Ecological detection system and method for restoring heavy metals in banana forest soil based on cultured earthworms

Abstract

The present invention provides an ecological detection system and method for restoring heavy metals in banana forest soil based on cultured earthworms, and relates to the fields of technical environmental science and ecological restoration. The present invention collects key characteristic parameters in banana forest soil before restoration, selects Eisenia deliciosa earthworms and adds biochar for restoration, measures the characteristic parameters after restoration after the experiment, calculates the difference between the parameters before and after restoration, and generates a heavy metal concentration index based on the differences, calculates the change in earthworm excrement and biochar, generates a heavy metal restoration index, and finally sets a heavy metal pollution threshold, uses the restoration index to correct the heavy metal concentration index, and compares it with the pollution threshold to judge the restoration effect. The present invention achieves the purpose of evaluating and optimizing the heavy metal restoration effect by restoring heavy metal pollution in banana forest soil using Eisenia deliciosa earthworms and biochar.

Description

Technical Field

[0001] The invention relates to the field of environmental science and ecological restoration technology, and in particular to an ecological detection system and method for restoring heavy metals in banana forest soil based on cultivating earthworms. Background Art

[0002] The development background of ecological detection methods for soil heavy metals stems from the increasingly serious problem of soil heavy metal pollution caused by agricultural production and industrial pollution worldwide. These pollutions not only damage the soil ecosystem, but also threaten human health and environmental safety through the food chain. Although traditional physical and chemical detection methods can accurately determine the content of heavy metals in soil, they cannot fully reflect their ecological risks and bioavailability. Therefore, ecological detection methods came into being, emphasizing the use of comprehensive indicators such as soil physical and chemical properties, enzyme activity, microbial community structure and bioremediation efficiency to evaluate the impact of heavy metals on soil health and ecological restoration potential, providing a scientific basis for the sustainable restoration of contaminated soil.

[0003] In existing technologies, soil heavy metal remediation mainly relies on physical and chemical analysis methods, such as spectral analysis and chromatography. Although these methods can accurately determine the content of heavy metals, they lack a comprehensive assessment of the ecological risk and bioavailability of heavy metals and cannot fully reflect the health status of soil and the potential for pollution remediation. At the same time, these methods usually require expensive instruments and equipment, complex operating procedures and long detection cycles, which limits their widespread application in actual remediation projects.

[0004] Secondly, the existing remediation methods lack a dynamic monitoring and evaluation mechanism for the remediation effect, and cannot reflect the changes in heavy metal concentrations and their ecological significance during the remediation process in real time and accurately. Traditional remediation methods, such as relying solely on chemical agents or physical means, are prone to secondary pollution or further damage to the soil ecological environment. In addition, current research rarely combines bioremediation with intelligent technology to quantitatively evaluate the remediation effect, which leads to a lack of scientificity and accuracy in the implementation of remediation measures and the evaluation of their effects, thus affecting the efficiency and sustainability of pollution control.

[0005] Therefore, it is necessary to provide an ecological detection system and method based on cultivating earthworms to repair heavy metals in banana forest soil to solve the above problems.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide an ecological detection system and method for repairing heavy metals in banana forest soil based on cultivating earthworms, so as to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] An ecological detection system based on cultivating earthworms to repair heavy metals in banana forest soil, the specific steps include:

[0010] A regional characteristic parameter acquisition module, which is used to collect heavy metal-related characteristic parameters in the soil of the banana forest before restoration, and the characteristic parameters include soil pH score, heavy metal ion concentration, soil mineral concentration and soil sand ratio;

[0011] A heavy metal concentration index generation module, wherein the heavy metal concentration index generation module is used to select Eisenia deliciosa, add biochar and place them in the banana forest restoration area for restoration. After the experiment is completed, characteristic parameter data after restoration is measured, and the difference between each characteristic parameter before and after restoration is calculated, and a heavy metal concentration index is generated according to the calculated difference between each characteristic parameter;

