CN115618076B - Hydrogeological data management method - Google Patents
Hydrogeological data management method Download PDFInfo
- Publication number
- CN115618076B CN115618076B CN202211387763.7A CN202211387763A CN115618076B CN 115618076 B CN115618076 B CN 115618076B CN 202211387763 A CN202211387763 A CN 202211387763A CN 115618076 B CN115618076 B CN 115618076B
- Authority
- CN
- China
- Prior art keywords
- hydrogeological
- interval
- curve
- candidate
- correlation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000013523 data management Methods 0.000 title claims abstract description 12
- 230000008859 change Effects 0.000 claims description 54
- 230000008569 process Effects 0.000 claims description 7
- 230000003313 weakening effect Effects 0.000 claims description 6
- 238000011156 evaluation Methods 0.000 abstract description 8
- 238000012545 processing Methods 0.000 abstract description 3
- 230000001174 ascending effect Effects 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 5
- 230000001419 dependent effect Effects 0.000 description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 4
- 230000002596 correlated effect Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000875 corresponding effect Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000001556 precipitation Methods 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/903—Querying
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/904—Browsing; Visualisation therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/907—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/909—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Library & Information Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application relates to the technical field of data processing, in particular to a hydrogeological data management method. The method comprises the following steps: acquiring hydrogeological data of a target area, and establishing a hydrogeological curve of each category according to the hydrogeological data and acquisition time of the hydrogeological data; obtaining the correlation degree between the target category and the first candidate category; determining a second candidate category from the first candidate category; acquiring the number of homogeneous extreme points and the number of non-homogeneous extreme points in a first hydrogeological curve of a target category and a second hydrogeological curve of a second candidate category; and correcting the correlation degree to obtain the influence degree of the target class on the second candidate class, and displaying the hydrogeological data and the influence degree of the second candidate class when the hydrogeological data of the target class is detected to be displayed. The method and the device improve the accuracy of the correlation evaluation between different types of hydrogeological data, and can evaluate the influence between the related hydrogeological data.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a hydrogeological data management method.
Background
The hydrogeological data is used for research work of hydrogeology such as water resource scheduling, water resource demonstration, water environment protection, flood prevention and drought resistance, the hydrogeological data in the hydrogeological data management system is large in data volume and multiple in category, the hydrogeological data needs to be managed, and in the management process, the related hydrogeological data needs to be managed so as to be convenient for a user to inquire.
In the prior art, a data scatter diagram is adopted to determine a pearson correlation coefficient between different types of hydrogeological data, and the correlation between the different types of hydrogeological data is represented by the pearson correlation coefficient, while the pearson correlation coefficient can only represent the linear correlation of the data, the accuracy of the evaluation of the correlation between the non-linearly correlated hydrogeological data is low, and the influence of one type of hydrogeological data on the change of the data of the other type of hydrogeological data is difficult to evaluate in the prior art for the hydrogeological data with the correlation.
Disclosure of Invention
In order to solve the above technical problem, an object of the present application is to provide a method for hydrogeological data management, which adopts the following technical solutions:
the application provides a hydrogeological data management method, which comprises the following steps:
acquiring hydrogeological data of a target area, wherein the hydrogeological data comprises a plurality of categories;
establishing a hydrogeological curve of each category according to the hydrogeological data and the acquisition time of the hydrogeological data;
obtaining a correlation degree between a target category and a first candidate category based on the change trend and the change amplitude of the hydrogeological curve, wherein the first candidate category is other categories except the target category;
determining the category of which the correlation degree is greater than a correlation degree threshold value from the first candidate category as a second candidate category;
acquiring the number of homogeneous extreme points and the number of non-homogeneous extreme points at the same position in the first hydrogeological curve of the target category and the second hydrogeological curve of the second candidate category;
correcting the correlation degree based on the number of the extreme points of the same kind and the number of the extreme points of non-same kind, obtaining the influence degree of the target category on the second candidate category;
and when the hydrogeological data of the target category is detected to be displayed, displaying the hydrogeological data of the second candidate category and the influence degree.
In some embodiments, the modifying the correlation based on the number of extreme points of same kind and the number of extreme points of non-same kind to obtain the influence of the target class on the second candidate class includes:
determining the correlation enhancement weight of the correlation according to the number of the like extreme points;
determining the correlation weakening weight of the correlation according to the number of the non-homogeneous extreme points;
and correcting the correlation degree according to the correlation degree enhancement weight and the correlation degree weakening weight to obtain the influence degree.
