A multi-frequency-based intelligent mud control method and system

Abstract

This invention provides a multi-frequency intelligent mud solids control method and system, including simultaneously acquiring multi-channel vibration signals at the mud inlet and slag outlet of the solids control equipment, correcting the coherence threshold between channels, and extracting vibration signal segments; calculating the power spectral density gradient norm and normalized frequency band energy entropy, and dividing the corresponding low, medium, and high frequency characteristic bands for equipment resonance, fluid sloshing, and particle impact; constructing a six-dimensional state vector composed of the above two parameters for each frequency band, and calculating the Mahalanobis distance between the vector and the standard state centroid of screen blockage and mud loss conditions; when the distance from the standard centroid of screen blockage is less than a first threshold, setting the operating frequency of the first vibration unit to be proportional to the gradient norm of the high frequency characteristic band, and determining the excitation phase of the second vibration unit; when the distance from the standard centroid of mud loss is less than a second threshold, setting the frequency reduction of the first vibration unit to be proportional to the energy entropy of the medium frequency characteristic band, and correcting the differential speed adjustment gain of the downstream centrifuge.

Description

Technical Field

[0001] This application belongs to the field of solids control, and in particular relates to a multi-frequency intelligent mud solids control method and system. Background Technology

[0002] In mud solids control systems, the function of a vibrating screen is to separate solid particles from the mud. However, due to variations in mud properties, discharge rates, and geological conditions, vibrating screens frequently experience abnormal conditions such as screen blockage and mud loss. Monitoring these abnormal conditions relies on the experience of on-site operators or on physical quantity monitoring equipment such as level sensors, which is insufficient to meet the demands of modern drilling operations for solids control system management. Different abnormal conditions exhibit different spectral characteristics in the equipment's vibration response: screen blockage manifests as high-frequency impacts and friction between fine particles and the screen; mud loss is related to the fluid sloshing pattern of mud on the screen surface; and the structural resonance of the equipment itself exists within a relatively fixed low-frequency range. Using a single-frequency excitation mode or feedback adjustment based on sensor signals may exacerbate equipment resonance or mud sloshing due to improper adjustment. Therefore, a new mud solids control method is urgently needed that can analyze equipment vibration information, identify different operating conditions, and thereby achieve multi-dimensional, differentiated, and collaborative control. Summary of the Invention

[0003] This invention proposes a multi-frequency-based intelligent mud solids control method to address the problems of existing methods failing to deeply analyze equipment vibration information, struggling to identify different working conditions, and failing to achieve multi-dimensional, differentiated, and collaborative control of mud solids. The method includes: Multi-channel vibration signals are simultaneously collected at the slurry inlet and slag outlet of the solids control equipment. Based on the normalized frequency band energy entropy of the mid-frequency characteristic frequency band determined in the previous analysis cycle, the coherence threshold of the signals between channels is corrected to extract the vibration signal segment. The vibration signal segment is subjected to short-time Fourier transform to calculate the power spectral density gradient norm and normalized frequency band energy entropy. The low, medium and high frequency characteristic bands corresponding to equipment resonance, fluid sloshing and particle impact are defined with the local maxima of the gradient norm as the center. A six-dimensional state vector is constructed, consisting of the power spectral density gradient norm of each characteristic frequency band and the normalized frequency band energy entropy. The Mahalanobis distance between the vector and the standard state centroid of the preset screen blockage and mud loss conditions is calculated. When the Mahalanobis distance with the standard centroid of screen blockage is less than a first threshold, the operating frequency of the first vibration unit is set to be proportional to the power spectral density gradient norm of the high-frequency characteristic frequency band, and the excitation phase of the second vibration unit is determined by looking up a table based on the normalized frequency band energy entropy of the frequency band. When the Mahalanobis distance from the standard centroid of the mud loss is less than the second threshold, the downward adjustment of the operating frequency of the first vibration unit is set to be proportional to the normalized frequency band energy entropy of the mid-frequency characteristic band, and the differential speed adjustment gain of the downstream centrifuge is corrected by using the power spectral density gradient norm of the band.