[0012] A dynamic analysis module for earthworm excrement and biochar, which is used to calculate the change in earthworm excrement and the change in biochar, and to generate a heavy metal remediation index by combining the change in earthworm excrement and the change in biochar;

[0013] The threshold correction and restoration effect evaluation module is used to establish a heavy metal pollution threshold, and use the calculated heavy metal restoration index to correct the heavy metal concentration index to obtain a heavy metal concentration index correction value, and compare the obtained heavy metal concentration index correction value with the heavy metal pollution threshold to determine the restoration effect of heavy metals in banana forest soil.

[0014] Furthermore, the difference between each characteristic parameter before and after the restoration is calculated, and the heavy metal concentration index is generated according to the calculated difference between each characteristic parameter, and the method is as follows:

[0015] First, perform minimum-maximum normalization on the feature data, scale the data to the range of [0,1], so that all feature data have the same scale, and then perform data cleaning on the feature data, including detection and deletion of outliers and duplicate data, and processing of missing values. Statistical methods are used to identify outliers and duplicate data in the feature data, delete outliers and duplicate data in the feature data, and use the mean, median or mode of the feature data to fill in the missing values ​​in the feature data;

[0016] The difference between each characteristic parameter before and after repair is calculated based on the following formula:

[0017] G dev =G now -Gbefore

[0018] ND dev =ND before -ND now

[0019] KW dev =KW before -KW now

[0020] STB dev =STB before -STB now

[0021] Among them, ND dev , KW dev , STB dev represents the difference in heavy metal ion concentration, soil mineral concentration, and soil sand ratio before and after banana forest restoration, ND before , KW before , STB before , G before represents the heavy metal ion concentration, soil mineral concentration, soil sand-soil ratio and soil pH score in the banana forest soil before restoration, ND now , KW now , STB now , G now represent the heavy metal ion concentration, soil mineral concentration, soil sand-soil ratio and soil pH score in the restored banana forest soil, respectively. dev It is the difference between the soil pH score after and before restoration;

[0022] The standard of pH value scoring is: the pH value score of the soil is set to 10 points. When the pH value is 7, the corresponding pH value score G = 10; when the pH value is in [4,7) or (7,10], the corresponding pH value score G = 7; when the pH value is in [1,4) or (10,13], the corresponding pH value score G = 4; when the pH value is less than 1 or greater than 13, the corresponding pH value score G = 1;

[0023] The heavy metal concentration index is generated based on the formula:

[0024]

[0025] Among them, R en Indicates the generated heavy metal concentration index.

[0026] Furthermore, the heavy metal remediation index was generated based on the following method:

[0027] The earthworm excrement content and biochar content before and after restoration in the restoration area were obtained, and the changes in earthworm excrement and biochar were calculated to generate the heavy metal restoration index. The calculation formula of the heavy metal restoration index is:

[0028]

[0029] Among them, F represents the heavy metal remediation index, PX1 and PX2 are the earthworm excrement contents before and after remediation, respectively, and swt1 and swt2 are the biochar contents before and after remediation, respectively.

[0030] Furthermore, the heavy metal remediation index obtained by calculation is used to correct the heavy metal concentration index to obtain a corrected value of the heavy metal concentration index, and the formula based on this is:

[0031] R en ′=R en *(1+F)

[0032] Among them, R en ′ represents the corrected value of heavy metal concentration index, R en Is the heavy metal concentration index.

[0033] Furthermore, the obtained heavy metal concentration index correction value was compared with the heavy metal pollution threshold to determine the restoration effect of heavy metals in banana forest soil. The logical formula was as follows:

[0034]

[0035] Among them, Q represents the logical value for judging the remediation effect of heavy metals in banana forest soil. When Q=0, the corrected value of the heavy metal concentration index is greater than the heavy metal pollution threshold, indicating that the heavy metal concentration in the banana forest soil is still exceeded and remediation treatment is still required; when Q=1, the heavy metal concentration index does not exceed the heavy metal pollution threshold, indicating that the heavy metal concentration in the banana forest soil is at a normal level and the remediation effect is good. WR is the heavy metal pollution threshold.

