A method and system for measuring hydrogeological parameters on-site

Abstract

The present invention relates to a method and system for measuring hydrogeological parameters in on-site detection. The method includes: obtaining the detection data of the existing detection point i; for each detection point i, calculating a spatio-temporal correlation degree sequence formed by the k-step spatio-temporal correlation degree between it and any other detection point j; during on-site detection, determining the spatio-temporal correlation degree distribution of the detection points based on the k-step spatio-temporal correlation degree sequence to determine the on-site detection location; and setting a measuring device at the on-site detection location to conduct hydrogeological detection. The present invention supports on-site detection based on big data analysis of hydrogeological detection, forms an effective supplement to the detection method of fixed detection points, can better cover the measurement of hydrogeological parameters in the area to be detected in terms of space and time, and improves the efficiency of on-site detection.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent detection, and particularly relates to a method and a system for measuring hydrogeological parameters in on-site detection. Background Art

[0002] Hydrogeology is a discipline that studies the natural phenomena of groundwater and its interaction with the geographical environment. However, traditional hydrogeological research methods mainly rely on manual exploration and empirical judgment, which are not only inefficient but also difficult to guarantee the prediction accuracy. Moreover, regarding the research on groundwater hydrology, it is mostly focused on the study of the groundwater level. In fact, the change of the groundwater level is a process that varies based on time and space, and the existing technologies also lack comprehensive research on the detection of the groundwater level and water quality at different groundwater levels; traditional on-site detection methods for hydrogeological parameters, such as hydrogeological mapping, geophysical exploration, drilling, etc., although they can directly and real-time reflect the true state of the underground environment, also have many limitations, such as being interfered by various factors during the testing process, high cost, long cycle, etc.

[0003] At present, a large amount of detection data is usually a large amount of data obtained when detection points are fixedly set, and is transmitted to a server through the Internet of Things for relevant processing. However, for hydrogeological data, it will be affected by various factors such as natural factors. Natural factors include the complexity of the geological structure itself, changes in climate and hydrological conditions, and the dynamic characteristics of groundwater. For example, faults, fractures in the geological structure, and differences in the permeability of different rock layers will cause the data to vary greatly at different locations and depths. Climatic factors such as rainfall, evaporation, and seasonal changes will also directly affect the water level and water quality of groundwater. In terms of human factors, human activities such as pumping, pollution, and engineering construction will significantly change the groundwater system. For example, over-exploitation leads to a drop in the water level, and the discharge of pollutants changes the water quality, all of which will make the data unstable. Then, from a spatial perspective, relying solely on the method of fixedly setting detection points cannot achieve full coverage of the area to be detected. Secondly, from a temporal perspective, when the location, quantity, discharge volume, and discharge speed of pollution sources change, effective and comprehensive coverage cannot be achieved. Currently, numerical simulation methods are often used to simulate hydrogeological data based on big hydrogeological data. As an important tool for modern scientific computing and engineering analysis, the numerical simulation method integrates the construction of hydrogeological models with computer technology. With the rapid development of computer technology, the application field of the numerical simulation method is becoming increasingly wide; in addition, there is the method of directly selecting a site on-site and installing a measuring device to detect the actual situation of groundwater quality in real time based on the latest on-site conditions; on-site testing usually requires a large amount of manpower, material resources, and financial resources, and the testing cycle is relatively long, making it difficult to obtain comprehensive underground information in a short time. In addition, for some special geological environments, such as karst areas and alpine mountainous areas, the difficulty and risk of on-site testing are significantly increased. Therefore, it is necessary to effectively cherish and utilize on-site detection resources; based on the above problems, the present invention supports on-site detection based on big data analysis of hydrogeological detection, forms an effective supplement to the detection method of fixedly set detection points, can better cover the determination of hydrogeological parameters in the area to be detected in terms of time and space, effectively cherish and utilize on-site detection resources, and improves the efficiency of on-site detection. Summary of the Invention

[0004] In order to solve the above problems in the prior art, the present invention proposes a method and system for determining hydrogeological parameters in on-site detection, and the method includes:

[0005] Step S1: Obtain the detection data of the existing detection point i , where: is the detection data of the i-th detection point at the t-th moment, ; T is the time span;

[0006] Step S2: Calculate the k-step spatio-temporal correlation degree between each detection point i and any other detection point j, and form a spatio-temporal correlation degree sequence ; where: the spatio-temporal correlation degree at k steps is used to indicate the k-step correlation degree of the detection data at the detection points i, j on the time axis; specifically, it includes the following steps:

[0007] Step S21: Perform stationarity processing on the detection data at the detection points i, j;

[0008] Step S22: Calculate the spatio-temporal correlation degree at k steps using the following formula (5)

[0009] (5);

[0010] Step S23: For each detection point i, arrange the spatio-temporal correlation degrees with another detection point j in sequence to form the spatio-temporal correlation degree sequence of the detection point i

[0011]

[0012] The value range of is [0, 1];

[0013] Step S3: During on-site detection, determine the spatio-temporal correlation degree distribution of the detection points based on the spatio-temporal correlation degree sequence to determine the on-site detection location;

[0014] Step S4: Set a measuring device at the on-site detection location to conduct hydrogeological detection.

[0015] Furthermore, the specific steps of Step S3 include:

[0016] Step S31: Obtain an unprocessed detection point i and its spatio-temporal correlation degree sequence ; construct spatio-temporal matrix , set the spatio-temporal correlation degree sequence related to the detection point j to form the j-th column in the spatio-temporal matrix of the detection point i; the elements with a spatio-temporal step of k form the k-th row in the spatio-temporal matrix of the detection point i;

[0017] Step S32: Calculate the matrix characteristics of the spatio-temporal matrix of the detection point i ;

[0018] Step S33: Judge whether all detection points i have been processed. If so, enter the next step; otherwise, return to Step S31;

[0019] Step S34: Determine the on-site detection location; specifically: delineate the on-site detection insertion range; based on the position coordinates and matrix characteristics of all detection points is within the insertion range determine the specific on-site detection location within the insertion range;

[0020] Determining a specific on-site detection location within the insertion range, specifically: setting an insertion weight of for all detection points is within the insertion range; based on the position coordinates of the detection points and their weight values to determine the centroid coordinates as the on-site detection location ; calculating the centroid coordinates of the on-site detection location using the following formulas (6)(7) ;

[0021] (6);

[0022] (7).

[0023] Furthermore, the type of the hydrogeological parameters is one or more.

[0024] Furthermore, delineating the on-site detection insertion range; specifically: delineating the on-site insertion range based on the on-site detection capability and the on-site detection plan.

[0025] Furthermore, a measuring device is provided among the detection points, and the measuring device is used to collect the hydrogeological parameters in the area to be detected.

[0026] Furthermore, the detection data is hydrogeological big data including the latest historical detection data.

[0027] Furthermore, K is a preset value.

[0028] Furthermore, the historical detection data is obtained by performing real-time detection once a day.

[0029] Furthermore, K = 30.

[0030] A measuring system for on-site detection of hydrogeological parameters, characterized in that the measuring system for on-site detection of hydrogeological parameters is used to implement the method for on-site detection of hydrogeological parameters described above.

[0031] The beneficial effects of the present invention include:

[0032] (1) Supporting on-site detection based on hydrogeological detection big data analysis, eliminating the influence of time and environmental factors on the detection big data through the k-step correlation coefficient, discovering the distribution of detection points within the time and space range based on the discovered correlation between the detection data of different detection points, starting from sparsity, self-uniformity, and overall uniformity, forming an effective supplement to the detection method of fixed detection points through on-site detection, being able to better cover the determination of hydrogeological parameters in the area to be detected in terms of time and space, and effectively and cherishingly utilizing on-site detection resources;

[0033] (2)Set up a multi-dimensional analysis method for multi-scale change analysis, enabling on-site adjustment according to different on-site conditions and detection plans; the adaptable spatio-temporal range can be dynamically adjusted to assist in determining on-site detection locations, facilitating and being able to improve the efficiency of on-site detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings described herein are used to provide a further understanding of the present invention, form a part of this application, but do not constitute an improper limitation of the present invention. In the drawings:

[0035] Figure 1 It is a schematic diagram of the method for measuring hydrogeological parameters in on-site detection provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] The present invention will be described in detail below in conjunction with the drawings and specific embodiments, where the illustrative embodiments and descriptions are only used to explain the present invention, but do not limit the present invention.

