Dynamic water quality monitoring method and system based on groundwater level

Abstract

The invention provides a water quality dynamic monitoring method and system based on underground water level, and relates to the technical field of water quality dynamic monitoring, the method comprises the following steps: calculating a correction coefficient based on historical data, seasonal change, rainfall and geological activity; adjusting the annual average change rate through the correction coefficient to obtain a final underground water level change trend; according to the final groundwater level change trend, setting a water quality abnormity monitoring point in the region where the groundwater level is abnormally changed, so as to obtain corresponding abnormal water quality data; according to the abnormal water quality data, regularly collecting water samples in the abnormal water quality data, detecting various indexes of the water samples, including chloride content, conductivity and total dissolved solids, and analyzing the change condition of the water quality so as to evaluate the water quality condition of the underground water. According to the method, the robustness to the data quality problem is improved, so that the actual condition of the underground water quality is reflected more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of water quality dynamic monitoring, and in particular to a method and system for dynamic monitoring of water quality based on groundwater level. Background Art

[0002] In the field of groundwater monitoring, although traditional data analysis methods have been widely used, their accuracy and real-time performance are still limited in some specific application scenarios. The dynamics and complexity of groundwater systems require more sophisticated and timely monitoring technologies to ensure water quality safety.

[0003] Especially in areas with abundant groundwater resources and extremely sensitive to water quality changes, such as agricultural irrigation areas, industrial concentration areas and drinking water sources, there are extremely high requirements for the stability of groundwater quality. In these areas, any slight change in water level or water quality may have a significant impact on local agricultural production, industrial activities or residents' lives. Therefore, methods that can capture and accurately analyze these changes in real time are particularly important.

[0004] In addition, some traditional data analysis methods are difficult to cope with in areas with complex geological structures and variable groundwater flow paths. In these areas, groundwater levels and water quality are more susceptible to rapid and unpredictable changes due to external factors (such as geological activities, climate change, human interference, etc.). This requires monitoring methods to be not only highly accurate, but also flexible and real-time enough to respond and adjust monitoring strategies in a timely manner.

[0005] Although traditional data analysis methods can make trend predictions based on historical data, some of them ignore the direct impact of abnormal groundwater level changes on water quality dynamics. These abnormal change points, as areas of significant changes in groundwater flow, recharge or discharge, are key observation points for water quality changes. A rapid drop in water levels may accelerate the dissolution of minerals in aquifers and affect water quality, while a rise in water levels may introduce new sources of pollution. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a method and system for dynamic monitoring of water quality based on groundwater level, so as to improve the robustness to data quality issues and thus more accurately reflect the actual situation of groundwater quality.

[0007] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0008] In a first aspect, a method for dynamic monitoring of water quality based on groundwater level is provided, the method comprising:

[0009] Step 1: In the area to be monitored, groundwater monitoring wells are laid out according to the topography, groundwater burial conditions and groundwater storage media. The types of monitoring wells include automatic monitoring wells and manual monitoring wells, so that the monitoring network covers the entire area to be monitored to obtain groundwater level and water quality data;

[0010] Step 2: Calculate the average annual change rate of the groundwater level in each monitoring well to determine the change trend of the groundwater level; calculate the correction coefficient based on historical data, seasonal changes, rainfall and geological activities; adjust the average annual change rate by the correction coefficient to obtain the final groundwater level change trend;

[0011] Step 3: according to the final groundwater level change trend, in the area where the groundwater level has abnormal changes, set water quality abnormal monitoring points to obtain corresponding abnormal water quality data;

[0012] Step 4: Based on the abnormal water quality data, water samples in the abnormal water quality data are collected regularly, and various indicators of the water samples are tested, including chloride content, electrical conductivity and total dissolved solids, and the changes in water quality are analyzed to evaluate the water quality of groundwater.

[0013] Furthermore, in the area to be monitored, groundwater monitoring wells are laid out according to the topography, groundwater burial conditions and groundwater storage media, including:

[0014] Determine the location of the monitoring well according to the topography, groundwater burial conditions and groundwater storage medium of the area to be monitored;

[0015] The parameters of the gray wolf optimization algorithm are set, including the size of the gray wolf population, the number of iterations, and the search space; each individual in the gray wolf population represents a monitoring well layout scheme, that is, each individual is a combination of a set of monitoring well locations;

[0016] According to the search strategy of the Gray Wolf optimization algorithm, the corresponding monitoring well layout scheme is found in the search space;

[0017] A fitness function is set to evaluate the pros and cons of each layout scheme; through the iterative process of the gray wolf optimization algorithm, the location of the monitoring well is continuously adjusted and optimized to optimize the fitness function;

[0018] When the preset number of iterations is reached, the iteration is stopped and the current α wolf position is output as the corresponding monitoring well layout optimization plan;

[0019] According to the optimization plan for the layout of monitoring wells, automatic monitoring wells and manual monitoring wells are laid out in the area to be monitored.

[0020] Furthermore, the annual average change rate of the groundwater level in each monitoring well is calculated to determine the change trend of the groundwater level, including:

[0021] Obtain historical data on groundwater levels from each monitoring well to obtain observations over many consecutive years;

[0022] Preprocess the observations of consecutive years to determine a time period that reflects the water level changes;

[0023] Use (final water level - initial water level) / years to calculate the average annual rate of change of groundwater level in each monitoring well during the time period;

[0024] According to the actual situation, set the threshold [E, L] for judging the water level change trend;

[0025] The time period is divided into several sub-segments, and the average annual change rate of the groundwater level data in each sub-segment is calculated respectively; if the average annual change rate of the sub-segment is greater than L, the trend of the sub-segment is judged to be an upward trend; if the average annual change rate of the sub-segment is less than E, the trend of the sub-segment is judged to be a downward trend; if the average annual change rate of the sub-segment is between [E, L], the trend of the sub-segment is judged to be a stable trend.

