A mine groundwater chemical evolution trend prediction method based on multi-source heterogeneous data fusion and related device

Abstract

The application discloses a mine area groundwater chemical evolution trend prediction method based on multi-source heterogeneous data fusion, a mine area groundwater chemical evolution trend prediction device, a prediction equipment and a computer readable storage medium. The method obtains groundwater chemical properties, dynamic states, monitoring well spatial attributes, hydrogeological backgrounds, mining disturbance and environmental supply data of a target mine area; after standardization processing of monitoring periods, spatial attributions and aquifer partitioning, water chemical evolution characteristics are extracted; then, fusion discrimination is performed in combination with hydrogeological correlation rules, so that a groundwater chemical evolution state is obtained, and a subsequent trend is predicted based on a historical evolution stage sequence, and a risk level, an influence range and a monitoring and management suggestion are output. The application can improve the accuracy and interpretability of mine area groundwater chemical evolution trend prediction.

Description

Technical Field

[0001] This invention belongs to the field of intelligent prediction technology of groundwater environment, and more specifically, relates to a method for predicting the chemical evolution trend of groundwater in mining areas based on multi-source heterogeneous data fusion, a device for predicting the chemical evolution trend of groundwater in mining areas based on multi-source heterogeneous data fusion, a prediction device, and a computer-readable storage medium. Background Technology

[0002] Predicting the chemical evolution trend of groundwater in mining areas falls under the field of hydrogeological monitoring and groundwater environmental assessment, and is particularly applicable to mining areas in high-altitude, cold, and high-evaporation environments. High-altitude mining areas such as Qinghai are significantly affected by seasonal precipitation, snowmelt, freeze-thaw cycles, evaporation concentration, and complex geological structures, resulting in marked changes in groundwater recharge, runoff, and discharge conditions. Simultaneously, mining activities such as pit excavation, drainage, tailings storage, and spoil heap infiltration also alter groundwater dynamics and water-rock interactions, causing phased changes in groundwater mineralization, major ions, acidity / alkalinity, and characteristic components.

[0003] In related technologies, many rely on single or periodic hydrochemical detection results, combined with limited water level data for manual analysis. This makes it difficult to uniformly correlate discrete sampling data with continuous water level, water temperature, meteorological recharge, aquifer structure, and mining disturbance information, resulting in a lack of effective spatiotemporal correspondence between data from different sources. Furthermore, in high-altitude mining areas, snowmelt recharge may cause ion dilution, strong evaporation may increase mineralization, and mining disturbances may cause abnormal enhancement of characteristic components. Existing methods struggle to accurately distinguish between natural seasonal fluctuations and the effects of human disturbances, easily leading to misjudgments of trends.

[0004] Therefore, how to form continuous and interpretable prediction results of groundwater chemical evolution trends through multi-source heterogeneous data fusion and hydrogeological correlation discrimination is a key issue of concern to those skilled in the art. Summary of the Invention

[0005] The purpose of this application is to provide a method for predicting the chemical evolution trend of groundwater in mining areas based on multi-source heterogeneous data fusion, a device for predicting the chemical evolution trend of groundwater in mining areas based on multi-source heterogeneous data fusion, a prediction device, and a computer-readable storage medium. Through multi-source heterogeneous data fusion and hydrogeological correlation discrimination, continuous and interpretable prediction results of groundwater chemical evolution trend are formed.

[0006] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a method for predicting the chemical evolution trend of groundwater in mining areas based on multi-source heterogeneous data fusion, comprising: S1, acquire multi-source heterogeneous monitoring data of the target mining area; wherein, the multi-source heterogeneous monitoring data includes: groundwater hydrochemical detection data, groundwater dynamic state data, monitoring well spatial attribute data, hydrogeological background data, mining disturbance data, and environmental recharge data; S2, based on preset monitoring cycle division rules, spatial assignment division rules, and aquifer zoning rules, the multi-source heterogeneous monitoring data is standardized to obtain standardized groundwater state data corresponding to each monitoring unit; wherein, the monitoring unit is determined by the monitoring well, aquifer attributes, and spatial location. S3. Based on the standardized groundwater state data, extract the groundwater chemical evolution characteristics of each monitoring unit to obtain a hydrochemical feature set; wherein, the hydrochemical feature set is used to characterize the groundwater chemical components, hydrochemical types and their changes with the monitoring cycle; S4. Based on the preset hydrogeological association rules, the hydrochemical feature set is fused and judged with the groundwater dynamic changes, environmental recharge changes, mining disturbance events, hydrogeological background, and changes of adjacent monitoring units of the corresponding monitoring unit to obtain the groundwater chemical evolution state of the corresponding monitoring unit. S5. Based on the groundwater chemical evolution state and the historical evolution stage sequence of the corresponding monitoring unit, predict the groundwater chemical evolution trend of the corresponding monitoring unit in the next monitoring cycle or a set prediction cycle, and output the groundwater chemical evolution trend result.

[0007] Optionally, in S1, the groundwater hydrochemical detection data includes: sampling time, sampling depth, acidity / alkalinity, conductivity, mineralization, major anions and cations, and characteristic pollutants; the groundwater dynamic state data includes: groundwater level data and water temperature data; the monitoring well spatial attribute data includes: monitoring well coordinates, elevation, well depth, screen tube location, and corresponding aquifer information; the hydrogeological background data includes: lithological distribution, aquitard location, fault structure, aquifer connectivity, and groundwater flow direction; the mining disturbance data includes: pit advancement location, dewatering period, drainage volume change, mining depth change, and tailings or spoil heap area; the environmental recharge data includes: precipitation, air temperature, evaporation, snowmelt period, and freeze-thaw transition period.

[0008] Optionally, S2 includes: According to the monitoring cycle division rules, multi-source heterogeneous monitoring data with different acquisition frequencies are matched to the same monitoring cycle; According to the aforementioned spatial allocation rules, based on the spatial attribute data of the monitoring wells and the direction of groundwater flow, the monitoring wells are assigned to the corresponding spatial monitoring areas; According to the aquifer zoning rules, the aquifer unit corresponding to each monitoring well is determined based on the location of the monitoring well screen pipe, the aquifer properties, and the location of the aquitard. After completing the matching of monitoring cycles, the division of spatial monitoring areas, and the determination of aquifer units, the data is standardized by unifying units, identifying missing data, identifying abnormal records, and identifying high-altitude environmental impacts, thus obtaining the standardized groundwater status data.

