Analysis method of microbial flora compound in mine environment governance

Abstract

The application is suitable for the field of environmental remediation technology, and particularly relates to a method for analyzing microbial flora compound in mine environmental governance, which comprises the following steps: obtaining multi-dimensional time sequence monitoring data under in-situ rainfall-oxidation reduction intermittent disturbance; extracting core repair parameters based on time sequence segmentation characteristics of the multi-dimensional time sequence monitoring data; obtaining corresponding parameter contribution based on the core repair parameters; adding the parameter contribution and the basic repair benchmark to obtain real-time comprehensive repair efficiency of the contaminated soil; and screening and determining the optimal flora compound scheme corresponding to the heavy metal pollution gradient based on the real-time comprehensive repair efficiency under different heavy metal pollution gradients. The method realizes accurate adaptation of flora compound and dynamic disturbance environment of the mine in-situ, and improves the planting stability, multiple metal synchronous solidification effect and overall repair efficiency of the compound flora.

Description

Technical Field

[0001] This application belongs to the field of environmental remediation technology, and in particular relates to an analytical method for the compounding of microbial communities in mine environmental remediation. Background Technology

[0002] The release of heavy metals from power plant slag in mines, coupled with soil acidification, leads to complex pollution from multiple metals such as lead, cadmium, and arsenic, accompanied by soil nutrient depletion, microecological imbalance, and structural deterioration. Heavy metal ions continuously migrate and diffuse into surrounding soils and groundwater through rainwater leaching and natural weathering, not only disrupting regional vegetation growth and ecological balance but also posing a long-term threat to the safety of surrounding residential environments. Therefore, mine ecological environment restoration is a core component of national land space ecological protection and restoration. Microbial remediation, with its advantages of being environmentally friendly, low-cost, producing no secondary pollution, and simultaneously reconstructing the soil microecology, has become the mainstream technology for treating mine-contaminated soil. Compared to single-function bacterial strains, multi-strain microbial communities can leverage synergistic effects between strains to improve the efficiency of heavy metal immobilization, soil improvement, and ecological restoration, making it a current research focus in the field of mine microbial remediation.

[0003] However, mine remediation is not conducted under steady-state conditions, but rather is subject to intermittent disturbances from in-situ rainfall and redox conditions. This causes the soil's physicochemical state, heavy metal occurrence patterns, and microbial metabolic activity to be constantly and dynamically changing in real time. For any given heavy metal pollution gradient, it remains impossible to effectively determine which microbial community combination can truly exert a sustained and stable optimal remediation effect. This predicament constitutes a fundamental obstacle to the precise application and engineering reliability of microbial community-based remediation technologies. Summary of the Invention

[0004] This application provides an analytical method for the compounding of microbial communities in mine environmental remediation, which can improve the problem of unstable remediation by compounding microbial communities.

[0005] In a first aspect, embodiments of this application provide an analytical method for the compounding of microbial communities in mine environmental remediation, including:

[0006] Acquire multi-dimensional time-series monitoring data under in-situ rainfall-redox intermittent disturbance; wherein, the multi-dimensional time-series monitoring data includes soil physicochemical time-series change data, heavy metal speciation time-series data, indigenous microbial community colonization activity time-series data, and exogenous functional microbial community iron and sulfur metabolism time-series data;

[0007] Based on the time-series segmentation features of the multi-dimensional time-series monitoring data, core repair parameters are extracted; wherein, the core repair parameters include in-situ adaptation parameters of indigenous microbial communities, metabolic synergy parameters of functional microbial communities, and heavy metal mineralization stability parameters of microbial communities.

[0008] Based on the core repair parameters, the corresponding parameter contribution amounts are obtained; wherein, the parameter contribution amounts include the indigenous microbial community adaptation contribution amount, the functional microbial community metabolic contribution amount, and the microbial community mineralization stability contribution amount.

[0009] The contribution of the parameters is summed with the baseline remediation amount to obtain the real-time comprehensive remediation efficiency of the contaminated soil.

[0010] Based on the real-time comprehensive remediation efficacy under different heavy metal pollution gradients, the optimal microbial community combination scheme corresponding to the heavy metal pollution gradient was screened and determined.

[0011] The technical solutions described in this application embodiment have at least the following technical effects:

[0012] The analytical method for microbial community compatibility in mine environmental remediation provided in this application obtains multi-dimensional time-series monitoring data, including soil physicochemical time-series changes, heavy metal speciation time-series data, indigenous microbial community colonization activity time-series data, and exogenous functional microbial community iron and sulfur metabolism time-series data, under in-situ rainfall-redox intermittent disturbance. This allows for the direct acquisition of comprehensive pollution and microbial community-related information under real dynamic disturbance at the mine site, providing accurate and comprehensive basic data support for subsequent remediation parameter extraction and scheme selection. Then, based on the time-series segmentation characteristics of the multi-dimensional time-series monitoring data, core remediation parameters, including in-situ adaptation parameters of indigenous microbial communities, metabolic synergy parameters of functional microbial communities, and heavy metal mineralization stability parameters of microbial communities, can be extracted. This enables the assessment of microbial community adaptation ability and metabolic synergy level. The system achieves precise quantification of the stabilization effect of heavy metal mineralization. Based on core remediation parameters, it derives parameter contributions including the adaptation contribution of indigenous microbial communities, the metabolic contribution of functional microbial communities, and the mineralization stabilization contribution of microbial communities. This clearly quantifies the remediation role of each of the three core parameters, reducing misjudgments of remediation efficacy caused by single-parameter evaluation. The parameter contributions are then summed with the baseline remediation amount to obtain the real-time comprehensive remediation efficacy of contaminated soil. This enables precise, real-time quantitative assessment of the remediation effect of contaminated soil, improving the comprehensiveness and accuracy of remediation efficacy evaluation. Finally, based on the real-time comprehensive remediation efficacy under different heavy metal pollution gradients, the optimal microbial community combination scheme adapted to the corresponding heavy metal pollution gradient is screened and determined, achieving precise matching of the microbial community combination scheme with different pollution gradients. This method achieves precise adaptation of microbial community compounding to the dynamic disturbance environment in situ of mines, improves the colonization stability of compound microbial communities, the simultaneous solidification effect of multiple metals, and the overall remediation efficiency. It solves the technical pain points of traditional microbial community compounding methods, such as poor adaptability, unstable remediation effect, and weak engineering feasibility. Ultimately, it achieves the goal of efficient, long-term, and precise remediation of heavy metal contaminated soil in mines, and enhances the engineering application value of microbial remediation technology in mine environmental governance.

[0013] Secondly, embodiments of this application provide an analytical system for the compounding of microbial communities in mountain environment management, comprising:

[0014] The acquisition module is used to acquire multi-dimensional time-series monitoring data under in-situ rainfall-redox intermittent disturbance; wherein, the multi-dimensional time-series monitoring data includes soil physicochemical time-series change data, heavy metal speciation time-series data, indigenous microbial community colonization activity time-series data, and exogenous functional microbial community iron and sulfur metabolism time-series data.

[0015] The extraction module is used to extract core repair parameters based on the time-series segmentation features of the multi-dimensional time-series monitoring data; wherein, the core repair parameters include in-situ adaptation parameters of indigenous microbial communities, metabolic synergy parameters of functional microbial communities, and heavy metal mineralization stability parameters of microbial communities.

[0016] The analysis module is used to obtain the corresponding parameter contribution based on the core repair parameters; wherein, the parameter contribution includes the contribution of indigenous microbial community adaptation, the contribution of functional microbial community metabolism, and the contribution of microbial community mineralization stability.

[0017] The processing module is used to sum the contribution of the parameters with the basic remediation benchmark to obtain the real-time comprehensive remediation efficiency of the contaminated soil.

[0018] The screening module is used to screen and determine the optimal microbial community combination scheme that is suitable for the corresponding heavy metal pollution gradient based on the real-time comprehensive remediation efficacy under different heavy metal pollution gradients.

[0019] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect above.

[0020] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0021] Fifthly, embodiments of this application provide a computer program product that, when run on an electronic device, causes the electronic device to execute the analytical method for the compounding of microbial communities in mine environmental remediation described in the first aspect above.

[0022] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating the analytical method for the compound microbial community in mine environmental remediation provided in the embodiments of this application;

[0025] Figure 2 This is a schematic diagram illustrating the effect of multi-dimensional time-series monitoring data in the analytical method for microbial community compounding in mine environmental remediation provided in the embodiments of this application;

[0026] Figure 3 This is a schematic diagram of the implementation process of step S300 in the analytical method for the compounding of microbial communities in mine environmental remediation provided in the embodiments of this application;

[0027] Figure 4 This is a schematic diagram of the structure of the analysis system for the compound microbial community in mountain environmental management provided in the embodiments of this application;

[0028] Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0029] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0030] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0031] The release of heavy metals from power plant slag in mines, coupled with soil acidification, leads to complex pollution from multiple metals such as lead, cadmium, and arsenic, accompanied by soil nutrient depletion, microecological imbalance, and structural deterioration. Heavy metal ions continuously migrate and diffuse into surrounding soils and groundwater through rainwater leaching and natural weathering, not only disrupting regional vegetation growth and ecological balance but also posing a long-term threat to the safety of surrounding residential environments. Therefore, mine ecological environment restoration is a core component of national land space ecological protection and restoration. Microbial remediation, with its advantages of being environmentally friendly, low-cost, producing no secondary pollution, and simultaneously reconstructing the soil microecology, has become the mainstream technology for treating mine-contaminated soil. Compared to single-function bacterial strains, multi-strain microbial communities can leverage synergistic effects between strains to improve the efficiency of heavy metal immobilization, soil improvement, and ecological restoration, making it a current research focus in the field of mine microbial remediation.

