Drilling machine working condition monitoring method and system based on fusion of voiceprint and drill rod motion trail

Abstract

The embodiment of the invention discloses a drilling machine working condition monitoring method and system based on fusion of voiceprint and drill rod motion trail, and the method comprises the steps: combining the monitoring of the drill rod motion trail with the voiceprint analysis, effectively making up for the defects in the prior art, achieving the real-time and precise judgment of the working condition of a drilling machine, and improving the working efficiency of the drilling machine. The voiceprint serves as a unique sound feature generated during operation of the drilling equipment and contains rich equipment state information, sound combination and energy differences exist under different working conditions, the unique sound information is fully utilized, the problem that misjudgment is prone to occurring due to only single-dimension drill rod track data is solved, equipment does not need to be additionally arranged, and the drilling efficiency is improved. The problems that monitoring is high in cost, large in workload and prone to being interfered are solved, more comprehensive and accurate monitoring can be provided through mutual cross verification of the voiceprint and the motion trail, the accuracy and authenticity of drilling data are guaranteed, and then the quality and efficiency of drilling work are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of drilling monitoring, and particularly to a method and system for monitoring the working conditions of a drilling rig based on the fusion of voiceprint and drill pipe movement trajectory. Background Art

[0002] In engineering exploration drilling work, many problems urgently need to be solved in the existing technology. Currently, engineering drilling generally adopts a contracting and outsourcing model, which makes it difficult to effectively guarantee key indicators such as the number of drill holes, depth, and core-taking quality. Traditional manual supervision and video monitoring methods not only have a large workload, consume a lot of manpower, but also have poor monitoring effects and cannot comprehensively and accurately control the drilling process.

[0003] In terms of obtaining formation strength information, existing methods such as the core-taking method have the problem of long time consumption, and other auxiliary means are also required, resulting in low drilling efficiency. In the field of borehole depth monitoring, the traditional drill string echo detection method for seismic while drilling is seriously interfered by mechanical noise, and the measurement results are extremely unstable, making it difficult to accurately obtain borehole depth information.

[0004] To solve some problems, there are currently relevant patents on methods and devices for determining the vertical movement trajectory of a drilling rig based on GNSS—RTK and GNSS—IR, which can accurately determine the vertical movement trajectory information of the drill pipe, with a measurement accuracy reaching the centimeter level. By analyzing the measured periodic drill pipe trajectory diagram, the number of times of lowering the drill and the depth of each lowering are statistically determined to determine the borehole depth. However, it is found in actual applications that when only relying on the drill pipe trajectory diagram to judge the working conditions of the drilling rig, misjudgment of working conditions will occur. For example, when the drill pipe trajectory curve is downward, it may be that the drilling rig is actually drilling, or the drill pipe is only being lowered, or the construction personnel are artificially dry drilling or false drilling; when the drill pipe hovers and the drill pipe trajectory curve is a horizontal line, the drilling rig may be performing a core-taking task, or it may be in working conditions such as replacing the drill pipe or adding drill pipe. The method of using the monitoring video to compare with the drill pipe trajectory diagram to judge the working conditions of the drilling rig is feasible, but additional equipment needs to be added, which increases the cost, and the monitoring workload is large, the efficiency is low, and it is also easily interfered by humans.

[0005] Generally speaking, currently in the monitoring of the working conditions of drilling equipment, there is a lack of effective means to monitor the working conditions of the drilling rig in real time and accurately, resulting in difficulty in guaranteeing the accuracy of drilling data, which in turn affects the quality and efficiency of drilling work. Specifically, misjudgment is likely to occur when only relying on the drill pipe trajectory diagram to judge the working conditions, while the method combining the monitoring video has problems such as high cost, large workload, and easy interference. Summary of the Invention

[0006] Based on this, it is necessary to propose a monitoring method and system for the working conditions of a drilling rig based on the fusion of voiceprint and drill pipe movement trajectory, which effectively makes up for the deficiencies of the existing technology and realizes the real-time and accurate determination of the working conditions of the drilling rig.

[0007] To achieve the above object, in the first aspect, the present invention provides a monitoring method for the working conditions of a drilling rig based on the fusion of voiceprint and drill pipe movement trajectory, and the method includes:

[0008] Obtain the current trajectory information and the current voiceprint vibration signal of the vertical movement of the drilling rig;

[0009] Input the current trajectory information and the current voiceprint vibration signal into a preset drilling rig working condition discrimination model to obtain the current working condition result of the drilling rig.

[0010] Optionally, the step of inputting the current trajectory information and the current voiceprint vibration signal into a preset drilling rig working condition discrimination model to obtain the current working condition result of the drilling rig includes:

[0011] Determine the time-frequency spectrum energy distribution according to the current voiceprint vibration signal;

[0012] Input the time-frequency spectrum energy distribution and the current trajectory information into the preset drilling rig working condition discrimination model to obtain the current working condition result.

[0013] Optionally, the time-frequency spectrum energy distribution includes a first time-frequency spectrum energy distribution and / or a second time-frequency spectrum energy distribution, and the step of determining the time-frequency spectrum energy distribution according to the current voiceprint vibration signal includes:

[0014] Use the Hilbert-Huang transform to determine the first time-frequency spectrum energy distribution according to the current voiceprint vibration signal; and / or,

[0015] Use the short-time Fourier transform to determine the second time-frequency spectrum energy distribution according to the current voiceprint vibration signal.

[0016] Optionally, the step of using the Hilbert-Huang transform to determine the first time-frequency spectrum energy distribution according to the current voiceprint vibration signal includes:

[0017] Use empirical mode decomposition to decompose the current voiceprint vibration signal into multiple IMF components;

[0018] Use the Hilbert transform to perform a transform on each IMF component to obtain a plurality of Hilbert spectra;

[0019] Determine the first time-frequency spectrum energy distribution according to all the Hilbert spectra.

[0020] Optionally, the use of the short-time Fourier transform to determine the second time-frequency spectrum energy distribution according to the current voiceprint vibration signal includes:

[0021] Using a sliding window function, the current voiceprint vibration signal is segmented into voiceprint vibration signals of multiple short time periods;

[0022] Using the fast Fourier transform, each short-time period voiceprint vibration signal is transformed to obtain the Fourier spectrum of each short time period;

[0023] Determine the second time-frequency spectrum energy distribution according to the Fourier spectra of all short time periods.

[0024] Optionally, the method further includes:

[0025] Obtain the historical trajectory information and historical voiceprint vibration signal of the vertical movement of the drill rig;

[0026] Using the Hilbert-Huang transform, determine the first historical time-frequency spectrum energy distribution according to the historical voiceprint vibration signal, and / or using the short-time Fourier transform, determine the second historical time-frequency spectrum energy distribution according to the historical voiceprint vibration signal;

[0027] Determine the historical working condition result according to the historical trajectory information and the first historical time-frequency spectrum energy distribution and / or the second historical time-frequency spectrum energy distribution;

[0028] Input the historical working condition result, the historical trajectory information, and the first historical time-frequency spectrum energy distribution and / or the second historical time-frequency spectrum energy distribution into the initial drill rig working condition discrimination model for training to obtain the preset drill rig working condition discrimination model.

[0029] Optionally, obtaining the current trajectory information of the vertical movement of the drill rig includes:

[0030] Obtain the network RTK signal and dual-frequency GNSS signal at each moment of the monitoring instrument, and the monitoring instrument has been installed at the top of the drill string of the drill rig;

[0031] Based on the trajectory determination of GNSS-RTK, determine the current trajectory information according to the network RTK signal and dual-frequency GNSS signal at all moments;

[0032] Or,

[0033] Obtain the direct GNSS signal and reflected GNSS signal at each moment of the monitoring instrument;

[0034] Based on the trajectory determination of GNSS-IR, determine the current trajectory information according to the direct GNSS signal and reflected GNSS signal at all moments.

[0035] Optionally, obtaining the current acoustic fingerprint vibration signal of the vertical movement of the drilling rig includes:

[0036] Obtaining the vibration signal of each moment of the monitoring instrument, where the monitoring instrument is installed at the top end of the drill string of the drilling rig;

[0037] Determining the current acoustic fingerprint vibration signal from the vibration signals of all moments.

