Permeability coefficient optimization method and system based on offshore pumping test tide influence

Abstract

The invention discloses a seaborne water pumping test tide influence-based permeability coefficient optimization method and system, and belongs to the technical field of seaborne water pumping tests. Sea level height data are obtained through tide monitoring equipment, an equally-spaced sampling sequence is generated, and the water level and the underground water ion concentration in the pumping well are synchronously collected; dynamically tracking a tidal signal dominant frequency and quantifying a time domain translation scale, and estimating a real-time permeability coefficient based on a Darcy law; constructing a fuzzy error prediction function and a Kalman filtering optimization model, and predicting a permeability coefficient adjustment amount; key parameters are dynamically optimized through the discrete time parameter updating model and fed back to the system. According to the method, the tidal dynamic influence and the groundwater response characteristics are fused, the problem of permeability coefficient calculation errors caused by tidal fluctuation in a traditional water pumping test is solved, the tidal influence can be quickly responded, and the precision and the real-time performance of offshore hydrogeological parameter evaluation are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of offshore pumping tests, and specifically to an optimization method and system for permeability coefficient based on the tidal influence of offshore pumping tests. Background Art

[0002] In offshore pumping tests, the permeability coefficient is a key parameter for evaluating the hydrogeological characteristics of aquifers. However, traditional methods usually ignore the periodic interference of tidal dynamic changes on the groundwater level, resulting in significant errors in the estimation of the permeability coefficient; since the sea level fluctuations caused by tides will indirectly affect the water level changes in the pumping well through the hydraulic connection between the aquifer and seawater, if the tidal signal and the pumping test data are not effectively separated, it will directly affect the applicability of Darcy's law and the calculation results. In addition, existing technologies mostly adopt static correction or simple filtering methods, which are difficult to track the adaptive response optimization of pumping tests under the main frequency changes of tides in real time, and lack the fusion analysis of the dynamic response of groundwater ion concentration, resulting in limited optimization effects. Summary of the Invention

[0003] The purpose of the present invention is to provide an optimization method and system for permeability coefficient based on the tidal influence of offshore pumping tests to solve the problems raised in the above background art.

[0004] To solve the above technical problems, the present invention provides the following technical solutions:

[0005] An optimization system for permeability coefficient based on the tidal influence of offshore pumping tests, the system includes: a data acquisition module, a permeability coefficient estimation module, an error correction module, and a parameter state update feedback module;

[0006] The data acquisition module obtains sea level height data through tidal monitoring equipment, maps it into a dynamic waveform including time and amplitude in a two-dimensional coordinate system to generate an equally spaced sampling sequence; high-precision water level sensors and conductivity sensors are deployed in the pumping test well to collect water level data and groundwater ion concentration in real time;

[0007] The permeability coefficient estimation module dynamically tracks the main frequency of the tidal signal through time domain translation based on the dynamic waveform of the sea level height data, and quantifies the time domain translation scale corresponding to different tidal periods; using the time domain translation scale as the sliding window scale, the conductivity sensor is instructed to collect groundwater ion concentration within each sliding window, and the high-precision water level sensor is instructed to collect the water level data in the pumping well, and the real-time permeability coefficient is estimated based on Darcy's law;

[0008] The error correction module is used to construct a fuzzy error prediction function, fuse the real-time frequency deviation and cumulative frequency error of the main frequency of the tidal signal and the water level fluctuation frequency, so as to characterize the response frequency error in the pumping test under the influence of tides; construct a dynamic optimization model of the permeability coefficient based on Kalman filtering to predict the adjustment amount of the permeability coefficient.

[0009] The parameter status update feedback module is used to construct a parameter update model based on discrete time, update the fuzzy adaptive gain coefficient, the fuzzy error switching gain and the linear proportional coefficient of the fuzzy error prediction function, and feedback the update results to the fuzzy error prediction function and the dynamic optimization model of the permeability coefficient.

[0010] Further, the data acquisition module includes a tidal information acquisition unit and a pumping test data acquisition unit;

[0011] The tidal information acquisition unit is used to perform time synchronization processing on the acquired sea level height data by means of time domain equidistant interpolation in the dynamic waveform of the sea level height data, so as to generate an equidistant sampling sequence;

[0012] The pumping test data acquisition unit respectively and real-time collects the water level data and the groundwater ion concentration through a high-precision water level sensor and a conductivity sensor.