[0032] However, mine remediation is not conducted under steady-state conditions, but rather is subject to intermittent disturbances from in-situ rainfall and redox conditions. This causes the soil's physicochemical state, heavy metal occurrence patterns, and microbial metabolic activity to be constantly and dynamically changing in real time. For any given heavy metal pollution gradient, it remains impossible to effectively determine which microbial community combination can truly exert a sustained and stable optimal remediation effect. This predicament constitutes a fundamental obstacle to the precise application and engineering reliability of microbial community-based remediation technologies.

[0033] To address the aforementioned issues, this application provides an analytical method for the compounding of microbial communities in mine environmental remediation. This method acquires multi-dimensional time-series monitoring data under in-situ rainfall-redox intermittent disturbance, including soil physicochemical time-series changes, heavy metal speciation time-series data, indigenous microbial community colonization activity time-series data, and exogenous functional microbial community iron-sulfur metabolism time-series data. This allows for the direct acquisition of comprehensive pollution and microbial community-related information under real dynamic disturbance at the mine site, providing accurate and comprehensive basic data support for subsequent remediation parameter extraction and scheme selection. Then, based on the time-series segmentation characteristics of the multi-dimensional time-series monitoring data, core remediation parameters are extracted, including indigenous microbial community in-situ adaptation parameters, functional microbial community metabolic synergy parameters, and microbial community heavy metal mineralization stability parameters. This enables the assessment of microbial community adaptation ability, metabolic synergy level, and heavy metal mineralization stabilization effect. Precise quantification is achieved by first obtaining the contribution of indigenous microbial communities, the metabolic contribution of functional microbial communities, and the mineralization stability contribution of microbial communities based on core remediation parameters. This clearly quantifies the remediation effects of each of the three core parameters, reducing misjudgments of remediation efficacy caused by single-parameter evaluation. Then, the parameter contribution is summed with the basic remediation benchmark to obtain the real-time comprehensive remediation efficacy of contaminated soil. This enables precise and real-time quantitative assessment of the remediation effect of contaminated soil, improving the comprehensiveness and accuracy of remediation efficacy evaluation. Finally, based on the real-time comprehensive remediation efficacy under different heavy metal pollution gradients, the optimal microbial community combination scheme adapted to the corresponding heavy metal pollution gradient is screened and determined, enabling precise matching of the microbial community combination scheme with different pollution gradients. This method achieves precise adaptation of microbial community compounding to the dynamic disturbance environment in situ of mines, improves the colonization stability of compound microbial communities, the simultaneous solidification effect of multiple metals, and the overall remediation efficiency. It solves the technical pain points of traditional microbial community compounding methods, such as poor adaptability, unstable remediation effect, and weak engineering feasibility. Ultimately, it achieves the goal of efficient, long-term, and precise remediation of heavy metal contaminated soil in mines, and enhances the engineering application value of microbial remediation technology in mine environmental governance.

[0034] The analytical method for the compound microbial community in mine environmental remediation provided in this application embodiment can be applied to electronic devices. In this case, the electronic device is the executing subject of the analytical method for the compound microbial community in mine environmental remediation provided in this application embodiment. This application embodiment does not impose any restrictions on the specific type of electronic device.

[0035] For example, electronic devices can be mobile phones, tablets, wearable devices, augmented reality (AR) / virtual reality (VR) devices, laptops, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs), desktop computers, smart screens, smart TVs, and other terminal devices; handheld devices with wireless communication capabilities; computing devices or other processing devices connected to a wireless modem; Internet of Things (IoT) terminals; computers; laptops; handheld communication devices; handheld computing devices; satellite wireless devices; wireless modem cards; set-top boxes (STBs); customer premises equipment (CPEs); and / or other devices used for communication over wireless systems, as well as next-generation communication systems, such as mobile terminals in 5G networks or mobile terminals in future evolved Public Land Mobile Networks (PLMNs).

[0036] To better understand the analytical method for the compounding of microbial communities in mine environmental remediation provided in the embodiments of this application, the specific implementation process of the analytical method for the compounding of microbial communities in mine environmental remediation provided in the embodiments of this application will be described by way of example below.

[0037] Figure 1 This paper illustrates a schematic flowchart of an analytical method for the complex composition of microbial communities in mine environmental remediation, provided in an embodiment of this application. The analytical method for the complex composition of microbial communities in mine environmental remediation includes:

[0038] S100 acquires multi-dimensional time-series monitoring data under in-situ rainfall-redox intermittent disturbance; among which, the multi-dimensional time-series monitoring data includes soil physicochemical time-series changes, heavy metal speciation time-series data, indigenous microbial community colonization activity time-series data, and exogenous functional microbial community iron and sulfur metabolism time-series data.

[0039] It is understandable that in-situ rainfall-redox intermittent disturbance is the most typical natural environmental characteristic of mine-contaminated soil. Specifically, when natural rainfall occurs, the soil moisture content rises rapidly, leaching is enhanced, and the soil redox potential (ORP) fluctuates violently, forming an intermittent disturbance state in which oxidized and reduced states alternate. This state directly determines the survival of indigenous microbial communities, the metabolic activity of exogenous functional microbial communities, and the pattern of heavy metal transformation. Soil physicochemical time-series data specifically include continuous changes in key physicochemical indicators such as soil pH, electrical conductivity (EC), organic matter content, redox potential, moisture content, and porosity over time. The data collection frequency matches the rainfall disturbance cycle, with short-term data collected at the minute level and long-term data collected at the hourly and daily levels. Heavy metal speciation time-series data can be obtained using the BCR continuous extraction method, detecting changes in the content of five heavy metals (lead, cadmium, and arsenic) in the soil at time-series intervals: exchangeable, carbonate-bound, iron-manganese oxidized, organically bound, and residual forms, to reflect the migration and transformation patterns of heavy metals under disturbed conditions. Indigenous microbial community colonization activity time-series data can be obtained using in situ plate counting, quantitative PCR (qPCR), and 16S rRNA assays. High-throughput sequencing technology is used to detect indicators such as viable cell count, community abundance, colonization survival rate, and metabolic activity of indigenous microorganisms over time, in order to observe the survival and adaptation capabilities of indigenous microbial communities in in-situ disturbed environments. Exogenous functional microbial iron-sulfur metabolism time-series data is used to detect temporal changes in metabolic indicators such as ferric reductase activity, sulfur oxidase activity, ferrous ion concentration, and sulfate ion concentration in iron-sulfur metabolic functional bacteria. Iron-sulfur metabolism is the core pathway for heavy metal immobilization in this type of microbial community, and time-series data can directly reflect the dynamic performance of its metabolic functions. Multidimensional time-series monitoring data can be collected using a combination of in-situ online monitoring equipment (such as online soil physicochemical index sensors, in-situ heavy metal online monitors, in-situ microbial metabolic activity monitoring probes, online redox potential analyzers, and continuous soil temperature and humidity sensors) + periodic field sampling + laboratory analysis.

[0040] For example, an initial time-series dataset of in-situ disturbance remediation can be obtained, and target data segments reflecting the transition of soil from a rainfall disturbance period to a static recovery period can be identified based on time-series segmentation features. When the number of consecutive occurrences of a target data segment reaches a preset threshold, the target data segment is used as valid multi-dimensional time-series monitoring data. Alternatively, a sliding time window can be used to traverse and filter the initial time-series dataset, and the completeness of the disturbance cycle of the data within each window can be determined by combining time-series segmentation features. Noise removal, outlier detection, and data quality scoring can be completed simultaneously. The sliding window data with a data quality score higher than a preset threshold and containing a complete rainfall disturbance-static recovery cycle can be integrated and processed as valid multi-dimensional time-series monitoring data, and so on, but not limited to these.

[0041] In one possible implementation, please refer to Figure 2 In step S100, multi-dimensional time-series monitoring data is acquired, including:

[0042] S110: Obtain the initial time-series dataset for in-situ disturbance remediation, and identify the target data segment reflecting the transition of soil from the rainfall disturbance period to the static recovery period based on the time-series segmentation features.

[0043] It is understandable that the initial time-series dataset consists of raw time-series data directly collected by in-situ monitoring equipment. This data has not undergone filtering, noise reduction, or segmentation. It contains invalid information such as environmental noise, equipment acquisition errors, random disturbance signals, and incomplete disturbance cycle data, making it unsuitable for direct extraction and calculation of core remediation parameters. Therefore, precise identification and filtering can be performed based on time-series segmentation features. Time-series segmentation features are key characteristic indicators extracted from multi-dimensional time-series data that can distinguish between rainfall-induced disturbance and static recovery states. Specifically, these can include abrupt changes in soil moisture content, fluctuations in redox potential, abrupt changes in heavy metal leaching concentration, and sudden changes in microbial activity. These features can be used to divide continuous time-series data into different time-series units. The rainfall-induced disturbance period refers to the period after natural rainfall, when soil moisture content rapidly increases, redox potential fluctuates drastically, heavy metal leaching intensifies, and microbial activity is suppressed. The static recovery period refers to the period after rainfall stops, when the soil gradually dehydrates and dries, redox potential stabilizes, heavy metal leaching weakens, and microbial activity gradually recovers. The target data segment refers to a time-series data segment that includes the entire process of rainfall disturbance from initiation to duration, termination, and recovery. It can fully reflect the complete impact of intermittent disturbance on the soil environment, microbial activity, and heavy metal speciation. The identification process can be carried out by combining threshold determination method with time-series segmentation algorithm. Abrupt change thresholds for moisture content, redox potential, and heavy metal concentration are set. When the data indicators exceed the threshold, it is determined to be the disturbance stage. When they fall back to the threshold range and remain stable, it is determined to be the recovery stage. In this way, the target data segment that meets the requirements is identified, and incomplete, irregular, and noisy invalid data segments are eliminated.

