Intelligent adjustment method, system and device for cutterhead of tunnel boring machine

Abstract

The application discloses a cutter head intelligent adjusting method, system and equipment for a tunnel boring machine, relates to the related technical field of the tunnel boring machine, and comprises the following steps: acquiring tunneling soil layer characteristic data and performing deep layer detection through a multispectral sensor; tunneling soil layer is derived to construct a tunneling soil layer characteristic space; a dynamic safety boundary constraint set is established, including a tunneling machine hard limit boundary, a geological mutation boundary, a settlement control boundary and a cutter life boundary; cutter head parameter optimization is performed with the multiple targets of minimum specific energy, minimum cutter wear and minimum settlement risk; and cutter head execution parameters are output for cutter head adjustment. The application solves the technical problems of insufficient dynamic sensing of geological conditions, poor adaptability of dynamic safety constraints, and difficult balance of multi-target optimization conflicts in the prior art, and improves the tunneling efficiency, stability, construction safety and cutter life.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tunnel boring machine, and particularly relates to a cutter head intelligent adjustment method, system and device for a tunnel boring machine. BACKGROUND

[0002] Tunneling engineering is faced with increasingly complex geological conditions and higher construction requirements. As the core equipment of modern tunnel construction, the intelligent adjustment of the cutter head of a tunnel boring machine is crucial to improving the tunneling efficiency, reducing the construction risk and prolonging the service life of the cutter. However, the existing methods still have deficiencies in deep geological feature perception, dynamic safety boundary modeling and multi-objective collaborative optimization, for example, the water content, cohesion, abrasiveness and lithology distribution of different soil layers are significantly different, and there may be unexplored weak interlayer or hard rock mutation zone. The traditional tunnel boring machine cutter head adjustment is difficult to adapt to complex and variable stratum conditions, directly affecting the stress state of the cutter head, the tunneling stability and the tunneling efficiency, and the existing cutter head control is difficult to balance multiple targets such as energy efficiency, cost and safety.

[0003] Therefore, in the related art at present, there are technical problems of insufficient dynamic perception of geological conditions, poor adaptability of dynamic safety constraints, and difficult balance of multi-objective optimization conflicts, resulting in low tunneling efficiency and stability, cutter life and construction safety. SUMMARY

[0004] The present application provides a cutter head intelligent adjustment method, system and device for a tunnel boring machine, which solves the technical problems of insufficient dynamic perception of geological conditions, poor adaptability of dynamic safety constraints, and difficult balance of multi-objective optimization conflicts in the prior art, resulting in low tunneling efficiency and stability, cutter life and construction safety, and achieves the technical effects of improving the tunneling efficiency, stability, construction safety and prolonging the service life of the cutter.

[0005] The present application provides a cutter head intelligent adjustment method for a tunnel boring machine, which comprises: acquiring tunneling soil layer feature data, including water content, cohesion, abrasiveness index and lithology distribution, and performing deep detection through a multispectral sensor to obtain feature change distribution; performing tunneling soil layer deduction according to the tunneling soil layer feature data and the feature change distribution to construct a tunneling soil layer feature space; establishing a dynamic safety boundary constraint set, including a tunnel boring machine hard limit boundary, a geological mutation boundary, a settlement control boundary and a cutter life boundary; in the tunneling soil layer feature space, taking minimum specific energy, minimum cutter wear and minimum settlement risk as multi-objectives, performing cutter head parameter optimization according to cutter head adjustment constraints, and using the safety boundary constraint set to clip the optimization solution set obtained by optimization, and outputting cutter head execution parameters for cutter head adjustment.

[0006] In a possible implementation, the method for intelligent adjustment of a cutterhead of a tunnel boring machine, the tunneling soil layer feature data, including water content, cohesion, abrasiveness index and lithology distribution, are acquired, and deep layer detection is performed through a multispectral sensor to obtain a feature change distribution, including: soil quality feature analysis is performed on the tunneling surface soil layer, including the water content, cohesion, abrasiveness index and lithology distribution, standard soil layer data are obtained, multispectral signal feature conversion is performed on the standard soil layer data, and an initial standard spectrum is set; multispectral signals collected from deep soil layers are fitted and compared with the initial standard spectrum to obtain signal change gradients of each soil layer, and the feature change distribution is obtained.

[0007] In a possible implementation, the method for intelligent adjustment of a cutterhead of a tunnel boring machine, tunneling soil layer derivation is performed according to the tunneling soil layer feature data and the feature change distribution, a tunneling soil layer feature space is constructed, including: a mapping conversion relationship between the initial standard spectrum and the soil quality feature is established; the feature change distribution is taken as mapping input based on the signal change gradients of each soil layer, soil quality feature mapping output is performed through the mapping conversion relationship, and soil quality distribution features of each soil layer are obtained; the soil quality distribution features of each soil layer are spatially embedded and fused according to spatial coordinates, and the tunneling soil layer feature space is constructed.

[0008] In a possible implementation, the method for intelligent adjustment of a cutterhead of a tunnel boring machine, a dynamic safety boundary constraint set is established, including: the machine hard limit boundary is set according to the equipment hard limit of the tunneling machine, based on rated parameters and a tolerance threshold; a constraint relationship between a tunneling broken degree index and a pushing speed is established, and the geological mutation boundary is set; the settlement control boundary is determined according to the constraint threshold of the stratum loss rate and the soil chamber pressure fluctuation; the cutter life boundary is determined according to the cutter distribution structure and the cutter performance of the cutterhead, in combination with the tunneling soil layer tunneling constraint.

[0009] In a possible implementation, the method for intelligent adjustment of a cutterhead of a tunnel boring machine determines a cutter life boundary according to a cutter distribution structure of the cutterhead, cutter performance, and in combination with tunneling constraints of a tunneling soil layer, and includes the following steps: a dynamic model describing a mapping relationship between a cutter wear rate and tunneling parameters is constructed based on cutter material performance test data and historical tunneling data, the tunneling parameters including a local thrust, a cutterhead rotation speed, and a rock and soil abrasiveness index; the cutterhead is divided into N sector pressure regions in a radial direction according to the cutter distribution structure of the cutterhead, N being a positive integer greater than or equal to 4; a cutter wear rate of each pressure region is calculated based on the dynamic model in combination with current tunneling parameters, and a wear space distribution thermal map is generated; critical working conditions of the cutter performance are analyzed according to characteristic parameters of the tunneling soil layer, including a maximum allowable contact stress, an abrasiveness index threshold, and a temperature limit; and a minimum cutter residual life value of the entire cutterhead is determined through a shortest plate effect with mechanical load deviation rate, the wear space distribution thermal map, and tunneling constraints as inputs, and a threshold of the tunneling parameters corresponding to the minimum cutter residual life value is set as the cutter life boundary.