[0038] To achieve the above object, the present invention provides, in a second aspect, a drilling rig condition monitoring system based on the fusion of acoustic fingerprint and drill pipe movement trajectory, and the system includes a monitoring instrument and a server;

[0039] The monitoring instrument is connected to the server, and the monitoring instrument is installed at the top end of the drill string of the drilling rig;

[0040] The monitoring instrument is used to transmit the network RTK signal and dual-frequency GNSS signal, or the direct GNSS signal and reflected GNSS signal of each moment to the server;

[0041] The monitoring instrument is also used to transmit the vibration signal of each moment to the server;

[0042] The server is used to execute the method described in any item of the first aspect.

[0043] Optionally, the monitoring instrument includes a main control chip, a GNSS module, an excitation and reception module, and a communication module;

[0044] The main control chip is respectively connected to the GNSS module, the excitation and reception module, and the communication module, and the communication module is connected to the server;

[0045] The main control chip is used to control the GNSS module to obtain the dual-frequency GNSS signal of each moment, control the communication module to obtain the network RTK signal of each moment, and control the communication module to transmit the network RTK signal and dual-frequency GNSS signal of each moment to the server; or, control the GNSS module to obtain the direct GNSS signal and reflected GNSS signal of each moment, and control the communication module to transmit the direct GNSS signal and reflected GNSS signal of each moment to the server;

[0046] The main control chip is also used to control the excitation and reception module to generate elastic waves, to obtain the vibration signal of each moment based on the elastic waves, and to control the communication module to transmit the vibration signal of each moment to the server.

[0047] To achieve the above object, the present invention provides, in a third aspect, a drilling rig condition monitoring device based on the fusion of acoustic fingerprint and drill pipe movement trajectory, and the device includes:

[0048] An acquisition module for acquiring current trajectory information and current acoustic fingerprint vibration signals of the vertical movement of the drilling rig;

[0049] A working condition discrimination module for inputting the current trajectory information and the current acoustic fingerprint vibration signals into a preset drilling rig working condition discrimination model to obtain the current working condition result of the drilling rig.

[0050] To achieve the above object, in a fourth aspect of the present invention, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the processor is caused to execute the method as described in any one of the first aspects.

[0051] To achieve the above object, in a fifth aspect of the present invention, a computer device is provided, including a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor is caused to execute the method as described in any one of the first aspects.

[0052] Adopting the embodiments of the present invention has the following beneficial effects: The above method obtains the current trajectory information and current acoustic fingerprint vibration signals of the vertical movement of the drilling rig, and then inputs the current trajectory information and current acoustic fingerprint vibration signals into a preset drilling rig working condition discrimination model to obtain the current working condition result of the drilling rig; that is, by combining the monitoring of the drill pipe movement trajectory and acoustic fingerprint analysis, it effectively makes up for the deficiencies of the prior art and realizes the real-time and accurate determination of the working condition of the drilling rig. The acoustic fingerprint, as a unique sound feature generated during the operation of the drilling equipment, contains rich equipment state information. There are differences in the sound combination and energy under different working conditions. This application makes full use of these unique sound information, avoids the problem of misjudgment that is prone to occur with only single-dimensional drill pipe trajectory data, and also avoids the problems of high cost, large workload, and susceptibility to interference in monitoring due to the need not to add additional equipment. Moreover, the cross-verification between the acoustic fingerprint and the movement trajectory can provide more comprehensive and accurate monitoring, ensuring the accuracy and authenticity of the drilling data, and thus improving the quality and efficiency of the drilling work. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0054] Among them:

[0055] Figure 1 It is a schematic diagram of the method for monitoring the working condition of a drilling rig based on the fusion of acoustic fingerprint and drill pipe movement trajectory in the embodiments of the present application;

[0056] Figure 2 It is a schematic diagram of the current trajectory information in the embodiment of the present application;

[0057] Figure 3 It is a schematic diagram of the first time-frequency spectrum energy distribution of the Hilbert-Huang transform under normal drilling conditions in the embodiment of the present application;

[0058] Figure 4 It is a schematic diagram of the second time-frequency spectrum energy distribution of the short-time Fourier transform under normal drilling conditions in the embodiment of the present application;

[0059] Figure 5 It is a schematic diagram of the structure of the initial drill rig condition discrimination model in the embodiment of the present application;

[0060] Figure 6 It is a schematic diagram of the monitoring instrument installed at the top of the drill string of the drill rig in the embodiment of the present application;

[0061] Figure 7 It is a schematic diagram of the drill rig condition monitoring system based on the fusion of voiceprint and drill pipe movement trajectory in the embodiment of the present application;

[0062] Figure 8 It is another schematic diagram of the drill rig condition monitoring system based on the fusion of voiceprint and drill pipe movement trajectory in the embodiment of the present application;

[0063] Figure 9 It is a schematic diagram of the drill rig condition monitoring device based on the fusion of voiceprint and drill pipe movement trajectory in the embodiment of the present application;

[0064] Figure 10 It is the internal structure diagram of the computer device in some embodiments. Detailed implementation manners

[0065] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0066] In engineering exploration drilling work, many problems that need to be solved urgently have emerged in the existing technology. Currently, engineering drilling generally adopts the contracting outsourcing model, which makes it difficult to effectively guarantee key indicators such as the number of drill holes, depth, and coring quality. The traditional manual supervision and video monitoring methods not only have a large workload, consume a lot of manpower, but also have poor monitoring effects and cannot comprehensively and accurately control the drilling process.

[0067] In terms of obtaining formation strength information, existing methods such as the coring method have the problem of long time consumption and also require other auxiliary means, resulting in low drilling efficiency. In the field of borehole depth monitoring, the traditional seismic while-drilling drill string echo detection method is severely interfered by mechanical noise, and the measurement results are extremely unstable, making it difficult to accurately obtain borehole depth information.

[0068] To solve some problems, there are currently patents related to methods and devices for determining the vertical movement trajectory of a drilling rig based on GNSS—RTK and GNSS—IR, which can accurately determine the vertical movement trajectory information of the drill pipe, with a measurement accuracy reaching the centimeter level. By analyzing the measured periodic drill pipe trajectory diagram, the number of times of lowering the drill and the depth of each lowering are statistically analyzed to determine the borehole depth. However, it is found in actual applications that when only relying on the drill pipe trajectory diagram to judge the working conditions of the drilling rig, there will be misjudgments of the working conditions. For example, when the drill pipe trajectory curve goes down, it may be that the drilling rig is actually lowering the drill, or the drill pipe is only being lowered, or the construction personnel are artificially dry drilling or false drilling; when the drill pipe hovers and the drill pipe trajectory curve is a horizontal line, the drilling rig may be performing a coring task, or it may be in working conditions such as replacing the drill pipe or adding drill pipe. The method of using the monitoring video to compare with the drill pipe trajectory diagram to judge the working conditions of the drilling rig is feasible, but it requires additional equipment, increasing the cost, and has a large monitoring workload, low efficiency, and is also easily affected by human interference.

[0069] Generally speaking, currently in the aspect of monitoring the working condition status of drilling equipment, there is a lack of effective means to monitor the working conditions of the drilling rig in real time and accurately, resulting in the difficulty of ensuring the accuracy of drilling data, and further affecting the quality and efficiency of drilling work. Specifically, relying solely on the drill pipe trajectory diagram to judge the working conditions is prone to misjudgment, while the method combining the monitoring video has problems such as high cost, large workload, and being easily affected by interference.

[0070] To solve the above problems, this application proposes a method and system for monitoring the working conditions of a drilling rig based on the fusion of voiceprint and drill pipe movement trajectory, effectively making up for the deficiencies of the existing technology and realizing real-time and accurate judgment of the working conditions of the drilling rig. The specific implementation principle will be described in detail in the following embodiments.

[0071] This application provides a method for monitoring the working conditions of a drilling rig based on the fusion of voiceprint and drill pipe movement trajectory in the first aspect.

