type of relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " in this article generally indicates that the related objects before and after it are in an "or" relationship.

[0061] In this article, the term "at least one" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, "at least one of A and B" can mean: A exists alone, A and B exist simultaneously, or B exists alone.

[0062] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion.

[0063] Example 1

[0064] This embodiment mainly describes a method for navigation suitability testing and evaluation of inland waterways using multi-source sensing and spatiotemporal matching, such as... Figure 1 As shown, it specifically includes:

[0065] S1: Deploy shore-based monitoring units on the selected inland waterway test section and integrate shipborne sensing units on the test vessel; establish a unified spatiotemporal reference covering the shore-based monitoring units and shipborne sensing units;

[0066] S2: Control the test vessel to navigate along the preset route in the test section, and use the shipborne sensing unit to collect the dynamic response data of the vessel in real time, while using the shore-based monitoring unit to collect the environmental data of the test section in real time.

[0067] S3: Based on the unified spatiotemporal benchmark, the collected ship dynamic response data and environmental data are spatiotemporally matched and fused to construct a ship response-environmental excitation coupled dataset containing ship motion parameters and corresponding location environmental parameters.

[0068] S4: Based on the coupled dataset, construct an airworthiness performance evaluation system including a target layer, a criterion layer, and an indicator layer; use an improved fuzzy hierarchical analysis method to determine the weight of each level of indicator, and combine cloud model theory to quantify the indicators, calculate the comprehensive airworthiness performance evaluation value, and determine the airworthiness effect level of the waterway accordingly.

[0069] Furthermore, such as Figure 2As shown, the shore-based monitoring unit deployed in the test section consists of an underwater topographic mapping module, a hydrological current velocity monitoring module, and a meteorological environment monitoring module. The underwater topographic mapping module uses a multibeam echo sounder to perform full-coverage scanning of the test section with a grid resolution of 0.5m × 0.5m, forming a three-dimensional topographic data field. It can generate no less than 400,000 water depth coordinate points per kilometer section. The hydrological current velocity monitoring module uses a bottom-mounted acoustic Doppler current profiler, deployed in layers of 0.5m along the water depth direction, with no less than 40 vertical layers. The sampling frequency is set to 1Hz, and it can continuously output longitudinal, lateral, and vertical three-dimensional current velocity components, generating no less than 3,600 sets of current velocity profile data per hour. The meteorological environment monitoring module is fixed at a high point in the section, with a sampling frequency set to 0.1Hz, continuously collecting four environmental parameters: wind speed, wind direction, air temperature, and air pressure, ensuring complete recording of the environmental field boundary conditions.

[0070] Furthermore, the shipborne sensing unit adopts a distributed installation structure, with a high-precision differential GPS positioning module deployed at both the bow and stern of the ship. The positioning data output frequency is 20Hz, and it can generate 72,000 positioning records per hour. The ship attitude reference system maintains the same clock source as the positioning module, synchronously outputting roll, pitch, and bow angular velocities. The attitude data sampling frequency is also 20Hz, which can completely record the ship's attitude changes during navigation. The Automatic Identification System (AIS) receiver collects dynamic information of the ship and surrounding vessels at a frequency of 1Hz. The main engine condition monitoring module is connected to the ship's control system, collecting rudder angle, propeller speed, and main engine power data at a frequency of 10Hz. All shipborne sensing devices are connected to a unified acquisition terminal to ensure consistent data triggering times.

[0071] The establishment of a unified spatiotemporal benchmark involves two technical aspects: time synchronization and spatial unification. Time synchronization is achieved using GPS time synchronization combined with a precise time protocol, aligning the system clocks of shore-based monitoring units and shipborne sensing units to Coordinated Universal Time (UTC), with time synchronization errors controlled within 1ms for all devices. Spatial unification involves converting all sensor data to the CGCS2000 National Geodetic Coordinate System and converting elevation data to the 1985 National Elevation Datum, ensuring accurate correspondence between topographic data, ship position data, and current velocity data within the same spatial framework, with spatial positioning deviations controlled within 1m, providing a unified benchmark for subsequent spatiotemporal matching and fusion.

