Drilling camera dynamic self-cleaning method and system based on multi-modal sensor linkage

Abstract

The application discloses a kind of based on multi-modal sensing linkage's borehole camera dynamic self-cleaning method and system, and the MEMS micro force sensor for being laid in the front end of borehole camera device obtains collision or attachment pressure data, is combined with image definition feature extraction module, realizes multi-source sensing data fusion and pollution discrimination using optimization algorithm based on fouling area;In the case where lens pollution is judged, control embedded system drive gas-liquid two-phase nozzle, with the set spray frequency, angle and pressure parameter carry out lens cleaning;Subsequently, according to the feedback result of image quality change judgment cleaning effect, if it does not reach the set definition threshold, then adjust spray strategy again until cleaning is completed.The application realizes dynamic pollution identification, adaptive cleaning decision and multi-cycle feedback regulation in the process of borehole camera, effectively improves the image acquisition efficiency and quality under the poor environment of coal mine deep well, has good engineering practicability and popularization value.

Description

Technical Field

[0001] This invention relates to the field of underground borehole imaging technology in coal mines, specifically a dynamic self-cleaning method and system for borehole cameras based on multimodal sensor linkage. Background Technology

[0002] As coal mining extends deeper, the underground geological environment exhibits significant characteristics such as high temperature (≥75℃), high pressure (≥200MPa), high dust levels, and high humidity. Especially during deep-hole drilling, high-resolution rock strata identification, structural fracture tracking, and prediction of water and mud inrush risks often rely on borehole camera systems. However, in these extreme environments, camera lenses are highly susceptible to contamination by coal dust, water mist, mud, and other particulate matter, forming a severe adhesion layer. Actual measurements show that lens imaging contrast can decrease by more than 80% within a short period after contamination, or even completely lose usable images, severely restricting the continuity and reliability of exploration missions.

[0003] Currently, manual cleaning is the most common method used in engineering projects. This involves stopping and retrieving the drill bit, then removing, disassembling, and cleaning it. This process takes 30-45 minutes per cycle, severely impacting operational efficiency. Furthermore, it poses risks such as scratches to the lens coating caused by high-pressure water jets and damage to the lens sealant due to residual corrosion solution. In addition, manual operation in a high-temperature, high-pressure environment inherently carries safety risks.

[0004] To reduce the frequency of manual intervention, technicians have attempted to introduce automated cleaning technologies. Existing representative solutions include:

[0005] 1. Timed spray cleaning solution method: The cleaning cycle is set to clean at regular intervals, but the actual state of contamination cannot be detected, resulting in either "over-cleaning" which wastes resources or "under-cleaning" which is not effective enough.

[0006] 2. Non-contact contamination identification method based on optical transmittance: It detects the change in the intensity of incident light transmitted through the lens, but the signal drifts severely in environments such as strong light straying and high humidity condensation in the well, resulting in poor stability and a high false judgment rate.

[0007] 3. Single gas pulse purging method: Compressed gas is used to blow air onto the lens, but it has limited effect on removing oily mud or dried dust, and has the problem of high air consumption. Summary of the Invention

[0008] The purpose of this invention is to address the shortcomings of existing technologies by providing a dynamic self-cleaning method and system for borehole cameras based on multimodal sensing linkage. By combining a probabilistic decision model, the system intelligently determines the timing and intensity of cleaning, avoiding resource waste. Employing gas-liquid two-phase flow nozzle pulse cleaning effectively removes various contaminants without damaging the lens surface. A closed-loop image sharpness feedback and online adaptive adjustment mechanism ensure continuous and stable cleaning results. The system has a compact overall structure, is suitable for the complex environment of deep coal mines, improves operational efficiency and safety, and guarantees the continuity and reliability of borehole imaging.

[0009] To achieve the above objectives, the present invention adopts the following technical solution.

[0010] A dynamic self-cleaning method for borehole imaging based on multimodal sensing linkage includes the following steps:

[0011] Step S1: Device deployment and system initialization;

[0012] The retractable push rod assembly is installed in the designated borehole through the support structure. When drilling and recording, the explosion-proof borehole camera is connected to the gas-liquid supply hose, power supply and signal acquisition cable. The explosion-proof borehole camera is then manually pushed into the target area through the retractable push rod assembly. The system power is turned on and the control module in the system completes initialization. The initialization includes camera system self-test, sensor calibration, network connection test and threshold preset loading.