[0037] The present invention provides a method and system for measuring hydrogeological parameters in on-site detection. As shown in the attached Figure 1 figures, the method includes the following steps:

[0038] Step S1: Obtain the detection data of the existing detection point i , where: is the detection data of the i-th detection point at the t-th moment, ; T is the time span;

[0039] Preferably: The area to be detected contains multiple existing detection points. These existing detection points are subjected to real-time detection to obtain hydrogeological parameters as detection data; here, the real-time detection is carried out at regular time intervals, and the detection data of all detection points need to be time-aligned; the types of the hydrogeological parameters are one or more, including: water level, soil type, rainfall, air humidity, water temperature, pH value, conductivity, permeability, flow velocity and direction, river flow rate, and / or river pollution degree; when there are multiple types, each type is determined in turn.

[0040] Preferably: A measuring device is provided at the detection point, and the measuring device is used to collect hydrogeological parameters in the area to be detected.

[0041] Preferably: The detection data is big hydrogeological data containing the latest historical detection data; T can be set to a relatively large value, for example, The time span is one year, taking into account seasonal factors. Of course, when conducting supplementary on-site inspections, a smaller time span can be selected. When re-evaluating existing inspection points, multi-scale inspection data can be selected, considering both long-span and short-span historical inspection data.

[0042] Alternatively: The inspection data is a part of the recent historical inspection data. When the part selected from the historical inspection data is similar to the current inspection environment, it is used as the analysis basis. Since hydrogeological inspections usually involve the interaction of groundwater, soil water, and surface water, as well as the influence of geological structures on water distribution and flow, environmental factors need to consider climate factors such as precipitation, evaporation, and temperature; topographic features such as slope, altitude, and landform type, which affect water flow and accumulation; geological structures such as rock type, faults, and fractures, which affect water penetration and storage; and hydrological factors such as the presence of surface water bodies (rivers, lakes), the flow direction and velocity of groundwater; and soil characteristics such as soil type, permeability, and water content, which affect water infiltration and pollutant migration. In addition, ecological factors and human activities can also be referred to.

[0043] Step S2: For each inspection point i, calculate the spatio-temporal correlation degree sequence formed by the k-step spatio-temporal correlation degree between it and any other inspection point j ; where: the k-step spatio-temporal correlation degree is used to indicate the k-step correlation degree of the inspection data of inspection point i and inspection point j on the time axis.

[0044] Specifically, it includes the following steps:

[0045] Step S21: Perform stationarity processing on the inspection data of inspection point i and inspection point j; specifically: Use the following formula (1)(2) or (3)(4) for differencing processing to eliminate the random trend; use the random trend to simulate the random sewage discharge behavior of the pollution source; considering that the discharge of pollutants is generally random, using the random trend can better simulate.

[0046]

[0047] Step S22: Calculate the k-step spatio-temporal correlation degree between any group of inspection points i, j; the k-step spatio-temporal correlation degree is used to indicate the k-step correlation degree of the inspection data of inspection point i and inspection point j on the time axis; specifically: Use the following formula (5) to calculate the k-step spatio-temporal correlation degree

[0048] (5);

[0049] Preferably: K is a preset value; of course, it can be considered that the maximum value of the value is T, that is, the analysis is carried out within the full span of the historical detection data; in addition, the K value can be determined according to the number of detection points N, the size of the on-site detection space where the detection points are arranged and the number of detection points N it contains, the time interval of the detection data, the number of detection data, the calculation overhead of the detection data, the storage overhead, etc.; for example: set the K value to 30, ; obviously, the larger the K value and the N value, the greater the computational load. However, the larger the K value, the larger the time range considered, and the larger the N value, the larger the space range considered. Here, a trade-off needs to be considered;