[0004] Furthermore, the present invention also relates to a multi-frequency-based intelligent mud solids control system, comprising the following modules: The interception module is used to simultaneously collect multi-channel vibration signals at the slurry inlet and slag outlet of the solids control equipment, and to correct the coherence threshold of the signals between channels and intercept vibration signal segments based on the normalized frequency band energy entropy of the mid-frequency characteristic frequency band determined in the previous analysis cycle. The defining module is used to perform short-time Fourier transform on the vibration signal segment, calculate the power spectral density gradient norm and normalized frequency band energy entropy, and define the low, medium and high frequency characteristic frequency bands corresponding to equipment resonance, fluid sloshing and particle impact, respectively, with the local maxima of the gradient norm as the center. The determination module is used to construct a six-dimensional state vector composed of the power spectral density gradient norm of each characteristic frequency band and the normalized frequency band energy entropy, and to calculate the Mahalanobis distance between the vector and the standard state centroid of the preset screen blockage and mud loss conditions; when the Mahalanobis distance with the standard centroid of screen blockage is less than a first threshold, the operating frequency of the first vibration unit is set to be proportional to the power spectral density gradient norm of the high-frequency characteristic frequency band, and the excitation phase of the second vibration unit is determined by looking up a table according to the normalized frequency band energy entropy of the frequency band. The correction module is used to set the downward adjustment of the operating frequency of the first vibration unit to be proportional to the normalized frequency band energy entropy of the mid-frequency characteristic band when the Mahalanobis distance from the standard centroid of the mud loss is less than the second threshold, and to correct the differential speed adjustment gain of the downstream centrifuge using the power spectral density gradient norm of the frequency band.

[0005] This invention, through in-depth spectral analysis of vibration signals at the equipment inlet and outlet, identifies low, medium, and high frequency characteristic bands representing equipment resonance, fluid sloshing, and particle impact, respectively. Utilizing a multidimensional state vector composed of the power spectral density gradient norm and frequency band energy entropy, along with Mahalanobis distance, it identifies screen blockage and slurry loss conditions. A mapping relationship is established between the condition identification results and a multivariate control strategy: for screen blockage, the excitation frequency and phase of the dual vibration units are synergistically adjusted to clear the screen with high-frequency vibration; for slurry loss, while reducing the dominant vibration frequency to stabilize the slurry flow state, the operating parameters of downstream equipment are corrected. This invention, based on mechanism-driven differentiated and synergistic control, improves the handling effect of abnormal conditions and ensures the stability and economy of the solids control system. Attached Figure Description

[0006] Figure 1A flowchart of the first embodiment; Figure 2 This is a schematic diagram illustrating the definition of the characteristic frequency band; Figure 3 A schematic diagram of the control strategy under screen clogging conditions; Figure 4 This is a schematic diagram of the control strategy under mud loss conditions. Detailed Implementation

[0007] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0008] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0009] In the first embodiment, the present invention proposes a multi-frequency-based intelligent mud solids control method, such as... Figure 1 ,include: S1, Simultaneously collect multi-channel vibration signals at the slurry inlet and slag outlet of the solids control equipment, and correct the coherence threshold of the inter-channel signals to extract vibration signal segments based on the normalized frequency band energy entropy of the mid-frequency characteristic frequency band determined in the previous analysis cycle. Piezoelectric accelerometers are installed on the inlet and outlet boxes of the vibrating screen, respectively. Data is acquired using a multi-channel data acquisition card at the same sampling frequency and start / stop clock to ensure signal synchronization. An initial coherence threshold is set, for example, 0.7. After each analysis cycle, the mid-frequency characteristic band of that cycle is retained, reflecting the intensity of mud sloshing. The normalized frequency band energy entropy of this band is calculated. If the energy entropy value of the previous cycle is high, for example, close to 1, it indicates severe mud sloshing and increased signal randomness. In this cycle, the coherence threshold is appropriately lowered, for example, to 0.6, to retain more potentially correlated signals. If the energy entropy value is low, for example, close to 0, it indicates stable fluid movement and good signal correlation. In this cycle, the coherence threshold is raised, for example, to 0.8, to filter out noise. The coherence coefficient of the signals from the inlet and outlet is calculated segment by segment, and only signal segments with a coherence coefficient greater than the corrected threshold are retained as vibration signals.

[0010] In an optional embodiment, the step of correcting the coherence threshold of the inter-channel signal to extract the vibration signal segment based on the normalized frequency band energy entropy of the mid-frequency characteristic frequency band determined in the previous analysis cycle includes: The normalized frequency band energy entropy of the mid-frequency characteristic band determined in the above analysis period As input, calculate the coherence threshold Th for the current period using the following formula: ; Calculate the coherence coefficient of the signals from the slurry inlet and slag outlet channels, and retain only the time periods with a coherence coefficient greater than Th as vibration signal segments.

[0011] Specifically, obtain the normalized frequency band energy entropy of the mid-frequency characteristic band calculated in the previous analysis period, such as the first 1 second. Assuming the previous cycle The value is 0.7, reflecting the degree of disorder in the slurry sloshing at the previous moment. The controller substitutes this value into a preset formula to calculate the coherence threshold used for signal filtering in the current cycle. Based on example data, the calculated threshold Th is 0.66. This threshold will be adjusted according to changes in operating conditions; when the fluid sloshing energy in the previous cycle is dispersed... When the threshold is larger, it decreases, allowing more potential signals to pass through.

