Iron ore tailing heavy metal chromium long-acting solid storage repairing method based on dynamic regulation and control of rhizosphere microorganism network

Abstract

The invention provides an iron ore tailing heavy metal chromium long-acting solid storage restoration method based on a dynamic regulation and control rhizosphere microorganism network, and relates to the technical field of environmental data processing. And a Cr long-acting solid storage prediction model and a dynamic rhizosphere microorganism network model are used for decision making and regulation and control. The method comprises the specific steps of data extraction and fusion, microorganism application and feedback data acquisition, and finally updating a model to reflect the states of microorganisms and heavy metal Cr, so that effective solid storage and management of the heavy metal Cr in the iron ore tailings are realized, and the ecological restoration of mines and the safety of ecological environments are promoted.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ore dressing, in particular to a long-acting heavy metal chromium fixation and remediation method for iron ore tailings based on dynamic regulation of rhizosphere microbial network. BACKGROUND

[0002] Iron ore tailings are solid wastes generated in the process of iron ore dressing, often containing high concentrations of heavy metal chromium (Cr), especially highly toxic hexavalent chromium [Cr (VI)], which is extremely easy to leach into water bodies and soil, causing ecological risks and health hazards. At present, the remediation of heavy metal chromium pollution in iron ore tailings is mainly based on a combination of plant remediation, chemical passivation and engineering covering. On the one hand, materials such as phosphate, lime and bentonite are added to change the pH and adsorption properties of the iron ore tailings matrix to reduce the effective content of heavy metal chromium. On the other hand, hyperaccumulating plants with the ability to accumulate heavy metals such as chromium are selected to absorb, migrate and accumulate heavy metal chromium within a certain time scale, achieve long-term storage of chromium, and form stable mineral forms such as chromium iron ore, thereby preventing its release into the environment again. With the development of Internet of Things and intelligent control technology, some technologies have begun to introduce online monitoring of soil moisture, nutrients and pH, and use drip irrigation and sprinkler devices for fine water and fertilizer management to improve plant growth and remediation efficiency. In rhizosphere biology research, mycorrhizal fungi, growth-promoting bacteria and chelating bacteria are widely used to improve the rhizosphere environment and enhance plant tolerance. Existing technologies introduce a certain amount of beneficial microbial inoculants through seed dressing, root irrigation and planting substrate mixing to improve the tolerance threshold and accumulation capacity of the root system to heavy metal chromium, and perform differential inoculation at different development stages or different root segment positions of the root system, trying to construct an artificial rhizosphere microbial community to some extent, in order to form a relatively stable remediation microenvironment.

[0003] With the rapid development of sensing technology, high-throughput omics detection and machine learning methods, iron ore tailings remediation gradually transitions from experience-driven to data-driven. On the one hand, rhizosphere pH, conductivity, moisture, temperature, and soil nitrogen, phosphorus, and potassium physicochemical indicators can be monitored by multi-parameter sensor networks at high frequency and in layers. On the other hand, microbial community structure and functional gene information can be obtained by metagenomic sequencing or high-throughput sequencing technology, providing a fine-grained data basis for depicting the rhizosphere microbial network. On this basis, some studies have begun to explore the use of machine learning methods to evaluate and predict plant screening, multi-factor stress domestication response, and remediation effect, and to build regression models or classification models to assist in screening efficient remediation plant combinations and microbial agent combinations. Some technologies also attempt to introduce artificial intelligence algorithms to optimize irrigation, fertilizer application, and plant configuration to some extent, making the remediation process evolve towards intelligent and precise direction. The overall trend is that heavy metal chromium remediation is developing from static governance to dynamic regulation, from single-object control to system optimization, and from end-point effect evaluation to life-cycle risk management.

[0004] However, although the existing technology has introduced some data collection and intelligent control means, the overall data processing still remains at the level of local parameter adjustment and experience-based auxiliary decision-making, and has not yet formed a systematic data-driven method for rhizosphere microbial networks and long-term immobilization of heavy metal chromium. Specifically, most methods regard rhizosphere microorganisms as a number of independent microbial groups or simple community collections, lack the abstraction of a functional network model composed of nodes and edges, and cannot utilize network structure and dynamic characteristics to represent their influence on heavy metal chromium morphological transformation and long-term immobilization. The rhizosphere physicochemical data, plant growth data, and microbiome data collected by sensors are mostly used separately, lacking multi-source heterogeneous data fusion and joint modeling steps, making it difficult to accurately predict the risk of heavy metal chromium re-migration and re-release in the time and space dimensions. Moreover, existing rhizosphere microbial inoculation, nutrient supply, and plant succession regulation are mostly based on pre-set rules or limited parameter optimization, and have not yet established a closed-loop control mechanism with prediction, decision-making, and feedback correction. When the physicochemical properties of iron ore tailings substrates or external climate conditions fluctuate greatly, static or semi-static microbial group configuration and nutrient management strategies are difficult to respond in time, which can easily lead to rhizosphere microecological imbalance and heavy metal chromium effective state rebound. SUMMARY

[0005] To overcome the shortcomings of the prior art, the purpose of the present application is to provide a method for long-term immobilization of heavy metal chromium in iron ore tailings based on dynamic regulation of rhizosphere microbial networks. The present application solves the problem of lack of dynamic modeling of rhizosphere microbial networks and long-term immobilization of heavy metal chromium in iron ore tailings remediation based on data processing in the prior art.

