Municipal drilling positioning method

Abstract

The embodiment of the invention provides a municipal drilling positioning method, and the method comprises the steps: transmitting a specific type of preset detection signal to a to-be-drilled region, and receiving the specific type of feature signals generated by different types of objects in the to-be-drilled region based on the preset detection signal. Furthermore, signal analysis is performed on the characteristic signals of the specific type through a preset neural network, so that the probability of drilling obstruction in the to-be-drilled area is determined, whether underground metal objects or cultural relics possibly appear in the to-be-drilled area or not can be effectively perceived, damage to the underground metal objects or cultural relics in the drilling process is avoided, and the drilling efficiency of the to-be-drilled area is improved. The equipment safety of the drill bit in the drilling positioning process and the safety of urban underground metal objects and cultural relics are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mechanical control, and more specifically, to a municipal drilling positioning method. Background Art

[0002] In the current positioning drilling process for municipal projects, the drilling equipment and drilling methods used in field drilling are usually directly applied to the drilling work for municipal projects. However, in the drilling work for municipal projects, since the drilling environment is often in the city, it is necessary to consider the metal objects (seismometers, strong seismometers, etc.) buried underground in the city, cables, and possible cultural relics in the positioning drilling process. These factors are not within the scope of consideration for field drilling. Therefore, directly applying the drilling equipment and drilling methods used in field drilling to the drilling work based on the urban environment is likely to cause damage to the underground metal objects or cultural relics in the city. Summary of the invention

[0003] In view of the above problems, in order to prevent drilling work based on urban environments from causing damage to underground equipment or cultural relics in the city, an embodiment of the present application provides a municipal drilling positioning method.

[0004] In a first aspect, an embodiment of the present application provides a municipal drilling positioning method, comprising: Transmitting a preset detection signal to the area to be drilled through the detection module to receive a characteristic signal fed back in response to the preset detection signal; Performing signal analysis on the characteristic signal according to a preset neural network model to determine the drilling obstruction probability; the drilling obstruction probability is used to characterize the probability of drilling obstruction occurring during the drilling process; The drill bit is driven to perform drilling control according to the drilling obstruction probability.

[0005] In a possible implementation, the detection module includes: a GRP detection unit and a NLJD detection unit; the preset detection signal includes: a high-frequency electromagnetic wave signal and a fundamental wave signal; the characteristic signal corresponding to the high-frequency electromagnetic wave signal is a reflected wave signal, and the characteristic signal corresponding to the fundamental wave signal is a harmonic signal; The GRP detection unit is used to transmit the high-frequency electromagnetic wave signal to the area to be drilled to receive the reflected wave signal; The NLJD detection unit is used to transmit the fundamental wave signal to the area to be drilled to receive the harmonic signal.

[0006] In a possible implementation, the GRP detection unit and the NLJD detection unit are configured to start working alternately, and the preset neural network model includes: an image recognition neural network; The performing signal analysis on the characteristic signal according to the preset neural network model includes: When the GRP detection unit is turned on, performing image data conversion on the reflected wave signal to obtain a reflected grayscale image; Performing image analysis on the reflected grayscale image by using the image recognition neural network to obtain a grayscale image analysis result; Based on the grayscale image analysis result and the real-time on time of the GRP detection unit, the operating states of the GRP detection unit and the NLJD detection unit are controlled.

[0007] In a possible implementation, the preset neural network model includes: a signal analysis neural network; the drilling obstruction probability includes: a metal object obstruction probability; The controlling the operating states of the GRP detection unit and the NLJD detection unit based on the grayscale image analysis result and the real-time opening duration of the GRP detection unit specifically includes: When the grayscale image analysis result shows that there is a bright spot area, the GRP detection unit is turned off, and the NLJD detection unit is driven to transmit the fundamental wave signal to the area to be drilled, so as to receive the corresponding harmonic signal; Performing signal analysis on the harmonic signal through the signal analysis neural network to obtain the probability of metal object obstruction; If the metal object obstruction probability is not less than the preset first threshold, the drilling speed of the driving drill bit is reduced according to the metal object obstruction probability.

[0008] In a possible implementation manner, after obtaining the metal object obstruction probability, the method further includes: When the metal object obstruction probability is less than the preset first threshold, if the real-time on time of the GRP detection unit is less than the preset second threshold, the NLJD detection unit is turned off, and the GRP detection unit is driven to operate based on its corresponding remaining on time; the remaining on time is obtained based on the first preset on time corresponding to the GRP detection unit and the real-time on time of the GRP detection unit; When the probability of metal object obstruction is less than the preset first threshold, if the real-time activation time of the GRP detection unit is not less than the preset second threshold, the remaining activation time of the GRP detection unit is skipped, and the NLJD detection unit is driven to operate based on its corresponding second preset activation time.

[0009] In a possible implementation, the drilling obstruction probability includes: a non-metallic object obstruction probability; The controlling the operating states of the GRP detection unit and the NLJD detection unit based on the grayscale image analysis result and the real-time opening duration of the GRP detection unit further includes: If the grayscale image analysis result shows that there is no bright spot area, the obstruction probability analysis is performed on the reflection grayscale image through the image recognition neural network to obtain the obstruction probability of the non-metallic object.

[0010] In a possible implementation, the step of controlling the drilling drive of the drill bit according to the drilling obstruction probability includes: When the drilling obstruction probability is greater than a preset third threshold, the drilling process of the driving drill bit is terminated.

[0011] In a possible implementation manner, there is a preset buffer phase between the end of the start-up phase of either the GRP detection unit or the NLJD detection unit and the start-up phase of the other.

[0012] In a possible implementation, the ratio of the first preset activation time, the second preset activation time, and the duration of the preset buffer phase is 16:4:1.

[0013] In a possible implementation, the step of controlling the drilling drive of the drill bit according to the drilling obstruction probability includes: The drilling speed of the driving drill bit is controlled according to the drilling obstruction probability; the drilling obstruction probability is inversely proportional to the drilling speed.

[0014] The technical solution provided by the embodiment of the present invention may include the following beneficial effects: The embodiment of the present application provides a municipal drilling positioning method, in which, first, a preset detection signal is transmitted to the area to be drilled by a detection module to receive a characteristic signal fed back for the preset detection signal. Subsequently, the characteristic signal is subjected to signal analysis according to a preset neural network model to determine the probability of drilling obstruction. Among them, the drilling obstruction probability is used to characterize the probability of drilling obstruction in the area to be drilled. Finally, the driving drill is controlled to drill according to the drilling obstruction probability. In this way, by transmitting a specific type of preset detection signal to the area to be drilled, a specific type of characteristic signal generated by different types of objects in the area to be drilled based on the preset detection signal can be received. Further, by performing signal analysis on a specific type of characteristic signal through a preset neural network, the probability of the existence of a drilling obstruction in the area to be drilled can be determined, and whether underground metal objects or cultural relics may appear in the area to be drilled can be effectively sensed to avoid damage to them during the drilling process, and the equipment safety of the drill bit itself during the drilling positioning process, as well as the safety of underground metal objects and cultural relics in the city, can be effectively improved.

[0015] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 A schematic diagram of a flow chart of a municipal drilling positioning method provided in an embodiment of the present application; Figure 2 This is a schematic diagram of the structure of a detection module according to an embodiment of the present application; Figure 3 A flow chart of a characteristic signal analysis method provided in an embodiment of the present application; Figure 4 A flowchart of a method for controlling a GRP detection unit and a NLJD detection unit provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings. It should be particularly noted that the embodiments described in the embodiments of the present application are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0019] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be the usual meanings understood by people with ordinary skills in the field to which the present application belongs. The "first", "second" and similar words used in the embodiments of the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0020] As mentioned above, in the current positioning drilling process for municipal projects, the drilling equipment and drilling methods used in field drilling are usually directly applied to the drilling work for municipal projects. However, in the drilling work for municipal projects, since the drilling environment is often in the city, it is necessary to consider the metal objects (seismometers, strong seismometers, etc.) buried underground in the city, cables, and possible cultural relics in the positioning drilling process. These factors are not within the scope of consideration for field drilling. Therefore, directly applying the drilling equipment and drilling methods used for field drilling to drilling work based on the urban environment is likely to cause damage to underground metal objects or cultural relics in the city.