[0010] In a possible implementation, the method for intelligent adjustment of a cutterhead of a tunnel boring machine performs cutterhead parameter optimization according to cutterhead adjustment constraints with the minimum specific energy, the minimum cutter wear, and the minimum subsidence risk as multiple objectives in the characteristic space of the tunneling soil layer, and includes the following steps: an evaluation function relationship between soil layer characteristics and cutterhead parameters is established with the minimum specific energy, the minimum cutter wear, and the minimum subsidence risk as multiple objectives; cutterhead parameter multidirectional search is performed within the cutterhead adjustment constraint range based on soil layer characteristics in the characteristic space of the tunneling soil layer, and search parameters are evaluated by using the multiple objective evaluation function to find a multiple optimization solution set as output.

[0011] In a possible implementation, the method for intelligent adjustment of a cutterhead of a tunnel boring machine further includes: a three-dimensional characteristic tensor space is established with a tunneling axis as a reference, wherein an X axis is a tunneling mileage, a y axis is a radial depth, and a z axis is a characteristic parameter value; soil quality characteristic vectors of each soil layer are mapped to grid nodes in the three-dimensional characteristic tensor space according to spatial distribution coordinates; and cutterhead parameters searched are fitted in terms of tunneling mileage and radial depth based on the three-dimensional characteristic tensor space to construct complete time sequence cutterhead parameters, and the multiple optimization solution set is generated.

[0012] In a possible implementation, the method for intelligent adjustment of a cutterhead of a tunnel boring machine includes the following steps in mapping soil quality characteristic vectors of each soil layer to grid nodes in the three-dimensional characteristic tensor space according to spatial distribution coordinates: a number of grids in X and Y axis directions in the three-dimensional characteristic tensor space is established, wherein the number of grids in the X axis direction corresponds to a tunneling process, and the number of grids in the Y axis direction corresponds to a radial depth; a Euclidean distance from a grid node to a soil layer detection point is calculated, a characteristic value is calculated according to an inverse distance weighting, and the characteristic value is assigned to a Z axis characteristic channel corresponding to the grid node.

[0013] The application also provides a cutter head intelligent adjustment system for a tunnel boring machine, which is used to implement the cutter head intelligent adjustment method for a tunnel boring machine, and comprises: a deep exploration module, configured to obtain excavation soil layer characteristic data, including water content, cohesion, abrasion index and lithology distribution, and perform deep exploration through a multispectral sensor to obtain characteristic change distribution; an excavation soil layer deduction module, configured to deduce the excavation soil layer according to the excavation soil layer characteristic data and the characteristic change distribution, and construct an excavation soil layer characteristic space; a safety boundary constraint configuration module, configured to establish a dynamic safety boundary constraint set, including a tunnel boring machine hard limit boundary, a geological mutation boundary, a settlement control boundary and a cutter life boundary; and a cutter head parameter optimization module, configured to perform cutter head parameter optimization in the excavation soil layer characteristic space with the multiple targets of minimum specific energy, minimum cutter wear and minimum settlement risk, according to cutter head adjustment constraints, and clip an optimization solution set obtained by optimization using the safety boundary constraint set, and output cutter head execution parameters for cutter head adjustment.

[0014] The application also provides an electronic device, comprising: a memory configured to store executable instructions; and a processor configured to execute the executable instructions stored in the memory to implement the cutter head intelligent adjustment method for a tunnel boring machine.

[0015] The cutter head intelligent adjustment method, system and device for a tunnel boring machine provided by the application can obtain excavation soil layer characteristic data and perform deep exploration through a multispectral sensor, deduce the excavation soil layer to construct an excavation soil layer characteristic space, establish a dynamic safety boundary constraint set including a tunnel boring machine hard limit boundary, a geological mutation boundary, a settlement control boundary and a cutter life boundary, perform cutter head parameter optimization with the multiple targets of minimum specific energy, minimum cutter wear and minimum settlement risk, clip an optimization solution set, and output cutter head execution parameters for cutter head adjustment. The technical problems of insufficient dynamic sensing of geological conditions, poor adaptability of dynamic safety constraints, and difficult balance of multi-target optimization conflicts in the prior art, which result in low tunneling efficiency and stability, cutter life and construction safety, are solved, and the technical effects of improving tunneling efficiency, stability, construction safety and extending cutter life are achieved. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly introduced below. The flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the foregoing or the following operations are not necessarily performed in sequence. On the contrary, various steps can be processed in reverse order or simultaneously as needed. Meanwhile, other operations can be added to these processes, or a step or several steps can be removed from these processes.

[0017] Figure 1 A flowchart of a method for intelligent adjustment of a cutterhead of a tunnel boring machine is provided in an embodiment of the present application.

[0018] Figure 2 A structure diagram of a system for intelligent adjustment of a cutterhead of a tunnel boring machine is provided in an embodiment of the present application.

[0019] Figure 3 A structure diagram of an electronic device is provided in an embodiment of the present application.

[0020] Legend: deep exploration module 10, tunneling soil layer derivation module 20, safety boundary constraint configuration module 30, cutterhead parameter optimization module 40, input device 401, processor 402, memory 403, output device 404. DETAILED DESCRIPTION

[0021] The above description is only a summary of the technical solutions of the present application. In order to make the technical solutions of the present application more clear, the following detailed description of the embodiments of the present application can be implemented according to the content of the description, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following detailed description of the embodiments of the present application.

[0022] In order to make the technical solutions, technical scheme and advantages of the present application more clear, the following will combine the drawings to make a further detailed description of the present application. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by those skilled in the art without making creative efforts belong to the scope of protection of the present application.

[0023] In the following description, "some embodiments" are described, which describe a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subset of all possible embodiments, and can be combined with each other without conflict. The term "first\second" is only to distinguish similar objects, and does not represent a specific order of the objects. The terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or modules not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by those skilled in the art in the technical field to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application.

[0024] The embodiments of the present application provide a method for intelligent adjustment of a cutterhead of a tunnel boring machine, as shown in Figure 1 The method comprises the following steps:

[0025] In step S100, the characteristics of the tunneling soil layer are obtained, including water content, cohesion, abrasion index, and lithology distribution, and deep exploration is performed through a multispectral sensor to obtain the distribution of characteristic changes.

