Intelligent Data Acquisition and Optimization Control System Applied to Mine Backfilling System

Abstract

The present invention discloses an intelligent data acquisition and optimization control system applied to a mine filling system, which relates to the technical field of computer automatic control. The system includes: an ore body stress analysis unit, a solid-phase mass analysis unit of the filling slurry, a physical property analysis unit of the filling body, and a filling control and analysis unit; the ore body stress analysis unit is used to establish a three-dimensional ore body geometric model according to the ore body geological exploration results, divide the three-dimensional ore body geometric model into finite element meshes, and calculate the solid-phase mass concentration of the filling slurry; the physical property analysis unit of the filling body is used to establish a rheological model of the filling slurry according to the solid-phase mass concentration of the filling slurry; the filling control and analysis unit is used to calculate the stability of the filling according to the three-dimensional stress tensor field of the ore body and the pore water pressure of the filling slurry, and obtain a stability coefficient. The present invention can intelligently control the filling speed, evaluate the construction stability in real time, and ensure the safety and efficiency of the filling construction process.

Description

Technical Field

[0001] The present invention relates to the technical field of computer automatic control, and particularly to an intelligent data acquisition and optimization control system applied to a mine filling system. Background Art

[0002] The underground cemented filling technology is a green mining method widely used in mine exploitation. By backfilling the goaf with slurry, it can support the rock formation, reduce surface subsidence, and control the deformation of the ore body, thereby improving the safety and environmental protection of the mining process. With the increasing depletion of mine resources, the demand for deep mining has gradually increased, and at the same time, the requirements for green mining and sustainable development have become increasingly strict. This has led to the gradual development of the underground cemented filling technology from simple manual control to a complex system relying on automation and intelligent control. However, although the existing technology has made remarkable progress in aspects such as filling material selection, slurry conveying equipment, and construction technology, there are still many deficiencies in system integration, dynamic control, and adaptability under complex ore body conditions.

[0003] In the traditional underground cemented filling technology, the commonly used filling materials mainly include cement-based slurry, fly ash-based slurry, and tailings-based slurry. These materials are injected into the goaf by means of pumping or gravity conveying. The existing filling construction mostly adopts fixed-parameter construction, that is, the slurry concentration, pumping speed, and filling volume are set according to experience, and usually lack the ability to respond to complex working conditions in real time during the construction process. A typical cemented filling system includes slurry preparation equipment, conveying equipment, grouting equipment, etc. The operation of the system depends on manual monitoring, and its automation and intelligent level are relatively low. In terms of filling design, the existing technology usually uses a simplified mechanical model to preliminarily estimate the ore body stress and filling body strength. For example, in some studies, the initial in-situ stress of the ore body is estimated using the buried depth and lithology data of the ore body, and for the filling body strength, it is usually designed according to the empirical values of the solid phase concentration and cement ratio measured by experiments. However, this simplified model ignores the complexity of the three-dimensional stress field of the ore body and fails to fully consider the dynamic coupling effect of ore body stress, pore water pressure, and mechanical properties of the filling body. Summary of the Invention

[0004] The object of the present invention is to provide an intelligent data acquisition and optimization control system applied to a mine filling system. The present invention can intelligently control the filling speed, evaluate the construction stability in real time, and ensure the safety and efficiency of the filling construction process.

[0005] To solve the above technical problems, the present invention provides an intelligent data acquisition and optimization control system applied to a mine filling system. The system includes: an ore body stress analysis unit, a solid phase quality analysis unit of filling slurry, a physical property analysis unit of filling body, and a filling control and analysis unit; the ore body stress analysis unit is used to establish a three-dimensional ore body geometric model according to the ore body geological exploration results, divide the three-dimensional ore body geometric model into finite element meshes, and calculate the three-dimensional stress tensor field of the ore body; the solid phase quality analysis unit of filling slurry is used to calculate the solid phase mass concentration of the filling slurry according to the three-dimensional stress tensor field of the ore body; the physical property analysis unit of filling body is used to establish a rheological model of the filling slurry according to the solid phase mass concentration of the filling slurry; combining the solid phase mass concentration of the filling slurry and the rheological model of the filling slurry, construct a filling body strength prediction model to predict the filling body strength of the filling slurry at each time; and calculate the pore water pressure of the filling slurry at each time according to the predicted filling body strength of the filling slurry at each time; the filling control and analysis unit is used to calculate the filling stability according to the three-dimensional stress tensor field of the ore body and the pore water pressure of the filling slurry, and obtain a stability coefficient; the stability coefficient describes the ability of the ore body and the filling body to withstand external stress, and the higher the value, the stronger the ability of the ore body and the filling body to withstand external stress; according to the stability coefficient, the filling body strength and the pore water pressure, use a preset filling speed control model to calculate the current optimal filling speed, and use the optimal filling speed to control the filling of the filling slurry.

[0006] Further, the ore body stress analysis unit divides the three-dimensional ore body geometric model into finite element meshes, and the unit volume of each finite element mesh is ; calculate the three-dimensional stress tensor field of the ore body through the following formula :

[0007] ;

[0008] Among them, is the rock unit weight of the ore body, indicating the weight of the rock per unit volume in the ore body; is the buried depth, indicating the thickness of the overlying strata above the ore body; represents the initial in-situ stress coefficient, the ratio of the horizontal stress to the vertical stress, obtained through in-situ stress measurement; is the elastic modulus of the ore body, a material property reflecting the rock's ability to resist deformation in the ore body, determined by laboratory rock uniaxial compression test or acoustic wave method; is the Poisson's ratio, describing the ratio of the lateral deformation to the longitudinal deformation of the rock, with a value of 0.2 or 0.4, determined by triaxial loading experiment; represents the displacement at the position coordinate of , obtained through a numerical simulation method based on the finite element method; is the Laplace operator represents the stress at the position coordinate ; is the divergence operator; is the total volume of all finite element meshes.

[0009] Furthermore, the solid-phase mass analysis unit of the filling slurry calculates the solid-phase mass concentration of the filling slurry according to the three-dimensional stress tensor field of the ore body through the following formula :

[0010] ;

[0011] where is the temperature; is the temperature influence coefficient, which is a preset value; is the residual yield stress, which describes the residual shear stress generated by the particle contact force in the filling slurry and is measured through a rheological experiment; is the maximum yield stress, which is the maximum allowable shear stress and is a set value; is the pressure influence coefficient, which is a set value; is the critical pressure, which represents the maximum allowable pressure; is the porosity of the filling slurry; is the water density; is the solid-phase density; is the standard pressure, which is a set value; is the maximum value of; is the median particle size, which is measured by a laser particle size analyzer or a sieving method; is the reference particle size; is the pressure.

