Method, system, device and medium for rapid detection of heavy metal content in groundwater

Abstract

The application provides a groundwater heavy metal content rapid detection method, system, device and medium, the groundwater sample is divided into a detection sample and a blank sample through a microporous filter membrane; an interference correction coefficient when ions compete for adsorption appears under a water quality type of the detection sample is generated; current response data in a working electrode and electrochemical response data in an interference compensation electrode are scanned, the competition adsorption effect in the detection sample is dynamically corrected through the interference correction coefficient, the current response data and the electrochemical response data, and a corrected current value of the detection sample is obtained; baseline analysis is performed on all corrected current values according to the blank sample, and a net response signal of heavy metals in the groundwater sample is generated; and the heavy metal content of the groundwater sample is rapidly detected based on a standard working curve of the heavy metals in a standard solution and the net response signal. According to the scheme, the detection signal can be dynamically corrected based on the ion competition adsorption effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of heavy metal content detection, and more particularly to a method, system, device and medium for rapid detection of heavy metal content in underground water. BACKGROUND

[0002] Heavy metal content detection is a detection method for converting trace heavy metal elements in a sample into quantifiable data through professional analysis technology. It usually relies on pretreatment systems, detection instrument platforms and multi-dimensional feature data, and combines atomic absorption, inductively coupled plasma mass spectrometry, anodic stripping voltammetry and other core technologies and intelligent algorithms for quantitative analysis and result presentation. Modern heavy metal content detection integrates sample pretreatment parameters, instrument calibration data, matrix interference compensation models and operation standardization specifications to achieve accurate determination of heavy metal content in soil water, agricultural products and electronic waste and pollution risk assessment output.

[0003] Rapid detection of heavy metal content in underground water is a detection method for converting heavy metal elements in underground water into instant readable results through portable analysis equipment. It relies on on-site pretreatment devices and portable detection instruments, and combines the matrix characteristics of underground water and the chemical properties of target heavy metals for quantitative analysis. For pollution investigation scenarios, rapid detection of heavy metal content in underground water can screen for elements exceeding the standard and pollution range in real time. In environmental emergency monitoring, instant detection data can quickly determine the pollution diffusion trend, thereby providing timely basis for pollution control decision-making and risk warning. In existing methods for rapid detection of heavy metal content in underground water, the influence of interfering ions in water (such as calcium and magnesium ions, organic complex compounds and coexisting heavy metals) is usually only corrected by fixed correction formula or simple blank control, which is difficult to accurately eliminate the detection deviation caused by ion competitive adsorption (for example, the traditional method may calculate the response signal according to the “basic correction coefficient” for high-concentration competitive ions and low-concentration target heavy metals, resulting in that the oxidation peak current of the target heavy metal is covered, and the detection result appears positive or negative deviation), thereby causing quantitative distortion of the detection result of heavy metal content in underground water. Therefore, how to dynamically correct the detection signal based on ion competitive adsorption effect has become a problem faced by the industry. SUMMARY

[0004] The present application provides a method, system, device and medium for rapid detection of heavy metal content in underground water, which can dynamically correct the detection signal based on ion competitive adsorption effect.

[0005] In a first aspect, the present application provides a method for rapid detection of heavy metal content in underground water, wherein the heavy metal content in an underground water sample is detected by a content detection instrument, and the content detection instrument includes a microporous filter membrane, a working electrode and an interference compensation electrode. The method includes the following steps:

[0006] Collecting a groundwater sample in a target area, and dividing the groundwater sample into a detection sample and a blank sample through a microporous filter membrane;

[0007] Generating an interference correction coefficient of ion competitive adsorption under a water quality type of the detection sample according to a solution pH and ionic strength of the detection sample and a deep learning algorithm;

[0008] Synchronously scanning heavy metals and competitive ions in the detection sample to obtain current response data of heavy metal oxidation peaks in a working electrode and electrochemical response data of competitive ions in an interference compensation electrode, dynamically correcting competitive adsorption effects in the detection sample through the interference correction coefficient, the current response data, and the electrochemical response data, and obtaining corrected current values of heavy metals in the detection sample at different oxidation peaks;

[0009] Generating a net response signal of heavy metals in the groundwater sample according to baseline analysis of all corrected current values of the blank sample;

[0010] Rapidly detecting a heavy metal content of the groundwater sample based on a standard working curve of heavy metals in a standard solution and the net response signal.

[0011] In some embodiments, generating an interference correction coefficient of ion competitive adsorption under a water quality type of the detection sample according to a solution pH and ionic strength of the detection sample and a deep learning algorithm specifically includes:

[0012] Collecting a solution pH and ionic strength of the detection sample;

[0013] Obtaining a plurality of water quality type labels of groundwater in the target area;

[0014] Generating an ion competition simulation model in different water quality types based on all water quality type labels and a deep learning algorithm;

[0015] Inputting the solution pH and ionic strength of the detection sample into the ion competition simulation model, and then outputting ion competitive adsorption intensity under a water quality type of the detection sample;

[0016] Generating an interference correction coefficient of ion competitive adsorption under a water quality type of the detection sample according to the ion competitive adsorption intensity and a preset correction coefficient mapping rule.

[0017] In some embodiments, synchronously scanning heavy metals and competitive ions in the detection sample to obtain current response data of heavy metal oxidation peaks in a working electrode and electrochemical response data of competitive ions in an interference compensation electrode specifically includes:

[0018] Injecting a detection sample into an electrochemical detection cell, and then synchronously applying a preset deposition potential to a working electrode and an interference compensation electrode to pre-enrich target heavy metals and competitive ions;

[0019] After the pre-enrichment, performing an anodic linear sweep on the deposition potential, and synchronously recording current-potential curves of the working electrode and the interference compensation electrode;

[0020] Identifying and extracting an oxidation peak potential and a corresponding peak current of the heavy metal ions from the current-potential curve of the working electrode to form current response data of the heavy metal oxidation peak in the working electrode;

[0021] Identifying and extracting a characteristic oxidation peak potential and a corresponding peak current of the competitive ions from the current-potential curve of the interference compensation electrode to form electrochemical response data of the competitive ions in the interference compensation electrode.

[0022] In some embodiments, the competitive adsorption effect in the detection sample is dynamically corrected by the interference correction coefficient, the current response data, and the electrochemical response data to obtain corrected current values of the heavy metals in the detection sample at different oxidation peaks, which specifically includes:

[0023] Performing linear analysis on the electrochemical response data to obtain an interference intensity index of the competitive ions in the detection sample;

[0024] Coupling and mapping the interference correction coefficient and the interference intensity index to obtain a signal suppression amount in the ion competitive adsorption process;

[0025] Dynamically compensating the current response data by the signal suppression amount to obtain the corrected current values of the heavy metals in the detection sample at different oxidation peaks.

