In-situ groundwater remediation method and device based on microbial agent

Abstract

The invention relates to an in-situ groundwater remediation method and device based on a microbial agent. The method comprises the steps that monitoring points are connected according to the flowing direction of groundwater to form a directed graph; performing breadth-first traversal on the directed graph, and dividing a diffusion edge between the nodes into one or more diffusion edges based on the repair load; determining a setting range of a microbial agent based on the directed graph; the setting range comprises one or more nodes. Under the condition of limited remediation resources such as monitoring data, the microbial agent device can be optimally arranged in a difference and adaptability manner in a three-dimensional space range, and is dynamically matched with the action period of the microbial agent and the pollutant diffusion mode, so that the in-situ remediation efficiency of the microbial agent device is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of groundwater remediation, and particularly relates to an in-situ groundwater remediation method and device based on microbial agents. Background Art

[0002] Microbial remediation is most suitable for biodegradable organic pollutants, such as petroleum hydrocarbons (benzene, diesel), chlorinated solvents (trichloroethylene), nitrates, etc. For example, hydrocarbons can be oxidized by aerobic bacteria, and chlorinated hydrocarbons can be reductively dechlorinated by anaerobic bacteria. In practical applications, the effects of microbial agents need to be verified through laboratory bench-scale tests and field pilot tests. For example, in a case of diesel leakage at a gas station, the injection of petroleum-degrading bacteria (Pseudomonas) in combination with a slow-release oxygen agent reduced the pollutant concentration by 90% within 3 months; while at a chlorinated hydrocarbon-contaminated site, the addition of dechlorinating bacteria (Dehalococcoides) and sodium lactate achieved complete dechlorination within 1 year. If the environmental conditions are not ideal (such as low temperature or low-permeability formations), biostimulation (addition of nutrients) or engineering means (electrokinetic assistance) can be combined to improve the remediation efficiency. Using a microbial agent device for in-situ groundwater remediation has the characteristics of economy and safety. The cost of microbial remediation is usually lower than that of chemical oxidation or pump-and-treat, especially suitable for large-scale low-concentration pollution, and there is no risk of secondary pollution, making it applicable to ecologically sensitive areas (such as near drinking water sources). The action time of some microbial agents is limited, but their effectiveness can be significantly extended through scientific selection of bacteria and environmental optimization. Its applicability needs to be judged comprehensively based on the type of pollutants, site conditions, and remediation goals to ensure that the technology is feasible, economical, and efficient.

