A water quality dynamic monitoring method and system based on groundwater level

Abstract

The present invention provides a method and system for dynamic water quality monitoring based on groundwater level, which relates to the technical field of dynamic water quality monitoring. The method includes: calculating a correction coefficient based on historical data, seasonal variations, rainfall, and geological activities; adjusting the annual average change rate by the correction coefficient to obtain the final groundwater level change trend; setting water quality anomaly 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; regularly collecting water samples from the abnormal water quality data according to the abnormal water quality data, detecting various indicators of the water samples, including chloride content, conductivity, and total dissolved solids, and analyzing the changes in water quality to evaluate the water quality status of groundwater. The present invention improves the robustness to data quality problems, thereby more accurately reflecting the actual situation of groundwater quality.

Description

Technical Field

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

[0002] In the field of groundwater monitoring, although traditional data analysis methods have been widely applied, their accuracy and real-time performance still have limitations in some specific application scenarios. The dynamic and complex nature of the groundwater system requires more refined and timely monitoring technologies to ensure water quality safety.

[0003] Especially in those areas with rich groundwater resources and extremely sensitive to water quality changes, such as agricultural irrigation areas, industrial concentration areas, and domestic drinking water sources, there are extremely high requirements for the stability of groundwater quality. In these areas, any minor changes 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, for those areas with complex geological structures and variable groundwater flow paths, some traditional data analysis methods are difficult to cope with. In these areas, the groundwater level and water quality are more easily affected by external factors (such as geological activities, climate change, human interference, etc.) and undergo rapid and unpredictable changes. This requires that the monitoring method not only has high accuracy, but also has sufficient flexibility and real-time performance to respond and adjust the monitoring strategy in a timely manner.

[0005] Traditional data analysis methods, although able to perform trend prediction based on historical data, some ignore the direct impact of abnormal groundwater level change points on water quality dynamics. These abnormal change points, as significant areas of change in groundwater flow, recharge, or discharge, are key observation points for water quality changes. A rapid drop in water level may accelerate the dissolution of minerals in the aquifer and affect water quality; while a rise in water level may introduce new pollution sources. Summary of the Invention

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

[0007] To solve the above technical problem, the technical solution of the present invention is as follows:

[0008] In the first aspect, a water quality dynamic monitoring method based on groundwater level, the method includes:

[0009] Step 1, in the area to be monitored, according to the topography, groundwater burial conditions and groundwater occurrence media, groundwater monitoring wells are arranged. 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 the water level and water quality data of groundwater;

[0010] Step 2, calculate the average annual change rate of the groundwater level of each monitoring well to judge 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;

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

[0012] Step 4, according to the abnormal water quality data, regularly collect water samples from the abnormal water quality data, detect various indicators of the water samples, including chloride content, conductivity and total dissolved solids, analyze the change of water quality to evaluate the water quality status of groundwater.

[0013] Further, in the area to be monitored, according to the topography, groundwater burial conditions and groundwater occurrence media, groundwater monitoring wells are arranged, including:

[0014] Determine the positions of the monitoring wells according to the topography, groundwater burial conditions and groundwater occurrence media of the area to be monitored;

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

[0016] According to the search strategy of the grey wolf optimization algorithm, find the corresponding monitoring well layout scheme in the search space;

[0017] Set a fitness function for evaluating the advantages and disadvantages of each layout scheme; through the iterative process of the grey wolf optimization algorithm, continuously adjust and optimize the positions of the monitoring wells to optimize the fitness function;

[0018] When the preset number of iterations is reached, stop the iteration and output the current position of the alpha wolf as the corresponding optimized monitoring well layout scheme;

[0019] According to the optimized monitoring well layout scheme, arrange automatic monitoring wells and manual monitoring wells in the area to be monitored.

[0020] Further, calculate the average annual change rate of the groundwater level of each monitoring well to judge the change trend of the groundwater level, including:

[0021] Obtain historical data of groundwater levels from each monitoring well to obtain observation values for consecutive years.

[0022] Preprocess the observation values for consecutive years to determine a time period reflecting the water level changes.

[0023] Use (ending water level - starting water level) / number of years to calculate the average annual change rate of groundwater levels for each monitoring well within the time period.

[0024] Set thresholds [E, L] for judging the trend of water level changes according to the actual situation.

[0025] Divide the time period into several sub - segments, and calculate the average annual change rate for the groundwater level data in each sub - segment respectively; if the average annual change rate of a sub - segment > L, the trend of this sub - segment is judged as an upward trend; if the average annual change rate of a sub - segment < E, the trend of this sub - segment is judged as a downward trend; if the average annual change rate of a sub - segment is between [E, L], the trend of this sub - segment is judged as a stable trend.

[0026] Furthermore, setting the thresholds [E, L] for judging the trend of water level changes according to the actual situation includes:

[0027] Obtain long - time - series groundwater level data and perform preprocessing.

[0028] Determine the goal of threshold setting, that is, upward trend, downward trend or stable trend.

[0029] Encode threshold E and threshold L into genes of the genetic algorithm using binary encoding.

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

[0031] Design a fitness function to evaluate the quality of each group of threshold settings.

[0032] According to the fitness function, select corresponding individuals to enter the next generation through roulette wheel selection.

[0033] Perform crossover operations on the selected individuals to generate new threshold combinations.