[0026] In some embodiments, the baseline analysis of all corrected current values is performed according to a blank sample to generate a net response signal of the heavy metals in the groundwater sample, which specifically includes:

[0027] Performing electrochemical scanning on the blank sample under the same conditions as the detection sample to obtain a baseline response signal of the blank sample;

[0028] Extracting multiple baseline oxidation current values of the heavy metals from the baseline response signal based on the oxidation peak potential interval of the heavy metals;

[0029] Performing point-by-point difference analysis on all corrected current values and all baseline oxidation current values, and then generating the net response signal of the heavy metals in the groundwater sample through the difference result.

[0030] In some embodiments, the heavy metal content of the groundwater sample is rapidly detected based on a standard working curve of the heavy metals in a standard solution combined with the net response signal, which specifically includes:

[0031] obtaining a standard working curve of the target heavy metal in a concentration standard solution;

[0032] extracting a peak current value of a heavy metal characteristic oxidation peak from the net response signal;

[0033] linear fitting the peak current value into the standard working curve to obtain a heavy metal concentration estimated value corresponding to the current value as a rapid detection result of the heavy metal content of the groundwater sample.

[0034] In some embodiments, the blank sample is a heavy metal-free groundwater matrix blank sample verified by inductively coupled plasma mass spectrometry.

[0035] In a second aspect, the present application provides a rapid detection system for heavy metal content of groundwater, comprising:

[0036] a collection module configured to collect a groundwater sample in a target area, and divide the groundwater sample into a detection sample and a blank sample by a microporous filter membrane;

[0037] a processing module configured to generate an interference correction coefficient when ion competitive adsorption occurs in a water quality type of the detection sample according to a solution pH and ion strength of the detection sample and a deep learning algorithm;

[0038] The processing module is further configured to perform synchronous scanning on heavy metals and competitive ions in the detection sample to obtain current response data of heavy metal oxidation peaks in a working electrode and electrochemical response data of competitive ions in an interference compensation electrode, and to perform dynamic correction on competitive adsorption effects in the detection sample by the interference correction coefficient, the current response data and the electrochemical response data to obtain corrected current values of the heavy metals in the detection sample at different oxidation peaks.

[0039] The processing module is further configured to perform baseline analysis on all corrected current values according to the blank sample to generate a net response signal of heavy metals in the groundwater sample.

[0040] an execution module configured to perform rapid detection on the heavy metal content of the groundwater sample based on a standard working curve of the heavy metal in a standard solution and the net response signal.

[0041] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements steps of the rapid detection method for heavy metal content of groundwater when executing the computer program.

[0042] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the rapid detection method for heavy metal content in underground water.

[0043] The technical scheme provided by the embodiments disclosed in the present application has the following beneficial effects:

[0044] In the rapid detection method, system, device and medium for heavy metal content in underground water provided by the present application, the underground water sample of the target area is collected, and the underground water sample is divided into a detection sample and a blank sample by a microporous filter film; the interference correction coefficient when the ion competitive adsorption occurs under the water quality type of the detection sample is generated according to the solution pH and ion strength of the detection sample and a deep learning algorithm; the heavy metal and the competitive ion in the detection sample are synchronously scanned to obtain the current response data of the heavy metal oxidation peak in the working electrode and the electrochemical response data of the competitive ion in the interference compensation electrode; the competitive adsorption effect in the detection sample is dynamically corrected by the interference correction coefficient, the current response data and the electrochemical response data to obtain the corrected current value of the heavy metal in the detection sample at different oxidation peaks; the baseline analysis of all the corrected current values is performed according to the blank sample to generate the net response signal of the heavy metal in the underground water sample; and the heavy metal content of the underground water sample is rapidly detected based on the standard working curve of the heavy metal in the standard solution and the net response signal.

[0045] Therefore, in this application, firstly, after collecting the solution pH and ionic strength data of the test sample, a deep learning algorithm is used to perform correlation analysis on these key water quality parameters, and interference correction coefficients for corresponding water quality types are derived by combining historical interference patterns, thereby achieving accurate quantitative modeling of ion competitive adsorption intensity. The interference correction coefficients reflect the adsorption trend of competing ions and target heavy metals in a specific water quality environment, making interference quantification more consistent with actual water quality characteristics, especially significantly improving the targeting and accuracy of interference assessment in complex matrices such as high salinity and strong acids / alkalis. Compared with traditional methods relying on fixed correction formulas, this method enhances the coupling relationship between correction coefficients and water quality parameters, thereby more effectively capturing the differences in ion competition in different groundwater environments, providing a basis for dynamic analysis based on ion competitive adsorption effects. The state correction provides a scientific and quantitative foundation. Then, based on the interference correction coefficient, after determining the quantitative weight of ion competition in the detection sample, the analytical framework composed of the heavy metal oxidation peak current response of the working electrode and the competing ion electrochemical response of the interference compensation electrode can accurately express the degree of interference (signal deviation) of competitive adsorption on the signal in different detection stages. This scheme calculates the current response data of each oxidation peak by synchronous scanning and dynamically corrects it by combining the correction coefficient. It can control the current distortion correction amplitude caused by interference in real time without losing the target signal, thereby improving the authenticity and stability of the corrected signal. Finally, a net response signal is generated based on all corrected current values. In summary, this scheme can accurately correct the detection signal based on the dynamic changes of the ion competitive adsorption effect. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating a rapid detection method for heavy metal content in groundwater according to some embodiments of this application;

[0047] Figure 2 This is a flowchart illustrating the determination of the net response signal according to some embodiments of this application;

[0048] Figure 3 This is a flowchart illustrating the construction of a linear expression according to some embodiments of this application;

[0049] Figure 4 This is a schematic diagram of the structure of a rapid detection system for heavy metal content in groundwater according to some embodiments of this application;

[0050] Figure 5 This is an internal structural diagram of a computer device for implementing a rapid detection method for heavy metal content in groundwater, according to some embodiments of this application. Detailed Implementation

[0051] For better understanding of the technical solutions in the present embodiment, the technical solutions in the present embodiment will be described in detail below in combination with the drawings in the specification and specific implementation manners.

[0052] Referring to Figure 1 The figure is a flowchart of a rapid detection method for heavy metal content in groundwater according to some embodiments of the present application. The rapid detection method for heavy metal content in groundwater mainly includes the following steps:

[0053] In step 101, groundwater samples in a target area are collected, and the groundwater samples are divided into detection samples and blank samples by using a microporous filter membrane.

[0054] In specific implementation, a sufficient amount of sample can be collected in situ at a groundwater monitoring point (such as a well mouth or a monitoring well) in the target area by using a portable sterile sampling bottle. When sampling, the sampling bottle is slowly filled along the container wall to avoid the generation of air bubbles due to agitation. Then, a portable manual filtration device is used to filter in situ through a disposable cellulose acetate microporous filter membrane with a pore size of 0.45 μm. Then, the filtrate after filtration is immediately divided into two groups of sterile polyethylene centrifuge tubes, one group as a detection sample and the other group as a blank sample.