[0012] Furthermore, the physical property analysis unit of the filling body establishes a rheological model of the filling slurry according to the solid-phase mass concentration of the filling slurry through the following formula

[0013] ;

[0014] where is the shear stress at time ; is the flow behavior index; is the pressure loss; is the pipe length; is the time when the filling speed of the filling slurry; is the pipe radius; is the solid-phase volume fraction, which represents the proportion of solid particles in the volume of the filling slurry; is the preset maximum solid-phase volume fraction; is the preset minimum porosity; is the activation energy of the filling slurry.

[0015] Furthermore, the pressure loss is calculated using the following formula:

[0016] ;

[0017] wherein, is the gas constant.

[0018] Furthermore, the filling body physical property analysis unit constructs a filling body strength prediction model through the following formula, in combination with the solid-phase mass concentration of the filling slurry and the rheological model of the filling slurry,

[0019] ;

[0020] wherein, represents the maximum filling body strength when the solid-phase mass concentration of the filling slurry is ; is the hydration reaction characteristic time of the filling slurry, and the time when the hydration reaction of the filling slurry reaches 50% is determined through a hydration heat release experiment; represents the hydration rate when the time , the shear stress is , and the temperature is , ; is the reference hydration rate.

[0021] Furthermore, the filling body physical property analysis unit calculates the pore water pressure of the filling slurry at each time through the following formula according to the predicted filling body strength of the filling slurry at each time:

[0022] ;

[0023] wherein, is the pore water pressure at time ; represents the permeability coefficient when the filling body strength is , obtained according to the pre-established relationship between the filling body strength and the permeability coefficient; is the volume compressibility coefficient of the filling slurry, representing the ratio of the actual volume of the filling slurry to the volume of the pipeline; is the unit weight of water.

[0024] Furthermore, the filling control and analysis unit calculates the stability of the filling using the following formula according to the three-dimensional stress tensor field of the ore body and the pore water pressure of the filling slurry, and obtains the stability coefficient :

[0025] ;

[0026] Among them, is the critical pore water pressure, which is a set value.

[0027] Furthermore, the filling control and analysis unit uses the following formula to calculate the current optimal filling speed according to the stability coefficient, filling body strength, and pore water pressure, using a preset filling speed control model :

[0028] .

[0029] The intelligent data acquisition and optimization control system applied to the mine filling system of the present invention has the following beneficial effects:

[0030] The present invention accurately calculates the three-dimensional stress tensor field of the ore body through the ore body stress analysis unit, making up for the limitations of the traditional two-dimensional simplified model. By using the finite element method in combination with the ore body geometry, fault distribution, lithology difference, and goaf characteristics, the stress distribution of the ore body under complex mining conditions is comprehensively described. The calculation of the three-dimensional stress tensor field provides a scientific basis for the filling body design, especially for the accurate prediction of the filling strength requirements in high stress concentration areas, improving the pertinence and safety of the filling body design. In addition, this accurate stress field analysis can dynamically reflect the redistribution of the ore body stress, providing a basis for real-time adjustment during the construction process. The present invention optimizes the efficiency and resource utilization rate of the filling construction while ensuring the construction safety. For example, through the filling speed control model, the present invention can dynamically optimize the construction speed on the premise of ensuring the performance of the filling body, avoiding unnecessary material waste; through the accurate calculation of the solid phase mass concentration of the slurry, the overuse of high-concentration slurry is reduced, thereby reducing the material cost.

[0031] In addition, the precise construction control of the present invention reduces the disturbance to the ore body and the surrounding environment, helping to reduce the surface settlement and environmental pollution during the ore body mining process, meeting the requirements of modern mine mining for green environmental protection. The present invention establishes a filling body strength prediction model through the filling body physical property analysis unit in combination with the solid phase mass concentration and rheological model, fundamentally solving the static limitations of the filling body mechanical property prediction in the traditional technology. This strength prediction model comprehensively considers the dynamic effects of multiple factors such as shear stress, temperature, and hydration reaction characteristics on the filling body strength development, and can accurately describe the mechanical property changes of the filling body during the curing process. In particular, through the hydration rate model and the dynamic calculation of the pore water pressure, the present invention can reasonably predict the hydration reaction and pore water pressure accumulation problems under complex deep high stress environments, providing scientific support for the whole process optimization of the filling construction. Description of the Drawings

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0033] Figure 1 It is a schematic structural diagram of an intelligent data acquisition and optimization control system applied to a mine filling system provided by an embodiment of the present invention. Specific embodiments

[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0035] Embodiment 1, refer to Figure 1 : An intelligent data acquisition and optimization control system applied to a mine filling system, the system includes: an ore body stress analysis unit, a solid phase quality analysis unit of filling slurry, a physical property analysis unit of filling body, and a filling control and analysis unit; the ore body stress analysis unit is used to establish a three-dimensional ore body geometric model according to the ore body geological exploration results, divide the three-dimensional ore body geometric model into finite element meshes, and calculate the three-dimensional stress tensor field of the ore body; the solid phase quality analysis unit of filling slurry is used to calculate the solid phase mass concentration of the filling slurry according to the three-dimensional stress tensor field of the ore body; the physical property analysis unit of filling body is used to establish a rheological model of the filling slurry according to the solid phase mass concentration of the filling slurry; combining the solid phase mass concentration of the filling slurry and the rheological model of the filling slurry, construct a filling body strength prediction model to predict the filling body strength of the filling slurry at each time; and according to the predicted filling body strength of the filling slurry at each time, calculate the pore water pressure of the filling slurry at each time; the filling control and analysis unit is used to calculate the filling stability according to the three-dimensional stress tensor field of the ore body and the pore water pressure of the filling slurry to obtain a stability coefficient; the stability coefficient describes the ability of the ore body and the filling body to withstand external stresses, and the higher the value, the stronger the ability of the ore body and the filling body to withstand external stresses; according to the stability coefficient, the filling body strength, and the pore water pressure, use a preset filling speed control model to calculate the current optimal filling speed, and use this optimal filling speed to control the filling slurry for filling.