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

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

[0036]

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

[0038]

[0039] where f i represents the occurrence frequency of the i-th geological event; s i represents the intensity of the i-th geological event; m i represents the weight of the i-th geological event on groundwater; a i and b i are exponential parameters respectively; k ij represents the interaction coefficient of the j-th geological event on the i-th geological event; p ij and q ij are 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.

[0040] Furthermore, the annual average change rate is adjusted by a correction coefficient to obtain the final groundwater level change trend, including:

[0041] Multiply the annual average change rate by the correction coefficient to obtain the adjusted annual average change rate;

[0042] Obtain the latest groundwater level data, denoted as W, representing the groundwater level of the current year;

[0043] Determine the future year t to be predicted;

[0044] For each set future year t, calculate the corresponding predicted water level H through H = W + A×(t - B), where W represents the known current water level, A is the adjusted annual average change rate; B represents the current year; (t - B) represents the number of years from the current year to the predicted year.

[0045] Furthermore, according to the abnormal water quality data, water samples in the abnormal water quality data are regularly collected, and various indexes of the water samples are detected, including chloride content, conductivity and total dissolved solids, and the change of water quality is analyzed to evaluate the water quality status of groundwater, including:

[0046] Determine the initial temperature T0. The initial temperature T0 is a parameter in the simulated annealing algorithm and represents the initial heat.

[0047] Define a temperature reduction strategy, that is, the temperature is multiplied by a cooling coefficient θ after each iteration, where 0 < θ < 1.

[0048] Transform the water quality assessment problem into an optimization problem, where each state represents a combination of water quality parameters, and the water quality parameters include chloride content, conductivity and total dissolved solids.

[0049] Define an objective function to evaluate the quality of the combination of water quality parameters.

[0050] Obtain real-time water quality data. When the water quality data exceeds the preset safety range, it is marked as abnormal data.

[0051] Randomly generate an initial state, that is, an initial estimate of a set of water quality parameters. At the current temperature, randomly perturb the current state to generate a new state, and use the objective function to evaluate the quality of the new state.

[0052] Decide whether to accept the new state according to the Metropolis criterion, update the current state to the accepted new state, reduce the temperature, and update the current temperature value according to the annealing plan to obtain the final state.

[0053] When the termination condition is met, stop the iteration. After identifying the abnormal data, guide the selection of sampling points and sampling frequency according to the final state.

[0054] Send the collected water samples to the laboratory to detect the chloride content, conductivity and total dissolved solids indexes to obtain the laboratory test results.

[0055] Compare and analyze the laboratory test results with the final state to evaluate the water quality status of groundwater.

[0056] In a second aspect, a water quality dynamic monitoring system based on the groundwater level includes:

[0057] 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 occurrence 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 the groundwater level and water quality data.

[0058] A calculation module, configured to calculate the annual average change rate of the groundwater levels of each monitoring well to determine the change trend of the groundwater levels; calculate a correction coefficient based on historical data, seasonal variations, rainfall, and geological activities; adjust the annual average change rate through the correction coefficient to obtain the final change trend of the groundwater levels;

[0059] An acquisition module, configured to set water quality anomaly monitoring points in areas where the groundwater levels have abnormal changes according to the final change trend of the groundwater levels, so as to acquire corresponding abnormal water quality data;

[0060] An evaluation module, configured to regularly collect water samples from the abnormal water quality data according to the abnormal water quality data, detect various indicators of the water samples, including chloride content, conductivity, and total dissolved solids, and analyze the change situation of the water quality to evaluate the water quality status of the groundwater.

[0061] In a third aspect, a computing device includes:

[0062] One or more processors;

[0063] A storage device, configured to store one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the method described above.

[0064] In a fourth aspect, a computer-readable storage medium stores a program, which when executed by a processor, implements the method described above.

[0065] The above solution of the present invention has at least the following beneficial effects:

[0066] By deploying automatic monitoring wells and manual monitoring wells, a network covering the entire area to be monitored is constructed, which can obtain the groundwater level and water quality data in real time or near real time. This greatly improves the monitoring efficiency and data timeliness, and helps to detect and handle water quality problems in a timely manner. The method calculates the annual average change rate of the groundwater levels of each monitoring well, and calculates a correction coefficient in combination with factors such as historical data, seasonal variations, rainfall, and geological activities, so as to more accurately reflect the true change trend of the groundwater levels. This practice of comprehensively considering various influencing factors significantly improves the accuracy and reliability of data analysis.

[0067] According to the corrected change trend of the groundwater levels, areas with abnormal water level changes can be accurately identified, and water quality anomaly monitoring points are set in these areas. This targeted monitoring strategy helps to more effectively capture and analyze water quality anomaly data.

[0068] By regularly collecting water samples from abnormal water quality data and detecting multiple indicators including chloride content, conductivity, and total dissolved solids, this method can comprehensively and deeply analyze the changes in water quality, which helps to accurately evaluate the water quality status of groundwater. Brief Description of the Drawings

[0069] Figure 1 is a schematic flow chart of a water quality dynamic monitoring method based on groundwater level provided by an embodiment of the present invention.

