Filter screen life prediction method, device and storage medium for drill pipe rust prevention equipment

Abstract

The application discloses a filter screen life prediction method and device for a drill rod rust prevention equipment and a storage medium, and relates to the technical field of industrial equipment intelligent maintenance and data processing. The method comprises the following steps: monitoring the running control instruction of the drill rod rust prevention equipment in real time, and synchronously collecting fluid pressure data and fluid medium temperature data on the inlet side of the filter screen; in response to a pump stopping instruction of the rust prevention equipment, triggering a transient characteristic capturing mode, and intercepting a pressure relaxation sequence of the filter screen inlet side pressure from the working steady-state pressure decay to the ambient back pressure at a preset high-frequency sampling rate; based on the fluid impedance principle, extracting the characteristics of the pressure relaxation sequence, and calculating the pressure decay time constant representing the damping characteristics of the filter screen pore structure; the application solves the problems that the traditional steady-state pressure difference detection method is greatly disturbed by the temperature variation characteristics of high-viscosity rust prevention oil, cannot perceive deep colloidal blockage and physical perforation, and has the characteristics of strong anti-interference ability and high prediction precision.

Description

Technical Field

[0001] This invention relates to the field of intelligent maintenance and data processing technology for industrial equipment, specifically to a method, device, and storage medium for predicting the lifespan of filter screens in drill pipe rust prevention equipment. Background Technology

[0002] In oil drilling and geological exploration operations, rust prevention and maintenance of drill pipes is a key process for extending the life of drilling tools. Rust prevention equipment typically uses a high-pressure pump to spray high-viscosity rust-preventive oil or cleaning fluid onto the inner and outer surfaces of the drill pipe. Precision filters are installed in the circulation pipeline to intercept metal cutting chips, formation sand particles, and oil sludge impurities carried in by the drill pipe.

[0003] Existing filter clogging detection technologies primarily rely on steady-state differential pressure monitoring, which involves monitoring the pressure difference between the filter inlet and outlet during equipment operation and triggering an alarm when the pressure difference exceeds a set threshold. However, this existing technology has significant drawbacks in drill pipe rust prevention scenarios:

[0004] The significant interference of fluid viscosity-temperature characteristics: Rust-preventive oils are typically high-viscosity non-Newtonian fluids, extremely sensitive to temperature. During low-temperature startup in winter or when ambient temperature drops sharply, the fluid viscosity increases exponentially, causing a substantial increase in steady-state pressure differential even if the filter is clean. Conversely, at high temperatures, even if the filter is partially clogged, the reduced viscosity may mask abnormal pressure differentials, leading to operational hazards.

[0005] Unable to identify deep colloidal blockages: Impurities generated during drill pipe operations include not only hard particles but also a large amount of colloidal sludge. This sludge adheres deep into the filter screen pores, forming an elastic deposit layer. The steady-state pressure difference method can only reflect flow resistance and cannot detect the fluid capacitive characteristics caused by the elasticity of the deposit layer, resulting in low lifetime prediction accuracy.

[0006] Lack of damage detection mechanism: The sharp threaded end of the drill pipe can easily puncture the filter screen. Once the filter screen is damaged, the pressure differential will drop instantly. The existing logic will misjudge this as the filter screen being extremely clear, thus causing large particles of impurities to directly damage the expensive nozzle system.

[0007] Therefore, it is necessary to design a method, device, and storage medium for predicting the life of filter screens for drill pipe rust prevention equipment that can decouple the influence of fluid viscosity and accurately invert the internal state of the filter screen using the transient characteristics of shutdown. Summary of the Invention

[0008] The purpose of this invention is to provide a method, device, and storage medium for predicting the lifespan of filter screens in drill pipe rust prevention equipment, in order to solve the problems mentioned in the background art.