[0003] The microbial agent is closely related to the spread of pollutants and requires a relatively strict monitoring data basis. ArcGIS or Surfer can be used to draw the isoconcentration map of groundwater pollutants for the setting of the microbial agent device. However, when using these automated tools to determine the spread of pollutants, there are high requirements for monitoring data. The quality and spatial distribution of the input data directly affect the accuracy and reliability of the mapping, which is contrary to the fact that the microbial agent is suitable for the remediation of large-scale low-concentration pollution. Obtaining the spread of low-concentration pollutants over a large area requires a large monitoring cost. For two-dimensional plane maps: at least 5 to 8 monitoring points are required to generate basic contour lines, but the accuracy is low. For three-dimensional distribution maps: ≥10 monitoring points are required, and there are layered data at different depths. If reliable mapping suggestions are needed, in a small-scale site (<1 km²): 15 to 20 monitoring points. In a large-scale site (>1 km²): at least 10 points per square kilometer, or increase according to the complexity of the pollution plume. In addition, there are also high requirements for the spatial distribution of the detection points; the monitoring points need to evenly cover the pollution plume: the monitoring points should be distributed in the pollution source, the core area of the plume, the head and the background area; if the pollution plume has bifurcations or crosswise migrations, additional monitoring points need to be added, which significantly increases the difficulty of implementing the microbial agent method. In addition, the use method of the microbial agent device requires strong flexibility and needs to be dynamically and flexibly adapted when implementing the prevention and control strategy. This is because the action time of the microbial agent in groundwater remediation is usually limited, and the specific duration depends on the characteristics of the strain, environmental conditions and the nature of the pollutants. After the liquid microbial agent is directly injected into the aquifer, the microorganisms can usually maintain their activity for 1 to 3 months, and then gradually become inactivated due to nutrient depletion, environmental stress or hydraulic scouring. Solid slow-release microbial agents (such as biochar or gel microspheres encapsulating bacteria) can extend the action time to 6 to 12 months and achieve long-term remediation by slowly releasing bacteria and nutrients. If continuous effects are required, the microbial agent or nutrients need to be monitored and supplemented regularly, which brings more difficulties to large-scale precise monitoring and the economic cost is very high. However, along with the daily use and protection of groundwater, there are also many monitoring points that can provide real-time pollutant monitoring data. Although these monitoring data cannot reach the level of accurately drawing the diffusion distribution map, they can be used to roughly determine the scope of the pollution prevention and control area and provide the approximate scope of the pollution source and the head area of the pollution plume. And this provides a basis for the layout and effective use of the microbial agent device. In the head area of the pollution plume, the pollutant concentration sharply transitions from high (the core area of the pollution plume) to low (the uncontaminated background water body), which is the key part to control the spread of pollution and is also the area that can be initially determined through limited monitoring data. Reasonably arranging the microbial agent in the pollution head can effectively prevent the expansion of the pollution range, which is more economical and flexible than setting up treatment methods such as biological curtains or reaction walls for the high-concentration core area.For the above reasons, the prior art often adopts a setting method with a fixed spacing for the microbial inoculant device. For example, a grid width of 5 - 20 m is set for grid layout, and the grid spacing is uniformly reduced when the permeability is poor or the pollutants are difficult to degrade. Such a setting method treats the groundwater prevention and control area as a two-dimensional plane. In fact, the groundwater prevention and control area is a three-dimensional area, and the pollutant concentration in each area is different. How to deploy the microbial inoculant device with differences and adaptability within the three-dimensional space range under the condition of limited monitoring data, and be able to quickly adapt to the desired prevention and control strategy is a technical problem to be solved. Based on the above problems, the present invention can optimize the deployment of the microbial inoculant device with differences and adaptability within the three-dimensional space range under the condition of limited repair resources such as monitoring data, and dynamically adapt to the action cycle of the microbial inoculant and the pollutant diffusion mode, so as to improve the in-situ repair efficiency of the microbial inoculant device. Summary of the Invention

[0004] In order to solve the above problems in the prior art, the present invention proposes an in-situ groundwater remediation method and device based on microbial inoculants, and the method includes: Step S1: Connect the monitoring points according to the groundwater flow direction to form a directed graph; the nodes are the monitoring points, and the edges are the diffusion edges from the upstream monitoring point to the downstream monitoring point; Step S2: Perform a breadth-first traversal on the directed graph, and divide the diffusion edges between the nodes into one or more diffusion edges based on the repair load; the repair load indicates the repair amount of the prevention and control space represented by the diffusion edge; the corresponding groundwater diffusion space is divided into one or more spatial grids with the same repair load; Step S3: Determine the setting range of the microbial inoculant based on the directed graph; the setting range includes one or more nodes; specifically: Step S31: Obtain any node in the directed graph, and determine the length set formed by the lengths of the shortest paths from each starting node to this node; the length is the number of diffusion edges passed from the starting node to the node; Step S32: Calculate the eigenvalue of the length set, and use the eigenvalue of the length set as the diffusion coefficient of this node; Step S33: Put the node into the repair set corresponding to the diffusion coefficient interval into which its diffusion coefficient falls; Step S34: Select one or more repair sets as the target repair set according to the pollutant type and / or pollution occurrence stage targeted by the repair strategy; Step S35: Select all the nodes in the target repair set or select some nodes in the target repair set at uniform intervals to form the setting range; deploy the microbial inoculant around the setting range.

[0005] Furthermore, the monitoring points dynamically monitor the pollutant concentration, groundwater depth, and groundwater flow direction.

[0006] Furthermore, a part of the nodes in the target repair set is selected at equal intervals to form a setting range, specifically: under the constraint of the number of setting positions of the repair strategy, a part of the nodes in the target repair set is selected at equal intervals to form a setting range.

[0007] Furthermore, the eigenvalue is the mean or minimum value of the lengths in the length set.

[0008] Furthermore, the association relationship between the repair set and its diffusion coefficient interval is preset.

[0009] Furthermore, the diffusion coefficient intervals do not overlap.

[0010] Furthermore, the action time length of the microbe agent deployed at one time is limited.

[0011] An in-situ groundwater remediation system based on a microbe agent, and the in-situ groundwater remediation system based on a microbe agent is used to implement the above-mentioned in-situ groundwater remediation method based on a microbe agent.

[0012] An in-situ groundwater remediation device based on a microbe agent, and the in-situ groundwater remediation device based on a microbe agent is used to implement the above-mentioned in-situ groundwater remediation method based on a microbe agent.

[0013] An in-situ groundwater remediation platform based on a microbe agent, and the in-situ groundwater remediation platform based on a microbe agent is used to implement the above-mentioned in-situ groundwater remediation method based on a microbe agent.

[0014] The beneficial effects of the present invention include: (1) On the basis of using a directed graph to describe the action area of the microbe agent, uniform and consistent grid division is performed on the three-dimensional space range based on the prevention and control load, which supports further quantitative analysis of the prevention and control area, realizes the optimized layout of the microbe agent device with differences and adaptability in the three-dimensional space range, dynamically adapts to the action cycle of the microbe agent and the pollutant diffusion mode, optimizes the layout method of the microbe agent device, and thus improves the repair efficiency; (2) Obtain the diffusion coefficient corresponding to the node based on the directed graph analysis to adapt to the prevention and control strategy; support that while ensuring that the layout density of the pollutant microbial agent device increases the barrier size as the pollution plume expands in the spatial range, with the progress of the remediation time, with the continuous diffusion of pollutants, and with the continuous retreat and expansion of the plume area, the microbial agent device barrier retreats, forming a process of dynamic adaptation following the diffusion progress of pollutants. This dynamic adaptation process is also adapted to the effective time length of the microbial agent device, avoiding poor remediation effects caused by insufficient agent action time length and agent migration. Description of the Drawings

[0015] The drawings described herein are used to provide a further understanding of the present invention, form a part of this application, but do not constitute an improper limitation to the present invention. In the drawings: Figure 1 Schematic diagram of the in-situ groundwater remediation method based on microbial agents provided by the present invention. Specific Embodiments

[0016] The present invention will be described in detail below in conjunction with the drawings and specific embodiments. The illustrative embodiments and descriptions are only used to explain the present invention, but do not limit the present invention.

[0017] The present invention proposes an in-situ groundwater remediation method and device based on microbial agents, as shown in the attached Figure 1 figure. The method includes the following steps: Step S1: Obtain the pollutant parameters of each monitoring point in the prevention and control area; connect the monitoring points according to the groundwater flow direction to form a directed graph G; the nodes in the directed graph are the monitoring points, and the edges are the diffusion edges from the upstream monitoring point to the downstream monitoring point.

[0018] Preferably: The monitoring points can dynamically monitor information such as pollutant concentration, groundwater depth, and flow direction; since there is no need to seek the absolute relationship between pollutant parameters from monitoring point to monitoring point and the actual location of the pollution source, the directed graph can be used to describe multi-source pollution and cross-groundwater flow and pollutant diffusion patterns, and perform parallel control of multi-source pollution accordingly; therefore, the prevention and control area can be the area where the microbial agent device needs to be arranged based on the remediation strategy, and the prevention and control area can be determined across administrative regions; when there is no upstream and downstream relationship between two nodes, there is no sequential relationship between these two nodes; the prevention and control area targeted by the microbial agent mainly includes the diffusion area of the pollution plume. Therefore, the starting node can be a node in the upstream area that has no sequential relationship with other nodes.