[0036] The present invention also provides an ecological detection method for restoring heavy metals in banana forest soil based on cultivating earthworms, and the ecological detection method is used to execute the above-mentioned ecological detection system for restoring heavy metals in banana forest soil based on cultivating earthworms, comprising:

[0037] Step 1: Collecting heavy metal-related characteristic parameters in the banana forest soil before restoration, the characteristic parameters include soil pH score, heavy metal ion concentration, soil mineral concentration and soil sand ratio;

[0038] Step 2: Select Eisenia deltaica and add biochar to the banana forest restoration area for restoration. After the experiment, measure the characteristic parameter data after restoration, and calculate the difference between each characteristic parameter before and after restoration. The heavy metal concentration index is generated based on the calculated difference between each characteristic parameter.

[0039] Step 3: Calculate the change in earthworm excrement and biochar, and combine the change in earthworm excrement and biochar to generate a heavy metal remediation index;

[0040] Step 4: Establish a heavy metal pollution threshold, use the calculated heavy metal remediation index to correct the heavy metal concentration index to obtain a corrected value of the heavy metal concentration index, compare the corrected value of the heavy metal concentration index with the heavy metal pollution threshold, and determine the remediation effect of heavy metals in banana forest soil.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] First, the present invention introduces Eisenia fetida and biochar, and utilizes their ecological characteristics and adsorption capacity to efficiently repair heavy metals in banana forest soil, significantly improving the shortcomings of traditional methods. This bioremediation strategy can not only reduce the secondary pollution that may be caused by chemical remediation methods, but also enhance the ecological health and sustainable utilization capacity of the soil. At the same time, the bioremediation process combines dynamic data collection and intelligent analysis to achieve real-time dynamic monitoring of heavy metal concentrations and remediation effects, ensuring that the remediation measures are scientific, efficient and eco-friendly;

[0043] Secondly, the present invention breaks through the limitations of traditional remediation technology in evaluating remediation effects through the collection and processing of characteristic parameters, the calculation of heavy metal concentration index, and the intelligent determination of remediation effects. This systematic and intelligent detection method not only improves the efficiency and accuracy of heavy metal pollution remediation, but also provides a basis for scientific decision-making on remediation effects, opening up a new direction for research and practice in the field of pollution control;

[0044] The present invention realizes the efficient restoration and intelligent detection of heavy metals in banana forest soil by introducing Eisenia fetida earthworms, biochar and artificial intelligence technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the system module flow of the present invention.

[0046] Figure 2 It is a schematic diagram of the overall method flow of the present invention. DETAILED DESCRIPTION

[0047] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0048] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0049] Example:

[0050] See also Figure 1 , an ecological detection system based on cultivating earthworms to repair heavy metals in banana forest soil, the specific steps include:

[0051] A regional characteristic parameter acquisition module, which is used to collect heavy metal-related characteristic parameters in the soil of the banana forest before restoration, and the characteristic parameters include soil pH score, heavy metal ion concentration, soil mineral concentration and soil sand ratio;

[0052] A heavy metal concentration index generation module, wherein the heavy metal concentration index generation module is used to select Eisenia deliciosa, add biochar and place them in the banana forest restoration area for restoration. After the experiment is completed, characteristic parameter data after restoration is measured, and the difference between each characteristic parameter before and after restoration is calculated, and a heavy metal concentration index is generated according to the calculated difference between each characteristic parameter;

[0053] A dynamic analysis module for earthworm excrement and biochar, which is used to calculate the change in earthworm excrement and the change in biochar, and to generate a heavy metal remediation index by combining the change in earthworm excrement and the change in biochar;

[0054] The threshold correction and restoration effect evaluation module is used to establish a heavy metal pollution threshold, and use the calculated heavy metal restoration index to correct the heavy metal concentration index to obtain a heavy metal concentration index correction value, and compare the obtained heavy metal concentration index correction value with the heavy metal pollution threshold to determine the restoration effect of heavy metals in banana forest soil.