[0026] Preferably, during tunneling, the cutting performance of the cutter head, the tunneling efficiency, and the safety of the equipment are highly dependent on real-time sensing of geological conditions. Key characteristic data of the tunneling soil layer, including water content, cohesion, abrasion index, and lithology distribution, are dynamically obtained, and further deep exploration is performed to construct a characteristic change distribution, thereby achieving precise geological modeling and intelligent adjustment of the cutter head. The characteristic data of the tunneling soil layer includes water content, cohesion, abrasion index, and lithology distribution. Specifically, water content refers to the mass proportion of water in the soil layer, reflecting the permeability and stability of the stratum. High water content may cause the cutter head to slip and mud cake to adhere, while low water content may increase the cutting resistance. Cohesion refers to the cohesive strength between soil particles, affecting the shear resistance of the soil body. Strata with high cohesion require greater cutter head thrust, while loose sand layers are prone to cause cutter idling or settling. The abrasion index represents an index of the degree of tool wear caused by rock and soil, which is usually related to quartz content and particle hardness. High abrasion strata such as sandstone and granite can accelerate tool wear, and the speed and thrust need to be optimized to prolong the tool life. Lithology distribution refers to the type, thickness, and spatial distribution of different rocks or soil layers on the tunneling section. Lithology mutations can easily cause cutter head vibration or unbalanced loading, and parameters need to be dynamically adjusted to maintain smooth tunneling.

[0027] Preferably, grid measurement points are arranged in the front area of tunneling. The water content distribution of the soil layer is inverted through spectral reflectance differences, a quantitative relationship model between water content and reflectance intensity is established, and the accuracy can reach ±1.5%. The soil layer water content is then calculated. Near-infrared bands (1400-1900nm) are used to extract spectral characteristics of soil particle adsorption properties, and mechanical test calibration is combined with drilling core sampling to achieve non-contact cohesion estimation with an error of less than 15kPa. The cohesion is then measured and obtained. Through heavy metal ions such as Fe³⁺ and SO4²⁻, characteristic absorption peaks at specific bands 450-550nm and 2100-2300nm, spectral differential technology is used to enhance weak signals, a database of the relationship between corrosive substance content and spectral characteristics is constructed, and by comparing real-time spectra with database characteristics, the corrosive grade of the soil layer can be quickly determined, and the abrasion index can be determined, providing a basis for tool material selection and corrosion prevention measures. Based on multispectral imaging and machine learning algorithms, convolutional neural networks are used for lithology classification of spectral images, with an accuracy of more than 90%, capable of identifying rock block distribution with a particle size of more than 5cm, and finally determining the lithology distribution.

[0028] Preferably, the unmanned aerial vehicle is equipped with a high-precision multispectral sensor, which realizes deep detection of the characteristics of the excavated soil layer through multi-band spectral analysis, including 10-20 working bands covering visible light, near-infrared, short-wave infrared, etc. It can simultaneously obtain spatial information and spectral information of the soil sample and process them to generate a three-dimensional geological feature map, for example, through infrared band difference to identify the boundary of the aquifer and form a water rate gradient, or through a hardness inversion model to predict the abrasion index distribution, to intuitively show the trend of soil layer changes in the excavation direction.

[0029] Further, step S100 further includes step S110, soil quality characteristic analysis of the excavation surface soil layer, including the water content, cohesion, abrasion index and lithology distribution, obtaining standard soil layer data, and performing multispectral signal feature conversion on the standard soil layer data, and setting an initial standard spectrum; step S120, fitting and comparing the multispectral signals collected from the deep soil layer with the initial standard spectrum to obtain the signal change gradient of each soil layer and obtain the characteristic change distribution.

[0030] Preferably, the soil quality characteristic analysis of the excavated section of the soil layer contacted by the cutterhead of the tunneling machine includes water content, cohesion, abrasion index and lithology distribution, a standard soil layer database is established, i.e., standard soil layer data is obtained; the reflectance / absorption spectrum signal of the standard soil layer is collected by a multispectral sensor, these soil quality parameters are associated with the reflectance data of the multispectral sensor, and an initial standard spectrum is set; the multispectral sensor continuously collects the spectrum signal of the deep soil layer in front of the excavation, such as within a range of 5-30 meters from the excavation face, and performs nonlinear fitting comparison with the initial standard spectrum, the offset degree of different band signals is analyzed, such as the absorption enhancement of the near-infrared band caused by the increase of water content, and then the signal change gradient of each soil layer is calculated to quantitatively analyze the spatial variation of geological parameters; finally, based on the signal gradient data, the spectrum difference is mapped into the continuous distribution of geological parameters by combining the inversion model, for example, a sudden drop in mid-infrared band reflectance at a certain depth and a gradient value exceeding a threshold value, which is determined as a high-cohesion hard rock layer; a linearly increasing gradient of thermal infrared signal is speculated as a seepage zone with increasing water content with depth, to ensure the precise dynamic optimization of the cutterhead parameters.

[0031] Step S200, according to the excavation soil layer characteristic data and the characteristic change distribution, the excavation soil layer is derived, and the excavation soil layer characteristic space is constructed.

[0032] The step S200 further comprises a step S210 of establishing a mapping conversion relationship between the initial standard spectrum and the soil property feature; a step S220 of taking the signal change gradient of each soil layer as a mapping input, and performing soil property feature mapping output through the mapping conversion relationship to obtain the soil property distribution feature of each soil layer; and a step S230 of performing spatial embedding fusion on the soil property distribution features of the soil layers according to spatial coordinates to construct the tunneling soil layer feature space.

[0033] Preferably, the multispectral detection data is converted into a tunneling soil layer feature space that can be used for intelligent decision-making by establishing a spectrum-soil property feature mapping model, combining signal gradient analysis and spatial fusion. Specifically, based on laboratory or historical data, a quantitative mapping relationship between the initial standard spectrum and the soil property feature is established through a neural network, a support vector regression or a physical inversion model. For example, the absorption peak intensity of a specific wave band is linearly related to the water content, and the shape of the spectral reflectance curve can reflect the lithology category. The signal change gradient of each soil layer refers to the change rate of the spectral features of different depth soil layers with the tunneling depth or spatial position when detected by the multispectral sensor, which reflects the change speed of the attenuation, scattering or reflection intensity of electromagnetic wave signals of different wave bands when penetrating the soil layer.

