A method, system, device and storage medium for detecting heavy metal content in a river

Abstract

The present application provides a method, system, device and storage medium for detecting heavy metal content in a river, by obtaining the historical absorbance of each heavy metal ion, and then determining the fluctuation characteristics corresponding to each heavy metal ion, and determining the content fluctuation loss of each heavy metal ion when performing absorbance content detection according to the fluctuation characteristics corresponding to each heavy metal ion; obtaining the historical ion concentration of each heavy metal ion, for each heavy metal ion, determining the concentration-absorbance fitting ratio of the heavy metal ion, and determining the goodness of fit of the heavy metal ion concentration by the concentration-absorbance fitting ratio; determining the variance expansion factor after correction of the heavy metal ion, so as to obtain the variance expansion factor after correction of each heavy metal ion; and realizing spectrophotometric nondestructive detection according to each corrected variance expansion factor. By adopting the scheme of the present application, it is possible to reduce the deviation between the detected ion concentration and the actual ion concentration under the influence of the collinearity of multiple heavy metal ions.

Description

Technical Field

[0001] The present application relates to the technical field of heavy metal content detection, and more specifically, to a method, system, device and storage medium for detecting heavy metal content in a river. Background Art

[0002] Heavy metal content detection refers to the determination of the amount or concentration of heavy metal components in a substance. It has important applications in various fields, such as environmental monitoring, food safety, drug analysis, etc. It provides pollution concentration information of key components through heavy metal content detection, which can provide data support for scientific decision-making or optimize industrial operations. It can also help identify and control potential hazards through heavy metal content detection, such as identifying toxic substances, pollutants and illegal ingredients, thereby enhancing the safety of products and the environment and protecting health and safety.

[0003] The detection of heavy metal content in rivers is to evaluate the concentration of heavy metals in river water to understand their potential impact on ecosystems and human health. Heavy metals such as lead, cadmium, mercury, copper, zinc, chromium and nickel usually come from industrial wastewater, agricultural fertilization, mining and other activities. These heavy metals are highly toxic and cumulative, posing a threat to aquatic ecosystems and human health. The detection of heavy metal content in rivers can effectively monitor heavy metal pollution in rivers, protect aquatic ecosystems and public health. In the existing technology, spectrophotometry is used to measure the degree of absorption (absorbance) of heavy metal ions in river water samples to light of a specific wavelength. Determine the concentration of the corresponding heavy metal ions. However, this method will cause overlapping absorption peaks (i.e., collinearity between heavy metal ions) due to the existence of the same or similar wavelength ranges between different heavy metal ions. This makes it impossible for the instrument to distinguish the specific absorption contribution of the absorbance of each heavy metal ion (i.e., the contribution of the absorbance of each heavy metal ion to the overall absorbance), resulting in a deviation between the final detected ion concentration and the actual ion concentration. Therefore, how to reduce the deviation between the detected ion concentration and the actual ion concentration under the influence of the collinearity of multiple heavy metal ions has become a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides a method, system, device and storage medium for detecting heavy metal content in a river, which can reduce the deviation between the detected ion concentration and the actual ion concentration under the influence of the collinearity of multiple heavy metal ions.

[0005] In a first aspect, the present application provides a method for detecting heavy metal content in a river, comprising the following steps:

[0006] Initiate heavy metal content detection of the target river, obtain the historical absorbance of each heavy metal ion in the target river, and then determine the fluctuation characteristics corresponding to each heavy metal ion based on the historical absorbance;

[0007] Determining the content fluctuation loss of each heavy metal ion when performing absorbance content detection according to the fluctuation characteristics corresponding to each heavy metal ion;

[0008] Obtain the historical ion concentration of each heavy metal ion in the target river, and for each heavy metal ion, determine the concentration-absorbance fitting ratio of the heavy metal ion according to the historical ion concentration of the heavy metal ion and the historical absorbance corresponding to the heavy metal ion, and then perform characteristic fitting on the ion concentration obtained by the absorbance content detection through the concentration-absorbance fitting ratio to obtain the goodness of fit of the heavy metal ion concentration;

[0009] Correcting the variance inflation factor of the absorbance of the heavy metal ions in the multiple regression model based on the content fluctuation loss corresponding to the heavy metal ions and the goodness of fit, thereby obtaining the corrected variance inflation factor of each heavy metal ion;

[0010] The corresponding heavy metal content in the target river is nondestructively tested by spectrophotometry based on the corrected variance inflation factors.

[0011] In some embodiments, determining the fluctuation characteristics corresponding to each heavy metal ion according to the historical absorbance specifically includes:

[0012] For each heavy metal ion, determining the absorbance trend amount of each heavy metal ion according to the historical absorbance corresponding to each heavy metal ion;

[0013] Determining the relative fluctuation ratio of absorbance in the historical absorbance corresponding to each heavy metal ion;

[0014] The fluctuation characteristics corresponding to each heavy metal ion are determined according to the absorbance trend quantity and the relative fluctuation ratio.

[0015] In some embodiments, determining the content fluctuation loss of each heavy metal ion when performing absorbance content detection according to the fluctuation characteristics corresponding to each heavy metal ion specifically includes:

[0016] Obtain the fluctuation influencing factors during absorbance content detection;

[0017] For each heavy metal ion, the content fluctuation loss of each heavy metal ion when performing absorbance content detection is determined by using the fluctuation influence factor and the fluctuation characteristics corresponding to each heavy metal ion.

[0018] In some embodiments, determining the concentration-absorbance fitting ratio of heavy metal ions according to the historical ion concentration of heavy metal ions and the historical absorbance corresponding to the heavy metal ions specifically includes:

[0019] Determine the concentration-absorbance linear factor according to the historical ion concentration of the heavy metal ion and the historical absorbance corresponding to the heavy metal ion;

[0020] The concentration-absorbance fitting ratio of the heavy metal ions is extracted from the historical ion concentration according to the concentration-absorbance linear factor.

[0021] In some embodiments, the ion concentration obtained by the absorbance content detection is characteristically fitted by the concentration-absorbance fitting ratio to obtain the goodness of fit of the heavy metal ion concentration, which specifically includes:

[0022] Determine the detection ion concentration data of heavy metal ions according to the historical absorbance of heavy metal ions;

[0023] Determining fitted ion concentration data of heavy metal ions according to the historical absorbance of heavy metal ions and the concentration-absorbance fitting ratio;

[0024] The goodness of fit of the heavy metal ion concentration is determined according to the detected ion concentration data and the fitted ion concentration data.

