Method, system and platform for rapidly detecting rainfall state based on group well effect

Abstract

The invention discloses a rapid rainfall state detection method, system and platform based on a group well effect, and the method comprises the steps: generating and obtaining corresponding first parameter data in real time based on the group well effect; according to the first reference data, generating and obtaining second parameter data which are located in precipitation wells in a target area and correspond to the first reference data; based on the first parameter data, processing second parameter data corresponding to each precipitation well in the target area in combination with a Kriging method and Monte Carlo simulation, and generating corresponding third parameter data; generating precipitation state prediction data corresponding to each precipitation well in the target area according to the third parameter data in combination with a mapping function; according to the system and the platform corresponding to the method, the stability of the precipitation effect can be guaranteed, and meanwhile construction safety is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of detection and processing of the precipitation state of a group of wells, and particularly relates to a method, a system and a platform for quickly detecting the precipitation state based on the group well effect. Background Art

[0002] At present, the group well effect refers to the phenomenon that during the precipitation process of engineering construction such as foundation pits, due to the simultaneous extraction of groundwater by multiple precipitation wells, the precipitation funnels of each precipitation well overlap with each other, thus affecting the precipitation effect of each well and the flow state of groundwater. Specifically, when the funnels of multiple precipitation wells are close to or overlap with each other, the flow path of groundwater will change, and the interference between adjacent precipitation wells may cause the pumping volume of some wells to decrease significantly, or even backwater phenomenon may occur. This interference effect will affect the balance and stability of the entire precipitation system, resulting in a decrease in the pumping capacity of the wells and a reduction in the drawdown of the water level. At the same time, the range of the groundwater level drop will be larger than that of a single well, and this expansion effect will affect the groundwater level and groundwater flow in the surrounding area, and may affect nearby buildings and the ecological environment.

[0003] Therefore, in view of the above technical problems and defects that when the funnels of multiple precipitation wells are close to or overlap with each other, the flow path of groundwater will change, the interference between adjacent precipitation wells may cause the pumping volume of some wells to decrease significantly, or even backwater phenomenon may occur, and this interference effect will affect the balance and stability of the entire precipitation system, resulting in a decrease in the pumping capacity of the wells and a reduction in the drawdown of the water level, it is urgently necessary to design and develop a method, a system and a platform for quickly detecting the precipitation state based on the group well effect. Summary of the Invention

[0004] In order to overcome the deficiencies and difficulties existing in the above-mentioned prior art, the purpose of the present invention is to provide a method, a system and a platform for quickly detecting the precipitation state based on the group well effect, so as to quickly evaluate the precipitation effect considering the group well effect, ensure the stability of the precipitation effect, and at the same time ensure the construction safety.

[0005] The first object of the present invention is to provide a method for quickly detecting the precipitation state based on the group well effect; the second object of the present invention is to provide a system for quickly detecting the precipitation state based on the group well effect; the third object of the present invention is to provide a platform for quickly detecting the precipitation state based on the group well effect.

[0006] The first object of the present invention is achieved as follows: The method includes the following steps:

[0007] Based on the group well effect, generate and obtain corresponding first parameter data in real time, and generate and obtain second parameter data corresponding to each precipitation well in the target area and corresponding to the first reference data according to the first reference data; wherein, the first parameter data is precipitation well layout scheme data corresponding to the group well effect; the second parameter data is the original groundwater level data corresponding to the positions of each precipitation well in the target area;

[0008] Based on the first parameter data, combine the Kriging method and Monte Carlo simulation to process the second parameter data corresponding to each precipitation well in the target area, and generate corresponding third parameter data; wherein, the third parameter data is groundwater level sample data corresponding to the second parameter data;

[0009] According to the third parameter data, and in combination with a mapping function, generate precipitation state prediction data corresponding to each precipitation well in the target area; wherein, the mapping function includes linear functions, exponential functions, and logarithmic functions.

[0010] Furthermore, the step of generating and obtaining corresponding first parameter data in real time based on the group well effect, and generating and obtaining second parameter data corresponding to each precipitation well in the target area and corresponding to the first reference data according to the first reference data further includes:

[0011] Generate and obtain preset water level acquisition frequency data, and generate water level change data corresponding to the positions of each precipitation well according to the preset water level acquisition frequency data;

[0012] Verify and process the water level change data, and remove abnormal data in the processed water level change data to generate corresponding second parameter data.

[0013] Furthermore, the step of combining the Kriging method and Monte Carlo simulation to process the second parameter data corresponding to each precipitation well in the target area based on the first parameter data and generating corresponding third parameter data further includes:

[0014] Construct a variogram model for describing the correlation between spatial variables;

[0015] Based on the variogram model, construct a Kriging system corresponding to the variogram model in real time, and generate corresponding Kriging weight data;

[0016] According to the Kriging weight data, perform weighted summation processing on the observation data corresponding to the groundwater level of the precipitation well, and generate third parameter data corresponding to the interpolation points of each precipitation well in the target area.

[0017] Furthermore, the step of constructing a variogram model for describing the correlation between spatial variables further includes:

[0018] Calculate and generate an experimental variogram corresponding to the position of the precipitation well, and generate a corresponding experimental variogram curve according to the experimental variogram; wherein, the calculation formula is specifically as follows:

[0019]

[0020] In the formula, h is the lag distance, N(h) is the number of observation pairs at a distance of h, and z(x i ) is the groundwater level value at the position x i of the precipitation well;

[0021] According to the experimental variogram curve, and in combination with the variogram model, fit and process the experimental variogram.

