A flexible slope support optimization design system

Abstract

The application discloses a flexible slope support optimization design system and relates to the technical field of flexible slope support. A data acquisition module is constructed, dynamic data and image data of a slope surface are collected based on a plurality of array sensors arranged on the slope surface, and the dynamic data is transmitted to a dynamic relational database. An instability model construction module is constructed, which is used for analyzing dynamic data to generate an instability model of the slope surface, generating a stress analysis report of the slope surface based on the instability model. A support interference field module is constructed, which generates a support interference field based on the stress analysis report, and generates an optimization scheme of flexible slope support based on the interaction between the support interference field and the instability model. A visualization module is constructed, which is used for visually displaying the instability model and the optimization scheme of flexible slope support. The scheme realizes efficient optimization of slope support from passive response to active foresight through the construction of an intelligent analysis closed loop of deep coupling of data driving and mechanical models.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of flexible slope support, in particular to a flexible slope support optimization design system. BACKGROUND

[0002] Currently, the optimization design of flexible slope support engineering mainly relies on periodic manual survey and static monitoring data of limited points, and the traditional optimization method is usually based on discrete, historical geological survey reports and simplified mechanical models;

[0003] Then, the prior art has some deficiencies, the traditional monitoring system can only provide isolated physical measurement values, such as displacement or stress data of a single point, and lacks the ability to effectively associate and fuse multi-source, heterogeneous monitoring data with environmental factors, and the understanding of the slope state is insufficient, which leads to that the data analysis stays on the surface phenomenon, and the underlying mechanical mechanism and evolution process of the rock mass cannot be revealed, so that the judgment of the potential instability mode seriously depends on the personal experience of engineers, and the objectivity and systematization are insufficient; at the same time, the traditional support design scheme is usually fixed once determined, and it is difficult to adjust in real time according to the dynamic response of the slope under the action of the external environment; its optimization process is an offline, non-continuous static calculation, and a dynamic model reflecting the interaction of the internal state of the slope and the propagation path of the instability risk cannot be constructed, so that it is difficult to realize the precise and active matching of the support structure and the slope instability process in time and space, which leads to that the support scheme may lack foresight and adaptability;

[0004] Therefore, it is of great significance to develop an intelligent flexible slope support optimization design system. SUMMARY

[0005] The purpose of the present application is to provide a flexible slope support optimization design system to solve the problems in the background art.

[0006] In order to achieve the above purpose, the present application provides the following technical scheme: a flexible slope support optimization design system, comprising:

[0007] The data acquisition module acquires dynamic data and image data of the slope surface based on a plurality of array sensors deployed on the slope surface, and transmits the dynamic data to a dynamic relational database;

[0008] The instability model construction module is connected with the data acquisition module, and is used for analyzing the dynamic data to generate an instability model of the slope surface, and generating a stress analysis report of the slope surface based on the instability model;

[0009] The support interference field module is connected with the instability model construction module, generates a support interference field based on the stress analysis report, and generates an optimization scheme of the flexible slope support based on the interaction of the support interference field and the instability model.

[0010] visualization module: connected with the supporting interference field module, used for visualizing display of the instability model and the optimization scheme of the flexible slope supporting.

[0011] In a preferred embodiment, the data acquisition module comprises:

[0012] a multi-array sensing unit, distributedly arranged on the surface and inside of the slope, used for collecting dynamic data of the slope, and labeling time stamp and three-dimensional geographic coordinates of the dynamic data to generate multi-source time-series dynamic data;

[0013] an environment monitoring unit, used for synchronously collecting environment state data of the environment where the slope is located;

[0014] transmitting the dynamic data and the environment state data to a dynamic relational database to generate an event data group.

[0015] In a preferred embodiment, the step of transmitting the dynamic data to the dynamic relational database to generate the event data group comprises:

[0016] the dynamic relational database comprises a real-time data caching unit, an environment modulation unit and a dynamic relationship construction unit;

[0017] the real-time data caching unit receives the multi-source time-series dynamic data in real time and stores them in a cache space by constructing a plurality of sub-databases;

[0018] the environment modulation unit is used for receiving the environment state data and converting the environment state data into an environment state vector;

[0019] the dynamic relationship construction unit comprises a feature rule library, a relationship calculation sub-unit and a structured event encapsulation unit;

[0020] the feature rule library pre-stores feature extraction operators based on the principles of rock and soil mechanics and space-time correlation rules;

[0021] the relationship calculation sub-unit continuously calls the feature extraction operators to perform parallel scanning and calculation on the multi-source time-series dynamic data and the environment state vector to generate relationship tuples;

[0022] the relationship tuples comprise the multi-source time-series dynamic data, the environment state vector, a relationship type identifier and a relationship strength quantitative value;

[0023] the structured event encapsulation unit analyzes the relationship tuples through a neural network, and generates the event data group based on the space-time correlation rules and the relationship type identifier.