[0009] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for predicting the lifespan of a filter screen in a drill pipe rust prevention device, comprising the following steps:

[0010] Step S1: Monitor the operation control commands of the drill pipe rust prevention equipment in real time, and simultaneously collect fluid pressure data and fluid medium temperature data at the filter inlet side;

[0011] Step S2: In response to the pump stop command of the rust prevention equipment, trigger the transient feature capture mode, and capture the pressure relaxation sequence of the filter screen inlet side pressure from the working steady state pressure to the ambient back pressure at a preset high frequency sampling rate;

[0012] Step S3: Based on the principle of fluid impedance, feature extraction is performed on the pressure relaxation sequence, and the pressure decay time constant characterizing the damping characteristics of the filter pore structure is calculated;

[0013] Step S4: Call the preset fluid viscosity-temperature compensation model, use the fluid medium temperature data to standardize and correct the pressure decay time constant, and generate a standardized clogging index that is decoupled from viscosity interference;

[0014] Step S5: Input the standardized clogging index into the life prediction model, calculate the remaining effective operating cycle of the filter, and output graded maintenance instructions or shutdown protection instructions based on the prediction results.

[0015] According to the above technical solution, the specific method for triggering the transient feature capture mode in step S2 is as follows:

[0016] Set a sliding time window to monitor the falling edge of the main pump control signal in real time;

[0017] When a falling edge trigger is detected, the delay is... To avoid electromagnetic interference, high-frequency acquisition was then initiated and continuous recording was performed. Duration of stress data ;

[0018] The raw pressure data collected Kalman filtering is performed to remove high-frequency noise, resulting in a smoothed pressure relaxation sequence.

[0019] According to the above technical solution, the specific method for calculating the pressure decay time constant in step S3 is as follows:

[0020] Construct an equivalent fluid RC attenuation model for the filter system, with the pressure attenuation equation as follows:

[0021] ;

[0022] in, for Instantaneous pressure at any moment The initial pressure at the moment the pump stops. The pressure decay time constant to be determined;

[0023] Performing a logarithmic transformation on the pressure relaxation sequence yields a linear sequence. ;

[0024] The linear sequence was fitted using the least squares method to extract the slope. The real-time pressure decay time constant was calculated. Wherein, the pressure decay time constant It is positively correlated with the thickness of the surface deposit layer of the filter and the degree of pore blockage.

[0025] According to the above technical solution, the specific method for generating the standardized congestion index in step S4 is as follows:

[0026] Get the current temperature of the fluid medium. ;

[0027] Calculate the current viscosity correction factor based on the Arrhenius viscosity equation. :

[0028] ;

[0029] in, Forward factor, For fluid activation energy, The viscosity is the gas constant; the viscosity correction coefficient is used to... Normalization is performed to calculate the standardized congestion index. : ;

[0030] in, This serves as the reference time constant for the clean filter. This is the normalization factor for flow conditions.

[0031] According to the above technical solution, the method further includes a filter damage identification step based on abnormal attenuation characteristics:

[0032] After calculating the real-time pressure decay time constant Then, compare it with the preset integrity threshold. Perform a comparison;

[0033] like And continue In each operating cycle, if it is determined that the filter screen has physical perforation or bypass leakage, a highest priority emergency stop command is generated, which has a higher priority than the graded maintenance command.

[0034] Wherein, the integrity threshold Less than the reference time constant of the clean filter .

[0035] According to the above technical solution, after step S5, the method further includes a step of updating the model in reverse using maintenance feedback data:

[0036] After performing filter replacement and maintenance, obtain the actual weight of the deposits on the removed filter. ;

[0037] Retrieve the standardized congestion index from the last calculation before the replacement. Calculate and predict the amount of dirt buildup. ;

[0038] Calculate prediction bias ;

[0039] If there is a deviation If the parameters exceed the preset tolerance range, the weight parameters in the lifetime prediction model are corrected using the gradient descent method. The updated formula is:

[0040] ;

[0041] in, The learning rate is used to achieve adaptive iteration of the properties of different batches of rust-preventive oil.