[0012] Time-series data of vibration sensor signals from both the slurry inlet and slag outlet were simultaneously acquired. A sliding time window method was used, for example, a window function with 1024 data points and a 50% overlap rate, to calculate the squared amplitude coherence coefficient between the two signals segment by segment, resulting in a time-varying coherence coefficient value sequence. This coherence coefficient sequence was compared with a calculated threshold of 0.66. Time segments with coherence coefficient values ​​greater than 0.66 were identified as vibration signal segments and retained, while time segments below the threshold were considered noise or irrelevant interference and discarded.

[0013] S2, Perform a short-time Fourier transform on the vibration signal segment to calculate the power spectral density gradient norm and the normalized frequency band energy entropy, and define the low, medium and high frequency characteristic bands corresponding to equipment resonance, fluid sloshing and particle impact respectively, centered on the local maxima of the gradient norm. A Hanning window is applied to the captured vibration signal segments, and overlapping segments are performed. A Fast Fourier Transform is then performed on each segment to obtain the time spectrum. The time spectrum is averaged along the time axis to obtain the power spectral density curve. The first difference of the power spectral density curve is calculated along the frequency axis, and its absolute value is the gradient norm. Local maxima of the gradient norm curve are searched in the full frequency domain. Based on the physical characteristics of the vibrating screen, the lowest frequency maximum usually corresponds to the inherent structural resonance of the equipment, the intermediate frequency maximum corresponds to the sloshing of the mud fluid, and the highest frequency maximum corresponds to the impact of drill cuttings on the screen. Assuming that the three identified center frequencies are 15Hz, 40Hz, and 100Hz, three characteristic frequency bands are defined. For example, based on the center frequencies, a fixed bandwidth is set to form a low-frequency band of 5Hz to 25Hz, a mid-frequency band of 30Hz to 50Hz, and a high-frequency band of 90Hz to 110Hz.

[0014] In an optional embodiment, defining the low, medium, and high frequency characteristic bands corresponding to device resonance, fluid sloshing, and particle impact, respectively, centered on the local maxima of the gradient norm, includes: Within the 0-500Hz range, three local maxima of the power spectral density gradient norm were identified, and the frequencies corresponding to these points were denoted as follows: , , ; Centered on the three frequencies, a frequency band with a width of 20% of the center frequency is defined as the low, mid, and high frequency characteristic bands, respectively; that is, the low frequency band is... The mid-frequency band is High frequency band is .

[0015] A Fourier transform is performed on the extracted vibration signal segment to calculate the power spectral density curve, which shows the distribution of signal power at different frequencies. The gradient of this power spectral density curve with respect to frequency is calculated, and the norm of the gradient is determined to obtain a power spectral density gradient norm curve. The peak point of this new curve corresponds to the frequency point on the original power spectral density curve where the energy change is most dramatic; this point is a key frequency representing the core physical process. Local maxima of this gradient norm curve are searched within the frequency range of 0 to 500 Hz.

[0016] Assume that after the search, three local maxima are identified, corresponding to frequencies of 60Hz, 180Hz, and 350Hz. These three frequencies are designated as the low-frequency center frequencies. Intermediate frequency center frequency and high frequency center frequency Based on the center frequencies of the three identified frequencies, characteristic frequency bands are defined. The low-frequency characteristic band is defined as the range of 54Hz to 66Hz. Similarly, the mid-frequency characteristic band is 162Hz to 198Hz, and the high-frequency characteristic band is 315Hz to 385Hz. This definition method allows the frequency band division to adapt to characteristic frequency drift caused by changes in material properties or equipment conditions, such as... Figure 2 .

[0017] In an optional embodiment, the truncated vibration signal segment is taken, and after applying a Hanning window, it is segmented according to a window length of 256 points and an overlap rate of 50%. A fast Fourier transform is performed on each segment to obtain the time spectrum. The time spectrum is averaged along the time axis to obtain the power spectral density (PSD) curve for the 0-500Hz frequency band. The PSD difference between adjacent frequency points is calculated along the frequency axis and the absolute value is taken to obtain the power spectral density gradient norm curve. The center frequency is determined by searching for the local maxima of this curve. , , The low / mid / high frequency bands are defined as [54Hz, 66Hz], [162Hz, 198Hz], and [315Hz, 385Hz], respectively. The frequency band energy is then calculated by integrating the PSD curves within each band. The total energy of the entire frequency band is the sum of the energies of each band. The ratio of the energy of each band to the total energy is used as a probability distribution and substituted into the Shannon entropy formula to calculate the original frequency band energy entropy. The normalized frequency band energy entropy is then obtained by normalizing the original entropy by ln3, ultimately yielding the low-frequency energy entropy. , intermediate frequency ,high frequency .

