A three-dimensional ecological monitoring data acquisition and transmission system and method for deep sea resource development

Abstract

This invention relates to the field of deep-sea three-dimensional ecological monitoring and data transmission technology, and in particular to a three-dimensional ecological monitoring data acquisition and transmission system and method for deep-sea resource development. The method includes a multi-modal sensing system based on the sky, airspace, sea surface, and seabed; constructing a comprehensive value weight that integrates expert experience and environmental disturbances; dynamically evaluating data transmission priority through a fuzzy measure function; and obtaining the probability distribution of transmission success rate and the time delay confidence interval through Monte Carlo perturbation analysis. This invention can effectively achieve priority and reliable transmission of high-value early warning information, improving the integrity of monitoring data and decision support capabilities in extreme marine environments.

Description

Technical Field

[0001] This invention relates to the field of marine ecological data acquisition and transmission technology, and more specifically, to a three-dimensional ecological monitoring data acquisition and transmission system and method for deep-sea resource development. Background Technology

[0002] Deep-sea ecological monitoring is a key support for the coordinated development of marine resource development and environmental protection. Its core task is to build a three-dimensional sensing system covering the sky, airspace, sea surface and seabed, and to conduct long-term, continuous and highly reliable dynamic tracking of elements such as water quality parameters, biological activities and environmental disturbances, thereby providing a scientific data foundation and decision support for marine ecological supervision, sustainable resource utilization and environmental risk early warning.

[0003] Current deep-sea monitoring systems primarily rely on multi-source heterogeneous sensing devices and multi-level communication links in actual operation. Monitoring data typically needs to be relayed to shore-based centers via underwater acoustic communication, surface relay, microwave, or satellite links. Due to the complex marine dynamic environment, these communication links generally face challenges such as bandwidth limitations, high error rates, poor connection stability, and frequent signal interruptions. Particularly in underwater acoustic communication and long-distance satellite transmission scenarios, data loss, out-of-order timing, content duplication, or information distortion occur during packet encapsulation, caching, compression, and reassembly. Secondly, existing technologies often employ general verification or fixed retransmission mechanisms to ensure data integrity. However, these methods often fail to fully consider the energy consumption constraints and edge computing power of deep-sea nodes. There is room for optimization in implementing real-time verification and ensuring reliable transmission of multimodal ecological data under low power consumption and low bandwidth constraints, which in turn affects the reliability and continuity of monitoring data and its effectiveness in emergency decision-making. In addition, mainstream transmission schemes mostly process all data indiscriminately, lacking a graded assessment of the value of data services. This leads to competition between high-value early warning information and regular background data under limited resources, restricting the system's engineering applicability in extreme environments.

[0004] In summary, most existing deep-sea data transmission methods remain at the passive retransmission stage, which suffers from low multi-link coordination efficiency and insufficient data verification reliability. There is an urgent need for a system solution that can combine edge computing and data value assessment to perform deeper hierarchical verification and intelligent transmission coordination, ultimately achieving a balance between data integrity, backhaul efficiency, and energy conservation in complex environments. Summary of the Invention

[0005] In view of this, the present invention proposes a three-dimensional ecological monitoring data acquisition and transmission system and method for deep-sea resource development, aiming to solve the problems of data loss and out-of-order transmission caused by limited communication link bandwidth, high bit error rate, and poor connection stability in current deep-sea monitoring, as well as the existing verification mechanism not fully combining energy consumption constraints and edge computing power, and the lack of data service value classification assessment, which leads to limited competition for the transmission of high-value early warning information.

[0006] This invention proposes a three-dimensional ecological monitoring data acquisition and transmission method for deep-sea resource development, characterized by comprising: Based on the three-dimensional perception dimensions of the sky, airspace, sea surface and seabed, a deep-sea ecological monitoring indicator system is constructed, a multimodal ecological monitoring factor set is obtained, an interval value judgment matrix is ​​constructed according to the business value attributes of each monitoring factor in the multimodal ecological monitoring factor set, and the interval value judgment matrix is ​​converted into a deterministic value matrix using the interval median. A least squares optimization model based on energy efficiency constraints is established based on the deterministic value matrix. The subjective value weights of each monitoring factor are obtained, and the deterministic value matrix is ​​dynamically verified based on the subjective value weights. The deterministic value matrix is ​​then iteratively adjusted based on the verification results. Historical environmental disturbance data and communication link status data are acquired. Objective information entropy weights are obtained based on historical environmental disturbance data. Based on subjective value weights and objective information entropy weights according to a preset fusion coefficient, the comprehensive value weight of monitoring data is obtained. The comprehensive value weight is used as the fuzzy density, and the interaction coefficient between monitoring factors and transmission environment is solved based on the fuzzy measure equation. The fuzzy measure function of monitoring data transmission priority is constructed based on the interaction coefficient. Acquire real-time ecological monitoring factor data of the area to be monitored, obtain the monitoring factor feature vector of the area to be monitored based on the real-time ecological monitoring factor data, and obtain the comprehensive transmission priority evaluation value of the monitoring data packet based on the fuzzy measure function and the monitoring factor feature vector. The statistical distribution between the comprehensive transmission priority assessment value and the link bandwidth resources is obtained. Based on the statistical distribution and Monte Carlo sampling, the real-time ecological monitoring factor data and the link bandwidth resources are subjected to multiple random perturbations and the hierarchical verification and packet transmission calculation are repeatedly performed to obtain the success rate probability distribution of the back transmission of the monitored data and the confidence interval of the transmission delay.

[0007] Furthermore, based on the three-dimensional perception dimensions of the sky, airspace, sea surface, and seabed, a deep-sea ecological monitoring indicator system is constructed, resulting in a multimodal ecological monitoring factor set, including: Based on the physical spatial distribution of the deep-sea sensing system, water quality parameters, biological activities and environmental disturbance factors are modeled in layers. The system acquires satellite remote sensing parameters, UAV inspection images, buoy hydrological data, and seabed sensor array signals from the preset monitoring configuration, and extracts the perception dimensions in the preset monitoring configuration based on these parameters. Based on the mechanism of deep-sea ecological evolution, a correlation analysis was conducted on the monitoring indicators of each dimension in the perception dimension, and the key monitoring indicators of each dimension in the perception dimension were extracted. Key monitoring indicators across various dimensions are subjected to feature reduction and standardization, and the set is determined as a multimodal ecological monitoring factor set.

[0008] Furthermore, when constructing an interval value judgment matrix based on the operational value attributes of each monitoring factor in the multimodal ecological monitoring factor set, and converting this interval value judgment matrix into a deterministic value matrix using the interval median, the following steps are taken: Based on the interval number, the business value of each monitoring factor in the multimodal ecological monitoring factor set is compared pairwise. The comparison results are determined as interval value elements, and an interval value judgment matrix is ​​constructed based on each interval value element. Based on the arithmetic mean relationship between the upper and lower bounds of the value elements in each interval, the median of the value elements in each interval is obtained. Replace each interval value element in the interval value judgment matrix with the interval midpoint of each interval value element, and determine the matrix after replacement as the deterministic value matrix.

[0009] Furthermore, based on the deterministic value matrix, a least-squares optimization model constrained by energy efficiency is established to obtain the subjective value weights of each monitoring factor. When performing dynamic consistency verification of the deterministic value matrix based on these subjective value weights, the following steps are taken: Obtain the element deviation values ​​between each element and its corresponding weight ratio in the deterministic value matrix, and establish an objective function for the sum of squared errors based on the edge-side computational overhead based on the element deviation values. A least squares optimization model is established based on the objective function of sum of squared errors, non-negativity of weights, normalization of weights, and preset nodal energy efficiency constraints. Based on the numerical iterative solution of the least squares optimization model, the subjective value weight vector that minimizes the judgment bias in the deterministic value matrix is ​​obtained. Based on the product relationship between the subjective value weight vector and the deterministic value matrix, the dynamic consistency index of the deterministic value matrix is ​​determined, and based on the quantitative relationship between the dynamic consistency index and the configured preset consistency threshold, it is determined whether the deterministic value matrix should be modified. When the dynamic consistency index is greater than or equal to the preset consistency threshold, the original parameters for maintaining the deterministic value matrix are determined. When the dynamic consistency index is less than the preset consistency threshold, the parameters of the deterministic value matrix are adjusted.

[0010] Furthermore, when the dynamic consistency index is less than the preset consistency threshold, the determination value matrix is ​​corrected, including: Obtain the ideal ratio matrix of the deterministic value matrix based on subjective value weights; Obtain the element-wise difference between each element of the deterministic value matrix and the corresponding ideal ratio in the ideal ratio matrix, map the element-wise difference to the corresponding feedback adjustment coefficient, and update the value of the element according to the feedback adjustment coefficient, wherein: The feedback adjustment coefficient is configured such that when the element difference is greater than the preset difference threshold, the feedback adjustment coefficient is less than 1; when the element difference is less than the preset difference threshold, the feedback adjustment coefficient is greater than 1; and when the element difference is equal to the preset difference threshold, the feedback adjustment coefficient is 1. The deterministic value matrix is ​​updated based on the updated elements, and the transpose elements at the corresponding positions in the deterministic value matrix are updated simultaneously. Based on the updated deterministic value matrix, a least squares optimization model is constructed for the second time, and subjective value weights are extracted again based on the constructed least squares optimization model. The updated deterministic value matrix is ​​then reviewed for consistency indicators based on the re-extracted subjective value weights until the dynamic consistency indicators reach the preset consistency threshold.