[0009] Optionally, S3 includes: Based on the changes in the main anions and cations in the standardized groundwater state data, the ion dominance type change characteristics of the corresponding monitoring units are identified; Based on the changes in mineralization, conductivity, and acidity / alkalinity in the standardized groundwater state data, the mineralization change characteristics and acidity / alkalinity shift characteristics of the corresponding monitoring units are identified. Based on the changes in sulfate, chloride ions and characteristic pollutants in the standardized groundwater state data, the enhanced characteristics of characteristic components of the corresponding monitoring units are identified. Based on the changes in the ion dominance type, the changes in mineralization, the acid-base shift, and the enhancement of the characteristic components, combined with the changes in groundwater dynamics and environmental recharge within the corresponding monitoring period, a set of hydrochemical characteristics is formed, including recharge dilution characteristics, evaporation concentration characteristics, water-rock interaction enhancement characteristics, and mining disturbance response characteristics.

[0010] Optionally, in S4, the hydrogeological association rules include: upstream and downstream association rules, same aquifer association rules, aquitard barrier rules, fracture water conduction association rules, disturbance source proximity association rules, and adjacent monitoring cycle continuation rules. Accordingly, the fusion discrimination includes: determining the spatial transmission relationship of hydrochemical changes based on the upstream and downstream association rules and the same aquifer association rules; determining the degree of hydraulic connection between different monitoring units based on the aquitard barrier rule and the fracture water conduction association rule; and determining the response relationship between hydrochemical changes and mining disturbance events based on the disturbance source proximity association rule and the adjacent monitoring cycle continuation rule.

[0011] Optionally, in S4, the groundwater chemical evolution state includes: background stable state, seasonal recharge and dilution state, evaporation and concentration state, enhanced water-rock interaction state, mining disturbance response state, and abnormal diffusion risk state; wherein, when the water level rises, the mineralization decreases, and the overall major ions decrease, and this corresponds to the precipitation, snowmelt, or freeze-thaw transition period, it is identified as the seasonal recharge and dilution state; when evaporation increases, the water level falls, and the mineralization or conductivity increases, but the characteristic pollutants do not increase synchronously, it is identified as the evaporation and concentration state; when a mining disturbance event occurs, and the corresponding monitoring unit shows a drop in water level, an increase in characteristic components, or a shift in acidity or alkalinity, and adjacent monitoring units show related changes in subsequent monitoring cycles, it is identified as the mining disturbance response state or the abnormal diffusion risk state.

[0012] Optionally, S5 includes: The chemical evolution status of groundwater obtained in the current monitoring period is connected with the historical evolution status of the same monitoring unit in chronological order to form the historical evolution stage sequence; Based on the historical evolution stage sequence, identify the state continuation trend, state recovery trend, or state diffusion trend of the corresponding monitoring unit; Based on the state continuation trend, state recovery trend, or state diffusion trend, and combined with the spatial location of the corresponding monitoring unit, the aquifer connectivity, and changes in adjacent monitoring units, the main change direction, risk level, impact range, and credibility indicator in the groundwater chemical evolution trend results are determined. Based on the main direction of change, risk level, scope of impact, and credibility indicator, corresponding monitoring or governance recommendations are output.

[0013] This application also provides a device for predicting the chemical evolution trend of groundwater in mining areas based on multi-source heterogeneous data fusion, including: The monitoring data acquisition module is used to acquire multi-source heterogeneous monitoring data of the target mining area; wherein, the multi-source heterogeneous monitoring data includes: groundwater hydrochemical detection data, groundwater dynamic state data, monitoring well spatial attribute data, hydrogeological background data, mining disturbance data, and environmental recharge data; The standardization processing module is used to standardize the multi-source heterogeneous monitoring data based on preset monitoring cycle division rules, spatial attribution division rules, and aquifer zoning rules to obtain standardized groundwater state data corresponding to each monitoring unit; wherein, the monitoring unit is determined by the monitoring well, aquifer attributes, and spatial location. The chemical feature acquisition module is used to extract the groundwater chemical evolution features of each monitoring unit based on the standardized groundwater state data to obtain a water chemical feature set; wherein, the water chemical feature set is used to characterize the groundwater chemical composition, water chemical type and its state of change with the monitoring period; The rule discrimination module is used to fuse and discriminate the hydrochemical feature set with the groundwater dynamic changes, environmental recharge changes, mining disturbance events, hydrogeological background, and changes of adjacent monitoring units of the corresponding monitoring unit based on preset hydrogeological association rules, so as to obtain the groundwater chemical evolution state of the corresponding monitoring unit. The trend evolution module is used to predict the groundwater chemical evolution trend of the corresponding monitoring unit in the next monitoring cycle or a set prediction cycle based on the groundwater chemical evolution state and the historical evolution stage sequence of the corresponding monitoring unit, and output the groundwater chemical evolution trend result.

[0014] This application also provides a prediction device, which further includes: Memory, used to store computer programs; A processor is used to execute the computer program to implement the steps of the method for predicting the chemical evolution trend of groundwater in mining areas as described above.

[0015] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method for predicting the chemical evolution trend of groundwater in mining areas as described above.

[0016] This application provides a method for predicting the chemical evolution trend of groundwater in mining areas based on multi-source heterogeneous data fusion, comprising: S1, acquiring multi-source heterogeneous monitoring data of the target mining area; wherein, the multi-source heterogeneous monitoring data includes: groundwater hydrochemical detection data, groundwater dynamic state data, monitoring well spatial attribute data, hydrogeological background data, mining disturbance data, and environmental recharge data; S2, standardizing the multi-source heterogeneous monitoring data based on preset monitoring cycle division rules, spatial attribution division rules, and aquifer zoning rules to obtain standardized groundwater state data corresponding to each monitoring unit; wherein, the monitoring unit is jointly determined by the monitoring well, aquifer attributes, and spatial location; S3, based on the standardized groundwater state data... According to the data, the groundwater chemical evolution characteristics of each monitoring unit are extracted to obtain a hydrochemical feature set; wherein, the hydrochemical feature set is used to characterize the groundwater chemical composition, hydrochemical type and its state of change with the monitoring cycle; S4, based on the preset hydrogeological association rules, the hydrochemical feature set is fused and discriminated with the groundwater dynamic changes, environmental recharge changes, mining disturbance events, hydrogeological background and changes of adjacent monitoring units of the corresponding monitoring unit to obtain the groundwater chemical evolution state of the corresponding monitoring unit; S5, based on the groundwater chemical evolution state and the historical evolution stage sequence of the corresponding monitoring unit, the groundwater chemical evolution trend of the corresponding monitoring unit in the next monitoring cycle or the set prediction cycle is predicted, and the groundwater chemical evolution trend result is output.