[0050] Step S23: For each detection point i, arrange the k-step spatio-temporal correlation degree between it and another detection point j in sequence (for example: detection point number, distance from detection point i) to form the spatio-temporal correlation degree sequence of detection point i The value range of is [0, 1], indicating that the two spatio-temporal correlation degree sequences change completely synchronously when the time lags or advances by k steps, while , indicating that the two are also completely uncorrelated when lagging or advancing by k steps. Setting k = 0~K can be used to indicate the correlation situation within the time span range of 0~K;

[0051] Alternatively: The specific content of step S23 is: For each detection point i, arrange the k-step spatio-temporal correlation degree between it and another detection point j in sequence (for example: detection point number, distance from detection point i) to form the spatio-temporal correlation degree sequence of detection point i ; put detection point i itself into the sequence, and set its spatio-temporal correlation degree to 1;

[0052] Preferably: This spatio-temporal correlation degree sequence changes dynamically as the detection progresses or the historical detection data changes;

[0053] Step S3: When conducting on-site detection, determine the spatio-temporal correlation degree distribution of the detection points based on the k-step spatio-temporal correlation degree sequence to determine the on-site detection location; here, a one-dimensional folding processing method is used, which is applicable to the situation where the range to be detected is small and the amount of detection point data is small;

[0054] Specifically, it includes the following steps:

[0055] Step S31: Obtain an unprocessed detection point i and its spatio-temporal correlation degree sequence ; construct spatio-temporal matrix , and set the spatio-temporal correlation degree sequence related to detection point j The j-th column in the spatio-temporal matrix that constitutes the detection point i; at this time, the elements with a spatio-temporal step of k constitute the k-th row in the spatio-temporal matrix of the detection point i; when the detection point i itself is also in the spatio-temporal matrix , the constructed spatio-temporal matrix

[0056] is ; however, when determining one-dimensional folding, whether it includes itself has no practical impact;

[0057] Step S32: Calculate the matrix characteristics of the spatio-temporal matrix of the detection point i ; specifically: Calculate the characteristic points of the spatio-temporal matrix elements as the matrix characteristics of the detection point i ;

[0058] Alternatively: The characteristic points are one or more of mean, gradient, sum, extreme value, trace, determinant, rank, norm, etc.; when the matrix characteristics do not show a normalized feature, it is necessary to re-normalize the matrix characteristics;

[0059] Step S33: Determine whether all detection points i have been processed. If so, proceed to the next step; otherwise, return to Step S31;

[0060] Step S34: Determine the on-site detection location; specifically: Encircle the on-site detection insertion range; based on the position coordinates and their matrix characteristics of all detection points is within the insertion range Determine the specific on-site detection location within the insertion range;

[0061] The determination of the specific on-site detection location within the insertion range is specifically: Set the insertion weight for all detection points is located within the insertion range to be ; Based on the position coordinates of the detection point and its weight value, determine the centroid coordinates as the on-site detection location ; Calculate the centroid coordinates of the on-site detection location using the following formulas (6)(7) ;

[0062] (6);

[0063] (7);

[0064] Define the on-site detection insertion range; specifically: Define the on-site insertion range based on on-site detection capabilities and on-site detection plans; Considering actual constraints (cost, accessibility) and management requirements (regulations, safety), determine the optimal solution through multi-factor trade-offs; In addition, topographical features also need to be considered: Avoid difficult-to-operate areas such as steep slopes and swamps, and prefer flat and stable sites; Consider surface cover: Avoid areas with dense vegetation (interfering with equipment installation) or hardened ground (such as concrete, which may hinder sampling); And traffic conditions: Ensure that the detection points are convenient for equipment transportation and personnel access (such as roads, power supply); Of course, an alternative approach is to, after determining the on-site detection location, make a final determination around this on-site detection location according to the above constraints and requirements;

[0065] Alternatively, define the on-site detection insertion range; specifically: Divide the on-site detection area into multiple sub-areas according to the on-site detection plan, and take each sub-area as a defined detection insertion range; Determine the on-site detection location for each defined detection insertion range; When there is an on-site detection plan, the determination of the insertion range and on-site detection location can be arranged according to the plan;

