CN116610897A - Tailing pond drainage data fitting method, system, equipment and storage medium - Google Patents
Tailing pond drainage data fitting method, system, equipment and storage medium Download PDFInfo
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Abstract
The application provides a tailing pond drainage data fitting method, a system, equipment and a storage medium, wherein the method comprises the following steps: preprocessing original drainage data by utilizing a hydraulic physical model to obtain an initial drainage data point set, carrying out interpolation calculation on the initial drainage data point set, obtaining inflection points of the initial drainage data point set according to calculation results, grouping the initial drainage data point set according to the inflection points to obtain a plurality of grouping data, respectively fitting the plurality of grouping data to obtain a plurality of fitting function groups, combining two adjacent fitting functions in each fitting function group to obtain a plurality of combination functions, sequentially carrying out residual calculation on each combination function, and taking a target combination function with the minimum residual as a target fitting function of the initial drainage data point set. According to the application, discrete data points can be fitted into a continuous function, and meanwhile, through residual analysis, the fitting precision is improved, so that the flood regulating calculation result is more accurate, and the method is more suitable for programmed calculation of the flood regulating calculation of the tailing pond.
Description
Technical Field
The application relates to the field of flow monitoring, in particular to a tailing pond drainage data fitting method, a system, equipment and a storage medium.
Background
The flood control algorithm is an important work for ensuring flood control safety of the tailing pond in the flood season, and the flood control algorithm is changed from traditional manual calculation to programmed calculation under the background of continuous development of intelligent automation technology. The characteristics of programmed calculation determine each step of the flood control calculation of the tailing pond, such as flood calculation, drainage calculation, water level-reservoir capacity curve and the like, a certain analytical formula is needed, the drainage flow is used as one of key parameters of the flood control calculation, two common calculation modes are an empirical formula method and a physical experiment method. For general flood drainage systems, an analytic formula for programmed calculation can be directly obtained by adopting an empirical formula method, but for complex systems, an empirical formula cannot be directly adopted, and a hydraulic physical model test needs to be carried out to determine the relationship between the water level and the drainage flow.
The data obtained by the hydraulic physical model test is generally the corresponding relation between the water head and the leakage flow, generally the scattered points, and cannot be directly brought into programmed calculation. On the one hand, if the leakage flow is obtained by adopting a method of adjacent point difference values, calculation is inaccurate possibly due to insufficient data point quantity; on the other hand, the flow state of the water flow of the complex flood drainage system is complex, if scattered point fitting is directly adopted, an analytical formula can be obtained, but the accuracy is low, and the result of flood control calculation is inaccurate.
Disclosure of Invention
In view of the above, the application aims to overcome the defects in the prior art and provide a tailing pond drainage data fitting method, a tailing pond drainage data fitting system, tailing pond drainage data fitting equipment and a storage medium.
The application provides the following technical scheme:
in a first aspect, the application provides a method for fitting drainage data of a tailing pond, which comprises the following steps:
preprocessing original drainage data by utilizing a hydraulic physical model to obtain an initial drainage data point set;
performing interpolation calculation on the initial drain data point set, and obtaining an inflection point of the initial drain data point set according to a calculation result;
grouping the initial drainage data point set according to the turning points to obtain a plurality of grouping data;
fitting is carried out on the plurality of grouping data respectively, so that a plurality of fitting function sets are obtained;
combining two adjacent fitting functions in each fitting function group to obtain a plurality of combined functions;
and carrying out residual calculation on each combination function in sequence, and taking the target combination function with the minimum residual as a target fitting function of the initial drainage data point set.
In one embodiment, the original drainage data includes a plurality of pressure heads and a plurality of spills, the preprocessing of the original drainage data by using a hydraulic physical model to obtain an initial drainage data point set includes:
and determining the flood discharge amount corresponding to each pressure water head through the hydraulic physical model, and constructing the initial flood discharge data point set according to the flood discharge amount corresponding to each pressure water head.