[0011] Furthermore, when acquiring historical environmental disturbance data and communication link status data, obtaining objective information entropy weights based on the historical environmental disturbance data, and obtaining the comprehensive value weight of the monitoring data based on the subjective value weights and objective information entropy weights according to a preset fusion coefficient, the process includes: The link quality data of deep-sea acoustic communication, satellite transmission and microwave relay in various historical periods are obtained, and the link quality data is normalized to construct a link status standardization matrix. The probability distribution of each monitoring factor under different link states is obtained based on the link state normalization matrix, and the entropy value of each monitoring factor is obtained based on the information entropy formula. The objective information entropy weight of each monitoring factor is obtained based on the entropy value, and the subjective value weight and the objective information entropy weight are linearly weighted according to the preset weight fusion coefficient to obtain the initial weight vector. The weight fusion coefficient is configured to be dynamically set based on the real-time priority of the monitoring task and the remaining power of the edge node. The initial weight vector is processed using a normalization algorithm to obtain the comprehensive value weight of the monitoring data.

[0012] Furthermore, based on the comprehensive value weight as the fuzzy density, and solving the interaction coefficient between the monitoring factor and the transmission environment based on the fuzzy measure equation, when constructing the fuzzy measure function for the priority of monitoring data transmission based on the interaction coefficient, it includes: The comprehensive value weight is used as the marginal fuzzy density of each monitoring factor to construct an initial fuzzy density vector; Based on the marginal fuzzy density of each monitoring factor, a fuzzy measure interaction equation is constructed regarding link bandwidth, bit error rate, and energy consumption cost. By nonlinearly solving the fuzzy measure interaction equation, the interaction coefficients between each monitoring factor and the transmission environment are obtained. Based on the marginal fuzzy density and interaction coefficient, the fuzzy measure value of any subset of monitoring factors is obtained according to the λ-fuzzy measure generation formula, and the fuzzy measure function is constructed.

[0013] Furthermore, real-time ecological monitoring factor data of the area to be monitored is obtained; monitoring factor feature vectors of the area to be monitored are obtained based on the real-time ecological monitoring factor data; and when obtaining the comprehensive transmission priority evaluation value of the monitoring data packet based on the fuzzy measure function and the monitoring factor feature vectors, the following steps are taken: Multi-level filtering and noise reduction, outlier masking, and missing data interpolation are performed on the real-time ecological monitoring factor data of the monitoring area to obtain a purified monitoring factor dataset. The purified monitoring factor dataset is normalized in terms of dimensions, and the normalized monitoring factors are combined according to the preset semantic importance order to form a real-time monitoring factor feature vector. Incremental contribution integral calculation is performed on the feature vector of real-time monitoring factors based on fuzzy measure function and Choquet fuzzy integral calculation formula; The result of the integral calculation is determined as the comprehensive transmission priority evaluation value of the data packets to be monitored.

[0014] Furthermore, when obtaining the statistical distribution between the comprehensive transmission priority assessment value and link bandwidth resources, the following is included: Obtain the correlation sample pairs between the comprehensive transmission priority evaluation value and the actual transmission success rate within the preset historical observation period, and perform time benchmark alignment and outlier removal on the correlation sample pairs to construct a joint statistical matrix; Based on the kernel density estimation method, the joint probability density between the comprehensive transmission priority evaluation value and the available bandwidth of the link in the joint statistical matrix is ​​estimated, and a statistical model of transmission performance is established. Based on the transmission performance statistical model, the mutual information, covariance, and nonlinear correlation structural features between the comprehensive transmission priority evaluation value and bandwidth resources are extracted. Based on the extracted features, a statistical distribution between the comprehensive transmission priority evaluation value and link bandwidth resources is constructed. Based on the constructed statistical distribution, a pseudo-random perturbation sequence that conforms to the dynamic environmental characteristics of the deep sea is generated, and the pseudo-random perturbation sequence is superimposed on the real-time ecological monitoring factor data and link bandwidth parameters. Perform hierarchical verification on the data after superimposed perturbation, including a first-level cyclic redundancy check and a second-level semantic logic check based on business value. According to the comprehensive transmission priority evaluation value from high to low, the monitoring data packets are mapped to different communication link fragments, and the feedback status of each simulated transmission is recorded. By repeatedly performing simulated transmission a preset number of times, a sample set of monitoring data return success rate is obtained, and the probability distribution function of the return success rate and the time delay confidence interval under a preset confidence level are calculated based on the sample set.

[0015] Compared with existing technologies, the advantages of this invention are as follows: By utilizing the subjective value weights obtained from the interval value judgment matrix and the least squares optimization model, the professional experience of deep-sea ecological experts can participate in the value definition of monitoring data in a structured and verifiable manner. Furthermore, dynamic consistency verification and feedback adjustment enhance the reliability of the weight construction in complex marine dynamic environments. Simultaneously, the objective information entropy weights extracted from historical environmental disturbances and link status data can accurately reflect the differences in information contribution of each monitoring factor under different transmission conditions, enabling the weight allocation to be adaptively adjusted according to link quality, thus constructing a comprehensive value weight system that considers both expert experience and objective environmental characteristics. Secondly, by introducing fuzzy density and fuzzy measurement functions to solve for the interaction coefficients between monitoring factors and the transmission environment, the limitation of traditional fixed-priority transmission in expressing the coupling relationship between environmental disturbances and data value is overcome. This effectively characterizes transmission needs under extreme conditions such as "high bit error rate + high-value early warning data," enabling transmission priority assessment to accurately reflect the urgency of data return under nonlinear environments, thereby improving the data reliability of the deep-sea monitoring system in extreme environments. Finally, by constructing statistical distributions and performing Monte Carlo sampling to analyze the perturbations of real-time monitoring factors and bandwidth resources, the probability distribution and confidence interval of transmission success rate were obtained, thereby upgrading data transmission assurance from "passive retransmission" to "proactive risk assessment." This mechanism enhances the system's operational robustness under low power consumption and low bandwidth constraints, providing highly reliable data support and decision-making basis for deep-sea resource development.

[0016] On the other hand, this application also provides a three-dimensional ecological monitoring data acquisition and transmission system for deep-sea resource development, including: The three-dimensional perception module is used to construct a deep-sea ecological monitoring indicator system, obtain a multimodal ecological monitoring factor set, generate a deterministic value matrix, and solve for subjective value weights. The value assessment module is connected to the three-dimensional perception module. It is used to obtain the objective information entropy weight based on historical data and combine it with the subjective value weight to obtain the comprehensive value weight of the monitoring data, thereby constructing a fuzzy measure function for the priority of monitoring data transmission. The intelligent transmission module is connected to the value assessment module to obtain the feature vector of real-time monitoring factors, calculate the comprehensive transmission priority assessment value, and obtain the success rate probability distribution of data return and the confidence interval of transmission delay based on statistical distribution and Monte Carlo sampling. The three-dimensional perception module includes an acoustic sensor array set on the seabed, a multi-functional buoy set on the sea surface, a drone inspection unit set in the airspace, and a remote sensing data receiving terminal set in the sky. The value assessment module is integrated into the edge computing unit inside the multi-functional buoy, which is connected to the sensor hub inside the multi-functional buoy via an RS485 bus.

[0017] It is understood that the three-dimensional ecological monitoring data acquisition and transmission system and method for deep-sea resource development in the above embodiments of this application have the same beneficial effects, and will not be described in detail here. Attached Figure Description

[0018] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating a three-dimensional ecological monitoring data acquisition and transmission method for deep-sea resource development, provided in an embodiment of the present invention; Figure 2 A logical structure diagram of the deep-sea three-dimensional ecological monitoring index system provided in this embodiment of the invention; Figure 3 A flowchart for determining the comprehensive value weight and consistency correction of monitoring data provided in this embodiment of the invention; Figure 4 A schematic diagram of the transmission priority evaluation model architecture based on fuzzy integrals provided in an embodiment of the present invention; Figure 5 This is a functional module block diagram of a three-dimensional ecological monitoring data acquisition and transmission system for deep-sea resource development, provided in an embodiment of the present invention. Detailed Implementation

[0019] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] Goal Level: This represents the overall goal that the entire evaluation system ultimately aims to achieve, and is the core evaluation result that all evaluation steps are geared towards.

[0021] Criteria Level: This level is used to structurally decompose the target level into the main evaluation dimensions or categories that affect the overall target. It is an intermediate level that connects the overall target with specific indicators.

[0022] Indicator Level: This layer is used to further refine the criteria layer, forming specific risk factor indicators that are quantifiable and observable. It is the most basic and directly collectable data layer in the assessment system.

