Coal gangue hill composite ecological restoration method based on plant-microorganism cooperation

Abstract

The application discloses a coal gangue hill composite ecological restoration method based on plant-microorganism cooperation and relates to the technical field of coal gangue hill ecological restoration.The method comprises the following steps: obtaining regional characteristic information set of the divided region set of the coal gangue hill; determining the region microorganism agent-planting plant group set; planting plants, and configuring microorganism root irrigation liquid according to the corresponding microorganism agent; after the microorganism root irrigation liquid is applied to the plant rhizosphere region in a root irrigation mode, performing surface soil covering; obtaining an analysis result set; adjusting the direction of the microorganism root irrigation liquid applied to the divided region set according to the analysis result set, and performing secondary root irrigation ecological restoration on the corresponding divided region according to the adjusted microorganism root irrigation liquid. The application solves the technical problem that the existing technology lacks dynamic and comprehensive understanding of the restoration of the coal gangue hill, resulting in poor ecological restoration effect. The technical effect of improving the ecological restoration quality is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ecological restoration of coal gangue dumps, in particular to a composite ecological restoration method for coal gangue dumps based on plant-microorganism cooperation. BACKGROUND

[0002] At present, coal gangue dumps have become a serious source of ecological pollution due to the presence of a large amount of harmful substances (such as heavy metals and salts) and the lack of organic matter in the soil. Traditional restoration methods for coal gangue dumps mostly rely on single plants or single microorganisms for restoration, but these methods often fail to quickly and effectively improve soil quality and promote vegetation growth, and the restoration effect is not sustainable. Moreover, the use of single-time restoration state analysis in the restoration process lacks dynamic analysis of the restoration process, resulting in a large gap between the restoration scheme and the actual situation of the coal gangue dump. SUMMARY

[0003] The present application provides a composite ecological restoration method for coal gangue dumps based on plant-microorganism cooperation, which is used to solve the technical problem of poor ecological restoration effect caused by the lack of dynamic and comprehensive understanding of the restoration of coal gangue dumps in the prior art.

[0004] In view of the above problems, the present application provides a composite ecological restoration method for coal gangue dumps based on plant-microorganism cooperation, which comprises: obtaining a set of regional characteristic information of a set of divided regions of the coal gangue dump; screening microorganism inoculants and planting plants based on the set of regional characteristic information, and determining a set of regional microorganism inoculant-planting plant groups; planting plants in the set of divided regions according to the set of microorganism inoculant-planting plant groups, and configuring a microorganism root drenching liquid according to the corresponding microorganism inoculants; after applying the microorganism root drenching liquid to the rhizosphere region of the plants by root drenching, covering the surface with soil; performing a two-dimensional and spatial interaction analysis of plant growth and soil environment improvement in a preset monitoring window for the set of divided regions, and obtaining a set of analysis results; adjusting the direction of the microorganism root drenching liquid applied to the set of divided regions according to the set of analysis results, and performing secondary root drenching ecological restoration on the corresponding divided regions according to the adjusted microorganism root drenching liquid.

[0005] Preferably, the set of divided regions includes bare areas, weathered areas, waterlogged areas, shallow erosion areas on slopes, and areas with excessive heavy metals.

[0006] Preferably, the double-dimensional spatio-temporal interaction analysis of the plant growth and soil environment improvement of the divided region set in the preset monitoring window obtains an analysis result set, including: extracting a first divided region from the divided region set; performing plant root system development index and soil environment improvement index extraction on the first divided region respectively in the preset monitoring window according to a preset monitoring frequency to obtain a first root length-root system volume-root hair quantity sequence and a first soil heavy metal concentration-nitrogen and phosphorus element content-pH value sequence; performing spatio-temporal interaction analysis on the first root length-root system volume-root hair quantity sequence and the first soil heavy metal concentration-nitrogen and phosphorus element content-pH value sequence respectively to obtain a first updated root system development time sequence feature vector and a first updated soil environment improvement time sequence feature vector; identifying the first updated root system development time sequence feature vector and the first updated soil environment improvement time sequence feature vector by using an ecological restoration analyzer to obtain a first analysis result; and performing double-dimensional spatio-temporal interaction analysis of the plant growth and soil environment improvement of the divided region set in the preset monitoring window to obtain the analysis result set.