[0070] Figure 2 is a schematic diagram of a water quality dynamic monitoring system based on groundwater level provided by an embodiment of the present invention. Detailed Embodiments

[0071] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the 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 so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0072] As Figure 1 shown, an embodiment of the present invention provides a water quality dynamic monitoring method based on groundwater level, and the method includes the following steps:

[0073] Step 1, in the area to be monitored, according to the topography, groundwater burial conditions, and groundwater occurrence medium, groundwater monitoring wells are arranged. 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 the groundwater level and water quality data;

[0074] Step 2, calculate the annual average change rate of the groundwater level of each monitoring well to judge the change trend of the groundwater level; calculate the correction coefficient based on historical data, seasonal changes, rainfall, and geological activities; adjust the annual average change rate through the correction coefficient to obtain the final change trend of the groundwater level;

[0075] Step 3, according to the final change trend of the groundwater level, in the area where the groundwater level has abnormal changes, set water quality abnormal monitoring points to obtain corresponding abnormal water quality data;

[0076] Step 4, according to the abnormal water quality data, regularly collect water samples from the abnormal water quality data, detect various indicators of the water samples, including chloride content, conductivity, and total dissolved solids, analyze the changes in water quality to evaluate the water quality status of groundwater.

[0077] In an embodiment of the present invention, step 1: reasonably arrange monitoring wells according to the topographical features, groundwater burial conditions, and occurrence media to ensure that the monitoring network can comprehensively cover the area to be monitored, so as to effectively obtain the water level and water quality data of groundwater in the entire area. By combining automatic monitoring wells and manual monitoring wells, real-time monitoring and transmission of data can be achieved, and regular on-site inspections and sampling can be carried out, improving the flexibility of monitoring and the reliability of data. Step 2: By calculating the annual average change rate, the long-term change trend of the groundwater level can be scientifically and objectively judged; by introducing correction factors such as historical data, seasonal changes, rainfall, and geological activities, the interference of external factors on the water level change is effectively eliminated, making the final water level change trend more accurate and credible. Step 3: According to the corrected groundwater level change trend, special water quality anomaly monitoring points are set in the abnormal change area, realizing key monitoring of potential pollution or water quality problem areas; by adding monitoring points in the water level abnormal area, water quality anomalies can be detected earlier. Step 4: Regularly collect water samples from abnormal water quality data and detect multiple indicators including chloride content, conductivity, and total dissolved solids, etc., so as to comprehensively and deeply understand the specific water quality status of groundwater.

[0078] In a preferred embodiment of the present invention, in step 1 above, within the area to be monitored, groundwater monitoring wells are arranged according to the topographical features, groundwater burial conditions, and groundwater occurrence media, including:

[0079] According to the topographical features, groundwater burial conditions, and groundwater occurrence media of the area to be monitored, determine the positions of the monitoring wells, specifically including: collecting topographical feature data (such as elevation, slope, etc.), groundwater burial condition data (such as water level depth, aquifer thickness, etc.), and groundwater occurrence media data (such as soil type, permeability, etc.) of the area to be monitored; preprocess the collected data, including data cleaning, format conversion, etc., to ensure data quality and consistency. Then, conduct data analysis to identify key areas that may affect groundwater flow and quality; based on the data analysis results, combined with expert knowledge and experience, preliminarily determine the candidate positions of the monitoring wells. These positions should be able to representatively reflect the dynamic changes of groundwater.

[0080] Set the parameters of the Grey Wolf Optimization algorithm, including the size of the grey wolf population, the number of iterations, and the search space; represent each individual in the grey wolf population as a monitoring well layout plan, that is, each individual is a combination of a set of monitoring well locations, specifically including: initialize the parameters of the Grey Wolf Optimization algorithm according to the scale and complexity of the problem. This includes the size of the grey wolf population (i.e., the number of individuals), the number of iterations (i.e., the maximum number of iteration rounds for the algorithm to run), and the search space (i.e., the possible range or boundary of the solution); represent each grey wolf individual 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 coding should be able to cover all the location information of the monitoring wells;

[0081] According to the search strategy of the Grey Wolf Optimization algorithm, find the corresponding monitoring well layout plan in the search space, specifically including: randomly generate an initial grey wolf population within the set search space. Each individual represents a possible monitoring well layout plan.

[0082] Set a fitness function for evaluating the quality of each layout plan; through the iterative process of the Grey Wolf Optimization algorithm, continuously adjust and optimize the positions of the monitoring wells to optimize the fitness function, specifically including: evaluate the fitness value of each individual through the fitness function. The fitness function can quantitatively reflect the quality of the monitoring well layout plan. For example, factors such as the coverage range, representativeness, and cost of the monitoring wells can be considered; update the position of each grey wolf individual according to the search strategy of the Grey Wolf Optimization algorithm (such as the guided search based on the α, β, δ wolves);

[0083] When the preset number of iterations is reached, stop the iteration and output the current position of the α wolf as the corresponding optimized monitoring well layout plan, specifically including: this is equivalent to finding a better monitoring well layout plan in the solution space; repeat the steps of fitness evaluation and grey wolf position update until the preset number of iterations is reached or other stopping conditions are met; after the iteration ends, select the optimal grey wolf individual according to the fitness value as the optimized plan for the monitoring well layout. Usually, the optimal solution corresponds to the individual with the highest (or lowest, depending on the definition of the fitness function) fitness value. Decode the grey wolf individual corresponding to the optimal solution to restore it to the specific monitoring well layout plan. This includes determining the exact position, type (automatic or manual), etc. information of each monitoring well. Finally, output this optimized layout plan.

