Adaptive abnormality identification method and system for running conditions of a roadheader

Abstract

The application provides an adaptive abnormality identification method and system for a tunneling machine operating condition, and relates to the technical field of mining engineering tunneling. The method comprises the following steps: when a lithology mutation event is captured, a plurality of associated devices are driven to perform condition collection based on a preset cooperative sampling rule, the lithology mutation event is matched and traced back in time sequence, a real-time lithology transition stage is dynamically called to determine a device associated judgment model, a correlation number matrix is calculated, and is loaded into the device associated judgment model to make a space-time associated coupling decision; when the decision result is associated coupling failure, a failure characteristic vector is analyzed to perform fault direction probability matching, and a real-time fault device group is output. The technical problems that the prior art has single or fixed parameter monitoring of the tunneling machine state, resulting in insufficient timeliness and accuracy of abnormality identification are solved. The technical effects of improving the comprehensiveness, real-time performance and accuracy of tunneling machine abnormality identification, and ensuring efficient and safe operation of the tunneling machine are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mining engineering tunneling, in particular to an adaptive abnormality identification method and system for running conditions of a tunneling machine. BACKGROUND

[0002] In the field of mining engineering tunneling, the stability and reliability of the running conditions of a tunneling machine as core construction equipment are directly related to the progress and quality of the project. However, the traditional abnormality identification technology for the tunneling machine is often limited to the monitoring and analysis of a single device or fixed parameters, and does not fully consider the dynamic influence of lithology mutation, a key variable, under complex geological conditions on the running state of the device. Lithology mutation can cause rapid changes in the running conditions of the device, and the traditional technology is difficult to capture such changes in real time and accurately identify the correlation between devices, resulting in insufficient timeliness and accuracy of abnormality identification, which is difficult to meet the needs of efficient and safe tunneling operations.

[0003] The prior art has the technical problem of insufficient timeliness and accuracy of abnormality identification due to single or fixed parameter monitoring of the running conditions of the tunneling machine. SUMMARY

[0004] The present application provides an adaptive abnormality identification method and system for the running conditions of a tunneling machine, which is used to solve the technical problem of insufficient timeliness and accuracy of abnormality identification due to single or fixed parameter monitoring of the running conditions of the tunneling machine in the prior art.

[0005] In view of the above problems, the present application provides an adaptive abnormality identification method and system for the running conditions of a tunneling machine.

[0006] In a first aspect, the present application provides an adaptive abnormality identification method for the running conditions of a tunneling machine, which comprises: triggering the collection of the running conditions of a plurality of associated devices in the tunneling machine when a lithology mutation event is captured; based on a preset coordinated sampling rule, driving a plurality of distributed edge collection nodes of the plurality of associated devices to perform millisecond-level synchronous condition collection and generate multi-channel running condition time series data; performing time series feature matching backtracking on the lithology mutation event to locate a real-time lithology transition stage; dynamically calling a device association judgment model according to the real-time lithology transition stage; after calculating a mutual relationship matrix based on the multi-channel running condition time series data, loading the mutual relationship matrix into the device association judgment model for spatio-temporal association coupling judgment; if the spatio-temporal association coupling judgment result is association coupling failure, resolving a failure feature vector from the mutual relationship matrix; performing fault pointing probability matching according to the failure feature vector to output a real-time fault device group, wherein the real-time fault device group has an adaptive hierarchical fault level identifier.

[0007] Preferably, the lithology mutation event is matched back in time to locate a real-time lithology transition stage, and the method comprises: taking the timestamp of the lithology mutation event as a reference point, calling time sequence data before and after a preset backtracking time window, and obtaining a multi-channel device working condition time sequence segment; by solving the multi-channel device working condition time sequence segment, a multi-modal lithology response feature is output, wherein the multi-modal lithology response feature includes a push speed drop rate, a cutter torque fluctuation amplitude, and a vibration signal main frequency energy proportion; the multi-modal lithology response feature is used to traverse and compare a plurality of standard modal feature templates of a plurality of standard transition stages in a lithology transition feature library to locate the real-time lithology transition stage.

[0008] Preferably, according to the real-time lithology transition stage, a device correlation judgment model is dynamically called, and the method comprises: interactively obtaining a plurality of device parameter correlation rules of the plurality of standard transition stages; after configuring a plurality of multi-task comparison engines for the plurality of device parameter correlation rules, a plurality of distributed parallel processing architecture configurations of the plurality of multi-task comparison engines are performed to complete the construction of a plurality of standard correlation judgment models; the plurality of standard transition stages are used as indexes to index the plurality of standard correlation judgment models to generate a correlation judgment model library; according to the real-time lithology transition stage, the device correlation judgment model is dynamically called from the correlation judgment model library.

[0009] Preferably, after calculating a cross-correlation number matrix based on the multi-channel running condition time sequence data, the cross-correlation number matrix is loaded into the device correlation judgment model for spatio-temporal correlation coupling decision, and the method comprises: combining and enumerating the multi-channel running condition time sequence data to obtain a plurality of groups of two-channel running condition time sequence data; taking a preset sliding segmentation window and a sliding segmentation step as a calculation reference, calculating the maximum cross-correlation number and the working condition time delay of the plurality of groups of two-channel running condition time sequence data, and outputting a plurality of symmetric relation matrices; according to the channel pair topological relationship of the plurality of groups of two-channel running condition time sequence data, the plurality of symmetric relation matrices are aggregated to generate the cross-correlation number matrix; the cross-correlation number matrix is loaded into the device correlation judgment model for spatio-temporal correlation coupling decision to output a spatio-temporal correlation coupling decision result.

[0010] Preferably, a failure feature vector is parsed from the cross-correlation number matrix, and the method comprises: according to the device parameter correlation rule of the device correlation judgment model, a plurality of channel pair relationship data fragments are extracted from the cross-correlation number matrix; the plurality of channel pair relationship data fragments are loaded into the multi-task comparison engine of the device correlation judgment model to perform parallel spatio-temporal coupling double-threshold decision, and a plurality of failure state sets are output; according to the rule ID index of the plurality of channel pair relationship data fragments, the plurality of failure state sets are dynamically aggregated to output the failure feature vector.

[0011] Preferably, when a lithology mutation event is captured, the operation condition collection of a preset plurality of associated devices in the tunneling machine is triggered, and the method comprises: based on a sampling window, performing sliding window standard deviation demodulation on a propulsion speed time series signal of the tunneling machine, outputting a propulsion speed standard deviation; performing local extreme value density analysis on a tunneling machine cutter torque signal, outputting a cutter torque extreme value density, wherein the propulsion speed time series signal demodulation and the cutter torque signal analysis are performed synchronously; when the propulsion speed standard deviation increases by more than 30% of the historical mean value baseline and meets P sampling windows, and the cutter torque extreme value density reaches more than Q times of the historical mean density, a lithology mutation event trigger instruction is generated; the lithology mutation event trigger instruction is sent to the plurality of distributed edge collection nodes based on a unified timestamp, triggering the operation condition collection of the plurality of associated devices.