[0007] Preferably, the spatio-temporal interaction analysis of the first root length-root system volume-root hair quantity sequence and the first soil heavy metal concentration-nitrogen and phosphorus element content-pH value sequence respectively to obtain the first updated root system development time sequence feature vector and the first updated soil environment improvement time sequence feature vector includes: performing time sequence feature analysis and spatial feature analysis on the first root length-root system volume-root hair quantity sequence by using a time sequence channel and a spatial channel respectively to obtain a first root system development time sequence feature vector and a first root system development spatial feature vector; updating the attention weight of the time sequence channel based on the first root system development time sequence feature vector and the first root system development spatial feature vector to obtain an updated time sequence channel; and performing feature analysis on the first root length-root system volume-root hair quantity sequence by using the updated time sequence channel to obtain the first updated root system development time sequence feature vector; and performing spatio-temporal interaction analysis on the first soil heavy metal concentration-nitrogen and phosphorus element content-pH value sequence to obtain the first updated soil environment improvement time sequence feature vector.

[0008] Preferably, the updating of the attention weight of the time sequence channel based on the first root system development time sequence feature vector and the first root system development spatial feature vector to obtain the updated time sequence channel includes: interacting the first root system development time sequence feature vector and the first root system development spatial feature vector by using a spatio-temporal feature interaction function to obtain a spatio-temporal interaction feature vector; updating and identifying the attention weight of the time sequence channel based on the spatio-temporal interaction feature vector and the first root system development time sequence feature vector to obtain an updated attention weight; and updating the time sequence channel according to the updated attention weight to obtain the updated time sequence channel.

[0009] Preferably, the spatio-temporal feature interaction function is:

[0010] ;

[0011] wherein, is a spatiotemporal interaction feature vector, is a first root development time sequence feature vector, is an element mapping similarity normalized value according to the first root development spatial feature vector and the first root development time sequence feature vector, is a transpose of the first root development spatial feature vector, is a dimension of the spatial feature vector, is used to introduce a prior position, which is a bias term.

[0012] Preferably, the plant root development indicators include root length, root system volume and root hair number; and the soil environment improvement indicators include soil heavy metal concentration, nitrogen and phosphorus element content and pH value.

[0013] Preferably, the microbe root drench liquid applied to the divided region set is directionally adjusted according to the analysis result set, and the corresponding divided regions are subjected to secondary root drenching ecological restoration according to the adjusted microbe root drench liquid, including: obtaining a target ecological restoration result, comparing the target ecological restoration result and the analysis result set respectively to obtain a restoration deviation set; adjusting the microbe root drench liquid applied to the divided region set in the direction of reducing the restoration deviation set to obtain an adjusted microbe root drench liquid set; and using the adjusted microbe root drench liquid set to perform secondary root drenching ecological restoration on the corresponding divided regions in the divided region set.

[0014] The application further provides an electronic device, including:

[0015] The memory is configured to store executable instructions, and the processor is configured to execute the executable instructions stored in the memory to implement the coal gangue hill composite ecological restoration method based on plant-microbe cooperation.

[0016] The application further provides a computer readable storage medium, including:

[0017] A computer program is stored thereon, and the program is executed by a processor to implement the coal gangue hill composite ecological restoration method based on plant-microbe cooperation.

[0018] One or more technical solutions provided in the application have at least the following technical effects or advantages:

[0019] The application obtains the region characteristic information set of the divided region set of the coal gangue dump, then performs microbial agent and planting plant screening based on the region characteristic information set, determines the region microbial agent-planting plant group set, and then performs plant planting on the divided region set according to the microbial agent-planting plant group set, configures microbial root irrigation liquid according to the corresponding microbial agent, applies the microbial root irrigation liquid to the rhizosphere region of the plant by the root irrigation mode, performs surface soil covering, performs plant growth and soil environment improvement double-dimensional space-time interaction analysis on the divided region set in a preset monitoring window, obtains an analysis result set, then adjusts the direction of the microbial root irrigation liquid applied to the divided region set according to the analysis result set, and performs secondary root irrigation ecological restoration on the corresponding divided region according to the adjusted microbial root irrigation liquid. The technical effect of improving the ecological restoration quality is achieved by performing double-dimensional space-time interaction analysis on the restoration process. BRIEF DESCRIPTION OF DRAWINGS

[0020] FIG. 1 is a flowchart of a coal gangue dump composite ecological restoration method based on plant-microorganism cooperation provided by an embodiment of the application. Figure 1

[0021] FIG. 2 is a flowchart of obtaining an analysis result set in the coal gangue dump composite ecological restoration method based on plant-microorganism cooperation provided by an embodiment of the application. Figure 2

[0022] Figure 3 FIG. 3 is a structural schematic diagram of an electronic device provided by an embodiment of the application.

[0023] FIG. 4 is a structural schematic diagram of an electronic device provided by an embodiment of the application. DETAILED DESCRIPTION

[0024] The application will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the application and not used to limit the scope of the application. In addition, it should be understood that those skilled in the art can make various modifications or changes to the application after reading the content taught by the application, and these equivalent forms also fall within the scope defined by the application.

