Methods and systems for detecting the migration of heavy metal pollution in abandoned mining areas

Abstract

This application provides a method and system for detecting heavy metal pollution migration in abandoned mining areas. The method involves collecting soil electrical impedance data from the abandoned mining area to the observation area using an intelligent impedance sensor. Based on this data, the impedance change information of each potential pollution unit is determined, and a heavy metal pollution migration model is constructed. Dynamic migration resistance for different potential migration channels is generated based on the pollution migration model. Source similarity is determined based on all isotope ratio information. Time-varying path inversion is performed on the pollution migration model based on all dynamic migration resistances and source similarity to obtain the dynamic migration risk of heavy metals in multiple main migration channels. The detection strategy for heavy metal pollution migration in each main migration channel is adaptively adjusted based on all dynamic migration risks. Using the scheme of this application, detection lag caused by dynamic changes in environmental factors can be avoided.

Description

Technical Field

[0001] This application relates to the field of heavy metal pollution migration detection technology, and more specifically, to a method and system for detecting heavy metal pollution migration in abandoned mining areas. Background Technology

[0002] Heavy metal pollution migration detection refers to the systematic monitoring and analysis of the existence state and dynamic distribution changes of heavy metal pollutants in the environment to reveal the migration paths, rates, and impact ranges of heavy metals in media such as soil, water, and atmosphere. Heavy metal pollution migration detection not only focuses on the concentration changes of the pollutants themselves, but also emphasizes the interaction between their migration mechanisms and environmental factors, aiming to achieve accurate prediction and effective control of pollution diffusion, and provide scientific support for protecting the ecological environment and safeguarding public health.

[0003] Heavy metal pollution migration detection in abandoned mining areas refers to the use of systematic monitoring and technical methods to dynamically track and analyze the migration patterns and diffusion trends of heavy metals in soil, water, and underground media in mining areas and surrounding abandoned land. Current heavy metal pollution migration detection methods largely rely on static models (such as constructing fixed migration paths based on data at a specific moment). However, heavy metal migration is significantly affected by environmental factors (such as precipitation and temperature) (e.g., increased porosity during the rainy season leads to expanded migration paths). Static models cannot reflect these dynamic changes in environmental factors, resulting in detection results lagging behind actual migration status. Therefore, avoiding detection lag caused by dynamic changes in environmental factors and achieving synchronous detection of pollution migration paths has become a challenge for the industry. Summary of the Invention

[0004] This application provides a method and system for detecting the migration of heavy metal pollution in abandoned mining areas, which can avoid detection delays caused by dynamic changes in environmental factors.

[0005] Firstly, this application provides a method for detecting the migration of heavy metal pollution in abandoned mining areas, comprising the following steps:

[0006] Soil electrical impedance data from the abandoned mining area to the observation area is collected using an intelligent impedance sensor, wherein the abandoned mining area to the observation area contains multiple potential pollution units;

[0007] Based on the soil electrical impedance data, the electrical impedance change information of each potential pollution unit is determined. Then, by combining the electrical impedance change information with the soil sound wave propagation speed, a heavy metal pollution migration model between the abandoned land in the mining area and the observation area is constructed. Based on the pollution migration model and the path search algorithm, dynamic migration resistance of different potential migration channels between the abandoned land in the mining area and the observation point is generated.

[0008] Collect isotope ratio information of heavy metals in different potential migration channels, and then determine the source similarity of heavy metals between the observation area and the abandoned land in the mining area in each potential migration channel based on all isotope ratio information.

[0009] Based on all dynamic migration resistances and source similarity, time-varying path inversion was performed on the pollution migration model to obtain the dynamic migration risk of heavy metals in multiple major migration channels.

[0010] The detection strategies for heavy metal pollution migration in each major migration pathway are adaptively adjusted based on all dynamic migration risks.

[0011] In some embodiments, determining the impedance change information for each potentially contaminated unit based on the soil electrical impedance data specifically includes:

[0012] Multiple sampling time points were used to obtain the soil electrical impedance data;

[0013] Select one potential contamination unit as the selected potential contamination unit;

[0014] The rate of change of electrical impedance of the selected potentially contaminated unit at different sampling time points was determined using the soil electrical impedance data.

[0015] Based on all impedance change rates, generate impedance change information for the selected potential contamination unit;

[0016] Continue to determine the impedance changes of the remaining potentially contaminated units.

[0017] In some embodiments, constructing a heavy metal pollution migration model between abandoned mining areas and the observation area by combining the electrical impedance change information with the soil acoustic wave propagation velocity specifically includes:

[0018] Obtain information on the sound wave propagation speed of all potentially contaminated units;

[0019] The sound velocity gradient of each potential contamination unit is determined using the sound wave propagation speed information.

[0020] A heavy metal pollution migration model between the abandoned land in the mining area and the observation area is constructed based on the sound velocity gradient and the rate of change of electrical impedance.

[0021] In some embodiments, the dynamic migration resistance generated based on the pollution migration model combined with the path search algorithm for different potential migration pathways from abandoned land in the mining area to the observation point specifically includes:

[0022] Based on the pollution migration model and the path search algorithm, multiple potential migration channels for heavy metals between abandoned areas in the mining cluster and the observation points are generated.

[0023] Dynamic migration resistance is generated for different potential migration channels between abandoned areas in the mining cluster and the observation area.

[0024] In some embodiments, determining the source similarity of heavy metals between the observed area and the abandoned land in the mineralized area in each potential migration channel based on all isotope ratio information specifically includes:

[0025] Obtain information on the isotope ratios of heavy metals in different potential migration pathways;

[0026] The isotopic deviation between the observation area and the abandoned land in the mineral cluster area in each potential migration channel was determined by all isotopic ratio information.