In one embodiment, the interpolating calculation is performed on the initial drain data point set, and the inflection point of the initial drain data point set is obtained according to the calculation result, including:
dividing two data points with the initial drainage data point concentration distance of a preset interval into a group to obtain a plurality of data point groups;
calculating a predicted pressure water head corresponding to each data point group according to a linear interpolation method;
and calculating according to the predicted pressure water head to obtain predicted flood discharge, and determining the inflection point according to the difference value of the predicted flood discharge and the actual flood discharge.
In one embodiment, the grouping the initial drain data point set according to the turning point to obtain a plurality of grouping data includes:
and grouping the initial drain flow data point set according to the quantity of inflection points to obtain a plurality of grouping data.
In one embodiment, the fitting is performed on the plurality of grouping data to obtain a plurality of fitting function sets, including:
and fitting each grouping data by using a polynomial function to obtain a plurality of fitting function sets.
In one embodiment, the combining two adjacent fitting functions in each fitting function set to obtain a plurality of combining functions includes:
and combining two adjacent fitting functions in each fitting function group, and carrying out connection treatment on each two combined fitting functions at the inflection point to obtain a plurality of combined functions.
In one embodiment, the calculating a residual error for each of the combination functions sequentially takes the target combination function with the smallest residual error as the target fitting function of the initial leakage flow data point set, and the method includes:
and carrying out residual analysis on each combination function, calculating the accumulated residual of each combination function, and taking the target combination function with the smallest accumulated residual as the target fitting function.
In a second aspect, the present application also provides a tailings pond drainage data fitting system, including:
the preprocessing module is used for preprocessing the original drainage data by utilizing the hydraulic physical model to obtain an initial drainage data point set;
the inflection point acquisition module is used for carrying out interpolation calculation on the initial drainage data point set and obtaining inflection points of the initial drainage data point set according to a calculation result;
the grouping module is used for grouping the initial drainage data point set according to the turning points to obtain a plurality of grouping data;
the fitting module is used for respectively fitting the plurality of grouping data to obtain a plurality of fitting function groups;
the combination module is used for combining two adjacent fitting functions in each fitting function group to obtain a plurality of combination functions;
and the analysis module is used for sequentially carrying out residual calculation on each combination function, and taking the target combination function with the minimum residual as the target fitting function of the initial leakage flow data point set.
In a third aspect, the present application also provides a computer device comprising a memory storing a computer program and at least one processor for executing the computer program to implement the tailings pond drainage data fitting method as described in the first aspect.
In a fourth aspect, the present application also provides a computer readable storage medium storing a computer program which, when executed, implements the tailings pond drainage data fitting method according to the first aspect.
The embodiment of the application has the following beneficial effects:
the fitting method for the drainage data of the tailing pond can fit discrete data points into a continuous function, can realize program calculation, realizes the optimization of the fitting function through residual analysis, further improves the fitting precision, ensures that the result of the flood control algorithm is more accurate, and is more suitable for programmed calculation of the drainage algorithm of the tailing pond.
In order to make the above objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 shows a flow chart of a method for fitting drainage data of a tailings pond;
FIG. 2 shows a flow chart of an inflection point acquisition method;
FIG. 3 shows a flow chart of a fitting function join processing method;
FIG. 4 shows a graph of the fitting effect of an objective fitting function;
fig. 5 shows a framework structure diagram of a tailings pond drainage data fitting system.
Description of main reference numerals:
500. a tailing pond drainage data fitting system; 501. a preprocessing module; 502. an inflection point acquisition module; 503. a grouping module; 504. fitting a module; 505. a combination module; 506. and an analysis module.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the application.
It will be understood that when an element is referred to as being "fixed to" another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. In contrast, when an element is referred to as being "directly on" another element, there are no intervening elements present. The terms "vertical," "horizontal," "left," "right," and the like are used herein for illustrative purposes only.
