Method, system and device for monitoring multifunctional parameters of direct-current drilling machine

Abstract

The invention discloses a method, a system and a device for monitoring multifunctional parameters of a direct-current drilling machine, and relates to the technical field of manufacturing of industrial automatic control system devices. The method, system and device for monitoring the multifunctional parameters of the direct-current drilling machine comprises the steps that S1, various data are collected and subjected to standardization and normalization processing, and a standardized working condition data set is constructed; s2, multi-dimensional disturbance characteristics are analyzed, and the stability level of a drilling system is quantified; s3, evaluating a dynamic evolution trend of a working condition, and updating a risk level, a response strategy and a monitoring priority; and S4, identifying an abnormal state based on the key disturbance value and the trend evolution value, and generating a monitoring report. The problems that an existing direct current drilling machine display device is insufficient in key working condition feature extraction capacity, deep understanding and trend analysis of the equipment operation state are difficult to support, and then the early warning timeliness and judgment accuracy of the abnormal state are limited are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of manufacturing industrial automatic control system devices, and particularly to a multi-functional parameter monitoring method, system and device for a DC drilling machine. Background Art

[0002] During the operation of existing DC drilling machine equipment, a fixed sampling frequency and basic display instruments are usually used to simply monitor the voltage, current, load, impact, and temperature rise during the drilling process. There is a lack of fine recognition and trend analysis capabilities for the multi-dimensional disturbance state of the drilling system, making it difficult to support precise state perception and operation decision-making assistance under complex drilling conditions. Traditional drilling monitoring systems mostly rely on single-type sensors and fixed-point data display methods, and are unable to achieve integrated analysis and dynamic modeling of the equipment operation state, structural response state, and operation environment state. This results in a lag in the recognition of early abnormal states, untimely risk warnings, and insufficient judgment basis, presenting problems such as potential safety hazards and efficiency fluctuations. At the data processing level, existing technologies lack a unified data standard, preprocessing process, and deep integration mechanism for various types of drilling data from different subsystems and monitoring terminals, and are unable to achieve effective alignment and dynamic collaboration of cross-modal data, restricting the precise quantification of working condition disturbances, real-time modeling of trend evolution, and comprehensive evaluation capabilities of the stability of the drilling system. Meanwhile, the recognition of abnormal states still mainly relies on static threshold rules and one-dimensional fluctuation overlimit judgments, and fails to integrate multi-dimensional disturbance characteristics and trend deviation behaviors for dynamic response and level recognition, making it difficult to meet the requirements for real-time diagnosis and structured report generation of systematic abnormal states in complex drilling environments. Therefore, in view of the deficiencies of the existing technologies, there is an urgent need for a multi-functional parameter monitoring method, system and device for a DC drilling machine. Summary of the Invention

[0003] Technical Problems to be Solved In view of the deficiencies of the existing technologies, the present invention provides a multi-functional parameter monitoring method, system and device for a DC drilling machine, which solves the problem that the key working condition feature extraction ability of the existing DC drilling machine display device is insufficient, making it difficult to support in-depth understanding and trend analysis of the equipment operation state, and further restricting the timeliness of abnormal state warning and the accuracy of judgment.

[0004] Technical Solutions To achieve the above object, the present invention is implemented through the following technical solutions: A multi-functional parameter monitoring method, system and device for a DC drilling machine, including S1, collecting drilling process condition data, structural operation state data and operation environment data of the current drilling task, and performing standardization and normalization preprocessing on the collected drilling process condition data, structural operation state data and operation environment data to construct a standardized condition data set; S2, analyzing the multi-dimensional disturbance characteristics of the current equipment operation state based on the standardized condition data set, and quantifying the stability level and risk level of the drilling system based on the analysis results of the multi-dimensional disturbance characteristics; S3, evaluating the dynamic evolution trend of the current condition state based on the standardized condition data set, and then updating the risk assessment level, disturbance response strategy and dynamic monitoring priority configuration of each core parameter; S4, taking the disturbance value of the key conditions of the current batch and the condition trend evolution value as inputs, comprehensively analyzing the abnormal state response of the current batch under the multi-dimensional disturbance intensity and trend deviation path, and generating an abnormal identification determination result and a structured monitoring report based on the abnormal state response.

[0005] Further, the specific steps of collecting the drilling process condition data, structural operation state data and operation environment data of the current drilling task, and performing standardization and normalization preprocessing on the collected drilling process condition data, structural operation state data and operation environment data to construct a standardized condition data set are as follows: Collecting drilling process condition data including motor voltage, drilling depth, axial impact intensity and energy consumption change rate through a drilling operation monitoring terminal, and at the same time recording the measurement unit, vibration intensity value, relative deviation rate of the current measurement value from the historical fluctuation range, instantaneous mutation amplitude and corresponding acquisition time of each condition data; Collecting structural operation state data including abnormal vibration density, historical maximum recorded value, historical minimum recorded value and cumulative operation duration of each monitoring item through a device operation state monitoring device; Collecting operation environment data including drilling area temperature value, load value and spatial stability around the equipment through an operation environment monitoring module, and at the same time recording the environmental monitoring frequency, monthly historical mean value and characteristic baseline mean value under historical stable conditions; Recording the original processed data including the data continuity integrity rate of each condition parameter, historical load mean value and sampling abnormality ratio; Through a unified high-precision clock synchronization mechanism, performing standardization processing on the drilling process condition data, structural operation state data and operation environment data collected during drilling, cleaning the existing missing items, unifying the field naming method, and performing normalization processing on the drilling process condition data, structural operation state data and operation environment data after cleaning and unit conversion to eliminate the dimension and scale differences caused by equipment model differences and sensor specification differences; Storing the standardized and normalized drilling process condition data, structural operation state data and operation environment data to construct a standardized condition data set.

[0006] Further, the specific steps for analyzing the multi-dimensional disturbance characteristics of the current equipment operating state based on the standardized working condition dataset are as follows: Extract the load value of the current batch and the historical load average value from the standardized working condition dataset, calculate the difference between the load value of the current batch and the historical load average value and take its absolute value to obtain the load value change rate; Extract the vibration intensity values of all working condition parameters and accumulate them to obtain the total short-period vibration value; Extract the temperature value at the current moment, take the first derivative and take the absolute value to obtain the temperature change rate at the current moment; Extract the axial impact intensity at the current moment, calculate the derivative of the axial impact intensity and take the absolute value to obtain the second-order change rate of the axial force; Add the load value change rate, the total short-period vibration value, the temperature change rate and the second-order change rate of the axial force to obtain the key working condition disturbance value during the drilling operation of the current batch.

[0007] Further, the specific steps for quantifying the stability level and risk level of the drilling system based on the analysis results of the multi-dimensional disturbance characteristics are as follows: Compare the key working condition disturbance value with the disturbance level threshold in real time. The disturbance level threshold includes the first disturbance threshold and the second disturbance threshold: When the key working condition disturbance value is less than or equal to the second disturbance threshold, it is determined as a low disturbance section, maintain the existing data acquisition strategy, directly execute the operation optimization process based on the current characteristic value, and do not introduce an additional compensation mechanism; When the key working condition disturbance value is greater than the second disturbance threshold and less than or equal to the first disturbance threshold, it is determined as a medium disturbance section, enter the disturbance warning state, and execute the dynamic steady-state compensation strategy, including adjusting the sampling period of the vibration and temperature parameters and shortening the sliding mean window length. At the same time, introduce a micro-amplitude correction factor in the construction of relevant parameters to weaken the influence of outliers and enhance the numerical stability of the characteristic value; When the key working condition disturbance value is greater than the first disturbance threshold, it is determined as a high disturbance section, immediately suspend the operation adjustment and optimization derivation based on this characteristic value, trigger the numerical anomaly emergency response mechanism, call the parameter records in multiple historical stable drilling tasks to construct the dynamic weighted average working condition characteristics, replace the current abnormal parameters for temporary calculation, and mark this task as an abnormal data batch, according to the quality key inspection list, and record the deviation characteristics and instability paths of the corresponding disturbance dimensions.

[0008] Further, the specific steps for evaluating the dynamic evolution trend of the current working condition state based on the standardized working condition dataset are as follows: Extract the energy consumption change rate at the current moment, the relative deviation rate of the current measurement value from the historical fluctuation range, and the characteristic baseline average value under the historical stable working condition from the standardized working condition dataset, subtract the relative deviation rate from the characteristic baseline average value and take the absolute value to obtain the trend offset; Extract the instantaneous mutation amplitude at the current moment and take the power of the mutation response adjustment factor to obtain the amplitude regulation value; Add the trend offset and the amplitude regulation value to obtain the disturbance reference value, divide the energy consumption change rate by the disturbance reference value and then add one and take the logarithm to obtain the working condition trend evolution value at the current moment.