[0025] It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] Embodiments, such as the accompanying drawings Figure 1 ​​As shown, the present application provides a coal gangue mountain composite ecological restoration method based on plant-microorganism cooperation, wherein the method comprises:

[0027] S1: Obtain a region characteristic information set of a divided region set of the coal gangue mountain;

[0028] Further, the divided region set comprises exposed regions, weathered regions, waterlogged regions, shallow slope erosion regions and heavy metal exceeding regions.

[0029] In one possible embodiment, the coal gangue mountain is a mountain formed by accumulation of discarded coal gangue. The coal gangue mountain is divided into regions by means of remote sensing monitoring and geological data retrieval, and each region has different soil characteristics, pollution levels and restoration requirements. The divided region set comprises exposed regions, weathered regions, waterlogged regions, shallow slope erosion regions and heavy metal exceeding regions. The region characteristic information set reflects the basic conditions of different divided regions, providing data support for subsequent screening of microbial agents and planting plants. Each region characteristic information comprises soil characteristics, pollution levels and restoration requirements of the region.

[0030] Preferably, the region characteristic information of different divided regions is as shown in Table 1:

[0031] Table 1: Region characteristic information table

[0032] Region Type Soil Characteristics Contamination Level Remediation Needs Bare Area No plant cover, soil surface directly exposed, easy water and wind erosion, loose soil High heavy metal pollution, high salt content, lack of organic matter Increase organic matter, plant cover, reduce wind and water erosion, heavy metal passivation, salt control Weathered Area Loose soil, mineral weathering, often lower soil density Heavy metal pollution, acidic environment, mineral loss Soil structure restoration, acidic soil adjustment, plant root fixation, microorganisms promote plant growth Waterlogged Area Soil is wet, lack of oxygen, tendency of salinization, poor soil permeability Too high salt content, too much water, lack of nitrogen and phosphorus Reduce salt content, drain water, improve soil permeability, increase nitrogen and phosphorus fertilizer Slope Shallow Erosion Area Weak soil, surface often eroded, serious loss High soil erosion, possible heavy metal accumulation Restore soil structure, reduce erosion, promote vegetation growth, microorganisms fix heavy metals Heavy Metal Exceeding Standard Area Soil is severely contaminated, may be acidic or alkaline High concentration of heavy metals (such as Pb, As, Cd, etc.) pollution Heavy metal passivation, microbial remediation, selection of heavy metal tolerant plants, plant hyperaccumulation remediation

[0033] S2: Screen microbial agents and planting plants based on the region characteristic information set, and determine a region microbial agent-planting plant group set;

[0034] In one embodiment, the microbial agent refers to a product containing specific microbial populations, including probiotics, soil restoration bacterial flora, nitrogen-fixing bacteria, phosphorus-dissolving bacteria, salt-tolerant bacteria and the like, which can improve the soil environment through various mechanisms. The planting plant refers to a plant selected and planted in a specific region during the restoration process. These plants need to have strong adaptability and pollution tolerance, and optionally, the planting plant comprises a hyperaccumulator, a salt-tolerant plant, a heavy metal-tolerant plant and the like.

[0035] Obtain a screening identifier, and input the region characteristic information set into the screening identifier for intelligent screening of microbial agents and planting plants, to obtain the region microbial agent-planting plant group set. Each region microbial agent-planting plant group in the region microbial agent-planting plant group set is a combination of a microbial agent and a planting plant suitable for each region determined according to the analysis result of the region characteristic information set.

[0036] Preferably, a plurality of sample area characteristic information and a plurality of sample area microbial inoculant-planting plant groups are obtained as training data, the framework based on the feedforward neural network is supervised trained by using the training data, the mapping relationship between the area characteristic information and the area microbial inoculant-planting plant group is learned, and the network parameters of the framework are updated according to the output condition in the training until the trained screening identifier is obtained. The screening identifier is used for intelligently determining the microbial inoculant and the planting plant.

[0037] For example, the area microbial inoculant-planting plant group set corresponding to different areas is shown in Table 2.

