Mine remediation effect prediction method and system based on soil data analysis

Abstract

The invention discloses a mine restoration effect prediction method and system based on soil data analysis, and relates to the field of mine ecological restoration, and the method comprises the steps: S1, deploying a bionic sensor network with a self-adaptive triggering capability in a restoration region, obtaining soil multi-modal data before, during and after restoration, and synchronously obtaining background data of a reference ecological region; and the bionic sensor network adaptively switches a monitoring mode and a sampling frequency according to the soil temperature and humidity, a biological disturbance signal or a preset repair event. According to the method, the bionic adaptive sensor network is introduced to actively capture soil-biological interaction key signals, a seed bank-soil-microorganism collaborative response model is constructed to reveal an ecological restoration internal mechanism, and the problems that in the prior art, data and the ecological process are disjointed, the model generalization ability is weak, and prediction lags are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of mine ecological restoration, specifically to a method and system for predicting the effects of mine restoration based on soil data analysis. Background Technology

[0002] Mining activities have caused severe damage to regional ecosystems, and ecological restoration is crucial to restoring their environmental functions and ecological value. However, mine restoration is a complex and time-consuming systemic project with significant investment, and its effectiveness is dynamically influenced by multiple factors, including soil, hydrology, biology, and climate. Currently, the management and evaluation of restoration projects largely rely on phased manual monitoring and expert judgment, lacking scientific, dynamic, and quantifiable technical means to predict the final results during the restoration process. This leads to delayed adjustments in restoration strategies, inaccurate resource allocation, and difficulty in achieving efficient and optimal restoration goals.

[0003] Existing technologies for assessing and predicting the effectiveness of mine remediation typically rely on periodic sampling and laboratory analysis of soil physicochemical indicators (such as pH, heavy metal content, and organic matter), combined with macro-ecological indicators like vegetation cover for static evaluation or simple regression prediction. Predictive models often employ machine learning methods or mechanistic models based on historical statistical data, attempting to establish a mapping relationship between soil indicators and remediation outcomes. Data collection primarily depends on traditional sensors or manual sampling at fixed frequencies, resulting in limited data dimensionality and insufficient capture of dynamic responses to ecological processes. When remediation encounters bottlenecks, the selection of intervention measures often relies on general experience, lacking precise decision support tailored to specific remediation stages and site conditions.

[0004] Despite some progress in existing technologies, significant shortcomings remain: First, traditional data acquisition methods are passive and isolated, making it difficult to capture key biological and ecological signals that truly reflect the early responses and interactions of the "soil-microbe-plant" system in real time and in situ. This leads to a disconnect between the input features of the prediction model and the intrinsic driving forces of ecological restoration. Second, existing prediction models are often "black boxes" or require large amounts of labeled data, exhibiting poor generalization and interpretability, making them difficult to adapt to the unique site conditions of different mines, especially when data is scarce in the early stages of remediation projects. Finally, existing technological systems typically separate "monitoring and prediction" from "engineering intervention." Prediction results cannot be directly and quickly transformed into actionable and precise optimization measures, creating a lagging decision-making cycle of "seeing the problem and then studying countermeasures," failing to form a closed-loop intelligent system where prediction guides intervention, and the intervention effect provides feedback to optimize prediction. Therefore, there is an urgent need for an integrated technological solution that can deeply integrate ecological mechanisms, intelligently sense key processes, and achieve linkage between prediction and dynamic optimization. Summary of the Invention