[0023] Risk Factor: A quantifiable element or characteristic that can affect the level of road safety, used to reflect the intensity and changes of potential risk sources.

[0024] Judgment Matrix: A matrix structure used to compare the relative importance of each evaluation element in pairs, reflecting the weight relationship between different elements through expert scoring results.

[0025] Least Squares Optimization Model: This refers to a mathematical model that finds the optimal weight vector by minimizing the sum of squared deviations of the judgment matrix, and is used to obtain weight results that are most consistent with expert judgment.

[0026] Objective Entropy Weight: This refers to the objective weight calculated based on the difference and information content of each indicator data in the sample, and is used to reflect the magnitude of the indicator's contribution to the uncertainty of the system.

[0027] Fuzzy density: In fuzzy measure theory, it refers to the parameter used to characterize the importance and interaction strength of a single risk factor or combination of factors, and is the basis for constructing fuzzy measure functions.

[0028] Fuzzy Measure Equation: A mathematical equation used to solve for the fuzzy measure values ​​of each risk factor and its combination based on the fuzzy density, in order to characterize the nonlinear coupling relationship and overall importance between factors.

[0029] Risk Factor Data: refers to the raw or processed numerical information used to characterize the current state of various risk factors of a road, and is the basic input for calculating the comprehensive risk value of a road.

[0030] Confidence interval: refers to a numerical range obtained based on statistical inference, used to describe the range in which the comprehensive risk value may fall at a given confidence level, thus reflecting the degree of uncertainty of the result.

[0031] The mechanism of road safety influence refers to the principles and intrinsic relationships by which various road risk factors affect road safety levels through structural characteristics, environmental conditions, and traffic behavior.

[0032] Interval Midpoint: The average of the two endpoints of a numerical interval. It is used to convert the interval judgment result into a deterministic value for subsequent calculation and analysis.

[0033] Dynamic Consistency Index: This index measures the consistency of weight changes in a judgment matrix over multiple rounds of adjustments or dynamic updates, ensuring that the weight system remains stable and reasonable in a dynamic environment.

[0034] Relative Contribution: refers to the proportion of importance of a certain risk factor in the overall assessment results, and is used to measure the degree of influence of the factor on the overall risk level.

[0035] The Choquet Fuzzy Integral Formula is a formula for nonlinearly aggregating a risk factor vector f given a fuzzy measure μ. Its mathematical expression is as follows: (Calculation formula) f(1)≤f(2)≤ f(n) represents the ascending order of risk factors; A(i) = {(i), (i+1), ..., (n)} represents the set consisting of the i-th factor and all subsequent factors after the ordering; μ(A(i)) is the fuzzy measure of the set A(i); f(0) = 0. This formula is used to comprehensively consider the interaction effects between risk factors, achieving a more realistic nonlinear risk fusion than linear weighting.

[0036] Sample pair: In data analysis or model training, a pair of related samples is used for comparison, correlation analysis, or to build judgment relationships, such as paired comparison samples used to build a judgment matrix.

[0037] Empirical distribution estimation is a nonparametric method that directly estimates the distribution of a random variable using sample data, without relying on any specific distribution assumptions.

[0038] Kernel density estimation is a nonparametric statistical method that estimates the true probability density of data by smoothing the samples using a kernel function without requiring a predefined distribution.

[0039] Gaussian Mixture Model (GMM): A statistical model that treats data as a mixture of multiple Gaussian distributions, used to characterize complex distribution structures and perform clustering, density estimation, or probability prediction.

[0040] Marginal distribution: refers to the probability distribution obtained when considering one (or some) dimensions of a multidimensional random variable in isolation. It is a univariate or low-dimensional probability distribution obtained by integrating or summing over the other dimensions.

[0041] Joint distribution: refers to the probability distribution when two or more random variables take values ​​together in the same probability space. It is used to describe the overall dependency relationship and joint probability of occurrence between variables.

[0042] A statistical model is a model that uses mathematical structures and probabilistic laws to describe and infer the data generation process, and is used to characterize the relationship between variables, predict results, or explain data behavior.

[0043] Alignment / Geometric Feature: refers to the geometric orientation attributes of a road in space, such as curve radius, longitudinal slope, and horizontal curve change rate, which are used to reflect the impact of road geometry on driving safety.

[0044] Fuzzy Integral Computation: refers to a method based on fuzzy measures to perform nonlinear weighted aggregation of multiple risk factors, used to characterize the interaction effects between factors and obtain comprehensive evaluation results.

[0045] Random perturbation refers to the introduction of random variations into system inputs or parameters to simulate uncertainty, test model robustness, or perform probability distribution analysis.

[0046] Monte Carlo sampling is a method that uses a large number of random samples to simulate and repeatedly compute to estimate the probability distribution, expected value, or uncertainty index of a complex system.

[0047] like Figures 1-2 As shown in some embodiments of this application, this embodiment provides a method for acquiring and transmitting three-dimensional ecological monitoring data for deep-sea resource development, including: Step S100: Construct a deep-sea ecological monitoring indicator system based on the three-dimensional perception dimensions of the sky, airspace, sea surface and seabed, obtain a multimodal ecological monitoring factor set, construct an interval value judgment matrix based on the business value attributes of each monitoring factor in the multimodal ecological monitoring factor set, and convert the interval value judgment matrix into a deterministic value matrix using the interval median.

[0048] Specifically, a deep-sea ecological monitoring indicator system is constructed based on the three-dimensional perception dimensions of the sky, airspace, sea surface, and seabed. When obtaining the multimodal ecological monitoring factor set, the following steps are taken: hierarchical modeling of water quality parameters, biological activities, and environmental disturbance elements is performed according to the physical spatial distribution of the deep-sea perception system; satellite remote sensing parameters, UAV inspection images, buoy hydrological data, and seabed sensor array signals are acquired from the preset monitoring configuration, and the perception dimensions in the preset monitoring configuration are extracted based on these parameters; correlation analysis is performed on the monitoring indicators of each dimension in the perception dimensions based on the deep-sea ecological evolution mechanism, and key monitoring indicators of each dimension are extracted; feature dimensionality reduction and standardization are performed on the key monitoring indicators of each dimension, and the set is determined as the multimodal ecological monitoring factor set.

[0049] Specifically, when constructing an interval value judgment matrix based on the operational value attributes of each monitoring factor in the multimodal ecological monitoring factor set, and converting the interval value judgment matrix into a deterministic value matrix using the interval median, the process includes: performing pairwise operational value comparisons of each monitoring factor in the multimodal ecological monitoring factor set based on interval numbers, determining the comparison results as interval value elements, and constructing an interval value judgment matrix based on each interval value element; obtaining the interval median of each interval value element based on the arithmetic mean relationship between the upper and lower bounds of each interval value element; replacing each interval value element in the interval value judgment matrix with the interval median of each interval value element, and determining the matrix after replacement as the deterministic value matrix.

[0050] Pre-set monitoring configurations can be implemented in accordance with industry standards such as the "Marine Observation Specifications" (GB / T 12763-2007) and the "Technical Guidelines for Deep-Sea Ecological Monitoring".

[0051] Understandably, this approach uses a three-dimensional perception system encompassing the sky, airspace, sea surface, and seabed to hierarchically model deep-sea ecological elements, structuring and layering complex ecological monitoring tasks. This top-down method decomposes the overall ecological security goal into perception criteria across different spatial dimensions, further refining them into quantifiable monitoring indicators. This results in a clear hierarchical structure for the overall monitoring system, facilitating systematic analysis and quantitative processing. Secondly, for each perception dimension, key monitoring indicators are acquired from multi-source information such as satellite remote sensing, UAV images, buoy data, and seabed sensor signals through the mechanisms of deep-sea ecological evolution. These indicators undergo feature reduction and standardization, forming a set of multimodal ecological monitoring factors that can be directly used for calculation. This process unifies heterogeneous and multi-source ecological information into quantifiable monitoring factors, providing foundational data for subsequent value weight calculation and transmission priority fusion. Finally, in the value definition stage, an interval value judgment matrix is ​​used to compare monitoring factors pairwise to express the fuzziness or uncertainty of the operational value of different factors. By calculating the median of the value elements in each interval, the interval value judgment matrix is ​​transformed into a deterministic value matrix, achieving a smooth transition from fuzzy evaluation to deterministic evaluation, and providing an operable mathematical basis for further weight calculation and consistency verification.

[0052] Step S200: Establish a least squares optimization model based on energy efficiency constraints according to the deterministic value matrix, obtain the subjective value weights of each monitoring factor, and perform dynamic consistency verification on the deterministic value matrix according to the subjective value weights. Iteratively adjust the deterministic value matrix according to the verification results.

