Integrated attitude control and adjustment system and method for green support shield equipment

Abstract

The invention belongs to the technical field of shield tunneling machines, and particularly relates to an integral attitude control and adjustment system and method for green support shield equipment, and the method comprises the steps: collecting attitude parameters, stratum parameters and equipment state data of a shield tunneling machine, and constructing a weighted directed graph through a mutual information algorithm; on the basis of the output parameters of the weighted directed graph, a control signal is generated, and the output force of the differential multi-cylinder linkage mechanism is adjusted through the control signal; based on the control signal, a synchronous grouting regulation and control unit and a regenerated material injection device mechanically connected with the synchronous grouting regulation and control unit are controlled, and the regenerated material injection device is driven by an air cylinder linkage mechanism; utilizing a neural network model integrated by the remote intelligent regulation and control center to predict stratum change and correct a control strategy; and a closed-loop pressure loop is formed between the hydraulic output pipeline of the differential multi-cylinder linkage mechanism and the shield tunneling machine thrust oil cylinder group. According to the invention, the precision, the efficiency and the environmental protection property of shield tunneling can be improved, and the method is particularly applied to complex stratum conditions.

Description

Technical Field

[0001] The present invention belongs to the technical field of shield machines, and particularly relates to an integral attitude control and adjustment system and method for a green support shield equipment. Background Art

[0002] In modern tunnel engineering, shield tunneling method is widely used due to its advantages such as high efficiency and safety. During the tunneling process of a shield machine, the precise control of its attitude (including inclination angle, deflection angle, roll angle, etc.) is crucial for ensuring the accuracy of the tunnel axis, reducing stratum disturbance, and improving construction quality.

[0003] Problems existing in the prior art: Traditional shield machine attitude control methods usually rely on experienced operators to manually adjust according to measurement data, or adopt an automated system based on simple feedback control (such as PID control). There are some deficiencies in the existing shield machine attitude control technologies. First of all, the underground geological conditions are complex and variable. Multiple factors such as surrounding rock strength, groundwater pressure, and soil layer type will affect the tunneling attitude of the shield machine. Traditional control methods often have difficulty comprehensively and real-time perceiving and analyzing the interaction between these complex environmental factors and the state of the shield machine itself (such as tool wear, hydraulic system pressure, etc.), resulting in limitations in the accuracy and response speed of attitude adjustment. For example, when encountering hard rock formations or abrupt strata, traditional control systems may not be able to adjust the thrust and torque in a timely and accurate manner, easily leading to attitude deviation from the designed axis; Secondly, existing shield machine attitude control systems usually regard processes such as attitude adjustment and support (such as synchronous grouting, segment erection) as relatively independent links. Attitude adjustment mainly relies on the telescoping of propulsion cylinders, while the support process is carried out according to experience or preset parameters. This separated control method is difficult to achieve the coordinated optimization of attitude adjustment and support process, and may lead to problems such as ground settlement and circumferential joint misalignment. For example, in soft soil layers, if the attitude adjustment does not match the grouting volume, it is easy to cause ground deformation; Therefore, in terms of the attitude control of shield equipment in the prior art, there is a lack of an integral solution that can comprehensively perceive multi-dimensional information, deeply analyze the complex relationships between various dimensions, realize the coordination of attitude adjustment and green support, and can effectively utilize waste and reduce carbon emissions. Summary of the Invention

[0004] The purpose of the present invention is to provide an integral attitude control and adjustment system and method for a green support shield equipment, which can improve the accuracy, efficiency, and environmental protection of shield tunneling, especially in the application under complex stratum conditions.

[0005] The technical solutions adopted by the present invention are specifically as follows: In a possible implementation, an integral attitude control and adjustment method for a green support shield equipment is provided, including the following steps: Collect the attitude parameters, formation parameters and equipment status data of the shield machine, and based on the collected data, use the mutual information algorithm to construct a weighted directed graph; Generate a control signal based on the output parameters of the weighted directed graph, and adjust the output force of the differential multi-cylinder linkage mechanism through the control signal; Based on the control signal, control the synchronous grouting control unit and the recycled material injection device mechanically connected to the synchronous grouting control unit, wherein the recycled material injection device is driven by a cylinder linkage mechanism; Optimize the segment assembly angle, and establish two-way communication between the shield machine and the remote intelligent control center through the communication module; Use the neural network model integrated in the remote intelligent control center to predict the formation change and correct the control strategy; Realize the sharing of data, control signals and correction strategies through the data bus; Form a closed-loop pressure circuit between the hydraulic output pipeline of the differential multi-cylinder linkage mechanism and the shield machine propulsion cylinder group.

[0006] In a possible implementation, the step of collecting the attitude parameters, formation parameters and equipment status data of the shield machine includes: Obtain the angle, displacement, sectional hardness and equipment status data in real time through multiple sensors.

[0007] In a possible implementation, the step of constructing a weighted directed graph based on the collected data using the mutual information algorithm includes: Construct a weighted directed graph based on the mutual information algorithm to quantify the correlation strength between any two dimensions.

[0008] In a possible implementation, the step of generating a control signal includes: Dynamically allocate weights according to the formation type, and generate a control signal based on the output parameters of the weighted directed graph; Among them, the step of controlling the synchronous grouting control unit and the recycled material injection device mechanically connected to the synchronous grouting control unit based on the control signal includes: Dynamically adjust the synchronous grouting volume and the recycled material injection volume according to the formation type, wherein the grouting volume is increased in soft soil layers, and the proportion of recycled materials is increased in hard rock layers.

[0009] In a possible implementation, the step of adjusting the output force of the differential multi-cylinder linkage mechanism includes: Adjust the output force of the differential multi-cylinder linkage mechanism according to the control signal through the PID closed-loop feedback algorithm; Monitor the actual output force of the differential multi-cylinder linkage mechanism, compare it with the target output force, and adjust the control signal according to the deviation.

[0010] According to another aspect of the embodiments of the present invention, an electronic device is further provided. The electronic device includes a memory and a processor; the memory is used for storing programs; the processor executes the programs to implement the method of any one of the foregoing.

[0011] According to another aspect of the embodiments of the present invention, a computer-readable storage medium is further provided. The storage medium stores a computer program, and when the computer program is executed by a processor, the method of any one of the foregoing is implemented.

[0012] According to another aspect of the embodiments of the present invention, a computer program product is further provided, including a computer program, and when the computer program is executed by a processor, the method of any one of the foregoing is implemented.

[0013] In a possible implementation manner, an integrated attitude control and adjustment system for a green support shield equipment is provided, including: A six-degree-of-freedom interaction monitoring module, including a data input interface, a data processing unit, and a storage unit. The data input interface is used to receive the shield machine attitude parameters, formation parameters, and equipment status data output by multiple sensors. The data processing unit executes the mutual information algorithm and generates a weighted directed graph. The storage unit is used to store data and the weighted directed graph; A differential multi-cylinder linkage mechanism, which includes a main hydraulic cylinder and an auxiliary cylinder array. The hydraulic pipeline of the main hydraulic cylinder is connected to the hydraulic pipeline of the shield machine propulsion cylinder group through a pressure sensor to form a closed-loop pressure circuit, and is differentially connected to a proportional relief valve through an electromagnetic directional valve; A vector adaptive regulation unit, including a control signal generation module and a PID feedback control module. The control signal generation module receives the output parameters of the weighted directed graph and generates a control signal. The PID feedback control module receives the output force feedback signal and the control signal of the differential multi-cylinder linkage mechanism, and outputs an adjustment signal to the differential multi-cylinder linkage mechanism according to the PID closed-loop feedback algorithm; A green support cooperation module, including a synchronous grouting regulation unit and a segment assembly angle optimization unit; A recycled material injection device, and a cylinder linkage mechanism mechanically connected thereto; A mechanical coupling structure that connects the recycled material injection device to the auxiliary cylinder array of the differential multi-cylinder linkage mechanism; The cylinder linkage mechanism receives the control signal of the vector adaptive regulation unit through the signal input port, and drives the recycled material injection device through the mechanical connection structure; The remote intelligent control center establishes two-way communication with the six-degree interactive monitoring module and the vector adaptive control unit through the communication module. The remote intelligent control center integrates an LSTM neural network model to predict formation changes; The data bus connects the six-degree interactive monitoring module, the vector adaptive control unit and the remote intelligent control center to form a real-time sharing channel for multi-source data.

