Integrated definition method and system for multi-dimensional scheme of disaster physics simulation test

Abstract

The application provides a kind of disaster physics simulation test multidimensional scheme integration definition method and system, it is related to deep engineering disaster physics simulation and test scheme definition technical field.Based on the technical architecture of process driving, sequential forced, data linkage and space-time unification, a set of standardized definition process covering geological conditions, environmental application, engineering activities and monitoring arrangement is constructed.Through establishing strict logical dependence and data transmission mechanism, it is ensured that subsequent steps must be executed on the basis of data generated in previous steps, fundamentally eliminating arbitrariness and dispersion in the process of scheme definition, ensuring the overall improvement of test scheme in systematicness, consistency and feasibility, and providing a reliable basis for deep engineering disaster physics simulation test.

Description

Technical Field

[0001] This invention relates to the field of deep engineering disaster physical simulation and test scheme definition technology, and in particular to an integrated definition method and system for multi-dimensional disaster physical simulation test schemes. Background Technology

[0002] Frequent disasters in deep engineering projects have become a major challenge to the national energy and resource strategic security. Researching the mechanisms of these disasters is crucial for ensuring the safe construction and operation of deep engineering projects. To this end, my country has constructed an ultra-large-scale physical simulation facility for deep engineering disasters. This facility, through core technological processes including geological reconstruction, environmental occurrence, engineering activities, and multi-source monitoring, accurately recreates the complex geological structures, high ground stress, and engineering disturbances of real deep engineering projects at the laboratory level, becoming a core experimental means to reveal the mechanisms of deep engineering disaster formation.

[0003] Before formal testing, deep engineering disaster physical simulation facilities require the standardized, structured, instantiated, and digitized construction and specification of multi-dimensional test schemes, encompassing geological reconstruction, environmental conditions, engineering activities, and multi-source monitoring, based on the test objectives. This process is known as "test scheme parameter definition and configuration." Specifically, this process includes setting rock mass properties, planning environmental boundary conditions such as gradient loading or stress wave disturbance, designing the spatiotemporal paths of engineering activities such as excavation, and deploying a sensor network to accurately capture disaster-inducing information. The coordinated configuration of these multi-source heterogeneous parameters is a prerequisite for successfully triggering and capturing specific disaster modes.

[0004] Currently, the definition of experimental schemes mainly adopts a parameter configuration method based on distributed independent subsystems. Specifically, in geological reconstruction, geometric modeling and mechanical parameter definition of geological bodies are usually completed using tools such as CAD; in environmental remediation, parameters such as load magnitude are set through dedicated control software; in engineering activities, motion trajectories and process parameters are usually planned in professional systems such as robot programming; and in information monitoring, sensor topology design is usually completed in independent deployment software.

[0005] The current methods for configuring and defining experimental parameters have the following main shortcomings: (1) The separation of dimensions and sequence in the configuration of test parameters leads to the distortion of the physical meaning of the test.

[0006] Current methods, when designing schemes for core experimental components such as geological reconstruction, environmental occurrence, engineering activities, and multi-source monitoring, generally employ fragmented approaches or independent tools, lacking a unified framework for integrating and configuring multi-dimensional parameters. For example, CAD software is used to geometrically model deep engineering tunnels to determine their suitability for physical simulation experiments; independent loading control software is used to configure the load parameters of the loading device; and dedicated sensor placement tools are used to plan monitoring points. The design of experimental schemes in these dimensions often lacks process constraints and system integration, and the sequential arrangement is usually not mandatory, relying heavily on the subjective experience of researchers to conduct experiments in parallel or arbitrarily. This leads to experiments deviating from the real geological environment in order to conform to the design scheme, losing the true meaning of physical simulation experiments. A typical example is planning the excavation outline first and then working backward to deduce the geological model and load conditions, significantly weakening the realism and scientific rigor of the physical simulation.

[0007] (2) Inefficiency of cross-dimensional experimental design parameter coordination.

[0008] Current methods lack automatic correlation capabilities for parameter transfer and coupling mechanisms across different dimensions, including geological reconstruction, environmental occurrence, engineering activities, and multi-source monitoring. Parameter coordination heavily relies on manual operation, and there is a lack of facility-level integration and correlation capabilities between multi-dimensional test parameters. For example, load parameters set in the environmental occurrence test, such as stress thresholds, cannot be automatically correlated and transferred to the engineering activity test, resulting in the inability to adjust key test parameters such as excavation sequence in a timely manner. Once the test plan for one dimension changes, such as adjusting the tunnel outline or dimensions, other related dimensions, such as the monitoring point layout plan, cannot achieve automatic response and real-time updates, still requiring operators to manually perform data conversion, transfer, and coordination. This process not only greatly increases the workload and time cost but also easily introduces transcription errors, parameter redundancy, and even logical conflicts, severely restricting the design efficiency and reliability of complex test plans and affecting the scientific validity and accuracy of test results.

[0009] (3) The spatiotemporal reference is heterogeneous and lacks a unified spatiotemporal framework.

[0010] Currently, there is a lack of unified standards for the selection and use of spatiotemporal references across the dimensions of geological reconstruction, environmental occurrence, engineering activities, and multi-source monitoring. These dimensions are established on independent local coordinate systems and time-series systems. For example, the geological reconstruction dimension uses the geometric center of the rock mass or specific geological markers as the spatial reference and the experimental phase as the time reference; the monitoring dimension typically uses the location of the first sensor deployment as the spatial origin and the start time of its operation as the time starting point. This fragmented reference strategy necessitates complex and frequent spatiotemporal coordinate transformations and alignments during the fusion of multi-dimensional data. This significantly increases the complexity and computational cost of data processing, easily introduces transformation errors, reduces data consistency and comparability, and ultimately affects the accuracy of disaster gestation process analysis and the reliability of simulation results. The lack of a globally unified spatiotemporal framework has become a key bottleneck restricting multi-dimensional collaboration.

[0011] These shortcomings severely restrict the design efficiency and reliability of complex experimental schemes, making it difficult to meet the accuracy requirements of multi-parameter coordinated configuration in ultra-large-scale physical simulation experiments. Summary of the Invention

[0012] The technical problem to be solved by this invention is to address the shortcomings of the prior art by providing an integrated definition method and system for multi-dimensional schemes of disaster physics simulation experiments. By establishing a strict logical dependency and data transmission mechanism, it ensures that subsequent steps must be executed based on the data generated in the preceding steps, fundamentally eliminating arbitrariness and discreteness in the scheme definition process, ensuring the overall improvement of the experimental scheme in terms of systematicness, consistency and feasibility, and providing a reliable foundation for disaster physics simulation experiments in deep engineering.

[0013] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: On the one hand, this invention provides an integrated definition method for multi-dimensional schemes in disaster physics simulation experiments, including the following steps: Step 1: Construct a multi-source information database to achieve multi-source information data storage and standardized management; The multi-source information database is used to centrally store and manage all experimental parameters input by users in subsequent stages, ensuring that various user-defined parameters can be stored safely and orderly, and providing data support for subsequent scheme definition stages; Step 2: Construct a unified spatiotemporal framework to achieve global unification of time and space dimensions; The unified spatiotemporal framework aims to establish a unified spatiotemporal reference benchmark for the entire experimental scheme definition process, ensuring that all dimensional parameters can be defined and expressed in a consistent spatiotemporal system. The unified spatiotemporal framework provides standardized spatiotemporal description specifications for various dimensions such as geological conditions, environmental conditions, engineering activities, and monitoring arrangements by defining a global coordinate system and a time reference system. Regarding spatial reference, a spatial reference system is established with the global coordinate system as the primary reference and the subsystem coordinate system as the secondary reference. The specific coordinate system design is as follows: The global coordinate system takes the geometric center of the geological model as the origin, establishes the units and directions of the X, Y, and Z axes, and uses reference points for auxiliary positioning; the subsystem coordinate system, representing specific equipment components, uses known global reference points in the global coordinate system as anchor points to calculate the displacement and rotation angle of the subsystem in space, thereby transforming the subsystem into the global coordinate system; all subsequent input spatial parameters will be defined and stored based on this global coordinate system. Regarding the time reference, a relative time system with the start time of the test as the origin is established as a unified time reference, and the time unit and time counting method are clearly defined. A relative counting architecture based on the BeiDou absolute time scale is adopted, with the absolute timestamp of the test zero point as the anchor point. The IEEE1588v2 protocol is used to ensure that the logical time of all network nodes is highly consistent at the nanosecond level resolution. All time-related parameters will be expressed in accordance with this unified time reference. Step 3: Correlation, Coordination, and Verification of Cross-Dimensional Experimental Parameters; When defining a multi-dimensional experimental plan, the correlation, coordination, and verification process for cross-dimensional parameters is performed simultaneously, specifically including the following steps: Step 3.1: Through the parameter correlation analysis engine, automatically establish the intrinsic relationship between geological parameters, environmental parameters, engineering parameters and monitoring parameters to form a parameter correlation network of "geology-environment-engineering-monitoring"; Step 3.2: Based on the pre-set engineering mechanics rules and experimental logic, establish a parameter association rule base to perform consistency verification on multi-dimensional parameters, including spatial position conflict detection, temporal logic verification, and mechanical rationality check; the rule base consists of three core verification logic groups, covering three dimensions: spatial position, temporal logic, and mechanical rationality. Step 3.3: For parameter conflicts or logical anomalies discovered during the verification process, visual alerts are provided to support users in timely adjusting parameter settings, ensuring the integrity and feasibility of the entire test plan in terms of physical meaning and engineering logic. The early warning mechanism adopts a three-level circuit breaker-style early warning. Once the verification logic in the association rule base is determined to be abnormal, the system automatically matches the early warning level and conflict type based on the anomaly and highlights it. The early warning levels include blue, orange, and red warnings. In addition, specific parameter adjustment schemes are provided based on the inversion algorithm. Step 4: Construct a rigorous, sequentially executed integrated definition process for multi-dimensional test schemes, encompassing geological conditions, environmental application, engineering activities, and monitoring deployment. This process follows the inherent scientific logic of foundation construction, input loading, response excitation, and state capture, with each subsequent step requiring the physical environment and boundary conditions defined in the preceding step. Users define and configure test parameters based on their experimental needs through this integrated multi-dimensional test scheme definition process. During the multi-dimensional test scheme definition, the system simultaneously performs cross-dimensional parameter correlation, collaboration, and verification. Through a parameter correlation analysis engine, it automatically establishes the intrinsic connections between geological parameters, environmental parameters, engineering parameters, and monitoring parameters, forming a "geology-environment-engineering-monitoring" parameter correlation network. Specifically, this includes the following steps: Step 4.1: The user defines the geological condition test plan; the user submits the geological structure map model file through the upload interface. The model input supports glTF, OBJ or FBX format; after parsing the file, the geometric center of the geological structure map model is directly aligned with the origin of the global coordinate system of the unified spatiotemporal framework, and its spatial units are uniformly converted to the reference units defined by the system. Step 4.2: The user defines the environmental load test scheme; based on the defined geological conditions and a unified spatiotemporal framework, the user inputs the environmental load parameters. Step 4.3: The user defines the engineering activity test plan; based on the aforementioned geological conditions, the user sets the engineering activity parameters according to a unified spatiotemporal framework; specifically, this includes defining the tunnel / cavity group robot excavation plan, filling plan, ventilation plan, temperature application plan, fluid environment application plan, vertical shaft drilling plan, horizontal well drilling plan, fracturing plan, injection and production plan, and reservoir creation plan. The system assigns plan definition configuration items to the user according to the scenario; Step 4.4: The user defines the monitoring deployment test plan; after completing the definition of all other plans, the user deploys the monitoring system according to the unified spatiotemporal framework; Step 5: Conduct multi-dimensional consistency verification of the integrated complete test plan to ensure that all parameters remain coordinated and consistent under a unified spatiotemporal framework, including comprehensive verification of spatial location matching, temporal sequence coherence, and mechanical logic rationality; after verification, generate and output the multi-dimensional integrated definition result of the test plan to support the loading of test equipment and execution of the test process in subsequent tests.