[0053] Specifically, the process involves establishing a least-squares optimization model based on energy efficiency constraints using a deterministic value matrix, obtaining the subjective value weights of each monitoring factor, and performing dynamic consistency verification of the deterministic value matrix based on these subjective value weights. This includes: obtaining the element deviation values ​​between each element in the deterministic value matrix and the ratio of its corresponding weight; establishing an error sum of squares objective function based on the element deviation values ​​and the computational overhead at the edge; establishing a least-squares optimization model based on the error sum of squares objective function, weight non-negativity constraints, weight normalization constraints, and preset node energy efficiency constraints; obtaining the subjective value weight vector that minimizes the judgment deviation in the deterministic value matrix through numerical iterative solution of the least-squares optimization model; determining the dynamic consistency index of the deterministic value matrix based on the product relationship between the subjective value weight vector and the deterministic value matrix; determining whether to modify the deterministic value matrix based on the quantitative relationship between the dynamic consistency index and the configured preset consistency threshold; maintaining the original parameters of the deterministic value matrix when the dynamic consistency index is greater than or equal to the preset consistency threshold; and modifying the parameters of the deterministic value matrix when the dynamic consistency index is less than the preset consistency threshold.

[0054] Specifically, when the dynamic consistency index is less than a preset consistency threshold, the deterministic value matrix is ​​corrected, including: obtaining an ideal ratio matrix of the deterministic value matrix based on subjective value weights; obtaining the element difference between each element of the deterministic value matrix and the corresponding ideal ratio in the ideal ratio matrix, mapping the element difference to the corresponding feedback adjustment coefficient, and updating the element's value based on the feedback adjustment coefficient, wherein: the feedback adjustment coefficient is configured such that when the element difference is greater than a configured preset difference threshold, the feedback adjustment coefficient is determined to be less than 1; when the element difference is less than a configured preset difference threshold, the feedback adjustment coefficient is determined to be less than 1. If the time limit is reached, the feedback adjustment coefficient is determined to be greater than 1; if the element difference is equal to the configured preset difference threshold, the feedback adjustment coefficient is determined to be 1; the deterministic value matrix is ​​updated according to the updated elements, and the transpose elements at the corresponding positions in the deterministic value matrix are updated simultaneously; a least squares optimization model is constructed for the second time based on the updated deterministic value matrix, and subjective value weights are extracted again according to the second-constructed least squares optimization model; the consistency index of the updated deterministic value matrix is ​​reviewed according to the extracted subjective value weights until the dynamic consistency index reaches the preset consistency threshold.

[0055] Understandably, the subjective value weights of monitoring factors are obtained by processing the deterministic value matrix using a least-squares optimization model. Specifically, the deviation between each element in the value matrix and its corresponding weight ratio is used as the optimization objective. An error sum-of-squares objective function based on edge computing overhead is constructed, and the optimal weight vector is solved under constraints of weight non-negativity, normalization, and node energy efficiency. This method can scientifically quantify the relative business value of each monitoring factor while preserving expert subjective judgment information, making the weight solution mathematically optimal and environmentally adaptable. Secondly, to ensure the consistency between the weights and the value matrix, a dynamic consistency index is introduced to verify the deterministic value matrix. By comparing the dynamic consistency index with a preset consistency threshold, it is determined whether the matrix needs adjustment. When consistency is insufficient, an ideal ratio matrix is ​​constructed, and the difference between the matrix elements and the ideal values ​​is calculated. A feedback adjustment coefficient is generated to quantitatively correct the matrix elements, and the transposed elements are updated simultaneously. This process achieves dynamic optimization of the value matrix, ensuring that the subjective value weights are coordinated with the value matrix and reducing inconsistencies caused by subjective biases. Finally, the adjusted value matrix is ​​input twice into the least squares optimization model to recalculate the subjective value weights, and a consistency check is performed again, forming a closed-loop iteration until the dynamic consistency index reaches the preset threshold. This cyclic optimization mechanism can adaptively correct the value matrix, ensuring that the final weights conform to both expert judgment and the rationality requirements under energy efficiency constraints, thereby improving the reliability of weight allocation and the stability of the monitoring system.

[0056] Step S300: Obtain historical environmental disturbance data and communication link status data; obtain objective information entropy weight based on historical environmental disturbance data; obtain comprehensive value weight of monitoring data based on subjective value weight and objective information entropy weight according to preset fusion coefficients; use comprehensive value weight as fuzzy density, and solve the interaction coefficient between monitoring factors and transmission environment based on fuzzy measurement equation; construct fuzzy measurement function of monitoring data transmission priority based on interaction coefficients.

[0057] Specifically, the process of acquiring historical environmental disturbance data and communication link status data, obtaining objective information entropy weights based on historical environmental disturbance data, and obtaining the comprehensive value weight of monitoring data based on subjective value weights and objective information entropy weights according to a preset fusion coefficient includes: acquiring link quality data of deep-sea acoustic communication, satellite transmission, and microwave relay for each historical period, and performing normalization processing on the link quality data to construct a standardized link status matrix; obtaining the probability distribution of each monitoring factor under different link states based on the standardized link status matrix, and obtaining the entropy value of each monitoring factor based on the information entropy formula; obtaining the objective information entropy weight of each monitoring factor based on the entropy value, and linearly weighting the subjective value weights and objective information entropy weights according to a preset weight fusion coefficient to obtain an initial weight vector, wherein the weight fusion coefficient is configured to be dynamically set based on the real-time priority of the monitoring task and the remaining power of the edge node; and processing the initial weight vector according to the normalization algorithm to obtain the comprehensive value weight of the monitoring data.

[0058] Specifically, when constructing a fuzzy measure function for monitoring data transmission priority based on the interaction coefficients between monitoring factors and the transmission environment by using the comprehensive value weight as the fuzzy density and the fuzzy measure equation, the process includes: using the comprehensive value weight as the marginal fuzzy density of each monitoring factor to construct an initial fuzzy density vector; constructing fuzzy measure interaction equations for link bandwidth, bit error rate, and energy consumption cost based on the marginal fuzzy density of each monitoring factor; obtaining the interaction coefficients between each monitoring factor and the transmission environment by nonlinearly solving the fuzzy measure interaction equations; and obtaining the fuzzy measure values ​​of any subset of monitoring factors according to the λ-fuzzy measure generation formula based on the marginal fuzzy density and interaction coefficients, thus constructing a fuzzy measure function.

[0059] It is understandable that objective information entropy weights for each monitoring factor are obtained through historical environmental disturbances and link status data. Specifically, link quality data from each historical period is normalized to construct a standardized link status matrix, and the probability distribution and entropy value of each monitoring factor under different link states are calculated. The entropy value objectively reflects the differences in information contribution of each monitoring factor under historical link conditions, thus obtaining the objective weight of each factor. This process introduces objective laws from historical links into the value assessment system, compensating for potential biases arising from relying solely on subjective expert judgment. Secondly, subjective value weights and objective information entropy weights are fused, achieving linear weighting through a preset weight fusion coefficient to obtain the final comprehensive value weight vector. The fusion coefficient can be dynamically set according to the monitoring task priority and the remaining power of edge nodes, ensuring that the final weights consider both expert experience and link status and energy constraints, thereby improving the scientific rigor and adaptability of data value assessment. The comprehensive value weights are then normalized to ensure their applicability to subsequent fuzzy measurement calculations. Finally, a fuzzy density and interaction model between monitoring factors and the transmission environment are constructed based on the comprehensive value weights. The comprehensive value weight is considered as the marginal fuzzy density of each monitoring factor. The interaction coefficients between each monitoring factor and link bandwidth, bit error rate, and energy consumption cost are solved through fuzzy measure interaction equations. These interaction coefficients can quantify the coupling effect of different monitoring factors under specific transmission environments, such as the priority guarantee requirement for high-value early warning data under high bit error rate links, thus reflecting the complex value-environment interaction relationship in the real marine environment. Finally, using the marginal fuzzy density and interaction coefficients, the fuzzy measure value of any subset of monitoring factors is calculated through the λ-fuzzy measure generation formula, constructing a complete fuzzy measure function. This function can nonlinearly integrate the interaction effects of each monitoring factor and its transmission environment, providing a basis for the subsequent fuzzy integral calculation of data packet transmission priority, enabling the evaluation results to more accurately reflect the overall urgency of backhauling deep-sea monitoring data.

[0060] Step S400: Obtain real-time ecological monitoring factor data of the area to be monitored; obtain the monitoring factor feature vector of the area to be monitored based on the real-time ecological monitoring factor data; and obtain the comprehensive transmission priority evaluation value of the monitoring data packet based on the fuzzy measure function and the monitoring factor feature vector.

[0061] Specifically, the process of acquiring real-time ecological monitoring factor data of the area to be monitored, obtaining monitoring factor feature vectors of the area to be monitored based on the real-time ecological monitoring factor data, and obtaining the comprehensive transmission priority evaluation value of the monitoring data packet based on the fuzzy measure function and the monitoring factor feature vectors includes: performing multi-level filtering and noise reduction, outlier masking, and missing data interpolation processing on the real-time ecological monitoring factor data of the area to be monitored to obtain a purified monitoring factor dataset; performing dimensional normalization processing on the purified monitoring factor dataset, and combining the normalized monitoring factors according to a preset semantic importance order to form a real-time monitoring factor feature vector; performing incremental contribution integral calculation on the real-time monitoring factor feature vector based on the fuzzy measure function and the Choquet fuzzy integral calculation formula; and determining the integral calculation result as the comprehensive transmission priority evaluation value of the monitoring data packet.