[0072] Please refer to Figure 1 , which is a schematic diagram of the method for monitoring the working conditions of a drilling rig based on the fusion of voiceprint and drill pipe movement trajectory in the embodiment of this application. The method includes:

[0073] Step 110: Obtain the current trajectory information and current voiceprint vibration signal of the vertical movement of the drilling rig.

[0074] It should be noted that both the current trajectory information and the current acoustic fingerprint vibration signal of the drilling rig's vertical movement are curves that change over time. For example, please refer to Figure 2 , which is a schematic diagram of the current trajectory information in the embodiments of this application.

[0075] Regarding the acquisition method of the current trajectory information, in some embodiments, the current trajectory information of the drilling rig's vertical movement can be obtained based on GNSS-RTK trajectory determination or GNSS-IR trajectory determination. Of course, in other embodiments, some other existing methods can also be used for acquisition.

[0076] Regarding the acquisition method of the current acoustic fingerprint vibration signal, in some embodiments, sensors can be installed at the top of the drill string of the drilling rig, and the current acoustic fingerprint vibration signal of the drilling rig's vertical movement can be obtained based on the sensors.

[0077] In other embodiments, after obtaining the current trajectory information and the current acoustic fingerprint vibration signal of the drilling rig's vertical movement, it is also necessary to preprocess the current trajectory information and the current acoustic fingerprint vibration signal respectively. The preprocessing includes but is not limited to filtering, smoothing, etc.

[0078] It can be understood that by preprocessing the current trajectory information, the accuracy and smoothness of the trajectory information of the drilling rig's vertical movement can be further improved. And by preprocessing the current acoustic fingerprint vibration signal, low-frequency mechanical noise can be eliminated.

[0079] Step 120: Input the current trajectory information and the current acoustic fingerprint vibration signal into a preset drilling rig working condition discrimination model to obtain the current working condition result of the drilling rig.

[0080] Among them, the preset drilling rig working condition discrimination model here refers to a trained deep learning model that can be directly used to predict and output the working condition result of the drilling rig based on the input trajectory information and acoustic fingerprint vibration signal. The current working condition result may have the following results: normal drilling (also known as lowering the drill), hovering (also known as idling or dry drilling), pulling out the drill, and anomalies (including dropping the drill, moving up and down).

[0081] Regarding the training method of the preset drilling rig working condition discrimination model, in some embodiments, a large amount of trajectory information and acoustic fingerprint vibration signals, as well as the working condition results corresponding to the trajectory information and acoustic fingerprint vibration signals, can be obtained. Then, the trajectory information, acoustic fingerprint vibration signals, and the corresponding working condition results are input into an initial deep learning model for training. After training to a certain extent, the trained preset drilling rig working condition discrimination model can be obtained. Among them, the corresponding working condition result can be used as the true value of the initial deep learning model. That is, by comparing this true value with the working condition result output during the training process one by one, it can be determined whether the initial deep learning model is trained well and meets the expected requirements.

[0082] In the embodiments of the present application, by combining the monitoring of the drill pipe movement trajectory and voiceprint analysis, the deficiencies of the prior art are effectively made up, and the real-time and accurate determination of the working conditions of the drilling rig is realized. As a unique sound feature generated during the operation of the drilling equipment, the voiceprint contains rich equipment status information. There are differences in the sound combination and energy under different working conditions. The present application makes full use of these unique sound information, avoiding the problem of misjudgment that is prone to occur in the single-dimensional drill pipe trajectory data. Moreover, since there is no need to add additional equipment, the problems of high cost, large workload, and susceptibility to interference in monitoring are also avoided. In addition, the cross-verification between the voiceprint and the movement trajectory can provide more comprehensive and accurate monitoring, ensuring the accuracy and authenticity of the drilling data, and thus improving the quality and efficiency of the drilling work.

[0083] In addition, the proposed method for monitoring the working conditions of a drilling rig based on the fusion of voiceprint and drill pipe movement trajectory has the following advantages in addition to the beneficial effects mentioned above: Detecting abnormal working conditions in a timely manner: By accurately determining the working conditions of the drilling rig in real time, abnormal working conditions such as drill pipe dropping and moving up and down can be quickly identified. If these abnormal working conditions are not discovered and handled in a timely manner, they may lead to serious safety accidents such as equipment damage and borehole collapse. For example, if the drill pipe dropping is not detected in time, the drill pipe may get stuck in the hole, and subsequent handling will not only be difficult but may also trigger other chain reactions, threatening the safety of construction workers and equipment. However, this method can give early warnings, enabling construction workers to have enough time to take countermeasures and avoid accidents; Ensuring personnel safety: Accurately grasping the working conditions of the drilling rig helps to reasonably arrange the working positions and tasks of construction workers. When an abnormal working condition is detected, relevant personnel can be notified to evacuate the dangerous area in time, reducing the time that personnel are exposed to the dangerous environment and lowering the risk of personnel casualties; Reasonably arranging the drilling plan: By understanding the working conditions of the drilling rig in real time, such as normal drilling and hovering, construction managers can flexibly adjust the drilling plan according to the actual situation. For example, when it is monitored that the drilling rig is in a normal drilling state and has high efficiency, the drilling time can be appropriately extended. If it is found that the drilling rig hovers frequently, it may mean that there are equipment problems or changes in formation conditions. At this time, maintenance or adjustment of drilling parameters can be arranged in time to avoid unnecessary shutdowns and delays and improve the overall drilling efficiency; Reducing ineffective operations: Accurately determining the working conditions can avoid ineffective operations such as dry drilling and false drilling. Dry drilling not only wastes time and resources but also increases equipment wear. By detecting and correcting these ineffective operations in time through this method, the effectiveness and economy of drilling work can be effectively improved; Discovering potential equipment faults in advance: The voiceprint contains information about the operating state of the equipment. The sound changes under different working conditions can reflect potential faults of the equipment. For example, abnormal friction sounds may imply increased wear of the drill pipe or drill bit. By monitoring the voiceprint and combining it with the drill pipe movement trajectory, these potential faults can be discovered in advance, and maintenance and component replacement can be arranged in time to avoid major repairs or replacements caused by equipment failures and reduce maintenance costs; Optimizing equipment use: Accurately grasping the working conditions of the equipment helps to reasonably arrange the use time and intensity of the equipment, avoiding overuse or unreasonable use of the equipment. For example, according to the working condition monitoring results, the start and stop times of the drilling rig can be reasonably arranged to reduce the idling and downtime of the equipment, extend the service life of the equipment, and reduce the depreciation cost of the equipment; Ensuring the accuracy of drilling data: The cross-verification of the voiceprint and movement trajectory can provide more comprehensive and accurate monitoring data, ensuring the authenticity and reliability of the drilling data. Accurate drilling data is of great significance for subsequent geological analysis, engineering design, etc., and can provide a scientific basis for relevant decisions;Support data in-depth analysis: The rich working condition monitoring data makes it possible for in-depth data analysis. By mining and analyzing a large amount of working condition data, the optimal drilling parameters and equipment operation state laws under different formation conditions can be summarized, providing data support for the improvement and innovation of drilling technology and promoting the technological progress of the drilling industry; Improve project quality: This method improves the quality and efficiency of drilling work, ensuring that the drilling project can be completed with high quality according to the design requirements. In the market competition, high-quality project results can enhance the reputation and competitiveness of the enterprise, attracting more customers and projects; Reduce project costs: By optimizing the drilling process, reducing ineffective operations, and lowering maintenance costs, this method helps to reduce the cost of the entire drilling project. During the bidding and project implementation processes, lower costs can bring greater profit margins for the enterprise, enhancing its price competitiveness in the market.

[0084] In a feasible implementation manner, for step 120 in the above embodiment, input the current trajectory information and the current acoustic fingerprint vibration signal into a preset drilling rig working condition discrimination model to obtain the current drilling rig working condition result of the drilling rig, including: determining the time-frequency spectrum energy distribution according to the current acoustic fingerprint vibration signal; inputting the time-frequency spectrum energy distribution and the current trajectory information into the preset drilling rig working condition discrimination model to obtain the current working condition result.