[0021] In order to solve this problem, an embodiment of the present application provides a municipal drilling positioning method, in which a preset detection signal is first transmitted to the area to be drilled by a detection module to receive a characteristic signal fed back for the preset detection signal. Subsequently, the characteristic signal is subjected to signal analysis according to a preset neural network model to determine the drilling obstruction probability. The drilling obstruction probability is used to characterize the probability of the existence of drilling obstacles in the area to be drilled. Finally, the driving drill is controlled to drill according to the drilling obstruction probability. In this way, by transmitting a preset detection signal of a specific type to the area to be drilled, a specific type of characteristic signal generated by different types of objects in the area to be drilled based on the preset detection signal can be received. Furthermore, by performing signal analysis on a specific type of characteristic signal through a preset neural network, the probability of the existence of drilling obstacles in the area to be drilled can be determined, and whether underground metal objects or cultural relics may appear in the area to be drilled can be effectively sensed to avoid damage to them during the drilling process, and the equipment safety of the drill bit itself during the drilling positioning process, as well as the safety of underground metal objects and cultural relics in the city, can be effectively improved.

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0023] See also Figure 1 , which is a flow chart of a municipal drilling positioning method provided in an embodiment of the present application, and specifically includes the following steps: S101: transmitting a preset detection signal to the area to be drilled through a detection module to receive a characteristic signal fed back in response to the preset detection signal.

[0024] When the drill is driven to drill or before drilling, the detection module installed on the drill is controlled to transmit a preset detection signal to the area to be drilled. A corresponding signal receiving device is also provided on the drill. If there are obstacles such as underground metal objects or cultural relics in the area to be drilled, the signal receiving device will receive the characteristic signals reflected by the obstacles, so as to analyze whether these obstacles will bring safety risks to the drilling process based on the characteristic signals.

[0025] In an embodiment of the present application, the detection module is divided into a GRP (Ground Radar Probe Unit) detection unit and a NLJD (Non-Linear Junction Detector) detection unit. Among them, the NLJD detection unit is used to transmit a fundamental wave signal, and the fundamental wave signal is used to detect whether there are metal objects unique to urban underground, such as seismographs, optical fiber sensing systems, etc. in the area to be drilled (underground). When the fundamental wave signal encounters similar metal objects containing nonlinear electronic components (such as diodes, transistors), these electronic components will generate harmonic signals due to their own nonlinear characteristics. Therefore, using the harmonic signal as a characteristic signal fed back after transmitting the fundamental wave signal can effectively determine whether there are underground metal objects in the area to be drilled.

[0026] Accordingly, the GRP detection unit is used to emit high-frequency electromagnetic wave signals, and the high-frequency electronic wave signals are used to detect whether there are non-metallic objects in the area to be drilled, such as historical relics, pipelines, stones, etc. When the high-frequency electromagnetic wave signal contacts such non-metallic objects, a reflected wave signal is generated. Similarly to the above, the reflected wave signal generated by such non-metallic objects is used as a characteristic signal, and then it can be determined whether there are non-metallic objects in the area to be drilled.