[0019] Preferably, when constructing a directed graph, the initial node (the starting position of pollutant diffusion upstream) and the target node (the ending position of pollutant diffusion, where the pollutant parameters drop to nearly 0) are determined based on the geographical information of the prevention and control area and the existing groundwater distribution information within the prevention and control area. Further, the diffusion edges between the nodes are determined based on the existing groundwater flow direction information. Among them, the geographical information of the prevention and control area includes information such as the geographical location distribution, landform, transportation, facilities, buildings, meteorology, water system, and vegetation of the prevention and control area. The diffusion edge corresponds to the prevention and control area where it is located and the three-dimensional space where it is located. To a certain extent, the geographical information of the diffusion edge can represent the geographical information of the three-dimensional space within the prevention and control space where it is located or within a certain range of the two-dimensional space containing the diffusion edge.

[0020] Alternatively, when the existing groundwater flow direction information is insufficient, after determining the initial node and the target node, diffusion edges are set between all adjacent nodes where there may be pollutant diffusion relationships based on the insufficient existing groundwater flow direction information. Further, the relationship information between the pollutant parameters of any two monitoring points with diffusion edges is analyzed to determine whether to retain the diffusion edge. When there is an association relationship (or a strong association relationship) between the pollutant parameters of the two in the historical monitoring data, the diffusion edge is retained; otherwise, the diffusion edge is deleted. An association relationship means that there has been an association relationship between the pollutant parameter information obtained from multiple dynamic monitoring. A strong association relationship indicates that there have been multiple association relationships between the two, or there is always an association relationship. In this way, a relatively dense-covered directed graph can be formed under the condition of limited or sparse monitoring point deployment, regardless of the specific groundwater flow direction and formation relationship information.

[0021] Preferably, the prevention and control area is the initially determined head of the pollution plume, which is the foremost area of pollutant migration and diffusion in the groundwater pollution plume, that is, the front boundary of the pollution zone in the groundwater flow direction. When pollutants diffuse within the groundwater prevention and control area, in the head area of the pollution plume, the pollutant concentration shows a sharp transition from high (the core area of the pollution plume) to low (the uncontaminated background water body). It is the key part to control pollution diffusion and is also the area that can be initially determined through limited monitoring data. Reasonable layout of microbial agents in the pollution head can effectively prevent the expansion of the pollution range, which is more economical and flexible than methods such as setting up biological curtains or reaction walls to treat the high-concentration core area.

[0022] Step S2: Traverse the directed graph and divide the diffusion edges between nodes into one or more diffusion edges according to the repair capacity. Specifically: perform a breadth-first traversal on the directed graph, and divide the diffusion edges between nodes into one or more diffusion edges based on the repair capacity; the corresponding groundwater diffusion space is divided into one or more spatial grids with the same repair capacity; if the diffusion edge is divided into one diffusion edge, that is, the diffusion edge does not need to be divided; while when the diffusion edge is divided into multiple diffusion edges, new nodes will be generated along with the division of the diffusion edge. Update the directed graph based on the new nodes and the generated diffusion edges; use the newly generated diffusion edges and their nodes to replace the diffusion edges before division to obtain the updated directed graph.

[0023] The specific steps of step S2 are as follows: Step S21: Put all starting nodes into the queue to be processed; a node without incoming edges is a starting node; a node without outgoing edges is a leaf node.

[0024] Preferably: Initialize the queue to be processed as empty; the starting node is or is not the location of the pollution source; however, the starting node is closer to the location of the pollution source.

[0025] Preferably: When there are multiple starting nodes, put all starting nodes into the queue to be processed in a randomly sorted order.