[0024] In a preferred embodiment, the instability model construction module comprises:

[0025] The slope digital model construction unit constructs a three-dimensional slope digital model based on dynamic data and image data;

[0026] The distributed event analysis unit extracts three-dimensional geographic coordinate distribution characteristics of the event data group, dynamically generates an event analysis subunit based on the three-dimensional geographic coordinate distribution characteristics, and is used for receiving and analyzing the event data group to generate a local instability feature vector;

[0027] The instability knowledge base unit pre-stores an instability mode rule set based on the principles of rock and soil mechanics, and the instability mode rule set is used to define the mapping relationship between different local instability feature vector combinations and potential instability modes;

[0028] The node collaborative reasoning unit is connected with each event analysis subunit and the instability knowledge base unit, performs collaborative reasoning on the local instability feature vector, and synthesizes an instability model describing the slope stability state;

[0029] Based on the instability model, a stress analysis report of the slope surface is generated.

[0030] In a preferred embodiment, the step of connecting with each event analysis subunit and the instability knowledge base unit, performing collaborative reasoning on the local instability feature vector, and synthesizing an instability model describing the slope stability state is:

[0031] The node collaborative reasoning unit receives the local instability feature vector from each event analysis subunit and performs spatiotemporal alignment processing on all local instability feature vectors;

[0032] The converged local instability feature vector set is matched with the instability mode rule set in the instability knowledge base unit, the matching degree of the feature vector and the instability mode rule set is calculated, and the corresponding potential instability mode is activated;

[0033] Based on the three-dimensional geographic coordinates in the local instability feature vector, the correlation strength between different position local instability features is analyzed, each event analysis subunit is taken as a node, the correlation strength is taken as an edge, and a correlation network representing the instability propagation path is constructed;

[0034] Combined with the activated potential instability mode and the correlation network, an instability model of the slope is synthesized through a weighted fusion algorithm;

[0035] Based on the instability model, a stress analysis report of the slope surface is generated, and the stress analysis report includes potential sliding surface geometric parameters, safety factor distribution, stress-strain field data, support resistance requirement distribution, and dynamic evolution prediction.

[0036] In a preferred embodiment, the support intervention field module includes:

[0037] a field mapping unit configured to receive the force analysis report and map the support resistance demand distribution, the potential sliding surface geometric parameters and the stress-strain field data in the report to an initial interference field overlaid on the instability model;

[0038] a dynamic field generator configured to generate a dynamic field generator based on the corresponding position of the three-dimensional geographic coordinates of the event analysis subunit on the initial interference field and connect with the event analysis subunit through a dynamic channel;

[0039] a field model interaction engine configured to build a field model in the dynamic field generator, internally set an optimization objective function, receive the local instability feature vector from the event analysis subunit through the dynamic channel, input the local instability feature vector into the optimization objective function, and dynamically correct the parameters of the initial interference field through an iterative adjustment algorithm;

[0040] a channel monitoring unit configured to continuously monitor the real-time load of all dynamic channels, entity the dynamic channels based on a preset super-fusion threshold, and generate anchors of the initial interference field;

[0041] obtain the corrected parameters of the initial interference field and the anchors to generate a support interference field, and discretize the continuous support interference field into spatial layout coordinates, force parameters and construction timing of the flexible support member through field streamline tracking technology.

[0042] In a preferred embodiment, the step of continuously monitoring the real-time load of all dynamic channels, entitying the dynamic channels based on a preset super-fusion threshold, and generating anchors of the flexible support net is as follows:

[0043] The channel monitoring unit continuously monitors the real-time load of all data channels and sets a preset overload threshold;

[0044] When it is found that the real-time load of one or more dynamic channels in a preset range continuously exceeds the preset overload threshold within a preset time, the area is determined as a high-energy core area;

[0045] The channel monitoring unit sends a freezing application to the dynamic channels in the high-energy core area, the dynamic channels receiving the freezing application are entityed to generate anchors and record anchor parameters, and the anchor data is transmitted to the field model interaction engine;

[0046] The anchor parameters include spatial positioning parameters, mechanical property parameters and dynamic correlation parameters.

[0047] In a preferred embodiment, the visualization module comprises:

[0048] a model rendering unit configured to generate a three-dimensional visualization scene based on the instability model and the support interference field, and real-time render the geometric shape, instability area and support structure of the slope;

[0049] A data superposition unit is configured to superimpose and display dynamic data, stress analysis reports and optimization schemes in a layer form.

[0050] A pre-warning display unit is configured to dynamically mark high-risk areas based on a destabilization model and highlight pre-warning information in the form of color or animation.

[0051] In the above technical solution, the present application has the following technical effects and advantages:

[0052] 1. The present application continuously collects dynamic data and image data through the multi-array sensors deployed on the slope surface, and efficiently processes the data in combination with a dynamic relational database. The system can capture subtle changes in the slope state in real time. The destabilization model construction module integrates multi-source data to generate a comprehensive destabilization model through the distributed event analysis unit and the node collaborative reasoning unit, and accurately reflects the stress state of the slope. The support intervention field module converts the stress analysis into an initial intervention field through the field mapping unit, and dynamically corrects the parameters through the interaction between the dynamic field generator and the event analysis subunit. The channel monitoring unit monitors the data load and automatically generates anchor nails to ensure the stability of the support structure. The intelligent algorithm based on the principles of rock and soil mechanics automatically generates an optimization scheme, reduces the subjective errors of manual design, improves the reliability and economy of the scheme, and greatly reduces the probability of slope destabilization accidents through the active monitoring and rapid intervention mechanism, thereby improving the intelligent level of flexible slope support.