[0027] It should be noted that the high-frequency electromagnetic wave signal emitted by the GRP detection unit has a frequency range of 10 MHz–2.6 GHz (microwave); this high-frequency electromagnetic wave signal has short pulses and wide bandwidth, and is used for the signal characteristics of reflection imaging. The fundamental wave signal (usually a low-frequency continuous wave) emitted by the NLJD detection unit has a frequency range of 1 MHz–3 GHz; this fundamental wave signal has a continuous wave, which is designed to stimulate the signal characteristics of the target harmonic response. In this way, by emitting a specific type of detection signal through a specific detection unit, it is possible to effectively determine whether there are metal objects and non-metal objects unique to urban drilling scenarios in the area to be drilled, thereby preventing the drilling process from causing damage to metal objects or cultural relics underground in the city.

[0028] S102: performing signal analysis on the characteristic signal according to a preset neural network model to determine a drilling obstruction probability; the drilling obstruction probability is used to characterize the probability of the existence of a drilling obstruction in the area to be drilled.

[0029] If a characteristic signal (i.e., a reflected wave signal or a harmonic signal) generated by a metal object or a non-metal object is received, the characteristic signal is analyzed by a preset neural network model to determine the probability of being blocked by an obstacle during the drilling process. Among them, the preset neural network model is divided into two types according to the type of characteristic signal. For harmonic signals, since traditional harmonic signal data is displayed in the form of signal waveform and signal power, the corresponding preset neural network model is a signal analysis neural network. Similarly, for reflected wave signals, by converting the reflected wave signal image data, a grayscale image corresponding to the reflected wave signal can be obtained, thereby determining whether there is a non-metallic object in the grayscale image. Therefore, the preset neural network model corresponding to the reflected wave signal is an image recognition neural network.

[0030] S103: Controlling the driving drill bit for drilling according to the drilling obstruction probability.

[0031] Finally, the drilling speed of the drill bit is controlled in real time according to the drilling obstruction probability output by the preset neural network model. Among them, the drilling obstruction probability is inversely proportional to the drilling speed. The higher the drilling obstruction probability, the lower the drilling speed. When the drilling obstruction probability is too high to exceed the preset third threshold, it indicates that there is a risk that obstacles in the area to be drilled will hinder the drilling of the drill bit. At this time, the drilling process of the driving drill bit needs to be terminated to prevent the driving drill bit from damaging the metal objects or cultural relics underground, while ensuring the safety of the drill bit equipment during the drilling process.

[0032] In addition, the drilling obstruction probability in the embodiment of the present application is subdivided into metal object obstruction probability and non-metal object obstruction probability according to the need to prevent damage to underground electronic equipment and cultural relics in the embodiment of the present application. Its specific application will be introduced below.

[0033] See also Figure 2 , this figure is a schematic diagram of the structure of a detection module in an embodiment of the present application. It can be seen from the figure that in actual application scenarios, the GRP detection unit and the NLJD detection unit use different transmission links, that is, the transmission links corresponding to the transmission of fundamental wave signals and the transmission of high-frequency electromagnetic wave signals are different, and the corresponding receiving links are also different. When the NLJD detection unit is running, sometimes part of the fundamental wave signal it transmits will be reflected to the receiving link of the GRP detection unit, causing the GRP detection unit to make a misjudgment. Therefore, in order to prevent the two from influencing each other when transmitting detection signals, the embodiment of the present application configures the GRP detection unit and the NLJD detection unit to work alternately. Only when any one of the detection units is completely closed can the other detection unit be turned on, so as to prevent the occurrence of misjudgments.

[0034] Specifically, when the GRP detection unit and the NLJD detection unit work alternately, after the start-up phase of any one of them ends and before the other enters the start-up phase, both need to go through a preset buffer phase. There is a difference in the preset start-up durations of the GRP detection unit and the NLJD detection unit, so it is necessary to ensure that the detection unit is completely turned off or turned on after its start-up duration reaches the corresponding preset start-up duration. The working sequence between the two is as follows: after the GRP detection unit is turned on for the first preset start-up duration, the GRP detection unit enters the preset buffer phase (the NLJD detection unit is turned off at this time), and after the GRP detection unit is completely turned off, the NLJD detection unit is turned on based on the second preset start-up duration, and enters the preset buffer phase when the second preset duration is reached (the GRP detection unit is turned off at this time), and so on and so forth.

