Solar energy water navigation platform operation capacity evaluation method

Abstract

This invention provides a method for assessing the operational capability of a solar-powered seaplane platform. The method includes: constructing a joint state space based on the hardware health status and battery charge status of the solar-powered seaplane platform; determining the availability vector of the solar-powered seaplane platform within the joint state space; determining the reliability matrix of the solar-powered seaplane platform based on the probability of the platform transitioning from an initial joint state to a joint state within the joint state space during a commercial marine survey mission; determining the capability vector of the solar-powered seaplane platform using a hierarchical method; and assessing the operational capability of the solar-powered seaplane platform based on its availability vector, reliability matrix, and capability vector. This invention improves the accuracy of operational capability assessment for solar-powered seaplane platforms and can systematically obtain the comprehensive operational capability of solar-powered seaplane platforms in performing commercial marine survey missions.

Description

Technical Field

[0001] This invention relates to the field of equipment performance evaluation technology, and in particular to a method for evaluating the operational capabilities of a solar-powered waterborne navigation platform. Background Technology

[0002] Solar-powered maritime navigation platforms, as a new type of marine equipment utilizing renewable energy and possessing long endurance and autonomous operation capabilities, have shown broad application prospects in civilian fields such as marine resource exploration, hydro-meteorological monitoring, marine ecological surveys, offshore wind farm operation and maintenance, and submarine cable inspection, by carrying mission payloads. During operation, the operational capabilities of solar-powered maritime navigation platforms are highly dependent on unstable solar energy input. Accurate assessment of their operational capabilities is a key link in achieving long-term, reliable, and economical autonomous marine observation, reducing the cost of offshore wind farm operation and maintenance, and improving the return on investment in civilian marine assets.

[0003] In the field of comprehensive operational capability assessment, the ADC (Availability, Dependability, and Capability) model has been widely used due to its clear concepts, rigorous structure, distinct levels, and highly operable indicators. Its assessment results have significant reference value in product development and mission execution. However, the operational capability of solar-powered maritime platforms is highly dependent on unstable solar energy input (manifested as dynamic fluctuations in battery state of charge (SOC)) and complex marine environmental conditions (directly affecting hull structure and equipment reliability). Traditional assessment methods struggle to accurately characterize the combined impact of these multiple factors on operational capability.

[0004] Classical ADC models typically simplify the system state to a binary "intact / faulty" form and assume that the capability vector remains constant during the mission. This differs significantly from the characteristics of solar-powered unmanned aerial vehicles (UAVs), where the energy state continuously changes and the system state is dynamically affected by the mission process. Therefore, it is necessary to improve traditional ADC models to more accurately reflect the actual behavioral characteristics of such energy-dependent unmanned systems, thereby improving the accuracy of operational capability assessment. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a method for evaluating the operational capabilities of a solar-powered waterborne navigation platform.

[0006] This invention provides a method for evaluating the operational capability of a solar-powered maritime navigation platform, comprising:

[0007] A joint state space is constructed based on the hardware health status and battery charge status of the solar-powered seaplane platform, and the availability vector of the solar-powered seaplane platform is determined in the joint state space.

[0008] The reliability matrix of the solar-powered seaplane platform is determined based on the probability that it transitions from an initial joint state to a joint state in the joint state space during the execution of a commercial marine survey mission.

[0009] The capability vector of the solar-powered maritime navigation platform is determined based on the hierarchical CRITIC method.

[0010] The operational capability of the solar-powered seaplane platform is evaluated based on its availability vector, reliability matrix, and capability vector.

[0011] The present invention also provides a device for evaluating the operational capability of a solar-powered maritime navigation platform, comprising:

[0012] The first determining module is used to determine the availability vector of the solar-powered seaplane platform based on the hardware health status and battery charge status of the solar-powered seaplane platform.

[0013] The second determining module is used to determine the reliability matrix of the solar-powered seaplane platform based on the state transition probability of the solar-powered seaplane platform during mission execution.

[0014] The third determining module is used to determine the capability vector of the solar-powered waterborne navigation platform based on the hierarchical CRITIC method.

[0015] The operational capability assessment module is used to assess the operational capability of the solar-powered seaplane platform based on its availability vector, reliability matrix, and capability vector.

[0016] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the operational capability assessment method for solar-powered seaplane navigation platforms as described above.

[0017] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for evaluating the operational capabilities of a solar-powered seaplane platform as described above.

[0018] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for evaluating the operational capabilities of a solar-powered seaplane platform.

[0019] The present invention provides a method for assessing the operational capability of solar-powered maritime navigation platforms. This method considers hardware status and energy level, and uses an improved ADC assessment model to evaluate operational capability. At the availability analysis level, an energy status dimension is introduced to construct a joint "hardware-energy" state space and its initial probability distribution. At the reliability analysis level, a state transition probability matrix is ​​constructed that comprehensively considers the dynamic changes in failure rate caused by factors such as energy consumption, resupply, and sea conditions. At the capability analysis level, a hierarchical CRITIC method is used for objective weighting, overcoming the limitations of traditional subjective evaluation methods, improving the scientific rigor and interpretability of capability assessment, and thus enhancing the accuracy of operational capability assessment. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in this invention 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 invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating the method for evaluating the operational capabilities of a solar-powered waterborne navigation platform provided by the present invention.