[0084] According to the optimized monitoring well layout plan, automatic monitoring wells and manual monitoring wells are arranged in the area to be monitored. Specifically, it includes: According to the optimized monitoring well layout plan, automatic monitoring wells and manual monitoring wells are actually arranged in the area to be monitored, which includes work such as site selection, drilling, and installation of monitoring equipment. After the arrangement 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 detections are carried out on the manual monitoring wells to verify their effectiveness; all the arranged 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 water quality dynamic monitoring and analysis work.

[0085] In the embodiment of the present invention, the Grey Wolf Optimization Algorithm can find the optimal monitoring well layout plan in the search space. This means that the positions of the monitoring wells are no longer randomly selected based on experience or simple rules, but the results of precise calculation and optimization by the algorithm, thus ensuring that the monitoring wells can more effectively cover key areas and improve the accuracy and representativeness of monitoring. The positions of the monitoring wells determined by the Grey 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 plan 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 ensure that each monitoring well can play its maximum utility and avoid waste of resources. The Grey Wolf Optimization Algorithm has strong global search ability and fast convergence speed, and can adapt to different topographies, groundwater burial conditions, and occurrence media. This enables the method to still find a relatively ideal monitoring well layout plan when facing complex and changeable groundwater environments. The monitoring well layout plan based on the Grey Wolf Optimization Algorithm can provide more accurate and comprehensive groundwater dynamic information for decision-makers.

[0086] Among them, the calculation formula of the fitness function F is:

[0087]

[0088] Among them, A c represents the area of the covered area; A t represents the total area; d i represents the distance from the i-th 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 j-th type of equipment; q j represents the quantity of the j-th type of equipment; sj denote the installation and maintenance costs of the j-th type of equipment; m represents the total number of equipment types; i and j represent index values.

[0089] In a preferred embodiment of the present invention, calculating the average annual change rate of the groundwater level of each monitoring well to judge the change trend of the groundwater level includes:

[0090] Obtain historical data of the groundwater level from each monitoring well to obtain observation values for consecutive years, specifically including: accessing the data recording system of each monitoring well or using dedicated data acquisition equipment to extract historical data of the groundwater level from each monitoring well; organizing 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 convenient for analysis, such as a CSV or Excel file.

[0091] Preprocess the observation values for consecutive years to determine a time period reflecting the water level change, specifically including: removing or correcting outliers, missing values, and duplicate values to ensure the reliability of the data; according to the research purpose, select a time period that can reflect the water level change, which involves analyzing the multi-year data to determine the most representative time period; for missing data points, interpolation methods (such as linear interpolation, polynomial interpolation, etc.) can be used for filling.

[0092] Use (ending water level - initial water level) / number of years to calculate the average annual change rate of the groundwater level of each monitoring well within the time period, specifically including: within the selected time period, find the water level readings corresponding to the start time and end time; use the formula "(ending water level - initial water level) / number of years" to calculate the average annual change rate.

[0093] According to the actual situation, set the threshold [E, L] for judging the water level change trend;

[0094] Divide the time period into several sub-segments, and calculate the average annual change rate for the groundwater level data within each sub-segment respectively; if the average annual change rate of the sub-segment > L, the trend of the sub-segment is judged as an upward trend; if the average annual change rate of the sub-segment < E, the trend of the sub-segment is judged as 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 as a stable trend.

[0095] In the embodiments of the present invention, the method is based on the historical groundwater level data of consecutive years obtained from each monitoring well, 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 annual average change rate, the change of the groundwater level can be accurately quantified. Combining the set threshold [E, L], the rising, falling or stable trend of the water level can be clearly judged, which helps to give early warnings and plan countermeasures. Dividing the overall time period into several sub-periods for analysis not only reveals the long-term trend but also captures the short-term change dynamics. This flexibility makes the analysis more meticulous and can adapt to the management needs of different time scales. Accurately judging the change trend of the groundwater level helps to find a balance between protection and utilization. For areas with a rising trend, the utilization amount can be appropriately increased; for areas with a falling trend, protection measures need to be strengthened to avoid over-exploitation. The stability of the groundwater level is crucial for maintaining the ecological environment. Through this method, abnormal changes in the water level can be detected in a timely manner, so as to take effective measures to protect the ecological environment and prevent ecological problems caused by water level fluctuations.

[0096] 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:

[0097] Obtain long-term groundwater level data and perform preprocessing, specifically including: obtaining long-term groundwater level data from groundwater monitoring wells or relevant databases, which are recorded in the form of a time series, including dates and water level values. Check and remove outliers, missing values or duplicate records in the data. For missing values, interpolation methods can be considered for filling; 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. To reduce the noise and fluctuations in the data, moving average or other smoothing techniques can be used to process the data.

[0098] Determine the goal of threshold setting, that is, the rising trend, falling trend or stable trend, specifically including: determining the goal of setting the threshold, that is, to identify the rising trend, falling trend or stable trend of the water level. This is usually determined based on actual needs and analysis purposes; through preliminary analysis of the preprocessed data, understand the overall change trend of the water level to provide a reference for subsequent threshold setting.