[0006] To achieve the above-mentioned purpose, the present application provides the following solutions:

[0007] A long-acting remediation method for heavy metal chromium in iron ore tailings based on dynamic regulation of rhizosphere microbial network, comprising:

[0008] Based on the preset remediation target, the matrix construction treatment and environmental monitoring layout treatment are performed on the iron ore tailings to be repaired to obtain a composite matrix layer, and the environmental parameters are collected by using the sensing device arranged in the composite matrix layer to obtain an iron ore tailings matrix environment data set with space-time labels;

[0009] The heavy metal chromium remediation plant community is configured in the composite matrix layer, and the rhizosphere microbial flora is inoculated, the rhizosphere samples are collected at a preset period to extract rhizosphere microbial information, the rhizosphere microbial information is associated with the iron ore tailings matrix environment data set and the heavy metal chromium remediation plant growth data, and a rhizosphere microbial original data set is constructed;

[0010] Based on the rhizosphere microbial original data set, a rhizosphere microbial network model is constructed, and the rhizosphere microbial network model is updated by using newly acquired rhizosphere microbial data and iron ore tailings matrix environment data during the remediation process to obtain a dynamic rhizosphere microbial network model;

[0011] The network state data of the dynamic rhizosphere microbial network model is extracted, and the network state data is feature fused with the iron ore tailings matrix environment data set and the heavy metal chromium remediation plant growth data to form a rhizosphere comprehensive state input feature data set to construct a heavy metal chromium long-acting fixation prediction model;

[0012] Based on the real-time collected iron ore tailings matrix environment data, the heavy metal chromium long-acting fixation prediction model is used to predict the current rhizosphere comprehensive state input feature data set to obtain a heavy metal chromium re-release risk assessment result, and the rhizosphere microbial network regulation decision data is generated according to the heavy metal chromium re-release risk assessment result;

[0013] According to the rhizosphere microbial network regulation decision data, the inoculation device or the injection device is controlled to perform rhizosphere microbial flora application operation in the root zone of the heavy metal chromium remediation plant community, and the regulated rhizosphere environment data, rhizosphere microbial data and heavy metal chromium form data are collected as feedback data;

[0014] The feedback data is used to update the dynamic rhizosphere microbial network model and the heavy metal chromium long-acting fixation prediction model to maintain the long-acting fixation state of the heavy metal chromium in the iron ore tailings.

[0015] The following technical effects are disclosed:

[0016] The application provides a long-acting fixation and repair method for heavy metal chromium in iron mine tailings based on dynamic regulation of rhizosphere microbial network. Traditional heavy metal chromium repair technologies often face problems such as chromium re-release, valence state conversion and unstable repair effect, and cannot effectively and durably reduce the content of heavy metal chromium. The application realizes precise control and optimization of the repair process by real-time monitoring of the iron mine tailings substrate environment and dynamically updating the rhizosphere microbial network model, ensuring the effectiveness and stability of the rhizosphere microorganisms. In addition, using the formed rhizosphere comprehensive state input feature data set, a long-acting fixation prediction model can be established to assess the risk of heavy metal chromium re-release in advance and provide a scientific basis for subsequent regulation. This method not only improves the effect of heavy metal chromium repair, but also takes into account environmental and economic benefits, providing an innovative solution for iron mine tailings treatment and repair, and helping to achieve green mine construction and more sustainable mine repair and management goals. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings described below are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0018] Figure 1 A long-acting fixation and repair method for heavy metal chromium in iron mine tailings based on dynamic regulation of rhizosphere microbial network is provided. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0020] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0021] As shown in Figure 1 The application provides a long-acting fixation and repair method for heavy metal chromium in iron mine tailings based on dynamic regulation of rhizosphere microbial network, comprising:

[0022] Step 100: Perform matrix construction processing and environmental monitoring layout processing on the iron ore tailings to be repaired based on the preset repair target, obtain a composite matrix layer, and collect environmental parameters using a sensing device arranged in the composite matrix layer to obtain an iron ore tailings matrix environment data set with a space-time label;

[0023] Step 200: Configure a heavy metal chromium repair plant community and inoculate a rhizosphere microbial flora in the composite matrix layer, collect rhizosphere samples at a preset period to extract rhizosphere microbial information, associate the rhizosphere microbial information with the iron ore tailings matrix environment data set and heavy metal chromium repair plant growth data, and construct a rhizosphere microbial original data set;

[0024] Step 300: Construct a rhizosphere microbial network model based on the rhizosphere microbial original data set, and update the rhizosphere microbial network model using newly acquired rhizosphere microbial data and iron ore tailings matrix environment data during the repair process to obtain a dynamic rhizosphere microbial network model;

[0025] Step 400: Extract network state data of the dynamic rhizosphere microbial network model, and perform feature fusion of the network state data with the iron ore tailings matrix environment data set and the heavy metal chromium repair plant growth data to form a rhizosphere comprehensive state input feature data set to construct a heavy metal chromium long-term immobilization prediction model;

[0026] Step 500: Based on the real-time collected iron ore tailings matrix environment data, use the heavy metal chromium long-term immobilization prediction model to predict the current rhizosphere comprehensive state input feature data set to obtain a heavy metal chromium re-release risk assessment result, and generate rhizosphere microbial network regulation and control decision data according to the heavy metal chromium re-release risk assessment result;

[0027] Step 600: According to the rhizosphere microbial network regulation and control decision data, control the inoculation device or injection device to perform rhizosphere microbial flora application operation in the root zone of the heavy metal chromium repair plant community, and collect the regulated rhizosphere environment data, rhizosphere microbial data and heavy metal chromium form data as feedback data;

[0028] Step 700: Update the dynamic rhizosphere microbial network model and the heavy metal chromium long-term immobilization prediction model using the feedback data to maintain the long-term immobilization state of heavy metal chromium in the iron ore tailings.

[0029] Further, in step 100, the embodiment first performs matrix construction processing and environmental monitoring layout processing on the iron ore tailings to be repaired according to the preset repair target, to form a composite matrix layer. Specifically, the embodiment first acquires source information and initial physicochemical parameter information of the iron ore tailings to be repaired. Based on the type, content range and target repair period of heavy metal chromium, the target particle size range, target pH range and target nutrient content range are determined. Then, the iron ore tailings to be repaired are crushed and sieved to ensure that the treated iron ore tailings meet the target particle size range, and then the iron ore tailings are mixed with passivation materials and improvement materials in a predetermined mass ratio to form a composite matrix layer that meets the target pH and nutrient content.

[0030] Next, in the formed composite matrix layer, the embodiment divides the monitoring subarea according to the vertical depth direction and the horizontal direction, and according to the functional requirements and representative positions of each monitoring subarea, a sensing device layout point is preset in each subarea, including a surface layer and a deep layer layout point. At least one environmental parameter sensing device is installed at each sensing device layout point, which is designed to collect environmental parameters such as pH, conductivity, moisture content, temperature and nutrient content in real time, and each sensing device is assigned a unique spatial identifier to facilitate subsequent data management and analysis.

[0031] Finally, in each environmental parameter collection period, the sensing device automatically collects the corresponding raw environmental parameter data, and binds these data with the corresponding spatial identifier and time stamp to generate records with spatial and time markers. Then, these records are classified according to the monitoring subarea, environmental parameter category and collection time, thereby constructing an iron ore tailings matrix environmental data set. Each data in the data set has a unique spatial marker and time marker, ensuring the traceability and accuracy of the data, and providing reliable data support for the subsequent repair process.

[0032] Further, in step 200, the embodiment first configures a heavy metal chromium repair plant community and inoculates rhizosphere microbial flora in the composite matrix layer. Specifically, according to the physicochemical properties of the composite matrix layer and the type of heavy metal chromium to be repaired, at least two heavy metal chromium repair plants with heavy metal chromium tolerance and enrichment ability are selected from plants with heavy metal chromium tolerance and enrichment ability, and are planted according to a predetermined row spacing and plant spacing to form a suitable heavy metal chromium repair plant community. This process not only improves the plant's ability to absorb heavy metal chromium, but also provides a good environment for the growth of rhizosphere microorganisms.

[0033] Then, within the root zone range of heavy metal chromium remediation plants, the embodiment divides the root system sampling zones according to different depths and levels, determines multiple rhizosphere functional zones, including the near-root zone and the far-root zone, and assigns a unique identifier to each zone. Subsequently, the embodiment carries out the application of rhizosphere microbial flora in these rhizosphere functional zones according to the preset inoculation method, ensuring the formation of a stable biological community of rhizosphere microorganisms in the composite substrate layer to promote the immobilization and remediation effect of heavy metal chromium.

[0034] Finally, in each sampling period, the embodiment collects rhizosphere soil samples and root surface adherent samples in each rhizosphere functional zone, numbers the collected rhizosphere samples, records the corresponding rhizosphere functional zone identifier, sampling time and heavy metal chromium remediation plant individual identifier. Next, the structural information and functional characteristic information of the rhizosphere microbial community are extracted to store as rhizosphere microbial information, and the environmental parameter data corresponding to the rhizosphere sample collection time and functional zone location in the iron ore tailings substrate environment data set are called, and the relevant heavy metal chromium remediation plant growth data are obtained. Finally, these information are associated to form a multi-source joint record containing rhizosphere spatial location, sampling time and plant individual identifier, and the joint records of each sampling period and each rhizosphere functional zone are summarized and stored to establish a complete rhizosphere microbial raw data set.

[0035] Further, in step 300, the embodiment first constructs a rhizosphere microbial network model based on the rhizosphere microbial raw data set. Specifically, the embodiment extracts rhizosphere microbial information and associates it with the iron ore tailings substrate environment data set and the heavy metal chromium remediation plant growth data to establish a diversified information structure. Through comprehensive analysis of the distribution, functional characteristics of rhizosphere microbial populations and their interaction with environmental factors, a preliminary rhizosphere microbial network model is formed. This model can reflect the dynamic relationship between rhizosphere microorganisms, plants and the environment, providing a solid foundation for further research.

[0036] Subsequently, during the remediation process, the embodiment continuously collects new rhizosphere microbial data and iron ore tailings substrate environment data. These newly acquired data cover the changes in rhizosphere microbial communities, dynamic fluctuations in environmental parameters and updates of plant growth status, ensuring that the data source for model updating is real and reliable. On this basis, the rhizosphere microbial network model is adjusted in real time according to the real-time monitoring data to reflect the changes between microbial communities and the environment during the remediation process. This dynamic adjustment mechanism significantly enhances the adaptability and accuracy of the model.

[0037] Finally, through continuous updating and optimization, the embodiment establishes a dynamic rhizosphere microbial network model. This model not only can simulate the dynamic changes of rhizosphere microorganisms in real time, but also can predict the response behavior of microorganisms under different environmental conditions and their immobilization capacity for heavy metal chromium. This dynamic network model provides a scientific basis for further rhizosphere microbial regulation and remediation strategies, and improves the long-term immobilization effect of heavy metal chromium in iron mine tailings. In practical application, the model can provide support for formulating more effective remediation schemes and improve the overall effectiveness of remediation measures.

[0038] Specifically, in this embodiment, the expression of the rhizosphere microbial network model describes the relationship between the number of rhizosphere microorganisms and various factors. Specifically, the number of rhizosphere microorganisms at a certain time depends on the amount of rhizosphere microorganisms applied, environmental suitability indicators, and the amount of heavy metal chromium re-released. The number of rhizosphere microorganisms represents the reproduction of microorganisms in the soil, while the amount of rhizosphere microorganisms applied reflects the amount of microorganisms introduced into the soil. Environmental suitability indicators assess the impact of soil environment on microbial growth and activity, considering factors such as temperature, humidity, and nutrient content. The amount of heavy metal chromium re-released refers to the amount of previously immobilized heavy metal chromium that is re-released back into the soil due to environmental changes, which will affect the survival of rhizosphere microorganisms.

[0039] In a certain experiment, the amount of rhizosphere microorganisms applied is 500 grams, indicating the number of active microorganisms introduced in the experiment. The environmental suitability index is determined to be 0.8, indicating that the soil environment in the experiment is relatively suitable for promoting the healthy growth of microorganisms. The amount of heavy metal chromium re-released is measured to be 200 milligrams, indicating that a large amount of heavy metal chromium is observed to be released during the experiment, which may inhibit the reproduction of microorganisms. Through these specific values, the number of rhizosphere microorganisms in the experimental process can be obtained, providing quantitative basis for understanding the role of microorganisms in soil restoration, and supporting the development of more effective remediation strategies.

[0040] More specifically, in this embodiment, the expression of the dynamic rhizosphere microbial network model is used to describe the dynamic rhizosphere microbial network state at a certain time. This model quantifies the influence of the number of rhizosphere microorganisms, substrate availability, and environmental stress on the microbial network. The dynamic rhizosphere microbial network state reflects the activity and survival of microorganisms at that time, and the number of rhizosphere microorganisms represents the total amount of microorganisms in the soil. Substrate availability refers to the degree of nutrients that can be utilized by microorganisms, directly affecting the growth and metabolic activity of microorganisms. Environmental stress may be caused by the presence of heavy metal chromium, abnormal soil pH, and other factors, which may inhibit the survival of microorganisms.

[0041] In the experiment, it is assumed that the rhizosphere microbial quantity is 800 units, representing the reproductive number of microorganisms in the soil; the substrate availability is 0.7, showing that the nutrient supply in the soil is relatively sufficient, which can promote the activity of microorganisms; the environmental stress value is 150 units, representing that there are certain harmful factors in the environment, which may inhibit the normal metabolic activity of microorganisms. Through these specific values, the state of the dynamic rhizosphere microbial network can be effectively analyzed, providing a scientific basis for improving soil ecological function and formulating effective microbial management strategies.

[0042] Further, in step 400, the embodiment first extracts the network state data of the dynamic rhizosphere microbial network model. These state data represent the activity level, population composition of rhizosphere microorganisms at a specific time, and their relationship with environmental factors. By comprehensively utilizing the information provided by the dynamic rhizosphere microbial network model, the performance of the microbial community under specific environmental conditions and its ability to immobilize heavy metal chromium can be obtained. The extraction of this data lays a solid foundation for subsequent analysis.

[0043] Subsequently, the extracted network state data is fused with the iron ore tailings matrix environmental data set and the heavy metal chromium remediation plant growth data. The iron ore tailings matrix environmental data set contains important environmental parameters that affect rhizosphere microbial activity, such as soil pH, humidity, and nutrient content, while the heavy metal chromium remediation plant growth data provides the growth conditions of plants during the remediation process, including biomass and health status. Through feature fusion, these data form a comprehensive rhizosphere comprehensive state input feature data set, enabling the information from different data sources to be effectively integrated in the same model.

[0044] Finally, by constructing a long-term heavy metal chromium immobilization prediction model, the embodiment can predict the immobilization effect of heavy metal chromium based on the rhizosphere comprehensive state input feature data set. This model will use the fused multi-dimensional data analysis to achieve a deep understanding of the relationship between microorganisms, environment, and plant growth status. Through this prediction model, scientific basis can be provided for the development of soil remediation and heavy metal chromium elimination schemes, promoting the development of long-term heavy metal chromium immobilization technology and achieving the goal of ecological restoration.