[0018] S3, construct a six-dimensional state vector composed of the power spectral density gradient norm of each characteristic frequency band and the normalized frequency band energy entropy, and calculate the Mahalanobis distance between the vector and the standard state centroid of the preset screen blockage and mud loss conditions; when the Mahalanobis distance with the standard centroid of screen blockage is less than a first threshold, set the operating frequency of the first vibration unit to be proportional to the power spectral density gradient norm of the high-frequency characteristic frequency band, and determine the excitation phase of the second vibration unit by looking up the table according to the normalized frequency band energy entropy of the frequency band; Normalized frequency band energy entropy was calculated for the low, medium, and high characteristic frequency bands, and the power spectral density gradient norm values ​​at the center frequencies of the three frequency bands were extracted. These six values ​​were combined into a six-dimensional state vector, which took the form of low-frequency gradient norm, low-frequency energy entropy, medium-frequency gradient norm, medium-frequency energy entropy, high-frequency gradient norm, and high-frequency energy entropy. A large amount of data was collected in advance under typical screen clogging conditions through experiments, and hundreds of six-dimensional state vectors were calculated. The mean vector of these vectors was taken as the standard centroid of screen clogging, and the covariance matrix was calculated. Similarly, under mud loss conditions, the standard centroid and covariance matrix of mud loss were obtained. In real-time monitoring, the currently measured six-dimensional state vectors were substituted into the Mahalanobis distance calculation formula to calculate the distances between the vectors and the standard centroids of screen clogging and mud loss.

[0019] The calculated Mahalanobis distance to the standard centroid of the screen blockage is compared with a preset empirical value, such as 3.5. If it is less than this value, screen blockage is determined to have occurred. At this time, the control system adjusts the first vibration unit located at the slag discharge end. The operating frequency is calculated using the formula F1 = Fbase + K1 × Ghigh frequency, where Fbase is the base frequency, such as 45Hz, K1 is the proportional gain coefficient, and Ghigh frequency is the power spectral density gradient norm of the current high-frequency characteristic band. Simultaneously, based on the normalized frequency band energy entropy Ehigh frequency of the current high-frequency characteristic band, a preset phase control table is consulted. This table maps the range of energy entropy to the phase difference. For example, when the energy entropy is between 0 and 0.3, it indicates that the blockage particles are concentrated, so the phase difference is set to 45° to enhance the conveying capacity; when the energy entropy is between 0.7 and 1.0, it indicates that the blockage is dispersed and random, so the phase difference is set to 0° to strengthen vertical vibration. The controller sets the excitation phase of the second vibration unit located at the slurry inlet end accordingly, such as... Figure 3 .

[0020] To create a state vector, in an optional embodiment, constructing a six-dimensional state vector consisting of the power spectral density gradient norm of each characteristic frequency band and the normalized band energy entropy includes: The power spectral density gradient norms of the low, medium, and high frequency characteristic bands are denoted as follows: The normalized frequency band energy entropy is denoted as follows: ;according to The order of these states forms the six-dimensional state vector.

[0021] Specifically, extract the three identified center frequencies. , , The corresponding power spectral density gradient norm value. The magnitude of this value reflects the intensity of the three physical phenomena: device resonance, fluid sloshing, and particle impaction. In this example, let's assume that the three values ​​are respectively... , and .

[0022] Calculate the total energy within the low, mid, and high characteristic frequency bands, i.e., the integral of the power spectral density curve within the corresponding frequency band. Assume the calculated energies of the three frequency bands are as follows: , , The three energy values ​​are normalized so that their sum is 1, resulting in a probability distribution, i.e. , , Using the method for calculating information entropy, the energy entropy of each frequency band is calculated separately, yielding... Assume the calculated value is , , The six calculated features are combined into a six-dimensional vector in a predetermined order. In this example, the state vector for the current period is... This indicates the characteristics of the screening process.

[0023] In an optional embodiment, a preset offline calibrated standard state centroid of screen clogging is established. The corresponding covariance matrix It is a 6×6 square matrix: Real-time acquisition of six-dimensional state vectors First calculate the difference vector Then, the inverse of the covariance matrix is ​​solved, and the Mahalanobis distance formula is used to calculate the Mahalanobis distance D=2.8. This value is less than the first threshold of 3.5, so it is determined to be a screen blockage condition.

[0024] To optimize the impact effect between the particles and the screen, in an optional embodiment, the operating frequency of the first vibration unit is adjusted according to the power spectral density gradient norm of the high-frequency characteristic band, including: The target operating frequency of the first vibration unit Calculate using the following formula: ,in, This is the dimensionless value of the power spectral density gradient norm of the high-frequency characteristic band. As the reference operating frequency, This is the frequency adjustment coefficient.