[0085] For the determination method of the time-frequency spectrum energy distribution, in some embodiments, time-frequency analysis technology can be used to analyze the current acoustic fingerprint vibration signal to obtain the time-frequency spectrum energy distribution.

[0086] In the embodiments of the present application, by determining the time-frequency spectrum energy distribution of the acoustic fingerprint vibration signal and using the time-frequency spectrum energy distribution to input into the model for discrimination, the accuracy and reliability of the drilling rig working condition determination are further improved.

[0087] It can be understood that enhancing the effectiveness of feature extraction: By using time-frequency analysis technology to determine the time-frequency spectrum energy distribution of the voiceprint vibration signal, the energy characteristics of the sound signal at different times and frequencies can be captured more meticulously. Under different working conditions, the energy distribution of the sound generated by the drill rig in the time-frequency domain has a unique pattern. This refined feature extraction method can provide richer and more discriminative information for the preset drill rig working condition discrimination model compared to directly using the original voiceprint vibration signal, helping the model to more accurately identify various working conditions and reduce the possibility of misjudgment; enhancing the model discrimination ability: Inputting the time-frequency spectrum energy distribution and the current trajectory information into the preset drill rig working condition discrimination model together realizes the fusion of multi-dimensional features. The time-frequency spectrum energy distribution reflects the changes in the operating state of the drill rig from the sound perspective, while the trajectory information provides the characteristics of the vertical movement of the drill rig from the kinematic perspective. The two complement each other, enabling the model to comprehensively consider information from both the sound and movement aspects, understand the drill rig working conditions more comprehensively and deeply, and thus significantly enhance the model's discrimination ability for drill rig working conditions and more accurately output the current working condition result; adapting to complex working condition changes: During actual drilling, the working conditions are complex and changeable, and the sound characteristics under different working conditions may be relatively similar, increasing the difficulty of discrimination. The time-frequency spectrum energy distribution can uncover more subtle differences in the sound signal, helping the model to accurately distinguish different working condition states under complex working conditions. For example, when distinguishing similar working conditions such as normal drilling and slight sticking, the detailed information provided by the time-frequency spectrum energy distribution enables the model to capture the subtle changes in the sound characteristics and thus make more accurate judgments, improving the adaptability and reliability of this method in complex actual environments.

[0088] In a feasible implementation manner, the time-frequency spectrum energy distribution in the above embodiments includes the first time-frequency spectrum energy distribution and / or the second time-frequency spectrum energy distribution.

[0089] Determining the time-frequency spectrum energy distribution according to the current voiceprint vibration signal in the above embodiments includes: using the Hilbert-Huang transform to determine the first time-frequency spectrum energy distribution according to the current voiceprint vibration signal; and / or using the short-time Fourier transform to determine the second time-frequency spectrum energy distribution according to the current voiceprint vibration signal.

[0090] It should be noted that by adopting the first time-frequency spectrum energy distribution of the Hilbert-Huang transform and the second time-frequency spectrum energy distribution of the short-time Fourier transform in this application, that is, adopting different types of time-frequency spectrum energy distributions, the characteristic information for model discrimination can be further enriched to help the model more accurately identify various working conditions, thereby reducing the possibility of misjudgment.

[0091] For example, the first time-frequency spectrum energy distribution and the second time-frequency spectrum energy distribution under the normal drilling working condition belong to different types of time-frequency spectrum energy distributions. Please refer to Figure 3 and Figure 4 , Figure 3Schematic diagram of the first time-frequency spectrum energy distribution of the Hilport-Huang transform under normal drilling conditions in an embodiment of the present application, Figure 4 It is a schematic diagram of the second time-frequency spectrum energy distribution of the short-time Fourier transform under normal drilling conditions in an embodiment of the present application.

[0092] In the embodiments of the present application, by adopting a variety of time-frequency analysis techniques to determine different types of time-frequency spectrum energy distribution, the characteristic information is further enriched, and the accuracy and adaptability of drilling rig working condition determination are significantly improved.

[0093] It is understandable that enriching the dimension of feature information: introducing the Hilport-Yellow transform to determine the first time-frequency spectrum energy distribution and the short-time Fourier transform to determine the second time-frequency spectrum energy distribution can analyze the soundprint vibration signal from different angles. The Hilport-Yellow transform is good at processing non-stationary signals and can accurately capture the local characteristics of the signal, while the short-time Fourier transform has a good balance between frequency and time resolution. Through these two methods, different types of time-frequency spectrum energy distributions are obtained, which provides richer and more comprehensive feature information for the preset drilling rig working condition discrimination model, which helps the model to understand the unique characteristics of sound signals under different working conditions in more detail; improve working conditions Discrimination accuracy: Different types of time-frequency spectrum energy distribution reflect the sound characteristics of the drilling rig from different aspects. The first time-frequency spectrum energy distribution and / or the second time-frequency spectrum energy distribution are input together with the current trajectory information into the preset drilling rig working condition discrimination model, so that the model can integrate more dimensional information for analysis and judgment. In the face of complex and changeable working conditions, these rich feature information can help the model more accurately identify various working conditions, effectively reduce the possibility of misjudgment, and significantly improve the accuracy of the drilling rig working condition judgment; Enhanced adaptability to complex working conditions: In the actual drilling process, the working conditions are complex and diverse, and the differences in sound characteristics under different working conditions may not be obvious. The time-frequency spectrum energy distribution determined by a variety of time-frequency analysis techniques can dig out more subtle and essential differences in sound signals. For example, under certain similar working conditions, the first time-frequency spectrum energy distribution and the second time-frequency spectrum energy distribution may provide key information from different angles, so that the model can capture these subtle differences and accurately distinguish different working conditions. This greatly improves the adaptability of this method in complex actual environments and ensures that the drilling rig working conditions can be reliably judged under various working conditions.

[0094] In a feasible implementation method, the Hilport-Huang transform in the above embodiment is used to determine the first time-frequency spectrum energy distribution according to the current voiceprint vibration signal, including: using empirical mode decomposition to decompose the current voiceprint vibration signal into multiple IMF components; using Hilport transform to transform each IMF component to obtain multiple Hilbert spectra; and determining the first time-frequency spectrum energy distribution according to all Hilbert spectra.

[0095] In the embodiments of the present application, the empirical mode decomposition and Hilbert transform are combined to determine the first time-frequency spectrum energy distribution, providing more accurate and detailed characteristic information for the discrimination of the drilling rig working conditions, and improving the discrimination accuracy and adaptability.

[0096] It can be understood that accurately capturing signal characteristics: Using empirical mode decomposition to decompose the current voiceprint vibration signal into multiple IMF components can adaptively decompose complex signals into a series of intrinsic mode functions with different characteristic scales. This method can accurately capture the characteristics of different frequency components in the voiceprint vibration signal, providing a clearer and more detailed signal basis for subsequent analysis. Different IMF components represent the components in different frequency ranges of the signal, making the analysis of the voiceprint vibration signal more comprehensive and in-depth; Obtaining rich time-frequency information: Performing Hilbert transform on each IMF component to obtain multiple Hilbert spectra. The Hilbert transform can convert a real signal into an analytic signal, thereby obtaining time-frequency information such as the instantaneous frequency and instantaneous amplitude of the signal. By performing Hilbert transform on each IMF component, the time-frequency distribution of the signal on different frequency components can be obtained, and these time-frequency information are crucial for understanding the sound characteristics generated by the drilling rig under different working conditions; Improving discrimination accuracy and adaptability: Determining the first time-frequency spectrum energy distribution according to all Hilbert spectra. This distribution synthesizes the time-frequency information of all IMF components and can more comprehensively reflect the energy characteristics of the voiceprint vibration signal in the time-frequency domain. Inputting the first time-frequency spectrum energy distribution and the current trajectory information into the preset drilling rig working condition discrimination model together provides richer and more discriminative characteristic information for the model. When facing complex and changeable working conditions, these rich characteristic information can help the model more accurately identify various working conditions, effectively reducing the possibility of misjudgment, and significantly improving the accuracy and adaptability of the determination of the drilling rig working conditions, ensuring that the working conditions of the drilling rig can be reliably determined under various working conditions.