[0055] It should be noted that the microporous filter membrane is used to quickly remove suspended particulate matter and colloidal impurities in situ to avoid the influence of impurity settlement on detection accuracy during transportation. The detection sample is directly used for subsequent in-situ electrochemical detection. The blank sample is a non-target heavy metal groundwater matrix blank sample verified by inductively coupled plasma mass spectrometry.

[0056] In step 102, an interference correction coefficient when ion competitive adsorption occurs under the water quality type of the detection sample is generated according to the solution pH and ionic strength of the detection sample and a deep learning algorithm.

[0057] In some embodiments, the interference correction coefficient when ion competitive adsorption occurs under the water quality type of the detection sample can be generated according to the solution pH and ionic strength of the detection sample and a deep learning algorithm by using the following steps:

[0058] The solution pH and ionic strength of the detection sample are collected;

[0059] A plurality of water quality type labels of groundwater in the target area are obtained;

[0060] An ion competition simulation model in different water quality types is generated based on all water quality type labels and a deep learning algorithm;

[0061] The solution pH and ionic strength of the detection sample are input into the ion competition simulation model, and then the ion competitive adsorption strength under the water quality type of the detection sample is output;

[0062] The interference correction coefficient of the ion competition adsorption of the detection sample is constructed according to the ion competition adsorption strength and a preset correction coefficient mapping rule.

[0063] It should be noted that the solution pH value of the detection sample in the present application refers to a water chemical parameter capable of representing the hydrogen ion activity level in the sample; the ion strength refers to a comprehensive index capable of reflecting the overall concentration effect of the solute ions in the sample; the solution pH value can be measured by using an acidity meter calibrated by a standard buffer solution, and the sample should be kept at a constant temperature during measurement, and the reading should be recorded after stabilization to ensure the accuracy of the pH value data; the ion strength can be obtained by detecting the overall conductivity of the sample using a conductivity meter calibrated by a standard solution, and converting according to the established correspondence between the conductivity and the total amount of ions in the solution; in other embodiments, the pH value can also be determined by an optical pH sensor or an ion selective electrode; the ion strength can also be obtained by ion chromatography or charge balance analysis method; the present application does not make any limitation in this regard.

[0064] In a specific implementation, the multiple water quality type labels of the underground water in the target area can be obtained by the following method: first, collect the solution pH value and ion strength data of multiple underground water samples in the target area to form a sample feature data set; second, divide the solution pH value into multiple intervals according to the national environmental protection standard or the industry water quality classification standard, such as weak acidity, neutrality, weak alkalinity, etc.; divide the ion strength into multiple intervals, such as low intensity, medium intensity, high intensity; then, determine the pH value interval to which the detection sample belongs according to the solution pH value of the detection sample, and determine the ion strength interval to which the detection sample belongs according to the ion strength value, to form a two-dimensional interval combination of pH value and ion strength; finally, classify the samples falling into the same two-dimensional interval combination as the same water quality type label, so as to obtain the multiple water quality type labels of the underground water in the target area; preferably, an unsupervised clustering algorithm can be used to assist in classifying the sample feature data set to optimize the accuracy of the interval division and the water quality type label.

[0065] It should be noted that the water quality type label in the present application refers to a classification identifier used to represent the acid-base characteristics and ion strength state of the underground water sample in the target area under different water chemical environments, wherein the label is divided according to the combination relationship of the solution pH value and the ion strength value of each sample in the preset interval, so as to reflect the belonging category of the underground water sample in the two-dimensional physicochemical feature space of pH value and ion strength.

[0066] In a specific implementation, the ion competition simulation model in different water quality types can be generated based on all water quality type labels and a deep learning algorithm in the following manner. First, the sample features corresponding to each water quality type label are input into the input layer of the deep neural network model. Second, the network structure is designed, including multiple hidden layers and activation functions, to capture the nonlinear relationship between the solution pH and ionic strength and the ion competition adsorption strength. Third, the labeled training samples are used to calculate the ion competition adsorption strength prediction value output by the model through forward propagation. Fourth, the loss function is used to measure the error between the prediction value and the actual ion competition adsorption strength, and the model weights are updated based on the error using the backpropagation algorithm. Fifth, the training process is repeated until the model converges, meets the preset error threshold, or reaches the maximum number of iterations. Finally, the trained deep neural network model is used as the ion competition simulation model in different water quality types.

[0067] It should be noted that the ion competition simulation model in the present application refers to a mathematical model for predicting the adsorption competition effect strength of target heavy metal ions in a complex ion environment based on the solution pH and ionic strength characteristics of groundwater samples under different water quality type labels and using a deep learning algorithm. The model fits the nonlinear mapping relationship between the solution physicochemical characteristics and the ion competition adsorption behavior to simulate the competition mechanism between heavy metal ions and coexisting ions under different water quality type conditions.

[0068] In a specific implementation, the solution pH and ionic strength of the detection sample are input into the ion competition simulation model, and the ion competition adsorption strength under the water quality type of the detection sample is output in the following manner. The solution pH and ionic strength of the detection sample are input into the input layer of the pre-trained ion competition simulation model. Then, the ion competition adsorption strength prediction value under the water quality type of the detection sample is calculated through the forward propagation process of the model. Finally, the output of the model is used as the ion competition adsorption strength under the water quality type of the detection sample.

[0069] It should be noted that the ion competition adsorption strength in the present application refers to a numerical indicator of the relative adsorption capacity size of target heavy metal ions and coexisting ions on the electrode surface or adsorption sites due to competition adsorption behavior under specific water quality type conditions. This numerical value is used to quantify the competition degree of different ions for adsorption sites to reflect the degree of interference of non-target ions on the electrochemical response of target heavy metal ions in the detection sample.