[0055] It should be noted that the accuracy and reliability of the experimental results can be ensured by cleaning, standardizing and matching the collected characteristic parameter data. Especially when studying the effect of soil heavy metal remediation, the actual effect of remediation measures and their impact on the environment can be more accurately evaluated. The propensity score matching method and linear adjustment can be used to further balance the data distribution, making the experimental analysis more rigorous and controllable.

[0056] Therefore, it is necessary to preprocess the collected characteristic parameter data, and the method is as follows:

[0057] Data cleaning of feature data includes detection and deletion of outliers and duplicate data, processing of missing values, using statistical methods to identify outliers and duplicate data in feature data, deleting outliers and duplicate data in feature data, and using the mean, median or mode of feature data to fill in missing values ​​in feature data;

[0058] The feature data is normalized as minimum-maximum normalization, scaling the data to the range of [0,1] so that all feature data have the same scale. The formula is:

[0059]

[0060] Among them, X′ is the normalized feature data, X is the original feature data, and X min is the minimum value of the same type of feature data in the data set, X max It is the maximum value of the same type of feature data in the dataset.

[0061] It should be noted that by calculating the difference in various characteristic parameters of the soil before and after remediation, a heavy metal concentration index is generated, which can effectively evaluate the changes in heavy metal pollution and the remediation effect during the soil remediation process. Factors such as the soil's heavy metal ion concentration, mineral concentration, and sand-to-soil ratio have an important impact on soil quality and plant growth, while the pH score is a key indicator for measuring soil health. By comprehensively considering these factors, the generated heavy metal concentration index can provide a quantitative basis for the effect of soil remediation, thereby providing scientific support for relevant decision-making and promoting the optimization of environmental governance and ecological restoration.

[0062] In the regional characteristic parameter acquisition module, the collected heavy metal concentration ions mainly include lead, cadmium, chromium, and mercury ions in the soil. The concentrations of these heavy metal ions are measured, and the average of these ion concentrations is used as the heavy metal ion concentration; according to the same method mentioned above, the collected soil mineral concentration mainly includes phosphorus, potassium, nitrogen, magnesium, and sulfur. The concentrations of these mineral ions are measured, and the average of these ion concentrations is used as the soil mineral concentration.

[0063] Therefore, the difference between each characteristic parameter before and after the restoration is calculated, and the heavy metal concentration index is generated according to the calculated difference between each characteristic parameter. The method is as follows:

[0064] First, perform minimum-maximum normalization on the feature data, scale the data to the range of [0,1], so that all feature data have the same scale, and then perform data cleaning on the feature data, including detection and deletion of outliers and duplicate data, and processing of missing values. Statistical methods are used to identify outliers and duplicate data in the feature data, delete outliers and duplicate data in the feature data, and use the mean, median or mode of the feature data to fill in the missing values ​​in the feature data;

[0065] The difference between each characteristic parameter before and after repair is calculated based on the following formula:

[0066] G dev =G now -G before

[0067] ND dev =ND before -ND now

[0068] KW dev =KW before -KW now

[0069] STB dev =STB before -STB now

[0070] Among them, ND dev , KW dev , STB dev represents the difference in heavy metal ion concentration, soil mineral concentration, and soil sand ratio before and after banana forest restoration, ND before , KW before , STB before , G before represents the heavy metal ion concentration, soil mineral concentration, soil sand-soil ratio and soil pH score in the banana forest soil before restoration, ND now , KW now , STB now , G now represent the heavy metal ion concentration, soil mineral concentration, soil sand-soil ratio and soil pH score in the restored banana forest soil, respectively. dev It is the difference between the soil pH score after and before restoration;