[0097] In a feasible implementation manner, using the short-time Fourier transform in the above embodiments to determine the second time-frequency spectrum energy distribution according to the current voiceprint vibration signal includes: using a sliding window function to divide the current voiceprint vibration signal into multiple short-time voiceprint vibration signals; using the fast Fourier transform to perform a transform on each short-time voiceprint vibration signal to obtain the Fourier spectrum of each short-time period; and determining the second time-frequency spectrum energy distribution according to the Fourier spectra of all short-time periods.

[0098] In the embodiments of the present application, the second time-frequency spectrum energy distribution is determined through a sliding window function and the fast Fourier transform, providing rich and accurate time-frequency characteristic information for the discrimination of the drilling rig working conditions, and effectively improving the discrimination accuracy and adaptability.

[0099] It can be understood that for precise segmented analysis of the signal: the current voiceprint vibration signal is segmented into voiceprint vibration signals of multiple short time periods by using a sliding window function. This method can conduct a detailed analysis of different time periods of the signal. In actual drilling, the voiceprint vibration signal changes continuously over time, and signals in different time periods may contain different working condition information. By segmenting with the sliding window function, these local features can be captured, providing a more accurate basis for subsequent analysis and helping to discover short-term and critical changes in the working condition features of the signal; obtaining detailed frequency domain information: performing a fast Fourier transform on the voiceprint vibration signal of each short time period to obtain the Fourier spectrum of each short time period. The fast Fourier transform is an efficient frequency domain analysis tool that can convert a time domain signal into a frequency domain signal, thereby obtaining the energy distribution of the signal at different frequencies. By performing the transformation on the signal of each short time period, the characteristics of the signal in the frequency domain within each time period can be understood in detail. There are differences in the energy distribution of the voiceprint vibration signal in the frequency domain under different working conditions, and these frequency domain information are crucial for distinguishing different working conditions; improving the discrimination accuracy and adaptability: determining the second time-frequency spectrum energy distribution according to the Fourier spectra of all short time periods. This distribution synthesizes the frequency domain information of all short time periods and can comprehensively reflect the energy characteristics of the voiceprint vibration signal in the time-frequency domain. Inputting the second time-frequency spectrum energy distribution and the current trajectory information into a preset drilling rig working condition discrimination model provides rich and distinguishable feature information for the model. When facing complex and variable working conditions, these rich feature information can help the model more accurately identify various working conditions, effectively reducing the possibility of misjudgment and significantly improving the accuracy and adaptability of the determination of the drilling rig working condition, ensuring reliable determination of the drilling rig working condition under various working conditions.

[0100] In a feasible implementation manner, the method in the above embodiment further includes: obtaining historical trajectory information and historical voiceprint vibration signals of the vertical movement of the drilling rig; using the Hilbert-Huang transform to determine the first historical time-frequency spectrum energy distribution according to the historical voiceprint vibration signal, and / or using the short-time Fourier transform to determine the second historical time-frequency spectrum energy distribution according to the historical voiceprint vibration signal; determining the historical working condition result according to the historical trajectory information and the first historical time-frequency spectrum energy distribution and / or the second historical time-frequency spectrum energy distribution; inputting the historical working condition result, the historical trajectory information, and the first historical time-frequency spectrum energy distribution and / or the second historical time-frequency spectrum energy distribution into an initial drilling rig working condition discrimination model for training to obtain a preset drilling rig working condition discrimination model.

[0101] The determination methods for the first historical time-frequency spectrum energy distribution and the second historical time-frequency spectrum energy distribution are similar to the determination methods for the first time-frequency spectrum energy distribution and the second time-frequency spectrum energy distribution above. For the relevant content of the determination methods for the first time-frequency spectrum energy distribution and the second time-frequency spectrum energy distribution above, reference can be made, and details will not be elaborated here.

[0102] For the determination method of historical working condition results, in some embodiments, the historical drilling rig working condition results can be determined according to the drilling speed, acceleration, and displacement mutation points in the historical trajectory information, and the comparison results between the energy in the first historical time-frequency spectrum energy distribution and / or the second historical time-frequency spectrum energy distribution and the corresponding energy thresholds.

[0103] Specifically, when the fluctuation of the drilling speed in the historical trajectory information satisfies the first stable fluctuation range, the acceleration is less than or equal to the acceleration threshold, there is no displacement mutation point, and the fluctuation of the energy in the first historical time-frequency spectrum energy distribution and / or the second historical time-frequency spectrum energy distribution satisfies the second stable fluctuation range, and there is energy less than the first energy threshold, the historical drilling rig working condition result is determined to be normal drilling; when the drilling speed in the historical trajectory information is equal to or approaches 0, the acceleration is equal to or approaches 0, there is no displacement mutation point, and there is an increase in the proportion of energy greater than the second energy threshold in the first historical time-frequency spectrum energy distribution and / or the second historical time-frequency spectrum energy distribution compared to the previous time, the historical drilling rig working condition result is determined to be hovering; when there is an instantaneous increase or decrease in the drilling speed in the historical trajectory information, the acceleration is greater than the acceleration threshold, there is a displacement mutation point, and there is an instantaneous energy drop in the first historical time-frequency spectrum energy distribution and / or the second historical time-frequency spectrum energy distribution, the historical drilling rig working condition result is determined to be pulling out the drill; when the drilling speed in the historical trajectory information is greater than the speed threshold, and there is energy greater than the third energy threshold in the first historical time-frequency spectrum energy distribution and / or the second historical time-frequency spectrum energy distribution, the historical drilling rig working condition result is determined to be abnormal; among them, the first stable fluctuation range, acceleration threshold, first energy threshold, second stable fluctuation range, second energy threshold, speed threshold, third energy threshold, etc. can all be set in advance by the operator based on a large amount of experience, experiments, or statistics. Of course, they can also be set by the operator according to actual needs.

[0104] For the determination method of historical working condition results, in some embodiments, professional technicians can also directly determine the historical working condition results according to the historical trajectory information, and the first historical time-frequency spectrum energy distribution and / or the second historical time-frequency spectrum energy distribution.

[0105] For the selection method of the initial drilling rig working condition discrimination model, in some embodiments, the initial drilling rig working condition discrimination model of the present application preferably uses a convolutional neural network. For example, please refer to Figure 5 , which is a schematic structural diagram of the initial drilling rig working condition discrimination model in the embodiments of the present application. The initial drilling rig working condition discrimination model shown in this schematic diagram includes a first convolutional layer, a pooling layer, a second convolutional layer, and a fully connected layer.

[0106] In an embodiment of the present application, historical data is obtained to train a preset drilling rig operating condition discrimination model, thereby improving the accuracy and adaptability of the model to the drilling rig operating condition judgment, providing strong support for real-time and accurate judgment of the drilling rig operating condition.

[0107] It is understandable that enriching the dimensions of training data: obtaining the historical trajectory information and historical soundprint vibration signals of the vertical movement of the drilling rig, using Hilport-Huang transform and / or short-time Fourier transform to determine the historical time-frequency spectrum energy distribution, and combining the historical trajectory information to determine the historical working condition results, and inputting these data into the initial model training. The rich historical data covers a variety of feature information under different working conditions, enabling the model to learn more comprehensive and detailed working condition characteristics, which helps to improve the model's recognition ability for various working conditions; accurately matching working condition characteristics: different types of historical time-frequency spectrum energy distributions reflect the sound characteristics of the drilling rig when it is running from different angles, and are used together with historical trajectory information as training data The model can integrate multi-dimensional information for analysis and judgment. When faced with complex and changeable working conditions, these rich feature information helps the model to more accurately capture the unique characteristics of different working conditions, effectively reduce the possibility of misjudgment, and significantly improve the accuracy of the judgment of the drilling rig working condition; Enhance model adaptability: The working conditions in the actual drilling process are complex and diverse. By training the model with a large amount of historical data, the model can be exposed to various possible working conditions. The model continuously adjusts parameters during the training process to adapt to the characteristic patterns under different working conditions, thereby enhancing the adaptability to complex working conditions and ensuring that the drilling rig working condition can be reliably judged under various actual working conditions, providing a solid guarantee for real-time and accurate monitoring.

[0108] In a feasible implementation, obtaining the current trajectory information of the vertical movement of the drilling rig in the above embodiment includes: obtaining the network RTK signal and dual-frequency GNSS signal of the monitoring instrument at each moment, the monitoring instrument has been installed at the top of the drill string of the drilling rig; trajectory measurement based on GNSS-RTK, determining the current trajectory information according to the network RTK signal and dual-frequency GNSS signal at all moments; or, obtaining the direct GNSS signal and reflected GNSS signal of the monitoring instrument at each moment; trajectory measurement based on GNSS-IR, determining the current trajectory information according to the direct GNSS signal and reflected GNSS signal at all moments.

[0109] For a schematic diagram of the monitoring instrument installation location, see Figure 6, which is a schematic diagram of the monitoring instrument installed at the top of the drill string of the drill rig in the embodiment of the present application. In this schematic diagram, 610 is the monitoring instrument, 620 is the original ball injection hole of the drill string of the drill rig (i.e., the top of the drill string of the drill rig), 630 is the card slot of the elevating device of the drill string of the drill rig, 640 is the new ball injection hole of the drill string of the drill rig, 650 is the water pipe of the drill string of the drill rig, 660 is the connector of the drill string of the drill rig, and 670 is the drill pipe of the drill string of the drill rig (i.e., the drill pipe closest to the top of the drill string of the drill rig).

[0110] It should be noted that only the top part of the entire drill string of the drill rig is non-rotating (i.e., installing the monitoring instrument here does not affect the monitoring task of the monitoring instrument). However, since the top of the drill string is the position of the ball injection hole, if the monitoring instrument is installed at the position of the ball injection hole, it is necessary to frequently open the monitoring instrument for ball injection operations. Moreover, since the drill rig often uses the elevating device to lift the drill string during the drilling work, it also restricts the installation position of the monitoring instrument and the size of the monitoring instrument. Therefore, the present application adds a new ball injection hole to the drill string of the drill rig, and uses screws to integrate the monitoring instrument with the top of the drill string of the drill rig (through screw integration, it will not affect the drilling work of the drill rig). In this way, the monitoring instrument can be made tight and firm, and it is not necessary to disassemble the monitoring instrument for ball injection operations, which improves the durability of the monitoring instrument, and can also accurately complete the monitoring task, enabling the monitoring instrument to work for a long time and stably, thereby ensuring the continuity and reliability of the monitoring data.

[0111] In the embodiment of the present application, by installing a monitoring instrument at the top of the drill string to obtain signals and measure the trajectory, the accuracy and stability of the trajectory information acquisition are improved, the continuity and reliability of the monitoring data are ensured, and a solid foundation is provided for the accurate determination of the working conditions of the drill rig.

[0112] It can be understood that for accurately obtaining trajectory information: install the monitoring instrument at the top of the drill string, and use GNSS-RTK technology to obtain the network RTK signal and dual-frequency GNSS signal at each moment to determine the current trajectory information, or use GNSS-IR technology to obtain the direct GNSS signal and reflected GNSS signal at each moment to determine the current trajectory information. This method can accurately measure the vertical movement trajectory information of the drill pipe, and the measurement accuracy reaches the centimeter level. Compared with traditional methods, it greatly improves the accuracy of obtaining trajectory information; to ensure monitoring stability: the top part of the drill string does not rotate, and installing the monitoring instrument here will not affect the monitoring task. At the same time, by adding a new ball injection hole to the drill string and using screws to integrate the monitoring instrument with the top of the drill string, the monitoring instrument is made tight and firm, avoiding the displacement or damage of the monitoring instrument caused by factors such as the vibration during the operation of the drilling rig, and ensuring that the monitoring instrument can work stably for a long time; to ensure data continuity: since the installation position of the monitoring instrument is reasonable and the fixing method is reliable, there is no need to frequently disassemble for ball injection operation, reducing the interference to the monitoring process caused by operating the monitoring instrument, thereby ensuring the continuity and reliability of the monitoring data. The stable monitoring data provides accurate basic information for the subsequent monitoring of the drilling rig working conditions based on the fusion of voiceprint and drill pipe movement trajectory, which helps to more accurately determine the working conditions of the drilling rig.

[0113] In a feasible implementation manner, obtaining the current voiceprint vibration signal of the vertical movement of the drilling rig in the above embodiment includes: obtaining the vibration signal at each moment of the monitoring instrument, and the monitoring instrument has been installed at the top of the drill string of the drilling rig; determining the current voiceprint vibration signal from the vibration signals at all moments.

[0114] For the schematic diagram of the installation position of the monitoring instrument, reference can be made to the above Figure 6 related content, which will not be elaborated here.

[0115] In the embodiment of the present application, by installing the monitoring instrument at the top of the drill string to directly obtain the vibration signal, the continuity and accuracy of the voiceprint vibration signal are ensured, providing high-quality input data for the accurate determination of the working conditions of the drilling rig.

[0116] It can be understood that directly obtaining high-quality signals: By installing the monitoring instrument at the top of the drill string, the vibration signals during the operation of the drilling rig can be directly obtained. These signals contain rich voiceprint information. Since the monitoring instrument is directly installed at the key position of the drill string, the attenuation and interference during signal transmission are reduced, ensuring the continuity and accuracy of the voiceprint vibration signals. This direct acquisition method has a higher signal-to-noise ratio and less distortion compared to signals obtained indirectly or converted by other means, providing a more reliable basis for subsequent analysis and discrimination; improving discrimination accuracy: The high-quality voiceprint vibration signals provide richer and more accurate feature information for the preset drilling rig condition discrimination model. Since the voiceprint features in the signals are clearer and more accurate, the model can more precisely identify the voice features under different conditions, thereby making more accurate condition judgments. This is of great significance for improving the quality and efficiency of drilling work. Especially in complex and variable drilling environments, it can more reliably determine the drilling rig conditions and reduce misjudgments and missed judgments; enhancing system robustness: By directly acquiring vibration signals and making condition judgments based on these signals, the robustness of the entire system is enhanced. Since the signal source is reliable and stable, the system's resistance to external interference is enhanced, and it can work stably under various conditions, ensuring the continuity and reliability of monitoring results. This is particularly important for long-term and large-scale drilling monitoring tasks, and can reduce the loss or error of monitoring data caused by system instability or signal interruption.

[0117] In a second aspect of the present application, a drilling rig condition monitoring system based on the fusion of voiceprint and drill pipe movement trajectory is provided.

[0118] Please refer to Figure 7 , which is a schematic diagram of the drilling rig condition monitoring system based on the fusion of voiceprint and drill pipe movement trajectory in the embodiment of the present application. The system includes a monitoring instrument 710 and a server 720.

[0119] Among them, the monitoring instrument 710 is connected to the server 720, and the monitoring instrument 710 is installed at the top of the drill string of the drilling rig.

[0120] In a feasible implementation manner, the monitoring instrument 710 is used to transmit the network RTK signal and dual-frequency GNSS signal, or the direct GNSS signal and reflected GNSS signal at each moment to the server 720; the monitoring instrument 710 is also used to transmit the vibration signal at each moment to the server 720; the server 720 is used to execute the method according to any one of the first aspects.

[0121] In some other embodiments, the monitoring instrument 710 can also be used to execute the method according to any one of the first aspects.

[0122] In the embodiment of the present application, by constructing an integrated monitoring system, seamless connection between signal acquisition and processing is achieved, data transmission efficiency and overall system performance are improved, and efficient support is provided for accurate determination of drilling rig working conditions.