[0022] Furthermore, the method of processing the second parameter data corresponding to each precipitation well in the target area and generating the corresponding third parameter data based on the first parameter data, in combination with the Kriging method and Monte Carlo simulation, further includes:

[0023] Calculate and generate Kriging weight data corresponding to the positions of each precipitation well in the target area; wherein, the calculation formula is specifically as follows:

[0024]

[0025] In the formula, λ j is the Kriging weight, μ is the Lagrange multiplier, N is the number of observation points, x i and x j are the positions of precipitation wells i and j, and x 0 is the position of the interpolation point;

[0026] Calculate and generate third parameter data corresponding to the interpolation points of each precipitation well in the target area; wherein, the calculation formula is specifically as follows:

[0027]

[0028] In the formula, z * (x 0 ) is the estimated groundwater level value at the interpolation point x 0 , λ j is the Kriging weight, N is the number of observation points, x j is the position of precipitation well j, x 0 is the position of the interpolation point, x 0 is the position of the interpolation point, σ 2 (x 0 ) is the variance of the estimated groundwater level value, and μ is the Lagrange multiplier.

[0029] Further, generating precipitation status prediction data corresponding to each precipitation well in the target area based on the third parameter data and in combination with a mapping function further includes:

[0030] Generating and obtaining a mapping function corresponding to the third parameter data;

[0031] According to the mapping function and based on the third parameter data, mapping the groundwater level change status to the corresponding precipitation amount data or precipitation rate data, and generating characterization parameter data corresponding to the precipitation effect.

[0032] The second object of the present invention is achieved as follows: The system is applied to the method for quickly detecting the precipitation status based on the group well effect, and the system includes:

[0033] A data acquisition and generation unit, configured to generate and obtain corresponding first parameter data in real time based on the group well effect, and generate and obtain second parameter data corresponding to the first reference data and located in each precipitation well in the target area according to the first reference data; wherein, the first parameter data is precipitation well layout plan data corresponding to the group well effect; the second parameter data is the original groundwater level data corresponding to the positions of each precipitation well in the target area;

[0034] A data processing and generation unit, configured to process the second parameter data corresponding to each precipitation well in the target area based on the first parameter data in combination with the Kriging method and Monte Carlo simulation, and generate corresponding third parameter data; wherein, the third parameter data is groundwater level sample data corresponding to the second parameter data;

[0035] A data prediction and generation unit, configured to generate precipitation status prediction data corresponding to each precipitation well in the target area according to the third parameter data and in combination with a mapping function; wherein, the mapping function includes a linear function, an exponential function, and a logarithmic function.

[0036] Further, the data acquisition and generation unit further includes:

[0037] A first data generation module, configured to generate and obtain water level acquisition preset frequency data, and generate water level change data corresponding to the positions of each precipitation well according to the water level acquisition preset frequency data;

[0038] A first processing and generation module, configured to verify and process the water level change data, remove abnormal data in the processed water level change data, and generate corresponding second parameter data;

[0039] And / or, the data processing and generation unit further includes:

[0040] A data model construction module for constructing a variogram model used to describe the correlation between spatial variables;

[0041] A data construction and generation module for constructing a Kriging system corresponding to the variogram model in real time based on the variogram model and generating corresponding Kriging weight data;

[0042] A second processing and generation module for performing weighted summation processing on the observation data corresponding to the groundwater level of the precipitation well according to the Kriging weight data and generating third parameter data corresponding to each interpolation point of the precipitation well in the target area;

[0043] And / or, the data prediction and generation unit further includes:

[0044] A second data generation module for generating and obtaining a mapping function corresponding to the third parameter data;

[0045] A third processing and generation module for mapping the groundwater level change state to the corresponding precipitation data or precipitation rate data according to the mapping function and based on the third parameter data, and generating characterization parameter data corresponding to the precipitation effect.

[0046] Further, the data model construction module further includes:

[0047] A first calculation and generation module for calculating and generating an experimental variogram corresponding to the position of the precipitation well and generating a corresponding experimental variogram curve according to the experimental variogram; the specific calculation formula is as follows:

[0048]

[0049] In the formula, h is the lag distance, N(h) is the number of observation pairs at a distance of h, and z(x i ) is the groundwater level value at the position x of the precipitation well i ;

[0050] A fourth processing and generation module for fitting the experimental variogram according to the experimental variogram curve and in combination with the variogram model;

[0051] And / or, the data processing and generation unit further includes:

[0052] A second calculation and generation module for calculating and generating Kriging weight data corresponding to the positions of each precipitation well in the target area; the specific calculation formula is as follows:

[0053]

[0054] In the formula, λ jwhere \( \lambda \) is the Kriging weight, \( \mu \) is the Lagrange multiplier, \( N \) is the number of observation points, \( x \) i and \( x \) j are the positions of precipitation wells \( i \) and \( j \), and \( x \) 0 is the position of the interpolation point;

[0055] A third calculation and generation module, configured to calculate and generate third parameter data corresponding to each precipitation well interpolation point within the target area; wherein, the specific calculation formula is as follows:

[0056]

[0057] In the formula, \( z \) * ( \( x \) 0 ) is the estimated value of the groundwater level at the interpolation point \( x \) 0 , \( \lambda \) j is the Kriging weight, \( N \) is the number of observation points, \( x \) j is the position of precipitation well \( j \), \( x \) 0 is the position of the interpolation point, \( x \) 0 is the position of the interpolation point, and \( \sigma \) 2 ( \( x \) 0 ) is the variance of the estimated value of the groundwater level, and \( \mu \) is the Lagrange multiplier.

[0058] The third object of the present invention is achieved as follows: including a processor, a memory, and a quick detection of precipitation status platform control program based on the group well effect; wherein the processor executes the quick detection of precipitation status platform control program based on the group well effect, the quick detection of precipitation status platform control program based on the group well effect is stored in the memory, and the quick detection of precipitation status platform control program based on the group well effect implements the quick detection of precipitation status method based on the group well effect.