[0005] Based on this, the purpose of this invention is to provide a method and system for predicting the effect of mine restoration based on soil data analysis, so as to solve the technical problems of insufficient perception of key signals in the early stage of ecological restoration, weak generalization ability of prediction models, and inability to directly guide precise dynamic intervention by traditional methods.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for predicting the effect of mine remediation based on soil data analysis, comprising the following steps: S1, deploying a biomimetic sensor network with adaptive triggering capability in the remediation area to acquire multimodal soil data before, during, and after remediation, and simultaneously acquiring background data of a reference ecological zone; the biomimetic sensor network adaptively switches monitoring modes and sampling frequencies according to soil temperature and humidity, biological disturbance signals, or preset remediation events; S2, based on the multimodal data, constructing a "seed bank-soil-microorganism" collaborative response model, which quantifies the germination strategy of artificially sown plant seeds, soil matrix evolution, and microbial community function under specific soil conditions. S3. Construct and train a mechanism-guided meta-learning prediction framework, which includes a physical information constraint module that encapsulates prior knowledge of soil ecology, and a meta-learning module that can quickly adjust the prediction strategy based on a small amount of new data from the remediation area; S4. Use the collaborative response model to enhance the features of the original data, and input the enhanced features into the meta-learning prediction framework to output a dynamic prediction curve of the remediation effect and a warning of the critical bottleneck period; S5. Based on the warning and prediction curve, generate a "prediction-intervention-reprediction" closed-loop optimization scheme, which includes precise adjustment suggestions for remediation measures such as seed bank components and the timing of microbial agent application.

[0007] The present invention is further configured such that the biomimetic sensor network with adaptive triggering capability includes: A root-like probe sensor, coated with a biocompatible material, can mimic the slow release of plant root exudates to attract and monitor the chemotactic response intensity of the rhizosphere microbiome. The soil animal-like disturbance sensor senses minute changes in soil porosity and structural strength through a miniature deformable structure, and triggers high-resolution physicochemical parameter acquisition only when a mechanical pattern resembling biological disturbance is detected.

[0008] The present invention is further configured such that constructing the “seed bank-soil-microorganism” synergistic response model specifically includes: S21, deconstructing the artificial seed bank into the proportion of different functional types of seeds, dormancy depth, and germination water requirement threshold; S22, using the multimodal data, identifying the dynamic changes in the concentration of key microbial signaling molecules that promote germination and the concentration of allelochemicals that inhibit germination in the soil; S23, establishing a dynamic probability model with soil hydrothermal conditions, signaling molecule and allelochemical concentrations as inputs, and the actual germination rate and seedling establishment rate of different functional types of seeds as outputs. This model also predicts the reaction of seedling establishment to the rhizosphere microbial community structure.

[0009] The present invention is further configured such that the construction and training method of the mechanism-guided meta-learning prediction framework is as follows: S31, the physical information constraint module is constructed as a set of differential equation soft constraints describing basic ecological principles such as soil material conservation and energy flow, and embedded in the forward propagation process of the neural network; S32, in the meta-learning stage, data from multiple historical mine restoration projects are used as meta-tasks for training, enabling the model to learn to extract common restoration patterns across projects; S33, for the target new restoration area, its initial small amount of data is used as a support set, and the model parameters are quickly adjusted through the meta-learning module to adapt to the specificity of the site, forming a "rapidly customized" prediction model.

[0010] The present invention is further configured such that the generation logic of the critical bottleneck period early warning is as follows: the dynamic prediction curve output by the prediction framework includes the first derivative curve of the remediation effect index changing over time; when the first derivative is continuously lower than a preset threshold, it is determined that the remediation process has entered a bottleneck period of diminishing returns; the system automatically backtracks the output of the "seed bank-soil-microorganism" collaborative response model within this period, locates the dominant limiting factor that leads to diminishing returns, and uses it as the core content of the early warning.

[0011] The present invention is further configured such that the generation of the closed-loop optimization scheme includes: S51, searching and matching in a case library of "remedial measures - restriction factor removal" constructed from historical data according to the dominant limiting factor; S52, using the adjusted "rapid customization" prediction model to simulate the effect of the matched potential intervention measures; S53, recommending the combination of intervention measures that can most effectively improve the first derivative and has the best overall cost, and presetting the trigger node for the next round of data collection and re-prediction after the intervention.

[0012] The present invention is further configured as a mine remediation effect prediction system based on soil data analysis, comprising: a biomimetic adaptive sensor network unit for performing data acquisition in step S1; a collaborative response modeling unit for constructing and running the “seed bank-soil-microorganism” collaborative response model; a meta-learning prediction engine for performing training and inference of the mechanism-guided meta-learning prediction framework; a bottleneck analysis and early warning unit for identifying key bottleneck periods and dominant limiting factors; and a closed-loop optimization scheme generation unit for generating the “prediction-intervention-reprediction” closed-loop optimization scheme.