[0072] The test vessel navigated along a pre-set route within the test section, performing various maneuvers including uphill, downhill, meeting, overtaking, and curve maneuvering. Maintaining a stable speed throughout the voyage, the entire system initiated data acquisition upon entering the initial section of the test segment and ceased acquisition upon leaving the final section. The shipborne sensing unit collected real-time dynamic response data including real-time coordinates, ground speed, water speed, heading angle, roll angle, pitch angle, bow rate, rudder angle, and propeller speed. All of this data was continuously stored using timestamps and spatial coordinates as indexes. The shore-based monitoring unit simultaneously collected environmental data including instantaneous water depth, three-dimensional current velocity, water level, wind speed, and wind direction. The environmental data and ship dynamic data used the same timestamp system to ensure synchronized acquisition.

[0073] Spatiotemporal matching and fusion of multi-source heterogeneous data are performed based on a unified spatiotemporal benchmark. Time matching uses the timestamp of shipborne data as the benchmark, and linear interpolation is performed on environmental data. The interpolation formula is as follows: In the formula, For a moment The corresponding environmental parameter interpolation results, For the previous sampling time, For the next sampling time, for Measured values ​​of environmental parameters at any given time. for Measured values ​​of environmental parameters at any given time.

[0074] Spatial matching uses the real-time planar coordinates of the ship as the retrieval basis, extracts the water depth value and velocity vector of the corresponding position from the three-dimensional terrain grid and velocity field. The water depth extraction adopts the grid bilinear interpolation method, and the velocity extraction is based on the water layer corresponding to the actual draft of the ship, thus completing the one-to-one binding of coordinate position and environmental parameters.

[0075] Furthermore, after spatiotemporal matching, a coupled dataset is constructed. Each fused data point contains 22 parameters: timestamp, planar coordinates, speed components, heading angle, attitude angle, rudder angle, rotational speed, water depth, three-dimensional current velocity, water level, wind speed, and wind direction. This comprehensively records the ship's motion state and corresponding environmental excitation information. Based on the coupled dataset, key ship navigation parameters can be directly calculated, including the drift angle calculation formula:

[0076]

[0077] In the formula, For the drift angle of the ship, The x-axis component of the ground speed. The y-axis component of the ground speed. The bow angle.

[0078] The width of the track zone is calculated based on the ship's dimensions and drift angle, using the following formula:

[0079]

[0080] In the formula, The width of the track band. For the overall length of the ship, For the ship's beam, This is the real-time drift angle.

[0081] Steering frequency is calculated by counting the number of times the rudder angle changes per unit time, using the following formula:

[0082]

[0083] In the formula, For steering frequency, This represents the total number of steering maneuvers within the statistical period. This is for the purpose of calculating the duration.

[0084] The root mean square deviation of the track reflects the degree of deviation between the measured track and the designed route. The calculation formula is as follows:

[0085]

[0086] In the formula, The root mean square of the track deviation. For the first The lateral deviation value of each sampling point This represents the total number of sampling points.

[0087] Furthermore, by introducing the Collision Risk Index (CRI) model in the shipbuilding field, based on two classic ship collision avoidance geometry parameters: the nearest encounter distance (DCPA) and the nearest encounter time (TCPA), the collision risk coefficient 'a' is obtained, as follows: In the formula, , These are the membership functions corresponding to DCPA and TCPA, respectively. , These are the weighting coefficients;

[0088] Based on an elliptical ship domain model, a ship domain proximity factor is introduced. The actual distance between the target ship's center of gravity and the ship's own center of gravity is d, and the polar radius angle between the target ship's center of gravity and the center of the elliptical domain is . The distance to the domain boundary in this direction is Then the domain proximity factor for: The larger of the two values ​​is used to determine the Collision Risk Index (CRI). Alternatively, a nonlinear risk synthesis formula may be used: ;

[0089] Shallow water resistance increase coefficient The formula is: In the formula, H is the channel depth and D is the ship's draft.