[0014] In a possible implementation, the six-degree interactive monitoring module receives output signals of the inclination sensor, the displacement sensor, the formation hardness sensor, and the tool force feedback unit through a data interface; The output signals include six-dimensional angle dimension, displacement vector dimension, partition hardness coefficient dimension, equipment status dimension, hydraulic cylinder linkage dimension and support adaptation dimension; The six-degree interactive monitoring module includes: a data fusion unit, a correlation analysis unit, a dynamic weight allocation unit and a prediction correction unit.

[0015] In a possible implementation, the data fusion unit includes a processing module, which executes a feature co-occurrence relationship modeling process. The data fusion unit also includes a node mapping module, a weight calculation module, and a topology structure optimization module.

[0016] In a possible implementation, the vector adaptive control unit includes: a formation type identification module, a weight adaptive adjustment module, a control signal generation module and a PID feedback control module.

[0017] In a possible implementation, the formation type identification module executes a formation type identification algorithm, and the formation type identification algorithm determines the current formation type based on the formation parameters output by the six-degree interactive monitoring module.

[0018] The technical effects achieved by the present invention are: The present invention is an integrated attitude control and adjustment system for shield equipment based on a six-degree interaction model. Multi-dimensional data such as the attitude, formation and equipment status of the shield machine are collected through a six-degree interaction monitoring module, and a weighted directed graph is constructed using a mutual information algorithm to quantify the correlation strength between dimensions. A vector adaptive control unit receives the output parameters of the weighted directed graph and generates a refined control signal. The control signal drives a differential multi-cylinder linkage mechanism to adjust the attitude of the shield machine, and realizes precise control of the propulsion force through a closed-loop pressure circuit formed by a main hydraulic cylinder and a propulsion cylinder group. At the same time, the auxiliary cylinder array is linked with a regenerative material injection device to dynamically inject regenerative materials according to formation changes to actively support the formation.

[0019] In the present invention, a control signal collaborates with a green support collaboration module to optimize the grouting strategy and the segment assembly angle. The remote intelligent control center communicates with the shield machine in real time through communication, uses the LSTM neural network model to predict the formation changes, and corrects the control strategy to ensure the predictability and adaptability of the shield machine attitude adjustment. The data bus ensures the real-time sharing of multi-source data, constructs an information closed-loop, and realizes the overall, adaptive, and collaborative control of the shield machine attitude under complex formation conditions through in-depth interactive analysis of multi-dimensional data, solving problems such as the difficulty of traditional control methods in dealing with complex formation changes, lagging attitude adjustment, and untimely support, and significantly improving the accuracy, safety, and efficiency of shield tunneling. Brief Description of the Drawings

[0020] Figure 1 is the method flow chart of the present invention; Figure 2 is the system structure schematic diagram of the present invention. Detailed Embodiments

[0021] In order to make the purpose and advantages of the present invention clearer, the present invention will be specifically described below in conjunction with embodiments. It should be understood that the following text is only used to describe one or several specific implementation manners of the present invention, and does not strictly limit the specific protection scope claimed by the present invention.

[0022] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order different from those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0023] According to an embodiment of the present invention, a method embodiment of an overall attitude control adjustment method for a green support shield equipment is provided. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described here can be executed in an order different from that here.

[0024] As Figure 1 shown, an overall attitude control adjustment method for a green support shield equipment includes the following steps: S1. Collect the attitude parameters, formation parameters, and equipment status data of the shield machine, and based on the collected data, use the mutual information algorithm to construct a weighted directed graph to reveal the complex associations between various dimensions; S2. Generate a control signal based on the output parameters of the weighted directed graph, and through the control signal, adjust the output force of the differential multi-cylinder linkage mechanism to achieve precise adjustment of the shield machine's attitude; S3. Based on the control signal, control the synchronous grouting regulation unit and the recycled material injection device mechanically connected to the synchronous grouting regulation unit, where the recycled material injection device is driven by a cylinder linkage mechanism; S4. Optimize the segment assembly angle, and establish two-way communication between the shield machine and the remote intelligent regulation center through the communication module; S5. Use the neural network model integrated in the remote intelligent regulation center to predict formation changes and correct the control strategy; S6. Through the data bus, realize the sharing of data, control signals, and correction strategies; S7. Form a closed-loop pressure circuit between the hydraulic output pipeline of the differential multi-cylinder linkage mechanism and the shield machine propulsion cylinder group to maintain the dynamic balance of the propulsion force.

[0025] According to the above steps, through the six-degree interaction model, it is possible to perceive multi-dimensional information such as the attitude of the shield machine, the formation, and the equipment status, and use the mutual information algorithm to quantify the association strength between various dimensions, construct a weighted directed graph as the control basis; and further realize the coordinated linkage of attitude adjustment, green support, and remote intelligent regulation, forming a closed-loop control process of "perception - analysis - execution - feedback", enabling the system running this method to more intelligently and efficiently cope with complex formation challenges and significantly improve the tunneling efficiency and tunnel forming quality.

[0026] According to the above step S1, receive the output signals from sensors such as the inclination sensor, displacement sensor, formation hardness sensor, and tool force feedback unit through the data input interface of the six-degree interaction monitoring module, and obtain six-degree dimension data such as the angle dimension, displacement vector dimension, partition hardness coefficient dimension, equipment status dimension, hydraulic cylinder linkage dimension, and support adaptation dimension.

[0027] Furthermore, it is executed by the data processing unit of the six-degree interaction monitoring module, using the mutual information algorithm: ; where X and Y are any two characteristic nodes (such as "tool force feedback" and "formation hardness"), used to quantify the non-linear association strength between any two dimensions, and construct a weighted directed graph with the six-degree dimensions as nodes and the mutual information value as the weight.

[0028] According to the above step S2, it is executed by the control signal generation module of the vector adaptive regulation unit, which receives output parameters such as the topological structure and edge weights of the weighted directed graph, and combines the recognition results of the formation type recognition module and the adjusted weights of the weight adaptive adjustment module to generate control signals for the differential multi-cylinder linkage mechanism and the green support coordination module.

[0029] Furthermore, it is executed by the PID feedback control module of the vector adaptive regulation unit, which receives the control signal and the output force feedback signal of the differential multi-cylinder linkage mechanism (obtained through a pressure sensor), calculates the adjustment instruction according to the PID closed-loop feedback algorithm, and outputs it to the control unit of the differential multi-cylinder linkage mechanism to adjust the oil or gas flow rate of the main hydraulic cylinder and the auxiliary cylinder array, thereby changing its output force.

[0030] According to the above step S3, it is executed by the synchronous grouting regulation unit of the green support coordination module, which receives the control signal and adjusts the flow rate, pressure and ratio of synchronous grouting. At the same time, it receives the control signal and drives the recycled material injection device (including the calcium carbonate-based cementitious material synthesis unit and the cylinder linkage nozzle) to inject recycled materials through the cylinder linkage mechanism.

[0031] According to the above step S4, it is executed by the segment assembly angle optimization unit of the green support coordination module, which calculates the optimal segment assembly angle based on the formation parameters and the shield machine attitude parameters, and is executed by the 5G communication module to realize the two-way transmission of data and instructions between the six-degree interaction monitoring module, the vector adaptive regulation unit on the shield machine side and the remote intelligent regulation center.

[0032] According to the above step S5, it is executed by the LSTM neural network model of the remote intelligent regulation center, which analyzes and predicts the received formation parameters, and sends the prediction results and the corrected control strategy back to the shield machine side through the 5G communication module.

[0033] According to the above step S6, it is realized by the data bus, which connects the six-degree interaction monitoring module, the vector adaptive regulation unit and the remote intelligent regulation center to ensure the real-time and efficient circulation of information between modules.

[0034] According to the above step S7, it is realized through physical connection, and the pressure difference is monitored by a pressure sensor in the closed-loop pressure circuit, and the signal is fed back to the vector adaptive regulation unit for adjustment.

[0035] Through the collaborative execution of the above steps, a comprehensive perception, intelligent analysis, precise control, and collaborative support for the shield tunneling process are achieved. The six-degree data collection and mutual information modeling provide an in-depth understanding of the complex environment; the vector adaptive regulation and green support collaboration realize the linkage optimization of the attitude and support; the remote intelligent regulation provides forward-looking prediction and strategy correction. The entire process forms an efficient and robust closed-loop control system, significantly improving the efficiency, safety, and tunnel forming quality of shield tunneling.