[0042] A filter life prediction system for drill pipe rust prevention equipment includes a multi-source sensing module configured to acquire high-frequency pressure signals, fluid temperature signals, and equipment start-up and shutdown control signals at the filter inlet and outlet in real time; a transient feature analysis module connected to the multi-source sensing module, configured to intercept pressure decay waveforms and calculate pressure decay time constants in response to shutdown signals; a viscosity-temperature compensation calculation unit connected to the transient feature analysis module, configured to calculate viscosity correction coefficients in real time based on fluid temperature and generate a standardized clogging index to eliminate viscosity interference; a life prediction and decision module configured to assess the remaining life of the filter based on the standardized clogging index and identify abnormal filter damage; and a self-learning optimization module configured to reverse-correct the internal parameters of the calculation model based on actual maintenance data.

[0043] According to the above technical solution, the multi-source sensing module includes a high-frequency pressure transmitter installed on the filter inlet side, and the sampling frequency of the high-frequency pressure transmitter is... Furthermore, the multi-source sensing module is equipped with an FPGA preprocessing unit, which is used to filter out periodic pulsation noise during pump operation before data is uploaded.

[0044] According to the above technical solution, the system is also equipped with a drill pipe operation fingerprint database, and the life prediction and decision module is also configured to identify the specifications of the drill pipe currently being processed, and dynamically adjust the consumption weight of the life prediction model according to the iron filings generation rate of different drill pipe specifications.

[0045] An electronic device, comprising:

[0046] Memory and processor;

[0047] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the method.

[0048] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method.

[0049] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: By extracting the transient pressure decay time constant during shutdown and combining it with a viscosity-temperature compensation model, this invention completely decouples the interference of fluid viscosity fluctuations on detection, effectively solving the problems of false alarms at low temperatures and missed detections of deep sludge. Simultaneously, the abnormal decay identification mechanism constructed in this invention can lock the risk of filter perforation in milliseconds and trigger an emergency stop, preventing damage to downstream precision equipment. Combined with a self-learning closed loop based on maintenance feedback, it achieves adaptive evolution of the prediction model as the operating environment changes, significantly improving the intelligence level and prediction accuracy of equipment maintenance. Attached Figure Description

[0050] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0051] Figure 1 This is a schematic diagram of the filter life prediction method for drill pipe rust prevention equipment of the present invention;

[0052] Figure 2 This is a schematic diagram of the filter life prediction system module for rust prevention equipment for drill pipes according to the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] Example 1

[0055] Please see Figure 1 This invention provides a method for predicting the lifespan of a filter screen in a drill pipe rust prevention device, comprising the following steps:

[0056] Step S1: Monitor the operation control commands of the drill pipe rust prevention equipment in real time, and simultaneously collect fluid pressure data and fluid medium temperature data at the filter inlet side.

[0057] In this embodiment of the invention, a high-frequency pressure transmitter (sampling frequency) is installed at the filter inlet of the drill pipe rust prevention equipment. A temperature sensor is installed in the storage tank or main circuit. The PLC controller collects the current fluid pressure in real time. Fluid temperature And the start / stop control signals (IO status) of the main pump.

[0058] Step S2: In response to the pump stop command of the rust prevention equipment, trigger the transient feature capture mode, and use a preset high-frequency sampling rate to capture the pressure relaxation sequence of the filter screen inlet side pressure from the working steady-state pressure to the ambient back pressure.

[0059] The specific method for triggering the transient feature capture mode in step S2 is as follows:

[0060] Set a sliding time window to monitor the falling edge of the main pump control signal in real time;

[0061] When a falling edge trigger is detected, the delay is... To avoid electromagnetic interference, high-frequency acquisition was then initiated and continuous recording was performed. Duration of stress data ;

[0062] The raw pressure data collected Kalman filtering is performed to remove high-frequency noise, resulting in a smoothed pressure relaxation sequence.

[0063] Specifically, system latency To avoid electromagnetic interference at the moment the contactor disconnects, the duration is then continuously recorded. The pressure data sequence records the complete physical process of the filter inlet pressure gradually releasing from the operating pressure (e.g., 2.5 MPa) to the ambient back pressure (0 MPa).