[0070] It should be noted that the preset correction coefficient mapping rule described in the present application refers to a mapping relationship for converting the ion competition adsorption intensity value of the detection sample under a specific water quality type into a corresponding interference correction coefficient; the mapping relationship is obtained by measuring the electrochemical signal suppression effect under different ion competition adsorption intensity levels under standardized experimental conditions, and establishing a corresponding table or function relationship between the signal suppression effect and the corresponding interference correction coefficient; the mapping rule can be expressed in a segmented table lookup, linear interpolation, polynomial fitting or nonlinear regression manner, for example, when expressed in a polynomial fitting manner, after obtaining a plurality of experimental data points of known ion competition adsorption intensity and corresponding interference correction coefficient, the data points are arranged in ascending order of ion competition adsorption intensity, and a polynomial curve is obtained by least squares fitting as a preset correction coefficient mapping rule; in other embodiments, the mapping rule can also be established by a machine learning regression model (such as random forest regression, support vector regression or neural network regression, etc.) to adapt to more complex water quality types and ion competition relationships, which is not limited in the present application; therefore, in specific implementation, the interference correction coefficient of the detection sample under the water quality type when ion competition adsorption occurs can be realized by the following way, that is: first, input the ion competition adsorption intensity value of the detection sample into the preset correction coefficient mapping rule under the corresponding water quality type; second, select the corresponding calculation method according to the specific expression form of the mapping rule, for example, when the mapping rule is in the form of segmented table lookup, retrieve the interval of ion competition adsorption intensity and obtain the corresponding interference correction coefficient, when the mapping rule is in the form of linear interpolation or polynomial fitting, substitute the adsorption intensity value into the corresponding function expression to calculate the interference correction coefficient; then, bind the calculated interference correction coefficient with the water quality type label of the detection sample to form the interference correction coefficient of the detection sample under the water quality type when ion competition adsorption occurs.

[0071] It should be noted that the interference correction coefficient described in the present application refers to a correction factor for quantifying the degree of current response signal suppression caused by coexisting ion competition adsorption in the process of electrochemical detection of target heavy metals under specific water quality type conditions, the value of which is used to compensate the electrochemical response signal in the detection sample to eliminate the interference of ion competition behavior on the current response of target heavy metals, thereby improving the accuracy and reliability of the detection result.

[0072] In step 103, the heavy metal and the competitive ion in the detection sample are synchronously scanned to obtain the current response data of the heavy metal oxidation peak in the working electrode and the electrochemical response data of the competitive ion in the interference compensation electrode, and the competitive adsorption effect in the detection sample is dynamically corrected by the interference correction coefficient, the current response data and the electrochemical response data to obtain the corrected current value of the heavy metal in the detection sample at different oxidation peaks.

[0073] In some embodiments, the synchronous scanning of the heavy metal and the competitive ion in the detection sample to obtain the current response data of the heavy metal oxidation peak in the working electrode and the electrochemical response data of the competitive ion in the interference compensation electrode can be achieved by the following steps:

[0074] The detection sample is injected into the electrochemical detection cell, and then a preset deposition potential is synchronously applied to the working electrode and the interference compensation electrode to pre-enrich the target heavy metal and the competitive ion;

[0075] After the pre-enrichment is completed, the deposition potential is anodically linearly scanned, and the current-potential curves of the working electrode and the interference compensation electrode are synchronously recorded;

[0076] The oxidation peak potential of the heavy metal ion and the corresponding peak current are identified and extracted from the current-potential curve of the working electrode to form the current response data of the heavy metal oxidation peak in the working electrode;

[0077] The characteristic oxidation peak potential of the competitive ion and the corresponding peak current are identified and extracted from the current-potential curve of the interference compensation electrode to form the electrochemical response data of the competitive ion in the interference compensation electrode.

[0078] In specific implementation, the detection sample is injected into the electrochemical detection cell, and then a preset deposition potential is synchronously applied to the working electrode and the interference compensation electrode to pre-enrich the target heavy metal and the competitive ion, which can be achieved by the following way, i.e., first, the detection sample is injected into the electrochemical detection cell equipped with a working electrode, a counter electrode and a reference electrode to ensure that the sample liquid completely covers the effective working area of the electrode; then, a preset constant deposition potential is applied to the working electrode and the interference compensation electrode by the electrochemical workstation to make the target heavy metal ion and the competitive ion in the solution migrate to the surface of the corresponding electrode and be deposited under the driving action of the electric field; the constant temperature condition and the stable stirring rate are maintained during the deposition process to enhance the mass transfer efficiency of the ions on the electrode surface and reduce the concentration polarization effect; after the deposition time is reached, the deposition potential is stopped, i.e., the pre-enrichment of the target heavy metal and the competitive ion is completed.

[0079] In a specific implementation, the anodic linear scanning of the deposition potential after the pre-concentration is completed, and the synchronous recording of the current-potential curves of the working electrode and the interference compensation electrode can be achieved in the following manner: first, after the pre-concentration of the target heavy metal and the competitive ions is completed, the potential scanning module of the electrochemical workstation is controlled to continuously increase the potential applied to the working electrode and the interference compensation electrode from the starting value of the deposition potential to the anode direction at a preset linear scanning rate; during the potential scanning process, the instantaneous current values of the working electrode and the interference compensation electrode at each potential point are collected simultaneously, and the potential-current correspondence is recorded as current-potential curve data; when the potential scanning reaches the preset termination potential, the scanning is ended, thereby obtaining the current-potential curve data of the working electrode and the interference compensation electrode.

[0080] It should be noted that the current-potential curve in the present application refers to the functional relationship data curve formed by applying a linearly changing potential scan to the working electrode or the interference compensation electrode and recording the corresponding instantaneous current values at different potential points during the electrochemical detection process; this curve reflects the electrochemical kinetic characteristics of the electrode surface redox reaction under specific test conditions.

[0081] In a specific implementation, the oxidation peak potential and the corresponding peak current of the heavy metal ions are identified and extracted from the current-potential curve of the working electrode to form the current response data of the heavy metal oxidation peak in the working electrode in the following manner: first, a peak detection algorithm based on signal processing is used to scan the current-potential curve of the working electrode to identify the peak points where the current reaches a local maximum and the corresponding potential range meets the preset heavy metal oxidation potential interval; then, the potential value of each identified peak point is extracted as the oxidation peak potential, and the corresponding peak current is extracted as the oxidation peak current response; finally, the set of all peak currents sorted from small to large is taken as the current response data of the heavy metal oxidation peak in the working electrode.

[0082] In a specific implementation, the characteristic oxidation peak potential and the corresponding peak current of the competitive ions are identified and extracted from the current-potential curve of the interference compensation electrode to form the electrochemical response data of the competitive ions in the interference compensation electrode in the following manner: first, a peak detection algorithm based on signal processing is used to scan the current-potential curve of the interference compensation electrode to identify the peak points where the current reaches a local maximum and the corresponding potential range meets the preset characteristic oxidation potential interval of the competitive ions; then, the potential value of each identified peak point is extracted as the oxidation peak potential, and the corresponding peak current is extracted as the oxidation peak current response; finally, the set of all peak currents sorted from small to large is taken as the electrochemical response data of the competitive ions in the interference compensation electrode.

[0083] It should be noted that the current response data of the heavy metal oxidation peak in the working electrode in the present application refers to a data set composed of one or more potential values of the heavy metal oxidation peak and corresponding peak currents identified by a peak detection algorithm based on the current-potential curve of the working electrode; the data reflects the current response characteristics of the target heavy metal in the electrochemical oxidation process, and can represent the oxidation activity and concentration information of the heavy metal ion; in addition, the electrochemical response data of the competitive ion in the interference compensation electrode refers to a data set composed of one or more potential values of the characteristic oxidation peak of the competitive ion and corresponding peak currents identified by a peak detection algorithm based on the current-potential curve of the interference compensation electrode; the data reflects the current response characteristics of the competitive ion in the electrochemical oxidation process.