[0025] The original The values ​​are dimensionless, making them suitable for control formulas. Dimensionlessness typically employs the max-min normalization method, determined based on historical operating data. The range of variation, for example, from 1.0 to 9.0. If the currently calculated... The value is 3.8, then the dimensionless value The calculated value is 0.35.

[0026] Dimensionless After setting the reference frequency, the controller calculates the target operating frequency of the first vibration unit according to the preset control law. In the formula, the reference operating frequency... Setting it to 50Hz is a standard initial setting. Frequency adjustment coefficient. Set to 5.0Hz, this determines the sensitivity of the controller response. Substituting the example data into the formula, we obtain the target frequency. Hz. The control system sends a command to the frequency converter driving the first vibration unit to adjust the output frequency to 51.75Hz, thereby achieving closed-loop optimization control of the screening efficiency.

[0027] In an optional embodiment, determining the excitation phase of the second vibration unit by looking up a table based on the normalized frequency band energy entropy of the frequency band includes: Based on the normalized frequency band energy entropy of the high-frequency characteristic frequency band The value of is obtained by looking up the corresponding excitation phase in the following preset table: when At that time, the phase is set to 15°; when At that time, the phase is set to 30°; when At that time, the phase was set to 45°.

[0028] Specifically, the movement trajectory and conveying speed of the material on the screen surface are controlled by adjusting the excitation phase of the second vibration unit, based on the normalized frequency band energy entropy of the high-frequency characteristic band. . The entropy value reflects the concentration of particle impact energy within the frequency band. A lower entropy value indicates concentrated impact energy and good screening performance, while a higher entropy value indicates dispersed energy, which may require adjustments to the material's trajectory to improve screening.

[0029] Extract from the current state vector The value. Assuming it is obtained from the state vector. The value is 0.45. The controller compares this value with a preset lookup table. The lookup table is structured as a piecewise function, which... The entire value range, from 0 to 1, is divided into three intervals, each corresponding to a preset excitation phase value. In this example, the value 0.45 falls within... Within the specified interval, according to the lookup table rules, the corresponding excitation phase is 30°. Therefore, the control system sends a command to the phase adjuster of the second vibration unit to set the excitation phase to 30°, thereby optimizing the transport and distribution of material on the screen.

[0030] S4, when the Mahalanobis distance from the standard centroid of the mud loss is less than the second threshold, the downward adjustment of the operating frequency of the first vibration unit is set to be proportional to the normalized frequency band energy entropy of the mid-frequency characteristic band, and the differential speed adjustment gain of the downstream centrifuge is corrected by using the power spectral density gradient norm of the band.

[0031] The calculated Mahalanobis distance from the standard centroid of mud loss is compared with a preset empirical value, such as 4.0. If it is less than this value, mud loss is determined to have occurred. At this time, the control system calculates the frequency reduction ΔF of the first vibration unit as K2 × Eintermediate frequency, where K2 is the proportional coefficient and Eintermediate frequency is the normalized frequency band energy entropy of the current intermediate frequency characteristic band. The new operating frequency is the current frequency minus the reduction ΔF. Simultaneously, a correction signal is fed forward to the downstream horizontal screw centrifuge controller. This correction signal is used to adjust the proportional gain in the PID controller for the speed difference between the centrifuge screw and the drum. A larger intermediate frequency gradient norm indicates more violent fluid sloshing on the screen surface, a higher risk of mud loss, and requires the centrifuge to quickly discharge the solid phase; therefore, the control gain is increased accordingly. Figure 4 .

[0032] In an optional embodiment, the step of setting the reduction in the operating frequency of the first vibration unit when the Mahalanobis distance from the standard centroid of mud loss is less than a second threshold to be proportional to the normalized frequency band energy entropy of the mid-frequency characteristic band includes: Under the condition that the Mahalanobis distance from the standard centroid of mud loss is less than the second threshold, the frequency down-adjustment is calculated according to the following formula. : ,in The normalized frequency band energy entropy of the mid-frequency characteristic band is... This is the frequency down-adjustment ratio; Subtract the current operating frequency of the first vibration unit A new operating frequency was obtained.

[0033] The current six-dimensional state vector is compared with the standard centroid of mud loss obtained from a large amount of experimental data on mud loss conditions, and the Mahalanobis distance between the two is calculated. Mahalanobis distance is a distance metric that takes into account the correlation between features. When the calculated Mahalanobis distance is less than a preset second threshold, such as 3, mud loss is determined, and a frequency reduction procedure is triggered.