[0064] Meanwhile, the collected raw data is subjected to Kalman filtering to remove high-frequency oscillation noise caused by the fluid water hammer effect, while retaining the main exponential decay trend.

[0065] Step S3: Based on the principle of fluid impedance, feature extraction is performed on the pressure relaxation sequence, and the pressure decay time constant characterizing the damping characteristics of the filter pore structure is calculated.

[0066] The specific method for calculating the pressure decay time constant in step S3 is as follows:

[0067] Construct an equivalent fluid RC attenuation model for the filter system, with the pressure attenuation equation as follows:

[0068] ;

[0069] in, for Instantaneous pressure at any moment The initial pressure at the moment the pump stops. , where R corresponds to the pore flow resistance of the filter screen, and C corresponds to the elastic energy storage effect of the filter screen structure and the oil sludge filter cake adhering to its surface; when the filter screen is clean, the fluid passes through rapidly, and the pressure is released instantaneously. Minimal; when the filter is clogged and covered with sticky sludge, the sludge layer exhibits a sponge effect, hindering pressure release and causing a slow pressure drop. Significantly increased;

[0070] Performing a logarithmic transformation on the pressure relaxation sequence yields a linear sequence. ;

[0071] The linear sequence was fitted using the least squares method to extract the slope. The real-time pressure decay time constant was calculated. Wherein, the pressure decay time constant It is positively correlated with the thickness of the surface deposit layer of the filter and the degree of pore blockage;

[0072] Compared to traditional detection methods that only show the steady-state value, similar to a doctor taking a patient's blood pressure, this invention captures transient characteristics, similar to the dynamic echo response a doctor listens to when listening to the heart. When the high-pressure pump of the drill pipe rust prevention equipment stops operating, the fluid at the filter inlet does not immediately stop, but undergoes a pressure release process. This process is like an inflated balloon deflating:

[0073] A clean filter screen acts like a large-aperture vent valve, allowing fluid to quickly flow back or release pressure through the mesh, resulting in a steep drop in the pressure curve and a short time constant. Extremely small (e.g., <50ms).

[0074] Filters clogged with hard particles: mesh size decreases, flow resistance increases. As the pressure increases, the rate of pressure release slows down.

[0075] Filter screens clogged with soft sludge: This is the most challenging situation for drill pipe rust prevention. Sludge and colloids brought in by the drill pipe form a porous, sponge-like filter cake on the filter screen surface. Upon pump shutdown, this high-pressure compressed porous sponge-like filter cake elastically rebounds, slowly releasing the adsorbed fluid. This physical phenomenon manifests as capacitance in fluid circuits. The discharge effect of "".

[0076] This invention is achieved through Extraction time constant In fact, it simultaneously captures two dimensions of characteristics: the reduction in porosity (increase in R) and the accumulation of sludge (increase in C). Therefore, The value is far more sensitive to the health status of the filter than to the steady-state pressure difference alone.

[0077] Step S4: Call the preset fluid viscosity-temperature compensation model, use the fluid medium temperature data to standardize and correct the pressure decay time constant, and generate a standardized blockage index that is decoupled from viscosity interference.

[0078] The specific method for generating the standardized congestion index in step S4 is as follows:

[0079] Get the current temperature of the fluid medium. ;

[0080] Calculate the current viscosity correction factor based on the Arrhenius viscosity equation. :

[0081] ;

[0082] in, Forward factor, For fluid activation energy, The viscosity is the gas constant; the viscosity correction coefficient is used to... Normalization is performed to calculate the standardized congestion index. : ;

[0083] in, This serves as the reference time constant for the clean filter. This is the normalization factor for flow conditions.