[0099] Encode the threshold E and threshold L into the genes of the genetic algorithm using binary coding, specifically including: determining the appropriate binary coding length according to the possible value ranges of the threshold E and L; converting the actual numerical values of the threshold E and L into the corresponding binary coding.

[0100] Randomly generate an initial set of threshold combinations [E, L] as the initial population. Each individual represents a set of threshold settings, specifically including: determining an appropriate population size, that is, the number of initial threshold combinations, according to the complexity of the problem and computational resources; using a random number generator to randomly generate an initial set of threshold combinations [E, L] as the initial population according to the encoding length determined in step three. Each individual (i.e., each set of threshold settings) is a randomly generated binary encoded string.

[0101] Design a fitness function to evaluate the quality of each set of threshold settings.

[0102] According to the fitness function, select the corresponding individuals to enter the next generation through roulette wheel selection, specifically including: calculating the probability of each individual being selected according to its fitness value. The higher the fitness value of an individual, the greater the probability of being selected; using the roulette wheel algorithm to randomly select a part of the individuals 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.

[0103] Perform crossover operations on the selected individuals to generate new threshold combinations, specifically including: randomly selecting two of the selected individuals as parents for pairing; randomly selecting one or more crossover points and swapping the gene segments of the two parents at these points, thereby generating new offspring individuals. This helps to introduce new genetic variations while retaining the excellent genes of the parents.

[0104] Perform mutation operations on the newly generated individuals, and repeat the selection, crossover, and mutation operations until the preset number of iterations is reached to obtain the corresponding individual as the final solution, that is, the final threshold settings [E, L], specifically including: performing mutation operations on the newly generated offspring individuals, that is, randomly changing the values of some of their gene positions (0 becomes 1 or 1 becomes 0) to increase the diversity of the population and prevent falling into local optimal solutions; repeating the selection, crossover, and mutation operations 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 time, the obtained individual is the final threshold settings [E, L].

[0105] In the embodiments of the present invention, the genetic algorithm is used to automatically find the optimal threshold setting, enabling the system to adaptively adjust the judgment criteria according to the specific characteristics of water level data, thereby improving the flexibility and accuracy of the system. Compared with the manually set threshold, the threshold obtained by the genetic algorithm is more objective and scientific, reducing the influence of human intervention and subjective judgment, and making the judgment of the water level change trend more reliable. The genetic algorithm can search for the optimal solution globally, avoiding being trapped in local optima, thus ensuring that the found threshold setting is optimal or nearly optimal globally. The entire threshold setting process is automatically completed by the algorithm, reducing the workload of manually setting the threshold and improving 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:

[0106] F(E,L) = w1·Acc - w2·Err + w3·Bal;

[0107] 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,

[0108]

[0109] Among them, 1(·) is an indicator function. If (T obs,i = T pred,i ) is true, then take 1, otherwise take 0; T obs,i is the trend (rising, falling, stable) of the actual groundwater level, and the value is {1, -1, 0}; the error calculation formula is:

[0110]

[0111] 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 pred,i calculated by the threshold [E, L] divides the water level change into the following three trends according to the change range of the groundwater level and the set threshold:

[0112] Rising trend (T pred = 1):

[0113] If the rising amplitude of the water level (h i+1 - h i ) is greater than the threshold E, it is judged that the water level is rising, that is:

[0114] Downward trend (T pred = -1):

[0115] If the decline amplitude of the water level (h i+1 -h i ) is less than the negative threshold -E, it is determined that the water level is declining, that is

[0116] Stable trend (T pred = 0):

[0117] If the change amplitude of the water level (h i+1 -h i ) is within the range of [-L, L], it is determined that the water level is stable, that is:

[0118] The calculation formula for the trend balance is:

[0119]

[0120] Among them, C1, C -1 and C0 are the quantities classified as upward, downward, and stable trends respectively.

[0121] In a preferred embodiment of the present invention, the calculation formula for the correction coefficient is:

[0122]

[0123] Among them, α1, β, γ, and δ are weight coefficients; p, q, and r are exponential parameters; H avg is the average value of the historical groundwater level; H ref is the reference water level value; S max is the maximum value of the seasonal water level; S min is the minimum value of the seasonal water level; S avg is the average value of the seasonal water level; R avg is the average annual rainfall; R ref is the reference rainfall value; G is the geological activity index; among them, the calculation formula for the geological activity index is:

[0124]

[0125] Among them, f i represents the occurrence frequency of the i-th geological event; s i represents the intensity of the i-th geological event; m i represents the weight of the i-th geological event on groundwater; a i and b i are exponential parameters respectively; k ijThe interaction coefficient representing the influence of the j-th geological event on the i-th geological event; p ij and q ij are 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 seismic and fault activities respectively; T1 and T2 are the seasonal periods of seismic and fault activities respectively.

[0126] In the 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, etc. The weight coefficients α1, β, γ, and δ and the exponential parameters (p, q, and r) in the formula can be adjusted according to actual situations to meet the groundwater level assessment requirements in different regions and under different conditions. By introducing the geological activity index G, this formula can quantify the influence of geological activities on the groundwater level. The calculation formula of the geological activity index considers the occurrence frequencies, intensities of various geological events, and their interactions, making the assessment of the influence of geological activities more accurate. In the calculation of the geological activity index, by introducing the seasonal influence amplitude coefficients ∈1 and ∈2 related to seismic and fault activities and their seasonal periods T1 and T2, this formula can better reflect the influence of seasonal changes on the groundwater level.