[0053] 2. The present application uses the feature extraction operator embedded with prior knowledge of rock and soil mechanics to perform parallel computing and correlation analysis on the spatiotemporal sequence dynamic data and environmental state vectors, generating relational tuples with clear physical meaning. Then, the neural network model is used for structured packaging to form event data groups representing specific rock and soil mechanics processes. This process essentially completes the paradigm shift from low-dimensional numerical perception to high-dimensional situation understanding, and improves discrete physical observation values to structured knowledge units containing causal logic. Such knowledge units not only represent the mechanical state changes in local areas, but also provide rich standardized inputs for subsequent distributed collaborative reasoning and destabilization mode recognition through their inherent spatiotemporal correlation properties, laying the foundation for the system's cognitive decision-making. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings described below are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0055] Figure 1 The system flowchart of the present application.

[0056] Figure 2A logic block diagram for the present application. DETAILED DESCRIPTION

[0057] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0058] Embodiment 1, please refer to Figure 1 and Figure 2 The flexible slope support optimization design system described in the embodiment includes:

[0059] The data acquisition module acquires dynamic data and image data of the slope surface based on a plurality of array sensors deployed on the slope surface, and transmits the dynamic data to a dynamic relational database;

[0060] The instability model construction module is connected with the data acquisition module, is used for analyzing the dynamic data to generate an instability model of the slope surface, and generates a stress analysis report of the slope surface based on the instability model;

[0061] The support interference field module is connected with the instability model construction module, generates a support interference field based on the stress analysis report, and generates an optimization scheme of the flexible slope support based on the interaction between the support interference field and the instability model;

[0062] The visualization module is connected with the support interference field module, is used for visually displaying the instability model and the optimization scheme of the flexible slope support;

[0063] Further, at present, the optimization design of the flexible slope support engineering mainly depends on periodic artificial survey and static monitoring data of limited points, and the traditional optimization method is usually established on the basis of discrete, historical geological survey reports and simplified mechanical models;

[0064] Then, the prior art has some deficiencies, the traditional monitoring system can only provide isolated physical measurement values, such as displacement or stress data of a single point, lacks the ability to effectively associate and fuse multiple sources of heterogeneous monitoring data with environmental factors, and lacks depth of understanding of the state of the slope, which leads to data analysis remaining at the surface phenomenon, unable to reveal the underlying mechanical mechanism and evolution process of the rock mass, making the judgment of the potential instability mode heavily dependent on the personal experience of engineers, lacking objectivity and systematicness; at the same time, once the traditional support design scheme is determined, it is usually fixed and unchanged within the construction period, and it is difficult to adjust in real time according to the dynamic response of the slope under the action of the external environment; the optimization process is an offline, non-continuous static calculation, and a dynamic model reflecting the interaction of the internal state of the slope and the propagation path of the instability risk cannot be constructed, so it is difficult to realize the precise and active matching of the support structure and the instability process of the slope in space and time, leading to the lack of foresight and adaptability of the support scheme;

[0065] The present application continuously collects dynamic data and image data by deploying multiple array sensors on the slope surface, and combines a dynamic relational database for efficient processing. The system can capture subtle changes in the state of the slope in real time. The instability model construction module integrates multiple sources of data to generate a comprehensive instability model through a distributed event analysis unit and a node collaborative reasoning unit, accurately reflecting the stress state of the slope. The support intervention field module uses a field mapping unit to convert stress analysis into an initial intervention field, and interacts with the dynamic field generator and the event analysis subunit to dynamically correct parameters. The channel monitoring unit monitors data load and automatically generates anchor nails to ensure the stability of the support structure. This mechanism generates an optimized scheme based on the intelligent algorithm of rock mechanics principles, reduces subjective errors of manual design, improves the reliability and economy of the scheme, and actively monitors and quickly intervenes to greatly reduce the probability of slope instability accidents and improve the intelligent level of flexible slope support.

[0066] Through the feature extraction operator embedded with prior knowledge of rock mechanics, parallel computing and correlation analysis are performed on the spatiotemporal sequence dynamic data and environmental state vectors to generate relationship tuples with clear physical meaning. Then, structured encapsulation is performed through a neural network model to form an event data group representing a specific rock mechanics process. This process essentially completes the paradigm shift from low-dimensional numerical perception to high-dimensional situation understanding, and improves discrete physical observation values to structured knowledge units containing causal logic. Such knowledge units not only represent the mechanical state changes in local areas, but also provide rich standardized inputs for subsequent distributed collaborative reasoning and instability mode recognition through their inherent spatiotemporal correlation attributes, laying the foundation for the cognitive decision-making of the whole system.

[0067] In one embodiment, the data acquisition module comprises:

[0068] A multi-array sensing unit is distributed on the surface and inside of the slope to collect dynamic data of the slope and generate multi-source time-series dynamic data by labeling time stamp and three-dimensional geographic coordinates for the dynamic data;

[0069] An environment monitoring unit is configured to synchronously collect environment state data of the environment where the slope is located;

[0070] The dynamic data and the environment state data are transmitted to a dynamic relational database to generate an event data group;