[0084] Drill pipe rust-preventive oil is a mixture of high-molecular-weight hydrocarbons, and its viscosity is extremely sensitive to temperature changes. In winter, its viscosity can be several times higher than in summer. Without compensation, low temperatures naturally increase fluid flow resistance, resulting in slower pressure decay. (If the value increases), the system is highly susceptible to misinterpreting it as a filter blockage. This invention introduces the Arrhenius viscosity equation as an interpreter, incorporating the real-time collected temperature data. Substitute into the model to calculate the current fluid flow factor. .

[0085] formula The physical essence is to forcibly convert test data under current operating conditions to equivalent values ​​at standard temperatures. This ensures the standardized congestion index. It depends only on the physical state of the filter itself, and has nothing to do with the temperature of the weather.

[0086] Step S5: Input the standardized clogging index into the life prediction model, calculate the remaining effective operating cycle of the filter, and output a graded maintenance instruction or a shutdown protection instruction based on the prediction result;

[0087] Specifically, the lifespan prediction model employs a dynamic extrapolation algorithm based on historical decay rates. Let the standardized clogging index threshold for a filter reaching complete failure (i.e., recommended replacement) be... (For example, set to 0.9). Record the current time. The standardized congestion index is and the last monitoring time Standardized congestion index Calculate the current congestion growth rate. :

[0088] ;

[0089] The remaining effective operating cycle of the filter. The calculation formula is:

[0090] ;

[0091] in, Safety factor for work intensity (value) This formula is used to cope with potential sudden high-load conditions in the future. Through this formula, the system can not only determine the current status but also provide a quantitative countdown, such as an estimated remaining operation time of 4.5 hours, facilitating on-site personnel in scheduling maintenance. Subsequently, based on... Values ​​and calculations Implement hierarchical decision-making:

[0092] For example, in this embodiment of the invention, the following settings are provided: Brand new Complete blockage.

[0093] when If the blockage is identified as minor, the system will display "It is recommended to arrange cleaning within 24 hours" on the human-machine interface and mark the batch of drill pipe currently being processed.

[0094] when or If the system detects a severe blockage, it will issue an audible and visual alarm and interlock to prevent the next drill rod from entering the spraying station, forcing the replacement of the filter.

[0095] Example 2

[0096] The method also includes a filter damage identification step based on abnormal attenuation characteristics:

[0097] After calculating the real-time pressure decay time constant Then, compare it with the preset integrity threshold. Perform a comparison;

[0098] like And continue In each operating cycle, if it is determined that the filter screen has physical perforation or bypass leakage, a highest priority emergency stop command is generated, which has a higher priority than the graded maintenance command.

[0099] Wherein, the integrity threshold Less than the reference time constant of the clean filter .

[0100] In this embodiment of the invention, when the filter screen is punctured by a sharp drill rod or the sealing ring fails, the fluid short-circuits, and the resistance drops sharply. At this time, the pressure will drop precipitously the moment the pump stops.

[0101] The system calculates in step S3 Then, add a logical judgment:

[0102] like (in The preset integrity threshold is usually set to the cleanness of the filter. (50%), and this phenomenon occurs continuously. One work cycle.

[0103] At this point, the system no longer calculates the lifespan but directly identifies it as "filter damage / leakage" and triggers the highest priority emergency stop command. This effectively prevents metal shavings from entering the subsequent precision nozzle system due to filter damage, avoiding more expensive equipment damage and effectively preventing the existing steady-state differential pressure method from only reporting a low differential pressure and mistakenly assuming the filter is clean.

[0104] Example 3

[0105] To enable the system to adapt to the differences in the properties of rust-preventive oils from different manufacturers and batches, this embodiment introduces a closed-loop feedback mechanism. Following step S5, the system further includes a step of using maintenance feedback data to update the model in reverse.

[0106] After performing filter replacement and maintenance, obtain the actual weight of the deposits on the removed filter. ;

[0107] Retrieve the standardized congestion index from the last calculation before the replacement. Calculate and predict the amount of dirt buildup. ;

[0108] Calculate prediction bias ;

[0109] If there is a deviation If the parameters exceed the preset tolerance range, the weight parameters in the lifetime prediction model are corrected using the gradient descent method. The updated formula is:

[0110] ;

[0111] in, The learning rate is used to achieve adaptive iteration of the properties of different batches of rust-preventive oil;

[0112] Through this embodiment, the system's prediction accuracy will continuously evolve as it is used over time and data is accumulated.