[0084] In some embodiments, the detection of the competitive adsorption effect in the detection sample is dynamically corrected by the interference correction coefficient, the current response data and the electrochemical response data, and the corrected current value of the heavy metal in the detection sample at different oxidation peaks can be achieved by the following steps:

[0085] linear analysis of the electrochemical response data to obtain the interference intensity index of the competitive ion in the detection sample;

[0086] coupling and mapping the interference correction coefficient and the interference intensity index to obtain the signal suppression amount in the ion competitive adsorption process;

[0087] dynamically compensating the current response data by the signal suppression amount to obtain the corrected current value of the heavy metal in the detection sample at different oxidation peaks.

[0088] In a specific implementation, the linear analysis of the electrochemical response data to obtain the interference intensity index of the competing ions in the detection sample can be achieved in the following manner: first, the peak current data of the characteristic oxidation peak of the competing ions in the current-potential curve of the interference compensation electrode is obtained; then, the peak current data is compared and calculated with a linear response equation between the standard concentration of the competing ions and the peak current, wherein the linear response equation can be obtained by measuring the peak current of the competing ions with different known concentrations in a standard solution and performing linear fitting on the concentration-current data points by the least square method; next, the peak current of the detection sample is substituted into the linear response equation to calculate the corresponding equivalent concentration of the competing ions; then, the equivalent concentration is subjected to ratio or difference operation with the set reference concentration to quantify the relative interference degree of the competing ions; preferably, the linear analysis can also use a multiple linear regression method, and the pH value and the ionic strength are introduced as auxiliary variables to correct the prediction deviation of the single concentration-current model under complex water quality conditions; in other embodiments, interval segmented linear fitting or sparse linear modeling based on regularization can also be used to adapt to different background electrochemical noise levels of the detection environment.

[0089] It should be noted that the interference intensity index of the competing ions in the present application refers to the quantitative index calculated based on the peak current of the characteristic oxidation peak of the competing ions after linear analysis of the electrochemical response data of the interference compensation electrode, which is used to reflect the interference degree of the competing ions in the detection sample on the electrochemical signal of the target heavy metal.

[0090] In a specific implementation, the interference correction coefficient and the interference intensity index are coupled and mapped to obtain the signal suppression amount in the ion competitive adsorption process. The following method can be used: first, the interference intensity index (such as the electrochemical response current of the competitive ion) is divided into several interference level intervals according to a preset standard, for example, a low interference interval and a high interference interval; similarly, the interference correction coefficient is divided into a weak correction level and a strong correction level; then, a two-dimensional level mapping table is constructed, taking the interference intensity level and the interference correction coefficient level as the input dimensions, and configuring the corresponding signal suppression amount for each level combination in the table, for example, medium interference + medium correction corresponds to a signal suppression amount of 0.15, strong interference + strong correction corresponds to a signal suppression amount of 0.32, and so on; in actual application, the interference intensity index and the interference correction coefficient corresponding to the detection sample are first located in the corresponding interval, and the corresponding signal suppression amount is obtained by looking up the table; preferably, to improve the calculation accuracy, when the input value is near the boundary of the adjacent interval, linear interpolation or fuzzy logic method can be used for continuous processing of the mapping value, so as to avoid sudden error; in other embodiments, a continuous function expression can also be obtained by least square fitting, polynomial regression or neural network regression according to the corresponding relationship among a large number of historical samples, and the interference intensity index and the interference correction coefficient are input into the function as independent variables to obtain the signal suppression amount, which is not limited in the present application.

[0091] It should be noted that the signal suppression amount in the present application refers to the parameter value quantifying the degree of suppression of the electrochemical signal in the ion competitive adsorption process after coupling and mapping the interference correction coefficient and the interference intensity index.

[0092] It should be noted that each peak current in the current response data corresponds to an oxidation peak of a heavy metal. Therefore, in specific implementation, the corrected current value of the heavy metal in the detected sample at different oxidation peaks can be obtained by dynamically compensating the current response data with the signal suppression amount as follows: First, obtain the peak current corresponding to the heavy metal at different oxidation peaks. Then, dynamically compensate the peak current with the signal suppression amount combined with a preset compensation rule. As a preferred embodiment, a correction function based on experimental data fitting can be set as the preset compensation rule (such as a compensation coefficient table, nonlinear function expression, etc.). Then, the signal suppression amount is input into the correction function to obtain the corresponding compensation ratio. Finally, the ratio is multiplied by the original peak current to generate the final corrected current value. Here, the correction function refers to the function used to compensate the peak current of the detected sample at different oxidation peaks. The signal suppression caused by competitive adsorption in the sample is mapped to a functional relationship of peak current compensation ratio. This functional relationship is pre-established through fitting experimental data, reflecting the compensation law of the influence of different signal suppression degrees on the heavy metal current response. The correction function can be expressed by table lookup, linear function, piecewise polynomial, or nonlinear expression. Preferably, a fitting model can be constructed based on the relationship between the measured signal suppression and the corresponding correction current ratio in samples of different water quality types. For example, in a nonlinear approach, a nonlinear expression such as an exponential function or sigmoid function can be obtained through least squares fitting. The function coefficients can be obtained by training with experimental data, thereby giving the fitting result good prediction accuracy within the target interval. In other implementations, the correction function can also be constructed through machine learning regression methods, which is not limited here.

[0093] It should be noted that the corrected current value mentioned in this application refers to the current response value obtained by dynamically compensating the original peak current after considering the influence of competitive adsorption effects in the sample. This value reflects the heavy metal oxidation peak current after interference correction, and can more accurately represent the true electrochemical response intensity of the target heavy metal in the sample, which can be used for subsequent concentration analysis and quantitative detection.

[0094] In step 104, baseline analysis is performed on all corrected current values ​​based on blank samples to generate net response signals for heavy metals in groundwater samples.

[0095] In some embodiments, reference Figure 2 As shown in the figure, this is a schematic flowchart illustrating the determination of the net response signal according to some embodiments of this application. The generation of the net response signal for heavy metals in a groundwater sample by performing baseline analysis on all corrected current values ​​based on a blank sample can be achieved through the following steps:

[0096] Firstly, in step 1041, the same condition electrochemical scanning as the detection sample is performed on the blank sample to obtain the baseline response signal of the blank sample;

[0097] Then, in step 1042, the baseline oxidation current values of the heavy metals are extracted from the baseline response signal based on the oxidation potential interval of the heavy metals;

[0098] Finally, in step 1043, all the corrected current values are point-by-point differentially analyzed with all the baseline oxidation current values, and then the net response signal of the heavy metals in the groundwater sample is generated through the differential results.