[0014] Furthermore, in step 3.1, the parameter association network, with quantitative relationships as its core, accurately depicts the constraint relationships between parameters of different dimensions. In the geological dimension, the surrounding rock classification and geological structural features not only constrain the tunnel excavation outline, excavation step distance, and rate in the engineering parameters, but also restrict the deployment location of monitoring equipment in the monitoring parameters. In the environmental dimension, the temperature field and seepage field restrict the grouting pressure in the engineering parameters and determine the alarm threshold of the monitoring equipment. In the engineering dimension, the excavation shape directly determines the sensor deployment density and spatial positioning in the monitoring scheme, and the monitoring parameters are also inversely correlated with the project progress. In the monitoring dimension, the multi-source heterogeneous monitoring data not only passively characterizes the comprehensive effects of the above-mentioned three-dimensional parameters of geology, environment, and engineering, but also utilizes the physical covariance relationship between different types of parameters to realize data verification and cleaning, and inverts the true state of the surrounding rock based on the cleaned data. The constraints between the aforementioned parameter networks are derived from three levels: spatial location, temporal logic, and mechanical response. These constraints are transformed into a multi-level mathematical model. Specifically, local constraints define the static boundary limitations of geological constitutive characteristics on engineering geometry and monitoring layout, the dynamic correction of construction loads and alarm thresholds by multi-field environmental coupling, and the direct driving force of engineering dynamics on the monitoring scheme. Based on this, global constraints are established. Through the tunnel surrounding rock fluid-structure coupling deformation response model and the inequality of excavation disturbance intensity constraints under load conditions, closed-loop control of the overall system stability is achieved. The specific constraint relationships and mathematical expressions of each dimension of the parameters are as follows: The constraints of geological parameters on other parameters are based on the parameter constraint matrix of geological constitutive characteristics; among them, formulas (1)-(3) quantify the boundary restrictions of geological classification on the geometric parameters of engineering activities, and formula (4) realizes the limitation of the deployment location of monitoring equipment through geological environment analysis; First, we define the set of geological state parameters G as the core constraint source: (1); In the formula, The rating of the surrounding rock represents the quality of the rock mass; This is a matrix representing geological structural features; This represents the initial geostress tensor. The geological parameters constrain the excavation profile; the geological structure determines the maximum permissible geometry of the excavation face and the effective reinforcement zone that the support must cover. (2); In the formula, This is the excavation area; The stable shape function is determined by the quality of the surrounding rock; The model constraining geological parameters on excavation step distance and rate indicates that the excavation step distance and rate are controlled by the surrounding rock scoring index rate and limited by the spacing of geological structures. (3); In the formula, This refers to the excavation rate; This refers to the excavation step distance; Spacing between geological structures; The surrounding rock score is determined by the quality of the surrounding rock and the spacing of geological structures. The model constraining the location of monitoring equipment based on geological parameters indicates that the non-uniformity of geological structures determines stress concentration zones and deformation-sensitive zones, thereby forcibly constraining the spatial coordinate set of the monitoring sensors. : (4); In the formula, To monitor the spatial coordinate geometry of the sensor; It is a geologically sensitive characteristic function. Represents the gradient of a function; Lower limit for monitoring sensor deployment; To establish standards for the deployment of monitoring sensors; The constraints of environmental parameters on other parameters are based on the nonlinear coupling model of the environmental field; among them, formula (5) realizes the constraints of water pressure and temperature in the environmental parameters on grouting in the engineering parameters; formula (6) is the constraint model of environmental parameters on the alarm threshold of monitoring equipment; The environmental parameter constraint model on grouting pressure indicates that the grouting pressure must overcome the environmental pore water pressure, but at the same time must be less than the ultimate pressure that may cause fracturing or uplift of the environmental formation: (5); In the formula, It is the horizontal principal stress; temperature; Pore ​​water pressure; Effective diffusion pressure difference; Grouting pressure; This refers to the formation fracturing pressure; Environmental parameters constrain the alarm threshold of monitoring equipment. The alarm threshold is not a fixed value, but rather decreases dynamically with the severity of the environment. High water pressure or extreme temperatures can reduce the structural load-bearing redundancy, therefore the alarm must be more sensitive. (6); In the formula, Monitor alarm thresholds; The theoretical threshold of the design specifications; Uniaxial compressive strength of rock mass; Reference temperature; Thermosensitive coefficient; Calculate the function for the early warning conditions; The constraints of engineering parameters on other parameters are realized through the engineering dynamics model; Formula (7) represents the constraints of engineering activities on the deployment density and spatial positioning of monitoring equipment; Engineering parameters constrain the density and spatial positioning of monitoring sensors; the spatial distribution of monitoring points must be located on the excavation outline boundary. (7); In the formula, Spatial distribution of monitoring points; To excavate the outline boundary; The overall constraint relationship between various parameters is characterized by the comprehensive representation of tunnel multi-source data, physical covariance data constraints, and inversion constraints based on the actual state of the surrounding rock. Multi-source data comprehensive characterization constraints describe the monitoring data as a function of the combined effects of geological, environmental, and engineering three-dimensional parameters: (8); In the formula, G is the set of monitoring data; E is the set of geological state parameters; P is the set of environmental parameters; and P is the set of engineering parameters. For multi-field mapping functions; This represents random noise in the system. Physical covariant data cleaning constraints describe how to apply constraints to data that conform to physical laws using the covariant relationships between physical parameters. (9); In the formula, Calculate the covariance; The theoretical correlation coefficient defined for physical theorems; Tolerance; This is the initial data set; A set of physical constraint data; The parameters are from the initial data set. and For the first data set The and the first One parameter; The inversion constraint for the true state of surrounding rock describes the process of inferring the true mechanical state of surrounding rock based on physical constraint data: (10); In the formula, This represents the actual surrounding rock condition. It is the inverse operator of the mapping function.

[0015] Furthermore, in step 3.2, the structure of the parameter association rule base is as follows: (1) Spatial location conflict detection: verify whether the geometric relationship between the four parties, namely engineering activities, monitoring equipment, geological structure and environmental objects, meets the safety constraints, including boundary positioning fit verification, excavation outline geometry verification and geological sensitive point layout verification; (2) Temporal logic verification: Review the causal sequence of construction procedures, monitoring and geological evolution on the time axis to ensure the temporal legality of the data flow, including dynamic rate verification and progress-monitoring reverse correlation; (3) Mechanical rationality check: Based on the physical constitutive relationship and safety reserve coefficient, a two-way boundary constraint check is performed on the engineering load, geological strength, environmental pressure and monitoring feedback, including grouting pressure window verification, alarm threshold dynamic verification and physical covariance data cleaning; The parameter association rule base implements multi-dimensional parameter compliance checks through a systematic validation rule table. This rule base defines clear computable conditions and handling strategies, as detailed below: The types of spatial location verification include boundary positioning fit verification, excavation outline geometry verification, and geologically sensitive point layout verification; The verification logic for the boundary positioning and fitting verification is as follows: The anomaly handling strategy is as follows: issue a warning for positioning deviation; if the monitoring point does not conform to the excavation outline boundary. It cannot reflect the true surface deformation and needs to be recalibrated. The verification logic for the excavation contour geometry verification is as follows: The anomaly handling strategy is as follows: prompt that the outline has exceeded the boundary; the current excavation shape exceeds the stable function domain determined by the surrounding rock quality RMR, and it is recommended to optimize the excavation cross section; The verification logic for the geologically sensitive site detection is as follows: The anomaly handling strategy is as follows: alert the system to monitoring point failure; the monitoring point is located in a geologically insensitive area, i.e., a gradient... If the value is below the threshold, it is recommended that the sensor be relocated to the construction zone. The verification types for sequential logic include dynamic rate verification and progress-monitoring reverse correlation; The verification logic for the dynamic rate verification is as follows: The anomaly handling strategy is as follows: determine if there is temporal / kinetic instability; if the product of the excavation rate and the step distance (time-varying disturbance intensity) exceeds the critical value, forcibly reduce the advance rate or shorten the step distance. The verification logic for the progress-monitoring reverse correlation is as follows: when When unstable, The anomaly handling strategy is as follows: trigger progress circuit breaker; based on the surrounding rock condition derived from the cleaned data. If the display is unstable, the project progress will be forcibly paused, i.e., the rate will be reduced to zero. The types of mechanical rationality verification include grouting pressure window verification, alarm threshold dynamic verification, and physical covariance data cleaning. The verification logic for the grouting pressure window verification is as follows: The abnormal handling strategy is as follows: early warning of grouting risk; if the pressure is too high, it will cause splitting, or if it is too low, it will prevent diffusion. The system will automatically recommend a safe pressure range based on the environmental field. The verification logic for the dynamic verification of the alarm threshold is as follows: The exception handling strategy is as follows: Indicate that the threshold has failed; the current threshold does not change with temperature. and intensity Dynamic correction, automatically generating new thresholds; The verification logic for the physical covariant data cleaning is as follows: The anomaly handling strategy is as follows: when the data error is too large, utilize covariant physical relationships. The theoretical values ​​of abnormal parameters are reconstructed based on normal parameters to fill the time series gaps.