[0035] In addition, in actual application scenarios, when judging whether there are non-metallic objects in the area to be drilled based on the reflected wave signal, it is necessary to involve the process of converting the reflected wave signal into a grayscale image and performing an obstruction probability analysis based on the grayscale image. The conversion analysis of the grayscale image often requires the emission of a long and continuous reflected wave. Compared with the fundamental wave signal, the corresponding harmonic signal acquisition often only needs to emit a short-time fundamental wave signal to obtain a harmonic signal sufficient for signal analysis. Therefore, the first preset opening time corresponding to the GRP detection unit needs to be much longer than the second preset opening time corresponding to the NLJD detection unit. In the application scenario of the embodiment of the present application, the ratio of the first preset opening time, the second preset opening time and the buffer stage time is 16:4:1, so that the GRP detection unit can have sufficient time to emit the reflected wave signal to ensure accurate detection of non-metallic objects.

[0036] In addition to being affected by the simultaneous opening of the NLJD detection unit, the GRP detection unit may also be affected by underground metal objects. As mentioned above, the GRP detection unit detects non-metallic objects such as underground cultural relics by emitting high-frequency electromagnetic wave signals. However, if there are also a large number of metal objects in the area to be drilled, the echoes generated by these metal objects for high-frequency electromagnetic waves may weaken the characteristic signals reflected by non-metallic objects. The echoes generated by these metal objects will appear in the form of large bright spots on the converted grayscale image, thereby affecting the obstruction probability analysis of the image recognition neural network for the grayscale image.

[0037] Therefore, in order to avoid this situation, the embodiment of the present application needs to control the opening of the GRP detection unit and the NLJD detection unit according to the actual situation of the grayscale image. If there is a certain bright spot area in the grayscale image, the NLJD detection unit needs to be started to determine whether the bright spot area in the image is generated by a metal object, so as to ensure the detection accuracy. Next, this process will be introduced in conjunction with the specific embodiment drawings.

[0038] See also Figure 3 , which is a flow chart of a characteristic signal analysis method provided in an embodiment of the present application, and specifically includes the following steps: S1021: when the GRP detection unit is turned on, convert the reflected wave signal into image data to obtain a reflected grayscale image; S1022: Performing image analysis on the reflective grayscale image through the image recognition neural network to obtain a grayscale image analysis result.

[0039] When the GRP detection unit transmits high-frequency transmission wave signals to the area to be drilled, the reflected wave signals are received in real time and converted into image data, and the reflected wave signals are converted into reflection recovery images. Subsequently, the reflected grayscale image is analyzed through the image recognition neural network, and the presence of bright spot areas is determined based on the corresponding grayscale image analysis results.

[0040] S1023: Based on the grayscale image analysis result and the real-time on time of the GRP detection unit, control the operating status of the GRP detection unit and the NLJD detection unit.

[0041] In the process of controlling the GRP detection unit and the NLJD detection unit according to whether there is a bright spot area in the grayscale image, if the grayscale image analysis result shows that there is no bright spot area in the grayscale image, the non-metallic object obstruction probability can be analyzed normally based on the image recognition network. If there is a bright spot area in the grayscale image, the NLJD detection unit needs to be run to determine whether the bright spot area in the grayscale image is caused by a metal object underground.

[0042] Since the first preset opening time of the GRP detection unit is much longer than the second preset opening time of the NLJD detection unit, in order to prevent the GRP detection unit from being interrupted prematurely, it is necessary to combine the real-time opening time of the two to make a control judgment. Next, this process will be introduced in conjunction with the specific embodiment drawings.