[0045] Specifically, in this embodiment, the expression of the long-term heavy metal chromium fixation prediction model is used to describe the long-term heavy metal chromium fixation amount at a specific time. The core of this model lies in the quantitative analysis of the influence of the effective concentration of heavy metal chromium, the re-release rate and the biological fixation rate on fixation. The long-term heavy metal chromium fixation amount represents the chromium content that is fixed in the soil for a long time, which has important ecological restoration significance. The effective concentration of heavy metal chromium refers to the concentration of heavy metal chromium that can be utilized by plants or microorganisms in the soil, reflecting the degree of environmental pollution of heavy metal chromium. The re-release rate of heavy metal chromium represents the rate at which the fixed heavy metal chromium is re-released into the soil as the environmental conditions change, which has a negative impact on the restoration effect of the soil. The biological fixation rate refers to the ability of plants or microorganisms to fix heavy metal chromium in their bodies through biochemical processes, which can effectively reduce the available heavy metal chromium concentration in the environment.

[0046] Assuming that in a certain experimental environment, the measured effective concentration of heavy metal chromium is 100 mg / L, indicating the amount of available heavy metal chromium present in the soil; the re-release rate is 20 mg / L, showing the amount of heavy metal chromium re-released due to environmental changes; and the biological fixation rate is 30%, indicating the proportion of heavy metal chromium that plants can fix through their physiological mechanisms during growth. These specific values are obtained through experiments or model derivation, forming the basis data for the long-term heavy metal chromium fixation prediction model. According to these values, the calculation result H(t) of the model can help evaluate the heavy metal chromium fixation effect, thereby providing data support for further ecological restoration strategies.

[0047] Further, in step 500, the embodiment uses the long-term heavy metal chromium fixation prediction model to predict the current rhizosphere comprehensive state input feature data set based on the real-time collection of iron ore tailings matrix environmental data, aiming to obtain the evaluation result of the heavy metal chromium re-release risk. First, the embodiment obtains the real-time iron ore tailings matrix environmental data corresponding to the current decision time, as well as the network state data of the dynamic rhizosphere microbial network model and the growth data of the heavy metal chromium remediation plant. These data provide the necessary background information for evaluation, so as to comprehensively analyze the mutual relationship between microorganisms, environment and plant growth state.

[0048] Next, the embodiment carries out consistent processing on real-time iron ore tailings matrix environmental data, network state data, and heavy metal chromium remediation plant growth data to ensure the comparability and effectiveness of each data source, thereby forming the current rhizosphere comprehensive state input feature dataset. In this step, data consistent processing is the key link to ensure that information at different time points and data forms can be effectively integrated. Subsequently, the current rhizosphere comprehensive state input feature dataset is input into the heavy metal chromium long-term storage prediction model for inference calculation to obtain heavy metal chromium re-release risk assessment results. The assessment results not only include the change trend information of the effective content of heavy metal chromium, but also provide heavy metal chromium re-release risk level information.

[0049] Subsequently, the embodiment compares the heavy metal chromium re-release risk assessment results with the preset long-term storage safety target, aiming to determine the rhizosphere microbial network regulation target. These regulation targets mainly include reducing the heavy metal chromium re-release risk level and reducing the rising amplitude of the effective content of heavy metal chromium. Based on these targets, the embodiment generates rhizosphere microbial network regulation decision data, including specific measures such as rhizosphere microbial flora application intensity, application combination, and application timing. These measures will be dynamically adjusted according to the heavy metal chromium re-release risk level and the effective content change to achieve optimized remediation effect, thereby enhancing the overall efficiency of the ecological remediation process.

[0050] Further, in step 600, according to the rhizosphere microbial network regulation decision data, the embodiment controls the inoculation device or injection device to perform rhizosphere microbial flora application operation in the root zone of the heavy metal chromium remediation plant community. This process first involves analyzing the regulation decision data to determine information related to the target application task, including the target root zone location, application intensity, application combination, and application timing. According to this information, the embodiment sets up application work units in the target root zone and assigns a unique identifier to each work unit to track and manage the application progress in subsequent operations.

[0051] Next, when the preset application timing is reached, the embodiment controls the inoculation device or injection device to apply rhizosphere microbial flora in the corresponding application work unit. The implementation of the application operation directly introduces rhizosphere microbial flora into the root zone of the heavy metal chromium remediation plant, which contacts and interacts with the rhizosphere medium. During the application process, the embodiment will monitor in real time according to the implementation feedback collection window to obtain the regulated rhizosphere environmental data, including important indicators such as the pH value, water content, conductivity, and temperature of the root zone.

[0052] Finally, within the feedback collection window, the embodiment will collect the rhizosphere samples after the regulation in detail and extract relevant rhizosphere microbial data, including rhizosphere microbial community structure and functional characteristic information. At the same time, for the root zone medium sample, heavy metal chromium form detection is carried out to obtain the regulation of heavy metal chromium form data, including available state content and proportion information of different forms. All collected data will be bound with operation unit identification and collection time to form a multi-element joint record feedback data. These feedback data provide an important basis for subsequent microbial regulation strategy, so that the effect of remediation measures can be evaluated and optimized.

