Risk warning method for the whole process of production project based on multi-source data mining

In order to realize the mixing jet of high temperature resistant surface materials, a modeling method based on multi-source data mining is proposed. According to the different position of high temperature resistant surface material in the jet, the high-speed jet is formed. The mechanical properties of high temperature resistant surface material plate were monitored by concentrated load moment analysis method. According to the measured data of the sensor, the grid characteristic test data of high temperature resistant surface material with multi-source data structure is fused. Then the characteristics of high temperature resistant surface materials were optimized. The mesh independence of heat-resistant surface material optimization is verified, and the optimization modeling of heat-resistant surface material is realized. Simulation analysis shows that the method has high stability and improves the preparation performance of high temperature resistant surface materials.

Different manufacturers achieve intelligent driving system function diversely, which imposes higher impartiality requirements for the evaluation method of the third party. To this end, this article presents a safety benefit evaluation method of intelligent driving systems based on multi-source data mining. On the basis of the discussion over the nature of general system identification, this approach uses neural network to learn the behavior of the evaluated object using the running vehicle data collected and provided by the manufacturer. Combined with the trained network controller, the test scenario model and the car following model extracted from the field operational tests data, and with the occupant injury model obtained from the accident data, Monte Carlo random simulation is used to calculate the injury risk with or without the evaluated system, then the safety benefit by comparison is estimated. In this article, the adaptive cruise system and the automatic emergency braking system are evaluated. The results show that the neural network can accurately imitate the behavior of the object to be evaluated. There is only 0.01 error between the evaluation results using this network and the real object.

In this article, we address the problem of data privacy in multisource data mining. To do it, we present a new multiparty fully homomorphic encryption (MP-FHE) scheme, in which all participants are completely fair to perform the same computation. At first, the proposed MP-FHE scheme is divided into five stages (i.e., calculation, configuration, recombination, resharing, and reconstruction stage) to achieve the unified computation form of addition and multiplication. Meanwhile, random bivariate polynomials and commutative encryption are used to achieve the degree reduction of polynomials and the continuity of computation. Moreover, we prove that the scheme meets result consistency and program termination under the fail-stop adversary model. Especially, three kinds of error detection criteria are presented to find errors in three different stages (i.e., recombination, resharing, and reconstruction stage), which provides the monitor basis for the fail-stop adversary model. In addition, the MP-FHE scheme is applied into privacy preserving k-means clustering algorithm. Finally, we evaluate the computation and communication performance of our scheme from both theoretical and experimental aspects, and the evaluation results show that the scheme is efficient enough for multisource data mining.

The accuracy of the distribution network topology relationship is the basic guarantee for the management of low-voltage and medium-voltage line losses. Due to many historical reasons and economic reforms of the power grid, the line-transformer-user file relationship is abnormal, making on-site investigation difficult. In view of the above problems, combined with the actual scenario of the distribution network, a distribution network topological anomaly recognition model based on multi-source data mining is established. Algorithms such as isolated forest, K-means feature cluster analysis, LSTM model are applied, and multi-dimensional feature fusion is used to identify features of line-transformer-user operation data. The value of intelligent distribution network data is deeply explored. Through the application of examples, the relationship of line-transformer-user in line loss management is realized, effectively improving the efficiency of line loss management.

In order to improve the accuracy and efficiency of talent training quality evaluation, a design method of quality evaluation model based on multi-source data mining is proposed. The data processing technology is used to preprocess the talent training data, the multi-source data mining method is used to classify the information in the evaluation database, and the information in the quality evaluation database is mapped to the evaluation index system. According to the diversity and complexity of knowledge modules required by aviation service talents, the talent evaluation system is divided into knowledge literacy module, interpersonal communication ability module, work status module, foreign language ability module and service literacy module to obtain various evaluation indicators. Set the evaluation standard, and use Likert scale method and standard deviation method to calculate the weight of each evaluation index. Finally, use BP neural network to build the talent training quality evaluation model and obtain the final evaluation result. The experimental results show that the proposed method has obvious advantages in evaluation accuracy and efficiency.

Risk warning method for the whole process of production project based on multi-source data mining

With the development of 3S technologies, a large quantity of spatial-temporal data related to land use has been accessed. Being scattered across different departments and lacking of relevant analysis tools made them utilize insufficiently. Although some experts have applied data mining to solve this problem, most of them have only provided one method for single task to build the mining systems. However, it is undesirable to use just one method to mine. In addition, the single function systems can not be used widely and conveniently. Hence, under full investigation on operations of land use, a multi-source data mining prototype system for land use is proposed by integrating of technologies of GIS and spatial data mining. According to the general data mining process, aiming at the multi-demands of land evaluation and land planning and so on, the system is developed by using ArcEngine 9.0 and VB.net. The system integrates basic geospatial data, land use/cover data, and thematic data as data sources, excavates different knowledge of land Quality, land use zoning rules, land use patterns and change rules and so on. Based on the types of knowledge, the system accordingly provides several different mining methods, including decision tree, support vector machine, artificial neural network, time series, spatial association rules, etc. Wide adaptability of the system is demonstrated by using some cases. The results of the system can meet multipurpose needs and be used to support decision-making of the land management department.

Research on Online Identification of Error Based on Multi Source Data Mining

The effectiveness of geological prospecting depends on the accuracy of the prediction of the prospecting target areas. In comparison with the conventional qualitative method (Mineral Exploration and Development), the use of big data concepts and methods for the in-depth analysis of the potential value of geological information has emerged as an effective way to improve the accuracy of prospecting target area predictions. The Beishan area in Gansu Province, China, is a prominent polymetallic metallogenic belt in northwest China. In recent years, geologists have encountered challenges in achieving effective breakthroughs in prospecting through conventional methods. In this study, we apply the big data concepts and methods to analyze the geochemical and aeromagnetic data of the Beishan area and utilize a series of self-developed software to rectify errors in the original data. A new geochemical remediation plan is proposed for the main elements of ore formation, and on this basis, a copper ore prospecting model based on multi-source data information mining is established. The prospecting model is used to predict the formation of copper ore in the Beishan area, and 100 level I and II preferred target areas with significant prospecting significance have been identified. Level I and II preferred target areas account for 2.7% of the study area. Verified by field sampling, the actual mineralization rate of the level I target area is 39.47%. This study proves the effectiveness of the proposed multi-source data mining method in improving the prediction accuracy of prospecting target areas.

Relevance analysis and short-term prediction of PM2.5 concentrations in Beijing based on multi-source data

This paper proposes the data mining method of voltage sag severity based on DHP algorithm and replaceable coefficient to assess the risk of voltage sag. The DHP (Direct Hashing and Pruning) is served to mine the relationship between voltage sag characteristic attribute (VSCA) in the fault scenario and the voltage sag severity (VSS) of node. Using direct hash pruning technology, frequent item sets can be found quickly and mining efficiency can be improved. The association rules are matched with the actual fault scenarios through replaceable coefficients to get the VSS of actual fault scenarios. Finally, the effectiveness and accuracy of this method are verified by simulation and examples.

Research on the evolution of public opinion and topic recognition based on multi-source data mining

The success of geological prospecting depends on the accuracy of target area prediction. Traditional qualitative research methods rooted in theoretical frameworks have shown significant limitations, especially in their inability to fully exploit the latent value of existing geological information. Applying big data concepts and methodologies to geological information mining has emerged as an effective way to improve the accuracy of prospecting target prediction. This study is founded on the core principle of geoscience big data: to “uncover correlations within data to address geological issues”. Taking geochemical prospecting and aeromagnetic data from the Beishan area in Gansu Province as a case in point, this study emphasizes the significance of meticulous data processing in averting potential errors. A suite of prospecting models was developed through multi-source data mining to identify potential gold deposits. Notably, aeromagnetic data were innovatively employed for the first time to predict the occurrence of non-magnetic minerals, which are primarily structurally altered rock-type and quartz vein-type gold deposits. The developed prospecting model was used to predict metallogenesis in the Beishan area of Gansu Province. The prospecting target area was delineated, accounting for 3.67% of the study area. Verification using field sampling data revealed that the actual mineralization rate in the level-I target area reached 52.6%. The research results suggest that this approach can substantially enhance the accuracy of prospecting target area prediction.

Research on the evolution of public opinion and topic recognition based on multi-source data mining

The growing discrepancy between the supply and demand of new accounting professionals underscores an urgent need for reform in the educational paradigms within higher education accounting programs. This paper introduces a comprehensive proposal for educational reform in accounting at the collegiate level, using ‘A school’ as a pilot site for our experimental teaching reform. Central to our methodology is the design of a multi-source data mining process, utilizing characteristics of educational information data. Employing the K-means clustering algorithm as a foundation, we further develop a Fuzzy C-Means (FCM) clustering algorithm to evaluate the impact of these pedagogical reforms. Subsequent to the reform, we collected and analyzed teaching data, establishing specific assessment metrics. The analysis, conducted via clustering, revealed significant improvements. Prior to the reform, the experimental class scored an average of 62.89, compared to the control class’s 63.05. Post-reform, the experimental class’s performance elevated to 74.57, while the control class saw a more modest increase to 65.14. This marked enhancement in the experimental class’s performance underscores the efficacy of the reform, aligning with our educational objectives, which anticipate an 80% qualification rate and a 42% excellence rate among accounting majors. This study not only advances the theoretical framework but also refines the practical approaches to cultivating accounting talent in collegiate settings. It offers valuable insights and serves as a reference for future reforms in accounting education at universities.