In some embodiments, the obtaining a correlation between the target category and the first candidate category based on the variation trend and the variation amplitude of the hydrogeological curve includes:
acquiring an extreme point in a first hydrogeological curve of the target category;
dividing the data range interval of the hydrogeological data into a plurality of candidate intervals by taking the extreme point as a reference;
determining a trend-related interval from the plurality of candidate intervals based on the variation trend of the hydrogeological curve, wherein the trend-related interval is an interval with the same variation trend between the first hydrogeological curve and a second hydrogeological curve of the first candidate category;
and acquiring the correlation degree between the target category and the first candidate category based on the trend correlation interval.
In some embodiments, the determining a trend-related interval from the plurality of candidate intervals based on the trend of change of the hydrogeological curve comprises:
for each candidate interval, judging whether the change trends of the first hydrogeological curve and the second hydrogeological curve of the first candidate category in the candidate interval are the same;
and if the change trends of the first hydrogeological curve and the second hydrogeological curve in the candidate interval are the same, determining the candidate interval as the trend correlation interval.
In some embodiments, the interval endpoints of the candidate interval include a first interval endpoint and a second interval endpoint, and the determining whether the first hydrogeological curve and the second hydrogeological curve have the same variation trend within the candidate interval includes:
acquiring first endpoint data of the first hydrogeological curve at the first interval endpoint and second endpoint data of the first hydrogeological curve at the second interval endpoint;
calculating the difference value of the first endpoint data and the second endpoint data to be a first interval endpoint difference value of the candidate interval, and taking the positive and negative of the first interval endpoint difference value as the change trend of the first hydrogeological curve in the candidate interval;
acquiring third endpoint data of the second hydrogeological curve at the first interval endpoint and fourth endpoint data of the second hydrogeological curve at the second interval endpoint;
calculating the difference value between the third end point data and the fourth end point data to be a second interval end point difference value of the candidate interval, and taking the positive and negative of the second interval end point difference value as the change trend of the second hydrogeological curve in the candidate interval;
if the first interval endpoint difference value and the second interval endpoint difference value are consistent in positive and negative, determining that the first hydrogeological curve and the second hydrogeological curve in the candidate interval have the same change trend;
and if the positive and negative of the first interval endpoint difference value and the second interval endpoint difference value are inconsistent, determining that the change trends of the first hydrogeological curve and the second hydrogeological curve in the candidate interval are different.
In some embodiments, the obtaining the correlation between the target category and the first candidate category based on the trend correlation interval includes:
acquiring the variation trend correlation degree and the variation amplitude correlation degree between the target category and the first candidate category based on the trend correlation interval;
and acquiring the correlation degree based on the variation trend correlation degree and the variation amplitude correlation degree.
In some embodiments, the obtaining of the correlation of the variation trend includes:
and calculating the interval length ratio of the trend correlation interval and the data range interval as the change trend correlation degree.
In some embodiments, the obtaining of the correlation of the variation amplitude includes:
determining the absolute value of the first interval endpoint difference value of the trend-related interval as a first amplitude value of the first hydrogeological curve in the trend-related interval, and calculating a first amplitude ratio of the first amplitude value to the peak-to-peak value of the first hydrogeological curve;
determining the absolute value of the endpoint difference value of the second interval of the trend-related interval as a second amplitude value of the second hydrogeological curve in the trend-related interval, and calculating a second amplitude ratio of the second amplitude value to the peak-to-peak value of the second hydrogeological curve;
and obtaining the change amplitude correlation degree based on the first amplitude ratio and the second amplitude ratio.
In some embodiments, said obtaining said variation amplitude correlation based on said first amplitude ratio and said second amplitude ratio comprises:
calculating the variation amplitude correlation according to a variation amplitude correlation formula, wherein the variation amplitude correlation formula comprises:
；
wherein,
in a degree dependent on the magnitude of the change>
Is a positive integer, is selected>
Is the first->
A first amplitude ratio, based on the trend-related interval>
Is the first->
A second magnitude ratio for a trend-related interval>
The number of trend correlated intervals.