[0059] Based on the group well effect, the method of the present invention generates and obtains corresponding first parameter data in real time, and generates and obtains second parameter data corresponding to each precipitation well in the target area and corresponding to the first reference data according to the first reference data; wherein, the first parameter data is precipitation well layout plan data corresponding to the group well effect; the second parameter data is the original groundwater level data corresponding to the positions of each precipitation well in the target area; based on the first parameter data, the second parameter data corresponding to each precipitation well in the target area is processed by combining the Kriging method and Monte Carlo simulation, and corresponding third parameter data is generated; wherein, the third parameter data is groundwater level sample data corresponding to the second parameter data; according to the third parameter data, and in combination with a mapping function, precipitation state prediction data corresponding to each precipitation well in the target area is generated; wherein, the mapping function includes linear functions, exponential functions and logarithmic functions, as well as the corresponding system and platform of the method, which can ensure the stability of the precipitation effect and ensure construction safety at the same time.

[0060] That is to say, the solution of the present invention provides a technical solution for the precipitation well layout plan. First, obtain the groundwater level parameters at the positions of the precipitation wells in the target site. Then, use the Kriging method and Monte Carlo simulation to generate samples of the groundwater level parameters at each point in the target site. Based on these samples, calculate the characterization parameters of the precipitation effect through a mapping function, and evaluate whether the precipitation well layout plan needs to be optimized according to this parameter. Therefore, the method and device provided by the present invention help to optimize the precipitation well layout plan, thereby saving construction costs and improving the project execution speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings.

[0062] Figure 1 Schematic flow chart of the precipitation effect evaluation method in the embodiment of a fast detection precipitation state method based on the group well effect of the present invention;

[0063] Figure 2 Schematic structural diagram of the precipitation effect evaluation device in the embodiment of a fast detection precipitation state method based on the group well effect of the present invention;

[0064] Figure 3 Schematic structural diagram of the precipitation effect evaluation equipment system in the embodiment of a fast detection precipitation state method based on the group well effect of the present invention;

[0065] Figure 4Schematic diagram of the step flow of a method for quickly detecting precipitation status based on the group well effect according to the present invention;

[0066] Figure 5 Schematic diagram of the system architecture of a system for quickly detecting precipitation status based on the group well effect according to the present invention;

[0067] Figure 6 Schematic diagram of the platform architecture of a platform for quickly detecting precipitation status based on the group well effect according to the present invention.

[0068] The realization of the object, functional features and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0069] To better understand the object, technical solution and advantages of the present invention more clearly, the present invention will be further described below in conjunction with the accompanying drawings and specific implementation manners. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification.

[0070] The present invention can also be implemented or applied through other different specific examples. Various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention.

[0071] It should be noted that if there are directional indications (such as up, down, left, right, front, back...) involved in the embodiments of the present invention, then the directional indications are only used to explain the relative positional relationship and movement conditions between components in a specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly.

[0072] In addition, if there are descriptions such as "first", "second", etc. involved in the embodiments of the present invention, then the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. Secondly, the technical solutions between various embodiments can be combined with each other, but it must be based on the fact that those skilled in the art can implement it. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present invention.

[0073] Preferably, a method for quickly detecting precipitation status based on the group well effect of the present invention is applied to one or more terminals or servers. The terminal is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0074] The terminal may be a computing device such as a desktop computer, notebook, palm computer, and cloud server. The terminal can interact with customers through means such as a keyboard, mouse, remote control, touchpad, or voice control device.

[0075] The present invention aims to implement a method, system, and platform for quickly detecting precipitation status based on the group well effect.

[0076] As Figure 4 shown, it is a flowchart of the method for quickly detecting precipitation status based on the group well effect provided by an embodiment of the present invention.

[0077] In this embodiment, the method for quickly detecting precipitation status based on the group well effect can be applied to a terminal with a display function or a fixed terminal, and the terminal is not limited to personal computers, smart phones, tablet computers, desktop computers or all-in-one computers equipped with cameras, etc.

[0078] The method for quickly detecting precipitation status based on the group well effect can also be applied to a hardware environment composed of a terminal and a server connected to the terminal through a network. The network includes but is not limited to: wide area network, metropolitan area network or local area network. The method for quickly detecting precipitation status based on the group well effect in the embodiment of the present invention can be executed by the server, can also be executed by the terminal, or can be jointly executed by the server and the terminal.

[0079] For example, for a precipitation status terminal that needs to perform fast detection based on the group well effect, the fast detection precipitation status function provided by the method of the present invention can be directly integrated on the terminal, or a client for implementing the method of the present invention can be installed. Again, the method provided by the present invention can also run on devices such as servers in the form of a Software Development Kit (SDK), providing an interface for the fast detection precipitation status function based on the group well effect in the form of an SDK. The terminal or other devices can implement the fast detection precipitation status function based on the group well effect through the provided interface. The present invention will be further described below with reference to the accompanying drawings.

[0080] As Figures 1 - 4 shown, the present invention provides a method for fast detecting precipitation status based on the group well effect, and the method includes the following steps:

[0081] S01. Based on the group well effect, generate and obtain corresponding first parameter data in real time, and generate and obtain second parameter data corresponding to each precipitation well in the target area and corresponding to the first reference data according to the first reference data; wherein, the first parameter data is precipitation well layout plan data corresponding to the group well effect; the second parameter data is original groundwater level data corresponding to the positions of each precipitation well in the target area;

[0082] S02. Based on the first parameter data, combine the Kriging method and Monte Carlo simulation to process the second parameter data corresponding to each precipitation well in the target area, and generate corresponding third parameter data; wherein, the third parameter data is groundwater level sample data corresponding to the second parameter data;

[0083] S03. According to the third parameter data, and in combination with a mapping function, generate precipitation status prediction data corresponding to each precipitation well in the target area; wherein, the mapping function includes a linear function, an exponential function, and a logarithmic function.

[0084] The step of generating and obtaining corresponding first parameter data in real time based on the group well effect, and generating and obtaining second parameter data corresponding to each precipitation well in the target area and corresponding to the first reference data according to the first reference data further includes:

[0085] S011. Generate and obtain preset frequency data for water level acquisition, and generate water level change data corresponding to the positions of each precipitation well according to the preset frequency data for water level acquisition;

[0086] S012. Verify and process the water level change data, and remove abnormal data in the processed water level change data to generate corresponding second parameter data.