[0071] Further, the multi-array sensing unit is composed of geological sensor arrays arranged on the surface and inside of the slope, specifically including micro-electro-mechanical system (MEMS) tilt sensors, fiber bragg grating (FBG) strain sensors, and resistance-type soil pressure cells, etc. These sensors are networked and communicated through a low-power wide-area network (LPWAN) protocol. The dynamic data of the slope collected based on the multi-array sensing unit includes deformation data, stress-strain data, and groundwater dynamic data. The deformation data is mainly obtained by a global navigation satellite system (GNSS) receiver and MEMS tilt sensors to acquire absolute displacement and tilt angle changes of the ground surface, and a buried inclinometer array is used to monitor deep horizontal displacement. The stress-strain data is measured by a distributed FBG sensor to measure the continuous strain field changes along the fiber path inside the rock-soil mass, and a resistance-type or vibrating wire soil pressure cell is used to record the contact stress at the key interface. The groundwater dynamic data is monitored by a network of osmometers to monitor the spatial and temporal distribution and fluctuation of pore water pressure, thereby reflecting the influence of hydrological conditions on the stability of the slope. Each sensing node is internally provided with a global positioning system (GPS) module and a miniature high-precision clock chip, which can automatically attach a nanosecond-level time stamp provided by the Beidou satellite navigation system and a centimeter-level three-dimensional geographic coordinate obtained based on a spatial interpolation algorithm to the data packet while collecting dynamic physical quantities such as vibration, displacement, and stress, thereby generating a multi-source time-series dynamic data stream with a unified space-time reference. The environment monitoring unit integrates weather stations, soil moisture meters, osmometers, and other devices to continuously collect environmental state data such as precipitation, temperature, humidity, and pore water pressure. After all the sensing data are preliminarily filtered and compressed by an edge computing gateway, they are transmitted to a cloud dynamic relational database via a 5G dedicated slice network. The dynamic relational database adopts a fusion architecture of time-series database and spatial database, and through an embedded rule engine and a stream processing platform, real-time space-time registration, correlation analysis, and feature extraction are performed on the injected heterogeneous data, and finally an event data group with complete semantic information is encapsulated to provide standardized data input for the upper analysis module.

[0072] In one embodiment, the step of transmitting the dynamic data to the dynamic relational database to generate the event data group comprises:

[0073] The dynamic relational database includes a real-time data caching unit, an environment modulation unit, and a dynamic relationship construction unit;

[0074] The real-time data caching unit is configured to receive multi-source time-series dynamic data in real time and store the multi-source time-series dynamic data in a cache space by constructing a plurality of sub-databases;

[0075] The environment modulation unit is configured to receive environment state data and convert the environment state data into an environment state vector;

[0076] The dynamic relationship construction unit comprises a feature rule library, a relationship calculation sub-unit, and a structured event encapsulation unit;

[0077] The feature rule library pre-stores feature extraction operators based on principles of geotechnical mechanics and space-time correlation rules;

[0078] The relationship calculation sub-unit continuously calls the feature extraction operators to perform parallel scanning and calculation on the multi-source time-series dynamic data and the environment state vector, and generates relationship tuples;

[0079] The relationship tuples comprise the multi-source time-series dynamic data, the environment state vector, a relationship type identifier, and a relationship strength quantification value;

[0080] The structured event encapsulation unit analyzes the relationship tuples by using a neural network, and generates event data groups based on the space-time correlation rules and the relationship type identifier;

[0081] Further, the generation of event data group relies on a multi-level dynamic relational database architecture. The real-time data caching unit adopts a distributed time series database such as InfluxDB as the core storage engine, and constructs a Redis cluster as a high-speed cache sub-database. Through a streaming processing platform, the sensor network is connected to realize high-throughput reception and temporary caching of multi-source time series dynamic data. The environmental modulation unit converts environmental state data into a numerical vector through feature engineering. Specifically, the discrete meteorological categories are processed by using hot encoding, and the continuous physical quantities are processed by using normalization method. Finally, the environmental state vector with fixed dimensions is generated. The core of the dynamic relationship construction unit is the feature rule base, in which the pre-stored feature extraction operators exist in the form of pre-compiled functions, such as the differential operator for calculating the displacement rate change, the spatial clustering operator for identifying the strain set, etc. The relationship calculation sub-unit is realized based on the Apache Flink streaming engine, which continuously calls these operators through a parallel pipeline to scan the dynamic data and environmental vector in the cache in real time, calculates the relationship strength by using the Pearson correlation coefficient algorithm, and outputs the relationship tuple containing the original data segment, the relationship type identifier and the quantitative strength value. Based on the obtained relationship triple, a recurrent neural network model is used to identify the spatiotemporal pattern of the input relationship tuple sequence. When a feature sequence that meets a specific instability precursor is identified, the encapsulation process is triggered to generate an event data group with complete semantic description. For example, under the condition of continuous rainfall, when the system monitors that the pore water pressure in the slope region X increases from 30 kPa to 60 kPa within 6 hours, the surface displacement rate accelerates from 0.5 mm / day to 3.0 mm / day, and the strain value in the deep strain concentration area increases from 10 micro-strain to 25 micro-strain, the system identifies the coordinated evolution of these parameters in space and time through the geotechnical mechanics operator: the water pressure rise triggers the deformation acceleration, and the strain concentration area completely coincides with the high-risk area. Based on this, the relationship tuple representing the "hydraulic-deformation-strain strong spatiotemporal coordination" is generated, and finally the neural network determines that the multi-parameter acceleration anomaly pattern meets the instability precursor characteristics, and encapsulates to generate the semantic event data group of "rainfall-induced shallow slip instability precursor in region X".