[0016] Furthermore, in step 3.3, the specific content of the inversion algorithm is as follows: Reconstruction strategy for spatial location conflicts: Set sensor coordinates as decision variables, use the geologically sensitive characteristic function as the gravitational field and the boundary of the engineering entity as the repulsive field, use the gradient descent method to search for the minimum point of the cost function in the feasible region, and automatically output the optimal coordinate solution that satisfies the safe distance and maximizes the monitoring efficiency. Reordering strategy for timing logic conflicts: Establish a time consumption function using Formula 3. The inverse function model calculates the maximum allowable excavation step distance and minimum necessary excavation rate under the current geological conditions, thereby matching the construction cycle and engineering procedures. The compensation strategy for mechanical rationality conflicts is as follows: the deviation between the measured values ​​and the theoretical calculation values ​​is used as the driving element for two-level iteration; in the first iteration, the residual is minimized using the Bayesian inference algorithm, and the uncertain parameters in the geological model are corrected in reverse to eliminate model errors; in the second iteration, based on the corrected model, the incremental engineering parameters required to meet the target safety factor are solved in reverse to achieve active restoration of mechanical equilibrium.

[0017] Further, step 4.2 specifically includes: Step 4.2.1: Static load settings; Based on the geological structure map model, users input static load parameters through the interface, including selecting the loading method, control mode, setting the target loading value and loading rate, and planning multiple loading stages; among them, the loading method is gradient loading or uniform loading, and the control mode is force control or displacement control; according to the user's input of the automatic unified unit of measurement, the display unit is switched to MPa or mm according to the selected control mode, and the corresponding stress application path diagram is generated. Step 4.2.2: Dynamic load setting; Users set dynamic load parameters according to test requirements, including selecting the disturbance wave function type, setting the frequency and amplitude values, and configuring the number of disturbances and the disturbance interval time of the cyclic disturbance mode; after receiving these parameters, they are incorporated into a unified time base to plan the dynamic load timing. Step 4.2.3: Internal stress wave generation / closed stress excitation settings; Users can specify the spatial location of stress wave generation points or closed stress excitation points in the geological structure map model, input blasting energy or stress magnitude and stress direction parameters, and set multiple blasts at a single point; the spatial coordinates input by the user are mapped to the global coordinate system, and the location and parameter information of these excitation points are visualized in the geological structure map model.

[0018] Furthermore, the definition of the engineering activity test plan in step 4.3 specifically includes: Step 4.3.1: Define the tunnel / cave group robot excavation scheme. Precisely define the spatial coordinates of the excavation starting point in the geological structure map model, select the circular or portal-shaped excavation cross-section shape, and set key geometric parameters in detail, including excavation depth, tunnel diameter, excavation direction angle, cross-section roll angle, and path curvature radius. Users can add multiple excavation steps through the graphical interface, flexibly adjust the execution order and spatial position relationship of each step, and generate the three-dimensional shape of the excavated body in real time and visualize it. Step 4.3.2: Define the filling scheme, set the quality standard for filling connection rate, determine the pipeline flow range, set the filling operation rate, and configure the pumping pressure parameters; automatically calculate the filling operation time through numerical simulation based on user input parameters, and evaluate the degree of matching between the filling effect and the project requirements; Step 4.3.3: Define the ventilation scheme, set the total ventilation volume requirement, determine the location coordinates of the ventilation shaft, set the working pressure of the ventilation system, configure the air intake flow rate, wind speed, wind direction, temperature, humidity and air pressure environmental parameters, specify the air door number and set the local air door opening control parameters, and establish a complete mine ventilation system scheme. Step 4.3.4: Define the temperature application scheme, set the temperature rise rate control parameters, determine the final target temperature value, formulate the temperature field spatiotemporal evolution scheme, and ensure that the temperature application process matches the project schedule; Step 4.3.5: Define the fluid environment application scheme, set the fluid heating temperature value, select the injection method as continuous injection or intermittent injection, set the injection pressure range, and formulate the spatiotemporal distribution scheme of fluid injection; Step 4.3.6: Define the vertical shaft drilling plan, set the vertical shaft design height, determine the drilling operation rate, select the wellbore structure dimensions, formulate a drilling schedule, and ensure that the drilling project is coordinated with the overall project schedule; Step 4.3.7: Define the horizontal well drilling plan, set the design length of the horizontal section, determine the horizontal drilling rate, select the wellbore completion size, formulate a horizontal well trajectory control plan, and ensure that the wellbore trajectory meets geological requirements; Step 4.3.8: Define the fracturing scheme, select the fracturing fluid injection method as single-stage injection or multi-stage injection, set the injection flow rate parameters, formulate the fracturing operation sequence plan, and ensure that the fracturing effect meets the engineering expectations; Step 4.3.9: Define the injection and production scheme, set the injection and production working mode, determine the pressure change rate, set the injection and production pressure target value, formulate the injection and production system timing scheme, and optimize the injection and production parameter ratio; Step 4.3.10: Define the reservoir creation scheme, select the reservoir stimulation method, set the injection rate parameters, formulate the reservoir creation operation plan, and ensure that the reservoir stimulation effect meets the engineering requirements.

[0019] Furthermore, step 4.4 specifically includes: Step 4.4.1: The user selects the monitoring point location in the geological structure map model, inputs the sensor spatial coordinates, selects the sensor type, and configures the monitoring channel parameters; based on the spatial coordinates input by the user, the sensor location is mapped to the global coordinate system; Step 4.4.2: The user confirms the sensor layout plan and checks the spatial distribution of the sensors in the geological structure model; performs a self-check on the working status of the deployed sensors and displays the self-check results to the user.

[0020] On the other hand, the present invention also provides an integrated definition system for multi-dimensional schemes of disaster physics simulation experiments, including: Data Layer: As the foundational support of the system architecture, the data layer is used to construct the multi-source information database described in step 1. It centrally manages the relevant information of the deep engineering disaster physical simulation test scheme definition input by the user based on the integrated definition process of the multi-dimensional test scheme in step 4. Specifically, it stores all scheme definition parameter information of geological conditions, environmental conditions, engineering activities and monitoring arrangements defined in steps 4.1 to 4.4, as well as the corresponding global coordinates established based on the unified spatiotemporal framework in step 2, the local coordinates and time plans generated in step 4 and applied to each system. Service Layer: Based on the standardized data provided by the data layer, the service layer provides the core calculation and analysis services required for defining the test scheme; the service layer provides the parameter correlation analysis service described in step 3.1, and establishes a "geology-environment-engineering-monitoring" parameter correlation network between geological condition parameters, environmental application parameters, engineering activity parameters and monitoring layout parameters; in addition, the service layer also provides the parameter correlation rule base in step 3.2 and the multi-dimensional verification service in step 5, and performs spatial location conflict detection, temporal logic verification and mechanical rationality check based on the engineering mechanics rules and test logic in the parameter correlation rule base; Functional Layer: The functional layer undertakes the task of correlation coordination and dynamic optimization of the test plan parameters described in step 3. After the user inputs a certain dimension of the test plan parameter in step 4, the functional layer can automatically identify other related dimension parameters based on the correlation analysis and verification results provided by the service layer based on steps 3.1 and 3.2, and perform linkage adjustment and consistency correction according to the mechanism in step 3.3. Through this process, the functional layer realizes real-time coordination and iterative optimization of multi-dimensional parameters, ensuring that the entire test plan meets the requirements of step 5. Interaction Layer: The interaction layer provides users with a unified entry point for the integrated definition and operation of the multi-dimensional test scheme described in step 4. Through the interaction layer, users can complete the definition and configuration of the multi-dimensional schemes for geological conditions, environmental conditions, engineering activities, and monitoring arrangements described in steps 4.1 to 4.4. The system calls the capabilities of the service layer and functional layer in the background, while the front end presents the results with an intuitive interactive interface and a three-dimensional visualization scene based on the unified spatiotemporal framework constructed in step 2. The interaction layer not only supports parameter input, scheme editing based on step 3.3, and viewing of the results generated in step 5, but also provides scheme pre-playback and dynamic simulation based on the time base of step 2, enabling users to perceive the global impact of parameter adjustments described in step 3 in real time during the interaction process.

[0021] Thirdly, this application proposes a computer-readable storage medium storing executable instructions that, when executed, cause a processor to perform the integrated definition method for multi-dimensional schemes of disaster physics simulation experiments.

[0022] Fourthly, this application proposes a computer program product, including a computer program or instructions, which, when executed by a processor, implements the integrated definition method for multi-dimensional schemes of disaster physics simulation experiments.

[0023] The beneficial effects of adopting the above technical solution are as follows: The integrated definition method and system for multi-dimensional disaster physical simulation experiments provided by this invention, by constructing a multi-dimensional collaborative definition system of "geology-environment-engineering-monitoring," systematizes and standardizes the complex disaster physical simulation experiment definition process, establishes a scalable technical system and methodological specifications, and realizes integrated scheme design throughout the entire process from geological condition construction, environmental load application, engineering activity planning to monitoring network deployment. This solves the problems of fragmented scheme definition process and discrete parameter configuration in traditional methods, providing a complete and reliable scheme foundation for deep engineering disaster physical simulation experiments. This invention establishes an intelligent association and collaboration mechanism among parameters in deep engineering disaster physical simulation experiments. Based on multi-source information fusion and parameter association rule base, it realizes automatic association and real-time collaboration of multi-dimensional test parameters such as geological attributes, environmental parameters, engineering activities, and monitoring layout, ensuring that any modification to any parameter can accurately trigger adaptive optimization and adjustment of related parameters, significantly improving the efficiency and consistency of scheme design, and avoiding parameter conflicts and logical errors caused by manual transmission. This invention constructs a unified spatiotemporal reference framework for deep engineering disaster physical simulation experiments. By defining a global spatiotemporal origin and coordinate transformation rules, this invention achieves accurate mapping and seamless fusion of multi-source heterogeneous data under a unified spatiotemporal benchmark, solving the transformation errors and accuracy losses caused by the coexistence of local coordinate systems in traditional methods. This provides a high-precision spatiotemporal foundation for multi-dimensional data collaboration and comprehensive analysis of experimental schemes. This invention also provides a traceable scheme management capability for the entire process of deep engineering disaster physical simulation experiments. Through a robust version control and data tracking mechanism, it records the modification history and relationships of each parameter in detail, ensuring the complete repeatability and verifiability of the experimental scheme, providing a reliable data foundation and quality assurance for the study of deep engineering disaster mechanisms. Furthermore, this invention constructs a unified multi-dimensional parameter integrated configuration system, realizing the integrated process of ultra-large-scale deep engineering disaster physical simulation experimental schemes from design and verification to output. This solves the core problems of scheme fragmentation, inefficient collaboration, and inconsistent benchmarks in traditional methods, significantly improving the scientific rigor, efficiency, and reliability of experimental design. In summary, the integrated definition method for multi-dimensional schemes of physical simulation experiments for ultra-large-scale deep engineering disasters proposed in this invention has significant beneficial effects in realizing integrated design of the entire experimental scheme process and intelligent coordination of multi-dimensional parameters. It also supports the unification of spatiotemporal benchmarks for parameter settings, providing important technical support and methodological guidance for the standardized and scientific development of physical simulation experiments for deep engineering disasters. Attached Figure Description