[0012] Preferably, the failure feature vector is matched for fault pointing probability, and a real-time fault device group is output, and the method comprises: obtaining a plurality of sets of sample feature vectors of the plurality of associated devices in a plurality of sets of sample fault states interactively; based on the plurality of associated devices, the plurality of sets of sample fault states and the plurality of sets of sample feature vectors, a three-level index tree is constructed; the failure feature vector is loaded into the three-level index tree, and a fault state coarse screening is performed based on a preselected similarity, to obtain a candidate fault state set; a fault state fine matching of the failure feature vector and the candidate fault state set is performed based on the Euclidean distance, to output a plurality of device fault association probability pairs; the plurality of device fault association probability pairs are ranked in descending order according to the device fault, and the real-time fault device group is output, wherein the real-time fault device group has an adaptive hierarchical fault level identifier.

[0013] Preferably, the adaptive anomaly recognition method for the operation condition of the tunneling machine further comprises: dynamically updating the historical mean baseline and the historical mean density based on a preset cumulative tunneling mileage.

[0014] Preferably, the adaptive anomaly recognition method for the operation condition of the tunneling machine further comprises: constructing a fault verification priority instruction of the real-time fault device group according to the adaptive hierarchical fault level identifier, and sending the fault verification priority instruction to a ground central control system.

[0015] In a second aspect of the present application, an adaptive abnormality identification system for running conditions of a tunneling machine is provided, which comprises: a condition acquisition triggering module configured to trigger acquisition of running conditions of a plurality of preset associated devices in the tunneling machine when a lithology mutation event is captured; a cooperative sampling execution module configured to drive a plurality of distributed edge sampling nodes of the plurality of associated devices to perform millisecond-level synchronous condition acquisition based on a preset cooperative sampling rule, and generate multi-channel running condition time series data; a time series feature backtracking module configured to perform time series feature matching backtracking on the lithology mutation event, and locate a real-time lithology transition stage; a model calling module configured to dynamically call a device association judgment model according to the real-time lithology transition stage; an associated coupling judgment module configured to load a correlation number matrix into the device association judgment model for spatio-temporal associated coupling judgment after the correlation number matrix is calculated based on the multi-channel running condition time series data; a failure feature analysis module configured to analyze a failure feature vector from the correlation number matrix if the spatio-temporal associated coupling judgment result is associated coupling failure; and a fault output module configured to perform fault pointing probability matching according to the failure feature vector, and output a real-time fault device group, wherein the real-time fault device group is provided with an adaptive hierarchical fault level identifier.

[0016] The one or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0017] The method provided by the embodiments of the present application triggers acquisition of running conditions of a plurality of preset associated devices in the tunneling machine when a lithology mutation event is captured; drives a plurality of distributed edge sampling nodes of the plurality of associated devices to perform millisecond-level synchronous condition acquisition based on a preset cooperative sampling rule, and generates multi-channel running condition time series data; performs time series feature matching backtracking on the lithology mutation event, and locates a real-time lithology transition stage; dynamically calls a device association judgment model according to the real-time lithology transition stage; loads a correlation number matrix into the device association judgment model for spatio-temporal associated coupling judgment after the correlation number matrix is calculated based on the multi-channel running condition time series data; analyzes a failure feature vector from the correlation number matrix if the spatio-temporal associated coupling judgment result is associated coupling failure; and performs fault pointing probability matching according to the failure feature vector, and outputs a real-time fault device group. The technical effect of improving the comprehensiveness, real-time performance and accuracy of abnormality identification of the tunneling machine is achieved, and the efficient and safe operation of the tunneling machine is ensured. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 A flowchart of an adaptive abnormality identification method for running conditions of a tunneling machine is provided.

[0019] Figure 2A structural schematic diagram of an adaptive abnormality identification system for a running condition of a heading machine is provided in the present application.

[0020] Reference signs: condition collection trigger module 10, cooperative sampling execution module 20, time sequence feature backtracking module 30, model calling module 40, correlation coupling decision module 50, failure feature analysis module 60, and fault output module 70. DETAILED DESCRIPTION

[0021] The present application provides an adaptive abnormality identification method and system for a running condition of a heading machine, which is used to solve the technical problem that a single or fixed parameter is used to monitor the state of the heading machine in the prior art, resulting in insufficient timeliness and accuracy of abnormality identification. The technical effect of improving the comprehensiveness, real-time performance and accuracy of abnormality identification of the heading machine is achieved, and the efficient and safe operation of the heading machine is ensured.

[0022] Hereinafter, the technical solutions in the present application will be described clearly and completely with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the scope of protection of the present application. In addition, it should be noted that, for convenience of description, only parts related to the present application are shown in the drawings, rather than all parts.

[0023] In one embodiment, as shown in the accompanying drawings, the present application provides an adaptive abnormality identification method for a running condition of a heading machine, which comprises the following steps. Figure 1

[0024] When a lithology mutation event is captured, the running conditions of a plurality of preset associated devices in the heading machine are triggered to be collected.

[0025] Specifically, the running condition of the heading machine is closely related to the lithology mutation event. When the heading machine cuts different rock layers, the working condition parameters such as the advancing speed, cutter torque and vibration of the heading machine are directly affected by the hardness, structure and integrity of the rock layers. When the lithology changes suddenly, the formation resistance and cutting conditions will change instantaneously, thereby causing significant fluctuations in these working condition parameters. Therefore, by capturing the lithology mutation event, the running conditions of a plurality of preset associated devices in the heading machine are collected. The plurality of preset associated devices are devices that are associated with the running conditions of the main drive system, cutting system, propulsion system and support system of the heading machine. The associated devices include, but are not limited to, a cutting motor, a propulsion oil cylinder and a conveyor. Based on this, rapid identification and response to geological changes are achieved, thereby ensuring accurate, comprehensive and timely collection of the running conditions of the heading machine.

[0026] ​Further, when a lithology mutation event is captured, the operation condition collection of a plurality of associated devices in the tunneling machine is triggered, and the method comprises: based on a sampling window, performing sliding window standard deviation demodulation on a propulsion speed time series signal of the tunneling machine, and outputting a propulsion speed standard deviation; performing local extreme value density analysis on a tunneling machine cutter torque signal, and outputting a cutter torque extreme value density, wherein the propulsion speed time series signal demodulation and the cutter torque signal analysis are synchronously performed; when the propulsion speed standard deviation suddenly increases by more than 30% of the historical average baseline and meets P sampling windows, and the cutter torque extreme value density reaches more than Q times of the historical average density, a lithology mutation event trigger instruction is generated; the lithology mutation event trigger instruction is sent to the plurality of distributed edge collection nodes based on a unified timestamp, and the operation condition collection of the plurality of associated devices is triggered.