[0034] Once the risk of mud loss is confirmed, the energy entropy of the normalized frequency band in the mid-frequency characteristic band is used. Obtain indicators representing the severity of the runaway. An increase in is usually related to the severity of slurry sloshing. Assume the current state vector contains... The value is 0.55, which is the frequency downsampling coefficient. The frequency is set to 5Hz. The controller calculates the amount of frequency reduction needed based on a formula. Hz. If the current stable operating frequency of the first vibration unit is 51.75Hz, the new target operating frequency will be set to 49.0Hz. The controller then instructs the frequency converter to reduce the frequency to 49.0Hz and maintain it for a short period of time. This low-frequency vibration helps to slow down the conveying speed of the slurry on the screen surface and reduce the loss of unscreened slurry.

[0035] In an optional embodiment, the step of correcting the differential speed regulation gain of the downstream centrifuge using the power spectral density gradient norm of the frequency band includes: The dimensionless value of the power spectral density gradient norm of the mid-frequency characteristic band. Calculate the corrected gain using the following formula. : ,in, This serves as the reference gain for differential speed regulation of downstream centrifuges. To correct the coefficient, update the proportional gain parameter in the differential speed PID controller of the centrifuge to... .

[0036] Specifically, the power spectral density gradient norm of the mid-frequency characteristic band This indicates the intensity of fluid sloshing on the screen, and is related to the solid content or viscosity of the discharged slurry. Assuming that historical data analysis is used to determine... The normal fluctuation range is 2.0 to 7.0. If the current state vector contains... The value is 4.2, then the dimensionless value is... It is 0.44.

[0037] Reference gain Set to 1.2, correction factor Set the value to 0.1. Substitute the data into the formula to calculate the new gain value. .when As the gain increases, the discharged slurry may thicken, requiring the centrifuge to respond quickly to maintain the dewatering effect; therefore, the gain is increased. The control system sends the calculated new gain value of 1.2528 to the centrifuge's programmable logic controller (PLC) via the industrial network, and uses the PLC to update the proportional term parameter in the differential proportional-integral-derivative (DI-DE) controller, thereby achieving predictive adjustment of downstream equipment.

[0038] In the second embodiment, the present invention also proposes a multi-frequency-based intelligent mud solids control system, comprising the following modules: The interception module is used to simultaneously collect multi-channel vibration signals at the slurry inlet and slag outlet of the solids control equipment, and to correct the coherence threshold of the signals between channels and intercept vibration signal segments based on the normalized frequency band energy entropy of the mid-frequency characteristic frequency band determined in the previous analysis cycle. The defining module is used to perform short-time Fourier transform on the vibration signal segment, calculate the power spectral density gradient norm and normalized frequency band energy entropy, and define the low, medium and high frequency characteristic frequency bands corresponding to equipment resonance, fluid sloshing and particle impact, respectively, with the local maxima of the gradient norm as the center. The determination module is used to construct a six-dimensional state vector composed of the power spectral density gradient norm of each characteristic frequency band and the normalized frequency band energy entropy, and to calculate the Mahalanobis distance between the vector and the standard state centroid of the preset screen blockage and mud loss conditions; when the Mahalanobis distance with the standard centroid of screen blockage is less than a first threshold, the operating frequency of the first vibration unit is set to be proportional to the power spectral density gradient norm of the high-frequency characteristic frequency band, and the excitation phase of the second vibration unit is determined by looking up a table according to the normalized frequency band energy entropy of the frequency band. The correction module is used to set the downward adjustment of the operating frequency of the first vibration unit to be proportional to the normalized frequency band energy entropy of the mid-frequency characteristic band when the Mahalanobis distance from the standard centroid of the mud loss is less than the second threshold, and to correct the differential speed adjustment gain of the downstream centrifuge using the power spectral density gradient norm of the frequency band.

[0039] In an optional embodiment, the step of correcting the coherence threshold of the inter-channel signal to extract the vibration signal segment based on the normalized frequency band energy entropy of the mid-frequency characteristic frequency band determined in the previous analysis cycle includes: The normalized frequency band energy entropy of the mid-frequency characteristic band determined in the above analysis period As input, calculate the coherence threshold Th for the current period using the following formula: ; Calculate the coherence coefficient of the signals from the slurry inlet and slag outlet channels, and retain only the time periods with a coherence coefficient greater than Th as vibration signal segments.

[0040] In an optional embodiment, defining the low, medium, and high frequency characteristic bands corresponding to device resonance, fluid sloshing, and particle impact, respectively, centered on the local maxima of the gradient norm, includes: Within the 0-500Hz range, three local maxima of the power spectral density gradient norm were identified, and the frequencies corresponding to these points were denoted as follows: , , ; Centered on the three frequencies, a frequency band with a width of 20% of the center frequency is defined as the low, mid, and high frequency characteristic bands, respectively; that is, the low frequency band is... The mid-frequency band is High frequency band is .

