Hydraulic conductivity optimization method and system based on tidal influence of offshore pumping test

Abstract

The present invention discloses a method and system for optimizing the permeability coefficient based on the tidal influence of offshore pumping tests, and belongs to the technical field of offshore pumping tests. The sea level height data is obtained through tidal monitoring equipment 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 permeability coefficient adjustment amount; key parameters are dynamically optimized through a discrete time parameter update model and fed back to the system. By integrating the dynamic influence of tides with the response characteristics of groundwater, the present invention solves the problem of permeability coefficient calculation errors caused by tidal fluctuations in traditional pumping tests and can quickly respond to tidal influences, significantly improving the accuracy and real-time performance of offshore hydrogeological parameter assessment.

Description

Technical Field

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

[0002] In offshore pumping tests, the permeability coefficient is a key parameter for evaluating the hydrogeological properties of aquifers. However, traditional methods often ignore the periodic interference of tidal dynamics on groundwater levels, resulting in significant errors in permeability coefficient estimation. Tidal sea level fluctuations indirectly affect water level changes in pumping wells through the hydraulic connection between the aquifer and the seawater. Failure to effectively separate tidal signals from pumping test data directly impacts the applicability and calculation results of Darcy's law. Furthermore, existing technologies often rely on static corrections or simple filtering methods, making it difficult to optimize the adaptive response of pumping tests in real-time under tidal dominant frequency conditions. Furthermore, they lack integrated analysis of the dynamic response of groundwater ion concentrations, resulting in limited optimization results. Summary of the Invention

[0003] The object of the present invention is to provide a method and system for optimizing the permeability coefficient based on the tidal influence of offshore pumping tests, so as to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0005] The permeability coefficient optimization system based on the tidal influence of offshore pumping test includes: data acquisition module, permeability coefficient estimation module, error correction module and parameter status update feedback module;

[0006] The data acquisition module acquires sea level data through tidal monitoring equipment and maps it into a dynamic waveform containing 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 sea level data, quantifies the time domain translation scale corresponding to different tidal cycles; uses the time domain translation scale as the sliding window scale, instructs the conductivity sensor to collect groundwater ion concentration within each sliding window, instructs the high-precision water level sensor to collect water level data in the pumping well, and estimates the real-time permeability coefficient based on Darcy's law;

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

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

[0010] Furthermore, 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 in the dynamic waveform of the sea level height data by means of time domain equal-interval interpolation to generate an equal-interval sampling sequence;

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

[0013] Furthermore, 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 and set the time domain translation scale, preset the superposition threshold, calculate the superposition 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 and set the instruction interval to instruct the conductivity sensor to collect groundwater ion concentration and the high-precision water level sensor to collect water level data in the pumping well, and estimate the real-time permeability coefficient based on Darcy's law.

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

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

[0018] 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.

[0019] Furthermore, the parameter state update feedback module includes a parameter update model unit and a parameter state 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 fuzzy error prediction function linear proportional coefficient;

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

[0022] The method for optimizing the hydraulic conductivity based on the tidal influence of offshore pumping tests includes the following steps:

[0023] Step S1: Acquire sea level data through tide monitoring equipment and map it into a dynamic waveform containing 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 data, the main frequency of the tidal signal is dynamically tracked by time domain translation, and the time domain translation scale corresponding to different tidal cycles is quantified; using the time domain translation scale as the sliding window scale, within each sliding window, the conductivity sensor is instructed to collect groundwater ion concentration, and the high-precision water level sensor is instructed to collect water level data in the pumping well, and the real-time permeability coefficient is estimated based on Darcy's law;

[0025] Step S3: Construct a fuzzy error prediction function, integrate the real-time frequency deviation and cumulative frequency error of the tidal signal main frequency and the water level floating 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 permeability coefficient adjustment amount;

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

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

[0028] In the dynamic waveform of sea level height data, the acquired sea level height data is time synchronized by time domain equal interval interpolation to generate an equal interval sampling sequence {(t i , hi )|i∈[1,n]}, where t i represents the i-th sampling time node, h i Indicates that at sampling time node t i The sea level height data is fitted in the dynamic waveform of the sea level height data below, and 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 and preset the superposition threshold to calculate the superposition of the dynamic waveform of the sea level height data under the time domain translation scale τ Let τ = τ + 1, and perform iterative calculation of the superposition degree under the time domain translation scale. When all the sample data in the equally spaced sampling sequence participate 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, the time domain translation scale corresponding to different tidal cycles is obtained;

[0031] Taking the time domain translation scale as the sliding window scale, in the e-th sliding window T e The internal command conductivity sensor collects the groundwater ion concentration C(t i ), instructing the high-precision water level sensor to collect the water level data H(t i ), 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, the real-time permeability coefficient is estimated:

[0032]

[0033] Where, 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 flowing through the porous medium collected in advance, A is the equivalent cross-sectional area of ​​the porous medium region in the pumping well, Δh is the head difference, γ is the preset conductivity sensitivity coefficient, and C ref is the standardized water ion concentration and is the historical average water ion concentration of the porous media area in the pumping well, C0 is the preset water ion concentration benchmark value, NUM(T e ) represents the sliding window T e The total number of sampling time nodes included.