[0113] Example 4

[0114] Please see Figure 2 The present invention also provides a filter life prediction system for drill pipe rust prevention equipment, comprising a multi-source sensing module configured to acquire high-frequency pressure signals, fluid temperature signals, and equipment start-up and shutdown control signals at the filter inlet and outlet in real time; a transient feature analysis module connected to the multi-source sensing module, configured to intercept pressure decay waveforms and calculate pressure decay time constants in response to shutdown signals; a viscosity-temperature compensation calculation unit connected to the transient feature analysis module, configured to calculate viscosity correction coefficients in real time based on fluid temperature and generate a standardized clogging index to eliminate viscosity interference; a life prediction and decision module configured to assess the remaining life of the filter based on the standardized clogging index and identify abnormal filter damage; and a self-learning optimization module configured to reverse-correct the internal parameters of the calculation model based on actual maintenance data.

[0115] The multi-source sensing module includes a high-frequency pressure transmitter installed on the filter inlet side, and the sampling frequency of the high-frequency pressure transmitter is... Furthermore, the multi-source sensing module is equipped with an FPGA preprocessing unit, which is used to filter out the periodic pulsation noise during pump operation before data is uploaded.

[0116] The system is also equipped with a drill pipe operation fingerprint database, and the life prediction and decision module is also configured to identify the specifications of the drill pipe currently being processed, and dynamically adjust the consumption weight of the life prediction model according to the iron filings generation rate of different drill pipe specifications.

[0117] Example 5

[0118] The present invention also provides an electronic device, comprising:

[0119] Memory and processor;

[0120] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the filter life prediction method for drill pipe rust prevention equipment as proposed in the above embodiments.

[0121] Example 6

[0122] The present invention also provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the method for predicting the lifespan of a filter screen for a drill pipe rust prevention device as described in the above embodiments.

[0123] The storage medium proposed in this embodiment and the filter life prediction method for drill pipe rust prevention equipment proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0124] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0125] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0126] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0127] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for predicting the lifespan of a filter screen in a drill pipe rust prevention device, characterized in that, The method includes the following steps: Step S1: Monitor the operation control commands of the drill pipe rust prevention equipment in real time, and simultaneously collect fluid pressure data and fluid medium temperature data at the filter inlet side; Step S2: In response to the pump stop command of the rust prevention equipment, trigger the transient feature capture mode, and capture the pressure relaxation sequence of the filter screen inlet side pressure from the working steady state pressure to the ambient back pressure at a preset high frequency sampling rate; Step S3: Based on the principle of fluid impedance, feature extraction is performed on the pressure relaxation sequence, and the pressure decay time constant characterizing the damping characteristics of the filter pore structure is calculated; Step S4: Call the preset fluid viscosity-temperature compensation model, use the fluid medium temperature data to standardize and correct the pressure decay time constant, and generate a standardized clogging index that is decoupled from viscosity interference; Step S5: Input the standardized clogging index into the life prediction model, calculate the remaining effective operating cycle of the filter, and output a graded maintenance instruction or a shutdown protection instruction based on the prediction result; The specific method for calculating the pressure decay time constant in step S3 is as follows: Construct an equivalent fluid RC attenuation model for the filter system, with the pressure attenuation equation as follows: ； in, for Instantaneous pressure at any moment The initial pressure at the moment the pump stops. The pressure decay time constant to be determined; Performing a logarithmic transformation on the pressure relaxation sequence yields a linear sequence. ; The linear sequence was fitted using the least squares method to extract the slope. The real-time pressure decay time constant was calculated. Wherein, the pressure decay time constant It is positively correlated with the thickness of the surface deposit layer of the filter and the degree of pore blockage.