[0044] S120: When the number of consecutive occurrences of the target data segment reaches a preset threshold, the target data segment is used as valid multi-dimensional time-series monitoring data.

[0045] It is understandable that the preset threshold is a pre-defined number of consecutive cycles based on the rainfall frequency, disturbance cycle, engineering practice experience, and statistical requirements of the in-situ mining site. For example, it can be set to 3-5 consecutive complete rainfall disturbance-restoration cycles. This threshold is not set randomly, but rather to exclude single target data segments caused by atypical disturbance factors such as accidental rainfall, short-term showers, and instantaneous equipment errors, so that the selected data can represent the long-term, stable, and typical intermittent disturbance patterns of the site. The continuous appearance of the target data segment means that multiple complete disturbance-restoration cycle data segments are sequentially connected without interruption, missing data, or abnormal abrupt changes, reflecting the periodic disturbance characteristics of the in-situ environment. If the number of consecutive occurrences of the target data segment does not reach the preset threshold, it indicates insufficient data representativeness, and the collection time needs to be extended until the threshold requirement is met. If the preset threshold is reached, this part of the data is integrated, denoised, and standardized to form a valid time-series dataset that can be directly used for subsequent calculations.

[0046] This approach, through a step-by-step process of acquiring the initial time-series dataset, identifying target data segments using time-series segmentation features, and filtering valid data using continuous thresholds, fundamentally addresses the industry pain points of high noise, excessive invalid information, and insufficient representativeness in in-situ mine environmental monitoring data. It also overcomes the shortcomings of traditional techniques that rely on static laboratory data or short-term random data. Identifying target data segments based on time-series segmentation features accurately matches the true patterns of in-situ rainfall-redox intermittent disturbances, ensuring a high degree of data consistency with site environmental characteristics. Filtering by a preset threshold for the number of consecutive segments effectively eliminates invalid data such as accidental disturbances, equipment errors, and short-term fluctuations, significantly improving the quality and reliability of valid time-series data. This provides accurate, reliable, and stable data support for subsequent extraction of core remediation parameters, calculation of parameter contributions, and comprehensive remediation effectiveness assessment.

[0047] S200 extracts core repair parameters based on the time-series segmentation features of multi-dimensional time-series monitoring data. Among them, the core repair parameters include the in-situ adaptation parameters of indigenous microbial communities, the metabolic synergy parameters of functional microbial communities, and the heavy metal mineralization stability parameters of microbial communities.

[0048] It can be understood that the temporal segmentation characteristics of multi-dimensional time-series monitoring data refer to the set of landmark changes extracted from four types of data: soil physicochemical temporal changes, heavy metal speciation transformation temporal changes, indigenous microbial community colonization activity temporal changes, and exogenous functional microbial community iron and sulfur metabolism temporal changes. These changes clearly distinguish and divide the rainfall disturbance period and the static recovery period. One of these features is the abrupt change in soil physicochemical state, namely, a rapid increase in soil moisture content, a significant decrease in redox potential (ORP), a sudden increase in electrical conductivity, and a change in soil pH during rainfall. The four main characteristics of the changes are: first, short-term fluctuations, with the above indicators gradually declining and stabilizing after the rainfall stops; second, fluctuations in heavy metal speciation and leaching, i.e., during the rainfall disturbance period, the exchangeable and leached forms of heavy metals increase rapidly, while the oxidized and organically bound forms of iron and manganese undergo short-term transformations, and during the static recovery period, the heavy metal speciation tends to stabilize and the leaching concentration continues to decrease; third, the response characteristics of indigenous microbial community colonization activity, i.e. during the rainfall disturbance period, the activity of indigenous microbial communities decreases and the colonization rate decreases due to drastic environmental changes and leaching erosion, and during the static recovery period, the activity gradually recovers and the colonization state is rebuilt as the environment stabilizes; and fourth, the rhythmic characteristics of exogenous functional microbial community iron and sulfur metabolism, i.e. during the rainfall disturbance period, the iron reduction and sulfur oxidation metabolic activities of functional microbial communities are inhibited by environmental fluctuations, and during the static recovery period, the metabolic indicators gradually recover and remain stable. These four characteristics exhibit synchronous response, distinct stages, repeatability, and quantifiability in time series, enabling the precise segmentation of continuous time series monitoring data into two independent and complete analysis units: the rainfall disturbance period and the static recovery period. This provides a unified, clear, and quantifiable standard and data boundary for the subsequent targeted extraction of in-situ adaptation parameters of indigenous microbial communities, metabolic synergy parameters of functional microbial communities, and heavy metal mineralization stability parameters of microbial communities.

[0049] In-situ adaptation parameters of indigenous microbial communities are core parameters used to quantify the survival and adaptation capabilities and colonization ability of indigenous microbial communities in lead / cadmium / arsenic compound acidic soils and under intermittent rainfall-redox disturbance environments. They reflect the tolerance and adaptability of indigenous microbial communities to extreme in-situ environments and are fundamental core parameters for microbial community formulation. Functional microbial community metabolic synergy parameters are core parameters used to quantify the metabolic synergy and functional complementarity between exogenous functional microbial communities and indigenous microbial communities. They reflect the degree of metabolic activity of the formulated microbial communities in the in-situ environment and reduce antagonistic effects between microbial communities that lead to remediation failure. Microbial community heavy metal mineralization stability parameters are core parameters used to quantify the ability of formulated microbial communities to mineralize, solidify, and stabilize the forms of lead, cadmium, and arsenic. They reflect the ability of microbial communities to transform active heavy metal forms into stable residual forms and are key parameters that determine the remediation effect.

[0050] For example, based on the temporal segmentation characteristics of multi-dimensional time-series monitoring data, the in-situ adaptation parameters of indigenous microbial communities can be determined. Microbial activity data corresponding to short-term and long-term disturbances based on the temporal segmentation characteristics can be extracted from the multi-dimensional time-series monitoring data. Through temporal decomposition of microbial community performance, the metabolic synergy parameters of functional microbial communities and the heavy metal mineralization stability parameters of microbial communities can be obtained. Alternatively, based on the temporal segmentation characteristics, the multi-dimensional time-series monitoring data can be uniformly calibrated and data normalized for the rainfall disturbance period and the static recovery period to construct a temporal feature matrix covering two time periods and multiple indicators. Then, based on this temporal feature matrix, the in-situ adaptation parameters of indigenous microbial communities, the metabolic synergy parameters of functional microbial communities, and the heavy metal mineralization stability parameters of microbial communities can be directly extracted by simultaneously performing adaptive fitting algorithms, metabolic synergy calculation algorithms, and mineralization stability quantification algorithms, etc., but not limited to these methods.

[0051] In one possible implementation, please refer to Figure 3 In step S200, based on the time-series segmentation features of multi-dimensional time-series monitoring data, core repair parameters are extracted, including:

[0052] S210, based on the time-series segmentation characteristics of multi-dimensional time-series monitoring data, determines the in-situ adaptation parameters of indigenous microbial communities.

[0053] It is understandable that the core of the in-situ adaptation parameters of indigenous microbial communities is to quantify the adaptation ability of indigenous microbial communities in dynamic disturbance rather than static environments. The value directly reflects the survival ability of indigenous microbial communities in in-situ extreme pollution and intermittent disturbance environments. The higher the value, the stronger the tolerance of indigenous microbial communities to acidity, heavy metal compound pollution, and rainfall disturbance, and the higher the survival rate of colonization after subsequent re-adaptation. For example, based on the temporal segmentation characteristics of multi-dimensional time-series monitoring data, the rainfall disturbance period and the static recovery period can be divided, and the heavy metal pollution gradient corresponding to each period can be matched. Then, based on the correspondence between the bacterial community activity and the heavy metal pollution gradient in each period, the native bacterial community adaptation curve under the continuous pollution gradient can be generated. Then, based on the native bacterial community adaptation curve, the in-situ adaptation parameters of the native bacterial community for the corresponding heavy metal pollution gradient can be determined. Alternatively, based on the temporal segmentation characteristics of multi-dimensional time-series monitoring data, the activity decay rate and minimum survival activity of the native bacterial community during the rainfall disturbance period, as well as the activity recovery rate and steady-state colonization activity of the native bacterial community during the static recovery period can be accurately extracted. Then, the above-mentioned time-series activity indicators can be correlated and fitted with the tolerance threshold of the corresponding heavy metal pollution gradient to construct an in-situ adaptation quantification model. The in-situ adaptation parameters of the native bacterial community under the corresponding pollution gradient can be directly calculated and output through the model, and so on, but not limited to these.

[0054] In one possible implementation, in step S210, based on the temporal segmentation characteristics of multi-dimensional temporal monitoring data, the in-situ adaptation parameters of the indigenous microbial community are determined, including:

[0055] S211, based on the time-series segmentation characteristics of multi-dimensional time-series monitoring data, divides the rainfall disturbance period and the static recovery period, and matches the heavy metal pollution gradient corresponding to each period.