[0090] Furthermore, the seaworthiness performance evaluation system adopts a three-layer structure. The target layer is the comprehensive seaworthiness performance index for inland waterway vessels; the criterion layer includes five indicators: basic seaworthiness, heading stability, roll comfort, shallow water adaptability, and risk control; and the indicator layer contains 19 underlying quantitative parameters, all of which can be directly calculated from the coupled dataset. An improved fuzzy hierarchical analysis method is used for weight calculation. First, a judgment matrix is ​​constructed using a three-scale method, with scale values ​​of -1, 0, and 1. The superiority value of each indicator is calculated based on the three-scale matrix, using the following formula:

[0091]

[0092] In the formula, For the first The goodness values ​​of each indicator The third scale matrix Line number Column elements.

[0093] Construct the optimal transfer matrix based on the goodness values. The formula for calculating the matrix elements is as follows:

[0094]

[0095] In the formula, To determine matrix elements, It is the difference between the maximum and minimum values ​​in the goodness value sequence.

[0096] The weights of the indicators are calculated using the square root method. First, the product of the elements in each row of the matrix is ​​calculated:

[0097]

[0098] The root of the product is obtained by taking the nth power.

[0099]

[0100] The final weights are obtained by normalizing the square root values:

[0101]

[0102] In the formula, For the first The weight values ​​of each indicator.

[0103] The underlying metrics are quantified using a cloud model, and cloud digital features, including expected values, are generated through a reverse cloud generator. ,entropy hyperentropy The expected value is calculated using the arithmetic mean of the measured data.

[0104]

[0105] Entropy is calculated using the standard deviation of measured data:

[0106]

[0107] The super-entropy is determined based on the range of entropy fluctuation, with the value range controlled between 0.02 and 0.1.

[0108] The weighted integrated cloud model is used to fuse layer by layer upwards to obtain the digital features of the target layer integrated cloud. The calculation formulas are as follows:

[0109]

[0110]

[0111]

[0112] In the formula, To achieve the overall expected value, For the comprehensive entropy, To integrate hyperentropy, This represents the global weight of the indicator.

[0113] The comprehensive expected value is compared with the preset level range, which is a continuous range of non-overlapping values. Each range corresponds to a level of airworthiness. The final level of airworthiness of the waterway is determined directly based on the range in which the comprehensive expected value is located, thus completing the entire process of technical implementation from multi-source data collection to level assessment.

[0114] After the seaworthiness level is determined, the system automatically generates restrictive navigation control recommendations for the waterway segment based on the comprehensive seaworthiness index and the evaluation results of each criterion level. These recommendations include speed restrictions, representative vessel type restrictions, time restrictions for navigation (all-weather navigation, daytime navigation only, navigation at specific water levels), distance requirements for navigation, and safety operation tips, forming a standardized control plan.

[0115] If the evaluation level is medium or poor, it indicates that the channel dimensions, water flow conditions, navigation stability or safety margin do not meet the design navigation requirements. The system will automatically output suggestions for channel engineering improvement, including channel dredging, channel widening and deepening, straightening of meandering river sections, optimization of navigation mark distribution, and addition of water flow control facilities, to provide a clear basis for subsequent channel improvement, navigation management and project acceptance.

[0116] This detailed implementation describes how multi-source sensor collaborative acquisition and unified spatiotemporal benchmark matching achieve precise fusion of ship navigation and waterway environment data. Relying on a three-layer evaluation system and an improved FAHP-cloud model, it completes objective quantitative calculations and can output stable and reproducible airworthiness rating conclusions, providing complete and reliable technical support for waterway testing and acceptance.