[0009] Furthermore, the specific steps for updating the risk assessment level, disturbance response strategy and dynamic monitoring priority configuration of each core parameter are as follows: real-time comparison of the operating condition trend evolution value and the trend stability threshold, the trend stability threshold includes a first trend threshold and a second trend threshold: when the operating condition trend evolution value is less than or equal to the second trend threshold, it is determined to be a trend highly stable section, the existing job scheduling and parameter calculation process is maintained unchanged, and the current trend state is marked as a stable sample input, participating in the update and correction of the long-term trend baseline, while reducing the trend calculation frequency and releasing computing resources for other real-time task processing; when the operating condition trend evolution value is greater than the second trend threshold and less than or equal to the first trend threshold, it is determined to be a trend transition In the segment, it enters the trend tracking enhancement mode, moderately improves the trend sampling resolution, narrows the window span to enhance the short-period disturbance response capability, and enables the trend evolution slope analysis at the same time to make real-time judgments on the directionality and acceleration of the changing trend, supporting the implementation of early intervention strategies. When the operating condition trend evolution value is greater than the first trend threshold, it is determined to be a trend-significant disturbance segment, and immediately switches to the trend disturbance buffer mechanism, dynamically adjusts the feature extraction parameter structure, introduces trend filtering and delay compensation processing to prevent high-frequency changes from causing decision fluctuations, and at the same time suspends the current parameter adjustment and strategy optimization operations, executes trend change isolation, and separately archives the trend slope, fluctuation period and slope curvature change characteristics of this segment, and enters the high-frequency evolution feature analysis queue.

[0010] Furthermore, the specific steps of taking the key operating condition disturbance value and the operating condition trend evolution value of the current batch as input and comprehensively analyzing the abnormal state response of the current batch under the multi-dimensional disturbance intensity and trend deviation path are as follows: obtaining the key operating condition disturbance value and the operating condition trend evolution value at each moment, multiplying the key operating condition disturbance value at each moment with the trend enhancement factor of the trend evolution value, and then multiplying it by the data continuity integrity rate corresponding to each operating condition parameter to obtain the trend disturbance value of the current operating condition parameter; extracting the abnormal vibration density of each operating condition parameter, multiplying the abnormal vibration density with the abnormal suppression adjustment factor, and then adding one to obtain the positive abnormal suppression value; dividing the trend disturbance value of each operating condition parameter by the positive abnormal suppression value to obtain the local abnormal response value of each operating condition parameter; and adding the local abnormal response values of all operating condition parameters to obtain the abnormal state response value at the current moment.

[0011] Furthermore, the specific steps for generating an anomaly recognition determination result and a structured monitoring report based on the anomaly status response are as follows: The anomaly status response value is compared with the anomaly level threshold in real time. The anomaly level threshold includes a first anomaly threshold and a second anomaly threshold: When the anomaly status response value is less than or equal to the second anomaly threshold, it is determined as a low response section. At this time, the current status summary is presented in an overview mode on the display device, marked with a normal operation identifier to avoid information interference. At the same time, the characteristic trend and disturbance path data corresponding to this status section are retained for subsequent dynamic update reference of the trend baseline, and the anomaly status operation frequency is delayed to improve the calculation resource scheduling efficiency; When the anomaly status response value is greater than the second anomaly threshold and less than or equal to the first anomaly threshold, it is determined as a medium response section. At this time, a multi-dimensional trend graph and an anomaly-sensitive parameter trajectory are automatically pushed through the display device, entering the trend tracking visualization mode, and the change slope, disturbance frequency, and change direction of key parameters are displayed in real time to assist the operator in judging the trend evolution. At the same time, a local parameter high-frequency sampling strategy is enabled and the data quality is dynamically corrected to enhance the observability and real-time interpretability of the anomaly trend; When the anomaly status response value is greater than the first anomaly threshold, it is determined as a high response section, and the deep anomaly warning mode is entered. At this time, the display device will switch to a hierarchical anomaly analysis interface, showing the constituent items of the anomaly response value, the ranking of key disturbance parameters, and the extreme value distribution of the trend slope. At the same time, the impact prediction information at the device component level is pushed, and the status prediction and scheduling derivation logic based on the current data is paused, switching to the anomaly isolation mode under the historical stable parameters, automatically recording the timestamp, parameter sequence, and trend path of the anomaly mutation point, and generating a structured anomaly monitoring report for quick traceability and risk intervention.

[0012] The second aspect of the present invention provides a multi-functional parameter monitoring system for a DC drilling machine, including: a data acquisition and preprocessing module, a working condition disturbance evaluation module, a trend analysis module, and an abnormal judgment and monitoring feedback module, where: the data acquisition and preprocessing module is used to collect the working condition data of the drilling process, the structural operation state data, and the working environment data of the current drilling task, and perform standardization and normalization preprocessing on the collected working condition data of the drilling process, the structural operation state data, and the working environment data to construct a standardized working condition data set; the working condition disturbance evaluation module is used to analyze the multi-dimensional disturbance characteristics of the current equipment operation state based on the standardized working condition data set, and quantify the stability level and risk level of the drilling system based on the analysis results of the multi-dimensional disturbance characteristics; the trend analysis module is used to evaluate the dynamic evolution trend of the current working condition state based on the standardized working condition data set, and then update the risk assessment levels, disturbance response strategies, and dynamic monitoring priority configurations of each core parameter; the abnormal judgment and monitoring feedback module is used to take the key working condition disturbance values and working condition trend evolution values of the current batch as inputs, comprehensively analyze the abnormal state response of the current batch under the multi-dimensional disturbance intensity and trend deviation path, and generate an abnormal recognition judgment result and a structured monitoring report based on the abnormal state response situation.

[0013] The third aspect of the present invention provides a multifunctional parameter monitoring device for a DC drilling machine, comprising: a drilling status monitoring terminal for collecting drilling working condition data including drilling depth, motor voltage, load change, impact strength, vibration amplitude, temperature rise parameters and axial force in real time during the drilling operation, and simultaneously collecting drill pipe deformation, drill bit disturbance and support structure response state parameters as well as environmental factor data of temperature, humidity, air pressure and dust concentration at the operation site, and uploading them to a data processor via an industrial bus; a data processor for performing field structure standardization, timestamp alignment, physical unit unification and dimension normalization on the received multi-source drilling data to construct a unified standardized working condition data set, and extracting key disturbance features, instantaneous fluctuation amplitude and data integrity rate indicators based on a sliding time window and historical comparison strategy, and sending the preprocessing results to Working condition analysis and trend assessment unit; The working condition analysis and trend assessment unit is used to perform feature recognition, coupling analysis and risk level calculation on the multi-dimensional disturbance characteristics in the current drilling task based on the standardized working condition data set, further construct the trend evolution sequence of each working condition parameter, evaluate the trend change rate, directionality and offset degree, dynamically update the monitoring priority, response adjustment strategy and intervention conditions of each parameter, and push the analysis results to the anomaly identification and reporting unit; The anomaly identification and report generation unit is used to construct multi-parameter fusion data with key disturbance values and trend evolution values as input, identify the abnormal evolution status and potential risk events in the current batch drilling process, output a structured monitoring report containing anomaly level, trigger parameters, impact path and time label, and push early warning instructions to the operation terminal when the threshold event is triggered, to assist the operator in responding and intervening accurately.

[0014] Beneficial Effects The present invention has the following beneficial effects: (1) This multifunctional parameter monitoring method, system and device for a DC drilling machine constructs a standardized working condition data set through multi-source fusion collection and standardized processing based on drilling process working condition data, structural operation status data and working environment data, thereby achieving comprehensive modeling and accurate expression of the drilling task status, effectively solving the problems of single data collection and one-sided status identification in the existing technology.

[0015] (2) This multifunctional parameter monitoring method, system and device for a DC drilling machine realizes graded identification and structured early warning of key risk states in the drilling process by jointly calculating abnormal response values based on the working condition disturbance intensity and trend evolution characteristics, thereby enhancing the system's ability to respond to sudden disturbances under complex working conditions and effectively solving the problems of abnormality identification lag and rough judgment standards in the existing technology.

[0016] (3) The multifunctional parameter monitoring method, system and device for a DC drilling machine realize real-time perception and intelligent feedback of the whole drilling operation process by constructing a dynamic monitoring mechanism including feature extraction, trend evaluation, risk quantification and priority adjustment. Furthermore, it improves the depth of system state analysis and parameter adaptive ability, and effectively solves the problems of shallow state perception and rigid control logic in the prior art.

[0017] (4) The multifunctional parameter monitoring method, system and device for a DC drilling machine achieve full-process closed-loop control from working condition acquisition, preprocessing, trend recognition to early warning report generation through an integrated device structure with multi-module division of labor and cooperation. Furthermore, it improves the data utilization efficiency of drilling operations and the intelligent level of operation decision-making, and effectively solves the problems of scattered device functions and disconnection between monitoring and feedback in the prior art.

[0018] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flowchart of a multifunctional parameter monitoring method for a DC drilling machine according to the present invention; Figure 2 It is a structural diagram of a multifunctional parameter monitoring system for a DC drilling machine according to the present invention; Figure 3 It is a bar chart of key working condition disturbance values related to the present invention; Figure 4 It is a line chart of working condition trend evolution values related to the present invention; Figure 5 It is a line chart of abnormal state response values related to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0021] Please refer to Figures 1 - 3, an embodiment of the present invention provides a technical solution: a multi-functional parameter monitoring method, system and device for a DC drilling machine, including S1, collecting the drilling process condition data, structural operation state data and operation environment data of the current drilling task, and performing standardization and normalization preprocessing on the collected drilling process condition data, structural operation state data and operation environment data to construct a standardized condition data set; S2, analyzing the multi-dimensional disturbance characteristics of the current equipment operation state based on the standardized condition data set, and quantifying the stability level and risk level of the drilling system based on the analysis results of the multi-dimensional disturbance characteristics; S3, evaluating the dynamic evolution trend of the current condition state based on the standardized condition data set, and then updating the risk assessment levels, disturbance response strategies and dynamic monitoring priority configurations of each core parameter; S4, taking the disturbance values and condition trend evolution values of the current batch of key conditions as inputs, comprehensively analyzing the abnormal state response of the current batch under the multi-dimensional disturbance intensity and trend deviation path, and generating an abnormal recognition determination result and a structured monitoring report based on the abnormal state response.