[0082] In one embodiment, the instability model construction module comprises:

[0083] A slope digital model construction unit constructs a three-dimensional slope digital model based on dynamic data and image data.

[0084] A distributed event analysis unit extracts the three-dimensional geographic coordinate distribution characteristics of the event data group, and dynamically generates an event analysis sub-unit based on the three-dimensional geographic coordinate distribution characteristics, which is used to receive and analyze the event data group to generate a local instability feature vector.

[0085] The instability knowledge base unit pre-stores an instability mode rule set based on the principle of geotechnical mechanics, and the instability mode rule set is used to define a mapping relationship between different local instability feature vector combinations and potential instability modes;

[0086] The node cooperative reasoning unit is connected with each event analysis sub-unit and the instability knowledge base unit, performs cooperative reasoning on the local instability feature vectors, and synthesizes an instability model describing the slope stability state;

[0087] Based on the instability model, a stress analysis report of the slope surface is generated;

[0088] Further, the technical implementation of the instability model construction module starts from the slope digital model construction unit, which establishes a three-dimensional geological entity model with centimeter-level precision by fusing laser point cloud scanning data and multi-view oblique photogrammetry images, and maps the rock and soil mechanics parameters as additional attributes to the model grid elements; The distributed event analysis unit instantiates lightweight edge computing nodes as event analysis sub-units at the corresponding positions of the slope digital model according to the spatial coordinate information of the event data group, each sub-unit analyzes the local event data group through the built-in feature extraction algorithm, and quantitatively generates local instability feature vectors containing displacement acceleration, strain energy density, and water coupling coefficient dimensions; The instability knowledge base unit adopts an expert system architecture, and its instability mode rule set defines the triggering conditions of various instability modes in the form of production rules, for example, when the feature vector combination simultaneously meets the conditions of local shear strain, displacement acceleration index exceeding the threshold value, and spatial coupling with high pore water pressure area, the circular arc sliding mode rule is activated; The node cooperative reasoning unit normalizes the feature vectors output by each sub-unit through a space-time alignment engine, constructs a topological network with analysis nodes as vertices and space-time correlation strength as edges using a graph neural network algorithm, realizes feature propagation and aggregation through a multi-layer message passing mechanism, finally matches the fused global features with the instability knowledge base, and outputs an instability model containing potential sliding surface geometric parameters, safety factor field distribution, and stress-strain cloud diagram, and generates a detailed stress analysis report accordingly.

[0089] In one embodiment, the step of connecting with each event analysis sub-unit and the instability knowledge base unit, performing cooperative reasoning on the local instability feature vectors, and synthesizing an instability model describing the slope stability state is as follows:

[0090] The node cooperative reasoning unit receives the local instability feature vectors from each event analysis sub-unit and performs space-time alignment processing on all local instability feature vectors;

[0091] The aggregated local instability feature vector set is matched with the instability mode rule set in the instability knowledge base unit, the matching degree of the feature vector and the instability mode rule set is calculated, and the corresponding potential instability mode is activated;

[0092] Based on the three-dimensional geographic coordinates in the local instability feature vector, the correlation strength between different position local instability features is analyzed, and a correlation network representing the instability propagation path is constructed with each event analysis subunit as a node and the correlation strength as an edge;

[0093] Combined with the activated potential instability mode and the correlation network, a weighted fusion algorithm is used to synthesize the instability model of the slope;

[0094] Based on the instability model, a stress analysis report of the slope surface is generated, including the potential sliding surface geometric parameters, safety factor distribution, stress-strain field data, support resistance requirement distribution and dynamic evolution prediction;

[0095] Further, the node collaborative inference unit first starts the space-time alignment engine to standardize the local instability feature vectors uploaded by the distributed event analysis unit; the engine uses the dynamic time warping algorithm to eliminate the time phase differences of each sensor data stream, and simultaneously maps the feature vectors of discrete coordinates to a unified three-dimensional grid node through the Kriging space interpolation method, to establish a feature vector matrix with space-time consistency. In the rule matching stage, the node collaborative inference unit matches the local instability feature vectors processed by the space-time alignment with the pre-stored instability mode rule set in the instability knowledge base unit. This matching process is implemented through a multi-level calculation framework. First, the matching core uses a vector similarity measurement algorithm, specifically to calculate the cosine similarity between each local instability feature vector and the pre-defined standard instability mode feature vector in the rule set, and generates an initial matching degree score. Then, the system calls the composite logical judgment conditions defined in the rule set. These conditions are scripts written based on the principles of rock mechanics, for example, a rule may require that the displacement acceleration index be greater than a set threshold, the strain concentration degree exceed a critical level, and the abnormal area overlap with a high-pore water pressure area in space, and other multiple Boolean conditions. The system will evaluate these conditions one by one and make a weighted correction to the initial matching degree to generate a potential instability mode. Based on the three-dimensional geographic coordinates in the local instability feature vector and the generated potential instability mode, the system calculates the statistical dependence of feature vectors at different positions through the mutual information entropy algorithm, quantifies the spatial correlation strength by combining the gravity model, and constructs a directed graph network with the event analysis subunit as the node and the spatial correlation strength as the edge. This network accurately represents the propagation path of the instability risk. The system integrates the activated instability mode and the correlation network through a multi-stage fusion process. First, a dynamic weight distribution mechanism is used to calculate a comprehensive weight coefficient based on the confidence value obtained by each instability mode in the rule matching stage and the topological centrality index of the mode corresponding node in the correlation network. Then, a slope stability control equation is established based on the theory of rock plasticity mechanics, and the slope digital model is converted into a numerical calculation grid through finite element discretization. In the solving process, the system embeds the potential slip path analyzed by the correlation network into the iterative calculation as a nonlinear constraint condition, and uses the strength reduction method to cyclically adjust the shear strength parameters of the rock mass, to monitor the state change of the global stiffness matrix of the model in real time. When the system stiffness matrix shows singularity characteristics, it is determined that the slope has reached a critical instability state, and the spatial coordinate parameters and geometric morphological features of the three-dimensional slip surface are accurately output at this time. The system synchronously generates complete mechanical field data including safety factor contour map, plastic strain distribution, and principal stress direction, and generates an instability model. Based on this, a stress analysis report is generated, including the geometric parameters of the slip surface, safety factor distribution, stress-strain field, support resistance requirement, and dynamic evolution prediction.