[0027] Based on the deviation of all isotopes, the source similarity of heavy metals in the observation area and abandoned land in the mining area in different potential migration channels was determined.

[0028] In some embodiments, time-varying path inversion is performed on the pollution migration model based on all dynamic migration resistances and source similarity to obtain the dynamic migration risk of heavy metals in multiple major migration channels; specifically including:

[0029] By performing resistance inversion on each potential pollution unit in the pollution migration model using all dynamic migration resistances and all source similarities, the local migration resistances between different potential pollution nodes are obtained.

[0030] Multiple main migration channels are generated based on all local migration resistances;

[0031] Determine the dynamic migration risk of heavy metals in each major migration pathway.

[0032] In some embodiments, the detection strategy for heavy metal pollution migration in each major migration pathway is adaptively adjusted based on all dynamic migration risks, specifically including:

[0033] Set a migration risk threshold for heavy metal pollution migration.

[0034] Risk assessment is performed on all dynamic migration risks based on the aforementioned migration risk threshold;

[0035] The detection strategy for heavy metal pollution migration in each major migration channel is adaptively adjusted based on the judgment results.

[0036] Secondly, this application provides a heavy metal pollution migration detection system for abandoned mining areas, comprising:

[0037] The acquisition module is used to acquire soil electrical impedance data from the abandoned mining area to the observation area through an intelligent impedance sensor, wherein the abandoned mining area to the observation area contains multiple potential pollution units;

[0038] The processing module is used to determine the impedance change information of each potential pollution unit based on the soil impedance data, and then construct a heavy metal pollution migration model between the abandoned land in the mining area and the observation area by combining the impedance change information with the soil sound wave propagation speed. Based on the pollution migration model and the path search algorithm, dynamic migration resistance of different potential migration channels between the abandoned land in the mining area and the observation point is generated.

[0039] The processing module is also used to collect isotope ratio information of heavy metals in different potential migration channels, and then determine the source similarity of heavy metals between the observation area and the abandoned land in the mining area in each potential migration channel based on all isotope ratio information.

[0040] The processing module is also used to perform time-varying path inversion on the pollution migration model based on all dynamic migration resistances and source similarity to obtain the dynamic migration risk of heavy metals in multiple main migration channels.

[0041] The execution module is used to adaptively adjust the detection strategy for heavy metal pollution migration in each major migration channel based on all dynamic migration risks.

[0042] Thirdly, this application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method for detecting the migration of heavy metal pollution in abandoned mining areas.

[0043] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for detecting the migration of heavy metal pollution in abandoned mining areas.

[0044] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0045] The method and system for detecting heavy metal pollution migration in abandoned mining areas provided in this application collects soil electrical impedance data from the abandoned mining area to the observation area using an intelligent impedance sensor. The area from the abandoned mining area to the observation area contains multiple potential pollution units. Based on the soil electrical impedance data, the impedance change information of each potential pollution unit is determined. Then, a heavy metal pollution migration model between the abandoned mining area and the observation area is constructed using the impedance change information combined with the soil sound wave propagation velocity. Based on the pollution migration model and a path search algorithm, dynamic migration resistance of different potential migration channels between the abandoned mining area and the observation point is generated. Isotope ratio information of heavy metals in different potential migration channels is collected. Then, based on all isotope ratio information, the source similarity of heavy metals between the observation area and the abandoned mining area in each potential migration channel is determined. Based on all dynamic migration resistances and source similarities, time-varying path inversion is performed on the pollution migration model to obtain the dynamic migration risk of heavy metals in multiple main migration channels. The detection strategy for heavy metal pollution migration in each main migration channel is adaptively adjusted based on all dynamic migration risks.

[0046] Therefore, in this application, firstly, after constructing a heavy metal pollution migration model between the abandoned mining area and the observation area by combining the impedance change information with the soil sound wave propagation velocity, the soil impedance change information obtained by the intelligent impedance sensor is dynamically fused with the sound wave propagation velocity data, so that the migration status in each potential pollution channel has an evolutionary characteristic that can be analyzed in real time. On this basis, a path search algorithm is introduced to dynamically update the resistance index of each migration channel, forming a migration resistance evolution map with time-series characteristics. This map breaks the limitation of the traditional model on the static qualitative nature of the migration path, so that the channel resistance can be updated in real time with changes in the external environment. For example, changes in soil moisture content, temperature, or compaction can be reflected in the fluctuation trend of the resistance value. Through multi-time period comparison and trend judgment, the dynamic quantification of the difficulty of pollutant migration is realized, thereby making up for the lack of traditional methods. The response lag caused by the real-time data feedback mechanism ensures that the system maintains the consistency of path identification and the continuity of detection even when faced with drastic changes in external conditions. Subsequently, based on the completion of the multi-channel dynamic migration resistance construction, the heavy metal isotope ratio characteristics obtained from different channels are integrated, and combined with the similarity assessment mechanism between pollutant source sites and observation points, a pollution migration risk index for each potential channel is constructed. The generation logic of pollution migration risk establishes the correlation mechanism between migration paths and source tracing signals, enabling the system to proactively respond to risk changes caused by precipitation, weathering, hydrodynamic changes, etc. By leveraging the distribution trend of the risk index over different time periods, the system can automatically adjust the detection frequency, assessment scale, and response strategy, avoiding the problem of delayed risk level judgment caused by environmental impacts. In summary, this scheme can avoid detection lag caused by dynamic changes in environmental factors. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating a method for detecting the migration of heavy metal pollution in abandoned mining areas, according to some embodiments of this application.