[0027] Specifically, a sampling window refers to a time interval during the continuous acquisition of tunneling machine operating condition signals, where the signal is segmented into segments with a fixed time length or a fixed number of data points. This segment is used to calculate local signal characteristic values, such as the standard deviation of the advance speed or the extreme value density of the cutterhead torque. The length of the sampling window is usually set according to the typical time scale of the tunneling machine's operating state changes. For example, for operating conditions with faster tunneling speeds and more frequent changes in geological conditions, the sampling window can be appropriately smaller, such as 0.5-2 seconds, to more sensitively capture signal changes. For operating conditions with slower tunneling speeds and relatively stable geological conditions, the sampling window can be larger, such as 2-5 seconds, to reduce noise interference and improve calculation stability. Based on a preset window, sliding window standard deviation demodulation is performed on the time-series signal of the tunneling machine's propulsion speed. The propulsion speed time-series signal refers to a continuous data sequence formed by the change of the distance moved by the propulsion device per unit time during the tunneling operation. The propulsion speed time-series signal can reflect the propulsion speed of the tunneling machine at different times in real time. Sliding window standard deviation demodulation refers to gradually sliding a fixed-length time window on the signal sequence and calculating the standard deviation of the data in each window. The trend of the standard deviation reflects the fluctuation range of the propulsion speed, thereby quantitatively analyzing the sensitivity to formation disturbance and obtaining the standard deviation of the propulsion speed. The larger the standard deviation of the propulsion speed, the more severe the fluctuation of the propulsion speed. Simultaneously, local extremum density analysis is performed on the tunneling machine cutterhead torque signal. The tunneling machine cutterhead torque signal refers to the data set consisting of the torque acting on the cutterhead over time as it rotates and cuts rock strata. The cutterhead torque signal reflects the magnitude of the resistance experienced by the cutterhead during cutting. Local extremum density refers to the ratio of the number of occurrences of local maximum or minimum values ​​of the torque signal to the duration within a given time window. It reflects the frequency of load fluctuations experienced by the cutterhead under changes in rock strata. Through local extremum density analysis of the cutterhead torque signal, the key indicator of cutterhead torque extremum density is output. A higher cutterhead torque extremum density value indicates more frequent occurrences of local extrema in the cutterhead torque, suggesting more frequent and drastic fluctuations in the cutterhead torque within a short period, reflecting the extreme instability of the load experienced by the cutterhead during rock cutting. The demodulation of the standard deviation of the propulsion speed and the cutterhead torque extremum density analysis are performed simultaneously. This parallel processing ensures high consistency between the two key indicators over time, enabling parallel monitoring of propulsion stability and cutting load fluctuations.

[0028] When the standard deviation of the advance speed increases by more than 30% relative to the historical mean baseline within P consecutive sampling windows, and the extreme value density of the cutterhead torque reaches more than Q times the historical mean density, the current operating state is determined to meet the triggering conditions for a lithological mutation event. A lithological mutation event triggering command is then generated immediately. The historical mean baseline is a long-term average value dynamically calculated based on a preset cumulative tunneling mileage, used to adapt to benchmark changes at different construction stages. The historical mean density corresponds to the frequency benchmark of local extreme values ​​under long-term stable operating conditions. P and Q are both positive integers, with P greater than or equal to 3 and Q greater than P. After the triggering command is generated, it is sent with a unified timestamp to multiple distributed edge acquisition nodes located at various positions on the tunneling machine. This allows each acquisition node to simultaneously initiate the acquisition of operating conditions for multiple related devices, including but not limited to the cutting motor, propulsion cylinder, and conveyor. By determining whether a lithological mutation event triggers the acquisition of operating conditions for multiple preset related devices in the tunneling machine, comprehensive, timely, and accurate operating information of key equipment can be obtained.

[0029] Based on preset collaborative sampling rules, multiple distributed edge acquisition nodes of the multiple associated devices are driven to perform millisecond-level synchronous operating condition acquisition and generate multi-channel operating condition time series data.

[0030] Specifically, upon receiving a lithological mutation event trigger command, multiple distributed edge acquisition nodes on various associated devices are driven to initiate operational condition data acquisition based on preset collaborative sampling rules. These preset collaborative sampling rules are sampling strategies constructed by comprehensively considering the physical connections, signal transmission characteristics, and business logic relationships between the various associated devices of the tunneling machine. This ensures the temporal and logical consistency of multi-source data. For example, the tunneling machine's cutting motor, propulsion cylinder, and conveyor interact and work collaboratively during operation. Changes in propulsion speed directly affect the feed rate of the cutting head, while the stress on the cutting head is fed back to the load on the propulsion cylinder. Simultaneously, the conveyor's operating speed needs to match the cutting motor's discharge speed. Furthermore, based on these relationships, the preset collaborative sampling rules clearly define the operational parameters that each associated device needs to collect under different operating conditions, as well as key information such as the collection frequency and time interval, comprehensively reflecting the equipment's operating status and performance. Distributed edge acquisition nodes are hardware units installed near various associated devices, possessing local signal acquisition and preliminary processing capabilities. Through a low-latency industrial bus or time synchronization network, they achieve high-precision clock alignment with the main control unit, enabling millisecond-level synchronous start-up of acquisition upon receiving a trigger command with a unified timestamp. Millisecond-level synchronization emphasizes that the deviation of the data acquisition start points of different devices on the time axis does not exceed milliseconds. In this way, each distributed edge acquisition node simultaneously acquires various types of operating condition signals, such as propulsion speed, cutterhead torque, hydraulic pressure, vibration acceleration, and motor current, and arranges them in chronological order to form multi-channel operating condition time-series data containing multiple data channels. Multi-channel refers to parallel data streams from different physical quantities and different devices, comprehensively reflecting the operating status of the tunneling machine within a certain time period.

[0031] The lithological abrupt change events are backtracked by time-series feature matching to locate the real-time lithological transition stage.

[0032] Specifically, for detected lithological abrupt events, feature data such as propulsion speed and cutterhead torque are extracted based on multi-channel operating condition time series data. The feature data are then matched with typical features of different lithological transition stages in historical data. The stage with the highest similarity is determined based on the degree of matching, thereby accurately locating the current real-time lithological transition stage.

[0033] Furthermore, the method involves performing time-series feature matching and backtracking on the lithological abrupt change event to locate the real-time lithological transition stage. This includes: using the timestamp of the lithological abrupt change event as a reference point, retrieving time-series data before and after a preset backtracking time window to obtain multi-channel equipment operating condition time-series segments; outputting multi-modal lithological response features by solving the multi-channel equipment operating condition time-series segments, wherein the multi-modal lithological response features include the propulsion speed reduction rate, cutterhead torque fluctuation amplitude, and the proportion of vibration signal dominant frequency energy; and using the multi-modal lithological response features to traverse and compare multiple standard modal feature templates of multiple standard transition stages in the lithological transition feature library to locate the real-time lithological transition stage.