[0013] Further, the permeability coefficient estimation module includes a sliding window quantization unit and a Darcy's law estimation unit;

[0014] The sliding window quantization unit is used to initialize the time domain translation scale, preset the superposition degree threshold, calculate the superposition degree of the dynamic waveform of the sea level height data under the time domain translation scale, and obtain the time domain translation scale corresponding to different tidal cycles by dynamically tracking the main frequency of the tidal signal;

[0015] The Darcy's law estimation unit is used to use the time domain translation scale as the sliding window scale, set the instruction interval, instruct the conductivity sensor to collect the groundwater ion concentration, instruct the high-precision water level sensor to collect the water level data in the pumping well, and estimate the real-time permeability coefficient based on Darcy's law.

[0016] Further, the error correction module includes a fuzzy error prediction model unit and a dynamic optimization model unit of the permeability coefficient;

[0017] The fuzzy error prediction model unit is used to construct a fuzzy error prediction function, fuse the real-time frequency deviation and cumulative frequency error of the main frequency of the tidal signal and the water level fluctuation frequency, so as to calculate the response frequency error;

[0018] The dynamic optimization model unit of the permeability coefficient is used to construct a dynamic optimization model of the permeability coefficient based on Kalman filtering to predict the adjustment amount of the permeability coefficient.

[0019] Further, the parameter status update feedback module includes a parameter update model unit and a parameter status feedback unit;

[0020] The parameter update model unit is used to construct a parameter update model based on discrete time to update the fuzzy adaptive gain coefficient, the fuzzy error switching gain, and the linear proportional coefficient of the fuzzy error prediction function;

[0021] The parameter status feedback unit is used to substitute the updated fuzzy adaptive gain coefficient, the fuzzy error switching gain, and the linear proportional coefficient of the fuzzy error prediction function into the fuzzy error prediction function and the dynamic optimization model of the permeability coefficient.

[0022] An optimization method for the permeability coefficient based on the tidal influence of the offshore pumping test, the method includes the following steps:

[0023] Step S1: Obtain the sea level height data through a tidal monitoring device, map it into a dynamic waveform including time and amplitude in a two-dimensional coordinate system to generate an equally spaced sampling sequence; deploy high-precision water level sensors and conductivity sensors in the pumping test well to collect water level data and groundwater ion concentration in real time;

[0024] Step S2: Based on the dynamic waveform of the sea level height data, dynamically track the main frequency of the tidal signal through time domain translation, and quantify the time domain translation scale corresponding to different tidal periods; use the time domain translation scale as the sliding window scale, and instruct the conductivity sensor to collect the groundwater ion concentration within each sliding window, instruct the high-precision water level sensor to collect the water level data in the pumping well, and estimate the real-time permeability coefficient based on Darcy's law;

[0025] Step S3: Construct a fuzzy error prediction function to fuse the real-time frequency deviation and the cumulative frequency error between the main frequency of the tidal signal and the water level fluctuation frequency to characterize the response frequency error during the pumping test under the influence of tides; construct a dynamic optimization model of the permeability coefficient based on Kalman filtering to predict the adjustment amount of the permeability coefficient;

[0026] Step S4: Construct a parameter update model based on discrete time to update the fuzzy adaptive gain coefficient, the fuzzy error switching gain, and the linear proportional coefficient of the fuzzy error prediction function, and feedback the update result to the fuzzy error prediction function and the dynamic optimization model of the permeability coefficient.

[0027] Further, the specific implementation process of generating the equally spaced sampling sequence includes:

[0028] In the dynamic waveform of the sea level height data, perform time synchronization processing on the obtained sea level height data through time domain equally spaced interpolation to generate an equally spaced sampling sequence {(t i ,hi )|i ∈ [1, n]}, where t i represents the i-th sampling time node, and h i represents the sea level height data fitted in the dynamic waveform of the sea level height data at the sampling time node t i . n represents the total number of sampling time nodes.