In the present application, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art according to the specific circumstances.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the templates herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
Example 1
Referring to fig. 1, fig. 1 is a flowchart of a method for fitting drainage data of a tailing pond, which is provided by an embodiment of the present application, and includes:
s101, preprocessing original drainage data by utilizing a hydraulic physical model to obtain an initial drainage data point set.
The original drainage data generally comprises pressure water heads and flood discharge amounts, after the original drainage data are obtained, the hydraulic physical model is utilized to determine the flood discharge amounts corresponding to each pressure water head, and an initial drainage data point set is constructed according to the flood discharge amounts corresponding to each pressure water head so as to provide a data basis for subsequent calculation.
S102, carrying out interpolation calculation on the initial drain flow data point set, and obtaining inflection points of the initial drain flow data point set according to a calculation result.
Assuming that the data point set obtained by the object model leakage flow experiment isThe method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>The unit is a pressure water head; />The unit is square meter/second for discharging flow; />Is a positive integer.
Referring to fig. 2, step S102 further includes:
s1021, dividing two data points with the initial drain data point concentration distance of a preset interval into a group to obtain a plurality of data point groups.
Two points with a spacing of 1 are grouped into one group, namely:for example: assuming 6 data points, the 6 points are divided into: four groups of points 1 and 3, points 2 and 4, points 3 and 5, and points 4 and 6, namely, one group of points are separated, and data points are grouped, so that subsequent data processing is facilitated.
S1022, calculating the predicted pressure water head corresponding to each data point group according to a linear interpolation method.
Solving by adopting a linear interpolation methodAt->Corresponding values on the straight line determined by the two points are recorded as。
S1023, calculating to obtain predicted flood discharge according to the predicted pressure water head, and determining the inflection point according to the difference value of the predicted flood discharge and the actual flood discharge.
Calculation ofAnd->Absolute difference between, namely: />，/>The leakage flow corresponding to the (m+1) th data point;
comparison ofValue size, ->Maximum value->Corresponding point->、/>2 nd maximum value->Corresponding point->；
The number of inflection points is set to be 1 or 2.
According to the leakage flow principle, the flow state is generally changed among free flow, half-pressure flow and pressure flow, 2 inflection points can basically cover most situations, and a user can select other numbers of inflection points according to actual situations.
When the number of inflection points is 1, the inflection points areWhen the number of inflection points is 2, the inflection point is +.>And->。
S103, grouping the initial drain flow data point set according to the turning points to obtain a plurality of grouping data.
And grouping the initial drain flow data point set according to the number of inflection points.
When the number of inflection points is 1, the inflection points areThen the initial set of drain data points is divided into 2 groups, wherein,
group 1:。
group 2:。
when the number of inflection points is 2, it is assumed that the 1 st inflection point isThe 2 nd inflection point is->Then the initial set of drain data points is divided into 3 groups.
First, determineAnd->Relative position on the coordinate axis, if +.>Is positioned at->Left side, then:
group 1:。
group 2:。
group 3:。
if it isIs positioned at->Right side, then:
group 1:。
group 2:。
group 3:。
grouping is carried out from the inflection points, and point sets at two sides of the inflection points are respectively fitted, so that the obtained function precision is higher.
And S104, fitting the plurality of grouping data respectively to obtain a plurality of fitting function sets.
And fitting each piece of grouping data by using a polynomial function to obtain a plurality of fitting function groups, wherein the degree of the polynomial is 1-4 times, and the degree of the fitting goodness is more than or equal to 0.98 in a fitting interval in a monotonically increasing manner.
The polynomial function is adopted as a fitting function form, the inflection points of the fitted function can be subjected to connection processing, and if other functions such as an exponential function, a power function and a logarithmic function are adopted for fitting, the inflection points cannot be subjected to connection processing after fitting.