[0056] It is understandable that by extracting temporal abrupt changes in characteristics such as moisture content, redox potential, heavy metal leaching concentration, and microbial activity, the continuous time-series data can be precisely divided into two distinct periods: the rainfall disturbance period, in which the soil is in a state of high moisture content, strong leaching, drastic fluctuations in redox potential, enhanced heavy metal activity, and suppressed microbial activity; and the static recovery period, in which soil moisture content gradually decreases, leaching weakens, redox potential stabilizes, heavy metal speciation tends to stabilize, and microbial activity gradually recovers. The heavy metal pollution gradient refers to the graded gradient of the total concentration and active form concentration of the three heavy metals (lead, cadmium, and arsenic) in the soil. Based on the pollution distribution characteristics of the mining site, it is divided into low-pollution gradient, medium-pollution gradient, and high-pollution gradient. Significant differences exist in the adaptability, metabolic activity, and mineralization effect of the microbial community under different gradients.

[0057] The specific matching process between each time period and the heavy metal pollution gradient is as follows: First, time period-data time sequence alignment is performed. Using the start and end timestamps of the defined rainfall disturbance period and resting recovery period as a benchmark, heavy metal speciation time sequence data and soil physicochemical time sequence data for the corresponding time periods are extracted from multi-dimensional time-series monitoring data. Then, heavy metal concentration calculations are performed within each time period. The average total concentration and average exchangeable (active) concentration of lead, cadmium, and arsenic are calculated for each time period. The total concentration reflects the pollution level, and the exchangeable concentration reflects the ecological risk and microbial inhibition intensity, thus obtaining the time-series data. The comprehensive pollution characteristic value of the segment is then determined; then, a pollution gradient classification judgment is performed, comparing the comprehensive pollution characteristic value with the preset low, medium, and high pollution gradient judgment thresholds. The thresholds are preset according to the engineering standards and remediation targets of the mine heavy metal complex pollution site. Low total concentration and active concentration are both low, indicating a low pollution gradient; medium concentration and active concentration are both medium, indicating a medium pollution gradient; and high concentration and active concentration are both high, indicating a high pollution gradient. Finally, the time period-gradient binding is completed, and the determined pollution gradient is directly marked as the heavy metal pollution gradient of the corresponding time period, forming a matching relationship between the rainfall disturbance period → corresponding pollution gradient and the static recovery period → corresponding pollution gradient.

[0058] S212 generates adaptation curves of indigenous microbial communities under continuous pollution gradients based on the correspondence between microbial community activity and heavy metal pollution gradients at different time periods.

[0059] It can be understood that the indigenous microbial community adaptation curve is a continuous curve with the heavy metal pollution gradient as the x-axis and the indigenous microbial community colonization activity / survival rate as the y-axis, reflecting the activity changes of indigenous microbial communities under different pollution gradients during rainfall disturbance and resting recovery periods. This step, based on the time-pollution gradient-microbial community activity correspondence data matched by S211, extracts indigenous microbial community activity data corresponding to low, medium, and high continuous pollution gradients during rainfall disturbance and resting recovery periods, respectively. Statistical methods such as multinomial fitting and nonlinear regression are used to generate indigenous microbial community adaptation curves under continuous pollution gradients. The indigenous microbial community adaptation curve can intuitively and continuously reflect the changing trend of indigenous microbial community activity with pollution gradient and disturbance state, clearly showing under which pollution gradient the indigenous microbial community has the strongest activity and optimal adaptation, and under which gradient its activity is significantly inhibited.

[0060] S213, Determine the in-situ adaptation parameters of indigenous microbial communities corresponding to heavy metal pollution gradients based on the adaptation curves of indigenous microbial communities.

[0061] It is understandable that, for different heavy metal pollution gradients, key numerical points corresponding to these points are extracted from the curves. Through weighted calculations and normalization, the in-situ adaptation parameters of indigenous microbial communities corresponding to each pollution gradient are quantitatively determined. Specifically, for each pollution gradient (low, medium, and high), the minimum microbial activity during rainfall disturbance and the maximum microbial activity during resting recovery are read from the adaptation curves. Core indicators such as activity recovery rate, disturbance inhibition rate, and steady-state survival rate are calculated. These indicators are then weighted and integrated to form the in-situ adaptation parameters of indigenous microbial communities under that gradient. These parameters are dimensionless values, and their ranges have been standardized to facilitate subsequent calculations of parameter contributions and horizontal comparisons. Higher parameter values ​​indicate stronger in-situ adaptation capabilities of indigenous microbial communities under that pollution gradient.

[0062] This setup, by segmenting time periods based on temporal characteristics, matching contamination gradients, generating adaptation curves, and determining adaptation parameters, upgrades the in-situ adaptation parameters of indigenous microbiota from qualitative descriptions to quantitative, precise, and gradient-based core parameters. This overcomes the limitations of traditional techniques that cannot quantify the in-situ adaptation capabilities of indigenous microbiota and rely solely on empirical judgment. These parameters are generated entirely based on in-situ dynamic time-series data, closely reflecting the real disturbance environment of mines, eliminating the biases of static adaptation parameters from laboratories, and significantly improving the in-situ applicability of the parameters. Furthermore, through continuous contamination gradient adaptation curves, precise customization of adaptation parameters under different contamination gradients is achieved.

[0063] S220 extracts microbial activity data corresponding to short-term and long-term disturbances based on time-series segmentation features from multi-dimensional time-series monitoring data. Through time-series decomposition of microbial performance, it obtains functional microbial metabolic synergy parameters and microbial heavy metal mineralization stability parameters.

[0064] It is understandable that short-term disturbances refer to short-duration showers or instantaneous disturbances, characterized by short duration and low intensity; while long-term disturbances refer to continuous rainfall or persistent disturbances, characterized by long duration and high intensity. These two types of disturbances have significantly different effects on the metabolic activity and heavy metal mineralization of the microbial community. The temporal decomposition of microbial community performance decomposes the overall performance of the microbial community into two independent dimensions: metabolic synergy and heavy metal mineralization stability. This process eliminates interfering factors such as environmental disturbances and the adaptability of native microbial communities, and separately quantifies the metabolic synergy and heavy metal mineralization stability of exogenous functional microbial communities.

[0065] This setup, using the temporal segmentation features of multi-dimensional time-series monitoring data as a unified extraction basis, can accurately determine the in-situ adaptation parameters of indigenous microbial communities, enabling quantitative characterization of their survival adaptation and colonization capabilities under in-situ dynamic disturbances and heavy metal compound pollution environments. Furthermore, it can divide short-term and long-term disturbances based on temporal segmentation features and extract corresponding microbial activity data. Then, through temporal decomposition of microbial performance, it obtains functional microbial metabolic synergy parameters and microbial heavy metal mineralization stability parameters. This not only distinguishes the performance differences of microbial communities under different disturbance durations but also deconstructs and separates the independent performance of microbial metabolic synergy and heavy metal mineralization stability, reducing parameter coupling interference. The entire process relies on unified temporal features to ensure consistent technical logic, achieving layered, precise, and efficient extraction of three core remediation parameters, fully reflecting the real environmental characteristics of in-situ rainfall-redox intermittent disturbances in mines.

[0066] In one possible implementation, the input data for the temporal decomposition of microbial community performance are all time-series data extracted based on time-series segmented features. The time-series data includes microbial community activity values ​​at the end of the resting recovery period, microbial community activity values ​​disturbed by short-term rainfall, microbial community activity values ​​disturbed by long-term rainfall, and disturbance intensity values. In step S220, through the temporal decomposition of microbial community performance, functional microbial community metabolic synergy parameters and microbial community heavy metal mineralization stability parameters are obtained, including:

[0067] S221, the first time series comprehensive value is calculated based on the bacterial activity value disturbed by short-term rainfall, the bacterial activity value at the end of the static recovery period, and the short-term disturbance intensity value.

[0068] It can be understood that the bacterial activity value at the end of the static recovery period is the basic activity value of the bacterial community under undisturbed steady state. As the benchmark value for calculation, the last 5 consecutive collection time points of the static recovery period can be determined according to the time series segmentation characteristics, and this position is defined as the end of the period. Then, the bacterial activity values ​​corresponding to these 5 time points are extracted from the time series data of indigenous bacterial community colonization activity and the time series data of exogenous functional bacterial community iron and sulfur metabolism. After removing the maximum and minimum values, the arithmetic mean is taken as the bacterial activity value at the end of the static recovery period in this cycle.

[0069] The short-term rainfall disturbance microbial community activity value is the activity value of the microbial community under short-term, low-intensity disturbance, reflecting the degree of influence of short-term disturbance on the microbial community. Based on the temporal segmentation characteristics, the duration interval of the short-term rainfall disturbance is first located (usually within 10-30 minutes after the start of the disturbance, and before the formation of a stage of continuous leaching and violent redox fluctuations). Within this interval, three consecutive time points with the greatest disturbance intensity and the most significant decrease in microbial community activity are selected, and the corresponding microbial community activity data are extracted. After smoothing and denoising, the average value is taken, which is the short-term rainfall disturbance microbial community activity value.