[0038] Table 2 Area microbial inoculant-planting plant group mapping table

[0039] Region Type Microbial Agent Planted Plant Bare Area Nitrogen-fixing Bacteria Switchgrass Weathered Area Sulfate-reducing Bacteria Amorpha fruticosa Waterlogged Area Pseudomonas Phragmites australis Slope Shallow Erosion Area Organic Matter-degrading Bacteria Amorpha fruticosa Heavy Metal Exceeding Standard Area Heavy Metal-fixing Bacteria Commelina communis

[0040] S3: According to the microbial inoculant-planting plant group set, the divided area set is respectively planted, and the microbial irrigation liquid is configured according to the corresponding microbial inoculant, and after the microbial irrigation liquid is applied to the rhizosphere area of the plant by the irrigation method, the surface is covered with soil;

[0041] In the embodiments of the present application, the microbial inoculant-planting plant group set selected according to the foregoing steps is respectively planted in different areas. For example, in the area with heavy heavy metal pollution, plants capable of hyperaccumulating heavy metals are selected for planting; in the waterlogged area, water-tolerant plants are selected for planting. The planting plants will help to restore the soil structure and improve the environmental conditions, and at the same time provide support for microorganisms.

[0042] Preferably, the position of the planting point is determined according to the area characteristic information of the divided area. For example, the selected planting point needs to have good drainage and suitable soil depth to avoid planting in areas with serious waterlogging or excessive barrenness. For weathered areas and bare areas, the soil usually needs to be loosened first to break the soil hardening, so that the root system can better take root. Manual or mechanical tools can be used for soil loosening to ensure the soil looseness and air permeability of the planting position. Preferably, if the soil is barren or seriously polluted, a proper amount of organic fertilizer, humus or other soil conditioner can be added before planting to improve the fertility and microbial activity of the soil and promote the growth of plant roots.

[0043] According to the size of the plant root system, a planting hole with appropriate depth and width is dug to ensure that the plant root system can fully expand. Optionally, the depth of the planting hole is about 1.5 times the root system of the plant, and the width is set by the person skilled in the art. The plant is placed in the planting hole to ensure that the root system is naturally stretched and not damaged. Cover the roots with soil and gently compact to ensure that the plant stands stably. Fill the soil around the planting hole to avoid gaps.

[0044] The microbial root irrigation liquid is configured by a person skilled in the art according to the matching requirements of the selected plant and microbial agent. Optionally, the microbial agent is expanded to a viable bacterial concentration of not less than 1 x 10 8 CFU / m by liquid culture to configure the microbial root irrigation liquid. The microbial root irrigation liquid is applied to the rhizosphere region of the plant by root irrigation. The root irrigation amount is usually 200-500 milliliters per plant. After root irrigation, the plant roots are covered with soil to ensure that the roots are protected and the soil is kept moist. The thickness of the soil covering is usually 5-10 centimeters to avoid exposing the plant roots. The surface soil is gently compacted to reduce water evaporation.

[0045] S4: performing double-dimensional spatiotemporal interaction analysis on the divided region set in the preset monitoring window for plant growth and soil environment improvement to obtain an analysis result set;

[0046] Further, as shown in FIG. 4, the double-dimensional spatiotemporal interaction analysis on the divided region set in the preset monitoring window for plant growth and soil environment improvement is performed to obtain an analysis result set. The step S4 of the embodiment of the present application further includes: Figure 2 extracting a first divided region from the divided region set;

[0047] extracting plant root system development indexes and soil environment improvement indexes of the first divided region respectively in the preset monitoring window according to a preset monitoring frequency to obtain a first root length-root system volume-root hair quantity sequence and a first soil heavy metal concentration-nitrogen and phosphorus element content-pH value sequence;

[0048] performing spatiotemporal interaction analysis on the first root length-root system volume-root hair quantity sequence and the first soil heavy metal concentration-nitrogen and phosphorus element content-pH value sequence respectively to obtain a first updated root system development time sequence feature vector and a first updated soil environment improvement time sequence feature vector;

[0049] identifying the first updated root system development time sequence feature vector and the first updated soil environment improvement time sequence feature vector by using an ecological restoration analyzer to obtain a first analysis result;

[0050] performing double-dimensional spatiotemporal interaction analysis on the divided region set in the preset monitoring window for plant growth and soil environment improvement respectively to obtain the analysis result set.

[0051] Further, the plant root system development indexes include root length, root system volume, and root hair quantity; and the soil environment improvement indexes include soil heavy metal concentration, nitrogen and phosphorus element content, and pH value.

[0052]

[0053] ​In a possible embodiment, the preset monitoring window is a monitoring time period preset by a person skilled in the art, which can be 5 days, 7 days, etc. The spatiotemporal interaction analysis of the two dimensions of plant growth and soil environment improvement is performed on each divided region to determine the ecological restoration of each divided region, and the hierarchical result set is obtained. The analysis result set reflects the ecological restoration degree of the divided region set after root irrigation once.