[0127] In a preferred embodiment of the present invention, the annual average change rate is adjusted by the correction coefficient to obtain the final groundwater level change trend, including:

[0128] Multiply the annual average change rate by the correction coefficient to obtain the adjusted annual average change rate;

[0129] Obtain the latest groundwater level data, denoted as W, representing the groundwater level of the current year;

[0130] Determine the future year t to be predicted;

[0131] For each set future year t, calculate the corresponding predicted water level H through H = W + A×(t - B), where W represents the currently known current water level, A is the adjusted annual average change rate; B represents the current year; (t - B) represents the number of years from the current year to the predicted year.

[0132] In the embodiments of the present invention, by introducing a correction coefficient to adjust the annual average change rate, the actual change trend of the groundwater level can be more accurately reflected. This adjustment takes into account various influencing factors, such as historical water levels, seasonal variations, rainfall, and geological activities, thereby improving the accuracy of prediction. After the dynamic adjustment of the annual average change rate by the correction coefficient, it can better adapt to the water level changes under different time and environmental conditions. This dynamic adjustment makes the prediction model more flexible and capable of dealing with various complex situations. Combining the latest groundwater level data, this method can quickly reflect the changes in the water level and provide timely information support for decision-makers. This helps decision-makers make rapid and accurate responses based on the actual situation. Accurate prediction of groundwater levels helps identify potential risk points, such as ecological environment problems or water resource shortages that may be caused by too rapid decline in water levels. By giving early warnings and taking corresponding preventive measures, the occurrence probability and impact degree of these risks can be reduced.

[0133] The above step 3, according to the final change trend of the groundwater level, in the areas where the groundwater level has abnormal changes, set water quality abnormal monitoring points to obtain corresponding abnormal water quality data, which may include:

[0134] Analyze the annual average change rate adjusted by the correction coefficient to identify those areas that show abnormal increases or decreases or large fluctuations. Combine historical data and geographical environment factors to evaluate whether these abnormal changes may indicate potential water quality problems; Mark those areas where the groundwater level has abnormal changes, including areas with rapid water level decline, rising areas, or areas with abnormal periodic fluctuations; In the marked areas of abnormal changes, select representative locations as water quality abnormal monitoring points according to the severity of the changes and the distribution of possible pollution sources, ensuring that the monitoring points can cover different types of abnormal changes, such as areas that may be polluted and ecologically sensitive areas; Install automatic or manual water quality monitoring equipment at the selected monitoring points, and these equipment should have the ability to measure key water quality parameters (such as pH value, dissolved oxygen, turbidity, chemical oxygen demand, heavy metal content, etc.); Configure the 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.

[0135] In a preferred embodiment of the present invention, according to the abnormal water quality data, regularly collect water samples in the abnormal water quality data, detect various indicators of the water samples, including chloride content, conductivity, and total dissolved solids, and analyze the changes in water quality to evaluate the water quality status of the groundwater, including:

[0136] Determine the initial temperature T0. The initial temperature T0 is a parameter in the simulated annealing algorithm, representing the initial heat. Specifically, it includes: setting an appropriate 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.

[0137] Define a temperature reduction strategy, that is, multiply the temperature by a cooling coefficient θ after each iteration, where 0 < θ < 1. Specifically, it includes: setting a cooling coefficient θ (0 < θ < 1), which determines the rate of temperature reduction. After each iteration, multiply the current temperature by this cooling coefficient to reduce the temperature, that is, the new temperature T_new = T_current × θ, where T_current represents the current temperature value in the simulated annealing algorithm.

[0138] Convert the water quality assessment problem into an optimization problem, where each state represents a combination of water quality parameters. The water quality parameters include 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. Regarding each set of water quality parameters (chloride content, conductivity, total dissolved solids) as a state, the entire optimization process is to find the optimal state.

[0139] Define an objective function to evaluate the quality of the water quality parameter combination. The formula for the objective function f is:

[0140]

[0141] where 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 ranges for chloride, conductivity, and total dissolved solids respectively; w1, w2, and w3 are weight coefficients; and σ1, σ2, and σ3 are the standard deviations controlling the fluctuations of each index.

[0142] Obtain real-time water quality data. When the water quality data exceeds the preset safety range, mark it as abnormal data. Specifically, it includes: obtaining real-time water quality data through water quality monitoring equipment, comparing these data with the preset safety range, and if it exceeds the range, mark it as abnormal data.

[0143] Randomly generate an initial state, that is, an initial estimate of a set of water quality parameters. At the current temperature, randomly perturb the current state to generate a new state, and use the objective function to evaluate the quality of the new state. Specifically, it includes: randomly generating an initial estimate of a set of water quality parameters as the initial state. At the current temperature, randomly perturb the current state (such as changing the values of chloride content, conductivity, or total dissolved solids) to generate a new state. Use the objective function defined in step 4 to evaluate the quality of the new state.

[0144] 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 schedule to obtain the final state, specifically including: calculating the difference ΔE between the objective function values of the new state and the current state; if ΔE < 0 (i.e., the new state is better), then unconditionally accept the new state. If ΔE > 0 (i.e., the new state is worse), then accept the new state with a certain probability where T is the current temperature; after accepting the new state, update the current state to the new state and lower the temperature.