[0070] The short-term disturbance intensity value is an indicator that quantifies the strength of short-term disturbances. It is calculated based on the changes in water content, the fluctuation range of redox potential, and the increase in heavy metal leaching concentration. After normalizing the three indicators to the interval [0, 1], they are weighted and summed according to preset weights (water content change range 0.4, redox potential fluctuation range 0.4, heavy metal leaching concentration increase 0.2) to finally obtain the disturbance intensity value in the interval [0, 1]. The closer the value is to 1, the stronger the disturbance; the closer it is to 0, the weaker the disturbance.

[0071] The activity values ​​of the microbial community disturbed by short-term rainfall, the activity values ​​of the microbial community at the end of the static recovery period, and the disturbance intensity value were weighted and summed. The weights were set according to the degree of influence of short-term disturbance on microbial community performance. Specifically, for the scenario of short-term rainfall disturbance, the Pearson correlation coefficients between the three indicators—the activity values ​​of the microbial community at the end of the static recovery period, the activity values ​​of the microbial community disturbed by short-term rainfall, and the disturbance intensity value—and the overall remediation performance of the microbial community were calculated. The absolute value of the correlation coefficient directly reflects the strength of the influence of the disturbance on the indicator. Secondly, sensitivity judgment was made in conjunction with the in-situ remediation mechanism of mines. The larger the correlation coefficient, the more sensitive the indicator is to short-term disturbance and the higher the degree of influence on the overall performance of the microbial community; the smaller the correlation coefficient, the less sensitive the indicator is to short-term disturbance and the lower the degree of influence on the overall performance of the microbial community. Based on this, the weights are determined according to the degree of influence following three rules: First, the indicator with the highest degree of influence is assigned the highest weight; second, the indicator with the second highest degree of influence is assigned the second highest weight; third, the indicator with the lowest degree of influence is assigned the lowest weight, and the sum of the weights of all indicators is 1. Finally, the dimensionless first time series comprehensive value is obtained through normalization. This value comprehensively reflects the overall performance of the microbial community's basic activity, perturbation response, and environmental tolerance under short-term perturbation.

[0072] S222, based on the bacterial community activity value disturbed by long-term rainfall, the bacterial community activity value at the end of the static recovery period, and the long-term disturbance intensity value, the second time series comprehensive value is calculated.

[0073] It is understandable that the activity value of microbial communities disturbed by long-term rainfall reflects the activity state of microbial communities under long-term high-intensity disturbance. Based on the temporal segmentation characteristics, the duration of long-term rainfall disturbance can be first located (usually the stage when continuous disturbance exceeds 60 minutes, soil moisture content is saturated, redox potential continues to decline, and heavy metal leaching reaches its peak). Within this interval, 3-5 consecutive time points with the most stable disturbance intensity and microbial community activity reduced to a relatively stable low point are selected, and the corresponding microbial community activity data are extracted. After smoothing and noise reduction, the average value is taken, which is the activity value of microbial communities disturbed by long-term rainfall.

[0074] The long-term disturbance intensity value is recalculated based on the duration and fluctuation amplitude of the long-term disturbance, reflecting the high intensity characteristics of the long-term disturbance. The calculation method of the long-term disturbance intensity value is similar to that of the disturbance intensity value in step S221. That is, it is calculated by comprehensively considering the change amplitude of water content, the fluctuation amplitude of redox potential, and the increase in heavy metal leaching concentration within the long-term rainfall disturbance interval. After normalizing the three indicators to the interval [0, 1], they are weighted and summed according to preset weights (water content change amplitude 0.4, redox potential fluctuation amplitude 0.4, heavy metal leaching concentration increase 0.2) to finally obtain the long-term disturbance intensity value within the interval [0, 1]. The closer the value is to 1, the stronger the disturbance; the closer it is to 0, the weaker the disturbance.

[0075] The activity values ​​of microbial communities disturbed by long-term rainfall, the activity values ​​of microbial communities at the end of the static recovery period, and the intensity value of long-term disturbance are weighted and summed. The weights are set according to the degree of influence of long-term disturbance on microbial community performance. Specifically, for the scenario of long-term rainfall disturbance, the Pearson correlation coefficients between the three indicators—the activity values ​​of microbial communities at the end of the static recovery period, the activity values ​​of microbial communities disturbed by long-term rainfall, and the intensity value of long-term disturbance—and the overall remediation performance of the microbial community are calculated. The absolute value of the correlation coefficient directly reflects the strength of the influence of disturbance on the indicator. Secondly, sensitivity judgment is made in combination with the in-situ remediation mechanism of mines. The larger the correlation coefficient, the more sensitive the indicator is to long-term disturbance and the higher the degree of influence on the overall performance of the microbial community; the smaller the correlation coefficient, the less sensitive the indicator is to long-term disturbance and the lower the degree of influence on the overall performance of the microbial community. Based on this, the weights are determined according to the degree of influence following three rules: First, the indicator with the highest degree of influence is assigned the highest weight; second, the indicator with the second highest degree of influence is assigned the second highest weight; third, the indicator with the lowest degree of influence is assigned the lowest weight, and the sum of the weights of all indicators is 1. Finally, the dimensionless second time series comprehensive value is obtained through normalization. This value comprehensively reflects the overall performance of the microbial community's basic activity, continuous resistance to disturbance, and long-term environmental tolerance under long-term disturbance.

[0076] S223, the heavy metal mineralization stability parameters of the microbial community are determined by the difference between the second time series comprehensive value and the first time series comprehensive value.

[0077] It is understandable that under short-term perturbation, the synergistic metabolic performance of the microbial community dominates, and its mineralization stability is not fully utilized; under long-term perturbation, the mineralization stability of the microbial community is fully manifested, becoming the core source of performance differences. Therefore, the difference between the two can accurately characterize the microbial community's heavy metal mineralization stability. After standardization, this difference becomes the microbial community's heavy metal mineralization stability parameter. The larger the parameter value, the stronger the stability of the microbial community in heavy metal mineralization and solidification under continuous perturbation, and the better the effect on the morphological stabilization of lead, cadmium, and arsenic.

[0078] S224. The metabolic synergy parameters of functional microbiota are determined by the difference between the first time-series composite value and the in-situ adaptation parameters of the indigenous microbiota.

[0079] It is understandable that the mineralization stability of the microbial community is relatively weak under short-term disturbances, and metabolic synergy is dominant. Therefore, this difference can accurately characterize the metabolic synergy between the functional microbial community and the indigenous microbial community. After standardization, the difference becomes the metabolic synergy parameter of the functional microbial community. The larger the parameter value, the stronger the metabolic synergy between the exogenous functional microbial community and the indigenous microbial community, the better the lack of antagonism and complementarity, and the higher the metabolic activity after combination.

[0080] This setup ensures that the input data for the temporal decomposition of microbial community performance are standardized time-series data extracted based on temporal segmentation features. It explicitly uses the activity values ​​of the microbial community at the end of the static recovery period, the activity values ​​of the microbial community disturbed by short-term rainfall, the activity values ​​of the microbial community disturbed by long-term rainfall, and the disturbance intensity value as the calculation basis. Then, the first and second time-series comprehensive values ​​are calculated sequentially. Through double-difference operations, the heavy metal mineralization stability parameters and functional microbial community metabolic synergy parameters of the microbial community are determined respectively. By comparing long and short-term disturbances, the influence of environmental interference and performance coupling can be effectively eliminated, accurately separating the heavy metal mineralization stability performance and metabolic synergy performance of the microbial community. This avoids confusion and calculation distortion between the two types of core parameters, significantly improving the accuracy, objectivity, and relevance of the calculation of functional microbial community metabolic synergy parameters and heavy metal mineralization stability parameters. The extracted core restoration parameters are more closely aligned with the actual working state of the microbial community under different durations of intermittent disturbances in the mine.

[0081] S300, based on the core repair parameters, yields the corresponding parameter contribution amounts; among which, the parameter contribution amounts include the contribution amount of indigenous microbial community adaptation, the contribution amount of functional microbial community metabolism, and the contribution amount of microbial community mineralization stability.

[0082] It can be understood that parameter contribution is an indicator that quantifies the actual contribution of each type of core parameter to the comprehensive soil remediation efficiency, and it corresponds one-to-one with the core remediation parameters. Indigenous microbial community in-situ adaptation parameters correspond to indigenous microbial community adaptation contribution, functional microbial community metabolic synergy parameters correspond to functional microbial community metabolic contribution, and microbial community heavy metal mineralization stabilization parameters correspond to microbial community mineralization stabilization contribution. Core remediation parameters are performance indicators, while parameter contribution is an efficiency indicator. The two are transformed through standardization, performance correction, and environmental constraints, converting dimensionless performance parameters into directly summable remediation efficiency contribution values.