[0017] It has the following beneficial effects: By acquiring multi-source data on groundwater hydrochemistry, dynamic state, spatial attributes, hydrogeological background, mining disturbance, and environmental recharge in mining areas, and standardizing the data by monitoring cycle, spatial attribution, and aquifer zoning, a comparable groundwater state basis is formed from data at different scales. Furthermore, by extracting hydrochemical evolution characteristics and combining them with hydrogeological correlation rules for fusion and discrimination, the influence of natural recharge, evaporation concentration, and mining disturbance can be distinguished, thereby improving the accuracy, continuity, and interpretability of groundwater chemical evolution trend prediction. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 A flowchart illustrating a method for predicting the chemical evolution trend of groundwater in mining areas based on multi-source heterogeneous data fusion, provided as an embodiment of this application; Figure 2 A schematic diagram of the structure of a device for predicting the chemical evolution trend of groundwater in a mining area based on multi-source heterogeneous data fusion, provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a prediction device provided in an embodiment of this application. Detailed Implementation

[0020] The purpose of this application is to provide a method for predicting the chemical evolution trend of groundwater in mining areas based on multi-source heterogeneous data fusion, a device for predicting the chemical evolution trend of groundwater in mining areas based on multi-source heterogeneous data fusion, a prediction device, and a computer-readable storage medium. Through multi-source heterogeneous data fusion and hydrogeological correlation discrimination, continuous and interpretable prediction results of groundwater chemical evolution trend are formed.

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0022] The following embodiment illustrates a method for predicting the chemical evolution trend of groundwater in mining areas based on multi-source heterogeneous data fusion, provided in this application.

[0023] Please refer toFigure 1 , Figure 1 A flowchart illustrating a method for predicting the chemical evolution trend of groundwater in mining areas based on multi-source heterogeneous data fusion, provided as an embodiment of this application.

[0024] In this embodiment, the method may include: S1, acquire multi-source heterogeneous monitoring data of the target mining area.

[0025] This embodiment uses a high-altitude mining area in Qinghai Province as an example. The target mining area may include the mining pit area, spoil heap area, tailings storage impact area, upstream recharge area, central runoff area, and downstream discharge area. Multiple groundwater monitoring wells are deployed within the mining area, each located at different hydrogeological locations and corresponding to different aquifer units. Because such mining areas are typically situated in high-altitude, frigid environments with strong evaporation and large diurnal temperature variations, groundwater chemical changes are influenced by natural factors such as precipitation, snowmelt, freeze-thaw cycles, and evaporation concentration, as well as by mining activities such as pit expansion, drainage, rock strata exposure, tailings storage and seepage, and spoil heap storage. Therefore, before trend prediction, it is necessary to acquire multi-source heterogeneous monitoring data that collectively reflect the groundwater chemical state, groundwater dynamic state, spatial location, geological background, mining disturbance, and environmental recharge processes.

[0026] The multi-source heterogeneous monitoring data in this embodiment includes groundwater hydrochemical detection data, groundwater dynamic state data, monitoring well spatial attribute data, hydrogeological background data, mining disturbance data, and environmental recharge data. Groundwater hydrochemical detection data is obtained through periodic sampling and testing from each monitoring well. This data includes sampling time, sampling depth, pH, conductivity, mineralization, major anions and cations, and characteristic pollutants. Major anions and cations may include basic components used to identify groundwater chemical types, such as calcium, magnesium, sodium, potassium, bicarbonate, sulfate, chloride, and nitrate ions. Characteristic pollutants may include fluoride, iron and manganese components, and other water quality factors related to mining activities, used to assist in determining the effects of tailings seepage, mine water impact, or enhanced water-rock interaction.

[0027] Groundwater dynamic state data includes groundwater level data and water temperature data. Groundwater level data is used to characterize changes in aquifer hydrodynamic conditions, such as water level rise due to enhanced recharge, water level drop due to drainage, and water level response in downstream discharge areas. Water temperature data is used to help determine seasonal freeze-thaw cycles, snowmelt recharge, and changes in groundwater circulation paths. Monitoring well spatial attribute data includes monitoring well coordinates, elevation, well depth, screen location, and corresponding aquifer information. Monitoring well coordinates and elevation are used to determine the monitoring well's location in the horizontal and vertical space of the mining area; well depth and screen location are used to determine the aquifer segment actually controlled by the monitoring well; and corresponding aquifer information is used to include the monitoring well in the corresponding aquifer unit.

[0028] Hydrogeological background data includes lithological distribution, aquitard location, fault structures, aquifer connectivity, and groundwater flow direction. Lithological distribution is used to determine the water-rock interaction background; aquitard location is used to determine the barrier relationship between different aquifers; fault structures are used to determine potential water-conducting channels; and aquifer connectivity and groundwater flow direction are used to determine possible spatial transmission paths for hydrochemical changes. Mining disturbance data includes pit advance location, dewatering period, drainage volume variation, mining depth variation, and tailings or spoil heap extent. Pit advance location and mining depth variation reflect changes in mining space boundaries; dewatering period and drainage volume variation reflect the process of anthropogenic alteration of groundwater dynamic conditions; and tailings or spoil heap extent reflects the location and extent of potential infiltration sources. Environmental recharge data includes precipitation, temperature, evaporation, snowmelt period, and freeze-thaw transition period, used to identify the contributions of seasonal recharge, evaporation concentration, and freeze-thaw effects to groundwater chemical changes in high-altitude mining areas.

[0029] The aforementioned data can originate from laboratory testing systems, automatic groundwater monitoring equipment, mine production scheduling systems, hydrogeological survey data, meteorological observation data, and remote sensing interpretation results. After data access, the system adds source, time, spatial, and physical meaning identifiers to each data point. For example, a specific hydrochemical test record corresponds to a specific monitoring well, sampling time, sampling depth, and aquifer; a drainage record corresponds to the location of the mining pit, drainage period, and changes in drainage volume; and a snowmelt record corresponds to the temperature rise and snowmelt process within a specific time period. Through this processing, previously discrete laboratory test data, continuous water level and temperature data, regional meteorological data, spatial geological structure data, and event-based mining disturbance data are all incorporated into the same data foundation, providing complete input for subsequent standardization processing. This step avoids trend judgment based solely on single hydrochemical test values, ensuring that groundwater chemical changes are supported by hydrodynamic, spatial, and disturbance backgrounds before entering the analysis stage.

[0030] S2, based on preset monitoring cycle division rules, spatial assignment division rules, and aquifer zoning rules, standardizes multi-source heterogeneous monitoring data to obtain standardized groundwater status data corresponding to each monitoring unit.