[0022] Figure 2 This is a schematic diagram of the Monte Carlo simulation process in the solar-powered waterborne navigation platform operation capability assessment method provided by the present invention;

[0023] Figure 3 This is a flowchart of the hardware status simulation submodule in the solar-powered waterborne navigation platform operation capability assessment method provided by the present invention;

[0024] Figure 4 This is a schematic diagram of the solar-powered waterborne navigation platform operation capability assessment index system in the solar-powered waterborne navigation platform operation capability assessment method provided by the present invention;

[0025] Figure 5 This is a schematic diagram of the structure of the solar-powered waterborne navigation platform operation capability assessment device provided by the present invention;

[0026] Figure 6 This is a schematic diagram of the SOC change at the start time of the daily task during the statistical period in the solar-powered waterborne navigation platform operation capability assessment method provided by the present invention;

[0027] Figure 7 This is a schematic diagram of the joint state probability and frequency distribution in the solar-powered waterborne navigation platform operation capability assessment method provided by the present invention;

[0028] Figure 8This is a heatmap of the mean transfer probability in the solar-powered waterborne navigation platform operational capability assessment method provided by this invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0030] The following is combined with Figure 1 The present invention describes a method for evaluating the operational capability of a solar-powered maritime navigation platform, comprising:

[0031] Step 101: Construct a joint state space based on the hardware health status and battery charge status of the solar-powered watercraft platform, and determine the availability vector of the solar-powered watercraft platform in the joint state space.

[0032] Step 102: Determine the credibility matrix of the solar-powered seaplane platform based on the probability that the platform will transition from its initial state to a joint state in the joint state space during the execution of a commercial marine survey mission.

[0033] Step 103: Determine the capability vector of the solar-powered maritime navigation platform based on the hierarchical CRITIC method;

[0034] Step 104: Evaluate the operational capability of the solar-powered seaplane platform based on its availability vector, reliability matrix, and capability vector.

[0035] The ADC model is used to quantitatively evaluate the extent to which equipment can perform a specified task under given conditions. This model incorporates the system's operational capability. Represented as an availability vector Credibility Matrix With ability vector The product of, i.e.:

[0036]

[0037] This model is a commonly used mathematical model in the field of equipment system mission operational capability assessment. Its core idea is to integrate operational capability (…) The decomposition consists of the product of three factors: the probability of the initial available state when the system begins to execute the task ( ), the reliability of the state during the task process ( ), and the ability to complete task objectives in various states ( This model has advantages such as clear structure, strong logic, and decomposable modeling, and therefore it is widely used in system design selection and operational capability analysis.

[0038] However, the standard ADC model has certain limitations. For long-endurance surface vessels that rely on solar energy, their operational capabilities are highly dependent on unstable energy supplies and complex marine environments. This makes it difficult for traditional methods to directly construct an objective and accurate capability matrix, and also makes it difficult to reflect the dynamic interaction between energy and system status.

[0039] This embodiment adapts and improves the model to suit the characteristics of solar-powered maritime navigation platforms. It fully incorporates the actual operation and working characteristics of energy-dependent unmanned maritime platforms, and constructs a more comprehensive and suitable improved ADC model for evaluating the operational capabilities of energy-dependent unmanned maritime platforms.

[0040] Traditional availability vectors only consider hardware failure states. If the system has n discrete hardware operating states, then the availability vector is:

[0041]

[0042] Satisfy the normalization condition:

[0043]

[0044] The improved availability vector considers both hardware state and initial energy level. Let the number of hardware failure states be... State set The battery state of charge (SOC) is discretized as follows: Each level, a set of states Define the joint state space. Total number of states .

[0045] The improved availability vector is:

[0046]

[0047] in:

[0048]

[0049] Satisfy the normalization condition:

[0050]

[0051] Credibility Matrix Describes the probability of a system transitioning from an initial state to other states during task execution; its dimensions are the same as the availability vector. The state space remains consistent, and it is usually assumed that the state transition probabilities are time-invariant constants.

[0052] Traditional reliability matrices are n-order matrices that only consider system failure states. For solar-powered maritime platforms, the state evolution during mission execution is driven by two coupled factors: first, the continuous change in battery state of charge (SOC) due to energy consumption and solar charging; and second, random failures of the hardware system caused by the marine environment (such as waves, salt spray, and wind speed) and cumulative fatigue. Traditional methods simplify the system state to a binary form of "intact / faulty" and ignore the influence of energy dynamics on transition probabilities, making it difficult to reflect the time-varying and non-stationary characteristics of actual missions.

[0053] Therefore, this embodiment makes the following improvements to the credibility matrix:

[0054] Let the total task duration be The improved credibility matrix is

[0055]

[0056] Among the elements This indicates that the system is in state at the initial moment. Under these conditions, the system transitions to a joint state after one mission cycle. The probability of.

[0057] Satisfy the normalization condition:

[0058]

[0059] In traditional ADC models, the capability vector This measures the system's ability to complete tasks in each joint state. However, the standard model does not specify this. In practical applications, subjective weighting methods such as the Analytic Hierarchy Process (AHP) are often used to determine the weights of each capability indicator. For solar-powered waterborne navigation platforms, their mission capabilities involve multiple dimensions such as endurance, payload operation capability, and communication reliability. There are often information overlaps and conflicts between the indicators, and it is difficult to guarantee the objectivity of weight assignment and the reproducibility of results by simply relying on expert experience.

