## 1 Introduction
At present, domestic and foreign scholars have carried out extensive research on the accuracy fusion evaluation of multisource data, such as field tests and semi-physical simulation tests. The main data fusion techniques include deep neural network methods [15], Bayesian inference [16], fuzzy logic [17] and the Dempster-Shafer evidence theory [18]. For example, to improve the accuracy of multisensor data fusion, [PERSON] et al. [19] proposes a data fusion algorithm based on a backpropagation neural network (BPNN), which can effectively address the problem of network nonconvergence and improve network performance. [PERSON] et al. [20] proposes a multisensor data fusion algorithm based on particle swarm optimization and an improved a backpropagation neural network by optimizing the network input, which improves the accuracy of sensor data fusion. [PERSON] et al. [21] and [PERSON] et al. [22] fused the data from a simulation model with real-time data based on the Bayesian entropy information fusion method and a posterior regularized Bayesian neural network method combining soft and hard knowledge constraints. [PERSON] et al. [23] and [PERSON] et al. [24] proposed a series of data fusion methods based on Bayesian reasoning, which have good fusion effects. [PERSON] et al. [25], [PERSON] et al. [26], and [PERSON] et al. [27] apply fuzzy set theory to multisensor data fusion, which reduces the uncertainty of sensor data information and has less computational complexity. [PERSON] et al. [28] and [PERSON] et al. [29] used the D-S evidence theory for multisource data fusion, which reduces the dependence on prior knowledge and demonstrates that a single-cycle fusion method has better fusion performance. In addition, some scholars have improved these basic data fusion methods. [PERSON] et al. [30] proposes a new method of optimizing data fusion based on a BPNN improved using a GSO algorithm, which improves the efficiency of data fusion and extends the network life. [PERSON] et al. [31] optimized the weights and thresholds of a neural network based on an artificial fish swarm algorithm and improved the data fusion accuracy of a wireless sensor network. [PERSON] et al. [32] used the beetle antennae search algorithm to improve a particle swarm optimization BPNN and applied it to multisource data fusion, which effectively improves the fusion efficiency. [PERSON] et al. [33] proposed an improved data fusion method based on a BPNN and applied it to the fire monitoring sensor network, which effectively improves the monitoring accuracy and the efficiency of a wireless sensor network for forest fire monitoring. [PERSON] et al. [34] proposed a multi-sensor fusion method to improve the Dempster-Shafer evidence theory, considering that the complex space environment of the battlefield has led to many difficulties in target recognition of flight equipment. [PERSON]. [PERSON] et al. [35] applied Dempster-Shafer evidence theory knowledge to information fusion and classification. [PERSON] et al. [36] put forward the theoretical method of reciprocal fuzzy closeness in calculating the weight of a data fusion model. The above methods are all aimed at improving fusion technology and do not start from the simulation data itself to study the validity, completeness, and credibility of the data source. The premise of data fusion is to ensure the validity and completeness of data to be fused. It is difficult for incomplete semi-physical simulation samples to fully characterize the landing information of an aircraft, which will affect the landing prediction accuracy of the fusion model. In this case, if an integrity detection of the semi-physical simulation data samples is carried out, supplementary test conditions are designed for the semi-physical simulation sample points that do not satisfy the completeness requirements, the test condition data corresponding to the placement points are supplemented, and the test is performed again. Then, a complete semi-physical simulation data source is obtained.