[0066] Alternatively, define the on-site detection insertion range; specifically: Define multiple on-site detection insertion ranges, and determine multiple on-site detection locations for each of the multiple insertion ranges; When no on-site detection plan has been formed, the on-site detection locations can be determined sequentially according to the settings of the existing detection locations; Obviously, when there are multiple insertion ranges, the on-site detection locations can be determined for each defined detection insertion range in sequence;

[0067] Define multiple on-site detection insertion ranges, specifically:

[0068] Step S34A1: Initialize the enclosure parameters; Initialize the set of candidate detection points as all detection points; Initialize the insertion range count value to 0;

[0069] Step S34A2: Determine whether the enclosure termination condition is met; If so, end, otherwise, proceed to the next step

[0070] Preferably: The termination condition is that the insertion range count value reaches a first preset value, and / or the number of detection points in the set of candidate detection points is less than a second preset value; For example: The first preset value is 1 - 5, and the second preset value is 3 - 8;

[0071] Alternatively: The termination condition is that the variance value between the insertion weight values of all detection points is less than 0.1;

[0072] Step S34A3: Obtain the weight value Of the lowest first detection point from the set of candidate detection points; Obtain the weight value Greater than the weight value Mean value Among the detection points greater than the weight value, the detection point with the closest geographical location to the first detection point is used as the second detection point; the geographical range including the first detection point and the second detection point is used as the delineated on-site detection insertion range; the first detection point and the second detection point are deleted from the candidate detection point set; the insertion range count value is incremented; return to step S34A2; in this way, on-site detection points that can act as a bridge are quickly constructed between the sparse detection point area and the dense detection point area to make up for possible omissions in on-site detection;

[0073] Preferably: the second detection point is one or more; usually when there are multiple second detection points with close weight values, setting multiple second detection points can provide a larger reasonable delineation range;

[0074] Preferably: the geographical range including the first detection point and the second detection point is the smallest geographical range of a certain shape; this shape is a closed shape without concave points; directly connecting the first detection point and the second detection point to form a polygon is the most direct choice;

[0075] In order to more accurately determine the detection position, it is necessary to make more full use of the distribution information contained in the spatio-temporal correlation sequence; perform two-dimensional folding determination. For the case of a large time and space span, two-dimensional folding determination can consider more spatio-temporal correlation information brought by time advance and lag; it can simultaneously discover uniformity and sparsity information, so as to guide the setting of on-site detection points; Alternatively: the specific steps of step S3 are as follows:

[0076] Step SE31: Obtain an unprocessed detection point i and its spatio-temporal correlation sequence ; construct a Spatio-temporal matrix for each detection point i, and set all or part of the spatio-temporal correlation sequences related to the detection point j1 to form the j1-th column in the spatio-temporal matrix of the detection point i; at this time, the elements with a spatio-temporal step of k1 form the k1-th row in the spatio-temporal matrix of the detection point i; K1 and N1 are preset values and K1 <= K, N1 <= N;

[0077] Preferably: before performing step SE31, determine the spatio-temporal matrix size values K1 and N1; for example: set K1 and N1 to preset values, for example: set K1 = K, N1 = N; when constructing the spatio-temporal matrix, it is necessary to intercept the element part in the spatio-temporal related sequence; it is applicable to the situation where the amount of detection point data is small and the time span is small. When the amount of detected large data is small, reduction of K and N may not be performed; while for the situation where the amount of detection point data is large and the time span is large, reduction of K and N is required; an optional reduction method is to traverse the spatio-temporal matrix of all detection points and perform reduction by finding the number of invalid elements (element values are very small and can be ignored) in the spatio-temporal matrix; the more the number of invalid elements, the smaller the values of K1 and N1 are set (the greater the reduction), otherwise, the fewer the number of invalid elements, the larger the values of K1 and N1 are set (the smaller the reduction); when constructing the spatio-temporal matrix after reduction, select the detection points involved in the larger element values and the part of the spatio-temporal correlation sequence involved in the spatio-temporal step length to construct the spatio-temporal matrix. The selected part can be the same (in this case, the detection data of some detection points j are not involved in the subsequent determination process at all) or different for different detection points i (for each detection point i, the detection points j it participates in are different, and the time spans it participates in are also the same or different); of course, the N value and the K value can be selected to be reduced independently or simultaneously;