[0029] Furthermore, the specific implementation process of step S2 includes:

[0030] Initialize the time domain translation scale τ, preset the superposition degree threshold, and calculate the superposition degree of the dynamic waveform of the sea level height data at the time domain translation scale τ Let τ = τ + 1, perform iterative calculation of the superposition degree at the time domain translation scale. When all the sample data in the equally spaced sampling sequence have participated in the iterative calculation, the iteration stops, and the main frequency of the tidal signal is captured By dynamically tracking the main frequency of the tidal signal, obtain the time domain translation scales corresponding to different tidal periods;

[0031] Using the time domain translation scale as the sliding window scale, within the e-th sliding window T e , instruct the conductivity sensor to collect the groundwater ion concentration C(t i ), instruct the high-precision water level sensor to collect the water level data H(t i ) in the pumping well. The instruction interval is set to Δt = t i+1 - t i , where t i , t i+1 ∈ T e . Based on Darcy's law, estimate the real-time permeability coefficient:

[0032]

[0033] In the formula, K(t i ) is the estimated real-time permeability coefficient, Q(T e ) is the water level change rate, L represents the actual flow distance of the water through the porous medium collected in advance, A is the equivalent cross-sectional area of the porous medium area in the pumping well, Δh is the head difference, γ is the preset conductivity sensitivity coefficient, C ref is the normalized water ion concentration and is the historical water ion concentration mean of the porous medium area in the pumping well, C0 is the preset water ion concentration reference value, NUM(T e ) represents the total number of sampling time nodes included in the sliding window T e .

[0034] Furthermore, the specific implementation process of step S3 includes:

[0035] Construct a fuzzy error prediction function V(T e), fuse the real-time frequency deviation and cumulative frequency error of the main frequency of the tidal signal and the water level floating frequency to calculate the response frequency error:

[0036]

[0037] In the formula, f tide (T e ) is the main frequency of the tidal signal within the sliding window T e , f gw (T e ) is the water level floating frequency calculated through the superposition degree algorithm model, f tide (T e ) - f gw (T e ) is the real-time frequency deviation, and α is the fuzzy adaptive gain coefficient;

[0038] Construct a dynamic optimization model of the permeability coefficient based on Kalman filtering to predict the adjustment amount of the permeability coefficient ΔK pred (t i ):

[0039] ΔK pred (t i ) = -β·sgn(V(T e )) - γ·V(T e ) + L(t i )·[Z(t i ) - K(t i )];

[0040] In the formula, if ΔK pred (t i ) is a positive value, it means to increase the permeability coefficient K(t i ), if ΔK pred (t i ) is a negative value, it means to decrease the permeability coefficient K(t i ), β is the fuzzy error switching gain, γ is the linear proportional coefficient of the fuzzy error prediction function, L(t i ) is the Kalman gain, and Z(t i ) is the observed value of the permeability coefficient.

[0041] Furthermore, the specific implementation process of step S4 includes:

[0042] Construct a parameter update model based on discrete time:

[0043]

[0044] In the formula, α(T e ), β(T e ) and γ(T e ) are respectively the sliding window Te The fuzzy adaptive gain coefficient, the fuzzy error switching gain, and the linear proportional coefficient of the fuzzy error prediction function within, where a1, a2, and a3 are preset adaptive adjustment coefficients and are respectively used to avoid overshoot of the fuzzy adaptive gain coefficient, the fuzzy error switching gain, and the linear proportional coefficient of the fuzzy error prediction function, and σ(T e ) is the standard deviation of the estimated real-time permeability coefficient within the sliding window T e , and σ max is the maximum standard deviation of the permeability coefficient allowed to be preset;

[0045] Substitute the updated fuzzy adaptive gain coefficient, the fuzzy error switching gain, and the linear proportional coefficient of the fuzzy error prediction function into the fuzzy error prediction function and the permeability coefficient dynamic optimization model.

[0046] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: In the permeability coefficient optimization method and system based on the tidal influence of the offshore pumping test provided by the present invention, sea level height data is obtained through a tidal monitoring device and an equally spaced sampling sequence is generated, and the water level and groundwater ion concentration in the pumping well are synchronously collected; the main frequency of the tidal signal is dynamically tracked and the time-domain translation scale is quantified, and the real-time permeability coefficient is estimated based on Darcy's law; a fuzzy error prediction function and a Kalman filter optimization model are constructed to predict the adjustment amount of the permeability coefficient; the key parameters are dynamically optimized through a discrete-time parameter update model and feedback to the system. By integrating the dynamic influence of tides and the response characteristics of groundwater, the present invention solves the problem of calculation error of the permeability coefficient caused by tidal fluctuations in traditional pumping tests and can quickly respond to tidal influence, significantly improving the accuracy and real-time performance of the evaluation of offshore hydrogeological parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification, and are used to explain the present invention together with the embodiments of the present invention, and do not constitute a limitation to the present invention.