[0060] Therefore, this embodiment adopts the hierarchical CRITIC (Criteria Importance Through Intercriteria Correlation) method, with the target layer being the comprehensive task capability. The criteria layer contains m capability sub-dimensions, each with several indicators, resulting in a total of p underlying indicators. The CRITIC method determines weights by comprehensively considering the degree of variation (standard deviation) within each indicator and the conflict between indicators (correlation coefficient), making it entirely data-driven. First, weights are assigned to the underlying indicators within each sub-dimension, then weights are assigned to each sub-dimension, ultimately synthesizing a comprehensive capability vector.

[0061] This embodiment considers hardware status and energy level, using an improved ADC evaluation model for operational capability assessment. At the availability analysis level, an energy status dimension is introduced, constructing a joint "hardware-energy" state space and its initial probability distribution. At the reliability analysis level, a state transition probability matrix is ​​constructed that comprehensively considers the dynamic changes in failure rate caused by factors such as energy consumption, resupply, and sea state. At the capability analysis level, a hierarchical CRITIC method is used for objective weighting, overcoming the limitations of traditional subjective evaluation methods and improving the scientific rigor and interpretability of capability assessment, thereby enhancing the accuracy of operational capability assessment. Based on this model, the success probability of a solar-powered maritime platform under a specified navigation mission can be quantitatively assessed, thus providing a more scientific and refined quantitative analysis framework for the comprehensive operational capability assessment of equipment.

[0062] Based on the above embodiments, this embodiment further includes the following before constructing the joint state space according to the hardware health status and battery charge status of the solar-powered watercraft platform:

[0063] Based on the failure interval of the solar-powered watercraft platform, determine the probability that the hardware health status of the solar-powered watercraft platform is normal.

[0064] Based on the probability that the hardware health status of the solar-powered water navigation platform is normal, determine whether the hardware health status is normal.

[0065] Based on the average repair time of the solar-powered watercraft platform, determine the probability that the hardware health status of the solar-powered watercraft platform is faulty;

[0066] Based on the probability that the hardware health status of the solar-powered watercraft platform is faulty, determine whether the hardware health status is faulty.

[0067] The initial state of a solar-powered waterborne navigation platform before it performs a mission is jointly determined by the hardware health status and the battery state of charge (SOC). This embodiment divides the hardware status into two categories: normal (…). ) and faults ( The probability is determined by the mean time between failures (MTBF). ) and mean repair time ( )Decide:

[0068]

[0069]

[0070] Satisfy the normalization condition:

[0071]

[0072] Based on the above embodiments, this embodiment constructs a joint state space according to the hardware health status and battery charge status of the solar-powered seaplane platform, and determines the availability vector of the solar-powered seaplane platform in the joint state space, including:

[0073] A joint state space is constructed by combining the discrete energy levels of the hardware health state and the battery state of charge.

[0074] Based on real irradiance data, Monte Carlo simulation is used to statistically analyze the frequency of each joint state in the joint state space, and the availability vector is obtained based on the frequency of each joint state.

[0075] The battery's SOC ranges from [0,1], and it is discretized as follows: Each level. Introduction There are thresholds that satisfy:

[0076]

[0077] Then the first Energy Level Corresponding SOC∈ ,in In this article, let The specific value is:

[0078]

[0079] The energy levels, from highest to lowest, are as follows: Corresponding to the highest energy range , correspond , correspond , correspond , correspond The joint state space has a total of There are several states. Sorted by hardware state priority: first... The states are: hardware is normal, with various energy levels superimposed (from high to low):

[0080]

[0081] The last five states represent hardware failures combined with various energy levels:

[0082]

[0083] The availability vector is then:

[0084]

[0085] Because solar-powered maritime navigation platforms may experience a random standby or preparation period before a mission is launched, during which hardware failures and repairs may occur, and the batteries are affected by fluctuations in solar radiation, the initial state of charge (SOC) exhibits randomness. Therefore, this embodiment employs the Monte Carlo simulation method to solve the probability distribution of the initial joint state according to the following procedure.

[0086] Overall Monte Carlo simulation framework (such as) Figure 2 First, set the system parameters, including the battery rated capacity. Charge and discharge efficiency and SOC upper and lower limits and Standby load power Maximum charge and discharge power and and hardware reliability parameters Total simulation duration Load hourly irradiance data for a typical meteorological year (TMY) and overlay random disturbances. The photovoltaic power generation sequence was converted into a photovoltaic power generation sequence through the photovoltaic model. Set the total number of simulations. For each loop, the hardware state simulation submodule is called sequentially to generate a fault state array, and then the energy management submodule is called to update the SOC periodically, recording the final SOC and hardware state after the warm-up period and the final SOC and hardware state at the end of the simulation. After all loops are completed, the frequency of each joint state (the combination of hardware normal / fault and the 5 SOC levels) is counted. Normalization yields the initial probability This yields the initial availability vector. .

[0087] Hardware state simulation submodule (such as) Figure 3 Initialize the current time and system status to normal, and set the total simulation duration. If the current state is normal, then generate a fault interval time that follows an exponential distribution. Calculate the fault start time ,like Then the normal state is recorded until It will then enter a fault state; otherwise, the normal state will continue until... And end; if the current state is faulty, then generate a repair time. Calculate the time when the repair is completed. ,like The fault status will be recorded until... After returning to normal, the current time will be updated to the next walk, and the above process will be repeated; otherwise, the fault state will continue until... And then it ends; finally, it outputs the hardware status (normal / fault) sequence for each discrete time step.