[0071] The standard of pH value scoring is: the pH value score of the soil is set to 10 points. When the pH value is 7, the corresponding pH value score G = 10; when the pH value is in [4,7) or (7,10], the corresponding pH value score G = 7; when the pH value is in [1,4) or (10,13], the corresponding pH value score G = 4; when the pH value is less than 1 or greater than 13, the corresponding pH value score G = 1;

[0072] In the above pH value scoring standard, when the pH value is 7, the corresponding pH value score G=10. A pH value of 7 indicates that the soil is neutral, which is generally considered to be an ideal environment for plant growth, because most plants can obtain the best nutrient absorption and growth conditions in neutral soil. Therefore, the pH value is 7, and the score is 10 points, indicating that the soil conditions are optimal; when the pH value is between [4,7) or (7,10], the corresponding pH value score G=7. These two intervals represent that the soil is slightly acidic or alkaline. Although this type of soil can still support the growth of most plants, it may affect the nutrient absorption or growth of some plants compared to neutral soil. In this case, the score is 7, indicating that the soil conditions are good, but not optimal; when the pH value is between [1,4) or (1 0,13], the corresponding pH score G = 4, soil with low or high pH is usually not conducive to plant growth, and soil that is too acidic or alkaline may cause certain nutrients such as nitrogen, phosphorus, and potassium to not be effectively absorbed by plants, thereby affecting plant growth. Therefore, when the soil pH is in this range, the score is 4 points, indicating that the soil conditions are poor and need to be repaired or improved; when the pH value is less than 1 or greater than 13, the corresponding pH score G = 1. This extreme soil acidity and alkalinity usually cannot support the growth of most plants and may also have a great impact on the environment. For example, soil that is too acidic or alkaline may cause serious damage to microorganisms and soil biological communities, thereby affecting soil health. Therefore, extremely acidic or alkaline soils are scored as 1 point, indicating that the soil quality is very poor.

[0073] This scoring standard can quantify soil pH changes, avoid the complexity of using pH values ​​directly, and can clearly and concisely reflect the impact of soil acidity and alkalinity on plant growth, thereby helping to evaluate the effectiveness of remediation measures. The scoring system helps to determine the goals and extent of remediation and ensure that soil pH is restored to the state most suitable for plant growth, especially a neutral soil environment.

[0074] The heavy metal concentration index is generated based on the formula:

[0075]

[0076] Among them, R en Indicates the generated heavy metal concentration index; in the above formula for calculating the heavy metal concentration index, when the difference in pH score Gdev The larger the value, the heavier the heavy metal concentration index R en decrease, which means that after restoration, the strong acid and strong alkaline state of the original banana forest soil has been improved to a neutral state; when the difference in heavy metal ion concentration ND dev When R en The concentration of heavy metal ions in the surface soil decreases, and the soil is effectively improved; when the difference in soil mineral concentration KW dev Increase, R en This indicates that in the process of earthworms repairing heavy metal soil, the consumption of organic matter increased, the mineral concentration decreased, and the degree of repair was greater; when the sand-soil ratio difference STB dev Increase, R en The decrease means that the current sand-to-soil ratio is reduced, and the sand content in the soil is reduced after restoration, and the restoration effect is obvious; In summary, the heavy metal concentration index R en The smaller the better, indicating a good degree of heavy metal soil remediation.

[0077] It should be noted that the method for generating the heavy metal remediation index can quantify and evaluate the effect of remediation measures on heavy metal pollution by calculating the changes in earthworm excrement and biochar content before and after remediation. The formula combines two key variables and fully reflects the comprehensive effect of bioremediation and material improvement on pollution remediation. The importance of this index lies in that, by accurately calculating the remediation effect, it can provide a scientific basis for the optimization of remediation strategies, promote the scientific and efficient pollution control, and provide an important reference indicator for the restoration of soil quality and environmental sustainable development.