[0026] Alternatively: When there are multiple starting nodes, sort the starting nodes according to the level of their pollutant parameters, the distance from the center of all starting nodes, or the distance from the center of the prevention and control area, so that the nodes sorted earlier enter the queue first, and the nodes sorted later enter the queue later; in the subsequent grid generation process, the diffusion edges processed earlier are more likely to form denser grids due to difficulty in achieving consistency; thus, it has more advantages in the subsequent repair process.

[0027] Step S22: Take out a node from the head of the queue to be processed as the current node; if the queue to be processed is empty, the grid generation ends; after the grid generation ends, the diffusion edges are divided into one or more diffusion edges with the same repair capacity; new nodes are formed at the division positions.

[0028] Step S23: Obtain an unprocessed child node of the current node as the current child node; put the current child node at the tail of the queue to be processed.

[0029] Preferably: Prioritize selecting unprocessed child nodes that are close to the current node to achieve fast convergence.

[0030] Step S24: Take the diffusion edge between the current node and the current child node as the current diffusion edge.

[0031] Step S25: Divide the current diffusion edge into one or more diffusion edges with the same repair load; the repair load indicates the repair amount of the prevention and control space represented by the diffusion edge.

[0032] The specific steps of step S25 are as follows: Step S251: Initialize the set to be divided as an empty set, and put the current diffusion edge into the set to be divided; Step S252: Obtain an unprocessed diffusion edge; Preferably: If the length of the unprocessed diffusion edge is less than the length threshold, set it as processed and return to step S252 to obtain again; the length threshold is a preset value, for example: 5m; Step S253: Divide the unprocessed diffusion edge into 2 diffusion edges on average according to the edge length ; where: is the starting node of the diffusion edge, is the ending node of the diffusion edge; the edge length is the distance between the two end nodes.

[0033] Preferably: Set attributes for the newly generated nodes; the attributes include pollutant parameters at the node position, node position, etc.; they can be obtained by actual measurement or calculation or prediction based on the attribute information at the end nodes.

[0034] Step S255: Judge whether the divided diffusion edges meet the constraint conditions, and put the diffusion edges that do not meet the constraint conditions into the set to be divided; the diffusion edges that meet the constraint conditions are the newly generated diffusion edges; the constraint condition is that the repair load size represented by the diffusion edge is within the repair load size threshold range.

[0035] Preferably: The repair load size is one of the comprehensive calculation values of the prevention and control space size represented by the diffusion edge, the diffusion edge length, the diffusion edge and the pollutant parameter concentration, the comprehensive calculation value of the prevention and control space size and the pollutant parameter concentration, etc.

[0036] Preferably: The space size threshold range and the pollutant concentration range threshold are preset values, for example: the space size threshold range is , that is, related to grid density and groundwater depth. The setting method of the threshold is related to the geographical location and climate of the prevention and control area. In humid areas such as the coast and along rivers, the groundwater depth is 0.5 - 5 m. In semi-arid areas such as plains and farmlands, the groundwater depth is 5 - 20 m. In arid areas such as deserts and plateaus, the groundwater depth is 20 - 100 m or deeper, and in urban areas, the groundwater depth is 3 - 15 m. Therefore, for different regions, the range of the spatial size threshold is different. Of course, the deeper the groundwater depth, the larger the threshold range; for example, the concentration of benzene is 0.01 mg / L, and the concentration of petroleum hydrocarbons is 0.1 mg / L - 1 mg / L; the threshold value of the comprehensive calculated value of the diffusion edge and pollutant parameter concentration is equal to .

[0037] Preferably: This step S255 further includes: determining whether the obtained diffusion edge satisfies one of the following formulas (1) (2) and one of (3) - (5). If not, it is placed in the set to be divided; the satisfied diffusion edge is the newly generated diffusion edge and will not be further divided; where: is the length of the diffusion edge; is the groundwater depth at is the pollutant concentration at are the upper and lower limit values of the consistency range; the consistency is judged through the following formula, making it easy to achieve the consistency of the load, spatial transition, and setting of the repair device in the divided grid space; .