[0034] Preferably, the signal change gradient and the feature change distribution of each soil layer are taken as mapping inputs, and the mapping conversion relationship is inputted to perform soil property feature mapping output, so that the corresponding soil property parameters are outputted layer by layer. For example, if the spectral gradient at a certain depth shows that the near-infrared absorption rate suddenly increases and the gradient value increases, the water content at this position is calculated through the water content mapping model; if the thermal infrared gradient suddenly changes, the lithology interface of sandstone→granite is identified through the lithology classifier, and then the soil property distribution feature of each soil layer is obtained, including the water content, cohesion and other parameters of each spatial point, so as to improve the soil property inversion accuracy.

[0035] Preferably, the soil property distribution features of each soil layer are associated with the milepost number, radial depth, ring angle and the like of the tunneling machine coordinate system to form a feature point cloud with spatial labels, and then the discrete points are fused into a continuous tunneling soil layer feature space through three-dimensional interpolation.

[0036] The step S300 comprises establishing a dynamic safety boundary constraint set, including a tunneling machine hard limit boundary, a geological mutation boundary, a settlement control boundary and a tool life boundary.

[0037] The step S300 further comprises a step S310 of setting the machine hard limit boundary of the tunneling machine based on the rated parameters and the tolerance threshold according to the equipment hard limit of the tunneling machine; a step S320 of establishing a constraint relationship between the tunneling broken degree index and the advancing speed to set the geological mutation boundary; a step S330 of determining the settlement control boundary according to the constraint threshold of the stratum loss rate and the soil chamber pressure fluctuation; and a step S340 of determining the cutter life boundary according to the cutter distribution structure and the cutter performance of the cutter head in combination with the tunneling constraint of the tunneling stratum.

[0038] Preferably, the dynamic safety boundary constraint set including the machine hard limit boundary, the geological mutation boundary, the settlement control boundary and the cutter life boundary is established through multi-dimensional dynamic constraint analysis to ensure the safe and efficient operation of the tunneling machine under complex geological conditions. Specifically, the machine hard limit boundary is an absolute safety threshold based on the mechanical performance of the equipment to prevent overload or structural damage. According to the design parameters of the tunneling machine such as the maximum torque, the rated thrust, the motor power and the like, the rated working interval is set, and then the tolerance threshold is set by adding engineering experience and safety specifications, for example, 90% of the rated torque is the pre-warning threshold and 105% is the hard limit shutdown threshold, which are used to monitor the cutter head drive system load in real time. If the parameters approach the hard limit, the speed reduction or shutdown protection is triggered immediately.

[0039] Preferably, the geological mutation boundary is a dynamic constraint for geological mutations such as alternating hard and soft strata and broken zones. The stratum stability is quantified by the rock mass fragmentation degree and the cutting specific energy mutation rate, and a negative correlation constraint between the tunneling broken degree index and the advancing speed is established. For example, when the tunneling broken degree index is greater than 0.8, the tunneling machine speed needs to be reduced to below 50% to avoid the cutter head jamming, cutter impact fracture or face collapse caused by geological mutations. The settlement control boundary is a dynamic threshold for ensuring that the ground settlement does not exceed the allowable value. Based on the stratum loss rate and the soil chamber pressure fluctuation, a double-index constraint is set. The stratum loss rate is the ratio of the excavation volume to the settlement tank volume. The tunneling parameters are dynamically adjusted by combining the requirements of urban environment to maintain the balance of the excavation face and prevent ground subsidence by adjusting the advancing force and the grouting pressure.

[0040] Preferably, the cutter life boundary is a replacement or maintenance threshold set according to the cutter wear prediction model. Based on the cutter distribution structure and the soil abrasiveness, the dynamic wear rate is calculated. The cutter distribution structure may include the distance between the cutters, the cutter edge material and the like. Then, the double thresholds of the wear amount and the tunneling mileage are set, for example, the cutter edge height loss of 15% and the forced inspection every 500 meters, to ensure the optimization of the cutter head speed / thrust combination and avoid unplanned shutdown caused by excessive cutter wear, thereby realizing accurate risk early warning, prolonging the cutter life and ensuring the continuity and stability of the tunneling.

[0041] Further, step S340 further comprises step S341 of constructing a dynamic model describing the mapping relationship between the tool wear rate and the tunneling parameters based on the tool material performance test data and the historical tunneling data, the tunneling parameters including the local thrust force, the cutterhead rotation speed and the rock and soil abrasiveness index; step S342 of dividing the cutterhead radially into N fan-shaped pressure regions according to the tool distribution structure of the cutterhead, N being a positive integer greater than or equal to 4; step S343 of calculating the tool wear rate of each pressure region based on the dynamic model in combination with the current tunneling parameters and generating a wear space distribution thermal map; step S344 of analyzing the critical working conditions of the tool performance according to the characteristic parameters of the tunneling stratum, including the maximum allowable contact stress, the abrasiveness index threshold and the temperature limit; and step S345 of taking the mechanical load deviation rate, the wear space distribution thermal map and the tunneling constraints as inputs, determining the minimum tool residual life value of the whole cutterhead through the shortest plate effect and setting the corresponding tunneling parameter threshold as the tool life boundary.

[0042] Preferably, the laboratory tool material performance test data is obtained, including the wear rate curve under different stresses and temperatures, such as the hardness alloy compression strength and heat resistance, and the historical tunneling data, such as the tool wear record under different rock strata, and then a dynamic model of the mapping relationship between the tool wear rate and the tunneling parameters is established based on nonlinear regression or neural network, so as to quantify the influence of the tunneling parameters on the tool life, wherein the tunneling parameters include the local thrust force, the cutterhead rotation speed and the rock and soil abrasiveness index, the local thrust force is the axial thrust force borne by a single tool or a local region on the cutterhead, insufficient thrust force will lead to reduced cutting efficiency, and excessive thrust force will accelerate tool wear and even breakage; the cutterhead rotation speed is the angular speed of the cutterhead rotation, excessively high rotation speed will increase the centrifugal force and lead to increased tool temperature, and excessively low rotation speed will reduce the tunneling efficiency and possibly cause cutterhead vibration.