[0099] As a preferred embodiment, the same condition electrochemical scanning process can be repeated according to the above-mentioned process of determining the current response data of the heavy metal oxidation peak from the detection sample, and the same deposition potential and scanning conditions as the detection sample are applied to the blank sample; then, the current-potential curve of the working electrode is synchronously collected; the baseline response signal of the blank sample can also be realized by other methods in other embodiments, which is not limited here; therefore, when specifically implemented, the baseline oxidation current values of the heavy metals can be extracted from the baseline response signal based on the oxidation potential interval of the heavy metals in the following manner, that is: first, according to the oxidation potential interval of the heavy metal identified in the foregoing detection sample, a characteristic potential window corresponding to each target heavy metal is preset, and the window size can be set as a value interval size composed of a certain potential offset before and after the center potential; then, on the current-potential curve of the blank sample, the oxidation potential interval of each heavy metal is located in the preset window, and the local maximum value or the interval average value in each interval is extracted as the baseline oxidation current value of the interval; thereby obtaining the baseline oxidation current values of the heavy metals.

[0100] It should be noted that the baseline response signal in the present application refers to the working electrode current-potential curve data collected in the electrochemical detection process of the blank sample, which reflects the electrochemical response characteristics of the electrode in the matrix background without target heavy metal ions, and is used as a reference benchmark for subsequent detection sample signal correction; the baseline oxidation current value refers to the local maximum current value or the interval average current value in the preset heavy metal oxidation potential interval, which is extracted from the baseline response signal, and is used to represent the background current response intensity of the blank sample in the potential interval as a baseline reference for heavy metal current signal purification.

[0101] In a specific implementation, all the corrected current values are subjected to a point-by-point difference analysis with all the baseline oxidation current values, and then the net response signal of the heavy metal in the groundwater sample is generated by the difference result. The following method can be used to achieve this, that is, first, according to the order of the oxidation peak potential from small to large, the corrected current value of the heavy metal in the detection sample is paired with the baseline oxidation current value at the corresponding position in the blank sample; then, the difference operation is performed point by point, that is, each corrected current value is subtracted by the corresponding baseline oxidation current value to obtain the net response current of the corresponding heavy metal at the oxidation peak position; finally, the set of all the net response current values after the difference is sorted according to the order of the corresponding oxidation peak potential to form the net response signal of the heavy metal in the groundwater sample.

[0102] It should be noted that the net response signal in the present application refers to the current difference set obtained by the point-by-point difference of the corrected current value of the heavy metal in the detection sample and the corresponding baseline oxidation current value of the blank sample. The net response signal reflects the characteristic electrochemical response intensity of the heavy metal after excluding the matrix background, and is used to accurately represent the actual electrochemical activity and concentration information of the target heavy metal in the groundwater sample.

[0103] In step 105, the heavy metal content of the groundwater sample is rapidly detected based on the standard working curve of the heavy metal in the standard solution combined with the net response signal.

[0104] In some embodiments, the rapid detection of the heavy metal content of the groundwater sample based on the standard working curve of the heavy metal in the standard solution combined with the net response signal can be achieved by the following steps:

[0105] Obtaining the standard working curve of the target heavy metal in the concentration standard solution;

[0106] Extracting the peak current value of the characteristic oxidation peak of the heavy metal from the net response signal;

[0107] Substituting the peak current value into the standard working curve for linear fitting to obtain the estimated value of the heavy metal concentration corresponding to the current value as the rapid detection result of the heavy metal content of the groundwater sample.

[0108] It should be noted that the standard working curve refers to the functional relationship curve between the oxidation peak current and the concentration of the target heavy metal in a series of known concentration standard solutions under the set electrochemical detection conditions. Therefore, when specifically implemented, the standard working curve of the target heavy metal in the concentration standard solution can be obtained in the following manner: first, a set of standard solutions covering the target concentration range is prepared, preferably, the concentration range should cover the actual concentration interval of the target heavy metal in the groundwater sample, and be distributed in a linear or logarithmic gradient; then, under the same electrochemical detection conditions as the detection sample, the set deposition potential and linear scanning are repeatedly applied to each standard solution, and the corresponding current-potential response curve is collected; then, according to the aforementioned signal processing method for identifying the oxidation peak current of the detection sample, the oxidation peak position and peak current of the target heavy metal are extracted from each response curve; finally, the extracted peak current is taken as the ordinate, and the corresponding concentration value is taken as the abscissa, and mathematical fitting is performed, preferably, the least square method can be used to establish a linear fitting relationship, or a polynomial function, a logarithmic function, an exponential function, etc. are selected according to the experimental characteristics to perform nonlinear fitting, and finally the standard working curve of the target heavy metal is obtained.

[0109] It should be noted that the characteristic oxidation peak refers to the main response peak selected from a plurality of possible oxidation peaks in the case of detecting only a single target heavy metal; in the specific implementation process, the target heavy metal may produce a plurality of oxidation peaks in the electrochemical oxidation process due to factors such as coordination environment, background matrix and reaction path, which are collectively referred to as oxidation peaks of the heavy metal; different oxidation peaks may correspond to different valence states, complex states or surface reaction kinetics of metal ions; preferably, a single peak with the strongest current response, the most stable potential position and the most clear and identifiable peak shape characteristics can be selected from the oxidation peaks as the characteristic oxidation peak, and the current value corresponding to the characteristic oxidation peak is taken as the peak current value of the heavy metal characteristic oxidation peak.

[0110] When specifically implemented, the peak current value is substituted into the standard working curve for linear fitting, and the heavy metal concentration estimate value corresponding to the peak current value is obtained as the rapid detection result of the heavy metal content of the groundwater sample in the following manner: first, the slope and intercept of the standard working curve are determined to form a linear expression of the standard working curve, then the peak current value is substituted into the linear expression to obtain the heavy metal concentration estimate value corresponding to the peak current value; finally, the heavy metal concentration estimate value is taken as the rapid detection result of the heavy metal content of the groundwater sample, thereby realizing the rapid detection of the heavy metal content of the groundwater sample; it should be noted that the process of determining the slope and intercept of the standard working curve to form the linear expression can be realized by the least square method linear regression method in the prior art; for reference Figure 3As shown, the figure is a flowchart of constructing a linear expression according to some embodiments of the present application, which specifically includes: first, constructing a two-dimensional data set by collecting the oxidation peak current and the corresponding concentration data points of the target heavy metal in different known concentration standard solutions; then, applying the least squares method algorithm to fit the two-dimensional data set, calculating the slope and intercept parameters of the fitted straight line, and then constructing the linear expression of the standard working curve through the slope and intercept parameters; preferably, it can be realized by mathematical software tools (such as Excel), and in other embodiments, other methods can also be used to determine, which is not limited here.