[0048] Figure 2 This is a schematic flowchart illustrating the construction of a pollution migration model according to some embodiments of this application;

[0049] Figure 3 This is a flowchart illustrating the risk assessment process according to some embodiments of this application;

[0050] Figure 4 This is a schematic diagram of the structure of a heavy metal pollution migration detection system in abandoned mining areas, as shown in some embodiments of this application.

[0051] Figure 5 This is an internal structural diagram of a computer device for implementing a method for detecting the migration of heavy metal pollution in abandoned mining areas, according to some embodiments of this application. Detailed Implementation

[0052] To better understand the technical solutions in this embodiment, the technical solutions in this embodiment will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0053] refer to Figure 1 The figure is a flowchart illustrating a method for detecting the migration of heavy metal pollution in abandoned mining areas, according to some embodiments of this application. The method mainly includes the following steps:

[0054] In step 101, soil electrical impedance data from the abandoned mining area to the observation area is collected using a smart impedance sensor, wherein the abandoned mining area to the observation area contains multiple potential pollution units.

[0055] In practice, multiple sets of intelligent impedance sensors can be deployed between the abandoned mining area and the observation area to collect data. The sensors are arranged in a grid pattern. Each set of intelligent impedance sensors periodically injects multi-frequency current signals into the ground and records the soil resistivity value of the corresponding potential pollution unit, forming an impedance time series dataset with time resolution as the soil resistivity data between the abandoned mining area and the observation area.

[0056] It should be noted that the intelligent impedance sensor is installed in multiple potentially polluted units between the abandoned mining area and the observation area to collect soil electrical impedance data of the unit in real time. The potentially polluted unit is a spatial unit divided in a grid, which constitutes the analysis nodes of the pollution migration path. The abandoned mining area is a historical tailings dump area that may contain heavy metal pollution sources, and the observation area is the downstream soil or ecologically sensitive area for real-time observation of the pollution migration status. Finally, the soil electrical impedance data is collected in a complete heavy metal migration cycle, which is preferably 7 days. In another preferred embodiment, the complete migration cycle can be shortened to 3 days to improve the monitoring time resolution.

[0057] In step 102, the impedance change information of each potential pollution unit is determined based on the soil impedance data. Then, the heavy metal pollution migration model between the abandoned mining area and the observation area is constructed by combining the impedance change information with the soil sound wave propagation speed. Based on the pollution migration model and the path search algorithm, the dynamic migration resistance of different potential migration channels between the abandoned mining area and the observation point is generated.

[0058] In some embodiments, determining the impedance change information for each potentially contaminated unit based on the soil electrical impedance data can be achieved using the following steps:

[0059] Multiple sampling time points were used to obtain the soil electrical impedance data;

[0060] Select one potential contamination unit as the selected potential contamination unit;

[0061] The rate of change of electrical impedance of the selected potentially contaminated unit at different sampling time points was determined using the soil electrical impedance data.

[0062] Based on all impedance change rates, generate impedance change information for the selected potential contamination unit;

[0063] Continue to determine the impedance changes of the remaining potentially contaminated units.

[0064] It should be noted that the multiple sampling time points mentioned in this application refer to the sampling time points corresponding to the soil electrical impedance data collection by the intelligent impedance sensor according to a preset cycle (such as every 3 days or 7 days) within a pollutant migration cycle; the sampling time points can be recorded by the sensor's local clock, or the sampling command can be uniformly issued and the time marked by the central control node; preferably, each sampling time point is located within the same migration cycle, which is used to reflect the electrical impedance change trend of the potential pollution unit within the cycle; in other embodiments, the sampling frequency can also be dynamically adjusted according to external factors such as meteorological changes and rainfall events, which is not limited in this application.

[0065] In specific implementation, determining the impedance change rate of a selected potential contamination unit at different sampling time points using the soil impedance data can be achieved in the following way: First, extract the soil impedance values ​​corresponding to each sampling time point within the migration cycle of the selected potential contamination unit; then, calculate the impedance change between two adjacent sampling time points in chronological order; then, using the impedance value of the previous sampling time point as a benchmark, determine the ratio of the impedance change to the benchmark value as the impedance change rate at the corresponding sampling time point, thereby obtaining the impedance change rate of the selected potential contamination unit at different sampling time points; as a preferred embodiment, the impedance change information of the selected potential contamination unit is generated based on all impedance change rates, that is, the sequence of all impedance change rates arranged in chronological order according to the sampling time points is used as the impedance change information of the selected potential contamination unit. In other embodiments, other methods can also be used, which are not limited here.

[0066] It should be noted that the impedance change rate mentioned in this application refers to a parameter value used to characterize the relative change of soil impedance at different sampling time points within the migration cycle of a selected potential contamination unit. The impedance change rate is obtained by calculating the ratio of the impedance change at two adjacent sampling time points to the impedance value at the previous sampling time point, in order to reflect the dynamic change characteristics of the impedance of the potential contamination unit at different sampling time points.

[0067] In some embodiments, reference Figure 2 As shown in the figure, this is a schematic flowchart illustrating the construction of a pollution migration model according to some embodiments of this application. The construction of a heavy metal pollution migration model between the abandoned mining area and the observation area by combining the electrical impedance change information with the soil sound wave propagation velocity can be achieved by the following steps:

[0068] First, in step 1021, the sound wave propagation speed information of all potential contamination units is obtained;

[0069] Then, in step 1022, the sound velocity gradient of each potential contamination unit is determined using the sound wave propagation velocity information;

[0070] Finally, in step 1023, a heavy metal pollution migration model between the abandoned land in the mining area and the observation area is constructed based on the sound velocity gradient and the rate of change of electrical impedance.