[0043] Preferably, the tangential force and the radial force borne by the tools at different radii are unevenly distributed, the cutterhead is divided radially into N fan-shaped regions based on the tool installation positions, such as the center tool, the normal tool and the edge tool, and the tool distribution structure, such as the radial / circumferential arrangement of the rolling cutter and the scraper, wherein N is a positive integer greater than or equal to 4, usually 8-12 regions, each region containing a plurality of tools, so as to realize fine spatial monitoring of tool wear and avoid that the global average value masks the local high-risk region. Based on the dynamic model, the current tunneling parameters and the soil abrasiveness are input in real time to calculate the instantaneous wear rate of each fan-shaped region, and a wear space distribution thermal map is generated through an interpolation algorithm to visually display the high wear region and the low wear region, wherein the high wear region is a red warning region, the edge tool has high linear speed and the wear is accelerated; and the low wear region is a blue safe region, the center tool has low rotation speed and the wear is slow.

[0044] Preferably, based on the characteristic parameters of the tunneling soil layer, the critical working conditions of the cutting tool performance are analyzed, including the maximum allowable contact stress, the abrasion index threshold, and the temperature limit. Specifically, the maximum allowable contact stress is set based on the material compressive strength test, setting the maximum stress limit per unit area of ​​the cutting edge, such as 2000 MPa. Exceeding this limit may lead to chipping. The abrasion index threshold is based on historical data statistics. When the abrasion index > 5, it indicates a highly abrasive formation, and the rotation speed is forcibly reduced by 20% to extend the tool's life. The temperature limit is a critical value used to ensure whether the cutting tool needs cooling. The cutting temperature of the cutting tool is monitored by infrared, and the cooling system is triggered when it exceeds 300°C to prevent the predicted results from deviating from the actual working conditions.

[0045] Preferably, the minimum remaining tool life of the entire cutterhead is determined by using the mechanical load deviation rate, wear spatial distribution heat map, and tunneling constraints as inputs, through the shortest board effect. The mechanical load deviation rate refers to the deviation between the actual load and the design rated value in each sector of the cutterhead's pressure zone, used to quantify the uniformity of load distribution. The wear spatial distribution heat map visually displays the real-time wear rate or cumulative wear in different areas of the cutterhead. Tunneling constraints refer to the engineering boundary conditions that must be met during tunnel construction, including rigid limitations such as mechanical performance, safety regulations, and schedule requirements. By traversing the wear data of all tools and combining it with their material property curves, the remaining life of each tool is calculated, i.e., the difference between the cutting edge height scrap threshold and the current measured value. The ratio of the wear rate to the remaining life is used to screen out the minimum remaining life and its corresponding cutting tool, such as the No. 3 side tool with 48 hours remaining. Then, based on the dynamic model of the cutting tool, the limit working parameters that enable its life to reach are solved in reverse. If the minimum remaining life under the current parameter combination is 48 hours, the tunneling parameter thresholds that maintain a safety margin of 72 hours or more for the minimum remaining life are obtained through model inversion, namely, the maximum allowable thrust of 1800kN and the maximum speed of 2.8rpm. Then, the tunneling parameter thresholds are compared with the tunneling constraints, and the more stringent value is taken as the final boundary. Finally, the corresponding tunneling parameter thresholds are set as the tool life boundary to ensure that the overall tool life is accurately extended and the safety and stability of tunnel excavation are guaranteed.

[0046] In step S400, within the characteristic space of the tunneling soil layer, with multiple objectives of minimizing specific energy, minimizing tool wear, and minimizing settlement risk, the cutterhead parameters are optimized based on the cutterhead adjustment constraints. The optimized solution set obtained by optimization is then trimmed using the safety boundary constraint set, and the cutterhead execution parameters are output for cutterhead adjustment.

[0047] Step S400 further includes step S410, which establishes an evaluation function relationship between soil layer characteristics and cutterhead parameters with the minimization of specific energy, the minimum of cutter wear, and the minimum of settlement risk as multiple objectives; step S420, which performs a multi-directional search of cutterhead parameters within the cutterhead adjustment constraint range based on the soil layer characteristics in the tunneling soil layer characteristic space, evaluates the search parameters using the multi-objective evaluation function, and finds a multi-optimized solution set that meets the objective evaluation results as the output.

[0048] Preferably, by establishing a quantitative mapping relationship between soil layer characteristics, cutterhead parameters, and construction objectives, and under the premise of meeting safety constraints, the system automatically searches for the optimal combination of tunneling parameters that comprehensively optimizes energy consumption, tool wear, and settlement risk. This enables intelligent and adaptive adjustment of the tunnel boring machine cutterhead. Specifically, energy consumption is the energy consumed per unit volume of tunneling. The energy minimization objective refers to establishing the physical relationship between soil hardness, moisture content, and other characteristics and energy consumption, focusing on reducing energy consumption. The tool wear minimization objective refers to mapping soil abrasiveness, propulsion force, and rotational speed to a wear rate index based on a wear dynamics model, aiming to minimize the wear rate. The settlement risk minimization objective refers to constructing a settlement risk scoring model using parameters such as ground loss rate and soil chamber pressure stability to suppress the risk of exceeding limits. Furthermore, a weighted comprehensive... A functional relationship between soil layer characteristics and cutterhead parameters is established. Then, in a multi-dimensional parameter space including cutterhead rotation speed, zone thrust, and torque, the cutterhead adjustment constraint range is defined, including red lines such as not exceeding the equipment's rated power and settlement threshold, and prioritizing the satisfaction of the cutter life boundary. Next, based on the soil layer characteristics in the tunneling soil layer characteristic space, a multi-directional search for cutterhead parameters is performed within the cutterhead adjustment constraint range. Specifically, hundreds of sets of randomly generated rotation speed and thrust parameter combinations are initialized for parameter evaluation. The evaluation score of each set of parameters is calculated using a multi-objective evaluation function, retaining high-quality solutions that are close to low specific energy, low wear, and low risk. Then, a new generation of parameter combinations is generated through simulation mutation and crossover, and the search parameter evaluation continues. Finally, a multi-optimized solution set that meets the target evaluation results is output.

[0049] Furthermore, step S420 also includes step S421, establishing a three-dimensional feature tensor space based on the tunneling axis, wherein the X-axis represents the tunneling mileage, the y-axis represents the radial depth, and the z-axis represents the feature parameter value; step S422, mapping the soil feature vectors of each soil layer to the grid nodes of the three-dimensional feature tensor space according to the spatial distribution coordinates; step S423, based on the three-dimensional feature tensor space, fitting the searched cutterhead parameters to the tunneling mileage and radial depth to construct complete time-series cutterhead parameters and generate the multi-optimization solution set.