[0083] For example, the bacterial activity recovery rate can be determined based on the bacterial activity value at the end of the static recovery period and the bacterial activity value disturbed by short-term rainfall. Then, based on the bacterial activity recovery rate and the in-situ adaptation parameters of the indigenous bacterial community, the elasticity coefficient of the indigenous bacterial community activity recovery under disturbance can be determined. Multiplying the in-situ adaptation parameters of the indigenous bacterial community by the elasticity coefficient of the indigenous bacterial community activity recovery yields the indigenous bacterial community adaptation contribution. Based on the bacterial activity recovery rate and the disturbance intensity value, the metabolic efficiency constraint factor under disturbance can be determined. Then, multiplying the functional bacterial community metabolic synergy parameters by the metabolic efficiency constraint factor yields the functional bacterial community metabolic contribution. Based on the in-situ adaptation parameters of the indigenous bacterial community and the bacterial community heavy metal mineralization stability parameters, the bacterial community synergy mineralization can be determined. The model first calculates the interference level, then corrects the heavy metal mineralization stability parameters of the microbial community based on the microbial community's synergistic mineralization interference level, thus obtaining the microbial community's mineralization stability contribution. Alternatively, it can first construct a unified parameter contribution quantification model based on time-series segmentation characteristics and heavy metal pollution gradients, then substitute the indigenous microbial community's in-situ adaptation parameters into the model and combine them with the in-situ disturbance tolerance coefficient to obtain the indigenous microbial community's adaptation contribution, substitute the functional microbial community's metabolic synergistic parameters into the model and combine them with the metabolic persistence factor calibration to obtain the functional microbial community's metabolic contribution, and substitute the microbial community's heavy metal mineralization stability parameters into the model and combine them with the multi-metal solidification synergistic weight correction to obtain the microbial community's mineralization stability contribution. At the same time, the three types of contributions are uniformly normalized to the same dimension range, and so on, but not limited to these methods.

[0084] In one possible implementation, in step S300, based on the core repair parameters, the corresponding parameter contribution is obtained, including:

[0085] S310, determine the bacterial activity recovery rate based on the bacterial activity value at the end of the static recovery period and the bacterial activity value disturbed by short-term rainfall.

[0086] It can be understood that the bacterial activity value at the end of the static recovery period is the steady-state activity value after the bacterial community is disturbed, while the bacterial activity value disturbed by short-term rainfall is the lowest activity value after the bacterial community is disturbed. The difference between the two reflects the extent of bacterial community activity recovery. Dividing this recovery extent by the duration of the disturbance yields the bacterial community activity recovery rate. The larger this rate value, the faster the bacterial community activity recovers after the disturbance, and the stronger its resistance to disturbance.

[0087] S320, based on the microbial community activity recovery rate and the in-situ adaptation parameters of the indigenous microbial community, determines the elasticity coefficient of the indigenous microbial community activity recovery under disturbance conditions.

[0088] It is understandable that the indigenous microbial community activity recovery elasticity coefficient is a key coefficient for correcting the contribution of indigenous microbial community adaptation. It is used to reflect the elasticity of indigenous microbial community activity recovery under perturbation. It is calculated by weighting the microbial community activity recovery rate and the indigenous microbial community in-situ adaptation parameters. Specifically, for the in-situ intermittent perturbation scenario in mines, the Pearson correlation coefficients between the microbial community activity recovery rate and the indigenous microbial community in-situ adaptation parameters and the actual repair efficiency after the indigenous microbial community perturbation are calculated. The absolute value of the correlation coefficient represents the degree of influence of each indicator on the activity recovery elasticity. The larger the correlation coefficient, the higher the degree of influence of the indicator on the recovery performance after the indigenous microbial community perturbation. Then, following the weight allocation rule, the indicator with a higher degree of influence is assigned a higher weight, and the sum of the weights of the two indicators is 1. After obtaining the initial elasticity coefficient based on weighted calculation, interval normalization calibration is performed in combination with the in-situ heavy metal complex pollution remediation scenario in mines. The final value is limited to the dimensionless interval [0, 1] to obtain the elasticity coefficient of indigenous microbial community activity recovery under disturbance. The larger the coefficient value, the stronger the activity recovery ability of the indigenous microbial community after disturbance and the better the adaptability to the in-situ environment.

[0089] S330, multiply the in-situ adaptation parameters of the indigenous microbial community by the elasticity coefficient of the recovery of the activity of the indigenous microbial community to obtain the adaptation contribution of the indigenous microbial community.

[0090] It is understandable that the in-situ adaptation parameters of the indigenous microbial community are the basic performance values, while the elasticity coefficient of the indigenous microbial community activity recovery is the dynamic correction value. Multiplying the two together can eliminate the deviation of the static parameters and fully reflect the actual contribution of the adaptation ability of the indigenous microbial community to the repair efficacy under dynamic disturbance.

[0091] This setup, through step-by-step calculation of the microbial community activity recovery rate and activity recovery elasticity coefficient, ultimately yields a precisely corrected contribution from the indigenous microbial community's adaptation. This overcomes the shortcomings of traditional techniques that rely solely on static adaptation parameters to calculate contribution, failing to reflect dynamic disturbance recovery capabilities. This contribution, combining static adaptation performance and dynamic recovery elasticity, comprehensively reflects the true remediation contribution of the indigenous microbial community under in-situ disturbance conditions, significantly improving the accuracy and applicability of the contribution.

[0092] In one possible implementation, step S300, based on the core repair parameters, obtains the corresponding parameter contribution amount, and further includes:

[0093] S340, based on the recovery rate of bacterial community activity and the long-term perturbation intensity value, determines the metabolic efficiency constraint factor under perturbation state.

[0094] It is understandable that the metabolic efficiency constraint factor is a key constraint coefficient used to correct the metabolic contribution of functional microbial communities, reflecting the degree of constraint of in-situ intermittent perturbation on the metabolic synergy of functional microbial communities. The greater the perturbation intensity and the slower the microbial community activity recovery rate, the smaller the metabolic efficiency constraint factor, indicating that the perturbation constrains metabolic performance more strongly; conversely, the metabolic efficiency constraint factor is larger. First, the two input data are subjected to dimensionless normalization, mapping the microbial community activity recovery rate to the [0, 1] standard interval (with the maximum measured activity recovery rate of the microbial community at the site as the normalization upper limit), and the long-term perturbation intensity value is mapped to the [0, 1] standard interval (with the maximum historical rainfall perturbation intensity at the site as the normalization upper limit), eliminating the interference of different dimensions and numerical magnitudes on the calculation results; then, a perturbation metabolic constraint function is constructed, with the normalized microbial community activity recovery rate as the positive compensation term and the normalized perturbation intensity value as the negative constraint term, using the core ratio. The initial constraint value is calculated using the formula: Initial metabolic efficiency constraint value = Normalized microbial community activity recovery rate ÷ (Normalized disturbance intensity value + 0.01), where 0.01 is added as a minimum constant coefficient. Finally, dynamic calibration is performed in conjunction with the engineering practice threshold of lead / cadmium / arsenic compound acidic soil, limiting the final result to the effective range of [0.2, 1.0]. When the disturbance intensity reaches the severe disturbance standard, the initial value is slightly corrected downwards, and when the microbial community activity recovery rate reaches the rapid recovery standard, it is slightly corrected upwards. The final value obtained after calibration is the metabolic efficiency constraint factor. The correction method is as follows: initial constraint value × (1 - disturbance attenuation coefficient), where the disturbance attenuation coefficient ranges from 0.1 to 0.2, and the closer the disturbance intensity is to 1, the larger the attenuation coefficient value. When the rapid recovery criterion is met, the initial constraint value is corrected upwards using a recovery gain coefficient, which is calculated as: initial constraint value × (1 + recovery gain coefficient), where the recovery gain coefficient ranges from 0.05 to 0.15, and the closer the bacterial community activity recovery rate is to 1, the larger the gain coefficient value. If both criteria are met simultaneously, the downward and upward corrections are performed sequentially. Finally, the corrected values ​​are truncated at the boundary: 0.2 is taken when the value is less than 0.2, and 1.0 is taken when the value is greater than 1.0, resulting in a metabolic efficiency constraint factor limited to the effective range of [0.2, 1.0].

[0095] S350, the metabolic contribution of functional microbiota is obtained by multiplying the metabolic synergy parameters of functional microbiota with the metabolic efficiency constraint factor.

[0096] It is understandable that the functional microbiota metabolic synergy parameter is a basal metabolic performance value, while the metabolic efficiency constraint factor is a correction value for the perturbation environment. Multiplying the two can reflect the actual contribution of the functional microbiota metabolic synergy performance to the repair efficiency under perturbation conditions, eliminating the influence of metabolic performance decline caused by environmental perturbation, and making the contribution more realistic and accurate. The larger the functional microbiota metabolic value, the greater the contribution of the functional microbiota metabolic synergy to the repair efficiency.

[0097] This setup, by determining the metabolic efficiency constraint factor under perturbation conditions based on the microbial community activity recovery rate and perturbation intensity, can accurately quantify the actual constraint effect of in-situ rainfall-redox intermittent perturbation on the metabolic synergy performance of functional microbial communities. Multiplying this constraint factor by the functional microbial community metabolic synergy parameters yields the metabolic contribution of the functional microbial community. This effectively eliminates the interference of metabolic efficiency attenuation caused by dynamic perturbation environments, truly reflecting the metabolic synergy repair contribution of functional microbial communities under in-situ compound pollution and intermittent perturbation scenarios in mines. It significantly improves the accuracy and authenticity of the calculation of the metabolic contribution of functional microbial communities, making the parameter contribution more closely aligned with actual repair conditions. This provides scientific and reliable metabolic contribution data support for the subsequent accurate calculation of real-time comprehensive repair efficiency and the selection of optimal microbial community combination schemes adapted to different perturbation intensities.

[0098] In one possible implementation, step S300, based on the core repair parameters, obtains the corresponding parameter contribution amount, and further includes:

[0099] S360 determines the degree of interference in synergistic mineralization of the microbial community based on the in-situ adaptation parameters of the indigenous microbial community and the stability parameters of heavy metal mineralization of the microbial community.

