A hydraulic support system design method for complex service environment of mine

Abstract

The application discloses a kind of mine complex service environment-oriented hydraulic support system design method, it is related to mine equipment design technical field, including: the mine service environment digital twin model of fusion multi-source data is constructed, based on field monitoring, historical data and similar simulation experiment, the multi-physical field-industrial large model mixed modeling framework including geology, load and medium parameter is established.The application is constructed by fusing multi-source data mine service environment digital twin model, realizes the simulation and real-time prediction of mechanical-corrosion-temperature multi-physical field coupling, can accurately capture transient and gradual environmental characteristics such as rock burst, corrosion medium diffusion, dynamically output the stress, concentration and temperature distribution of key parts, provide accurate boundary conditions for structural design and sealing system protection, and then can identify local partial load risk and aging weak area in advance, effectively avoid structural instability and sealing failure in field application.

Description

Technical Field

[0001] This invention relates to the field of mining equipment design technology, specifically to a systematic design method for hydraulic support systems designed for complex mining environments. Background Technology

[0002] Hydraulic supports, as the core equipment in fully mechanized coal mining, directly determine the safety, efficiency, and economy of mining operations. However, the service environment in mines is significantly complex and uncertain, specifically manifested in: diverse geological conditions (such as fluctuations in coal seam thickness, differences in roof lithology, and varying fault development), complex operating loads (including alternating static, dynamic, and impact loads), harsh environmental conditions (high humidity, dust erosion, and the presence of corrosive gases), and new demands brought about by iterative mining technologies (such as the new requirements for support structure and performance in high-extraction and intelligent mining).

[0003] Existing hydraulic support design methods are mostly based on empirical analogies or single-condition assumptions, which have the following shortcomings: First, the design process is disconnected from the actual service environment, failing to fully consider the coupled influence of environmental parameters on support performance, leading to problems such as insufficient structural strength, sealing failure, and poor adaptability in field applications; Second, there is a lack of a systematic environment-performance-structure correlation mechanism, with performance requirements determined by subjective judgment, resulting in long design iteration cycles and high costs; Third, a standardized environmental boundary condition library has not been established, making it difficult to reuse design data across different projects and hindering the accumulation and transfer of design knowledge; Furthermore, under complex geological conditions of active rockbursts and well-developed joints in the roof rock mass, conventional monitoring data and... The existing models struggle to accurately capture the spatiotemporal correlation characteristics of the coupled disaster of impact load and local roof instability, resulting in design deficiencies in the support structure's resistance to instantaneous eccentric loads and dynamic stability. Furthermore, in the extreme environments of deep mining, characterized by high temperature, high humidity, and non-uniform diffusion of corrosive gases, existing corrosion-aging models cannot characterize the spatial differences in temperature, humidity, and medium concentration fields and their gradient influence on the aging rate of sealing materials. This leads to a lack of accurate basis for predicting the lifespan of the sealing system and designing local protection, thus restricting the adaptability and reliability of the support under real, complex working conditions. Therefore, this paper proposes a systematic design method for hydraulic supports in complex mining environments to address the aforementioned problems. Summary of the Invention

[0004] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a systematic design method for hydraulic support systems oriented towards complex mining environments, comprising the following steps: Step 1: Construct a digital twin model of the mine service environment that integrates multi-source data. Based on on-site monitoring, historical data and similar simulation experiments, establish a multi-physics field-industrial large model hybrid modeling framework that includes geological, load and medium parameters. Step 2: Integrate the dynamic identification module for impact-roof coupling disasters, introduce a multi-scale temporal attention mechanism and graph convolutional network, fuse microseismic and roof delamination high-frequency data, extract the spatiotemporal characteristics of impact-roof instability, and generate a dynamic load distribution probability map. Step 3: Establish a corrosion-aging gradient prediction sub-model. Based on the physical information neural network, input non-uniform temperature and humidity, gas concentration gradient and micro-parameters of sealing material, solve the spatial distribution of aging rate at the sealing interface, and output the partition lifetime map. Step 4: Create an intelligent environmental boundary condition and case library, store it in a standardized three-dimensional system of geological type-mining process-environmental level, integrate rockburst and corrosion databases, and support intelligent retrieval and dynamic updates; Step 5: Construct an environment-performance-structure modular mapping model. Based on random forest and LSTM algorithms, this model enables the inverse calculation of performance requirements from environmental parameters and links them to the structural parameter design of the top beam, columns, and sealing modules. Step 6: Execute a multidisciplinary collaborative closed-loop iterative optimization process. Use the HLA architecture to integrate multidisciplinary tools and optimize through simulation-evaluation-correction loops until the overall performance meets the target rate of ≥95%. Output the final scaffold solution and provide feedback to update the environment library.

[0005] Preferably, step 1 specifically includes: Through on-site monitoring networks, mine historical databases, and laboratory similar simulations, three-dimensional spatiotemporal parameters of geology, dynamic loads, and corrosive media are collected simultaneously with a data acquisition frequency of no less than 1Hz, enabling real-time synchronous acquisition of environmental parameters and providing a high-fidelity data foundation for subsequent modeling. Based on the aforementioned three-dimensional spatiotemporal parameters, a multiphysics coupling model containing mechanical equilibrium, heat conduction, and corrosion diffusion control equations is constructed to quantify the coupling effect of the environment on the support, accurately characterize the interaction mechanism between the environment and the support, and support the quantitative analysis of the coupling effect. The industrial large-scale model using the Transformer architecture extracts features from historical fault time-series data and interacts with the multiphysics coupling model through a real-time data interface to dynamically correct and form a digital twin model of the mine service environment, realizing online adaptive updating of the model and improving the accuracy and robustness of environmental prediction.

[0006] Preferably, step 2 specifically includes: The system integrates a microseismic monitoring system and a roof delamination instrument to collect high-frequency time-series data on rockburst events and roof deformation at a frequency of no less than 10Hz, enabling high-resolution real-time monitoring of rockburst and roof deformation, and providing a precise data foundation for disaster identification. A spatiotemporal feature extraction model integrating multi-scale temporal attention mechanism and graph convolutional network was constructed to process the high-frequency temporal data, thereby improving the ability and modeling accuracy to extract impact-top spatiotemporal correlation features from multi-source data. Using the aforementioned spatiotemporal feature extraction model, the coupling and correlation characteristics between the impact load wave and the local instability of the top plate in time and space are identified, and a dynamic load probability distribution map for the anti-eccentric load design of the support is generated, which intuitively reveals the distribution of eccentric load risk and provides a quantitative basis for the local strengthening of the support structure and the optimization of the control system.