[0025] In some embodiments, the variance inflation factor of the absorbance of the heavy metal ions in the multiple regression model is corrected based on the content fluctuation loss corresponding to the heavy metal ions and the goodness of fit, so as to obtain the corrected variance inflation factor of each heavy metal ion, specifically including:

[0026] Obtain the variance inflation factor of heavy metal ions in multiple regression models;

[0027] Determine the collinear fluctuation coefficient of the heavy metal ions by the fluctuation loss corresponding to the heavy metal ions and the goodness of fit;

[0028] The variance inflation factor of the absorbance of heavy metal ions in the multiple regression model is corrected by the collinear fluctuation coefficient to obtain the variance inflation factor corrected for the heavy metal ions, thereby obtaining the variance inflation factor corrected for each heavy metal ion.

[0029] In some embodiments, performing spectrophotometric nondestructive testing of the corresponding heavy metal content in the target river according to each corrected variance inflation factor specifically includes:

[0030] Collinearity is judged for each heavy metal ion according to each corrected variance inflation factor, and all heavy metal ions are divided into multiple collinear ions and multiple orthogonal ions;

[0031] The content of each heavy metal in the target river was nondestructively detected by spectrophotometry using all colinear ions and all orthogonal ions.

[0032] In a second aspect, the present application provides a system for detecting heavy metal content in a river, comprising:

[0033] An acquisition module, used to acquire the historical absorbance of each heavy metal ion in the target river after starting the heavy metal content detection of the target river, and then determine the fluctuation characteristics corresponding to each heavy metal ion according to the historical absorbance;

[0034] A processing module, used to determine the content fluctuation loss of each heavy metal ion when performing absorbance content detection according to the fluctuation characteristics corresponding to each heavy metal ion;

[0035] The processing module is also used to obtain the historical ion concentration of each heavy metal ion in the target river, and for each heavy metal ion, determine the concentration-absorbance fitting ratio of the heavy metal ion according to the historical ion concentration of the heavy metal ion and the historical absorbance corresponding to the heavy metal ion, and then perform characteristic fitting on the ion concentration obtained by the absorbance content detection through the concentration-absorbance fitting ratio to obtain the goodness of fit of the heavy metal ion concentration;

[0036] The processing module is also used to correct the variance inflation factor of the absorbance of the heavy metal ions in the multiple regression model based on the content fluctuation loss corresponding to the heavy metal ions and the goodness of fit, so as to obtain the corrected variance inflation factor of each heavy metal ion;

[0037] The execution module is used to perform spectrophotometric nondestructive detection of the corresponding heavy metal content in the target river according to each corrected variance inflation factor.

[0038] In a third aspect, the present application provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned method for detecting heavy metal content in rivers when executing the computer program.

[0039] In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for detecting heavy metal content in a river.

[0040] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects:

[0041] In the method, system, device and storage medium for detecting heavy metal content in rivers provided by the present application, the historical absorbance of each heavy metal ion in the target river is obtained, and then the fluctuation characteristics corresponding to each heavy metal ion are determined based on the historical absorbance; the content fluctuation loss of each heavy metal ion when the absorbance content is detected is determined based on the fluctuation characteristics corresponding to each heavy metal ion; the historical ion concentration of each heavy metal ion in the target river is obtained, and for each heavy metal ion, the concentration-absorbance fitting ratio of the heavy metal ion is determined based on the historical ion concentration of the heavy metal ion and the historical absorbance corresponding to the heavy metal ion, and then the ion concentration obtained by the absorbance content detection is characteristically fitted through the concentration-absorbance fitting ratio to obtain the goodness of fit of the heavy metal ion concentration; the variance inflation factor of the absorbance of the heavy metal ion in the multiple regression model is corrected based on the content fluctuation loss corresponding to the heavy metal ion and the goodness of fit, so as to obtain the corrected variance inflation factor of each heavy metal ion; and the corresponding heavy metal content in the target river is non-destructively detected by spectrophotometry based on each corrected variance inflation factor.

[0042] It can be seen that in this application, after obtaining the historical absorbance of each heavy metal ion in the target river, first, the error amount (content fluctuation loss) when detecting the absorbance content of each heavy metal ion is determined according to the abnormal fluctuation of the corresponding absorbance data caused by the collinearity of each heavy metal ion (that is, the fluctuation characteristics corresponding to the heavy metal ions). Then, after obtaining the historical ion concentration of each heavy metal ion in the river water sample, the linear ratio (concentration-absorbance fitting ratio) between the historical ion concentration of the heavy metal ion and the corresponding historical absorbance is determined according to the historical ion concentration of the heavy metal ion and the corresponding historical absorbance, thereby obtaining the similarity between the heavy metal ion concentration obtained by absorbance detection (detected ion concentration data) and the heavy metal ion concentration obtained by fitting (fitted ion concentration data) (goodness of fit of concentration). Secondly, when judging the various heavy metal ions in the river water sample through the multiple regression model, the linear ratio between the historical ion concentration of the heavy metal ion and the corresponding historical absorbance is determined according to the historical ion concentration of the heavy metal ion and the corresponding historical absorbance. After the degree of collinearity between metal ions is determined, the collinearity degree of each heavy metal ion is corrected by the content fluctuation loss corresponding to the heavy metal ions and the goodness of fit, that is, the influence of the difference between the actual value and the detected value and the data fluctuation on the variance inflation factor (collinearity) is quantified, and the variance inflation factor is corrected according to the degree of influence. The variance inflation factor can be adjusted by comprehensively considering the characteristics of the data to make it more accurately reflect the true collinearity relationship between the variables (corrected variance inflation factor), thereby avoiding the influence of the collinearity of multiple heavy metal ions, reducing the deviation between the detected ion concentration and the actual ion concentration, and performing spectrophotometric non-destructive detection of the corresponding heavy metal content in the target river based on the result of the deviation correction; in summary, this scheme can reduce the deviation between the detected ion concentration and the actual ion concentration under the influence of the collinearity of multiple heavy metal ions. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of a flow chart of a method for detecting heavy metal content in a river according to some embodiments of the present application;