[0087] Based on the first parameter data, combining the Kriging method and Monte Carlo simulation to process the second parameter data corresponding to each precipitation well in the target area, and generating corresponding third parameter data, further comprising:

[0088] S021. Construct a variogram model for describing the correlation between spatial variables;

[0089] S022. Based on the variogram model, construct a Kriging system corresponding to the variogram model in real time, and generate corresponding Kriging weight data;

[0090] S023. According to the Kriging weight data, perform weighted summation processing on the observation data corresponding to the groundwater levels of the precipitation wells, and generate third parameter data corresponding to the interpolation points of each precipitation well in the target area.

[0091] The construction of a variogram model for describing the correlation between spatial variables further comprises:

[0092] S0211. Calculate and generate an experimental variogram corresponding to the position of the precipitation well, and generate a corresponding experimental variogram curve according to the experimental variogram; wherein, the calculation formula is as follows:

[0093]

[0094] In the formula, h is the lag distance, N(h) is the number of observation pairs at a distance of h, z(x i ) is the groundwater level value at the position x of the precipitation well i ;

[0095] S0212. According to the experimental variogram curve, and in combination with the variogram model, perform fitting processing on the experimental variogram.

[0096] Based on the first parameter data, combining the Kriging method and Monte Carlo simulation to process the second parameter data corresponding to each precipitation well in the target area, and generating corresponding third parameter data, further comprising:

[0097] S024. Calculate and generate Kriging weight data corresponding to the positions of each precipitation well in the target area; wherein, the calculation formula is as follows:

[0098]

[0099] In the formula, λ j is the Kriging weight, μ is the Lagrange multiplier, N is the number of observation points, x i and x j are the positions of precipitation wells i and j, and x 0 is the position of the interpolation point;

[0100] S025. Calculate and generate third parameter data corresponding to the interpolation points of each precipitation well in the target area; wherein the calculation formula is as follows:

[0101]

[0102] In the formula, z * (x 0 ) is the interpolation point x 0 The estimated value of groundwater level, λ j is the Kriging weight, N is the number of observation points, x j is the location of precipitation well j, x 0 is the position of the interpolation point, x 0 is the position of the interpolation point, σ 2 (x 0 ) is the variance of the groundwater level estimate and μ is the Lagrange multiplier.

[0103] The step of generating precipitation state prediction data corresponding to each precipitation well in the target area according to the third parameter data and in combination with a mapping function also includes:

[0104] S031. Generate and obtain a mapping function corresponding to the third parameter data;

[0105] S032. According to the mapping function and based on the third parameter data, the groundwater level change state is mapped to the corresponding precipitation data or precipitation rate data, and characterization parameter data corresponding to the precipitation effect is generated.

[0106] Specifically, in an embodiment of the present invention, the scheme of the present invention proposes a rapid evaluation scheme and device for dewatering effect taking into account the well group effect.

[0107] In a first aspect, an embodiment of the present invention provides a rapid evaluation scheme for precipitation effect taking into account the well group effect, the steps comprising:

[0108] S1. According to the precipitation well layout plan, collect and screen the groundwater level data of each precipitation well location in the target area;

[0109] S2, combining Kriging method and Monte Carlo simulation to estimate the groundwater level data of each point in the target area;

[0110] S3. Using the groundwater level data samples at each point in the target area, the mapping function is used to calculate the characterization parameters for evaluating the precipitation effect.

[0111] In a first possible implementation of the first aspect, the steps of collecting and screening groundwater level data at the locations of each precipitation well in the target area according to the precipitation well arrangement plan described in S1 include:

[0112] S11. Install groundwater level monitoring equipment at each precipitation well location according to the precipitation well layout plan.

[0113] S12. Regularly or continuously collect groundwater level data at a set frequency and record the water level changes at each precipitation well location.

[0114] S13. Transmit the collected groundwater level data to the data storage and regularly check and verify the accuracy and integrity of the data, exclude outliers, and ensure data quality.

[0115] In the second possible implementation manner of the first aspect, calculating the groundwater level data of each point in the target area according to the Kriging method and Monte Carlo simulation in S2 includes:

[0116] S21. Establish a variogram model for describing the correlation between spatial variables.

[0117] S22. Calculate the experimental variogram according to the following formula:

[0118]

[0119] where h is the lag distance, N(h) is the number of observation pairs at a distance of h, and z(x i ) is the groundwater level value at the precipitation well location x i . By calculating the variogram values corresponding to different lag distances, obtain the experimental variogram curve, and at the same time select a suitable theoretical variogram model to fit the experimental variogram.

[0120] S23. According to the variogram model, construct the Kriging system equation and solve the following system of equations to obtain the Kriging weights λ j :

[0121]

[0122] where λ j is the Kriging weight, μ is the Lagrange multiplier, N is the number of observation points, x i and x j are the positions of precipitation wells i and j, and x 0 is the position of the interpolation point.

[0123] S24. Use the Kriging weights λ j to perform weighted summation on the observed data to obtain the estimated groundwater level value of the interpolation point x 0 :

[0124]

[0125] At the same time, estimate the variance to represent the uncertainty of the interpolation:

[0126]

[0127] Among them, z * (x 0 ) is the estimated groundwater level at the interpolation point x 0 , λ j is the Kriging weight, N is the number of observation points, x j is the location of precipitation well j, x 0 is the location of the interpolation point, σ 2 (x 0 ) is the variance of the estimated groundwater level, and μ is the Lagrange multiplier.

[0128] S25. Based on the Kriging interpolation result, through Monte Carlo simulation, for each point in the site, based on the Kriging estimated value z * (x 0 ) and the variance σ 2 (x 0 ), generate a sufficient number of groundwater level data samples.