The application has the following beneficial effects:
in the embodiment of the application, the correlation degrees among different types of hydrogeological data are obtained according to the variation trend and the variation amplitude of different hydrogeological curves, the variation trend and the variation amplitude of the hydrogeological data are considered at the same time, and the correlation among the different types of hydrogeological data is comprehensively evaluated through the variation trend relation and the variation amplitude relation among the different types of hydrogeological data, so that the non-linear correlation evaluation among the different types of hydrogeological data is realized, and the accuracy of the correlation evaluation is improved. And correcting the correlation degree between different types of hydrogeological data according to the number of similar extreme points and the number of non-similar extreme points in different hydrogeological curves to obtain the influence degree between different types of hydrogeological data, wherein the influence degree characterizes the influence of one type of hydrogeological data on the data change of the other type of hydrogeological data, so that the influence evaluation between the related hydrogeological data is realized.
Drawings
In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flow chart of a hydrogeological data management method according to an embodiment of the present application.
Detailed Description
To further illustrate the technical means and effects of the present application for achieving the predetermined invention, the following detailed description of a hydrogeological data management method according to the present application, its specific implementation, structure, features and effects will be given in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
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 following specifically describes a specific scheme of the hydrogeological data management method provided by the present application with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a hydrogeological data management method according to an embodiment of the present disclosure. As shown in fig. 1, the method comprises the steps of:
s101, acquiring hydrogeological data of a target area, wherein the hydrogeological data comprises a plurality of categories.
The target area is any geographical area concerned by the embodiment of the present application, and is not limited to this. In some embodiments, a geographic area of 5km x 5km may be selected as the target area.
The categories may include, among other things, temperature, humidity, altitude, precipitation, water flow, etc.
The hydrogeological data of each category may be collected at set time intervals, where the set time intervals may be set according to actual needs, for example, the set time intervals may be set to 1 minute, 10 minutes, 1 hour, and the like, which is not limited in this respect.
It should be noted that, in the embodiment of the present application, the acquisition time of each category of hydrogeological data is consistent.
And S102, establishing a hydrogeological data curve of each category according to the hydrogeological data and the acquisition time of the hydrogeological data.
The acquisition time of hydrogeological data is used as an abscissa, the hydrogeological data is used as an ordinate, a coordinate system is constructed, and then all hydrogeological data in the coordinate system are connected through smooth curves to obtain hydrogeological data curves of each category.
S103, obtaining the correlation between the target category and a first candidate category based on the change trend and the change amplitude of the hydrogeological curve, wherein the first candidate category is other categories except the target category. It should be noted that the target category may be any of a plurality of categories of hydrogeological data.
The relevance score characterizes a degree of relevance between the target category and the first candidate category.
The variation trend comprises an ascending trend and a descending trend, and the variation amplitude is the amplitude of the ascending or descending of the hydrogeological data curve in different intervals.
For two types of hydrogeological data curves with similar change trends and similar change amplitudes, the hydrogeological data of the two types often have certain correlation, namely the hydrogeological data of one type influences the change of the hydrogeological data of the other type, or the hydrogeological data of the two types influence each other.
In the embodiment of the present application, the process of obtaining the correlation between the target category and the first candidate category based on the variation trend and the variation amplitude of the hydrogeological curve includes, but is not limited to, the following steps:
s201, obtaining extreme points in the first hydrogeological curve of the target category.
In some embodiments, the difference between each adjacent discrete point in the first hydrogeological curve may be calculated according to a set calculation order to obtain a difference sequence, and determine whether signs of adjacent difference values in the difference sequence are consistent, if not, the middle discrete point of the three discrete points forming the adjacent difference value is considered as an extreme point. For example, if the v-th difference is opposite in sign to the v + 1-th difference, the v + 1-th discrete point is an extreme point, where v is a positive integer.
It should be noted that the calculation order is set to include two calculation orders, i.e., a calculation order from a previous discrete point to a next discrete point, and a calculation order from a next discrete point to a previous discrete point, which may be optional, and is not limited in this respect.
In other embodiments, the variation trend of the first hydrogeological curve may be analyzed to determine an ascending trend interval and a descending trend interval in the first hydrogeological curve, and then a discrete point coinciding between the ascending trend interval and the descending trend interval, or a discrete point coinciding between the descending trend interval and the ascending trend interval may be used as an extreme point in the first hydrogeological curve.
It should be noted that the extreme points in the embodiments of the present application are discrete points in the hydrogeological data of the target region, and are obtained by using the method described in the embodiments of the present application, and are not described in detail in the following embodiments.
And S202, dividing the data range interval of the hydrogeological data into a plurality of candidate intervals by taking the extreme point as a reference.
The data range interval of the hydrogeological data is an interval of a first hydrogeological curve of a target category or a second hydrogeological curve of a first candidate category, and the interval ranges of the first hydrogeological curve and the second hydrogeological curve are consistent.