[0078] Step SE32: Calculate the element mean of each column of elements in the spatio-temporal matrix As the eigenvalue; construct the matrix eigenvector of detection point i

[0079] The selection method of other eigenvalues of the matrix is the same as before;

[0080] Step SE33: Arrange the elements in the matrix eigenvector of detection point i According to the numerical size of the elements therein to obtain the arranged eigenvector ; After arrangement, the element position has nothing to do with the identification of the detection point but is related to the numerical value;

[0081] Step SE34: Calculate the difference between the arranged eigenvectors of any two detection points i and j

[0082]

[0083] Step SE35: Calculate the deviation value between each detection point i and other detection points j

[0084]

[0085] Calculate the weight value of detection point i

[0086] Set the pointing value of detection point i

[0087] Wherein: is a function of the weight value, the deviation value, the sum of the weight value and the deviation value, and the weighted value of the weight value and the deviation value; the function is a linear or non-linear function;

[0088] Preferably: the step further includes calculating the self-deviation value of the detection point i ;

[0089] The self-deviation degree value is used to detect the distribution uniformity of the detection point itself;

[0090] Step SE36: Use the detection point i pointed to by the detection point i with the smallest pointing value as the target detection point;

[0091] Alternatively: among the first NX detection points with the smallest pointing value , use the detection point i with the largest deviation value, that is the detection point i pointed to by as the target detection point; NX is a preset value and can be set to .

[0092] Alternatively: the step SE36 is specifically: calculate the total deviation value ; among the first NX detection points with the smallest pointing value , use the detection point i with the largest total deviation value, that is the detection point i pointed to by as the target detection point; wherein: and are deviation coefficients;

[0093] Step SE37: Select a on-site detection location based on the target detection point; specifically: select a on-site detection location near the target detection point for continuous on-site detection;

[0094] The step of selecting a on-site detection location near the target detection point is specifically:

[0095] Step S371: Calculate the deviation value between the target detection point

[0096] and each detection point j; wherein:

[0097] Step S372: For the target detection point oi, use or The detected point j pointed to is used as the contralateral target detected point; a on-site detection position is selected between the target detected point and the contralateral target detected point for continuous on-site detection; and the distance between the on-site detection position and the target detected point is made less than the distance between the target detected point and the contralateral target detected point; of course, it is also possible to make a determination similar to the above based on calculating the weight value of the detected point i; after determining the on-site detection position, necessary factors such as safety and compliance must be considered. In terms of safety risks, high-voltage lines, underground pipelines, hazardous chemical storage areas, etc. need to be avoided; in terms of environmental protection, when laying points in ecologically sensitive areas (such as wetlands, protected areas), environmental protection regulations need to be complied with to reduce interference with the ecology; in terms of legal authority factors, it is necessary to ensure that the detected point is located in an area where legal access is possible (such as avoiding private land disputes).

[0098] Alternatively: The specific step S372 is as follows: For the target detected point i, the detected point j with a self-deviation value greater than the preset value (non-uniform distribution) is used as the contralateral target detected point; a closed area including the target detected point and all contralateral target detected points is constructed, and an on-site detection position is selected within this closed area for continuous on-site detection; and the distance between the on-site detection position and the target detected point is made less than the distance between the target detected point and any contralateral target detected point.

[0099] When the time span of the detection data has a greater impact on the detection data, multiple factors such as sparsity, self-uniformity, and distribution uniformity also need to be considered. To further improve the accuracy, three-dimensional determination can be selected. Alternatively: The specific step S3 is as follows:

[0100] Step SF31: Obtain an unprocessed detected point i and its spatio-temporal correlation sequence ; Construct a spatio-temporal matrix , and set the spatio-temporal correlation sequence related to the detected point j1 to form the j1-th column in the spatio-temporal matrix of the detected point i; at this time, the elements with a spatio-temporal step of k1 form the k1-th row in the spatio-temporal matrix of the detected point i. ;

[0101] Preferably: Before executing this step SF31, determine the spatio-temporal matrix size values K1 and N1; K1 and N1 are preset values, and the determination method is the same as before.