[0062] Understandably, for the real-time ecological monitoring factor data of the area to be monitored, multi-level filtering and noise reduction, outlier masking, and missing data interpolation methods are used to process the original data to form a reliable purification monitoring factor dataset. This step ensures the integrity and accuracy of the input data, providing a robust data foundation for subsequent priority calculations and effectively avoiding evaluation biases caused by data quality issues. Secondly, the processed monitoring factor data undergoes dimensional normalization and vectorization, combining each monitoring factor according to a preset semantic importance order to form a real-time monitoring factor feature vector. This step achieves a unified representation of multi-dimensional heterogeneous monitoring factors, enabling comprehensive calculations of various indicators under the same dimension and providing standardized input for fuzzy integral processing. Then, fuzzy measure functions and Choquet fuzzy integrals are used to calculate the monitoring factor feature vector item by item. Specifically, by calculating the incremental contribution of each monitoring factor and considering the interaction between the monitoring factor and the transmission environment, nonlinear weighted aggregation of the monitoring factors is achieved. This method can effectively reflect the comprehensive backhaul requirements of different monitoring factors and their combinations in a specific link environment, avoiding the problem of traditional fixed priority methods ignoring environmental coupling effects. Finally, the cumulative contribution of all increments yields a comprehensive transmission priority assessment value, which serves as a quantitative indicator of the urgency of the overall backhaul of the monitored data packets. This technical principle, by combining real-time data, fuzzy measures, and Choquet fuzzy integrals, achieves a dynamic and nonlinear comprehensive evaluation of multi-dimensional monitoring factors and their interaction with the transmission environment, providing a scientific and reliable decision-making basis for intelligent transmission of deep-sea data.

[0063] Step S500: Obtain the statistical distribution between the comprehensive transmission priority evaluation value and the link bandwidth resources, and based on the statistical distribution and Monte Carlo sampling, perform multiple random perturbations on the real-time ecological monitoring factor data and the link bandwidth resources, and repeatedly perform hierarchical verification and packet transmission calculation to obtain the success rate probability distribution of the data to be monitored and the confidence interval of the transmission delay.

[0064] Specifically, obtaining the statistical distribution between the comprehensive transmission priority assessment value and link bandwidth resources includes: obtaining the associated sample pairs of the comprehensive transmission priority assessment value and the actual transmission success rate within a preset historical observation period, and performing time benchmark alignment and outlier removal on the associated sample pairs to construct a joint statistical matrix; estimating the joint probability density between the comprehensive transmission priority assessment value and the available link bandwidth in the joint statistical matrix based on the kernel density estimation method to establish a transmission performance statistical model; extracting the mutual information, covariance, and nonlinear correlation structural features between the comprehensive transmission priority assessment value and bandwidth resources based on the transmission performance statistical model; and constructing the statistical distribution between the comprehensive transmission priority assessment value and link bandwidth resources based on the extracted features.

[0065] Understandably, a joint statistical matrix is ​​constructed using historical observation data. This involves aligning the comprehensive transmission priority assessment values ​​and actual transmission success rate samples from several historical observation periods according to a time benchmark and removing outliers, resulting in a joint statistical matrix containing multidimensional variable relationships. This step structures the historical transmission observation data, enabling subsequent statistical analysis to fully utilize past transmission information and reflect the actual correlation characteristics between priority assessment values ​​and link resources. Secondly, based on the joint statistical matrix, a kernel density estimation method is used to model the joint probability density of the comprehensive transmission priority assessment values ​​and available link bandwidth, constructing a transmission performance statistical model. This non-parametric method can characterize the complex probability distribution characteristics and nonlinear correlation between priority assessment values ​​and bandwidth resources, thus providing a reliable mathematical foundation for transmission reliability analysis. Subsequently, mutual information, covariance, and related structural characteristics are calculated based on the statistical model to form the statistical distribution of the comprehensive transmission priority assessment values ​​and link bandwidth resources. This statistical distribution not only reflects the central trend of transmission success rate but also characterizes the uncertainty and fluctuation characteristics of transmission performance, providing a quantitative basis for subsequent probabilistic inference. Finally, using Monte Carlo sampling and random perturbation methods, the real-time monitoring factor data and link bandwidth parameters were subjected to multiple random perturbations, and hierarchical verification and packet transmission calculations were repeatedly performed to obtain the probability distribution and confidence interval of the monitoring data return success rate. This step, by simulating dynamic disturbances in the marine environment and fluctuations in link resources, achieves a probabilistic description of the reliability of data return, ensuring that the evaluation results not only reflect the probability of return but also include uncertainty and confidence levels. This provides a more scientific and robust basis for the operation management and emergency decision-making of deep-sea monitoring systems.

[0066] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principle of this invention is further explained below in conjunction with a specific application scenario.

[0067] When establishing a deep-sea ecological monitoring indicator system and determining its comprehensive value weights, the following steps are taken: Based on a four-dimensional perception framework encompassing "sky-air-sea surface-seabed," and incorporating the principles of the analytic hierarchy process (AHP), the hierarchical relationships between each level are clarified. Specifically, the target layer should comprehensively measure the overall operational value of deep-sea ecological data; the criteria layer connects the target layer and the indicator layer, and, in conjunction with industry standards such as the "Marine Observation Specification" (GB / T 12763-2007), selects several core dimensions that play a decisive role in data value; the indicator layer is responsible for quantifying the value degree of each dimension in the criteria layer and is the direct data source for value assessment (e.g., ...). Figure 3 (As shown). After constructing the four-dimensional indicator system, the contribution of each level of indicators is quantified using an improved analytic hierarchy process (AHP). Traditional AHP suffers from strong subjectivity and rigid consistency checks, with a structure as shown... Figure 4 As shown, this module has been innovated and improved in the following aspects.

[0068] In traditional analytic hierarchy process (AHP), experts need to provide accurate numerical judgments, which often fail to accurately reflect the uncertainty in their thought process. To address this issue, this invention uses interval numbers to construct a value judgment matrix, better representing the ambiguity of expert judgments, improving the robustness of weight calculations, and making the results more robust and stable. The interval value judgment matrix can be represented as: in, This represents the interval judgment matrix, used to characterize the range of values ​​of the relative relationship between indicators under uncertain conditions; m is the order of the judgment matrix, indicating that the element in the i-th row and j-th column of the matrix is ​​a closed interval; This is the lower bound of the interval, representing the judgment value under the most unfavorable or lowest estimated condition; The upper bound of the interval represents the judgment value under the most favorable or highest estimated condition.

[0069] Secondly, this invention employs the least squares method to optimize the final weight allocation, transforming the weights into a planning problem seeking the "optimal fit." This solves the fundamental difficulty in traditional methods, where inconsistent judgment matrices prevent the determination of weights or result in unreasonable weights. The optimization model is as follows: Among them, v ij To determine the elements of a matrix; Let be the weight ratio of the i-th indicator to the j-th indicator; the constraint condition is: For weight normalization constraints; w i ≥0 is a positive constraint on the weight; For node energy efficiency constraints, E i E represents the unit transmission energy consumption of the i-th factor. maxThe maximum allowable energy consumption of the edge nodes is defined. By solving this optimization problem, a scientifically reasonable final weight vector is obtained.

[0070] This invention designs a dynamic consistency check mechanism to fundamentally solve the problem of rigid consistency check processes in traditional analytic hierarchy process (AHP). The formula is as follows: This invention designs a dynamic consistency check mechanism to fundamentally solve the problem of rigid consistency check processes in traditional analytic hierarchy process (AHP). The formula is as follows: DCI = SCI + α·σ w Wherein, SCI is the static consistency index; σ w α is the weighted standard deviation; α is the adjustment coefficient; DCI is the dynamic consistency index. When DCI < 0.1, the test is passed; otherwise, the judgment matrix is ​​automatically adjusted until the consistency requirements are met.

[0071] Furthermore, to overcome the limitations of subjective judgment and achieve a complementary advantage between expert experience and data objectivity, this invention employs the entropy weight method to correct subjective weights. The entropy weight method is based on the principle that "the greater the dispersion of indicator data, the stronger its ability to distinguish the evaluation object, and the higher its weight should be," extracting the objective importance of each indicator from the actually collected multidimensional monitoring factor data. The formula is as follows: W=β·W s +(1-β)·W e Where β is the weighting coefficient, used to balance the influence of subjective and objective weights, and is usually taken as 0.5–0.7; W s For subjective value weight; W e W represents the objective information entropy weight calculated using the entropy weight method; W is the final comprehensive value weight.

[0072] Furthermore, through the above steps, a complete monitoring indicator system and its comprehensive value weight vector W=[w1,w2,...,w] can be obtained. n This weight vector not only serves as the fuzzy density input for the stage-two fuzzy integral calculation, but can also be used independently for sensitivity analysis of monitoring factors, providing a basis for subsequent deep-sea ecological risk governance.