[0036] In the above steps, the collected data can include the output signals of inclination sensors, displacement sensors, formation hardness sensors, and cutter force feedback units; the constructed weighted directed graph can quantify the correlation strength between any two dimensions. For example, the mutual information weight between cutter force feedback and formation hardness ≥ 0.8; the generated control signal can dynamically allocate weights according to the formation type and, based on the output parameters of the weighted directed graph, apply a fuzzy control algorithm to generate a non-linear control strategy; the output force of the differential multi-cylinder linkage mechanism can be adjusted through a PID closed-loop feedback algorithm, thereby shortening the response time; the control of synchronous grouting and the injection of recycled materials can dynamically adjust the injection volume and proportion according to the formation type. For example, after the grouting volume in soft soil layers is increased, the proportion of recycled materials in hard rock layers is increased. In particular, through different grouting materials, the improvement of different properties of hard rock layers can be achieved, such as through pouring; the optimal segment assembly angle can be optimized using a preset mathematical model or optimization algorithm, such as calculating the optimal angle based on a geometric model or finite element analysis; 5G communication can use an industrial-grade 5G module with low latency and high bandwidth; the LSTM model can predict sudden changes in the hardness of the front formation to achieve early prediction and warning; in the closed-loop pressure circuit, the pressure difference between the main hydraulic cylinder and the propulsion cylinder group can be monitored and adjusted to maintain the balance of the propulsion force.

[0037] As an alternative embodiment, the steps of collecting the attitude parameters, formation parameters, and equipment status data of the shield machine include: Real-time obtain angle, displacement, sectional hardness, and equipment status data through multiple sensors.

[0038] According to the above steps, by using multiple sensors to obtain angle, displacement, partition hardness and equipment status data in real time, the comprehensiveness and real-time nature of data collection are further enhanced. A six-degree interaction model is constructed based on the collected data, which is also the basic data source for subsequent intelligent analysis and control decisions; the angle data reflects the pitch, roll and heading of the shield machine, and is obtained through the inclination sensor; the displacement data reflects the longitudinal, lateral and elevation deviations of the shield machine relative to the design axis, and is obtained through the displacement sensor; the partition hardness data reflects the softness and hardness of the stratum in different areas in front of the cutterhead, and is obtained through comprehensive analysis of the data from the stratum hardness sensor and the tool force feedback unit; the equipment status data reflects the operating conditions of key components of the shield machine, such as cutterhead torque, thrust, speed, tool wear, hydraulic system pressure flow, etc., and is obtained through the tool force feedback unit and other equipment-provided sensors; by collecting these data in real time through multiple sensors, the system can fully perceive the excavation environment and equipment status, providing a rich and reliable data basis for subsequent intelligent analysis and control.

[0039] This embodiment is completed by the data input interface of the six-degree interactive monitoring module and the connected multiple sensors. The multiple sensors include but are not limited to inclination sensors, displacement sensors, formation hardness sensors and tool force feedback units. The inclination sensor measures the pitch angle and roll angle of the shield machine in real time and outputs angle data. The displacement sensor measures the longitudinal and lateral deviations of the shield machine relative to the design axis in real time and outputs displacement data. The formation hardness sensor and the tool force feedback unit monitor the cutter disc cutting parameters in real time, comprehensively analyze and generate a partition hardness map of the area in front of the cutter disc, and output the partition hardness data. The tool force feedback unit and other equipment sensors monitor the cutter disc torque, thrust, speed, hydraulic system pressure flow, etc. in real time, and output equipment status data. These multi-sensor data are transmitted to the data input interface of the six-degree interactive monitoring module in real time at a high frequency (for example, 10-100 times per second).

[0040] Furthermore, through real-time collection of angle, displacement, partition hardness and equipment status data by multiple sensors, the comprehensiveness, timeliness and accuracy of the input information of the six-degree interactive model can be ensured. This comprehensive data perception capability is a prerequisite for achieving precise attitude control and collaborative support. For example, the partition hardness data acquired in real time can be directly used to adjust the cutting parameters and attitude adjustment force of the cutterhead; the equipment status data acquired in real time can be used to monitor the health of the equipment and adjust the excavation parameters when necessary. The real-time data collection method based on multiple sensors can improve the system's perception of complex geological environments and equipment operating status.

[0041] According to the above, the types and quantities of multi-sensors can be configured according to the model, size of the shield machine and the tunneling formation conditions. For example, gyro sensors can be added to obtain angular velocity information, acoustic sensors or seismic sensors can be added to detect the formation structure ahead, temperature sensors and vibration sensors can be added to monitor the operating status of the equipment, etc. The data acquisition system should have high synchronization to ensure the consistency of timestamps between data from different sensors, which is convenient for subsequent data fusion and correlation analysis. The data acquisition frequency should be high enough to capture the rapid changes in the formation and equipment status. The collected raw data can be stored in the storage unit of the six-degree interaction monitoring module for historical data analysis and model training.

[0042] As an alternative embodiment, the steps of constructing a weighted directed graph using the mutual information algorithm based on the collected data include: Construct a weighted directed graph based on the mutual information algorithm to quantify the association strength between any two dimensions.

[0043] According to the above, the mutual information algorithm is used to quantify the association strength between any two dimensions and a weighted directed graph is constructed based on this. Mutual information is a measure of the mutual dependence between two random variables and can capture linear and non-linear associations. By calculating the mutual information values between pairs of six-degree dimension data and using these values as the weights of the edges in the graph structure, a weighted directed graph reflecting the association strength and direction between the six-degree dimensions can be constructed. This graph structure intuitively shows the mutual influence relationship between each dimension and provides an important basis for the generation of subsequent control strategies.

[0044] Furthermore, it is executed by the data processing unit (especially the correlation analysis unit and the weight calculation module) of the six-degree interaction monitoring module. First, preprocess the collected six-degree dimension data, including data cleaning, standardization, and discretization (if necessary). Then, for any two dimensions X and Y (such as the "tool force feedback dimension" and the "formation hardness dimension"), calculate the mutual information value between them: , where p(x,y) is the joint probability distribution of X and Y, and p(x) and p(y) are the marginal probability distributions of X and Y respectively. The higher the mutual information value, the greater the association strength between the two dimensions. Then, using the six-degree dimensions as the nodes of the graph and the calculated mutual information values as the edge weights, construct a weighted directed graph. The direction of the edge can be determined based on experience or further causal relationship analysis. For example, if the change in the "tool force feedback dimension" usually precedes the change in the "formation hardness dimension", a directed edge from the tool force feedback to the formation hardness can be established. The output of this step is a weighted directed graph reflecting the correlation between the six-degree dimensions.

[0045] Furthermore, by using the mutual information algorithm to construct a weighted directed graph, the interdependence between the six-degree dimensions is more accurately quantified. For example, the mutual information algorithm can discover the non-linear relationship between the tool force feedback and the formation hardness, or the complex correlation between the groundwater pressure and the formation deformation. The constructed weighted directed graph intuitively shows these correlations, enabling the control system to understand the mutual influence paths and intensities between various factors. For example, the correlation path weight of "groundwater pressure → grouting flow rate → segment erection angle". This graph-structure-based correlation analysis provides a solid data foundation for subsequent control signal generation and strategy optimization.

[0046] It should be further noted that in the above scheme, the calculation of the mutual information value can adopt methods such as histogram estimation, kernel density estimation, or k-nearest neighbor estimation. In order to process continuous data, appropriate discretization binning can be performed first. When constructing a weighted directed graph, a threshold can be set to only retain the edges with mutual information values higher than the threshold. The topological structure and edge weights of the graph can be dynamically updated during the tunneling process to reflect the changes in the formation conditions and equipment status. In addition to the mutual information algorithm, methods such as Granger causality analysis and transfer entropy can also be combined to determine the edge direction of the graph and more accurate correlation metrics. The constructed weighted directed graph is an important input for the vector adaptive regulation unit to generate control signals.

[0047] As an alternative embodiment, the steps of generating a control signal include: Dynamically allocate weights according to the formation type and generate a control signal based on the output parameters of the weighted directed graph.