[0024] Figure 1 A flowchart of the integrated definition method for a multi-dimensional scheme of physical simulation test for disasters in ultra-large deep engineering provided in Embodiment 1 of the present invention; Figure 2This is a flowchart of the correlation, coordination, and verification process for cross-dimensional experimental parameters provided in Embodiment 1 of the present invention; Figure 3 This is the parameter association rule library for cross-dimensional experimental parameter association, collaboration, and verification provided in Embodiment 1 of the present invention; Figure 4 A flowchart is defined for the test scheme driven by the geological conditions-environmental application-engineering activities-monitoring layout process provided in Embodiment 1 of the present invention. Figure 5 This is a diagram of the integrated system architecture for a multi-dimensional scheme for physical simulation experiments of disasters in ultra-large-scale deep engineering provided in Embodiment 2 of the present invention. Detailed Implementation

[0025] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0026] Example 1: To address the issues of fragmented dimensions and sequences in current experimental scheme parameter configurations, this embodiment aims to provide an integrated scheme definition method that follows the inherent logic of "geology-environment-engineering-monitoring." By establishing a process-driven, mandatory definition sequence and data dependencies, it ensures that the experimental scheme construction is based on the scientific logic that geological conditions determine environmental conditions, environmental conditions constrain engineering activities, and engineering activities guide monitoring deployment. This achieves an integrated definition of the entire process from geological reconstruction, environmental loading, engineering excavation to monitoring deployment. It solves the problem of distorted physical meaning caused by arbitrary configuration sequences and dimensional fragmentation in traditional methods, providing a methodological foundation for constructing realistic and reliable physical simulation experiments of deep engineering disasters.

[0027] To address the limitations of current methods in terms of inefficient parameter coordination across dimensional experimental schemes, this embodiment aims to establish an automatic association and real-time coordination mechanism for cross-dimensional experimental parameters. By constructing a unified multi-source information data collaborative storage and management mechanism, bidirectional automatic driving and linked updates among heterogeneous parameters from geology, environment, engineering, and monitoring are achieved. This ensures that modifications to any dimension of the scheme automatically and accurately map and trigger adaptive optimization and adjustments in other related dimensions, replacing inefficient and error-prone manual transmission and coordination. This significantly improves the design efficiency, internal consistency, and reliability of complex experimental schemes, providing data collaboration support for high-fidelity simulation of the entire process of deep engineering disaster formation.

[0028] To address the limitations of current methods that lack a unified spatiotemporal framework, this embodiment aims to construct a globally unified spatiotemporal benchmark framework to eliminate benchmark differences among various dimensions such as geological reconstruction, environmental occurrence, engineering activities, and multi-source monitoring. By defining a unique and precise spatiotemporal coordinate origin and reference system, seamless fusion, accurate mapping, and dynamic correlation of multi-source heterogeneous data are achieved within a unified spatiotemporal framework. This resolves the transformation errors and accuracy losses caused by the coexistence of local coordinate systems, providing a high-precision and consistent spatiotemporal data foundation for multi-dimensional data fusion and time-series analysis throughout the entire disaster development process, and addressing a key bottleneck restricting multi-dimensional collaborative analysis.

[0029] like Figure 1 As shown, the method of this embodiment is described below.

[0030] Step 1: Construct a multi-source information database to achieve multi-source information data storage and standardized management.

[0031] The multi-source information database serves as the data foundation for the entire multi-dimensional integrated experimental scheme definition process, used to centrally store and manage all experimental parameters input by the user in subsequent stages. The core function of this platform is to provide a structured data storage environment, ensuring that various user-defined parameters are stored securely and systematically, and providing data support for subsequent scheme definition stages.

[0032] Step 2: Construct a unified spatiotemporal framework to achieve global unification of time and space dimensions.

[0033] The unified spatiotemporal framework aims to establish a unified spatiotemporal reference standard for the entire experimental scheme definition process, ensuring that all dimensional parameters can be defined and expressed within a consistent spatiotemporal system. By defining a global coordinate system and a time reference system, the unified spatiotemporal framework provides standardized spatiotemporal description specifications for various dimensions, including geological conditions, environmental conditions, engineering activities, and monitoring deployment. Regarding the spatial reference, a three-dimensional coordinate system is established with the geometric center of the geological model as the origin, clearly defining the coordinate units and directions. All subsequently input spatial parameters, such as the coordinates of the excavation start point and sensor deployment locations, will be defined and stored based on this global coordinate system. Regarding the time reference, a relative time system is established with the experimental start time as the origin, clearly defining the time units and time counting methods. All time-related parameters, such as the duration of the loading phase, will be expressed according to this unified time reference.

[0034] Step 3: Correlation, Coordination, and Verification of Cross-Dimensional Experimental Parameters. When defining the multi-dimensional experimental plan, the correlation, coordination, and verification process for cross-dimensional parameters is performed simultaneously, such as... Figure 2 As shown.

[0035] Step 3.1: Through the parameter correlation analysis engine, automatically establish the intrinsic relationship between geological parameters, environmental parameters, engineering parameters and monitoring parameters to form a parameter correlation network of "geology-environment-engineering-monitoring".

[0036] The parameter correlation network, centered on quantitative relationships, accurately characterizes the constraints between parameters across different dimensions. In the geological dimension, rock grading and geological structural features not only constrain the tunnel excavation profile, excavation step distance, and rate in engineering parameters, but also restrict the deployment location of monitoring equipment in monitoring parameters. In the environmental dimension, temperature and seepage fields constrain grouting pressure in engineering parameters and determine the alarm threshold of monitoring equipment. In the engineering dimension, the excavation shape directly determines the sensor deployment density and spatial positioning in the monitoring scheme, and monitoring parameters are also inversely correlated with project progress. In the monitoring dimension, multi-source heterogeneous monitoring data not only passively characterize the combined effects of the aforementioned geological, environmental, and engineering parameters, but also utilize the physical covariance relationships between different types of parameters to achieve data verification and cleaning, and invert the true state of the surrounding rock based on the cleaned data. All correlations provide core support for scheme consistency verification, real-time conflict early warning, and intelligent parameter collaboration.

[0037] The constraints between the aforementioned parameter networks are derived from three levels: spatial location, temporal logic, and mechanical response. These constraints are transformed into a multi-level mathematical model. Specifically, local constraints define the static boundary limitations of geological constitutive characteristics on engineering geometry and monitoring layout, the dynamic correction of construction loads and alarm thresholds by multi-field environmental coupling, and the direct driving force of engineering dynamics on the monitoring scheme. Based on this, global constraints are established. Through the tunnel surrounding rock fluid-structure coupling deformation response model and the inequality of excavation disturbance intensity constraints under load conditions, closed-loop control of the overall system stability is achieved. The specific constraint relationships and mathematical expressions of each dimension of the parameters are as follows: The constraints of geological parameters on other parameters are based on the parameter constraint matrix of geological constitutive characteristics; among them, formulas (1)-(3) quantify the boundary restrictions of geological classification on the geometric parameters of engineering activities, and formula (4) realizes the limitation of the deployment location of monitoring equipment through geological environment analysis; First, we define the set of geological state parameters G as the core constraint source: (1); In the formula, The rating of the surrounding rock represents the quality of the rock mass; This is a matrix representing geological structural features; This represents the initial geostress tensor. The geological parameters constrain the excavation profile; the geological structure determines the maximum permissible geometry of the excavation face and the effective reinforcement zone that the support must cover. (2); In the formula, This is the excavation area; The stable shape function is determined by the quality of the surrounding rock; The model constraining geological parameters on excavation step distance and rate indicates that the excavation step distance and rate are controlled by the surrounding rock scoring index rate and limited by the spacing of geological structures. (3); In the formula, This refers to the excavation rate; This refers to the excavation step distance; Spacing between geological structures; The surrounding rock score is determined by the quality of the surrounding rock and the spacing of geological structures. The model constraining the location of monitoring equipment based on geological parameters indicates that the non-uniformity of geological structures determines stress concentration zones and deformation-sensitive zones, thereby forcibly constraining the spatial coordinate set of the monitoring sensors. : (4); In the formula, To monitor the spatial coordinate geometry of the sensor; It is a geologically sensitive characteristic function. Represents the gradient of a function; Lower limit for monitoring sensor deployment; To establish standards for the deployment of monitoring sensors; The constraints of environmental parameters on other parameters are based on the nonlinear coupling model of the environmental field; among them, formula (5) realizes the constraints of water pressure and temperature in the environmental parameters on grouting in the engineering parameters; formula (6) is the constraint model of environmental parameters on the alarm threshold of monitoring equipment; The environmental parameter constraint model on grouting pressure indicates that the grouting pressure must overcome the environmental pore water pressure, but at the same time must be less than the ultimate pressure that may cause fracturing or uplift of the environmental formation: (5); In the formula, It is horizontal ground stress; temperature; Pore ​​water pressure; Effective diffusion pressure difference; Grouting pressure; This refers to the formation fracturing pressure; Environmental parameters constrain the alarm threshold of monitoring equipment. The alarm threshold is not a fixed value, but rather decreases dynamically with the severity of the environment. High water pressure or extreme temperatures can reduce the structural load-bearing redundancy, therefore the alarm must be more sensitive. (6); In the formula, Monitor alarm thresholds; The theoretical threshold of the design specifications; Uniaxial compressive strength of rock mass; Reference temperature; Thermosensitive coefficient; Calculate the function for the early warning conditions; The constraints of engineering parameters on other parameters are realized through the engineering dynamics model; Formula (7) represents the constraints of engineering activities on the deployment density and spatial positioning of monitoring equipment; Engineering parameters constrain the density and spatial positioning of monitoring sensors; the spatial distribution of monitoring points must be located on the excavation outline boundary. (7); In the formula, Spatial distribution of monitoring points; To excavate the outline boundary; The overall constraint relationship between various parameters is characterized by the comprehensive representation of tunnel multi-source data, physical covariance data constraints, and inversion constraints based on the actual state of the surrounding rock. Multi-source data comprehensive characterization constraints describe the monitoring data as a function of the combined effects of geological, environmental, and engineering three-dimensional parameters: (8); In the formula, G is the set of monitoring data; E is the set of geological state parameters; P is the set of environmental parameters; and P is the set of engineering parameters. For multi-field mapping functions; This represents random noise in the system. Physical covariant data cleaning constraints describe how to apply constraints to data that conform to physical laws using the covariant relationships between physical parameters. (9); In the formula, Calculate the covariance; The theoretical correlation coefficient defined for physical theorems; Tolerance; This is the initial data set; A set of physical constraint data; The parameters are from the initial data set. and For the first data set The and the first One parameter; The inversion constraint for the true state of surrounding rock describes the process of inferring the true mechanical state of surrounding rock based on physical constraint data: (10); In the formula, This represents the actual surrounding rock condition. It is the inverse operator of the mapping function.