The method comprises the steps of analyzing the shape characteristics of a large number of drainage curves, determining a scheme of the sectional fitting of the drainage model data points, finding inflection points of the curves by judging the change rate of slopes of adjacent data points, grouping the data points firstly and then fitting the data points by taking the inflection points as a boundary, and improving the fitting precision.
S105, combining two adjacent fitting functions in each fitting function group to obtain a plurality of combined functions.
Taking polynomial fitting and inflection point number of 1 as an example, the combination function is shown in the following table.
Referring to fig. 3, step S105 further includes:
s1051, combining two adjacent fitting functions in each fitting function group, and carrying out connection treatment on each two combined fitting functions at the inflection point to obtain a plurality of combined functions.
When the number of inflection points is 1:
(1) supposing inflection pointsTwo sets of data on the left and right, fitting function is +.>And->；
(2) Will beThe points are substituted with +.>And->Obtain->And->；
(3) Function ofRewritten as +.>；
When the number of inflection points is 2:
supposing inflection pointsAt inflection point->The fitting function of the three sets of data is +.about.>、And->；
Will beThe points are substituted with +.>And->Obtain->And->；
Function ofRewritten as +.>；
Will beCarry in->And->Obtain->And->；
Function ofRewritten as +.>。
By processing the fitting function at the inflection point, the function fitted by the data points of the leakage flow physical model test can be ensured to be continuous, so that the program calculation requirement is met.
S106, carrying out residual calculation on each combination function in sequence, and taking the target combination function with the minimum residual as a target fitting function of the initial drainage data point set.
Assumed point setThe piecewise function obtained after the piecewise fitting and the linking treatment is；
Will beRespectively substituting the function +.>Obtain->；
Cumulative residual isAnd selecting a target combination function with the smallest accumulated residual as a target fitting function of the initial drain flow data point set.
In the embodiment, the difference between the actual test value and the fitting value is counted, so that the best fitting function is optimized in a plurality of groups of fitting functions, and the accuracy of the fitting curve is further improved.
The following is a process for fitting and testing drainage data of a tailing pond by using the method of the application.
The physical model test results of the flood drainage system of a certain tailing pond are shown in table 1.
TABLE 1 physical model test values for tailings pond drainage
(1) The data point set is divided into 8 pairs in this example in two pairs of points spaced 1 apart, as shown in table 2.
Table 2 data point junction pairs
(2) Solving by adopting a linear interpolation methodAt->Corresponding values on the straight line determined by the two points are recorded asThe->As shown in table 3.
TABLE 3 Linear interpolation results
(3) Calculation ofAnd->Absolute difference between>。
In this exampleAs shown in table 4.
TABLE 4 Linear interpolation results
(4) An inflection point is determined, and when the number of inflection points is 1,corresponding->Maximum, then->Is an inflection point; when the number of inflection points is 2, the +.>Corresponding->Maximum (max)/(min)>Corresponding->Second, then->Andis an inflection point.
(5) According to the inflection point situation, data are grouped, and when the number of inflection points is 1, 2 groups are arranged:
group 1:
group 2:
when the number of inflection points is 2, 3 groups are divided, becauseAt->Left side, then:
group 1:
group 2:
group 3:
(6) taking the number of inflection points of 1 as an example, the data points of the groups are respectively fitted, and two groups of fitting functions are shown in table 5 and table 6.
Table 5 group 1 fitting function
Table 6 group 2 fitting function
(7) Excluding the fitting functions of group 1 and group 2, where the goodness of fit is < 0.98 and the non-monotonically increasing fitting functions, the 4 fitting functions resulting in group 1 and the 4 fitting functions of group 2 were combined separately to result in 6 piecewise functions, as shown in table 7.
TABLE 7 piecewise function combination
(7) Processing the joint points of the 6-group segment function, taking group 1 as an exampleTwo equations are carried in:
and->The difference between them is:
due toThe function after processing is:
namely:
the modified piecewise functions obtained after all the 6-component piecewise functions are joined together are shown in table 8.