[0043] See also Figure 4, which is a flow chart of a control method for a GRP detection unit and a NLJD detection unit provided in an embodiment of the present application. As shown in the figure, it is first necessary to determine whether there is a bright spot area in the grayscale image based on the grayscale image analysis result. If it is determined that the grayscale image analysis result shows that there is a bright spot area, it is necessary to turn off the GRP detection unit according to the mechanism of alternating work between the GRP detection unit and the NLJD detection unit, and drive the NLJD detection unit to transmit a fundamental wave signal to the area to be drilled, and directly detect whether there is a metal object in the area to be drilled to determine whether the bright spot area appearing in the grayscale image is caused by a metal object. If the NLJD detection unit receives the corresponding harmonic signal, the received harmonic signal is analyzed by a signal analysis neural network to determine the probability of a metal object obstructing the area to be drilled, that is, the metal object obstruction probability.

[0044] If the probability of obstruction by metal objects is not less than the preset first threshold, it indicates that there is indeed a risk of obstruction by metal objects in the area to be drilled. At this time, the drilling speed of the driving drill bit can be directly reduced based on the inverse proportional relationship between the obstruction probability and the drilling speed to prevent the drilling process from causing damage to underground electronic equipment such as underground metal objects.

[0045] On the contrary, if the probability of obstruction by a metal object is less than the preset first threshold, it indicates that the bright spots in the grayscale image are not caused by metal objects underground. Therefore, it is necessary to combine the real-time activation time of the GRP detection unit to determine whether the GRP detection unit needs to be restarted to complete its remaining activation time, so as to prevent the GRP detection unit from being interrupted prematurely.

[0046] If the real-time on time of the GRP detection unit is less than the preset second threshold, it indicates that the GRP detection unit is interrupted prematurely. Therefore, it is necessary to turn off the NLJD detection unit and re-drive the GRP detection unit to run based on its corresponding remaining on time, so as to ensure that a grayscale image can be generated by a reflection wave signal of sufficient duration. Correspondingly, if the real-time on time of the GRP detection unit is not less than the preset second threshold, it indicates that the GRP detection unit has been running for a period of time. At this time, even if the GRP detection unit is re-driven to turn on, the actual running time left for the GRP detection unit is not much because it still needs to go through the buffer stage. Therefore, in this case, the remaining on time of the GRP detection unit is directly skipped, and the NLJD detection unit is driven to operate normally, so as to comprehensively improve the detection efficiency.

[0047] The embodiment of the present application provides a municipal drilling positioning method, in which a preset detection signal is first transmitted to the area to be drilled by a detection module to receive a characteristic signal fed back for the preset detection signal. Subsequently, the characteristic signal is subjected to signal analysis according to a preset neural network model to determine the probability of drilling obstruction. The drilling obstruction probability is used to characterize the probability of drilling obstruction in the area to be drilled. Finally, the driving drill is controlled to drill according to the drilling obstruction probability. In this way, by transmitting a preset detection signal of a specific type to the area to be drilled, a specific type of characteristic signal generated by different types of objects in the area to be drilled based on the preset detection signal can be received. Furthermore, by performing signal analysis on a specific type of characteristic signal through a preset neural network, the probability of drilling obstruction in the area to be drilled can be determined, and whether underground metal objects or cultural relics may appear in the area to be drilled can be effectively sensed to avoid damage to them during the drilling process, and the equipment safety of the drill itself during the drilling positioning process, as well as the safety of underground metal objects and cultural relics in the city, can be effectively improved.

[0048] It should be noted that the various embodiments in this specification are described in a progressive manner, and the same and similar parts between the various embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the method, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. The method described above is only schematic, in which the units described as separate components may or may not be physically separated, and the components prompted as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative work.

[0049] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A municipal drilling positioning method, characterized in that: include: Transmitting a preset detection signal to the area to be drilled through the detection module to receive a characteristic signal fed back in response to the preset detection signal; Performing signal analysis on the characteristic signal according to a preset neural network model to determine the drilling obstruction probability; the drilling obstruction probability is used to characterize the probability of drilling obstruction occurring during the drilling process; The drill bit is driven to perform drilling control according to the drilling obstruction probability.