[0036] Specifically, the ore body stress analysis unit is the foundation of the entire system, and its core function is to comprehensively analyze the three-dimensional mechanical state of the underground ore body. During the longwall mining operation underground, the ore body often forms a complex stress distribution due to mining disturbances, and these stress states directly affect the stability of the stope and the stress conditions of the filling body. To solve this problem, the ore body stress analysis unit constructs a three-dimensional geometric model of the ore body based on geological exploration data. By collecting the physical property parameters, fault distribution, lithological interfaces, and goaf scope of the ore body, a digital model that can comprehensively reflect the actual state of the ore body is established. Subsequently, the system uses the finite element analysis method to divide the three-dimensional model of the ore body into multiple discrete small units. The purpose of this mesh division is to discretize the complex continuum problem, enabling the stress and deformation characteristics of each unit to be solved by numerical methods. By combining the mechanical equilibrium equations and constitutive relations within each unit, the three-dimensional stress tensor field of the entire ore body is finally formed through iterative calculations. The core of the three-dimensional stress tensor field lies in comprehensively describing the internal stress state of the ore body. For underground mining, the stress inside the ore body can be divided into principal stress components (i.e., the maximum and minimum normal stresses along a specific direction) and shear stress components (describing the shear effect within a plane). Through the calculation of the stress tensor field, the system can not only determine the location of high-stress concentration areas in the ore body but also identify the characteristics of stress redistribution caused by mining. This information directly provides guidance for filling design because the filling body needs to bear these stresses after formation, so the strength and stability design of the filling material must be highly matched with the mechanical state of the ore body. Compared with traditional technologies, the present invention provides a more efficient guidance scheme for the subsequent filling process through more accurate and dynamic stress field analysis. Based on the results of the ore body stress analysis, the solid-phase mass analysis unit of the filling slurry further calculates the solid-phase mass concentration of the filling slurry, which is an important indicator determining the mechanical properties of the filling body. The solid-phase mass concentration describes the mass ratio of solid particles in the slurry and directly affects the strength, fluidity, and consolidation properties of the slurry. In the present invention, the calculation of the solid-phase mass analysis unit is not only based on the physical properties of the solid material but also dynamically combines the distribution characteristics of the three-dimensional stress tensor field of the ore body. High-stress areas in the stress tensor field usually require a higher-strength filling body, which requires increasing the solid-phase concentration of the filling slurry. Low-stress areas allow a lower-concentration slurry to ensure the economy and construction efficiency of the filling process. Therefore, the present invention intelligently adjusts the solid-phase concentration of the slurry by analyzing the internal stress distribution of the ore body, enabling the filling design to meet the mechanical requirements while avoiding material waste.

[0037] The calculation process of the solid-phase mass concentration is actually a coupled optimization that combines multiple physical fields. The solid particles in the slurry need to form a stable suspension system under the action of water. Too low a solid-phase concentration may lead to insufficient strength of the slurry, while too high a concentration will increase the construction difficulty and may pose a risk of pipe blockage. Therefore, the solid-phase mass analysis unit introduces a dynamic adjustment mechanism. This mechanism takes the ore body stress tensor field as the input and obtains the optimal solid-phase concentration distribution required for each ore body unit through multiple iterative calculations. The system not only considers the requirement of slurry strength but also comprehensively evaluates the fluidity of the slurry in the pipeline, the pumping pressure, and the adaptability to the on-site construction conditions. For example, when the slurry needs to be transported over a long distance, the system will automatically reduce the solid-phase concentration to reduce pipe wear and pumping resistance; while in the high-stress concentration area, the solid-phase concentration will be significantly increased to enhance the compressive capacity of the filling body. It is worth mentioning that the design of the solid-phase concentration of the filling slurry also needs to match the subsequent rheological model. The rheological properties are directly affected by the solid-phase concentration. The yield stress and viscosity coefficient of the slurry change with the change of the solid-phase concentration, and these parameters further affect the operability of the slurry during construction and the strength performance after final consolidation. Therefore, when calculating the concentration, the solid-phase mass analysis unit will simultaneously predict the rheological parameters of the slurry to ensure the comprehensiveness and feasibility of the design scheme. Through this collaborative optimization of multiple parameters, the filling design of the present invention achieves the best balance between mechanical properties and construction efficiency.

[0038] The core task of the physical property analysis unit of the filling body is to comprehensively analyze the physical properties of the filling slurry from the perspective of materials science, especially its rheological properties, strength growth characteristics and the variation law of pore water pressure under different conditions. This unit first establishes its rheological model based on the solid-phase mass concentration of the slurry to describe the fluidity and stability of the slurry during construction. The long-distance underground transportation and complex spatial construction have put forward high requirements for the flow performance of the slurry, so the rheological model has become an important tool to ensure the smooth progress of construction. In the present invention, the rheological model comprehensively considers parameters such as the solid-phase particle distribution, viscosity, and yield stress of the slurry to reflect its flow characteristics under different shear conditions. The yield stress of the slurry is a key parameter in the model, which reflects the minimum shear force required for the slurry to start flowing. By predicting the yield stress, the system can evaluate whether the slurry has sufficient anti-segregation ability to prevent the influence of particle settlement on the uniformity of the slurry and the construction quality. In addition, the physical property analysis unit of the filling body also dynamically evaluates the consolidation performance of the slurry by introducing a strength prediction model. After the construction of the mine filling slurry is completed, it needs to be gradually consolidated to form a filling body with sufficient bearing capacity. During this process, the strength of the slurry increases over time and is affected by its composition, solid-phase mass concentration and surrounding environmental conditions. The system establishes a set of time-based strength growth prediction models through the combination of experiments and modeling. This model can accurately predict the strength of the slurry at different time points, guide the construction personnel to select reasonable construction intervals and operation rhythms, and at the same time avoid the damage of the filling body caused by premature loading. Through strength prediction, the system can further adjust the proportion of the slurry and the filling process to make the performance of the filling body more in line with the requirements of mine engineering.

[0039] At the same time, in order to comprehensively evaluate the possible problems that may occur during the consolidation process of the slurry, this unit specially introduces an analysis module for pore water pressure. During the filling process, the pore water pressure inside the slurry is one of the key factors affecting its consolidation speed and stability. Excessive pore water pressure may lead to difficult drainage of the slurry, thus delaying its consolidation process and even causing structural instability. The system constructs a dynamic calculation model of pore water pressure by comprehensively considering the material characteristics of the filling body and the construction environmental conditions to predict the pore water pressure distribution of the slurry at different consolidation stages. The accuracy of this model provides the necessary safety guarantee for the subsequent construction process.