[0034] Specifically, after a lithological abrupt change event is triggered and multi-channel operating condition data acquisition is completed, the detected lithological abrupt change event is backtracked using time-series feature matching to locate the current real-time lithological transition stage of the tunnel boring machine (TBM). First, using the unified timestamp of the lithological abrupt change event as a reference point, multi-channel equipment operating condition data segments covering a certain time period before and after that timestamp are retrieved. The preset backtracking time window is a time range set according to the typical lag characteristics of the TBM's response to strata changes, including both the signal change trend before the event and the dynamic response after the event, thus ensuring the completeness of feature extraction. Subsequently, the extracted multi-channel data segments are processed using a specific algorithm model, such as a time-series analysis algorithm based on signal processing, to output multimodal lithological response features. Multimodal lithological response features refer to a set of features that integrate multiple signal types from different physical quantities and different equipment, reflecting the changes in rock strata characteristics during the tunneling process. Multimodal lithological response characteristics include: the rate of decrease in advance speed, the amplitude of cutterhead torque fluctuation, and the proportion of dominant frequency energy in the vibration signal. The rate of decrease in advance speed, i.e., the relative decrease in advance speed per unit time, reflects the degree of increase in formation cutting resistance. The amplitude of cutterhead torque fluctuation, i.e., the peak-to-valley difference of the torque signal within the analysis window, is sensitive to changes in lithological hardness and heterogeneity. The proportion of dominant frequency energy in the vibration signal, i.e., the proportion of energy in the dominant frequency band of the vibration signal to the total energy, reveals changes in impact characteristics during the cutting process. Finally, the above multimodal lithological response characteristics are systematically compared with standard modal feature templates of multiple pre-established standard transition stages in the lithological transition feature library. The lithological transition feature library is a set of phased features constructed based on historical construction data and typical formation test data, covering different types of lithological transition modes, such as from soft rock to hard rock and from hard rock to fractured zones. Through similarity calculations, such as Euclidean distance and cosine similarity methods, the standard transition stage corresponding to the current multimodal characteristics is accurately matched, thereby achieving precise positioning of the real-time lithological transition stage.

[0035] Based on the real-time lithological transition stage, the equipment association judgment model is dynamically retrieved.

[0036] Specifically, based on the identified real-time lithological transition stage, a suitable equipment association judgment model is dynamically retrieved from a pre-set model library. This model comprehensively considers various key parameters and interrelationships of equipment operation during this lithological transition stage, enabling accurate and reliable assessment of the equipment's current state. This provides a reliable basis for subsequent adjustments to the tunneling machine's operation, ensuring efficient and safe operation.

[0037] Furthermore, based on the real-time lithological transition stage, the device association judgment model is dynamically retrieved. The method includes: interactively obtaining multiple device parameter association rules for the multiple standard transition stages; configuring multiple multi-task comparison engines for the multiple device parameter association rules, configuring multiple distributed parallel processing architectures for the multiple multi-task comparison engines, and completing the construction of multiple standard association judgment models; using the multiple standard transition stages as indexes, indexing and storing the multiple standard association judgment models to generate an association judgment model library; and dynamically retrieving the device association judgment model from the association judgment model library based on the real-time lithological transition stage.

[0038] Specifically, by leveraging expert experience, historical construction data, and experimental verification results, multiple equipment parameter association rules corresponding to standard lithological transition stages are interactively obtained. A standard transition stage refers to a predefined typical stage of transition between different lithologies, such as from soft rock to medium-hard rock, or from medium-hard rock to hard rock. Equipment parameter association rules refer to the correlation patterns of operating parameters between different devices under specific lithological transition stages, including parameter coupling direction, correlation coefficient range, and response delay characteristics. A multi-task comparison engine is configured for multiple equipment parameter association rules, implemented by mapping each association rule to an independent comparison task within the engine. Each task includes a specific combination of input data channels, feature calculation methods, and threshold judgment conditions. For example, if a rule requires monitoring "the correlation coefficient between the cutterhead torque and the propulsion cylinder pressure to be no less than 0.85 and the time delay no more than 200ms" during a certain lithological transition stage, a task can be created in the engine, specifying the input channels as the cutterhead torque signal and the propulsion cylinder pressure signal, calculating the maximum cross-correlation coefficient and phase delay of the sliding window, and setting corresponding thresholds for real-time judgment. For another rule, such as "the fluctuation amplitude of the main drive motor current and the change in the proportion of the cutterhead vibration main frequency energy must be positively correlated," another task can be configured, using the current fluctuation amplitude and the proportion of vibration energy as input, and using a trend correlation analysis algorithm for judgment. In this way, the multi-task comparison engine can run the calculation tasks corresponding to multiple rules in parallel at the same time, realizing the synchronous analysis and judgment of the relationship between multiple equipment parameters. The multi-task comparison engine refers to a computing module capable of processing multiple tasks simultaneously. Employing advanced multi-threading or distributed computing technologies, it can efficiently compare and analyze equipment parameters under different rules. It can be implemented using existing multivariate parallel analysis frameworks in industrial automation or condition monitoring systems, such as multi-channel correlation analysis engines used in SCADA or DCS systems, multi-channel vibration feature comparison algorithms used in condition monitoring systems, or multi-feature matching pipelines deployed using parallel computing frameworks like MATLAB Parallel Computing or Apache Spark. After configuration, multiple comparison engines are deployed in a distributed parallel processing architecture, allowing each engine to run simultaneously on multiple computing nodes, significantly improving processing speed and efficiency. This enables rapid processing of large amounts of equipment parameter data, thereby completing the construction of multiple standard correlation judgment models. Based on the above configuration, a standard correlation judgment model is constructed for each standard lithological transition stage, and the standard lithological transition stages are used as an index for indexed storage, forming a correlation judgment model library. The index is similar to a book's table of contents, allowing for quick location of specific standard correlation judgment models and improving model retrieval efficiency.Finally, once the real-time lithological transition stage is identified and matched, the corresponding equipment association judgment model is dynamically retrieved directly from the association judgment model library through the index. This avoids the accuracy loss of the general model under specific stages, thereby timely and accurately assessing the operating status of the equipment under the current lithological conditions. This enables the diagnostic process to adapt to formation changes and ensures that the optimal analysis model can be called for judgment under different lithological transition conditions.

[0039] After calculating the cross-correlation matrix based on the multi-channel operating condition time-series data, the cross-correlation matrix is ​​loaded into the equipment association judgment model for spatiotemporal association coupling judgment.

[0040] Specifically, based on the multi-channel operating condition time series data, the sliding window calculation is first performed on each pair of channels to obtain the maximum number of cross-correlation numbers and corresponding time delays for each pair of channels in each window. The results are then aggregated into a cross-correlation number matrix according to the channel topology order. This cross-correlation number matrix is ​​used as a feature input into the device association judgment model, and the spatiotemporal association coupling judgment result is output.

[0041] Furthermore, after calculating the cross-correlation matrix based on the multi-channel operating condition time-series data, the cross-correlation matrix is ​​loaded into the equipment association judgment model for spatiotemporal association coupling determination. The method includes: combining and enumerating the multi-channel operating condition time-series data to obtain multiple sets of dual-channel operating condition time-series data; using a preset sliding segmentation window and sliding segmentation step size as the calculation basis, calculating the maximum cross-correlation number and operating condition delay of the multiple sets of dual-channel operating condition time-series data, and outputting multiple symmetric relation matrices; aggregating the multiple symmetric relation matrices according to the channel pair topology of the multiple sets of dual-channel operating condition time-series data to generate the cross-correlation matrix; loading the cross-correlation matrix into the equipment association judgment model for spatiotemporal association coupling determination, and outputting the spatiotemporal association coupling determination result.