[0013] The invention is further configured to include: a mobile on-site micro pilot-scale platform, which integrates a small climate simulation chamber, a soil column test device and a rapid detection instrument, for rapidly verifying and fine-tuning the suggested intervention measures at the remediation site after receiving the closed-loop optimization scheme instructions, and feeding back the verification results to the system to strengthen the model.

[0014] The present invention is further configured such that the system is linked with the repair engineering equipment to form an intelligent repair execution system; the intervention measures suggestions output by the closed-loop optimization scheme generation unit can be automatically converted into executable operation instructions for "intelligent microbial agent spraying drone" or "precision reseeding robot".

[0015] In summary, the present invention has the following main beneficial effects: This invention introduces a biomimetic adaptive sensor network to actively capture key signals of soil-organism interaction, constructs a "seed bank-soil-microorganism" collaborative response model to reveal the intrinsic mechanism of ecological restoration, and adopts a mechanism-guided meta-learning framework to achieve accurate and customized predictions under small data conditions. This effectively solves the problems of data disconnection from ecological processes, weak model generalization ability, and prediction lag in existing technologies. Finally, by establishing an intelligent closed loop of "prediction-bottleneck early warning-dynamic optimization", the prediction results are directly transformed into executable and precise intervention instructions, realizing a fundamental shift in mine restoration from passive monitoring and assessment to active prediction and control, and significantly improving the scientific nature, efficiency, and cost-effectiveness of the restoration process. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a system framework diagram of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the following description is merely a preferred embodiment of the invention and is not intended to limit the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0018] This invention provides a method and system for predicting the effects of mine restoration based on soil data analysis. It aims to achieve scientific, dynamic prediction and precise control of the effects of mine ecological restoration through the deep integration of intelligent sensing, mechanism modeling and artificial intelligence prediction.

[0019] Example 1 refer to Figure 1 The flowchart shown illustrates the prediction method in this embodiment, which includes the following specific steps: Step S1: Deploy a biomimetic adaptive sensor network and acquire multimodal data.

[0020] In the target mine restoration area and surrounding reference ecological zone, a biomimetic sensor network with adaptive triggering capabilities is deployed. This network does not sample at a fixed frequency, but rather triggers intelligently based on environmental changes and ecological events. Specifically, each sensor node in the network has built-in environmental state judgment logic: when soil temperature and humidity reach a preset threshold range conducive to biological activity, or when a specific mechanical disturbance pattern caused by suspected earthworm or other soil animal activity is detected, or when the system receives preset restoration event commands such as "sowing" or "irrigation," the sensor node automatically switches from a low-power sleep mode to a high-frequency, high-precision monitoring mode.

[0021] The biomimetic sensor network comprises two types of core sensors: Root-like probe sensor: The main body of this sensor is a slender probe structure that can be inserted into the soil at different depths. Its surface is specially treated and coated or encapsulated with a biocompatible gel, which can slowly release specific organic substances (such as sugars and organic acids) that mimic plant root secretions. When these "mimic secretions" diffuse in the rhizosphere microdomain, they attract specific microbial communities to accumulate there and undergo metabolic responses. The sensor surface integrates miniature impedance spectroscopy or fluorescence detection units, which can monitor in situ, in real time, the intensity of changes in the electrochemical or optical signals of the microenvironment caused by microbial chemotaxis, aggregation, and metabolic activities. This intensity can indirectly quantify the activity and functional response of the rhizosphere microbial community.

[0022] Soil-like animal disturbance sensor: The core of this sensor is a flexible thin film or micro-cantilever beam structure that can detect minute deformations, embedded in the soil. When the soil undergoes microscopic changes in pore structure or mechanical properties (such as shear force) due to animal activities such as traversing and feeding, this structure deforms. The sensor's built-in pattern recognition algorithm can distinguish specific mechanical signal patterns caused by biological disturbances from non-biological disturbances such as wind and precipitation. Only when a pattern matching the characteristics of biological disturbance is identified will the sensor trigger a series of high-resolution samples from nearby traditional physicochemical parameter sensors such as soil temperature, humidity, pH, and conductivity, thereby efficiently capturing micro-changes in the physicochemical environment that occur in conjunction with biological activity.