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

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

[0036]

[0037] Where, f tide (T e ) is the sliding window T e The main frequency of the tidal signal within, f gw (T e ) is the water level fluctuation frequency calculated by the superposition algorithm model, f tide (T e )-f gw (T e ) is the real-time frequency deviation, α is the fuzzy adaptive gain coefficient;

[0038] Construct a dynamic optimization model of permeability coefficient based on Kalman filtering to predict the permeability coefficient adjustment 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, indicating an increase in the permeability coefficient K(t i ), if ΔK pred (t i ) is a negative value, indicating a decrease in 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, 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] Where, α(T e ),β(T e ) and γ(T e ) are sliding windows Te The fuzzy adaptive gain coefficient, fuzzy error switching gain and fuzzy error prediction function linear proportional coefficient are in the fuzzy adaptive gain coefficient, fuzzy error switching gain and fuzzy error prediction function linear proportional coefficient, a1, a2 and a3 are preset adaptive adjustment coefficients and are used to avoid overshoot of the fuzzy adaptive gain coefficient, fuzzy error switching gain and fuzzy error prediction function linear proportional coefficient, σ(T e ) is the sliding window T e Standard deviation of the estimated real-time permeability coefficient, σ max The maximum standard deviation of the permeability coefficient allowed by the preset;

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

[0046] Compared with the existing technology, 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 offshore pumping tests provided by the present invention, sea level height data is obtained through tidal monitoring equipment 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 Kalman filter optimization model are constructed to predict the permeability coefficient adjustment amount; key parameters are dynamically optimized through a discrete time parameter update model and fed back to the system. By integrating the dynamic influence of tides with the response characteristics of groundwater, the present invention solves the problem of permeability coefficient calculation errors caused by tidal fluctuations in traditional pumping tests and can quickly respond to tidal influences, significantly improving the accuracy and real-time performance of offshore hydrogeological parameter assessment. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0050] In the first embodiment, a permeability coefficient optimization system based on tidal influence of offshore pumping test 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 acquires sea level data through tidal monitoring equipment and maps it into a dynamic waveform containing 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 acquired sea level height data in the dynamic waveform of the sea level height data by means of time domain equal-interval interpolation to generate an equal-interval sampling sequence;

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

[0055] The permeability coefficient estimation module dynamically tracks the main frequency of the tidal signal through time domain translation based on the dynamic waveform of sea level data, quantifies the time domain translation scale corresponding to different tidal cycles; uses the time domain translation scale as the sliding window scale, instructs the conductivity sensor to collect groundwater ion concentration within each sliding window, instructs the high-precision water level sensor to collect 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 quantization unit and a Darcy's law estimation unit;

[0057] The sliding window quantization unit is used to initialize and set the time domain translation scale, preset the superposition threshold, calculate the superposition 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;

[0058] The Darcy's law estimation unit is used to use the time domain translation scale as the sliding window scale and set the instruction interval to instruct the conductivity sensor to collect groundwater ion concentration and the high-precision water level sensor to collect 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, integrating the real-time frequency deviation and cumulative frequency error of the tidal signal main frequency and the water level floating frequency to characterize the response frequency error during the pumping test under the influence of the tide; construct a dynamic optimization model of the permeability coefficient based on Kalman filtering to predict the permeability coefficient adjustment amount;

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

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

[0062] 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.

[0063] The parameter state 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 fuzzy error prediction function linear proportional coefficient, and feed back the update results to the fuzzy error prediction function and the permeability coefficient dynamic optimization model;

[0064] Exemplarily, the parameter state update 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 based on discrete time to update the fuzzy adaptive gain coefficient, the fuzzy error switching gain and the fuzzy error prediction function linear proportional coefficient;

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

[0067] See also Figure 1 In the second embodiment, a method for optimizing the permeability coefficient based on the tidal influence of an offshore pumping test is provided, which is applicable to the first embodiment. The method includes the following steps:

[0068] Step S1: Acquire sea level data through tide monitoring equipment and map it into a dynamic waveform containing 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] For example, in the dynamic waveform of the sea level data, the acquired sea level data is time-synchronized by time-domain equal-interval interpolation to generate an equal-interval sampling sequence {(t i , h i )|i∈[1,n]}, where t i represents the i-th sampling time node, h i Indicates that at sampling time node t i The sea level height data fitted in the dynamic waveform of the sea level height data below, n represents the total number of sampling time nodes;

[0070] For example, tidal data: tidal monitoring equipment is used to collect sea level data at 1-minute intervals to generate an equally spaced sampling sequence with a total sampling number n = 1440 (24-hour data); pumping test data: high-precision water level sensors (accuracy ±0.1cm) and conductivity sensors (accuracy ±0.01mS / cm) are deployed to synchronously collect water level and ion concentration at 5-minute intervals.