[0048] Figure 1 It is a schematic diagram of the steps of the permeability coefficient optimization method based on the tidal influence of the offshore pumping test of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0050] In the first embodiment: An optimization system for permeability coefficient based on the tidal influence of offshore pumping tests is provided. The system includes: a data acquisition module, a permeability coefficient estimation module, an error correction module, and a parameter status update feedback module.

[0051] The data acquisition module obtains sea level height data through tidal monitoring equipment, maps it into a dynamic waveform including time and amplitude in a two-dimensional coordinate system to generate an equally spaced sampling sequence; high-precision water level sensors and conductivity sensors are deployed in the pumping test well to collect water level data and groundwater ion concentration in real time.

[0052] Exemplarily, the data acquisition module includes a tidal information acquisition unit and a pumping test data acquisition unit.

[0053] The tidal information acquisition unit is used to perform time synchronization processing on the obtained sea level height data in the dynamic waveform of the sea level height data by means of time-domain equally spaced interpolation to generate an equally spaced sampling sequence.

[0054] The pumping test data acquisition unit respectively collects water level data and groundwater ion concentration in real time through high-precision water level sensors and conductivity sensors.

[0055] The permeability coefficient estimation module, based on the dynamic waveform of the sea level height data, dynamically tracks the main frequency of the tidal signal by means of time-domain translation, quantifies the time-domain translation scale corresponding to different tidal periods; uses the time-domain translation scale as the sliding window scale, and in each sliding window, commands the conductivity sensor to collect groundwater ion concentration, commands the high-precision water level sensor to collect the water level data in the pumping well, and estimates the real-time permeability coefficient based on Darcy's law.

[0056] Exemplarily, the permeability coefficient estimation module includes a sliding window quantification unit and a Darcy's law estimation unit.

[0057] The sliding window quantification unit is used to initialize the setting of the time-domain translation scale, preset the superposition degree threshold, calculate the superposition degree of the dynamic waveform of the sea level height data at the time-domain translation scale, and obtain the time-domain translation scale corresponding to different tidal periods by dynamically tracking the main frequency of the tidal signal.

[0058] The Darcy's law estimation unit is used to use the time-domain translation scale as the sliding window scale, set the command interval, command the conductivity sensor to collect groundwater ion concentration, command the high-precision water level sensor to collect the water level data in the pumping well, and estimate the real-time permeability coefficient based on Darcy's law.

[0059] The error correction module is used to construct a fuzzy error prediction function, fuse the real-time frequency deviation and cumulative frequency error of the main frequency of the tidal signal and the water level floating frequency, so as to characterize the response frequency error in the pumping test under the influence of tides; construct a dynamic optimization model of the permeability coefficient based on Kalman filtering to predict the adjustment amount of the permeability coefficient.

[0060] Exemplarily, the error correction module includes a fuzzy error prediction model unit and a dynamic optimization model unit of the permeability coefficient.

[0061] The fuzzy error prediction model unit is used to construct a fuzzy error prediction function, fuse the real-time frequency deviation and cumulative frequency error of the main frequency of the tidal signal and the water level floating frequency, so as to calculate the response frequency error.

[0062] The dynamic optimization model unit of the permeability coefficient is used to construct a dynamic optimization model of the permeability coefficient based on Kalman filtering to predict the adjustment amount of the permeability coefficient.

[0063] The parameter state update and feedback module is used to construct a parameter update model under discrete time, update the fuzzy adaptive gain coefficient, the fuzzy error switching gain and the linear proportional coefficient of the fuzzy error prediction function, and feedback the update results to the fuzzy error prediction function and the dynamic optimization model of the permeability coefficient.

[0064] Exemplarily, the parameter state update and feedback module includes a parameter update model unit and a parameter state feedback unit.

[0065] The parameter update model unit is used to construct a parameter update model under discrete time to update the fuzzy adaptive gain coefficient, the fuzzy error switching gain and the linear proportional coefficient of the fuzzy error prediction function.

[0066] The parameter state feedback unit is used to substitute the updated fuzzy adaptive gain coefficient, the fuzzy error switching gain and the linear proportional coefficient of the fuzzy error prediction function into the fuzzy error prediction function and the dynamic optimization model of the permeability coefficient.