[0088] The energy management submodule determines the charging and discharging status based on the relationship between photovoltaic power and load power. During charging, the actual charging energy is constrained by three factors: the theoretical charging energy after efficiency conversion, the remaining battery capacity, and the maximum allowable charging power. During discharging, the actual discharging energy is constrained by three factors: the theoretical discharging energy, the available battery capacity, and the maximum allowable discharging power. The system updates the State of Charge (SOC) based on these constraints and limits it within preset upper and lower threshold ranges before feeding it back to the main control module.

[0089] The control logic of the energy management submodule is as follows:

[0090] when When the system enters battery charging mode, the excess power is converted into charging energy, and the constraints are as follows:

[0091] ;

[0092] when When the system switches to battery discharge mode, the constraints are as follows:

[0093] ;

[0094] In the formula: for Simulate the power change energy of the system within a step size; This refers to the theoretical rechargeable energy after taking charging efficiency into account. This refers to the theoretical discharge energy required after taking into account discharge efficiency. This represents the maximum available capacity that can be released from the battery in its current state. This represents the maximum remaining chargeable capacity of the battery under its current state.

[0095] Based on the above embodiments, this embodiment determines the reliability matrix of the solar-powered seaplane platform according to the probability of the platform transitioning from an initial joint state to a joint state in the joint state space during mission execution, including:

[0096] The initial joint state of the solar-powered waterborne navigation platform is determined by sampling each joint state in the joint state space based on the availability vector.

[0097] Monte Carlo simulation was used to obtain the probability that a solar-powered waterborne platform would transition from an initial joint state to any joint state in the joint state space after one mission cycle.

[0098] Based on the probability that the solar-powered seaplane platform transitions from the initial joint state to any joint state, a reliability matrix for the solar-powered seaplane platform is constructed.

[0099] Because the energy changes of a solar-powered waterborne navigation platform are affected by irradiance, load power, and charging / discharging constraints, and because hardware failures and repair processes are random, the state transitions exhibit non-homogeneous Markov characteristics. This embodiment first analyzes the physical process of the state transitions, and then uses Monte Carlo simulation to solve for the transition probability matrix.

[0100] Within each discrete time step, the system's net power determines the direction and magnitude of the State of Charge (SOC) change. Constrained by charge / discharge efficiency, SOC upper and lower limits, and maximum charge / discharge power, continuous changes in SOC map to transitions between discrete energy levels. Current level Possibly transferred to , Alternatively, it may remain unchanged, but under strong radiation conditions, cross-level transitions may occur. Simultaneously, the hardware alternates between normal and fault states. Faults reduce load power (specific settings depend on actual conditions), and extremely low SOC may also trigger protection shutdowns. Therefore, the two sub-processes are coupled and need to be modeled uniformly in a joint state space.

[0101] Simulation duration is the same as task duration. The initial state is sampled based on the availability vector A, and the state transitions within each time step are statistically analyzed, i.e.:

[0102]

[0103] Based on the above embodiments, this embodiment uses the hierarchical CRITIC method to determine the capability vector of the solar-powered seaplane platform, including:

[0104] Establish a hierarchical capability indicator system, which includes multiple secondary indicators and multiple tertiary indicators under each secondary indicator;

[0105] Calculate the local weight of each tertiary indicator based on the dimensionless value of each indicator in each joint state.

[0106] Based on the local weights of the tertiary indicators under each secondary indicator and the dimensionless values ​​in each joint state, calculate the dimensionless value of each secondary indicator in each joint state.

[0107] Calculate the weight of each tertiary indicator based on the dimensionless value of each secondary indicator in each joint state;

[0108] Calculate the comprehensive capability value under each joint state based on the weight of each secondary indicator and its dimensionless value under each joint state.

[0109] The capability vector is constructed based on the comprehensive capability value under each joint state.

[0110] Based on typical operational tasks of solar-powered maritime navigation platforms, a hierarchical capability index system is established. This paper sets the number of secondary capability indicators to be [number missing] based on typical operational tasks. These are: civilian operational navigation and endurance capabilities, environmental perception and data acquisition capabilities, sample fidelity and analysis capabilities, data communication and data transmission capabilities, and autonomous operation and path optimization capabilities. Each secondary indicator has several quantifiable tertiary indicators, totaling... One, specifically as follows Figure 4 As shown.

[0111] For any joint state Its operational capability value This can be represented as a linear combination of the dimensionless values ​​of each secondary capability indicator and its weight:

[0112]

[0113] In the formula: For the first The weights of each secondary capability indicator satisfy the following: ; In a joint state Next The dimensionless value (range [0,1]) of each secondary indicator. Furthermore, the dimensionless value of each secondary indicator is obtained by weighting the dimensionless values ​​of its subordinate tertiary indicators:

[0114]

[0115] in, For the first The set of tertiary indicators contained in each secondary indicator. This refers to the local weight of the third-level indicator. This represents the dimensionless value of the third-level indicator (obtained after standardization). Combining the above two equations, we can obtain the direct expression for the comprehensive capability value:

[0116]

[0117] in, For the first The global weight of each tertiary indicator (i.e., the product of its local weight and the weight of its respective tertiary indicator). All The capability vector is obtained by arranging the capability values ​​of the joint states:

[0118]

[0119] Based on the above embodiments, this embodiment calculates the local weight of each tertiary indicator according to the dimensionless value of each tertiary indicator in each joint state, including:

[0120] Calculate the standard deviation of each tertiary indicator based on its dimensionless value under each joint state;

[0121] Calculate the conflict between each tertiary indicator and other tertiary indicators;

[0122] Calculate the information content of each tertiary indicator based on its standard deviation and corresponding conflict.

[0123] Calculate the local weight of each tertiary indicator based on the amount of information contained in each tertiary indicator.