[0041] In an optional embodiment, constructing a six-dimensional state vector consisting of the power spectral density gradient norm of each characteristic frequency band and the normalized frequency band energy entropy includes: The power spectral density gradient norms of the low, medium, and high frequency characteristic bands are denoted as follows: The normalized frequency band energy entropy is denoted as follows: ;according to The order of these states forms the six-dimensional state vector.

[0042] In an optional embodiment, adjusting the operating frequency of the first vibration unit according to the power spectral density gradient norm of the high-frequency characteristic band includes: The target operating frequency of the first vibration unit Calculate using the following formula: ,in, This is the dimensionless value of the power spectral density gradient norm of the high-frequency characteristic band. As the reference operating frequency, This is the frequency adjustment coefficient.

[0043] In an optional embodiment, determining the excitation phase of the second vibration unit by looking up a table based on the normalized frequency band energy entropy of the frequency band includes: Based on the normalized frequency band energy entropy of the high-frequency characteristic frequency band The value of is obtained by looking up the corresponding excitation phase in the following preset table: when At that time, the phase is set to 15°; when At that time, the phase is set to 30°; when At that time, the phase was set to 45°.

[0044] In an optional embodiment, the step of setting the reduction in the operating frequency of the first vibration unit when the Mahalanobis distance from the standard centroid of mud loss is less than a second threshold to be proportional to the normalized frequency band energy entropy of the mid-frequency characteristic band includes: Under the condition that the Mahalanobis distance from the standard centroid of mud loss is less than the second threshold, the frequency down-adjustment is calculated according to the following formula. : ,in The normalized frequency band energy entropy of the mid-frequency characteristic band is... This is the frequency down-adjustment ratio; Subtract the current operating frequency of the first vibration unit A new operating frequency was obtained.

[0045] In an optional embodiment, the step of correcting the differential speed regulation gain of the downstream centrifuge using the power spectral density gradient norm of the frequency band includes: The dimensionless value of the power spectral density gradient norm of the mid-frequency characteristic band. Calculate the corrected gain using the following formula. : ,in, This serves as the reference gain for differential speed regulation of downstream centrifuges. To correct the coefficient, update the proportional gain parameter in the differential speed PID controller of the centrifuge to... .

[0046] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0047] The functional modules shown in the above-described block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0048] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0049] The aspects of this application have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block in 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, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0050] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A multi-frequency-based intelligent mud solids control method, characterized in that, Includes the following steps: Multi-channel vibration signals are simultaneously collected at the slurry inlet and slag outlet of the solids control equipment. Based on the normalized frequency band energy entropy of the mid-frequency characteristic frequency band determined in the previous analysis cycle, the coherence threshold of the signals between channels is corrected to extract the vibration signal segment. The vibration signal segment is subjected to short-time Fourier transform to calculate the power spectral density gradient norm and normalized frequency band energy entropy. The low, medium and high frequency characteristic bands corresponding to equipment resonance, fluid sloshing and particle impact are defined with the local maxima of the gradient norm as the center. A six-dimensional state vector is constructed, consisting of the power spectral density gradient norm of each characteristic frequency band and the normalized frequency band energy entropy. The Mahalanobis distance between the vector and the standard state centroid of the preset screen blockage and mud loss conditions is calculated. When the Mahalanobis distance with the standard centroid of screen blockage is less than a first threshold, the operating frequency of the first vibration unit is adjusted according to the power spectral density gradient norm of the high-frequency characteristic frequency band, and the excitation phase of the second vibration unit is determined by looking up a table according to the normalized frequency band energy entropy of the frequency band. When the Mahalanobis distance from the standard centroid of the mud loss is less than the second threshold, the downward adjustment of the operating frequency of the first vibration unit is set to be proportional to the normalized frequency band energy entropy of the mid-frequency characteristic band, and the differential speed adjustment gain of the downstream centrifuge is corrected by using the power spectral density gradient norm of the band.

2. The method according to claim 1, characterized in that, The process of correcting the coherence threshold of inter-channel signals to extract vibration signal segments based on the normalized frequency band energy entropy of the mid-frequency characteristic frequency band determined in the previous analysis cycle includes: The normalized frequency band energy entropy of the mid-frequency characteristic band determined in the above analysis period As input, calculate the coherence threshold Th for the current period using the following formula: Calculate the coherence coefficient of the signals from the slurry inlet and slag outlet channels, and retain only the time periods with a coherence coefficient greater than Th as vibration signal segments.

3. The method according to claim 2, characterized in that, The definition of low, medium, and high frequency characteristic bands corresponding to equipment resonance, fluid sloshing, and particle impact, centered on the local maxima of the gradient norm, includes: Within the 0-500Hz range, three local maxima of the power spectral density gradient norm were identified, and the frequencies corresponding to these points were denoted as follows: , , ; Centered on the three frequencies, a frequency band with a width of 20% of the center frequency is defined as the low, mid, and high frequency characteristic bands, respectively; that is, the low frequency band is... The mid-frequency band is High frequency band is .