[0054] Preferably, a first divided region is extracted from the divided region set, and then, in a preset monitoring window, plant root development indexes and soil environment improvement indexes of the first divided region are extracted respectively according to a preset monitoring frequency, and a first root length-root system volume-root hair quantity sequence and a first soil heavy metal concentration-nitrogen and phosphorus element content-pH value sequence are obtained. The preset monitoring frequency is a monitoring time interval (such as 1 day, 2 days, etc.) preset by a person skilled in the art. The plant root development indexes include root length, root system volume, and root hair quantity; and the soil environment improvement indexes include soil heavy metal concentration, nitrogen and phosphorus element content, and pH value.

[0055] Preferably, the first root length-root system volume-root hair quantity sequence reflects the plant change of the first divided region in the preset monitoring window, and further reflects whether the plant root system grows healthily and can effectively absorb water and nutrients in the restoration process. The first soil heavy metal concentration-nitrogen and phosphorus element content-pH value sequence reflects the soil environment improvement of the first divided region in the preset monitoring window, and further reflects the pollution degree of the soil and whether the soil tends to be suitable for plant growth. The spatiotemporal interaction analysis refers to considering both time and space dimensions to analyze the dynamic process of plant growth and soil environment improvement changing with time and the spatial distribution among different regions. This analysis can reveal the restoration effect of different restoration regions and different time points, and provide support for further optimizing the restoration strategy.

[0056] The first updated root development time sequence feature vector is a feature vector that can reflect the growth state of the plant in the first divided region, which is extracted based on the spatiotemporal interaction analysis of the time sequence data related to root development (such as root length, root system volume, etc.). The first updated soil environment improvement time sequence feature vector is a feature vector extracted based on the spatiotemporal analysis of the soil environment improvement indexes (such as heavy metal concentration, nitrogen and phosphorus content, etc.), which represents the environmental improvement of the soil in the preset monitoring window. The ecological restoration analyzer is a functional module for intelligently analyzing the ecological restoration degree, the input data of which are the updated root development time sequence feature vector and the updated soil environment improvement feature vector, and the output data of which are the analysis results.

[0057] In one embodiment, multiple sample-updated root system development time series feature vectors and multiple sample-updated soil environment improvement feature vectors, as well as corresponding multiple analysis results, are obtained as an analyzer training data set. The analyzer training data set is divided into a training set and a validation set according to a division ratio preset by a person skilled in the art (e.g., 3:2). The training set is used to train a framework constructed based on a feedforward neural network to learn the two-to-one mapping relationship between the updated root system development time series feature vectors and the updated soil environment improvement feature vectors and the analysis results. After the training is completed, the multiple sample-updated root system development time series feature vectors and the multiple sample-updated soil environment improvement feature vectors in the validation set are input into the trained network to obtain multiple output analysis results. The similarity of the multiple output analysis results and the multiple sample analysis results in the validation set is compared to determine the number of similarities that meet the similarity threshold preset by a person skilled in the art. If the number is greater than the preset number, the verification is passed, and a trained ecological restoration analyzer is obtained.

[0058] The trained ecological restoration analyzer was used to identify the first-update root development time series feature vector and the first-update soil environment improvement time series feature vector, obtaining a first analysis result. Based on the same principles used to obtain the first analysis result, a two-dimensional spatiotemporal interactive analysis of plant growth and soil environment improvement was performed on the set of partitioned regions within a preset monitoring window to obtain the aforementioned analysis result set. This achieved the technical effect of analyzing the ecological restoration status of the different partitioned regions, providing data support for subsequent adjustments to the microbial root irrigation solution during secondary root irrigation.

[0059] Furthermore, a spatiotemporal interaction analysis is performed on the first root length-root volume-root hair number sequence and the first soil heavy metal concentration-nitrogen and phosphorus content-pH value sequence to obtain a first updated root development time series feature vector and a first updated soil environment improvement time series feature vector. In this embodiment, step S4 further includes:

[0060] The time series channel and space channel were used to analyze the time series characteristics of the first root length, root volume and root hair number sequence, and the time series characteristic vector and space series characteristic vector of the first root development were obtained.

[0061] Based on the first root system development temporal feature vector and the first root system development spatial feature vector, updating the attention weight of the temporal channel to obtain an updated temporal channel;

[0062] Performing feature analysis on the first root length-root volume-root hair number sequence using the updated time series channel to obtain a first updated root development time series feature vector;

[0063] The first soil heavy metal concentration-nitrogen and phosphorus element content-pH value sequence is subjected to spatiotemporal interaction analysis to obtain a first updated soil environment improvement time sequence characteristic vector.

[0064] Further, the attention weight of the time sequence channel is updated based on the first root development time sequence characteristic vector and the first root development spatial characteristic vector to obtain an updated time sequence channel.

[0065] The first root development time sequence characteristic vector and the first root development spatial characteristic vector are interacted by using a spatiotemporal feature interaction function to obtain a spatiotemporal interaction characteristic vector.