[0129] Combined with the first aspect or the first possible implementation manner of the first aspect, in the second possible implementation manner of the first aspect, the theoretical variogram model can select one of the exponential model, Gaussian model, spherical model, etc., to better fit the experimental variogram.

[0130] Combined with the first aspect or the second possible implementation manner of the first aspect, in the third possible implementation manner of the first aspect, in S3, using the groundwater level data samples of each point in the target area, the characterization parameters for evaluating the precipitation effect are calculated by using the mapping function, and the steps include:

[0131] S31. According to the specific situation, select different mapping functions, such as linear function, exponential function, logarithmic function, etc.

[0132] S32. According to the mapping function and the groundwater level data samples, map the groundwater level change to the precipitation amount or precipitation rate, and calculate the characterization parameters of the precipitation effect.

[0133] Second aspect, the embodiment of the present invention provides a rapid evaluation device for precipitation effect considering the group well effect, including:

[0134] An acquisition module, configured to collect and screen the groundwater level data at the positions of each precipitation well in the target area according to the precipitation well layout plan;

[0135] A processing module, which combines the Kriging method and Monte Carlo simulation to generate the groundwater level data of each point in the target area;

[0136] A calculation module, configured to use the groundwater level data samples of each point in the target area and calculate the characterization parameters for evaluating the precipitation effect by using a mapping function.

[0137] As Figure 1 shown, the flowchart of the rapid evaluation scheme for precipitation effect considering the group well effect provided by the embodiment of the present invention. The method steps of this embodiment include:

[0138] In S1, according to the precipitation well layout scheme, collecting and screening the groundwater level data at the positions of each precipitation well in the target area. The steps include:

[0139] S11. According to the precipitation well layout scheme, install groundwater level monitoring equipment at the position of each precipitation well.

[0140] S12. Regularly or continuously collect the groundwater level data at a set frequency, and record the water level changes at the positions of each precipitation well.

[0141] S13. Transmit the collected groundwater level data to the data storage, and regularly check and verify the accuracy and integrity of the data, exclude outliers, and ensure the data quality.

[0142] In S2, combining the Kriging method and Monte Carlo simulation to estimate the groundwater level data of each point in the target area, including:

[0143] S21. Establish a variogram model for describing the correlation between spatial variables.

[0144] S22. Calculate the experimental variogram according to the following formula:

[0145]

[0146] where h is the lag distance, N(h) is the number of observation pairs at a distance of h, and z(x i ) is the groundwater level value at the position x i of the precipitation well. By calculating the variogram values corresponding to different lag distances, an experimental variogram curve is obtained, and at the same time, a suitable theoretical variogram model is selected to fit the experimental variogram.

[0147] S23. According to the variogram model, construct the Kriging system equation and solve the following system of equations to obtain the Kriging weights λ j :

[0148]

[0149] where λ j is the Kriging weight, μ is the Lagrange multiplier, N is the number of observation points, x i and x j are the positions of precipitation wells i and j, x0 is the position of the interpolation point.

[0150] S24. Use the Kriging weight λ j to perform weighted summation on the observed data to obtain the estimated value of the groundwater level at the interpolation point x 0 :

[0151]

[0152] At the same time, estimate the variance to represent the uncertainty of the interpolation:

[0153]

[0154] where z * (x 0 ) is the estimated value of the groundwater level at the interpolation point x 0 , λ j is the Kriging weight, N is the number of observation points, x j is the position of the precipitation well j, x 0 is the position of the interpolation point, σ 2 (x 0 ) is the variance of the estimated value of the groundwater level, and μ is the Lagrange multiplier.

[0155] S25. Based on the Kriging interpolation result, through Monte Carlo simulation, for each point in the site, based on the Kriging estimated value z * (x 0 ) and the variance σ 2 (x 0 ), generate a sufficient number of groundwater level data samples.

[0156] In S3, using the groundwater level data samples of each point in the target area, the steps of calculating the characterization parameters for evaluating the precipitation effect by using the mapping function include:

[0157] S31. According to the specific situation, select different mapping functions, such as linear function, exponential function, logarithmic function, etc.

[0158] S32. According to the mapping function and the groundwater level data samples, map the change of the groundwater level to the precipitation amount or precipitation rate, and calculate the characterization parameters of the precipitation effect.

[0159] The embodiment of the present invention provides a technical solution for the layout of precipitation wells. First, the groundwater level parameters of the precipitation well locations in the target site are obtained. Then, the Kriging method and Monte Carlo simulation are used to generate samples of the groundwater level parameters at each point in the target site. Based on these samples, the characterization parameters of the precipitation effect are calculated by a mapping function, and the parameters are used to evaluate whether the layout of the precipitation wells needs to be optimized. Therefore, the method and device provided by the present invention are helpful to optimize the layout of the precipitation wells, thereby saving construction costs and improving the speed of project execution.

[0160] like Figure 2 As shown in FIG. 1 , it is a schematic diagram of the structure of a precipitation effect evaluation device provided by an embodiment of the present invention. Figure 2 As shown, the device of this embodiment may include: an acquisition module, a processing module and a calculation module. The acquisition module is used to collect and filter the groundwater level data of each precipitation well location in the target area according to the precipitation well layout plan; the processing module is used to calculate the groundwater level data of each point in the target area according to the Kriging method and Monte Carlo simulation; the calculation module is used to calculate and evaluate the characterization parameters of the precipitation effect of the precipitation well layout plan based on the samples of the groundwater level parameters of each point in the target site and the mapping function.

[0161] like Figure 3 As shown in FIG. 1 , it is a schematic diagram of the structure of an embodiment of a precipitation effect evaluation device provided by an embodiment of the present invention. Figure 3 As shown, the device is used to evaluate the effect of the dewatering well arrangement scheme, including at least one processing unit 301 (such as a central processing unit), a memory 302, and at least one communication bus 303, which is used to realize the connection and communication between the components. The processing unit 301 is used to execute the executable module stored in the memory 302, such as a computer program. The memory 302 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk memory.