[0007] Preferably, step 2 further includes: The multi-scale temporal attention mechanism is used to capture cross-scale temporal dependencies from millisecond-level impact to minute-level roof creep, achieving high-precision temporal correlation between impact and roof response, and improving the ability to identify disaster coupling features; The graph convolutional network transforms the sensor node topology of the hydraulic support area into graph data to model the spatial propagation path of shock waves in the support system, reveal the spatial attenuation law of shock waves in the support system, and enhance the prediction accuracy of local load distribution. By integrating temporal dependence and spatial propagation characteristics, a dynamic spectrum representing the probability and intensity distribution of impact-roof coupling disasters is output. This spectrum serves as a key boundary condition input for subsequent design, providing spatialized load probability input and supporting the structural optimization design of supports against eccentric loading and instability.

[0008] Preferably, step 3 specifically includes: Based on downhole environmental monitoring data, an environmental gradient field describing the non-uniform distribution of temperature, humidity and corrosive gas concentration in the space around the support is constructed to visualize the spatial heterogeneity of corrosive media and temperature and humidity, providing accurate input for local aging prediction. A corrosion-aging prediction model based on physical information neural network is established. Its governing equations are coupled with the dynamics of medium diffusion, heat conduction and material chemical reaction, so as to realize the physical consistency modeling of corrosion and aging processes and improve the reliability and interpretability of lifetime prediction. The environmental gradient field and the microscopic parameters of the sealing material are input into the corrosion-aging prediction model to solve for the distribution field of the aging rate of the sealing interface as a function of spatial location, and output a high-resolution aging rate map to support differentiated protection and precise life management of the sealing system.

[0009] Preferably, step 3 further includes: The loss function of the physical information neural network includes the residuals of the control equations, the residuals of the boundary conditions, and the errors of the sparse measured data points, so as to drive the model to conform to physical laws, ensure that the model not only follows the basic physical process, but also fits well with the measured data, thereby improving the physical consistency and generalization ability of the prediction. The corrosion-aging prediction model outputs aging rate cloud maps and predicted remaining life maps for key components of the sealing system. Based on the predicted remaining life map, regions with significant aging gradients are identified, providing quantitative basis for differentiated protection design and reliability assessment of the sealing structure. The output map can intuitively display high-risk aging areas, supporting targeted sealing enhancement design and extending the service life of key components.

[0010] Preferably, step 4 specifically includes: The environmental loads and environmental boundary conditions of the medium action output by the digital twin model of the mine service environment are standardized and feature-encoded to achieve unified and comparable environmental parameters, eliminate differences in dimensions and scales, and ensure consistency in subsequent modeling and retrieval. Based on a three-dimensional classification system of geological type, mining technology, and environmental level, standardized environmental boundary conditions and corresponding historical cases are stored in the database to build a structured knowledge base, which supports multi-dimensional rapid retrieval and reuse of historical cases, thereby improving design reference efficiency. An intelligent retrieval module based on vector similarity is constructed, and a dynamic update mechanism linked to the mining stage is established to form an intelligent environmental boundary condition and case library, enabling rapid matching of similar working conditions and dynamic optimization of library content, while maintaining the timeliness and engineering applicability of boundary conditions.

[0011] Preferably, step 5 specifically includes: Based on the random forest algorithm, the key environmental feature parameters that have the greatest impact on the performance of the stent are screened from the environmental boundary conditions. This accurately identifies the dominant factors, avoids secondary parameters from interfering with the design judgment, and improves the interpretability of the model. Using the key environmental feature parameters as input, an LSTM network is used to construct a performance requirement back-inference model, outputting a quantitative performance index vector and its design priority, thereby realizing automatic mapping from environment to performance, reducing reliance on human experience, and improving the scientific nature of the design. Establish a modular mapping rule base, which links each quantitative performance index vector to the specific structural, material, or hydraulic parameters of the top beam, column, seal, or base module, forming a structured design knowledge base that supports rapid parameter matching and modular combination, thereby improving design efficiency.

[0012] Preferably, step 5 further includes: The quantitative performance index vector includes at least impact resistance, eccentric load stiffness, aging resistance level of sealing interface, and the adaptability range of height adjustment mechanism, covering multi-dimensional performance requirements to ensure that the support fully meets functional and reliability requirements in complex environments. The modular mapping rule base defines 3-5 core performance interfaces for each module and associates them with multiple sets of optional implementation parameter combinations, clearly defining the module performance boundaries, supporting flexible parameter configuration and optimization iteration, and enhancing design adaptability; Based on the performance requirements, the output indicators and priorities of the reverse model are deduced, and the initial structural parameter schemes of each module are matched and generated from the modular mapping rule base. The initial design scheme is automatically generated, shortening the design cycle and reducing the amount of manual adjustment.

[0013] Preferably, step 6 specifically includes: A high-level system architecture is adopted to integrate multidisciplinary analysis tools for structural mechanics simulation, hydraulic system analysis and material life assessment, so as to realize real-time synchronization of multidisciplinary data and improve the consistency and collaborative efficiency of the simulation system. Based on the initial structural parameter scheme, multidisciplinary collaborative simulation is carried out, and the overall performance compliance rate is calculated to comprehensively evaluate the shortcomings of the support performance and provide quantitative basis for optimization. If the overall performance compliance rate does not reach 95%, the module parameters are adjusted in reverse by using the performance requirement reverse model and modular mapping rule base. The simulation-evaluation-correction loop is executed iteratively until the target is met, and the final support design scheme is output. The new data is fed back to the environment library to achieve closed-loop optimization and continuously improve the design accuracy and scheme adaptability.

[0014] This invention provides a systematic design method for hydraulic support systems designed for complex mining environments. It offers the following advantages: (I) This systematic design method for hydraulic support systems in complex mining environments integrates multi-source data to construct a digital twin model of the mining environment, enabling coupled simulation and real-time prediction of multi-physics fields such as mechanics, corrosion, and temperature. It can accurately capture transient and gradual environmental characteristics such as rockburst and the diffusion of corrosive media, and dynamically output the stress, concentration, and temperature distribution of key parts. This provides precise boundary conditions for structural design and sealing system protection, and can identify local off-center load risks and aging weak areas in advance, effectively avoiding structural instability and sealing failure in field applications.

[0015] (II) This systematic design method for hydraulic support systems in complex mining environments is based on random forest and LSTM algorithms to construct a performance requirement back-inference model. It automatically extracts key features from environmental parameters and outputs quantitative performance indicators and design priorities. Combined with a modular mapping rule base, it accurately associates performance indicators with the structural, material and hydraulic parameters of core modules such as top beams, columns and seals. It also supports multi-objective optimization and conflict resolution. It can automatically generate initial design schemes according to different working conditions, reduce the reliance on experience in the design process, improve design accuracy and first-time success rate, and shorten the design cycle.