[0031] In this embodiment, the monitoring unit is determined by the monitoring well, aquifer properties, and spatial location. That is, if the same monitoring well corresponds to different screen sections or different aquifers, separate monitoring units are established according to the actual controlled aquifer. Even if different monitoring wells are spatially close, if they are located in different aquifers with a stable impermeable layer in between, they are not directly assigned to the same monitoring unit. By defining the monitoring unit through the combined factors of the monitoring well, aquifer properties, and spatial location, subsequent hydrochemical evolution assessments have both spatial positioning and hydrogeological significance, avoiding the mixing and comparison of data from different aquifers or under different hydrodynamic conditions.

[0032] In the standardization process, firstly, according to the monitoring cycle division rules, multi-source heterogeneous monitoring data with different collection frequencies are matched to the same monitoring cycle. Water chemistry detection data are typically sampled monthly or quarterly, water level and temperature data can be recorded continuously hourly or daily, environmental recharge data have daily or process-scale characteristics, and mining disturbance data may be recorded as production events. To ensure that these data can participate in the same round of judgment, this embodiment determines the monitoring cycle based on the water chemistry sampling time, and includes water level changes, water temperature changes, precipitation processes, snowmelt processes, freeze-thaw transition processes, evaporation changes, and mining drainage processes that reflect the water sample formation environment before and after the sampling date into the same monitoring cycle. In this way, each water chemistry detection record not only corresponds to the detection value itself, but also to the groundwater dynamic conditions, environmental recharge conditions, and mining disturbance conditions within the sampling period.

[0033] Subsequently, according to the spatial allocation rules, the monitoring wells are assigned to corresponding spatial monitoring areas based on their spatial attribute data and groundwater flow direction. These spatial monitoring areas can include upstream recharge monitoring areas, mining disturbance monitoring areas, tailings storage impact monitoring areas, runoff transition monitoring areas, and downstream discharge monitoring areas. The system determines the spatial allocation of monitoring wells based on their coordinates, elevation, relative position to the mining pit or storage area, groundwater flow direction, and aquifer location. For example, monitoring wells located upstream of the mining area and far from mining disturbance sources can be assigned to the upstream recharge monitoring area; monitoring wells located near the mining pit boundary, the drainage impact range, or fault zones can be assigned to the mining disturbance monitoring area; monitoring wells located downstream of the tailings storage area and connected to shallow aquifers can be assigned to the tailings storage impact monitoring area; and monitoring wells located in the downstream discharge direction of the mining area can be assigned to the downstream discharge monitoring area.

[0034] Then, according to the aquifer zoning rules, the aquifer unit corresponding to each monitoring well is determined based on the location of the monitoring well screen, aquifer properties, and the location of the impermeable layer. For monitoring wells with screens located in the same aquifer and hydraulically connected, the system establishes a correlation within the same aquifer. For monitoring wells with stable impermeable layers between different aquifers, the system reduces their direct hydraulic connection. For monitoring wells located near fault structures, even if their aquifer vertical locations differ, potential connectivity markers are established based on the fault's water-conductivity potential. In this way, key hydrogeological constraints such as aquifer connectivity, impermeable layer barriers, and fault water-conductivity can be preserved in standardized data.

[0035] After completing the matching of monitoring cycles, the division of spatial monitoring areas, and the determination of aquifer units, the system performs unit standardization, missing data identification, anomaly identification, and high-altitude environmental impact identification on the data. Unit standardization is used to convert detection data from different sources into a comparable state, such as unifying ion concentration, conductivity, and mineralization to the same data caliber. Missing data identification is used to mark missing hydrochemical components, water level records, environmental recharge records, or mining disturbance records within a certain monitoring cycle, avoiding the misclassification of missing data as normal and stable data during subsequent fusion and discrimination. Anomaly identification is used to identify records with missing sampling times, inconsistent sampling well numbers, severe missing ion components, inconsistent detection units, and records that are clearly inconsistent with the sampling logic compared to adjacent cycles. High-altitude environmental impact identification reflects the potential impact of low-temperature freezing, evaporation during sample transportation, on-site instrument drift, periods of strong evaporation, and freeze-thaw transition periods on the data.

[0036] For example, if a monitoring well shows a synchronous increase in conductivity, mineralization, and major ions within the same monitoring period, but the water level does not decrease significantly, environmental recharge does not decrease significantly, and neighboring monitoring wells do not show the same changes, and this sampling process occurs after low-temperature freezing or long-distance sample transportation, the system will mark this record as an anomaly requiring verification, rather than directly using it as a basis for judging the risk of anomaly spread. Conversely, if a monitoring well shows a decrease in water level and an increase in characteristic components after enhanced mining drainage, and downstream monitoring wells in the same aquifer show similar changes in subsequent periods, such records will not be simply classified as sampling anomalies, but will serve as important input for subsequent fusion and discrimination.

[0037] After S2 processing, the system generates standardized groundwater state data corresponding to each monitoring unit. This standardized groundwater state data features a unified monitoring period, clear spatial attribution, clear aquifer attributes, complete data quality identification, and high-altitude environmental impact identification. Through this step, data from different sources, frequencies, and spatial scales are transformed into mutually corresponding groundwater state descriptions, solving the problem that discrete sampling data is difficult to analyze together with continuous monitoring data, hydrogeological data, and mining disturbance data.

[0038] S3, based on standardized groundwater state data, extracts the groundwater chemical evolution characteristics of each monitoring unit to obtain a set of hydrochemical characteristics.

[0039] In this embodiment, the hydrochemical feature set is used to characterize the chemical composition, hydrochemical type, and changes of groundwater over the monitoring period. The system does not directly use a single detection value as a trend prediction result, but first extracts features from standardized groundwater state data that reflect the direction of evolution, the causes of change, and the persistence of change.

[0040] First, the system identifies the changing characteristics of ion dominance types in corresponding monitoring units based on the changes in major anions and cations in standardized groundwater state data. Specifically, within the same monitoring unit, the system compares the changes in components such as calcium, magnesium, sodium, potassium, bicarbonate, sulfate, and chloride ions in adjacent monitoring periods and historical background periods to determine whether the dominant ion composition has shifted. For example, under long-term background conditions, a certain upstream recharge monitoring unit is dominated by bicarbonate and calcium and magnesium ions, indicating recharge runoff-type groundwater. If the dominance of sulfate or chloride ions in the hydrochemical type identification continues to increase in subsequent periods, the system identifies the changing characteristics of ion dominance types. This characteristic is used to reflect the potential transformation of groundwater from recharge runoff control to evaporation concentration, enhanced water-rock interaction, or the influence of mining disturbance.