[0040] All the analysis results generated by the physical property analysis unit of the filling body directly provide key data support for the filling control and analysis unit. The main task of the filling control and analysis unit is to perform real-time dynamic control of the filling process based on the ore body stress conditions, slurry characteristics, and construction progress, so as to optimize the filling efficiency and ensure construction safety. In the core control logic of this unit, the three-dimensional stress tensor field of the ore body is combined with the filling body strength and pore water pressure to form a set of stability evaluation mechanisms. The key index for stability evaluation is the stability coefficient, which reflects the ability of the filling body to withstand external stresses under the current stress conditions. By calculating the stability coefficient, the system can accurately judge whether the filling body meets the design requirements and avoid engineering safety problems caused by insufficient stability. In terms of filling control, the filling control and analysis unit dynamically adjusts the filling speed and slurry flow rate based on the stability coefficient, pore water pressure, and strength growth data calculated in real time. Determining the filling speed is a comprehensive optimization process that needs to consider multiple factors such as material strength growth, pore water pressure dissipation, and construction efficiency. By constructing a filling speed control model, the system can calculate the current optimal filling speed according to real-time parameters, thereby maximizing construction efficiency and ensuring that the performance of the filling body meets the design requirements. For example, in high-stress areas, since the external load borne by the filling body is large, the system will increase the solid-phase concentration of the slurry and appropriately reduce the filling speed to ensure that the strength growth rate of the filling body can keep up with the stress loading rhythm; while in low-stress areas, the concentration of the slurry can be appropriately reduced and the filling speed can be increased to improve the overall construction efficiency. The dynamic nature of filling speed control enables the system to flexibly respond to various complex mine working conditions, and this ability is exactly what the traditional fixed-speed filling method does not possess. The filling control and analysis unit also has intelligent decision-making capabilities. By interconnecting with other units of the entire system, it can achieve a comprehensive perception and rapid response to the complex mine environment. For example, when the system detects that the pore water pressure in a certain area of the ore body is too high, it can immediately issue an alarm and adjust the filling plan to reduce construction risks. At the same time, when the strength growth rate of the filling body is insufficient to meet the design requirements, the system will also optimize the filling effect by increasing the solid-phase concentration or extending the construction interval. This intelligent decision-making based on real-time data significantly improves the safety and reliability of the system.

[0041] Example 2: Set up an ore body stress analysis unit, divide the three-dimensional ore body geometric model into finite element meshes, and the unit volume of each finite element mesh is ; calculate the three-dimensional stress tensor field of the ore body through the following formula :

[0042] ;

[0043] Among them, is the rock unit weight of the ore body, indicating the weight of the rock per unit volume in the ore body; $\sigma_{0}$ represents the burial depth, which is the thickness of the overlying rock layer above the ore body; $K_{0}$ represents the initial in-situ stress coefficient, which is the ratio of the horizontal stress to the vertical stress and is obtained through in-situ stress measurement; $E$ is the elastic modulus of the ore body, which is a material property reflecting the rock's resistance to deformation in the ore body and is determined through laboratory uniaxial compression tests or acoustic wave methods; $\nu$ is the Poisson's ratio, which describes the ratio of the lateral deformation to the longitudinal deformation of the rock, with a value of 0.2 or 0.4 and is determined through triaxial loading experiments; $u(x,y,z)$ represents the displacement at the position with coordinates $(x,y,z)$ and is obtained through numerical simulation methods based on the finite element method; $\nabla^{2}$ is the Laplace operator $\sigma(x,y,z)$ represents the stress at the position with coordinates $(x,y,z)$; $\nabla\cdot$ is the divergence operator; $V$ is the total volume of all finite element meshes.

[0044] Specifically, the first part of the formula, $\sigma_{0}+K_{0}\gamma z$, represents the stress distribution of the ore body under static in-situ stress. This part is the basic stress field of the ore body and is directly related to the geological conditions. The parameter $\gamma$ is the unit weight of the rock mass, which represents the weight of the rock per unit volume. It is an important index describing the physical properties of the rock and is usually determined through laboratory tests. Its value directly affects the stress magnitude caused by the self-weight of the ore body. The burial depth $\sigma_{0}$ represents the thickness of the overlying rock layer above the ore body, which is an important determinant of the static stress of the ore body. As $\sigma_{0}$ increases, the vertical in-situ stress on the ore body increases linearly. In addition, the in-situ stress coefficient $K_{0}$ is the ratio of the horizontal stress to the vertical stress and is used to describe the anisotropy of the in-situ stress and is obtained through in-situ stress measurement. The value of $K_{0}$ is affected by the regional geological structure and the properties of the rock itself, and usually ranges from 0.4 to 1.0. The physical meaning of the static stress term is to describe the basic stress state of the ore body without external disturbances and provides a benchmark for the initial stress distribution of the ore body. The second part of the formula, namely the integral term, describes the additional stress distribution caused by the change of the displacement field $u(x,y,z)$ in the elastic deformation state of the ore body. This part incorporates the mechanical properties of the rock mass material into the formula by introducing the elastic modulus $E$ and the Poisson's ratio $\nu$. The elastic modulus $E$ is a characteristic parameter representing the rock's resistance to deformation and reflects the stiffness of the rock material under external forces. Its value is usually determined through uniaxial compression tests and is one of the bases for studying the mechanical properties of rocks. The Poisson's ratio Describes the lateral deformation characteristics of rocks under longitudinal compression, with values between 0.2 and 0.4, obtained through triaxial loading experiments. Combining the two describes the elastic response characteristics of the rock mass under external forces and converts these characteristics into calculation parameters for stress distribution.

[0045] In the integral term, the displacement field is the local deformation of the ore body caused by external loads and boundary conditions, representing the displacement changes of each point inside the ore body due to stress. Through the finite element method, the three-dimensional geometric model of the ore body is divided into finite grid elements with a volume of , and the displacement field of each element is solved by being driven by external forces and boundary conditions. The Laplace operator and the divergence operator describe the local bending and compression behaviors of the displacement field respectively. The Laplace operator represents the second-order change of the displacement field in local space, and its physical meaning is the stress distribution in the ore body caused by shear action; the divergence operator term is related to volume deformation and describes the expansion or compression effect of the ore body material under uniform loading. After combining these two terms, through the parameterization of the elastic modulus and the Poisson's ratio , the additional stress field in the elastic deformation state of the ore body is completely described. To obtain the stress distribution within the entire ore body, the formula uses an integral form to accumulate the contributions of all finite elements, representing the integral over the total grid volume . This calculation method can synthesize the local stress response of the ore body into an overall three-dimensional stress tensor field , thus accurately reflecting the stress distribution state of the ore body under the combined action of static in-situ stress and dynamic external forces. The results of this stress tensor field have clear engineering significance. First, through the calculation of the stress tensor field, the location and scope of stress concentration areas within the ore body can be determined. These areas are often the key parts prone to instability during the mining process. Second, the stress tensor field provides a scientific basis for the design of the filling body. In high-stress concentration areas, high-strength filling bodies are required to share the external forces and prevent the instability of the rock mass; while in low-stress areas, filling materials with lower strength but higher economy can be used. Third, the distribution of the stress tensor field also provides basic data for parameter control during the filling construction process. For example, the dynamic adjustment of the solid-phase mass concentration of the slurry can be optimized based on the requirements of the stress field. The formula comprehensively considers the influence of the internal physical properties of the ore body and external loads on the stress distribution through the coupling of the static in-situ stress term and the dynamic stress term. The introduction of the finite element method enables the formula to perform high-precision numerical solutions under complex geological conditions, especially suitable for the complex and variable stress fields of the ore body during longwall mining. The applicability of this calculation method is not only reflected in the solution of stress distribution in three-dimensional space but also can dynamically adjust the design parameters of the filling system by simulating the interaction between mining operations and the filling process to ensure the overall stability of the ore body.