[0022] Specifically, the specific steps of collecting the drilling process condition data, structural operation state data and operation environment data of the current drilling task, and performing standardization and normalization preprocessing on the collected drilling process condition data, structural operation state data and operation environment data to construct a standardized condition data set are as follows: collecting the drilling process condition data including motor voltage, current, power factor, rotation speed, drilling depth, drill bit temperature rise, axial impact intensity, energy consumption change rate, brush wear degree and related operation load characteristics through the drilling operation monitoring terminal to form a comprehensive quantitative perception of the electric drive performance, drilling process and load change; during the collection process, synchronously record the measurement unit, vibration intensity value, sensor model, sampling frequency, measurement accuracy, sampling period of each condition data, calculate the relative deviation rate, instantaneous mutation amplitude of the current measurement value and the historical fluctuation range and the corresponding collection time, and at the same time mark the drilling mode and drill bit type identification corresponding to the operation to achieve the complete correspondence of parameters and operation configuration.

[0023] Collect the structural operation status data including spindle load change, drive system temperature rise, cooling system efficiency, motor start-stop frequency, equipment vibration spectrum, system no-load energy consumption, real-time power density, and servo response delay through the equipment operation status monitoring device. At the same time, record the sampling stability, abnormal vibration density, historical maximum recorded value, historical minimum recorded value, abnormal marking status, and cumulative operation duration of each monitoring item to evaluate the current operation stability and failure risk of the equipment. Collect the operation environment data including drilling area temperature, environmental load, humidity, air pressure, dust concentration, electromagnetic interference intensity, and spatial stability around the equipment through the operation environment monitoring module. At the same time, record the environmental monitoring frequency, sensor type, monitoring period, monthly historical mean, and characteristic baseline mean under historical stable working conditions to assist in analyzing the correlation between drilling efficiency and environmental disturbance. Further record the original processing data including data continuity integrity rate, historical load mean, standard power consumption correction coefficient mean, equipment status record coverage rate, environmental sampling synchronization rate, and sampling anomaly ratio of each working condition parameter as the basic indicators for data integrity and quality control.

[0024] Through a unified high-precision clock synchronization mechanism, ensure the consistency and alignability of the working condition data, structural operation status data, and operation environment data in the time domain during the drilling process; then perform standardization processing on various types of original collected data, clean up the missing items, jump extreme values, and abnormal fields beyond the set detection range that appear during the collection process, unify the field naming methods and physical unit systems of each data source, and complete the scale unification and dimension equivalence between data; then perform normalization processing on the three types of data after cleaning and unit conversion respectively to eliminate the interference effects caused by differences in different equipment models, sampling frequencies, and sensor accuracies, and improve the horizontal comparability and vertical continuity of the data; finally, structurally store all the drilling process working condition data, structural operation status data, and operation environment data after standardization and normalization processing to construct a stable, high-quality, and traceable standard working condition data set as the basic input source for subsequent key disturbance feature identification and abnormal state response analysis.

[0025] In this implementation plan, a comprehensive, standardized, and traceable working condition data foundation system is constructed to solve the problems of heterogeneous multi-source data, asynchronous sampling, inconsistent units, and non-standard processing of missing and abnormal items in current drilling operations. Through multi-dimensional acquisition, cleaning, unified standardization, and normalization of the working condition data during the drilling process, the structural operation status data, and the working environment data, this step ensures the consistency of various core parameters in terms of time, space, and physical dimensions, improves the accuracy of data fusion and comparison, provides a reliable data foundation for subsequent key disturbance identification, trend evolution analysis, and abnormal state response, helps to accurately understand the drilling operation status, dynamically evaluate the operation stability, and intelligently trigger the early warning response strategy, thereby enhancing the adaptability of the overall system to complex drilling tasks and the operation safety level.

[0026] Specifically, the specific steps for analyzing the multi-dimensional disturbance characteristics of the current equipment operation status based on the standardized working condition data set are as follows: Extract the load value of the current batch and the historical load average value from the standardized working condition data set, calculate the difference between the load value of the current batch and the historical load average value and take its absolute value to obtain the load value change rate; Extract the vibration intensity values of all working condition parameters and accumulate them to obtain the total short-period vibration value; Extract the temperature value at the current moment, take the first derivative and its absolute value to obtain the temperature change rate at the current moment; Extract the axial impact intensity at the current moment, calculate the derivative of the axial impact intensity and take its absolute value to obtain the second-order change rate of the axial force; Add the load value change rate, the total short-period vibration value, the temperature change rate, and the second-order change rate of the axial force to obtain the key working condition disturbance value during the drilling operation of the current batch.

[0027] The calculation formula for the key working condition disturbance value is: ; In the formula, m represents the total number of working condition parameters; represents the load value at the current moment, which is used to reflect the instantaneous load level on the drilling motor, and is compared with the historical load average value to determine whether there are abnormal current fluctuations and overload phenomena; represents the historical load average value, which is used to describe the load reference level within a short period during the drilling process and reflects the steady-state operation trend of the system; represents the vibration intensity value of the jth working condition parameter, which is used to record the structural response amplitude of the drilling equipment at this moment and is the basic data for vibration risk assessment; represents the temperature value at the current moment, represents the temperature change rate at the current moment, which is used to measure the thermal stability during the drilling process; represents the axial impact intensity at the current moment, represents the second-order change rate of the axial force, which is used to identify whether there are sudden impacts, jams, and structural response mutations caused by rock layer faults during the drilling process of the drill bit, and reflects the impact disturbance intensity.

[0028] In this embodiment, the current load value at time point t1 is set to 120, the historical load average value is 100, the load change rate is 20, the vibration intensity value is 9.5, the current temperature is 36.5, the temperature change rate is 0.8, the impact intensity value is 8.5, and the second-order change rate of the axial force is 0.4; the current load value at time point t2 is set to 105, the historical load average value is 100, the load change rate is 5, the vibration intensity value is 8.0, the current temperature is 37.0, the temperature change rate is 0.5, the impact intensity value is 9.6, and the second-order change rate of the axial force is 0.4; the current load value at time point t3 is set to 130, the historical load average value is 100, the load change rate is 30, the vibration intensity value is 10.3, the current temperature is 38.2, the temperature change rate is 1.2, the impact intensity value is 10.4, and the second-order change rate of the axial force is 0.6; the current load value at time point t4 is set to 98, the historical load average value is 100, the load change rate is 2, the vibration intensity value is 6.4, the current temperature is 38.0, the temperature change rate is 0.2, the impact intensity value is 11.7, and the second-order change rate of the axial force is 0.1; the current load value at time point t5 is set to 115, the historical load average value is 100, the load change rate is 15, the vibration intensity value is 8.9, the current temperature is 39.0, the temperature change rate is 1.0, the impact intensity value is 11.5, and the second-order change rate of the axial force is 0.6. Calculate the key working condition disturbance values at each time point, as shown in Table 1, the data table of key working condition disturbance values.

[0029] Table 1 Data table of key working condition disturbance values Time point Current moment load value Historical load mean value Load deviation value Vibration intensity value Temperature value Temperature change rate Axial impact intensity Second - order change rate of axial force Key operating condition disturbance value t1 120 100 20 9.5 36.5 0.8 8.5 0.4 30.7 t2 105 100 5 8.0 37.0 0.5 9.6 0.4 13.9 t3 130 100 30 10.3 38.2 1.2 10.4 0.6 42.1 t4 98 100 2 6.4 38.0 0.2 11.7 0.1 8.7 t5 115 100 15 8.9 39.0 1.0 11.5 0.6 25.5 As Figure 3 shown, it is the bar chart of key working condition disturbance values provided by the example of this application. As shown in Table 1 and Figure 3 it can be seen that the key working condition disturbance value at time point t3 is the highest, indicating that its load change rate is large, vibration intensity is high, temperature change rate is fast, and impact response is intense at the current moment, comprehensively reflecting that the disturbance intensity of the drilling system is the most significant during this period, there are relatively high operation risks and regulation instabilities, and it should be preferentially included in the key monitoring scope and trigger the trend warning mechanism and operation parameter correction process in a timely manner; relatively speaking, the key working condition disturbance value at time point t4 is the lowest, with small load fluctuation amplitude, stable vibration and temperature changes, and mild impact response, and the overall state is relatively stable. It can be marked as a typical steady-state reference sample for updating the stable working condition baseline and optimizing the interference judgment threshold setting. The bar chart of key working condition disturbance values clearly reflects the differences in disturbance intensity of the drilling operation state in each period. The higher the disturbance value, the more intervention strategies need to be strengthened, and the lower the disturbance value, the more it can be used as the input of steady-state characteristics, which helps to improve the pertinence of the regulation strategy and the robustness of the system operation.