[0041] Secondly, based on standardized groundwater state data containing changes in mineralization, conductivity, and acidity / alkalinity, the system identifies the mineralization change characteristics and acidity / alkalinity shift characteristics of corresponding monitoring units. Mineralization characterizes the overall level of dissolved substances in groundwater, conductivity helps characterize changes in total ion content, and acidity / alkalinity reflects the groundwater reaction environment. For high-altitude mining areas in Qinghai, an increase in mineralization may be caused by strong evaporation, or by the mixing of deep, highly mineralized water, enhanced rock dissolution, or mining disturbance; a decrease in mineralization may be related to recharge and dilution from precipitation, snowmelt, or freeze-thaw cycles. Therefore, when identifying mineralization change characteristics, the system simultaneously records the direction of change, duration of change, corresponding monitoring period, and relevant water level background. Acidity / alkalinity shift characteristics are used to determine whether abnormal changes have occurred in the groundwater reaction environment, such as oxidation of sulfur-containing minerals, the influence of mine water, or tailings leaching, which may cause acidity / alkalinity shifts.

[0042] Furthermore, based on changes in sulfate, chloride, and characteristic pollutants in standardized groundwater state data, the system identifies enhancements in characteristic components for corresponding monitoring units. Sulfate enhancement can indicate increased oxidation of sulfur-containing minerals, enhanced water-rock interaction, or the impact of mine water; chloride enhancement can indicate evaporation concentration, deep brackish water intrusion, or salinization; and enhancements in characteristic pollutants can identify the impact of tailings storage seepage, mining drainage, or industrial activities. When identifying enhancements in characteristic components, the system combines data quality indicators within the monitoring period to eliminate spurious changes caused by sampling anomalies, unit errors, or missing measurements, while retaining the time, spatial location, and aquifer unit information of the changes.

[0043] Then, based on the characteristics of changes in ion dominance type, mineralization, acid-base shift, and enhancement of characteristic components, combined with groundwater dynamics and environmental recharge changes within the corresponding monitoring period, the system forms a set of hydrochemical characteristics, including recharge-dilution characteristics, evaporation-concentration characteristics, enhanced water-rock interaction characteristics, and mining disturbance response characteristics. Specifically, when precipitation increases, snowmelt or freeze-thaw transition is significant within a certain monitoring period, and groundwater levels rise while mineralization, conductivity, and the overall concentration of major ions decrease, the system generates a recharge-dilution characteristic. This characteristic indicates that groundwater chemical changes are mainly caused by enhanced external freshwater recharge.

[0044] When evaporation increases, temperature rises, or recharge decreases during a monitoring period, causing groundwater levels to drop or remain low, while mineralization and conductivity increase, but characteristic pollutants do not increase synchronously, the system exhibits evaporation concentration characteristics. This characteristic indicates that groundwater chemical changes are mainly related to high-altitude, high-evaporation environments. When shifts in sulfate, chloride, iron-manganese components, or acidity / alkalinity in groundwater correspond to aquifer lithology, fault structures, exposed rock layers from mining pits, or deep water mixing conditions, the system exhibits enhanced water-rock interaction characteristics. This characteristic indicates changes in groundwater-rock contact conditions or an increased degree of water-rock reaction. When mining dewatering, pit advancement, changes in mining depth, or tailings accumulation occur, and corresponding monitoring units show a drop in water level, enhanced characteristic components, acidity / alkalinity shifts, or a change in ion dominance types, the system exhibits mining disturbance response characteristics.

[0045] After S3 processing, each monitoring unit obtains a hydrochemical feature set. This hydrochemical feature set is not simply a collection of detection data, but a comprehensive set of characteristics including changes in chemical composition, hydrochemical type, direction of change, duration of change, possible causes, corresponding hydrodynamic background, and environmental recharge background. Through this step, the system can transform single-point numerical changes into evolutionary characteristics with hydrogeological significance, providing clear input for subsequent fusion and discrimination.

[0046] S4, based on preset hydrogeological association rules, integrates and distinguishes the hydrochemical feature set with the groundwater dynamic changes, environmental recharge changes, mining disturbance events, hydrogeological background, and changes of adjacent monitoring units of the corresponding monitoring unit to obtain the groundwater chemical evolution state of the corresponding monitoring unit.

[0047] The hydrogeological correlation rules in this embodiment include upstream-downstream correlation rules, same aquifer correlation rules, aquitard isolation rules, fault-conducting correlation rules, disturbance source proximity correlation rules, and adjacent monitoring cycle continuation rules. The upstream-downstream correlation rule is used to determine whether hydrochemical changes are transmitted from upstream to downstream along the groundwater flow direction. The same aquifer correlation rule is used to determine whether multiple monitoring units are located in the same aquifer and are comparable. The aquitard isolation rule is used to determine whether there is a stable aquitard between different aquifers, thereby limiting the vertical transmission of hydrochemical changes. The fault-conducting correlation rule is used to determine whether there may be abnormal hydraulic connections between monitoring units near fault structures. The disturbance source proximity correlation rule is used to determine the spatial relationship between hydrochemical changes and mining pits, tailings storage areas, spoil heaps, or drainage points. The adjacent monitoring cycle continuation rule is used to determine whether a change has continuous or phased transmission characteristics.

[0048] During the fusion and discrimination process, the system first determines the spatial transmission relationship of hydrochemical changes based on upstream-downstream correlation rules and correlation rules within the same aquifer. For example, if a monitoring unit in a mining-disturbed area first shows increased sulfate, acid-base shifts, and a drop in water level, and a downstream monitoring unit in the same aquifer shows similar but weaker changes in subsequent monitoring cycles, while no similar changes are observed in the upstream recharge area, the system judges that the hydrochemical change may be transmitted along the groundwater flow direction. Conversely, if multiple areas simultaneously show increased mineralization and conductivity during the same evaporation enhancement cycle, but there is no significant increase in characteristic pollutants upstream, in the mining-disturbed area, or downstream, the system tends to judge it as regional evaporative concentration rather than localized pollution diffusion.

[0049] The system then determines the degree of hydraulic connection between different monitoring units based on the aquitard barrier rule and the fracture water-conducting correlation rule. If two monitoring units belong to different aquifers and there is a continuous and stable aquitard in between, the system reduces the judgment of their direct hydraulic connection even if their hydrochemical changes are close in time. If the two monitoring units are located on opposite sides of a fracture structure, and the fracture zone matches the connectivity between the exposed area of ​​the mining pit or the aquifer, the system increases the judgment of their potential water-conducting correlation. This process avoids erroneous inferences about abnormal diffusion paths based solely on spatial proximity or similar change times.