[0071] It should be noted that the sound wave propagation speed information mentioned in this application refers to the speed parameter of sound wave propagation in the soil of each potential contaminated unit. This parameter can be obtained by measuring with an on-site sound wave detection instrument. Preferably, the measurement process includes emitting a sound wave signal into the soil, receiving the reflected or transmitted signal, and calculating the sound speed using the time difference. The sound wave propagation speed information is represented in the form of a numerical matrix, wherein each matrix element or array item corresponds to the sound wave propagation speed value of a potential contaminated unit, in meters per second. The spatial layout of the matrix corresponds to the grid division of the potential contaminated units, thereby ensuring that the sound speed information can accurately reflect the acoustic characteristic distribution of each spatial unit.

[0072] In specific implementation, determining the sound velocity gradient of each potential pollution unit through the sound wave propagation speed information can be achieved in the following way: First, obtain the sound wave propagation speed values ​​of the potential pollution unit and its adjacent potential pollution units in multiple spatial directions; then, calculate the sound wave propagation speed difference between the potential pollution unit and its adjacent potential pollution units in each direction; next, obtain the spatial distance between the potential pollution unit and its adjacent potential pollution units in each direction; then, divide the sound wave propagation speed difference in each direction by the corresponding spatial distance to obtain the sound velocity gradient value in each direction; finally, generate the sound velocity gradient of the potential pollution unit based on the sound velocity gradient values ​​in each direction; as a preferred embodiment, Euclidean norm calculation can be used, that is, summing the squares of the sound velocity gradient values ​​in each direction and taking the square root to obtain the sound velocity gradient of the potential pollution unit.

[0073] It should be noted that the sound velocity gradient mentioned in this application refers to a parameter value used to characterize the degree of change in the sound wave propagation speed of a potential pollution unit in multiple spatial directions, in order to reflect the spatial trend and intensity of the change in the sound wave propagation speed of the potential pollution unit.

[0074] In specific implementation, the heavy metal pollution migration model between the abandoned mining area and the observation area based on the sound velocity gradient and the rate of change of electrical impedance can be implemented in the following way: First, based on all potential pollution units divided in a grid, a set of nodes is constructed, with each node corresponding to a potential pollution unit; then, connecting edges between nodes are established according to spatial adjacency, forming a graph structure model representing the pollution migration path; next, for each edge, the sound velocity gradient and rate of change of electrical impedance of the potential pollution units corresponding to the nodes connecting the two ends of the edge are obtained; then, the weight value of the edge is calculated based on the sound velocity gradient and the rate of change of electrical impedance, which can be implemented by constructing a combination function. The edge weight is used to characterize the migration probability of pollution in the direction of the path; in a preferred embodiment, the edge weight can be the product of the sound velocity gradient and the rate of change of electrical impedance as a combination index to reflect the coupling effect of medium change and electrical response; in other embodiments, the two can also be normalized and linearly weighted, with the weight coefficient set according to different geological environment experience to balance the influence intensity of acoustic and electrical characteristics on the migration path. This application does not limit this.

[0075] It should be noted that the heavy metal pollution migration model described in this application refers to a graph structure model constructed based on potential pollution units, where nodes correspond to each potential pollution unit, edges represent the spatial adjacency relationship between potential pollution units, and edge weights are calculated by combining the sound velocity gradient and electrical impedance change rate of the potential pollution unit. This model is used to quantify the possibility and intensity of pollutant migration along different paths. The model reflects the coupling characteristics of the acoustic properties and electrical response of the medium.

[0076] In some embodiments, generating dynamic migration resistance for different potential migration pathways between abandoned mining areas and observation points based on the pollution migration model and path search algorithm can be achieved through the following steps:

[0077] Based on the pollution migration model and the path search algorithm, multiple potential migration channels for heavy metals between abandoned areas in the mining cluster and the observation points are generated.

[0078] Dynamic migration resistance is generated for different potential migration channels between abandoned areas in the mining cluster and the observation area.

[0079] In specific implementation, generating multiple potential migration channels for heavy metals between abandoned mining areas and observation points based on the pollution migration model and path search algorithm can be achieved in the following way: First, determine the starting node and target node in the pollution migration model, corresponding to the potential pollution source unit in the abandoned mining area and the potential pollution unit in the observation area, respectively; then, construct the pollution migration model as a weighted graph structure, where each node represents a potential pollution unit, and the edges between nodes represent migration channels between adjacent spatial units, with the edge weight represented by a combination of the sound velocity gradient and electrical impedance change rate of the corresponding potential pollution unit; next, apply the path search algorithm to the graph structure, starting from the starting node, to search for a set of migration paths to the target node, which serve as multiple potential migration channels between the abandoned mining area and the observation area; preferably, the A* algorithm can be used to implement the path search, i.e., starting from the starting node, initially... The minimum cumulative edge weight of all nodes is initialized, and at each step, the node with the smallest current cumulative edge weight is selected for expansion. The shortest path information of other nodes is updated step by step until the shortest path of the target node is determined. A potential migration channel is recorded for this path. In other embodiments, other methods can be used to achieve this, which are not limited here. As a preferred embodiment, the dynamic migration resistance of different potential migration channels between the abandoned mining area and the observation area can be generated in the following way: First, for each potential migration channel, the edge weights between all adjacent potential pollution units in the channel path are extracted. The edge weights are obtained by combining the sound velocity gradient and the rate of change of electrical impedance of the potential pollution units according to a preset rule. Then, according to the arrangement order of each edge in the path, the edge weights of all edges are weighted and accumulated to obtain the dynamic migration resistance of the corresponding potential migration channel, thereby generating the dynamic migration resistance of different potential migration channels between the abandoned mining area and the observation area.