[0044] Figure 2 is a schematic diagram of a process for determining fluctuation characteristics according to some embodiments of the present application;

[0045] Figure 3 is a schematic diagram of a process for determining goodness of fit according to some embodiments of the present application;

[0046] Figure 4 is a structural block diagram of a system for detecting heavy metal content in a river according to some embodiments of the present application;

[0047] Figure 5 It is a diagram of the internal structure of a computer device for implementing a method for detecting heavy metal content in a river according to some embodiments of the present application. DETAILED DESCRIPTION

[0048] The core of the present application is to obtain the historical absorbance of each heavy metal ion in the target river, and then determine the fluctuation characteristics corresponding to each heavy metal ion based on the historical absorbance; determine the content fluctuation loss of each heavy metal ion when the absorbance content is detected according to the fluctuation characteristics corresponding to each heavy metal ion; obtain the historical ion concentration of each heavy metal ion in the target river, for each heavy metal ion, determine the concentration-absorbance fitting ratio of the heavy metal ion according to the historical ion concentration of the heavy metal ion and the historical absorbance corresponding to the heavy metal ion, and then perform feature fitting on the ion concentration obtained by the absorbance content detection through the concentration-absorbance fitting ratio to obtain the goodness of fit of the heavy metal ion concentration; based on the content fluctuation loss corresponding to the heavy metal ion and the goodness of fit, correct the variance inflation factor of the absorbance of the heavy metal ion in the multiple regression model, so as to obtain the corrected variance inflation factor of each heavy metal ion; perform spectrophotometric non-destructive detection of the corresponding heavy metal content in the target river according to each corrected variance inflation factor; it can reduce the deviation between the detected ion concentration and the actual ion concentration under the influence of the collinearity of multiple heavy metal ions.

[0049] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 , which is a schematic flow chart of a method for detecting heavy metal content in a river according to some embodiments of the present application. The method 100 for detecting heavy metal content in a river mainly includes the following steps:

[0050] In step 101, the heavy metal content detection of the target river is started, the historical absorbance of each heavy metal ion in the target river is obtained, and then the fluctuation characteristics corresponding to each heavy metal ion are determined based on the historical absorbance.

[0051] In specific implementation, after initiating the heavy metal content detection of the target river, the historical absorbance of each heavy metal ion in the target river can be obtained through the historical monitoring database of the target river.

[0052] It should be noted that the historical monitoring database described in the present application includes the absorbance and detection ion concentration of various heavy metal ions in all historical river water samples of the target river. Among them, the present application precipitates all heavy metal ions in each historical river water sample by o-phenanthroline, and then uses the quenching property of the curved structure fluorescent group of the co-precipitated powder sample by graphene to keep the heavy metal ions in a free state, thereby purifying the heavy metal ions, and then uses a photometer to measure the absorbance of each heavy metal ion in each historical river water sample in the target river, so that the set of absorbances corresponding to each heavy metal ion is used as the historical absorbance of the corresponding heavy metal ion; in addition, the detection ion concentration refers to the concentration in the historical river water sample obtained by chemical detection. The present application obtains the detection ion concentration of the corresponding heavy metal ion in the historical river water sample by the Beer-Lambert law in the prior art after taking the absorbance of the corresponding heavy metal ion in the historical river water sample as input.

[0053] In some embodiments, reference Figure 2 As shown, this figure is a schematic diagram of the process of determining the fluctuation characteristics according to some embodiments of the present application. The fluctuation characteristics corresponding to each heavy metal ion can be determined according to the historical absorbance by the following steps:

[0054] First, in 1021, for each heavy metal ion, the absorbance trend amount of the heavy metal ion is determined according to the historical absorbance corresponding to each heavy metal ion;

[0055] Then, in 1022, the relative fluctuation ratio of the absorbance in the historical absorbance corresponding to each heavy metal ion is determined;

[0056] Finally, in 1023, the fluctuation characteristics corresponding to each heavy metal ion are determined according to the absorbance trend amount and the relative fluctuation ratio, so as to obtain the fluctuation characteristics corresponding to all heavy metal ions.

[0057] It should be noted that the absorbance trend amount in the present application characterizes the overall absorbance trend of the historical absorbance. The larger the absorbance trend amount, the higher the overall absorbance trend of the historical absorbance, and the smaller the absorbance trend amount, the lower the overall absorbance trend of the historical absorbance. As a preferred embodiment, determining the absorbance trend amount of heavy metal ions according to the historical absorbance corresponding to the heavy metal ions can be achieved in the following manner, namely: taking the average value of all absorbances in the historical absorbance as the absorbance trend amount of the heavy metal ions. In other embodiments, other methods can also be used for determination, which is not limited here.

[0058] It should also be noted that the relative fluctuation ratio in the present application characterizes the degree of discreteness of the historical absorbance. The larger the relative fluctuation ratio, the higher the degree of discreteness of the historical absorbance, and the smaller the relative fluctuation ratio, the lower the degree of discreteness of the historical absorbance. As a preferred embodiment, determining the relative fluctuation ratio of the absorbance in the historical absorbance corresponding to the heavy metal ions can be achieved in the following manner, namely: taking the standard deviation of all absorbances in the historical absorbance as the relative fluctuation ratio of the historical absorbance. In other embodiments, other methods can also be used to determine it, which is not limited here.

[0059] In addition, in the present application, when the collinearity of heavy metal ions is large, the absorbances of multiple heavy metal ions influence each other, resulting in an increase in the fluctuation of the absorbance data, thereby increasing the relative fluctuation ratio, and because the absorbance becomes more unstable, the degree of discreteness increases, that is, the fluctuation feature will be larger. Therefore, the fluctuation feature described in the present application characterizes the abnormal fluctuation degree of the corresponding absorbance data due to the collinearity of heavy metal ions. The larger the fluctuation feature, the higher the abnormal fluctuation degree of the corresponding absorbance data due to the collinearity of heavy metal ions. The smaller the fluctuation feature, the lower the abnormal fluctuation degree of the corresponding absorbance data due to the collinearity of heavy metal ions. As a preferred embodiment, determining the fluctuation feature corresponding to each heavy metal ion according to the absorbance trend amount and the relative fluctuation ratio can be achieved in the following manner, namely: taking the quotient of the relative fluctuation ratio of the historical absorbance corresponding to each heavy metal ion and the absorbance trend amount as the fluctuation feature corresponding to each heavy metal ion. In other embodiments, it can also be determined by other methods, which will not be repeated here.