And S203, determining a trend related interval from a plurality of candidate intervals based on the change trend of the hydrogeological curve, wherein the trend related interval is an interval with the same change trend between the first hydrogeological curve and the second hydrogeological curve of the first candidate category.
Optionally, for each candidate interval, determining whether the variation trends of the first hydrogeological curve in the candidate interval and the second hydrogeological curve in the first candidate category are the same, and if the variation trends of the first hydrogeological curve in the candidate interval and the second hydrogeological curve in the first candidate category are the same, determining that the candidate interval is a trend-related interval.
The interval endpoints of the candidate interval comprise a first interval endpoint and a second interval endpoint, wherein the first interval endpoint is a left endpoint of the candidate interval, the second interval endpoint is a right endpoint of the candidate interval, or the first interval endpoint is a right endpoint of the candidate interval, and the second interval endpoint is a left endpoint of the candidate interval.
In some embodiments, first endpoint data of the first hydrogeological curve at the end point of the first interval and second endpoint data of the first hydrogeological curve at the end point of the second interval are obtained, then a difference value between the first endpoint data and the second endpoint data is calculated to be a first interval endpoint difference value of the candidate interval, and the positive and negative of the first interval endpoint difference value is used as a change trend of the first hydrogeological curve in the candidate interval.
And acquiring third end point data of the second hydrogeological curve at the first interval end point and fourth end point data of the second hydrogeological curve at the second interval end point, then calculating the difference value of the third end point data and the fourth end point data as a second interval end point difference value of the candidate interval, and taking the positive and negative of the second interval end point difference value as the change trend of the second hydrogeological curve in the candidate interval.
If the positive and negative of the first interval endpoint difference value and the second interval endpoint difference value are consistent, determining that the change trends of the first hydrogeological curve and the second hydrogeological curve in the candidate interval are the same; and if the positive and negative of the first interval endpoint difference value and the second interval endpoint difference value are inconsistent, determining that the change trends of the first hydrogeological curve and the second hydrogeological curve in the candidate interval are different.
S204, acquiring the correlation degree between the target category and the first candidate category based on the trend correlation interval.
In the embodiment of the present application, the obtaining of the correlation between the target category and the first candidate category based on the trend correlation interval includes, but is not limited to, the following steps:
s301, based on the trend correlation interval, obtaining the change trend correlation degree and the change amplitude correlation degree between the target category and the first candidate category.
Optionally, the obtaining process of the correlation degree of the variation trend includes: and calculating the interval length ratio of the trend correlation interval and the data range interval as the correlation degree of the change trend.
In some implementations, the degree of correlation of the trend of change between the target category and the first candidate category may be calculated by the following formula (1):
；
wherein,
for a trend-dependent degree of change, a decision is made as to whether a change is positive or negative>
Is the number of the trend-related interval>
Is a positive integer, is selected>
Represents a trend-related interval, is>
Is the first->
The section length of the trend-related section->
Is the interval length of the data range interval.
In other implementations, if the change trends of the first hydrogeological curve and the second hydrogeological curve in the candidate interval are different, the candidate interval is determined to be a non-trend related interval, and after the non-trend related interval is determined, the change trend correlation degree between the target category and the first candidate category is calculated by the following formula (2):
wherein,
for a trend-dependent degree of change, a decision is made as to whether a change is positive or negative>
Number of non-trending relevant intervals>
Is a positive integer, <' > based on>
Represents a non-trend related interval, is>
Is the first->
The length of the interval not being trend-related, is->
Is the interval length of the data range interval.
Further, optionally, the obtaining process of the amplitude correlation degree includes: determining the absolute value of the endpoint difference value of the first interval of the trend-related interval as the first amplitude value of the first hydrogeological curve in the trend-related interval, calculating a first amplitude ratio of the first amplitude value to the peak-to-peak value of the first hydrogeological curve, determining the absolute value of the endpoint difference value of the second interval of the trend-related interval as the second amplitude value of the second hydrogeological curve in the trend-related interval, calculating a second amplitude ratio of the second amplitude value to the peak-to-peak value of the second hydrogeological curve, and acquiring the variation amplitude correlation degree based on the first amplitude ratio and the second amplitude ratio.
Wherein the peak-to-peak value is a difference between a maximum value and a minimum value of the hydrogeological data curve.
Optionally, the variation amplitude correlation is calculated according to a variation amplitude correlation formula, wherein the variation amplitude correlation formula (3) includes:
（3）
wherein,
for a degree of amplitude dependence, is>
Is a positive integer->
Is the first->
A first amplitude ratio, based on the trend-related interval>
Is the first->
A second amplitude ratio, based on the trend-related interval>
The number of trend related intervals.