[0030] This formula uniformly quantifies the degree of deviation and change trend of multiple parameters during the drilling process of the current batch, so as to accurately identify the possibility and intensity of abnormal working conditions, and support the construction of a high-sensitivity perception and response mechanism for the system to disturbance events. By comprehensively analyzing the absolute deviation degree between the real-time acquisition value and the historical mean value of each working condition parameter at the current moment, and combining the instantaneous fluctuation amplitude, change rate and stability factor, this formula normalizes and multi-dimensionally integrates the deviation characteristics of each parameter, and calculates a numerical result representing the overall working condition disturbance level. Its design logic not only considers the static offset of parameter values, but also introduces dynamic fluctuation trends and stability adjustment factors, so that the result can reflect the overall deviation state of the system under complex disturbances.

[0031] Specifically, based on the analysis results of multi-dimensional disturbance characteristics, the specific steps to quantify the stability level and risk level of the drilling system are as follows: Compare the key working condition disturbance value with the preset disturbance level threshold in real time. The disturbance level threshold is jointly composed of the first disturbance threshold and the second disturbance threshold. When the key working condition disturbance value is less than or equal to the second disturbance threshold, it is determined that the current is in the low disturbance section, indicating that the changes of each core working condition parameter are stable, the fluctuations are controllable, and the parameter deviation degree is within the historical standard range. The existing data acquisition frequency and sliding window length will be maintained, and the conventional optimization process of the drilling operation will be directly executed based on the currently constructed eigenvalue, without introducing any additional data correction and compensation mechanisms; When the key working condition disturbance value is greater than the second disturbance threshold and less than or equal to the first disturbance threshold, it is determined as the medium disturbance section and enters the disturbance warning state, indicating that some parameters have shown a trend of increased fluctuation and deviation from the baseline, but the overall situation is still controllable. At this time, the system will start the dynamic steady-state compensation strategy, including automatically adjusting the sampling period and shortening the sliding average window length for sensitive parameters such as vibration intensity and temperature rise change, so as to enhance the system's response ability to short-period drastic changes. At the same time, a micro amplitude numerical correction factor is introduced in the construction of key features to effectively weaken the interference of mutation outliers on the overall evaluation result and improve the numerical robustness and dynamic stability of eigenvalue calculation; When the key working condition disturbance value exceeds the first disturbance threshold, it is determined as the high disturbance section, indicating that there are serious deviations in the current working condition characteristics, which may involve serious working condition disturbance situations such as mechanical shock, energy consumption mutation and structural response abnormality. Immediately suspend the operation strategy adjustment and optimization derivation based on this eigenvalue, and instead trigger the numerical anomaly emergency response mechanism, automatically call the parameter records in multiple historical stable drilling tasks to construct a dynamically weighted average working condition feature, and replace the current abnormal parameters to participate in the temporary working condition evaluation calculation to ensure that the operation logic is not interrupted; At the same time, mark the current task as an abnormal data batch, include it in the key list for data quality investigation, and record the parameter dimensions, numerical deviation characteristics and possible instability paths involved in the current disturbance, providing basic data support for subsequent abnormal source tracing analysis and diagnostic optimization.

[0032] In this implementation plan, real-time monitoring and dynamic hierarchical response of the disturbance level in key working conditions during the drilling operation are realized, improving the recognition ability of abnormal disturbances and the emergency handling efficiency. By comparing the disturbance value of the key working condition obtained through real-time calculation with the set disturbance level threshold, the disturbance level section where the current operation state is located can be accurately determined, and the corresponding response mechanism can be triggered accordingly. When the disturbance is at a low level, the existing data processing and operation optimization strategies are maintained to ensure the calculation efficiency; when the disturbance is at a medium level, the early warning mode is automatically entered, and potential numerical instability problems are alleviated by adjusting the sampling parameters and introducing a correction mechanism; while when the disturbance reaches a high level, the abnormal handling mechanism is activated to avoid misleading subsequent decisions caused by abnormal data. This process effectively enhances the response elasticity and calculation robustness of the system under different disturbance intensities, contributing to ensuring the continuity of the drilling task and the stability of the operation results.

[0033] Specifically, the specific steps for evaluating the dynamic evolution trend of the current working condition state based on the standardized working condition dataset are as follows: extract the energy consumption change rate at the current moment, the relative deviation rate between the current measurement value and the historical fluctuation range, and the characteristic baseline mean value under the historical stable working condition from the standardized working condition dataset, and take the absolute value after subtracting the relative deviation rate from the characteristic baseline mean value to obtain the trend offset; extract the instantaneous mutation amplitude at the current moment and take the power of the mutation response adjustment factor to obtain the amplitude regulation value; add the trend offset and the amplitude regulation value to obtain the disturbance reference value, and divide the energy consumption change rate by the disturbance reference value and then add one and take the logarithm to obtain the working condition trend evolution value at the current moment.

[0034] The calculation formula for the working condition trend evolution value is: ; In the formula, represents the energy consumption change rate at the current moment, which is used to measure the fluctuation intensity of the key parameters of the drilling operation in a short period of time and reflects the activity degree of the disturbance amplitude of the current working condition; represents the relative deviation rate between the current measurement value and the historical fluctuation range, which is used to reflect the steady-state level of the drilling operation during this period and is an important basis for fluctuation evaluation and trend offset judgment; represents the characteristic baseline mean value under the historical stable working condition, which is used to construct the reference for trend deviation judgment; represents the instantaneous mutation amplitude, which is used to characterize the violent fluctuation behavior of the key parameters during the drilling process on an extremely short time scale; It represents the mutation response regulator, with a value range between 0.2 and 0.6, which is derived from the change rate characteristics of the trend mutation section in historical drilling tasks. First, the complete time series of trend evolution values is extracted from multiple historical stable batches, the time nodes with trend mutations are identified, and the trend change rates in adjacent periods before and after the mutation are calculated. Subsequently, the maximum value of the ratio of the front and rear rates among all mutation points is extracted as the response representative most sensitive to trend perturbations, and the ratios of the front and rear rates among all mutation points are normalized and mapped to the α value within the set range. When a certain indicator has a high sampling frequency and continuous monitoring performance in the trajectory chain, its weight factor is increased to enhance the indicator's perception ability of sudden evolution trends, ensuring that high-frequency and high-quality data are fully utilized in the calculation; if the sampling distribution of a certain indicator is sparse, the weight factor is lowered to reduce its dominant influence in the determination of the mutation amplitude, avoiding interference from low-reliability data in the accuracy judgment of the overall response value, thereby enhancing the system's robust perception ability and response stability to abnormal mutations.

[0035] In this embodiment, the abnormal adjustment factors at each time point are kept unchanged. The energy consumption change rate at time point T1 is set to 0.7576, the current deviation rate is 1.0724, the characteristic baseline mean value is 1.0976, and the instantaneous mutation amplitude is 0.3892; the energy consumption change rate at time point T2 is set to 1.0872, the current deviation rate is 0.9639, the characteristic baseline mean value is 1.0434, and the instantaneous mutation amplitude is 0.4221; the energy consumption change rate at time point T3 is set to 0.9435, the current deviation rate is 1.1717, the characteristic baseline mean value is 1.0662, and the instantaneous mutation amplitude is 0.3375; the energy consumption change rate at time point T4 is set to 0.1463, the current deviation rate is 0.9162, the characteristic baseline mean value is 0.9853, and the instantaneous mutation amplitude is 0.4726; the energy consumption change rate at time point T5 is set to 0.2766, the current deviation rate is 1.1346, the characteristic baseline mean value is 0.9446, and the instantaneous mutation amplitude is 0.4461; the energy consumption change rate at time point T6 is set to 1.0166, the current deviation rate is 0.8145, the characteristic baseline mean value is 0.9686, and the instantaneous mutation amplitude is 0.2286; the energy consumption change rate at time point T7 is set to 0.7068, the current deviation rate is 0.8372, the characteristic baseline mean value is 1.0925, and the instantaneous mutation amplitude is 0.3745; the energy consumption change rate at time point T8 is set to 1.1932, the current deviation rate is 1.0065, the characteristic baseline mean value is 1.0149, and the instantaneous mutation amplitude is 0.3415; the energy consumption change rate at time point T9 is set to 0.7481, the current deviation rate is 1.0387, the characteristic baseline mean value is 0.9061, and the instantaneous mutation amplitude is 0.3812; the energy consumption change rate at time point T10 is set to 0.1429, the current deviation rate is 0.8386, the characteristic baseline mean value is 1.0004, and the instantaneous mutation amplitude is 0.3021. Calculate the working condition trend evolution values at each time point, as shown in the data table of working condition trend evolution values in Table 2.