[0096] In one embodiment, the support intervention field module comprises:

[0097] a field mapping unit for receiving the force analysis report and mapping the support resistance demand distribution, potential sliding surface geometric parameters and stress-strain field data in the report to an initial interference field covering the instability model;

[0098] a dynamic field generator for generating a dynamic field generator based on the corresponding position of the three-dimensional geographic coordinates of the event analysis subunit on the initial interference field and connecting with the event analysis subunit through a dynamic channel;

[0099] a field model interaction engine in the dynamic field generator for building an optimization objective function, receiving a local instability feature vector from the event analysis subunit through the dynamic channel, inputting the local instability feature vector into the optimization objective function, and dynamically correcting the parameters of the initial interference field through an iterative adjustment algorithm;

[0100] a channel monitoring unit for continuously monitoring the real-time load of all dynamic channels, based on a preset super-fusion threshold, solidifying the dynamic channels, and generating anchor nails of the initial interference field;

[0101] obtaining the corrected parameters of the initial interference field and the anchor nails to generate a support interference field, and discretizing the continuous support interference field into spatial layout coordinates, force parameters and construction timing of the flexible support components through field streamline tracking technology;

[0102] Further, the construction process of the initial interference field is a key step of converting abstract mechanical data into concrete spatial force field. The field mapping unit first analyzes the multi-dimensional data of the force analysis report, extracts the key parameters of the field strength, and converts the support resistance demand distribution into scalar field strength data. The geometric parameters of the potential slip surface are used to define the direction reference of the vector field, and the stress-strain field data are used as the basis input of the tensor field. In the specific implementation, the system converts the discrete support resistance demand data into continuous field strength distribution based on the three-dimensional grid structure of the instability model through spatial interpolation algorithm. This process uses the improved Kriging interpolation method, which not only considers the spatial distance relationship, but also introduces the geotechnical mechanical parameters as the collaborative variables to ensure that the field strength distribution conforms to the geomechanics law. For the establishment of the vector field, the system generates the field direction distribution covering the entire model by using the tangent direction of the potential slip surface as the main reference benchmark and combining with the principal stress direction data through the vector synthesis algorithm. In order to achieve the real coverage effect, the system uses the hierarchical mapping technology to first establish the two-dimensional field distribution on the surface of the instability model, and then extends it to the depth direction according to the geological stratification data to form a three-dimensional field. The field parameters of each grid element are determined through weighted calculation, and the weight depends on the relative position of the element to the slip surface and its importance in the stress field. This depth-coupled mapping method ensures that the initial interference field is completely consistent with the instability model in geometry and mechanics, laying a solid foundation for subsequent dynamic optimization. The initial interference field generated based on this not only completely covers each geometric element of the instability model, but also realizes the continuous distribution of mechanical parameters at the physical level, forming a virtual support force field that accurately corresponds to the slope instability state. The dynamic field generator then instantiates multiple field generator instances at the spatial positions corresponding to the initial interference field according to the three-dimensional geographic coordinates of the event analysis sub-unit. Each instance establishes a dynamic channel connection with the corresponding event analysis sub-unit through a dedicated data link. In the field model interaction engine built inside the field generator, the optimization algorithm with the multi-objective function of minimizing the support structure weight, maximizing the safety factor, and optimizing the deformation coordination is implanted. The core of the field model interaction engine is a multi-objective optimization system, which aims to simultaneously achieve the minimization of the support structure weight, the maximization of the safety factor, and the optimization of the deformation coordination. The engine receives the local instability feature vector from the event analysis sub-unit in real time through the dynamic channel as the input data of the optimization process. The sequence quadratic programming algorithm based on sensitivity analysis is used for iterative calculation: the algorithm takes the field strength and direction parameters of the initial interference field as the design variables, calculates the sensitivity of the objective function to the variables in each iteration, constructs and solves the quadratic programming sub-problem to determine the parameter update direction, and then checks the safety constraints and adjusts the field distribution with new data.This process is executed in a loop until the optimal interference field parameters that satisfy the multi-objective balance are converged. The channel monitoring unit monitors the transmission load of all dynamic channels in parallel through packet rate detection and buffer depth monitoring. When the load of a specific regional channel continuously exceeds the preset threshold, the channel materialization process is triggered, and the high-load channel is converted into an anchor structure with specific mechanical properties. The system finally integrates the corrected interference field parameters and anchor spatial distribution, solves the potential energy field gradient to develop field streamline tracking, and automatically generates the spatial topology of the flexible support net according to the streamline density. The design inclination of the anchor rod is calculated according to the streamline curvature, the support component specification parameters are determined by the field strength value, and the phased construction timing is deduced in reverse according to the instability risk propagation path, completing the digital conversion from the continuous field model to the discrete support scheme and generating the design scheme of the flexible support.