[0026] Furthermore, according to the actual situation, the thresholds [E, L] for judging the water level change trend are set, including:

[0027] Obtain long-term series of groundwater level data and perform preprocessing;

[0028] Determine the target for threshold setting, i.e., an upward trend, a downward trend, or a stable trend;

[0029] The threshold E and threshold L are encoded as genes of the genetic algorithm using binary coding;

[0030] Randomly generate a set of initial threshold combinations [E, L] as the initial population, each individual represents a set of threshold settings;

[0031] Design a fitness function to evaluate the quality of each set of threshold settings;

[0032] According to the fitness function, the corresponding individuals are selected through roulette to enter the next generation;

[0033] Perform a crossover operation on the selected individuals to generate new threshold combinations;

[0034] The newly generated individuals are mutated, and the selection, crossover and mutation operations are repeated until the preset number of iterations is reached to obtain the corresponding individuals as the final solution, that is, the final threshold setting [E, L].

[0035] Furthermore, the calculation formula of the correction coefficient is:

[0036]

[0037] Among them, α1, β, γ and δ are weight coefficients; p, q and r are exponential parameters; H avg is the average historical groundwater level; H ref is the reference water level value; S max is the maximum seasonal water level; S min is the minimum seasonal water level; S avg is the average seasonal water level; R avg is the average annual rainfall; R ref is the reference rainfall value; G is the geological activity index; the calculation formula of the geological activity index is:

[0038]

[0039]

[0040] Among them, f i represents the occurrence frequency of the ith geological event; s i represents the intensity of the ith geological event; m i represents the weight of the impact of the ith geological event on groundwater; a i and b i are the exponential parameters; k ij represents the interaction coefficient of the impact of the j-th geological event on the i-th geological event; p ij and q ij are the exponential parameters respectively; c represents the adjustment constant of the basic geological activity level; ∈1 and ∈2 are the seasonal influence amplitude coefficients related to earthquake and fault activities respectively; T1 and T2 are the seasonal periods of earthquake and fault activities respectively.

[0041] Furthermore, the annual average change rate is adjusted by the correction coefficient to obtain the final groundwater level change trend, including:

[0042] Multiply the average annual change rate by the correction factor to obtain the adjusted average annual change rate;

[0043] Get the latest groundwater level data, denoted as W, which represents the groundwater level of the current year;

[0044] Determine the future year t to be predicted;

[0045] For each set future year t, the corresponding predicted water level H is calculated by H=W+A×(t-B), where W represents the current known water level, A is the adjusted average annual rate of change, B represents the current year, and (t-B) represents the difference in years from the current year to the predicted year.

[0046] Furthermore, based on the abnormal water quality data, water samples in the abnormal water quality data are collected regularly, and various indicators of the water samples are tested, including chloride content, conductivity and total dissolved solids, and the changes in water quality are analyzed to evaluate the water quality of groundwater, including:

[0047] Determine the initial temperature T0. The initial temperature T0 is a parameter in the simulated annealing algorithm, representing the initial heat;

[0048] Define a temperature drop strategy, that is, the temperature is multiplied by a cooling coefficient θ after each iteration, where 0<θ<1;

[0049] The water quality assessment problem is transformed into an optimization problem, where each state represents a combination of a set of water quality parameters, including chloride content, conductivity, and total dissolved solids;

[0050] Define an objective function to evaluate the quality of water quality parameter combinations;

[0051] Obtain real-time water quality data, and mark it as abnormal data when it exceeds the preset safety range;

[0052] Randomly generate an initial state, that is, an initial estimate of a set of water quality parameters, randomly perturb the current state at the current temperature, generate a new state, and use the objective function to evaluate the quality of the new state;

[0053] Decide whether to accept the new state according to the Metropolis criterion, update the current state to the accepted new state, lower the temperature, and update the current temperature value according to the annealing plan to obtain the final state;

[0054] When the termination condition is met, the iteration is stopped, and after the abnormal data is identified, the selection of sampling points and the sampling frequency are guided according to the final state;

[0055] The collected water samples are sent to the laboratory to test the chloride content, conductivity and total dissolved solids index to obtain laboratory test results;

[0056] Compare and analyze the laboratory test results with the final status to evaluate the water quality of groundwater.

[0057] In the second aspect, a water quality dynamic monitoring system based on groundwater level comprises:

[0058] A determination module is used to lay out groundwater monitoring wells in the area to be monitored according to the topography, groundwater burial conditions and groundwater storage medium. The types of monitoring wells include automatic monitoring wells and manual monitoring wells, so that the monitoring network covers the entire area to be monitored to obtain groundwater level and water quality data;

[0059] The calculation module is used to calculate the average annual change rate of the groundwater level in each monitoring well to determine the change trend of the groundwater level; calculate the correction coefficient based on historical data, seasonal changes, rainfall and geological activities; adjust the average annual change rate through the correction coefficient to obtain the final change trend of the groundwater level;

[0060] The acquisition module is used to set abnormal water quality monitoring points in areas where the groundwater level has abnormal changes according to the final groundwater level change trend to obtain corresponding abnormal water quality data;

[0061] The evaluation module is used to regularly collect water samples from abnormal water quality data, detect various indicators of water samples, including chloride content, electrical conductivity and total dissolved solids, analyze changes in water quality, and evaluate the water quality of groundwater.

[0062] According to a third aspect, a computing device includes:

[0063] one or more processors;

[0064] The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described.

[0065] In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described is implemented.

[0066] The above solution of the present invention includes at least the following beneficial effects:

[0067] By laying out automatic monitoring wells and artificial monitoring wells, a network covering the entire area to be monitored is constructed, which can obtain groundwater level and water quality data in real time or quasi-real time. This greatly improves the efficiency of monitoring and the real-time nature of data, and helps to promptly discover and deal with water quality problems. This method calculates the average annual change rate of groundwater levels in each monitoring well, and calculates the correction coefficient based on historical data, seasonal changes, rainfall, geological activities and other factors, thereby more accurately reflecting the true change trend of groundwater levels. This approach of comprehensively considering multiple influencing factors has significantly improved the accuracy and reliability of data analysis.

[0068] Based on the corrected groundwater level change trend, areas with abnormal water level changes can be accurately identified, and abnormal water quality monitoring points can be set in these areas. This targeted monitoring strategy helps to capture and analyze abnormal water quality data more effectively.