[0067] Please refer to Figure 1 , in the second embodiment: provide a method for optimizing the permeability coefficient based on the influence of tides on the offshore pumping test to apply to the above-mentioned first embodiment. The method includes the following steps:

[0068] Step S1: Obtain the sea level height data through the tidal monitoring device, map it into a dynamic waveform including time and amplitude in a two-dimensional coordinate system to generate an equally spaced sampling sequence; deploy high-precision water level sensors and conductivity sensors in the pumping test well to collect water level data and groundwater ion concentration in real time.

[0069] Exemplarily, in the dynamic waveform of sea level height data, time synchronization processing is performed on the obtained sea level height data by means of time domain equally spaced interpolation to generate an equally spaced sampling sequence \(\{(t i , h i )|i\in[1, n]\}\), where \(t i \) represents the \(i\)-th sampling time node, and \(h i \) represents the sea level height data fitted in the dynamic waveform of sea level height data at the sampling time node \(t i \), and \(n\) represents the total number of sampling time nodes;

[0070] For example, tidal data: The sea level height data is collected at 1-minute intervals using a tidal monitoring device to generate an equally spaced sampling sequence, and the total number of samples \(n = 1440\) (24-hour data); pumping test data: Deploy high-precision water level sensors (accuracy \(\pm0.1\) cm) and conductivity sensors (accuracy \(\pm0.01\) mS / cm), and synchronously collect the water level and ion concentration at 5-minute intervals.

[0071] Step S2: Based on the dynamic waveform of sea level height data, dynamically track the main frequency of the tidal signal by means of time domain translation, and quantify the time domain translation scale corresponding to different tidal periods; using the time domain translation scale as the sliding window scale, instruct the conductivity sensor to collect the groundwater ion concentration within each sliding window, and instruct the high-precision water level sensor to collect the water level data in the pumping well, and estimate the real-time permeability coefficient based on Darcy's law;

[0072] Exemplarily, initialize the time domain translation scale \(\tau\), and preset the superposition degree threshold, and calculate the superposition degree of the dynamic waveform of sea level height data at the time domain translation scale \(\tau Let \(\tau=\tau + 1\), perform iterative calculation of the superposition degree at the time domain translation scale. When all the sample data in the equally spaced sampling sequence have participated in the iterative calculation, the iteration stops, and the main frequency of the tidal signal is captured By dynamically tracking the main frequency of the tidal signal, obtain the time domain translation scale corresponding to different tidal periods;

[0073] For example, superposition degree calculation: Initialize the time domain translation scale \(\tau = 1\), preset the superposition degree threshold \(R_{th}=0.8\), and obtain \(\tau max = 745\) (corresponding to the semi-diurnal tide period of 12.4 hours);

[0074] Using the time domain translation scale as the sliding window scale, within the \(e\)-th sliding window \(T e \), instruct the conductivity sensor to collect the groundwater ion concentration \(C(t i )\), instruct the high-precision water level sensor to collect the water level data \(H(t i )\) in the pumping well, and the instruction interval is set to \(\Delta t=t i+1 -t i \), where \(ti , t i+1 ∈T e , estimate the real-time permeability coefficient based on Darcy's law:

[0075]

[0076] In the formula, K(t i ) is the estimated real-time permeability coefficient, Q(T e ) is the water level change rate, L represents the actual flow distance of the pre-collected water flow through the porous medium, A is the equivalent cross-sectional area of the porous medium area in the pumping well, Δh is the head difference, γ is the preset conductivity sensitivity coefficient, C ref is the normalized water ion concentration and is the average historical water ion concentration of the porous medium area in the pumping well, C0 is the preset water ion concentration reference value, NUM(T e ) represents the total number of sampling time nodes included in the sliding window T e ;

[0077] For example, Darcy's law parameters: sliding window scale Te = 12 hours, command interval Δt = 5 minutes; equivalent cross-sectional area A = 2.5m 2 , head difference Δh = 1.2m, flow distance L = 50m, conductivity sensitivity coefficient γ = 0.2, reference value C0 = 100mg / L.