[0111] In addition, another aspect of the present application, in some embodiments, the present application provides a rapid detection system for heavy metal content in groundwater, referring to Figure 4 The figure is a structural schematic diagram of a rapid detection system for heavy metal content in groundwater according to some embodiments of the present application, which includes a collection module 201, a processing module 202 and an execution module 203, which are described as follows:

[0112] The collection module 201 is mainly used for collecting groundwater samples in the target area in the present application, and the groundwater samples are divided into detection samples and blank samples by microporous filter membranes;

[0113] The processing module 202 is mainly used for generating interference correction coefficients when the detection sample appears ion competitive adsorption according to the solution pH and ionic strength of the detection sample and deep learning algorithm in the present application;

[0114] In addition, the processing module 202 in the present application is also used for synchronous scanning of heavy metals and competitive ions in the detection sample, to obtain current response data of heavy metal oxidation peak in the working electrode and electrochemical response data of competitive ions in the interference compensation electrode, and to dynamically correct the competitive adsorption effect in the detection sample through the interference correction coefficient, the current response data and the electrochemical response data, to obtain the corrected current value of heavy metals in the detection sample at different oxidation peaks;

[0115] In addition, the processing module 202 in the present application is also used for baseline analysis of all corrected current values according to the blank sample, to generate the net response signal of heavy metals in the groundwater sample;

[0116] The execution module 203 is mainly used for rapid detection of heavy metal content in the groundwater sample based on the standard working curve of heavy metals in the standard solution and the net response signal in the present application.

[0117] In addition, the application further provides a computer device, comprising a memory and a processor, the memory stores codes, and the processor is configured to acquire the codes and execute the groundwater heavy metal content rapid detection method.

[0118] In some embodiments, referring to Figure 5 , the figure is an internal structure diagram of a computer device for implementing the groundwater heavy metal content rapid detection method according to some embodiments of the application. The groundwater heavy metal content rapid detection method in the above embodiments can be implemented by the computer device shown in Figure 5 , which comprises at least one processor 301, a communication bus 302, a memory 303 and at least one communication interface 304.

[0119] The processor 301 can be a general central processing unit (CPU), an application-specific integrated circuit (ASIC) or one or more circuits for controlling the execution of the groundwater heavy metal content rapid detection method in the application.

[0120] The communication bus 302 is used for transmitting information between the above components.

[0121] The memory 303 can be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, an optical disk storage (including a compact disk, a laser disk, an optical disk, a digital versatile disk, a Blu-ray disk, etc.), a magnetic disk or other magnetic storage device, or any other medium capable of carrying or storing desired program codes in the form of instructions or data structures and capable of being accessed by a computer, but not limited to. The memory 303 can exist independently and be connected to the processor 301 through the communication bus 302. The memory 303 can also be integrated with the processor 301.

[0122] The memory 303 is configured to store program codes for implementing the solutions of the present application, and the processor 301 is configured to execute the program codes stored in the memory 303. The program codes can include one or more software modules. The above-mentioned groundwater heavy metal content rapid detection method can be implemented by the processor 301 and one or more software modules in the program codes in the memory 303.

[0123] The communication interface 304 is configured to communicate with other devices or communication networks, such as an Ethernet, a radio access network (RAN), a wireless local area network (WLAN), etc., using any transceiver-like mechanism.

[0124] In specific implementations, as an example, the computer device can include multiple processors, each of which can be a single-CPU processor or a multi-CPU processor. The processor herein can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0125] The above-mentioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a laptop computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. The embodiments of the present application do not limit the type of computer device.

[0126] In addition, the present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the above-mentioned groundwater heavy metal content rapid detection method.

[0127] In summary, in the groundwater heavy metal content rapid detection method, system, device and medium disclosed by the embodiment of the application, the groundwater sample of the target area is collected, and the groundwater sample is divided into a detection sample and a blank sample through a microporous filter film; the interference correction coefficient when the ion competitive adsorption occurs under the water quality type of the detection sample is generated according to the solution pH and ion strength of the detection sample and a deep learning algorithm; the heavy metal and competitive ions in the detection sample are synchronously scanned to obtain the current response data of the heavy metal oxidation peak in the working electrode and the electrochemical response data of the competitive ions in the interference compensation electrode; the competitive adsorption effect in the detection sample is dynamically corrected through the interference correction coefficient, the current response data and the electrochemical response data to obtain the corrected current value of the heavy metal in the detection sample at different oxidation peaks; the baseline analysis of all the corrected current values is performed according to the blank sample to generate the net response signal of the heavy metal in the groundwater sample; the heavy metal content of the groundwater sample is rapidly detected based on the standard working curve of the heavy metal in the standard solution and the net response signal; and the detection signal can be accurately corrected based on the dynamic change of the ion competitive adsorption effect.

[0128] Although preferred embodiments of the application have been described, those skilled in the art will be able to make additional modifications and variations without departing from the spirit and scope of the application. Accordingly, the appended claims are intended to encompass all such modifications and variations as falling within the scope of the application.

[0129] Obviously, various modifications and changes can be made to the present application without departing from the spirit and scope of the application. Accordingly, the present application intends to include all such modifications and changes as falling within the scope of the claims of the present application and their equivalents.