[0069] By regularly collecting water samples from abnormal water quality data and testing multiple indicators including chloride content, conductivity and total dissolved solids, this method can comprehensively and deeply analyze changes in water quality and help accurately assess the water quality status of groundwater. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a flow chart of a method for dynamic monitoring of water quality based on groundwater level provided by an embodiment of the present invention.

[0071] Figure 2 It is a schematic diagram of a water quality dynamic monitoring system based on groundwater level provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0072] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0073] like Figure 1 As shown, an embodiment of the present invention provides a method for dynamic monitoring of water quality based on groundwater level, the method comprising the following steps:

[0074] Step 1: In the area to be monitored, groundwater monitoring wells are laid out according to the topography, groundwater burial conditions and groundwater storage media. The types of monitoring wells include automatic monitoring wells and manual monitoring wells, so that the monitoring network covers the entire area to be monitored to obtain groundwater level and water quality data;

[0075] Step 2: Calculate the average annual change rate of the groundwater level in each monitoring well to determine the change trend of the groundwater level; calculate the correction coefficient based on historical data, seasonal changes, rainfall and geological activities; adjust the average annual change rate by the correction coefficient to obtain the final groundwater level change trend;

[0076] Step 3: according to the final groundwater level change trend, in the area where the groundwater level has abnormal changes, set water quality abnormal monitoring points to obtain corresponding abnormal water quality data;

[0077] Step 4: Based on the abnormal water quality data, water samples in the abnormal water quality data are collected regularly, and various indicators of the water samples are tested, including chloride content, electrical conductivity and total dissolved solids, and the changes in water quality are analyzed to evaluate the water quality of groundwater.

[0078] In the embodiment of the present invention, step 1, reasonably arrange monitoring wells according to the topography, groundwater burial conditions and storage medium, ensure that the monitoring network can fully cover the area to be monitored, so as to effectively obtain the groundwater level and water quality data in the entire area. Combining automatic monitoring wells and manual monitoring wells, it can not only realize real-time monitoring and transmission of data, but also conduct regular on-site inspections and sampling, which improves the flexibility of monitoring and the reliability of data. Step 2, by calculating the average annual change rate, the long-term change trend of the groundwater level can be scientifically and objectively judged; the introduction of correction coefficients such as historical data, seasonal changes, rainfall and geological activities effectively eliminates the interference of external factors on water level changes, making the final water level change trend more accurate and reliable. Step 3, according to the corrected groundwater level change trend, set a special water quality abnormality monitoring point in the abnormal change area, and realize the key monitoring of potential pollution or water quality problem areas; by adding monitoring points in the water level abnormal area, water quality abnormalities can be discovered earlier. Step 4: Regularly collect water samples from abnormal water quality data and test multiple indicators including chloride content, conductivity and total dissolved solids, so as to fully and deeply understand the specific water quality of groundwater.

[0079] In a preferred embodiment of the present invention, the above step 1, in the area to be monitored, according to the topography, groundwater burial conditions and groundwater storage medium, deploys groundwater monitoring wells, including:

[0080] The location of the monitoring well is determined according to the topography, groundwater burial conditions and groundwater storage media of the area to be monitored, including: collecting topography data (such as elevation, slope, etc.), groundwater burial condition data (such as water level depth, aquifer thickness, etc.) and groundwater storage media data (such as soil type, permeability, etc.) of the area to be monitored; preprocessing the collected data, including data cleaning and format conversion, to ensure data quality and consistency. Next, data analysis is performed to identify key areas that may affect groundwater flow and quality; based on the data analysis results, combined with expert knowledge and experience, the candidate locations of the monitoring wells are preliminarily determined. These locations should be able to representatively reflect the dynamic changes of groundwater.

[0081] Set the parameters of the gray wolf optimization algorithm, including the size of the gray wolf population, the number of iterations, and the search space; each individual in the gray wolf population represents a monitoring well layout plan, that is, each individual is a combination of a set of monitoring well locations, specifically including: initializing the parameters of the gray wolf optimization algorithm according to the scale and complexity of the problem. This includes the size of the gray wolf population (i.e., the number of individuals), the number of iterations (i.e., the maximum number of iterations of the algorithm), and the search space (i.e., the possible range or boundary of the solution); each gray wolf individual is represented as a monitoring well layout plan. Specifically, a coding method (such as binary coding, real number coding, etc.) can be used to represent each individual, where the length and structure of the code should be able to cover the location information of all monitoring wells;

[0082] According to the search strategy of the gray wolf optimization algorithm, the corresponding monitoring well layout scheme is found in the search space, specifically including: randomly generating an initial gray wolf population in the set search space. Each individual represents a possible monitoring well layout scheme.

[0083] Set a fitness function to evaluate the pros and cons of each deployment scheme; through the iterative process of the gray wolf optimization algorithm, continuously adjust and optimize the location of the monitoring wells to optimize the fitness function, specifically including: evaluating the fitness value of each individual through the fitness function. The fitness function can quantitatively reflect the pros and cons of the monitoring well deployment scheme, for example, it can consider factors such as the coverage, representativeness, and cost of the monitoring wells; according to the search strategy of the gray wolf optimization algorithm (such as guided search based on α, β, and δ wolves), update the location of each gray wolf individual;

[0084] When the preset number of iterations is reached, the iteration is stopped, and the current α wolf position is output as the corresponding monitoring well layout optimization plan, which specifically includes: this is equivalent to finding a better monitoring well layout plan in the solution space; repeating the fitness evaluation and gray wolf position update steps until the preset number of iterations is reached or other stop conditions are met; after the iteration, the optimal gray wolf individual is selected according to the fitness value as the optimization plan for monitoring well layout. Usually, the optimal solution corresponds to the individual with the highest fitness value (or the lowest, depending on the definition of the fitness function). The gray wolf individual corresponding to the optimal solution is decoded and restored to a specific monitoring well layout plan. This includes determining the exact location, type (automatic or manual) and other information of each monitoring well. Finally, the optimized layout plan is output.