Table 8 modified piecewise function
Residual analysis is performed on the 6 groups of modified piecewise functions, taking the 1 st group of modified piecewise functions as an example, and the original data point set of the physical model test is brought into the modified piecewise functions, so as to obtain the accumulated residual of the modified functions, which is shown in table 9.
Table 9 group 1 correction fit function cumulative residual
The 6 sets of modified piecewise function cumulative residuals are shown in table 10.
Table 10 correction fitting function cumulative residual
As can be seen from table 10, the fitting function modified in group 6 has the smallest cumulative residual, and the cumulative residual at all data points is 1.787. The fitting result when the number of inflection points is 2 is calculated by adopting the same method, and the minimum value of the accumulated residual errors of the corrected fitting function is 1.824 through calculation, so that the best fitting function (target fitting function) of the drainage curve physical model test data of the tailing pond is:
referring to fig. 4, fig. 4 is a graph of a fitting effect of a target fitting function according to an embodiment of the present application.
It can be seen that by the fitting method, discrete data points can be fitted into a continuous analytic function, so that the fitting precision is improved, and the result of flood control calculation is more accurate.
Example 2
Referring to fig. 5, the embodiment of the present application further provides a framework structure diagram of a tailing pond drainage data fitting system, where the tailing pond drainage data fitting system 500 includes:
the preprocessing module 501 is configured to preprocess original drainage data by using a hydraulic physical model to obtain an initial drainage data point set;
the inflection point obtaining module 502 is configured to perform interpolation calculation on the initial drain data point set, and obtain an inflection point of the initial drain data point set according to a calculation result;
a grouping module 503, configured to group the initial drain data point set according to the turning point, so as to obtain a plurality of group data;
a fitting module 504, configured to fit the plurality of packet data respectively, so as to obtain a plurality of fitting function sets;
a combination module 505, configured to combine two adjacent fitting functions in each fitting function set to obtain a plurality of combination functions;
and the analysis module 506 is configured to sequentially perform residual calculation on each of the combination functions, and use the target combination function with the smallest residual as the target fitting function of the initial leakage flow data point set.
It will be appreciated that the data processing apparatus described above corresponds to the tailings pond drainage data fitting method of example 1; any of the alternatives in embodiment 1 are also applicable to this embodiment and will not be described in detail here.
Example 3
The embodiment of the application also provides a computer device, for example, the computer device can be, but not limited to, a desktop computer, a notebook computer and the like, and the existence form of the computer device is not limited, and the computer device mainly depends on whether the computer device needs to support the interface display function of a browser webpage or not. The computer device illustratively comprises a memory storing a computer program and at least one processor for executing the computer program to implement the tailings pond blowdown data fitting method described in embodiment 1 above.
The processor may be an integrated circuit chip with signal processing capabilities. The processor may be a general purpose processor including at least one of a central processing unit (Central Processing Unit, CPU), a graphics processor (GraphicsProcessing Unit, GPU) and a network processor (Network Processor, NP), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like that may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present application.
The Memory may be, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-OnlyMemory, PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc. The memory is used for storing a computer program, and the processor can correspondingly execute the computer program after receiving the execution instruction.
Further, the memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for a function; the storage data area may store data created from the use of the computer device (e.g., iteration data, version data, etc.), and so on. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
Example 4
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores computer executable instructions, and the computer executable instructions, when being called and executed by a processor, cause the processor to execute the tailing pond drainage data fitting method in the embodiment 1.
The computer readable storage medium may be either a nonvolatile storage medium or a volatile storage medium. For example, the computer-readable storage medium may include, but is not limited to,: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus embodiments described above are merely illustrative, for example, of the flow diagrams and block diagrams in the figures, which illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules or units in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a smart phone, a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application.