[0036] Table 2 Data Table of Working Condition Trend Evolution Values Time point Energy consumption change rate Current deviation rate Characteristic baseline mean value Instantaneous mutation amplitude Abnormal regulation factor Trend evolution value t1 0.7576 1.0724 1.0976 0.3892 2 0.8329 t2 1.0872 0.9639 1.0434 0.4221 2 1.1162 t3 0.9435 1.1717 1.0662 0.3375 2 0.9103 t4 0.1463 0.9162 0.9853 0.4726 2 0.2298 t5 0.2766 1.1346 0.9446 0.4461 2 0.4027 t6 1.0166 0.8145 0.9686 0.2286 2 1.3059 t7 0.7068 0.8372 1.0925 0.3745 2 0.6889 t8 1.1932 1.0065 1.0149 0.3415 2 1.1685 t9 0.7481 1.0387 0.9061 0.3812 2 0.8989 t10 0.1429 0.8386 1.0004 0.3021 2 0.2606 As Figure 4 shown, it is the line graph of the working condition trend evolution values provided by the example of this application. As shown in Table 2 and Figure 4It can be seen that the trend evolution value at time point T6 is the highest, indicating that the corresponding energy consumption change rate is relatively high, the current deviation rate is relatively small, and the mutation amplitude is relatively low. Combining with the strengthening effect of the abnormal adjustment factor, the overall working condition state change trend in this period is the most stable, the parameter change tends to be predictable, and the system shows good disturbance suppression ability and trend ductility in this period, which can be used as a reference baseline for subsequent working condition evolution modeling and parameter optimization strategy formulation; the trend evolution value at time point T4 is the lowest. Although its mutation amplitude is large, the energy consumption change rate is low and the deviation rate remains at a medium level, indicating that there are external disturbances in this period, and the overall trend expression has a high degree of uncertainty. It is necessary to focus on the operation environment factors and equipment operation status corresponding to this section, and adjust the sampling density and abnormal detection strategy in this section in a timely manner. The broken line chart of the trend evolution value can intuitively reveal the dynamic change trend of the drilling working condition at each time point and the system response stability. The higher the value, the stronger the working condition stability, the clearer the parameter trend expression, and the more suitable it is as a reference basis for working condition optimization; while the lower the value, the stronger the mutation and disturbance of the working condition, and it is necessary to conduct in-depth analysis and compensation correction in combination with multi-source monitoring data.

[0037] Specifically, the specific steps for further updating the risk assessment level, disturbance response strategy, and dynamic monitoring priority configuration of each core parameter are as follows: Compare the working condition trend evolution value with the trend stability threshold in real time. The trend stability threshold includes the first trend threshold and the second trend threshold: When the working condition trend evolution value is less than or equal to the second trend threshold, it is determined as a highly stable trend section, indicating that the fluctuation range of each key parameter in the current drilling process is limited, the change direction is highly consistent, and the abnormal interference signal is scarce. Keep the existing operation scheduling and parameter calculation process unchanged. At the same time, mark the current trend state as a stable sample input and incorporate it into the rolling update of the long-term trend baseline for correcting the standard working condition range and state evaluation template; in addition, by reducing the time frequency and sampling density of the trend evolution value calculation, effectively release the computing resources to support the high-frequency real-time computing tasks of other modules and ensure the overall operation efficiency of the system; When the working condition trend evolution value is greater than the second trend threshold and less than or equal to the first trend threshold, it is determined as a trend transition section, indicating that there are certain disturbance signs in the current drilling process but have not entered the significant fluctuation state, and enter the trend tracking and strengthening mode; in this mode, appropriately improve the trend sampling resolution and shorten the sliding window span to enhance the sensitive capture ability of short-period disturbances. At the same time, enable the trend evolution slope analysis to calculate the directionality and change speed of the trend change in real time for the operation and maintenance personnel to intervene in advance to adjust the key control parameters and support the implementation of the preemptive intervention strategy; When the working condition trend evolution value is greater than the first trend threshold, it is determined as a significantly disturbed section of the trend, indicating that the drilling working condition has changed drastically and there is a continuous multi-dimensional abnormal coupling effect. At this time, immediately switch to the trend disturbance buffering mechanism, dynamically adjust the feature extraction parameter structure, introduce the trend filtering method and the delay compensation processing logic, and buffer the risk of strategy misjudgment caused by high-frequency fluctuations. At the same time, suspend the current parameter adjustment and strategy derivation operations based on the trend value, start the trend anomaly isolation process, mark this section as an abnormal evolution segment, independently archive the key feature fields of the trend slope, fluctuation period, and slope curvature change, and send them to the high-frequency evolution feature analysis queue for subsequent abnormal evolution model training and feature recognition rule optimization.

[0038] In this implementation plan, by comparing the working condition trend evolution value with the set trend stability threshold in real time, the automatic recognition and dynamic response of the current drilling working condition trend state are realized, enhancing the system's perception ability and regulation adaptability to trend disturbances. When the trend evolution value is at a low level and has high stability, the system marks this state as a stable section, maintains the original operation scheduling and parameter calculation strategy unchanged, and incorporates the current trend state into the stable sample set for the update and correction of the long-term trend baseline. At the same time, appropriately reduce the trend calculation frequency to release computing resources to support the execution of other tasks. When the trend evolution value is in the medium fluctuation range, it is determined as a trend transition state, and the system will enter the trend tracking enhancement mode, moderately increase the sampling resolution and analysis frequency, and introduce the directional discrimination mechanism of the trend slope and acceleration to provide a prediction basis for possible trend upgrades. When the trend evolution value exceeds the significant disturbance threshold, the system immediately starts the trend buffering mechanism, suspends the current parameter adjustment and strategy derivation, re-adjusts the feature extraction parameter structure, introduces the trend filtering and delay compensation processes, and at the same time isolates the trend data in this stage for subsequent high-frequency disturbance evolution analysis. Through this process, the response timeliness, decision-making stability, and regulation robustness of the drilling system to trend changes can be effectively improved.

[0039] Specifically, taking the key working condition disturbance value and the working condition trend evolution value of the current batch as inputs, and comprehensively analyzing the abnormal state response of the current batch under the multi-dimensional disturbance intensity and trend deviation path, the specific steps are as follows: Obtain the key working condition disturbance value and the working condition trend evolution value at each moment, multiply the key working condition disturbance value at each moment by the trend enhancement factor of the trend evolution value and then multiply by the data continuity integrity rate corresponding to each working condition parameter to obtain the trend disturbance value of the current working condition parameter. Extract the abnormal vibration density of each working condition parameter, multiply the abnormal vibration density by the abnormal suppression adjustment factor and then add one to obtain the positive abnormal suppression value. Divide the trend disturbance value of each working condition parameter by the positive abnormal suppression value to obtain the local abnormal response value of each working condition parameter. Add up the local abnormal response values of all working condition parameters to obtain the abnormal state response value at the current moment.

[0040] The calculation formula for the abnormal state response value is as follows: Where m represents the total number of working condition parameters; represents the key working condition disturbance value, reflecting its current disturbance intensity; represents the working condition trend evolution value, measuring its trend change speed and direction; represents the data continuity integrity rate of the j-th working condition parameter; represents the abnormal vibration density of the j-th working condition parameter; represents the abnormal suppression adjustment factor, with a value range between 1 and 5, which is derived from the signal-to-noise ratio statistical characteristics of the sampling fields in historical stable drilling tasks. Specifically, by extracting the working condition parameter sequences in the high-stability section from multiple historical batches, calculating the average signal-to-noise ratio of each type of parameter, and combining the signal-to-noise ratio level of the current sampling field, its abnormal susceptibility under standard deviation conditions is obtained; when a certain parameter frequently shows high-amplitude vibrations in multiple batches, the value of the abnormal suppression adjustment factor is increased to enhance the numerical suppression ability for such high-risk indicators and prevent the excessive proportion of abnormal disturbance terms in the formula from affecting the overall evaluation stability; if the historical performance of the working condition parameter is stable and the vibration density is low, the abnormal suppression adjustment factor takes a lower value to avoid excessive weakening of stable parameters and ensure the balance and robustness of the calculation results. represents the trend enhancement factor, with a value range between 1 and 3, which is obtained by analyzing the trend change sequences of all working condition parameters within a sliding time window, statistically calculating the average trend slope value of the continuous positive evolution sections, and calculating the deviation amplitude between the average trend slope value and the historical trend baseline. When a certain working condition parameter shows an obvious upward trend, the trend enhancement factor takes a larger value to enhance the expression ability of trend information in abnormal recognition; if the trend fluctuation is weak, the trend enhancement factor takes a smaller value to avoid the amplification distortion of the trend term to the overall evaluation value, thereby improving the response sensitivity and recognition accuracy for trend disturbance events.

[0041] In this embodiment, the key operating condition disturbance value of Example 1 is set to 30.7, the operating condition trend evolution value is 0.8329, the data continuity integrity rate is 0.95, the abnormal vibration density is 0.30, the abnormal suppression adjustment factor is 2.5, and the trend enhancement factor is 1.8; the key operating condition disturbance value of Example 2 is set to 13.9, the operating condition trend evolution value is 1.1162, the data continuity integrity rate is 0.89, the abnormal vibration density is 0.18, the abnormal suppression adjustment factor is 2.0, and the trend enhancement factor is 2.1; the key operating condition disturbance value of Example 3 is set to 42.1, the operating condition trend evolution value is 0.9103, the data continuity integrity rate is 0.97, the abnormal vibration density is 0.42, the abnormal suppression adjustment factor is 3.0, and the trend enhancement factor is 2.5; the key operating condition disturbance value of Example 4 is set to 8.7, the operating condition trend evolution value is 0.2298, the data continuity integrity rate is 0.92, the abnormal vibration density is 0.12, the abnormal suppression adjustment factor is 1.6, and the trend enhancement factor is 1.2; the key operating condition disturbance value of Example 5 is set to 25.5, the operating condition trend evolution value is 1.3059, the data continuity integrity rate is 0.88, the abnormal vibration density is 0.36, the abnormal suppression adjustment factor is 2.7, and the trend enhancement factor is 2.8. Calculate the abnormal state response values of each example, as shown in the data table of abnormal state response values in Table 3.