[0060] In step 102, the content fluctuation loss of each heavy metal ion when performing absorbance content detection is determined according to the fluctuation characteristics corresponding to each heavy metal ion.

[0061] In specific implementation, as a preferred embodiment, the content fluctuation loss of each heavy metal ion when performing absorbance content detection according to the fluctuation characteristics corresponding to each heavy metal ion can be determined by, for example, the following steps:

[0062] Obtain the fluctuation influencing factors during absorbance content detection;

[0063] For each heavy metal ion, the content fluctuation loss of each heavy metal ion when performing absorbance content detection is determined by the fluctuation influence factor and the fluctuation characteristics corresponding to each heavy metal ion, thereby obtaining the content fluctuation loss of all heavy metal ions when performing absorbance content detection.

[0064] It should be noted that the fluctuation impact factor in the present application is a quantitative value of the degree of influence of the fluctuation of the historical absorbance on the concentration of heavy metal ions. The larger the fluctuation impact factor, the higher the degree of influence of the fluctuation of the historical absorbance on the concentration of heavy metal ions; the smaller the fluctuation impact factor, the lower the degree of influence of the fluctuation of the historical absorbance on the concentration of heavy metal ions. In some preferred embodiments, after setting multiple control groups under different absorbances of the same heavy metal ion, the quantitative value of the degree of influence of the fluctuation of the historical absorbance on the concentration of heavy metal ions can be obtained by an experimental verification method, and the obtained quantitative value is used as the fluctuation impact factor in the present application.

[0065] It should also be noted that the content fluctuation loss in the present application characterizes the error amount of the detected ion concentration data caused by the fluctuation of the historical absorbance (the fluctuation characteristic of the absorbance). The greater the content fluctuation loss, the greater the error amount of the detected ion concentration data caused by the fluctuation of the historical absorbance, and the smaller the content fluctuation loss, the smaller the error amount of the detected ion concentration data caused by the fluctuation of the historical absorbance. As a preferred embodiment, the content fluctuation loss of heavy metal ions during absorbance content detection is determined by the fluctuation influence factor and the fluctuation characteristic corresponding to the heavy metal ions. The content fluctuation loss can be achieved in the following way, namely: the result of multiplying the fluctuation influence factor of the heavy metal ions and the fluctuation characteristic corresponding to the heavy metal ions is used as the content fluctuation loss of the heavy metal ions during absorbance content detection. In other embodiments, other methods can also be used for determination, which are not limited here.

[0066] In step 103, the historical ion concentration of each heavy metal ion in the target river is obtained. For each heavy metal ion, the concentration-absorbance fitting ratio of the heavy metal ion is determined according to the historical ion concentration of the heavy metal ion and the historical absorbance corresponding to the heavy metal ion. Then, the ion concentration obtained by the absorbance content detection is characteristically fitted using the concentration-absorbance fitting ratio to obtain the goodness of fit of the heavy metal ion concentration.

[0067] It should be noted that the historical ion concentration in the present application is a set of ion concentrations of corresponding heavy metal ions in various historical river water samples in the target river. In addition, the historical ion concentration refers to the true concentration of heavy metals in the historical river water samples (ie, actual concentration). As a preferred embodiment, multiple redundant detection ion concentrations of heavy metal ions in a historical river water sample can be obtained by redundantly measuring the heavy metal ions in the historical river water samples, and the average value of the redundant measurement results can be used as the ion concentration of the heavy metal ions in the target river corresponding to the historical river water sample. In other embodiments, it can also be determined by other methods, which will not be repeated here.

[0068] In addition, it should be noted that each ion concentration in the historical ion concentration in the present application has a unique corresponding historical river water sample, and each absorbance in the historical absorbance has a unique corresponding historical river water sample, that is, each historical ion concentration has a unique corresponding absorbance. Therefore, in some embodiments, the concentration-absorbance fitting ratio of heavy metal ions is determined according to the historical ion concentration of heavy metal ions and the historical absorbance corresponding to the heavy metal ions. The following steps can be used to achieve:

[0069] Determine the concentration-absorbance linear factor according to the historical ion concentration of the heavy metal ion and the historical absorbance corresponding to the heavy metal ion;

[0070] The concentration-absorbance fitting ratio of the heavy metal ions is extracted from the historical ion concentration according to the concentration-absorbance linear factor.

[0071] In addition, the concentration-absorbance linear factor in the present application characterizes the degree of linear correlation between the heavy metal ion concentration and the absorbance. The larger the concentration-absorbance linear factor is, the higher the degree of linear correlation between the heavy metal ion concentration and the absorbance is, and the smaller the concentration-absorbance linear factor is, the lower the degree of linear correlation between the heavy metal ion concentration and the absorbance is. As a preferred embodiment, determining the concentration-absorbance linear factor based on the historical ion concentration of heavy metal ions and the historical absorbance corresponding to the heavy metal ions can be achieved in the following manner, namely: first, determining the sum of the products of the ion concentration and the absorbance corresponding to each historical river water sample, then multiplying the above result by the total number of historical river water samples, and then determining the product of the sum of all ion concentrations in the historical ion concentration and the sum of all absorbances in the historical absorbance, and finally, taking the difference between the results obtained in the above two steps as the concentration-absorbance linear factor.

[0072] It should also be noted that the concentration-absorbance fitting ratio described in the present application is a quantitative value of the linear ratio between the ion concentration of heavy metal ions and the corresponding absorbance, which characterizes the degree of influence of the absorbance of heavy metal ions on the ion concentration. The larger the concentration-absorbance fitting ratio, the higher the degree of influence of the absorbance of heavy metal ions on the ion concentration, and the smaller the concentration-absorbance fitting ratio, the smaller the degree of influence of the absorbance of heavy metal ions on the ion concentration. As a preferred embodiment, the concentration-absorbance fitting ratio of heavy metal ions can be extracted from the historical ion concentration according to the concentration-absorbance linear factor. The following method can be used to achieve it, namely: first, determine the sum of the squares of all ion concentrations in the historical ion concentration, then determine the square of the sum of all ion concentrations in the historical ion concentration, and secondly, multiply the sum of squares by the total number of historical river water samples, and then subtract the square of the sum. Finally, the quotient of the concentration-absorbance linear factor and the obtained result is used as the concentration-absorbance fitting ratio of heavy metal ions. In other embodiments, other methods can also be used to determine it, which is not limited here.