[0100] It can be understood that the degree of interference in synergistic mineralization by microbial communities is a correction coefficient that quantifies the degree of interference of the synergistic effect between indigenous and functional microbial communities on the heavy metal mineralization process, reflecting the influence of synergistic / antagonistic effects between microbial communities on mineralization stability. The in-situ adaptation parameter of indigenous microbial communities reflects their survival and adaptation ability, while the heavy metal mineralization stability parameter reflects the mineralization and solidification ability of functional microbial communities. The synergistic effect of the two can produce mineralization interference: excessive adaptation of indigenous microbial communities may inhibit the mineralization performance of functional microbial communities, while insufficient adaptation will not provide synergistic support. Therefore, the degree of interference in synergistic mineralization by microbial communities can be calculated by the ratio of the in-situ adaptation parameter of indigenous microbial communities to the heavy metal mineralization stability parameter of microbial communities. The smaller the interference value, the better the synergistic mineralization effect and the smaller the interference; conversely, the larger the value, the greater the interference.

[0101] S370, based on the degree of interference of synergistic mineralization of the microbial community, corrects the stability parameters of heavy metal mineralization of the microbial community and obtains the contribution of microbial community to mineralization stability.

[0102] It is understandable that by correcting the bacterial community's heavy metal mineralization stability parameters through the degree of synergistic mineralization interference, the contribution of the bacterial community to mineralization stability can be obtained, thus realizing the transformation of mineralization performance parameters into contribution to remediation efficiency. The correction process is as follows: multiply the bacterial community's heavy metal mineralization stability parameters by (1 - degree of synergistic mineralization interference) to eliminate the mineralization interference caused by the synergistic effect of the bacterial community, and obtain the contribution of the bacterial community to mineralization stability that truly reflects the efficiency of heavy metal mineralization and solidification. This contribution quantifies the synchronous mineralization stability contribution of the compound bacterial community to lead, cadmium, and arsenic. The larger the value, the better the heavy metal solidification effect and the lower the migration risk.

[0103] This setup, by determining the degree of interference in synergistic mineralization of the microbial community based on the in-situ adaptation parameters of the indigenous microbial community and the heavy metal mineralization stability parameters of the microbial community, can accurately quantify the degree of mutual interference between the indigenous and functional microbial communities in the process of synergistically completing the mineralization stability of heavy metals. Based on this degree of interference, the heavy metal mineralization stability parameters of the microbial community are then corrected to obtain the contribution of the microbial community to mineralization stability. This can effectively reduce the calculation deviation of mineralization performance caused by the mismatch between the adaptation of the microbial communities and the imbalance of synergistic effects, and truly reflect the actual mineralization, solidification and remediation contribution of the compound microbial community to lead, cadmium and arsenic heavy metals under in-situ compound pollution and intermittent disturbance environments.

[0104] In one possible implementation, after extracting the core repair parameters in step S200, the method further includes:

[0105] S301, based on the end-of-life microbial activity value and the corresponding heavy metal pollution gradient during the static recovery period, the baseline remediation amount is obtained.

[0106] It can be understood that the baseline remediation quantity is the basic remediation efficacy value of the soil under conditions of no exogenous microbial enhancement, determined solely by the steady-state activity of indigenous microbial communities and the pollution gradient. The microbial activity value at the end of the static recovery period represents the steady-state baseline activity of indigenous microbial communities, indicating the soil's own basic remediation capacity. The corresponding heavy metal pollution gradient reflects the impact of pollution level on basic remediation capacity; the higher the pollution gradient, the lower the baseline remediation quantity, and vice versa. For example, a remediation gain value can be determined based on the heavy metal pollution gradient, and then the microbial activity value at the end of the static recovery period can be added to the remediation gain value to obtain the baseline remediation quantity. Alternatively, the baseline remediation attenuation coefficient can be matched according to different heavy metal pollution gradients, the microbial activity value at the end of the static recovery period can be normalized, multiplied by the baseline remediation attenuation coefficient of the corresponding pollution gradient, and then fine-tuned by the working condition calibration coefficient of in-situ compound pollution remediation in mines, ultimately obtaining the baseline remediation quantity adapted to the current heavy metal pollution gradient, and so on, but not limited to these methods.

[0107] This setup determines the baseline remediation quantity by combining the microbial activity value at the end of the static recovery period with the heavy metal pollution gradient. This provides a stable, objective, and unified baseline for calculating real-time comprehensive remediation effectiveness. The baseline quantity reflects the soil's own basic remediation capacity, fully combining in-situ steady-state microbial activity with the actual pollution gradient, and closely matches the real remediation scenario of mines.

[0108] In one possible implementation, in step S301, the baseline remediation amount is obtained based on the end-of-life microbial activity value and the corresponding heavy metal contamination gradient during the static recovery period, including:

[0109] S3011, Determining remediation gain value based on heavy metal pollution gradient.

[0110] It can be understood that the remediation gain value is a dimensionless environmental correction parameter used to quantify the additional contribution or reduction effect of the soil's inherent basic remediation capacity caused by the intensity of heavy metal toxicity stress, soil physicochemical environment, and differences in site microenvironment, excluding the end-stage homeostatic activity of indigenous microbiota during the static recovery period. It is used to calibrate the soil's inherent basic remediation potential under different pollution gradients. Different heavy metal pollution gradients correspond to a remediation gain value. Differentiated remediation gain values ​​are set for low, medium, and high pollution gradients: under low pollution gradients, the soil's self-remediation capacity is strong, resulting in a higher remediation gain value; under high pollution gradients, the soil's remediation capacity is inhibited, resulting in a lower remediation gain value; and for medium pollution gradients, an intermediate value is used. This method, considering the strong spatial heterogeneity of the mining environment and the large differences in pollution levels across different areas, does not determine the gradient based on the average pollution level of the entire mining area. Instead, it adopts a method of zonal sampling and zonal assignment in local pollution areas of the mine, dividing the mine into multiple independent remediation areas. Each area is individually assessed for heavy metal pollution gradients and matched with a corresponding remediation gain value. For example, matching can be performed in a composite database, or obtained through machine learning models, etc., but is not limited to these methods. A composite database refers to a database containing remediation gain values ​​corresponding to different heavy metal pollution gradients. This data can be obtained through laboratory experiments, field measurements and monitoring, and past experience. After acquisition, the collected data is organized, classified, and archived, useful information and patterns are extracted, and relevant data are saved into the database to form a composite database. Specifically, soil sampling points are first set up for each localized pollution area of ​​the mine, and the total concentrations of lead, cadmium, and arsenic, the concentration of active heavy metals, soil pH, and redox potential are measured in each area. Based on this, each area is divided into low, medium, and high heavy metal pollution zones. The remediation process involved several steps. First, for each pollution gradient zone, in-situ monitoring and indoor simulated remediation experiments were conducted. Exogenous microbial enhancement was excluded. The actual basic remediation efficiency of the soil relying solely on indigenous microbial communities was measured. Using the microbial activity at the end of the static recovery period as a benchmark, the difference between the actual basic remediation efficiency and the steady-state microbial activity was calculated. This difference represents the remediation gain value under the corresponding pollution gradient. Subsequently, the heavy metal pollution gradients and corresponding remediation gain values ​​obtained from the experiments for each zone were systematically compiled and archived. Historical experimental data for different mining conditions and pollution types were supplemented to establish a quantitative mapping relationship between gradient and gain, ultimately forming a standardized composite database. The machine learning model was trained using multiple sets of training data, each set including a heavy metal pollution gradient and its corresponding remediation gain value.

[0111] S3012, the baseline repair benchmark is obtained by adding the bacterial activity value at the end of the static recovery period to the repair gain value.

[0112] It can be understood that the baseline repair quantity = the bacterial activity value at the end of the static recovery period + the repair gain value.

[0113] This setup, by determining the remediation gain value based on the differential heavy metal pollution gradient, can accurately match the impact of different pollution levels on the soil's basic remediation capacity. Then, by adding the end-of-life microbial activity value during the static recovery period to this remediation gain value, the basic remediation benchmark can be obtained. This fully combines the dual factors of the native microbial community's steady-state activity and the actual heavy metal pollution gradient, quantifying the soil's basic remediation efficiency benchmark that fits the different pollution gradients in the mine site.

[0114] S400 sums the parameter contribution with the basic remediation baseline to obtain the real-time comprehensive remediation efficiency of contaminated soil.

[0115] It can be understood that real-time comprehensive repair efficiency = contribution of indigenous microbial community adaptation + contribution of functional microbial community metabolism + contribution of microbial community mineralization stability + basic repair benchmark.

[0116] S500, based on the real-time comprehensive remediation efficacy under different heavy metal pollution gradients, screens and determines the optimal microbial community compound scheme suitable for the corresponding heavy metal pollution gradient.

[0117] It is understandable that, based on the real-time comprehensive remediation efficacy obtained from S400 under different heavy metal pollution gradients (low, medium, and high), a horizontal comparison and screening were conducted. The bacterial community combination with the highest real-time comprehensive remediation efficacy value was determined as the optimal bacterial community combination scheme suitable for the corresponding pollution gradient. The screening process fully considers the in-situ rainfall-redox intermittent disturbance environment and the characteristics of lead / cadmium / arsenic complex acidic pollution to achieve precise customized combination schemes for each gradient: for low pollution gradients, a lightweight combination scheme with strong adaptability and good metabolic synergy is selected; for high pollution gradients, an enhanced combination scheme with strong mineralization stability and high colonization survival rate is selected; and for medium pollution gradients, a balanced combination scheme with the best overall performance is selected.