[0042] Table 3 Data Table of Abnormal State Response Values Instance number Key operating condition disturbance value Operating condition trend evolution value Data continuity integrity rate Abnormal vibration density Abnormal suppression regulation factor Trend enhancement factor Abnormal state response value Instance 1 30.7 0.8329 0.95 0.30 2.5 1.8 17.64 Instance 2 13.9 1.1162 0.89 0.18 2.0 2.1 11.89 Instance 3 42.1 0.9103 0.97 0.42 3.0 2.5 23.14 Instance 4 8.7 0.2298 0.92 0.12 1.6 1.2 4.22 Instance 5 25.5 1.3059 0.88 0.36 2.7 2.8 18.93 As Figure 5 shown, it is the line chart of abnormal state response values provided for the examples of this application. As shown in Table 3 and Figure 5 it can be seen that from the distribution of abnormal state response values, the response value of Example 3 is the highest, reaching 23.14, indicating that its key operating condition disturbance is strong, the trend evolution is obvious, and at the same time, the abnormal vibration density is large and the data continuity integrity rate is high. Although it has a certain data quality guarantee, due to the concentrated vibration disturbance and the significant upward trend, its proportion in the abnormal state assessment is the highest. The operating conditions corresponding to this example should be dynamically regulated and parameter corrected first to prevent the continuous spread of its disturbance and affect the overall system stability; while the abnormal state response value of Example 4 is the lowest, only 4.22. Although its data continuity is good, the disturbance intensity and trend evolution level are low, and the vibration density is the smallest, so the abnormal risk is low. It can be used as a low-priority monitoring object to avoid excessive resource allocation and improve the overall monitoring and regulation efficiency. The change trend of this response value intuitively reflects the degree of abnormal disturbance and data fluctuation characteristics faced by each example in the current operation stage. The higher the response value, the more unstable its operating condition, and the more it needs to be the focus of monitoring and regulation.

[0043] By integrating the key working condition disturbance values and the working condition trend evolution values of the current batch, this formula constructs a unified response evaluation index to quantitatively characterize the comprehensive influence degree of the current drilling task on the system stability under the multi-dimensional disturbance intensity and trend deviation path. This formula can be used to identify the abnormal evolution state level of the system in real time, reflect the superposition effect of the working condition changes in terms of both amplitude and direction, and thus provide a quantitative basis for the early warning judgment of abnormal events, the triggering of emergency strategies, and the allocation of monitoring resources.

[0044] Specifically, the specific steps for generating the abnormal recognition and determination results and the structured monitoring report based on the abnormal state response are as follows: Compare the abnormal state response value with the abnormal level threshold in real time. The abnormal level threshold includes the first abnormal threshold and the second abnormal threshold to achieve the automatic recognition and hierarchical response of the abnormal level of the current drilling system state, and improve the early warning accuracy of the system abnormal state and the intelligent adaptation ability of the display device. When the abnormal state response value is less than or equal to the second abnormal threshold, the system is determined to be in the low response section. In this stage, no excessive prompts and visual interferences are triggered. Only the current state summary is presented in the overview mode on the display device, accompanied by the "normal operation" label to enhance the clarity of the state perception. At the same time, the disturbance path and characteristic trend data of this state section are archived to participate in the subsequent update of the trend baseline, and the calculation period of the abnormal state response value is appropriately extended to reduce the operation load and optimize the resource allocation efficiency. When the abnormal state response value is between the second abnormal threshold and the first abnormal threshold, the system is determined to be in the medium response section and automatically enters the trend tracking visualization mode. The display device real-time pushes the multi-dimensional trend graph and the abnormal sensitive parameter trajectory to show the change process of the disturbance frequency, parameter slope, and fluctuation direction, assisting the operator to identify potential abnormal trends. At the same time, start the local parameter high-frequency sampling mechanism and dynamically correct the sampling data quality to improve the observability of the medium response section and the interpretability of the abnormal evolution. When the abnormal state response value is higher than the first abnormal threshold, the system is determined to be in the high response section and immediately switches to the deep abnormal early warning mode. The display device presents a hierarchical abnormal analysis interface, focusing on showing the constituent items of the abnormal response value, the ranking of key disturbance indicators, and the diagnostic information of the trend slope extreme value distribution. Synchronously push the influence prediction map of the equipment component level, pause the current state prediction process and parameter scheduling logic based on real-time data, and switch to the abnormal isolation strategy under the historical stable data. The system will automatically record the timestamp, index sequence, and trend path of the abnormal mutation point to generate a structured abnormal monitoring report to support subsequent rapid traceability and the implementation of abnormal intervention decisions. Through this mechanism, the precise management of multi-level, visual, and traceable abnormal responses is realized, comprehensively improving the operation safety of the drilling system and the intelligent level of state recognition.

[0045] In this implementation, a real-time comparison mechanism is established between the abnormal state response value and the classification threshold. The system status level is automatically divided according to different abnormal response intensities, and corresponding visualization display, data processing, and control strategies are executed according to the level, so as to achieve dynamic adaptation and intelligent hierarchical intervention of abnormal recognition. By segmentally comparing the abnormal state response value with the first and second abnormal thresholds, the system can avoid ineffective resource consumption in the low response state, enhance the trend recognition and abnormal warning capabilities in the medium response state, and timely interrupt the inference process and trigger in-depth abnormal isolation and structured report generation in the high response state. This mechanism effectively improves the system's sensitivity to sudden abnormalities, response flexibility, and decision-making closed-loop ability, and is an important basic process for ensuring the stability and safety of drilling operations.

[0046] In the second aspect of the present invention, a multi-functional parameter monitoring system for a DC drilling machine is provided, including a data acquisition and preprocessing module, a working condition disturbance evaluation module, a trend analysis module, and an abnormal judgment and monitoring feedback module. The data acquisition and preprocessing module, by deploying multi-type monitoring terminals covering vibration, electricity, temperature rise, energy consumption, displacement, and environmental perception on the drilling platform, can collect the drilling process working condition data, structural operation status data, and working environment data of the current drilling task in real time, including but not limited to motor voltage, current, drilling depth, equipment vibration spectrum, environmental temperature, humidity, and air pressure values. After collection, all data are uniformly subjected to field standardization, physical unit normalization, and abnormal cleaning processing, removing missing items, extreme points, and non-compliant data, and normalizing the processed data to a unified scale system to eliminate data heterogeneity problems caused by differences in sensor models and sampling resolutions, so as to construct a complete, consistent, and standard working condition dataset that can be used for cross-batch analysis.

[0047] Based on this standard working condition dataset, the working condition disturbance evaluation module comprehensively extracts disturbance indicators including electrical fluctuation intensity, abnormal vibration frequency density, and load change rate, constructs a multi-dimensional disturbance characteristic vector reflecting the system stability characteristics, and quantitatively analyzes the operation stability and system risk level of the equipment in the current drilling task by calculating the disturbance comprehensive value and the fluctuation trend slope, forming a risk level label and an intervention priority index.

[0048] The trend analysis module further models and analyzes the evolution path of each working condition parameter in the standard working condition dataset in the time dimension. Combining historical trend baselines, change directions, and slope curvature characteristics, it identifies the stability, directionality, and mutation probability of the working condition change trend, and accordingly dynamically updates the risk level of key working condition parameters, the configuration of disturbance response strategies, and the sampling frequency and priority of each monitoring point, realizing the adaptive adjustment of parameter control and monitoring resource allocation.

[0049] The anomaly judgment and monitoring feedback module takes the key working condition disturbance value and the working condition trend evolution value of the current batch as inputs, integrates historical stable state samples and deviation path models, evaluates the anomaly response intensity of the current working condition under the combined action of non-linear disturbances and trend offsets, forms an anomaly state response value, and drives a multi-level feedback mechanism according to the response level, including anomaly identification and annotation, parameter substitution calculation, trend isolation analysis, and structured monitoring report generation. Finally, it provides visual warning results and executable intervention suggestions to the control system and operation and maintenance personnel to support the intelligent risk control and scheduling optimization of the drilling task under complex working conditions.

[0050] In this implementation plan, the data acquisition and preprocessing module is used to comprehensively acquire and standardize multi-source working condition data during the drilling process. By deploying various types of monitoring terminals covering the drilling process, motor operation, structural vibration, and working environment, key parameters such as voltage, current, drilling depth, equipment temperature rise, impact intensity, energy consumption rate, and environmental temperature and humidity are acquired. After the acquisition is completed, the system uniformly performs data standardization processing, including unified field format, unit conversion, missing value cleaning, and outlier removal, and normalizes various types of data to a unified scale system to solve the sampling differences of multiple devices and the heterogeneity of multi-dimensional parameters. Finally, a standardized working condition dataset for trend analysis and risk assessment is constructed.

[0051] The working condition disturbance evaluation module conducts disturbance intensity analysis and stability evaluation on the working condition parameters in the standardized dataset. By extracting disturbance characteristics in dimensions such as electrical fluctuations, mechanical shocks, and environmental changes, multi-dimensional disturbance indicators are calculated, and based on this, the stable level of the current operating state of the equipment is quantified. Combining the disturbance value with the risk grading threshold, it evaluates whether there is an abnormal fluctuation risk in the current working condition and outputs a disturbance level label for guiding the setting of response strategies for subsequent trend tracking and parameter regulation.

[0052] The trend analysis module is used to identify and analyze the evolution trend of working condition parameters. Based on the standardized working condition dataset, an evolution path model of each core parameter is constructed, its slope, fluctuation period, and trend direction are evaluated, and the state of the working condition is judged. The module compares the trend evolution value with the set threshold, dynamically adjusts the sampling window, analysis period, and response strategy; at the same time, it updates the trend baseline, risk level, and monitoring priority to achieve precise tracking and forward-looking control of different working condition states.