[0162] In some embodiments, the memory 302 stores a program 304, which can be executed by the processing unit 301. The program 304 is used to implement a method for evaluating the dewatering effect of a dewatering well arrangement scheme, which includes:

[0163] S1. According to the precipitation well layout plan, collect and screen the groundwater level data of each precipitation well location in the target area;

[0164] S2, combining Kriging method and Monte Carlo simulation to estimate the groundwater level data of each point in the target area;

[0165] S3. Using the groundwater level data samples at each point in the target area, the mapping function is used to calculate the characterization parameters for evaluating the precipitation effect.

[0166] To achieve the above object, the present invention further provides a quick detection precipitation state system based on the group well effect, and the system is applied to the quick detection precipitation state method based on the group well effect, as Figure 5 shown, the system specifically includes:

[0167] A data acquisition and generation unit, configured to generate and acquire corresponding first parameter data in real time based on the group well effect, and generate and acquire second parameter data corresponding to each precipitation well in the target area and corresponding to the first reference data according to the first reference data; wherein, the first parameter data is precipitation well layout scheme data corresponding to the group well effect; the second parameter data is original groundwater level data corresponding to the positions of each precipitation well in the target area;

[0168] A data processing and generation unit, configured to process the second parameter data corresponding to each precipitation well in the target area based on the first parameter data, in combination with the Kriging method and Monte Carlo simulation, and generate corresponding third parameter data; wherein, the third parameter data is groundwater level sample data corresponding to the second parameter data;

[0169] A data prediction and generation unit, configured to generate precipitation state prediction data corresponding to each precipitation well in the target area according to the third parameter data and in combination with a mapping function; wherein, the mapping function includes a linear function, an exponential function, and a logarithmic function.

[0170] The data acquisition and generation unit further includes:

[0171] A first data generation module, configured to generate and acquire water level acquisition preset frequency data, and generate water level change data corresponding to the positions of each precipitation well according to the water level acquisition preset frequency data;

[0172] A first processing and generation module, configured to verify and process the water level change data, and remove abnormal data in the processed water level change data, and generate corresponding second parameter data;

[0173] And / or, the data processing and generation unit further includes:

[0174] A data model construction module, configured to construct a variogram model for describing the correlation between spatial variables;

[0175] A data construction and generation module, configured to construct a Kriging system corresponding to the variogram model in real time based on the variogram model, and generate corresponding Kriging weight data;

[0176] The second processing and generating module is configured to perform weighted summation processing on the observation data corresponding to the groundwater level of the precipitation wells according to the Kriging weight data, and generate third parameter data corresponding to each interpolation point of the precipitation wells in the target area;

[0177] And / or, the data prediction and generating unit further includes:

[0178] The second data generating module is configured to generate and obtain a mapping function corresponding to the third parameter data;

[0179] The third processing and generating module is configured to map the groundwater level change state to the corresponding precipitation data or precipitation rate data according to the mapping function and based on the third parameter data, and generate characterization parameter data corresponding to the precipitation effect.

[0180] The data model construction module further includes:

[0181] The first calculation and generating module is configured to calculate and generate an experimental variogram corresponding to the position of the precipitation well, and generate a corresponding experimental variogram curve according to the experimental variogram; wherein, the calculation formula is specifically as follows:

[0182]

[0183] In the formula, h is the lag distance, N(h) is the number of observation pairs at a distance of h, z(x i ) is the groundwater level value at the position x of the precipitation well i ;

[0184] The fourth processing and generating module is configured to fit the experimental variogram according to the experimental variogram curve and in combination with the variogram model;

[0185] And / or, the data processing and generating unit further includes:

[0186] The second calculation and generating module is configured to calculate and generate Kriging weight data corresponding to each precipitation well position in the target area; wherein, the calculation formula is specifically as follows:

[0187]

[0188] In the formula, λ j is the Kriging weight, μ is the Lagrange multiplier, N is the number of observation points, x i and x j are the positions of the precipitation wells i and j, and x 0 is the position of the interpolation point;

[0189] The third calculation and generating module is configured to calculate and generate third parameter data corresponding to each precipitation well interpolation point in the target area; wherein, the calculation formula is specifically as follows:

[0190]

[0191] In the formula, z * (x 0 ) is the estimated value of the groundwater level at the interpolation point x 0 , λ j is the Kriging weight, N is the number of observation points, x j is the location of precipitation well j, x 0 is the location of the interpolation point, x 0 is the location of the interpolation point, σ 2 (x 0 ) is the variance of the estimated value of the groundwater level, and μ is the Lagrange multiplier.

[0192] In the embodiment of the system solution of the present invention, the method steps involved in the quick detection of precipitation status based on the group well effect have been described in detail above. That is to say, the functional modules in the system are used to implement the steps or sub-steps in the above method embodiment, which will not be elaborated here.

[0193] To achieve the above object, the present invention also provides a quick detection precipitation status platform based on the group well effect, as Figure 6 shown, including a processor, a memory, and a quick detection precipitation status platform control program based on the group well effect; wherein, the processor executes the quick detection precipitation status platform control program based on the group well effect, and the quick detection precipitation status platform control program based on the group well effect is stored in the memory, and the quick detection precipitation status platform control program based on the group well effect realizes the method steps of the quick detection precipitation status based on the group well effect. For example:

[0194] S01. Based on the group well effect, generate and obtain corresponding first parameter data in real time, and generate and obtain second parameter data corresponding to each precipitation well in the target area and corresponding to the first reference data according to the first reference data; wherein, the first parameter data is the precipitation well layout plan data corresponding to the group well effect; the second parameter data is the original groundwater level data corresponding to the positions of each precipitation well in the target area;

[0195] S02. Based on the first parameter data, combine the Kriging method and Monte Carlo simulation to process the second parameter data corresponding to each precipitation well in the target area, and generate corresponding third parameter data; wherein, the third parameter data is the groundwater level sample data corresponding to the second parameter data;

[0196] S03. Generate precipitation status prediction data corresponding to each precipitation well in the target area according to the third parameter data and in combination with a mapping function, where the mapping function includes a linear function, an exponential function, and a logarithmic function.