[0066] The updated attention weight is obtained by updating the attention weight of the time sequence channel based on the spatiotemporal interaction characteristic vector, the first root development time sequence characteristic vector, and the first root development spatial characteristic vector.

[0067] The time sequence channel is updated according to the updated attention weight to obtain the updated time sequence channel.

[0068] Further, the spatiotemporal feature interaction function is as follows:

[0069] ;

[0070] wherein, is the spatiotemporal interaction characteristic vector, is the first root development time sequence characteristic vector, is an element mapping similarity degree normalization value according to the first root development spatial characteristic vector and the first root development time sequence characteristic vector, is a transpose of the first root development spatial characteristic vector, is a dimension of the spatial characteristic vector, is used to introduce a prior position, and is a bias term.

[0071] In the embodiments of the present application, a sample root length-root system volume-root hair number sequence set, a sample root system development time sequence feature vector set, and a sample root system development space feature vector set are obtained. The sequence set and the time sequence feature vector set are used to supervise the training of a framework based on a convolutional neural network until convergence, and the time sequence channel is obtained. The sequence set and the space feature vector set are used to supervise the training of a framework based on a convolutional neural network until convergence, and the space channel is obtained. The time sequence channel is used to perform convolutional analysis on information that changes significantly in the time dimension of the root length-root system volume-root hair number sequence, and the space channel is used to perform convolutional analysis on information that is relatively static in the space dimension of the root length-root system volume-root hair number sequence. The time sequence channel has a higher analysis frame rate than the space channel.

[0072] Preferably, the first root system development time sequence feature vector and the first root system development space feature vector are interacted in space and time, the features obtained under different convolution scales are fused and analyzed, and the attention weight of the time sequence channel is updated according to the fusion analysis result to obtain an attention weight of the time sequence channel that conforms to the space-time double dimension.

[0073] The first updated root system development time sequence feature vector is obtained by using the updated time sequence channel with the updated attention weight to analyze the first root length-root system volume-root hair number sequence. The first updated root system development time sequence feature vector is a feature vector obtained by analyzing the static information in space and the dynamic information in time. Based on the same principle as that for obtaining the first updated root system development time sequence feature vector, the first updated soil environment improvement time sequence feature vector is obtained by performing space-time interactive analysis on the first soil heavy metal concentration-nitrogen and phosphorus element content-pH value sequence.

[0074] Preferably, the space-time feature interaction function is a mathematical model for combining the time sequence feature vector and the space feature vector to generate a fused space-time interaction feature vector. The function reveals the relationship between the time sequence data and the space data by interacting with them, and can more accurately capture the comprehensive features of plant growth or soil improvement. The space-time interaction feature vector is a feature representation obtained by the space-time feature interaction function, which combines information from both time and space. Through this feature vector, the model can consider the changes in plant growth over time.

[0075] The similarity of each element in the spatiotemporal interaction feature vector and the element in the first root system development time sequence feature vector is identified to obtain an element similarity set. The similarity of any one element is divided by the sum of the element similarity set, and the difference between the calculation result and 1 is taken as the weight of the element to obtain an updated attention weight. The time sequence channel is updated using the updated attention weight to obtain the updated time sequence channel. In the updated time sequence channel, the model will preferentially process features with high weights in a specific period. This weighted feature analysis can further improve the repair effect. For example, if the analysis result shows that the plant root system grows very slowly in a certain period of time, the time sequence channel will increase attention to this feature through the updated weight, thereby dynamically adjusting the repair strategy (such as increasing the application amount of root irrigation liquid, improving the formula of microbial inoculant, etc.).

[0076] S5: According to the analysis result set, the direction of the microbial root irrigation liquid applied to the divided region set is adjusted, and the corresponding divided region is subjected to secondary root irrigation ecological restoration according to the adjusted microbial root irrigation liquid.

[0077] Further, according to the analysis result set, the direction of the microbial root irrigation liquid applied to the divided region set is adjusted, and the corresponding divided region is subjected to secondary root irrigation ecological restoration according to the adjusted microbial root irrigation liquid, and the embodiment S5 of the present application further comprises:

[0078] Obtain a target ecological restoration result, compare the target ecological restoration result and the analysis result set respectively, and obtain a repair deviation set;

[0079] Adjust the microbial root irrigation liquid applied to the divided region set in the direction of reducing the repair deviation set to obtain an adjusted microbial root irrigation liquid set;

[0080] The corresponding divided region in the divided region set is subjected to secondary root irrigation ecological restoration using the adjusted microbial root irrigation liquid set.