[0023] Background soil data from a reference ecological zone (an undisturbed or stably restored natural ecosystem) is acquired synchronously via the network to serve as a benchmark and control for assessing the remediation effectiveness. All data includes precise timestamps and three-dimensional spatial coordinates.

[0024] Step S2: Construct a “seed bank-soil-microorganism” collaborative response model.

[0025] This step aims to quantify how remediation measures (seeding) ultimately influence the critical early ecological process of plant establishment through interactions between the soil medium and the microbial community. The construction process is as follows: S21. Seed Bank Deconstruction: The artificially proportioned seed bank placed in the remediation area is digitally characterized. The proportion of seeds of each plant species (or functional type, such as nitrogen-fixing plants and deep-rooted plants), physical dormancy characteristics (such as the water requirement for breaking dormancy or mechanical treatment requirements corresponding to seed coat hardness), physiological dormancy characteristics (cumulative accumulated temperature or specific hormone stimulation required for germination), and minimum moisture threshold required for germination are analyzed and recorded.

[0026] S22. Key Signal Identification: Using the multimodal data obtained in step S1, especially the microbial response signals from root-like probe sensors, and soil solution composition data obtained through possible in-situ mass spectrometry or subsequent laboratory analysis, dynamically identify and quantify two types of key biochemical signals: one is the concentration of key microbial signal molecules that promote germination, such as plant hormones (e.g., IAA) produced by certain growth-promoting bacteria, or enzymes secreted by fungi that promote seed dormancy; the other is the concentration of allelochemicals that inhibit germination, which may come from the decomposition products of certain pioneer plant residues or specific microbial metabolites.

[0027] S23. Dynamic Probabilistic Modeling: Establish a dynamic probabilistic model driven by multiple factors. The model's inputs include real-time soil temperature, volumetric water content (from sensors), and the concentrations of promoting and inhibiting signal molecules identified in step S22. The model's output is the actual germination probability of seeds of different plant functional types within a specific time window, and the probability of successful seedling establishment (surviving beyond the critical period) after germination. This model is not static; it also simulates how, once the seedling root system is established, the exudates from its newly formed roots will react and alter the community structure and function of the rhizosphere microorganisms (feedback loop). This prediction result will serve as one of the inputs for the next round of model iteration.

[0028] Step S3: Construct and train the mechanism-guided meta-learning prediction framework.

[0029] This framework is used to learn and predict the long-term dynamics of macro-remediation effects, such as vegetation cover and soil organic matter increment.

[0030] S31. Construction of the Physical Information Constraint Module: This module embeds the fundamental physicochemical laws of soil ecology into the neural network in the form of "soft constraints." Specifically, during the forward propagation calculation of the loss function in the neural network, an additional "physical consistency loss" term is added. For example, according to the law of conservation of mass, the rate of change of the total amount of a certain heavy metal element in the soil should be approximately equal to the difference between the external input flux and the fluxes absorbed by plants and leached out. The various fluxes predicted by the network must roughly satisfy this constraint; otherwise, a penalty term will be generated. These constraints are embodied through a set of non-rigorous equations describing mass and energy balance using ordinary or partial differential equations.

[0031] S32. Meta-learning Training Phase: Collect a large amount (e.g., dozens) of completed historical mine restoration projects from different regions and mineral types, including full-cycle data (including multimodal data and final acceptance results). Treat each project as an independent "task." In meta-learning training, the learning objective of the model (called the "meta-model") is not to directly fit the data of a specific project, but to learn how to quickly adapt to a new project. During training, a historical project is randomly selected, and most of its data is used to simulate the "support set," while a small portion is used to simulate the "query set." After quickly adjusting certain internal parameters (such as the bias of the feature extractor) on the "support set," the meta-model needs to make accurate predictions on the "query set." Through repeated practice of this kind of "rapid adaptation and prediction" on massive historical tasks, the meta-model learns to extract universal restoration patterns and adaptation methods across projects.