[0042] Specifically, the multi-channel operating condition time series data is combined and enumerated, that is, all possible channel pairs are exhaustively enumerated in a pairwise manner to obtain multiple sets of dual-channel time series data for relation measurement. Each set of dual-channel data is segmented and analyzed based on a preset sliding segmentation window and sliding segmentation step size. The sliding segmentation window is an analysis interval that slides continuously at a fixed length from the original time series, and the sliding segmentation step size is the displacement length of adjacent analysis intervals, determining the distance the window slides each time. Based on a preset sliding window and sliding step size, the maximum cross-correlation coefficient and corresponding operating delay of multiple sets of dual-channel operating condition time series data are calculated within each window. The maximum cross-correlation coefficient is calculated, for example, by multiplying two dual-channel time series data sequences by different time offsets and then summing the results, obtaining a series of cross-correlation values. The maximum value, after normalization, is the maximum cross-correlation coefficient, reflecting the similarity between the two channel data at different time offsets. The value ranges from -1 to 1; the closer the absolute value is to 1, the stronger the correlation. During the calculation of the maximum cross-correlation coefficient, the time offset of the two time series data sequences when the maximum cross-correlation value is obtained is recorded. This offset is the operating delay, reflecting the time delay of one channel data change relative to the other. The results from each window are aggregated to form a symmetric relation matrix for each channel pair. Each symmetric relation matrix corresponds to a set of dual-channel data, and the elements of the symmetric relation matrix reflect the maximum cross-correlation coefficient and operating delay information of this pair of channel data.

[0043] Channel topology refers to the structural relationships, such as the physical location or functional associations of different channels within a tunneling machine, which can be obtained from equipment design documents. Based on these channel topology relationships, multiple symmetrical relationship matrices are aggregated, integrating scattered channel relationship information into a single matrix that comprehensively reflects the overall association characteristics of the equipment. For example, aggregation can be performed using K-means clustering or analytic hierarchy process (AHP) based on channel correspondence and functional affinity to obtain a cross-correlation matrix. This cross-correlation matrix integrates the maximum cross-correlation coefficients and operational delay information across all channels, comprehensively presenting the association characteristics between each channel. This cross-correlation matrix is ​​then loaded into the equipment association judgment model. This model, combined with pre-defined decision rules and algorithms, analyzes the association coupling of the equipment in different time and spatial dimensions, determining whether the associations between different parts of the equipment are normal and whether there are potential fault risks. Finally, it outputs a spatiotemporal association coupling judgment result, enabling adaptive anomaly identification and fault prediction during tunneling machine operation, improving real-time diagnostic capabilities and construction safety under complex geological conditions.

[0044] If the spatiotemporal correlation coupling determination result is a correlation coupling failure, then the failure feature vector is parsed from the cross-correlation matrix.

[0045] Specifically, when the spatiotemporal correlation coupling judgment result indicates a failure in correlation coupling, it means that during the operation of the tunneling machine, the correlation between the parameters monitored by different channels in the temporal and spatial dimensions has become abnormal, breaking the coupling mode that should exist under normal operating conditions. This indicates that the tunneling machine has potential faults or performance degradation problems. Based on this, the failure feature vector is accurately parsed from the cross-correlation matrix. In the parsing process, firstly, based on the pre-set rule ID index in the equipment correlation judgment model, the specific channel pair data area related to the correlation coupling failure in the cross-correlation matrix can be quickly located. Then, a weighted summation aggregation algorithm is used to process the fragmented relational data of each located channel pair to obtain the failure feature vector, realizing adaptive anomaly identification of the tunneling machine. This provides comprehensive and accurate data basis for tunneling machine fault diagnosis and prediction, which helps to improve the overall reliability and operating efficiency of the tunneling machine and reduce production losses and safety risks caused by equipment failures.

[0046] Furthermore, the method for parsing the failure feature vector from the cross-relation matrix includes: extracting multiple channel pair relationship data fragments from the cross-relation matrix according to the device parameter association rules of the device association judgment model; loading the multiple channel pair relationship data fragments into the multi-task comparison engine of the device association judgment model, performing parallel spatiotemporal coupled dual-threshold decision-making, and outputting multiple failure state sets; dynamically aggregating the multiple failure state sets according to the rule ID index of the multiple channel pair relationship data fragments, and outputting the failure feature vector.

[0047] Specifically, after inputting the cross-correlation matrix into the equipment association judgment model, the matrix is ​​first sliced ​​according to the pre-set equipment parameter association rules within the model. Several channel pair relationship data fragments are extracted based on channel pairs and rule constraints. That is, focusing on the tunneling machine equipment parameter pairs, a local subset of data relationships such as correlation strength and time delay is extracted from the cross-correlation matrix. By extracting data fragments, the channel pairs that need to be analyzed can be accurately located, avoiding the computational complexity and information interference caused by uniform processing of the entire matrix, thus improving the relevance and efficiency of the analysis.

[0048] Multiple channel pair relationship data shards are input into the multi-task comparison engine of the device association judgment model, and parallel spatiotemporal coupled dual-threshold decision-making is performed. This parallel spatiotemporal coupled dual-threshold decision-making method is based on spatiotemporal correlation and dual-threshold settings. Regarding spatiotemporal correlation, the parallel spatiotemporal coupled dual-threshold decision-making comprehensively considers the temporal and spatial changes of channel data to determine whether they meet the preset association pattern. The dual thresholds set boundary conditions for normal and failed states respectively; only when the data exceeds these two threshold ranges is the channel pair determined to be in a failed state. This decision-making method can accurately identify whether each channel pair has spatiotemporal correlation coupling problems and output multiple sets of failed states, each set corresponding to the failed state information of a channel pair relationship data shard. Finally, based on the rule ID index of multiple channel pair relationship data shards, multiple sets of failed states are dynamically aggregated, and a failed feature vector is output. The rule ID index is a unique identifier assigned to each device parameter association rule, which can accurately locate the corresponding channel pair relationship data shard and its failed state set. Dynamic aggregation refers to the process of integrating various failure state sets based on rule ID indexes. A weighted summation aggregation algorithm is used to aggregate scattered failure state information into a comprehensive failure feature vector. This algorithm considers the varying importance of different rules to the overall equipment performance, assigning appropriate weights to the channel-to-data fragments corresponding to each rule ID. The weights are typically determined based on expert experience, historical data statistical analysis, and the equipment's key performance indicators. For example, the main drive motor, as a core component of the tunneling machine, will have relatively high weights for its related rules. By using weighted summation, the failure state information scattered across the channel-to-data fragments is integrated to obtain a failure feature vector. This vector contains failure state information from all channels, presenting the spatiotemporal coupling failure characteristics of the tunneling machine as a whole. This enables adaptive anomaly identification for the tunneling machine, providing intuitive and comprehensive information for fault diagnosis and maintenance decisions, helping technicians quickly locate equipment problems, and improving the reliability and operating efficiency of the tunneling machine.

