A multifunctional parameter monitoring method, system and device for a DC drilling machine

Abstract

The present invention discloses a multifunctional parameter monitoring method, system and device for a DC drilling machine, and relates to the technical field of manufacturing industrial automatic control system devices. The multifunctional parameter monitoring method, system and device for a DC drilling machine include S1, collecting various types of data and performing standardization and normalization processing to construct a standardized working condition data set; S2, analyzing multidimensional disturbance characteristics and quantifying the stability level of the drilling system; S3, evaluating the dynamic evolution trend of the working condition, updating the risk level, response strategy and monitoring priority; S4, identifying abnormal conditions based on key disturbance values ​​and trend evolution values, and generating a monitoring report. This solves the problem that the existing DC drilling machine display device has insufficient key working condition feature extraction capabilities, is difficult to support in-depth understanding and trend analysis of the equipment's operating status, and thus limits the timeliness of warnings and the accuracy of judgments of abnormal conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automatic control system device manufacturing, and in particular to a multifunctional parameter monitoring method, system and device for a DC drilling machine. Background Art

[0002] Existing DC drilling equipment typically uses fixed sampling frequencies and basic display instruments to simply monitor voltage, current, load, impact, and temperature rise during drilling. These systems lack the ability to precisely identify and analyze trends in the drilling system's multi-dimensional disturbance states, making it difficult to accurately perceive and assist in operational decision-making under complex drilling conditions.

[0003] Traditional drilling monitoring systems rely on a single type of sensor and fixed-point data display, failing to integrate analysis and dynamic modeling of equipment operating status, structural response status, and operating environment status. This results in delayed recognition of early abnormal conditions, untimely risk warnings, and insufficient judgment basis, leading to safety hazards and efficiency fluctuations.

[0004] At the data processing level, existing technologies lack unified data standards, preprocessing processes, and deep fusion mechanisms for multiple types of drilling data from different subsystems and monitoring terminals. This makes it impossible to achieve effective alignment and dynamic collaboration of cross-modal data, limiting the ability to accurately quantify operating disturbances, model trend evolution in real time, and comprehensively assess drilling system stability.

[0005] At the same time, abnormal state identification is still mainly based on static threshold rules and single-dimensional fluctuation limit judgment, failing to integrate multi-dimensional disturbance characteristics and trend deviation behavior for dynamic response and level identification. This makes it difficult to meet the needs of real-time diagnosis and structured report generation of systemic abnormal states in complex drilling environments.

[0006] Therefore, in view of the shortcomings of the existing technology, there is an urgent need for a multifunctional parameter monitoring method, system and device for a DC drilling machine. Summary of the Invention

[0007] Technical problems solved

[0008] In response to the shortcomings of the existing technology, the present invention provides a multi-functional parameter monitoring method, system and device for a DC drilling machine, which solves the problem that the existing DC drilling machine display device has insufficient ability to extract key working condition features, making it difficult to support in-depth understanding and trend analysis of the equipment's operating status, thereby limiting the timeliness of warnings and accuracy of judgments of abnormal conditions.

[0009] Technical Solution

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multifunctional parameter monitoring method, system and device for a DC drilling machine, including S1, collecting the drilling process working condition data, structural operation status data and operating environment data of the current drilling task, and standardizing and normalizing the collected drilling process working condition data, structural operation status data and operating environment data to construct a standardized working condition data set; S2, analyzing the multidimensional disturbance characteristics of the current equipment operating state based on the standardized working condition data set, and quantifying the stability level and risk level of the drilling system based on the analysis results of the multidimensional disturbance characteristics; S3, evaluating the dynamic evolution trend of the current working condition based on the standardized working 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 current batch of key working condition disturbance values ​​and working condition trend evolution values ​​as input, comprehensively analyzing the abnormal state response of the current batch under multidimensional disturbance intensity and trend deviation path, and generating abnormal identification judgment results and structured monitoring reports based on the abnormal state response.

[0011] Furthermore, the drilling process working condition data, structural operation status data and working environment data of the current drilling task are collected, and the collected drilling process working condition data, structural operation status data and working environment data are standardized and normalized preprocessed to construct a standardized working condition data set. The specific steps are: collecting drilling process working condition data including motor voltage, drilling depth, axial impact strength and energy consumption change rate through the drilling operation monitoring terminal, and recording the measurement unit, vibration intensity value, relative deviation rate between the current measurement value and the historical fluctuation range, instantaneous mutation amplitude and corresponding collection time of each working condition data; collecting structural operation status data including abnormal vibration density, historical maximum record value, historical minimum record value and cumulative operation time of each monitoring item through the equipment operation status monitoring device; collecting data including drilling area temperature value, load value through the working environment monitoring module The operating environment data including the stability of the space around the equipment is recorded, and the environmental monitoring frequency, monthly historical average and characteristic baseline average under historical stable working conditions are recorded at the same time; the original processed data including the data continuity integrity rate of each working condition parameter, the historical load average and the sampling anomaly ratio are recorded; through a unified high-precision clock synchronization mechanism, the drilling process working condition data, structure operation status data and operating environment data collected during the drilling process are standardized, the missing items are cleaned, the field naming method is unified, and the drilling process working condition data, structure operation status data and operating 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 drilling process working condition data, structure operation status data and operating environment data after standardization and normalization are stored to construct a standardized working condition data set.

[0012] Furthermore, the specific steps of analyzing the multidimensional disturbance characteristics of the current equipment operating status based on the standardized working condition data set are: extracting the load value of the current batch and the historical load average from the standardized working condition data set, calculating the difference between the load value of the current batch and the historical load average and taking its absolute value to obtain the load value change rate; extracting the vibration intensity values ​​of all working condition parameters, accumulating them to obtain the total short-period vibration value; extracting the temperature value at the current moment, calculating the first-order derivative and taking the absolute value to obtain the temperature change rate at the current moment; extracting the axial impact strength at the current moment, calculating the derivative of the axial impact strength and taking the absolute value to obtain the second-order change rate of the axial force; adding 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 of the current batch during the drilling operation.