[0102] Step SF32: First, perform column sorting, calculate the sum value of each column element, and re-arrange each entire column in the order of the sum value size; then perform column permutation. For each column in the spatio-temporal matrix in turn, arrange its elements in ascending order of numerical value to obtain the arranged spatio-temporal matrix ;

[0103] Step SF33: Calculate the difference of the permutation spatio-temporal matrix between any two detection points i and j

[0104] The difference is used to consider spatio-temporal uniformity;

[0105] Step SF34: Calculate the deviation value between each detection point i and other detection points j

[0106]

[0107] Calculate the weight value of detection point i

[0108] Set the pointing value of detection point i ;

[0109] Wherein: is a function of the weighted value of the weight value, deviation value, sum of weight value and deviation value, or weighted value of weight value and deviation value; the function is a linear or non-linear function;

[0110] Preferably: The step further includes calculating the self-deviation value of detection point i

[0111]

[0112] Step SF35: Take the detection point i pointed by the detection point i with the largest pointing value as the target detection point;

[0113] Alternatively: The step SF35 is specifically: Take the detection point i with the largest deviation value among the top NX detection points with the largest pointing value, that is The detection point i pointed by as the target detection point; NX is a preset value and can be set to ;

[0114] Alternatively: The step SF35 is specifically: Calculate the total deviation value Take the detection point i with the largest total deviation value among the top NX detection points with the largest pointing value, that is The detection point i pointed by as the target detection point; wherein: and are deviation coefficients;

[0115] Step SF36: Select the on-site detection location based on the target detection point; specifically; Select the on-site detection location near the target detection point for continuous on-site detection; When determining in a three-dimensional manner, the operation method and step SE37 of selecting the on-site detection location near the target detection point are similar; Of course, it can be directly near the target detection point, and specific settings can be made based on necessary factors such as on-site detection difficulty, safety and compliance, environmental protection, and legal authority;

[0116] Step S4: Set up a measuring device at the on-site detection location for hydrogeological detection; specifically: Drill a hole according to the specification (the hole diameter and depth are determined according to the thickness of the aquifer) vertically at the on-site detection location, insert a PVC filter pipe, fill it with gravel, seal and stop the water; Install a water level gauge or a sampling pipe to ensure that the filter pipe section is aligned with the target aquifer; Fix the probe of the measuring device in the well pipe and calibrate the measuring device; Use a bailer or a submersible pump to extract groundwater and detect it after standing still and stabilizing; When the deviation value at the on-site detection location shown by continuous on-site detection decreases to non-maximum, set this on-site detection point as a new detection point to replace the existing detection point i;

[0117] After receiving the detection data, the server records the detection time, location, equipment model, environmental conditions (such as temperature, rainfall) using a standardized form; Associates and stores the detection data with the on-site photos or videos taken and the key geological features marked (such as lithological interfaces, springs);

[0118] Preferably: Repeat the measurement 3 times for the same on-site detection location, take the average value, and calculate the relative standard deviation RSD until it is qualified; For example: is qualified;

[0119] Preferably: For groundwater level detection, use a pressure type water level gauge, a float type water level gauge or an electronic data recorder; For water quality detection, use a portable multi-parameter water quality instrument (measuring pH, dissolved oxygen, conductivity, etc.); For permeability detection: a double-ring infiltrometer, a pumping test device or a permeameter; For flow velocity and direction determination, use a tracer test, an underground water flow velocity meter or a thermal pulse method instrument;

[0120] Preferably: Install a remote telemetry system (such as a GSM / GPRS transmission module) on the measuring device to achieve real-time data collection and transmission, and send the collected detection data to the server;

[0121] Based on the same inventive concept, the present invention also provides a measuring system for on-site detecting hydrogeological parameters, and the system is used to implement the above-mentioned method for measuring on-site hydrogeological parameters;

[0122] Based on the same inventive concept, the present invention also provides a measuring server for on-site detecting hydrogeological parameters, and the server is used to implement the above-mentioned method for measuring on-site hydrogeological parameters;

[0123] A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including assembly or interpreted languages, declarative or procedural languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may or may not correspond to a file in a file system. The program can be stored as part of a file that holds other programs or data (such as one or more scripts stored in a markup language document), in a single file dedicated to the program, or in multiple cooperating files (such as files that store one or more modules, subroutines, or portions of code). A computer program can be deployed to execute on one computer or on multiple computers located at one site or distributed across multiple sites and interconnected by a communication network.