[0013] Step S2: Real-time acquisition of multimodal sensor data and calculation of lens contamination probability;

[0014] During the drilling and imaging process, the image clarity feedback module collects and analyzes the image clarity index in real time, while the contact contamination detection unit continuously monitors the adhesion resistance and particle impact signal on the lens surface. After receiving the analysis and monitoring results from the image clarity feedback module and the contact contamination detection unit, the control module performs fusion analysis on the multimodal sensor data and calculates the probability of lens contamination by combining the built-in decision algorithm and probability assessment model.

[0015] Step S3: Self-cleaning trigger and gas-liquid two-phase flow cleaning;

[0016] When the probability of lens contamination calculated by the control module exceeds the preset contamination probability threshold, the control module activates the gas-liquid two-phase cleaning component to form a gas-liquid two-phase jet that sprays onto the lens surface.

[0017] Step S4: Post-cleaning feedback verification and iterative cleaning;

[0018] After the lens surface is cleaned, the image clarity feedback module collects and analyzes the image clarity index again. If the image clarity reaches the preset clarity threshold, the system continues the drilling and imaging task; otherwise, the image clarity feedback module sends a cleaning failure signal to the control module. The control module performs adaptive optimization based on the previous parameters, cleans the lens surface again, and executes step S5.

[0019] Step S5: Security and Redundancy Processing Mechanism;

[0020] If the image clarity still fails to reach the preset clarity threshold after multiple cleaning cycles, the data transmission module sends an alarm to the ground control terminal, prompting manual intervention.

[0021] Specifically, in step S2, the contact contamination detection unit continuously monitors the adhesion resistance and particle impact signal on the lens surface, as follows:

[0022] Step S21: The flexible MEMS micro-force sensor array of the contact contamination detection unit continuously collects the pressure signal on the surface of the explosion-proof drilling camera lens.

[0023] Step S22: Remove the flexible MEMS micro-force sensor using a low-pass filter. t Pressure signals collected at all times For high-frequency noise in the signal, the transfer function of the low-pass filter is expressed as:

[0024] ;

[0025] In the above formula, It is the input frequency of the pressure signal. It is the cutoff frequency of the low-pass filter;

[0026] Signal after removing high-frequency noise by a low-pass filter Represented as:

[0027] ;

[0028] Step S23: The pressure signal after removing high-frequency noise through low-pass filtering. Furthermore, mean filtering and smoothing are performed based on the sliding window algorithm, as expressed by the formula:

[0029] ;

[0030] In the above formula, It is a smoothed pressure signal; N This is the window size, representing the total number of sampling time points; It is the time point of signal acquisition;

[0031] Step S24: Process the smoothed pressure signal The normalization process is performed, and the formula for normalization is expressed as follows:

[0032] ;

[0033] In the above formula, It is the normalized pressure signal; , These are signal sequences The maximum and minimum values;

[0034] Step S25: Construct a two-dimensional pressure distribution matrix. The mathematical expression for the two-dimensional pressure distribution matrix is:

[0035] ;

[0036] In the above formula, Let be the azimuth angle of the Mth sensor in the flexible MEMS micro-force sensor array; For a point in time;

[0037] The two-dimensional pressure distribution matrix represents the pressure distribution on the surface of the explosion-proof drilling camera lens at different azimuth angles and different time points;

[0038] Step S26: The contact contamination detection unit determines whether there are contaminants adhering to the lens based on the pressure distribution on the surface of the explosion-proof drilling camera lens at different azimuth angles and at different time points.

[0039] Furthermore, in step S2, the image sharpness feedback module has a built-in image sharpness function. This function evaluates the edge sharpness of the image based on the Laplacian transform and outputs a sharpness score S.

[0040] ;

[0041] In the above formula, The Laplacian operator for an image; E The variance representing the grayscale variation at the image edges; V R This indicates the total lens area of ​​the drilling camera probe. , The radius of the lens; V r This represents the actual area used for calculating soiling. , The actual area radius calculated for the contamination; The correlation coefficient represents the degree of soiling, with a value ranging from 0% to 100%.

[0042] If the sharpness score S is below the set threshold for more than 3 consecutive frames, the lens is considered to be contaminated by pollutants.