[0073] In some embodiments, reference Figure 3 As shown in FIG. 1 , this figure is a schematic diagram of a process for determining goodness of fit according to some embodiments of the present application. The ion concentration obtained by the absorbance content detection is characteristically fitted by the concentration-absorbance fitting ratio, and the goodness of fit of the heavy metal ion concentration can be obtained by the following steps:

[0074] First, in 1031, the detection ion concentration data of the heavy metal ions is determined according to the historical absorbance of the heavy metal ions;

[0075] Then, in 1032, the fitted ion concentration data of the heavy metal ions is determined according to the historical absorbance of the heavy metal ions and the concentration-absorbance fitting ratio;

[0076] Finally, in 1033, the goodness of fit of the heavy metal ion concentration is determined based on the detected ion concentration data and the fitted ion concentration data.

[0077] In specific implementation, the detection ion concentration data of heavy metal ions can be determined based on the historical absorbance of heavy metal ions in the following manner, namely: first, the molar absorptivity of the heavy metal ions is obtained, and then, the historical absorbance of the heavy metal ions and the molar absorptivity of the heavy metal ions are used as input, and the Beer-Lambert law in the prior art is used to output the detection ion concentration corresponding to each absorbance in the historical absorbance of the heavy metal ions. Finally, all the detection ion concentrations are combined according to the order of the corresponding absorbances in the historical absorbance, and the obtained set is used as the detection ion concentration data in this application. In other embodiments, it can also be determined by other methods, which will not be repeated here.

[0078] In specific implementation, the fitting ion concentration data of heavy metal ions can be determined according to the historical absorbance of heavy metal ions and the concentration-absorbance fitting ratio in the following manner, namely: first, each absorbance in the historical absorbance is divided by the concentration-absorbance fitting ratio as the corresponding fitting ion concentration, and then, all the fitting ion concentrations are sorted in the order of the corresponding historical absorbances to form a set as the fitting ion concentration data in this application. In other embodiments, other methods can also be used for determination, which will not be repeated here.

[0079] It should be noted that the goodness of fit of the concentration described in the present application characterizes the similarity between the heavy metal ion concentration (detected ion concentration data) obtained by absorbance detection and the heavy metal ion concentration (fitted ion concentration data) obtained by fitting. The greater the goodness of fit, the higher the similarity between the heavy metal ion concentration (detected ion concentration data) obtained by absorbance detection and the heavy metal ion concentration (fitted ion concentration data). The smaller the goodness of fit, the lower the similarity between the heavy metal ion concentration (detected ion concentration data) obtained by absorbance detection and the heavy metal ion concentration (fitted ion concentration data). As a preferred embodiment, Determining the goodness of fit of heavy metal ion concentration based on the detected ion concentration data and the fitted ion concentration data can be achieved in the following manner, namely: first, select a historical river water sample, then obtain the detected ion concentration of the historical river water sample in the detected ion concentration data and the fitted ion concentration in the fitted ion concentration data, secondly, determine the square of the difference between the detected ion concentration and the fitted ion concentration, repeat the above steps to determine the results corresponding to the remaining historical river water samples, and finally, determine the sum of the results corresponding to all historical river water samples and divide it by the total number of river water samples, and use the obtained result as the goodness of fit of the heavy metal ion concentration in this application.

[0080] In step 104, the variance inflation factor of the absorbance of the heavy metal ions in the multiple regression model is corrected based on the content fluctuation loss corresponding to the heavy metal ions and the goodness of fit, thereby obtaining the corrected variance inflation factor of each heavy metal ion.

[0081] In some embodiments, the variance inflation factor of the absorbance of the heavy metal ions in the multiple regression model is corrected based on the content fluctuation loss corresponding to the heavy metal ions and the goodness of fit, so as to obtain the corrected variance inflation factor of each heavy metal ion. The following steps can be used to achieve this:

[0082] Obtain the variance inflation factor of the absorbance of heavy metal ions in the multiple regression model;

[0083] Determine the collinear fluctuation coefficient of the heavy metal ions by the fluctuation loss corresponding to the heavy metal ions and the goodness of fit;

[0084] The variance inflation factor of the absorbance of heavy metal ions in the multiple regression model is corrected by the collinear fluctuation coefficient to obtain the variance inflation factor corrected for the heavy metal ions, thereby obtaining the variance inflation factor corrected for each heavy metal ion.

[0085] It should be noted that the variance inflation factor is used to characterize the correlation between the concentration of heavy metal ions after absorbance content detection and the concentration of other heavy metal ions. The larger the variance inflation factor, the higher the correlation between the concentration of heavy metal ions and the concentration of other heavy metal ions. The smaller the variance inflation factor, the lower the correlation between the concentration of heavy metal ions and the concentration of other heavy metal ions. As a preferred embodiment, the historical ion concentration of heavy metal ions can be used as the independent variable and the historical ion concentration of other heavy metal ions as the dependent variable. Then, after modeling using the auxiliary regression model in the prior art, the determination coefficient of the obtained auxiliary regression model is determined. Finally, one minus the inverse of the determination coefficient is used as the variance inflation factor of the absorbance of heavy metal ions in the multiple regression model in this application.

[0086] It should also be noted that the collinear fluctuation coefficient in the present application reflects the degree of influence of the difference between the detected value and the actual value and the fluctuation of the data on the correlation between the concentration of heavy metal ions and the concentration of other heavy metal ions. The larger the collinear fluctuation coefficient, the higher the degree of influence of the difference between the detected value and the actual value and the fluctuation of the data on the correlation between the concentration of heavy metal ions and the concentration of other heavy metal ions. The smaller the collinear fluctuation coefficient, the lower the degree of influence of the difference between the detected value and the actual value and the fluctuation of the data on the correlation between the concentration of heavy metal ions and the concentration of other heavy metal ions. As a preferred embodiment, determining the collinear fluctuation coefficient of heavy metal ions by the fluctuation loss corresponding to heavy metal ions and the goodness of fit can be achieved in the following manner, namely: dividing the fluctuation loss corresponding to the heavy metal ions by the value of the goodness of fit is used as the collinear fluctuation coefficient in the present application. In other embodiments, it can also be determined by other methods, which are not limited here.