[0053] The anomaly assessment and monitoring feedback module integrates key disturbance values and trend evolution characteristics to determine whether the drilling task is currently in an abnormal state. By constructing an abnormal state response value, it comprehensively assesses the impact of multi-dimensional disturbances and trend deviations on system stability, and uses the set anomaly threshold to grade the anomaly. Based on the judgment results, the system triggers different levels of response mechanisms, including displaying a stable state summary, enabling trend visualization tracking, and entering an in-depth anomaly analysis mode. Simultaneously, it generates structured monitoring reports and intervention recommendations, enabling rapid identification, feedback, and regulatory support for abnormal conditions.

[0054] A third aspect of the present invention provides a multifunctional parameter monitoring device for a DC drilling machine, comprising: a drilling status monitoring terminal for high-frequency, real-time acquisition of multidimensional drilling condition data throughout the entire drilling operation, including drilling depth, motor voltage, current amplitude, load variation, impact intensity, vibration amplitude, temperature rise parameters, and axial force. The terminal also integrates multiple sensor types to acquire structural state parameters such as drill pipe deformation rate, drill bit disturbance frequency, and local strain and vibration response of the support structure. Furthermore, an environmental sensing module is deployed to collect information on external environmental factors such as temperature, humidity, air pressure, dust concentration, electromagnetic interference intensity, and background vibration intensity at the worksite. All data is uniformly encoded and synchronously uploaded to a data processor via a high-speed industrial bus, accompanied by a sampling timestamp, device number, sensor type, and sampling accuracy identifier, ensuring structural consistency and temporal coherence in subsequent processing.

[0055] The data processor is used to standardize the multi-dimensional field structure of the received heterogeneous drilling monitoring data stream, including field name unification, timestamp alignment, missing item filling, sampling frequency conversion and physical unit conversion, and construct a standardized working condition data set based on normalization rules; at the same time, a sliding time window mechanism is introduced, combined with the historical stable working condition baseline, to dynamically extract key disturbance feature points, instantaneous fluctuation amplitude, sampling continuity indicators and monitoring data integrity rate; data fields with abnormal mutations, value range drift and signal-to-noise ratio abnormalities are suppressed, corrected and labeled, and the cleaning and standardization results are transmitted to the working condition analysis and trend evaluation unit for subsequent state identification and evolutionary reasoning.

[0056] The working condition analysis and trend assessment unit is used to conduct in-depth fusion analysis on the multi-dimensional disturbance characteristics of electricity, machinery, and environment involved in the current drilling task based on the input standardized working condition data set. This unit integrates feature recognition algorithms and coupling modeling logics, automatically identifies typical disturbance patterns, evaluates the composite impact degree on the stability of the drilling system, and calculates the disturbance level and system risk score; at the same time, it constructs a trend evolution sequence for all key parameters, dynamically tracks the trend change rate, change direction, and trajectory deviation degree; according to the trend characteristics and disturbance response results, it real-time updates the monitoring priority ranking, response adjustment strategy, and abnormal intervention criterion of each parameter, and synchronously pushes the structured analysis results to the abnormal recognition and report generation unit.

[0057] The abnormal recognition and report generation unit is used to fuse key disturbance values, trend evolution characteristics, response amplitude, and slope information to construct a multi-dimensional abnormal response index system, and judge and classify the potential abnormal states and high-risk working condition sections in the current batch of drilling operations; after this unit identifies events such as significant trend deviation and disturbance level exceeding the threshold, it will automatically mark the corresponding triggering parameters, change trajectories, abnormal duration, and time tags, and output a structured monitoring report, including abnormal level, impact path, early warning suggestions, and diagnostic conclusions; when the abnormal response value exceeds the system-set threshold, the system will immediately push an abnormal early warning instruction to the operation control terminal to drive the operator to perform rapid intervention and dynamic adjustment operations to ensure the stability of the drilling process and the safe operation of the system.

[0058] In this implementation plan, the drilling state monitoring terminal is responsible for high-frequency collection of multi-dimensional working condition parameters throughout the drilling process, including drilling depth, voltage, current, load, impact intensity, vibration, temperature rise, and axial force, etc. At the same time, it integrates various types of sensors to monitor the dynamic response states of drill pipes, drill bits, and support structures; and deploys an environment perception module to obtain environmental information such as temperature, humidity, air pressure, dust, electromagnetic interference, and background vibration, and uploads it to the data processor through unified coding and timestamp synchronization to ensure the structural consistency and time series continuity of the data.

[0059] The data processor is used to perform structured standard processing on the received multi-source drilling data, including field naming specifications, time alignment, missing value filling, frequency conversion, and unit conversion, and construct a unified data set based on normalization rules; at the same time, it uses a sliding time window mechanism to extract key disturbance points and instantaneous fluctuation amplitudes, identify sampling continuity and data integrity rate, and suppress, correct, and mark abnormal values, and output structured cleaned data for subsequent analysis.

[0060] The working condition analysis and trend assessment unit conducts disturbance identification and trend modeling based on a standardized data set, performs in-depth disturbance analysis and coupled modeling by integrating electrical, mechanical, and environmental parameters, evaluates the disturbance level and risk score, forms a trend evolution sequence, and dynamically monitors its rate, direction, and deviation degree. Based on this, it updates the monitoring priorities and adjustment strategies of each parameter, providing a decision-making basis for the anomaly identification unit.

[0061] The anomaly identification and report generation unit is used to integrate disturbance values, trend characteristics, response intensity, and slope information to construct a multi-dimensional anomaly response system, automatically identify and classify potential abnormal states and high-risk working conditions. After identification, it outputs a structured monitoring report, marking the anomaly level, path, duration, and early warning suggestions, and pushing an early warning instruction to the control terminal when the anomaly value exceeds the limit, assisting the operator to achieve rapid response and system intervention, and ensuring the safety and stability of the drilling operation.

[0062] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise", or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article, or device.

[0063] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A multi-functional parameter monitoring method for a DC drilling machine, characterized in that Including: S1, collect the working condition data of the drilling process, the operating state data of the structure, and the working environment data of the current drilling task, the operating state data and the working environment data, and perform standardization and normalization preprocessing on the collected working condition data of the drilling process, the operating state data of the structure, and the working environment data to construct a standardized working condition data set; S2, analyze the multi-dimensional disturbance characteristics of the current equipment operating state based on the standardized working condition data set, and quantify the stability level and risk level of the drilling system based on the analysis results of the multi-dimensional disturbance characteristics; S3, evaluate the dynamic evolution trend of the current working condition state based on the standardized working condition data set, and then update the risk assessment level, disturbance response strategy, and dynamic monitoring priority configuration of each core parameter; S4, take the disturbance value of the key working conditions and the evolution value of the working condition trend of the current batch as inputs, and comprehensively analyze the abnormal state response of the current batch under the multi-dimensional disturbance intensity and trend deviation path: Multiply the disturbance value of the key working conditions at each moment by the power of the trend enhancement factor of the evolution value of the trend and then multiply by the data continuity integrity rate corresponding to each working condition parameter to obtain the trend disturbance value of the current working condition parameter; Extract the abnormal vibration density of each working condition parameter, multiply the abnormal vibration density by the abnormal suppression adjustment factor and then add one to obtain the positive abnormal suppression value; Divide the trend disturbance value of each working condition parameter by the positive abnormal suppression value to obtain the local abnormal response value of each working condition parameter, and add the local abnormal response values of all working condition parameters to obtain the abnormal state response value at the current moment; And generate an abnormal recognition determination result and a structured monitoring report based on the abnormal state response situation.

2. The multifunctional parameter monitoring method for a DC drilling machine according to claim 1, characterized in that: The specific steps of collecting the working condition data of the drilling process, the operating state data of the structure, and the working environment data of the current drilling task, and performing standardization and normalization preprocessing on the collected working condition data of the drilling process, the operating state data of the structure, and the working environment data to construct a standardized working condition data set are as follows: Collect the working condition data of the drilling process including motor voltage, drilling depth, axial impact intensity, and energy consumption change rate through the drilling operation monitoring terminal, and record the measurement unit, vibration intensity value, relative deviation rate of the current measurement value from the historical fluctuation range, instantaneous mutation amplitude, and corresponding collection time of each working condition data; collect the structure operating state data including abnormal vibration density, historical maximum record value, historical minimum record value, and cumulative operating duration of each monitoring item through the equipment operating state monitoring device; Collect the working environment data including the temperature value of the drilling area, load value, and spatial stability around the equipment through the working environment monitoring module, and record the environmental monitoring frequency, monthly historical average value, and characteristic baseline average value under historical stable working conditions; record the original processing data including the data continuity integrity rate, historical load average value, and sampling abnormality ratio of each working condition parameter. Through a unified high-precision clock synchronization mechanism, the working condition data, structural operation status data, and operation environment data collected during the drilling process are standardized, missing items are cleaned, the field naming method is unified, and the working condition data, structural operation status data, and operation environment data after cleaning and unit conversion are normalized to eliminate the dimension and scale differences caused by equipment model differences and sensor specification differences; the standardized and normalized working condition data, structural operation status data, and operation environment data during the drilling process are stored to construct a standardized working condition dataset.