[0124] In constructing capability vectors In the process, the original data of each tertiary indicator must first be converted into dimensionless values ​​to eliminate the influence of dimensions and orders of magnitude; then the hierarchical CRITIC method is used to objectively calculate the global weight of each indicator.

[0125] For each joint state and each tertiary indicator Raw data needs to be obtained first. Then, the range standardization method is used to map all indicators to the [0,1] interval, with larger standardized values ​​indicating stronger representation. For positive indicators (larger original values ​​indicate stronger representation), the standardization formula is:

[0126]

[0127] For negative indicators (smaller raw values ​​indicate stronger capabilities, such as turning radius), the standardized formula is:

[0128]

[0129] When the range of some tertiary indicators under a certain secondary indicator is zero across all joint states, this type of indicator lacks state-discriminating ability. Based on this, tertiary indicators are divided into variable and constant groups. Sensitivity analysis is then conducted by adjusting the proportion of constant indicators within the secondary indicators to verify the results. Robustness. In this embodiment, only data from the executable task state is used in the CRITIC weight calculation, excluding zero values ​​in low-energy standby and fault states, in order to avoid statistical distortion and ensure that the weights reflect the relative importance of the capability itself.

[0130] For the k-th secondary indicator, let its subordinate p k For each of the three-level indicators, based on the standardized data matrix of all N joint states, the following steps are performed:

[0131] (1) Calculate the standard deviation of the vth tertiary indicator. (Reflecting the intensity of contrast):

[0132]

[0133] (2) Calculate the conflict between the v-th tertiary indicator and other tertiary indicators:

[0134]

[0135] in, is the Pearson correlation coefficient between the tertiary index v and the tertiary index w.

[0136] (3) Calculate the amount of information:

[0137]

[0138] (4) Calculate the local weights:

[0139]

[0140] Local weights reflect the relative importance of each lower-level indicator to the higher-level indicator (i.e., the secondary indicator) within the same capability sub-dimension.

[0141] Similarly, each secondary indicator is treated as a composite indicator, and its value is the weighted sum of the dimensionless values ​​of the tertiary indicators under that secondary indicator (using the local weights obtained in the previous step):

[0142]

[0143] Therefore, all The state is in The score matrix for each secondary indicator is then applied again using the CRITIC method (steps as above) to calculate the weights of the secondary indicators. ,satisfy:

[0144]

[0145] The following describes the solar-powered seaplane platform operation capability assessment device provided by the present invention. The solar-powered seaplane platform operation capability assessment device described below can be referred to in correspondence with the solar-powered seaplane platform operation capability assessment method described above.

[0146] like Figure 5 As shown, the device includes a first determining module 501, a second determining module 502, a third determining module 503, and a work capability assessment module 504, wherein:

[0147] The first determining module 501 is used to determine the availability vector of the solar-powered seaplane platform based on the hardware health status and battery charge status of the solar-powered seaplane platform.

[0148] The second determining module 502 is used to determine the reliability matrix of the solar-powered seaplane platform based on the state transition probability of the solar-powered seaplane platform during mission execution.

[0149] The third determining module 503 is used to determine the capability vector of the solar-powered waterborne navigation platform based on the hierarchical CRITIC method;

[0150] The operational capability assessment module 504 is used to assess the operational capability of the solar-powered seaplane platform based on its availability vector, reliability matrix, and capability vector.

[0151] In the case study, a publicly available international standard set of nearshore environmental parameters was selected. Based on publicly available historical meteorological data for that location, the hourly irradiance sequence from 2021 to 2023 was used as the solar energy input benchmark, and Gaussian perturbations were superimposed to simulate actual weather fluctuations. The operational capability parameters of a certain type of solar-powered surface vessel suitable for routine commercial marine monitoring are shown in Table 1.

[0152] Table 1. Operational Capability Parameters of a Certain Type of Solar-Powered Floating Platform

[0153]

[0154] In this embodiment, a waterborne navigation platform equipped with a multi-parameter water quality monitor, an acoustic Doppler current profiler (ADCP), and a weather station is simulated to carry out a 10-hour (0800-1800) hydrological environment survey mission. This is the average power consumption of the aforementioned scientific research payload.

[0155] To eliminate the influence of the initial state of charge (SOC), a one-year (8760-hour) preheating period was set in the simulation. The SOC distribution at the end of the preheating period (hour 8761) is shown below. Figure 6 As shown, its mean The result shows a significant difference from the initial setpoint of 0.5. This indicates that the warm-up period has effectively eliminated the interference of artificial initial conditions, and the mean SOC after warm-up (0.61092) more accurately reflects the natural steady-state energy level of the system under given illumination, load, and fault parameters, thus possessing practical physical significance. All subsequent statistics are based on this distribution after warm-up.

[0156] Figure 7 Ten joint states are given. probability And its frequency of appearance in Monte Carlo simulations. The system has five energy levels when it is functioning normally. The energy level corresponding to system failure. The total statistical sample size is 17,520,000 hours of state records (data from the last two years of 1,000 independent simulations).

[0157] Therefore, we can obtain vector A = [0.06252, 0.07104, 0.25153, 0.44232, 0.14829, 2.56 × 10^24]. -4 4.89×10 -4 8.97×10 -4 ,0.0026,0.02007].

[0158] Based on the Monte Carlo simulation framework, the frequency of reaching the final state s′ after one task cycle from each initial state s is statistically analyzed, thereby obtaining the transition probability matrix D for task duration from 08:00 to 18:00.