[0087] In addition, the present application corrects the variance inflation factor through the collinear fluctuation coefficient, that is, it quantifies the impact of the difference between the actual value and the detected value and the data fluctuation on the variance inflation factor (collinearity), and corrects the variance inflation factor according to the degree of impact. The variance inflation factor can be adjusted by comprehensively considering the characteristics of the data so that it can more accurately reflect the true collinear relationship between the variables; as a preferred embodiment, the variance inflation factor of the absorbance of heavy metal ions in the multiple regression model is corrected through the collinear fluctuation coefficient, and the variance inflation factor after the heavy metal ion correction can be obtained in the following manner, namely: the product of the variance inflation factor and the collinear fluctuation coefficient is used as the variance inflation factor after the heavy metal ion correction. In other embodiments, it can also be determined by other methods, which will not be repeated here.

[0088] In step 105, a nondestructive spectrophotometric test is performed on the corresponding heavy metal content in the target river according to each corrected variance inflation factor.

[0089] In some embodiments, the following steps may be used to perform nondestructive spectrophotometric testing of the corresponding heavy metal content in the target river according to each corrected variance inflation factor:

[0090] Collinearity is judged for each heavy metal ion according to each corrected variance inflation factor, and all heavy metal ions are divided into multiple collinear ions and multiple orthogonal ions;

[0091] The content of each heavy metal in the target river was nondestructively detected by spectrophotometry using all colinear ions and all orthogonal ions.

[0092] In specific implementation, collinearity judgment is performed on each heavy metal ion according to each corrected variance inflation factor, and all heavy metal ions are divided into multiple collinear ions and multiple orthogonal ions. This can be achieved in the following manner, namely: first, a variance inflation threshold is obtained, and when the corrected variance inflation factor of the heavy metal ion is greater than or equal to the variance inflation threshold, the heavy metal ion is regarded as a collinear ion, and when the corrected variance inflation factor of the heavy metal ion is less than the variance inflation threshold, the heavy metal ion is regarded as an orthogonal ion, thereby obtaining multiple collinear ions and multiple orthogonal ions.

[0093] It should be noted that the colinear ions described in the present application refer to heavy metal ions whose colinearity is higher than the variance inflation threshold, that is, the linear correlation between the heavy metal ions and other heavy metal ions is too high. Similarly, the orthogonal ions refer to heavy metal ions whose colinearity is lower than the variance inflation threshold, that is, there is no obvious linear correlation between the heavy metal ions and other heavy metal ions.

[0094] In some embodiments, the nondestructive spectrophotometric detection of each heavy metal content in the target river by all colinear ions and all orthogonal ions can be achieved by the following steps:

[0095] Obtain multiple river water samples from target rivers;

[0096] Obtain the collinear ion concentration data of each collinear ion and the orthogonal ion concentration data of each orthogonal ion in all river water samples;

[0097] For each collinear ion, determining a principal component eigenvector of the collinear ion concentration data of the collinear ion;

[0098] Determine the collinear model parameter column by using the principal component eigenvector;

[0099] Determine the lossless ion concentration of the collinear ions in each river water sample according to the collinear model parameter column and the principal component eigenvector, thereby obtaining the lossless ion concentration of each collinear ion in each river water sample;

[0100] For each orthogonal ion, an orthogonal model parameter column is determined according to the orthogonal ion concentration data of the orthogonal ion;

[0101] Determine the lossless ion concentration of the orthogonal ions in each river water sample according to the orthogonal model parameter column and the orthogonal ion concentration data, thereby obtaining the lossless ion concentration of each orthogonal ion in each river water sample;

[0102] Based on the nondestructive ion concentrations corresponding to all colinear ions and all orthogonal ions in each river water sample, spectrophotometric nondestructive detection of each heavy metal content in the target river is achieved.

[0103] In specific implementation, the main component eigenvectors of the collinear ion concentration data of the collinear ions can be determined in the following manner, namely: first, the collinear ion concentration data of the collinear ions are standardized using the prior art to obtain standard concentration data, and then the principal component analysis algorithm in the prior art is used (that is, after determining the covariance matrix of the standard concentration data, the eigenvalue decomposition algorithm in the prior art is sampled to obtain multiple eigenvectors), and the eigenvector corresponding to the maximum eigenvalue in each eigenvector obtained is used as the main component eigenvector in this application. In other embodiments, other methods can also be used to determine it, which will not be repeated here.

[0104] In specific implementation, the collinear model parameter column can be determined by the principal component eigenvector in the following manner, namely: first, the principal component eigenvector is multiplied by each collinear ion concentration in the collinear ion concentration data of the collinear ions to obtain a plurality of characteristic ion concentrations; then, a linear regression model is established with all characteristic ion concentrations as input and ion concentration as output, and then fitting is performed using the least squares method in the prior art, namely: minimizing the residual sum of squares between the ion concentration and the characteristic ion concentration to obtain the optimized linear regression model, thereby taking the set of all regression coefficients in the linear regression model as the collinear model parameter column in the present application; other methods may also be used to determine the parameters in other embodiments, which are not limited here.

[0105] It should be noted that because the linear correlation between the collinear ions and other heavy metal ions is too high, the estimation of the regression coefficient in the linear regression model is unstable, making the linear regression model too sensitive to changes in the data, thereby reducing the accuracy of the ion concentration obtained by the linear regression model. Therefore, the present application uses the principal component analysis method in the prior art to linearly combine the original variables (collinear ion concentration data of collinear ions) to generate new variables (multiple characteristic ion concentrations), thereby ensuring that the generated principal components are orthogonal, that is, each principal component is independent of each other, thereby eliminating the collinearity problem.

[0106] In specific implementation, the lossless ion concentration of the colinear ions in each river water sample is determined according to the colinear model parameter column, so as to obtain the lossless ion concentration of each colinear ion in each river water sample. This can be achieved in the following manner, namely: first, a river water sample is selected, and then, the characteristic ion concentration of the colinear ions in the river water sample is obtained; secondly, the characteristic ion concentration is multiplied by the slope in the collinear model parameter column and then the result of adding the intercept in the collinear model parameter column is taken as the lossless ion concentration of the colinear ions in the river water sample; and the lossless ion concentration of the colinear ions in the remaining river water samples is continued to be determined, so as to obtain the lossless ion concentration of the colinear ions in each river water sample.