3. A multi-functional parameter monitoring method for a DC drilling machine according to claim 1, characterized in that: The specific steps for analyzing the multi-dimensional disturbance characteristics of the current equipment operation status based on the standardized working condition dataset are as follows: Extract the load value of the current batch and the historical load average value from the standardized working condition dataset, calculate the difference between the load value of the current batch and the historical load average value and take its absolute value to obtain the load value change rate; Extract the vibration intensity values of all working condition parameters and accumulate them to obtain the total short-period vibration value; Extract the temperature value at the current moment, take the first derivative and take the absolute value to obtain the temperature change rate at the current moment; Extract the axial impact intensity at the current moment, calculate the derivative of the axial impact intensity and take the absolute value to obtain the second-order change rate of the axial force; Add the load value change rate, the total short-period vibration value, the temperature change rate, and the second-order change rate of the axial force to obtain the key working condition disturbance value during the drilling operation of the current batch.

4. A multi-functional parameter monitoring method for a DC drilling machine according to claim 1, characterized in that: The specific steps for quantifying the stability level and risk level of the drilling system based on the analysis results of the multi-dimensional disturbance characteristics are as follows: Compare the key working condition disturbance value with the disturbance level threshold in real time. The disturbance level threshold includes the first disturbance threshold and the second disturbance threshold: When the key working condition disturbance value is less than or equal to the second disturbance threshold, it is determined as a low disturbance section, the existing data acquisition strategy is maintained, and the operation optimization process is directly executed based on the current characteristic value without introducing an additional compensation mechanism; When the key working condition disturbance value is greater than the second disturbance threshold and less than or equal to the first disturbance threshold, it is determined as a medium disturbance section, enter the disturbance warning state, and execute the dynamic steady-state compensation strategy, including adjusting the sampling period of vibration and temperature parameters and shortening the sliding mean window length, and at the same time introducing a micro-amplitude correction factor in the construction of relevant parameters to weaken the influence of outliers and enhance the numerical stability of the characteristic value; When the key working condition disturbance value is greater than the first disturbance threshold, it is determined as a high disturbance section, immediately suspend the operation adjustment and optimization derivation based on this characteristic value, trigger the numerical anomaly emergency response mechanism, call the parameter records in multiple historical stable drilling tasks to construct the dynamic weighted average working condition characteristics, replace the current abnormal parameters for temporary calculation, and at the same time mark this task as an abnormal data batch, according to the quality key inspection list, and record the deviation characteristics and instability paths of the corresponding disturbance dimensions.

5. A multifunctional parameter monitoring method for a DC drilling machine according to claim 1, characterized in that: The specific steps for evaluating the dynamic evolution trend of the current working condition based on the standardized working condition dataset are as follows: Extract the energy consumption change rate at the current moment, the relative deviation rate of the current measurement value from the historical fluctuation range, and the characteristic baseline mean under the historical stable working condition from the standardized working condition dataset, and take the absolute value after subtracting the relative deviation rate from the characteristic baseline mean to obtain the trend offset; Extract the instantaneous mutation amplitude at the current moment and take the power of the mutation response adjustment factor to obtain the amplitude regulation value; Add the trend offset and the amplitude regulation value to obtain the disturbance reference value, divide the energy consumption change rate by the disturbance reference value, add one, and take the logarithm to obtain the working condition trend evolution value at the current moment.

6. A multifunctional parameter monitoring method for a DC drilling machine according to claim 1, characterized in that: The specific steps for further updating the risk assessment level, disturbance response strategy, and dynamic monitoring priority configuration of each core parameter are as follows: Compare the working condition trend evolution value with the trend stability threshold in real time. The trend stability threshold includes a first trend threshold and a second trend threshold: When the working condition trend evolution value is less than or equal to the second trend threshold, it is determined as a highly stable trend section, and the existing operation scheduling and parameter calculation process remain unchanged. At the same time, mark the current trend state as a stable sample input to participate in the update and correction of the long-term trend baseline, and reduce the trend calculation frequency to release computing resources for other real-time task processing; When the working condition trend evolution value is greater than the second trend threshold and less than or equal to the first trend threshold, it is determined as a trend transition section, and enter the trend tracking enhancement mode. Appropriately improve the trend sampling resolution, narrow the window span to enhance the short-term disturbance response ability, and at the same time enable trend evolution slope analysis to make real-time discrimination on the directionality and acceleration of the change trend to support the implementation of early intervention strategies; When the working condition trend evolution value is greater than the first trend threshold, it is determined as a significantly disturbed trend section, and immediately switch to the trend disturbance buffer mechanism, dynamically adjust the characteristic extraction parameter structure, introduce trend filtering and delay compensation processing to prevent decision-making fluctuations caused by high-frequency changes. At the same time, suspend the current parameter adjustment and strategy optimization operations, perform trend anomaly isolation, and separately archive the trend slope, fluctuation period, and slope curvature change characteristics of this section, and enter the high-frequency evolution feature analysis queue.

7. A multi-functional parameter monitoring method for a DC drilling machine according to claim 1, characterized in that: The specific steps for generating an anomaly recognition determination result and a structured monitoring report based on the anomaly state response are as follows: Compare the anomaly state response value with the anomaly level threshold in real time. The anomaly level threshold includes a first anomaly threshold and a second anomaly threshold: When the anomaly state response value is less than or equal to the second anomaly threshold, it is determined as a low response section. At this time, synchronously present the current state summary in an overview mode on the display device, mark it with a normal operation identifier to avoid information interference, and at the same time retain the characteristic trend and disturbance path data corresponding to this state section for use as a reference for subsequent dynamic update of the trend baseline, and delay the anomaly state operation frequency to improve the computing resource scheduling efficiency; When the abnormal state response value is greater than the second abnormal threshold and less than or equal to the first abnormal threshold, it is determined to be in the medium response section. At this time, the multi-dimensional trend chart and abnormal sensitive parameter trajectory are automatically pushed through the display device, entering the trend tracking visualization mode, and displaying the change slope, disturbance frequency and change direction of key parameters in real time, assisting operators to judge the trend evolution. At the same time, the local parameter high-frequency sampling strategy is enabled and the data quality is dynamically corrected to improve the observability and real-time interpretability of abnormal trends; When the abnormal state response value is greater than the first abnormal threshold, it is determined to be a high response section and enters the deep abnormal warning mode. At this time, the display device will switch to the layered abnormality analysis interface, showing the abnormal response value components, key disturbance parameter rankings and trend slope extreme value distributions, and at the same time push the impact prediction information at the equipment component level, suspend the state prediction and scheduling deduction logic based on the current data, switch to the abnormal isolation mode under the historical stable parameters, automatically record the timestamp, parameter sequence and trend path of the abnormal mutation point, and generate a structured abnormality monitoring report for rapid tracing and risk intervention.

8. A system applying the multi-functional parameter monitoring method of a DC drilling machine according to any one of claims 1-7, characterized in that: include: The data acquisition and preprocessing module is used to collect the drilling process working condition data, structural operation status data and working environment data of the current drilling task, and standardize and normalize the collected drilling process working condition data, structural operation status data and working environment data to construct a standardized working condition data set; The operating disturbance assessment module is used to analyze the multi-dimensional disturbance characteristics of the current equipment operating status based on a standardized operating condition data set, and quantify the stability level and risk level of the drilling system based on the analysis results of the multi-dimensional disturbance characteristics; The trend analysis module is used to evaluate the dynamic evolution trend of the current operating condition based on the standardized operating condition data set, and then update the risk assessment level, disturbance response strategy and dynamic monitoring priority configuration of each core parameter; The abnormality judgment and monitoring feedback module is used to take the current batch's key operating condition disturbance values and operating condition trend evolution values as input, comprehensively analyze the abnormal state response of the current batch under multi-dimensional disturbance intensity and trend deviation path, and generate abnormality identification and judgment results and structured monitoring reports based on the abnormal state response.

9. An apparatus applying the DC drilling machine multi-functional parameter monitoring method according to any one of claims 1-7, characterized in that: include: The drilling status monitoring terminal is used to collect real-time drilling condition data during the drilling operation, including drilling depth, motor voltage, load change, impact intensity, vibration amplitude, temperature rise parameters, and axial force. It also collects drill pipe deformation, drill bit disturbance, and support structure response parameters, as well as environmental factors such as temperature, humidity, air pressure, and dust concentration at the operation site, and uploads the data to the data processor via the industrial bus. The data processor is used to standardize the field structure, align timestamps, unify physical units, and normalize dimensions of the received multi-source drilling data to construct a unified standardized working condition dataset. Based on a sliding time window and historical comparison strategy, it extracts key disturbance characteristics, instantaneous fluctuation amplitude, and data integrity rate indicators, and sends the preprocessing results to the working condition analysis and trend assessment unit. The working condition analysis and trend assessment unit is used to perform feature recognition, coupling analysis and risk level calculation on the multi-dimensional disturbance characteristics in the current drilling task based on the standardized working condition data set, further construct the trend evolution sequence of each working condition parameter, evaluate the trend change rate, directionality and deviation degree, dynamically update the monitoring priority, response adjustment strategy and intervention conditions of each parameter, and push the analysis results to the anomaly recognition and reporting unit; The anomaly recognition and reporting generation unit is used to construct multi-parameter fusion data with the key disturbance value and trend evolution value as inputs, identify the abnormal evolution state and potential risk events in the current batch of drilling process, output a structured monitoring report including the anomaly level, triggering parameters, impact path and time label, and push a warning instruction to the operation terminal when the triggering threshold event occurs to assist the operator to respond and intervene accurately.

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