[0046] Example 3: The solid-phase mass analysis unit of the filling slurry calculates the solid-phase mass concentration of the filling slurry according to the three-dimensional stress tensor field of the ore body through the following formula :

[0047] ;

[0048] where is the temperature; is the temperature influence coefficient, which is a preset value; is the residual yield stress, which describes the residual shear stress generated by the particle contact force in the filling slurry and is determined through rheological experiments; is the maximum yield stress, which is the maximum allowable shear stress and is a set value; is the pressure influence coefficient, which is a set value; is the critical pressure, which represents the maximum allowable pressure; is the porosity of the filling slurry; is the water density; is the solid-phase density; is the standard pressure, which is a set value; is the maximum value of; is the median particle size, which is measured by a laser particle size analyzer or sieving method; is the reference particle size; is the pressure.

[0049] Specifically, the first part of the formula represents the relationship between the solid-phase concentration of the filling slurry and the ratio of the maximum stress of the ore body. Here, is the maximum principal stress at a certain point in the three-dimensional stress tensor field of the ore body, which is obtained from the stress calculation formula in Example 2 and is a key parameter describing the degree of local stress concentration in the ore body. And is the standard pressure, which is used as a reference value for normalization so that the stress calculation results in different regions can be uniformly incorporated into the concentration calculation. Through this ratio, the requirements for high-stress areas of the ore body can be directly converted into the design basis for the solid-phase concentration of the slurry. Higher solid-phase concentrations are required in high-stress areas to provide sufficient strength. The second part introduces the particle characteristics and environmental factors of the filling slurry. This part mainly describes the adjustment mechanism of the solid-phase concentration from the aspects of particle distribution, porosity, and temperature conditions of the slurry. The parameters and are the densities of the solid-phase particles and water respectively. Their ratio determines the buoyancy effect of the solid phase in the slurry. The greater the density difference, the easier the particles are to settle, which requires a higher concentration to maintain the uniformity of the slurry. The porosity is the void ratio in the slurry, which describes the filling efficiency of the solid-phase particles and directly reflects the relationship between the particle gaps and the volume of solid particles. A lower porosity indicates close packing between particles, which can improve the overall strength of the slurry but may reduce its fluidity. Therefore, a trade-off is needed in the concentration calculation. The median particle size and the reference particle size are used to characterize the particle size distribution of the particles. Their ratio reflects the influence of changes in particle fineness on the properties of the slurry. Fine particles ( small) can usually fill the gaps between particles and improve the density, but they will increase the viscosity and pumping difficulty of the slurry. The temperature and the temperature influence coefficient further supplement the influence of the environment on the properties of the slurry. The fluidity and consolidation speed of the slurry may decrease in a low-temperature environment. Therefore, appropriate adjustments need to be made in the concentration calculation. The third part of the formula describes the influence of the residual yield stress generated by the contact force between particles in the slurry on the solid-phase concentration. The residual yield stress reflects the remaining shear strength of the slurry under shear action, which is usually determined by particle contact force and friction. The maximum yield stress is the maximum allowable shear strength in the design. The ratio of the two represents the proximity of the current shear stress of the slurry to its ultimate shear resistance. When the residual yield stress is high, the shear resistance between particles is significant, indicating that the fluidity of the slurry is poor. At this time, the solid-phase concentration needs to be reduced to improve its pumping performance. The last part To introduce the influence of construction pressure on the solid concentration. Construction pressure and the critical pressure The ratio describes the relative strength of the pressure borne by the current slurry. Pressure influence coefficient Determines the sensitivity of the slurry concentration adjustment to pressure changes. As the pressure increases, the particle arrangement inside the slurry tends to be compact, so it is necessary to appropriately reduce the solid concentration to avoid the risk of slurry blockage in the pipeline.

[0050] Example 4: The filling body physical property analysis unit establishes a rheological model of the filling slurry according to the solid-phase mass concentration of the filling slurry through the following formula;

[0051] ;

[0052] where, is the shear stress at time ; is the flow behavior index; is the pressure loss; is the pipeline length; is the filling speed of the filling slurry at time ; is the pipeline radius; is the solid-phase volume fraction, representing the proportion of solid particles in the volume of the filling slurry; is the preset maximum solid-phase volume fraction; is the preset minimum porosity; is the activation energy of the filling slurry.

[0053] Specifically, the first main part of the rheological model describes the basic shear stress characteristics of the slurry. Here, is the maximum allowable shear stress, representing the ultimate shear resistance of the slurry. It is a key value preset through the physical parameters of the slurry and construction requirements. The solid-phase volume fraction is the proportion of the volume of solid particles in the total volume of the slurry, which is the core parameter affecting the fluidity and strength of the slurry. When increases, the contact force and frictional force between particles increase, thus significantly improving the shear resistance of the slurry. To prevent the unlimited amplification of the shear stress by the solid-phase volume fraction, the formula uses as a normalization factor to ensure that the calculation result of the shear stress can be kept within a reasonable range. On this basis, the formula further introduces to describe the inhibitory effect of pressure loss on shear stress. Pressure loss is the energy consumption of the slurry due to flow resistance in the pipeline, and its value increases with the increase of flow velocity and pipeline length. The critical pressure is a preset limit value, representing the upper limit of the influence of pressure loss on shear stress. When increases, the energy consumption of the slurry increases, and this consumption will cause the shear stress of the slurry to decrease relatively, thus maintaining the fluidity of the slurry during construction. The introduction of the pressure loss term enables the formula to effectively adapt to the working conditions of long-distance pumping and complex pipeline structures.

[0054] The second part of the formula further describes the change of shear stress during the dynamic flow of the slurry. First of all, the shear rate is the key driving force for the flow change of the slurry, and it jointly determines the dynamic adjustment characteristics of shear stress with the flow behavior index . The flow behavior index is an important parameter characterizing the rheological properties of the slurry. When , the slurry behaves as a Newtonian fluid, and the shear stress is linearly related to the shear rate; when , the slurry behaves as a pseudoplastic fluid, that is, the shear stress decreases when the shear rate increases, and this characteristic is usually beneficial to construction; while indicates that the slurry is a dilatant fluid, and the higher the shear rate, the greater the shear stress. The exponential term synthesizes the influence of temperature, activation energy, filling speed and pipeline parameters on shear stress. First of all, the activation energy is the energy required for the slurry to overcome the adsorption force and binding force between particles during flow. It is determined by experiments and is an important parameter characterizing the interaction strength between slurry particles. When the temperature increases, the binding force between particles weakens, and the inhibitory effect of activation energy decreases, resulting in a decrease in the shear stress of the slurry. This relationship is described by the exponential term . At the same time, characterizes the influence of the filling speed and the solid-phase mass concentration on shear stress. When the filling speed is high, the interaction between particles increases, resulting in an increase in shear stress; while long pipelines and high pressure losses will weaken this effect, thus maintaining the fluidity of the slurry. The last part corrects the shear stress by introducing the slurry porosity . The porosity characterizes the void ratio between solid-phase particles, and it is directly related to the compactness and fluidity of the slurry. When the porosity is high, the friction between particles is low, and the shear stress decreases accordingly; while when the porosity approaches the minimum value When the particles are arranged more closely, the shear stress increases significantly. With this correction term, the formula can accurately reflect the influence of different particle distribution states on the rheological properties of the slurry.