[0080] It should be noted that the potential migration channels mentioned in this application refer to multiple possible pollutant migration paths obtained from potential pollution source units in abandoned mining areas to observed potential pollution units in the observation area through a path search algorithm based on a pollution migration model. Each path consists of sequentially connected potential pollution unit nodes and their connecting edges, used to characterize the specific route that pollutants may migrate along spatial nodes. The dynamic migration resistance refers to the weighted sum of the edge weights between adjacent potential pollution units in the potential migration channel path, used to quantify the magnitude of resistance during the migration of pollutants along a specific migration channel, reflecting the dynamic obstacle characteristics of the migration path.

[0081] In step 103, isotope ratio information of heavy metals in different potential migration channels is collected, and then the source similarity of heavy metals between the observation area and the abandoned land in the mining area in each potential migration channel is determined based on all isotope ratio information.

[0082] It should be noted that the isotope ratio information of heavy metals mentioned in this application refers to the ratio between the stable isotope abundances of a specified heavy metal element collected from multiple potential pollution units along different potential migration pathways, such as the abundance ratio of lead-206 to lead-207, the abundance ratio of copper-65 to copper-63, and the abundance ratio of zinc-66 to zinc-64. In specific implementation, an embedded monitoring system with a high-sensitivity detection module can be used to achieve dynamic updating and continuous monitoring of the isotope ratios, which is not limited in this application.

[0083] In some embodiments, determining the source similarity of heavy metals between the observed area and the abandoned land in the mineralized area in each potential migration channel based on all isotope ratio information can be achieved by the following steps:

[0084] Obtain information on the isotope ratios of heavy metals in different potential migration pathways;

[0085] The isotopic deviation between the observation area and the abandoned land in the mineral cluster area in each potential migration channel was determined by all isotopic ratio information.

[0086] Based on the deviation of all isotopes, the source similarity of heavy metals in the observation area and abandoned land in the mining area in different potential migration channels was determined.

[0087] In practice, determining the isotopic deviation between the observation area and the abandoned mining area in each potential migration channel using all isotopic ratio information can be achieved as follows: First, extract the isotopic ratio information of the potential pollution unit corresponding to the observation area and the potential pollution unit corresponding to the abandoned mining area in each potential migration channel; then, for each heavy metal element, calculate the difference between the corresponding isotopic ratio between the observation area and the abandoned mining area; finally, using the difference and the isotopic ratio of the abandoned mining area as a benchmark, calculate the relative deviation between the two. The relative deviation can be obtained by dividing the ratio difference by the benchmark value. Then, the relative deviation of each heavy metal element is weighted and summed according to a preset weighting coefficient to obtain the isotopic deviation between the observation area and the abandoned land in the corresponding potential migration channel. As a preferred embodiment, the sum of squares of the deviation values ​​of each element can be calculated and the square root can be taken using the Euclidean distance method to generate a comprehensive isotopic deviation value. In other embodiments, the deviation can also be measured and calculated based on Mahalanobis distance, Manhattan distance or cosine similarity, etc., and this application does not limit it in this way.

[0088] It should be noted that the isotopic deviation mentioned in this application refers to the information on the ratio of various heavy metal isotopes of the observed area and the abandoned land in the mining area in each potential migration channel, which is used to quantify the degree of difference in the heavy metal isotopic composition between the observed area and the abandoned land in the mining area in a specific migration channel.

[0089] In specific implementation, determining the source similarity of heavy metals in different potential migration channels between the observation area and the abandoned land in the mining area based on all isotope deviations can be achieved in the following way: First, obtain the corresponding isotope deviation information in each potential migration channel. The isotope deviation may include the isotope ratio deviation of heavy metal elements such as lead, zinc, and copper. Then, construct the functional relationship between isotope deviation and source similarity. Next, according to the preset mapping model, input the corresponding isotope deviation in each channel into the mapping model to calculate the source similarity value of the corresponding channel. As a preferred embodiment, an exponential function mapping method can be used, that is, multiply the isotope deviation by a preset coefficient and take the negative exponent as the source similarity of the channel. In other embodiments, the inverse distance function or a regression model based on machine learning can also be used to transform the deviation. This application does not limit this.

[0090] It should be noted that the source similarity mentioned in this application refers to the numerical value obtained by converting the isotopic deviation between the observation area and the abandoned land in the mining area in each potential migration channel through a preset function mapping relationship, which reflects the similarity between the two in terms of heavy metal isotopic composition.

[0091] In step 104, time-varying path inversion is performed on the pollution migration model based on all dynamic migration resistances and source similarity to obtain the dynamic migration risk of heavy metals in multiple main migration channels.

[0092] In some embodiments, the dynamic migration risk of heavy metals in multiple major migration channels can be obtained by performing time-varying path inversion on the pollution migration model based on all dynamic migration resistances and source similarity using the following steps:

[0093] By performing resistance inversion on each potential pollution unit in the pollution migration model using all dynamic migration resistances and all source similarities, the local migration resistances between different potential pollution nodes are obtained.

[0094] Multiple main migration channels are generated based on all local migration resistances;

[0095] Determine the dynamic migration risk of heavy metals in each major migration pathway.