[0048] Based on the above, during the shield tunneling process, under different formation conditions, the key factors affecting the attitude and support are different. By introducing a mechanism of dynamically allocating weights according to the formation type in the steps of generating a control signal and combining the output parameters of the weighted directed graph, the adaptive adjustment of the control strategy is realized. The currently identified formation type by the formation type recognition module is used by the dynamic weight allocation unit to adjust the priority weights of the six-degree dimensions in the control decision. For example, in hard rock formations, dimensions such as tool force feedback and partition hardness should have higher weights; in soft soil layers, dimensions such as formation deformation and hydraulic cylinder pressure should be more concerned. Then, the control signal generation module receives the output parameters of the weighted directed graph after weight adjustment (such as the values of key nodes, the weights of correlation paths, etc.) and generates a control signal based on these parameters. This control signal represents the control instructions for the differential multi-cylinder linkage mechanism, the synchronous grouting regulation unit, and the segment erection angle optimization unit. This way of dynamically allocating weights according to the formation type and combining the analysis results of the weighted directed graph to generate a control signal ensures that the control strategy can be optimized for the current actual situation, significantly improving the effectiveness of attitude control and coordinated support.

[0049] Furthermore, it is jointly completed by the control signal generation module, the formation type identification module, and the weight adaptive adjustment module of the vector adaptive regulation unit. First, the formation type identification module determines the formation type of the current tunneling. Then, the weight adaptive adjustment module dynamically adjusts the weights of the six-degree dimensions in the control decision according to the formation type from the preset weight mapping table or through an adaptive algorithm. For example, in hard rock layers, the tool force feedback weight is increased to 0.4, and in soft soil layers, the hydraulic cylinder pressure weight is increased to 0.5. Next, the control signal generation module receives the output parameters of the weighted directed graph, such as the current values of the key influencing factor nodes (identified by the topological structure optimization module), the current states of the important association paths (identified by the association analysis unit), etc. The control signal generation module combines these parameters with the dynamically assigned weights and generates a control signal through a control algorithm (such as fuzzy control, model predictive control, or a control algorithm based on graph neural networks). This control signal includes attitude adjustment instructions for the differential multi-cylinder linkage mechanism (such as the telescopic amount or pressure of each hydraulic cylinder), grouting instructions for the synchronous grouting regulation unit (such as grouting volume, pressure, ratio), and segment assembly angle instructions for the segment assembly angle optimization unit.

[0050] Furthermore, by dynamically allocating weights according to the formation type and combining the output parameters of the weighted directed graph to generate a control signal, the intelligence and adaptability of the control strategy are realized. The dynamic weight allocation ensures that under different formation conditions, the system can prioritize the most critical influencing factors, improving the efficiency and accuracy of control. Generating a control signal based on the output parameters of the weighted directed graph enables the control decision to make full use of the complex association information between the six-degree dimensions and achieve more refined and comprehensive control. For example, in sandy layers, the correlation between groundwater pressure and formation deformation is enhanced, and the system will increase the weights of groundwater pressure and formation deformation data and generate corresponding grouting and attitude adjustment instructions to prevent formation instability. This intelligent control signal generation method significantly improves the system's ability to cope with complex formation challenges.

[0051] In the above solution, the dynamic weight allocation can adopt a fuzzy rule base, an expert system, or an adaptive weight adjustment algorithm based on reinforcement learning. The weight mapping table can be constructed and optimized according to historical tunneling data and expert experience. The control signal generation algorithm can adopt a model based on fuzzy control, using the output parameters of the weighted directed graph as the input of the fuzzy controller and the formation type as the basis for selecting fuzzy rules to output control instructions, or adopt a model based on graph neural networks, using the weighted directed graph as the input and learning the mapping relationship from the graph structure to the control instructions through neural networks. The output frequency of the control signal should match the response speed of the actuator to ensure real-time control.

[0052] As an optional embodiment, the step of controlling the synchronous grouting control unit and the regeneration material injection device mechanically connected to the synchronous grouting control unit based on the control signal includes: According to the type of stratum, the synchronous grouting volume and recycled material injection volume are dynamically adjusted. The grouting volume is increased in soft soil layers, and the proportion of recycled materials is increased in hard rock layers.

[0053] According to the above, the specific process of grouting by controlling the synchronous grouting control unit and the recycled material injection device is as follows. Synchronous grouting is an important support method in the shield tunneling process, and its effect directly affects the tunnel forming quality and stratum stability. By dynamically adjusting the synchronous grouting amount and the recycled material injection amount according to the stratum type, the intelligent and adaptive grouting strategy is realized. This step is executed by the synchronous grouting control unit of the green support collaborative module, which receives the control signal generated by the vector adaptive control unit, and dynamically adjusts the grouting parameters in combination with the current stratum type identified by the stratum type identification module. In the soft soil layer, since the stratum is easy to deform and collapse, a larger grouting amount is required to quickly fill the voids and stabilize the surrounding rock, so the grouting amount is increased. In the hard rock layer, there are relatively few cracks and voids, and the recycled material has good filling and consolidation properties, which can increase the proportion of recycled materials in the injected material to realize resource recycling and enhance the support effect. The grouting amount and the proportion of recycled materials are dynamically adjusted according to the stratum type to ensure that the grouting strategy can best match the actual stratum conditions and improve the support efficiency and environmental benefits.

[0054] Furthermore, the synchronous grouting control unit of the green support collaborative module executes and links the recycled material injection device. The synchronous grouting control unit receives a control signal from the vector adaptive control unit, which includes instructions for the grouting amount and the recycled material injection amount. At the same time, the synchronous grouting control unit obtains the current formation type identified by the formation type identification module. According to the formation type, the synchronous grouting control unit dynamically adjusts the grouting amount and the recycled material injection amount parameters in the control signal. For example, when a soft soil layer is identified, the grouting control unit will increase the total grouting amount to ensure that the soil voids can be fully filled, prevent formation settlement, and increase the grouting amount. When a hard rock layer is identified, the grouting control unit will increase the proportion of recycled materials in the injected materials, increase the proportion of recycled materials, and reduce the use of original materials such as cement. The synchronous grouting control unit achieves precise control of the grouting amount and the recycled material injection amount by controlling the cylinder linkage nozzle of the grouting pump and the recycled material injection device.

[0055] In this embodiment, by dynamically adjusting the synchronous grouting volume and the injection volume of recycled materials according to the formation type, the intelligent and targeted grouting strategy is realized. Increasing the grouting volume in soft soil layers can effectively control the formation deformation and prevent collapse; increasing the proportion of recycled materials in hard rock layers not only realizes the recycling of resources, but also can utilize the good filling and consolidation properties of recycled materials to improve the fracture filling effect. The adaptive grouting strategy significantly improves the support effect and material utilization rate, and reduces resource waste and environmental pollution.

[0056] In the above solution, the dynamic adjustment of the grouting volume and the injection volume of recycled materials can adopt the look-up table method or the fuzzy control algorithm based on the formation type and formation parameters. For example, a grouting parameter rule base can be established, and the optimal grouting volume and the proportion of recycled materials can be determined according to different formation types and formation parameters (such as formation deformation rate, groundwater pressure). The injection volume of recycled materials can be expressed as a part of the total injection volume or injected in a certain proportion mixed with traditional slurry; the cylinder linkage nozzle can control the local injection volume by adjusting its movement speed and injection time, and the grouting process can be adjusted by feedback in combination with the formation deformation monitoring data. For example, when the formation settlement rate exceeds the threshold, the grouting volume is automatically increased.

[0057] As an alternative embodiment, the steps of adjusting the output force of the differential multi-cylinder linkage mechanism include: Adjusting the output force of the differential multi-cylinder linkage mechanism according to the control signal through the PID closed-loop feedback algorithm; Monitoring the actual output force of the differential multi-cylinder linkage mechanism, comparing it with the target output force, and adjusting the control signal according to the deviation.

[0058] According to the above, the differential multi-cylinder linkage mechanism is the actuator for the shield machine attitude adjustment, and the precise control of its output force is the key to realizing the precise attitude adjustment. By adopting the PID closed-loop feedback algorithm, according to the control signal generated by the vector adaptive control unit, the output force of the differential multi-cylinder linkage mechanism is precisely adjusted. The PID (Proportion-Integral-Differential) algorithm is a classic closed-loop control algorithm, which can calculate the control quantity according to the deviation between the system output and the target value to reduce the deviation and improve the response speed and stability of the system. In this step, the PID feedback control module of the vector adaptive control unit receives the control signal (representing the desired output force or attitude target) and the output force feedback signal of the differential multi-cylinder linkage mechanism (obtained through a pressure sensor or a force sensor), calculates the deviation between the current output force and the desired output force, and then calculates the adjustment signal according to the PID algorithm and outputs it to the control unit of the differential multi-cylinder linkage mechanism to control the electromagnetic directional valve and the proportional relief valve, and adjust the oil or gas flow of the main hydraulic cylinder and the auxiliary cylinder array, so as to change its output force and make it approach the desired value.

[0059] Further, it is executed by the PID feedback control module of the vector adaptive regulation unit. The PID feedback control module receives the control signal from the control signal generation module. This signal represents the desired output force of the differential multi-cylinder linkage mechanism or the force required to achieve the target posture. At the same time, the PID feedback control module receives the actual output force feedback signal of the differential multi-cylinder linkage mechanism. This signal is obtained through a pressure sensor or a force sensor. The PID feedback control module calculates the deviation between the desired output force and the actual output force. According to the proportional (P), integral (I), and derivative (D) control laws of the PID algorithm, it calculates the adjustment amount. The proportional term generates a control action according to the current deviation magnitude; the integral term generates a control action according to the accumulation of historical deviations, which is used to eliminate the steady-state error; the derivative term generates a control action according to the deviation change rate, which is used to suppress overshoot and improve the response speed. The calculated adjustment signal is output to the control unit of the differential multi-cylinder linkage mechanism, controlling the electromagnetic directional valve and the proportional relief valve, and adjusting the oil or gas flow rate of the main hydraulic cylinder and the auxiliary cylinder array, so as to change its output force and make it approach the desired value.

[0060] Furthermore, by adopting the PID closed-loop feedback algorithm to adjust the output force of the differential multi-cylinder linkage mechanism, the precise control of the attitude adjustment actuator is realized. The closed-loop feedback mechanism can monitor the actual output force in real time and make dynamic adjustments according to the deviation, overcoming the shortcoming that the open-loop control is sensitive to external disturbances. As a mature and stable control algorithm, the PID algorithm can effectively reduce the attitude deviation and improve the response speed and accuracy of attitude adjustment; for example, when there is a slight deviation in the attitude of the shield machine, the PID controller can accurately calculate the required small adjustment force according to the deviation magnitude and change rate, and quickly apply it through the differential multi-cylinder linkage mechanism to restore the attitude to the target position. This precise force control is the key to achieving millimeter-level or even sub-millimeter-level attitude adjustment.

[0061] It should be further noted that the parameters (Kp, Ki, Kd) of the PID controller can be tuned according to the dynamic characteristics and control requirements of the differential multi-cylinder linkage mechanism. The parameter tuning can adopt the empirical method, the Ziegler-Nichols method or other optimization algorithms. In order to improve the control performance, improved PID algorithms such as fuzzy PID, adaptive PID or the method of combining feedforward control and PID control can be adopted. The output force feedback signal can be obtained through the pressure sensors or force sensors installed on the main hydraulic cylinder and the auxiliary cylinders to ensure the accuracy of the feedback signal. The output frequency of the control signal should match the calculation speed of the PID controller and the response speed of the actuator.

[0062] It should be further noted that the actual output force of the monitored differential multi-cylinder linkage mechanism is compared with the target output force, and the control signal is adjusted according to the deviation. This step is executed by the PID feedback control module of the vector adaptive control unit. First, the actual output force signal of the differential multi-cylinder linkage mechanism is obtained through a pressure sensor or a force sensor. This signal reflects the actual thrust or lateral force exerted by the current differential multi-cylinder linkage mechanism on the attitude of the shield machine. Then, the actual output force signal is compared with the target output force represented by the control signal output by the control signal generation module of the vector adaptive control unit, and the deviation e(t) = target output force - actual output force is calculated. Next, the PID feedback control module calculates the adjustment signal according to the deviation e(t) and its integral and differential according to the PID control law: ; where u(t): the adjustment signal output by the controller (such as the opening of the electromagnetic directional valve, the pressure setting value of the proportional relief valve); e(t): the deviation at the current moment, that is, the difference between the target output force and the actual output force e(t)=target output force - actual output force; , , : proportional, integral, and differential gains, which need to be tuned according to the dynamic characteristics of the system; Finally, the calculated adjustment signal u(t) is output to the control unit of the differential multi-cylinder linkage mechanism. The control unit controls the electromagnetic directional valve and the proportional relief valve to adjust the oil or gas flow of the main hydraulic cylinder and the auxiliary cylinder array, thereby changing its output force. This process is carried out in a cycle, with real-time monitoring, comparison, and adjustment until the deviation is reduced to an allowable range.

[0063] Furthermore, by monitoring the actual output force, comparing it with the target value, and performing PID adjustment according to the deviation, precise closed-loop control of the output force of the differential multi-cylinder linkage mechanism is achieved, which can effectively overcome interference factors such as the nonlinearity of the hydraulic / pneumatic system, friction, and changes in formation reaction force, ensuring that the differential multi-cylinder linkage mechanism can accurately and quickly execute the attitude adjustment instructions issued by the vector adaptive control unit; for example, when the formation resistance suddenly increases and causes the actual output force to deviate from the target value, the PID controller can immediately detect the deviation and quickly adjust the control signal to increase the output force, so that the actual output force returns to the target value. This precise force control is an important guarantee for achieving high-precision attitude adjustment and maintaining the balance of the propulsion force.

[0064] In the above solution, the monitoring of the actual output force can be directly measured by force sensors installed on the piston rods of the main hydraulic cylinder and the auxiliary cylinders, or calculated by measuring the cavity pressure and combining with the effective area of the cylinder block (such as through a pressure sensor). The target output force is calculated and generated by the vector adaptive control unit according to the analysis results and control strategies of the six-degree interaction model. The calculation of the deviation and the implementation of the PID control algorithm can be completed in the processor of the vector adaptive control unit. The adjustment of the control signal can be a control current or voltage signal directly output to the electro-hydraulic proportional valve group. To improve the dynamic performance of the system, a combination of feedforward control and PID control can be adopted to adjust the control signal in advance according to the prediction results of the formation change.

[0065] As an alternative embodiment, the steps of monitoring the actual output force of the differential multi-cylinder linkage mechanism include: obtaining the actual output force of the differential multi-cylinder linkage mechanism through a pressure sensor or a force sensor.

[0066] Based on the above, obtaining the actual output force of the differential multi-cylinder linkage mechanism through a pressure sensor or a force sensor emphasizes the use of direct or high-precision sensing methods to obtain feedback signals. The pressure sensor can be installed in the oil or gas cavities of the main hydraulic cylinder and the auxiliary cylinder array. By measuring the pressure in the cavity and combining with the effective area of the piston, the output force of the cylinder block can be calculated. The force sensor can be directly installed at the connection between the cylinder block and the shield machine structure or on the piston rod to directly measure the force exerted by the cylinder block. By using these sensors, the system can obtain the actual output force of the differential multi-cylinder linkage mechanism in real time and accurately, providing a reliable feedback signal for the PID closed-loop feedback control to ensure the performance and stability of the control system.

[0067] As an alternative embodiment, the steps of the remote intelligent control center predicting the formation change and correcting and generating the control signal.

[0068] Based on the above, the steps of prospectively correcting the control strategy according to the formation prediction results of the remote intelligent control center. By using the LSTM neural network model integrated in the remote intelligent control center to predict the formation change and feeding the prediction results back to the shield machine side for correcting the control strategy. This step is carried out before or during the generation of the control signal. According to the predicted future formation change trend of the remote intelligent control center (such as an upcoming hard rock formation or a soft and water-rich formation ahead), the control signal generated by the vector adaptive control unit is corrected. For example, if it is predicted that the machine is about to enter a hard rock formation ahead, the system can adjust the control signal in advance, increase the propulsion force, adjust the cutter head speed and torque, and prepare the attitude adjustment and grouting strategies suitable for hard rock. This correction of the control strategy based on the prediction information enables the system to respond to formation mutations in advance, reduce the attitude deviation and safety risks, and improve the smoothness and efficiency of tunneling.

[0069] As an alternative embodiment, the optimal segment erection angle is calculated based on the formation parameters and the shield machine attitude parameters. Segment erection is a key link in tunnel formation. The accuracy of the erection angle directly affects the offset and leakage of the tunnel circumferential joints, and further affects the stability and waterproof performance of the tunnel structure. By calculating the optimal segment erection angle according to the real-time obtained formation parameters and shield machine attitude parameters, the formation parameters (such as formation deformation rate, formation stress) reflect the extrusion and deformation effects of the formation on the segment ring; the shield machine attitude parameters (such as shield machine pitch angle, roll angle, heading angle, axis deviation) reflect the installation position and direction of the segment ring. The purpose of optimizing the segment erection angle is to minimize the offset of the circumferential joints by adjusting the installation angle of the segment ring under the current formation conditions and shield machine attitude, and ensure the quality of tunnel formation. This step is executed by the segment erection angle optimization unit of the green support collaboration module, which uses a mathematical model or optimization algorithm to comprehensively consider factors such as formation deformation, shield machine attitude, geometric shape and connection method of the segment ring, and calculates the optimal erection angle of each segment.