[0055] Example 5: Pressure Loss It is calculated using the following formula:

[0056] ;

[0057] where is the gas constant.

[0058] Specifically, the first part of the formula describes the basic influence of flow velocity, pipe length, and diameter on pressure loss. First, represents the pipe length. As the pipe length increases, the frictional resistance that the slurry needs to overcome during transportation will increase linearly. Therefore, is a direct factor affecting pressure loss. Second, is the flow velocity of the slurry at time , which reflects the transportation rate of the slurry. High flow velocity is usually accompanied by greater internal flow resistance, resulting in higher pressure loss. The influence of the pipe diameter on pressure loss is non-linear. It is introduced in the form of , indicating that a decrease in pipe diameter will significantly increase pressure loss. A smaller pipe diameter will cause a local increase in the flow velocity of the slurry, thus significantly enhancing the effects of wall friction and fluid resistance. Therefore, in actual construction, to reduce pressure loss, a larger pipe diameter is usually selected. The second part of the formula further considers the correction effect of the flow behavior index on pressure loss. The flow behavior index is a parameter describing the rheological properties of the slurry, which determines the viscous behavior of the slurry in different flow states. When , the slurry behaves as a Newtonian fluid, and its viscous behavior is linearly related to the flow velocity; when , the slurry behaves as a pseudoplastic fluid, that is, as the flow velocity increases, its viscosity will decrease, thus reducing pressure loss; when , the slurry behaves as a dilatant fluid, and its viscosity increases with the increase in flow velocity, resulting in an increase in pressure loss. The correction factor The introduction ensures that the formula can dynamically adapt to slurries with different rheological properties. Especially under the working conditions of pseudoplastic fluids, this correction significantly reduces the calculated value of pressure loss, making the formula more in line with the physical phenomena in actual transportation. Generally speaking, by accurately describing the flow characteristics of slurries in pipeline transportation, the pressure loss formula organically combines the flow velocity, pipeline geometric parameters, and slurry rheological properties, providing a theoretical basis for parameter optimization in complex construction environments. Each part of the formula has a clear physical meaning: the pipeline length and diameter reflect the restriction of the pipeline geometric characteristics on the slurry flow; the flow velocity directly determines the magnitude of the hydrodynamic resistance; and the flow state index ensures that the formula can adapt to different types of filling slurries through the correction of viscous behavior.

[0059] Example 6: The physical property analysis unit of the filling body constructs a filling body strength prediction model through the following formula, combining the solid-phase mass concentration of the filling slurry and the rheological model of the filling slurry,

[0060] ;

[0061] wherein, represents the maximum filling body strength when the solid-phase mass concentration of the filling slurry is ; is the characteristic time of the hydration reaction of the filling slurry, and the time when the hydration reaction of the filling slurry reaches 50% is determined through the hydration heat release experiment; represents the hydration rate when the shear stress is , the temperature is , and the time is , ; is the reference hydration rate.

[0062] Specifically, first, represents the maximum strength of the filling body under a specific solid-phase mass concentration , which is a basic parameter in the model. The solid-phase mass concentration is the mass ratio of solid particles in the filling slurry and is one of the main factors determining the final strength. A high solid-phase concentration usually corresponds to a higher particle packing density and fewer porosities, thus forming a stronger consolidation structure. This item determines the corresponding maximum strength through experimental and numerical fitting methods, providing a clear goal for the slurry proportion design. The second part of the formula describes the control effect of the hydration reaction process on strength growth. The hydration reaction is the most critical chemical reaction in the solidification process of the filling slurry, and its characteristic time ​Represents the time required for the hydration reaction to reach 50% completion, determined through the hydration heat release experiment. This time constant reflects the reaction rate and activity of the hydrated substances in the slurry. Hydration rate is the core of this part, describing the situation at a specific time , shear stress and temperature under which the hydration reaction rate is. Specifically, contains multiple influencing factors. The reference hydration rate is the hydration rate of the slurry under standard conditions, describing the inherent reaction ability of the material. Temperature and activation energy reflect the influence of temperature on the hydration reaction rate through the classical Arrhenius formula. An increase in temperature will significantly accelerate the hydration rate, which is caused by the reduction of activation energy to overcome the chemical reaction barrier on the particle surface. At the same time, the ratio of shear stress and the maximum yield stress further corrects the hydration rate. A larger shear stress will accelerate hydration by increasing the contact frequency between particles and breaking the inert layer on the particle surface. The time integral term is a cumulative description of the degree of completion of the hydration reaction, indicating the total effect experienced by the hydration reaction before time . By raising the ratio of to the integral term to the power of , the formula effectively captures the dynamic control process of the hydration reaction on strength formation. The negative sign in the exponential term indicates that when the hydration reaction rate is low or the time is short, the strength growth will be significantly slowed down. The third part of the formula is the correction term for the influence of pressure loss on the filling body strength. Pressure loss is the pressure energy consumed by the slurry due to flow resistance during transportation, which may affect the uniformity of the slurry in the pipeline and the tightness of particle distribution. The critical pressure is a reference value for normalization to ensure that the influence of pressure loss under different construction conditions can be reasonably compared. When the pressure loss is small, the weakening effect of this term on strength is very small; but when the pressure loss approaches or exceeds the critical value, the non-uniformity of particle distribution and the shear effect of the slurry may significantly reduce the final strength.

[0063] Example 7: The physical property analysis unit of the filling body calculates the pore water pressure of the filling slurry at each time according to the predicted filling body strength of the filling slurry at each time through the following formula:

[0064] ;

[0065] wherein, is the pore water pressure at time ; represents the permeability coefficient when the filling body strength is , and is obtained according to the relationship between the filling body strength and the permeability coefficient established in advance; is the volume compressibility coefficient of the filling slurry, representing the ratio of the actual volume of the filling slurry to the volume of the pipeline; is the unit weight of water.