[0013] Furthermore, the specific steps of 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: real-time comparison of the key working condition disturbance value and the disturbance level threshold, the disturbance level threshold includes a first disturbance threshold and a second disturbance threshold: when the key working condition disturbance value is less than or equal to the second disturbance threshold, it is determined to be a low disturbance section, the existing data acquisition strategy is maintained, and the operation optimization process is directly executed according to 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 to be a medium disturbance section, enters the disturbance warning state, and executes the dynamic steady-state compensation strategy, including adjusting The sampling period of vibration and temperature parameters is shortened and the sliding mean window length is shortened. At the same time, a slight correction factor is introduced in the process of constructing relevant parameters to weaken the influence of outliers and enhance the numerical stability of eigenvalues. When the disturbance value of the key working condition is greater than the first disturbance threshold, it is judged as a high disturbance section, and the operation adjustment and optimization derivation based on the eigenvalue is immediately suspended, triggering the numerical anomaly emergency response mechanism, calling the parameter records of multiple historical stable drilling tasks to construct a dynamic weighted average working condition feature, replacing the current abnormal parameters for temporary calculation, and marking this task as an abnormal data batch. According to the quality focus check list, the deviation characteristics and instability path of the corresponding disturbance dimension are recorded.

[0014] Furthermore, the specific steps of evaluating the dynamic evolution trend of the current operating condition state based on the standardized operating condition data set are: extracting 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 under the historical stable operating condition from the standardized operating condition data set, subtracting the relative deviation rate from the characteristic baseline mean and taking the absolute value to obtain the trend offset; extracting the instantaneous mutation amplitude at the current moment and taking the power of the mutation response adjustment factor to obtain the amplitude control value; adding the trend offset to the amplitude control value to obtain the disturbance reference value, dividing the energy consumption change rate by the disturbance reference value and adding one to take the logarithm to obtain the operating condition trend evolution value at the current moment.

[0015] 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.

[0016] 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.

[0017] Furthermore, the specific steps of generating abnormal identification and judgment results and structured monitoring reports based on the abnormal state response situation are as follows: real-time comparison of the abnormal state response value and the abnormal level threshold, the abnormal level threshold includes a first abnormal threshold and a second abnormal threshold: when the abnormal state response value is less than or equal to the second abnormal threshold, it is determined to be a low response segment, and the current state summary is synchronously presented in an overview mode on the display device, marked as a normal operation mark to avoid information interference, and at the same time retains the characteristic trend and disturbance path data corresponding to the state segment, which are used as a reference for subsequent trend baseline dynamic update, and delays the abnormal 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 a medium response segment, and the multi-dimensional trend chart and abnormal sensitive parameter trajectory are automatically pushed through the display device to enter the trend Tracking visualization mode displays the change slope, disturbance frequency and change direction of key parameters in real time, assisting operators to judge the trend evolution, while enabling the local parameter high-frequency sampling strategy and dynamically correcting the data quality 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 judged as 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 to display the abnormal response value components, key disturbance parameter rankings and trend slope extreme value distributions, and push the impact prediction information at the equipment component level, and 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.

[0018] The second aspect of the present invention provides a multifunctional parameter monitoring system for a DC drilling machine, comprising: a data acquisition and preprocessing module, a working condition disturbance assessment module, a trend analysis module, and an abnormality judgment and monitoring feedback module, wherein: the data acquisition and preprocessing module is used to collect the drilling process working condition data, structure operation status data, and operating environment data of the current drilling task, and perform standardization and normalization preprocessing on the collected drilling process working condition data, structure operation status data, and operating environment data to construct a standardized working condition data set; the working condition disturbance assessment module is used to perform multidimensional disturbance characteristics of the current equipment operating state based on the standardized working condition data set. The system is used to analyze the current working condition 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 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; the abnormal judgment and monitoring feedback module is used to take the current batch of key working condition disturbance values ​​and working condition trend evolution values ​​as input, comprehensively analyze the abnormal state response of the current batch under the 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.

[0019] 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.

[0020] Beneficial effects

[0021] The present invention has the following beneficial effects:

[0022] (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.

[0023] (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.

[0024] (3) This multifunctional parameter monitoring method, system and device for a DC drilling machine realizes real-time perception and intelligent feedback of the entire drilling process by constructing a dynamic monitoring mechanism including feature extraction, trend assessment, risk quantification and priority adjustment, thereby improving the system's state analysis depth and parameter adaptation capabilities, and effectively solving the problems of shallow state perception and rigid control logic in existing technologies.

[0025] (4) This method, system and device for monitoring the multifunctional parameters of a DC drilling machine realizes closed-loop control of the entire process from working condition collection, preprocessing, trend identification to early warning report generation by integrating a multi-module division of labor and collaborative device structure, thereby improving the data utilization efficiency and intelligent level of operation decision-making of drilling operations, and effectively solving the problems of dispersed equipment functions and disconnection between monitoring and feedback in the existing technology.

[0026] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of a multifunctional parameter monitoring method for a DC drilling machine according to the present invention;

[0028] Figure 2 This is a structural diagram of a multifunctional parameter monitoring system for a DC drilling machine according to the present invention;

[0029] Figure 3 A bar graph of disturbance values ​​of key working conditions involved in the present invention;

[0030] Figure 4 A line graph showing the trend evolution of the working condition involved in the present invention;

[0031] Figure 5 This is a line graph of the abnormal state response value involved in the present invention. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0033] See also Figure 1-Figure 3, an embodiment of the present invention provides a technical solution: a multifunctional parameter monitoring method, system and device for a DC drilling machine, including S1, collecting drilling process working condition data, structural operation status data and operating environment data of the current drilling task, and standardizing and normalizing the collected drilling process working condition data, structural operation status data and operating environment data to construct a standardized working condition data set; S2, analyzing the multidimensional disturbance characteristics of the current equipment operating state based on the standardized working condition data set, and quantifying the stability level and risk level of the drilling system based on the analysis results of the multidimensional disturbance characteristics; S3, evaluating the dynamic evolution trend of the current working condition based on the standardized working 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 current batch of key working condition disturbance values ​​and working condition trend evolution values ​​as input, comprehensively analyzing the abnormal state response of the current batch under multidimensional disturbance intensity and trend deviation path, and generating abnormality identification judgment results and structured monitoring reports based on the abnormal state response.

[0034] Specifically, the drilling process working condition data, structural operation status data and operating environment data of the current drilling task are collected, and the collected drilling process working condition data, structural operation status data and operating environment data are standardized and normalized preprocessed to construct a standardized working condition data set. The specific steps are as follows: the drilling process working condition data including motor voltage, current, power factor, speed, drilling depth, drill bit temperature rise, axial impact strength, energy consumption change rate, brush wear degree and related operating load characteristics are collected through the drilling operation monitoring terminal to form a comprehensive quantitative perception of electric drive performance, drilling progress and load changes; during the collection process, the measurement unit, vibration intensity value, sensor model, sampling frequency, measurement accuracy, sampling period of each working condition data are synchronously recorded, and the relative deviation rate of the current measurement value and the historical fluctuation range, the instantaneous mutation amplitude and the corresponding collection time are calculated, and the drilling mode and drill bit type identification corresponding to the operation are marked to achieve complete correspondence between parameters and operation configuration.

[0035] The equipment's operating status monitoring device collects structural operating status data, including spindle load changes, 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. It also records the sampling stability, abnormal vibration density, historical maximum and minimum values, abnormal flag status, and accumulated operating time of each monitoring item to assess the equipment's current operating stability and failure risk. The operating environment monitoring module collects operating environment data, including drilling area temperature, environmental load, humidity, air pressure, dust concentration, electromagnetic interference intensity, and the stability of the space surrounding the equipment. It also records the environmental monitoring frequency, sensor type, monitoring period, monthly historical average, and characteristic baseline average under historical stable operating conditions to assist in analyzing the correlation between drilling efficiency and environmental disturbances. Raw processed data, including the data continuity completeness rate, historical load average, standard power consumption correction factor average, equipment status record coverage, environmental sampling synchronization rate, and sampling anomaly ratio, for each operating condition parameter is recorded as basic indicators of data integrity and quality control.

[0036] A unified high-precision clock synchronization mechanism is used to ensure the consistency and alignment of drilling process condition data, structural operation status data, and operating environment data in the time domain. Subsequently, all types of raw collected data are standardized, and missing items, jump extreme values, and abnormal fields that exceed the set detection range that appear during the collection process are cleaned. The field naming methods and physical unit systems of each data source are unified to achieve scale unification and dimensional equivalence between data. The three types of data after cleaning and unit conversion are then normalized separately to eliminate the interference caused by differences in equipment models, sampling frequencies, and sensor accuracy, thereby improving the horizontal comparability and vertical continuity of the data. Finally, all standardized and normalized drilling process condition data, structural operation status data, and operating environment data are structured and stored to construct a stable, high-quality, and traceable standardized working condition data set, which serves as the basic input source for subsequent key disturbance feature identification and abnormal state response analysis.

[0037] 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 irregular handling of missing abnormal items in current drilling operations. Through the multi-dimensional collection, cleaning, unified standardization, and normalization of drilling process working condition data, structural operating status data, and operating environment data, this step ensures the consistency of various core parameters in time, space, and physical dimensions, improves the accuracy of data fusion and comparison, and provides a reliable data foundation for subsequent key disturbance identification, trend evolution analysis, and abnormal state response. It helps to achieve accurate understanding of drilling operation status, dynamic evaluation of operational stability, and intelligent triggering of early warning response strategies, thereby improving the overall system's adaptability to complex drilling tasks and operational safety level.

[0038] Specifically, the specific steps for analyzing the multidimensional disturbance characteristics of the current equipment operating 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 from the standardized working condition data set, calculate the difference between the load value of the current batch and the historical load average and take its absolute value to obtain the load value change rate; extract the vibration intensity values ​​of all working condition parameters, accumulate them to obtain the total short-period vibration value; extract the temperature value at the current moment, calculate the first-order derivative and take the absolute value to obtain the temperature change rate at the current moment; extract the axial impact strength at the current moment, calculate the derivative of the axial impact strength 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 of the current batch during the drilling operation.

[0039] The calculation formula for the key operating condition disturbance value is:

[0040] ;

[0041] Where m represents the total number of operating parameters; Indicates the current load value, which is used to reflect the instantaneous load level on the drilling motor. It is compared with the historical load average to determine whether there is abnormal current fluctuation and overload. Indicates the historical load average, which is used to describe the load benchmark level within a short period during the drilling process and reflect the steady-state operation trend of the system; The vibration intensity value of the jth working condition parameter is used to record the structural response amplitude of the drilling equipment at that moment and is the basic data for vibration risk assessment; Indicates the current temperature value. Indicates the temperature change rate at the current moment, which is used to measure the thermal stability during the drilling process; Indicates the axial impact strength at the current moment, It represents the second-order rate of change of the axial force and is used to identify whether the drill bit experiences sudden structural response changes caused by sudden impact, sticking, and rock faults during the drilling process, reflecting the intensity of the impact disturbance.

[0042] In this embodiment, the current load value at time point t1 is set to 120, the historical load average 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 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 is 100, the load change rate is 30, the vibration intensity value is The load value at time point t4 is set to 98, the historical load average 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 load value at time point t5 is set to 115, the historical load average 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. The disturbance values ​​of the key working conditions at each time point are calculated, as shown in Table 1.

[0043] Table 1 Key operating condition disturbance value data table

[0044] Time point Current load value Historical load average Load deviation value Vibration intensity value Temperature value Temperature change rate Axial impact strength Second-order rate of change 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

[0045] like Figure 3 As shown in Table 1 and Figure 3It can be seen that the key operating condition disturbance value at time t3 is the highest, indicating a large load change rate, high vibration intensity, rapid temperature change rate, and severe shock response at this moment. This comprehensively reflects that the drilling system's disturbance intensity is the most significant during this period, posing high operational risks and control instability. This period should be prioritized for key monitoring, with timely triggering of trend warning mechanisms and operational parameter correction processes. In contrast, the key operating condition disturbance value at time t4 is the lowest, with small load fluctuations, stable vibration and temperature changes, and a mild shock response. This indicates a relatively stable overall state. This can be marked as a typical steady-state reference sample for updating the stable operating condition baseline and optimizing the setting of the disturbance judgment threshold. The key operating condition disturbance value histogram clearly reflects the differences in disturbance intensity of the drilling operation state during each time period. Higher disturbance values ​​indicate a more stringent intervention strategy, while lower disturbance values ​​can be used as steady-state feature inputs, helping to improve the targeted control strategy and the robustness of system operation.

[0046] This formula uniformly quantifies the degree of deviation and change trend of multiple parameters in the current batch drilling process, thereby accurately identifying the possibility and intensity of abnormal working conditions and supporting the construction of a highly sensitive perception and response mechanism for disturbance events in the system. This formula comprehensively analyzes the absolute degree of deviation between the real-time collected value of each working condition parameter at the current moment and the historical average, and combines the instantaneous fluctuation amplitude, change rate and stability factor to normalize and multi-dimensionally integrate the deviation characteristics of each parameter to calculate a numerical result representing the overall working condition disturbance level. Its design logic not only takes into account the static offset of the parameter value, but also introduces dynamic fluctuation trends and stability adjustment factors, so that the results can reflect the overall deviation state of the system under complex disturbances.

[0047] Specifically, the stability level and risk level of the drilling system are quantified based on the analysis results of the multi-dimensional disturbance characteristics. The specific steps are: real-time comparison of the key working condition disturbance value with the preset disturbance level threshold, which is 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 state is in the low disturbance section, indicating that the changes in the core working condition parameters 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 characteristic value, without the need to introduce any additional data correction and compensation mechanism.

[0048] When the disturbance value of the key operating condition is greater than the second disturbance threshold and less than or equal to the first disturbance threshold, it is judged to be a 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 are still controllable overall. At this time, the system will initiate a dynamic steady-state compensation strategy, including automatically adjusting the sampling period for sensitive parameters such as vibration intensity and temperature rise changes, shortening the sliding average window length, to enhance the system's response ability to short-term drastic changes. At the same time, a small numerical correction factor is introduced in the key feature construction process to effectively weaken the interference of sudden abnormal values ​​on the overall evaluation results, thereby improving the numerical robustness and dynamic stability of the eigenvalue calculation;

[0049] When the disturbance value of the key working condition exceeds the first disturbance threshold, it is determined to be a high disturbance section, indicating that there is a serious deviation in the current working condition characteristics, which may involve serious working condition disturbances such as mechanical impact, sudden change in energy consumption and abnormal structural response. The operation strategy adjustment and optimization derivation based on the characteristic value will be immediately suspended, and the numerical abnormality emergency response mechanism will be triggered instead. The parameter records in multiple historical stable drilling tasks will be automatically called to construct a dynamic weighted average working condition characteristic, which will replace the current abnormal parameters in the temporary working condition assessment calculation to ensure that the operation logic is not interrupted. At the same time, the current task will be marked as an abnormal data batch and included in the data quality key inspection list. The parameter dimensions, numerical deviation characteristics and possible instability paths involved in the current disturbance will be recorded to provide basic data support for subsequent abnormality tracing analysis and diagnosis optimization.

[0050] In this implementation scheme, real-time monitoring and dynamic graded response to the disturbance level of key working conditions during the drilling operation are achieved, thereby improving the ability to identify abnormal disturbances and the efficiency of emergency handling. By comparing the disturbance value of key working conditions calculated in real time with the set disturbance level threshold, the disturbance level section of the current operating state 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 computing 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 sampling parameters and introducing correction mechanisms; and when the disturbance reaches a high level, the abnormal handling mechanism is activated to prevent abnormal data from misleading subsequent decisions. This process effectively enhances the response elasticity and computational robustness of the system under different disturbance intensities, which helps to ensure the continuity of the drilling task and the stability of the operation results.

[0051] Specifically, the specific steps for evaluating the dynamic evolution trend of the current operating condition state based on the standardized operating condition data set 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 under the historical stable operating condition from the standardized operating condition data set, subtract the relative deviation rate from the characteristic baseline mean 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 control value; add the trend offset and the amplitude control value to obtain the disturbance baseline value, divide the energy consumption change rate by the disturbance baseline value and add one to take the logarithm to obtain the operating condition trend evolution value at the current moment.

[0052] The calculation formula for the working condition trend evolution value is:

[0053] ;

[0054] Where, Indicates the energy consumption change rate at the current moment, which is used to measure the fluctuation intensity of key parameters of drilling operations in a short period of time and reflect the activity of the current working condition disturbance amplitude; Indicates 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 the period and is an important basis for fluctuation assessment and trend deviation judgment; It represents the characteristic baseline mean under historical stable working conditions and is used to build a benchmark reference for trend deviation judgment; The instantaneous mutation amplitude is used to characterize the dramatic fluctuation behavior of key parameters in the drilling process within a very short time scale; α represents the mutation response adjustment factor, ranging from 0.2 to 0.6. It is derived from the rate of change characteristics of trend mutation segments in historical drilling missions. First, a complete time series of trend evolution values ​​is extracted from multiple historical stable batches. The time nodes where trend mutations occur are identified, and the trend change rates of the adjacent time periods before and after the mutation are calculated. The maximum value of the ratio of the before-after rate across all mutation points is then extracted as the most sensitive response representative to trend perturbations. These ratios are then normalized and mapped to an α value within a set range. When an indicator has a high sampling frequency and continuous monitoring performance in the trajectory chain, its weight factor is increased to enhance its ability to perceive sudden evolutionary trends and ensure that high-frequency, high-quality data is fully utilized in the calculation. If an indicator has a sparse sampling distribution, its weight factor is lowered to reduce its dominant influence in determining the mutation amplitude, preventing low-reliability data from interfering with the accuracy of the overall response value, thereby improving the system's robust perception of abnormal mutations and response stability.

[0055] In this embodiment, the abnormal adjustment factor of each time point is kept unchanged, and 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 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 is 1.0434, and the instantaneous mutation amplitude is 0.4221; the energy consumption change rate at time point T3 is set to 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 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 is 0.9446, and the instantaneous mutation amplitude is 0. The 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 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 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 0.8145, the characteristic baseline mean is 0.9686, and the instantaneous mutation amplitude is 0.2286. 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 is 0.9061, and the transient 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 is 1.0004, and the transient mutation amplitude is 0.3021. The operating condition trend evolution values ​​at each time point are calculated, as shown in Table 2.

[0056] Table 2 Working condition trend evolution value data table

[0057] Time point Energy consumption change rate Current deviation rate Characteristic baseline mean Instantaneous mutation amplitude Abnormal regulatory factors 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

[0058] like Figure 4 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 its corresponding energy consumption change rate is high, the current deviation rate is small, and the mutation amplitude is low. Combined with the strengthening effect of the abnormal adjustment factor, the overall working condition change trend during this period is the most stable, and the parameter changes tend to be predictable. The system shows good disturbance suppression ability and trend ductility during 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 is external disturbance during this period. The overall trend expression is highly uncertain. It is necessary to focus on the corresponding operating environment factors and equipment operating status of this section, and adjust the sampling density and anomaly detection strategy of this section in a timely manner. The trend evolution value line chart can intuitively reveal the dynamic change trend of the drilling working conditions and the stability of the system response at each time point. The higher the value, the stronger the working condition stability and the clearer the parameter trend expression, and the more suitable it is as a reference for working condition optimization; the lower the value, the stronger the mutation and disturbance of the working condition, and the need for in-depth analysis and compensation correction combined with multi-source monitoring data.

[0059] Specifically, the 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 with the trend stability threshold, which includes the first trend threshold and the second trend threshold:

[0060] When the working condition trend evolution value is less than or equal to the second trend threshold, it is determined to be a highly stable trend section, indicating that the fluctuation range of key parameters in the current drilling process is limited, the change direction is highly consistent, and abnormal interference signals are rare, maintaining the existing operation scheduling and parameter calculation process unchanged. At the same time, the current trend status is marked as a stable sample input and included in the rolling update of the long-term trend baseline to revise the standard working condition range and status assessment template; in addition, by reducing the time frequency and sampling density of the trend evolution value calculation, computing resources are effectively released to support high-frequency real-time computing tasks of other modules, ensuring the overall operating efficiency of the system;

[0061] 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 in a trend transition section, indicating that the current drilling process has some signs of disturbance but has not yet entered a significant fluctuation state, and enters the trend tracking enhancement mode. In this mode, the trend sampling resolution is appropriately improved and the sliding window span is shortened to enhance the sensitivity to short-term disturbances. At the same time, trend evolution slope analysis is enabled to calculate the direction and speed of trend changes in real time, allowing operation and maintenance personnel to intervene in advance to adjust key control parameters and support the implementation of preemptive intervention strategies.

[0062] When the working condition trend evolution value is greater than the first trend threshold, it is determined to be a trend-significant disturbance section, indicating that the drilling condition has changed dramatically and there is a continuous multi-dimensional abnormal coupling effect; at this time, it immediately switches to the trend disturbance buffer mechanism, and by dynamically adjusting the feature extraction parameter structure, introduces the trend filtering method and delay compensation processing logic to buffer the risk of strategy misjudgment caused by high-frequency fluctuations; at the same time, the current parameter adjustment and strategy deduction operations based on the trend value are suspended, and the trend anomaly isolation process is started. The section is marked as an abnormal evolution fragment, and the key feature fields of trend slope, fluctuation period, and slope curvature change are independently archived and sent to the high-frequency evolution feature analysis queue for subsequent abnormal evolution model training and feature recognition rule optimization.

[0063] In this implementation, by comparing the trend evolution value of the drilling condition in real time with a set trend stability threshold, the system automatically identifies and dynamically responds to the current drilling condition trend state, enhancing the system's perception of trend disturbances and its control adaptability. When the trend evolution value is low and the stability is high, the system marks this state as a stable segment, maintains the original job scheduling and parameter calculation strategy, and incorporates the current trend state into the stable sample set for updating and correcting the long-term trend baseline. At the same time, the trend calculation frequency is appropriately reduced to free up computing resources for other tasks. When the trend evolution value is within a medium fluctuation range, it is determined to be a trend transition state. The system enters a trend tracking enhancement mode, moderately increasing the sampling resolution and analysis frequency, and introducing a directional discrimination mechanism for trend slope and acceleration to provide a basis for predicting possible trend escalation. When the trend evolution value exceeds the significant disturbance threshold, the system immediately activates the trend buffer mechanism, suspends the current parameter adjustment and strategy derivation, readjusts the feature extraction parameter structure, introduces trend filtering and delay compensation processes, and isolates the trend data for this stage and archives it separately for subsequent high-frequency disturbance evolution analysis. Through this process, the drilling system's response timeliness to trend changes, decision-making stability and control robustness can be effectively improved.

[0064] Specifically, taking the key operating condition disturbance value and the operating condition trend evolution value of the current batch as input, a comprehensive analysis is conducted on the abnormal state response of the current batch under the multi-dimensional disturbance intensity and trend deviation path. The specific steps are: 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 with 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; adding the local abnormal response values ​​of all operating condition parameters to obtain the abnormal state response value at the current moment.

[0065] The calculation formula for the abnormal state response value is:

[0066]

[0067] Where m represents the total number of operating parameters; Indicates the disturbance value of key working conditions, reflecting its current disturbance intensity; Indicates the trend evolution value of the working condition, measuring its trend change speed and direction; Indicates the data continuity completeness rate of the j-th operating condition parameter; Indicates the abnormal vibration density of the jth operating condition parameter; It represents the anomaly suppression adjustment factor, with a value range of 1 to 5. It is derived from the signal-to-noise ratio statistical characteristics of the sampling field in the historical stable drilling task. Specifically, it extracts the operating parameter sequence in the high stability section from multiple historical batches, calculates the average signal-to-noise ratio of each type of parameter, and combines it with the signal-to-noise ratio level of the current sampling field to obtain its abnormal susceptibility under standard deviation conditions. When a certain parameter frequently vibrates with high amplitude in multiple batches, the value of the anomaly suppression adjustment factor is increased to enhance the numerical suppression ability of such high-risk indicators and prevent the abnormal disturbance term from accounting for too large a proportion in the formula and affecting the overall evaluation stability. If the operating parameter has a stable historical performance and the vibration density is low, the anomaly suppression adjustment factor takes a lower value to avoid excessive weakening of the stable parameter and ensure the balance and robustness of the calculation results. It represents the trend enhancement factor, which ranges from 1 to 3. It is obtained by analyzing the trend change sequence of all operating parameters in the sliding time window, counting the average trend slope value of the continuous positive evolution segment, and calculating the deviation between the average trend slope value and the historical trend baseline. When a certain operating parameter shows an obvious upward trend, the trend enhancement factor takes a larger value to enhance the expression ability of trend information in anomaly identification; if the trend fluctuation is weak, the trend enhancement factor takes a smaller value to avoid the amplification and distortion of the overall evaluation value by the trend item, thereby improving the response sensitivity and identification accuracy to trend disturbance events.

[0068] 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, and the data continuity integrity rate is 0. The 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. For example 4, the key operating condition disturbance value 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. For example 5, the key operating condition disturbance value 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. The abnormal state response values ​​of each example are calculated, as shown in Table 3.

[0069] Table 3 Abnormal state response value data table

[0070] Instance number Key operating condition disturbance value Working condition trend evolution value Data continuity integrity rate Abnormal vibration density Abnormal inhibitory regulatory factor Trend Enhancer Abnormal status response value Example 1 30.7 0.8329 0.95 0.30 2.5 1.8 17.64 Example 2 13.9 1.1162 0.89 0.18 2.0 2.1 11.89 Example 3 42.1 0.9103 0.97 0.42 3.0 2.5 23.14 Example 4 8.7 0.2298 0.92 0.12 1.6 1.2 4.22 Example 5 25.5 1.3059 0.88 0.36 2.7 2.8 18.93

[0071] like Figure 5 As shown in Table 3 and Figure 5 From the distribution of abnormal state response values, it can be seen that instance 3 has the highest response value, reaching 23.14, indicating that its key operating condition has strong disturbances and a clear trend evolution. It also has a high abnormal vibration density and a high continuity integrity rate. Although it has certain data quality assurance, the concentrated vibration disturbances and a significantly increasing trend result in it accounting for the highest proportion in the abnormal state assessment. Dynamic control and parameter correction should be prioritized for the operating condition corresponding to this instance to prevent its disturbances from continuing to spread and affecting the overall system stability. On the other hand, instance 4 has the lowest abnormal state response value, at only 4.22. Although its data continuity is good, its disturbance intensity and trend evolution level are low, its vibration density is minimal, and its abnormal risk is low. Therefore, it can be designated as a low-priority monitoring target to avoid resource overallocation and improve overall monitoring and regulation efficiency. This response value trend intuitively reflects the degree of abnormal disturbances and data fluctuation characteristics faced by each instance in the current operating stage. The higher the response value, the more unstable its operating condition is, and the more important it needs to be monitored and controlled.

[0072] This formula integrates the current batch of key operating condition disturbance values ​​and operating condition trend evolution values ​​to construct a unified response evaluation index, quantitatively characterizing the comprehensive impact of the current drilling task on system stability under multi-dimensional disturbance intensity and trend deviation paths. This formula can be used to identify the system's abnormal evolution level in real time, reflecting the cumulative effects of operating condition changes in both magnitude and direction, thereby providing a quantitative basis for early warning and determination of abnormal events, triggering emergency strategies, and allocating monitoring resources.

[0073] Specifically, the specific steps for generating abnormal identification and judgment results and structured monitoring reports based on the abnormal state response are as follows: real-time comparison of the abnormal state response value with the abnormal level threshold, which includes the first abnormal threshold and the second abnormal threshold, to achieve automatic identification and graded response to the abnormal level of the current drilling system state, improve the accuracy of the early warning of the system abnormal state and the intelligent adaptability 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 a low response section. At this stage, no excessive prompts and visual interference are triggered. Only the current state summary is presented in an overview mode on the display device, with a "normal operation" logo attached to enhance the clarity of state perception; at the same time, the disturbance path and characteristic trend data of the state section are archived to participate in the subsequent update of the trend baseline, and the calculation cycle of the abnormal state response value is appropriately extended to reduce the computing load and optimize resource allocation efficiency. When the abnormal state response value is between the second and first abnormal thresholds, the system identifies the segment as medium response and automatically enters trend tracking visualization mode. The display device pushes a multi-dimensional trend chart and abnormality-sensitive parameter trajectory in real time, showing the changes in disturbance frequency, parameter slope, and fluctuation direction, assisting operators in identifying potential abnormal trends. Simultaneously, a local parameter high-frequency sampling mechanism is activated and the sampling data quality is dynamically corrected to improve the observability of the medium response segment and the interpretability of abnormal evolution. When the abnormal state response value exceeds the first abnormal threshold, the system identifies the segment as high response and immediately switches to deep abnormality warning mode. The display device presents a layered abnormality analysis interface, focusing on diagnostic information such as the components of the abnormal response value, the ranking of key disturbance indicators, and the distribution of extreme trend slope values. Simultaneously, an impact prediction map is pushed to the device component level, suspending the current state prediction process and parameter scheduling logic based on real-time data and switching to an abnormality isolation strategy based on historical stable data. The system automatically records the timestamps, indicator sequences, and trend paths of abnormal mutation points, generating structured abnormality monitoring reports to support subsequent rapid tracing and abnormal intervention decision-making. Through this mechanism, multi-level, visual, and traceable precise management of abnormal responses is achieved, which comprehensively improves the operational safety of the drilling system and the intelligent level of status identification.

[0074] In this implementation plan, a real-time comparison mechanism between abnormal state response values ​​and grading thresholds is established to automatically divide the system state levels according to different abnormal response intensities, and execute corresponding visualization, data processing and control strategies according to the levels, thereby achieving dynamic adaptation of abnormal identification and intelligent hierarchical intervention. By comparing the abnormal state response value with the first and second abnormal thresholds in sections, the system can avoid ineffective resource consumption in low-response states, enhance trend identification and abnormality early warning capabilities in medium-response states, and promptly interrupt the reasoning process and trigger deep abnormality isolation and structured report generation in high-response states. This mechanism effectively improves the system's perception sensitivity, response flexibility and decision-making closed-loop capability for sudden abnormalities, and is an important basic process for ensuring the stability and safety of drilling operations.

[0075] The second aspect of the present invention provides a multifunctional parameter monitoring system for a DC drilling machine, including a data acquisition and preprocessing module, a working condition disturbance assessment module, a trend analysis module and an abnormality judgment and monitoring feedback module. The data acquisition and preprocessing module deploys multiple types of monitoring terminals covering vibration, electrical, temperature rise, energy consumption, displacement and environmental perception on the drilling platform to 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, ambient temperature and humidity and air pressure values; after collection, all data are uniformly subjected to field standardization, physical unit normalization and abnormality cleaning processing, and missing items, extreme points and non-standard data are eliminated, and the processed data are normalized to a unified scale system to eliminate the data heterogeneity problem caused by differences in sensor models and sampling resolution, thereby constructing a complete, consistent and standardized working condition data set that can be used for cross-batch analysis.

[0076] Based on the standardized working condition data set, the working condition disturbance assessment module comprehensively extracts disturbance indicators including electrical fluctuation intensity, vibration frequency abnormal density, and load change rate, constructs a multi-dimensional disturbance characteristic vector reflecting the system stability characteristics, and quantitatively analyzes the operating stability and system risk level of the equipment in the current drilling task by calculating the comprehensive disturbance value and the fluctuation trend slope, forming a risk level label and intervention priority index.

[0077] The trend analysis module further models and analyzes the evolution path of each operating condition parameter in the standardized operating condition data set in the time dimension, and combines the historical trend baseline, change direction and slope curvature characteristics to identify the stability, direction and mutation probability of the operating condition change trend. Based on this, it dynamically updates the risk level of key operating condition parameters, the disturbance response strategy configuration and the sampling frequency and priority of each monitoring point, realizing adaptive adjustment of parameter control and monitoring resource allocation.

[0078] The abnormal judgment and monitoring feedback module takes the current batch of key operating condition disturbance values ​​and operating condition trend evolution values ​​as input, integrates historical stable state samples and deviation path models, evaluates the abnormal response intensity of the current operating condition under the combined action of nonlinear disturbance and trend deviation, forms an abnormal state response value, and drives a multi-level feedback mechanism based on the response level, including abnormal identification and labeling, parameter substitution calculation, trend isolation analysis and structured monitoring report generation. Ultimately, it provides visual early warning results and executable intervention suggestions to the control system and operation and maintenance personnel, supporting intelligent risk control and scheduling optimization of drilling tasks under complex working conditions.

[0079] In this implementation plan, the data acquisition and preprocessing module is used to achieve comprehensive collection and standardized processing of multi-source working condition data during the drilling process. By deploying multiple types of monitoring terminals covering the drilling process, motor operation, structural vibration, and working environment, key parameters including voltage, current, drilling depth, equipment temperature rise, impact strength, energy consumption rate, and ambient temperature and humidity are collected. After the collection is completed, the system uniformly performs data standardization processing, including field format unification, unit conversion, missing value cleaning, and outlier removal, and normalizes all types of data to a unified scale system to solve the problems of multi-device sampling differences and multi-dimensional parameter heterogeneity, and finally constructs a standardized working condition data set that can be used for trend analysis and risk assessment.

[0080] The operating disturbance assessment module analyzes the disturbance intensity and evaluates the stability of operating parameters in a standardized data set. It extracts disturbance characteristics related to electrical fluctuations, mechanical shock, and environmental changes, calculates multidimensional disturbance indicators, and uses these indicators to quantify the stability of the equipment's current operating state. Combining the disturbance value with risk classification thresholds, it assesses whether the current operating condition presents the risk of abnormal fluctuations and outputs a disturbance level label to guide subsequent trend tracking and parameter control response strategy setting.

[0081] The trend analysis module identifies and analyzes the evolutionary trends of operating parameters. Based on standardized operating condition datasets, it constructs evolutionary path models for each core parameter, assessing their slope, fluctuation period, and trend direction to determine the current operating condition. The module compares trend evolution values ​​with set thresholds and dynamically adjusts the sampling window, analysis period, and response strategy. It also updates trend baselines, risk levels, and monitoring priorities, enabling precise tracking and proactive control of different operating conditions.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] The operating condition analysis and trend assessment unit is used to conduct a deep, integrated analysis of the electrical, mechanical, and environmental multi-dimensional disturbance characteristics involved in the current drilling task based on the input standardized operating condition data set. This unit integrates feature recognition algorithms and coupled modeling logic to automatically identify typical disturbance patterns, assess their combined impact on the stability of the drilling system, and calculate the disturbance level and system risk score. It also constructs a trend evolution sequence for all key parameters, dynamically tracking the rate of trend change, direction of change, and degree of trajectory deviation. Based on trend characteristics and disturbance response results, it updates the monitoring priority ranking, response adjustment strategy, and abnormal intervention criteria for each parameter in real time, and simultaneously pushes the structured analysis results to the abnormality identification and report generation unit.

[0086] The anomaly identification and report generation unit is used to integrate key disturbance values, trend evolution characteristics, response amplitude and slope information, build a multi-dimensional abnormal response indicator system, and judge and grade the potential abnormal states and high-risk working conditions in the current batch of drilling operations; after identifying significant trend deviations and disturbance level over-threshold events, the unit will automatically mark the corresponding trigger parameters, change trajectories, abnormal duration and time labels, and output a structured monitoring report including abnormality level, impact path, warning suggestions and diagnostic conclusions; when the abnormal response value exceeds the system-set threshold, the system will immediately push an abnormal warning instruction to the operation control terminal, driving 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.

[0087] In this implementation plan, the drilling status monitoring terminal is responsible for high-frequency collection of multi-dimensional working parameters throughout the drilling process, including drilling depth, voltage, current, load, impact strength, vibration, temperature rise and axial force, while integrating multiple types of sensors to monitor the dynamic response status of the drill rod, drill bit and supporting structure; and deploying an environmental perception module to obtain environmental information such as temperature, humidity, air pressure, dust, electromagnetic interference and background vibration, and upload it to the data processor synchronously through unified coding and timestamps to ensure the structural consistency and temporal continuity of the data.

[0088] 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 completion, frequency conversion and unit conversion, and to build a unified data set based on normalization rules; at the same time, a sliding time window mechanism is used to extract key disturbance points and instantaneous fluctuation amplitudes, identify sampling continuity and data integrity, suppress, correct and mark outliers, and output structured cleaned data for subsequent analysis.

[0089] The operating condition analysis and trend assessment unit performs disturbance identification and trend modeling based on standardized data sets, integrates electrical, mechanical, and environmental parameters to conduct in-depth disturbance analysis and coupling modeling, evaluates disturbance levels and risk scores, forms trend evolution sequences, and dynamically monitors their rate, direction, and degree of deviation; accordingly, it updates the monitoring priority and adjustment strategy of each parameter, providing a decision-making basis for the anomaly identification unit.

[0090] The anomaly identification and report generation unit is used to integrate disturbance values, trend characteristics, response intensity and slope information to build a multi-dimensional anomaly response system, and 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 pushes early warning instructions 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 drilling operations.

[0091] It should be noted that, in this document, relational terms such as first and second, etc., are used only 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 "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0092] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A multifunctional parameter monitoring method for a DC drilling machine, characterized in that: include: S1, collecting drilling process working condition data, structural operation status data and operating environment data of the current drilling task, and standardizing and normalizing the collected drilling process working condition data, structural operation status data and operating environment data to construct a standardized working condition data set; S2, analyzes the multi-dimensional disturbance characteristics of the current equipment operating status based on the standardized working condition data set, and quantifies the stability level and risk level of the drilling system based on the analysis results of the multi-dimensional disturbance characteristics; S3, based on the standardized working condition data set, evaluates the dynamic evolution trend of the current working condition state, and then updates the risk assessment level, disturbance response strategy and dynamic monitoring priority configuration of each core parameter; S4, taking the current batch’s key operating condition disturbance value and operating condition trend evolution value as input, comprehensively analyzes the abnormal state response of the current batch under multi-dimensional disturbance intensity and trend deviation path: The key operating condition disturbance value at each moment is multiplied by the trend enhancement factor of the trend evolution value, and then multiplied by the data continuity integrity rate corresponding to each operating condition parameter to obtain the trend disturbance value of the current operating condition parameter; Extract the abnormal vibration density of each working condition parameter, multiply the abnormal vibration density by the abnormal suppression adjustment factor and add one to obtain the positive abnormal suppression value; The trend disturbance value of each operating parameter is divided by the positive abnormal suppression value to obtain the abnormal response local value of each operating parameter, and the local abnormal response values ​​of all operating parameters are added together to obtain the abnormal state response value at the current moment; And generate abnormal identification and judgment results and structured monitoring reports based on abnormal state responses; The specific steps of generating abnormality identification and determination results and structured monitoring reports based on the abnormal state response are as follows: Real-time comparison of the abnormal state response value and the abnormal level threshold. The abnormal level threshold includes the first abnormal threshold and the second abnormal threshold: When the abnormal state response value is less than or equal to the second abnormal threshold, it is determined to be a low response segment. At this time, the current state summary is synchronously presented in an overview mode on the display device, marked as a normal operation mark to avoid information interference. At the same time, the characteristic trend and disturbance path data corresponding to the state segment are retained and used as a reference for subsequent trend baseline dynamic updates. The frequency of abnormal state operations is also delayed to improve the efficiency of computing resource scheduling; 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.

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

3. The multifunctional parameter monitoring method for a DC drilling machine according to claim 1, characterized in that: The specific steps of analyzing the multi-dimensional disturbance characteristics of the current equipment operating state based on the standardized working condition data set are as follows: Extract the load value of the current batch and the historical load average from the standardized working condition data set, calculate the difference between the load value of the current batch and the historical load average, and take the absolute value to obtain the load value change rate; Extract the vibration intensity values ​​of all working condition parameters and add them up to get the total short-period vibration value; Extract the current temperature value, find the first derivative, and take the absolute value to get the temperature change rate at the current moment; Extract the axial impact strength at the current moment, calculate the derivative of the axial impact strength and take the absolute value to obtain the second-order change rate of the axial force; 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 are added together to obtain the key working condition disturbance value of the current batch during the drilling operation.

4. The multifunctional parameter monitoring method for a DC drilling machine according to claim 1, characterized in that: The specific steps of 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 disturbance value of key working conditions with the disturbance level threshold in real time. The disturbance level threshold includes the first disturbance threshold and the second disturbance threshold: When the disturbance value of the key working condition is less than or equal to the second disturbance threshold, it is determined to be a low-disturbance section. The existing data collection 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 disturbance value of the key operating condition is greater than the second disturbance threshold and less than or equal to the first disturbance threshold, it is determined to be a medium disturbance section and enters the disturbance warning state. The dynamic steady-state compensation strategy is implemented, including adjusting the sampling period of vibration and temperature parameters and shortening the sliding mean window length. At the same time, a slight correction factor is introduced in the process of constructing relevant parameters to weaken the influence of outliers and enhance the numerical stability of eigenvalues. When the disturbance value of the key working condition is greater than the first disturbance threshold, it is determined to be a high disturbance section, and the operation adjustment and optimization derivation based on the characteristic value are immediately suspended, triggering the numerical abnormality emergency response mechanism, calling the parameter records of multiple historical stable drilling tasks to construct a dynamic weighted average working condition feature, replacing the current abnormal parameters for temporary calculation, and marking this task as an abnormal data batch. According to the quality focus check list, the deviation characteristics and instability path of the corresponding disturbance dimension are recorded.

5. The multifunctional parameter monitoring method for a DC drilling machine according to claim 1, characterized in that: The specific steps of evaluating the dynamic evolution trend of the current working condition based on the standardized working condition data set are as follows: Extract the current energy consumption change rate, the relative deviation rate between the current measurement value and the historical fluctuation range, and the characteristic baseline mean under historical stable conditions from the standardized operating condition data set. Subtract the relative deviation rate from the characteristic baseline mean 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 control value; The trend offset is added to the amplitude control value to obtain the disturbance reference value. The energy consumption change rate is divided by the disturbance reference value and then added to take the logarithm to obtain the operating condition trend evolution value at the current moment.

6. The multifunctional parameter monitoring method for a DC drilling machine according to claim 1, characterized in that: The specific steps for updating the risk assessment level, disturbance response strategy and dynamic monitoring priority configuration of each core parameter are as follows: Compare the operating condition trend evolution value with the trend stability threshold in real time, where 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 in a highly stable trend segment. The existing job scheduling and parameter calculation processes are maintained unchanged, and the current trend state is marked as a stable sample input. It participates in the update and correction of the long-term trend baseline. At the same time, the trend calculation frequency is reduced to free up 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 section and enters the trend tracking enhancement mode. The trend sampling resolution is appropriately improved and the window span is narrowed to enhance the short-period disturbance response capability. At the same time, trend evolution slope analysis is enabled to perform real-time judgment on the direction 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 section, and the trend disturbance buffer mechanism is immediately switched to. The feature extraction parameter structure is dynamically adjusted, and trend filtering and delay compensation processing are introduced to prevent high-frequency changes from causing decision fluctuations. At the same time, the current parameter adjustment and strategy optimization operations are suspended, and trend fluctuation isolation is executed. The trend slope, fluctuation period, and slope curvature change characteristics of this section are archived separately and enter the high-frequency evolution feature analysis queue.

7. A system using the multifunctional parameter monitoring method for a DC drilling machine according to any one of claims 1 to 6, 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.

8. A device using the multifunctional parameter monitoring method for a DC drilling machine according to any one of claims 1 to 6, 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. It further constructs the trend evolution sequence of each working condition parameter, evaluates the trend change rate, directionality, and degree of deviation, dynamically updates the monitoring priority, response adjustment strategy, and intervention conditions of each parameter, and pushes 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 abnormal evolution status and potential risk events in the current batch drilling process, and output a structured monitoring report containing anomaly level, trigger parameters, impact path and time label. When a threshold event is triggered, it pushes early warning instructions to the operation terminal to assist operators in responding and intervening accurately.

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