[0016] (III) This systematic design method for hydraulic support systems in complex mining environments classifies and stores standardized environmental data according to a three-dimensional system of geological type, mining process, and environmental level. It integrates historical cases of rockburst and corrosion, and the intelligent retrieval module based on cosine similarity can match similar working conditions within 3 seconds, providing a reliable reference for current design. The case library supports a dynamic update mechanism linked with the mining stage to ensure the timeliness of data, realizes the standardized accumulation and efficient reuse of design knowledge, and effectively solves the problem of data silos between different projects. Attached Figure Description

[0017] Figure 1 This is a schematic diagram illustrating the workflow of a systematic design method for hydraulic support systems designed for complex mining environments, as described in this invention. Figure 2 This is a schematic diagram of the process flow for a systematic design method of hydraulic support systems for complex service environments in mines, as described in this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1, please refer to Figure 1 , Figure 2 This invention provides a technical solution: a systematic design method for hydraulic support systems oriented towards complex mining environments, comprising the following steps: Step 1: Construct a digital twin model of the mine service environment integrating multi-source data. Based on field monitoring, historical data, and similar simulation experiments, establish a hybrid modeling framework of multiphysics field-industrial large model that includes geological, load, and medium parameters. Through field monitoring network, mine historical database, and laboratory similar simulation, three-dimensional spatiotemporal parameters of geology, dynamic load, and corrosive medium are collected synchronously with a data acquisition frequency of no less than 1Hz to achieve real-time synchronous acquisition of environmental parameters, providing a high-fidelity data foundation for subsequent modeling. Based on the three-dimensional spatiotemporal parameters, construct a multiphysics field coupling model that includes mechanical equilibrium, heat conduction, and corrosion diffusion control equations to quantify the coupling effect of the environment on the support, accurately characterize the interaction mechanism between the environment and the support, and support the quantitative analysis of coupling effects. Use the industrial large model with Transformer architecture to extract features from historical fault time series data, and interact with the multiphysics field coupling model through a real-time data interface to dynamically correct and form a digital twin model of the mine service environment, realizing online adaptive updating of the model and improving the accuracy and robustness of environmental prediction. Mechanical equilibrium is the same as the equilibrium equation in solid mechanics (describing the effects of impact loads), and its expression is: ; in, For stress tensor, For material density, It is a volume force vector. For displacement vectors, For time; The heat conduction equation (describing temperature distribution) is expressed as follows: ; in, For specific heat capacity, For temperature, Thermal conductivity, The intensity of the internal heat source.