[0053] Further, in step 700, the embodiment updates the dynamic rhizosphere microbial network model and the heavy metal chromium long-term immobilization prediction model using the feedback data collected from the regulation operation to ensure the long-term immobilization state of heavy metal chromium in the iron ore tailings. First, the embodiment cleanses and unifies the feedback data. This process aims to remove redundant and erroneous information to ensure data consistency and comparability, thereby forming a clear feedback joint record to provide a reliable data basis for subsequent model updating.

[0054] Next, based on the feedback joint record, the embodiment generates model update data for the dynamic rhizosphere microbial network model. These update data contain the real-time state of rhizosphere microbes reflected by the feedback information, which are used to update the dynamic rhizosphere microbial network model to obtain new network state data. In addition, the embodiment also generates model update samples for the heavy metal chromium long-term immobilization prediction model based on the feedback joint record. These samples include input rhizosphere comprehensive state input feature data and output heavy metal chromium form data. Through the updated heavy metal chromium long-term immobilization prediction model, not only can the immobilization state of heavy metal chromium be more accurately predicted, but also future immobilization strategies can be optimized.

[0055] Finally, the embodiment performs effectiveness verification on the updated dynamic rhizosphere microbial network model and the heavy metal chromium long-term immobilization prediction model to ensure the reliability of model updating. The effectiveness verification standard is evaluated according to the preset requirements, and the acceptance criteria may involve the prediction accuracy and stability of the model. If the updated model meets the conditions of effectiveness verification, it will be applied to the next decision cycle to maintain and manage the long-term immobilization state of heavy metal chromium in the iron ore tailings. This process ensures the dynamic adaptability of the model, which can respond to environmental changes and regulation effects in real time, providing scientific basis for continuous ecological restoration.

[0056] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between various embodiments can be referred to each other.

[0057] The principles and implementation manners of the present application are described by using specific examples in the present application, and the above examples are only used to help understand the method of the present application and its core idea; meanwhile, for the general technical personnel in the art, the specific implementation manners and application ranges will be changed according to the idea of the present application. In conclusion, the content of the present specification should not be understood as the limitation of the present application.

Claims

1. A method for long-term consolidation and remediation of heavy metal chromium in iron ore tailings based on dynamic regulation of rhizosphere microbial networks, characterized in that, include: Based on the preset remediation goals, the iron ore tailings to be remediated are subjected to matrix construction and environmental monitoring deployment to obtain a composite matrix layer. Environmental parameters are collected using sensors deployed in the composite matrix layer to obtain an iron ore tailings matrix environmental dataset with spatiotemporal labels. A heavy metal chromium remediation plant community is configured in the composite matrix layer and inoculated with rhizosphere microbial community. Rhizosphere samples are collected at a preset period to extract rhizosphere microbial information. The rhizosphere microbial information is correlated with the iron ore tailings matrix environment dataset and the growth data of heavy metal chromium remediation plants to construct the original rhizosphere microbial dataset. A rhizosphere microbial network model is constructed based on the original rhizosphere microbial dataset. During the remediation process, the rhizosphere microbial network model is updated using newly acquired rhizosphere microbial data and iron ore tailings matrix environment data to obtain a dynamic rhizosphere microbial network model. The network state data of the dynamic rhizosphere microbial network model is extracted, and the network state data is fused with the iron ore tailings matrix environment dataset and the heavy metal chromium remediation plant growth data to form a rhizosphere comprehensive state input feature dataset, so as to construct a long-term heavy metal chromium retention prediction model. Based on real-time collected iron ore tailings matrix environment data, the long-term preservation prediction model for heavy metal chromium is used to predict the current rhizosphere integrated state input feature dataset, obtain the risk assessment result of heavy metal chromium re-release, and generate rhizosphere microbial network regulation decision data based on the heavy metal chromium re-release risk assessment result. Based on the rhizosphere microbial network regulation decision data, the inoculation device or injection device is controlled to perform rhizosphere microbial community application operations in the root zone of the heavy metal chromium remediation plant community, and the regulated rhizosphere environment data, rhizosphere microbial data and heavy metal chromium speciation data are collected as feedback data. The dynamic rhizosphere microbial network model and the long-term consolidation prediction model for heavy metal chromium are updated using the feedback data to maintain the long-term consolidation of heavy metal chromium in iron ore tailings.

2. The method for long-term consolidation and remediation of heavy metal chromium in iron ore tailings based on dynamic regulation of rhizosphere microbial networks according to claim 1, characterized in that, The process involves matrix construction and environmental monitoring deployment of the iron ore tailings to be remediated based on preset remediation targets, resulting in a composite matrix layer. Environmental parameters are collected using sensors deployed within the composite matrix layer, yielding a spatiotemporally labeled iron ore tailings matrix environmental dataset, including: Based on the preset remediation target, obtain the source information and initial physicochemical parameter information of the iron ore tailings to be remediated, and determine the target particle size range, target pH range and target nutrient content range based on the type of heavy metal chromium, the range of heavy metal chromium content and the target remediation period. The iron ore tailings to be repaired are crushed and screened to control the treated iron ore tailings within the target particle size range. The iron ore tailings are then mixed evenly with passivating materials and improving materials according to a preset mass ratio to form a composite matrix layer that meets the target pH range and the target nutrient content range. The composite matrix layer is divided into monitoring zones along the vertical depth direction and the horizontal direction. According to the functional requirements and representative locations of each monitoring zone, sensor deployment points are preset in each monitoring zone. The sensor deployment points include surface deployment points and deep deployment points. At least one environmental parameter sensing device is installed at each sensing device deployment point, wherein the environmental parameter sensing device is used to collect at least one environmental parameter among pH, conductivity, water content, temperature and nutrient content, and a unique spatial identifier is assigned to each environmental parameter sensing device. During each environmental parameter acquisition cycle, the environmental parameter sensing device automatically collects the corresponding raw environmental parameter data and binds each raw environmental parameter data with the corresponding spatial identifier and timestamp to generate an environmental parameter data record with spatial and time markers. Environmental parameter data records with spatial and temporal markers are categorized according to monitoring zones, environmental parameter categories, and collection times to construct an iron ore tailings matrix environment dataset. Each data record in the iron ore tailings matrix environment dataset has a unique spatial and temporal marker.