[0107] In specific implementation, the orthogonal model parameter column can be determined based on the orthogonal ion concentration data of the orthogonal ions in the following manner, namely: first, the orthogonal ion concentration data is used as input and the ion concentration is used as output. After the linear regression model is established, it is fitted by the least squares method in the prior art, that is, minimizing the residual sum of squares between the ion concentration and the characteristic ion concentration to obtain the optimized linear regression model, thereby taking the set of all regression coefficients in the linear regression model as the orthogonal model parameter column in this application. In other embodiments, other methods can also be used for determination, which are not limited here.

[0108] In specific implementation, the lossless ion concentration of the orthogonal ions in each river water sample is determined according to the orthogonal model parameter column and the orthogonal ion concentration data, so as to obtain the lossless ion concentration of each orthogonal ion in each river water sample. This can be achieved in the following manner, namely: first, a river water sample is selected, and then, the orthogonal ion concentration of the orthogonal ions in the river water sample is obtained; secondly, the orthogonal ion concentration is multiplied by the slope in the orthogonal model parameter column and then added to the intercept in the orthogonal model parameter column to obtain the lossless ion concentration of the orthogonal ions in the river water sample; and the lossless ion concentration of the orthogonal ions in the remaining river water samples is continued to be determined, so as to obtain the lossless ion concentration of the orthogonal ions in each river water sample.

[0109] It should be noted that the lossless ion concentration described in the present application refers to the actual ion concentration obtained after eliminating the collinearity between heavy metal ions. Using the lossless ion concentration as the actual ion concentration of the corresponding heavy metal ions can avoid the deviation between the detected ion concentration and the actual ion concentration caused by the collinearity between heavy metal ions. Therefore, the lossless ion concentrations corresponding to all collinear ions and all orthogonal ions in the river water sample are used as the actual ion concentrations of the corresponding heavy metal ions, thereby realizing spectrophotometric non-destructive detection of the content of each heavy metal in the target river.

[0110] In addition, in another aspect of the present application, in some embodiments, the present application provides a system for detecting heavy metal content in a river, referring to Figure 4 , which is a structural block diagram of a river heavy metal content detection system according to some embodiments of the present application, the river heavy metal content detection system 200 includes: an acquisition module 201, a processing module 202 and an execution module 203, which are described as follows:

[0111] Acquisition module 201, in the present application, acquisition module 201 is mainly used to acquire the historical absorbance of each heavy metal ion in the target river after starting the heavy metal content detection of the target river, and then determine the fluctuation characteristics corresponding to each heavy metal ion according to the historical absorbance;

[0112] Processing module 202, in the present application, processing module 202 is mainly used to determine the content fluctuation loss of each heavy metal ion when performing absorbance content detection according to the fluctuation characteristics corresponding to each heavy metal ion;

[0113] In addition, the processing module 202 in the present application is also used to obtain the historical ion concentration of each heavy metal ion in the target river. For each heavy metal ion, the concentration-absorbance fitting ratio of the heavy metal ion is determined according to the historical ion concentration of the heavy metal ion and the historical absorbance corresponding to the heavy metal ion, and then the ion concentration obtained by the absorbance content detection is characteristically fitted by the concentration-absorbance fitting ratio to obtain the goodness of fit of the heavy metal ion concentration;

[0114] In addition, the processing module 202 in the present application is also used to correct the variance inflation factor of the absorbance of the heavy metal ions in the multiple regression model based on the content fluctuation loss corresponding to the heavy metal ions and the goodness of fit, so as to obtain the corrected variance inflation factor of each heavy metal ion;

[0115] Execution module 203, in the present application, execution module 203 is mainly used to perform spectrophotometric non-destructive detection of the corresponding heavy metal content in the target river according to each corrected variance inflation factor.

[0116] In addition, the present application also provides a computer device, which includes a memory and a processor, the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned method for detecting heavy metal content in rivers.

[0117] In some embodiments, reference Figure 5 , which is an internal structure diagram of a computer device for implementing a method for detecting heavy metal content in a river according to some embodiments of the present application. The method for detecting heavy metal content in a river in the above embodiments can be Figure 5 The computer device 300 shown in the figure is implemented, and the computer device 300 includes at least one processor 301, a communication bus 302, a memory 303 and at least one communication interface 304.

[0118] The processor 301 may be a general-purpose central processing unit (CPU), or an application-specific integrated circuit (ASIC) or one or more processors for controlling the execution of the method for detecting heavy metal content in a river in the present application.

[0119] The communication bus 302 is used to transmit information between the above components.

[0120] The memory 303 may 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, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 303 may exist independently and be connected to the processor 301 via the communication bus 302. The memory 303 may also be integrated with the processor 301.

[0121] The memory 303 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the program code stored in the memory 303. The program code may include one or more software modules. The method for detecting heavy metal content in a river in the above embodiment can be implemented by the processor 301 and one or more software modules in the program code in the memory 303.

[0122] The communication interface 304 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0123] In a specific implementation, as an embodiment, a computer device may include multiple processors, each of which may be a single-CPU processor or a multi-CPU processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0124] The above-mentioned computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a portable 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 embodiment of the present application does not limit the type of computer device.

[0125] In addition, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned method for detecting heavy metal content in a river is implemented.

[0126] In summary, in the method, system, device and storage medium for detecting heavy metal content in a river disclosed in the embodiments of the present application, the historical absorbance of each heavy metal ion in the target river is obtained, and then the fluctuation characteristics corresponding to each heavy metal ion are determined based on the historical absorbance; the content fluctuation loss of each heavy metal ion during absorbance content detection is determined based on the fluctuation characteristics corresponding to each heavy metal ion; in addition, the historical ion concentration of each heavy metal ion in the target river is obtained, and for each heavy metal ion, the concentration-absorbance simulation of the heavy metal ion is determined based on the historical ion concentration of the heavy metal ion and the historical absorbance corresponding to the heavy metal ion. The concentration-absorbance fitting ratio is used to perform characteristic fitting on the ion concentration obtained by the absorbance content detection, and the goodness of fit of the heavy metal ion concentration is obtained; based on the content fluctuation loss corresponding to the heavy metal ions and the goodness of fit, the variance inflation factor of the absorbance of the heavy metal ions in the multiple regression model is corrected, so as to obtain the corrected variance inflation factor of each heavy metal ion; the corresponding heavy metal content in the target river is non-destructively detected by spectrophotometry according to each corrected variance inflation factor; the deviation between the detected ion concentration and the actual ion concentration can be reduced under the influence of the collinearity of multiple heavy metal ions.