[0159] D=

[0160] Its structure presents a clear block-like form:

[0161]

[0162] Subscript This indicates that the hardware is working properly. ), Indicates a hardware failure ( ). This describes the evolution of energy levels under normal hardware conditions. This is a leakage path from normal to faulty. This represents energy migration within a fault state; the zero block in the lower left corner indicates that the fault is unrecoverable during the mission. The mean heatmap of the transition probability is shown below. Figure 8 As shown.

[0163] Capability Vector This indicates that the system is in various joint states. The ability to complete specified tasks on time is measured. Based on the established hierarchical indicator system, and using the indicators given in Table 2... to The raw data of the three-level indicators for four normal states were used to determine the weights using the hierarchical CRITIC method, and the comprehensive capability value of each state was synthesized. For the normal states with lower energy... and hardware failure status to In actual missions, the capability can be considered zero, and this invention uniformly sets its capability value to zero. This conservative approach does not affect the relative order of the components in the capability vector, and it is consistent with engineering practice (under extremely low energy or fault conditions, solar-powered waterborne navigation platforms are unable to complete the prescribed scientific research tasks).

[0164] For 10 joint states to The raw values ​​of 16 tertiary indicators were obtained through dynamic simulation of the waterborne navigation platform, mission load modeling, and measured data.

[0165] Table 2. Marine Environmental Monitoring Operation Capability Index System for a Certain Type of Solar-Powered Floating Platform

[0166]

[0167] against to The raw data for 16 tertiary indicators were obtained from four joint states, as shown in Table 3. Among them, the turning radius and storage capacity are constant values ​​in all states.

[0168] Table 3 Standardized Level 3 Indicator Values ​​( – )

[0169]

[0170] The weights of each indicator are calculated using the stratified CRITIC method. For constant indicators such as radius of gyration and sample cold storage time, the CRITIC method cannot directly handle them because their standard deviation is zero. This invention employs a strategy of "equal-weighted basic allocation + sensitivity analysis": first, the basic weights of constant indicators are allocated equally according to the total number of tertiary indicators under the secondary indicator; then, a sensitivity scan is performed on the weights of the constant indicators.

[0171] Taking "civilian operational navigation and endurance" as an example, it comprises five tertiary indicators, four of which are variable (economic cruising speed, continuous operational endurance (pure solar power), continuous operational endurance (hybrid power), and energy self-sufficiency days), and one is constant (turning radius). First, the CRITIC method (based on Table 3) is applied to the four variable indicators. – The standard deviation, conflict, information content, and CRITIC weights were calculated from the data, and the results are shown in Table 4.

[0172] Table 4. CRITIC Weights of Variable Indicators under Civil Operation Navigation and Endurance Capabilities

[0173]

[0174] The local weights of the three sub-indicators under the Civil Operation Navigation and Endurance Capability are determined as follows: The total weight of the variable indicators is taken as follows: Local weights of each variable index = Local weights of the constant index (radius of gyration) are taken as follows: The results are shown in Table 5.

[0175] Table 5. Partial Weights of Third-Level Indicators for Civilian Operational Navigation and Endurance Capability (Basic Scheme)

[0176]

[0177] Similarly, calculate the local weights of the tertiary indicators under the other secondary indicators. Among them, the four indicators under "Environmental Perception and Data Acquisition Capability" are all variable, and the CRITIC results are used directly; among the two indicators under "Sample Fidelity and Analysis Capability," the in-situ water quality analysis time is variable, while the sample cold storage time is constant, and the weights are calculated according to... Weights were assigned to constant and variable indicators; the two indicators under "Data Communication and Backhaul Capability" were both variable and directly CRITIC; the three indicators under "Autonomous Operation and Path Optimization Capability" were all variable and directly CRITIC. The summarized results are shown in Table 6.

[0178] Table 6. Local Weights of Tertiary Indicators under Each Secondary Indicator (Basic Scheme)

[0179]

[0180] First, calculate each secondary indicator. – The score The 4×5 secondary indicator score matrix is ​​obtained (as shown in Table 7).

[0181] Table 7. Scores of Secondary Indicators under Normal Conditions (Basic Scheme)

[0182]

[0183] The CRITIC method was applied to Table 7 (all secondary indicators are variable) to calculate the standard deviation, conflict, information content, and weight of each secondary indicator. The results are shown in Table 8.

[0184] Table 8 Weights of Secondary Indicators (Basic Scheme)

[0185]

[0186] No. Global weight of each tertiary indicator Table 9 lists the global weights for all tertiary indicators.

[0187] Table 9 Global Weights of Level 3 Indicators (Basic Scheme)

[0188]

[0189] for to Overall ability value .for (Normal but energy level is lower) ) and fault status to Since the capability is extremely low in actual tasks, this paper uniformly sets its capability value to zero. Therefore, the capability vector is:

[0190]

[0191] Because the constant indicators (radius of gyration, sample refrigerated storage time) are assigned non-zero weights in the basic scheme, but their standardized values ​​are always 1, their contribution to the capability values ​​is a constant offset. To examine the impact of these constant indicator weights on capability value ranking and operational capability assessment results, this paper designs three sets of sensitivity analysis experiments:

[0192] Option A (Basic Option): Constant indicators are allocated with equal weights (as above).

[0193] Option B (Low Constant Weight): Compress the total weight of constant indicators to 50% of their original value, and increase the weight of the remaining variable indicators proportionally.