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

[0072] For example, the temporal translation scale τ is initialized and the superposition threshold is preset to calculate the superposition of the dynamic waveform of the sea level height data under the temporal translation scale τ. Let τ = τ + 1, and perform iterative calculation of the superposition degree under the time domain translation scale. When all the sample data in the equally spaced sampling sequence participate 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, the time domain translation scale corresponding to different tidal cycles is obtained;

[0073] For example, the calculation of the degree of superposition is as follows: initialize the time domain translation scale τ = 1, preset the degree of superposition threshold Rth = 0.8, and obtain τ max =745 (corresponding to a semidiurnal tidal period of 12.4 hours);

[0074] Taking the time domain translation scale as the sliding window scale, in the e-th sliding window T e The internal command conductivity sensor collects the groundwater ion concentration C(t i ), instructing the high-precision water level sensor to collect the water level data H(t i ), the instruction interval is set to Δt=t i+1 -t i , where ti , t i+1 ∈T e , based on Darcy's law, the real-time permeability coefficient is estimated:

[0075]

[0076] Where, 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 flowing through the porous medium collected in advance, A is the equivalent cross-sectional area of ​​the porous medium region in the pumping well, Δh is the head difference, γ is the preset conductivity sensitivity coefficient, and C ref is the standardized water ion concentration and is the historical average water ion concentration of the porous media area in the pumping well, C0 is the preset water ion concentration benchmark value, NUM(T e ) represents the sliding window T e The total number of sampling time nodes included;

[0077] For example, Darcy's law parameters: sliding window size Te = 12 hours, instruction 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, integrate the real-time frequency deviation and cumulative frequency error of the tidal signal main frequency and the water level floating 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 permeability coefficient adjustment amount;

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

[0080]

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

[0082] Construct a dynamic optimization model of permeability coefficient based on Kalman filtering to predict the permeability coefficient adjustment 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 a positive value, indicating an increase in the permeability coefficient K(t i ), if ΔK pred (t i ) is a negative value, indicating a decrease in 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, Z(t i ) is the observed value of the permeability coefficient.

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

[0086] For example, a parameter update model based on discrete time is constructed:

[0087]

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

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

[0090] It should be noted that, in this document, relational terms such as first and second, etc., are used only 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 terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0091] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the permeability coefficient based on the tidal influence of offshore pumping tests, characterized in that: The method comprises the following steps: Step S1: Acquire sea level data through tide monitoring equipment and map it into a dynamic waveform containing 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; Step S2: Based on the dynamic waveform of the sea level data, the main frequency of the tidal signal is dynamically tracked by time domain translation, and the time domain translation scale corresponding to different tidal cycles is quantified; using the time domain translation scale as the sliding window scale, within each sliding window, the conductivity sensor is instructed to collect groundwater ion concentration, and the high-precision water level sensor is instructed to collect water level data in the pumping well, and the real-time permeability coefficient is estimated based on Darcy's law; Step S3: Construct a fuzzy error prediction function, integrate the real-time frequency deviation and cumulative frequency error of the tidal signal main frequency and the water level floating 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 permeability coefficient adjustment amount; Step S4: Construct a parameter update model based on discrete time, update the fuzzy adaptive gain coefficient, fuzzy error switching gain and fuzzy error prediction function linear proportional coefficient, and feed back the update results to the fuzzy error prediction function and the permeability coefficient dynamic optimization model.

2. The method for optimizing the permeability coefficient based on the tidal influence of offshore pumping test according to claim 1, characterized in that: The specific implementation process of generating the equally spaced sampling sequence includes: In the dynamic waveform of sea level height data, the acquired sea level height data is time synchronized by time domain equal interval interpolation to generate an equal interval sampling sequence {(t i , h i )|i∈[1,n]}, where t i represents the i-th sampling time node, h i Indicates that at sampling time node t i The sea level height data is fitted in the dynamic waveform of the sea level height data below, and n represents the total number of sampling time nodes.