[0032] S33. Rapid Customization of New Remediation Areas: For a new target remediation area, only a small amount of data (such as monitoring data from the first three months) is acquired at the initial stage of the project as a "support set." Using the meta-learning module trained in step S32, the parameters of the pre-trained basic prediction model (i.e., the meta-model) are quickly adjusted to generate a "rapidly customized" prediction model specific to this target area. This model inherits general knowledge while also possessing site-specific characteristics.

[0033] Step S4: Feature enhancement and dynamic prediction.

[0034] The raw multimodal data obtained in step S1 is input into the "seed bank-soil-microbe" synergistic response model constructed in step S2. This model, acting as a powerful feature generator, outputs a series of high-order features with clear ecological significance, such as "the theoretical promoting strength of current microbial activity on seed germination" and "the current risk index of allelopathic inhibition." These features more directly reflect the driving process of ecological restoration than the original temperature and pH values. These enhanced features are then used as input to the "rapidly customized" prediction model obtained in step S3. This model outputs dynamic prediction curves of core restoration effect indicators (such as vegetation index) over a future period (e.g., the next 6 months to 2 years).

[0035] Simultaneously, the system analyzes the first derivative of the predicted curve (i.e., the instantaneous rate of change of the remediation effect). A "healthy rate of change" threshold is set based on historical experience. When the system detects that the predicted first derivative curve is continuously below this threshold for more than a preset duration (e.g., two consecutive months in the predicted future), it determines that the remediation process is about to enter or has already entered a bottleneck period of diminishing returns. The system immediately and automatically backtracks the intermediate calculation results of the "seed bank-soil-microorganism" synergistic response model during the period that triggered the warning, identifies which input factor(s) (e.g., "concentration of promoting signaling molecules below the critical value" or "excessive fluctuation in soil moisture") is the dominant limiting factor causing the decline in the rate of change, and generates a clear warning containing this factor.

[0036] Step S5: Generate a closed-loop optimization scheme of "prediction-intervention-re-prediction".

[0037] After receiving a bottleneck warning, the system initiates the optimization plan generation process: S51. Case Matching: Based on the dominant limiting factor in the early warning information (e.g., "insufficient activity of beneficial rhizosphere bacteria"), a search is performed in the pre-built historical "remediation measures - limiting factor removal" case database. This case database stores a large number of related records from historical projects, detailing "what problems were encountered, what measures were taken (e.g., applying specific microbial agents, adjusting irrigation plans), and how the problems were alleviated."

[0038] S52. Measure Simulation: Match several potential intervention measure candidates from the case library (such as "bacterial agent A", "bacterial agent B", "add water-retaining materials"). Use each measure as a new input condition and substitute it into the "rapid customization" prediction model of the current site to perform "hypothesis" simulation, predicting the change of the repair effect curve after taking the measure, especially the improvement of its first derivative.

[0039] S53. Solution Recommendation and Closed-Loop Design: By comprehensively comparing the "potential for improving effectiveness" and "implementation cost" of each simulated measure, an optimal or second-best combination of intervention measures is recommended (e.g., "before the next rain, use a drone to spray fungicide A at a dose of X grams per square meter"). Simultaneously, the system will preset a post-intervention effect verification time point (e.g., 30 days after intervention) and automatically generate an instruction to trigger a new round of data collection and analysis at that time point (i.e., return to step S1), forming a closed loop of "prediction → early warning → intervention → re-monitoring → re-prediction".

[0040] Example 2 refer to Figure 2 The system framework diagram shown in this embodiment illustrates the system used to implement the above method, and includes the following units: The biomimetic adaptive sensor network unit is physically composed of various biomimetic sensor nodes, gateways, and communication modules distributed at the repair site. It is responsible for data acquisition, preliminary processing, and wireless transmission in step S1.

[0041] Collaborative Response Modeling Unit: This is a software module deployed on a server or in the cloud, which embeds the algorithm program of the "seed bank-soil-microorganism" collaborative response model. It is responsible for executing step S2 and performing modeling calculations on the received data.

[0042] Meta-learning prediction engine: This is the core computing software module, which includes a physical information constraint encoder, meta-learning algorithms (such as MAML and Reptile), and a basic neural network model. It is responsible for training and updating the framework in step S3 and for quickly adapting to the new repair area.

[0043] Bottleneck Analysis and Early Warning Unit: This is a logic judgment and early warning generation module that continuously monitors the dynamic curve output by the prediction engine, executes the first derivative calculation, threshold comparison, and backtracking location logic in step S4, and generates an early warning signal.

[0044] Closed-loop optimization scheme generation unit: This is a decision support software module that integrates case library management, measure simulation, and optimization algorithms. It is responsible for executing step S5 and generating an optimization scheme report that includes specific measures, dosages, and timing.

[0045] In a preferred embodiment, the system further includes: Mobile on-site micro-pilot platform: This is a rapid validation unit integrated into a mobile vehicle or container. Internally, it integrates a small climate simulation chamber (with adjustable temperature, humidity, and light), multiple test devices for filling on-site soil columns, and rapid soil nutrient and heavy metal detectors. Once the system generates an optimized plan (e.g., recommending the use of a new microbial agent), the platform can immediately obtain soil samples from the remediation site, set conditions similar to future weather forecasts in the climate simulation chamber, conduct small-scale control experiments (e.g., setting up a microbial agent application group and a control group), and use rapid detectors to evaluate the preliminary effects and optimal parameters of the measures within days or weeks. The validation results are fed back to the system for revising model parameters or supplementing the case library.

[0046] In another preferred embodiment, the system can be linked with intelligent equipment for remediation projects to form an execution closed loop. The structured scheme output by the closed-loop optimization scheme generation unit can be automatically converted into control commands for the intelligent equipment via a communication interface. For example, the command "spray Y microbial agent at a dosage of Z within the X coordinate range" can be directly sent to a microbial agent intelligent spraying drone. The drone automatically plans its flight path, loads the microbial agent, and completes precise spraying according to the command. Alternatively, the command "reseed B grass seeds at a density of C in area A" can drive a precision reseeding robot to a designated location to perform trenching, sowing, and covering operations. After execution, the equipment feeds back a completion signal to the system, which then updates the status of the remediation measures and sets subsequent monitoring tasks.

[0047] Example 3 This embodiment provides a computer-readable storage medium, such as a server hard drive, USB flash drive, or optical disc, on which a computer program (instruction code) is stored. When the program is loaded and executed by one or more processors (such as the CPU of a cloud server or edge computing device), it can control the corresponding hardware system (such as a sensor network, server, or intelligent equipment) to complete all or part of the steps described in Embodiment 1, thereby realizing the mine restoration effect prediction and optimization function of the present invention.

[0048] The steps of the methods or algorithms described in this application can be directly embedded in hardware, software units executed by a processor, or a combination of both. Exemplarily, a storage medium can be connected to a processor so that the processor can read information from and write information to the storage medium. Optionally, the storage medium can also be integrated into the processor. Optionally, the processor and the storage medium can also be located in different components within a terminal. These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0049] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely illustrative examples of this application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for predicting the effect of mine remediation based on soil data analysis, characterized in that, Includes the following steps: S1. Deploy a biomimetic sensor network with adaptive triggering capability in the remediation area to acquire multimodal soil data before, during and after remediation, and simultaneously acquire background data of the reference ecological zone; the biomimetic sensor network adaptively switches monitoring modes and sampling frequencies according to soil temperature and humidity, biological disturbance signals or preset remediation events. S2. Based on the multimodal data, a "seed bank-soil-microbe" collaborative response model is constructed. This model quantifies the feedback loop relationship among artificially sown plant seed bank germination strategies, soil matrix evolution, and microbial community function activation under specific soil conditions. S3. Construct and train a mechanism-guided meta-learning prediction framework, which includes a physical information constraint module that encapsulates prior knowledge of soil ecology, and a meta-learning module that can quickly adjust the prediction strategy based on a small amount of new data from the remediation area. S4. Use the collaborative response model to enhance the features of the original data, and input the enhanced features into the meta-learning prediction framework to output the dynamic prediction curve of the repair effect and the early warning of the critical bottleneck period. S5. Based on the warning and prediction curves, generate a closed-loop optimization scheme of "prediction-intervention-reprediction", which includes precise adjustment suggestions for remediation measures such as seed bank components and timing of microbial agent addition.