[0194] Option C (High Constant Weight): Increase the total weight of constant indicators to 150% of the original value, and compress the remaining variable indicators proportionally to ensure that the total weight is 1.

[0195] Option D (Zero Constant Weight): Completely ignore constant indicators (i.e., directly assign 0 weights in the traditional CRITIC method), and only use variable indicators to calculate the capability value.

[0196] Calculate the four schemes – The ability values ​​are shown in Table 10.

[0197] Table 10 Comparison of Ability Values ​​under Different Constant Index Weights

[0198]

[0199] As can be seen from Table 10, the ranking of capability values ​​is completely consistent across all schemes. Furthermore, the difference between adjacent states remains stable. Option D (ignoring constant indices) leads to... The capability value is 0.0000, while Scheme C (high constant) gives 0.1118, but the relative order remains unchanged. This indicates that while the constant index affects the absolute value of the capability, it does not change the relative superiority or inferiority relationship between the components of the capability vector. Due to the final operational capability... It is a weighted sum, and and The probability of medium-to-high energy states is dominant. Changes in the weights of constant indicators only linearly scale the overall range of operational capability values ​​and do not alter the operational capability ranking of different design schemes. Therefore, the capability vector given by the basic scheme is sufficiently robust and can be used for subsequent operational capability assessments.

[0200] Based on the ADC model, the operational capability (E) of this solar-powered maritime platform in performing marine environmental monitoring tasks is:

[0201] ;

[0202] Where, )(A) is the availability row vector ( (D) is the reliability transition matrix ( (C) is the capability column vector ( Substituting the obtained (A), (D), and (C) into the equation, we get:

[0203] = 0.4377;

[0204] To quantitatively separate the independent contributions of dynamic energy state evolution and random hardware failures to operational capability assessment, two sets of comparative simulation scenarios were set up:

[0205] Scenario 1: To independently assess the impact of hardware reliability on operational success, this scenario interrupts the solar-battery energy closed-loop feedback. It assumes no battery SOC degradation throughout the operation, ensuring all hardware maintains its theoretical peak capacity under normal operating conditions, and that hardware failure logic and probability remain unchanged. The calculated operational capability value is:

[0206] 1.0000 ;

[0207] Scenario 2: To quantify the dominant impact of the dynamic interplay between solar power supply fluctuations and operational energy consumption on the system's operational capability, this scenario controls for random hardware failures and treats the system as ideally reliable. Based on this, a Monte Carlo simulation is re-executed using the simulation framework of this invention. First, the energy level distribution under normal hardware conditions at the initial moment of the task is obtained, and after normalization, the initial state vector is obtained:

[0208] ;

[0209] Then, a reliability submatrix containing only energy transfers between normal states is constructed:

[0210] ;

[0211] The task capability vector is extracted by taking the first 5 components corresponding to the normal hardware state from the original capability vector, denoted as:

[0212] ;

[0213] Substitute the above three factors into the work capacity calculation formula:

[0214] 0.4281;

[0215] By comparing the relative differences among the three, the different weights of energy management and hardware reliability in ensuring the success rate of commercial operations can be quantified, providing decision support for equipment selection and reducing operating costs.

[0216] Ignoring energy state evolution, the operational capability estimate surges to 0.9757, a relative increase of 123% compared to the baseline. This significant deviation indicates that assuming a constant battery SOC and peak capability throughout the mission would severely overestimate the actual mission completion capability of the solar-powered maritime platform. Continuous power consumption during the mission causes the energy level to drift to lower levels, which photovoltaic refueling cannot fully compensate for, resulting in a substantial decrease in capability.

[0217] Ignoring hardware failures, the operational capability only decreased slightly to 0.4281, a reduction of approximately 2.3%. This is because the probability of failure in a single task is extremely low (approximately 0.5%), and low-energy state failures have already dominated the capability decay, thus masking the marginal impact of hardware failures.

[0218] This invention addresses the challenge of dynamically coupling hardware status and energy dynamics in the operational capability assessment of solar-powered maritime platforms, proposing an assessment method based on an improved ADC model. The main work and conclusions are as follows:

[0219] (1) In availability analysis, we break through the traditional binary assumption of "intact / faulty" and construct a joint state space (10 states in total) that includes hardware state and 5 levels of battery state of charge (SOC). Based on real irradiance data and Monte Carlo simulation, we solve the probability distribution of the initial joint state, eliminate the influence of artificially set initial SOC, and make the availability vector more physically meaningful.

[0220] (2) In the credibility analysis, a transition probability matrix that comprehensively considers energy evolution and random hardware failures was established. Simulation results show that hardware failures are unrecoverable during the mission, the energy state exhibits dissipative characteristics of drifting towards the medium to low range, and the failure probability is independent of the energy level, verifying the rationality of the model assumptions.

[0221] (3) In the capability analysis, a hierarchical system consisting of 5 secondary indicators and 16 tertiary indicators was constructed. The hierarchical CRITIC method was used for objective weighting, and sensitivity analysis was conducted on constant indicators. The results show that the capability value decreases monotonically with the decrease of energy level, the capability in the fault state is zero, and the change of weight does not change the capability ranking, proving that the assessment results are robust.

[0222] (4) Based on the ADC model, the synthesized job capabilities are obtained as the job capability values ​​under typical task conditions. Comparative analysis shows that if energy dynamics or hardware failures are ignored, the operational capability will be overestimated by approximately 123%, highlighting the necessity of the improved model presented in this paper.