[0070] Furthermore, a preset mathematical model or optimization algorithm is used to calculate the optimal segment erection angle. This calculation is completed using a preset mathematical model or optimization algorithm. The preset mathematical model can be a model based on geometry, mechanics or finite element theory, which is used to describe the relationship between formation deformation, shield machine attitude and segment ring shape and position, and establish an optimization function with the offset of the circumferential joint as the target. The optimization algorithm is used to solve this optimization function and find the segment erection angle combination that minimizes the offset of the circumferential joint. For example, a geometric model based on the axis deviation of the shield machine, the deviation of the ring surface and the formation convergence deformation can be established to calculate how to adjust the segment angle in the current state to make the circumferential joints flush, or optimization algorithms such as genetic algorithm and particle swarm algorithm can be used, with the minimum offset of the circumferential joint as the objective function and the segment angle as the optimization variable to search for the optimal solution. The application of these mathematical models and optimization algorithms makes the determination of the segment erection angle no longer rely on experience, but on scientific calculations, significantly improving the erection accuracy.

[0071] As an alternative embodiment, in the closed-loop pressure circuit, the pressure difference between the main hydraulic cylinder and the propulsion cylinder group is monitored and adjusted. The monitoring and adjustment of the pressure difference between the main hydraulic cylinder and the propulsion cylinder group in the closed-loop pressure circuit are as follows. During the tunneling process of the shield machine, the main hydraulic cylinder is part of the differential multi-cylinder linkage mechanism, and its output force and the output force of the propulsion cylinder group together constitute the total propulsion force of the shield machine. The force balance relationship between the two directly affects the propulsion state and the effect of attitude adjustment of the shield machine. In the closed-loop pressure circuit, a pressure sensor is used to monitor the pressure difference between the main hydraulic cylinder and the propulsion cylinder group, and this pressure difference is used as a feedback signal for adjustment. Monitoring the pressure difference can understand the force distribution between the two in real time. Adjusting the pressure difference aims to maintain the force balance between the two, ensuring that when the main hydraulic cylinder adjusts its output force for attitude adjustment, it will not have an adverse impact on the normal propulsion of the propulsion cylinder group, or when the propulsion cylinder group encounters changes in formation resistance, the main hydraulic cylinder can make corresponding coordination. This monitoring and adjustment mechanism helps to maintain the stability of the overall propulsion force of the shield machine and the smoothness of attitude adjustment.

[0072] According to another aspect of the embodiments of the present invention, an electronic device is further provided. The electronic device includes a memory and a processor; the memory is used to store programs; the processor executes the programs to implement the method of any one of the foregoing.

[0073] According to another aspect of the embodiments of the present invention, a computer-readable storage medium is further provided. The storage medium stores a computer program, and when the computer program is executed by a processor, it implements the method of any one of the foregoing.

[0074] According to another aspect of the embodiments of the present invention, a computer program product is further provided, including a computer program, and when the computer program is executed by a processor, it implements the method of any one of the foregoing.

[0075] Please refer to Figure 2 , an integrated attitude control and adjustment system for a green support shield equipment, including: A six-degree-of-freedom interaction monitoring module, including a data input interface, a data processing unit, and a storage unit. The data input interface is used to receive the attitude parameters of the shield machine, formation parameters, and equipment status data output by multiple sensors. The data processing unit executes the mutual information algorithm and generates a weighted directed graph. The storage unit is used to store data and the weighted directed graph; A differential multi-cylinder linkage mechanism, which includes a main hydraulic cylinder and an auxiliary cylinder array. The hydraulic pipeline of the main hydraulic cylinder is connected to the hydraulic pipeline of the shield machine propulsion cylinder group through a pressure sensor, forming a closed-loop pressure circuit, and is differentially connected to the proportional overflow valve through an electromagnetic reversing valve; The vector adaptive control unit includes a control signal generation module and a PID feedback control module. The control signal generation module receives the output parameters of the weighted directed graph and generates control signals. The PID feedback control module receives the output force feedback signal and the control signal of the differential multi-cylinder linkage mechanism, and outputs an adjustment signal to the differential multi-cylinder linkage mechanism according to the PID closed-loop feedback algorithm; The green support cooperation module includes a synchronous grouting control unit and a segment assembly angle optimization unit; The recycled material injection device and the cylinder linkage mechanism mechanically connected thereto; The mechanical coupling structure connects the recycled material injection device to the auxiliary cylinder array of the differential multi-cylinder linkage mechanism; The cylinder linkage mechanism receives the control signal of the vector adaptive control unit through the signal input port and drives the recycled material injection device through the mechanical connection structure; The remote intelligent control center establishes two-way communication with the six-degree interaction monitoring module and the vector adaptive control unit through the communication module. The remote intelligent control center is integrated with an LSTM neural network model for predicting formation changes; The data bus connects the six-degree interaction monitoring module, the vector adaptive control unit and the remote intelligent control center to form a real-time multi-source data sharing channel.

[0076] Based on the above, the six-degree interaction model comprehensively senses multi-source heterogeneous data, uses the mutual information algorithm to quantify the non-linear correlation between data, constructs a weighted directed graph, provides more accurate input for attitude control. The differential multi-cylinder linkage mechanism combines hydraulics and pneumatics to provide more flexible attitude adjustment capabilities. The integration of the green support cooperation module and the recycled material injection device realizes the recycling of resources, synthesizes calcium carbonate-based cementitious materials from shield waste, improves the material utilization rate, reduces CO2 emissions. The LSTM model of the remote intelligent control center predicts formation changes, predicts and warns in advance, reduces the frequency of manual intervention, improves the intelligent level of the system and the ability to respond to complex formation mutations. The data bus realizes real-time sharing of multi-module data, ensures the coordinated linkage of all parts of the system, and the closed-loop pressure circuit maintains the dynamic balance of the propulsion force, improving the tunneling stability. The mechanical coupling structure realizes the synchronization of attitude adjustment and support material injection, and optimizes the tunnel forming quality.

[0077] It should be further noted that the main hydraulic cylinder and the auxiliary cylinder array of the differential multi-cylinder linkage mechanism can be custom-designed according to the size and tunneling force requirements of the shield machine. The auxiliary cylinder array can be composed of multiple independently controlled cylinders to provide more refined differential force control. The electromagnetic directional valve and the proportional relief valve can be industrial-grade valves with high response speed and high precision. The control signal generation module of the vector adaptive regulation unit can generate control signals based on various control algorithms, such as fuzzy control, neural network control, or model predictive control, in combination with the mutual information algorithm. The parameters of the PID feedback control module can be adjusted online adaptively according to the formation type and tunneling state. The synchronous grouting regulation unit of the green support coordination module can achieve precise control of the grouting volume and rate by using a flow meter and a proportional control valve. The optimal segment assembly angle unit can calculate the optimal assembly angle based on three-dimensional scan data and a preset mathematical model. The calcium carbonate-based cementitious material synthesis unit of the recycled material injection device can adjust the reaction conditions and catalyst types according to the waste residue composition and the required material properties. The cylinder linkage nozzle can be designed as a multi-hole or adjustable nozzle to adapt to different injection requirements and formation conditions. The cylinder linkage mechanism can be replaced by an electric push rod or a hydraulic cylinder to provide different driving forces and control precisions. The mechanical coupling structure can adopt various forms such as a linkage mechanism, a gear drive, or a belt drive to ensure reliable linkage between the recycled material injection device and the auxiliary cylinder array of the differential multi-cylinder linkage mechanism. The remote intelligent regulation center can be deployed on a cloud platform or a local server, and the LSTM neural network model can be continuously trained and optimized based on historical tunneling data. The 5G communication module can be an industrial-grade module supporting low latency and high bandwidth. The data bus can use industrial Ethernet or a fiber optic network to ensure the stability and real-time nature of data transmission. The pressure sensor of the closed-loop pressure circuit can be a high-precision pressure sensor, and the monitoring range can cover the maximum working pressure of the shield machine propulsion cylinder group.

[0078] As an alternative embodiment, the six-degree interaction monitoring module receives the output signals of the inclination sensor, the displacement sensor, the formation hardness sensor, and the tool force feedback unit through a data interface.

