# Development of a Multifunctional Unmanned Boat Platform for Aquaculture Automation
## 2 Complete Machine Structure and Working Principle
### Working Principle
#### 2.2.2 Water Quality Evaluation System Based on Crowdsource Sensing  
Since the data acquired by the unmanned boat during the cruising process is discrete point data, this paper first converts the discrete point data into continuously distributed spatial data by the method of Kriging interpolation. Secondly, this paper establishes the membership function for different types of water quality data. The membership function sets a quantitative range for each water quality indicator, transforming subjective sensory judgments into statistically and mathematically quantifiable analysis. Finally, this paper constructed a comprehensive evaluation model of water quality based on pH value, water temperature, and TDS value. The model comprehensively considered the influence of each indicator on the environmental quality of the water body. It utilized the fuzzy  
Figure 7: Flowchart of the monte carlo simulation algorithm. comprehensive evaluation method [26; 27; 28] to assess the suitability of the fishing area, and calculated and gave the reference suggestions. (1) Kriging interpolation  
Obtaining a continuous spatial data distribution is essential in water quality monitoring and assessment. However, due to technical and resource limitations, water quality data acquisition is usually discrete. To obtain continuous water quality data within the entire fishing area, this paper employs the kriging interpolation method to acquire continuous spatial data. The kriging interpolation method has been widely applied in the fields of marine environment and water quality monitoring. Currently, there are various kriging interpolation methods, such as ordinary kriging, simple kriging, and universal kriging. Based on previous research, this paper adopts the ordinary kriging method, as shown in Equation (1), which has been validated as the most effective method in previous studies [29; 30; 31]. \[\hat{Z}(x_{0})=\sum_{i=1}^{n}\lambda_{i}Z(x_{i}), \tag{1}\]  
where \(\hat{Z}(x_{0})\) is the predicted value at \(x_{0}\), \(Z(x_{i})\) is the observed value at a known point, \(\lambda_{i}\) is the weighting factor, and \(n\) is the number of known points. (2) Establishment of the Membership function  
Water quality evaluation typically involves multiple indicators, each with different units, ranges, and levels of importance. Membership functions can unify these diverse indicators into fuzzy sets, thereby eliminating unit differences in comprehensive evaluations and facilitating unified calculation and analysis. Therefore, the membership function, as a fuzzy mathematical tool [32; 33], provides an effective way to quantify the uncertainty and continuity of water quality indicators. Based on the water quality criteria for fisheries aquaculture, this paper constructs the membership functions for three major indicators (Figure 8). (3) Comprehensive Evaluation Model  
Fuzzy comprehensive evaluation is a comprehensive evaluation method based on fuzzy set theory, which mainly solves the problem of uncertainty and ambiguity in the evaluation process. It is suitable for dealing with evaluation problems that have incomplete  
Figure 8: Indicator membership function. information or are not easy to quantify. The basic idea of fuzzy comprehensive evaluation is to use the membership function of fuzzy sets to describe the degree to which things belong to a certain category. It then conducts a comprehensive evaluation of multiple factors through fuzzy transformation, with the specific steps as follows:  
Determine the set of evaluation factors: first you need to determine the factors of the evaluation object \(U=\{u_{1},u_{2},\ldots,u_{n}\}\), which is a collection of factors that affect the evaluation object. Determine the set of rubrics: the set of rubrics \(V=\{v_{1},v_{2},\ldots,v_{n}\}\) is the set of possible evaluation results for the subject of the evaluation.