Claims

1. A method for rapid detection of heavy metal content in groundwater, wherein, The method comprises the following steps: collecting a groundwater sample in a target area, dividing the groundwater sample into a detection sample and a blank sample by a microporous filter membrane; generating an interference correction coefficient when ion competitive adsorption occurs in a water quality type of the detection sample according to the solution pH and ion strength of the detection sample and a deep learning algorithm; performing synchronous scanning on heavy metals and competitive ions in the detection sample to obtain current response data of heavy metal oxidation peaks in a working electrode and electrochemical response data of competitive ions in an interference compensation electrode; dynamically correcting competitive adsorption effects in the detection sample by the interference correction coefficient, the current response data and the electrochemical response data to obtain corrected current values of heavy metals in the detection sample at different oxidation peaks; performing baseline analysis on all corrected current values according to the blank sample to generate a net response signal of heavy metals in the groundwater sample; and rapidly detecting the heavy metal content of the groundwater sample based on a standard working curve of heavy metals in a standard solution and the net response signal. The method comprises the following steps: collecting a groundwater sample in a target area, dividing the groundwater sample into a detection sample and a blank sample by a microporous filter membrane; generating an interference correction coefficient when ion competitive adsorption occurs in a water quality type of the detection sample according to the solution pH and ion strength of the detection sample and a deep learning algorithm; performing synchronous scanning on heavy metals and competitive ions in the detection sample to obtain current response data of heavy metal oxidation peaks in a working electrode and electrochemical response data of competitive ions in an interference compensation electrode; dynamically correcting competitive adsorption effects in the detection sample by the interference correction coefficient, the current response data and the electrochemical response data to obtain corrected current values of heavy metals in the detection sample at different oxidation peaks; performing baseline analysis on all corrected current values according to the blank sample to generate a net response signal of heavy metals in the groundwater sample; and rapidly detecting the heavy metal content of the groundwater sample based on a standard working curve of heavy metals in a standard solution and the net response signal. The method comprises the following steps: collecting a groundwater sample in a target area, dividing the groundwater sample into a detection sample and a blank sample by a microporous filter membrane; generating an interference correction coefficient when ion competitive adsorption occurs in a water quality type of the detection sample according to the solution pH and ion strength of the detection sample and a deep learning algorithm; performing synchronous scanning on heavy metals and competitive ions in the detection sample to obtain current response data of heavy metal oxidation peaks in a working electrode and electrochemical response data of competitive ions in an interference compensation electrode; dynamically correcting competitive adsorption effects in the detection sample by the interference correction coefficient, the current response data and the electrochemical response data to obtain corrected current values of heavy metals in the detection sample at different oxidation peaks; performing baseline analysis on all corrected current values according to the blank sample to generate a net response signal of heavy metals in the groundwater sample; and rapidly detecting the heavy metal content of the groundwater sample based on a standard working curve of heavy metals in a standard solution and the net response signal. The method comprises the following steps: collecting a groundwater sample in a target area, dividing the groundwater sample into a detection sample and a blank sample by a microporous filter membrane; generating an interference correction coefficient when ion competitive adsorption occurs in a water quality type of the detection sample according to the solution pH and ion strength of the detection sample and a deep learning algorithm; performing synchronous scanning on heavy metals and competitive ions in the detection sample to obtain current response data of heavy metal oxidation peaks in a working electrode and electrochemical response data of competitive ions in an interference compensation electrode; dynamically correcting competitive adsorption effects in the detection sample by the interference correction coefficient, the current response data and the electrochemical response data to obtain corrected current values of heavy metals in the detection sample at different oxidation peaks; performing baseline analysis on all corrected current values according to the blank sample to generate a net response signal of heavy metals in the groundwater sample; and rapidly detecting the heavy metal content of the groundwater sample based on a standard working curve of heavy metals in a standard solution and the net response signal. The method comprises the following steps: collecting a groundwater sample in a target area, dividing the groundwater sample into a detection sample and a blank sample by a microporous filter membrane; generating an interference correction coefficient when ion competitive adsorption occurs in a water quality type of the detection sample according to the solution pH and ion strength of the detection sample and a deep learning algorithm; performing synchronous scanning on heavy metals and competitive ions in the detection sample to obtain current response data of heavy metal oxidation peaks in a working electrode and electrochemical response data of competitive ions in an interference compensation electrode; dynamically correcting competitive adsorption effects in the detection sample by the interference correction coefficient, the current response data and the electrochemical response data to obtain corrected current values of heavy metals in the detection sample at different oxidation peaks; performing baseline analysis on all corrected current values according to the blank sample to generate a net response signal of heavy metals in the groundwater sample; and rapidly detecting the heavy metal content of the groundwater sample based on a standard working curve of heavy metals in a standard solution and the net response signal. ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 2. The method of claim 1, wherein, ​ The detection sample is injected into an electrochemical detection cell, and then a preset deposition potential is synchronously applied to the working electrode and the interference compensation electrode to pre-enrich target heavy metals and competitive ions; After the pre-enrichment is completed, an anodic linear sweep is performed on the deposition potential, and the current-potential curves of the working electrode and the interference compensation electrode are recorded synchronously; The oxidation peak potential and the corresponding peak current of the heavy metal ions are identified and extracted from the current-potential curve of the working electrode to form the current response data of the heavy metal oxidation peak in the working electrode; The characteristic oxidation peak potential and the corresponding peak current of the competitive ions are identified and extracted from the current-potential curve of the interference compensation electrode to form the electrochemical response data of the competitive ions in the interference compensation electrode.

3. The method of claim 1, wherein, The baseline analysis of all corrected current values is performed according to the blank sample to generate the net response signal of the heavy metals in the groundwater sample, which specifically includes: The blank sample is subjected to electrochemical scanning under the same conditions as the detection sample to obtain the baseline response signal of the blank sample; Based on the oxidation peak potential interval of the heavy metals, a plurality of baseline oxidation current values of the heavy metals are extracted from the baseline response signal; All corrected current values are subjected to point-by-point difference analysis with all baseline oxidation current values, and then the net response signal of the heavy metals in the groundwater sample is generated through the difference results.

4. The method of claim 1, wherein, The heavy metal content of the groundwater sample is rapidly detected based on the standard working curve of the heavy metals in the standard solution combined with the net response signal, which specifically includes: The standard working curve of the target heavy metals in the concentration standard solution is obtained; The peak current value of the characteristic oxidation peak of the heavy metals is extracted from the net response signal; The peak current value is substituted into the standard working curve for linear fitting to obtain the heavy metal concentration estimate value corresponding to the current value as the rapid detection result of the heavy metal content of the groundwater sample.

5. The method of claim 1, wherein, The blank sample is a target heavy metal-free groundwater matrix blank sample verified by inductively coupled plasma mass spectrometry.

6. A system for rapid detection of heavy metal content in groundwater, which adopts the method according to any one of claims 1 to 5 to rapidly detect the heavy metal content in groundwater, characterized in that, The system includes: The acquisition module is used to collect the groundwater sample of the target area, and the groundwater sample is divided into a detection sample and a blank sample through a microporous filter membrane; The processing module is used to generate the interference correction coefficient when ion competition adsorption occurs in the water quality type of the detection sample according to the solution pH and ionic strength of the detection sample combined with a deep learning algorithm; The processing module is also used to perform synchronous scanning on the heavy metals and competitive ions in the detection sample to obtain the current response data of the heavy metal oxidation peak in the working electrode and the electrochemical response data of the competitive ions in the interference compensation electrode, dynamically correct the competitive adsorption effect in the detection sample through the interference correction coefficient, the current response data, and the electrochemical response data, and obtain the corrected current value of the heavy metals in the detection sample at different oxidation peaks; The processing module is also used to perform baseline analysis on all corrected current values according to the blank sample to generate the net response signal of the heavy metals in the groundwater sample; The execution module is used to rapidly detect the heavy metal content of the groundwater sample based on the standard working curve of the heavy metals in the standard solution combined with the net response signal. 7.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-6 when the computer program is executed by the processor. The computer program is executed by the processor to realize the steps of the rapid detection method of heavy metal content in groundwater according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, the computer-readable storage medium comprising: The computer program is executed by the processor to realize the steps of the rapid detection method of heavy metal content in groundwater according to any one of claims 1 to 5.

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