[0079] Based on the above, by introducing various sensors such as inclination sensors, displacement sensors, formation hardness sensors, and cutter force feedback units, a comprehensive perception of the shield machine's attitude, formation characteristics, and equipment operating status has been achieved. The data of the sensors constitute the basic input of the six-degree interaction model, providing a rich and reliable information source for subsequent data processing and precise control. The inclination sensor is used to monitor the pitch angle and roll angle of the shield machine in real time. The displacement sensor is used to monitor the longitudinal and lateral displacements of the shield machine. The formation hardness sensor is used to detect the hardness distribution of the front formation. The cutter force feedback unit provides the reaction force data during cutter cutting, reflecting the formation's acting force on the cutter head and the wear status of the cutters. The fusion of these multi-source data enables the system to more accurately understand the current tunneling environment and equipment status, laying a foundation for realizing refined and adaptive attitude control.

[0080] Furthermore, the output signals include the angle dimension, displacement vector dimension, partition hardness coefficient dimension, equipment status dimension, hydraulic cylinder linkage dimension, and support adaptation dimension in the six-degree dimension.

[0081] Based on the above, six key dimensions are defined: the angle dimension, displacement vector dimension, partition hardness coefficient dimension, equipment status dimension, hydraulic cylinder linkage dimension, and support adaptation dimension, which comprehensively cover the key factors affecting shield tunneling and attitude control. The angle dimension and displacement vector dimension reflect the spatial attitude and position deviation of the shield machine; the partition hardness coefficient dimension and equipment status dimension describe the formation conditions and the operating status of the equipment itself; the hydraulic cylinder linkage dimension and support adaptation dimension are related to the attitude adjustment actuator and the support process; by abstracting these seemingly independent factors into a unified six-degree dimension and conducting correlation analysis, the present invention can construct an interaction model closer to the actual complex process, providing a comprehensive data basis for realizing integral attitude control adjustment.

[0082] Even further, the six-degree interaction monitoring module includes: a data fusion unit, a correlation analysis unit, a dynamic weight assignment unit, and a prediction correction unit.

[0083] According to the above, by constructing a data processing flow, the aim is to extract deep - level information from six - degree - dimension data, construct an accurate interaction model, and achieve the prediction of future strata changes. The data fusion unit is responsible for integrating the raw data from different sensors, eliminating redundancy and noise, and performing preliminary feature extraction; the correlation analysis unit uses tools such as the mutual information algorithm to quantify the correlation strength between different dimensions, constructs a weighted directed graph, and reveals complex coupling relationships; the dynamic weight allocation unit dynamically adjusts the weights of each dimension in the control decision according to the current strata type and tunneling state to ensure that key information is processed first; the prediction correction unit uses prediction algorithms such as the LSTM neural network model to predict the strata change trend and correct the model parameters and control strategies according to the prediction results; the collaborative work of the above - mentioned units enables the six - degree interaction monitoring module to extract information crucial for attitude control and support coordination from massive data, significantly improving the intelligent level of the system and its ability to cope with complex environments.

[0084] As an alternative embodiment, the data fusion unit includes a processing module that executes the feature co - occurrence relationship modeling process. The data fusion unit also includes a node mapping module, a weight calculation module, and a topology optimization module.

[0085] According to the above, in this embodiment, by introducing the feature co - occurrence relationship modeling process and setting up a processing module, a node mapping module, a weight calculation module, and a topology optimization module in the data fusion unit, the aim is to systematically construct and optimize a model that reflects the correlation between six - degree dimensions; the processing module is responsible for executing the entire feature co - occurrence relationship modeling process; the node mapping module abstracts and maps the collected six - degree - dimension data into feature nodes in the graph structure to prepare for subsequent correlation analysis; the weight calculation module calculates the correlation strength between any two feature nodes based on methods such as the mutual information algorithm and uses it as the edge weight in the graph structure to construct a weighted directed graph. The topology optimization module optimizes the constructed weighted directed graph. For example, it uses the PageRank algorithm to identify key influencing factor nodes, dynamically adjusts the data processing priority, and ensures that the system can quickly focus on the most important information; the collaborative effect of the above - mentioned modules enables the data fusion unit to efficiently and accurately capture the complex correlations in the six - degree - dimension data, provides high - quality input for the correlation analysis unit and the dynamic weight allocation unit, and significantly improves the analysis ability of the six - degree interaction model and the performance of the overall control system.

[0086] As an alternative embodiment, the recycled material injection device includes: a calcium carbonate-based cementitious material synthesis unit and a cylinder-linked nozzle. The calcium carbonate-based cementitious material synthesis unit is a chemical reaction device that receives fine-grained muck, cooling water, and externally supplied CO2 gas from the shield muck removal system. By controlling the reaction conditions (such as temperature, pressure, and reaction time), the calcium components in the waste muck react with CO2 to form calcium carbonate crystals and form a cementitious material with other components. The cylinder-linked nozzle is connected to the discharge port of the calcium carbonate-based cementitious material synthesis unit and is located at the tail of the shield machine or the segment erection area. The cylinder-linked nozzle is driven by a cylinder linkage mechanism and can adjust the position, angle, and injection rate of the nozzle to ensure that the recycled material can be accurately injected into the formation area that needs to be supported.

[0087] Furthermore, the calcium carbonate-based cementitious material synthesis unit is used to synthesize calcium carbonate-based cementitious materials. Using the waste muck generated during shield tunneling as the main raw material, a calcium carbonate-based material with cementitious properties is synthesized through specific chemical reactions. This material can replace or partially replace traditional grouting materials for formation support, void filling, and surrounding rock improvement. The synthesis process utilizes industrial by-products or waste (such as CO2 gas and shield muck), converting low-value waste into high-value engineering materials, achieving efficient recycling of resources, and significantly reducing the carbon footprint and environmental impact of the project.

[0088] Even further, the calcium carbonate-based cementitious material synthesis unit can be in the form of a stirred reactor, a tubular reactor, or a fluidized bed reactor, etc., and is equipped with a heating, pressurizing, and gas injection control system. The reaction conditions can be optimized to muck particles:cooling water:CO2 gas = 3:2:1 (mass or volume ratio), temperature 80 - 100°C, pressure gradient 0.2 MPa / min, and 0.5% nano-CaO catalyst is incorporated to improve the reaction efficiency and material strength. The compressive strength of the synthesized calcium carbonate-based cementitious material can reach ≥20 MPa (traditional cement slurry is 15 MPa); the cylinder-linked nozzle can be designed as a single or multiple nozzles, and the diameter and shape of the nozzle can be adjusted according to the viscosity of the injected material and the formation conditions. The cylinder-linked nozzle can control the injection pressure (such as 2.5 MPa) and flow rate (such as 0.8 - 1.2 m³ / h) through a control unit connected to an electro-hydraulic proportional valve group to achieve the mixed injection or separate injection of the recycled material and traditional slurry.

[0089] As an alternative embodiment, the vector adaptive regulation unit includes: a formation type identification module, a weight adaptive adjustment module, a control signal generation module, and a PID feedback control module.

[0090] According to the above, through the formation type identification module and the weight adaptive adjustment module, the control strategy can be adaptively adjusted according to the real-time formation information; the formation type identification module is responsible for judging the formation type of the current tunneling (such as soft soil, sand layer, hard rock, etc.) according to the formation parameters provided by the six-degree interaction monitoring module; the weight adaptive adjustment module dynamically adjusts the weights of the input parameters in the control signal generation module or the parameters of the control algorithm according to the identified formation type and the analysis results of the six-degree interaction model to optimize the control effect; the control signal generation module receives the output parameters of the six-degree interaction model after weight adjustment and generates control instructions; the PID feedback control module receives the feedback signal of the actuator (differential multi-cylinder linkage mechanism) and the generated control signal for closed-loop regulation; the coordinated work of the above modules enables the vector adaptive control unit to adjust the control strategy in real time according to the complex formation conditions and equipment status, realizing precise and robust control of the shield machine attitude.

[0091] As an optional embodiment, the formation type identification module executes the formation type identification algorithm, and the formation type identification algorithm determines the current formation type based on the formation parameters output by the six-degree interaction monitoring module.