[0124] Those skilled in the art will appreciate that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

[0125] The present invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart illustrations and / or block diagrams, and combinations of flows and / or blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device create means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0126] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0127] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the steps of the function specified in one process or a plurality of processes and / or blocks Figure 1 in one block or a plurality of blocks Figure 1 in the process.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A method for measuring hydrogeological parameters on site, characterized in that: The method comprises: Step S1: Obtain the detection data of the existing detection point i ,in: is the detection data of the i-th detection point at the t-th moment, ; T is the time span; Step S2: Calculate the k-step spatiotemporal correlation between each detection point i and any other detection point j, and form a spatiotemporal correlation sequence ; Among them: k-step spatiotemporal correlation It is used to indicate the k-step correlation degree of the detection data of the detection points i and j on the time axis; it specifically includes the following steps: Step S21: Perform stationarity processing on the detection data of detection points i and j; Step S22: Calculate the k-step spatiotemporal correlation using the following formula (5): ; （5）； Step S23: for each detection point i, arrange its k-step spatiotemporal correlation with another detection point j in order to form a spatiotemporal correlation sequence of detection point i The value range of is [0, 1]; Step S3: When performing on-site detection, determine the on-site detection position by determining the distribution of the spatiotemporal correlation of the detection point based on the k-step spatiotemporal correlation sequence; Step S4: Setting up a measuring device at the on-site testing location to perform hydrogeological testing.

2. The method for determining hydrogeological parameters on site according to claim 1, characterized in that: The step S3 specifically includes: Step S31: Obtain an unprocessed detection point i and its spatiotemporal correlation sequence ; Build Space-time matrix , set the spatiotemporal correlation sequence related to the detection point j The elements with a spatiotemporal step length of k constitute the kth row in the spatiotemporal matrix of the detection point i. Step S32: Calculate the matrix characteristics of the spatiotemporal matrix of the detection point i ; Step S33: determine whether all detection points i have been processed, if yes, proceed to the next step, otherwise, return to step S31; Step S34: determine the on-site detection position; specifically: circle the on-site detection insertion range; based on the position coordinates of all detection points is within the insertion range and their matrix features Determine the specific on-site detection location within the insertion range; The specific on-site detection position is determined within the insertion range, specifically: the insertion weight is set to be ; Based on the position coordinates of the detection point and its weight value to determine the center of gravity coordinates as the on-site detection position ; Use the following formula (6) (7) to calculate the centroid coordinates of the on-site detection position ; (6); (7)。 3. The method for determining hydrogeological parameters on site according to claim 2, characterized in that: The type of the hydrogeological parameter is one or more.

4. The method for determining hydrogeological parameters on site according to claim 3, characterized in that: The on-site inspection insertion range is delineated; specifically: the on-site insertion range is delineated based on the on-site inspection capability and the on-site inspection plan.

5. The method for determining hydrogeological parameters on site according to claim 4, characterized in that: A measuring device is provided in the detection point, and the measuring device is used to collect hydrogeological parameters in the area to be detected.

6. The method for determining hydrogeological parameters on site according to claim 5, characterized in that: The detection data is hydrogeological big data including recent historical detection data.

7. The method for determining hydrogeological parameters on site according to claim 6, characterized in that: K is a preset value.

8. The method for determining hydrogeological parameters on site according to claim 7, characterized in that: The historical detection data is obtained by performing real-time detection once a day.

9. The method for determining hydrogeological parameters on site according to claim 8, characterized in that: K=30。 10. A system for measuring hydrogeological parameters on site, characterized in that: The system for measuring hydrogeological parameters detected on site is used to implement the method for measuring hydrogeological parameters detected on site as described in any one of claims 1 to 9.

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