With the increase in environmental awareness worldwide, the filling mining method has attracted extensive attention because this method can realize safe and green mining in underground metal mines. Recycling waste tailings in stopes to control underground rock movement, and surface settlement can reduce waste environmental pollution while ensuring mining safety. Although many test data are required to support the formulation of the mine backfill scheme, the advanced management tool of backfill test data is insufficient. In this study, a new data management method that is suitable for backfill experiments is proposed. First, this study analyses the main system requirements, including experimental business process modeling, experimental process combing, and a multidimensional query of experimental data. Then, the backfill test business flow and data flow are summarized to establish the backfill test business model and experimental index system. Then, many system functions are designed, including backfill experiment management, experimental data query, backfill knowledge maintenance, and system management. Finally, a backfill test data management system is developed based on B/S architecture. Developing a data interface, having a built‐in test formula and customizing a multidimensional data analysis enable the system to solve the problems in data collection, data accounting, and data analysis. After being put into use in the Backfilling Engineering Laboratory of a group in Shandong, this system improved the data‐sharing rate and utilization rate and provided a convenient data management tool for the laboratory.

The backfill mining method is adopted in many mines around the world because it can reliably handle underground mine tailings and eliminate dangers in goafs. It is necessary to improve backfilling resource allocation and efficiency, thereby eliminating safety hazards and providing reliable support for the next stage of mining as quickly as possible. In this paper, we propose a backfill-scheduling optimization model that considers multiple processes, resource constraints, and operating capabilities. The purpose of this model is to minimize the exposure time of goafs. This NP-hard (Nondeterministic Polynomial-time hard) problem has a non-inferior implemented solution through multiple iterations of genetic, crossover, and mutation operations of the genetic algorithm. The results show that the model significantly reduces the backfilling-delay time and backfilling-operation time.

Addressing the resource constraints, such as manpower and equipment, faced by mine backfilling operations, this study proposed an optimization model for backfill scheduling based on the Resource-Constrained Project Scheduling Problem (RCPSP). The model considered backfilling’s multi-process, multi-task, and multi-resource characteristics, aiming to minimize total delay time. Constraints included operational limits, resource requirements, and availability. The goal was to determine optimal resource configurations for each stope’s backfilling steps. A heuristic genetic algorithm (GA) was employed for solution. To handle equipment unavailability, a new encoding/decoding algorithm ensured resource availability and continuous operations. Case verification using real mine data highlights the advantages of the model, showing a 20.6% decrease in completion time, an 8 percentage point improvement in resource utilization, and a 47.4% reduction in overall backfilling delay time compared to traditional methods. This work provides a reference for backfilling scheduling in similar mines and promotes intelligent mining practices.