[0038] Preferably: is a preset value; for example: .

[0039] Step S256: Determine whether there are still unprocessed diffusion edges. If so, return to step S252; Step S26: If the current node still has unprocessed child nodes, return to step S23; otherwise, return to step S22.

[0040] Step S3: Determine the setting range of the microbial inoculant based on the directed graph; specifically: for any node in the directed graph, determine the length of the shortest path from any starting node to this node; determine the diffusion coefficient of this node based on this length; divide the node into one or more repair sets based on this diffusion coefficient; select one or more repair sets from these repair sets as the target repair sets based on the repair strategy, and select one or more nodes from the target repair sets to form the setting range.

[0041] The specific steps of the said step S3 include the following steps: Step S31: Obtain any node c in the directed graph, and determine the lengths of the shortest paths from each starting node o to this node c to form a length set; is the number of diffusion edges passed from the starting node o to the node c.

[0042] Preferably: When there is 1 starting node, use the nodes with a 1-step length from this starting node as the starting nodes.

[0043] Step S32: Determine the diffusion coefficient of this node based on this length set; specifically: Calculate the eigenvalue of this length set, and use the eigenvalue of this length set as the diffusion coefficient of this node.

[0044] Preferably: The eigenvalue is one or more of the mean value, minimum value, maximum value, sum value, etc. of the lengths in the length set; Step S33: Put the node into the repair set corresponding to the diffusion coefficient interval into which its diffusion coefficient falls.

[0045] Preferably: Divide the diffusion coefficient into one or more diffusion coefficient intervals, and preset the association relationship between the repair set and its diffusion coefficient interval; the diffusion coefficient intervals do not overlap.

[0046] Step S34: Select one or more repair sets from this repair set as the target repair set based on the repair strategy; specifically: Select one or more repair sets as the target repair set according to the pollutant type and / or pollution occurrence stage targeted by the repair strategy.

[0047] The selection of one or more repair sets as the target repair set according to the pollutant type targeted by the repair strategy; specifically: For petroleum hydrocarbon pollution, select the repair sets with small and large diffusion coefficient interval values; for chlorinated hydrocarbon pollution, select the repair set with a medium diffusion coefficient interval value; for nitrate pollution, select the repair set with a small diffusion coefficient interval value; for heavy metal pollution, use all repair sets as the target repair set.

[0048] Preferably: For petroleum hydrocarbon pollution, use Pseudomonas and slow-release oxygen agents; for chlorinated hydrocarbon pollution, use Dehalococcoides and sodium lactate; for nitrate pollution, use Paracoccus and methanol; for heavy metal pollution, use sulfate-reducing bacteria.

[0049] Select one or more repair sets as the target repair set according to the pollution occurrence stage targeted by the repair strategy; specifically: in the early repair stage when the pollutants have not migrated on a large scale, select the repair set with a small numerical value in the diffusion coefficient interval; in the middle and late repair stages when the pollution has spread, select the repair set with a moderate or large numerical value in the diffusion coefficient interval; of course, in the case of unlimited repair resources, extensive deployment of microbial agent devices can be carried out without considering the selection of the repair set.

[0050] Step S35: Select one or more nodes from the target repair set to form a setting range; specifically: select all the nodes in the target repair set or select some nodes in the target repair set at equal intervals to form a setting range.

[0051] Alternatively: The specific step S35 is: Under the constraint of the number of setting positions of the repair strategy, select some nodes in the target repair set at equal intervals to form a setting range.

[0052] Alternatively: The specific step S35 is: Based on the constraint of the number of setting positions of the repair strategy and the progress time of the repair strategy, select one or more nodes from the target repair set to form a setting range, and adjust the distribution of the one or more nodes in the target repair set as time progresses.