[0078] Therefore, it is necessary to generate a heavy metal remediation index based on the following method:

[0079] The earthworm excrement content and biochar content before and after restoration in the restoration area were obtained, and the changes in earthworm excrement and biochar were calculated to generate the heavy metal restoration index. The calculation formula of the heavy metal restoration index is:

[0080]

[0081] Among them, F represents the heavy metal remediation index, PX1 and PX2 are the contents of earthworm excrement before and after remediation, swt1 and swt2 are the contents of biochar before and after remediation, respectively; in the above formula, ePX 2-PX1In this part of the formula, an exponent with base e is used to represent the effect of the change in earthworm excrement before and after restoration on the heavy metal restoration index F, that is, the larger the PX2-PX1, the smaller the F, which means that the earthworms absorb and digest more heavy metal soil, which leads to an increase in excrement, indicating that the restoration effect is good; ln(swt1-set2) In this part of the formula, a logarithmic function is used to characterize the effect of biochar content before and after restoration on the heavy metal restoration index F, that is, the larger the swt1-swt2, the smaller F, which means that in the process of eliminating the remaining heavy metal residues in the earthworm excrement and removing the remaining heavy metal residues, the biochar consumption increases, indicating that the restoration effect is good and the amount of heavy metal soil restored is large; therefore, the smaller F is, it means that the heavy metal concentration in the soil or environment after restoration is lower, which shows that the restoration measures have significant effects on the control of heavy metal pollution.

[0082] It should be noted that the revised heavy metal concentration index correction value obtained through the correction formula can more comprehensively and dynamically reflect the real risk level of heavy metal pollution in the environment, and can combine environmental remediation capabilities and pollution characteristics to avoid the errors caused by traditional fixed threshold assessments. This method can provide a scientific basis for pollution risk assessment and governance strategies, thereby optimizing the allocation of remediation resources, improving the efficiency of pollution control, and ultimately helping to achieve sustainable development of the ecological environment.

[0083] Therefore, it is necessary to use the calculated heavy metal remediation index to correct the heavy metal concentration index to obtain the corrected value of the heavy metal concentration index. The formula is:

[0084] R en ′=R en *(1+F)

[0085] Among them, R en ′ represents the corrected value of heavy metal concentration index, R en Is the heavy metal concentration index.

[0086] In the above formula, R en The reason for the correction is that since environmental changes are uncertain factors, the preset threshold value often changes due to external factors, and the smaller the heavy metal concentration index output in the heavy metal detection model, the better the remediation effect, so it is necessary to further limit R en By reducing the specific value of the heavy metal concentration index, we can more sensitively and dynamically capture the changes in the heavy metal concentration index and promptly determine whether the soil remediation effect meets the standards.

[0087] It should be noted that it is of great significance to use the heavy metal remediation index to correct the heavy metal pollution threshold, because this method can more comprehensively and dynamically reflect the real risk level of heavy metal pollution in the environment. By introducing the remediation index F, the fixed preset threshold Ren Corrected to a more adaptive correction value R en ′, effectively combining the environmental restoration capacity and pollution characteristics, avoiding the deviation that may be caused by traditional fixed threshold assessment, and then correcting the heavy metal concentration index R by judging en ′ and the pollution threshold, and scientifically quantify the restoration results. When Q=1, it indicates that the restoration effect is good; when Q=0, it indicates that the pollution still exceeds the standard and needs further restoration. This method provides a scientific basis for pollution risk assessment and governance optimization, improves resource utilization efficiency, and promotes the sustainable development of the ecological environment.