[0050] Preferably, a three-dimensional feature tensor space is established with the X-axis as the tunneling mileage, the y-axis as the radial depth, and the z-axis as the feature parameter value, based on the tunneling axis. The X-axis is based on the tunnel axis and is divided into grids per meter / ring; the y-axis extends from the excavation surface to the depth of the strata, reflecting the range of advance detection; the z-axis is a normalized multi-dimensional geological feature, such as water content of 0% to 100% and abrasion index of 1 to 10. Then, the soil layer feature vectors obtained from multispectral detection are inserted into the corresponding grid nodes in the three-dimensional feature tensor space according to their spatial distribution coordinates, and Kriging interpolation is used to fill in the undetected areas to form a continuous three-dimensional tensor field. Next, based on the three-dimensional feature tensor space, the searched cutterhead parameters are fitted with the tunneling mileage and radial depth, that is, the spatial dimension is added according to the cutter rotation speed and thrust, including radial adaptation and mileage prediction. Radial adaptation refers to matching the strata at different depths on the y-axis according to the radial position of the cutterhead partition, and mileage prediction refers to preloading the strata features 50m ahead along the X-axis in advance to pre-generate parameter plans. Then, dynamic fitting is performed, that is, extracting the features of the directly excavated area and loading the features of the area to be excavated during the tunneling process, and generating the corresponding complete time-series cutterhead parameters. Finally, in the three-dimensional tensor space, the local optimal parameters are solved in parallel with the feature values ​​of each grid node as input, and multiple optimized solution sets are output, which may include parameters matching different radial depths for each mileage segment, thereby ensuring the stability and reliability of the tunnel boring machine operation.

[0051] Furthermore, step S422 also includes step S4221, establishing the number of grids in the X and Y axes of the three-dimensional feature tensor space, where the number of grids on the X axis corresponds to the tunneling process and the number of grids on the Y axis corresponds to the radial depth; step S4222, calculating the Euclidean distance from the grid node to the soil layer detection point, calculating the feature value according to the inverse distance weight, and assigning the feature value to the Z-axis feature channel corresponding to the grid node.

[0052] Preferably, by discretizing the tunneling environment into a structured grid and accurately interpolating the geological features of discrete detection points to the full-space grid nodes based on spatial distance weights, a quantifiable three-dimensional digital twin geological model is constructed, providing high-precision spatial data for cutterhead parameter optimization. Specifically, in the X-axis tunneling mileage direction, divided according to construction ring number or mileage station, the number of grids in the X-axis direction in the three-dimensional feature tensor space is the ratio of the total tunneling length to the grid spacing. For example, if the total tunnel length is 1000 meters and the grid spacing is 0.5 meters, then the number of grids in the X-axis = 2000. In the Y-axis radial depth direction, divided from the excavation face to the depth of the stratum, the number of grids in the Y-axis direction in the three-dimensional feature tensor space is the ratio of the detection depth to the layer thickness. For example, if the detection depth is 30 meters and each meter is divided into 1 layer, the number of grids in the Y-axis is 30. Key areas can be densified, such as dividing 0-5 meters into 10 layers, to reflect the differences in the cutting strata of the cutterhead at different positions. Then, for each grid node, the Euclidean distance to the soil detection point is calculated, and an eigenvalue is calculated according to the inverse distance weighting, that is, the reciprocal of the Euclidean distance is used as the eigenvalue of the node. The closer the grid node is to the detection point, the greater the influence. Finally, the eigenvalue is assigned to the corresponding Z-axis feature channel of the grid node. Each grid node can contain multiple Z-axis feature channels. By adjusting the grid density to balance calculation efficiency and control accuracy, the operational stability and reliability of the tunnel boring machine are ensured.

[0053] Preferably, the optimized solution set obtained through optimization is pruned using a set of safety boundary constraints. Specifically, tunneling time-series simulation is performed based on the three-dimensional feature tensor space and multiple optimized solution sets to analyze ground loss, tunneling fracturing, and cutter and equipment wear during the tunneling process. This includes ground loss simulation, rock fracturing analysis, and wear accumulation calculation. Ground loss simulation calculates the over-excavation rate based on the soil chamber pressure and advance speed to predict the development of surface settlement troughs. For example, a thrust of 2000 kN and a rotation speed of 3 rpm in a sand layer indicates a predicted settlement of 8 mm, close to the control boundary. Rock fracturing analysis assesses the rock mass fracturing degree by inverting the rock mass fracturing degree through cutting energy. Collapse risks, such as when the fragmentation index is >0.7, are marked as potential spalling zones. The cumulative wear calculation is based on the dynamic model to iteratively calculate the tool wear depth, for example, the cumulative wear of the side tool in the granite layer is 0.3mm / ring. Then, the simulation results are checked ring by ring using the hard limit boundary of the tunnel boring machine, the geological mutation boundary, the settlement control boundary and the tool life boundary in the safety boundary constraint set. The cutterhead parameters that cause any boundary to exceed the limit are eliminated and the final cutterhead execution parameters are output for the cutterhead adjustment of the tunnel boring machine, thereby improving the tunnel excavation efficiency, stability, construction safety and extending the tool life and reducing tool consumption.

[0054] In the above text, refer to Figure 1 A method for intelligent adjustment of the cutterhead of a tunnel boring machine according to an embodiment of the present invention is described in detail. Next, reference will be made to... Figure 2A cutterhead intelligent adjustment system for a tunnel boring machine is described according to an embodiment of the present invention.

[0055] The intelligent cutterhead adjustment system for tunnel boring machines according to embodiments of the present invention addresses the technical problems in existing technologies, such as insufficient dynamic perception of geological conditions, poor adaptability to dynamic safety constraints, and difficulty in balancing multi-objective optimization conflicts, leading to low tunneling efficiency and stability, cutter life, and construction safety. It achieves the technical effects of improving tunneling efficiency, stability, construction safety, and extending cutter life. Figure 2 As shown, the intelligent cutterhead adjustment system for tunnel boring machines includes: a deep detection module 10, a soil layer deduction module 20, a safety boundary constraint configuration module 30, and a cutterhead parameter optimization module 40.