[0053] Specifically, it includes the following steps: Step S351: Obtain the constraint N on the number of setting positions of the repair strategy, the number K of target repair sets, and the current progress time t of the repair strategy; where: the progress time is the number of unit time lengths; for example: if the unit time length is 1 month, then t = 2 means the progress time length is 2 months; where: the smaller the number k of the target repair set, the smaller the corresponding numerical value in the diffusion coefficient interval.

[0054] Step S352: Divide the position number constraint N into K positive integers based on the progress time; expressed as ; The division makes .

[0055] Step S353: For the k-th target repair set, select nodes at equal intervals as the setting positions; after the selection, the selected A set of nodes constitutes a setting range; microbial inoculant devices are arranged around each node position within the setting range. Then, while ensuring that the density of the pollutant inoculant devices increases the barrier size as the pollution plume expands in the spatial range, as the remediation time progresses, as the pollutants continue to spread, and as the plume area retreats and expands, the microbial inoculant device barrier retreats, forming a process of dynamic adaptation following the diffusion progress of the pollutants. This dynamic adaptation process is also compatible with the effective time length of the microbial inoculant devices, avoiding poor remediation effects caused by insufficient inoculant action time length and inoculant migration.

[0056] The microbial inoculant is arranged around each node position within the setting range; specifically: the setting method of the microbial inoculant is selected based on the groundwater depth at the node position. The microbial inoculant is arranged longitudinally at the node position with a large groundwater depth, allowing the microbial inoculant to diffuse radially; while at the node position with a small groundwater depth, the microbial inoculant is arranged transversely, so as to adapt to the spatial remediation load represented by the extended side. A suitable arrangement position is selected within the vicinity of the node position for arranging the microbial inoculant device.

[0057] Step S3 further includes: when the remediation strategy is time-constrained or setting position quantity-constrained, determining the fastest setting range of the microbial inoculant based on the directed graph; specifically: obtaining the shortest path from the starting node to the target node, and taking the node positions on the shortest path as the fastest setting range; specifically including the following steps: Step S3X1: Select the target node; set the fastest setting range to be empty; specifically: all leaf nodes are used as target nodes.

[0058] Alternatively: The leaf nodes falling within the key prevention and control area are used as target nodes; the key prevention and control area is the remediation focus area of the remediation strategy.

[0059] Alternatively: Some leaf nodes are selected at intervals from the leaf nodes as target nodes.

[0060] Step S3X2: Obtain any starting node; obtain the shortest path from this starting node to each target node, and put this shortest path into the shortest path set; when there is no path from this starting node to a target node, the path length is infinite.

[0061] Step S3X3: Obtain the one with the shortest shortest path length from this set of shortest paths (the path length is the number of diffusion edges in the path). When the start node and the target node of the one with the shortest shortest path length are not within the fastest setting range, put all the nodes on the path of the one with the shortest shortest path length into the fastest setting range; delete the one with the shortest shortest path length from the set of shortest paths; repeat this step until the set of shortest paths is empty; since the fastest setting range is set management, the repeated addition of nodes will not cause an increase in the nodes in the set.

[0062] Of course, re-selection and position screening of nodes can be performed within the fastest setting range according to the actual needs of the repair strategy. For example, re-selection of nodes can be carried out from perspectives such as the interval distance and the actual difficulty of setting, which will not be elaborated here.

[0063] Preferably: The microbial inoculant is arranged on the functional inoculant carrier to form a microbial inoculant device; the functional inoculant carrier is a porous material such as biochar, activated carbon, zeolite, etc., and a nutrient solution storage tank containing essential elements for microbial growth such as N, P, K, etc. is also provided; in some water quality environments, it can be arranged in a slow-release capsule to slowly release the microorganisms and extend the action time; when arranging the microbial inoculant device, a high-pressure injection pump can be selected according to the groundwater depth to inject the liquid inoculant into the deep aquifer, or a permeable diffuser such as a porous pipe or a sieve can be used to uniformly release the inoculant to avoid local blockage.

[0064] Based on the same inventive concept, the present invention also provides a microbial inoculant system, and the system is used to implement the above-mentioned microbial inoculant method.