[0197] The specific details of the steps have been elaborated above and will not be repeated here.

[0198] In the embodiment of the present invention, the built-in processor of the quick detection precipitation status platform based on the group well effect can be composed of integrated circuits. For example, it can be composed of a single packaged integrated circuit, or can be composed of multiple integrated circuits with the same or different functions packaged, including the combination of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips, etc. The processor uses various interfaces and lines to connect to each component, and by running or executing the programs or units stored in the memory, as well as calling the data stored in the memory, it can execute various functions of the quick detection precipitation status based on the group well effect and process data.

[0199] The memory is used to store program codes and various data, is installed in the quick detection precipitation status platform based on the group well effect, and can achieve high-speed and automatic access to programs or data during operation.

[0200] The memory includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electrically-erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc memories, magnetic disk memories, tape memories, or any other computer-readable medium that can be used to carry or store data.

[0201] Based on the group well effect, the method of the present invention generates and obtains corresponding first parameter data in real time, and generates and obtains second parameter data corresponding to each precipitation well in the target area and corresponding to the first reference data according to the first reference data; wherein, the first parameter data is the precipitation well layout plan data corresponding to the group well effect; the second parameter data is the original groundwater level data corresponding to the positions of each precipitation well in the target area; based on the first parameter data, the second parameter data corresponding to each precipitation well in the target area is processed by combining the Kriging method and Monte Carlo simulation, and corresponding third parameter data is generated; wherein, the third parameter data is the groundwater level sample data corresponding to the second parameter data; according to the third parameter data, and in combination with the mapping function, precipitation state prediction data corresponding to each precipitation well in the target area is generated; wherein, the mapping function includes linear function, exponential function and logarithmic function, as well as the corresponding system and platform of the method, which can ensure the stability of the precipitation effect and ensure construction safety at the same time.

[0202] That is to say, the solution of the present invention provides a technical solution for the precipitation well layout plan. First, obtain the groundwater level parameters at the positions of the precipitation wells in the target site. Then, use the Kriging method and Monte Carlo simulation to generate samples of the groundwater level parameters at each point in the target site. Based on these samples, calculate the characterization parameters of the precipitation effect through the mapping function, and evaluate whether the precipitation well layout plan needs to be optimized according to this parameter. Therefore, the method and device provided by the present invention help to optimize the precipitation well layout plan, thereby saving construction costs and improving the project execution speed.

[0203] In other words, the embodiment of the present invention provides a rapid evaluation solution and device for precipitation effect considering the group well effect, including: S1. According to the precipitation well layout plan, collect and screen the groundwater level data at the positions of each precipitation well in the target area; S2. Combine the Kriging method and Monte Carlo simulation to calculate the groundwater level data at each point in the target area; S3. Use the groundwater level data samples at each point in the target area and apply the mapping function to calculate the characterization parameters for evaluating the precipitation effect. Therefore, the rapid evaluation solution and device for precipitation effect considering the group well effect provided by the present invention help to reasonably cope with the group well effect, can ensure the safety and effect of the precipitation project, and reduce the construction risk.

[0204] The above embodiments only represent several implementation manners of the present invention, and the description thereof is relatively specific and detailed, but it should not be construed as a limitation to the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

Claims

1. A method for quickly detecting precipitation status based on the well group effect, characterized in that: The method comprises the steps of: Based on the well group effect, corresponding first parameter data is generated and obtained in real time, and second parameter data corresponding to the first reference data and located in each precipitation well in the target area is generated and obtained according to the first reference data; wherein the first parameter data is precipitation well arrangement scheme data corresponding to the well group effect; and the second parameter data is the original groundwater level data corresponding to the location of each precipitation well in the target area; Based on the first parameter data, the second parameter data corresponding to each precipitation well in the target area is processed by combining the Kriging method and the Monte Carlo simulation, and the corresponding third parameter data is generated; wherein the third parameter data is the groundwater level sample data corresponding to the second parameter data; According to the third parameter data and in combination with a mapping function, precipitation state prediction data corresponding to each precipitation well in the target area is generated; wherein the mapping function includes a linear function, an exponential function and a logarithmic function.

2. According to claim 1, a method for quickly detecting precipitation status based on the well group effect is characterized in that: Based on the well group effect, the corresponding first parameter data is generated and obtained in real time, and the second parameter data corresponding to the first reference data and located in the target area of ​​the precipitation wells is generated and obtained according to the first reference data, and further includes: Generate and obtain water level collection preset frequency data, and generate water level change data corresponding to the position of each precipitation well according to the water level collection preset frequency data; The water level change data is verified and processed, and abnormal data in the water level change data is removed and processed to generate corresponding second parameter data.

3. According to claim 1, a method for quickly detecting precipitation status based on the well group effect is characterized in that: Based on the first parameter data, combining the Kriging method and the Monte Carlo simulation to process the second parameter data corresponding to each precipitation well in the target area and generate the corresponding third parameter data, further comprising: Construct a variogram model to describe the correlation between spatial variables; Based on the variation function model, a Kriging system corresponding to the variation function model is constructed in real time, and corresponding Kriging weight data is generated; According to the Kriging weight data, the observation data corresponding to the groundwater level of the precipitation well is processed by weighted summation, and the third parameter data corresponding to the interpolation points of each precipitation well in the target area are generated.

4. According to claim 3, a method for quickly detecting precipitation status based on the well group effect is characterized in that: The construction of a variogram model for describing the correlation between spatial variables also includes: The experimental variation function corresponding to the location of the precipitation well is calculated and generated, and the corresponding experimental variation function curve is generated according to the experimental variation function; wherein the calculation formula is as follows: Where h is the lag distance, N(h) is the number of observations with distance h, and z(x i ) is the location of the precipitation well x i The groundwater level at The experimental variogram is processed according to the experimental variogram curve and in combination with the variogram model fitting.

