## 4 Multi-Source Data Fusion Technology and Algorithm  
### Principles and Methods of Data Fusion Technology  
Data fusion refers to the integration of data from different sources, formats, scales, dimensions, and properties to form a unified and coherent data set, thereby providing more comprehensive and accurate information support for real estate management and urban optimization [21]. The principle of data fusion technology is mainly to improve data quality and analysis accuracy by preprocessing and matching multi-source data [22]. Data preprocessing is the basis of multi-source data fusion, which aims to eliminate inconsistency, redundancy, and noise in the data and ensure the reliability of the analysis results. The process includes data cleaning, integration, conversion, and normalization to solve the problem of data inconsistency from different sources. For example, ensuring data integrity involves removing duplicate records and correcting erroneous data through a combination of automated checks, such as range validation and consistency checks, and manual verification by experts. This process efficiently identifies data errors by comparing the data against expected patterns, known values, and logical constraints. Data integration addresses the semantic heterogeneity of different data sources, such as sensors and GIS data, by standardizing data formats and creating a common vocabulary or schema. Data conversion then adjusts the original data to a format suitable for analysis, ensuring that the data are not only clean but also compatible with the analytical tools and methods used in our study. In addition, feature extraction is an important part of improving analysis efficiency, and key patterns are extracted by applying techniques such as principal component analysis. Data dimensionality reduction and outlier detection can further improve computing efficiency, ensure data quality, and provide a solid foundation for subsequent analysis [23]. In the process of data matching and association, the key is to effectively identify and link information from different sources to ensure the accuracy and completeness of the data. This link uses algorithms such as pattern recognition, similarity measurement, and entity resolution to establish a unified data view, eliminate data silos, and improve the effectiveness of decision support [24]. Data matching requires careful preprocessing, and then rule-based, similarity-based, or machine-learning methods can be used to address the challenges of data format heterogeneity, semantic differences, and large data size. With the development of technology, researchers have developed advanced algorithms such as distributed computing and graph computing for real-time dynamic data matching while also paying attention to the need to protect personal privacy during data fusion. In short, data fusion technology provides strong data support for real estate management and urban optimization by effectively integrating information from different data sources and promoting the sustainable development of smart cities. In the construction of smart cities, multi-source data fusion technology is crucial, and data fusion algorithms are the key means to achieve this goal. These algorithms effectively integrate, complement, and optimize multi-source data to improve their availability, accuracy, and reliability [25]. The main types of data fusion algorithms include methods based on probability statistics, evidence theory, fuzzy set theory, neural networks, and machine learning. Each method has its own characteristics and is suitable for different scenarios and data types [26]. The types and comparisons of data fusion algorithms are shown in Table 2.  
These data fusion algorithms provide strong support for the construction of smart cities. Through reasonable selection and application, the application value of data can be significantly improved [34]. However, they still face challenges in implementation, such as data quality, consistency, and real-time performance.