3. The method for long-term consolidation and remediation of heavy metal chromium in iron ore tailings based on dynamic regulation of rhizosphere microbial networks according to claim 1, characterized in that, The process involves configuring a heavy metal chromium remediation plant community in the composite matrix layer and inoculating it with rhizosphere microorganisms. Rhizosphere samples are collected at preset intervals to extract rhizosphere microbial information. This rhizosphere microbial information is then correlated with the iron ore tailings matrix environment dataset and the heavy metal chromium remediation plant growth data to construct an original rhizosphere microbial dataset, including: Based on the physicochemical properties of the composite matrix layer and the type of heavy metal chromium to be repaired, at least two types of heavy metal chromium repair plants with heavy metal chromium tolerance and enrichment capacity are selected and planted in the composite matrix layer according to the preset row spacing and plant spacing to form a heavy metal chromium repair plant community. Within the root zone of the chromium-remediated plant, the root sampling depth was stratified and the root horizontal partition was divided. Multiple rhizosphere functional partitions, including the near-root zone and the far-root zone, were identified, and a unique partition identifier was assigned to each rhizosphere functional partition. Rhizosphere microbial communities are applied to the rhizosphere functional zones according to a preset inoculation method, so that the rhizosphere microbial communities form a stable rhizosphere microbial community in the composite matrix layer. During each sampling cycle, rhizosphere soil samples and root surface attachment samples were collected in each rhizosphere functional zone. The collected rhizosphere samples were numbered and the corresponding rhizosphere functional zone identifier, sampling time, and individual heavy metal chromium remediation plant identifier were recorded. Extract rhizosphere microbial community structure information and rhizosphere microbial functional characteristic information, and store the rhizosphere microbial community structure information and rhizosphere microbial functional characteristic information as rhizosphere microbial information; The environmental parameter data corresponding to the rhizosphere sample collection time and rhizosphere functional zoning location in the iron ore tailings matrix environmental dataset are called, and the heavy metal chromium remediation plant growth data corresponding to the rhizosphere sample are obtained. The rhizosphere microbial information, the corresponding environmental parameter data and the corresponding heavy metal chromium remediation plant growth data are associated to form a multi-source joint record containing rhizosphere spatial location, sampling time and plant individual identifier. The multi-source joint records obtained from each sampling period and each rhizosphere functional zone are summarized and stored to construct the original rhizosphere microbial dataset.

4. The method for long-term consolidation and remediation of heavy metal chromium in iron ore tailings based on dynamic regulation of rhizosphere microbial networks according to claim 1, characterized in that, The expression for the rhizosphere microbial network model is: ； in, For at any time The number of rhizosphere microorganisms; For at any time The quality of the rhizosphere microorganisms applied; For at any time Environmental suitability indicators; For at any time The amount of heavy metal chromium re-released; and These are parameter coefficients corresponding to microbial growth, environmental adaptation, and inhibitory effects.

5. The method for long-term consolidation and remediation of heavy metal chromium in iron ore tailings based on dynamic regulation of rhizosphere microbial networks according to claim 1, characterized in that, The expression for the dynamic rhizosphere microbial network model is: ； in, For at any time The dynamic state of the rhizosphere microbial network; For at any time The number of rhizosphere microorganisms; For at any time Substrate availability; and These are parameter coefficients corresponding to microbial proliferation, substrate utilization efficiency, and the inhibitory effect of environmental pressure.

6. The method for long-term consolidation and remediation of heavy metal chromium in iron ore tailings based on dynamic regulation of rhizosphere microbial networks according to claim 1, characterized in that, The expression for the prediction model of long-term solidification of heavy metal chromium is as follows: ； in, For at any time Long-term solid reserves of heavy metal chromium; For at any time The effective concentration of the heavy metal chromium; For at any time The rate of re-release of heavy metal chromium; For at any time biological fixation rate; and The parameter coefficients correspond to the concentration of available heavy metal chromium, the effects of re-release, and biological fixation.