[0038] Step 3.2: Based on pre-defined engineering mechanics rules and experimental logic, establish a parameter association rule base to perform consistency verification on multi-dimensional parameters, including spatial location conflict detection (e.g., conflict between excavation area and sensor placement location), temporal logic verification (e.g., temporal matching between loading stage and excavation steps), and mechanical rationality checks (e.g., the degree of matching between rock mass strength and applied load), etc., for multi-dimensional collaborative verification. The parameter association rule base design is as follows: Figure 3 As shown.

[0039] The parameter association rule base uses a systematic set of verification rule tables to perform multi-dimensional parameter compliance checks, covering three core verification types: spatial location, temporal logic, and mechanical rationality. The structure of the parameter association rule base is as follows: (1) Spatial location conflict detection: verify whether the geometric relationship between the four parties, namely engineering activities, monitoring equipment, geological structure and environmental objects, meets the safety constraints, including boundary positioning fit verification, excavation outline geometry verification and geological sensitive point layout verification; (2) Temporal logic verification: Review the causal sequence of construction procedures, monitoring and geological evolution on the time axis to ensure the temporal legality of the data flow, including dynamic rate verification and progress-monitoring reverse correlation; (3) Mechanical rationality check: Based on the physical constitutive relationship and safety reserve coefficient, a two-way boundary constraint check is performed on the engineering load, geological strength, environmental pressure and monitoring feedback, including grouting pressure window verification, alarm threshold dynamic verification and physical covariance data cleaning.

[0040] The parameter association rule base implements multi-dimensional parameter compliance checks through a systematic validation rule table. This rule base defines clear computable conditions and handling strategies, as detailed below: The types of spatial location verification include boundary positioning fit verification, excavation outline geometry verification, and geologically sensitive point layout verification; The verification logic for the boundary positioning and fitting verification is as follows: The anomaly handling strategy is as follows: issue a warning for positioning deviation; if the monitoring point does not conform to the excavation outline boundary. It cannot reflect the true surface deformation and needs to be recalibrated. The verification logic for the excavation contour geometry verification is as follows: The anomaly handling strategy is as follows: prompt that the outline has exceeded the boundary; the current excavation shape exceeds the stable function domain determined by the surrounding rock quality RMR, and it is recommended to optimize the excavation cross section; The verification logic for the geologically sensitive site detection is as follows: The anomaly handling strategy is as follows: alert the system to monitoring point failure; the monitoring point is located in a geologically insensitive area, i.e., a gradient... If the value is below the threshold, it is recommended that the sensor be relocated to the construction zone. The verification types for sequential logic include dynamic rate verification and progress-monitoring reverse correlation; The verification logic for the dynamic rate verification is as follows: The anomaly handling strategy is as follows: determine if there is temporal / kinetic instability; if the product of the excavation rate and the step distance (time-varying disturbance intensity) exceeds the critical value, forcibly reduce the advance rate or shorten the step distance. The verification logic for the progress-monitoring reverse correlation is as follows: when When unstable, The anomaly handling strategy is as follows: trigger progress circuit breaker; based on the surrounding rock condition derived from the cleaned data. If the display is unstable, the project progress will be forcibly paused, i.e., the rate will be reduced to zero. The types of mechanical rationality verification include grouting pressure window verification, alarm threshold dynamic verification, and physical covariance data cleaning. The verification logic for the grouting pressure window verification is as follows: The abnormal handling strategy is as follows: early warning of grouting risk; if the pressure is too high, it will cause splitting, or if it is too low, it will prevent diffusion. The system will automatically recommend a safe pressure range based on the environmental field. The verification logic for the dynamic verification of the alarm threshold is as follows: The exception handling strategy is as follows: Indicate that the threshold has failed; the current threshold does not change with temperature. and intensity Dynamic correction, automatically generating new thresholds; The verification logic for the physical covariant data cleaning is as follows: The anomaly handling strategy is as follows: when the data error is too large, utilize covariant physical relationships. The theoretical values ​​of abnormal parameters are reconstructed based on normal parameters to fill the time series gaps; The above rules all operate using a closed-loop mechanism of pre-defined conditions and anomaly handling. Specifically, this mechanism automatically generates a set of parameter validity data, including physical quantity values, logical timing, and associated covariant boundaries, based on an engineering mechanics rule base. Then, an event-driven conflict scan is initiated, monitoring parameter changes in real time and performing rule traversal and differential comparison. If the calculation result does not meet the pre-defined conditions, the abnormal object is locked and transmission is blocked. For abnormal states, the system implements a risk-quantified tiered handling strategy: issuing warnings for minor deviations, generating intelligent correction suggestions for logical errors, and activating mandatory interlocking and operation freeze for severe conflicts. Finally, the mechanism performs regression verification on the corrected parameters, checking for secondary conflicts through a second full-traversal scan, ensuring that the anomaly marker is removed after all associated rule verifications are passed, and generating a compliant data packet to complete adaptive closed-loop control.

[0041] Step 3.3: For parameter conflicts or logical anomalies discovered during the verification process, visual alerts are provided to support users in timely adjusting parameter settings, ensuring the integrity and feasibility of the entire test plan in terms of physical meaning and engineering logic. The early warning mechanism adopts a three-level circuit breaker approach. Once the verification logic (the aforementioned formulas and rules) in the association rule base is determined to be abnormal, the system automatically matches the early warning level and conflict type based on the anomaly and highlights it. The early warning levels include blue, orange, and red. In addition, based on the inversion algorithm, the system can provide specific parameter adjustment schemes. The specific content of the inversion algorithm is as follows: (1) Reconstruction strategy for spatial location conflict: The sensor coordinates are set as decision variables, the geological sensitivity characteristic function (see Formula 4) is used as the gravitational field, and the boundary of the engineering entity is used as the repulsive field. The gradient descent method is used to search for the minimum point of the cost function in the feasible region, and the optimal coordinate solution that satisfies the safety distance and maximizes the monitoring efficiency is automatically output.

[0042] (2) Rearrangement strategy for timing logic conflicts: Establish the time consumption function through formula 3. The inverse function model calculates the maximum allowable excavation step distance and minimum necessary excavation rate under the current geological conditions, thereby matching the construction cycle and engineering procedures.

[0043] (3) Compensation strategy for mechanical rationality conflict: The deviation between the measured value and the theoretical calculation value is used as the driving element for two-level iteration. In the first iteration, the Bayesian inference algorithm is used to minimize the residual and correct the uncertain parameters (such as modulus and geostress) in the geological model in reverse to eliminate model error; in the second iteration, based on the corrected model, the incremental engineering parameters required to meet the target safety factor are solved in reverse to achieve active restoration of mechanical equilibrium.

[0044] Step 4: Construct a rigorous, sequentially executed integrated definition process for multi-dimensional test schemes encompassing geological conditions, environmental application, engineering activities, and monitoring deployment. This process follows the inherent scientific logic of foundation construction, input loading, response excitation, and state capture. Each subsequent step must be performed within the physical environment and boundary conditions defined in the preceding step, ensuring the integrity, consistency, and scientific rigor of the test scheme. Users define and configure test parameters based on their experimental needs using this integrated multi-dimensional test scheme definition process. During the multi-dimensional test scheme definition, the system simultaneously performs cross-dimensional parameter correlation, collaboration, and verification processes. Through a parameter correlation analysis engine, it automatically establishes the intrinsic connections between geological parameters, environmental parameters, engineering parameters, and monitoring parameters, forming a "geology-environment-engineering-monitoring" parameter correlation network.

[0045] like Figure 4 As shown, the specific steps include the following: Step 4.1: The user defines the geological condition test plan. The user submits the geological structure map model file through the upload interface. The model input supports glTF, OBJ, or FBX formats. After parsing the file, the geometric center of the geological structure map model is directly aligned with the origin of the global coordinate system of the unified spatiotemporal framework, and its spatial units are uniformly converted to the system-defined reference units.

[0046] Step 4.2: The user defines the environmental load test scheme. Based on the defined geological conditions and a unified spatiotemporal framework, the user inputs the environmental load parameters.

[0047] Step 4.2.1: Static load setting.

[0048] Based on the geological structure map model, users input static load parameters through the interface, including selecting the loading method (gradient loading or uniformly distributed loading), control mode (force control or displacement control), setting the target loading value and loading rate, and planning multiple loading stages. The system automatically unifies the units of measurement according to the user input, switches the display unit to MPa or mm according to the selected control mode, and generates the corresponding stress application path diagram.

[0049] Step 4.2.2: Dynamic load setting.

[0050] Users set dynamic load parameters according to test requirements, including selecting the disturbance wave function type, setting the frequency and amplitude values, and configuring the number of disturbances and the disturbance interval time for the cyclic disturbance mode. After receiving these parameters, the system incorporates them into a unified time base and plans the dynamic load timing.

[0051] Step 4.2.3: Internal stress wave generation / closed stress excitation settings.

[0052] Users specify the spatial location of stress wave generation points or closed stress excitation points in the geological structure map model, input blasting energy or stress magnitude and direction parameters, and can set up multiple blasts at a single point. The system maps the user-input spatial coordinates to the global coordinate system, visually displaying the location and parameter information of these excitation points in the geological structure map model.