[0073] Furthermore, this invention enables precise calculation of the comprehensive transmission priority of monitoring data. To overcome the limitations of traditional fixed priority models that ignore the dynamic interaction between monitoring factors and the transmission environment, this invention proposes a nonlinear fusion mechanism based on an improved fuzzy integral fusion model. Using multidimensional monitoring data and the comprehensive value weights obtained in Phase 1 as inputs, it achieves fuzzy integral fusion of monitoring factors and calculation of data packet transmission priority.

[0074] Furthermore, in traditional fuzzy analysis, the setting of fuzzy density largely relies on manual setting or experience, which is highly subjective. Therefore, this method uses the comprehensive value weight of the monitoring factors determined in stage one as the fuzzy density of the fuzzy integral, thereby achieving a seamless transition from the analytic hierarchy process (AHP) to the fuzzy integral fusion model in terms of model structure.

[0075] First, establish a fuzzy measurement system: Among them, w i The comprehensive value weight of the i-th monitoring factor satisfies .

[0076] This approach allows fuzzy density to have a clear physical meaning, avoiding the problem of setting fuzzy density in traditional methods, and ensuring that the fuzzy fusion process has theoretical consistency and comparability at the weight level.

[0077] Furthermore, there are often complex correlations between monitoring factors and the transmission environment. For example, when "high bit error rate + high-value early warning data" coexist, the transmission assurance requirements are far higher than the result of simply adding the two together. To accurately characterize this interaction, this invention introduces a fuzzy measurement equation: μ(A∪B)=μ(A)+μ(B)+λ·μ(A)·μ(B) Wherein, λ is the interaction coefficient, reflecting the direction and strength of the interaction between the monitoring factor and the transmission environment. When λ>0, it indicates a positive correlation (value-environment synergistic enhancement); when λ<0, it indicates a negative correlation (value-environment mutual inhibition); and when λ=0, it indicates an independent relationship.

[0078] Furthermore, based on the fuzzy measurement system obtained in the second step, this invention uses fuzzy integrals to achieve nonlinear fusion calculation of comprehensive transmission priority. First, the standardized values ​​of various monitoring factors are arranged in ascending order as f. (1) ≤f (2) ≤ ≤f (n) Its overall transmission priority R is expressed as: In actual calculations, discrete fuzzy integrals are used: Where, Δfi=f (i) -f (i-1) f (0) =0.

[0079] This nonlinear integral calculation method incorporates the ranking and weight of each monitoring factor into the calculation process through a fuzzy measure function, thereby achieving a nonlinear comprehensive evaluation of multidimensional monitoring factors.

[0080] Furthermore, to ensure the reliability of the fusion computation, this method employs Monte Carlo simulation to quantify uncertainty. By repeatedly sampling the input data and weights within their possible distribution ranges and repeatedly calculating the integrated transmission priority, the final output priority and its confidence interval are provided, offering an intuitive reference regarding the reliability of the evaluation results. Specifically: Through multiple simulations, the probability distribution and confidence interval of transmission priority can be obtained, thereby quantifying the stability and reliability of the evaluation results. The final output of stage two is a comprehensive transmission priority evaluation value in the interval [0,1].

[0081] In the above embodiments, by utilizing the subjective value weights obtained from the interval value judgment matrix and the least squares optimization model, the business experience of deep-sea ecological experts can participate in the value definition of monitoring data in a structured and verifiable manner. Dynamic consistency verification and feedback adjustment enhance the reliability of the weight construction in complex marine dynamic environments. Simultaneously, the objective information entropy weights extracted from historical environmental disturbances and link status data can accurately reflect the differences in information contribution of each monitoring factor under different transmission conditions, enabling the weight allocation to be adaptively adjusted according to link quality, thus constructing a comprehensive value weight system that considers both expert experience and objective environmental characteristics. Secondly, by introducing fuzzy density and fuzzy measure functions to solve for the interaction coefficients between monitoring factors and the transmission environment, the limitation of traditional fixed-priority transmission in expressing the coupling relationship between environmental disturbances and data value is overcome. This effectively characterizes transmission needs under extreme conditions such as "high bit error rate + high-value early warning data," enabling transmission priority assessment to accurately reflect the urgency of data backhaul under nonlinear environments, thereby improving the data reliability of the deep-sea monitoring system in extreme environments. Finally, by constructing statistical distributions and performing Monte Carlo sampling to analyze the perturbations of real-time monitoring factors and bandwidth resources, the probability distribution and confidence interval of transmission success rate were obtained, thereby upgrading data transmission assurance from "passive retransmission" to "proactive risk assessment." This mechanism enhances the system's operational robustness under low power consumption and low bandwidth constraints, providing highly reliable data support and decision-making basis for deep-sea resource development.

[0082] In the above embodiments, by utilizing the subjective value weights obtained from the interval value judgment matrix and the least squares optimization model, the business experience of deep-sea ecological experts can participate in the value definition of monitoring data in a structured and verifiable manner. Dynamic consistency verification and feedback adjustment enhance the reliability of the weight construction in complex marine dynamic environments. Simultaneously, the objective information entropy weights extracted from historical environmental disturbances and link status data can accurately reflect the differences in information contribution of each monitoring factor under different transmission conditions, enabling the weight allocation to be adaptively adjusted according to link quality, thus constructing a comprehensive value weight system that considers both expert experience and objective environmental characteristics. Secondly, by introducing fuzzy density and fuzzy measure functions to solve for the interaction coefficients between monitoring factors and the transmission environment, the limitation of traditional fixed-priority transmission in expressing the coupling relationship between environmental disturbances and data value is overcome. This effectively characterizes transmission needs under extreme conditions such as "high bit error rate + high-value early warning data," enabling transmission priority assessment to accurately reflect the urgency of data backhaul under nonlinear environments, thereby improving the data reliability of the deep-sea monitoring system in extreme environments. Finally, by constructing statistical distributions and performing Monte Carlo sampling to analyze the perturbations of real-time monitoring factors and bandwidth resources, the probability distribution and confidence interval of transmission success rate were obtained, thereby upgrading data transmission assurance from "passive retransmission" to "proactive risk assessment." This mechanism enhances the system's operational robustness under low power consumption and low bandwidth constraints, providing highly reliable data support and decision-making basis for deep-sea resource development.

[0083] In another preferred embodiment based on the above embodiments, such as Figure 5 As shown, this embodiment provides a three-dimensional ecological monitoring data acquisition and transmission system for deep-sea resource development, including: a three-dimensional sensing module, a value assessment module, and an intelligent transmission module.

[0084] Specifically, the three-dimensional perception module is configured to construct a deep-sea ecological monitoring indicator system based on three-dimensional perception dimensions of the sky, airspace, sea surface, and seabed, obtaining a multimodal ecological monitoring factor set. The module is also configured to construct an interval value judgment matrix based on the operational value attributes of each monitoring factor in the multimodal ecological monitoring factor set, and convert this interval value judgment matrix into a deterministic value matrix using the interval median. Furthermore, the module is configured to establish a least-squares optimization model based on energy efficiency constraints using the deterministic value matrix, obtain the subjective value weights of each monitoring factor, and perform dynamic consistency verification on the deterministic value matrix based on the subjective value weights, iteratively adjusting the deterministic value matrix based on the verification results. The value assessment module is signal-connected to the three-dimensional perception module. This module is configured to acquire historical environmental disturbance data and communication link status data, obtain objective information entropy weights based on the historical environmental disturbance data, and obtain monitoring data based on the subjective value weights and objective information entropy weights according to a preset fusion coefficient. The value assessment module is configured to use the comprehensive value weight as the fuzzy density and solve the interaction coefficient between the monitoring factors and the transmission environment based on the fuzzy measure equation. It then constructs a fuzzy measure function for the priority of monitoring data transmission based on the interaction coefficient. The intelligent transmission module is signal-connected to the value assessment module. It is configured to acquire real-time ecological monitoring factor data of the area to be monitored and obtain the monitoring factor feature vector of the area based on the real-time ecological monitoring factor data. The intelligent transmission module is also configured to obtain the comprehensive transmission priority assessment value of the monitoring data packet based on the fuzzy measure function and the monitoring factor feature vector. Furthermore, the intelligent transmission module is configured to obtain the statistical distribution between the comprehensive transmission priority assessment value and the link bandwidth resources. Based on the statistical distribution and Monte Carlo sampling, it performs multiple random perturbations on the real-time ecological monitoring factor data and the link bandwidth resources, repeatedly executing hierarchical verification and packet transmission calculations to obtain the success rate probability distribution of the data to be monitored and the confidence interval of the transmission delay.

[0085] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principle of this invention is further explained below in conjunction with a specific application scenario.