[0050] The system further determines the response relationship between hydrochemical changes and mining disturbance events based on the proximity association rules of disturbance sources and the continuation rules of adjacent monitoring cycles. For example, if a monitoring unit near the mine pit experiences a drop in water level, an increase in sulfate, a shift in acidity / alkalinity, and an increase in iron and manganese components after enhanced drainage, and a downstream adjacent monitoring unit exhibits similar changes in the subsequent monitoring cycle, the system establishes a response relationship between this change and the mining disturbance event. If a monitoring unit only shows an increase in characteristic components in a single sampling, but lacks corresponding water level changes, responses from adjacent monitoring units, and support from mining disturbance events, the system lowers the evolution status level of this anomaly and associates it with data verification recommendations.

[0051] Through the above-mentioned fusion discrimination, the system classifies the groundwater chemical evolution state of the corresponding monitoring units into four categories: background stable state, seasonal recharge and dilution state, evaporation and concentration state, enhanced water-rock interaction state, mining disturbance response state, and anomalous diffusion risk state. When the water chemical components fluctuate around the historical background range over multiple monitoring periods, and neither groundwater dynamic changes nor environmental recharge changes show any anomalies, and no continuous anomalies are observed in adjacent monitoring units, the system classifies it as a background stable state. When the water level rises, mineralization decreases, and the overall level of major ions decreases, and this corresponds to periods of precipitation, snowmelt, or freeze-thaw transition, the system classifies it as a seasonal recharge and dilution state. This state indicates that the groundwater chemical changes are mainly caused by enhanced natural recharge, which can avoid misjudging dilution phenomena caused by snowmelt or precipitation as pollution control effects or the disappearance of anomalies.

[0052] When evaporation increases, water level drops, and mineralization or conductivity rises, but characteristic pollutants do not increase synchronously, the system identifies it as an evaporative concentration state. This state indicates that groundwater chemical changes are mainly influenced by the high-altitude, high-evaporation environment, avoiding the simplistic attribution of regional concentration phenomena to mining pollution diffusion. When shifts in sulfate, chloride, iron-manganese components, or acidity / alkalinity in groundwater correspond to lithological distribution, exposed rock strata in mining pits, fracture water-conducting conditions, or the mixing of deep water bodies, the system identifies it as an enhanced water-rock interaction state. This state reflects changes in groundwater-rock contact conditions or an increased degree of reaction.

[0053] When a mining disturbance event occurs, and the corresponding monitoring unit shows a drop in water level, an increase in characteristic components, or a shift in acidity / alkalinity, and adjacent monitoring units show related changes in subsequent monitoring cycles, the system classifies it as either a mining disturbance response state or an abnormal diffusion risk state. If the changes are mainly concentrated near the disturbance source and their spatial transmission is not yet obvious, it is classified as a mining disturbance response state; if the changes continue downstream along the groundwater flow direction or through fractured water-conducting channels to adjacent monitoring units, it is classified as an abnormal diffusion risk state. Through this step, the system can cross-reference hydrochemical characteristics, water level changes, environmental recharge, mining disturbance events, and hydrogeological background, thereby distinguishing between natural seasonal fluctuations, evaporation concentration, enhanced water-rock interaction, and anomalies caused by human disturbance.

[0054] S5, based on the groundwater chemical evolution status and the historical evolution stage sequence of the corresponding monitoring unit, predicts the groundwater chemical evolution trend of the corresponding monitoring unit in the next monitoring cycle or the set prediction cycle, and outputs the groundwater chemical evolution trend results.

[0055] In this embodiment, the system first connects the groundwater chemical evolution state obtained in the current monitoring cycle with the historical evolution state of the same monitoring unit in chronological order to form a historical evolution stage sequence. This historical evolution stage sequence is used to record the state change process of the same monitoring unit in continuous monitoring cycles. For example, a monitoring unit may sequentially experience a background stable state, a seasonal recharge and dilution state, and a background recovery state, or it may sequentially experience a background stable state, a mining disturbance response state, and an abnormal diffusion risk state. By forming a time-continuous stage sequence, the system can avoid making trend judgments based solely on the current single-cycle state.

[0056] Subsequently, based on the historical evolutionary stage sequence, the system identifies the state continuation trend, state recovery trend, or state diffusion trend of the corresponding monitoring units. When a monitoring unit maintains the same abnormal state for multiple consecutive monitoring cycles, such as increased mineralization, enhanced sulfate, and persistent acid-base shifts without any signs of recovery, the system identifies it as a state continuation trend. When a monitoring unit experiences a recharge and dilution state during snowmelt or precipitation periods, and then gradually returns to the historical background range as recharge weakens, the system identifies it as a state recovery trend. When a monitoring unit first exhibits a mining disturbance response state, and then downstream monitoring units in the same aquifer or fracture-guided water-conducting zone show similar changes, the system identifies it as a state diffusion trend.

[0057] Next, based on the trends of state continuation, state recovery, or state diffusion, and combined with the spatial location of the corresponding monitoring unit, aquifer connectivity, and changes in adjacent monitoring units, the system determines the main change direction, risk level, impact range, and credibility indicator in the groundwater chemical evolution trend results. The main change direction can include trends of increasing mineralization, increased sulfate, increased chloride, acid-base shift, recharge-dilution, background recovery, and anomalous diffusion. The risk level is determined based on the persistence of change, spatial transmissibility, degree of anomaly of characteristic factors, correlation with mining disturbance events, and aquifer connectivity. The impact range is estimated based on upstream and downstream monitoring relationships, connectivity within the same aquifer, fracture water-conducting relationships, and aquitard boundaries. The credibility indicator indicates the degree of data support for the trend results. A high credibility indicator is indicated when hydrochemical changes, groundwater level changes, mining disturbance events, and changes in adjacent monitoring units all corroborate each other; a low credibility indicator is indicated when only a single hydrochemical anomaly exists without supporting hydrodynamic changes, spatial transmissibility, or disturbance events.

[0058] Finally, based on the main direction of change, risk level, scope of impact, and credibility indicator, the system outputs corresponding monitoring or remediation recommendations. For areas identified as being in a mining disturbance response state with potential downstream spread, the system outputs recommendations such as increasing monitoring frequency, increasing sampling at downstream control wells, verifying drainage paths in mining pits, inspecting fractured water-conducting channels, and assessing the seepage risk of tailings stockpiles. For areas identified as being in an abnormal spread risk state, the system can output a list of key monitoring wells, the potential scope of impact, key anomaly factors, and a recommended retesting cycle. For areas identified as being in an evaporation and concentration state, the system outputs recommendations for continuous observation in conjunction with the background of strong evaporation seasons to avoid taking unnecessary pollution control measures. For areas identified as being in a seasonal replenishment and dilution state, the system outputs recommendations for periodic review in conjunction with snowmelt seasons, rainy seasons, and freeze-thaw transition periods. For trend results with low credibility, the system outputs recommendations for re-sampling, continuous monitoring of replenished water levels, or verification of testing units.