S302, obtaining the correlation degree based on the variation trend correlation degree and the variation amplitude correlation degree.
Specifically, the correlation between the target category and the first candidate category may be calculated by the following formula (4):
；
wherein,
is related, is>
For a trend-dependent degree of change, a decision is made as to whether a change is positive or negative>
Is the amplitude of variation correlation.
As a possible case, different weights may be respectively given to the variation trend correlation degree and the variation amplitude correlation degree according to the influence degree of the variation trend correlation degree and the variation amplitude correlation degree on the correlation, and the product of the variation trend correlation degree and the variation amplitude correlation degree after the weights are given is used as the correlation degree.
And S104, determining the category with the correlation degree larger than the correlation degree threshold value from the first candidate category as a second candidate category.
It should be noted that the correlation threshold may be set according to actual scene requirements, and is not limited herein, and optionally, the correlation threshold is 0.5.
After obtaining the correlation degree between the target category and the first candidate category, determining whether the correlation degree is greater than a correlation degree threshold value, if so, determining that the hydrogeological data of the target category has the correlation with the first candidate category, and taking the first candidate category as a second candidate category, and if not, determining that the hydrogeological data of the target category does not have the correlation with the first candidate category.
After determining the second candidate category, the second candidate category may be associated with the target category, to facilitate management and querying of the hydrogeological data of the target category and the hydrogeological data of the second candidate category.
In the above embodiment, it has been determined that there is a correlation between the target category and the second candidate category, that is, a change in the hydrogeological data of the target category can affect a change in the hydrogeological data of the second candidate category, but the hydrogeological data of the target category affects the hydrogeological data of each second candidate category to a different extent, and therefore, it is also necessary to determine the degree of the influence of the hydrogeological data of the target category on the hydrogeological data of each second candidate category.
S105, acquiring the number of homogeneous extreme points and the number of non-homogeneous extreme points at the same position in the first hydrogeological curve of the target category and the second hydrogeological curve of the second candidate category. The extreme point category comprises a maximum point, a minimum point and a non-extreme point.
And taking the abscissa position of each extreme point in the first hydrogeological curve as a reference, comparing the extreme point in the first hydrogeological curve with the discrete point at the same position in the second hydrogeological curve, if the extreme point and the discrete point are both the extreme points, determining that the extreme point is the same-class extreme point, and if the extreme point is the maximum point, determining that the discrete point is the minimum point or the off-line point is not the extreme point, determining that the extreme point is the non-same-class extreme point.
For example, assume that the extreme point A in the first hydrogeological curve is
Point B in the second hydrogeological curve is ^>
If the point A is a maximum value point and the point B is a maximum value point, determining that the point A is a similar maximum value point; if the point A is a maximum value point and the point B is a minimum value point, determining the point A to be a non-homogeneous extreme value point; and if the point A is the maximum point and the point B is not the extreme point, determining that the point A is the non-homogeneous extreme point.
And S106, correcting the correlation degree based on the number of the similar extreme points and the number of the non-similar extreme points to obtain the influence degree of the target class on the second candidate class.
The influence degree characterizes the influence degree of the target category on the second candidate category, namely the influence of the data change of the hydrogeological data of the target category on the data change of the hydrogeological data of the second candidate category.
In the embodiment of the application, the influence degrees of the hydrogeological data of the target category on the hydrogeological data of different second candidate categories are different, the correlation degree can be corrected to obtain the corrected influence degree, and the influence degrees of the hydrogeological data of the target category on the second hydrogeological data of different second candidate categories are characterized through the influence degrees.
Optionally, the correlation enhancement weight of the correlation is determined according to the number of the similar extreme points, the correlation weakening weight of the correlation is determined according to the number of the non-similar extreme points, and the correlation is corrected according to the correlation enhancement weight and the correlation weakening weight to obtain the influence degree.
Specifically, the degree of influence of the target class on the second candidate class may be obtained by correcting the correlation between the target class and the second candidate class according to the following formula (5):
wherein,
to influence the degree>
Is related, is>
Is the number of the same extreme points, and is used for judging whether the corresponding extreme points are present>
Number of non-homogeneous extreme points, based on the number of the most recent extreme points>
To adjust the coefficient, optionally>
Wherein is present>
Is the interval length of the data range interval,
represents a relevance enhancing weight, <' > based on the correlation>
Indicating a correlation decreasing weight.