[0049] Based on the failure feature vector, fault pointing probability matching is performed to output a real-time fault device group, wherein the real-time fault device group has an adaptive hierarchical fault level identifier.

[0050] Furthermore, based on the failure feature vectors, fault pointing probability matching is performed to output a real-time fault device group. The method includes: interactively obtaining multiple sets of sample feature vectors of the multiple associated devices under multiple sample fault states; constructing a three-level index tree based on the multiple associated devices, multiple sets of sample fault states, and multiple sets of sample feature vectors; loading the failure feature vectors into the three-level index tree, performing coarse screening of fault states based on pre-selected similarity to obtain a candidate fault state set; performing fine matching of the failure feature vectors and the candidate fault state set based on Euclidean distance to output multiple device fault association probability pairs; and performing device fault descending classification based on the multiple device fault association probability pairs to output the real-time fault device group, wherein the real-time fault device group has an adaptive classification fault level identifier.

[0051] Specifically, by using a human-computer interaction interface or an automated data acquisition and transmission system, characteristic data of various related devices under different preset fault states are obtained, and after data cleaning and feature extraction algorithms, multiple sets of sample feature vectors are formed. Then, a three-level index tree is constructed based on multiple related devices, multiple sets of sample fault states, and multiple sets of sample feature vectors. This three-level index tree is a hierarchical data structure for rapid retrieval. Multiple related devices serve as first-level index nodes, with each device considered an independent branch. This allows for quick location of relevant data sets for specific devices; for example, key devices such as the cutting motor, propulsion cylinder, and conveyor of a tunneling machine can be designated as first-level nodes. Next, for each device node, a second-level index is constructed based on multiple sets of sample fault states. Different preset fault states under the same device, such as overload and short circuit of the cutting motor, and abnormal pressure and leakage of the hydraulic system, are designated as child nodes under that device node. This method allows for precise identification of the data range of the device under specific fault states. Finally, under each sample fault state node, multiple sets of sample feature vectors serve as the third-level index, attaching specific vector data reflecting the device characteristics under that fault state to the corresponding fault state node. For example, feature vectors such as temperature, current, and speed corresponding to an overload fault state of the cutting motor. This hierarchical index structure significantly improves the efficiency of data retrieval and matching, thereby increasing the speed of the entire fault analysis process.

[0052] Then, the failure feature vector is loaded into the three-level index tree. Based on a pre-set similarity threshold, the similarity between the failure feature vector and the sample feature vectors in the three-level index tree is initially determined, i.e., a coarse screening of faults is performed. By calculating the similarity between the failure feature vector and the sample feature vectors under each level of the index, fault states with similarity exceeding the pre-selected threshold are filtered out to form a candidate fault state set. Based on this, the scope of fault analysis can be quickly narrowed, and obviously irrelevant fault states can be eliminated, improving the efficiency and accuracy of subsequent fine matching. After obtaining the candidate fault state set, the failure feature vector is finely matched with the fault states in the candidate fault state set based on Euclidean distance. Euclidean distance is a commonly used method to measure the distance between two vectors. In fault matching, by calculating the Euclidean distance between the failure feature vector and each sample feature vector in the candidate fault state set, the smaller the distance, the more similar the two are, that is, the greater the probability that the fault state corresponding to the sample feature vector is related to the current equipment failure. Based on the Euclidean distance calculation, the fault association probability between each candidate fault state and the corresponding equipment with the failure feature vector is obtained, forming equipment fault association probability pairs. These pairs are then sorted in descending order of probability, with higher probabilities indicating a higher likelihood of fault occurrence. A real-time fault equipment group is output. This real-time fault equipment group refers to the set of currently faulty equipment dynamically determined during tunneling machine operation, based on the matching analysis of failure feature vectors and preset fault modes. The real-time fault equipment group includes an adaptive fault level identifier, dynamically generated according to the magnitude of the equipment fault association probability and pre-defined grading rules, such as minor faults, general faults, and severe faults. This adaptive grading method can flexibly adjust according to the actual fault association situation of the equipment, more accurately reflecting the severity of equipment faults. It provides maintenance personnel with clear and intuitive equipment fault information, facilitating timely maintenance measures and improving the reliability and operating efficiency of the tunneling machine.

[0053] The historical average baseline and historical average density are dynamically updated based on the preset cumulative tunneling mileage.

[0054] Specifically, the preset cumulative tunneling mileage is a pre-defined threshold that can be set based on actual needs and expert experience. This preset cumulative tunneling mileage serves as the trigger for updates. When the tunneling machine's actual cumulative tunneling mileage reaches the preset threshold, the update process is initiated, dynamically updating the historical mean baseline and mean density. The specific update process is as follows: Key parameter data of the tunneling machine at different operating stages are continuously recorded, such as the temperature of the cutting motor and the pressure of the propulsion cylinder. When the cumulative tunneling mileage reaches the preset value, all parameter data recorded during this period are automatically extracted. Using statistical mean calculation methods, a new average value is calculated for each parameter's data, thereby updating the historical mean baseline. Simultaneously, the data for each parameter is grouped and statistically analyzed to calculate the frequency of each group's data. Then, a new density curve is plotted based on the frequency distribution, thus updating the historical mean density. Dynamic updates improve the adaptability of anomaly identification. The reference standard is automatically adjusted according to the actual use of the tunneling machine, avoiding misjudgments and omissions caused by factors such as equipment aging and changes in the working environment. At the same time, the dynamic update mechanism ensures that historical data always matches the current equipment status, providing accurate and effective reference for anomaly identification and improving the overall operational reliability and efficiency of the tunneling machine.

[0055] Based on the adaptive hierarchical fault level identifier, a fault verification priority instruction for the real-time fault equipment group is constructed, and the fault verification priority instruction is sent to the ground central control system.

[0056] Specifically, the adaptive hierarchical fault level identifier reflects the severity of each device's fault in the real-time faulty equipment group. Based on the adaptive hierarchical fault level identifier, fault verification priority instructions are constructed for devices with different fault levels, such as high priority for severe faults and low priority for minor faults. These fault verification priority instructions are then sent to the ground control system. The ground control system, located on the ground during tunneling machine operation, is the core control platform that receives these instructions and coordinates personnel and resources to sequentially verify and process the real-time faulty equipment group. This process enables the control system to arrange personnel to verify faulty equipment in an orderly manner according to priority, improving fault handling efficiency and ensuring the stable operation of the tunneling machine.