5. A method for quickly detecting precipitation status based on the well group effect according to claim 1 or 3, characterized in that: Based on the first parameter data, combining the Kriging method and the Monte Carlo simulation to process the second parameter data corresponding to each precipitation well in the target area and generate the corresponding third parameter data, further comprising: Calculate and generate Kriging weight data corresponding to the location of each precipitation well in the target area; the calculation formula is as follows: In the formula, λ j is the Kriging weight, μ is the Lagrange multiplier, N is the number of observation points, x i and x j is the location of precipitation wells i and j, and x0 is the location of the interpolation point; The third parameter data corresponding to the interpolation points of each precipitation well in the target area are calculated and generated; wherein the calculation formula is as follows: In the formula, z * (x0) is the estimated groundwater level at the interpolation point x0, λ j is the Kriging weight, N is the number of observation points, x j is the location of the precipitation well j, x0 is the location of the interpolation point, x0 is the location of the interpolation point, σ 2 (x0) is the variance of the estimated groundwater level and μ is the Lagrange multiplier.

6. According to claim 1, a method for quickly detecting precipitation status based on the well group effect is characterized in that: The step of generating precipitation state prediction data corresponding to each precipitation well in the target area according to the third parameter data and in combination with a mapping function also includes: Generate and obtain a mapping function corresponding to the third parameter data; According to the mapping function and based on the third parameter data, the groundwater level change state is mapped to the corresponding precipitation data or precipitation rate data, and characterization parameter data corresponding to the precipitation effect is generated.

7. A quick detection system for precipitation status based on the well group effect, characterized in that: The system is applied to the method for quickly detecting precipitation status based on the well group effect as claimed in any one of claims 1 to 6, and the system comprises: A data acquisition and generation unit is used to generate and acquire corresponding first parameter data in real time based on the well group effect, and to generate and acquire second parameter data corresponding to the first reference data for each precipitation well located in the target area according to the first reference data; wherein the first parameter data is precipitation well arrangement scheme data corresponding to the well group effect; and the second parameter data is original groundwater level data corresponding to the location of each precipitation well in the target area; A data processing and generating unit, configured to process the second parameter data corresponding to each precipitation well in the target area based on the first parameter data in combination with the Kriging method and the Monte Carlo simulation, and generate corresponding third parameter data; wherein the third parameter data is groundwater level sample data corresponding to the second parameter data; A data prediction generation unit is used to generate precipitation state prediction data corresponding to each precipitation well in the target area based on the third parameter data and in combination with a mapping function; wherein the mapping function includes a linear function, an exponential function and a logarithmic function.

8. A quick detection system for precipitation status based on the well group effect according to claim 7, characterized in that: The data acquisition generation unit further includes: A first data generation module is used to generate and obtain water level acquisition preset frequency data, and generate water level change data corresponding to the position of each precipitation well according to the water level acquisition preset frequency data; A first processing and generating module, used for verifying and processing the water level change data, removing abnormal data in the water level change data, and generating corresponding second parameter data; And / or, the data processing and generating unit further includes: A data model building module is used to build a variogram model for describing the correlation between spatial variables; A data construction and generation module is used to construct a Kriging system corresponding to the variogram model in real time based on the variogram model, and generate corresponding Kriging weight data; A second processing and generating module is used to perform weighted summation processing on the observation data corresponding to the groundwater level of the precipitation well according to the Kriging weight data, and generate third parameter data corresponding to the interpolation points of each precipitation well in the target area; And / or, the data prediction generation unit further includes: A second data generating module, used to generate and obtain a mapping function corresponding to the third parameter data; The third processing generation module is used to map the groundwater level change state to corresponding precipitation data or precipitation rate data according to the mapping function and based on the third parameter data, and generate characterization parameter data corresponding to the precipitation effect.

9. A quick detection system for precipitation status based on the well group effect according to claim 7 or 8, characterized in that: The data model building module further includes: The first calculation and generation module is used to calculate and generate an experimental variation function corresponding to the location of the precipitation well, and generate a corresponding experimental variation function curve according to the experimental variation function; wherein the calculation formula is as follows: Where h is the lag distance, N(h) is the number of observations with distance h, and z(x i ) is the location of the precipitation well x i The groundwater level at A fourth processing and generating module, used for fitting and processing the experimental variogram according to the experimental variogram curve and in combination with the variogram model; And / or, the data processing and generating unit further includes: The second calculation generation module is used to calculate and generate Kriging weight data corresponding to the location of each precipitation well in the target area; wherein the calculation formula is as follows: In the formula, λ j is the Kriging weight, μ is the Lagrange multiplier, N is the number of observation points, x i and x j is the location of precipitation wells i and j, and x0 is the location of the interpolation point; The third calculation generation module is used to calculate and generate third parameter data corresponding to the interpolation points of each precipitation well in the target area; wherein the calculation formula is as follows: In the formula, z * (x0) is the estimated groundwater level at the interpolation point x0, λ j is the Kriging weight, N is the number of observation points, x j is the location of the precipitation well j, x0 is the location of the interpolation point, x0 is the location of the interpolation point, σ 2 (x0) is the variance of the estimated groundwater level and μ is the Lagrange multiplier.

10. A quick detection platform for precipitation status based on the well group effect, characterized in that: It includes a processor, a memory, and a platform control program for quickly detecting the state of precipitation based on a group of wells effect; wherein the platform control program for quickly detecting the state of precipitation based on a group of wells effect is executed by the processor, and the platform control program for quickly detecting the state of precipitation based on a group of wells effect is stored in the memory, and the platform control program for quickly detecting the state of precipitation based on a group of wells effect implements the method for quickly detecting the state of precipitation based on a group of wells effect as described in any one of claims 1 to 6.

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