3. The method for optimizing the permeability coefficient based on the tidal influence of offshore pumping test according to claim 2, characterized in that: The specific implementation process of step S2 includes: Initialize the time domain translation scale τ and preset the superposition threshold to calculate the superposition of the dynamic waveform of the sea level height data under the time domain translation scale τ Let τ = τ + 1, and perform iterative calculation of the superposition degree under the time domain translation scale. When all the sample data in the equally spaced sampling sequence participate 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, the time domain translation scale corresponding to different tidal cycles is obtained; Taking the time domain translation scale as the sliding window scale, in the e-th sliding window T e The internal command conductivity sensor collects the groundwater ion concentration C(t i ), instructing the high-precision water level sensor to collect the water level data H(t i ), 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, the real-time permeability coefficient is estimated: Where, 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 flowing through the porous medium collected in advance, A is the equivalent cross-sectional area of ​​the porous medium region in the pumping well, Δh is the head difference, γ is the preset conductivity sensitivity coefficient, and C ref is the standardized water ion concentration and is the historical average water ion concentration of the porous media area in the pumping well, C0 is the preset water ion concentration benchmark value, NUM(T e ) represents the sliding window T e The total number of sampling time nodes included.

4. The method for optimizing the permeability coefficient based on the tidal influence of offshore pumping test according to claim 3 is characterized in that: The specific implementation process of step S3 includes: Construct the fuzzy error prediction function V(T e ), integrating the real-time frequency deviation and cumulative frequency error of the tidal signal main frequency and the water level floating frequency to calculate the response frequency error: Where, f tide (T e ) is the sliding window T e The main frequency of the tidal signal within, f gw (T e ) is the water level fluctuation frequency calculated by the superposition algorithm model, f tide (T e )-f gw (T e ) is the real-time frequency deviation, α is the fuzzy adaptive gain coefficient; Construct a dynamic optimization model of permeability coefficient based on Kalman filtering to predict the permeability coefficient adjustment 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 )]; In the formula, if ΔK pred (t i ) is a positive value, indicating an increase in the permeability coefficient K(t i ), if ΔK pred (t i ) is a negative value, indicating a decrease in 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, Z(t i ) is the observed value of the permeability coefficient.

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

6. A hydraulic conductivity optimization system based on tidal influence of offshore pumping test, which executes the hydraulic conductivity optimization method according to any one of claims 1 to 5, characterized in that: The system includes: a data acquisition module, a permeability coefficient estimation module, an error correction module and a parameter state update feedback module; The data acquisition module acquires sea level data through tidal monitoring equipment and maps it into a dynamic waveform containing 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; The permeability coefficient estimation module dynamically tracks the main frequency of the tidal signal through time domain translation based on the dynamic waveform of sea level data, quantifies the time domain translation scale corresponding to different tidal cycles; uses the time domain translation scale as the sliding window scale, instructs the conductivity sensor to collect groundwater ion concentration within each sliding window, instructs the high-precision water level sensor to collect 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, integrating the real-time frequency deviation and cumulative frequency error of the tidal signal main frequency and the water level floating frequency to characterize the response frequency error during the pumping test under the influence of the tide; construct a dynamic optimization model of the permeability coefficient based on Kalman filtering to predict the permeability coefficient adjustment amount; The parameter state update feedback module is used to construct a parameter update model based on discrete time, update the fuzzy adaptive gain coefficient, fuzzy error switching gain and fuzzy error prediction function linear proportional coefficient, and feed back the update results to the fuzzy error prediction function and the permeability coefficient dynamic optimization model.

7. The permeability coefficient optimization system based on tidal influence of offshore pumping test according to claim 6 is 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 in the dynamic waveform of the sea level height data by means of time domain equal-interval interpolation to generate an equal-interval sampling sequence; The pumping test data acquisition unit collects water level data and groundwater ion concentration in real time through a high-precision water level sensor and a conductivity sensor respectively.

8. The permeability coefficient optimization system based on tidal influence of offshore pumping test according to claim 6 is characterized in that: 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 threshold, calculate the superposition 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; The Darcy's law estimation unit is used to use the time domain translation scale as the sliding window scale and set the instruction interval to instruct the conductivity sensor to collect groundwater ion concentration and the high-precision water level sensor to collect 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 tidal influence of offshore pumping test according to claim 6 is 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, integrating the real-time frequency deviation and cumulative frequency error of the tidal signal main frequency and the water level floating frequency 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 hydraulic conductivity optimization system based on tidal influence of offshore pumping test according to claim 6, characterized in that: The parameter state update feedback module includes a parameter update model unit and a parameter state 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 fuzzy error prediction function linear proportional coefficient; The parameter state feedback unit is used to substitute the updated fuzzy adaptive gain coefficient, fuzzy error switching gain and fuzzy error prediction function linear proportional coefficient into the fuzzy error prediction function and the permeability coefficient dynamic optimization model.

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