[0145] When the termination condition is met, stop the iteration. After identifying the abnormal data, guide the selection of sampling points and sampling frequency according to the final state, specifically including: setting the iteration stop condition, such as reaching the maximum number of iterations, the temperature dropping below a certain threshold, or no obvious improvement in the state in consecutive multiple iterations, etc. When the termination condition is met, stop the iteration. According to the water quality parameter combination of the final state, guide the selection of sampling points and sampling frequency to more accurately understand the water quality status of the abnormal area.

[0146] Send the collected water samples to the laboratory to detect the chloride content, conductivity, and total dissolved solids indicators to obtain the laboratory test results, specifically including: sending the collected water samples to the laboratory, and using professional testing equipment and methods to measure indicators such as chloride content, conductivity, and total dissolved solids to obtain accurate laboratory test results.

[0147] Compare and analyze the laboratory test results with the final state to evaluate the water quality status of the groundwater, specifically including: comparing and analyzing the laboratory test results with the final state obtained by the simulated annealing algorithm. By comparing the differences between the two, the water quality status of the groundwater can be more accurately evaluated, including identifying potential water quality problems, pollution sources, etc. This information can provide an important basis for subsequent water resource management and protection decisions.

[0148] In the embodiments of the present invention, chloride, conductivity, and total dissolved solids are important indicators for evaluating the water quality status of groundwater because they can comprehensively reflect the chemical characteristics and pollution degree of groundwater. The chloride content is usually related to the salinity of groundwater, and its abnormal increase may indicate seawater intrusion, industrial pollution, or the 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 dissolved solids in water, including inorganic salts and organic substances, etc., and its level directly affects the taste and use value of water.

[0149] When conducting an evaluation, the water quality status of groundwater 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 trend of indicators over time, it is possible to predict the possible development direction of water quality.

[0150] In the embodiments of the present invention, by regularly collecting water samples from 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 precisely evaluate the water quality status of groundwater and timely detect potential water quality problems. Combining with the simulated annealing algorithm, according to the characteristics of the water quality assessment problem, the combination of water quality parameters can be optimized, thereby guiding the selection of sampling points and sampling frequencies. 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 detected in a timely manner. Comparing and analyzing the laboratory test results with the final state obtained by the simulated annealing algorithm helps decision-makers make more reasonable and effective water resource management and protection decisions. Introducing optimization technologies such as the simulated annealing algorithm 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.

[0151] As Figure 2 shown, the embodiments of the present invention also provide a water quality dynamic monitoring system based on the groundwater level, including:

[0152] A determination module, configured to arrange groundwater monitoring wells in the area to be monitored according to the topography, groundwater burial conditions, and groundwater occurrence 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;

[0153] A calculation module, configured to calculate the average annual change rate of the groundwater level of each monitoring well to judge the changing trend of the groundwater level; calculate a 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 changing trend of the groundwater level;

[0154] An acquisition module, configured to set water quality anomaly monitoring points in the area where the groundwater level has abnormal changes according to the final changing trend of the groundwater level to obtain corresponding abnormal water quality data;

[0155] An evaluation module, configured to regularly collect water samples from abnormal water quality data according to the abnormal water quality data, detect various indexes of the water samples, including chloride content, conductivity and total dissolved solids, analyze the change of water quality, so as to evaluate the water quality status of groundwater.

[0156] It should be noted that this device corresponds to the above method. All implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0157] An embodiment of the present invention further provides a computing device, including: a processor and a memory storing a computer program. When the computer program is run by the processor, the above method is executed. All implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0158] An embodiment of the present invention further provides a computer-readable storage medium storing instructions. When the instructions are run on a computer, the computer is made to execute the above method. All implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

Claims

1. A water quality dynamic monitoring method based on groundwater level, characterized in that, The method includes: Step 1, in the area to be monitored, according to the topography, groundwater burial conditions and groundwater occurrence media, arrange groundwater monitoring wells, including: determining the positions of the monitoring wells according to the topography, groundwater burial conditions and groundwater occurrence media of the area to be monitored; setting the parameters of the Grey Wolf Optimization Algorithm, including the size of the grey wolf population, the number of iterations and the search space; representing each individual in the grey wolf population as a monitoring well arrangement plan, that is, each individual is a combination of a set of monitoring well positions; searching for the corresponding monitoring well arrangement plan in the search space according to the search strategy of the Grey Wolf Optimization Algorithm; setting a fitness function for evaluating the pros and cons of each arrangement plan; continuously adjusting and optimizing the positions of the monitoring wells through the iterative process of the Grey Wolf Optimization Algorithm to optimize the fitness function; when the preset number of iterations is reached, stop the iteration and output the position of the alpha wolf as the corresponding optimized monitoring well arrangement plan; arranging automatic monitoring wells and manual monitoring wells in the area to be monitored according to the optimized monitoring well arrangement plan; 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 of each monitoring well to judge 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; Step 3, according to the final change trend of the groundwater level, set water quality anomaly monitoring points in the areas where the groundwater level has abnormal changes to obtain the corresponding abnormal water quality data; Step 4, according to the abnormal water quality data, regularly collect water samples from the abnormal water quality data, detect various indicators of the water samples, and analyze the change of the water quality to evaluate the water quality status of the groundwater.