[0043] Specifically, in step S3, the control module receives the evaluation and judgment results from the image clarity feedback module and the contact contamination detection unit, and performs calculations using a decision algorithm and a probability assessment model. The calculation process is as follows:

[0044] The decision algorithm generates a unified pollution confidence index by weighted fusion of multimodal sensing data from the image sharpness module and the contact pollution detection unit. The probability of lens contamination is expressed by the following formula:

[0045] ;

[0046] In the above formula, It represents the probability of lens contamination given sensor data; It is the intercept term, which represents the basic pollution probability under zero sensor data input; Corresponding features The regression coefficients; It is a sensor t Pressure values ​​collected at specific time points; It is an exponential function;

[0047] The probability assessment model is based on Bayesian theory and calculates the posterior probability of lens contamination by combining prior probability and conditional probability.

[0048] ;

[0049] In the above formula, Given sensor data X The posterior probability of lens contamination represents the likelihood of contamination occurring. Sensor data was observed under polluted conditions. X The likelihood function represents the probability of sensor data occurring under pollution conditions; It is the prior probability of pollution occurring, representing the probability of pollution occurring. Sensor data X The total probability is a set constant;

[0050] When the probability of lens contamination calculated by the control module exceeds a preset contamination probability threshold, the probability of lens contamination calculated by the control module is set to... and The larger value in;

[0051] when When the pollution probability threshold is greater than or equal to the preset threshold, the control module issues a cleaning command.

[0052] Specifically, in step S5, the control module performs adaptive optimization based on the previous parameters. The formula for adaptive optimization is expressed as follows:

[0053] ;

[0054] In the above formula, K Clean the control module and determine the parameters; To optimize the proportion j The percentage of soiled items at that time; For the first i The optimized eigenvalues ​​in the sub-optimization; To optimize the total number of self-cleaning cycles, It is a positive integer; G The nozzle spray pressure is kept constant during each cleaning process to ensure a consistent amount of cleaning agent is sprayed. To adjust the spraying time of the cleaning agent.

[0055] Based on the above technical solutions, the present invention also provides a drilling camera dynamic self-cleaning system based on multimodal sensing linkage, which is used to realize the above-mentioned drilling camera dynamic self-cleaning method. The system includes an explosion-proof drilling camera, a retractable push rod assembly, a contact contamination detection unit, an image clarity feedback module, a control module, a gas-liquid two-phase cleaning assembly, and a data transmission module.

[0056] The explosion-proof drilling camera integrates a front glass cover, an infrared supplementary light device, and a video sensor for explosion-proof drilling video recording.

[0057] The retractable push rod assembly is used to connect the explosion-proof drilling camera. When drilling and recording, the explosion-proof drilling camera is manually pushed into the target area.

[0058] The contact contamination detection unit includes a flexible MEMS micro-force sensor array located around the lens of the explosion-proof drilling camera, used to determine whether there are contaminants attached to the lens and to feed the determination result back to the control module;

[0059] The image sharpness feedback module has a built-in image sharpness function, which is used to automatically evaluate the image quality of the current borehole camera and feed the evaluation result back to the control module;

[0060] The control module has a built-in decision algorithm and probability evaluation model, which is used to receive the evaluation results from the image clarity feedback module and the contact pollution detection unit, calculate the posterior probability of the pollution state based on the evaluation results, and determine whether to issue a cleaning command based on the calculation results.

[0061] The gas-liquid two-phase cleaning assembly includes a gas source, an atomizing liquid storage tank, a mixer, and a nozzle, and is used to dynamically clean the lens surface of the explosion-proof drilling camera when a cleaning command is received from the control module.

[0062] The data transmission module is used to upload image and lens status information to the ground control terminal.

[0063] Specifically, the flexible MEMS micro-force sensor array has a sampling frequency of not less than 100Hz and an error of not more than ±5%.

[0064] Specifically, the spraying duration of the gas-liquid two-phase cleaning component is 2-5 seconds, and the maximum working pressure is 0.8 MPa; the image clarity feedback module will pause feedback for 0.5-1 seconds after each spraying.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] 1. The method of the present invention collects multimodal sensor data during the drilling and imaging process, designs decision-making algorithms and probability evaluation models, and uses cross-validation of multimodal sensor data to obtain the probability of lens contamination, which effectively solves the problem that traditional methods using only a single sensor are prone to misjudgment due to environmental factors.

[0067] 2. The method of the present invention adopts an adaptive optimization method based on iterative learning and weighted experience fusion. Through the execution-evaluation-adjustment closed-loop control strategy, the spraying parameters can be dynamically adjusted according to the historical cleaning effect, thereby optimizing resource consumption while ensuring the cleaning effect and avoiding over-cleaning or under-cleaning.