[0117] Based on Embodiment 1, this embodiment details the specific verification of the inland waterway navigation suitability test and evaluation method based on multi-source sensing and spatiotemporal matching, using a dredging project on a curved section of the upper Yangtze River as an example. Figure 3 As shown, the test section is approximately 5 kilometers long, including one bend and one estuary. The waterway is dredged to Class III standards, and the representative vessel type is a 1000-ton cargo ship (85.0m in total length, 10.8m in beam, and 2.0m in full load draft). Before the test, a multi-source sensor network was constructed and a unified spatiotemporal reference was established: an R2Sonic 2026 multibeam echo sounder and four bottom-mounted ADCPs were deployed on shore to monitor underwater topography and three-dimensional current velocity; a NovAtel OEM7 differential GPS and an iXBluePhins fiber optic gyroscope were integrated on the test vessel. All devices were synchronized to UTC via the PTP protocol, with the synchronization error controlled within 0.5 milliseconds, and the spatial data was unified to the CGCS2000 coordinate system. During the test, the ship sailed along the designed route at speeds of 8, 10, and 12 knots, and real-time dynamic response data (such as position, attitude, and rudder angle) and environmental data (water depth and current velocity) were collected, with sampling frequencies of no less than 20 Hz and 1 Hz, respectively.

[0118] Table 1: Evaluation Index System and Allocation Table

[0119] Based on Table 1 above, the system uses a reverse cloud generator to convert the measured data into quantitative cloud digital features (expectation Ex, entropy En, hyperentropy He).

[0120] Calculate the local weights of all indicator layers, the local weight w_local of each indicator, and the global weight w_global relative to the target layer, as shown in Table 2 below.

[0121] Table 2: Local and Global Weights of the Indicator Layer

[0122] Simultaneously, based on the measured data from the actual ship trials, cloud digital features (Ex, En, He) are generated for each underlying indicator.

[0123] Ex (Expected): The value that best represents the qualitative concept of the indicator. For measurable indicators, the mean of the measured data is usually taken.

[0124] Entropy: A measure of uncertainty in qualitative concepts, i.e., the range of values ​​for the indicator. It can be determined based on the standard deviation of measured data and expert experience.

[0125] He (hyperentropy): The entropy of entropy, reflecting the dispersion of cloud droplets and the randomness of expert judgment. It is usually taken as a small value (such as 0.02-0.1), or estimated based on data volatility.

[0126] The following are the feature values ​​of the indicator cloud model based on measured data, as shown in Table 3:

[0127] Table 3: Feature values ​​of the indicator cloud model based on measured data

[0128] The measured channel depth margin was between 2.5 and 3.3 meters, which is better than the standard requirements. The system assigned an expected value Ex of 85. The measured maximum drift angle was 10.5°, corresponding to x of 78. Table 4 shows the generation of cloud model feature values ​​for some key underlying indicators. These values ​​will serve as the input basis for subsequent comprehensive evaluation.

[0129] Table 4: Data Feature Generation Table for Underlying Indicators

[0130] The system employs a weighted integrated cloud model algorithm to comprehensively calculate the underlying indicators upwards, deriving the integrated cloud digital characteristics of the target layer. The calculated Integrated Airworthiness Index (IWSI) Extotal for this flight segment is 81.00, Entropy Entropy is 0.98, and Hyperentropy Hetotal is 0.068. Based on the preset grading standard (Good: [80, 90]), the final evaluation level for this flight segment is Good. To visually demonstrate the performance differences across various dimensions, the system generates an airworthiness performance evaluation and analysis radar chart, such as... Figure 4 As shown in the figure, the score distribution of the five criterion layers is presented in a pentagonal structure: B1, the basic airworthiness index, scores 82.75 (Good), indicating good channel-scale conditions; B3, the roll comfort index, scores the highest at 83.90 (Good), indicating smooth and comfortable navigation; B5, the risk control index, scores 82.45 (Good), indicating sufficient safety margin. However, the B2 heading stability index in the radar chart scores 76.55 (Medium), the lowest among the five dimensions. This is mainly attributed to the larger measured maximum drift angle (10.5°) and higher steering frequency (13 times / min) in the curve section, indicating that this area is a weakness in airworthiness and requires close attention. Through this entire process, the present invention successfully achieved a closed loop from multi-source data acquisition to quantitative evaluation, verifying the effectiveness of the method.