[0057] Example 2, based on the same inventive concept as the adaptive anomaly identification method for tunneling machine operating conditions in the foregoing examples, such as... Figure 2 As shown, this application provides an adaptive anomaly identification system for tunneling machine operating conditions, the system comprising:

[0058] The working condition acquisition trigger module 10 is used to trigger the acquisition of the operating conditions of multiple pre-set associated equipment in the tunneling machine when a lithological abrupt change event is captured. The collaborative sampling execution module 20 is used to drive multiple distributed edge acquisition nodes of the multiple associated equipment to perform millisecond-level synchronous working condition acquisition based on pre-set collaborative sampling rules, generating multi-channel operating condition time-series data. The time-series feature backtracking module 30 is used to perform time-series feature matching backtracking on the lithological abrupt change event to locate the real-time lithological transition stage. The model retrieval module 40 is used to dynamically retrieve the equipment association judgment model based on the real-time lithological transition stage. The association coupling judgment module 50 is used to calculate the cross-correlation matrix based on the multi-channel operating condition time-series data, and then load the cross-correlation matrix into the equipment association judgment model for spatiotemporal association coupling judgment. The failure feature parsing module 60 is used to determine if the spatiotemporal association coupling judgment result is an association coupling failure, and then parse the failure feature vector from the cross-correlation matrix. The fault output module 70 is used to perform fault pointing probability matching based on the failure feature vector and output a real-time fault device group, wherein the real-time fault device group has an adaptive hierarchical fault level identifier.

[0059] The time-series feature backtracking module 30 is further configured to: use the timestamp of the lithological abrupt change event as a reference point to call the time-series data before and after a preset backtracking time window to obtain a multi-channel equipment operating condition time-series segment; output multi-modal lithological response features by solving the multi-channel equipment operating condition time-series segment, wherein the multi-modal lithological response features include the propulsion speed reduction rate, the cutterhead torque fluctuation amplitude, and the proportion of vibration signal main frequency energy; and use the multi-modal lithological response features to traverse and compare multiple standard modal feature templates of multiple standard transition stages in the lithological transition feature library to locate the real-time lithological transition stage.

[0060] The model retrieval module 40 is further configured to: interactively obtain multiple device parameter association rules for the multiple standard transition stages; after configuring multiple multi-task comparison engines for the multiple device parameter association rules, configure multiple distributed parallel processing architectures for the multiple multi-task comparison engines to complete the construction of multiple standard association judgment models; use the multiple standard transition stages as indexes to index and store the multiple standard association judgment models to generate an association judgment model library; and dynamically retrieve the device association judgment model from the association judgment model library according to the real-time lithology transition stage.

[0061] The correlation coupling decision module 50 is further configured to: combine and enumerate the multi-channel operating condition time series data to obtain multiple sets of dual-channel operating condition time series data; calculate the maximum cross-correlation number and operating condition delay of the multiple sets of dual-channel operating condition time series data based on a preset sliding segmentation window and sliding segmentation step size, and output multiple symmetric relationship matrices; aggregate the multiple symmetric relationship matrices according to the channel pair topology of the multiple sets of dual-channel operating condition time series data to generate the cross-correlation number matrix; load the cross-correlation number matrix into the equipment correlation judgment model to perform spatiotemporal correlation coupling judgment, and output the spatiotemporal correlation coupling judgment result.

[0062] The failure feature parsing module 60 is further configured to: extract multiple channel pair relationship data fragments from the cross-relationship matrix according to the device parameter association rules of the device association judgment model; load the multiple channel pair relationship data fragments into the multi-task comparison engine of the device association judgment model, perform parallel spatiotemporal coupled dual threshold decision-making, and output multiple failure state sets; dynamically aggregate the multiple failure state sets according to the rule ID index of the multiple channel pair relationship data fragments, and output the failure feature vector.

[0063] The operating condition acquisition triggering module 10 is also used to perform the following: based on the sampling window, perform sliding window standard deviation demodulation on the propulsion speed time-series signal of the tunneling machine and output the propulsion speed standard deviation; perform local extreme value density analysis on the cutterhead torque signal of the tunneling machine and output the cutterhead torque extreme value density, wherein the propulsion speed time-series signal demodulation and the cutterhead torque signal analysis are performed synchronously; when the propulsion speed standard deviation suddenly increases by more than 30% of the historical average baseline, satisfying P sampling windows, and the cutterhead torque extreme value density reaches more than Q times the historical average density, a lithological mutation event triggering command is generated; the lithological mutation event triggering command is sent to the multiple distributed edge acquisition nodes based on a unified timestamp to trigger the acquisition of the operating conditions of the multiple associated devices.

[0064] The fault output module 70 is further configured to perform the following: interactively obtain multiple sets of sample feature vectors of the multiple associated devices under multiple sets of sample fault states; construct a three-level index tree based on the multiple associated devices, multiple sets of sample fault states, and multiple sets of sample feature vectors; load the failure feature vectors into the three-level index tree, perform coarse screening of fault states based on pre-selected similarity, and obtain a candidate fault state set; perform fine matching of the failure feature vectors and the candidate fault state set based on Euclidean distance, and output multiple device fault association probability pairs; perform device fault descending classification according to the multiple device fault association probability pairs, and output the real-time fault device group, wherein the real-time fault device group has an adaptive classification fault level identifier.

[0065] The system is also used to perform: dynamic updates of the historical average baseline and historical average density based on a preset cumulative tunneling mileage.

[0066] The system is also used to execute: constructing a fault verification priority instruction for the real-time fault equipment group based on the adaptive hierarchical fault level identifier, and sending the fault verification priority instruction to the ground central control system.

[0067] In summary, any of the methods or steps described above can be stored as computer instructions or programs in various types of computer memory, and the computer instructions or programs can be recognized by various types of computer processors to implement any of the above methods or steps.

[0068] Based on the above specific embodiments of the present invention, any improvements and modifications made to the present invention by those skilled in the art without departing from the principle of the present invention shall fall within the patent protection scope of the present invention.

Claims

1. A method for adaptive anomaly recognition for operating conditions of a roadheader, characterized in that, The method comprises: When a lithology mutation event is captured, triggering acquisition of running conditions of a plurality of preset associated devices in a heading machine; Based on a preset collaborative sampling rule, driving a plurality of distributed edge collection nodes of the plurality of associated devices to perform millisecond-level synchronous condition collection, and generating multi-channel running condition time series data; Performing time series feature matching backtracking on the lithology mutation event to locate a real-time lithology transition stage; According to the real-time lithology transition stage, dynamically calling a device association judgment model; After calculating a correlation number matrix based on the multi-channel running condition time series data, loading the correlation number matrix to the device association judgment model to make a spatio-temporal association coupling decision; If the spatio-temporal association coupling decision result is association coupling failure, resolving a failure feature vector from the correlation number matrix; According to the failure feature vector, performing fault pointing probability matching to output a real-time fault device group, wherein the real-time fault device group has an adaptive hierarchical fault level identifier; According to the real-time lithology transition stage, dynamically calling a device association judgment model, comprising: Interactively obtaining a plurality of device parameter association rules of a plurality of standard transition stages; After configuring a plurality of multi-task comparison engines for the plurality of device parameter association rules, performing a plurality of distributed parallel processing architecture configurations of the plurality of multi-task comparison engines to complete construction of a plurality of standard association judgment models; Taking the plurality of standard transition stages as indexes, indexing the plurality of standard association judgment models to generate an association judgment model library; According to the real-time lithology transition stage, dynamically calling the device association judgment model from the association judgment model library; After calculating a correlation number matrix based on the multi-channel running condition time series data, loading the correlation number matrix to the device association judgment model to make a spatio-temporal association coupling decision, comprising: Combining and enumerating the multi-channel running condition time series data to obtain a plurality of groups of two-channel running condition time series data; Taking a preset sliding segmentation window and a sliding segmentation step as a calculation reference, performing maximum correlation number and condition time delay calculation of the plurality of groups of two-channel running condition time series data to output a plurality of symmetric relation matrices; According to a channel pair topology relationship of the plurality of groups of two-channel running condition time series data, aggregating the plurality of symmetric relation matrices to generate the correlation number matrix; Loading the correlation number matrix to the device association judgment model to make a spatio-temporal association coupling decision and output a spatio-temporal association coupling decision result.

2. The method for adaptive anomaly recognition of operating conditions of a roadheader according to claim 1, characterized in that, Performing time series feature matching backtracking on the lithology mutation event to locate a real-time lithology transition stage, the method comprising: Taking a timestamp of the lithology mutation event as a reference point, performing pre-set backtracking time window before and after time series data calling to obtain multi-channel device condition time series segments; By solving the multi-channel device condition time series segments, outputting multi-modal lithology response features, wherein the multi-modal lithology response features comprise a pushing speed drop rate, a cutter torque fluctuation amplitude, and a vibration signal main frequency energy proportion; The multi-modal lithology response characteristics are used to traverse multiple standard modal characteristic templates of multiple standard transition stages in a lithology transition characteristic library, and the real-time lithology transition stage is located.

3. The method for adaptive anomaly recognition of operating conditions of a roadheader according to claim 1, characterized in that, The failure feature vector is parsed from the correlation number matrix, and the method comprises: According to the device parameter association rule of the device association judgment model, multiple channel pair relationship data fragments are extracted from the correlation number matrix; The multiple channel pair relationship data fragments are loaded into the multi-task comparison engine of the device association judgment model, and parallel space-time coupling double-threshold decision is executed, and multiple failure state sets are output; According to the rule ID index of the multiple channel pair relationship data fragments, the multiple failure state sets are dynamically aggregated, and the failure feature vector is output.

4. The method for adaptive anomaly identification of operating conditions of a roadheader according to claim 1, characterized in that, When a lithology mutation event is captured, the running conditions of a plurality of associated devices in the tunneling machine are triggered to be collected, and the method comprises: Based on the sampling window, the standard deviation of the advancing speed time sequence signal of the tunneling machine is demodulated by a sliding window, and the advancing speed standard deviation is output; The local extreme value density of the tunneling machine cutter torque signal is analyzed, and the cutter torque extreme value density is output, wherein the advancing speed time sequence signal demodulation and the cutter torque signal analysis are executed synchronously; When the advancing speed standard deviation suddenly increases by more than 30% of the historical mean value baseline and meets P sampling windows, and the cutter torque extreme value density reaches more than Q times of the historical mean density, a lithology mutation event trigger instruction is generated; The lithology mutation event trigger instruction is sent to the multiple distributed edge collection nodes based on a unified timestamp, and the running conditions of the multiple associated devices are triggered to be collected.

5. The method for adaptive anomaly recognition of operating conditions of a roadheader according to claim 1, characterized in that, According to the failure feature vector, a real-time fault device group is output by performing fault direction probability matching, and the method comprises: A plurality of sample feature vectors of the plurality of associated devices in a plurality of sample fault states are interactively obtained; Based on the plurality of associated devices, a plurality of sample fault states and a plurality of sample feature vectors, a three-level index tree is constructed; The failure feature vector is loaded into the three-level index tree, and a fault state coarse screening is performed based on a preselected similarity, to obtain a candidate fault state set; Based on the Euclidean distance, a fault state fine matching is performed between the failure feature vector and the candidate fault state set, and a plurality of device fault association probability pairs are output; According to the plurality of device fault association probability pairs, a device fault descending order classification is performed, and the real-time fault device group is output, wherein the real-time fault device group has an adaptive hierarchical fault level identifier.

6. The method for adaptive anomaly identification of operating conditions of a roadheader according to claim 4, characterized in that, The historical mean value baseline and the historical mean density are dynamically updated based on a preset cumulative tunneling mileage.

7. The method for adaptive anomaly identification of operating conditions of a heading machine according to claim 1, characterized in that, According to the adaptive hierarchical fault level identifier, a fault verification priority instruction of the real-time fault device group is constructed, and the fault verification priority instruction is sent to a ground control system.

8. An adaptive anomaly detection system for a machine operating condition, characterized by, Steps for implementing the method of any one of claims 1 to 7, comprising: A working condition collection trigger module (10) is configured to trigger the collection of the running conditions of a plurality of associated devices in a tunneling machine when a lithology mutation event is captured. The cooperative sampling execution module (20) is configured to drive a plurality of distributed edge collection nodes of the plurality of associated devices to perform millisecond-level synchronous working condition collection based on a preset cooperative sampling rule, and generate multi-channel running working condition time series data. The time series feature backtracking module (30) is configured to perform time series feature matching backtracking on the lithology mutation event, and locate a real-time lithology transition stage. The model calling module (40) is configured to dynamically call a device association judgment model according to the real-time lithology transition stage. The association coupling decision module (50) is configured to load the interrelation number matrix to the device association judgment model for spatio-temporal association coupling decision after calculating the interrelation number matrix based on the multi-channel running working condition time series data. The failure feature analysis module (60) is configured to analyze a failure feature vector from the interrelation number matrix if the spatio-temporal association coupling decision result is association coupling failure. The fault output module (70) is configured to perform fault pointing probability matching according to the failure feature vector, and output a real-time fault device group, wherein the real-time fault device group has an adaptive hierarchical fault level identifier. The model calling module (40) is further configured to: interactively obtain a plurality of device parameter association rules of a plurality of standard transition stages; configure a plurality of multi-task comparison engines for the plurality of device parameter association rules, perform a plurality of distributed parallel processing architecture configurations of the plurality of multi-task comparison engines, complete construction of a plurality of standard association judgment models; index the plurality of standard association judgment models by taking the plurality of standard transition stages as indexes, and generate an association judgment model library; and dynamically call the device association judgment model from the association judgment model library according to the real-time lithology transition stage. The association coupling decision module (50) is further configured to: combine and enumerate the multi-channel running working condition time series data to obtain a plurality of groups of two-channel running working condition time series data; calculate maximum interrelation numbers and working condition time delays of the plurality of groups of two-channel running working condition time series data by taking a preset sliding segmentation window and a sliding segmentation step length as calculation bases, and output a plurality of symmetric relation matrices; aggregate the plurality of symmetric relation matrices to generate the interrelation number matrix according to a channel pair topology relationship of the plurality of groups of two-channel running working condition time series data; and load the interrelation number matrix to the device association judgment model for spatio-temporal association coupling decision, and output a spatio-temporal association coupling decision result.

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