[0065] Based on the same inventive concept, the present invention also provides a microbial inoculant device, and the device is used to implement the above-mentioned microbial inoculant method.

[0066] A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including assembly or interpreted languages, declarative or procedural languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may or may not correspond to a file in a file system. The program can be stored as part of a file that holds other programs or data (such as one or more scripts in a markup language document), in a single file dedicated to the program, or in multiple cooperating files (such as files that store one or more modules, subroutines, or code portions). A computer program can be deployed to execute on one computer or on multiple computers located at one site or distributed across multiple sites and interconnected by a communication network.

[0067] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0068] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0069] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0070] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable devices provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: still can modify the specific implementation manners of the present invention or make equivalent substitutions, and any modification or equivalent substitution that does not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. An in-situ groundwater remediation method based on microbial inoculum, characterized in that, The method includes: Step S1: Connect the monitoring points according to the underground water flow direction to form a directed graph; the nodes are the monitoring points, and the edges are the diffusion edges from the upstream monitoring point to the downstream monitoring point. Step S2: Perform a breadth-first traversal on the directed graph, and divide the diffusion edges between the nodes into one or more diffusion edges based on the repair load. The repair load indicates the repair amount of the prevention and control space represented by the diffusion edge; the corresponding groundwater diffusion space is divided into one or more spatial grids with the same repair load. Step S3: Determine the setting range of the microbial agent based on the directed graph; the setting range includes one or more nodes; specifically: Step S31: Obtain any node in the directed graph, and determine the length set composed of the lengths of the shortest paths from each starting node to this node. The length is the number of diffusion edges passed from the starting node to the node. Step S32: Calculate the eigenvalue of the length set, and use the eigenvalue of the length set as the diffusion coefficient of this node. Step S33: Put the node into the repair set corresponding to the diffusion coefficient interval into which its diffusion coefficient falls. Step S34: Select one or more repair sets as the target repair set according to the pollutant type and / or pollution occurrence stage targeted by the repair strategy. Step S35: Select all the nodes in the target repair set or select some nodes in the target repair set at uniform intervals to form the setting range; arrange the microbial agent around the setting range.

2. The in-situ groundwater remediation method based on microbial inoculum according to claim 1, wherein The monitoring points dynamically monitor the pollutant concentration, groundwater depth, and underground water flow direction.

3. The in-situ groundwater remediation method based on microbial inoculum according to claim 2, characterized in that, The selection of some nodes in the target repair set at uniform intervals to form the setting range is specifically: under the constraint of the number of setting positions of the repair strategy, select some nodes in the target repair set at uniform intervals to form the setting range.

4. The in-situ groundwater remediation method based on microbial inoculum according to claim 3, characterized in that, The eigenvalue is the mean or minimum value of the lengths in the length set.

5. The in-situ groundwater remediation method based on microbial inoculum according to claim 4, characterized in that, Preset the association relationship between the repair set and its diffusion coefficient interval in advance.

6. The in-situ groundwater remediation method based on microbial inoculum according to claim 5, wherein, The diffusion coefficient intervals do not overlap with each other.

7. The in-situ groundwater remediation method based on microbial inoculum according to claim 6, characterized in that, The action time length of the microbial agent arranged at one time is limited.

8. An in-situ groundwater remediation system based on microbial inoculum, characterized in that, The in-situ groundwater repair system based on the microbial agent is used to implement the in-situ groundwater repair method based on the microbial agent described in any one of claims 1-7 above.

9. An in-situ groundwater remediation device based on microbial agents, characterized in that, The in-situ groundwater repair device based on the microbial agent is used to implement the in-situ groundwater repair method based on the microbial agent described in any one of claims 1-7 above.

10. An in-situ groundwater remediation platform based on microbial agents, characterized in that, The in-situ groundwater repair platform based on the microbial agent is used to implement the in-situ groundwater repair method based on the microbial agent described in any one of claims 1-7 above.

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