[0092] According to the above, during the shield tunneling process, the formation conditions are the key factors affecting the tunneling parameters and attitude control strategy; different types of formations (such as soft soil, sand layer, hard rock, composite formation, etc.) have significant differences in the forces, deformations and support requirements of the shield machine; the formation type identification module is configured to execute the formation type identification algorithm and automatically determine the formation type where the current shield machine is located based on the real-time formation parameters output by the six-degree interaction monitoring module; the six-degree interaction monitoring module provides rich formation-related parameters, such as the partition hardness coefficient dimension, groundwater pressure, formation deformation rate, etc.; the formation type identification algorithm comprehensively analyzes these parameters and uses pattern recognition or classification techniques to classify the current formation into the preset formation types; this real-time, multi-parameter-based formation type identification ability provides accurate formation information input for the subsequent weight adaptive adjustment and control strategy generation of the vector adaptive control unit, ensuring that the control system can adopt the optimal control strategy for different formations and significantly improving the tunneling efficiency and safety.

[0093] In addition, the differential multi-cylinder linkage mechanism includes a control unit connected to the electromagnetic directional valve and the proportional relief valve, and the control unit is configured to output a control signal to adjust the oil or gas flow of the main hydraulic cylinder and the auxiliary cylinder array, and the main hydraulic cylinder and the auxiliary cylinder array form a differential connection structure through hydraulic pipelines and pneumatic pipelines.

[0094] As an alternative embodiment, the pressure sensor in the closed-loop pressure circuit is connected to the signal input port of the vector adaptive control unit. The pressure sensor is used to monitor the pressure difference between the main hydraulic cylinder and the propulsion cylinder group. The vector adaptive control unit is configured to receive the pressure difference signal and make adjustments.

[0095] Furthermore, a pressure sensor is provided in the closed-loop pressure circuit to monitor the pressure difference between the main hydraulic cylinder and the propulsion cylinder group, and input the pressure difference signal to the signal input port of the vector adaptive control unit. The vector adaptive control unit is configured to receive the pressure difference signal and make adjustments according to this signal. By monitoring and adjusting the pressure difference between the main hydraulic cylinder and the propulsion cylinder group, the system can achieve the dynamic balance of the output forces of the two, ensure the stability of the overall propulsion force during the attitude adjustment process, and improve the smoothness of tunneling and the quality of tunnel formation.

[0096] It should be noted that the above-mentioned various modules can be implemented by software or hardware. For the latter, it can be achieved in the following ways, but not limited to this: all the above-mentioned modules are located in the same processor; or, the above-mentioned various modules are respectively located in different processors in any combination form.

[0097] The above is only the preferred embodiment of the present invention. It should be pointed out that for those of ordinary skill in the art of this technology, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention. The structures, devices, and operation methods not specifically described and explained in the present invention are implemented according to the conventional means in the art without special instructions and limitations.

Claims

1. An integral attitude control and adjustment method for a green support shield equipment, characterized in that, It includes the following steps: Collect the attitude parameters, formation parameters and equipment status data of the shield machine, and based on the collected data, use the mutual information algorithm to construct a weighted directed graph; Generate a control signal based on the output parameters of the weighted directed graph, and adjust the output force of the differential multi-cylinder linkage mechanism through the control signal; Based on the control signal, control the synchronous grouting control unit and the recycled material injection device mechanically connected to the synchronous grouting control unit, wherein the recycled material injection device is driven by a cylinder linkage mechanism; Optimize the segment assembly angle and establish two-way communication between the shield machine and the remote intelligent control center through the communication module; Use the neural network model integrated in the remote intelligent control center to predict the formation change and correct the control strategy; Realize the sharing of data, control signals and correction strategies through the data bus; Form a closed-loop pressure circuit between the hydraulic output pipeline of the differential multi-cylinder linkage mechanism and the shield machine propulsion cylinder group.

2. The overall attitude control and adjustment method of a green support shield equipment according to claim 1, characterized in that The steps of collecting the attitude parameters, formation parameters and equipment status data of the shield machine include: Obtain angle, displacement, sectional hardness and equipment status data in real time through multiple sensors.

3. An integral attitude control and adjustment method for a green support shield equipment according to claim 1, characterized in that, The steps of constructing a weighted directed graph based on the collected data using the mutual information algorithm include: Construct a weighted directed graph based on the mutual information algorithm to quantify the correlation strength between any two dimensions.

4. A method for overall attitude control and adjustment of a green support shield equipment according to claim 1, characterized in that, The steps of generating the control signal include: Dynamically allocate weights according to the formation type and generate a control signal based on the output parameters of the weighted directed graph; Among them, the steps of controlling the synchronous grouting control unit and the recycled material injection device mechanically connected to the synchronous grouting control unit based on the control signal include: Dynamically adjust the synchronous grouting volume and the recycled material injection volume according to the formation type. Among them, increase the grouting volume in soft soil layers and increase the proportion of recycled materials in hard rock layers.

5. The integral attitude control and adjustment method of a green support shield equipment according to claim 1, characterized in that The steps of adjusting the output force of the differential multi-cylinder linkage mechanism include: Adjust the output force of the differential multi-cylinder linkage mechanism according to the control signal through the PID closed-loop feedback algorithm; Monitor the actual output force of the differential multi-cylinder linkage mechanism, compare it with the target output force, and adjust the control signal according to the deviation.

6. An integral attitude control and adjustment system for a green support shield equipment, which uses the integral attitude control and adjustment method for the green support shield equipment as described in any one of claims 1 to 5, is characterized in that, It includes: Six-degree interaction monitoring module, including a data input interface, a data processing unit and a storage unit. The data input interface is used to receive the attitude parameters, formation parameters and equipment status data output by multiple sensors. The data processing unit executes the mutual information algorithm and generates a weighted directed graph. The storage unit is used to store data and the weighted directed graph; Differential multi-cylinder linkage mechanism, the differential multi-cylinder linkage mechanism includes a main hydraulic cylinder and an auxiliary cylinder array. The hydraulic pipeline of the main hydraulic cylinder is connected to the hydraulic pipeline of the shield machine propulsion cylinder group through a pressure sensor to form a closed-loop pressure circuit, and a differential connection structure is formed through an electromagnetic directional valve and a proportional overflow valve; Vector adaptive control unit, including a control signal generation module and a PID feedback control module. The control signal generation module receives the output parameters of the weighted directed graph and generates a control signal. The PID feedback control module receives the output force feedback signal and the control signal of the differential multi-cylinder linkage mechanism, and outputs an adjustment signal to the differential multi-cylinder linkage mechanism according to the PID closed-loop feedback algorithm; The green support collaborative module includes a synchronous grouting control unit and a segment assembly angle optimization unit; A recycled material injection device and a cylinder linkage mechanism mechanically connected thereto; A mechanical coupling structure connects the recycled material injection device to the auxiliary cylinder array of the differential multi-cylinder linkage mechanism; The cylinder linkage mechanism receives the control signal of the vector adaptive control unit through the signal input port and drives the recycled material injection device through the mechanical connection structure; The remote intelligent control center establishes two-way communication with the six-degree interaction monitoring module and the vector adaptive control unit through the communication module. The remote intelligent control center integrates an LSTM neural network model for predicting formation changes; The data bus connects the six-degree interaction monitoring module, the vector adaptive control unit and the remote intelligent control center to form a real-time multi-source data sharing channel.

7. An integral attitude control and adjustment system for a green support shield equipment according to claim 6, characterized in that: The six-degree interaction monitoring module receives the output signals of the inclination sensor, the displacement sensor, the formation hardness sensor and the tool force feedback unit through the data interface; The output signals include six degrees of dimensions: the angle dimension, the displacement vector dimension, the partition hardness coefficient dimension, the equipment state dimension, the hydraulic cylinder linkage dimension and the support adaptation dimension; The six-degree interaction monitoring module includes: a data fusion unit, a correlation analysis unit, a dynamic weight distribution unit and a prediction correction unit.

8. The integrated attitude control and adjustment system for a green support shield equipment according to claim 7, characterized in that, The data fusion unit includes a processing module, and the processing module executes a feature co-occurrence relationship modeling process. The data fusion unit also includes a node mapping module, a weight calculation module and a topology structure optimization module.

9. The integrated attitude control and adjustment system for a green support shield equipment according to claim 6, wherein, The vector adaptive control unit includes: a formation type identification module, a weight adaptive adjustment module, a control signal generation module and a PID feedback control module.

10. An integrated attitude control and adjustment system for a green support shield equipment according to claim 9, characterized in that: The formation type identification module executes a formation type identification algorithm, and the formation type identification algorithm determines the current formation type based on the formation parameters output by the six-degree interaction monitoring module.

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