[0088] Therefore, it is necessary to compare the obtained heavy metal concentration index correction value with the heavy metal pollution threshold to determine the restoration effect of heavy metals in banana forest soil. The logical formula is as follows:

[0089]

[0090] Among them, Q represents the logical value for judging the remediation effect of heavy metals in banana forest soil. When Q=0, the corrected value of the heavy metal concentration index is greater than the heavy metal pollution threshold, indicating that the heavy metal concentration in the banana forest soil is still exceeded and remediation treatment is still required; when Q=1, the heavy metal concentration index does not exceed the heavy metal pollution threshold, indicating that the heavy metal concentration in the banana forest soil is at a normal level and the remediation effect is good. WR is the heavy metal pollution threshold.

[0091] See also Figure 2 The present invention also provides an ecological detection method for restoring heavy metals in banana forest soil based on cultivating earthworms, and the ecological detection method is used to execute the above-mentioned ecological detection system for restoring heavy metals in banana forest soil based on cultivating earthworms, comprising:

[0092] Step 1: Collecting heavy metal-related characteristic parameters in the banana forest soil before restoration, the characteristic parameters include soil pH score, heavy metal ion concentration, soil mineral concentration and soil sand ratio;

[0093] Step 2: Select Eisenia deltaica and add biochar to the banana forest restoration area for restoration. After the experiment, measure the characteristic parameter data after restoration, and calculate the difference between each characteristic parameter before and after restoration. The heavy metal concentration index is generated based on the calculated difference between each characteristic parameter.

[0094] Step 3: Calculate the change in earthworm excrement and biochar, and combine the change in earthworm excrement and biochar to generate a heavy metal remediation index;

[0095] Step 4: Establish a heavy metal pollution threshold, use the calculated heavy metal remediation index to correct the heavy metal concentration index to obtain a corrected value of the heavy metal concentration index, compare the corrected value of the heavy metal concentration index with the heavy metal pollution threshold, and determine the remediation effect of heavy metals in banana forest soil.

[0096] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0097] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0098] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0099] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. An ecological detection system for repairing heavy metals in banana forest soil based on cultivating earthworms, characterized in that: The specific steps include: A regional characteristic parameter acquisition module, which is used to collect heavy metal-related characteristic parameters in the soil of the banana forest before restoration, and the characteristic parameters include soil pH score, heavy metal ion concentration, soil mineral concentration and soil sand ratio; A heavy metal concentration index generation module, wherein the heavy metal concentration index generation module is used to select Eisenia deliciosa, add biochar and place them in the banana forest restoration area for restoration. After the experiment is completed, characteristic parameter data after restoration is measured, and the difference between each characteristic parameter before and after restoration is calculated, and a heavy metal concentration index is generated according to the calculated difference between each characteristic parameter; A dynamic analysis module for earthworm excrement and biochar, which is used to calculate the change in earthworm excrement and the change in biochar, and to generate a heavy metal remediation index by combining the change in earthworm excrement and the change in biochar; A threshold correction and restoration effect evaluation module, which is used to set a heavy metal pollution threshold, use the calculated heavy metal restoration index to correct the heavy metal concentration index to obtain a heavy metal concentration index correction value, compare the obtained heavy metal concentration index correction value with the heavy metal pollution threshold, and judge the restoration effect of heavy metals in banana forest soil; The method for generating the heavy metal remediation index is based on: The earthworm excrement content and biochar content before and after restoration in the restoration area were obtained, and the changes in earthworm excrement and biochar were calculated to generate the heavy metal restoration index. The calculation formula of the heavy metal restoration index is: Among them, F represents the heavy metal remediation index, PX1 and PX2 are the earthworm excrement contents before and after remediation, swt1 and swt2 are the biochar contents before and after remediation, respectively; The heavy metal remediation index calculated is used to correct the heavy metal concentration index to obtain the heavy metal concentration index correction value, based on the formula: R en ′=R en *(1+F) Among them, R en ′ represents the corrected value of heavy metal concentration index, R en is the heavy metal concentration index.