[0066] Specifically, the permeability coefficient in the formula is the core parameter describing the dissipation rate of pore water pressure, and it is dynamically correlated with the filling body strength . The permeability coefficient reflects the flow ability of water in the filling body and is usually obtained through the pre-established strength-permeability relationship curve. When the filling body strength is low (at the initial stage of curing), the permeability coefficient is large, and the pore water is more easily discharged; as the strength increases (the degree of curing improves), the slurry gradually forms a dense consolidation structure, and the permeability coefficient decreases significantly, thereby slowing down the dissipation rate of pore water pressure. By expressing the permeability coefficient as a function of strength, the formula can dynamically adjust the evolution process of pore water pressure to adapt to the curing process of the filling body. The core expression of the formula describes the migration characteristics of pore water affected by the mechanical behavior of the filling body in three-dimensional space. Among them, is the displacement field inside the ore body, which is calculated by the finite element method in Example 2 and represents the local deformation distribution caused by the ore body stress and external forces in the three-dimensional coordinate system. The introduction of the filling body strength further reflects the constraint effect of mechanical parameters on water migration during the curing process. The second-order partial derivative in the expression represents the gradient distribution of pore water diffusion on the plane, while the vertical derivative reflects the flow trend of water in the vertical direction. This coupling relationship in the spatial dimension comprehensively characterizes the variation law of pore water pressure under the combined action of the displacement field and the strength field. The exponential term describes the process of pore water pressure dissipation over time, and this term directly introduces the volume compressibility coefficient , the unit weight of water , the permeability coefficient and time . The volume compressibility coefficient is the volume response parameter of the filling slurry, representing the degree of volume change of the slurry under pressure, usually determined by experiments. A larger indicates that the slurry has higher compressibility, resulting in an accelerated dissipation rate of pore water pressure. The unit weight of water is a physical property parameter, characterizing the contribution of the gravitational effect of water to pressure. As time As time progresses, the negative exponent in the exponential term causes the pore water pressure to exhibit an exponential decay characteristic, reflecting the kinetic law of the osmotic effect. The formula expresses the instantaneous change rate of the pore water pressure through the time derivative and comprehensively describes the evolution dynamics of the pore water pressure during the solidification process of the filling body in combination with the above parameters. In the initial stage, the pore water pressure rapidly decreases due to osmosis and moisture migration; as time goes by, the increase in the degree of solidification reduces the permeability coefficient, and the decay of the pore water pressure gradually stabilizes. Through the precise modeling of this dynamic process, the formula can reflect the transformation process of the filling slurry from liquid to consolidation in real time, providing important data support for the adjustment of construction parameters and the stability assessment.

[0067] Example 8: The filling control and analysis unit uses the following formula to calculate the filling stability and obtain the stability coefficient according to the three-dimensional stress tensor field of the ore body and the pore water pressure of the filling slurry :

[0068] ;

[0069] where is the critical pore water pressure, which is a set value.

[0070] Specifically, the first key part of the formula represents the ratio of the filling body strength to the ore body stress action. This ratio reflects the resistance level of the filling body under the current stress conditions. The filling body strength is the compressive strength when the filling slurry is solidified to time and its calculation is based on the strength prediction model in Example 6. reflects the mechanical properties that gradually increase during the solidification process of the slurry with the progress of the hydration reaction. The ore body stress is the external load distribution on the ore body during mining and construction, which is determined by the three-dimensional stress tensor field calculated by the finite element method in Example 2. The formula directly quantifies the bearing capacity of the filling body under the action of the ore body stress through the ratio of the two: when is higher and is lower, the filling body has a higher bearing margin and the stability coefficient is correspondingly larger; conversely, when is lower or is higher, the bearing capacity of the filling body is insufficient and the stability coefficient decreases. The second part is the correction term for the stability of the pore water pressure . The pore water pressure is the hydrostatic pressure generated by the incomplete discharge of the internal moisture of the filling body during the solidification process, and its value is determined by the pore water pressure calculation formula in Example 7. The critical pore water pressure is a safety reference value set during the design process, used to define the upper limit of the influence of pore water pressure on the stability of the filling body. When the pore water pressure approaches the critical value , approaches 1, and the correction term tends to 0, indicating that the weakening effect of pore water pressure on stability is significant. This term reflects the dynamic influence of hydraulic factors on the mechanical properties of the filling body: high pore water pressure may lead to a decrease in the shear strength of the filling body, uneven consolidation, or internal cracking, significantly weakening its stability; while when the pore water pressure is low, the correction term tends to 1, indicating that its influence on stability can be ignored. The third part of the formula is the correction term for the dynamic growth of the filling body strength, used to describe the relationship between the current strength of the filling body and its theoretical maximum strength . is the maximum theoretical strength of the filling body when the solid phase mass concentration is , determined by the strength model of Example 6. The ratio in the exponential term indicates the proportion of the current strength to the theoretical maximum strength: when approaches , the exponential term tends to , indicating that the strength of the filling body has approached its theoretical limit, and further enhancement has limited influence on stability; when is much smaller than , the value of the exponential term approaches 1, indicating that the current strength is still in the rapid growth stage, and its influence on stability is relatively significant.

[0071] Example 9: The filling control and analysis unit uses the following formula to calculate the current optimal filling speed according to the stability coefficient, the strength of the filling body, and the pore water pressure, using a preset filling speed control model :

[0072] .

[0073] Specifically, the first correction term of the formula is the square root of the ratio of the strength of the filling body to the ore body stress, directly describing the bearing capacity of the filling body under the current external stress conditions. The strength of the filling body is the compressive strength when the filling slurry solidifies to time , calculated by the strength prediction model of Example 6, and its growth rate is affected by hydration reaction, solid phase concentration, and environmental conditions. The ore body stress It is the three-dimensional stress distribution formed by the external force during the mining process of the ore body and is obtained by calculating the stress tensor field in Embodiment 2. The larger this ratio is, the relatively stronger the compressive capacity of the filling body is, and it can withstand a higher construction speed; conversely, when the ratio is small, the filling body is in a weak state, and the construction speed needs to be appropriately reduced to avoid excessive load on the filling body. The second correction term combines the restrictions of pressure loss and the ore body displacement field on the speed. Pressure loss is the energy loss caused by the friction of the pipeline inner wall and the viscosity of the slurry during the transportation of the slurry, and is closely related to the construction parameters and the geometric characteristics of the pipeline. Critical pressure is the preset safety upper limit used to limit the adverse effects of high pressure loss on construction. When the pressure loss approaches or exceeds the critical value, tends to 1, and the role of the correction term will significantly reduce the filling speed to prevent the pipeline from being blocked or the fluidity of the slurry from being limited due to excessive conveying pressure. The exponential term further considers the dynamic influence of the internal displacement field of the ore body. The displacement field is jointly determined by the ore body stress and external forces. A larger displacement indicates that there may be potential instability risks in the ore body structure. At this time, the construction speed needs to be appropriately reduced to avoid further disturbance to the ore body structure. The third correction term introduces the ratio of the filling body strength to its theoretical ultimate strength, and the influence of the solid-phase mass concentration on the construction speed. Theoretical ultimate strength is the maximum strength when the filling slurry solidifies to a complete state under the condition that the solid-phase mass concentration is , and is predetermined by the model in Embodiment 6. The ratio represents the proportion of the current strength to the ultimate strength, reflecting the curing process of the filling body. When the current strength approaches the theoretical limit, the room for increasing the construction speed is limited; while in the early stage of curing, this ratio is small, and the speed needs to be appropriately reduced to provide sufficient consolidation time for the slurry. In the correction term describes the combined effect of the solid-phase mass concentration and the pressure loss. When is high, the density and strength growth of the slurry are relatively fast, but the conveying pressure loss will also increase accordingly; at this time, the speed needs to be appropriately reduced to balance the contradiction between strength formation and conveying efficiency. The fourth correction term describes the influence of pore water pressure on the speed through the permeability coefficient and the unit weight of water . Permeability coefficient It is a function of the strength of the filling body and gradually decreases with the increase in strength, reflecting the dynamic change of the moisture migration ability. When the permeability coefficient is low, it is more difficult for the pore water pressure inside the slurry to dissipate. An excessively high construction speed may lead to the accumulation of internal pressure, thus having an adverse impact on the stability and uniformity of the filling body.