[0068] 3. The method of the present invention has a safety and redundancy processing mechanism. The system will run autonomously in most cases. When continuous cleaning fails, the redundancy protection mechanism will be triggered to request manual intervention to prevent invalid cycles, which significantly improves the robustness and practicality of the system in complex drilling environments. Attached Figure Description

[0069] Figure 1 This is a flowchart of a dynamic self-cleaning method for borehole imaging based on multimodal sensing linkage according to the present invention;

[0070] Figure 2 This is an architecture diagram of a drilling camera dynamic self-cleaning system based on multimodal sensing linkage according to the present invention.

[0071] Figure 3 This is a diagram showing the arrangement of the micro-force sensor of the present invention around the lens of an explosion-proof drilling camera;

[0072] In the figure: 1. Flexible MEMES micro-force sensor. Detailed Implementation

[0073] To facilitate understanding and implementation of the present invention by those skilled in the art, the various steps of the method proposed in this invention are described in detail below. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various modifications or alterations to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0074] Example 1

[0075] like Figure 1 As shown, this embodiment discloses a dynamic self-cleaning method for borehole cameras based on multimodal sensing linkage, including the following steps:

[0076] Step S1: Device deployment and system initialization;

[0077] The retractable push rod assembly is installed in the designated borehole through the support structure. When drilling and recording, the explosion-proof borehole camera is connected to the gas-liquid supply hose, power supply and signal acquisition cable. The explosion-proof borehole camera is then manually pushed into the target area through the retractable push rod assembly. The system power is turned on and the control module in the system completes initialization. The initialization includes camera system self-test, sensor calibration, network connection test and threshold preset loading.

[0078] Step S2: Real-time acquisition of multimodal sensor data and calculation of lens contamination probability;

[0079] During the drilling and imaging process, the image clarity feedback module collects and analyzes the image clarity index in real time, while the contact contamination detection unit continuously monitors the adhesion resistance and particle impact signal on the lens surface. After receiving the analysis and monitoring results from the image clarity feedback module and the contact contamination detection unit, the control module performs fusion analysis on the multimodal sensor data and calculates the probability of lens contamination by combining the built-in decision algorithm and probability assessment model.

[0080] Step S3: Self-cleaning trigger and gas-liquid two-phase flow cleaning;

[0081] When the probability of lens contamination calculated by the control module exceeds the preset contamination probability threshold, the control module activates the gas-liquid two-phase cleaning component to form a gas-liquid two-phase jet that sprays onto the lens surface.

[0082] Step S4: Post-cleaning feedback verification and iterative cleaning;

[0083] After the lens surface is cleaned, the image clarity feedback module collects and analyzes the image clarity index again. If the image clarity reaches the preset clarity threshold, the system continues the drilling and imaging task; otherwise, the image clarity feedback module sends a cleaning failure signal to the control module. The control module performs adaptive optimization based on the previous parameters, cleans the lens surface again, and executes step S5.

[0084] Step S5: Security and Redundancy Processing Mechanism;

[0085] If the image clarity still fails to reach the preset clarity threshold after multiple cleaning cycles, the data transmission module sends an alarm to the ground control terminal, prompting manual intervention.

[0086] Specifically, in step S2, the contact contamination detection unit continuously monitors the adhesion resistance and particle impact signal on the lens surface, as follows:

[0087] Step S21: The flexible MEMS micro-force sensor array of the contact contamination detection unit continuously collects the pressure signal on the surface of the explosion-proof drilling camera lens.

[0088] Step S22: Remove the flexible MEMS micro-force sensor using a low-pass filter. t Pressure signals collected at all times For high-frequency noise in the signal, the transfer function of the low-pass filter is expressed as:

[0089] ;

[0090] In the above formula, It is the input frequency of the pressure signal. It is the cutoff frequency of the low-pass filter;

[0091] Signal after removing high-frequency noise by a low-pass filter Represented as:

[0092] ;

[0093] Step S23: The pressure signal after removing high-frequency noise through low-pass filtering. Furthermore, mean filtering and smoothing are performed based on the sliding window algorithm, as expressed by the formula:

[0094] ;

[0095] In the above formula, It is a smoothed pressure signal; N This is the window size, representing the total number of sampling time points; It is the time point of signal acquisition;