[0020] The corrosion diffusion equation (describing the penetration of media such as H2S) is expressed as follows: ; in, The concentration of the corrosive medium, Where is the diffusion coefficient. The reaction rate constant; The specific work involves: Deploying a distributed monitoring network consisting of microseismic sensors, stress gauges, temperature and humidity sensors, and gas composition analyzers at the working face of the mine where hydraulic supports are in service. This network collects real-time geological structural information (coal seam dip angle, roof lithology thickness, fault distribution density, etc.), dynamic load data (including roof static load, peak value, frequency, and duration of mining-induced impact loads, etc.), and environmental media parameters (H2S concentration gradient, humidity variation range, dust particle size distribution, etc.). The data acquisition frequency is uniformly set to 1Hz to ensure the capture of transient events such as rockbursts and continuous changes in environmental parameters. Simultaneously, it integrates existing historical geological databases, mine pressure monitoring records, and laboratory simulation data (drop hammer tests simulating rockbursts, accelerated aging tests in corrosive environments) to form a three-dimensional spatiotemporal parameter dataset covering geology, load, and media. Based on this dataset, a multi-physics coupled numerical model is constructed, centered on solid mechanics equilibrium equations, transient heat conduction equations, and corrosion medium diffusion-reaction control equations. The model is solved using the finite element method, with the mesh size set from 5mm to 20mm according to the support structure characteristics. The time step is synchronized with the data acquisition frequency. With a step size of 1 second, it achieves synchronous simulation and quantitative analysis of the stress field, temperature field, and corrosion concentration field of the support under complex environments. Based on the construction of a multi-physics coupling model, a pre-trained industrial large model based on the Transformer architecture is introduced. This industrial large model uses nearly ten years of accumulated hydraulic support fault maintenance records, working condition logs, and historical time-series data of sensors in the mine as training sets. Through a self-attention mechanism, it extracts the time-series feature patterns of load abrupt changes and abnormal medium concentration before the fault occurs. The industrial large model uses a 1-hour time window and a 1-minute sliding step size to perform online feature extraction and anomaly scoring on the real-time monitoring data. Through a standardized MQTT data interface, the potential anomaly patterns (correlation features between impact loads of specific frequencies and increased corrosive gas concentrations) identified by the industrial large model are transmitted to the multi-physics coupling numerical model in real time. The boundary conditions are dynamically adjusted according to the potential anomaly patterns to achieve online correction and adaptive evolution of the model, forming a digital twin model of the mine service environment that is updated synchronously with the physical environment and has predictive functions. It can output the boundary conditions of the environmental load spectrum and medium concentration field distribution for the next 1 to 8 hours.During system operation, the digital twin model of the mine service environment is driven by the collected and fused data, and performs real-time calculations and status updates at a frequency of no less than 1Hz. The core outputs include: dynamic stress cloud maps of key parts of the support for structural strength assessment, concentration gradient data of corrosive media near the contact surface of the sealing ring for sealing system analysis, and temperature distribution field of structural components for thermal load assessment. All output data are published through the data bus in a standardized engineering data format. At the same time, the difference between the predicted status of the digital twin model of the mine service environment and the actual monitoring feedback is continuously compared. When the relative error exceeds 5% continuously for more than 10 minutes, the model parameter calibration process is automatically triggered, and historical similar working condition data is called to fine-tune the boundary conditions in the model to ensure the prediction accuracy and reliability of the digital twin model of the mine service environment. Step 2: Integrate the dynamic identification module for impact-roof coupling disasters, introduce a multi-scale temporal attention mechanism and graph convolutional network, fuse high-frequency data of microseismic events and roof delamination, extract the spatiotemporal characteristics of impact-roof instability, generate a dynamic load distribution probability map, integrate the microseismic monitoring system and roof delamination instrument, collect high-frequency time-series data of rockburst events and roof deformation at a frequency of not less than 10Hz, realize high-resolution real-time monitoring of impact and roof deformation, and provide a precise data foundation for disaster identification. Construct a spatiotemporal feature extraction model that integrates multi-scale temporal attention mechanism and graph convolutional network to process high-frequency time-series data, improve the ability to extract the spatiotemporal correlation features of impact-roof from multi-source data and the modeling accuracy. Using the spatiotemporal feature extraction model, identify the coupling correlation features of impact load wave and local roof instability in time and space, and generate a dynamic load probability distribution map for the support anti-eccentric load design, intuitively reveal the distribution of eccentric load risk, and provide quantitative basis for local strengthening of support structure and optimization of control system. The specific work content is as follows: Within the working face supported by hydraulic supports, a microseismic monitoring system and a roof separation meter are deployed. The microseismic sensors are spaced 15 meters apart, using data acquisition cards with a precision of no less than 16 bits. The roof separation meter is installed at two monitoring points, one at a depth of 3 meters and the other at a depth of 2 meters, in the roof rock strata to capture deformation differences at different depths of the rock mass. All sensors collect real-time data on rockburst events and roof displacement time series at a sampling frequency of no less than 10 Hz. The data is collected by an intrinsically safe mining data acquisition unit and transmitted to a ground data processing server via industrial Ethernet. During the process, a timestamp alignment and outlier removal preprocessing mechanism was adopted to ensure the spatiotemporal synchronization and effectiveness of impact event and roof deformation data. A spatiotemporal feature extraction model integrating a multi-scale temporal attention mechanism and a graph convolutional network was constructed. In this model, the multi-scale temporal attention module sets three time scale windows: 100 milliseconds, 1 second, and 10 seconds, to simultaneously capture the impact transient waveform, short-term roof response, and long-term creep trend. The graph convolutional network uses the spatial locations of the microseismic sensor and the roof delamination meter as nodes, with node features being real-time data from each sensor. An adjacency matrix is ​​constructed between points based on the actual sensor spacing and the rock mechanics transmission path. The model input is a preprocessed high-frequency time-series data stream. After feature extraction, the output is a spatiotemporal correlation tensor of impact-roof coupling. The tensor dimension is time step × number of sensor nodes × feature dimension, which includes energy, frequency, displacement gradient, and co-correlation index. Using the spatiotemporal feature extraction model, the impact event and roof deformation data are analyzed in real time to identify the coupling correlation characteristics between the two, namely the time delay relationship and the spatial propagation path. Based on the identified coupling correlation characteristics, a dynamic load probability distribution map is generated. The map represents the probability of off-center load occurrence at different locations within the support area in the next time window in grid form. The grid resolution is 0.5 m × 0.5 m. The off-center load occurrence probability is calculated based on the impact intensity, roof displacement acceleration, and historical case matching degree. This dynamic load probability distribution map is output in real time in the form of a heat map to guide the local reinforcement of key load-bearing parts, the optimization of hydraulic cylinder layout, and the adjustment of control system parameters in the design of the support anti-off-center load structure, thereby improving the dynamic stability and overall reliability of the support under impact-roof coupling disasters. The formula for calculating the probability of off-center loading is as follows: ; ; In the formula: Indicates time Time, coordinates within the support area Probability of off-center loading at the location; Indicates in Location and time The impact energy intensity at any given moment is obtained by spatial interpolation of the seismic wave energy extracted by the microseismic monitoring system within a local grid. Indicates in Location and time The roof displacement acceleration at any given time is obtained by second-order differentiation from the roof delamination instrument monitoring data, reflecting the dynamic rate of change of local roof instability; This represents the historical case matching function, used to quantify the similarity between current spatiotemporal features and historical biased cases; Indicates the current time The spatiotemporal feature vector at the location has a dimension of It includes multi-dimensional features such as impact energy, frequency, displacement gradient, and co-correlation index; Indicates the first case in the historical case library The spatiotemporal feature vectors of each off-load case, totaling... A historical case; Indicates the first The weighting coefficients for each historical case are set based on the time of occurrence, severity, and geological similarity of the case. The Gaussian kernel width parameter is used to control the similarity decay rate and is calibrated according to the scale range of the feature vector. The weighting coefficients for the impact strength term, the roof acceleration term, and the historical matching term are respectively, satisfying... ; and These represent the maximum values ​​of impact energy intensity and top plate displacement acceleration in all grids within the current time window, respectively, for normalization processing; Furthermore, step 2 also includes: a multi-scale temporal attention mechanism is used to capture cross-scale temporal dependencies from millisecond-level impact to minute-level roof creep, to achieve high-precision temporal correlation of impact-roof response, and to improve the ability to identify disaster coupling features. The graph convolutional network transforms the sensor node topology of the hydraulic support area into graph data to model the spatial propagation path of impact waves in the support system, reveal the spatial attenuation law of impact waves in the support system, enhance the prediction accuracy of local load distribution, and fuse temporal dependencies with spatial propagation features to output a dynamic spectrum characterizing the probability and intensity distribution of impact-roof coupling disasters. This spectrum serves as a key boundary condition input for subsequent design, providing spatialized load probability input to support the structural optimization design of the support against eccentric loading and instability. The specific work involves: In practical operation, a multi-scale temporal attention mechanism is implemented by constructing a deep neural network module with parallel processing capabilities. This deep neural network module has three independent feature extraction channels, corresponding to three fixed time windows of 100 milliseconds, 1 second, and 10 seconds, respectively. Each channel is configured with a bidirectional long short-term memory network structure, with 128 hidden layer neurons to fully extract local temporal features at their respective scales. When the multi-scale temporal attention mechanism is executed, the pre-processed raw sensor data stream is received in real time at a sampling frequency of 10 Hz, and synchronous sliding segments are performed according to the fixed time window length. For the 100-millisecond window, the energy spectral density and instantaneous frequency of the impact waveform are calculated. A 1-second window is used to analyze the statistical characteristics of roof displacement velocity and acceleration; a 10-second window is used to track cumulative displacement and creep rate; the feature vectors extracted from each channel are adaptively