[0074] The present invention has been introduced in detail above. Specific examples are used herein to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention. It should be noted that for those of ordinary skill in the art of this technology, without departing from the principle of the present invention, several improvements and modifications can still be made to the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

Claims

1. Intelligent data acquisition and optimization control system applied to mine filling system, characterized by: The system comprises: an ore body stress analysis unit, a filling slurry solid phase quality analysis unit, a filling body physical property analysis unit and a filling control and analysis unit; the ore body stress analysis unit is used to establish a three-dimensional ore body geometric model according to the geological exploration results of the ore body, divide the three-dimensional ore body geometric model into finite element grids, and calculate the three-dimensional stress tensor field of the ore body; the filling slurry solid phase quality analysis unit is used to calculate the solid phase mass concentration of the filling slurry according to the three-dimensional stress tensor field of the ore body; the filling body physical property analysis unit is used to establish a rheological model of the filling slurry according to the solid phase mass concentration of the filling slurry; combining the solid phase mass concentration of the filling slurry and the rheological model of the filling slurry, constructing a filling body strength prediction model, predicting the filling body strength of the filling slurry at each time; and calculating the pore water pressure of the filling slurry at each time according to the predicted filling body strength of the filling slurry at each time; the filling control and analysis unit is used to calculate the filling stability according to the three-dimensional stress tensor field of the ore body and the pore water pressure of the filling slurry, and obtain the stability coefficient : ; in, is the critical pore water pressure, is the set value; For time The filling strength at The position coordinates are stress at For time The pore water pressure at Indicates that the solid mass concentration of the filling slurry is The maximum filling strength at the time of filling; the stability coefficient describes the ability of the ore body and the filling body to withstand external stress. The higher the value, the stronger the ability of the ore body and the filling body to withstand external stress; the preset filling speed control model is used to calculate the current optimal filling speed : ; in, For time The filling speed of the filling slurry at ; is the pressure loss; is the critical pressure, indicating the maximum allowable pressure; The filling strength is The permeability coefficient at the time of filling is obtained according to the pre-established relationship between the filling body strength and the permeability coefficient; is the specific gravity of water; The optimum filling speed is used to control the filling slurry for filling.

2. The intelligent data acquisition and optimization control system for mine filling system according to claim 1, characterized in that: Assume an ore body stress analysis unit and divide the three-dimensional ore body geometric model into finite element grids. The unit volume of each finite element grid is ; The three-dimensional stress tensor field of the ore body is calculated by the following formula : ； in, is the rock mass density of the ore body, which indicates the weight per unit volume of rock in the ore body; is the burial depth, which indicates the thickness of the rock layer covering the ore body; It represents the initial geostress coefficient, the ratio of horizontal stress to vertical stress, obtained through geostress measurement; The elastic modulus of the ore body is a material property that reflects the ability of the rock in the ore body to resist deformation. It is measured by laboratory uniaxial compression tests of rocks or by the sonic method. is Poisson's ratio, which describes the ratio of the lateral deformation to the longitudinal deformation of the rock, and takes a value of 0.2 or 0.4, which is determined by triaxial loading experiments; The position coordinates are The displacement at is obtained by numerical simulation based on the finite element method; is the Laplace operator; is the divergence operator; The total volume of all finite element meshes.

3. The intelligent data acquisition and optimization control system for mine filling system according to claim 2, characterized in that: The filling slurry solid phase mass analysis unit calculates the solid phase mass concentration of the filling slurry according to the three-dimensional stress tensor field of the ore body through the following formula: : ； in, is temperature; is the temperature influence coefficient, which is the preset value; is the residual yield stress, which describes the residual shear stress generated by the contact force of particles in the filling slurry and is determined by rheological experiments; is the maximum yield stress, is the maximum allowable shear stress, and is the set value; is the pressure influence coefficient, is the set value; is the porosity of the filling slurry; is the water density; is the solid phase density; is the standard pressure, is the set value; for The maximum value of is the median particle size, measured by laser particle size analyzer or sieving method; is the reference particle size; For pressure.

4. The intelligent data acquisition and optimization control system for mine filling system according to claim 3, characterized in that: The filling body physical property analysis unit establishes a rheological model of the filling slurry according to the solid mass concentration of the filling slurry through the following formula; ； in, For time Shear stress at is the flow index; is the length of the pipeline; is the pipe radius; is the solid phase volume fraction, which indicates the proportion of solid particles in the volume of the filling slurry; is the preset maximum solid volume fraction; is the preset minimum porosity; is the activation energy of the filling slurry.

5. The intelligent data acquisition and optimization control system for mine filling system according to claim 4, characterized in that: Pressure loss Calculated using the following formula: ； in, is the gas constant.

6. The intelligent data acquisition and optimization control system for mine filling system according to claim 5, characterized in that: The filling body physical property analysis unit constructs a filling body strength prediction model by combining the solid phase mass concentration of the filling slurry and the rheological model of the filling slurry through the following formula: ； in, is the characteristic time of the hydration reaction of the filling slurry, and the time when the hydration reaction of the filling slurry reaches 50% is determined by the hydration heat release experiment; Indicates when When the shear stress is , the temperature is The hydration rate at ; is the baseline hydration rate.

7. The intelligent data acquisition and optimization control system for mine filling system according to claim 6, characterized in that: The filling body physical property analysis unit calculates the pore water pressure of the filling slurry at each time according to the predicted filling body strength of the filling slurry at each time by the following formula: ； in, It is the volume compression coefficient of the filling slurry, which represents the ratio of the actual volume of the filling slurry to the volume of the pipeline.

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