[0103] In one embodiment, the step of continuously monitoring the real-time load of all dynamic channels, materializing dynamic channels based on a preset overload threshold, and generating anchors for the flexible support net is:

[0104] The channel monitoring unit continuously monitors the real-time load of all data channels and sets a preset overload threshold.

[0105] When it is found that the real-time load of one or more dynamic channels within a preset range continuously exceeds the preset overload threshold within a preset time, it is determined that the area is a high-energy core area.

[0106] The channel monitoring unit sends a freezing application to the dynamic channels in the high-energy core area, and the dynamic channels that receive the freezing application are materialized to generate anchors and record anchor parameters. The anchor data is transmitted to the field model interaction engine.

[0107] The anchor parameters include spatial positioning parameters, mechanical property parameters, and dynamic correlation parameters.

[0108] Further, the channel monitoring unit continuously collects transmission state indicators of each dynamic channel through the probe program deployed in the data link layer, including but not limited to data packet transmission rate, bit error rate and buffer queue depth. The system preset load threshold is a multi-dimensional dynamic threshold value, the value of which is determined by the channel base bandwidth and the real-time transmitted data type. When the monitoring unit detects that more than half of the dynamic channels in a certain geographical area continuously exceed the upper limit of the threshold value for three sampling periods, the area will be marked as a high-energy core area. At this time, the monitoring unit will send a freezing instruction frame containing a time stamp to the target channel, and the channel receiving the instruction will immediately start the materialization process: first, it stops the normal transmission of the data stream, and then converts the channel state parameters at the current time into the mechanical properties of the anchor, wherein the spatial positioning parameters are determined by analyzing the three-dimensional coordinates of the channel terminal device, the mechanical performance parameters are converted into tensile strength and anchoring force according to the historical load peak value of the channel, and the dynamic correlation parameters are inherited from the original topological connection relationship of the channel. These quantified anchor parameter sets are packaged as structured data packets and transmitted in real time to the field model interaction engine through a special interface, providing key spatial anchoring point data for dynamic optimization of flexible support schemes.

[0109] In one embodiment, the visualization module comprises:

[0110] a model rendering unit for generating a three-dimensional visualization scene based on the instability model and the support interference field, and rendering the geometric shape, instability area and support structure of the slope in real time;

[0111] a data superposition unit for superimposed display of dynamic data, stress analysis report and optimization scheme in the form of layers;

[0112] a warning display unit for dynamically marking high-risk areas based on the instability model and highlighting the warning information in the form of color or animation;

[0113] Further, the visualization module constructs a three-dimensional scene using a physics-based rendering engine, which generates a slope visualization model with real lighting and material properties by fusing the triangular mesh data of the instability model with the vector field data of the support interference field. The model rendering unit dynamically adjusts the model accuracy at different viewing distances using level-of-detail techniques, and implements semi-transparent rendering of the instability area based on depth information through shader programming, and parameterized instantiation rendering of the support structure. The data overlay unit uses a multi-layer management architecture, aligns the real-time sensor data with the contour maps in the stress analysis report using a spatial registration algorithm, displays dynamic data changes using an interactive heat map layer, overlays the mechanical parameter labels of the support components using a vector layer, and realizes visual comparison of different optimization schemes through a time axis controller. The early warning display unit analyzes the safety factor field data in the instability model in real time, uses an elevation gradient-based color mapping technique to layer the risk areas, automatically triggers a pulse warning animation when the safety factor falls below the critical threshold, simulates the propagation path of the instability trend using a particle system, and generates a dynamic sign containing a risk level text description. All early warning elements support real-time interaction and perspective focusing operations in three-dimensional space.