[0085] According to the optimization plan for the layout of monitoring wells, automatic monitoring wells and manual monitoring wells are laid out in the area to be monitored, which specifically includes: according to the optimized layout plan for monitoring wells, automatic monitoring wells and manual monitoring wells are actually laid out in the area to be monitored, which includes site selection, drilling, installation of monitoring equipment and other work. After the layout is completed, the equipment in the automatic monitoring wells is debugged and calibrated to ensure that it can operate normally and accurately collect data. At the same time, preliminary water quality and water level tests are carried out on the manual monitoring wells to verify their effectiveness; all the laid monitoring wells are connected to form a groundwater monitoring network covering the entire area to be monitored. Ensure that data can be transmitted and shared in real time for subsequent dynamic water quality monitoring and analysis.

[0086] In an embodiment of the present invention, the gray wolf optimization algorithm can find the optimal monitoring well layout scheme in the search space. This means that the location of the monitoring well is no longer a random choice based on experience or simple rules, but the result of precise calculation and optimization by the algorithm, so as to ensure that the monitoring well can cover the key area more effectively and improve the accuracy and representativeness of monitoring. The location of the monitoring well determined by the gray wolf optimization algorithm can more accurately capture the dynamic changes of groundwater. This targeted layout method not only reduces unnecessary monitoring points, but also ensures the effective coverage of key areas, thereby improving the overall monitoring efficiency. The optimized monitoring well layout scheme can reduce the number of unnecessary monitoring wells, which not only reduces the layout and maintenance costs, but also saves resources. At the same time, through reasonable layout, it can be ensured that each monitoring well can play the greatest role and avoid waste of resources. The gray wolf optimization algorithm has a strong global search capability and a fast convergence speed, and can adapt to different topography, groundwater burial conditions and storage media. This enables the method to still find a more ideal monitoring well layout scheme when facing a complex and changeable groundwater environment. The monitoring well layout plan based on the Gray Wolf optimization algorithm can provide decision makers with more accurate and comprehensive groundwater dynamic information.

[0087] Among them, the calculation formula of the fitness function F is:

[0088]

[0089] Among them, A c Indicates the area of ​​the covered area; A t represents the total area; d i represents the distance from the ith monitoring well to the nearest key hydrogeological feature; ∈ represents a constant to avoid division by zero; n represents the total number of monitoring wells; c u represents the unit drilling cost; h represents the drilling depth; f d represents the drilling diameter coefficient; p j represents the unit price of the jth type of equipment; q j represents the number of the jth type of equipment; sj represents the installation and maintenance cost of the j-th type of equipment; m represents the total number of equipment types; i and j represent index values.

[0090] In a preferred embodiment of the present invention, the annual average change rate of the groundwater level in each monitoring well is calculated to determine the change trend of the groundwater level, including:

[0091] Obtain historical data of groundwater levels from each monitoring well to obtain observations over many consecutive years, including: accessing the data recording system of each monitoring well or using dedicated data acquisition equipment to extract historical data of groundwater levels from each monitoring well; collating the collected data to ensure the integrity and accuracy of the data. This may include checking the timestamps of data records, water level readings, etc.; if the data is not stored in a unified format, it needs to be converted into a format that is easy to analyze, such as a CSV or Excel file.

[0092] Preprocess the observations from multiple consecutive years to determine a time period that reflects water level changes, including: removing or correcting outliers, missing values, and duplicate values ​​to ensure data reliability; select a time period that can reflect water level changes based on the purpose of the study, which involves analyzing multiple years of data to determine the most representative time period; for missing data points, interpolation methods (such as linear interpolation, polynomial interpolation, etc.) can be used to fill them.

[0093] Use (final water level - initial water level) / years to calculate the average annual rate of change of groundwater level in each monitoring well within the time period, specifically including: finding the water level readings corresponding to the start time and end time within the selected time period; using the formula "(final water level - initial water level) / years" to calculate the average annual rate of change.

[0094] According to the actual situation, set the threshold [E, L] for judging the water level change trend;

[0095] The time period is divided into several sub-segments, and the average annual change rate of the groundwater level data in each sub-segment is calculated respectively; if the average annual change rate of the sub-segment is greater than L, the trend of the sub-segment is judged to be an upward trend; if the average annual change rate of the sub-segment is less than E, the trend of the sub-segment is judged to be a downward trend; if the average annual change rate of the sub-segment is between [E, L], the trend of the sub-segment is judged to be a stable trend.

[0096] In an embodiment of the present invention, the method is based on the historical data of groundwater levels obtained from each monitoring well for many consecutive years, ensuring the comprehensiveness and depth of the analysis. This data-driven method provides a solid information basis for decision makers, making water resource management decisions more reasonable. By calculating the average annual change rate, the change of groundwater levels can be accurately quantified. Combined with the set threshold [E, L], the rising, falling or stable trend of the water level can be clearly judged, which is helpful for early warning and planning response measures. Dividing the overall time period into several sub-segments for analysis not only reveals the long-term trend, but also captures the dynamic changes in the short term. This flexibility makes the analysis more detailed and can adapt to management needs at different time scales. Accurately judging the changing trend of groundwater levels helps to find a balance between protection and utilization. For areas with an upward trend, the utilization can be appropriately increased; for areas with a downward trend, protection measures need to be strengthened to avoid over-exploitation. The stability of groundwater levels is crucial to maintaining the ecological environment. Through this method, abnormal changes in water levels can be discovered in a timely manner, so that effective measures can be taken to protect the ecological environment and prevent ecological problems caused by water level fluctuations.

[0097] In a preferred embodiment of the present invention, according to the actual situation, the threshold [E, L] for judging the water level change trend is set, including:

[0098] Obtain long-term series of groundwater level data and perform preprocessing, including: obtaining long-term series of groundwater level data from groundwater monitoring wells or related databases, which are recorded in the form of time series, including date and water level values. Check and remove outliers, missing values ​​or duplicate records in the data. For missing values, consider using interpolation methods to fill them; in order to make the data easier to process and analyze, the water level data can be standardized, such as scaling the data to between 0 and 1 or performing Z-score standardization. In order to reduce noise and fluctuations in the data, the data can be processed using sliding average or other smoothing techniques.