[0096] Step S24: Process the smoothed pressure signal The normalization process is performed, and the formula for normalization is expressed as follows:

[0097] ;

[0098] In the above formula, It is the normalized pressure signal; , These are signal sequences The maximum and minimum values;

[0099] Step S25: Construct a two-dimensional pressure distribution matrix. The mathematical expression for the two-dimensional pressure distribution matrix is:

[0100] ;

[0101] In the above formula, Let be the azimuth angle of the Mth sensor in the flexible MEMS micro-force sensor array; For a point in time;

[0102] The two-dimensional pressure distribution matrix represents the pressure distribution on the surface of the explosion-proof drilling camera lens at different azimuth angles and different time points;

[0103] Step S26: The contact contamination detection unit determines whether there are contaminants adhering to the lens based on the pressure distribution on the surface of the explosion-proof drilling camera lens at different azimuth angles and at different time points.

[0104] Furthermore, in step S2, the image sharpness feedback module has a built-in image sharpness function. This function evaluates the edge sharpness of the image based on the Laplacian transform and outputs a sharpness score S.

[0105] ;

[0106] In the above formula, The Laplacian operator for an image; E The variance representing the grayscale variation at the image edges; V R This indicates the total lens area of ​​the drilling camera probe. , The radius of the lens; V r This represents the actual area used for calculating soiling. , The actual area radius calculated for the contamination; The correlation coefficient represents the degree of soiling, with a value ranging from 0% to 100%.

[0107] If the sharpness score S is lower than the set threshold for 3 consecutive frames or more, the lens is considered to be contaminated by pollutants.

[0108] In this embodiment, based on past engineering experience, the selected sharpness score S is set to a threshold between 300 and 400.

[0109] Specifically, in step S3, the control module receives the evaluation and judgment results from the image clarity feedback module and the contact contamination detection unit, and performs calculations using a decision algorithm and a probability assessment model. The calculation process is as follows:

[0110] The decision algorithm generates a unified pollution confidence index by weighted fusion of multimodal sensing data from the image sharpness module and the contact pollution detection unit. The probability of lens contamination is expressed by the following formula:

[0111] ;

[0112] In the above formula, It represents the probability of lens contamination given sensor data; It is the intercept term, which represents the basic pollution probability under zero sensor data input; Corresponding features The regression coefficients; It is a sensor t Pressure values ​​collected at specific time points; It is an exponential function;

[0113] The probability assessment model is based on Bayesian theory and calculates the posterior probability of lens contamination by combining prior probability and conditional probability.

[0114] ;

[0115] In the above formula, Given sensor data X The posterior probability of lens contamination represents the likelihood of contamination occurring. Sensor data was observed under polluted conditions. X The likelihood function represents the probability of sensor data occurring under pollution conditions; It is the prior probability of pollution occurring, representing the probability of pollution occurring. Sensor data X The total probability is a set constant;

[0116] When the probability of lens contamination calculated by the control module exceeds a preset contamination probability threshold, the probability of lens contamination calculated by the control module is set to... and The larger value in;

[0117] In this embodiment, based on past engineering experience, the setting is as follows: When the value is ≥0.85, the lens surface is considered to be contaminated, and the control module issues a cleaning command.

[0118] Specifically, in step S5, the control module performs adaptive optimization based on the previous parameters. The formula for adaptive optimization is expressed as follows:

[0119] ;

[0120] In the above formula, K Clean the control module and determine the parameters; To optimize the proportion j The percentage of soiled items at that time; For the first i The optimized eigenvalues ​​in the sub-optimization; To optimize the total number of self-cleaning cycles, It is a positive integer; G The nozzle spray pressure is kept constant during each cleaning process to ensure a consistent amount of cleaning agent is sprayed. This refers to the spraying time of the cleaning agent.

[0121] In this embodiment, the nozzle ejection pressure is set. G =0.2MPa, the cleaning agent spraying time is 1s; as shown in Table 1 below, the results of the self-cleaning optimization are given. i and the corresponding optimization ratio j Cleaning judgment parameters of the control module under changing conditions.

[0122] Table 1. Cleaning judgment parameters of the control module under continuous adaptive optimization

[0123] ;

[0124] As can be seen from Table 1 above, during the first adaptive optimization, the optimization ratio was... j =20%, at this time the control module's cleaning judgment parameter is... K =5.3, the improvement in cleaning effect on lens surface contamination is small, so to avoid the optimization ratio from the previous adaptive optimizations. j Too small a size leads to unsatisfactory cleaning results and increases the total number of cleaning cycles. In this embodiment, the proportion is optimized. j Starting with 20%, the value is selected with a gradient of 10%.