[0118] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0119] Corresponding to the analytical method for the combination of microbial communities in mine environmental remediation described in the above embodiments, this application also provides an analytical system for the combination of microbial communities in mine environmental remediation. Each module of this system can realize each step of the analytical method for the combination of microbial communities in mine environmental remediation. Figure 4 The diagram shows a structural block diagram of the analytical system for the compounding of microbial communities in mountain environmental management provided in the embodiments of this application. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0120] Reference Figure 4The analytical system for the complex microbial community in the environmental remediation of this mountain includes:

[0121] The acquisition module is used to acquire multi-dimensional time-series monitoring data under in-situ rainfall-redox intermittent disturbances. The multi-dimensional time-series monitoring data includes soil physicochemical time-series changes, heavy metal speciation time-series data, indigenous microbial community colonization activity time-series data, and exogenous functional microbial community iron and sulfur metabolism time-series data.

[0122] The extraction module is used to extract core repair parameters based on the time-series segmentation features of multi-dimensional time-series monitoring data. Among them, the core repair parameters include the in-situ adaptation parameters of indigenous microbial communities, the metabolic synergy parameters of functional microbial communities, and the heavy metal mineralization stability parameters of microbial communities.

[0123] The analysis module is used to obtain the corresponding parameter contribution based on the core repair parameters. The parameter contribution includes the contribution of indigenous microbial community adaptation, the contribution of functional microbial community metabolism, and the contribution of microbial community mineralization stability.

[0124] The processing module is used to sum the parameter contribution amount with the basic remediation benchmark amount to obtain the real-time comprehensive remediation efficiency of contaminated soil.

[0125] The screening module is used to screen and determine the optimal microbial community combination scheme suitable for the corresponding heavy metal pollution gradient based on the real-time comprehensive remediation efficacy under different heavy metal pollution gradients.

[0126] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0127] Those skilled in the art will understand that, for the sake of convenience and brevity, the above-described module division is merely an example. In practical applications, the above functions can be assigned to different modules as needed, that is, the internal structure of the system can be divided into different modules to complete all or part of the functions described above. The modules in the embodiments can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0128] This application also provides an electronic device. Figure 5 This is a schematic diagram of the structure of an electronic device 6 provided in an embodiment of this application. Figure 5As shown, the electronic device 6 of this embodiment includes: at least one processor 60 ( Figure 5 Only one is shown in the image), at least one memory 61 ( Figure 5 (Only one is shown in the image) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, it causes the electronic device 6 to perform the steps in any of the above-described methods for analyzing the compound microbial community in mine environmental management, or to perform the functions of each module in the above-described device embodiments.

[0129] For example, the computer program 62 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 62 in the electronic device 6.

[0130] The electronic device 6 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. This electronic device may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will understand that... Figure 5 This is merely an example of electronic device 6 and does not constitute a limitation on electronic device 6. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0131] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An analytical method for the compounding of microbial communities in mine environmental remediation, characterized in that, include: Acquire multi-dimensional time-series monitoring data under in-situ rainfall-redox intermittent disturbance; wherein, the multi-dimensional time-series monitoring data includes soil physicochemical time-series change data, heavy metal speciation time-series data, indigenous microbial community colonization activity time-series data, and exogenous functional microbial community iron and sulfur metabolism time-series data; Based on the time-series segmentation features of the multi-dimensional time-series monitoring data, core repair parameters are extracted; wherein, the core repair parameters include in-situ adaptation parameters of indigenous microbial communities, metabolic synergy parameters of functional microbial communities, and heavy metal mineralization stability parameters of microbial communities. Based on the core repair parameters, the corresponding parameter contribution amounts are obtained; wherein, the parameter contribution amounts include the indigenous microbial community adaptation contribution amount, the functional microbial community metabolic contribution amount, and the microbial community mineralization stability contribution amount. The contribution of the parameters is summed with the baseline remediation amount to obtain the real-time comprehensive remediation efficiency of the contaminated soil. Based on the real-time comprehensive remediation efficacy under different heavy metal pollution gradients, the optimal microbial community combination scheme corresponding to the heavy metal pollution gradient was screened and determined.

2. The analytical method for the compound microbial community in mine environmental remediation as described in claim 1, characterized in that, The core repair parameters are extracted based on the time-series segmentation features of the multi-dimensional time-series monitoring data, including: Based on the temporal segmentation characteristics of the multi-dimensional temporal monitoring data, the in-situ adaptation parameters of the indigenous microbial community are determined. Extract microbial activity data corresponding to short-term and long-term disturbances based on time-series segmentation features from the multi-dimensional time-series monitoring data. Through time-series decomposition of microbial performance, obtain functional microbial metabolic synergy parameters and microbial heavy metal mineralization stability parameters.

3. The analytical method for the complex microbial community in mine environmental remediation as described in claim 2, characterized in that, The determination of the in-situ adaptation parameters of the indigenous microbial community based on the time-series segmentation features of the multi-dimensional time-series monitoring data includes: Based on the time-series segmentation characteristics of the multi-dimensional time-series monitoring data, the rainfall disturbance period and the static recovery period are divided, and the heavy metal pollution gradient corresponding to each period is matched. Based on the correspondence between bacterial community activity and the heavy metal pollution gradient at different time periods, an adaptation curve of indigenous bacterial communities under a continuous pollution gradient is generated. Based on the adaptation curve of the indigenous microbial community, the in-situ adaptation parameters of the indigenous microbial community corresponding to the heavy metal pollution gradient are determined.

4. The analytical method for the compound microbial community in mine environmental remediation as described in claim 2, characterized in that, The input data for the temporal decomposition of the microbial community performance are all time-series data extracted based on time-series segmented features. The time-series data includes the microbial community activity value at the end of the static recovery period, the microbial community activity value disturbed by short-term rainfall, the microbial community activity value disturbed by long-term rainfall, the short-term disturbance intensity value, and the long-term disturbance intensity value. The temporal decomposition of microbial community performance yields functional microbial community metabolic synergy parameters and microbial community heavy metal mineralization stability parameters, including: Based on the short-term rainfall disturbance bacterial activity value, the end-of-rest recovery period bacterial activity value, and the short-term disturbance intensity value, a first time-series comprehensive value is calculated. The second time-series comprehensive value is calculated based on the bacterial community activity value disturbed by the long-term rainfall, the bacterial community activity value at the end of the static recovery period, and the long-term disturbance intensity value. The difference between the second time-series composite value and the first time-series composite value is used to determine the heavy metal mineralization stability parameters of the microbial community. The metabolic synergy parameters of the functional microbial community are determined by the difference between the first time-series composite value and the in-situ adaptation parameters of the indigenous microbial community.

5. The analytical method for the compound microbial community in mine environmental remediation as described in claim 4, characterized in that, The process of obtaining the corresponding parameter contribution based on the core repair parameters includes: The bacterial activity recovery rate is determined based on the bacterial activity value at the end of the static recovery period and the bacterial activity value disturbed by short-term rainfall. Based on the bacterial community activity recovery rate and the indigenous bacterial community in situ adaptation parameters, the elasticity coefficient of indigenous bacterial community activity recovery under disturbance is determined; The adaptation contribution of the indigenous microbial community is obtained by multiplying the in-situ adaptation parameters of the indigenous microbial community with the elasticity coefficient of the activity recovery of the indigenous microbial community.

6. The analytical method for the compound microbial community in mine environmental remediation as described in claim 5, characterized in that, The process of obtaining the corresponding parameter contribution based on the core repair parameters also includes: The metabolic efficiency constraint factor under the perturbation state is determined based on the bacterial community activity recovery rate and the long-term perturbation intensity value. The metabolic contribution of the functional microbial community is obtained by multiplying the metabolic synergy parameter of the functional microbial community with the metabolic efficiency constraint factor.

7. The analytical method for the compound microbial community in mine environmental remediation as described in claim 6, characterized in that, The process of obtaining the corresponding parameter contribution based on the core repair parameters also includes: The degree of synergistic mineralization interference of the microbial community is determined based on the in-situ adaptation parameters of the indigenous microbial community and the heavy metal mineralization stability parameters of the microbial community. Based on the synergistic mineralization interference degree of the bacterial community, the heavy metal mineralization stability parameters of the bacterial community are corrected to obtain the mineralization stability contribution of the bacterial community.

8. The analytical method for the compound microbial community in mine environmental remediation as described in claim 4, characterized in that, After extracting the core repair parameters, the method further includes: The baseline remediation amount is obtained based on the end-of-life microbial activity value and the corresponding heavy metal pollution gradient during the static recovery period.

9. The analytical method for the compound microbial community in mine environmental remediation as described in claim 8, characterized in that, The basic remediation baseline quantity is obtained based on the end-of-life microbial activity value and the corresponding heavy metal pollution gradient during the static recovery period, including: The remediation gain value is determined based on the aforementioned heavy metal pollution gradient; The baseline repair value is obtained by adding the bacterial activity value at the end of the static recovery period to the repair gain value.

10. The analytical method for the compound microbial community in mine environmental remediation as described in claim 1, characterized in that, The acquisition of multi-dimensional time-series monitoring data under in-situ rainfall-redox intermittent perturbation includes: The initial time-series dataset of in-situ rainfall-redox intermittent disturbance remediation was obtained, and the target data segment reflecting the transition of soil from the rainfall disturbance period to the static recovery period was identified based on the time-series segmentation features. When the number of consecutive occurrences of the target data segment reaches a preset threshold, the target data segment is considered as valid multi-dimensional time-series monitoring data.

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