weighted and fused using a trainable attention weight matrix. The weight matrix is ​​trained using historical data and periodically fine-tuned online using newly generated working condition data to ensure that the model can continuously and accurately capture cross-scale temporal correlation patterns from transient impact to slow creep; the microseismic sensors and roof delamination meters deployed on the working face are abstracted as nodes in a graph structure to realize the spatial propagation model of impact waves within the support system. The initial feature vector of each node consists of the physical quantities collected in real time by the corresponding sensor and their first-order difference values. The connection relationships between nodes are the edges of the graph. The connection is defined based on the actual physical location of the sensors and the mechanical properties of the rock strata: if the Euclidean distance between two sensors is less than a pre-set threshold of 30 meters and they are within the same lithological stratum, a connection is established. The connection weights in the adjacency matrix are determined by comprehensively calculating the reciprocal of the sensor spacing and the dynamic elastic modulus of the rock mass in the area (obtained through previous geological exploration, with typical values ​​in the range of 10-30 GPa). The constructed graph data is input into a neural network containing two layers of graph convolution operations. The first layer of this neural network has an output dimension of 64, and the second layer has an output dimension of 32. The activation function is ReLU, and the graph convolution kernel aggregation method is mean aggregation. The goal of training the neural network is to learn the impact event from the epicenter location to the surrounding sensors. The energy attenuation and waveform distortion patterns of nodes are quantitatively characterized to represent the propagation path and spatial influence range of fluctuations in complex rock strata-support structure systems. The fusion of temporal and spatial features is achieved through tensor splicing and fully connected layer mapping. Specifically, the temporal feature tensor output by the multi-scale temporal attention module (dimension: time step × feature dimension) is aligned and combined with the node feature matrix output by the graph convolutional network (dimension: number of sensor nodes × spatial feature dimension) to form a unified spatiotemporal feature tensor. This spatiotemporal feature tensor is input into a fully connected layer with 256 neurons for nonlinear transformation and information compression, and then mapped to each preset evaluation grid (0.5 m × 0.5 m) through a softmax output layer.The probability of disaster occurrence (5 meters) and the estimated intensity level are calculated, and the resulting dynamic map is pushed to the design platform in the form of a heat map through the mining industrial ring network at a refresh rate of no less than 1 frame / second. This dynamic map contains two core data layers: one is the disaster occurrence probability layer; the other is the coupled disaster intensity layer. The formula for calculating the probability of a disaster is as follows: ; ; ; ; In the formula: Indicates time Spatial location The probability of a shock-roof coupling disaster occurring at the site is expressed by the Sigmoid function. Normalized output; Represents the spatiotemporal fusion feature vector, composed of temporal features Spatial features It is formed by concatenating along the feature dimension; This represents a multi-scale temporal feature vector, obtained by weighted fusion of three time-scale channels; Indicates the first Each timescale channel in time The attention weights are calculated using a trainable weight matrix and the current temporal state. Indicates the first BiLSTM in each channel at time The hidden state vector has a dimension of 256 (concatenated from two bidirectional 128-dimensional vectors). Represents spatial feature vectors, corresponding to sensor nodes. In time The graph convolution output; Represents the forward propagation function of a two-layer graph convolutional network; Indicates time The feature matrix of all sensor nodes, with dimension . , For the number of nodes, Input feature dimension; The adjacency matrix of the graph is determined by both the sensor spacing and the elastic modulus of the rock mass; Indication of spatial location The index of the nearest sensor node; , These are the weight matrix and bias vector of the fully connected layer, respectively, obtained through training with historical data; The formula for calculating the intensity of coupled disasters is as follows: ; ; In the formula: Indicates time ,Location The coupled disaster intensity value at the location is expressed in dimensionless relative intensity, and its non-negativity is guaranteed by the Softplus function. , These are the weight matrix and bias vector of the intensity prediction branch, which share the underlying features with the probability branch but are trained independently. As an activation function, it maps the linear output to non-negative intensity values ​​to avoid negative intensity. for ; Step 3: Establish a corrosion-aging gradient prediction sub-model. Based on a physical information neural network, input non-uniform temperature and humidity, gas concentration gradient, and microscopic parameters of the sealing material to solve the spatial distribution of aging rate at the sealing interface and output a regional life map. Based on downhole environmental monitoring data, construct an environmental gradient field describing the non-uniform distribution of temperature, humidity, and corrosive gas concentration around the support, realizing the visualization of the spatial heterogeneity of corrosive media and temperature and humidity, providing accurate input for local aging prediction. Establish a corrosion-aging prediction model based on a physical information neural network. Its governing equations couple medium diffusion, heat conduction, and material chemical reaction kinetics to achieve physical consistency modeling of corrosion and aging processes, improving the reliability and interpretability of life prediction. Input the environmental gradient field and microscopic parameters of the sealing material into the corrosion-aging prediction model to solve the distribution field of aging rate at the sealing interface with spatial location, and output a high-resolution aging rate map to support differentiated protection and precise life management of the sealing system. The specific work involves: In the underground mine environmental monitoring network, several temperature and humidity sensors and gas composition detection points are arranged along the hydraulic support structure. The sensors are installed near the key sealing interfaces of the support columns, top beams, and bases, with an installation spacing of no more than 2 meters. The measurement accuracy reaches ±0.5℃, ±2%RH, and ±5ppm, respectively, with a data acquisition frequency of 1Hz. The monitoring system performs spatial interpolation of environmental data at a frequency of no less than 4 hours / time to construct an environmental gradient field of temperature, humidity, and corrosive gas concentration around the support. This gradient field is generated using the Kriging interpolation method, with a grid resolution of 0.1m × 0.1m × 0.1m, capable of characterizing local temperature and humidity differences and gas concentration gradients. Based on the environmental gradient field of temperature, humidity, and corrosive gas concentration around the support, a corrosion-aging prediction model based on a physical information neural network is constructed. The governing equations of the corrosion-aging prediction model include three terms: one is an unsteady-state diffusion equation describing the diffusion of corrosive gases in porous media, where the diffusion coefficient is a function of temperature and takes a value between 1.0 × 10⁻⁶. -8 Up to 5.0×10-7 The model consists of three parts: 1) a thermal conductivity coefficient between m² / s and 2) a thermal conductivity coefficient between m² / s and 0.2-0.5 W / (m·K) for steel and 0.2-0.5 W / (m·K) for sealing materials; 2) a first-order kinetic equation describing the chemical aging reaction of sealing materials under humid heat and corrosive media, with the reaction rate constant related to temperature via the Arrhenius formula; 3) a multi-objective loss function including physical equation residuals, boundary condition errors, and measured data loss for training the corrosion-aging prediction model, and an L-BFGS optimizer for parameter optimization to ensure that the prediction results simultaneously satisfy physical laws and measured data; 4) an environmental gradient field and microscopic parameters of the sealing material (including material porosity, crosslinking density, initial antioxidant content, etc.) are input to the trained corrosion-aging prediction model for forward solving, and the model output is the aging rate distribution field at each location of the sealing interface, with a spatial resolution consistent with the environmental grid (0.1 m × 0.1 m × 0.1 m), and the aging rate unit is % per hour, representing the instantaneous rate of material performance degradation. Furthermore, step 3 also includes: the loss function of the physical information neural network includes the residuals of the control equations, the residuals of the boundary conditions, and the errors of the sparse measured data points, so as to drive the model to conform to physical laws, ensure that the model not only follows the basic physical process, but also fits well with the measured data, improve the physical consistency and generalization ability of the prediction, and output the corrosion-aging prediction model as the aging rate cloud map and the predicted remaining life map of each key part of the sealing system. Based on the predicted remaining life map, the significant aging gradient region is identified, providing a quantitative basis for the differentiated protection design and reliability assessment of the sealing structure. The output map can intuitively display the high-risk aging region, support the targeted sealing reinforcement design, and extend the service life of key components. The specific work involves: based on the aging rate distribution field and combined with the initial performance threshold of the material, the predicted remaining life map of the key parts of the sealing system is obtained by integral calculation. Each grid cell in the map corresponds to a remaining life value, reflecting the differences in the aging process of different regions. Based on the predicted remaining life map, regions with significant aging gradients, i.e. regions with remaining life below the set threshold, are automatically identified. For regions with significant aging gradients, differentiated protection design suggestions are generated, including locally increasing the thickness of the sealing material, using a high corrosion-resistant coating, or optimizing the sealing structure. The formula for predicting remaining lifespan is as follows: ; ; In the formula: Indicates position in three-dimensional space The predicted remaining lifetime at this location corresponds to the value of each grid cell in the lifetime map; This indicates the material performance failure threshold, which is the threshold at which the material ages. When this threshold is reached, the sealing function is considered to have failed. Indicates from the initial time up to the current moment During this period, at the location The degree of aging that has accumulated; Indicates the location The current aging rate at the location is output by the corrosion-aging prediction model; The lower limit protection value for the aging rate is a very small positive number used to avoid the denominator being zero, ensuring that the formula remains numerically stable in the region where the aging rate is close to zero. This indicates the starting point for life calculation, which is usually the time when the sealing system is put into use; Indicates the current evaluation time; Denotes the integral variable, representing the process from... arrive A point in time between; Step 4: Create an intelligent environmental boundary condition and case library, store it in a standardized three-dimensional system of geological type-mining process-environmental level, integrate rockburst and corrosion databases, and support intelligent retrieval and dynamic updates; Step 5: Construct an environment-performance-structure modular mapping model. Based on random forest and LSTM algorithms, this model enables the inverse calculation of performance requirements from environmental parameters and links them to the structural parameter design of the top beam, columns, and sealing modules. Step 6: Execute a multidisciplinary collaborative closed-loop iterative optimization process. Use the HLA architecture to integrate multidisciplinary tools and optimize through simulation-evaluation-correction loops until the overall performance meets the target rate of ≥95%. Output the final scaffold solution and provide feedback to update the environment library.