[0086] When deploying a three-dimensional ecological monitoring system in a deep-sea oil and gas field development area, the system first receives chlorophyll a concentration and sea surface temperature inversion data from ocean color satellites via a remote sensing data receiving terminal 101 in the sky. In the airspace, a UAV inspection unit 102, equipped with a multispectral camera and lidar, performs periodic low-altitude patrols to acquire images of red tide algae distribution and information on oil slick thickness on the sea surface. On the sea surface, a multi-functional buoy 103 integrates a temperature, salinity, and depth gauge, a dissolved oxygen sensor, and a wave meter to collect hydrodynamic parameters. On the seabed, an acoustic sensor array 104 continuously monitors acoustic signals from cetaceans and micro-seismic activity from seabed geological activity through a network of hydrophones deployed in the seabed. These four-dimensional sensing devices correspond to... Figure 5 The raw data from components 101, 102, 103, and 104 are aggregated via underwater acoustic communication unit 105 and satellite antenna 106 to the edge computing unit 201 inside the multi-functional buoy 103.

[0087] The 200-level stereo perception module performs structured processing on the aforementioned raw data based on the "Marine Observation Standard" (GB / T 12763-2007): satellite remote sensing parameters are assigned to the "water quality anomaly" indicator under the "sky domain" criterion layer; UAV image analysis is assigned to the "biological disturbance" indicator under the "air domain" criterion layer; buoy hydrological data is mapped to the "physical environment" indicator under the "sea surface" criterion layer; and seabed acoustic signals are converted to the "ecological response" indicator under the "seabed" criterion layer. After feature reduction, a multimodal ecological monitoring factor set containing 12 dimensions is formed, including chlorophyll a concentration, oil film area ratio, dissolved oxygen saturation, and whale call frequency. The expert team performs pairwise comparisons of these 12 factors, constructs a 6×6 interval value judgment matrix using interval numbers [1,3], [2,5], etc., and then replaces the original interval elements with the median values ​​of each interval, such as 2 and 3.5, to obtain a deterministic value matrix.

[0088] The value assessment module 300 accesses the historical link logs stored in the edge computing unit 201 for the past six months. These logs record the bit error rate (BER) of each underwater acoustic communication, the available bandwidth of the satellite link, and the remaining power of the nodes. After normalizing the link quality data, a link state standardization matrix is ​​constructed, and the probability distribution of each monitoring factor under different link states is calculated. For example, when the underwater acoustic BER is higher than 10... -3 At that time, the data packet loss rate of the "whale cry event frequency" factor increased significantly, its information entropy value decreased, and the corresponding objective information entropy weight increased. The subjective value weight W_s and the objective information entropy weight W_e were linearly weighted with β=0.6 to obtain the comprehensive value weight vector W=[0.12, 0.08, ..., 0.15]. This weight vector was used as the fuzzy density input λ-fuzzy measure equation, and the interaction coefficient λ=0.32 was obtained, indicating that there is a positive synergistic effect between high-value early warning data and the harsh link environment.

[0089] The intelligent transmission module 400 receives the monitoring factor data in real time, performs wavelet threshold noise filtering on the drift data of the dissolved oxygen sensor caused by biological attachment, and uses spatiotemporal interpolation to complete the cloud-obscured areas in the satellite image, forming a purified monitoring factor dataset. After Min-Max normalization, the data is arranged in a preset semantic order as a feature vector f=[0.85,0.32, ..., 0.91]. Substituting f and the fuzzy measure function μ into the Choquet fuzzy integral formula, f is first sorted in ascending order to obtain f_(1)=0.32, f_(2)=0.85, ..., f_(12)=0.91, and then Δf_i·μ(A_i) is calculated sequentially. The summation yields a comprehensive transmission priority evaluation value R=0.78.

[0090] Subsequently, the system extracted 150 sample pairs of R values ​​and actual transmission success rates from the historical database over the past 30 days. After removing outliers with timestamp deviations exceeding 5 minutes, a joint statistical matrix was constructed. A Gaussian kernel function was used for kernel density estimation to obtain the joint probability density function of R and link bandwidth. Based on this statistical distribution, the Monte Carlo sampling module generated 1000 sets of random perturbation sequences, each perturbation simultaneously affecting real-time factor data and bandwidth parameters. Two levels of verification were performed on each set of perturbation data: the first level used CRC-32 checksums to verify data packet integrity, and the second level performed semantic verification based on the business rule that "if chlorophyll a concentration > threshold and oil film area > threshold, then trigger a red tide-oil spill composite early warning." Based on the verification results and link allocation strategy, the success or failure of each simulated transmission and its latency were recorded. Finally, the results of 1000 simulations were statistically analyzed, yielding a return transmission success rate of 87.2%, and the transmission delay confidence interval was calculated at a 95% confidence level as [4.2s, 6.8s].

[0091] The above process fully realizes a closed-loop operation from multi-source data acquisition, value weight fusion, nonlinear priority assessment to transmission reliability quantification, with each step relying on... Figure 5 The hardware modules and data flow paths shown are executed without introducing external assumptions or undisclosed technical means.

[0092] It is understood that the three-dimensional ecological monitoring data acquisition and transmission system and method for deep-sea resource development in the above embodiments of this application have the same beneficial effects, and will not be described in detail here.

[0093] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0094] This application is illustrated by referring to flowchart illustrations of methods, apparatus (systems), and computer programs according to embodiments of this application. Or a block diagram. It should be understood that flowcharts and... can be implemented by computer program instructions. Or each process in the block diagram and Or boxes, and flowcharts and Or the process in the block diagram and Or a combination of boxes. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which are executable by the processor of the computer or other programmable data processing device, produce instructions for implementing the process. Figure 1 One or more processes and or box Figure 1 A device that provides the functions specified in one or more boxes.

[0095] 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 it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for acquiring and transmitting three-dimensional ecological monitoring data for deep-sea resource development, characterized in that, include: Based on the three-dimensional perception dimensions of the sky, airspace, sea surface and seabed, a deep-sea ecological monitoring indicator system is constructed, a multimodal ecological monitoring factor set is obtained, an interval value judgment matrix is ​​constructed according to the business value attributes of each monitoring factor in the multimodal ecological monitoring factor set, and the interval value judgment matrix is ​​converted into a deterministic value matrix using the interval median. A least squares optimization model based on energy efficiency constraints is established based on the deterministic value matrix. The subjective value weights of each monitoring factor are obtained, and the deterministic value matrix is ​​dynamically verified based on the subjective value weights. The deterministic value matrix is ​​then iteratively adjusted based on the verification results. Historical environmental disturbance data and communication link status data are acquired. Objective information entropy weights are obtained based on historical environmental disturbance data. Based on subjective value weights and objective information entropy weights according to a preset fusion coefficient, the comprehensive value weight of monitoring data is obtained. The comprehensive value weight is used as the fuzzy density, and the interaction coefficient between monitoring factors and transmission environment is solved based on the fuzzy measure equation. The fuzzy measure function of monitoring data transmission priority is constructed based on the interaction coefficient. Acquire real-time ecological monitoring factor data of the area to be monitored, obtain the monitoring factor feature vector of the area to be monitored based on the real-time ecological monitoring factor data, and obtain the comprehensive transmission priority evaluation value of the monitoring data packet based on the fuzzy measure function and the monitoring factor feature vector. The statistical distribution between the comprehensive transmission priority assessment value and the link bandwidth resources is obtained. Based on the statistical distribution and Monte Carlo sampling, the real-time ecological monitoring factor data and the link bandwidth resources are subjected to multiple random perturbations and the hierarchical verification and packet transmission calculation are repeatedly performed to obtain the success rate probability distribution of the back transmission of the monitored data and the confidence interval of the transmission delay.

2. The method for acquiring and transmitting three-dimensional ecological monitoring data for deep-sea resource development as described in claim 1, characterized in that, A deep-sea ecological monitoring indicator system is constructed based on the three-dimensional perception dimensions of the sky, airspace, sea surface, and seabed. When obtaining the multimodal ecological monitoring factor set, it includes: Based on the physical spatial distribution of the deep-sea sensing system, water quality parameters, biological activities and environmental disturbance factors are modeled in layers. The system acquires satellite remote sensing parameters, UAV inspection images, buoy hydrological data, and seabed sensor array signals from the preset monitoring configuration, and extracts the perception dimensions in the preset monitoring configuration based on these parameters. Based on the mechanism of deep-sea ecological evolution, a correlation analysis was conducted on the monitoring indicators of each dimension in the perception dimension, and the key monitoring indicators of each dimension in the perception dimension were extracted. Key monitoring indicators across various dimensions are subjected to feature reduction and standardization, and the set is determined as a multimodal ecological monitoring factor set.

3. The method for acquiring and transmitting three-dimensional ecological monitoring data for deep-sea resource development as described in claim 2, characterized in that, When constructing an interval value judgment matrix based on the operational value attributes of each monitoring factor in the multimodal ecological monitoring factor set, and converting this interval value judgment matrix into a deterministic value matrix using the interval median, the following steps are taken: Based on the interval number, the business value of each monitoring factor in the multimodal ecological monitoring factor set is compared pairwise. The comparison results are determined as interval value elements, and an interval value judgment matrix is ​​constructed based on each interval value element. Based on the arithmetic mean relationship between the upper and lower bounds of the value elements in each interval, the median of the value elements in each interval is obtained. Replace each interval value element in the interval value judgment matrix with the interval midpoint of each interval value element, and determine the matrix after replacement as the deterministic value matrix.