[0081] In one possible embodiment, the target ecological restoration result is a restoration target set by those skilled in the art, including improvement of soil quality, improvement of plant growth, and other indicators. The target ecological restoration result provides a reference for the adjustment of the subsequent restoration scheme. By comparing the target ecological restoration result and the analysis result set, a repair deviation set is obtained. The repair deviation set reflects the problems existing in the repair process, such as slower plant growth in some areas or unsatisfactory improvement of the soil environment. These deviations will guide the adjustment of the restoration scheme to ensure the refinement of the repair process. By reducing the repair deviation set, accurate directions can be provided for subsequent restoration.

[0082] According to the repair deviation set, the formula and application method of the microbial root irrigation liquid are optimized, and the concentration, application frequency, etc. of the microbial agent are adjusted. This adjustment can supplement the short board in the repair process, such as enhancing the activity of certain microorganisms, improving the soil improvement speed, etc. After the adjustment of the microbial root irrigation liquid, secondary root irrigation repair is carried out in each region. The secondary root irrigation can further strengthen the effect of microorganisms, make up for the deficiency after the first repair, ensure the healthy growth of plants, and optimize the soil quality. This step ensures the continuity and long-term effect of the repair process. By comparing the target and actual repair results, the deviations in the repair process can be accurately identified, and adjustments can be made accordingly. This process improves the flexibility and adaptability of the repair scheme.

[0083] In summary, the embodiments of the present application have at least the following technical effects:

[0084] According to the specific needs of the region, the present application accurately selects appropriate microbial agents and planting plants, forms a set of regional microbial agent-planting plant groups, and in the repair process, through the double-dimensional space-time interaction analysis of plant growth and soil environment improvement in the divided region within the preset monitoring window, the repair progress and effect of the region are obtained. This space-time interaction analysis not only can monitor the plant growth in real time, but also can monitor the soil improvement process, provide timely feedback for subsequent repair, ensure the dynamic optimization of the repair scheme, and achieve the technical effects of improving the repair efficiency and durability.

[0085] Based on the foregoing embodiments, the embodiments of the present application also provide an electronic device and a computer readable storage medium having a computer program stored therein, which, when executed by a processor of the electronic device, can implement the method of any one of the foregoing embodiments.

[0086] Figure 3 FIG. 1 is a structural schematic diagram of an electronic device provided by an embodiment of the present application, which shows a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present application. Figure 3 The electronic device shown is merely an example and should not impose any limitation on the functions and use range of the embodiments of the present application. The electronic device is in the form of a general computing device, and its components can include but are not limited to an input device 301, a processor 302, a memory 303, and an output device 304. The processor 302 can be one or more; the memory 303 can include a computer readable medium and at least one program product, which has a set of (at least one) program modules configured to perform the functions of the embodiments of the present application.

[0087] The memory 303 shown in the embodiments of the present application can adopt any combination of one or more computer readable media; the computer readable storage medium can be, but is not limited to, an infrared ray, a semiconductor system, a device or a means, or a combination of any of the above, for storing software programs, computer executable programs and modules, such as the program instructions / modules corresponding to the coal gangue hill composite ecological restoration method based on plant-microorganism cooperation in the embodiments of the present application. The processor 302 executes various functions and data processing of the computer device by running the software programs, instructions and modules stored in the memory 303, that is, realizes the above-mentioned coal gangue hill composite ecological restoration method based on plant-microorganism cooperation.

[0088] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the above describes specific embodiments of the present application. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

[0089] The above only describes the preferred embodiments of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0090] The present application and the drawings are only exemplary descriptions of the present application, and are considered to cover any and all modifications, changes, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art can make various modifications and changes to the present application without departing from the scope of the present application. Thus, if these modifications and changes of the present application belong to the scope of the present application and its equivalent technology, the present application intends to include these modifications and changes.