[0078] Step S3: Construct a fuzzy error prediction function to fuse the real-time frequency deviation and cumulative frequency error of the main frequency of the tidal signal and the water level fluctuation frequency to characterize the response frequency error during the pumping test under the influence of tides; construct a dynamic optimization model of the permeability coefficient based on Kalman filtering to predict the adjustment amount of the permeability coefficient;

[0079] Exemplarily, construct a fuzzy error prediction function V(T e ) to fuse the real-time frequency deviation and cumulative frequency error of the main frequency of the tidal signal and the water level fluctuation frequency to calculate the response frequency error:

[0080]

[0081] In the formula, f tide (T e ) is the main frequency of the tidal signal within the sliding window T e , f gw (T e ) is the water level fluctuation frequency calculated by the superposition degree algorithm model, f tide (T e ) - f gw (T e ) is the real-time frequency deviation, and α is the fuzzy adaptive gain coefficient;

[0082] Build a dynamic optimization model of permeability coefficient based on Kalman filter to predict the adjustment amount of permeability coefficient ΔK pred (t i ):

[0083] ΔK pred (t i )=-β·sgn(V(T e ))-γ·V(T e )+L(t i )·[Z(t i )-K(t i )];

[0084] In the formula, if ΔK pred (t i ) is positive, it means increasing the permeability coefficient K(t i ), if ΔK pred (t i ) is negative, it means decreasing the permeability coefficient K(t i ), β is the fuzzy error switching gain, γ is the linear proportional coefficient of the fuzzy error prediction function, L(t i ) is the Kalman gain, and Z(t i ) is the observed value of the permeability coefficient.

[0085] Step S4: Build a parameter update model based on discrete time to update the fuzzy adaptive gain coefficient, the fuzzy error switching gain, and the linear proportional coefficient of the fuzzy error prediction function, and feedback the update results to the fuzzy error prediction function and the dynamic optimization model of the permeability coefficient;

[0086] Exemplarily, build a parameter update model based on discrete time:

[0087]

[0088] In the formula, α(T e ), β(T e ) and γ(T e ) are the fuzzy adaptive gain coefficient, the fuzzy error switching gain, and the linear proportional coefficient of the fuzzy error prediction function within the sliding window T e respectively, a1, a2, and a3 are preset adaptive adjustment coefficients and are used to avoid overshoot of the fuzzy adaptive gain coefficient, the fuzzy error switching gain, and the linear proportional coefficient of the fuzzy error prediction function respectively, σ(T e ) is the standard deviation of the estimated real-time permeability coefficient within the sliding window T e , and σ max is the maximum standard deviation of the preset allowable permeability coefficient;

[0089] Substitute the updated fuzzy adaptive gain coefficient, fuzzy error switching gain, and linear proportional coefficient of the fuzzy error prediction function into the fuzzy error prediction function and the dynamic optimization model of the permeability coefficient.

[0090] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0091] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An optimization method for permeability coefficient based on the tidal influence of offshore pumping tests, characterized in that, The method includes the following steps: Step S1: Obtain sea level height data through a tidal monitoring device, map it into a dynamic waveform including time and amplitude in a two-dimensional coordinate system to generate an equally-spaced sampling sequence; deploy high-precision water level sensors and conductivity sensors in a pumping test well to collect water level data and groundwater ion concentration in real time; Step S2: Based on the dynamic waveform of the sea level height data, dynamically track the main frequency of the tidal signal through time-domain translation, and quantify the time-domain translation scale corresponding to different tidal cycles; use the time-domain translation scale as the sliding window scale, and instruct the conductivity sensor to collect groundwater ion concentration within each sliding window, instruct the high-precision water level sensor to collect the water level data in the pumping well, and estimate the real-time permeability coefficient based on Darcy's law; Step S3: Construct a fuzzy error prediction function to fuse the real-time frequency deviation and cumulative frequency error between the main frequency of the tidal signal and the water level fluctuation frequency to characterize the response frequency error during the pumping test under the influence of tides; construct a dynamic optimization model of the permeability coefficient based on Kalman filtering to predict the adjustment amount of the permeability coefficient; Step S4: Construct a parameter update model based on discrete time to update the fuzzy adaptive gain coefficient, fuzzy error switching gain, and linear proportional coefficient of the fuzzy error prediction function, and feedback the update results to the fuzzy error prediction function and the dynamic optimization model of the permeability coefficient.