Further, in order to improve the accuracy of the influence degree, the correlation degrees may be arranged in order from large to small to obtain a correlation degree sequence, then the correlation degrees in the correlation degree sequence are grouped to obtain a plurality of groups, and the variable in the formula (5) is determined according to the group in which each correlation degree is located
And a variable->
Is constrained to avoid overcorrecting the correlation.
Illustratively, assume a maximum correlation of
With a minimum correlation ≦>
The correlation in the correlation sequence is divided into five groups, the first group:
And, a second group:
and the third group:
and the fourth group:
and the fifth group:
。
the variables in the above equation (5)
And a variable->
The following constraints are satisfied:
；
where H is the packet sequence number.
Specifically, if the correlation is in the first group, then
Maximum value of 0, is greater than or equal to>
The maximum value is 4; if the degree of correlation is in a second packet, then->
Maximum value of 1, is greater than or equal to>
The maximum value is 3; if the correlation is in the third group, then
Maximum value of 2, is greater or less than>
The maximum value is 2; if the degree of correlation is in the fourth packet, then ^ er>
The maximum value is 3, and the maximum value is,
the maximum value is 1; if the degree of correlation is in a fifth group, then +>
Maximum value of 4->
The maximum value is 0;
it should be noted that, according to the constraint condition, when the number of like extreme points is less than or equal to the constrained maximum value,
equal to the number of like extreme points; when the number of like extreme points is greater than the maximum value to be restricted, then>
Equal to the constrained maximum value. When the number of non-homogeneous extrema points is less than or equal to the constrained maximum value, then->
Equal to the number of like extreme points; when the number of like extreme points is greater than the maximum value to be restricted, then>
Equal to the constrained maximum value.
S107, when the hydrogeological data of the target category is detected to be displayed, displaying the hydrogeological data and the influence degree of the second candidate category.
When the user inquires the hydrogeological data, the hydrogeological data inquired by the user is detected, and when the hydrogeological data of the target category is detected to be displayed, the hydrogeological data of the second candidate category and the influence degree of the target category on the second candidate category are displayed, so that the data inquiry of the user and the mastering of the data correlation can be facilitated.
In summary, in the embodiment of the application, the correlation degrees between different types of hydrogeological data are obtained according to the variation trend and the variation amplitude of different hydrogeological curves, the variation trend and the variation amplitude of the hydrogeological data are considered at the same time, and the correlation between different types of hydrogeological data is comprehensively evaluated through the variation trend relationship and the variation amplitude relationship between different types of hydrogeological data, so that the non-linear correlation evaluation between different types of hydrogeological data is realized, and the accuracy of the correlation evaluation is improved. And correcting the correlation degree between different types of hydrogeological data according to the number of similar extreme points and the number of non-similar extreme points in different hydrogeological curves to obtain the influence degree between different types of hydrogeological data, wherein the influence degree characterizes the influence of one type of hydrogeological data on the data change of the other type of hydrogeological data, so that the influence evaluation between related hydrogeological data is realized.
It should be noted that: the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only a preferred embodiment of the present application and should not be taken as limiting the present application, and any modifications, equivalents, improvements and the like that are made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (8)
1. A hydrogeological data management method, characterized in that it comprises:
acquiring hydrogeological data of a target area, wherein the hydrogeological data comprises a plurality of categories;
establishing a hydrogeological curve of each category according to the hydrogeological data and the acquisition time of the hydrogeological data;
obtaining the correlation degree between a target category and a first candidate category based on the change trend and the change amplitude of the hydrogeological curve, wherein the first candidate category is other categories except the target category;
determining the category of which the correlation degree is greater than a correlation degree threshold value from the first candidate category as a second candidate category;
acquiring the number of homogeneous extreme points and the number of non-homogeneous extreme points at the same position in the first hydrogeological curve of the target category and the second hydrogeological curve of the second candidate category;
correcting the correlation degree based on the number of the extreme points of the same kind and the number of the extreme points of the non-same kind to obtain the influence degree of the target class on the second candidate class;
when the hydrogeological data of the target category is detected to be displayed, displaying the hydrogeological data of the second candidate category and the influence degree;
the obtaining of the correlation between the target category and the first candidate category based on the variation trend and the variation amplitude of the hydrogeological curve comprises:
acquiring extreme points in a first hydrogeological curve of the target category;
dividing the data range interval of the hydrogeological data into a plurality of candidate intervals by taking the extreme point as a reference;
determining a trend-related interval from the plurality of candidate intervals based on the variation trend of the hydrogeological curve, wherein the trend-related interval is an interval with the same variation trend between the first hydrogeological curve and a second hydrogeological curve of the first candidate category;
and acquiring the correlation degree between the target category and the first candidate category based on the trend correlation interval.