4. The method for acquiring and transmitting three-dimensional ecological monitoring data for deep-sea resource development as described in claim 3, characterized in that, A least-squares optimization model based on energy efficiency constraints is established according to the deterministic value matrix. The subjective value weights of each monitoring factor are obtained, and dynamic consistency verification of the deterministic value matrix is ​​performed based on these subjective value weights, including: Obtain the element deviation values ​​between each element and its corresponding weight ratio in the deterministic value matrix, and establish an objective function for the sum of squared errors based on the edge-side computational overhead based on the element deviation values. A least squares optimization model is established based on the objective function of sum of squared errors, non-negativity of weights, normalization of weights, and preset nodal energy efficiency constraints. Based on the numerical iterative solution of the least squares optimization model, the subjective value weight vector that minimizes the judgment bias in the deterministic value matrix is ​​obtained. Based on the product relationship between the subjective value weight vector and the deterministic value matrix, the dynamic consistency index of the deterministic value matrix is ​​determined, and based on the quantitative relationship between the dynamic consistency index and the configured preset consistency threshold, it is determined whether the deterministic value matrix should be modified. When the dynamic consistency index is greater than or equal to the preset consistency threshold, the original parameters for maintaining the deterministic value matrix are determined. When the dynamic consistency index is less than the preset consistency threshold, the parameters of the deterministic value matrix are adjusted.

5. The method for acquiring and transmitting three-dimensional ecological monitoring data for deep-sea resource development as described in claim 4, characterized in that, When the dynamic consistency index is less than the preset consistency threshold, the determination value matrix is ​​corrected, including: Obtain the ideal ratio matrix of the deterministic value matrix based on subjective value weights; Obtain the element-wise difference between each element of the deterministic value matrix and the corresponding ideal ratio in the ideal ratio matrix, map the element-wise difference to the corresponding feedback adjustment coefficient, and update the value of the element according to the feedback adjustment coefficient, wherein: The feedback adjustment coefficient is configured such that when the element difference is greater than the preset difference threshold, the feedback adjustment coefficient is less than 1; when the element difference is less than the preset difference threshold, the feedback adjustment coefficient is greater than 1; and when the element difference is equal to the preset difference threshold, the feedback adjustment coefficient is 1. The deterministic value matrix is ​​updated based on the updated elements, and the transpose elements at the corresponding positions in the deterministic value matrix are updated simultaneously. Based on the updated deterministic value matrix, a least squares optimization model is constructed for the second time, and subjective value weights are extracted again based on the constructed least squares optimization model. The updated deterministic value matrix is ​​then reviewed for consistency indicators based on the re-extracted subjective value weights until the dynamic consistency indicators reach the preset consistency threshold.

6. The method for acquiring and transmitting three-dimensional ecological monitoring data for deep-sea resource development as described in claim 1, characterized in that, When acquiring historical environmental disturbance data and communication link status data, obtaining objective information entropy weights based on the historical environmental disturbance data, and then obtaining the comprehensive value weight of the monitoring data based on the subjective value weights and objective information entropy weights according to a preset fusion coefficient, the following steps are taken: The link quality data of deep-sea acoustic communication, satellite transmission and microwave relay in various historical periods are obtained, and the link quality data is normalized to construct a link status standardization matrix. The probability distribution of each monitoring factor under different link states is obtained based on the link state normalization matrix, and the entropy value of each monitoring factor is obtained based on the information entropy formula. The objective information entropy weight of each monitoring factor is obtained based on the entropy value, and the subjective value weight and the objective information entropy weight are linearly weighted according to the preset weight fusion coefficient to obtain the initial weight vector. The weight fusion coefficient is configured to be dynamically set based on the real-time priority of the monitoring task and the remaining power of the edge node. The initial weight vector is processed using a normalization algorithm to obtain the comprehensive value weight of the monitoring data.

7. The method for acquiring and transmitting three-dimensional ecological monitoring data for deep-sea resource development as described in claim 6, characterized in that, When using the comprehensive value weight as the fuzzy density, and solving for the interaction coefficient between the monitoring factor and the transmission environment based on the fuzzy measure equation, the fuzzy measure function for prioritizing monitoring data transmission is constructed based on the interaction coefficient, including: The comprehensive value weight is used as the marginal fuzzy density of each monitoring factor to construct an initial fuzzy density vector; Based on the marginal fuzzy density of each monitoring factor, a fuzzy measure interaction equation is constructed regarding link bandwidth, bit error rate, and energy consumption cost. By nonlinearly solving the fuzzy measure interaction equation, the interaction coefficients between each monitoring factor and the transmission environment are obtained. Based on the marginal fuzzy density and interaction coefficient, the fuzzy measure value of any subset of monitoring factors is obtained according to the λ-fuzzy measure generation formula, and the fuzzy measure function is constructed.

8. The method for acquiring and transmitting three-dimensional ecological monitoring data for deep-sea resource development as described in claim 7, characterized in that, When acquiring real-time ecological monitoring factor data of the area to be monitored, obtaining the monitoring factor feature vector of the area to be monitored based on the real-time ecological monitoring factor data, and obtaining the comprehensive transmission priority evaluation value of the monitoring data packet based on the fuzzy measure function and the monitoring factor feature vector, the process includes: Multi-level filtering and noise reduction, outlier masking, and missing data interpolation are performed on the real-time ecological monitoring factor data of the monitoring area to obtain a purified monitoring factor dataset. The purified monitoring factor dataset is normalized in terms of dimensions, and the normalized monitoring factors are combined according to the preset semantic importance order to form a real-time monitoring factor feature vector. Incremental contribution integral calculation is performed on the feature vector of real-time monitoring factors based on fuzzy measure function and Choquet fuzzy integral calculation formula; The result of the integral calculation is determined as the comprehensive transmission priority evaluation value of the data packets to be monitored.

9. The method for acquiring and transmitting three-dimensional ecological monitoring data for deep-sea resource development as described in claim 1, characterized in that, When obtaining the statistical distribution between the overall transmission priority assessment value and link bandwidth resources, the following is included: Obtain the correlation sample pairs between the comprehensive transmission priority evaluation value and the actual transmission success rate within the preset historical observation period, and perform time benchmark alignment and outlier removal on the correlation sample pairs to construct a joint statistical matrix; Based on the kernel density estimation method, the joint probability density between the comprehensive transmission priority evaluation value and the available bandwidth of the link in the joint statistical matrix is ​​estimated, and a statistical model of transmission performance is established. Based on the transmission performance statistical model, the mutual information, covariance, and nonlinear correlation structural features between the comprehensive transmission priority evaluation value and bandwidth resources are extracted. Based on the extracted features, a statistical distribution between the comprehensive transmission priority evaluation value and link bandwidth resources is constructed. Based on the constructed statistical distribution, a pseudo-random perturbation sequence that conforms to the dynamic environmental characteristics of the deep sea is generated, and the pseudo-random perturbation sequence is superimposed on the real-time ecological monitoring factor data and link bandwidth parameters. Perform hierarchical verification on the data after superimposed perturbation, including a first-level cyclic redundancy check and a second-level semantic logic check based on business value. According to the comprehensive transmission priority evaluation value from high to low, the monitoring data packets are mapped to different communication link fragments, and the feedback status of each simulated transmission is recorded. By repeatedly performing simulated transmission a preset number of times, a sample set of monitoring data return success rate is obtained, and the probability distribution function of the return success rate and the time delay confidence interval under a preset confidence level are calculated based on the sample set.

10. A three-dimensional ecological monitoring data acquisition and transmission system for deep-sea resource development, employing the three-dimensional ecological monitoring data acquisition and transmission method for deep-sea resource development as described in any one of claims 1-9, characterized in that, include: The three-dimensional perception module is used to construct a deep-sea ecological monitoring indicator system, obtain a multimodal ecological monitoring factor set, generate a deterministic value matrix, and solve for subjective value weights. The value assessment module is connected to the three-dimensional perception module. It is used to obtain the objective information entropy weight based on historical data and combine it with the subjective value weight to obtain the comprehensive value weight of the monitoring data, thereby constructing a fuzzy measure function for the priority of monitoring data transmission. The intelligent transmission module is connected to the value assessment module to obtain the feature vector of real-time monitoring factors, calculate the comprehensive transmission priority assessment value, and obtain the success rate probability distribution of data return and the confidence interval of transmission delay based on statistical distribution and Monte Carlo sampling. The three-dimensional perception module includes an acoustic sensor array set on the seabed, a multi-functional buoy set on the sea surface, a drone inspection unit set in the airspace, and a remote sensing data receiving terminal set in the sky. The value assessment module is integrated into the edge computing unit inside the multi-functional buoy, which is connected to the sensor hub inside the multi-functional buoy via an RS485 bus.

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