2. The permeability coefficient optimization method based on the tidal influence of offshore pumping tests according to claim 1, wherein The specific implementation process of generating the equally-spaced sampling sequence includes: In the dynamic waveform of sea level height data, time synchronization processing is performed on the obtained sea level height data by means of equidistant interpolation in the time domain to generate an equidistant sampling sequence {(t i , h i )|i ∈ [1, n]}, where t i represents the i-th sampling time node, and h i represents the sea level height data fitted in the dynamic waveform of sea level height data at the sampling time node t i , and n represents the total number of sampling time nodes.

3. The permeability coefficient optimization method based on the tidal influence of the offshore pumping test according to claim 2, wherein The specific implementation process of Step S2 includes: Initialize the time-domain translation scale τ, preset the superposition degree threshold, and calculate the superposition degree of the dynamic waveform of sea level height data at the time-domain translation scale τ Let τ = τ + 1, perform iterative calculation of the superposition degree at the time-domain translation scale. When all the sample data in the equally spaced sampling sequence have participated in the iterative calculation, the iteration stops, and the main frequency of the tidal signal is captured By dynamically tracking the main frequency of the tidal signal, obtain the time-domain translation scales corresponding to different tidal periods; Taking the time-domain translation scale as the sliding window scale, within the e-th sliding window T e instruct the electrical conductivity sensor to collect the groundwater ion concentration C(t i ), instruct the high-precision water level sensor to collect the water level data H(t i ) in the pumping well, and set the instruction interval as Δt = t i+1 -t i , where t i , t i+1 ∈T e , and estimate the real-time permeability coefficient based on Darcy's law: Wherein, K(t i ) is the estimated real-time permeability coefficient, Q(T e ) is the water level change rate, L represents the actual flow distance of the pre-collected water flow through the porous medium, A is the equivalent cross-sectional area of the porous medium area in the pumping well, Δh is the head difference, γ is the preset conductivity sensitivity coefficient, C ref is the normalized water ion concentration and is the mean value of the historical water ion concentration in the porous medium area in the pumping well, C0 is the preset water ion concentration reference value, NUM(T e ) represents the total number of sampling time nodes included in the sliding window T e ).

4. The permeability coefficient optimization method based on the tidal influence of the offshore pumping test according to claim 3, wherein The specific implementation process of Step S3 includes: Construct a fuzzy error prediction function V(T e ), and fuse the real-time frequency deviation and cumulative frequency error of the main frequency of the tidal signal and the water level floating frequency to calculate the response frequency error: where f tide (T e ) is the main frequency of the tidal signal within the sliding window T e , f gw (T e ) is the water level floating frequency calculated by the superposition degree algorithm model, f tide (T e ) - f gw (T e ) is the real-time frequency deviation, and α is the fuzzy adaptive gain coefficient; Build a dynamic optimization model of permeability coefficient based on Kalman filter to predict the adjustment amount of permeability coefficient ΔK pred (t i ): ΔK pred (t i ) = -β·sgn(V(T e )) - γ·V(T e ) + L(t i )·[Z(t i ) - K(t i )]; where, if ΔK pred (t i ) is positive, it means an increase in the permeability coefficient K(t i ), and if ΔK pred (t i ) is negative, it means a decrease in the permeability coefficient K(t i ), β is the fuzzy error switching gain, γ is the linear proportionality coefficient of the fuzzy error prediction function, L(t i ) is the Kalman gain, and Z(t i ) is the observed value of the permeability coefficient.

5. The permeability coefficient optimization method based on the tidal influence of the offshore pumping test according to claim 4, wherein, The specific implementation process of Step S4 includes: Construct a parameter update model based on discrete time: Wherein, α(T e ), β(T e ), and γ(T e ) are respectively the fuzzy adaptive gain coefficient, the fuzzy error switching gain, and the linear proportional coefficient of the fuzzy error prediction function within the sliding window T e . a1, a2, and a3 are preset adaptive adjustment coefficients and are respectively used to avoid overshoot of the fuzzy adaptive gain coefficient, the fuzzy error switching gain, and the linear proportional coefficient of the fuzzy error prediction function. σ(T e ) is the standard deviation of the estimated real-time permeability coefficient within the sliding window T e , and σ max is the maximum standard deviation of the permeability coefficient allowed by preset; Substitute the updated fuzzy adaptive gain coefficient, fuzzy error switching gain, and linear proportional coefficient of the fuzzy error prediction function into the fuzzy error prediction function and the dynamic optimization model of the permeability coefficient.