[0021] Example 2, as Figure 1 , Figure 2 As shown, based on Embodiment 1, the present invention provides a technical solution: Step 4 specifically includes: standardizing and feature-encoding the environmental loads and environmental boundary conditions of the medium action output by the digital twin model of the mine service environment to achieve unified and comparable environmental parameters, eliminate differences in dimensions and scales, and ensure consistency in subsequent modeling and retrieval. According to the three-dimensional classification system of geological type-mining process-environmental level, the standardized environmental boundary conditions and corresponding historical cases are stored in the database to build a structured knowledge base, which supports multi-dimensional rapid retrieval and reuse of historical cases, improves design reference efficiency, builds an intelligent retrieval module based on vector similarity, and establishes a dynamic update mechanism linked with the mining stage to form an intelligent environmental boundary condition and case library, realizes rapid matching of similar working conditions and dynamic optimization of library content, and maintains the timeliness and engineering applicability of boundary conditions. The specific work involves: after acquiring the environmental load and medium action data output from the digital twin model of the mine service environment, performing standardized preprocessing; for the load data, including roof static load, peak impact load, frequency, and duration, dimensional normalization and outlier removal are performed according to the "Mine Pressure Monitoring Specification" (MT / T878-2000); load parameters are uniformly converted to megapascals, and time parameters are converted to seconds; medium parameters such as H2S concentration, relative humidity, and dust concentration are calibrated according to the "Air Quality Standard for Underground Coal Mines" (GB20426-2006) and standardized using the Z-score method, and then feature encoding is performed on the standardized multidimensional parameters. Discrete geological classification variables were processed using one-hot encoding, and continuous environmental variables were processed using piecewise multinomial encoding, generating 128-dimensional feature vectors. All processing was automated using Python scripts. The processed feature vectors were encapsulated in JSON format, with timestamps and data quality identifiers. The encoded environmental boundary condition feature vectors were stored in a MySQL relational database according to a three-dimensional classification system of geological type-mining technology-environmental level. Geological types were classified according to the "Coal Seam Geological Classification Specification" (DZ / T0215-2002) into gently dipping coal seams (dip angle < 25°), dipping coal seams (25°~45°), and steeply dipping coal seams (dip angle > 45°). Mining technology... Based on the fully mechanized mining process, it is divided into high-depth fully mechanized mining, top-coal caving fully mechanized mining, and thin coal seam fully mechanized mining. Environmental levels are classified according to Appendix D of the 2022 edition of the "Coal Mine Safety Regulations" into general environment (no impact and corrosion level I), complex environment (impact or corrosion level II), and extreme environment (strong impact and corrosion level III). Each record in the database table includes a feature vector, three-dimensional classification label, mining stage (initial mining / normal / final mining), timestamp, and a unique case identifier. The database is deployed on a mine explosion-proof server, supporting bidirectional synchronization and off-site disaster recovery backup to ensure data security and traceability. Based on the stored case library, an intelligent retrieval module with cosine similarity as the core algorithm is constructed, with the target mine as the retrieval input. The system calculates the similarity between the current environmental feature vector and all case vectors in the database. A similarity threshold of ≥0.85 is set, and matching cases are returned. The retrieval response time is designed to be ≤3 seconds, supporting both batch retrieval and real-time streaming retrieval modes. Simultaneously, a dynamic update mechanism linked to the mining stage is established: during the initial mining stage, new monitoring data is automatically retrieved every 24 hours, triggering model recalibration; during normal mining, the case database is incrementally updated every 72 hours; and during the final mining stage or when encountering special conditions such as faults or ground pressure, real-time data synchronization and immediate case entry into the database are initiated. All update operations are logged, and version rollback is supported to ensure the timeliness, accuracy, and engineering usability of the intelligent environmental boundary conditions and case database. Step 5 specifically includes: Based on the random forest algorithm, the key environmental feature parameters that have the greatest impact on the performance of the support are selected from the environmental boundary conditions. The dominant factors are accurately identified, and the interference of secondary parameters with design judgment is avoided, thereby improving the interpretability of the model. Using the key environmental feature parameters as input, the performance requirement back-inference model is constructed using the LSTM network. The output is a quantitative performance index vector and its design priority, realizing automatic mapping from environment to performance, reducing reliance on human experience, improving the scientific nature of the design, and establishing a modular mapping rule base. Each quantitative performance index vector is associated with the specific structural, material, or hydraulic parameters of the top beam, column, seal, or base module, forming a structured design knowledge base that supports rapid parameter matching and modular combination, thereby improving design efficiency. Furthermore, step 5 also includes: quantifying the performance index vector to include at least impact strength, eccentric load stiffness, aging resistance level of the sealing interface, and the adaptability range of the height adjustment mechanism, covering multi-dimensional performance requirements, ensuring that the support fully meets functional and reliability requirements in complex environments, defining 3-5 core performance interfaces for each module in the modular mapping rule library, associating multiple sets of optional implementation parameter combinations, clarifying module performance boundaries, supporting flexible parameter configuration and optimization iteration, enhancing design adaptability, back-deriving the indicators and priorities output by the model based on performance requirements, matching and generating initial structural parameter schemes for each module from the modular mapping rule library, automatically generating initial design schemes, shortening the design cycle, and reducing the workload of manual adjustments; The specific work involves: after obtaining standardized environmental boundary condition data, using a random forest algorithm to evaluate and screen feature importance in order to identify environmental parameters that have a dominant impact on the performance of hydraulic supports. Specifically, the random forest model is set to contain 100 decision trees, with a maximum depth of no more than 20 layers per tree and a minimum number of samples for node splits set to 5. This ensures that the model can fully learn the nonlinear relationship between environmental parameters and historical performance data without overfitting. The historical dataset used for model training covers at least 500 sets of environment-performance pairing records under different working conditions, encompassing 42 initial environmental features across three categories: geology, load, and medium. After training, the importance score of each feature is calculated based on the Gini impurity reduction, and the top 15 most important features are selected as key environmental feature parameters. These include roof static load and impact load. Peak values, H2S concentration, coal seam dip angle, and roof lithological compressive strength were selected. The screening process was automated using Python's scikit-learn library. The results were output as a list ranked by feature importance, and the system was retrained and updated every six months based on new data to ensure the timeliness and representativeness of key features. Using the selected key environmental feature parameters as input, a performance requirement backpropagation model based on a Long Short-Term Memory (LSTM) network was constructed to achieve a quantitative mapping from environmental conditions to performance indicators. This LSTM network adopted a three-layer hidden layer structure, with 128, 64, and 32 neurons in each layer, respectively. The dropout rate was set to 0.2 to prevent overfitting, and the time step was set to 60 based on the data collection frequency, corresponding to a 1-minute environmental data sequence. The model training used mean squared error as the loss function and the Adam optimizer was used for parameter updates, with an initial learning rate of 0.001, and during training, a learning rate decay strategy is used. The model outputs a 10-dimensional quantitative performance index vector, specifically including indicators such as the impact resistance of the top beam, the compressive stiffness of the column, the aging resistance level of the sealing material, and the response time of the hydraulic system. It also outputs design priority scores for each indicator, calculated based on the frequency and severity of various performance defects in historical fault data. The entropy weight method is used to determine the weight of each indicator. The model undergoes incremental training quarterly using the latest operating data to ensure its predictive accuracy. A structured modular mapping rule base is established to convert the quantitative performance index vector into specific design parameters. This modular mapping rule base associates the quantitative performance index vector with the top beam, column, sealing, and other components according to the functional division of the hydraulic support. The base consists of four core modules, each with 3 to 5 core performance interfaces. The top beam module is associated with impact strength, bending stiffness, and local corrosion resistance; the column module is associated with compressive stiffness, lateral force resistance, and fatigue life; the sealing module is associated with aging resistance level, compression set, and media compatibility; and the base module is associated with torsional stiffness, ground pressure, and corrosion fatigue resistance. Each mapping rule in the modular mapping rule base clearly defines the matching relationship between performance index thresholds and corresponding structural, material, or hydraulic parameters. The mapping process is implemented using a rule-based inference engine, supporting multi-objective optimization and conflict resolution. It can automatically generate initial design parameter combinations for each module based on performance priorities and output them in JSON format to subsequent design iterations. It also supports manual adjustments by engineers and online updates to the rule base. Step 6 specifically includes: adopting a high-level system architecture to integrate multidisciplinary analysis tools for structural mechanics simulation, hydraulic system analysis, and material life assessment, achieving real-time synchronization of multidisciplinary data, improving the consistency and collaborative efficiency of the simulation system, conducting multidisciplinary collaborative simulation based on the initial structural parameter scheme, calculating the overall performance compliance rate, comprehensively evaluating the performance shortcomings of the support, providing quantitative basis for optimization, if the overall performance compliance rate does not reach 95%, then adjusting the module parameters in reverse through the performance requirement back-engineering model and modular mapping rule library, iteratively executing the simulation-evaluation-correction loop until the target is met, outputting the final support design scheme, and feeding the new data back to the environment library to achieve closed-loop optimization, continuously improving design accuracy and scheme adaptability; The specific work involves: building a multidisciplinary collaborative simulation platform based on a high-level HLA (High Level Architecture) system architecture, integrating ANSYS Mechanical, AMESim, and a self-developed material aging assessment module (based on Python 3.9); achieving data interaction between tools through a standardized FMI interface, with a data exchange frequency set to no less than 10 Hz; automatically generating a parametric finite element model based on the initial structural parameter scheme output from the modular mapping rule library before simulation starts; using SOLID187 elements for the structural mesh, with mesh size adaptively divided between 5mm and 20mm according to structural characteristics; selecting hydraulic system pipeline diameters between 10mm and 32mm based on the flow-pressure matching curve; using the Mooney-Rivlin hyperelastic model as the constitutive model for the sealing material; and calling simulation boundary conditions in real time from the intelligent environment boundary condition library, including top plate load spectrum and medium concentration gradient field, to ensure the simulation environment is consistent with real service conditions; and implementing multidisciplinary simulation... After true synchronous execution, key performance indicators are automatically extracted, including structural strength, hydraulic system performance, and material life, to analyze the overall performance compliance rate. Specifically, for structural strength, the maximum equivalent stress of the top beam (allowable value 345MPa) and the buckling safety factor of the column (threshold ≥1.8) are extracted through finite element post-processing. For the hydraulic system, the pressure overshoot during the frame movement process (allowable range ±15%) and the leakage of the sealing system (threshold ≤0.5mL / min) are calculated. For material life, the aging rate of the sealing interface is output based on the corrosion-aging sub-model (prediction error ≤8%), thus determining the overall performance compliance rate. The formula is as follows: ; in, To achieve the overall performance compliance rate, The total number of performance indicators. For the first The compliance coefficient of each indicator (1 for compliance, 0 for non-compliance) is used to calculate the overall performance compliance rate. At this time, the reverse parameter tuning process is triggered: the performance requirement reverse model outputs a corrected performance target vector based on the type and deviation of the non-compliant items; the modular mapping rule base generates adjustment suggestions through rule reasoning, and the adjusted parameters are automatically updated to the parameterized model, starting a new round of simulation iteration; during the iterative optimization process, the overall performance compliance rate is monitored in real time, and when the compliance rate improvement of three consecutive iterations is less than 0.5%, the local fine simulation mode is automatically started (mesh size is refined to 2 mm, time step is shortened to 0.1s), achieving... After the convergence condition is met, the final design package is output, which includes: detailed engineering drawings (DWG format), hydraulic schematic diagram (PDF format), bill of materials (XML format), and simulation verification report (DOCX format). All process data (including input parameters, simulation results, and parameter tuning records for each iteration) are automatically and structurally stored in the MySQL case library. At the same time, the incremental update mechanism of the environment library is triggered: the environment-performance mapping relationship verified in this design is used as a new case and stored in the library according to the classification system of geological type-mining process-environmental level. The case library version management adopts a Git-like mechanism, which supports backtracking any historical design scheme by timestamp.