2. The ecological detection system for repairing heavy metals in banana forest soil based on cultivating earthworms according to claim 1 is characterized in that: The difference between each characteristic parameter before and after the restoration is calculated, and the heavy metal concentration index is generated according to the calculated difference between each characteristic parameter. The method is as follows: First, perform minimum-maximum normalization on the feature data, scale the data to the range of [0,1], so that all feature data have the same scale, and then perform data cleaning on the feature data, including detection and deletion of outliers and duplicate data, and processing of missing values. Statistical methods are used to identify outliers and duplicate data in the feature data, delete outliers and duplicate data in the feature data, and use the mean, median or mode of the feature data to fill in the missing values ​​in the feature data; The difference between each characteristic parameter before and after repair is calculated based on the following formula: G dev =G now -G before ND dev =ND before -ND now KW dev =KW before -KW now Etc. dev =etc. before -etc now Among them, ND dev , KW dev , STB dev They represent the difference in heavy metal ion concentration, soil mineral concentration, and soil sand ratio before and after banana forest restoration, respectively. before , KW before , STB before , G before represents the heavy metal ion concentration, soil mineral concentration, soil sand-soil ratio and soil pH score in the banana forest soil before restoration, ND now , KW now , STB now , G now represent the heavy metal ion concentration, soil mineral concentration, soil sand-soil ratio and soil pH value score in the restored banana forest soil, respectively. dev It is the difference between the soil pH score after and before restoration; The standard of pH value scoring is: the pH value score of the soil is set to 10 points. When the pH value is 7, the corresponding pH value score G = 10; when the pH value is in [4,7) or (7,10], the corresponding pH value score G = 7; when the pH value is in [1,4) or (10,13], the corresponding pH value score G = 4; when the pH value is less than 1 or greater than 13, the corresponding pH value score G = 1; The heavy metal concentration index is generated based on the formula: Among them, R en Indicates the generated heavy metal concentration index.

3. The ecological detection system for repairing heavy metals in banana forest soil based on cultivating earthworms according to claim 1 is characterized in that: The obtained heavy metal concentration index correction value was compared with the heavy metal pollution threshold to determine the restoration effect of heavy metals in banana forest soil. The logical formula was as follows: Among them, Q represents the logical value for judging the remediation effect of heavy metals in banana forest soil. When Q=0, the corrected value of the heavy metal concentration index is greater than the heavy metal pollution threshold, indicating that the heavy metal concentration in the banana forest soil is still exceeded and remediation treatment is still required; when Q=1, the heavy metal concentration index does not exceed the heavy metal pollution threshold, indicating that the heavy metal concentration in the banana forest soil is at a normal level and the remediation effect is good. WR is the heavy metal pollution threshold.

4. An ecological detection method for repairing heavy metals in banana forest soil based on cultivating earthworms, characterized in that: The ecological detection method is used to implement the ecological detection system for restoring heavy metals in banana forest soil based on cultured earthworms according to any one of claims 1 to 3, comprising: Step 1: Collecting heavy metal-related characteristic parameters in the banana forest soil before restoration, the characteristic parameters include soil pH score, heavy metal ion concentration, soil mineral concentration and soil sand ratio; Step 2: Select Eisenia deltaica and add biochar to the banana forest restoration area for restoration. After the experiment, measure the characteristic parameter data after restoration, and calculate the difference between each characteristic parameter before and after restoration. The heavy metal concentration index is generated based on the calculated difference between each characteristic parameter. Step 3: Calculate the change in earthworm excrement and biochar, and combine the change in earthworm excrement and biochar to generate a heavy metal remediation index; Step 4: Establish a heavy metal pollution threshold, use the calculated heavy metal remediation index to correct the heavy metal concentration index to obtain a corrected value of the heavy metal concentration index, compare the corrected value of the heavy metal concentration index with the heavy metal pollution threshold, and determine the remediation effect of heavy metals in banana forest soil.

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