6. An optimization system for permeability coefficient based on the tidal influence of offshore pumping tests, which executes the permeability coefficient optimization method described in any one of claims 1-5, characterized in that, The system includes: a data acquisition module, a permeability coefficient estimation module, an error correction module, and a parameter status update feedback module; The data acquisition module obtains sea level height data through a tidal monitoring device, maps it into a dynamic waveform including time and amplitude in a two-dimensional coordinate system to generate an equally-spaced sampling sequence; deploys high-precision water level sensors and conductivity sensors in a pumping test well to collect water level data and groundwater ion concentration in real time; The permeability coefficient estimation module, based on the dynamic waveform of the sea level height data, dynamically tracks the main frequency of the tidal signal through time-domain translation, quantifies the time-domain translation scale corresponding to different tidal cycles; uses the time-domain translation scale as the sliding window scale, and instructs the conductivity sensor to collect groundwater ion concentration within each sliding window, instructs the high-precision water level sensor to collect the water level data in the pumping well, and estimates the real-time permeability coefficient based on Darcy's law; The error correction module is used to construct a fuzzy error prediction function to fuse the real-time frequency deviation and cumulative frequency error between the main frequency of the tidal signal and the water level fluctuation frequency to characterize the response frequency error during the pumping test under the influence of tides; construct a dynamic optimization model of the permeability coefficient based on Kalman filtering to predict the adjustment amount of the permeability coefficient; The parameter status update and feedback module is used to construct a parameter update model based on discrete time, update the fuzzy adaptive gain coefficient, the fuzzy error switching gain, and the linear proportionality coefficient of the fuzzy error prediction function, and feedback the update results to the fuzzy error prediction function and the permeability coefficient dynamic optimization model.

7. The permeability coefficient optimization system based on the tidal influence of the offshore pumping test according to claim 6, characterized in that, The data acquisition module includes a tidal information acquisition unit and a pumping test data acquisition unit; The tidal information acquisition unit is used to perform time synchronization processing on the acquired sea level height data by means of time-domain equally spaced interpolation in the dynamic waveform of the sea level height data, so as to generate an equally spaced sampling sequence; The pumping test data acquisition unit respectively and real-time acquires the water level data and the groundwater ion concentration through a high-precision water level sensor and a conductivity sensor.

8. The permeability coefficient optimization system based on the tidal influence of the offshore pumping test according to claim 6, wherein, The permeability coefficient estimation module includes a sliding window quantization unit and a Darcy's law estimation unit; The sliding window quantization unit is used to initialize and set the time-domain translation scale, preset the superposition degree threshold, calculate the superposition degree of the dynamic waveform of the sea level height data under the time-domain translation scale, and obtain the time-domain translation scale corresponding to different tidal periods by dynamically tracking the main frequency of the tidal signal; The Darcy's law estimation unit is used to use the time-domain translation scale as the sliding window scale, set the command interval, command the conductivity sensor to collect the groundwater ion concentration, command the high-precision water level sensor to collect the water level data in the pumping well, and estimate the real-time permeability coefficient based on Darcy's law.

9. The permeability coefficient optimization system based on the tidal influence of the offshore pumping test according to claim 6, characterized in that, The error correction module includes a fuzzy error prediction model unit and a permeability coefficient dynamic optimization model unit; The fuzzy error prediction model unit is used to construct a fuzzy error prediction function, fuse the real-time frequency deviation and the cumulative frequency error between the main frequency of the tidal signal and the water level fluctuation frequency, so as to calculate the response frequency error; The permeability coefficient dynamic optimization model unit is used to construct a permeability coefficient dynamic optimization model based on Kalman filtering to predict the permeability coefficient adjustment amount.

10. The permeability coefficient optimization system based on the tidal influence of the offshore pumping test according to claim 6, characterized in that: The parameter status update and feedback module includes a parameter update model unit and a parameter status feedback unit; The parameter update model unit is used to construct a parameter update model based on discrete time to update the fuzzy adaptive gain coefficient, the fuzzy error switching gain, and the linear proportionality coefficient of the fuzzy error prediction function; The parameter status feedback unit is used to substitute the updated fuzzy adaptive gain coefficient, the fuzzy error switching gain, and the linear proportionality coefficient of the fuzzy error prediction function into the fuzzy error prediction function and the permeability coefficient dynamic optimization model.

Images