7. The method for long-term consolidation and remediation of heavy metal chromium in iron ore tailings based on dynamic regulation of rhizosphere microbial networks according to claim 1, characterized in that, The method uses real-time collected iron ore tailings matrix environment data and the long-term sequestration prediction model for heavy metal chromium to predict the current rhizosphere integrated state input feature dataset, obtaining a risk assessment result for heavy metal chromium re-release. Based on this risk assessment result, rhizosphere microbial network regulation decision data is generated, including: Based on the current decision-making time, acquire real-time iron ore tailings matrix environment data, network state data of dynamic rhizosphere microbial network model, and heavy metal chromium remediation plant growth data corresponding to the current decision-making time. The real-time iron ore tailings matrix environment data, the network status data, and the heavy metal chromium remediation plant growth data are processed to obtain the current rhizosphere integrated status input feature dataset. The current rhizosphere integrated state input feature dataset is input into the long-term solidification prediction model of heavy metal chromium for inference calculation, and the risk assessment result of heavy metal chromium re-release corresponding to the current decision time is obtained. The risk assessment result of heavy metal chromium re-release includes: the changing trend information of the effective content of heavy metal chromium and the risk level information of heavy metal chromium re-release. The risk assessment results of the re-release of heavy metal chromium are compared with the preset long-term preservation safety target to determine the rhizosphere microbial network regulation target, wherein the rhizosphere microbial network regulation target includes: reducing the risk level of heavy metal chromium re-release and reducing the increase in the content of bioavailable heavy metal chromium. Rhizosphere microbial network regulation decision data are generated based on the aforementioned rhizosphere microbial network regulation objectives.

8. The method for long-term consolidation and remediation of heavy metal chromium in iron ore tailings based on dynamic regulation of rhizosphere microbial networks according to claim 1, characterized in that, The rhizosphere microbial network regulation decision-making data includes: The application intensity, combination, and timing of rhizosphere microbial community application are defined, wherein the application intensity of the rhizosphere microbial community is positively correlated with the risk level of chromium re-release, and the application timing is determined based on the changing trend information of the available chromium content.

9. A method for long-term consolidation and remediation of heavy metal chromium in iron ore tailings based on dynamically regulated rhizosphere microbial networks, as described in claim 1, is characterized in that... The process involves controlling the inoculation or injection device to apply rhizosphere microbial community data to the root zone of the heavy metal chromium remediation plant community based on the rhizosphere microbial network regulation decision data, and collecting post-regulation rhizosphere environmental data, rhizosphere microbial data, and heavy metal chromium speciation data as feedback data, including: By analyzing the rhizosphere microbial network regulation decision data, the target root zone location, rhizosphere microbial community application intensity, rhizosphere microbial community application combination, and rhizosphere microbial community application timing corresponding to the target application task are obtained. Application work units are established in the root zone of the heavy metal chromium remediation plant community, and each application work unit is assigned a work unit identifier, wherein the application work unit includes: a spatial range corresponding to the location of the target root zone; When the application time for the rhizosphere microbial community is reached, the inoculation device or injection device is controlled to perform the application operation of the rhizosphere microbial community in the application operation unit corresponding to the operation unit identifier, so that the rhizosphere microbial community enters the root zone of the heavy metal chromium remediation plant community and comes into contact with the rhizosphere medium. Based on the application of the rhizosphere microbial community, a feedback collection window is determined, and rhizosphere environmental data after regulation is collected within the feedback collection window. The rhizosphere environmental data is then bound to the operation unit identifier and the collection time. The rhizosphere environmental data includes: root zone pH, water content, electrical conductivity, and temperature. Within the feedback acquisition window, rhizosphere samples after regulation are collected and rhizosphere microbial data after regulation are extracted. The rhizosphere microbial data is then bound to the work unit identifier and the acquisition time. The rhizosphere microbial data includes: rhizosphere microbial community structure information and rhizosphere microbial functional characteristic information. Within the feedback acquisition window, a sample of the root region medium after regulation is acquired and the speciation of heavy metal chromium is detected to obtain the speciation data of heavy metal chromium. The speciation data of heavy metal chromium is then bound to the work unit identifier and the acquisition time. The speciation data of heavy metal chromium includes: the content of effective form of heavy metal chromium and the proportion of different forms. The rhizosphere environment data, the rhizosphere microbial data, and the heavy metal chromium speciation data are summarized to generate the feedback data, wherein the feedback data includes: multi-source joint records corresponding to the same work unit identifier and the same collection time.

10. A method for long-term consolidation and remediation of heavy metal chromium in iron ore tailings based on dynamically regulated rhizosphere microbial networks, as described in claim 1, is characterized in that... The step of updating the dynamic rhizosphere microbial network model and the long-term chromium sequestration prediction model using the feedback data to maintain the long-term sequestration of chromium in iron ore tailings includes: The feedback data is cleaned and standardized to obtain a joint feedback record; The model update data of the dynamic rhizosphere microbial network model is generated based on the feedback joint recording, and the dynamic rhizosphere microbial network model is updated using the model update data to obtain the updated dynamic rhizosphere microbial network model and the network status data corresponding to the collection time. Based on the feedback joint record, a model update sample is generated for the prediction model of long-term heavy metal chromium retention. The model update sample is then used to update the prediction model of long-term heavy metal chromium retention, resulting in an updated prediction model of long-term heavy metal chromium retention. The model update sample includes: input rhizosphere integrated state input feature data and output heavy metal chromium morphology data. The updated dynamic rhizosphere microbial network model and the updated prediction model for long-term chromium consolidation were validated. If the validation met the preset requirements, the updated dynamic rhizosphere microbial network model and the updated prediction model for long-term chromium consolidation were used in the next decision cycle to maintain the long-term consolidation of chromium in iron ore tailings.

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