[0127] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0128] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for detecting heavy metal content in a river, characterized in that: The steps include: Initiate heavy metal content detection of the target river, obtain the historical absorbance of each heavy metal ion in the target river, and then determine the fluctuation characteristics corresponding to each heavy metal ion based on the historical absorbance; Determining the content fluctuation loss of each heavy metal ion when performing absorbance content detection according to the fluctuation characteristics corresponding to each heavy metal ion; Obtain the historical ion concentration of each heavy metal ion in the target river, and for each heavy metal ion, determine the concentration-absorbance fitting ratio of the heavy metal ion according to the historical ion concentration of the heavy metal ion and the historical absorbance corresponding to the heavy metal ion, and then perform characteristic fitting on the ion concentration obtained by the absorbance content detection through the concentration-absorbance fitting ratio to obtain the goodness of fit of the heavy metal ion concentration; Based on the content fluctuation loss corresponding to the heavy metal ions and the goodness of fit, the variance inflation factor of the absorbance of the heavy metal ions in the multiple regression model is corrected, thereby obtaining the corrected variance inflation factor of each heavy metal ion; The corresponding heavy metal content in the target river is nondestructively tested by spectrophotometry based on the corrected variance inflation factors.

2. The method according to claim 1, characterized in that Determining the fluctuation characteristics corresponding to each heavy metal ion based on the historical absorbance specifically includes: For each heavy metal ion, determining the absorbance trend amount of each heavy metal ion according to the historical absorbance corresponding to the heavy metal ion; Determining the relative fluctuation ratio of absorbance in the historical absorbance corresponding to each heavy metal ion; The fluctuation characteristics corresponding to each heavy metal ion are determined according to the absorbance trend quantity and the relative fluctuation ratio.

3. The method according to claim 1, characterized in that Determining the content fluctuation loss of each heavy metal ion when performing absorbance content detection according to the fluctuation characteristics corresponding to each heavy metal ion specifically includes: Obtain the fluctuation influencing factors during absorbance content detection; For each heavy metal ion, the content fluctuation loss of each heavy metal ion when performing absorbance content detection is determined by using the fluctuation influence factor and the fluctuation characteristics corresponding to each heavy metal ion.

4. The method according to claim 1, characterized in that Determining the concentration-absorbance fitting ratio of heavy metal ions according to the historical ion concentration of heavy metal ions and the historical absorbance corresponding to the heavy metal ions specifically includes: Determine the concentration-absorbance linear factor according to the historical ion concentration of the heavy metal ion and the historical absorbance corresponding to the heavy metal ion; The concentration-absorbance fitting ratio of the heavy metal ions is extracted from the historical ion concentration according to the concentration-absorbance linear factor.

5. The method according to claim 1, characterized in that The characteristic fitting of the ion concentration obtained by the absorbance content detection is performed by the concentration-absorbance fitting ratio to obtain the goodness of fit of the heavy metal ion concentration, which specifically includes: Determine the detection ion concentration data of heavy metal ions according to the historical absorbance of heavy metal ions; Determining fitted ion concentration data of heavy metal ions according to the historical absorbance of heavy metal ions and the concentration-absorbance fitting ratio; The goodness of fit of the heavy metal ion concentration is determined according to the detected ion concentration data and the fitted ion concentration data.

6. The method according to claim 1, characterized in that Based on the content fluctuation loss corresponding to the heavy metal ions and the goodness of fit, the variance inflation factor of the absorbance of the heavy metal ions in the multiple regression model is corrected, so that the corrected variance inflation factor of each heavy metal ion is obtained, which specifically includes: Obtain the variance inflation factor of heavy metal ions in multiple regression models; Determine the collinear fluctuation coefficient of the heavy metal ions by the fluctuation loss corresponding to the heavy metal ions and the goodness of fit; The variance inflation factor of the absorbance of heavy metal ions in the multiple regression model is corrected by the collinear fluctuation coefficient to obtain the variance inflation factor corrected for the heavy metal ions, thereby obtaining the variance inflation factor corrected for each heavy metal ion.

7. The method according to claim 1, characterized in that The spectrophotometric nondestructive testing of the corresponding heavy metal content in the target river based on each corrected variance inflation factor specifically includes: Collinearity is judged for each heavy metal ion according to each corrected variance inflation factor, and all heavy metal ions are divided into multiple collinear ions and multiple orthogonal ions; The content of each heavy metal in the target river was nondestructively detected by spectrophotometry using all colinear ions and all orthogonal ions.

8. A system for detecting heavy metal content in a river, characterized in that: include: An acquisition module, used to acquire the historical absorbance of each heavy metal ion in the target river after starting the heavy metal content detection of the target river, and then determine the fluctuation characteristics corresponding to each heavy metal ion according to the historical absorbance; A processing module, used to determine the content fluctuation loss of each heavy metal ion when performing absorbance content detection according to the fluctuation characteristics corresponding to each heavy metal ion; The processing module is also used to obtain the historical ion concentration of each heavy metal ion in the target river, and for each heavy metal ion, determine the concentration-absorbance fitting ratio of the heavy metal ion according to the historical ion concentration of the heavy metal ion and the historical absorbance corresponding to the heavy metal ion, and then perform characteristic fitting on the ion concentration obtained by the absorbance content detection through the concentration-absorbance fitting ratio to obtain the goodness of fit of the heavy metal ion concentration; The processing module is further used to correct the variance inflation factor of the absorbance of the heavy metal ions in the multiple regression model based on the content fluctuation loss corresponding to the heavy metal ions and the goodness of fit, so as to obtain the corrected variance inflation factor of each heavy metal ion; The execution module is used to perform spectrophotometric nondestructive detection of the corresponding heavy metal content in the target river according to each corrected variance inflation factor.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for detecting heavy metal content in a river according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for detecting heavy metal content in a river as described in any one of claims 1 to 7 are implemented.

Images