[0096] In specific implementation, the local migration resistance between different potential pollution nodes can be obtained by performing resistance inversion on each potential pollution unit in the pollution migration model using all dynamic migration resistances and all source similarities. This can be achieved in the following way: First, extract the dynamic migration resistance and source similarity corresponding to all potential migration channels in the pollution migration model; then, represent each potential migration channel as a path formed by the sequential connection of multiple adjacent potential pollution units, and identify the connection relationships between all potential pollution units in each path; next, based on the dynamic migration resistance and source similarity, determine the local migration resistance between each potential pollution unit in the path according to its contribution to the overall migration process. The process involves allocating local resistance to the connections between potential contamination units. Specifically, in a preferred embodiment, the resistance inversion process can be implemented using the weighted allocation method in the prior art. That is, the dynamic migration resistance of each potential migration channel is taken as the total amount, and the source similarity value of the connection segments between potential contamination units in the path is normalized to obtain the weight coefficient of each connection segment. Then, the total dynamic migration resistance is allocated to each potential contamination unit according to the weight ratio to obtain the corresponding local migration resistance. In other embodiments, the least squares method or regularization optimization algorithm can also be used to numerically invert the local migration resistance, and this application does not limit this.

[0097] It should be noted that the local migration resistance mentioned in this application refers to the resistance value allocated to the connection segment between adjacent potential pollution units after weighted allocation or numerical inversion of the dynamic migration resistance of potential migration channels in the pollution migration model and source similarity, reflecting the magnitude of the resistance of the connection segment in the overall pollution migration process; the local migration resistance is used to quantify the ease or difficulty of pollutant migration between spatial units, thereby providing fine-grained resistance information for pollution migration path analysis.

[0098] In specific implementation, generating multiple main migration channels based on all local migration resistances can be achieved in the following way: First, a pollution migration graph model is constructed based on the local migration resistances obtained from resistance inversion, where each potential pollution unit is treated as a node in the graph model, and the spatial connection relationship between adjacent potential pollution units is treated as an edge in the graph model, with the weight of the edge being the corresponding local migration resistance; then, pollution source nodes in the abandoned land of the mining area are set as starting nodes, and observation point nodes in the observation area are set as target nodes; then, multiple paths between the starting node and the target node are calculated in the graph model using existing path search algorithms, where each path consists of a sequence of sequentially connected nodes, and the weight of the path is the sum of the weights of all edges on the path; next, several paths with smaller total weights are selected as multiple main migration channels, where each main migration channel contains multiple potential pollution unit nodes, representing possible migration routes of pollutants; in other embodiments, path optimization methods such as heuristic search, ant colony algorithm, or genetic algorithm can also be used to generate multiple main migration channels, and this application does not limit this.

[0099] It should be noted that the main migration channels described in this application are pollution migration map models constructed based on local migration resistance obtained through resistance inversion, and a set of multiple edge weights and low-resistance paths selected using a path search algorithm. Compared with the potential migration channels, the main migration channels combine the actual spatial distribution information of local migration resistance, which can more accurately reflect the resistance characteristics in the pollutant migration process, thereby effectively filtering out paths with high resistance and low migration probability. By identifying these low-resistance paths, the main migration channels in this application can more accurately locate the main migration routes of pollutants, improve the pertinence and reliability of migration path analysis, and help achieve more effective pollution monitoring and risk management.

[0100] In practice, determining the dynamic migration risk of heavy metals in each major migration channel can be achieved in the following way: First, for each major migration channel, the dynamic migration resistance of the selected major migration channel is calculated based on the local migration resistance corresponding to the potential pollution unit constituting the selected major migration channel. Specifically, the local migration resistances of all potential pollution units are accumulated or weighted and summed. Then, the source similarity corresponding to each major migration channel is obtained, wherein the source similarity is calculated based on the aforementioned isotope deviation information. Next, for each major migration channel, the product of the corresponding dynamic migration resistance and the corresponding source similarity is determined as the dynamic migration risk of heavy metals in the corresponding major migration channel, thereby obtaining the dynamic migration risk of heavy metals in each major migration channel.

[0101] It should be noted that the dynamic migration risk mentioned in this application refers to an index calculated based on the product of the cumulative local migration resistance of potential pollution units in the main migration channel and the source similarity corresponding to that channel. It is used to comprehensively reflect the migration resistance and source similarity of heavy metal pollutants on the migration path, and thus assess the risk level of pollution migration.

[0102] In step 105, the detection strategy for heavy metal pollution migration in each major migration pathway is adaptively adjusted based on all dynamic migration risks.

[0103] In some embodiments, the detection strategy for heavy metal pollution migration in each major migration pathway can be adaptively adjusted based on all dynamic migration risks using the following steps:

[0104] Set a migration risk threshold for heavy metal pollution migration.

[0105] Risk assessment is performed on all dynamic migration risks based on the aforementioned migration risk threshold;

[0106] The detection strategy for heavy metal pollution migration in each major migration channel is adaptively adjusted based on the judgment results.

[0107] It should be noted that the migration risk threshold mentioned in this application refers to a numerical boundary used to distinguish between low-risk and high-risk migration channels. The migration risk threshold can be determined comprehensively based on historical pollution event data, environmental monitoring standards, and pollutant safety evaluation indicators. This threshold can be obtained through statistical analysis methods (such as quantile analysis and cluster analysis) or expert experience, and can be dynamically adjusted according to different regional characteristics and pollution risk levels. As a preferred embodiment, the migration risk threshold can be set as the median or mean of dynamic migration risk multiplied by a certain coefficient. In other embodiments, the threshold can also be automatically generated by combining the risk distribution predicted by machine learning models, which is not limited in this application.