[0053] Step 4.3: The user defines the engineering activity test plan. Based on the aforementioned geological conditions, the user sets the engineering activity parameters according to a unified spatiotemporal framework. This specifically includes the following plan definitions; the system assigns plan definition configuration items to the user based on the scenario: Step 4.3.1: Define the robot excavation scheme for tunnels / cave clusters. Precisely define the spatial coordinates of the excavation starting point in the geological structure model, select a circular or portal-shaped excavation cross-section, and set detailed key geometric parameters such as excavation depth, tunnel diameter, excavation direction angle, cross-sectional roll angle, and path curvature radius. Users can add multiple excavation steps through a graphical interface, flexibly adjusting the execution order and spatial relationship of each step. The system generates the 3D morphology of the excavated body in real time and provides a visual display.

[0054] Step 4.3.2: Define the filling scheme, set the filling connection rate quality standard, determine the pipeline flow range, set the filling operation rate, and configure the pumping pressure parameters; the system automatically calculates the filling operation time through numerical simulation based on the user input parameters, and evaluates the degree of matching between the filling effect and the project requirements.

[0055] Step 4.3.3: Define the ventilation scheme, set the total ventilation volume requirement, determine the location coordinates of the ventilation shaft, set the working pressure of the ventilation system, configure environmental parameters such as air intake flow rate, wind speed, wind direction, temperature, humidity and air pressure, specify the air door number and set the local air door opening control parameters, and establish a complete mine ventilation system scheme.

[0056] Step 4.3.4: Define the temperature application scheme, set the temperature rise rate control parameters, determine the final target temperature value, formulate the temperature field spatiotemporal evolution scheme, and ensure that the temperature application process matches the project schedule.

[0057] Step 4.3.5: Define the fluid environment application scheme, set the fluid heating temperature value, select the injection method (continuous injection or intermittent injection), set the injection pressure range, and formulate the spatiotemporal distribution scheme of fluid injection.

[0058] Step 4.3.6: Define the vertical shaft drilling plan, set the vertical shaft design height, determine the drilling operation rate, select the wellbore structure size, formulate a drilling schedule, and ensure that the drilling project is coordinated with the overall project schedule.

[0059] Step 4.3.7: Define the horizontal well drilling plan, set the design length of the horizontal section, determine the horizontal drilling rate, select the wellbore completion size, formulate a horizontal well trajectory control plan, and ensure that the wellbore trajectory meets geological requirements.

[0060] Step 4.3.8: Define the fracturing scheme, select the fracturing fluid injection method (single-stage or multi-stage injection), set the injection flow rate parameters, formulate the fracturing operation sequence plan, and ensure that the fracturing effect meets the engineering expectations.

[0061] Step 4.3.9: Define the injection and production scheme, set the injection and production working mode, determine the pressure change rate, set the injection and production pressure target value, formulate the injection and production system timing scheme, and optimize the injection and production parameter ratio.

[0062] Step 4.3.10: Define the reservoir creation scheme, select the reservoir stimulation method, set the injection rate parameters, formulate the reservoir creation operation plan, and ensure that the reservoir stimulation effect meets the engineering requirements.

[0063] Step 4.4: The user defines the monitoring deployment test plan. After completing the definition of all other plans, the user deploys the monitoring system according to the unified spatiotemporal framework.

[0064] Step 4.4.1: The user selects the monitoring point location in the geological structure map model, inputs the sensor's spatial coordinates, selects the sensor type, and configures the monitoring channel parameters. The system maps the sensor location to the global coordinate system based on the user-input spatial coordinates.

[0065] Step 4.4.2: The user confirms the sensor layout plan and views the spatial distribution of the sensors in the geological structure model. The system performs a self-check on the operational status of the deployed sensors and displays the results visually to the user.

[0066] Step 5: The system performs multi-dimensional consistency verification on the integrated complete test plan to ensure that all parameters remain consistent within a unified spatiotemporal framework, including comprehensive verification of spatial location matching, temporal sequence coherence, and mechanical logic rationality. Upon successful verification, the system generates and outputs the multi-dimensional integrated definition results of the test plan, supporting the loading of test equipment and the execution of the test process in subsequent experiments.

[0067] Example 2 An integrated definition system for multi-dimensional schemes of physical simulation experiments for disasters in ultra-large-scale deep engineering projects, such as... Figure 5 As shown, it includes: Data Layer: As the foundational support of the system architecture, the data layer is used to construct the multi-source information database described in step 1. It centrally manages information related to the deep engineering disaster physical simulation test scheme definitions input by users based on the integrated definition process of multi-dimensional test schemes in step 4. Specifically, it stores all scheme definition parameter information in the geological conditions, environmental conditions, engineering activities, and monitoring arrangements defined in steps 4.1 to 4.4, as well as the corresponding global coordinates established based on the unified spatiotemporal framework in step 2, and the local coordinates and time plans generated in step 4 applied to various systems. Through unified data standards and structured storage schemes, the data layer achieves efficient organization and standardized management of test parameters, providing complete, accurate, and consistent data resource support for the upper-level scheme definition and verification analysis, ensuring the high availability and traceability of multi-dimensional test data.

[0068] Service Layer: Based on the standardized data provided by the data layer, the service layer provides the core calculation and analysis services required for defining experimental schemes. The service layer provides the parameter correlation analysis service described in step 3.1, establishing a "geological-environment-engineering-monitoring" parameter correlation network between geological condition parameters, environmental application parameters, engineering activity parameters, and monitoring layout parameters. Furthermore, the service layer also provides the parameter correlation rule base from step 3.2 and the multi-dimensional verification service from step 5, performing spatial location conflict detection, temporal logic verification, and mechanical rationality checks based on the engineering mechanics rules and experimental logic in the parameter correlation rule base.

[0069] Functional Layer: The functional layer undertakes the task of correlation, coordination, and dynamic optimization of the test plan parameters described in step 3. After the user inputs a certain dimension of the test plan parameter in step 4, the functional layer can automatically identify other related dimension parameters based on the correlation analysis and verification results provided by the service layer based on steps 3.1 and 3.2, and perform linkage adjustment and consistency correction according to the mechanism in step 3.3. Through this process, the functional layer achieves real-time coordination and iterative optimization of multi-dimensional parameters, ensuring that the entire test plan meets the requirements of step 5 in terms of logical consistency and engineering feasibility.

[0070] Interaction Layer: The interaction layer provides users with a unified entry point for defining and operating the multi-dimensional experimental schemes described in step 4. Users complete the definition and configuration of the multi-dimensional schemes for geological conditions, environmental conditions, engineering activities, and monitoring arrangements described in steps 4.1 to 4.4 through the interaction layer. The system calls upon the capabilities of the service and functional layers in the background, while the front end presents the results with an intuitive interactive interface and a 3D visualization scene based on the unified spatiotemporal framework constructed in step 2. The interaction layer not only supports parameter input, scheme editing based on step 3.3, and viewing the results generated in step 5, but also provides scheme pre-playback and dynamic simulation based on the time reference of step 2, enabling users to perceive the global impact of parameter adjustments described in step 3 in real time during the interaction process. Through this integrated design of functionality and interaction, the interaction layer effectively reduces operational complexity and improves the scientific rigor, controllability, and intuitiveness of experimental scheme design.

[0071] Example 3 This embodiment proposes a computer-readable storage medium that stores executable instructions. When these instructions are executed, if they are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0072] The computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the integrated definition method for multi-dimensional schemes of disaster physical simulation experiments described in various embodiments of this application.

[0073] The aforementioned storage media include: flash memory, hard disks, multimedia cards, card-type memory (e.g., SD (Secure Digital Memory Card) or DX (Memory Data Register, MDR) memory), random access memory (RAM), static random-access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, disks, optical discs, servers, APP (Application) application stores, and other media capable of storing program verification codes. These media store computer programs, which, when executed by a processor, can implement the various steps of the integrated definition method for multi-dimensional disaster physics simulation experiments described above.

[0074] Example 4 This embodiment proposes a computer program product, including a computer program or instructions, which, when executed by a processor, implements the integrated definition method for multi-dimensional schemes of disaster physics simulation experiments.

[0075] Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a computer program product.

[0076] The various embodiments in this application are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope defined by the present invention.

Claims

1. A method for defining an integrated multi-dimensional scheme for disaster physics simulation experiments, characterized in that: Includes the following steps: Step 1: Construct a multi-source information database to achieve multi-source information data storage and standardized management; The multi-source information database is used to centrally store and manage all experimental parameters input by users in subsequent stages, ensuring that various user-defined parameters can be stored safely and orderly, and providing data support for subsequent scheme definition stages; Step 2: Construct a unified spatiotemporal framework to achieve global unification of time and space dimensions; The unified spatiotemporal framework aims to establish a unified spatiotemporal reference benchmark for the entire experimental scheme definition process, ensuring that all dimensional parameters can be defined and expressed in a consistent spatiotemporal system. The unified spatiotemporal framework provides standardized spatiotemporal description specifications for various dimensions such as geological conditions, environmental conditions, engineering activities, and monitoring arrangements by defining a global coordinate system and a time reference system. Regarding spatial reference, a spatial reference system is established with the global coordinate system as the primary reference and the subsystem coordinate system as the secondary reference. The specific coordinate system design is as follows: The global coordinate system takes the geometric center of the geological model as the origin, establishes the units and directions of the X, Y, and Z axes, and uses reference points for auxiliary positioning; the subsystem coordinate system, representing specific equipment components, uses known global reference points in the global coordinate system as anchor points to calculate the displacement and rotation angle of the subsystem in space, thereby transforming the subsystem into the global coordinate system; all subsequent input spatial parameters will be defined and stored based on this global coordinate system. Regarding the time reference, a relative time system with the start time of the test as the origin is established as a unified time reference, and the time unit and time counting method are clearly defined. A relative counting architecture based on the BeiDou absolute time scale is adopted, with the absolute timestamp of the test zero point as the anchor point. The IEEE1588v2 protocol is used to ensure that the logical time of all network nodes is highly consistent at the nanosecond level resolution. All time-related parameters will be expressed in accordance with this unified time reference. Step 3: Correlation, Coordination, and Verification of Cross-Dimensional Experimental Parameters; When defining a multi-dimensional experimental plan, the correlation, coordination, and verification process for cross-dimensional parameters is performed simultaneously, specifically including the following steps: Step 3.1: Through the parameter correlation analysis engine, automatically establish the intrinsic relationship between geological parameters, environmental parameters, engineering parameters and monitoring parameters to form a "geology-environment-engineering-monitoring" parameter correlation network; Step 3.2: Based on the pre-set engineering mechanics rules and experimental logic, establish a parameter association rule base to perform consistency verification on multi-dimensional parameters, including spatial position conflict detection, temporal logic verification, and mechanical rationality check; the rule base consists of three core verification logic groups, covering three dimensions: spatial position, temporal logic, and mechanical rationality. Step 3.3: Provide visual alerts for parameter conflicts or logical anomalies found during the verification process, and support users to adjust parameter settings in a timely manner to ensure the integrity and feasibility of the entire test plan in terms of physical meaning and engineering logic; The early warning mechanism adopts a three-level circuit breaker system. Once the verification logic in the association rule base is determined to be abnormal, the system automatically matches the early warning level and conflict type based on the abnormality and highlights it. The early warning levels include blue, orange and red. In addition, specific parameter adjustment schemes are given based on the inversion algorithm. Step 4: Construct a rigorous, sequentially executed integrated definition process for multi-dimensional test schemes encompassing geological conditions, environmental application, engineering activities, and monitoring deployment. This process follows the inherent scientific logic of foundation construction, input loading, response excitation, and state capture, with each subsequent step requiring the physical environment and boundary conditions defined in the preceding step. Users define and configure test parameters based on their experimental needs through this integrated multi-dimensional test scheme definition process. During the multi-dimensional test scheme definition, the system simultaneously performs cross-dimensional parameter correlation, collaboration, and verification processes. Through a parameter correlation analysis engine, it automatically establishes the intrinsic connections between geological parameters, environmental parameters, engineering parameters, and monitoring parameters, forming a "geology-environment-engineering-monitoring" parameter correlation network. Specifically, this includes the following steps: Step 4.1: The user defines the geological condition test plan; the user submits the geological structure map model file through the upload interface. The model input supports glTF, OBJ or FBX format; after parsing the file, the geometric center of the geological structure map model is directly aligned with the origin of the global coordinate system of the unified spatiotemporal framework, and its spatial units are uniformly converted to the reference units defined by the system. Step 4.2: The user defines the environmental load test scheme; based on the defined geological conditions and a unified spatiotemporal framework, the user inputs the environmental load parameters. Step 4.3: The user defines the engineering activity test plan; based on the aforementioned geological conditions, the user sets the engineering activity parameters according to a unified spatiotemporal framework; specifically, this includes defining the tunnel / cavity group robot excavation plan, filling plan, ventilation plan, temperature application plan, fluid environment application plan, vertical shaft drilling plan, horizontal well drilling plan, fracturing plan, injection and production plan, and reservoir creation plan. The system assigns plan definition configuration items to the user according to the scenario; Step 4.4: The user defines the monitoring deployment test plan; after completing the definition of all other plans, the user deploys the monitoring system according to the unified spatiotemporal framework; Step 5: Conduct multi-dimensional consistency verification of the integrated complete test plan to ensure that all parameters remain coordinated and consistent under a unified spatiotemporal framework, including comprehensive verification of spatial location matching, temporal sequence coherence, and mechanical logic rationality; after verification, generate and output the multi-dimensional integrated definition result of the test plan to support the loading of test equipment and execution of the test process in subsequent tests.

2. The integrated definition method for multi-dimensional schemes of disaster physics simulation experiments according to claim 1, characterized in that: In step 3.1, the parameter association network, with quantitative relationships as its core, accurately depicts the constraint relationships between parameters of different dimensions. In the geological dimension, the surrounding rock classification and geological structure characteristics not only constrain the tunnel excavation outline, excavation step distance, and rate in the engineering parameters, but also restrict the deployment location of monitoring equipment in the monitoring parameters. In the environmental dimension, the temperature field and seepage field restrict the grouting pressure in the engineering parameters and determine the alarm threshold of the monitoring equipment. In the engineering dimension, the excavation shape directly determines the sensor deployment density and spatial positioning in the monitoring scheme, and the monitoring parameters are also inversely correlated with the project progress. In the monitoring dimension, the multi-source heterogeneous monitoring data not only passively characterizes the comprehensive effects of the above-mentioned three-dimensional parameters of geology, environment, and engineering, but also utilizes the physical covariance relationship between different types of parameters to realize data verification and cleaning, and inverts the true state of the surrounding rock based on the cleaned data. The constraints between the aforementioned parameter networks are derived from three levels: spatial location, temporal logic, and mechanical response. These constraints are transformed into a multi-level mathematical model. Specifically, local constraints define the static boundary limitations of geological constitutive characteristics on engineering geometry and monitoring layout, the dynamic correction of construction loads and alarm thresholds by multi-field environmental coupling, and the direct driving force of engineering dynamics on the monitoring scheme. Based on this, global constraints are established. Through the tunnel surrounding rock fluid-structure coupling deformation response model and the inequality of excavation disturbance intensity constraints under load conditions, closed-loop control of the overall system stability is achieved. The specific constraint relationships and mathematical expressions of each dimension of the parameters are as follows: The constraints of geological parameters on other parameters are based on the parameter constraint matrix of geological constitutive characteristics; among them, formulas (1)-(3) quantify the boundary restrictions of geological classification on the geometric parameters of engineering activities, and formula (4) realizes the limitation of the deployment location of monitoring equipment through geological environment analysis; First, we define the set of geological state parameters G as the core constraint source: （1）； In the formula, The rating of the surrounding rock represents the quality of the rock mass; This is a matrix representing geological structural features; This represents the initial geostress tensor; The geological parameters constrain the excavation profile; the geological structure determines the maximum permissible geometry of the excavation face and the effective reinforcement zone that the support must cover. （2）； In the formula, This is the excavation area; The stable shape function is determined by the quality of the surrounding rock; The model constraining geological parameters on excavation step distance and rate indicates that the excavation step distance and rate are controlled by the surrounding rock scoring index rate and limited by the spacing of geological structures. （3）； In the formula, This refers to the excavation rate; This refers to the excavation step distance; Spacing between geological structures; The surrounding rock score is determined by the quality of the surrounding rock and the spacing of geological structures. The model constraining the location of monitoring equipment based on geological parameters indicates that the non-uniformity of geological structures determines stress concentration zones and deformation-sensitive zones, thereby forcibly constraining the spatial coordinate set of the monitoring sensors. : （4）； In the formula, To monitor the spatial coordinate geometry of the sensor; It is a geologically sensitive characteristic function. Represents the gradient of a function; Lower limit for monitoring sensor deployment; To establish standards for the deployment of monitoring sensors; The constraints of environmental parameters on other parameters are based on the nonlinear coupling model of the environmental field; among them, formula (5) realizes the constraints of water pressure and temperature in the environmental parameters on grouting in the engineering parameters; formula (6) is the constraint model of environmental parameters on the alarm threshold of monitoring equipment; The environmental parameter constraint model on grouting pressure indicates that the grouting pressure must overcome the environmental pore water pressure, but at the same time must be less than the ultimate pressure that may cause fracturing or uplift of the environmental formation: （5）； In the formula, It is the horizontal principal stress; temperature; Pore ​​water pressure; Effective diffusion pressure difference; Grouting pressure; This refers to the formation fracturing pressure; Environmental parameters constrain the alarm threshold of monitoring equipment. The alarm threshold is not a fixed value, but rather decreases dynamically with the severity of the environment. High water pressure or extreme temperatures can reduce the structural load-bearing redundancy, therefore the alarm must be more sensitive. （6）； In the formula, Monitor alarm thresholds; The theoretical threshold of the design specification; Uniaxial compressive strength of rock mass; Reference temperature; Thermosensitive coefficient; Calculate the function for the early warning conditions; The constraints of engineering parameters on other parameters are realized through the engineering dynamics model; Formula (7) represents the constraints of engineering activities on the deployment density and spatial positioning of monitoring equipment; Engineering parameters constrain the density and spatial positioning of monitoring sensors; the spatial distribution of monitoring points must be located on the excavation outline boundary. （7）； In the formula, Spatial distribution of monitoring points; To excavate the outline boundary; The overall constraint relationship between various parameters is characterized by the comprehensive representation of tunnel multi-source data, physical covariance data constraints, and inversion constraints based on the actual state of the surrounding rock. Multi-source data comprehensive characterization constraints describe the monitoring data as a function of the combined effects of geological, environmental, and engineering three-dimensional parameters: （8）； In the formula, G is the set of monitoring data; E is the set of geological state parameters; P is the set of environmental parameters; and P is the set of engineering parameters. For multi-field mapping functions; This represents random noise in the system. Physical covariant data cleaning constraints describe how to apply constraints to data that conform to physical laws using the covariant relationships between physical parameters. （9）； In the formula, Calculate the covariance; The theoretical correlation coefficient defined for physical theorems; Tolerance; This is the initial data set; A set of physical constraint data; The parameters are from the initial data set. and For the first data set The and the first One parameter; The inversion constraint for the true state of surrounding rock describes the process of inferring the true mechanical state of surrounding rock based on physical constraint data: （10）； In the formula, This represents the actual surrounding rock condition. It is the inverse operator of the mapping function.

3. The integrated definition method for multi-dimensional schemes of disaster physics simulation experiments according to claim 2, characterized in that: In step 3.2, the structure of the parameter association rule base is as follows: (1) Spatial location conflict detection: verify whether the geometric relationship between the four parties, namely engineering activities, monitoring equipment, geological structure and environmental objects, meets the safety constraints, including boundary positioning fit verification, excavation outline geometry verification and geological sensitive point layout verification; (2) Temporal logic verification: Review the causal sequence of construction procedures, monitoring and geological evolution on the time axis to ensure the temporal legality of the data flow, including dynamic rate verification and progress-monitoring reverse correlation; (3) Mechanical rationality check: Based on the physical constitutive relationship and safety reserve coefficient, a two-way boundary constraint check is performed on the engineering load, geological strength, environmental pressure and monitoring feedback, including grouting pressure window verification, alarm threshold dynamic verification and physical covariance data cleaning; The parameter association rule base implements multi-dimensional parameter compliance checks through a systematic validation rule table. This rule base defines clear computable conditions and handling strategies, as detailed below: The types of spatial location verification include boundary positioning fit verification, excavation outline geometry verification, and geologically sensitive point layout verification; The verification logic for the boundary positioning and fitting verification is as follows: The anomaly handling strategy is as follows: issue a warning for positioning deviation; if the monitoring point does not conform to the excavation outline boundary. It cannot reflect the true surface deformation and needs to be recalibrated. The verification logic for the excavation contour geometry verification is as follows: The anomaly handling strategy is as follows: prompt that the outline has exceeded the boundary; the current excavation shape exceeds the stable function domain determined by the surrounding rock quality RMR, and it is recommended to optimize the excavation cross section; The verification logic for the geologically sensitive site detection is as follows: The anomaly handling strategy is as follows: alert the system to monitoring point failure; the monitoring point is located in a geologically insensitive area, i.e., a gradient... If the value is below the threshold, it is recommended that the sensor be relocated to the construction zone. The verification types for sequential logic include dynamic rate verification and progress-monitoring reverse correlation; The verification logic for the dynamic rate verification is as follows: The anomaly handling strategy is as follows: determine if there is temporal / kinetic instability; if the product of the excavation rate and the step distance (time-varying disturbance intensity) exceeds the critical value, forcibly reduce the advance rate or shorten the step distance. The verification logic for the progress-monitoring reverse correlation is as follows: when When unstable, The exception handling strategy is to trigger a progress circuit breaker. Rock condition derived from cleaned data If the display is unstable, the project progress will be forcibly paused, i.e., the rate will be reduced to zero. The types of mechanical rationality verification include grouting pressure window verification, alarm threshold dynamic verification, and physical covariance data cleaning. The verification logic for the grouting pressure window verification is as follows: The abnormal handling strategy is as follows: early warning of grouting risk; if the pressure is too high, it will cause splitting, or if it is too low, it will prevent diffusion. The system will automatically recommend a safe pressure range based on the environmental field. The verification logic for the dynamic verification of the alarm threshold is as follows: The exception handling strategy is to indicate that the threshold has failed. The current threshold does not change with temperature. and intensity Dynamic correction, automatically generating new thresholds; The verification logic for the physical covariant data cleaning is as follows: The anomaly handling strategy is as follows: when the data error is too large, utilize covariant physical relationships. The theoretical values ​​of abnormal parameters are reconstructed based on normal parameters to fill the time series gaps.

4. The integrated definition method for multi-dimensional schemes of disaster physics simulation experiments according to claim 3, characterized in that: In step 3.3, the specific content of the inversion algorithm is as follows: Reconstruction strategy for spatial location conflicts: Set sensor coordinates as decision variables, use the geologically sensitive characteristic function as the gravitational field and the boundary of the engineering entity as the repulsive field, use the gradient descent method to search for the minimum point of the cost function in the feasible region, and automatically output the optimal coordinate solution that satisfies the safe distance and maximizes the monitoring efficiency. Reordering strategy for timing logic conflicts: Establish a time consumption function using Formula 3. The inverse function model calculates the maximum allowable excavation step distance and minimum necessary excavation rate under the current geological conditions, thereby matching the construction cycle and engineering procedures. Compensation strategy for mechanical rationality conflicts: Use the deviation between the measured value and the theoretical calculation value as the driving element for two-level iteration; The first iteration uses a Bayesian inference algorithm to minimize the residuals, and then reverses the uncertain parameters in the geological model to eliminate model errors. The second iteration, based on the corrected model, reverse-engineers the incremental engineering parameters required to meet the target safety factor, thereby achieving the active restoration of mechanical equilibrium.

5. The integrated definition method for multi-dimensional schemes of disaster physics simulation experiments according to claim 4, characterized in that: Step 4.2 specifically includes: Step 4.2.1: Static load settings; Based on the geological structure map model, users input static load parameters through the interface, including selecting the loading method, control mode, setting the target loading value and loading rate, and planning multiple loading stages; among them, the loading method is gradient loading or uniform loading, and the control mode is force control or displacement control; according to the user's input of the automatic unified unit of measurement, the display unit is switched to MPa or mm according to the selected control mode, and the corresponding stress application path diagram is generated. Step 4.2.2: Dynamic load setting; Users set dynamic load parameters according to test requirements, including selecting the disturbance wave function type, setting the frequency and amplitude values, and configuring the number of disturbances and the disturbance interval time of the cyclic disturbance mode; after receiving these parameters, they are incorporated into a unified time base to plan the dynamic load timing. Step 4.2.3: Internal stress wave generation / closed stress excitation settings; Users can specify the spatial location of stress wave generation points or closed stress excitation points in the geological structure map model, input blasting energy or stress magnitude and stress direction parameters, and set multiple blasts at a single point; the spatial coordinates input by the user are mapped to the global coordinate system, and the location and parameter information of these excitation points are visualized in the geological structure map model.

6. The integrated definition method for multi-dimensional schemes of disaster physics simulation experiments according to claim 5, characterized in that: The definition of the engineering activity test plan in step 4.3 specifically includes: Step 4.3.1: Define the tunnel / cave group robot excavation scheme. Precisely define the spatial coordinates of the excavation starting point in the geological structure map model, select the circular or portal-shaped excavation cross-section shape, and set key geometric parameters in detail, including excavation depth, tunnel diameter, excavation direction angle, cross-section roll angle, and path curvature radius. Users can add multiple excavation steps through the graphical interface, flexibly adjust the execution order and spatial position relationship of each step, and generate the three-dimensional shape of the excavated body in real time and visualize it. Step 4.3.2: Define the filling scheme, set the quality standard for filling connection rate, determine the pipeline flow range, set the filling operation rate, and configure the pumping pressure parameters; automatically calculate the filling operation time through numerical simulation based on user input parameters, and evaluate the degree of matching between the filling effect and the project requirements; Step 4.3.3: Define the ventilation scheme, set the total ventilation volume requirement, determine the location coordinates of the ventilation shaft, set the working pressure of the ventilation system, configure the air intake flow rate, wind speed, wind direction, temperature, humidity and air pressure environmental parameters, specify the air door number and set the local air door opening control parameters, and establish a complete mine ventilation system scheme. Step 4.3.4: Define the temperature application scheme, set the temperature rise rate control parameters, determine the final target temperature value, formulate the temperature field spatiotemporal evolution scheme, and ensure that the temperature application process matches the project schedule; Step 4.3.5: Define the fluid environment application scheme, set the fluid heating temperature value, select the injection method as continuous injection or intermittent injection, set the injection pressure range, and formulate the spatiotemporal distribution scheme of fluid injection; Step 4.3.6: Define the vertical shaft drilling plan, set the vertical shaft design height, determine the drilling operation rate, select the wellbore structure dimensions, formulate a drilling schedule, and ensure that the drilling project is coordinated with the overall project schedule; Step 4.3.7: Define the horizontal well drilling plan, set the design length of the horizontal section, determine the horizontal drilling rate, select the wellbore completion size, formulate a horizontal well trajectory control plan, and ensure that the wellbore trajectory meets geological requirements; Step 4.3.8: Define the fracturing scheme, select the fracturing fluid injection method as single-stage injection or multi-stage injection, set the injection flow rate parameters, formulate the fracturing operation sequence plan, and ensure that the fracturing effect meets the engineering expectations; Step 4.3.9: Define the injection and production scheme, set the injection and production working mode, determine the pressure change rate, set the injection and production pressure target value, formulate the injection and production system timing scheme, and optimize the injection and production parameter ratio; Step 4.3.10: Define the reservoir creation scheme, select the reservoir stimulation method, set the injection rate parameters, formulate the reservoir creation operation plan, and ensure that the reservoir stimulation effect meets the engineering requirements.

7. The integrated definition method for multi-dimensional schemes of disaster physics simulation experiments according to claim 6, characterized in that: Step 4.4 specifically includes: Step 4.4.1: The user selects the monitoring point location in the geological structure map model, inputs the sensor spatial coordinates, selects the sensor type, and configures the monitoring channel parameters; based on the spatial coordinates input by the user, the sensor location is mapped to the global coordinate system; Step 4.4.2: The user confirms the sensor layout plan and checks the spatial distribution of the sensors in the geological structure model; performs a self-check on the working status of the deployed sensors and displays the self-check results to the user.

8. An integrated definition system for multi-dimensional schemes in disaster physics simulation experiments, characterized in that: The method for implementing the integrated definition of multi-dimensional schemes for disaster physics simulation experiments as described in any one of claims 1 to 7 includes: Data Layer: As the foundational support of the system architecture, the data layer is used to construct the multi-source information database described in step 1. It centrally manages the relevant information of the deep engineering disaster physical simulation test scheme definition input by the user based on the integrated definition process of the multi-dimensional test scheme in step 4. Specifically, it stores all scheme definition parameter information of geological conditions, environmental conditions, engineering activities and monitoring arrangements defined in steps 4.1 to 4.4, as well as the corresponding global coordinates established based on the unified spatiotemporal framework in step 2, the local coordinates and time plans generated in step 4 and applied to each system. Service Layer: Based on the standardized data provided by the data layer, the service layer provides the core calculation and analysis services required for defining the test scheme; the service layer provides the parameter correlation analysis service described in step 3.1, and establishes a "geology-environment-engineering-monitoring" parameter correlation network between geological condition parameters, environmental application parameters, engineering activity parameters and monitoring layout parameters; in addition, the service layer also provides the parameter correlation rule base in step 3.2 and the multi-dimensional verification service in step 5, and performs spatial location conflict detection, temporal logic verification and mechanical rationality check based on the engineering mechanics rules and test logic in the parameter correlation rule base; Functional Layer: The functional layer undertakes the task of correlation coordination and dynamic optimization of the test plan parameters described in step 3. After the user inputs a certain dimension of the test plan parameter in step 4, the functional layer can automatically identify other related dimension parameters based on the correlation analysis and verification results provided by the service layer based on steps 3.1 and 3.2, and perform linkage adjustment and consistency correction according to the mechanism in step 3.

3. Through this process, the functional layer realizes real-time coordination and iterative optimization of multi-dimensional parameters, ensuring that the entire test plan meets the requirements of step 5. Interaction Layer: The interaction layer provides users with a unified entry point for the integrated definition and operation of the multi-dimensional test scheme described in step 4. Through the interaction layer, users can complete the definition and configuration of the multi-dimensional schemes for geological conditions, environmental conditions, engineering activities, and monitoring arrangements described in steps 4.1 to 4.

4. The system calls the capabilities of the service layer and functional layer in the background, while the front end presents the results with an intuitive interactive interface and a three-dimensional visualization scene based on the unified spatiotemporal framework constructed in step 2. The interaction layer not only supports parameter input, scheme editing based on step 3.3, and viewing of the results generated in step 5, but also provides scheme pre-playback and dynamic simulation based on the time base of step 2, enabling users to perceive the global impact of parameter adjustments described in step 3 in real time during the interaction process.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores executable instructions that, when executed, cause a processor to perform the integrated definition method for multi-dimensional schemes of disaster physics simulation experiments as described in any one of claims 1 to 7.

10. A computer program product, characterized in that: It includes a computer program or instructions that, when executed by a processor, implement the integrated definition method for multi-dimensional schemes of disaster physical simulation experiments as described in any one of claims 1 to 7.

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