[0099] Determine the goal of setting the threshold, i.e., an upward trend, a downward trend, or a stable trend. Specifically, determine the goal of setting the threshold, i.e., to identify an upward trend, a downward trend, or a stable trend in the water level. This is usually determined based on actual needs and analysis purposes; by conducting a preliminary analysis of the preprocessed data, understand the overall trend of the water level change, so as to provide a reference for subsequent threshold setting.

[0100] The threshold E and the threshold L are encoded as genes of the genetic algorithm using binary coding, which specifically includes: determining a suitable binary coding length according to the possible value ranges of the thresholds E and L; and converting the actual values ​​of the thresholds E and L into corresponding binary codes.

[0101] A set of initial threshold combinations [E, L] is randomly generated as the initial population, and each individual represents a set of threshold settings. Specifically, the process includes: determining an appropriate population size, i.e., the number of initial threshold combinations, based on the complexity of the problem and computing resources; using a random number generator, according to the code length determined in step 3, randomly generating a set of initial threshold combinations [E, L] as the initial population. Each individual (i.e., each set of threshold settings) is a randomly generated binary code string.

[0102] Design a fitness function to evaluate the quality of each set of threshold settings;

[0103] According to the fitness function, the corresponding individuals are selected to enter the next generation through roulette, which includes: calculating the probability of being selected according to the fitness value of each individual. The higher the fitness value, the greater the probability of being selected; using the roulette algorithm, a part of the individuals are randomly selected to enter the next generation according to the selection probability of each individual. This ensures that excellent individuals have a greater chance of being retained.

[0104] The selected individuals are crossovered to generate new threshold combinations, including: randomly selecting two from the selected individuals as parents for pairing; randomly selecting one or more crossover points, exchanging the gene fragments of the two parents at these points, and thus generating new offspring individuals. This helps to introduce new genetic variations while retaining the excellent genes of the parents.

[0105] The newly generated individuals are mutated, and selection, crossover and mutation operations are repeated until the preset number of iterations is reached to obtain the corresponding individuals as the final solution, that is, the final threshold setting [E, L]. Specifically, the following steps are performed: the newly generated offspring individuals are mutated, that is, the values ​​of some gene positions are randomly changed (0 to 1 or 1 to 0) to increase the diversity of the population and prevent falling into the local optimal solution; selection, crossover and mutation operations are repeated until the preset number of iterations is reached or other termination conditions are met (such as finding a solution that meets the accuracy requirements, the fitness value reaches the preset threshold, etc.). At this point, the individual obtained is the final threshold setting [E, L].

[0106] In an embodiment of the present invention, the optimal threshold setting is automatically found through a genetic algorithm, so that the system can adaptively adjust the judgment criteria according to the specific characteristics of the water level data, thereby improving the flexibility and accuracy of the system. Compared with the threshold set manually, the threshold obtained by the genetic algorithm is more objective and scientific, reducing the influence of human intervention and subjective judgment, making the judgment of the water level change trend more reliable. The genetic algorithm can search for the optimal solution on a global scale and avoid falling into the local optimum, thereby ensuring that the threshold setting found is optimal or close to optimal on a global scale. The entire threshold setting process is automatically completed by the algorithm, which reduces the workload of manually setting the threshold and improves work efficiency. Through the iterative optimization of the genetic algorithm, a more accurate threshold setting can be found, making the judgment of the water level change trend more accurate, which helps to timely discover potential water resource problems and take corresponding countermeasures. Among them, the calculation formula of the fitness function is:

[0107] F(E,L)=w1·Acc-w2·Err+w3·Bal;

[0108] Among them, Acc is the accuracy of trend classification; Err is the error; Bal is the trend balance; w1, w2 and w3 represent weight coefficients; among them,

[0109]

[0110] Among them, 1(·) is the indicator function. If (T obs,i =T pred,i ) is true, then it takes 1, otherwise it takes 0; T obs,i is the trend of the actual groundwater level (rising, falling, stable), and its value is {1, -1, 0}; the error calculation formula is:

[0111]

[0112] Among them, h i+1 -h i is the water level change; T pred,i L is the change predicted by the threshold; T pred,i is the predicted trend calculated by the threshold [E, L]; N is the total number of data points; the predicted trend T calculated by the threshold [E, L] pred,i According to the change range of groundwater level and the set threshold, the water level change is divided into the following three trends:

[0113] Upward trend (T pred =1):

[0114] If the water level rise (h i+1 -h i ) is greater than the threshold E, it is judged that the water level is rising, that is:

[0115] Downward trend (T pred =-1):

[0116] If the water level drops (h i+1 -h i ) is less than the negative threshold -E, it is judged that the water level is dropping, that is,

[0117] Stable trend (T pred =0):

[0118] If the water level change (h i+1 -h i ) is within the range of [-L, L], the water level is considered stable, that is:

[0119] The calculation formula for trend balance is:

[0120]

[0121] Among them, C1, C -1 and C0 are the numbers classified as rising, falling, and stable trends, respectively.

[0122] In a preferred embodiment of the present invention, the calculation formula of the correction coefficient is:

[0123]

[0124] Among them, α1, β, γ and δ are weight coefficients; p, q and r are exponential parameters; H avg is the average historical groundwater level; H ref is the reference water level value; S max is the maximum seasonal water level; S min is the minimum seasonal water level; S avg is the average seasonal water level; R avg is the average annual rainfall; R ref is the reference rainfall value; G is the geological activity index; the calculation formula of the geological activity index is:

[0125]

[0126] Among them, f i represents the occurrence frequency of the ith geological event; s i represents the intensity of the ith geological event; m i represents the weight of the impact of the ith geological event on groundwater; a i and b iare the exponential parameters; k ij represents the interaction coefficient of the impact of the j-th geological event on the i-th geological event; p ij and q ij are the exponential parameters respectively; c represents the adjustment constant of the basic geological activity level; ∈1 and ∈2 are the seasonal influence amplitude coefficients related to earthquake and fault activities respectively; T1 and T2 are the seasonal periods of earthquake and fault activities respectively.