Claims

1. A composite ecological restoration method for coal gangue heaps based on plant-microorganism collaboration, characterized in that: The method comprises: Obtaining a set of regional characteristic information of a set of divided regions of the gangue mountain; Screening microbial agents and colonizing plants based on the regional characteristic information set to determine a regional microbial agent-colonizing plant group set; Planting is performed on the divided area sets according to the microbial agent-planting plant group set, and a microbial root irrigation solution is prepared according to the corresponding microbial agent, and the microbial root irrigation solution is applied to the rhizosphere area of ​​the plant by root irrigation, and then the surface is covered with soil; Conduct a two-dimensional spatiotemporal interactive analysis of plant growth and soil environment improvement on the divided area set within a preset monitoring window to obtain a set of analysis results; According to the analysis results, the direction of the microbial root irrigation solution applied in the divided areas is adjusted, and the secondary root irrigation ecological restoration is carried out in the corresponding divided areas according to the adjusted microbial root irrigation solution; The method is characterized in that a two-dimensional spatiotemporal interactive analysis of plant growth and soil environment improvement is performed on a set of divided areas in a preset monitoring window to obtain a set of analysis results, including: extracting a first divided area from the divided area set; Within the preset monitoring window, plant root development indicators and soil environment improvement indicators are extracted for the first divided area according to the preset monitoring frequency, and the first root length-root volume-root hair number sequence and the first soil heavy metal concentration-nitrogen and phosphorus content-pH value sequence are obtained; The first root length-root volume-root hair number sequence and the first soil heavy metal concentration-nitrogen and phosphorus content-pH value sequence were analyzed for spatiotemporal interaction, respectively, to obtain the first-update root development time series characteristic vector and the first-update soil environment improvement time series characteristic vector. Using an ecological restoration analyzer, identifying a first-update root system development time series feature vector and a first-update soil environment improvement time series feature vector to obtain a first analysis result; Performing a two-dimensional spatiotemporal interactive analysis of plant growth and soil environment improvement on the divided area set within a preset monitoring window to obtain the analysis result set; The plant root development indicators include root length, root volume and root hair number; the soil environment improvement indicators include soil heavy metal concentration, nitrogen and phosphorus content and pH value; Among them, the direction of the microbial root irrigation solution applied in the divided areas is adjusted according to the analysis results, and the secondary root irrigation ecological restoration is carried out in the corresponding divided areas according to the adjusted microbial root irrigation solution, including: Obtaining a target ecological restoration result, and comparing the target ecological restoration result with the analysis result set to obtain a restoration deviation set; The microbial root irrigation solution applied to the divided area set is adjusted with the reduction of the repair deviation set as the adjustment direction to obtain an adjusted microbial root irrigation solution set; The adjusted microbial root irrigation liquid set is used to perform secondary root irrigation ecological restoration on the corresponding divided areas in the divided area set.

2. The method for composite ecological restoration of coal gangue heaps based on plant-microorganism synergy according to claim 1, characterized in that: The divided area set includes exposed areas, weathered areas, waterlogged areas, shallow slope scour areas and areas with excessive heavy metals.

3. The method for composite ecological restoration of coal gangue heaps based on plant-microorganism synergy according to claim 1, characterized in that: The first root length-root volume-root hair number sequence and the first soil heavy metal concentration-nitrogen and phosphorus content-pH value sequence were analyzed for spatiotemporal interaction, and the first-update root development time series feature vector and the first-update soil environment improvement time series feature vector were obtained, including: The time series channel and space channel were used to analyze the time series characteristics of the first root length, root volume and root hair number sequence, and the time series characteristic vector and space series characteristic vector of the first root development were obtained. Based on the first root system development temporal feature vector and the first root system development spatial feature vector, updating the attention weight of the temporal channel to obtain an updated temporal channel; Performing feature analysis on the first root length-root volume-root hair number sequence using an updated time series channel to obtain a first updated root development time series feature vector; A spatiotemporal interaction analysis was performed on the first soil heavy metal concentration-nitrogen and phosphorus content-pH value sequence to obtain the first updated soil environment improvement time series feature vector.

4. The method for composite ecological restoration of coal gangue heaps based on plant-microorganism synergy according to claim 3, characterized in that: Based on the first root system development temporal feature vector and the first root system development spatial feature vector, the attention weight of the temporal channel is updated to obtain an updated temporal channel, including: Using a spatiotemporal feature interaction function to interact the first root system development temporal feature vector and the first root system development spatial feature vector to obtain a spatiotemporal interaction feature vector; Performing updating and identification based on the spatiotemporal interaction feature vector, the first root system development time series feature vector, and the attention weight of the time series channel to obtain an updated attention weight; The timing channel is updated according to the updated attention weight to obtain the updated timing channel.

5. The method for composite ecological restoration of coal gangue heaps based on plant-microorganism synergy according to claim 4, characterized in that: The spatiotemporal feature interaction function is: Among them, f s ′ is the spatiotemporal interaction feature vector, f s is the first root system development time series eigenvector, is the normalized value of the similarity mapping between the first root system development space feature vector and the first root system development time series feature vector, is the transpose of the first root system development space eigenvector, d K is the dimension of the spatial feature vector, M is used to introduce the prior position, and is the bias term.

6. An electronic device, characterized in that: The electronic device comprises: a memory for storing executable instructions; The processor is configured to implement the plant-microorganism synergistic composite ecological restoration method for coal gangue heaps as described in any one of claims 1 to 5 when executing the executable instructions stored in the memory.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the plant-microorganism synergistic composite ecological restoration method for coal gangue heaps is implemented as described in any one of claims 1 to 5.

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