[0125] Furthermore, in Table 1, after three consecutive adaptive optimization iterations, the control module cleans and determines the parameters. K The range of value variation has gradually decreased, indicating that continuing adaptive optimization will hardly achieve a more significant cleaning effect on lens surface contamination. Therefore, in this embodiment, the value is set to... i =1,2,3, corresponding optimization percentages j=20%, 30%, 40%, meaning that if the image clarity still does not reach the preset clarity threshold after three consecutive adaptive optimization cleaning cycles, the data transmission module will send an alarm to the ground control terminal, prompting manual intervention.

[0126] Example 2

[0127] like Figure 2 As shown, this embodiment discloses a drilling camera dynamic self-cleaning system based on multimodal sensing linkage, which is used to implement the drilling camera dynamic self-cleaning method described in Embodiment 1. The system includes an explosion-proof drilling camera, a telescopic push rod assembly, a contact contamination detection unit, an image clarity feedback module, a control module, a gas-liquid two-phase cleaning assembly, and a data transmission module.

[0128] The explosion-proof drilling camera integrates a front glass cover, an infrared supplementary light device, and a video sensor for explosion-proof drilling video recording.

[0129] The retractable push rod assembly is used to connect the explosion-proof drilling camera. When drilling and recording, the explosion-proof drilling camera is manually pushed into the target area.

[0130] like Figure 3 As shown, the contact contamination detection unit includes a flexible MEMS micro-force sensor array located around the lens of the explosion-proof drilling camera, which is used to determine whether there are contaminants attached to the lens and to feed the determination result back to the control module; Figure 2 Lens diameter of explosion-proof drilling camera The lens diameter is 32mm, a key parameter derived from optimization experiments. This 32mm lens diameter maintains image clarity while reducing the probability of contaminants adhering to the lens and achieving optimal cleaning results.

[0131] The image sharpness feedback module has a built-in image sharpness function, which is used to automatically evaluate the image quality of the current borehole camera and feed the evaluation result back to the control module;

[0132] The control module has a built-in decision algorithm and probability evaluation model, which is used to receive the evaluation results from the image clarity feedback module and the contact pollution detection unit, calculate the posterior probability of the pollution state based on the evaluation results, and determine whether to issue a cleaning command based on the calculation results.

[0133] The gas-liquid two-phase cleaning assembly includes a gas source, an atomizing liquid storage tank, a mixer, and nozzles. The nozzle assembly is mounted on the outer protective cover of the drilling camera and includes a central jet port (gas supply) and an annular liquid nozzle (liquid supply). The nozzle front end is equipped with a piezoelectric atomization unit, which can make the cleaning liquid form fine droplets to enhance the cleaning effect. The spraying medium in the atomizing liquid storage tank includes compressed gas (generally nitrogen, with a storage pressure of 0.6~0.8MPa) and cleaning liquid (mining-grade non-corrosive neutral surfactant solution, with an adjustable flow rate of 50~150mL / min). The mixing method adopts a Y-type mixing tube to make the gas and liquid form turbulence and generate a two-phase flow. The gas-liquid two-phase cleaning assembly is used to dynamically clean the lens surface of the explosion-proof drilling camera when it receives a cleaning command from the control module.

[0134] The data transmission module is used to upload image and lens status information to the ground control terminal.

[0135] Specifically, the flexible MEMS micro-force sensor array has a sampling frequency of not less than 100Hz and an error of not more than ±5%.

[0136] Specifically, the spraying duration of the gas-liquid two-phase cleaning component is 2-5 seconds, and the maximum working pressure is 0.8 MPa; the image clarity feedback module will pause feedback for 0.5-1 seconds after each spraying.

[0137] The method of this invention first collects multimodal sensor data during the drilling and imaging process, designs a decision-making algorithm and a probability evaluation model, and uses cross-validation of the multimodal sensor data to obtain the probability of lens contamination, effectively solving the problem that traditional methods using only a single sensor are prone to misjudgment due to environmental factors. Secondly, it adopts an adaptive optimization method based on iterative learning and weighted experience fusion. Through an execution-evaluation-adjustment closed-loop control strategy, it can dynamically adjust the spraying parameters according to historical cleaning effects, thereby optimizing resource consumption while ensuring cleaning effect and avoiding over-cleaning or under-cleaning. Finally, the system has a safety and redundancy processing mechanism. In most cases, the system will run autonomously. When continuous cleaning fails, the redundancy protection mechanism will be triggered to request manual intervention to prevent invalid loops, which significantly improves the robustness and practicality of the system in complex drilling environments.