[0123] It can be understood that the integrated system design: the drilling rig working condition monitoring system based on the fusion of voiceprint and drill rod motion trajectory proposed in this application integrates two core components: monitoring instrument 710 and server 720. The monitoring instrument 710 is installed at the top of the drill string of the drilling rig, and can directly collect network RTK signals, dual-frequency GNSS signals, direct GNSS signals, reflected GNSS signals and vibration signals, and transmit these signals to the server 720 in real time for processing through the connection with the server 720. This integrated system design realizes the seamless connection between signal acquisition and processing, reduces the attenuation and interference in the signal transmission process, and improves the data transmission efficiency; efficient data transmission: the connection between the monitoring instrument 710 and the server 720 ensures efficient data transmission. The monitoring instrument 710 transmits the collected various signals to the server 720 in real time. The server 720 can quickly receive and process these data, thereby realizing real-time and accurate judgment of the drilling rig working condition. This efficient data transmission mechanism ensures the timeliness and accuracy of the monitoring data, and provides timely decision support for construction management personnel; improve the overall performance of the system: by installing the monitoring instrument 710 at the top of the drill string and directly connecting to the server 72 0 connection, the system can more accurately obtain the motion trajectory and soundprint vibration signals of the drilling rig. These high-quality signals provide a reliable basis for subsequent analysis and judgment, which helps to improve the overall performance of the system. At the same time, as the core of data processing, the server 720 can make full use of its powerful computing power to quickly and accurately process the input signals, thereby further improving the accuracy and efficiency of working condition judgment; Enhance system scalability: The monitoring system design has good scalability. With the continuous development of drilling technology, the system can be upgraded and expanded according to actual needs. For example, more sensors can be added to collect different types of signals, or the server 720 can be upgraded to improve data processing capabilities. This scalability enables the system to adapt to the needs of future drilling technology and maintain its advancement and competitiveness; Simplify system deployment and maintenance: The integrated system design simplifies the system deployment and maintenance process. The connection between the monitoring instrument 710 and the server 720 is simple and clear, which reduces the complexity and cost of system deployment. At the same time, due to the fewer system components, maintenance is more convenient, reducing maintenance costs and workload, which helps to promote the application of the monitoring system and increase its popularity in the drilling industry.

[0124] based on Figure 7 , see Figure 8, which is another schematic diagram of the drilling rig working condition monitoring system based on the fusion of voiceprint and drill pipe movement trajectory in the embodiments of the present application. The monitoring instrument 710 in the above embodiments includes a main control chip 716, a GNSS module 712, an excitation and reception module 718, and a communication module 714.

[0125] Among them, the main control chip 716 is respectively connected to the GNSS module 712, the excitation and reception module 718, and the communication module 714, and the communication module 714 is connected to the server 720.

[0126] In a feasible implementation manner, the main control chip 716 is used to control the GNSS module 712 to obtain dual-frequency GNSS signals at each moment, control the communication module 714 to obtain network RTK signals at each moment, and control the communication module 714 to transmit the network RTK signals and dual-frequency GNSS signals at each moment to the server 720; or, control the GNSS module 712 to obtain direct GNSS signals and reflected GNSS signals at each moment, and control the communication module 714 to transmit the direct GNSS signals and reflected GNSS signals at each moment to the server 720; the main control chip 716 is also used to control the excitation and reception module 718 to generate elastic waves, obtain vibration signals at each moment based on the elastic waves, and control the communication module 714 to transmit the vibration signals at each moment to the server 720.

[0127] It should be noted that since the monitoring instrument 710 is installed at the top of the drill string of the drilling rig, after the excitation and reception module 718 generates elastic waves, the elastic waves will be affected by the vibration of the drill string of the drilling rig. Therefore, based on the generated elastic waves, the elastic waves affected by the vibration of the drill string of the drilling rig can be received and analyzed, and then the vibration signals can be determined.

[0128] Please continue to refer to Figure 8 , the monitoring instrument 710 may further include a GNSS antenna 711, a communication antenna 713, a battery 715, a signal amplification module 717, and a screw rod 719. The excitation and reception module 718 includes an exciter 718A and a receiver 718B.

[0129] In some embodiments, the main control chip 716 is also used to control the exciter 718A to generate elastic waves, the receiver 718B to obtain vibration signals based on the elastic waves, and perform signal amplification through the signal amplification module 717, and control the communication module 714 and the communication antenna 713 to transmit the vibration signals to the server 710.

[0130] In some embodiments, the battery 715 is used to supply power to each module, and the screw rod 719 is used to integrally install the monitoring instrument 710 at the top of the drill string of the drilling rig with screws.

[0131] In some embodiments, the excitation receiving module 718 may be a piezoelectric ceramic sheet, that is, the exciter 718A and the receiver 718B may be piezoelectric ceramic sheets; it can be understood that using piezoelectric ceramic sheets can emit elastic waves to complete the recording of the vibration signals of the drill string.

[0132] In the embodiments of the present application, by modularizing the monitoring instrument 710, the efficient integration of signal acquisition and processing is realized, the data transmission efficiency and system flexibility are improved, and strong support is provided for the accurate determination of the working conditions of the drill rig.

[0133] It can be understood that modular design improves integration: the monitoring instrument 710 adopts modular design, including core components such as the main control chip 716, the GNSS module 712, the excitation receiving module 718 and the communication module 714. This design enables the various modules to work together efficiently to achieve the integration of signal acquisition, processing and transmission. The main control chip 716 serves as the control center and is responsible for coordinating the work of various modules to ensure the accuracy and timeliness of signal acquisition. The GNSS module 712 is responsible for acquiring GNSS signals, the excitation receiving module 718 is responsible for generating elastic waves and acquiring vibration signals, and the communication module 714 is responsible for converting the acquired signals into real-time signals. The collected signals are transmitted to the server 720. This modular design improves the integration of the monitoring instrument 710, reduces the system complexity, and makes the system more stable and reliable. Efficient signal acquisition and processing: The main control chip 716 can accurately control the work of the GNSS module 712 and the excitation receiving module 718 to ensure the accuracy and integrity of signal acquisition. The GNSS module 712 can obtain high-precision dual-frequency GNSS signals and direct / reflected GNSS signals to provide accurate basic data for trajectory determination. The excitation receiving module 718 can obtain high-precision signals by generating elastic waves and receiving the signals affected by the vibration of the drill string. The vibration signal of high quality provides a reliable data source for voiceprint analysis, and the communication module 714 is responsible for transmitting these signals to the server 720 in real time to ensure the timeliness and accuracy of the data; improve data transmission efficiency: the connection between the communication module 714 and the server 720 ensures efficient data transmission. The main control chip 716 controls the communication module 714 to transmit the collected network RTK signal, dual-frequency GNSS signal, direct GNSS signal, reflected GNSS signal and vibration signal to the server 720 in real time. The server 720 can quickly receive and process these data, thereby realizing real-time and accurate judgment of the drilling rig working condition. This efficient data transmission mechanism reduces the delay and packet loss during data transmission, improves data transmission efficiency, and provides timely decision support for construction management personnel; enhance system flexibility: the modular design makes the monitoring instrument 710 have good scalability and flexibility. With the continuous development of drilling technology, the monitoring instrument 710 can be upgraded and expanded according to actual needs. For example, more sensors can be added to collect different types of signals, or existing modules can be upgraded to improve performance. This flexibility enables the monitoring instrument 710 to adapt to the needs of future drilling technology and maintain its advancement and competitiveness;Simplify system deployment and maintenance: The modular design simplifies the deployment and maintenance process of the monitoring instrument 710. Standardized interfaces are used to connect between modules, making the system deployment more convenient and fast. At the same time, due to fewer system components and clear structure, maintenance is also easier. Once a failure occurs or maintenance is required, the problem can be quickly located and repaired, reducing the maintenance cost and workload. This helps to promote the application of the monitoring system and increase its popularity in the drilling industry.;

[0134] In a third aspect of the present application, a drilling rig condition monitoring device based on the fusion of voiceprint and drill pipe movement trajectory is provided.

[0135] Please refer to Figure 9 , which is a schematic diagram of the drilling rig condition monitoring device based on the fusion of voiceprint and drill pipe movement trajectory in the embodiment of the present application. The device 910 includes:

[0136] An acquisition module 911, configured to acquire the current trajectory information and the current voiceprint vibration signal of the vertical movement of the drilling rig;

[0137] A working condition discrimination module 912, configured to input the current trajectory information and the current voiceprint vibration signal into a preset drilling rig working condition discrimination model to obtain the current working condition result of the drilling rig.

[0138] In the embodiment of the present application, the relevant content of the above acquisition module 911 and working condition discrimination module 912 can refer to the content in the embodiment shown in Figure 1 and will not be elaborated here.

[0139] It should be noted that the device 910 of the present application further includes some other modules. It can be understood that there is a one-to-one correspondence between the method and the device 910 of the present application. Therefore, some other modules of the device 910 of the present application are the corresponding content of the method of the present application in the above embodiment.

[0140] In the embodiment of the present application, by combining the monitoring of the drill pipe movement trajectory and the voiceprint analysis, the deficiencies of the existing technology are effectively made up, and the real-time and accurate determination of the drilling rig working condition is realized. The voiceprint, as the unique sound feature generated during the operation of the drilling equipment, contains rich equipment status information. There are differences in the sound combination and energy under different working conditions. The present application makes full use of these unique sound information, avoids the problem of misjudgment that is prone to occur with only single-dimensional drill pipe trajectory data, and also avoids the problems of high cost, large workload, and susceptibility to interference in monitoring due to the need to add no equipment. Moreover, the cross-verification between the voiceprint and the movement trajectory can provide more comprehensive and accurate monitoring, ensuring the accuracy and authenticity of the drilling data, and thus improving the quality and efficiency of the drilling work.

[0141] In a fourth aspect, the present application further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to execute the drill rig condition monitoring method based on the fusion of voiceprint and drill pipe movement trajectory in the above method embodiments.

[0142] In a fifth aspect, the present application further provides a computer device, including a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to execute the drill rig condition monitoring method based on the fusion of voiceprint and drill pipe movement trajectory in the above method embodiments.

[0143] Figure 10 The internal structure diagram of the computer device in some embodiments is shown. The computer device may specifically be a terminal, a server, or a gateway. As Figure 10 shown, the computer device includes a processor, a memory, and a network interface connected through a system bus.

[0144] Among them, the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program, which, when executed by the processor, enables the processor to implement each step in the above method embodiments. The internal memory may also store a computer program, which, when executed by the processor, enables the processor to execute each step in the above method embodiments. Those skilled in the art can understand that Figure 10 the structure shown in

[0145] is only a block diagram of some structures related to the solution of the present application and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0146] Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in this application may include non-volatile and / or volatile memories. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0147] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0148] The above-described embodiments merely represent several implementation manners of this application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of this application. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several modifications and improvements can still be made, and these all belong to the protection scope of this application. Therefore, the protection scope of the patent of this application shall be subject to the appended claims.

Claims

1. A drilling rig working condition monitoring method based on the fusion of voiceprint and drill rod motion trajectory, characterized in that: The method comprises: Obtain the current trajectory information and current soundprint vibration signal of the vertical movement of the drilling rig; The current trajectory information and the current soundprint vibration signal are input into a preset drilling rig working condition discrimination model to obtain the current working condition result of the drilling rig.

2. The method according to claim 1, characterized in that The step of inputting the current trajectory information and the current soundprint vibration signal into a preset drilling rig working condition discrimination model to obtain the current drilling rig working condition result of the drilling rig includes: Determine the spectrum energy distribution according to the current voiceprint vibration signal; The time-frequency spectrum energy distribution and the current trajectory information are input into the preset drilling rig working condition discrimination model to obtain the current working condition result.

3. The method according to claim 2, characterized in that The time-frequency spectrum energy distribution includes a first time-frequency spectrum energy distribution and / or a second time-frequency spectrum energy distribution, and determining the time-frequency spectrum energy distribution according to the current voiceprint vibration signal includes: Determine the first time-frequency spectrum energy distribution according to the current voiceprint vibration signal using Hilport-Huang transform; and / or, The second time-frequency spectrum energy distribution is determined according to the current voiceprint vibration signal by using short-time Fourier transform.

4. The method according to claim 3, characterized in that The determining the first time-frequency spectrum energy distribution according to the current voiceprint vibration signal by using the Hilport-Huang transform includes: Decomposing the current voiceprint vibration signal into multiple IMF components by using empirical mode decomposition; Using the Hilbert transform, each IMF component is transformed to obtain multiple Hilbert spectra; The first time-frequency spectrum energy distribution is determined according to all Hilbert spectra.

5. The method according to claim 3, characterized in that: The determining the second time-frequency spectrum energy distribution according to the current voiceprint vibration signal by using short-time Fourier transform includes: Using a sliding window function, the current voiceprint vibration signal is divided into a plurality of voiceprint vibration signals of short time periods; Using fast Fourier transform, the voiceprint vibration signal of each short time period is transformed to obtain the Fourier spectrum of each short time period; The second time spectrum energy distribution is determined according to the Fourier spectra of all short time segments.

6. The method according to claim 3, characterized in that The method further comprises: Acquire historical trajectory information and historical soundprint vibration signals of the vertical movement of the drilling rig; Determine a first historical time-frequency spectrum energy distribution according to the historical voiceprint vibration signal by using a Hilport-Huang transform, and / or determine a second historical time-frequency spectrum energy distribution according to the historical voiceprint vibration signal by using a short-time Fourier transform; Determine a historical operating condition result according to the historical trajectory information, and the first historical time-frequency spectrum energy distribution and / or the second historical time-frequency spectrum energy distribution; The historical operating condition results and the historical trajectory information, as well as the first historical time-frequency spectrum energy distribution and / or the second historical time-frequency spectrum energy distribution are input into an initial drilling rig operating condition discrimination model for training to obtain the preset drilling rig operating condition discrimination model.

7. The method according to claim 1, characterized in that Get the current trajectory information of the vertical movement of the drilling rig, including: Obtaining a network RTK signal and a dual-frequency GNSS signal at each moment of a monitoring instrument, wherein the monitoring instrument has been installed at the top of the drill string of the drilling rig; Based on GNSS-RTK trajectory determination, the current trajectory information is determined according to the network RTK signal and dual-frequency GNSS signal at all times; or, Obtaining the direct GNSS signal and the reflected GNSS signal of the monitoring instrument at each moment; Based on GNSS-IR trajectory determination, the current trajectory information is determined according to the direct GNSS signal and the reflected GNSS signal at all times.

8. The method according to claim 1, characterized in that Get the current soundprint vibration signal of the vertical movement of the drilling rig, including: Obtaining a vibration signal at each moment of a monitoring instrument, wherein the monitoring instrument has been installed at the top of a drill string of the drilling rig; The current voiceprint vibration signal is determined by the vibration signals at all times.

9. A drilling rig working condition monitoring system based on the fusion of voiceprint and drill rod motion trajectory, characterized in that: The system includes a monitoring instrument and a server; The monitoring instrument is connected to the server, and the monitoring instrument is installed at the top of the drill string of the drilling rig; The monitoring instrument is used to transmit the network RTK signal and the dual-frequency GNSS signal, or the direct GNSS signal and the reflected GNSS signal at each moment to the server; The monitoring instrument is also used to transmit the vibration signal at each moment to the server; The server is used to execute the method according to any one of claims 1 to 8.

10. The system according to claim 9, characterized in that The monitoring instrument includes a main control chip, a GNSS module, an excitation receiving module and a communication module; The main control chip is connected to the GNSS module, the excitation receiving module, and the communication module respectively, and the communication module is connected to the server; The main control chip is used to control the GNSS module to obtain the dual-frequency GNSS signal at each moment, control the communication module to obtain the network RTK signal at each moment, and control the communication module to transmit the network RTK signal and the dual-frequency GNSS signal at each moment to the server; or control the GNSS module to obtain the direct GNSS signal and the reflected GNSS signal at each moment, and control the communication module to transmit the direct GNSS signal and the reflected GNSS signal at each moment to the server; The main control chip is also used to control the excitation receiving module to generate elastic waves, so as to obtain the vibration signal at each moment based on the elastic waves, and to control the communication module to transmit the vibration signal at each moment to the server.

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