[0131] This embodiment details how, through real-ship testing, this application successfully constructed a multi-source data spatiotemporal matching and FAHP-cloud model evaluation framework, achieving precise quantification of the navigation effectiveness of inland waterways. The test measured a comprehensive navigation index of 81.00, with an evaluation level of "good," and accurately identified heading stability as a weakness. This effectively solves the problems of strong subjectivity and low data utilization in traditional evaluations, significantly improving the objectivity and scientific nature of waterway evaluation.

[0132] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any changes, modifications, substitutions, integrations, and parameter changes made to these embodiments within the spirit and principles of the present invention, without departing from the principles and spirit of the present invention, through conventional substitutions or to achieve the same function, fall within the scope of protection of the present invention.

Claims

1. A method for navigation suitability testing and evaluation of inland waterways using multi-source sensing and spatiotemporal matching, characterized in that, Includes the following steps: S1: Deploy shore-based monitoring units on the selected inland waterway test section and integrate shipborne sensing units on the test vessel; establish a unified spatiotemporal reference covering the shore-based monitoring units and shipborne sensing units; S2: Control the test vessel to navigate along the preset route in the test section, and use the shipborne sensing unit to collect the dynamic response data of the vessel in real time, while using the shore-based monitoring unit to collect the environmental data of the test section in real time. S3: Based on the unified spatiotemporal benchmark, the collected ship dynamic response data and environmental data are spatiotemporally matched and fused to construct a ship response-environmental excitation coupled dataset containing ship motion parameters and corresponding location environmental parameters. S4: Based on the coupled dataset, construct an airworthiness performance evaluation system that includes a target layer, a criterion layer, and an indicator layer; An improved fuzzy hierarchical analysis method was used to determine the weights of indicators at each level, and cloud model theory was combined to quantify the indicators. The comprehensive evaluation value of airworthiness performance was calculated, and the airworthiness effect level of the waterway was determined accordingly.

2. The method for navigation testing and evaluation of inland waterways using multi-source sensing and spatiotemporal matching according to claim 1, characterized in that, In step S1, establishing a unified spatiotemporal reference covering both the shore-based monitoring unit and the shipborne sensing unit specifically includes: By using a high-precision clock synchronization server and employing a precise time protocol or GPS timing method, the system time of the shore-based monitoring unit and the shipborne sensing unit is synchronized to the same standard time to eliminate time synchronization errors between the sensors. All raw data coordinates collected by sensors were uniformly converted to the national geodetic coordinate system, and elevation data were uniformly converted to the national elevation datum to achieve spatial uniformity.

3. The method for navigation testing and evaluation of inland waterways using multi-source sensing and spatiotemporal matching according to claim 1, characterized in that, In step S2, the test vessel is controlled to navigate along a preset route on the test section. The shipborne sensing unit collects real-time dynamic response data of the vessel, while the shore-based monitoring unit collects real-time environmental data for the test section. Specifically: The ship dynamic response data includes at least the ship's real-time position coordinates, ground speed, heading angle, roll angle, pitch angle, yaw rate, rudder angle, and propeller speed. The environmental data includes at least the instantaneous water depth distribution, three-dimensional current velocity distribution, water level, wind speed, and wind direction of the test segment; The shipborne sensing unit and the shore-based monitoring unit record data synchronously according to a preset sampling frequency.