[0138] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A dynamic self-cleaning method for borehole imaging based on multimodal sensing linkage, characterized in that, Includes the following steps: Step S1: Device deployment and system initialization; The explosion-proof drilling camera is placed into the drilling target area through the retractable push rod assembly, and the drilling camera system initialization is completed. Step S2: Real-time acquisition of multimodal sensor data and calculation of lens contamination probability; During the drilling and imaging process, the image clarity feedback module collects and analyzes the image clarity index in real time, while the contact contamination detection unit continuously monitors the adhesion resistance and particle impact signal on the lens surface. After receiving the analysis and monitoring results from the image clarity feedback module and the contact contamination detection unit, the control module performs fusion analysis on the multimodal sensor data and calculates the probability of lens contamination by combining the built-in decision algorithm and probability assessment model. The contact contamination detection unit continuously monitors the adhesion resistance and particle impact signal on the lens surface, as follows: Step S21: The flexible MEMS micro-force sensor array of the contact contamination detection unit continuously collects the pressure signal on the surface of the explosion-proof drilling camera lens. Step S22: Remove the flexible MEMS micro-force sensor using a low-pass filter. t Pressure signals collected at all times For high-frequency noise in the signal, the transfer function of the low-pass filter is expressed as: ； In the above formula, It is the input frequency of the pressure signal. It is the cutoff frequency of the low-pass filter; Signal after removing high-frequency noise by a low-pass filter Represented as: ； Step S23: The pressure signal after removing high-frequency noise through low-pass filtering. Furthermore, mean filtering and smoothing are performed based on the sliding window algorithm, as expressed by the formula: ； In the above formula, It is a smoothed pressure signal; N This is the window size, representing the total number of sampling time points; It is the time point of signal acquisition; Step S24: Process the smoothed pressure signal The normalization process is performed, and the formula for normalization is expressed as follows: ； In the above formula, It is the normalized pressure signal; , These are signal sequences The maximum and minimum values; Step S25: Construct a two-dimensional pressure distribution matrix. The mathematical expression for the two-dimensional pressure distribution matrix is: ； In the above formula, Let be the azimuth angle of the Mth sensor in the flexible MEMS micro-force sensor array; For a point in time; The two-dimensional pressure distribution matrix represents the pressure distribution on the surface of the explosion-proof drilling camera lens at different azimuth angles and different time points; Step S26: The contact contamination detection unit determines whether there are contaminants attached to the lens based on the pressure distribution on the surface of the explosion-proof drilling camera lens at different azimuth angles and at different time points. Step S3: Self-cleaning trigger and gas-liquid two-phase flow cleaning; When the probability of lens contamination calculated by the control module exceeds the preset contamination probability threshold, the control module activates the gas-liquid two-phase cleaning component to form a gas-liquid two-phase jet that sprays onto the lens surface. Step S4: Post-cleaning feedback verification and iterative cleaning; After the lens surface is cleaned, the image clarity feedback module collects and analyzes the image clarity index again. If the image clarity reaches the preset clarity threshold, the system continues the drilling and imaging task; otherwise, the image clarity feedback module sends a cleaning failure signal to the control module. The control module performs adaptive optimization based on the previous parameters, cleans the lens surface again, and executes step S5. Step S5: Security and Redundancy Processing Mechanism; If the image clarity still fails to reach the preset clarity threshold after multiple cleaning cycles, the data transmission module sends an alarm to the ground control terminal, prompting manual intervention.

2. The drilling camera dynamic self-cleaning method based on multimodal sensing linkage according to claim 1, characterized in that, In step S2, the image sharpness feedback module has a built-in image sharpness function. This function evaluates the edge sharpness of the image based on the Laplacian transform and outputs a sharpness score S. ； In the above formula, The Laplacian operator for an image; E The variance representing the grayscale variation at the image edges; V R This indicates the total lens area of ​​the drilling camera probe. , The radius of the lens; V r This represents the actual area used for calculating soiling. , The actual area radius calculated for the contamination; The correlation coefficient represents the degree of soiling, with a value ranging from 0% to 100%. If the sharpness score S is below the set threshold for more than 3 consecutive frames, the lens is considered to be contaminated by pollutants.