2. The method according to claim 1, wherein the modifying the correlation based on the number of homogeneous extreme points and the number of non-homogeneous extreme points to obtain the influence of the target class on the second candidate class comprises:
determining the correlation enhancement weight of the correlation according to the number of the like extreme points;
determining the correlation weakening weight of the correlation according to the number of the non-homogeneous extreme points;
and correcting the correlation degree according to the correlation degree enhancement weight and the correlation degree weakening weight to obtain the influence degree.
3. The method of claim 1, wherein determining a trend-relevant interval from the plurality of candidate intervals based on the trend of change of the hydrogeological curve comprises:
for each candidate interval, judging whether the change trends of the first hydrogeological curve and the second hydrogeological curve of the first candidate category in the candidate interval are the same;
and if the change trends of the first hydrogeological curve and the second hydrogeological curve in the candidate interval are the same, determining the candidate interval as the trend correlation interval.
4. The method according to claim 3, wherein the interval endpoints of the candidate interval comprise a first interval endpoint and a second interval endpoint, and the determining whether the variation trends of the first hydrogeological curve and the second hydrogeological curve in the candidate interval are the same comprises:
acquiring first endpoint data of the first hydrogeological curve at the first interval endpoint and second endpoint data of the first hydrogeological curve at the second interval endpoint;
calculating the difference value of the first endpoint data and the second endpoint data to be a first interval endpoint difference value of the candidate interval, and taking the positive and negative of the first interval endpoint difference value as the change trend of the first hydrogeological curve in the candidate interval;
acquiring third endpoint data of the second hydrogeological curve at the first interval endpoint and fourth endpoint data of the second hydrogeological curve at the second interval endpoint;
calculating the difference value of the third end point data and the fourth end point data to be a second interval end point difference value of the candidate interval, and taking the positive and negative of the second interval end point difference value as the change trend of the second hydrogeological curve in the candidate interval;
if the first interval endpoint difference value and the second interval endpoint difference value are consistent in positive and negative, determining that the first hydrogeological curve and the second hydrogeological curve in the candidate interval have the same change trend;
and if the positive and negative of the first interval endpoint difference value and the second interval endpoint difference value are inconsistent, determining that the change trends of the first hydrogeological curve and the second hydrogeological curve in the candidate interval are different.
5. The method according to claim 1, wherein the obtaining the correlation degree between the target category and the first candidate category based on the trend correlation interval comprises:
acquiring the variation trend correlation degree and the variation amplitude correlation degree between the target category and the first candidate category based on the trend correlation interval;
and acquiring the correlation degree based on the variation trend correlation degree and the variation amplitude correlation degree.
6. The method according to claim 5, wherein the obtaining process of the variation trend correlation degree comprises:
and calculating the interval length ratio of the trend correlation interval and the data range interval as the change trend correlation degree.
7. The method according to claim 5, wherein the obtaining process of the variation amplitude correlation comprises:
determining the absolute value of the first interval endpoint difference value of the trend-related interval as a first amplitude value of the first hydrogeological curve in the trend-related interval, and calculating a first amplitude ratio of the first amplitude value to the peak-to-peak value of the first hydrogeological curve;
determining the absolute value of the endpoint difference value of the second interval of the trend-related interval as a second amplitude value of the second hydrogeological curve in the trend-related interval, and calculating a second amplitude ratio of the second amplitude value to the peak-to-peak value of the second hydrogeological curve;
and obtaining the change amplitude correlation degree based on the first amplitude ratio and the second amplitude ratio.