2. The method for predicting the lifespan of a filter screen in a drill pipe rust prevention device according to claim 1, characterized in that: The specific method for triggering the transient feature capture mode in step S2 is as follows: Set a sliding time window to monitor the falling edge of the main pump control signal in real time; When a falling edge trigger is detected, the delay is... To avoid electromagnetic interference, high-frequency acquisition was then initiated and continuous recording was performed. Duration of stress data ; The raw pressure data collected Kalman filtering is performed to remove high-frequency noise, resulting in a smoothed pressure relaxation sequence.

3. The method for predicting the lifespan of a filter screen in a drill pipe rust prevention device according to claim 1, characterized in that: The specific method for generating the standardized congestion index in step S4 is as follows: Get the current temperature of the fluid medium. ; Calculate the current viscosity correction factor based on the Arrhenius viscosity equation. : ； in, Forward factor, For fluid activation energy, The viscosity is the gas constant; the viscosity correction coefficient is used to... Normalization is performed to calculate the standardized congestion index. : ; in, This serves as the reference time constant for the clean filter. This is the normalization factor for flow conditions.

4. The method for predicting the lifespan of a filter screen for rust prevention equipment of drill pipes according to claim 3, characterized in that: The method also includes a filter damage identification step based on abnormal attenuation characteristics: After calculating the real-time pressure decay time constant Then, compare it with the preset integrity threshold. Perform a comparison; like And continue In each operating cycle, if it is determined that the filter screen has physical perforation or bypass leakage, a highest priority emergency stop command is generated, which has a higher priority than the graded maintenance command. Wherein, the integrity threshold Less than the reference time constant of the clean filter .

5. The method for predicting the lifespan of a filter screen for rust prevention equipment of drill pipes according to claim 1, characterized in that: Following step S5, the method further includes a step of updating the model in reverse using maintenance feedback data: After performing filter replacement and maintenance, obtain the actual weight of the deposits on the removed filter. ; Retrieve the standardized congestion index from the last calculation before the replacement. Calculate and predict the amount of dirt buildup. ; Calculate prediction bias ; If there is a deviation If the weight parameters in the lifetime prediction model exceed the preset tolerance range, the gradient descent method is used to correct them. The updated formula is: ； in, The learning rate is used to achieve adaptive iteration of the properties of different batches of rust-preventive oil.

6. A filter screen life prediction system for drill pipe rust prevention equipment, characterized in that: For performing the method as described in any one of claims 1 to 5, the system includes a multi-source sensing module configured to acquire high-frequency pressure signals, fluid temperature signals, and equipment start-stop control signals in real time at the filter inlet and outlet. The transient feature analysis module, connected to the multi-source sensing module, is configured to intercept the pressure decay waveform and calculate the pressure decay time constant in response to a shutdown signal; the viscosity-temperature compensation calculation unit, connected to the transient feature analysis module, is configured to calculate the viscosity correction coefficient in real time based on the fluid temperature and generate a standardized clogging index that eliminates viscosity interference; the life prediction and decision module is configured to assess the remaining life of the filter screen based on the standardized clogging index and identify abnormal filter screen damage. The self-learning optimization module is configured to correct the internal parameters of the calculation model in reverse based on actual maintenance data.

7. The filter life prediction system for drill pipe rust prevention equipment according to claim 6, characterized in that: The multi-source sensing module includes a high-frequency pressure transmitter installed on the filter inlet side, and the sampling frequency of the high-frequency pressure transmitter is... Furthermore, the multi-source sensing module is equipped with an FPGA preprocessing unit, which is used to filter out the periodic pulsation noise during pump operation before data is uploaded. The system is also equipped with a drill pipe operation fingerprint database, and the life prediction and decision module is also configured to identify the specifications of the drill pipe currently being processed, and dynamically adjust the consumption weight of the life prediction model according to the iron filings generation rate of different drill pipe specifications.

8. An electronic device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the method as described in any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5.

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