3. The drilling camera dynamic self-cleaning method based on multimodal sensing linkage according to claim 2, characterized in that, In step S3, the control module receives the evaluation and judgment results from the image clarity feedback module and the contact contamination detection unit, and performs calculations using a decision algorithm and a probability assessment model. The calculation process is as follows: The decision algorithm generates a unified pollution confidence index by weighted fusion of multimodal sensing data from the image sharpness module and the contact pollution detection unit. The probability of lens contamination is expressed by the following formula: ； In the above formula, It represents the probability of lens contamination given sensor data; It is the intercept term, which represents the basic pollution probability under zero sensor data input; Corresponding features The regression coefficients; It is a sensor t Pressure values ​​collected at specific time points; It is an exponential function; The probability assessment model is based on Bayesian theory and calculates the posterior probability of lens contamination by combining prior probability and conditional probability. ； In the above formula, Given sensor data X The posterior probability of lens contamination represents the likelihood of contamination occurring. Sensor data was observed under polluted conditions. X The likelihood function represents the probability of sensor data occurring under pollution conditions; It is the prior probability of pollution occurring, representing the probability of pollution occurring; Sensor data X The total probability is a set constant; When the probability of lens contamination calculated by the control module exceeds a preset contamination probability threshold, the probability of lens contamination calculated by the control module is set to... and The larger value in; when When the pollution probability threshold is greater than or equal to the preset threshold, the control module issues a cleaning command.

4. The drilling camera dynamic self-cleaning method based on multimodal sensing linkage according to claim 3, characterized in that, In step S5, the control module performs adaptive optimization based on the previous parameters. The formula for adaptive optimization is expressed as follows: ； In the above formula, K Clean the control module and determine the parameters; To optimize the proportion j The percentage of soiled area at that time; For the first i The optimized eigenvalues ​​in the sub-optimization; To optimize the total number of self-cleaning cycles, It is a positive integer; G The nozzle spray pressure is kept constant during each cleaning process to ensure a consistent amount of cleaning agent is sprayed. t This refers to the spraying time of the cleaning agent.

5. A drilling camera dynamic self-cleaning system based on multimodal sensing linkage, used to implement the drilling camera dynamic self-cleaning method based on multimodal sensing linkage as described in any one of claims 1-4, characterized in that, It includes an explosion-proof drilling camera, a retractable push rod assembly, a contact-type contamination detection unit, an image clarity feedback module, a control module, a gas-liquid two-phase cleaning assembly, and a data transmission module; The explosion-proof drilling camera integrates a front glass cover, an infrared supplementary light device, and a video sensor for explosion-proof drilling video recording. The retractable push rod assembly is used to connect the explosion-proof drilling camera. When drilling and recording, the explosion-proof drilling camera is manually pushed into the target area. The contact contamination detection unit includes a flexible MEMS micro-force sensor array located around the lens of the explosion-proof drilling camera, used to determine whether there are contaminants attached to the lens and to feed the determination result back to the control module; The image sharpness feedback module has a built-in image sharpness function, which is used to automatically evaluate the image quality of the current borehole camera and feed the evaluation result back to the control module; The control module has a built-in decision algorithm and probability evaluation model, which is used to receive the evaluation results from the image clarity feedback module and the contact pollution detection unit, calculate the posterior probability of the pollution state based on the evaluation results, and determine whether to issue a cleaning command based on the calculation results. The gas-liquid two-phase cleaning assembly includes a gas source, an atomizing liquid storage tank, a mixer, and a nozzle, and is used to dynamically clean the lens surface of the explosion-proof drilling camera when a cleaning command is received from the control module. The data transmission module is used to upload image and lens status information to the ground control terminal.

6. The drilling camera dynamic self-cleaning system based on multimodal sensing linkage according to claim 5, characterized in that, The flexible MEMS micro-force sensor array has a sampling frequency of not less than 100Hz and an error of not more than ±5%.

7. A drilling camera dynamic self-cleaning system based on multimodal sensing linkage according to claim 5, characterized in that, The spraying duration of the gas-liquid two-phase cleaning component is 2-5 seconds, and the maximum working pressure is 0.8 MPa; the image clarity feedback module will pause feedback for 0.5-1 seconds after each spraying.

Images