[0022] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0023] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A systematic design method for hydraulic support systems designed for complex mining environments, characterized in that, Includes the following steps: Step 1: Construct a digital twin model of the mine service environment that integrates multi-source data. Based on on-site monitoring, historical data and similar simulation experiments, establish a multi-physics field-industrial large model hybrid modeling framework that includes geological, load and medium parameters. Step 2: Integrate the dynamic identification module for impact-roof coupling disasters, introduce a multi-scale temporal attention mechanism and graph convolutional network, fuse microseismic and roof delamination high-frequency data, extract the spatiotemporal characteristics of impact-roof instability, and generate a dynamic load distribution probability map. Step 3: Establish a corrosion-aging gradient prediction sub-model. Based on the physical information neural network, input non-uniform temperature and humidity, gas concentration gradient and micro-parameters of sealing material, solve the spatial distribution of aging rate at the sealing interface, and output the partition lifetime map. Step 4: Create an intelligent environmental boundary condition and case library, store it in a standardized three-dimensional system of geological type-mining process-environmental level, and integrate the rockburst and corrosion database; Step 5: Construct an environment-performance-structure modular mapping model. Based on random forest and LSTM algorithms, this model enables the inverse deduction of performance requirements from environmental parameters and their correlation with the structural parameter design of each module. Step 6: Execute a multidisciplinary collaborative closed-loop iterative optimization process. Use the HLA architecture to integrate multidisciplinary tools and optimize through simulation-evaluation-correction loops until the overall performance meets the target rate of ≥95%. Output the final scaffold solution and provide feedback to update the environment library.

2. The systematic design method for hydraulic support systems oriented towards complex mining environments according to claim 1, characterized in that: Step 1 specifically includes: Three-dimensional spatiotemporal parameters of geology, dynamic loads, and corrosive media are collected simultaneously through on-site monitoring networks, mine history databases, and laboratory similar simulations. Based on the aforementioned three-dimensional spatiotemporal parameters, a multiphysics coupling model containing mechanical equilibrium, heat conduction, and corrosion diffusion control equations is constructed to quantify the coupling effect of the environment on the support. The industrial large model using the Transformer architecture extracts features from historical fault time-series data and interacts with the multi-physics coupling model through a real-time data interface to dynamically correct and form a digital twin model of the mine service environment.

3. The systematic design method for hydraulic support systems oriented towards complex mining service environments according to claim 1, characterized in that: Step 2 specifically includes: The system integrates a microseismic monitoring system and a roof delamination instrument to collect high-frequency time-series data on rockburst events and roof deformation at a frequency of no less than 10 Hz. A spatiotemporal feature extraction model integrating multi-scale temporal attention mechanism and graph convolutional network is constructed to process the high-frequency temporal data; Using the aforementioned spatiotemporal feature extraction model, the coupling and correlation characteristics between the impact load wave and the local instability of the top plate in time and space are identified, and a dynamic load probability distribution map for the anti-eccentric load design of the support is generated.

4. The systematic design method for hydraulic support systems oriented towards complex mining environments according to claim 3, characterized in that: Step 2 also includes: The multi-scale temporal attention mechanism is used to capture cross-scale temporal dependencies ranging from millisecond-level impacts to minute-level roof creep; The graph convolutional network transforms the sensor node topology of the hydraulic support area into graph data to model the spatial propagation path of impact waves in the support system. By fusing temporal dependence and spatial propagation characteristics, a dynamic spectrum representing the probability and intensity distribution of impact-roof coupled disasters is output.

5. The systematic design method for hydraulic support systems oriented towards complex mining service environments according to claim 1, characterized in that: Step 3 specifically includes: Based on downhole environmental monitoring data, an environmental gradient field describing the non-uniform distribution of temperature, humidity, and corrosive gas concentration in the space surrounding the support is constructed. A corrosion-aging prediction model based on physical information neural network was established, whose governing equations are coupled with medium diffusion, heat conduction and material chemical reaction kinetics. The environmental gradient field and the microscopic parameters of the sealing material are input into the corrosion-aging prediction model to solve for the distribution field of the aging rate of the sealing interface as a function of spatial location, and the aging rate spectrum is output.

6. The systematic design method for hydraulic support systems oriented towards complex mining service environments according to claim 5, characterized in that: Step 3 also includes: The loss function of the physical information neural network includes the residuals of the control equations, the residuals of the boundary conditions, and the errors of the sparse measured data points, so as to drive the model to conform to physical laws. The corrosion-aging prediction model outputs aging rate cloud maps and predicted remaining life maps for key parts of the sealing system, and identifies regions with significant aging gradients based on the predicted remaining life maps.

7. The systematic design method for hydraulic support systems oriented towards complex mining service environments according to claim 1, characterized in that: Step 4 specifically includes: The environmental loads and environmental boundary conditions of the medium action output by the digital twin model of the mine service environment are standardized and feature-encoded. Based on a three-dimensional classification system of geological type, mining technology, and environmental level, standardized environmental boundary conditions and corresponding historical cases are stored in the database. An intelligent retrieval module based on vector similarity is constructed, and a dynamic update mechanism linked to the mining stage is established to form an intelligent environmental boundary condition and case library.

8. The systematic design method for hydraulic support systems oriented towards complex mining service environments according to claim 1, characterized in that: Step 5 specifically includes: Based on the random forest algorithm, the key environmental feature parameters that have the greatest impact on the performance of the scaffold are selected from the environmental boundary conditions; Using the key environmental feature parameters as input, an LSTM network is used to construct a performance requirement back-inference model, which outputs a quantitative performance index vector and its design priority. Establish a modular mapping rule base to vectorize each quantitative performance index to the specific structural, material, or hydraulic parameters of the top beam, column, seal, or base module.

9. A systematic design method for hydraulic support systems oriented towards complex mining environments, as described in claim 8, characterized in that: Step 5 further includes: The quantitative performance index vector includes at least impact resistance, eccentric load stiffness, aging resistance level of the sealing interface, and the adaptability range of the height adjustment mechanism. The modular mapping rule base defines 3-5 core performance interfaces for each module and associates them with multiple sets of optional implementation parameter combinations; Based on the performance requirements, the output indicators and priorities of the model are deduced, and the initial structural parameter schemes of each module are matched and generated from the modular mapping rule base.

10. A systematic design method for hydraulic support systems oriented towards complex mining environments, as described in claim 1, characterized in that: Step 6 specifically includes: A high-level system architecture is adopted to integrate multidisciplinary analysis tools for structural mechanics simulation, hydraulic system analysis, and material life assessment. Based on the initial structural parameter scheme, a multidisciplinary collaborative simulation was conducted, and the overall performance compliance rate was calculated. If the overall performance compliance rate does not reach 95%, the module parameters are adjusted in reverse by using the performance requirement reverse model and modular mapping rule base. The simulation-evaluation-correction cycle is executed iteratively until the target is met, at which point the final stent design scheme is output and the new data is fed back to the environment library.

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