2. The method according to claim 1, characterized in that: The detection module includes: a GRP detection unit and a NLJD detection unit; the preset detection signal includes: a high-frequency electromagnetic wave signal and a fundamental wave signal; the characteristic signal corresponding to the high-frequency electromagnetic wave signal is a reflected wave signal, and the characteristic signal corresponding to the fundamental wave signal is a harmonic signal; The GRP detection unit is used to transmit the high-frequency electromagnetic wave signal to the area to be drilled to receive the reflected wave signal; The NLJD detection unit is used to transmit the fundamental wave signal to the area to be drilled to receive the harmonic signal.

3. The method according to claim 2, characterized in that The GRP detection unit and the NLJD detection unit are configured to be turned on alternately, and the preset neural network model includes: an image recognition neural network; The performing signal analysis on the characteristic signal according to the preset neural network model includes: When the GRP detection unit is turned on, performing image data conversion on the reflected wave signal to obtain a reflected grayscale image; Performing image analysis on the reflected grayscale image by using the image recognition neural network to obtain a grayscale image analysis result; Based on the grayscale image analysis result and the real-time on time of the GRP detection unit, the operating states of the GRP detection unit and the NLJD detection unit are controlled.

4. The system according to claim 3, characterized in that The preset neural network model includes: a signal analysis neural network; the drilling obstruction probability includes: a metal object obstruction probability; The controlling the operating states of the GRP detection unit and the NLJD detection unit based on the grayscale image analysis result and the real-time opening duration of the GRP detection unit specifically includes: When the grayscale image analysis result shows that there is a bright spot area, the GRP detection unit is turned off, and the NLJD detection unit is driven to transmit the fundamental wave signal to the area to be drilled, so as to receive the corresponding harmonic signal; Performing signal analysis on the harmonic signal through the signal analysis neural network to obtain the probability of metal object obstruction; If the metal object obstruction probability is not less than the preset first threshold, the drilling speed of the driving drill bit is reduced according to the metal object obstruction probability.

5. The method according to claim 4, characterized in that After obtaining the metal object obstruction probability, the method further includes: When the metal object obstruction probability is less than the preset first threshold, if the real-time on time of the GRP detection unit is less than the preset second threshold, the NLJD detection unit is turned off, and the GRP detection unit is driven to operate based on its corresponding remaining on time; the remaining on time is obtained based on the first preset on time corresponding to the GRP detection unit and the real-time on time of the GRP detection unit; When the probability of metal object obstruction is less than the preset first threshold, if the real-time activation time of the GRP detection unit is not less than the preset second threshold, the remaining activation time of the GRP detection unit is skipped, and the NLJD detection unit is driven to operate based on its corresponding second preset activation time.

6. The method according to claim 3, characterized in that The drilling obstruction probability includes: non-metallic object obstruction probability; The controlling the operating states of the GRP detection unit and the NLJD detection unit based on the grayscale image analysis result and the real-time opening duration of the GRP detection unit further includes: If the grayscale image analysis result shows that there is no bright spot area, the obstruction probability analysis is performed on the reflection grayscale image through the image recognition neural network to obtain the obstruction probability of the non-metallic object.

7. The method according to claim 1, characterized in that The drilling control of the driving drill bit according to the drilling obstruction probability includes: When the drilling obstruction probability is greater than a preset third threshold, the drilling process of the driving drill bit is terminated.

8. The method according to claim 5, characterized in that There is a preset buffer phase between the end of the start-up phase of either the GRP detection unit or the NLJD detection unit and the start-up phase of the other.

9. The method according to claim 8, characterized in that The ratio of the first preset start-up time, the second preset start-up time, and the duration of the preset buffering phase is 16:4:

1.

10. The method according to claim 1, characterized in that The drilling control of the driving drill bit according to the drilling obstruction probability includes: The drilling speed of the driving drill bit is controlled according to the drilling obstruction probability; the drilling obstruction probability is inversely proportional to the drilling speed.

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