8. The method of claim 7, wherein obtaining the change magnitude correlation based on the first magnitude ratio and the second magnitude ratio comprises:
calculating the variation amplitude correlation degree according to a variation amplitude correlation degree formula, wherein the variation amplitudeThe degree correlation formula includes:
；
wherein,
for a degree of amplitude dependence, is>
Is a positive integer, is selected>
Is the first->
First amplitude ratio of trend correlation interval
Is the first->
A second amplitude ratio, based on the trend-related interval>
The number of trend related intervals. />
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211387763.7A CN115618076B (en) | 2022-11-07 | 2022-11-07 | Hydrogeological data management method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211387763.7A CN115618076B (en) | 2022-11-07 | 2022-11-07 | Hydrogeological data management method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115618076A CN115618076A (en) | 2023-01-17 |
| CN115618076B true CN115618076B (en) | 2023-04-18 |
Family
ID=84878785
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211387763.7A Active CN115618076B (en) | 2022-11-07 | 2022-11-07 | Hydrogeological data management method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115618076B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117390471B (en) * | 2023-12-12 | 2024-03-26 | 山东省地质矿产勘查开发局第八地质大队(山东省第八地质矿产勘查院) | Hydrogeological data management method and system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020087829A1 (en) * | 2018-10-31 | 2020-05-07 | 深圳壹账通智能科技有限公司 | Data trend analysis method and system, computer device and readable storage medium |
| CN114740979A (en) * | 2022-04-19 | 2022-07-12 | 腾讯科技（深圳）有限公司 | Method and device for representing change trend of service curve and readable storage medium |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150088424A1 (en) * | 2013-09-20 | 2015-03-26 | Schlumberger Technology Corporation | Identifying geological formation depth structure using well log data |
| CN106452934B (en) * | 2015-08-10 | 2020-02-11 | 中国移动通信集团公司 | Method and device for analyzing network performance index change trend |
| CN107609327A (en) * | 2017-08-24 | 2018-01-19 | 武汉大学 | A kind of basic hydrologic variation Measure Indexes and analysis of Influential Factors monitoring method |
| CN111028087B (en) * | 2019-12-05 | 2023-09-22 | 中国银行股份有限公司 | Information display method, device and equipment |
| CN112926386B (en) * | 2021-01-21 | 2022-11-01 | 徐一帆 | Method for identifying aquifer and water-resisting layer in hydrogeology |
| CN115062100B (en) * | 2022-08-11 | 2022-11-01 | 国网浙江省电力有限公司湖州供电公司 | Geological information analysis method based on multi-dimensional induction data and split type drilling machine |
-
2022
- 2022-11-07 CN CN202211387763.7A patent/CN115618076B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020087829A1 (en) * | 2018-10-31 | 2020-05-07 | 深圳壹账通智能科技有限公司 | Data trend analysis method and system, computer device and readable storage medium |
| CN114740979A (en) * | 2022-04-19 | 2022-07-12 | 腾讯科技（深圳）有限公司 | Method and device for representing change trend of service curve and readable storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115618076A (en) | 2023-01-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105787104B (en) | Method and device for acquiring user attribute information | |
| CN104793224B (en) | A kind of GPS location method for correcting error and device | |
| CN110967028B (en) | Navigation map construction method and device, robot and storage medium | |
| CN110375753A (en) | Map-matching method, device, server and storage medium | |
| CN115618076B (en) | Hydrogeological data management method | |
| CN104702685B (en) | Pollution sources method for tracing and its system based on back trajectca-rles | |
| US8332458B2 (en) | Monitoring threshold functions over distributed data sets | |
| CN103514199A (en) | Method and device for POI data processing and method and device for POI searching | |
| CN108550400B (en) | Method for evaluating influence of air pollutants on number of respiratory disease patients | |
| WO2012042521A2 (en) | Detecting change points in data streams | |
| CN105512206A (en) | Outlier detection method based on clustering | |
| CN112217650B (en) | Network blocking attack effect evaluation method, device and storage medium | |
| CN111178653A (en) | Method and device for determining a contaminated area | |
| CN110222312A (en) | Processing method, device and the server of user's evaluation data | |
| Shongwe et al. | On monitoring the process mean of autocorrelated observations with measurement errors using the w‐of‐w runs‐rules scheme | |
| US7623998B1 (en) | System and method for identifying Bezier curves from a shape | |
| US10796036B2 (en) | Prediction of inhalable particles concentration | |
| CN116826724A (en) | Wind power interval prediction method based on multiple data screening and related equipment | |
| CN104392000B (en) | Determine the method and apparatus that mobile site captures quota | |
| CN111222672B (en) | Air Quality Index (AQI) prediction method and device | |
| CN114170455A (en) | Object gathering method and device, electronic equipment and storage medium | |
| CN116539831B (en) | Water environment data monitoring processing method based on big data analysis | |
| CN117649061B (en) | Multi-node networking electricity analysis method and system for environmental protection monitoring | |
| CN116415527B (en) | Urban block wind environment assessment method and system | |
| KR101460129B1 (en) | Method and apparatus for surveying efficiency of keyword advertisement |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |