Method and system for determining a cutter exchange combination for a tunnel boring machine cutter head

Abstract

Embodiments of the present application relate to the field of electric digital data processing, and particularly relate to a method and system for determining a combination of cutter replacement for a tunnel boring machine. The method and system can determine a plurality of possible candidate combinations of cutter replacement, and obtain an evaluation feature of each candidate combination of cutter replacement in combination with the overall cutter wear degree, so as to avoid damaging the overall balance of the cutter disc by only focusing on a single severely worn cutter. Furthermore, in combination with the evaluation feature of the candidate combination of cutter replacement and the running state feature, the method and system can predict the corresponding tunneling performance improvement degree of each candidate combination of cutter replacement, so as to convert the performance of different combinations of cutter replacement into quantifiable comparison indexes, so as to directly determine the preferred combination of cutter replacement according to the tunneling performance improvement degree. Compared with the experience-based judgment of performing the cutter replacement operation, the method and system can accurately replace the cutter according to the specific wear condition, and can also avoid excessive cutter replacement to increase the cost or insufficient cutter replacement to cause further damage to the cutter, thereby effectively improving the tunneling speed and the reliability of the cutter replacement.

Description

Technical Field

[0001] The embodiments of this application relate to the field of electronic digital data processing, and in particular to a method and system for determining the cutterhead changing combination of a tunnel boring machine. Background Technology

[0002] The statements herein are provided merely as background information in connection with this application and do not necessarily constitute prior art.

[0003] Full-face hard rock tunnel boring machines (TBMs) primarily rely on multiple cutters on the cutterhead to break the rock mass during the tunneling process. However, the installation radius, stress state, rock mass conditions, and service history of each cutter on the cutterhead are different, resulting in significant differences in the wear degree of cutters at different positions. Some cutters may also suffer damage such as uneven wear, chipping, and bearing malfunctions, which in turn affect the stress state of the cutterhead and the rock-breaking efficiency. Therefore, it is necessary to replace the cutters appropriately according to their condition. Summary of the Invention

[0004] A brief overview of this application is provided below to offer a basic understanding of certain aspects thereof. It should be understood that this overview is not an exhaustive summary of the application. It is not intended to identify key or essential parts of the application, nor is it intended to limit its scope. Its purpose is merely to present certain concepts in a simplified form as a prelude to the more detailed description that follows.

[0005] This application provides a method for determining cutterhead replacement combinations for a tunnel boring machine (TBM), comprising the following steps: S10: acquiring stable tunneling operation data of the TBM before cutterhead replacement and the state data of the cutterheads on the cutterhead; S20: determining the operating state characteristics of the TBM at the moment when cutterhead replacement is required based on the stable tunneling operation data and the cutterhead state data; S30: determining the wear degree of the entire cutterhead before cutterhead replacement based on the cutterhead state data; S40: determining candidate cutterhead replacement combinations from the cutterheads based on the cutterhead state data; S50: determining the evaluation characteristics of the candidate cutterhead replacement combinations based on the candidate cutterhead replacement combinations and the wear degree of the entire cutterhead before cutterhead replacement determined in step S30; S60: determining the degree of tunneling performance improvement corresponding to different candidate cutterhead replacement combinations based on the evaluation characteristics of the candidate cutterhead replacement combinations and the operating state characteristics determined in step S20; S70: determining the TBM cutterhead replacement combination based on the degree of tunneling performance improvement corresponding to different candidate cutterhead replacement combinations.

[0006] The method for determining cutterhead replacement combinations for tunnel boring machines (TBMs) provided in this application identifies multiple possible candidate combinations and combines them with the overall cutterhead wear level to obtain evaluation characteristics for each candidate combination. This avoids focusing solely on a single severely worn cutterhead, which could disrupt the overall cutterhead balance. Furthermore, by combining the evaluation characteristics of each candidate combination with the operating characteristics of the TBM before the cutterhead replacement, the degree of improvement in tunneling performance corresponding to each candidate combination can be predicted. This transforms the performance of different cutterhead replacement combinations into quantifiable and comparable indicators, allowing for the direct determination of the preferred cutterhead replacement combination based on the degree of improvement in tunneling performance. Compared to relying on experience to perform cutterhead replacement operations, this method allows for precise cutterhead replacement based on specific wear conditions. It also avoids excessive cutterhead replacement, which increases costs, or insufficient replacement, which could lead to further cutter damage, effectively improving tunneling speed and the reliability of cutterhead replacement.

[0007] This application, in another aspect, provides a system for determining cutterhead replacement combinations for a tunnel boring machine (TBM), comprising: a data acquisition unit for acquiring stable tunneling operation data of the TBM before cutterhead replacement and state data of the cutters on the cutterhead; and a processor configured to: determine the operating state characteristics of the TBM at the moment when cutterhead replacement is required based on the stable tunneling operation data and the cutterhead state data; determine the wear degree of the entire cutterhead before cutterhead replacement based on the cutterhead state data; determine candidate cutterhead replacement combinations from the cutters based on the cutterhead state data; determine the evaluation characteristics of the candidate cutterhead replacement combinations based on the candidate cutterhead replacement combinations and the wear degree of the entire cutterhead before cutterhead replacement; determine the degree of tunneling performance improvement corresponding to different candidate cutterhead replacement combinations based on the evaluation characteristics of the candidate cutterhead replacement combinations and the operating state characteristics; and determine the TBM cutterhead replacement combination based on the degree of tunneling performance improvement corresponding to different candidate cutterhead replacement combinations.

[0008] The system for determining cutterhead replacement combinations for tunnel boring machines (TBMs) provided in this application identifies multiple possible candidate combinations and combines them with the overall cutterhead wear level to obtain evaluation characteristics for each candidate combination. This avoids focusing solely on a single severely worn cutterhead, which could disrupt the overall cutterhead balance. Furthermore, by combining the evaluation characteristics of each candidate combination with the operating characteristics of the TBM before the cutterhead replacement, the system can predict the degree of improvement in tunneling performance corresponding to each candidate combination. This transforms the performance of different cutterhead replacement combinations into quantifiable and comparable indicators, allowing for the direct determination of the preferred cutterhead replacement combination based on the degree of improvement in tunneling performance. Compared to relying on experience to perform cutterhead replacement operations, this system can precisely replace cutters based on specific wear conditions. It also avoids excessive cutterhead replacement, which increases costs, or insufficient replacement, which could lead to further cutter damage, effectively improving tunneling speed and the reliability of cutterhead replacement. Attached Figure Description

[0009] To further illustrate the above and other advantages and features of this application, the specific embodiments of this application will be described in more detail below with reference to the accompanying drawings. The drawings, together with the following detailed description, are included in and form a part of this specification. Elements having the same function and structure are indicated by the same reference numerals. It should be understood that these drawings only depict typical examples of this application and should not be considered as limiting the scope of this application.

[0010] Figure 1 This is a comparative diagram showing the improvement in tunneling performance after using different cutterhead combinations. Detailed Implementation

[0011] Exemplary embodiments of this application will be described below with reference to the accompanying drawings. For clarity and brevity, not all features of actual implementations are described in the specification. However, it should be understood that many implementation-specific decisions must be made in the development of any such actual embodiment to achieve the developer's specific goals, such as complying with constraints related to the system and business, and these constraints may vary depending on the implementation. Furthermore, it should be understood that while development work can be very complex and time-consuming, such development work is merely a routine task for those skilled in the art who benefit from the content of this application.

[0012] It should also be noted that, in order to avoid obscuring this application with unnecessary details, only the equipment structure and / or processing steps closely related to the solution according to this application are shown in the accompanying drawings, while other details that are not closely related to this application are omitted.

[0013] The following disclosure provides several different implementations or examples for carrying out this application. To simplify the disclosure of this application, specific examples of components and methods are described below. Of course, these are merely examples and are not intended to limit this application. In the description of the embodiments of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0014] Currently, the maintenance of cutterheads during tunneling typically focuses on cutter wear detection, remaining life prediction, single-cutter replacement thresholds, cutter arrangement, and cutter disassembly / reassembly operations. However, since tunnel boring machines (TBMs) have numerous cutterheads on their cutterheads, sometimes multiple cutterheads need to be replaced simultaneously before proceeding to the next stage of tunneling. Relying solely on the wear of a single cutterhead or a single ranking result makes it difficult to directly characterize the change in the overall cutterhead wear state after a specific cutterhead combination undergoes a cutter replacement. It is also difficult to uniformly compare the differences between different combinations in terms of cutterhead space coverage, wear uniformity, and overall wear reduction.

[0015] To address the aforementioned problems, one embodiment of this application provides a method for determining cutterhead replacement combinations for a tunnel boring machine (TBM), comprising the following steps: S10: acquiring stable tunneling operation data of the TBM before cutterhead replacement and the state data of the cutterheads on the cutterhead; S20: determining the operating state characteristics of the TBM at the moment when cutterhead replacement is required based on the stable tunneling operation data and the cutterhead state data; S30: determining the wear degree of the entire cutterhead before cutterhead replacement based on the cutterhead state data; S40: determining candidate cutterhead replacement combinations from the cutterheads based on the cutterhead state data; S50: determining the evaluation characteristics of the candidate cutterhead replacement combinations based on the candidate cutterhead replacement combinations and the wear degree of the entire cutterhead before cutterhead replacement determined in step S30; S60: determining the degree of tunneling performance improvement corresponding to different candidate cutterhead replacement combinations based on the evaluation characteristics of the candidate cutterhead replacement combinations and the operating state characteristics determined in step S20; S70: determining the TBM cutterhead replacement combination based on the degree of tunneling performance improvement corresponding to different candidate cutterhead replacement combinations.

[0016] The method for determining cutterhead replacement combinations for tunnel boring machines (TBMs) provided in this application identifies multiple possible candidate combinations and combines them with the overall cutterhead wear level to obtain evaluation characteristics for each candidate combination. This avoids focusing solely on a single severely worn cutterhead, which could disrupt the overall cutterhead balance. Furthermore, by combining the evaluation characteristics of each candidate combination with the operating characteristics of the TBM before the cutterhead replacement, the degree of improvement in tunneling performance corresponding to each candidate combination can be predicted. This transforms the performance of different cutterhead replacement combinations into quantifiable and comparable indicators, allowing for the direct determination of the preferred cutterhead replacement combination based on the degree of improvement in tunneling performance. Compared to relying on experience to perform cutterhead replacement operations, this method allows for precise cutterhead replacement based on specific wear conditions. It also avoids excessive cutterhead replacement, which increases costs, or insufficient replacement, which could lead to further cutter damage, effectively improving tunneling speed and the reliability of cutterhead replacement.

[0017] In some embodiments, step S60 specifically includes the following steps: S61: Determine a predetermined evaluation method based on historical actual cutter replacement event data; S62: Determine a candidate evaluation vector corresponding to each candidate cutter replacement combination based on the evaluation characteristics and operating status characteristics of the candidate cutter replacement combinations; S63: Determine the degree of tunneling performance improvement corresponding to different candidate cutter replacement combinations based on the candidate evaluation vectors and the predetermined evaluation method.

[0018] The evaluation method is determined based on historical data of actual cutter replacement events, providing practical experience reference for the performance evaluation of each candidate cutter replacement combination. The degree of tunneling performance improvement corresponding to each candidate cutter replacement combination is determined by combining the candidate evaluation vector. This ensures that the tunneling performance improvement prediction of different candidate cutter replacement combinations is supported by historical data and takes into account the current specific working conditions, making the performance improvement prediction results of each candidate cutter replacement combination closer to reality.

[0019] In some embodiments, in step S61, the data of historical actual cutterhead replacement events includes the penetration depth before and after the cutterhead replacement and the total thrust before and after the cutterhead replacement for each historical actual cutterhead replacement event. Step S61 specifically includes the following steps: S611: Determine the standardized logarithmic penetration ratio after the cutterhead replacement based on the penetration depth before and after the cutterhead replacement and the total thrust before and after the cutterhead replacement for each historical actual cutterhead replacement event; S612: Determine the degree of improvement in tunneling performance for each historical actual cutterhead replacement event based on the standardized logarithmic penetration ratio after the cutterhead replacement; S613: Determine the binary classification label for each historical actual cutterhead replacement event based on the degree of improvement in tunneling performance and a preset performance improvement threshold; S614: Determine a predetermined evaluation method based on the data of historical actual cutterhead replacement events and the binary classification label.

[0020] Based on the penetration depth and total thrust before and after each historical cutterhead replacement event, the standardized logarithmic penetration ratio after the cutterhead replacement is determined. This eliminates the influence of thrust differences and provides a unified benchmark for the penetration depth changes under different working conditions. The degree of tunneling performance improvement after each historical cutterhead replacement event is determined based on the standardized logarithmic penetration ratio and compared with a preset performance improvement threshold. Each historical cutterhead replacement event is assigned a binary classification label, which transforms the continuous performance changes after each historical cutterhead replacement event into a binary judgment of whether the corresponding cutterhead replacement combination has met or not met expectations. The predetermined evaluation method thus determined can predict whether each candidate cutterhead replacement combination has met or not met expectations. This evaluation method is based on a large amount of historical data, and when subsequently used to evaluate candidate cutterhead replacement combinations, the judgment results are closer to real working conditions, making the process of selecting the best cutterhead replacement combination from the candidates repeatable.

[0021] Figure 1 This is a comparative diagram showing the improvement in tunneling performance after using different cutterhead combinations, such as... Figure 1 As shown in the figure, there are two cutter replacement schemes: the historical actual cutter replacement combination and the preferred cutter replacement combination of this application. The blue bars represent the probability that the tunneling performance improvement reaches the preset performance improvement threshold after using different cutter replacement combinations, and the orange bars represent the tunneling performance improvement degree of different cutter replacement combinations. It can be seen that the cutter replacement combination determined by the method provided in this application is higher than the historical actual cutter replacement combination in terms of both the probability of achieving the target and the cutter replacement gain, and the improvement effect on tunneling performance is more obvious.

[0022] In some embodiments, the degree of improvement in tunneling performance in step S612 is determined as follows: ;in, Indicates the degree of improvement in tunneling performance; This represents the standardized logarithmic penetration ratio after tool change. The above setting converts the logarithmic difference in penetration efficiency before and after tool change into an intuitive percentage, making the improvement effect of each historical actual hobbing tool change event a directly comparable value. It also facilitates subsequent comparison with preset performance improvement thresholds to assign binary classification labels, thereby making the evaluation of candidate tool change combinations more intuitive.

[0023] In some embodiments, in step S611, the normalized logarithmic penetration ratio after tool change is determined as follows: ;in, This represents the standardized logarithmic penetration ratio after tool change; and These represent the penetration depth before and after tool change, respectively. and These represent the total propulsion force before and after tool change, respectively; β represents the propulsion force correction coefficient.

[0024] The above settings utilize the thrust correction coefficient β to eliminate the influence of thrust fluctuations on penetration, so that the penetration efficiency changes under different thrust conditions have a unified comparison benchmark, and the penetration efficiency changes of each historical actual cutter replacement event can be quantified for horizontal comparison.

[0025] In some embodiments, the propulsion correction factor may be determined based on historical data, engineering experience, or sensitivity analysis.

[0026] In some embodiments, in step S613, when the tunneling performance improvement reaches a preset performance improvement threshold, the corresponding historical actual cutter replacement event can be marked as an event that has achieved the expected cutter replacement effect; otherwise, it can be marked as an event that has not achieved the expected cutter replacement effect.

[0027] In some embodiments, in step S611, the penetration depth is used to characterize the actual axial tunneling amount corresponding to each rotation of the cutterhead. Its physical definition is the ratio of the actual tunneling distance in the stable tunneling section to the cumulative number of cutterhead rotations in that stable tunneling section, and it is determined as follows: Where P represents the penetration depth; L represents the actual tunneling distance of the stable tunneling section; n represents the average cutterhead rotation speed of the stable tunneling section; and T represents the tunneling duration of the stable tunneling section.

[0028] By determining the penetration depth using three measured physical quantities—actual tunneling distance, average cutterhead rotation speed, and tunneling duration—the influence of abnormal working conditions and operational fluctuations can be eliminated. This allows the penetration depth to more accurately reflect the rock-breaking efficiency under pure tunneling conditions, thus making the penetration depth comparable under different times and working conditions.

[0029] In some embodiments, when the cutterhead rotation speed changes with time, n is the average cutterhead rotation speed obtained by time weighting within a stable tunneling section, such that nT / 60 is equal to the cumulative number of cutterhead rotations corresponding to that stable tunneling section.

[0030] Understandably, in step S611, the penetration before tool change... The penetration depth is determined by the actual tunneling distance, average cutterhead speed, and tunneling duration of the stable tunneling section before cutterhead replacement; the penetration depth after cutterhead replacement... It is determined by the actual tunneling distance of the stable tunneling section after cutterhead replacement, the average cutterhead rotation speed, and the tunneling duration.

[0031] In some embodiments, in step S614, the sample input for each historical actual hobbing tool replacement event can be determined based on the operating state characteristics before tool replacement and the evaluation characteristics of the corresponding actual tool replacement combination; the classification result for each historical actual hobbing tool replacement event can be determined based on the binary classification label of each historical actual hobbing tool replacement event determined in step S613; the historical samples for each historical actual hobbing tool replacement event, consisting of the sample input and classification result of a single event, can be determined based on the classification result and the corresponding sample input; all historical samples can be divided into a training set and a test set using historical actual hobbing tool replacement events as the dividing unit; and missing value imputation and feature standardization processing can be performed on the sample inputs in the training set and the test set.

[0032] Based on the sample input of the training set and the corresponding classification results, a pre-set threshold classification model is trained to initially determine the classification method; based on the sample input of the training set and the degree of tunneling performance improvement determined in step S612, a pre-set continuous effect model is trained to initially determine the continuous prediction method; based on the sample input of the test set and the corresponding classification results, the initially determined classification method is verified; based on the sample input of the test set and the corresponding degree of tunneling performance improvement, the initially determined continuous prediction method is verified; the verified classification method and continuous prediction method are used together as the predetermined evaluation method.

[0033] The predetermined evaluation method includes at least one of a threshold classification model and a continuous effect model. When both models are used simultaneously, the threshold classification model is used to determine whether the candidate cutter change combination has reached the preset performance improvement threshold, and the continuous effect model is used to obtain the predicted value of the tunneling performance improvement of each candidate cutter change combination, and to assist in the ranking of the candidate cutter change combinations that meet the engineering constraints.

[0034] The training and test sets are divided based on historical actual cutter replacement events. Each historical actual cutter replacement event corresponds to one actual cutter replacement operation performed on-site, ensuring that data corresponding to the same historical actual cutter replacement event does not appear simultaneously in the training and test data. Model parameter selection is completed using only the training set. The threshold classification model can adopt an L2 regularized logistic regression model, and the continuous effect model can adopt a ridge regression model. However, the candidate scheme evaluation model of this invention is not limited to the specific model forms mentioned above, and the model training process is feasible for those skilled in the art.

[0035] Understandably, the actual historical hob change event's post-change operation data is only used to determine the predetermined evaluation method, and is not used as part of the candidate evaluation vector corresponding to the candidate tool change combination.

[0036] In some embodiments, in step S62, combining the evaluation features of the candidate tool change combination with the operating state features before the tool change to form a candidate evaluation vector is something that can be achieved by those skilled in the art.

[0037] In some embodiments, in step S63, the candidate evaluation vector formed by combining steps S62 can be used as input to the predetermined evaluation method determined in step S61, so as to determine the degree of tunneling performance improvement of each candidate tool change combination or whether each candidate tool change combination reaches a preset performance improvement threshold.

[0038] In some embodiments, step S40 specifically includes the following steps: S41: Determine the current wear amount of each hob on the cutter head based on the hob status data; S42: Determine a basic candidate set of hobs that need to be replaced based on the current wear amount of each hob; S43: Determine the hobs that need to be forcibly replaced based on the hob status data, and add the hobs that need to be forcibly replaced to the basic candidate set; S44: Determine the hobs that cannot be replaced based on the hob status data; S45: Remove the hobs that cannot be replaced based on step S44 from the basic candidate set determined in step S43, and obtain a candidate hob set; S46: Determine the candidate tool replacement combination based on the candidate hob set.

[0039] By identifying hobs that need to be replaced and eliminating those that cannot be replaced, the resulting candidate tool replacement combinations take into account both the actual wear level and the mandatory requirements under special working conditions, ensuring the reliability and feasibility of the candidate tool replacement combinations in actual operation.

[0040] In some embodiments, in step S42, a preset number of hobs can be selected in descending order of current wear to form a basic candidate set.

[0041] In some embodiments, in step S43, some hobs on the cutter head have little current wear so that they are not in the basic candidate set, but may have abnormal damage (such as uneven wear, breakage, ring slippage, etc.) or meet the conditions for forced replacement (such as due to chipping, bearing abnormality, or maintenance requirements, etc.). As hobs that need to be forcibly replaced, they also need to be forcibly incorporated into the basic candidate set.

[0042] In some embodiments, in step S45, some hobs in the basic candidate set cannot be replaced due to factors such as structural safety, limited operating conditions, or unavailability of the hob to be replaced. Therefore, these hobs need to be removed from the basic candidate set so that the final candidate hob set meets both the objective wear level required for replacement and the practical conditions for performing the replacement operation. This facilitates the simplification of the number of candidate tool replacement combinations and reduces the amount of computation.

[0043] In some embodiments, in step S45, the hob size of the candidate hob set can be set according to the total number of hobs on the cutter head, the allowable downtime, spare parts conditions, and the calculation scale.

[0044] In some embodiments, in step S46, within a preset replacement quantity range (which can be determined according to actual conditions), multiple candidate tool replacement combinations are generated from the candidate hobbing cutter set as follows: Each candidate tool replacement combination includes the specific hobbing cutter number of one or more hobbing cutters to be replaced. For a candidate hobbing cutter set containing multiple hobbing cutters, when each tool replacement combination is allowed to replace hobbing cutters within the preset replacement quantity range, multiple candidate tool replacement combinations can be formed by combination enumeration. The candidate tool replacement combinations enumerated at the moment when tool replacement is needed belong to the tool replacement scheme to be evaluated and are not used as newly added historical actual hobbing cutter replacement event samples, thus maintaining the boundary of real event samples.

[0045] In some embodiments, step S50 specifically includes the following steps: S51: determining the initial wear amount of the hob included in each candidate tool change combination; S52: determining the wear degree of the entire cutter disc after tool change for each candidate tool change combination after tool change based on the initial wear amount of the hob included in each candidate tool change combination determined in step S51 and the wear degree of the entire cutter disc before tool change; S53: determining the evaluation characteristics of the candidate tool change combination based on each candidate tool change combination, the wear degree of the entire cutter disc before tool change, and the wear degree of the entire cutter disc after tool change.

[0046] By determining the degree of wear on the entire cutterhead after each candidate tool change combination, the impact of each candidate tool change combination on the overall wear balance of the cutterhead is considered, thus avoiding the destruction of the overall load distribution by only considering local repairs.

[0047] In some embodiments, in step S30, the current wear of each hob at the moment when tool replacement is required can be arranged according to the hob number to form a full tool disc wear vector before tool replacement, so as to characterize the wear degree of the full tool disc before tool replacement.

[0048] In some embodiments, in step S52, for any candidate tool change combination, the current wear amount corresponding to the current position of each hob in the combination is replaced with the initial wear amount of the corresponding hob to be replaced, while keeping the wear amounts of the remaining hobs unchanged, to obtain the overall cutter head wear degree after tool change corresponding to the candidate tool change combination. It can be understood that "before and after tool change" here refers to simulating hob replacement to update the overall cutter head wear state, rather than reading actual operating or wear data after tool change in the field. Therefore, even without performing tool change in the field, the simulated overall cutter head wear state corresponding to multiple candidate tool change combinations can be obtained and compared against the state baseline at the moment when tool change is required.

[0049] In some embodiments, in step S53, the evaluation characteristics of the candidate tool change combination may include at least the number of hobs included in the candidate tool change combination, the number of cutter head space partitions covered by the candidate tool change combination, the wear percentile statistics of each hob in the candidate tool change combination in the overall cutter head wear sequence before replacement, the standard deviation of the overall cutter head wear after simulated tool change, and the percentage reduction in total wear of the entire cutter head. The wear percentile statistics can be the mean of the wear percentiles of each hob in the candidate tool change combination. Using the spatial coverage characteristics of the candidate tool change combination on the cutter head and the overall cutter head wear distribution change characteristics together for the evaluation of the candidate tool change combination can characterize the impact of joint replacement of multiple hobs on the overall cutter head wear state, enhancing the scientificity and reliability of the finally obtained hob replacement combination.

[0050] The cutter head space can be divided according to at least one of the hob installation radius and hob type. For example, the cutter head can be divided into a central area, inner ring area, middle ring area, outer ring area, and edge area according to the installation radius; other zoning methods can also be used according to the actual cutter head structure. For any candidate replacement combination, the number of different space zones involved is counted to characterize the spatial coverage of the candidate tool replacement combination on the cutter head.

[0051] The total wear reduction ratio R of the entire cutter head can be determined as follows: ; To replace the wear of the i-th hob on the front cutter head; To simulate the wear of the i-th hob on the cutter head after a tool change; M is the total number of hobs on the cutter head.

[0052] In some embodiments, step S70 specifically includes the following steps: S71: Remove the cutter replacement combinations that are prohibited from being replaced from different candidate cutter replacement combinations to obtain updated cutter replacement combinations; S72: Determine the cutter replacement combinations that include the cutter rollers that need to be forcibly replaced in step S43 from the updated cutter replacement combinations to obtain actual candidate cutter replacement combinations; S73: Sort the degree of tunneling performance improvement corresponding to each actual candidate cutter replacement combination; S74: Determine the cutter roller replacement combination of the tunnel boring machine based on the sorting result determined in step S73 and the preset performance improvement threshold.

[0053] Removing prohibited cutter replacement combinations from the pool of candidate options—such as those that cannot be implemented due to construction conditions, equipment status, or safety regulations—ensures the updated cutter replacement combinations are engineering-feasible. From this pool, candidate cutter replacement combinations that include the mandatory cutter replacements identified in step S43 are determined, ensuring that critical cutters requiring replacement are replaced. The resulting candidate combinations are then ranked according to their perceived improvement in tunneling performance, allowing for direct comparison of the performance of different cutter replacement schemes and avoiding uncertainties arising from subjective judgment or empirical inference. Prohibited cutter replacement combinations and cutters requiring mandatory replacement can be determined by those skilled in the art during actual operation.

[0054] In some embodiments, in step S74, the actual candidate cutter change combination with the highest degree of tunneling performance improvement has the best performance improvement effect. When it simultaneously meets the engineering constraints and the degree of tunneling performance improvement is higher than the preset performance improvement threshold, it can be used as the preferred cutter change combination for the moment when cutter change is required.

[0055] Specifically, in step S74, for actual candidate tool change combinations that meet the engineering constraints, it can be determined whether the tool change has reached the preset performance improvement threshold based on the classification results of the threshold classification model. For tool change combinations that have reached the preset performance improvement threshold, they are further sorted according to the predicted values ​​of the tunneling performance improvement of each actual candidate tool change combination obtained from the continuous effect model to obtain the tool change combination with the best performance improvement effect. When there are no actual candidate tool change combinations that simultaneously meet the engineering constraints and the preset performance improvement threshold, no specific tool change combination is output, and it is determined that tool change is not recommended at the current moment.

[0056] The engineering constraints may include at least hob safety constraints, forced replacement constraints, prohibited replacement constraints, and hob availability constraints. Specific constraints can be determined by those skilled in the art based on practice. These multiple constraints ensure that the output hob change combination directly corresponds to a specific hob number, and discourage tool changes when output conditions are not met.

[0057] In some embodiments, in step S10, the stable tunneling operation data before cutterhead replacement is the operation data within the stable tunneling section, which may include at least the total thrust, cutterhead torque, cutterhead speed, actual tunneling distance, and tunneling time. The cutterhead status data may include the cutterhead number, installation position, current wear amount, and initial wear amount of the cutterhead to be replaced. The cutterhead to be replaced may be a new cutterhead, or a spare cutterhead with a known non-zero initial wear amount that meets the usage conditions, or a used cutterhead. The cutterhead status data may also include cutterhead type, installation radius, cutterhead space partitioning, abnormal damage indicator, forced replacement indicator, prohibited replacement indicator, and the availability status of the cutterhead to be replaced, providing a direct basis for determining the subsequent candidate cutterhead set. The stable tunneling section may be determined based on the fluctuation degree and duration of operating parameters such as cutterhead speed, total thrust, and cutterhead torque within a preset time window.

[0058] The above settings ensure that the determination and selection of cutter replacement combinations only use stable tunneling operation data, cutter wear status, and information on the cutter to be replaced that can be obtained before the current cutter replacement time, avoiding the use of data that can only be obtained after on-site implementation as decision input, thus reducing the cost of cutter replacement decision.

[0059] In some embodiments, in step S20, the stable tunneling operation data can be processed to obtain the operating state characteristics that characterize the tunneling state of the tunnel boring machine at the moment when a cutterhead replacement is required. Preferably, a first quartile Q1, a third quartile Q3, and an interquartile range IQR = Q3 - Q1 are determined for the total thrust and cutterhead torque in the stable tunneling operation data, respectively. When the total thrust or cutterhead torque of any sampled record exceeds the corresponding range of [Q1 - k·IQR, Q3 + k·IQR], the sampled record is removed, where k is a preset outlier discrimination coefficient, preferably 1.5. Stable tunneling sections with zero actual tunneling distance are not included in subsequent calculations. By removing outlier sampled values ​​for the total thrust and cutterhead torque using the interquartile range method and excluding invalid sections with zero tunneling distance, the retained operating state characteristics more accurately reflect the actual working conditions before the cutterhead replacement, providing reliable basic data for subsequent evaluation.

[0060] In some embodiments, in step S20, the operating status characteristics of the tunnel boring machine at the moment when a cutterhead replacement is required may include penetration depth, total thrust statistics, cutterhead torque statistics, maximum wear of the entire cutterhead, and standard deviation of wear of the entire cutterhead.

[0061] Another aspect of this application provides a system for determining cutterhead replacement combinations for a tunnel boring machine (TBM), comprising: a data acquisition unit for acquiring stable tunneling operation data of the TBM before cutterhead replacement and state data of the cutters on the cutterhead; and a processor configured to: determine the operating state characteristics of the TBM at the moment when cutterhead replacement is required based on the stable tunneling operation data and the cutterhead state data; determine the wear degree of the entire cutterhead before cutterhead replacement based on the cutterhead state data; determine candidate cutterhead replacement combinations from the cutters based on the cutterhead state data; determine the evaluation characteristics of the candidate cutterhead replacement combinations based on the candidate cutterhead replacement combinations and the wear degree of the entire cutterhead before cutterhead replacement; determine the degree of tunneling performance improvement corresponding to different candidate cutterhead replacement combinations based on the evaluation characteristics of the candidate cutterhead replacement combinations and the operating state characteristics; and determine the TBM cutterhead replacement combination based on the degree of tunneling performance improvement corresponding to different candidate cutterhead replacement combinations.

[0062] The system for determining cutterhead replacement combinations for tunnel boring machines (TBMs) provided in this application identifies multiple possible candidate combinations and combines them with the overall cutterhead wear level to obtain evaluation characteristics for each candidate combination. This avoids focusing solely on a single severely worn cutterhead, which could disrupt the overall cutterhead balance. Furthermore, by combining the evaluation characteristics of each candidate combination with the operating characteristics of the TBM before the cutterhead replacement, the system can predict the degree of improvement in tunneling performance corresponding to each candidate combination. This transforms the performance of different cutterhead replacement combinations into quantifiable and comparable indicators, allowing for the direct determination of the preferred cutterhead replacement combination based on the degree of improvement in tunneling performance. Compared to relying on experience to perform cutterhead replacement operations, this system can precisely replace cutters based on specific wear conditions. It also avoids excessive cutterhead replacement, which increases costs, or insufficient replacement, which could lead to further cutter damage, effectively improving tunneling speed and the reliability of cutterhead replacement.

[0063] Regarding the embodiments of this application, it should also be noted that, without conflict, the embodiments of this application and the features in the embodiments can be combined with each other to obtain new embodiments.

[0064] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. The scope of protection of this application shall be determined by the scope of the claims.

Claims

1. A method for determining the cutterhead changing combination of a tunnel boring machine, characterized in that, It includes the following steps: S10: Obtain stable tunneling operation data of the tunnel boring machine before cutter replacement and status data of the roller cutter on the cutterhead; S20: Based on the stable tunneling operation data and the cutterhead status data, determine the operating status characteristics of the tunnel boring machine at the moment when a cutterhead replacement is required; S30: Determine the wear level of the entire cutter head before tool replacement based on the hob status data; S40: Based on the hob's state data, determine candidate tool change combinations from the hobs; S50: Based on the candidate tool change combination and the wear degree of the entire tool disc before tool change determined in step S30, determine the evaluation characteristics of the candidate tool change combination; S60: Based on the evaluation characteristics of the candidate cutter change combinations and the operating state characteristics determined in step S20, determine the degree of tunneling performance improvement corresponding to different candidate cutter change combinations; S70: Determine the cutterhead replacement combination of the tunnel boring machine based on the degree of improvement in tunneling performance corresponding to different candidate cutterhead replacement combinations.

2. The method according to claim 1, characterized in that, Step S60 specifically includes the following steps: S61: Determine the predetermined evaluation method based on historical data of actual hobbing machine replacement events; S62: Based on the evaluation characteristics of the candidate tool change combinations and the operating state characteristics, determine the candidate evaluation vector corresponding to each candidate tool change combination; S63: Based on the candidate evaluation vector and the predetermined evaluation method, determine the degree of tunneling performance improvement corresponding to different candidate cutter change combinations.

3. The method according to claim 2, characterized in that, In step S61, the data of the historical actual hob change events includes the penetration depth before and after the tool change and the total propulsion force before and after the tool change for each historical actual hob change event. Step S61 specifically includes the following steps: S611: Determine the standardized logarithmic penetration ratio after tool change based on the penetration before and after tool change and the total propulsion force before and after tool change for each historical actual hob change event. S612: Determine the degree of tunneling performance improvement of historical actual cutter replacement events based on the standardized logarithmic penetration ratio after the cutter replacement; S613: Determine the binary label of each historical actual cutter replacement event based on the degree of tunneling performance improvement and the preset performance improvement threshold; S614: Determine the predetermined evaluation method based on the data of the historical actual cutter replacement events and the binary classification labels.

4. The method according to claim 3, characterized in that, In step S612, the degree of improvement in tunneling performance is determined as follows: ； in, This indicates the degree of improvement in tunneling performance; This represents the standardized logarithmic penetration ratio after the tool change.

5. The method according to claim 3, characterized in that, In step S611, the normalized logarithmic penetration ratio after tool change is determined as follows: ； in, This represents the standardized logarithmic penetration ratio after the tool change; and These represent the penetration depth before and after tool change, respectively. and These represent the total propulsion force before and after tool change, respectively; β represents the propulsion force correction coefficient.

6. The method according to claim 5, characterized in that, In step S611, the penetration depth is determined as follows: ； Wherein, P represents the penetration depth; L represents the actual tunneling distance of the stable tunneling section; n represents the average cutterhead rotation speed of the stable tunneling section; and T represents the tunneling duration of the stable tunneling section.

7. The method according to claim 1, characterized in that, Step S40 specifically includes the following steps: S41: Determine the current wear amount of each hob on the cutter head based on the hob status data; S42: Based on the current wear of each hob, determine the basic candidate set of hobs that need to be replaced; S43: Based on the status data of the hob, determine the hob that needs to be forcibly replaced, and add the hob that needs to be forcibly replaced to the basic candidate set; S44: Based on the hob status data, determine the hob that cannot be replaced; S45: Remove the hobs that cannot perform the replacement operation as determined in step S44 from the basic candidate set determined in step S43, and obtain a candidate hob set. S46: Determine the candidate tool change combination based on the candidate hobbing cutter set.

8. The method according to claim 1, characterized in that, The S50 step specifically includes the following steps: S51: Determine the initial wear of the hob included in each candidate tool change combination; S52: Based on the initial wear of the hob included in each candidate tool change combination determined in step S51 and the wear degree of the entire tool disc before tool change, determine the wear degree of the entire tool disc after tool change for each candidate tool change combination. S53: Determine the evaluation characteristics of the candidate tool change combination based on each candidate tool change combination, the wear degree of the entire tool disc before tool change, and the wear degree of the entire tool disc after tool change.

9. The method according to claim 7, characterized in that, Step S70 specifically includes the following steps: S71: Remove prohibited tool change combinations from different candidate tool change combinations to obtain updated tool change combinations; S72: Determine from the updated tool change combinations the tool change combinations that include the hob that needs to be forcibly replaced in step S43, and obtain the actual candidate tool change combinations; S73: Sort the degree of tunneling performance improvement corresponding to each actual candidate cutter change combination; S74: Based on the sorting results determined in step S73 and the preset performance improvement threshold, determine the cutterhead replacement combination of the tunnel boring machine.

10. A system for determining the cutterhead changing combination of a tunnel boring machine, characterized in that, It includes: The data acquisition component is used to acquire stable tunneling operation data of the tunnel boring machine before cutter replacement and the status data of the roller cutters on the cutterhead; The processor is configured to: Based on the stable tunneling operation data and the cutterhead status data, the operating status characteristics of the tunnel boring machine before cutterhead replacement are determined; Based on the hob's condition data, determine the wear level of the entire cutter head before tool replacement; Based on the hob's status data, candidate tool change combinations are determined from the hobs; The evaluation characteristics of the candidate tool change combinations are determined based on the candidate tool change combinations and the wear degree of the entire tool disc before the tool change. Based on the evaluation characteristics of the candidate cutter change combinations and the operating status characteristics, the degree of tunneling performance improvement corresponding to different candidate cutter change combinations is determined. The cutterhead replacement combination of the tunnel boring machine is determined based on the degree of improvement in tunneling performance corresponding to different candidate cutterhead replacement combinations.

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