[0059] In this embodiment, the method can be implemented using related devices deployed in a mining area groundwater environment monitoring platform, geological exploration data center, or mine ecological environment management system. These related devices may include a data access unit, a data preprocessing unit, a feature extraction unit, a fusion discrimination unit, a trend prediction unit, and a result output unit. The data access unit receives multi-source heterogeneous monitoring data provided by laboratory testing systems, automatic groundwater monitoring equipment, mine production systems, meteorological data systems, and remote sensing interpretation results. The data preprocessing unit performs monitoring cycle matching, spatial attribution, aquifer unit determination, unit unification, missing data identification, abnormal record identification, and high-altitude environmental impact identification. The feature extraction unit extracts ion dominance type variation characteristics, mineralization variation characteristics, acid-base shift characteristics, characteristic component enhancement characteristics, recharge dilution characteristics, evaporation concentration characteristics, water-rock interaction enhancement characteristics, and mining disturbance response characteristics from standardized groundwater state data. The fusion discrimination unit determines the groundwater chemical evolution state based on upstream-downstream correlation rules, same aquifer correlation rules, aquitard barrier rules, fracture water conduction correlation rules, disturbance source proximity correlation rules, and adjacent monitoring cycle continuity rules. The trend prediction unit is used to predict the evolution trend of the next monitoring period or within a set prediction period based on the groundwater chemical evolution status and historical evolution stage sequence. The results output unit is used to output groundwater chemical evolution trend map, monitoring well risk classification map, list of abnormal factor changes, credibility indicator, and monitoring or remediation recommendations.

[0060] Through the complete process of this embodiment, groundwater hydrochemical monitoring data from the target mining area is no longer used in isolation for judgment, but rather forms a closed-loop analysis chain together with groundwater dynamic state, monitoring well spatial attributes, hydrogeological background, mining disturbance events, and environmental recharge processes. This method can continuously and interpretably identify the chemical evolution state of groundwater under the superposition of natural seasonal fluctuations and mining disturbances in high-altitude mining areas, and predict the direction of change, risk level, and scope of impact in the next monitoring cycle or a set prediction cycle. This provides a technical basis for groundwater protection, drainage regulation, pollution risk prevention and control, and optimization of monitoring schemes in mining areas.

[0061] In summary, this embodiment acquires multi-source data on groundwater hydrochemistry, dynamic state, spatial attributes, hydrogeological background, mining disturbance, and environmental recharge in the mining area. It then standardizes the data by monitoring cycle, spatial attribution, and aquifer zoning, enabling data at different scales to form a comparable groundwater state basis. Furthermore, by extracting hydrochemical evolution characteristics and combining them with hydrogeological correlation rules for fusion and discrimination, it can distinguish between natural recharge, evaporation concentration, and the impact of mining disturbance, thereby improving the accuracy, continuity, and interpretability of groundwater chemical evolution trend prediction.

[0062] The following describes a device for predicting the chemical evolution trend of groundwater in mining areas based on multi-source heterogeneous data fusion, provided in an embodiment of this application. The device for predicting the chemical evolution trend of groundwater in mining areas based on multi-source heterogeneous data fusion and the method for predicting the chemical evolution trend of groundwater in mining areas based on multi-source heterogeneous data fusion described below can be referred to in correspondence with each other.

[0063] Please refer to Figure 2 , Figure 2 This is a schematic diagram of a device for predicting the chemical evolution trend of groundwater in mining areas based on multi-source heterogeneous data fusion, provided in an embodiment of this application.

[0064] In this embodiment, the device may include: The monitoring data acquisition module 100 is used to acquire multi-source heterogeneous monitoring data of the target mining area; wherein, the multi-source heterogeneous monitoring data includes: groundwater hydrochemical detection data, groundwater dynamic state data, monitoring well spatial attribute data, hydrogeological background data, mining disturbance data, and environmental recharge data; The standardization processing module 200 is used to standardize the multi-source heterogeneous monitoring data based on preset monitoring cycle division rules, spatial attribution division rules, and aquifer zoning rules to obtain standardized groundwater state data corresponding to each monitoring unit; wherein, the monitoring unit is jointly determined by the monitoring well, aquifer attributes, and spatial location; The chemical feature acquisition module 300 is used to extract the groundwater chemical evolution features of each monitoring unit based on the standardized groundwater state data to obtain a water chemical feature set; wherein, the water chemical feature set is used to characterize the groundwater chemical composition, water chemical type and its state of change with the monitoring period; The rule discrimination module 400 is used to fuse and discriminate the hydrochemical feature set with the groundwater dynamic changes, environmental recharge changes, mining disturbance events, hydrogeological background, and changes of adjacent monitoring units of the corresponding monitoring unit based on preset hydrogeological association rules, so as to obtain the groundwater chemical evolution state of the corresponding monitoring unit. The trend evolution module 500 is used to predict the groundwater chemical evolution trend of the corresponding monitoring unit in the next monitoring cycle or a set prediction cycle based on the groundwater chemical evolution state and the historical evolution stage sequence of the corresponding monitoring unit, and output the groundwater chemical evolution trend result.

[0065] This application also provides a prediction device, please refer to... Figure 3 , Figure 3 This is a schematic diagram of the structure of a prediction device provided in an embodiment of this application. The prediction device may include: Memory, used to store computer programs; The processor, when executing a computer program, can implement the steps of any of the above-described methods for predicting the chemical evolution trend of groundwater in mining areas based on the fusion of multi-source heterogeneous data.

[0066] like Figure 3 The diagram shows the structural composition of a prediction device, which may include a processor 10, a memory 11, a communication interface 12, and a communication bus 13. The processor 10, memory 11, and communication interface 12 communicate with each other via the communication bus 13.

[0067] In this embodiment, the processor 10 may be a central processing unit (CPU), an application-specific integrated circuit, a digital signal processor, a field-programmable gate array, or other programmable logic devices.

[0068] The processor 10 can call the program stored in the memory 11. Specifically, the processor 10 can execute the operations in the embodiment of the abnormal IP identification method.