[0127] In an embodiment of the present invention, the correction coefficient formula comprehensively considers various factors affecting the groundwater level, including historical groundwater level, seasonal water level changes, average annual rainfall, and geological activity index. The weight coefficients α1, β, γ, and δ and the index parameters (p, q, and r) in the formula can be adjusted according to actual conditions to meet the needs of groundwater level assessment in different regions and under different conditions. By introducing the geological activity index G, the formula can quantify the impact of geological activities on groundwater levels. The calculation formula of the geological activity index takes into account the frequency, intensity, and interaction of various geological events, making the assessment of the impact of geological activities more accurate. In the calculation of the geological activity index, by introducing the seasonal impact amplitude coefficients ∈1 and ∈2 related to earthquake and fault activities and their seasonal cycles T1 and T2, the formula can better reflect the impact of seasonal changes on groundwater levels.

[0128] In a preferred embodiment of the present invention, the annual average change rate is adjusted by a correction coefficient to obtain the final groundwater level change trend, including:

[0129] Multiply the average annual change rate by the correction factor to obtain the adjusted average annual change rate;

[0130] Get the latest groundwater level data, denoted as W, which represents the groundwater level of the current year;

[0131] Determine the future year t to be predicted;

[0132] For each set future year t, the corresponding predicted water level H is calculated by H=W+A×(t-B), where W represents the current known water level, A is the adjusted average annual rate of change, B represents the current year, and (t-B) represents the difference in years from the current year to the predicted year.

[0133] In an embodiment of the present invention, by introducing a correction coefficient to adjust the average annual change rate, the actual change trend of the groundwater level can be more accurately reflected. This adjustment takes into account a variety of influencing factors, such as historical water levels, seasonal changes, rainfall, and geological activities, thereby improving the accuracy of the prediction. After the average annual change rate is dynamically adjusted by the correction coefficient, it can better adapt to the changes in water levels under different time and environmental conditions. This dynamic adjustment makes the prediction model more flexible and able to cope with various complex situations. Combined with the latest groundwater level data, the method can quickly reflect the changes in water levels and provide timely information support for decision makers. This helps decision makers make quick and accurate responses based on actual conditions. Accurate groundwater level prediction helps identify potential risk points, such as ecological and environmental problems or water shortages that may be caused by a rapid drop in water levels. The probability and impact of these risks can be reduced by early warning and taking corresponding preventive measures.

[0134] The above step 3, according to the final groundwater level change trend, sets abnormal water quality monitoring points in areas where the groundwater level has abnormal changes to obtain corresponding abnormal water quality data, which may include:

[0135] Analyze the average annual rate of change adjusted by the correction coefficient, identify those areas that show abnormal increase or decrease or large fluctuations, and combine historical data and geographical environmental factors to evaluate whether these abnormal changes may indicate potential water quality problems; mark those areas where groundwater levels change abnormally, including areas with rapid water level decline, rise, or abnormal periodic fluctuations; in the marked abnormal change areas, select representative locations as water quality abnormality monitoring points based on the severity of the changes and the distribution of possible pollution sources, and ensure that the monitoring points can cover different types of abnormal changes, such as potentially polluted areas, ecologically sensitive areas, etc.; install automatic or manual water quality monitoring equipment at the selected monitoring points. These equipment should have the ability to measure key water quality parameters (such as pH, dissolved oxygen, turbidity, chemical oxygen demand, heavy metal content, etc.); configure equipment to automatically collect water samples regularly (such as daily, weekly or monthly), and conduct real-time or regular water quality analysis. Establish a data quality control process to clean, verify and standardize the collected data. Use statistical methods to detect outliers in the collected water quality data.

[0136] In a preferred embodiment of the present invention, based on the abnormal water quality data, water samples in the abnormal water quality data are collected regularly, various indicators of the water samples are tested, including chloride content, conductivity and total dissolved solids, and changes in water quality are analyzed to evaluate the water quality of groundwater, including:

[0137] Determine the initial temperature T0. The initial temperature T0 is a parameter in the simulated annealing algorithm, which represents the initial heat. Specifically, it includes: setting a suitable initial temperature T0 according to the complexity and scale of the problem. This temperature value is usually determined through experience or experiments to ensure that the algorithm has enough "heat" at the beginning to jump out of the local optimal solution.

[0138] Define a temperature drop strategy, that is, after each iteration, the temperature is multiplied by a cooling coefficient θ, where 0<θ<1, specifically including: setting a cooling coefficient θ (0<θ<1), which determines the rate of temperature drop. After each iteration, the current temperature is multiplied by the cooling coefficient to reduce the temperature, that is, the new temperature T_new=T_current×θ, T_current represents the current temperature value in the simulated annealing algorithm.

[0139] The water quality assessment problem is transformed into an optimization problem, where each state represents a combination of a set of water quality parameters, including chloride content, conductivity, and total dissolved solids. Specifically, it includes: determining the goal of water quality assessment, such as minimizing a certain combination of chloride content, conductivity, and total dissolved solids. Each set of water quality parameters (chloride content, conductivity, total dissolved solids) is regarded as a state, and the entire optimization process is to find the optimal state.

[0140] Define an objective function to evaluate the quality of the water quality parameter combination, where the formula of the objective function f is:

[0141]

[0142] Among them, Cl, Ec and TDS represent the chloride content, conductivity and total dissolved solids in the current state respectively; C1, E1 and T1 are the standard values ​​of the preset safety range of chloride, conductivity and total dissolved solids respectively; w1, w2 and w3 are weight coefficients; σ1, σ2 and σ3 are the standard deviations for controlling the fluctuations of each indicator.

[0143] Acquire real-time water quality data. When the water quality data exceeds the preset safety range, mark it as abnormal data. Specifically, acquire water quality data in real time through water quality monitoring equipment, compare the data with the preset safety range, and mark it as abnormal data if it exceeds the range.

[0144] Randomly generate an initial state, that is, a set of initial estimated values ​​of water quality parameters, randomly perturb the current state at the current temperature to generate a new state, and use the objective function to evaluate the quality of the new state, specifically including: randomly generate a set of initial estimated values ​​of water quality parameters as the initial state, randomly perturb the current state at the current temperature (such as changing the chloride content, conductivity or total dissolved solids value) to generate a new state. Use the objective function defined in step 4 to evaluate the quality of the new state.