[0223] The method proposed in this invention provides a scientific quantitative tool for assessing the operational capabilities of energy-dependent unmanned systems. The research results can directly guide the optimization of energy management strategies for solar-powered maritime platforms, the formulation of preventative hardware maintenance plans, and the economic evaluation of commercial operations. Future research can further incorporate key factors such as sea state dynamics, the diversity of commercial payloads, and multi-platform cluster collaborative monitoring to further expand the model's applicability and scope in the fields of civilian marine engineering and operational support.

[0224] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the operational capability of a solar-powered maritime navigation platform, characterized in that, include: A joint state space is constructed based on the hardware health status and battery charge status of the solar-powered seaplane platform, and the availability vector of the solar-powered seaplane platform is determined in the joint state space. The reliability matrix of the solar-powered seaplane platform is determined based on the probability that it transitions from an initial joint state to a joint state in the joint state space during the execution of a commercial marine survey mission. The capability vector of the solar-powered maritime navigation platform is determined based on the hierarchical CRITIC method. The operational capability of the solar-powered seaplane platform is evaluated based on its availability vector, reliability matrix, and capability vector. A joint state space is constructed based on the hardware health status and battery state of charge of the solar-powered seaplane platform, and the availability vector of the solar-powered seaplane platform is determined in the joint state space, including: A joint state space is constructed by combining the discrete energy levels of the hardware health state and the battery state of charge. Based on real irradiance data, Monte Carlo simulation was used to statistically analyze the frequency of each joint state in the joint state space, and the availability vector was obtained based on the frequency of each joint state. Based on the probability that the solar-powered seaplane platform transitions from an initial joint state to a joint state in the joint state space during the execution of a commercial marine survey mission, a reliability matrix for the solar-powered seaplane platform is determined, including: The initial joint state of the solar-powered waterborne navigation platform is determined by sampling each joint state in the joint state space based on the availability vector. Monte Carlo simulation was used to obtain the probability that the solar-powered seaplane platform would transition from the initial joint state to any joint state in the joint state space after completing a commercial marine survey mission cycle. Based on the probability that the solar-powered seaplane platform transitions from the initial joint state to any joint state, a reliability matrix for the solar-powered seaplane platform is constructed.

2. The method for evaluating the operational capability of a solar-powered maritime navigation platform according to claim 1, characterized in that, Before constructing the joint state space based on the hardware health status and battery state of charge of the solar-powered maritime navigation platform, the following steps are also included: Based on the failure interval of the solar-powered watercraft platform, determine the probability that the hardware health status of the solar-powered watercraft platform is normal. Based on the probability that the hardware health status of the solar-powered water navigation platform is normal, determine whether the hardware health status is normal. Based on the average repair time of the solar-powered watercraft platform, determine the probability that the hardware health status of the solar-powered watercraft platform is faulty; Based on the probability that the hardware health status of the solar-powered watercraft platform is faulty, determine whether the hardware health status is faulty.

3. The method for evaluating the operational capability of a solar-powered maritime navigation platform according to claim 1, characterized in that, Based on the hierarchical CRITIC method, the capability vector of the solar-powered maritime navigation platform is determined, including: Establish a hierarchical capability indicator system, which includes multiple secondary indicators and multiple tertiary indicators under each secondary indicator; Calculate the local weight of each tertiary indicator based on the dimensionless value of each indicator in each joint state. Based on the local weights of the tertiary indicators under each secondary indicator and the dimensionless values ​​in each joint state, calculate the dimensionless value of each secondary indicator in each joint state. Calculate the weight of each tertiary indicator based on the dimensionless value of each secondary indicator in each joint state; Calculate the comprehensive capability value under each joint state based on the weight of each secondary indicator and its dimensionless value under each joint state. The capability vector is constructed based on the comprehensive capability value under each joint state.

4. The method for evaluating the operational capability of a solar-powered maritime navigation platform according to claim 3, characterized in that, Based on the dimensionless value of each tertiary indicator in each joint state, calculate the local weight of each tertiary indicator, including: Calculate the standard deviation of each tertiary indicator based on its dimensionless value under each joint state; Calculate the conflict between each tertiary indicator and other tertiary indicators; Calculate the information content of each tertiary indicator based on its standard deviation and corresponding conflict. Calculate the local weight of each tertiary indicator based on the amount of information contained in each tertiary indicator.

5. The method for evaluating the operational capability of a solar-powered maritime navigation platform according to claim 3, characterized in that, The secondary indicators include civilian operational navigation and endurance capabilities, environmental perception and data acquisition capabilities, sample fidelity and analysis capabilities, data communication and data transmission capabilities, and autonomous operation and path optimization capabilities.

6. A device for evaluating the operational capability of a solar-powered maritime navigation platform, characterized in that, The method for evaluating the operational capability of a solar-powered maritime navigation platform as described in any one of claims 1-5 includes: The first determining module is used to determine the availability vector of the solar-powered seaplane platform based on the hardware health status and battery charge status of the solar-powered seaplane platform. The second determining module is used to determine the credibility matrix of the solar-powered seaplane platform based on the state transition probability of the solar-powered seaplane platform during the execution of commercial marine survey missions. The third determining module is used to determine the capability vector of the solar-powered waterborne navigation platform based on the hierarchical CRITIC method. The operational capability assessment module is used to assess the operational capability of the solar-powered seaplane platform based on its availability vector, reliability matrix, and capability vector.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for evaluating the operational capabilities of a solar-powered seaplane platform as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for evaluating the operational capabilities of a solar-powered seaplane platform as described in any one of claims 1 to 5.

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