[0056] The deep exploration module 10 is used to acquire characteristic data of the tunneling soil layer, including water content, cohesion, abrasive index, and lithological distribution, and to obtain the characteristic variation distribution through deep exploration using a multispectral sensor. The tunneling soil layer derivation module 20 is used to deduce the tunneling soil layer based on the tunneling soil layer characteristic data and characteristic variation distribution, and to construct the tunneling soil layer characteristic space. The safety boundary constraint configuration module 30 is used to establish a dynamic safety boundary constraint set, including the tunneling machine hard limit boundary, geological abrupt change boundary, settlement control boundary, and cutter life boundary. The cutterhead parameter optimization module 40 is used to optimize the cutterhead parameters in the tunneling soil layer characteristic space with multiple objectives, including minimizing specific energy, minimizing cutter wear, and minimizing settlement risk, based on the cutterhead adjustment constraints, and to prune the optimized solution set obtained by optimization using the safety boundary constraint set, and output the cutterhead execution parameters for cutterhead adjustment.

[0057] The specific configuration of the deep detection module 10 will be described in detail below. The deep detection module 10 further includes: performing soil characteristic analysis on the excavation surface soil layer, including the water content, cohesion, abrasion index, and lithological distribution, obtaining standard soil layer data, and performing multispectral signal feature conversion on the standard soil layer data to set an initial standard spectrum; fitting and comparing the multispectral signals collected from the deep soil layer with the initial standard spectrum to obtain the signal change gradient of each soil layer and obtain the characteristic change distribution.

[0058] The specific configuration of the tunneling soil layer derivation module 20 will be described in detail below. The tunneling soil layer derivation module 20 further includes: establishing a mapping transformation relationship between the initial standard spectrum and the soil characteristics; based on the signal change gradient of each soil layer, using the characteristic change distribution as a mapping input, and performing soil characteristic mapping output through the mapping transformation relationship to obtain the soil distribution characteristics of each soil layer; and spatially embedding and fusing the soil distribution characteristics of each soil layer according to spatial coordinates to construct the tunneling soil layer feature space.

[0059] The specific configuration of the safety boundary constraint configuration module 30 will be described in detail below. The safety boundary constraint configuration module 30 further includes: setting the hard limit boundary of the tunneling machine based on its equipment hard limits, rated parameters, and tolerance thresholds; establishing a constraint relationship between the tunneling fragmentation index and the advance speed, and setting the geological abrupt change boundary; determining the settlement control boundary according to the constraint thresholds of the formation loss rate and soil pressure fluctuation; and determining the cutter life boundary based on the cutter distribution structure and cutter performance of the cutterhead, combined with the tunneling constraints of the tunneling soil layer.

[0060] The specific configuration of the safety boundary constraint configuration module 30 will be described in detail below. The safety boundary constraint configuration module 30 further includes: constructing a dynamic model describing the mapping relationship between tool wear rate and tunneling parameters based on tool material performance test data and historical tunneling data, wherein the tunneling parameters include local thrust, cutterhead rotation speed, and soil abrasiveness index; dividing the cutterhead radially into N sector-shaped pressure regions according to the tool distribution structure of the cutterhead, where N is a positive integer ≥ 4; calculating the tool wear rate of each pressure region based on the dynamic model and current tunneling parameters, and generating a wear spatial distribution heat map; analyzing the critical working conditions of tool performance based on the characteristic parameters of the tunneling soil layer, including maximum allowable contact stress, abrasiveness index threshold, and temperature limit; using mechanical load deviation rate, wear spatial distribution heat map, and tunneling constraints as inputs, determining the minimum remaining tool life value of the entire cutterhead through the shortest board effect, and setting the corresponding tunneling parameter threshold as the tool life boundary.

[0061] The specific configuration of the cutterhead parameter optimization module 40 will be described in detail below. The cutterhead parameter optimization module 40 further includes: establishing an evaluation function relationship between soil layer characteristics and cutterhead parameters with multiple objectives, including minimizing specific energy, minimizing tool wear, and minimizing settlement risk; performing multi-directional search of cutterhead parameters within the cutterhead adjustment constraint range based on the soil layer characteristics in the tunneling soil layer characteristic space; evaluating the search parameters using the multi-objective evaluation function; and finding a multi-optimized solution set that meets the objective evaluation results as the output.

[0062] The following will describe the specific configuration of the cutterhead parameter optimization module 40 in detail. The cutterhead parameter optimization module 40 further includes: establishing a three-dimensional feature tensor space based on the tunneling axis, where the X-axis represents the tunneling mileage, the y-axis represents the radial depth, and the z-axis represents the feature parameter values; mapping the soil feature vectors of each soil layer to the grid nodes of the three-dimensional feature tensor space according to their spatial distribution coordinates; and fitting the searched cutterhead parameters to the tunneling mileage and radial depth based on the three-dimensional feature tensor space to construct complete time-series cutterhead parameters and generate the multi-optimization solution set.

[0063] The specific configuration of the cutterhead parameter optimization module 40 will be described in detail below. The cutterhead parameter optimization module 40 further includes: establishing the number of grids in the X and Y axes of the three-dimensional feature tensor space, where the number of grids on the X axis corresponds to the corresponding tunneling process, and the number of grids on the Y axis corresponds to the corresponding radial depth; calculating the Euclidean distance from the grid node to the soil layer detection point, calculating feature values ​​according to inverse distance weights, and assigning the feature values ​​to the Z-axis feature channel corresponding to the grid node.

[0064] The intelligent cutterhead adjustment system for tunnel boring machines provided in this embodiment of the invention can execute the intelligent cutterhead adjustment method for tunnel boring machines provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0065] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention. This electronic device is in the form of a general-purpose computing device, and its components may include, but are not limited to, an input device 401, a processor 402, a memory 403, and an output device 404. The processor 402 may be one or more; the memory 403 may include a computer-readable medium and at least one program product having a set (at least one) of program modules configured to perform the functions of the embodiments of this application.

[0066] The memory 403 shown in this embodiment of the invention can be any combination of one or more computer-readable media. The computer-readable storage medium can be, but is not limited to, infrared, semiconductor systems, devices or components, or any combination thereof, used to store software programs, computer-executable programs and modules, such as the program instructions / modules corresponding to the intelligent cutterhead adjustment method for tunnel boring machines in this embodiment of the invention. The processor 402 executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory 403, thereby realizing the above-mentioned intelligent cutterhead adjustment method for tunnel boring machines.