[0145] According to the Metropolis criterion, decide whether to accept the new state, update the current state to the accepted new state, lower the temperature, and update the current temperature value according to the annealing plan to obtain the final state, which includes: calculating the difference ΔE between the objective function value of the new state and the current state; if ΔE<0 (that is, the new state is better), then accept the new state unconditionally. If ΔE>0 (that is, the new state is worse), then with a certain probability Accept the new state, where T is the current temperature; after accepting the new state, update the current state to the new state and reduce the temperature.

[0146] When the termination condition is met, the iteration is stopped. After the abnormal data is identified, the selection of sampling points and the sampling frequency are guided according to the final state, including: setting the iteration stop condition, such as reaching the maximum number of iterations, the temperature drops below a certain threshold, or the state does not improve significantly in multiple consecutive iterations. When the termination condition is met, the iteration is stopped. According to the combination of water quality parameters in the final state, the selection of sampling points and the sampling frequency are guided to more accurately understand the water quality status of the abnormal area.

[0147] The collected water samples are sent to the laboratory to test the chloride content, conductivity and total dissolved solids to obtain laboratory test results, which specifically includes: sending the collected water samples to the laboratory, using professional testing equipment and methods to measure indicators such as chloride content, conductivity and total dissolved solids to obtain accurate laboratory test results.

[0148] Compare and analyze the laboratory test results with the final state to evaluate the groundwater quality, including: compare and analyze the laboratory test results with the final state obtained by the simulated annealing algorithm. By comparing the differences between the two, the groundwater quality can be more accurately evaluated, including identifying potential water quality problems, pollution sources, etc. This information can provide important basis for subsequent water resources management and protection decisions.

[0149] In the embodiments of the present invention, chloride, conductivity and total dissolved solids are important indicators for evaluating the quality of groundwater, because they can comprehensively reflect the chemical characteristics and pollution degree of groundwater. Chloride content is usually related to the salinity of groundwater, and its abnormal increase may indicate seawater intrusion, industrial pollution or dissolution of natural salt mines. Conductivity reflects the total concentration of ions in water and is an important parameter for evaluating water purity. Total dissolved solids (TDS) represents the total amount of all soluble solids in water, including inorganic salts and organic matter, etc., and its level directly affects the taste and use value of water.

[0150] When conducting an evaluation, the groundwater quality can be judged based on the specific values ​​and changing trends of these indicators. For example, by comparing historical data with standard limits, it is possible to assess whether the current water quality is within the normal range; by observing the changing trends of indicators over time, it is possible to predict the possible development direction of water quality.

[0151] In an embodiment of the present invention, by regularly collecting water samples in abnormal water quality data and detecting indicators such as chloride content, conductivity and total dissolved solids, more detailed and accurate water quality information can be obtained. This helps to more accurately evaluate the water quality of groundwater and promptly discover potential water quality problems. Combined with the simulated annealing algorithm, the water quality parameter combination can be optimized according to the characteristics of the water quality assessment problem, thereby guiding the selection of sampling points and the sampling frequency. This optimized sampling strategy can improve the pertinence and efficiency of sampling, reduce unnecessary sampling work, and save resources and costs. By obtaining real-time water quality data and comparing it with the preset safety range, water quality anomalies can be discovered in time. Comparing and analyzing the laboratory test results with the final state obtained by the simulated annealing algorithm can help decision makers make more reasonable and effective water resources management and protection decisions. The introduction of optimization technologies such as simulated annealing algorithms can improve the intelligence and automation level of water quality monitoring. This can not only reduce the workload of staff and improve work efficiency, but also reduce the impact of human factors on water quality assessment results.

[0152] like Figure 2 As shown, an embodiment of the present invention further provides a water quality dynamic monitoring system based on groundwater level, comprising:

[0153] A determination module is used to lay out groundwater monitoring wells in the area to be monitored according to the topography, groundwater burial conditions and groundwater storage medium. The types of monitoring wells include automatic monitoring wells and manual monitoring wells, so that the monitoring network covers the entire area to be monitored to obtain groundwater level and water quality data;

[0154] The calculation module is used to calculate the average annual change rate of the groundwater level in each monitoring well to determine the change trend of the groundwater level; calculate the correction coefficient based on historical data, seasonal changes, rainfall and geological activities; adjust the average annual change rate through the correction coefficient to obtain the final change trend of the groundwater level;

[0155] The acquisition module is used to set abnormal water quality monitoring points in areas where the groundwater level has abnormal changes according to the final groundwater level change trend to obtain corresponding abnormal water quality data;

[0156] The evaluation module is used to regularly collect water samples from abnormal water quality data, detect various indicators of water samples, including chloride content, electrical conductivity and total dissolved solids, analyze changes in water quality, and evaluate the water quality of groundwater.

[0157] It should be noted that the device is a device corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0158] The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0159] The embodiment of the present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

Claims

1. A method for dynamic monitoring of water quality based on groundwater level, characterized in that: The method comprises: Step 1: In the area to be monitored, groundwater monitoring wells are laid out according to the topography, groundwater burial conditions and groundwater storage media. The types of monitoring wells include automatic monitoring wells and manual monitoring wells, so that the monitoring network covers the entire area to be monitored to obtain groundwater level and water quality data; Step 2: Calculate the average annual change rate of the groundwater level in each monitoring well to determine the change trend of the groundwater level; calculate the correction coefficient based on historical data, seasonal changes, rainfall and geological activities; adjust the average annual change rate by the correction coefficient to obtain the final groundwater level change trend; Step 3: according to the final groundwater level change trend, in the area where the groundwater level has abnormal changes, set water quality abnormal monitoring points to obtain corresponding abnormal water quality data; Step 4: Based on the abnormal water quality data, regularly collect water samples from the abnormal water quality data, test various indicators of the water samples, analyze changes in water quality, and evaluate the water quality of groundwater.