2. The water quality dynamic monitoring method based on the groundwater level according to claim 1, characterized in that, Calculating the average annual change rate of the groundwater level of each monitoring well to judge the change trend of the groundwater level includes: Obtaining the historical data of the groundwater level from each monitoring well to obtain the observed values for consecutive years; Preprocessing the observed values for consecutive years to determine a time period reflecting the water level change; Using (ending water level - initial water level) / number of years to calculate the average annual change rate of the groundwater level of each monitoring well within the time period; Setting the threshold [E, L] for judging the water level change trend according to the actual situation; Dividing the time period into several sub - segments, and calculating the average annual change rate of the groundwater level data within each sub - segment respectively; if the average annual change rate of the sub - segment > L, the trend of this sub - segment is judged as an upward trend; if the average annual change rate of the sub - segment < E, the trend of this sub - segment is judged as a downward trend; if the average annual change rate of the sub - segment is between [E, L], the trend of this sub - segment is judged as a stable trend.

3. A water quality dynamic monitoring method based on the groundwater level according to claim 2, characterized in that Setting the threshold [E, L] for judging the water level change trend according to the actual situation includes: Obtaining the long - time - series groundwater level data and preprocessing it; Determining the goal of threshold setting, that is, upward trend, downward trend or stable trend; Encoding the threshold E and threshold L into the genes of the genetic algorithm using binary coding; Randomly generate an initial set of threshold combinations [E, L] as the initial population, where 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, select the corresponding individuals into the next generation through roulette wheel selection; Perform crossover operations on the selected individuals to generate new threshold combinations; Perform mutation operations on the newly generated individuals, and repeat the selection, crossover, and mutation operations until the preset number of iterations is reached to obtain the corresponding individual as the final solution, that is, the final threshold settings [E, L].

4. A water quality dynamic monitoring method based on the groundwater level according to claim 3, characterized in that, The various indicators of the water sample include chloride content, conductivity, and total dissolved solids.

5. A water quality dynamic monitoring method based on groundwater level according to claim 4, characterized in that, Adjust the annual average change rate through a correction coefficient to obtain the final groundwater level change trend, including: Multiply the annual average change rate by the correction coefficient to obtain the adjusted annual average change rate; Obtain the latest groundwater level data, denoted as W, representing the groundwater level of the current year; Determine the future year t to be predicted; For each set future year t, calculate the corresponding predicted water level H through H = W + A×(t - B), where W represents the known current water level, A is the adjusted annual average change rate; B represents the current year; (t - B) represents the number of years from the current year to the predicted year.

6. The water quality dynamic monitoring method based on the groundwater level according to claim 5, characterized in that, According to the abnormal water quality data, regularly collect water samples from the abnormal water quality data, detect the various indicators of the water samples, including chloride content, conductivity, and total dissolved solids, and analyze the changes in water quality to evaluate the water quality status of the groundwater, including: Determine the initial temperature T0. The initial temperature T0 is a parameter in the simulated annealing algorithm, representing the initial heat; Define a temperature reduction strategy, that is, multiply the temperature by a cooling coefficient θ after each iteration, where 0 < θ < 1; Convert the water quality assessment problem into an optimization problem, where each state represents a combination of water quality parameters, and the water quality parameters include chloride content, conductivity, and total dissolved solids; Define an objective function to evaluate the quality of the water quality parameter combination; Obtain real-time water quality data. When the water quality data exceeds the preset safety range, mark it as abnormal data; Randomly generate an initial state, that is, an initial estimate of a set of water quality parameters. At the current temperature, perform a random perturbation on the current state to 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, reduce the temperature, and update the current temperature value according to the annealing plan to obtain the final state; When the termination condition is met, stop the iteration. After identifying the abnormal data, guide the selection of sampling points and sampling frequency according to the final state; Send the collected water samples to the laboratory to detect the chloride content, conductivity, and total dissolved solids indicators to obtain the laboratory test results; Compare and analyze the laboratory test results with the final state to evaluate the water quality status of the groundwater.

7. A water quality dynamic monitoring system based on the groundwater level, characterized in that, Applied in the method according to any one of claims 1 to 6, including: A determination module, configured to arrange groundwater monitoring wells in the area to be monitored according to the topography, groundwater burial conditions and groundwater occurrence 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, in order to obtain the water level and water quality data of groundwater; A calculation module, configured to calculate the annual average change rate of the groundwater level of each monitoring well to judge the change trend of the groundwater level; calculate a correction coefficient based on historical data, seasonal changes, rainfall and geological activities; adjust the annual average change rate through the correction coefficient to obtain the final change trend of the groundwater level; An acquisition module, configured to set water quality anomaly monitoring points in the area where the groundwater level has abnormal changes according to the final change trend of the groundwater level, in order to obtain corresponding abnormal water quality data; An evaluation module, configured to regularly collect water samples from the abnormal water quality data according to the abnormal water quality data, detect various indexes of the water samples, and analyze the change of the water quality, in order to evaluate the water quality status of the groundwater.

8. A computing device, characterized in that, Comprising: One or more processors; A storage device, configured 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 according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, A program is stored in the computer-readable storage medium, and when the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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