4. The method for navigation testing and evaluation of inland waterways using multi-source sensing and spatiotemporal matching according to claim 1, characterized in that, In step S3, the collected ship dynamic response data and environmental data are spatiotemporally matched and fused, specifically including: Using the timestamp of the data collected by the shipborne sensing unit as the reference time point, an interpolation algorithm is used to process the environmental data to obtain the environmental parameter values ​​corresponding to the reference time point; Based on the real-time position coordinates of the ship at the reference time point, the water depth and current velocity vector at that coordinate position are extracted from the environmental data. The matched time, ship position, ship motion parameters, and corresponding water depth and current velocity vectors are integrated into a single fused data record to generate the coupled dataset.

5. The method for navigation testing and evaluation of inland waterways using multi-source sensing and spatiotemporal matching according to claim 1, characterized in that, The airworthiness performance evaluation system constructed in S4 includes: The target layer is the comprehensive index of the seaworthiness performance of vessels in inland waterways; The criteria layer includes basic airworthiness indicators, heading stability indicators, roll comfort indicators, shallow water adaptability indicators, and risk control indicators; The indicator layer includes several underlying quantitative indicators belonging to each criterion layer. These underlying quantitative indicators include channel depth margin, maximum drift angle, root mean square of roll angle, and minimum ship-to-shore distance.

6. The method for navigation testing and evaluation of inland waterways using multi-source sensing and spatiotemporal matching according to claim 5, characterized in that, In step S4, after constructing the evaluation system, the method further includes a step of calculating the ship dynamic response parameters in the index layer based on the coupled dataset, wherein the drift angle formula is: in, Indicates the drift angle. and These represent the components of the ship's speed relative to the ground along the x-axis and y-axis of the coordinate system, respectively. Indicates the bow angle of the ship.

7. The method for navigation testing and evaluation of inland waterways using multi-source sensing and spatiotemporal matching according to claim 5, characterized in that, The method of determining the weights of indicators at each level using an improved fuzzy hierarchical analysis method specifically includes: When constructing the judgment matrix, the three-scale method is used instead of the traditional nine-scale method, and the relative importance of each indicator is compared and assigned a value of -1, 0 or 1. The optimal transfer matrix algorithm is used to transform the three-scale matrix into a judgment matrix that meets the consistency requirements, and the weight vector of each index is calculated to reduce the bias caused by subjective judgment.

8. The method for navigation testing and evaluation of inland waterways using multi-source sensing and spatiotemporal matching according to claim 5, characterized in that, The quantitative processing of indicators based on cloud model theory specifically includes: For each underlying quantitative indicator, based on the distribution characteristics of the measured data, a reverse cloud generator is used to generate the cloud digital features of that indicator. These cloud digital features include the expected value. ,entropy and hyperentropy ; Among them, expected value The central tendency of the evaluation value of the characterization index, entropy Uncertainty range of the characterization index, hyperentropy Characterizes the degree of dispersion of entropy.

9. The method for navigation testing and evaluation of inland waterways using multi-source sensing and spatiotemporal matching according to claim 8, characterized in that, The calculated comprehensive airworthiness performance evaluation value is obtained by using a weighted integrated cloud model algorithm, which integrates the cloud digital features of each underlying quantitative indicator upwards to calculate the comprehensive cloud digital features of the target layer. The formula is as follows: in, For the first The global weight of each indicator, , , The first Cloud digital characteristics of each indicator This represents the total number of indicators involved in the calculation.

10. The method for navigation testing and evaluation of inland waterways using multi-source sensing and spatiotemporal matching according to claim 1, characterized in that, The determination of the navigational suitability level specifically includes: Based on the expected value in the comprehensive cloud digital characteristics of the target layer obtained by calculation, it is compared with the preset airworthiness effect classification standard. The preset airworthiness rating standard includes multiple non-overlapping numerical ranges, each of which corresponds to a specific airworthiness rating. If the expected value falls into any of the multiple non-overlapping numerical intervals, then the specific airworthiness level corresponding to that interval is determined as the waterway airworthiness level.

Images