Claims (10)
1. The tailing pond drainage data fitting method is characterized by comprising the following steps of:
preprocessing original drainage data by utilizing a hydraulic physical model to obtain an initial drainage data point set;
performing interpolation calculation on the initial drain data point set, and obtaining an inflection point of the initial drain data point set according to a calculation result;
grouping the initial drainage data point set according to the turning points to obtain a plurality of grouping data;
fitting is carried out on the plurality of grouping data respectively, so that a plurality of fitting function sets are obtained;
combining two adjacent fitting functions in each fitting function group to obtain a plurality of combined functions;
and carrying out residual calculation on each combination function in sequence, and taking the target combination function with the minimum residual as a target fitting function of the initial drainage data point set.
2. The method for fitting drainage data of a tailings pond according to claim 1, wherein the original drainage data comprises a plurality of pressure heads and a plurality of spillways, the preprocessing of the original drainage data by using a hydraulic physical model to obtain an initial drainage data point set comprises the following steps:
and determining the flood discharge amount corresponding to each pressure water head through the hydraulic physical model, and constructing the initial flood discharge data point set according to the flood discharge amount corresponding to each pressure water head.
3. The method for fitting drainage data of a tailings pond according to claim 2, wherein the interpolating the initial drainage data point set to obtain inflection points of the initial drainage data point set according to a calculation result comprises:
dividing two data points with the initial drainage data point concentration distance of a preset interval into a group to obtain a plurality of data point groups;
calculating a predicted pressure water head corresponding to each data point group according to a linear interpolation method;
and calculating according to the predicted pressure water head to obtain predicted flood discharge, and determining the inflection point according to the difference value of the predicted flood discharge and the actual flood discharge.
4. A tailings pond drainage data fitting method according to claim 3, wherein the grouping the initial drainage data point set according to the turning points to obtain a plurality of grouping data comprises:
and grouping the initial drain flow data point set according to the quantity of inflection points to obtain a plurality of grouping data.
5. The method for fitting drainage data of a tailings pond according to claim 1, wherein the fitting is performed on the plurality of grouping data respectively to obtain a plurality of fitting function sets, and the method comprises the steps of:
and fitting each grouping data by using a polynomial function to obtain a plurality of fitting function sets.
6. The method for fitting tailings pond drainage data according to claim 1, wherein the step of combining two adjacent fitting functions in each fitting function set to obtain a plurality of combining functions comprises:
and combining two adjacent fitting functions in each fitting function group, and carrying out connection treatment on each two combined fitting functions at the inflection point to obtain a plurality of combined functions.
7. The method for fitting drainage data of a tailings pond according to claim 1, wherein the step of sequentially performing residual calculation on each of the combination functions, and taking the target combination function with the minimum residual as the target fitting function of the initial drainage data point set comprises the steps of:
and carrying out residual analysis on each combination function, calculating the accumulated residual of each combination function, and taking the target combination function with the smallest accumulated residual as the target fitting function.
8. A tailings pond drainage data fitting system, comprising:
the preprocessing module is used for preprocessing the original drainage data by utilizing the hydraulic physical model to obtain an initial drainage data point set;
the inflection point acquisition module is used for carrying out interpolation calculation on the initial drainage data point set and obtaining inflection points of the initial drainage data point set according to a calculation result;
the grouping module is used for grouping the initial drainage data point set according to the turning points to obtain a plurality of grouping data;
the fitting module is used for respectively fitting the plurality of grouping data to obtain a plurality of fitting function groups;
the combination module is used for combining two adjacent fitting functions in each fitting function group to obtain a plurality of combination functions;
and the analysis module is used for sequentially carrying out residual calculation on each combination function, and taking the target combination function with the minimum residual as the target fitting function of the initial leakage flow data point set.
9. A computer device comprising a memory storing a computer program and at least one processor for executing the computer program to implement the tailings pond blowdown data fitting method of any one of claims 1 to 7.
10. A computer readable storage medium, wherein the computer readable storage medium stores a computer program which, when executed, implements the tailings pond drainage data fitting method of any one of claims 1 to 7.
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