[0114] The above merely provides a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A flexible slope support optimization design system, characterized in that, a data acquisition module: based on a multi-array sensor deployed on the slope surface, the dynamic data and image data of the slope surface are collected, and the dynamic data are transmitted to a dynamic relational database; the data acquisition module comprises: a multi-array sensor unit, which is distributed on the surface and inside of the slope, for collecting dynamic data of the slope, and labeling time stamp and three-dimensional geographic coordinates for the dynamic data to generate multi-source time sequence dynamic data; an environmental monitoring unit for synchronously collecting environmental state data of the environment where the slope is located; transmitting the dynamic data and the environmental state data to the dynamic relational database to generate event data groups; a failure model construction module: connected with the data acquisition module, for analyzing the dynamic data to generate a failure model of the slope, and generating a stress analysis report of the slope based on the failure model; the failure model construction module comprises: a distributed event analysis unit, which extracts the three-dimensional geographic coordinate distribution characteristics of the event data group, dynamically generates an event analysis subunit based on the three-dimensional geographic coordinate distribution characteristics, for receiving and analyzing the event data group to generate a local failure feature vector; a support interference field module: connected with the failure model construction module, for generating a support interference field based on the stress analysis report, and generating an optimization scheme of the flexible slope support by interacting the support interference field with the failure model; the support interference field module comprises: a field mapping unit, for receiving the stress analysis report, and mapping the support resistance demand distribution, the potential sliding surface geometric parameters and the stress-strain field data in the report to an initial interference field covering on the failure model; a dynamic field generator, which generates a dynamic field generator based on the corresponding position of the three-dimensional geographic coordinates of the event analysis subunit on the initial interference field, and connects with the event analysis subunit through a dynamic channel; a dynamic field generator constructs a field model interaction engine, which has an optimization objective function built in, receives the local failure feature vector from the event analysis subunit through the dynamic channel, inputs the local failure feature vector into the optimization objective function, and dynamically corrects the parameters of the initial interference field through an iterative adjustment algorithm; a channel monitoring unit, which continuously monitors the real-time load of all dynamic channels, and based on a preset super fusion threshold, the dynamic channels are materialized to generate anchor nails of the initial interference field; obtaining the corrected parameters of the initial interference field and the anchor nails to generate a support interference field, and through field streamline tracing technology, the continuous support interference field is discretized into spatial layout coordinates, stress parameters and construction sequence of the flexible support components; a visualization module: connected with the support interference field module, for visualizing and displaying the failure model and the optimization scheme of the flexible slope support.

2. The flexible slope support optimization design system of claim 1, wherein, the step of transmitting the dynamic data to the dynamic relational database to generate event data groups is: the dynamic relational database comprises a real-time data caching unit, an environmental modulation unit and a dynamic relationship construction unit; the real-time data caching unit receives multi-source time sequence dynamic data in real time and stores it in the cache space by constructing multiple sub-databases; the environmental modulation unit is used for receiving environmental state data and converting the environmental state data into an environmental state vector; The dynamic relationship construction unit comprises a feature rule library, a relationship calculation subunit, and a structured event packaging unit; The feature rule library pre-stores feature extraction operators based on the principles of geotechnical mechanics and space-time correlation rules; The relationship calculation subunit continuously calls the feature extraction operators to perform parallel scanning and calculation on the multi-source time-series dynamic data and the environmental state vector, and generates relationship tuples; The relationship tuples comprise the multi-source time-series dynamic data, the environmental state vector, a relationship type identifier, and a relationship strength quantization value; The structured event packaging unit analyzes the relationship tuples through a neural network, generates event data groups based on the space-time correlation rules and the relationship type identifier.

3. The flexible slope support optimization design system of claim 1, wherein, The instability model construction module further comprises: A slope digital model construction unit that constructs a three-dimensional slope digital model based on dynamic data and image data; An instability knowledge base unit that pre-stores an instability mode rule set based on the principles of geotechnical mechanics, the instability mode rule set being used to define the mapping relationship between different local instability feature vector combinations and potential instability modes; A node collaborative reasoning unit that is connected to each event analysis subunit and the instability knowledge base unit, performs collaborative reasoning on the local instability feature vectors, and synthesizes an instability model describing the stability state of the slope; A force analysis report of the slope surface is generated based on the instability model.

4. The flexible slope support optimization design system of claim 3, wherein, The step of connecting the node collaborative reasoning unit to each event analysis subunit and the instability knowledge base unit, performing collaborative reasoning on the local instability feature vectors, and synthesizing an instability model describing the stability state of the slope comprises: The node collaborative reasoning unit receives the local instability feature vectors from each event analysis subunit, and performs space-time alignment processing on all the local instability feature vectors; The aggregated local instability feature vector set is matched with the instability mode rule set in the instability knowledge base unit, the matching degree of the feature vector and the instability mode rule set is calculated, and the corresponding potential instability mode is activated; Based on the three-dimensional geographic coordinates in the local instability feature vectors, the correlation strength between different position local instability features is analyzed, an association network representing the instability propagation path is constructed with each event analysis subunit as a node and the correlation strength as an edge, and a weighted fusion algorithm is used to synthesize the instability model of the slope in combination with the activated potential instability mode and the association network; A force analysis report of the slope surface is generated based on the instability model. The step of continuously monitoring the real-time load of all dynamic channels, and based on a preset overload threshold, solidifying the dynamic channels to generate the anchors of the flexible support net comprises:

5. The flexible slope support optimization design system of claim 1, wherein, A channel monitoring unit continuously monitors the real-time load of all data channels, and a preset overload threshold is set; When it is found that the real-time load of one or more dynamic channels in a preset range continuously exceeds the preset overload threshold within a preset time, the area is determined to be a high-energy core area; The channel monitoring unit sends a freezing application to the dynamic channels in the high-energy core area, the dynamic channels receiving the freezing application are solidified to generate anchors and record anchor parameters, and the anchor data is transmitted to the field model interaction engine; The anchor parameters include spatial positioning parameters, mechanical performance parameters, and dynamic correlation parameters. The visualization module comprises:

6. The flexible slope support optimization design system of claim 1, wherein, ​ A model rendering unit is configured to generate a three-dimensional visual scene based on the instability model and the support interference field, and to render the geometric shape, instability area and support structure of the slope in real time; A data superimposition unit is configured to superimpose and display the dynamic data, stress analysis report and optimization scheme in the form of layers; An early warning display unit is configured to dynamically mark the high-risk area based on the instability model, and to highlight the early warning information in the form of color or animation.

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