4. The method according to claim 1, characterized in that, The construction of the six-dimensional state vector, consisting of the power spectral density gradient norm of each characteristic frequency band and the normalized frequency band energy entropy, includes: The power spectral density gradient norms of the low, medium, and high frequency characteristic bands are denoted as follows: The normalized frequency band energy entropy is denoted as follows: ;according to The order of these states forms the six-dimensional state vector.

5. The method according to claim 1, characterized in that, Adjusting the operating frequency of the first vibration unit according to the power spectral density gradient norm of the high-frequency characteristic band includes: The target operating frequency of the first vibration unit Calculate using the following formula: ,in, This is the dimensionless value of the power spectral density gradient norm of the high-frequency characteristic band. As the reference operating frequency, This is the frequency adjustment coefficient.

6. The method according to claim 5, characterized in that, The step of determining the excitation phase of the second vibration unit by looking up a table based on the normalized frequency band energy entropy of the frequency band includes: Based on the normalized frequency band energy entropy of the high-frequency characteristic frequency band The value of is obtained by looking up the corresponding excitation phase in the following preset table: when At that time, the phase is set to 15°; when At that time, the phase is set to 30°; when At that time, the phase was set to 45°.

7. The method according to claim 1, characterized in that, When the Mahalanobis distance from the standard centroid of mud loss is less than the second threshold, the amount by which the operating frequency of the first vibration unit is reduced is set to be proportional to the normalized frequency band energy entropy of the mid-frequency characteristic band, including: Under the condition that the Mahalanobis distance from the standard centroid of mud loss is less than the second threshold, the frequency down-adjustment is calculated according to the following formula. : ,in The normalized frequency band energy entropy of the mid-frequency characteristic band is... This is the frequency down-adjustment ratio; Subtract the current operating frequency of the first vibration unit A new operating frequency was obtained.

8. The method according to claim 1, characterized in that, The method of correcting the differential speed adjustment gain of the downstream centrifuge using the power spectral density gradient norm of the frequency band includes: The dimensionless value of the power spectral density gradient norm of the mid-frequency characteristic band. Calculate the corrected gain using the following formula. : ,in, This serves as the reference gain for differential speed regulation of downstream centrifuges. To correct the coefficient, update the proportional gain parameter in the differential speed PID controller of the centrifuge to... .

9. A multi-frequency-based intelligent mud solids control system, characterized in that, Includes the following modules: The interception module is used to simultaneously collect multi-channel vibration signals at the slurry inlet and slag outlet of the solids control equipment, and to correct the coherence threshold of the signals between channels and intercept vibration signal segments based on the normalized frequency band energy entropy of the mid-frequency characteristic frequency band determined in the previous analysis cycle. The defining module is used to perform short-time Fourier transform on the vibration signal segment, calculate the power spectral density gradient norm and normalized frequency band energy entropy, and define the low, medium and high frequency characteristic frequency bands corresponding to equipment resonance, fluid sloshing and particle impact, respectively, with the local maxima of the gradient norm as the center. The determination module is used to construct a six-dimensional state vector composed of the power spectral density gradient norm of each characteristic frequency band and the normalized frequency band energy entropy, and to calculate the Mahalanobis distance between the vector and the standard state centroid of the preset screen blockage and mud loss conditions; when the Mahalanobis distance with the standard centroid of screen blockage is less than a first threshold, the operating frequency of the first vibration unit is set to be proportional to the power spectral density gradient norm of the high-frequency characteristic frequency band, and the excitation phase of the second vibration unit is determined by looking up a table according to the normalized frequency band energy entropy of the frequency band. The correction module is used to set the downward adjustment of the operating frequency of the first vibration unit to be proportional to the normalized frequency band energy entropy of the mid-frequency characteristic band when the Mahalanobis distance from the standard centroid of the mud loss is less than the second threshold, and to correct the differential speed adjustment gain of the downstream centrifuge using the power spectral density gradient norm of the frequency band.

10. The system according to claim 9, characterized in that, The process of correcting the coherence threshold of inter-channel signals to extract vibration signal segments based on the normalized frequency band energy entropy of the mid-frequency characteristic frequency band determined in the previous analysis cycle includes: The normalized frequency band energy entropy of the mid-frequency characteristic band determined in the above analysis period As input, calculate the coherence threshold Th for the current period using the following formula: ; Calculate the coherence coefficient of the signals from the slurry inlet and slag outlet channels, and retain only the time periods with a coherence coefficient greater than Th as vibration signal segments.

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