[0067] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0068] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for intelligent adjustment of a cutterhead of a tunnel boring machine, characterized in that, The method comprises: obtaining tunneling soil layer characteristic data, including water content, cohesion, abrasion index and lithology distribution, and performing deep layer detection through a multispectral sensor to obtain characteristic change distribution, including: performing soil quality characteristic analysis on the tunneling surface soil layer, including the water content, cohesion, abrasion index and lithology distribution, obtaining standard soil layer data, and performing multispectral signal characteristic conversion on the standard soil layer data, and setting an initial standard spectrum; fitting and comparing the multispectral signals collected from the deep layer soil with the initial standard spectrum to obtain the signal change gradient of each soil layer, and obtaining the characteristic change distribution; deriving the tunneling soil layer according to the tunneling soil layer characteristic data and the characteristic change distribution to construct a tunneling soil layer characteristic space; establishing a dynamic safety boundary constraint set, including a tunneling machine hard limit boundary, a geological mutation boundary, a settlement control boundary and a tool life boundary; in the tunneling soil layer characteristic space, taking minimum specific energy, minimum tool wear and minimum settlement risk as multiple objectives, performing cutter head parameter optimization according to cutter head adjustment constraints, and using the safety boundary constraint set to clip the optimization solution set obtained by optimization to output cutter head execution parameters for cutter head adjustment; wherein, in the tunneling soil layer characteristic space, taking minimum specific energy, minimum tool wear and minimum settlement risk as multiple objectives, performing cutter head parameter optimization according to cutter head adjustment constraints, comprising: taking the minimum specific energy, the minimum tool wear and the minimum settlement risk as multiple objectives to establish an evaluation function relationship between the soil layer characteristics and the cutter head parameters; based on the soil layer characteristics in the tunneling soil layer characteristic space, performing cutter head parameter multidirectional search within the cutter head adjustment constraint range, using the evaluation function of the multiple objectives to evaluate the search parameters, and finding a multiple optimization solution set that meets the objective evaluation results as output; further comprising: establishing a three-dimensional feature tensor space with the tunneling axis as the reference, wherein the X-axis is the tunneling mileage, the y-axis is the radial depth, and the z-axis is the feature parameter value; mapping the soil quality characteristic vectors of each soil layer to the grid nodes of the three-dimensional feature tensor space according to the spatial distribution coordinates; based on the three-dimensional feature tensor space, fitting the searched cutter head parameters with the tunneling mileage and the radial depth to construct complete time sequence cutter head parameters, and generating the multiple optimization solution set; wherein, mapping the soil quality characteristic vectors of each soil layer to the grid nodes of the three-dimensional feature tensor space according to the spatial distribution coordinates, comprises: establishing the grid numbers of the X and Y axes in the three-dimensional feature tensor space, wherein the X-axis grid number corresponds to the tunneling mileage, and the Y-axis grid number corresponds to the radial depth; calculating the Euclidean distance from the grid nodes to the soil layer detection points, calculating the feature values according to the inverse distance weighting, and assigning the feature values to the Z-axis feature channels corresponding to the grid nodes.

2. The intelligent adjustment method for a cutterhead of a tunnel boring machine according to claim 1, characterized in that, deriving the tunneling soil layer according to the tunneling soil layer characteristic data and the characteristic change distribution to construct a tunneling soil layer characteristic space, comprising: establishing a mapping conversion relationship between the initial standard spectrum and the soil quality characteristics; based on the signal change gradient of each soil layer, taking the characteristic change distribution as the mapping input, performing soil quality characteristic mapping output through the mapping conversion relationship to obtain the soil quality distribution characteristics of each soil layer; The soil quality distribution characteristics of each soil layer are spatially embedded and fused according to spatial coordinates to construct a feature space of the tunneling soil layer.

3. The intelligent cutterhead adjustment method for a tunneling machine of claim 1, wherein, A dynamic safety boundary constraint set is established, including: According to the equipment hard limit of the tunneling machine, the hard limit boundary of the tunneling machine is set based on the rated parameters and the tolerance threshold; A constraint relationship between the tunneling broken degree index and the advancing speed is established to set the geological mutation boundary; According to the constraint threshold of the strata loss rate and the soil bin pressure fluctuation, the settlement control boundary is determined; According to the cutter distribution structure and the cutter performance of the cutter head, the cutter life boundary is determined in combination with the tunneling constraints of the tunneling soil layer.

4. The intelligent cutterhead adjustment method for a tunneling machine of claim 3, wherein, According to the cutter distribution structure and the cutter performance of the cutter head, the cutter life boundary is determined in combination with the tunneling constraints of the tunneling soil layer, including: Based on the cutter material performance test data and the historical tunneling data, a dynamic model describing the mapping relationship between the cutter wear rate and the tunneling parameters is constructed, the tunneling parameters including the local advancing force, the cutter head speed and the rock and soil abrasiveness index; According to the cutter distribution structure of the cutter head, the cutter head is divided into N fan-shaped pressure regions in the radial direction, N being a positive integer greater than or equal to 4; Based on the dynamic model, the cutter wear rate of each pressure region is calculated in combination with the current tunneling parameters, and a wear space distribution thermal map is generated; According to the feature parameters of the tunneling soil layer, the critical working conditions of the cutter performance are analyzed, including the maximum allowable contact stress, the abrasiveness index threshold and the temperature limit; Taking the mechanical load deviation rate, the wear space distribution thermal map and the tunneling constraint as inputs, the minimum cutter residual life value of the whole cutter head is determined through the shortest plate effect, and the corresponding tunneling parameter threshold is set as the cutter life boundary.

5. A cutterhead intelligent adjustment system for a tunnel boring machine, characterized in that, The system is used to implement the cutter head intelligent adjustment method for a tunnel boring machine according to any one of claims 1 to 4, and the system comprises: A deep exploration module is configured to obtain tunneling soil layer feature data, including water content, cohesion, abrasiveness index and lithology distribution, and to obtain feature change distribution through multi-spectral sensors; A tunneling soil layer derivation module is configured to derive the tunneling soil layer based on the tunneling soil layer feature data and the feature change distribution, and to construct a tunneling soil layer feature space; A safety boundary constraint configuration module is configured to establish a dynamic safety boundary constraint set, including a tunneling machine hard limit boundary, a geological mutation boundary, a settlement control boundary and a cutter life boundary; A cutter head parameter optimization module is configured to perform cutter head parameter optimization in the tunneling soil layer feature space with the multi-objective of minimum specific energy, minimum cutter wear and minimum settlement risk, and to crop the optimization solution set obtained by optimization using the safety boundary constraint set, and output cutter head execution parameters for cutter head adjustment.

6. An electronic device, comprising: The electronic device comprises: A memory for storing executable instructions; A processor for executing the executable instructions stored in the memory to implement the cutter head intelligent adjustment method for a tunneling machine according to any one of claims 1 to 4.

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