2. A method for dynamic monitoring of water quality based on groundwater level according to claim 1, characterized in that: In the area to be monitored, groundwater monitoring wells are laid out according to the topography, groundwater burial conditions and groundwater storage media, including: Determine the location of the monitoring well according to the topography, groundwater burial conditions and groundwater storage medium of the area to be monitored; The parameters of the gray wolf optimization algorithm are set, including the size of the gray wolf population, the number of iterations, and the search space; each individual in the gray wolf population represents a monitoring well layout scheme, that is, each individual is a combination of a set of monitoring well locations; According to the search strategy of the Gray Wolf optimization algorithm, the corresponding monitoring well layout scheme is found in the search space; A fitness function is set to evaluate the pros and cons of each layout scheme; through the iterative process of the gray wolf optimization algorithm, the location of the monitoring well is continuously adjusted and optimized to optimize the fitness function; When the preset number of iterations is reached, the iteration is stopped and the current α wolf position is output as the corresponding monitoring well layout optimization plan; According to the optimization plan for the layout of monitoring wells, automatic monitoring wells and manual monitoring wells are laid out in the area to be monitored.

3. A method for dynamic monitoring of water quality based on groundwater level according to claim 2, characterized in that: Calculate the annual average change rate of groundwater level in each monitoring well to determine the change trend of groundwater level, including: Obtain historical data on groundwater levels from each monitoring well to obtain observations over many consecutive years; Preprocess the observations of consecutive years to determine a time period that reflects the water level changes; Use (final water level - initial water level) / years to calculate the average annual rate of change of groundwater level in each monitoring well during the time period; According to the actual situation, set the threshold [E, L] for judging the water level change trend; The time period is divided into several sub-segments, and the average annual change rate of the groundwater level data in each sub-segment is calculated respectively; if the average annual change rate of the sub-segment is greater than L, the trend of the sub-segment is judged to be an upward trend; if the average annual change rate of the sub-segment is less than E, the trend of the sub-segment is judged to be a downward trend; if the average annual change rate of the sub-segment is between [E, L], the trend of the sub-segment is judged to be a stable trend.

4. A method for dynamic monitoring of water quality based on groundwater level according to claim 3, characterized in that: According to the actual situation, set the threshold [E, L] for judging the water level change trend, including: Obtain long-term series of groundwater level data and perform preprocessing; Determine the target for threshold setting, i.e., an upward trend, a downward trend, or a stable trend; The threshold E and threshold L are encoded as genes of the genetic algorithm using binary coding; Randomly generate a set of initial threshold combinations [E, L] as the initial population, each individual represents a set of threshold settings; Design a fitness function to evaluate the quality of each set of threshold settings; According to the fitness function, the corresponding individuals are selected through roulette to enter the next generation; Perform a crossover operation on the selected individuals to generate new threshold combinations; The newly generated individuals are mutated, and the selection, crossover and mutation operations are repeated until the preset number of iterations is reached to obtain the corresponding individuals as the final solution, that is, the final threshold setting [E, L].

5. A method for dynamic monitoring of water quality based on groundwater level according to claim 4, characterized in that: The water samples were tested for chloride content, electrical conductivity and total dissolved solids.

6. A method for dynamic monitoring of water quality based on groundwater level according to claim 5, characterized in that: The annual average change rate is adjusted by the correction coefficient to obtain the final groundwater level change trend, including: Multiply the average annual change rate by the correction factor to obtain the adjusted average annual change rate; Get the latest groundwater level data, denoted as W, which represents the groundwater level of the current year; Determine the future year t to be predicted; For each set future year t, the corresponding predicted water level H is calculated by H=W+A×(tB), where W represents the current known water level, A is the adjusted average annual rate of change, B represents the current year, and (tB) represents the difference in years from the current year to the predicted year.

7. A method for dynamic monitoring of water quality based on groundwater level according to claim 6, characterized in that: Based on the abnormal water quality data, water samples are collected regularly to test various indicators of water samples, including chloride content, conductivity and total dissolved solids, and analyze the changes in water quality to assess the water quality of groundwater, including: Determine the initial temperature T0, which is a parameter in the simulated annealing algorithm and represents the initial heat; Define a temperature drop strategy, that is, the temperature is multiplied by a cooling coefficient θ after each iteration, where 0<θ<1; The water quality assessment problem is transformed into an optimization problem, where each state represents a combination of a set of water quality parameters, including chloride content, conductivity, and total dissolved solids; Define an objective function to evaluate the quality of water quality parameter combinations; Obtain real-time water quality data, and mark it as abnormal data when it exceeds the preset safety range; Randomly generate an initial state, that is, an initial estimate of a set of water quality parameters, randomly perturb the current state at the current temperature, generate a new state, and use the objective function to evaluate the quality of the new state; Decide whether to accept the new state according to the Metropolis criterion, update the current state to the accepted new state, lower the temperature, and update the current temperature value according to the annealing plan to obtain the final state; When the termination condition is met, the iteration is stopped, and after the abnormal data is identified, the selection of sampling points and the sampling frequency are guided according to the final state; The collected water samples are sent to the laboratory to test the chloride content, conductivity and total dissolved solids index to obtain laboratory test results; Compare and analyze the laboratory test results with the final status to evaluate the water quality of groundwater.

8. A water quality dynamic monitoring system based on groundwater level, characterized in that: Applied to the method according to any one of claims 1 to 7, comprising: A determination module is used to lay out groundwater monitoring wells in the area to be monitored according to the topography, groundwater burial conditions and groundwater storage medium. The types of monitoring wells include automatic monitoring wells and manual monitoring wells, so that the monitoring network covers the entire area to be monitored to obtain groundwater level and water quality data; The calculation module is used to calculate the average annual change rate of the groundwater level in each monitoring well to determine the change trend of the groundwater level; calculate the correction coefficient based on historical data, seasonal changes, rainfall and geological activities; adjust the average annual change rate through the correction coefficient to obtain the final change trend of the groundwater level; The acquisition module is used to set abnormal water quality monitoring points in areas where the groundwater level has abnormal changes according to the final groundwater level change trend to obtain corresponding abnormal water quality data; The evaluation module is used to regularly collect water samples from abnormal water quality data, detect various indicators of water samples, analyze changes in water quality, and evaluate the water quality of groundwater.

9. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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