[0108] For specific implementation, refer to Figure 3As shown in the figure, this is a flowchart illustrating the risk assessment process in some embodiments of this application. Risk assessment of all dynamic migration risks based on the migration risk threshold can be achieved in the following manner: First, obtain the dynamic migration risks corresponding to each major migration channel; then, compare the dynamic migration risks with a preset migration risk threshold; then, for each major migration channel, if the dynamic migration risk value is greater than or equal to the migration risk threshold, the migration risk of that channel is determined to be high risk; if the dynamic migration risk value is less than the migration risk threshold, the migration risk of that channel is determined to be low risk. In other embodiments, multi-level migration risk thresholds can also be set to divide dynamic migration risks into high-risk, medium-risk, and low-risk levels to achieve more refined risk classification; or a fuzzy judgment method can be used to perform probabilistic risk assessment of dynamic migration risks, thereby improving the flexibility and accuracy of risk assessment. This application does not limit this approach. In a preferred embodiment, the detection strategy for heavy metal pollution migration in each major migration channel can be adaptively adjusted based on the judgment results in the following manner: First, obtain the risk judgment results corresponding to each major migration channel; then, for major migration channels judged as high-risk, increase the detection frequency and accuracy of heavy metal pollution migration in the corresponding major migration channel, which may include shortening the sampling interval, adding sensor points, or using detection equipment with higher sensitivity; for major migration channels judged as low-risk, reduce the detection frequency of heavy metal pollution migration in the corresponding major migration channel or simplify the detection methods to save monitoring resources; in other embodiments, a medium-intensity detection strategy can also be formulated for medium-risk channels to achieve dynamic matching between risk level and detection intensity; in addition, the detection strategy parameters can be dynamically adjusted by combining historical monitoring data and environmental change factors to achieve accurate monitoring of heavy metal pollution migration and reasonable allocation of resources, which is not limited in this application.

[0109] In another aspect, in some embodiments, this application provides a heavy metal pollution migration detection system for abandoned mining areas, with reference to... Figure 4 The figure is a schematic diagram of the structure of a heavy metal pollution migration detection system in a mining area wasteland according to some embodiments of this application. The heavy metal pollution migration detection system 200 in the mining area wasteland includes: a data acquisition module 201, a processing module 202, and an execution module 203, which are described below:

[0110] The acquisition module 201 in this application is mainly used to acquire soil electrical impedance data from the abandoned land in the mining area to the observation area through an intelligent impedance sensor. The abandoned land in the mining area to the observation area contains multiple potential pollution units.

[0111] Processing module 202, in this application, is mainly used to determine the impedance change information of each potential pollution unit based on the soil impedance data, and then construct a heavy metal pollution migration model between the abandoned land in the mining area and the observation area by combining the impedance change information with the soil sound wave propagation speed. Based on the pollution migration model and combined with the path search algorithm, dynamic migration resistance of different potential migration channels between the abandoned land in the mining area and the observation point is generated.

[0112] In addition, the processing module 202 in this application is also used to collect isotope ratio information of heavy metals in different potential migration channels, and then determine the source similarity of heavy metals between the observation area and the abandoned land of the mining area in each potential migration channel based on all isotope ratio information.

[0113] In addition, the processing module 202 in this application is also used to perform time-varying path inversion on the pollution migration model based on all dynamic migration resistance and source similarity to obtain the dynamic migration risk of heavy metals in multiple main migration channels.

[0114] The execution module 203 in this application is mainly used to adaptively adjust the detection strategy for heavy metal pollution migration in each major migration channel based on all dynamic migration risks.

[0115] In addition, this application also provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to acquire the code and execute the above-described method for detecting the migration of heavy metal pollution in abandoned mining areas.

[0116] In some embodiments, reference Figure 5 This figure is an internal structural diagram of a computer device for implementing a method for detecting heavy metal pollution migration in abandoned mining areas, according to some embodiments of this application. The method for detecting heavy metal pollution migration in abandoned mining areas described in the above embodiments can be implemented through... Figure 5 The computer device shown is used to implement this, and the computer device 300 includes at least one processor 301, a communication bus 302, a memory 303, and at least one communication interface 304.

[0117] The processor 301 may be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more devices used to control the execution of the heavy metal pollution migration detection method in the mining area wasteland of this application.

[0118] The communication bus 302 is used to transmit information between the aforementioned components.

[0119] Memory 303 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CDROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory 303 may exist independently and be connected to processor 301 via communication bus 302. Memory 303 may also be integrated with processor 301.

[0120] The memory 303 stores program code for executing the scheme of this application, and its execution is controlled by the processor 301. The processor 301 executes the program code stored in the memory 303. The program code may include one or more software modules. In the above embodiment, the method for detecting the migration of heavy metal pollution in abandoned mining areas can be implemented by the processor 301 and one or more software modules in the program code in the memory 303.

[0121] Communication interface 304 uses any transceiver-like device to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0122] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core processor or a multi-core processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0123] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device may be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0124] In addition, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method for detecting the migration of heavy metal pollution in abandoned mining areas.

[0125] In summary, the heavy metal pollution migration detection method and system for abandoned mining areas disclosed in this application collects soil electrical impedance data from the abandoned mining area to the observation area using an intelligent impedance sensor. The area from the abandoned mining area to the observation area contains multiple potential pollution units. Based on the soil electrical impedance data, the impedance change information of each potential pollution unit is determined. Then, by combining the impedance change information with the soil sound wave propagation velocity, a heavy metal pollution migration model between the abandoned mining area and the observation area is constructed. Based on this pollution migration model and a path search algorithm, a migration path from the abandoned mining area to the observation area is generated. The study investigates the dynamic migration resistance of different potential migration channels between points; collects isotope ratio information of heavy metals in different potential migration channels, and then determines the source similarity of heavy metals between the observation area and the abandoned land in the mining area in each potential migration channel based on all isotope ratio information; performs time-varying path inversion on the pollution migration model based on all dynamic migration resistance and source similarity to obtain the dynamic migration risk of heavy metals in multiple main migration channels; adaptively adjusts the detection strategy of heavy metal pollution migration in each main migration channel based on all dynamic migration risks; and avoids detection lag caused by dynamic changes in environmental factors.