2. The method according to claim 1, characterized in that, In step S1, the bionic sensor network with adaptive triggering capability includes: A root-like probe sensor, coated with a biocompatible material, can mimic the slow release of plant root exudates to attract and monitor the chemotactic response intensity of the rhizosphere microbiome. The soil animal-like disturbance sensor senses minute changes in soil porosity and structural strength through a miniature deformable structure, and triggers high-resolution physicochemical parameter acquisition only when a mechanical pattern resembling biological disturbance is detected.

3. The method according to claim 1, characterized in that, In step S2, constructing the "seed bank-soil-microorganism" synergistic response model specifically includes: S21. Deconstruct the artificial seed bank into the proportion of different plant functional types of seeds, dormancy depth and germination water requirement threshold; S22. Using the multimodal data, identify the dynamic changes in the concentrations of key microbial signaling molecules that promote germination and allelochemicals that inhibit germination in the soil. S23. Establish a dynamic probabilistic model with soil hydrothermal conditions, signal molecules and allelochemical concentrations as inputs, and actual germination rate and seedling establishment rate of different functional seeds as outputs. This model also predicts the reaction of seedling establishment to the rhizosphere microbial community structure.

4. The method according to claim 1, characterized in that, In step S3, the method for constructing and training the mechanism-guided meta-learning prediction framework is as follows: S31. The physical information constraint module is constructed as a set of differential equation soft constraints describing basic ecological principles such as soil material conservation and energy flow, and is embedded into the forward propagation process of the neural network. S32. In the meta-learning stage, data from multiple historical mine restoration projects are used as meta-tasks for training, enabling the model to learn to extract common restoration patterns across projects. S33. For the target new restoration area, use its initial small amount of data as a support set, and quickly adjust the model parameters through the meta-learning module to adapt it to the specificity of the site, forming a "rapidly customized" prediction model.

5. The method according to claim 1, characterized in that, In step S4, the logic for generating the critical bottleneck period early warning is as follows: The dynamic prediction curve output by the prediction framework includes the first derivative curve of the repair effect index over time. When the first derivative is continuously lower than a preset threshold, the repair process is determined to have entered a bottleneck period of diminishing returns. The system automatically backtracks the output of the "seed bank-soil-microorganism" collaborative response model within that time period, identifies the dominant limiting factors that lead to diminishing returns, and uses them as the core content of the early warning.

6. The method according to claim 5, characterized in that, In step S5, the generation of the closed-loop optimization scheme includes: S51. Based on the dominant limiting factor, perform a search and matching in the "Remedial Measures - Limiting Factor Removal" case library constructed from historical data; S52. Using the adjusted "rapid customization" prediction model, simulate the effects of the matched potential intervention measures; S53. Recommend the combination of intervention measures that can most effectively improve the first derivative and have the best overall cost, and preset the trigger node for the next round of data collection and re-prediction after the intervention.

7. A mine remediation effect prediction system based on soil data analysis, characterized in that, include: A biomimetic adaptive sensor network unit is used to perform data acquisition in step S1; The collaborative response modeling unit is used to construct and run the "seed bank-soil-microorganism" collaborative response model. A meta-learning prediction engine is used to perform training and inference of the mechanism-guided meta-learning prediction framework. The bottleneck analysis and early warning unit is used to identify critical bottleneck periods and dominant limiting factors; The closed-loop optimization scheme generation unit is used to generate the "prediction-intervention-re-prediction" closed-loop optimization scheme.

8. The system according to claim 7, characterized in that, Also includes: A mobile, on-site micro-pilot platform integrates a small climate simulation chamber, a soil column test device, and a rapid testing instrument. After receiving instructions from the closed-loop optimization scheme, the platform is used to quickly verify and fine-tune the suggested intervention measures at the remediation site and feed the verification results back to the system to strengthen the model.

9. The system according to claim 7, characterized in that, The system works in conjunction with the repair engineering equipment to form an intelligent repair execution system; the intervention measures suggestions output by the closed-loop optimization scheme generation unit can be automatically converted into executable operation instructions for "intelligent microbial agent spraying drones" or "precision reseeding robots".

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6.

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