[0126] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0127] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for detecting the migration of heavy metal pollution in abandoned mining areas, characterized in that, Includes the following steps: Soil electrical impedance data from the abandoned mining area to the observation area is collected using an intelligent impedance sensor, wherein the abandoned mining area to the observation area contains multiple potential pollution units; Based on the soil electrical impedance data, the electrical impedance change information of each potential pollution unit is determined. Then, by combining the electrical impedance change information with the soil sound wave propagation speed, a heavy metal pollution migration model between the abandoned land in the mining area and the observation area is constructed. Based on the pollution migration model and the path search algorithm, dynamic migration resistance of different potential migration channels between the abandoned land in the mining area and the observation point is generated. Collect isotope ratio information of heavy metals in different potential migration channels, and then determine the source similarity of heavy metals between the observation area and the abandoned land in the mining area in each potential migration channel based on all isotope ratio information. Based on all dynamic migration resistances and source similarity, time-varying path inversion was performed on the pollution migration model to obtain the dynamic migration risk of heavy metals in multiple major migration channels. The detection strategies for heavy metal pollution migration in each major migration pathway are adaptively adjusted based on all dynamic migration risks.

2. The method as described in claim 1, characterized in that, The determination of impedance change information for each potentially contaminated unit based on the soil electrical impedance data specifically includes: Multiple sampling time points were used to obtain the soil electrical impedance data; Select one potential contamination unit as the selected potential contamination unit; The rate of change of electrical impedance of the selected potentially contaminated unit at different sampling time points was determined using the soil electrical impedance data. Based on all impedance change rates, generate impedance change information for the selected potential contamination unit; Continue to determine the impedance changes of the remaining potentially contaminated units.

3. The method as described in claim 1, characterized in that, The model for the migration of heavy metal pollution between abandoned mining areas and the observation area is constructed by combining the electrical impedance change information with the soil sound wave propagation velocity. Specifically, this includes: Obtain information on the sound wave propagation speed of all potentially contaminated units; The sound velocity gradient of each potential contamination unit is determined using the sound wave propagation speed information. A heavy metal pollution migration model between the abandoned land in the mining area and the observation area is constructed based on the sound velocity gradient and the rate of change of electrical impedance.

4. The method as described in claim 1, characterized in that, Based on the pollution migration model and path search algorithm, the dynamic migration resistance generated for different potential migration channels between abandoned mining areas and observation points specifically includes: Based on the pollution migration model and the path search algorithm, multiple potential migration channels for heavy metals between abandoned areas in the mining cluster and the observation points are generated. Dynamic migration resistance is generated for different potential migration channels between abandoned areas in the mining cluster and the observation area.

5. The method as described in claim 1, characterized in that, Based on all isotope ratio information, the source similarity of heavy metals between the observed area and the abandoned land in the mineralized area in each potential migration channel is determined, specifically including: Obtain information on the isotope ratios of heavy metals in different potential migration pathways; The isotopic deviation between the observation area and the abandoned land in the mineral cluster area in each potential migration channel was determined by all isotopic ratio information. Based on the deviation of all isotopes, the source similarity of heavy metals in the observation area and abandoned land in the mining area in different potential migration channels was determined.

6. The method as described in claim 1, characterized in that, Based on all dynamic migration resistances and source similarity, time-varying path inversion was performed on the pollution migration model to obtain the dynamic migration risks of heavy metals in several major migration channels, specifically including: By performing resistance inversion on each potential pollution unit in the pollution migration model using all dynamic migration resistances and all source similarities, the local migration resistances between different potential pollution nodes are obtained. Multiple main migration channels are generated based on all local migration resistances; Determine the dynamic migration risk of heavy metals in each major migration pathway.

7. The method as described in claim 1, characterized in that, The detection strategies for heavy metal pollution migration in each major migration pathway are adaptively adjusted based on all dynamic migration risks, specifically including: Set a migration risk threshold for heavy metal pollution migration. Risk assessment is performed on all dynamic migration risks based on the aforementioned migration risk threshold; The detection strategy for heavy metal pollution migration in each major migration channel is adaptively adjusted based on the judgment results.

8. A heavy metal pollution migration detection system for abandoned mining areas, characterized in that, include: The acquisition module is used to acquire soil electrical impedance data from the abandoned mining area to the observation area through an intelligent impedance sensor, wherein the abandoned mining area to the observation area contains multiple potential pollution units; The processing module is used to determine the impedance change information of each potential pollution unit based on the soil impedance data, and then construct a heavy metal pollution migration model between the abandoned land in the mining area and the observation area by combining the impedance change information with the soil sound wave propagation speed. Based on the pollution migration model and the path search algorithm, dynamic migration resistance of different potential migration channels between the abandoned land in the mining area and the observation point is generated. The processing module is also used to collect isotope ratio information of heavy metals in different potential migration channels, and then determine the source similarity of heavy metals between the observation area and the abandoned land in the mining area in each potential migration channel based on all isotope ratio information. The processing module is also used to perform time-varying path inversion on the pollution migration model based on all dynamic migration resistances and source similarity to obtain the dynamic migration risk of heavy metals in multiple main migration channels. The execution module is used to adaptively adjust the detection strategy for heavy metal pollution migration in each major migration channel based on all dynamic migration risks.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for detecting the migration of heavy metal pollution in abandoned mining areas as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for detecting the migration of heavy metal pollution in abandoned mining areas as described in any one of claims 1 to 7.

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