[0069] The memory 11 is used to store one or more programs. The programs may include program code, which includes computer operation instructions. In this embodiment, the memory 11 stores at least a program for implementing the following functions: S1, acquire multi-source heterogeneous monitoring data of the target mining area; wherein, the multi-source heterogeneous monitoring data includes: groundwater hydrochemical detection data, groundwater dynamic state data, monitoring well spatial attribute data, hydrogeological background data, mining disturbance data, and environmental recharge data; S2, based on preset monitoring cycle division rules, spatial assignment division rules, and aquifer zoning rules, the multi-source heterogeneous monitoring data is standardized to obtain standardized groundwater state data corresponding to each monitoring unit; wherein, the monitoring unit is determined by the monitoring well, aquifer attributes, and spatial location. S3. Based on the standardized groundwater state data, extract the groundwater chemical evolution characteristics of each monitoring unit to obtain a hydrochemical feature set; wherein, the hydrochemical feature set is used to characterize the groundwater chemical components, hydrochemical types and their changes with the monitoring cycle; S4. Based on the preset hydrogeological association rules, the hydrochemical feature set is fused and judged with the groundwater dynamic changes, environmental recharge changes, mining disturbance events, hydrogeological background, and changes of adjacent monitoring units of the corresponding monitoring unit to obtain the groundwater chemical evolution state of the corresponding monitoring unit. S5. Based on the groundwater chemical evolution state and the historical evolution stage sequence of the corresponding monitoring unit, predict the groundwater chemical evolution trend of the corresponding monitoring unit in the next monitoring cycle or a set prediction cycle, and output the groundwater chemical evolution trend result.

[0070] In one possible implementation, the memory 11 may include a program storage area and a data storage area, wherein the program storage area may store the operating system and applications required for at least one function; and the data storage area may store data created during use.

[0071] In addition, memory 11 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device or other volatile solid-state storage device.

[0072] Communication interface 12 can be an interface for the communication module, used to connect with other devices or systems.

[0073] Of course, it should be noted that, Figure 3 The structure shown does not constitute a limitation on the prediction device in the embodiments of this application. In practical applications, a prediction device may include more than Figure 3 More or fewer components as shown, or combinations of certain components.

[0074] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the steps of any of the above-described methods for predicting the chemical evolution trend of groundwater in mining areas based on multi-source heterogeneous data fusion.

[0075] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0076] For a description of the computer-readable storage medium provided in this application, please refer to the above method embodiments; further details will not be repeated here.

[0077] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0078] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0079] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0080] The foregoing has provided a detailed description of a method for predicting the chemical evolution trend of groundwater in mining areas based on multi-source heterogeneous data fusion, a device for predicting the chemical evolution trend of groundwater in mining areas based on multi-source heterogeneous data fusion, a prediction device, and a computer-readable storage medium. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

Claims

1. A method for predicting the chemical evolution trend of groundwater in mining areas based on multi-source heterogeneous data fusion, characterized in that, include: S1, acquire multi-source heterogeneous monitoring data of the target mining area; wherein, the multi-source heterogeneous monitoring data includes: groundwater hydrochemical detection data, groundwater dynamic state data, monitoring well spatial attribute data, hydrogeological background data, mining disturbance data, and environmental recharge data; S2, based on preset monitoring cycle division rules, spatial assignment division rules, and aquifer zoning rules, the multi-source heterogeneous monitoring data is standardized to obtain standardized groundwater state data corresponding to each monitoring unit; wherein, the monitoring unit is determined by the monitoring well, aquifer attributes, and spatial location. S3. Based on the standardized groundwater state data, extract the groundwater chemical evolution characteristics of each monitoring unit to obtain a hydrochemical feature set; wherein, the hydrochemical feature set is used to characterize the groundwater chemical components, hydrochemical types and their changes with the monitoring cycle; S4. Based on the preset hydrogeological association rules, the hydrochemical feature set is fused and judged with the groundwater dynamic changes, environmental recharge changes, mining disturbance events, hydrogeological background, and changes of adjacent monitoring units of the corresponding monitoring unit to obtain the groundwater chemical evolution state of the corresponding monitoring unit. S5. Based on the groundwater chemical evolution state and the historical evolution stage sequence of the corresponding monitoring unit, predict the groundwater chemical evolution trend of the corresponding monitoring unit in the next monitoring cycle or a set prediction cycle, and output the groundwater chemical evolution trend result.

2. The method for predicting the chemical evolution trend of groundwater in mining areas according to claim 1, characterized in that, In S1, the groundwater hydrochemical detection data includes: sampling time, sampling depth, acidity / alkalinity, conductivity, mineralization, major anions and cations, and characteristic pollutants; the groundwater dynamic state data includes: groundwater level data and water temperature data; the monitoring well spatial attribute data includes: monitoring well coordinates, elevation, well depth, screen tube location, and corresponding aquifer information; the hydrogeological background data includes: lithological distribution, aquitard location, fault structure, aquifer connectivity, and groundwater flow direction; the mining disturbance data includes: pit advancement location, dewatering period, drainage volume variation, mining depth variation, and tailings or spoil heap area; the environmental recharge data includes: precipitation, air temperature, evaporation, snowmelt period, and freeze-thaw transition period.

3. The method for predicting the chemical evolution trend of groundwater in mining areas according to claim 1, characterized in that, S2 includes: According to the monitoring cycle division rules, multi-source heterogeneous monitoring data with different acquisition frequencies are matched to the same monitoring cycle; According to the aforementioned spatial allocation rules, based on the spatial attribute data of the monitoring wells and the direction of groundwater flow, the monitoring wells are assigned to the corresponding spatial monitoring areas; According to the aquifer zoning rules, the aquifer unit corresponding to each monitoring well is determined based on the location of the monitoring well screen pipe, the aquifer properties, and the location of the aquitard. After completing the matching of monitoring cycles, the division of spatial monitoring areas, and the determination of aquifer units, the data is standardized by unifying units, identifying missing data, identifying abnormal records, and identifying high-altitude environmental impacts, thus obtaining the standardized groundwater status data.

4. The method for predicting the chemical evolution trend of groundwater in mining areas according to claim 1, characterized in that, S3 includes: Based on the changes in the main anions and cations in the standardized groundwater state data, the ion dominance type change characteristics of the corresponding monitoring units are identified; Based on the changes in mineralization, conductivity, and acidity / alkalinity in the standardized groundwater state data, the mineralization change characteristics and acidity / alkalinity shift characteristics of the corresponding monitoring units are identified. Based on the changes in sulfate, chloride ions and characteristic pollutants in the standardized groundwater state data, the enhanced characteristics of characteristic components of the corresponding monitoring units are identified. Based on the changes in the ion dominance type, the changes in mineralization, the acid-base shift, and the enhancement of the characteristic components, combined with the changes in groundwater dynamics and environmental recharge within the corresponding monitoring period, a set of hydrochemical characteristics is formed, including recharge dilution characteristics, evaporation concentration characteristics, water-rock interaction enhancement characteristics, and mining disturbance response characteristics.

5. The method for predicting the chemical evolution trend of groundwater in mining areas according to claim 1, characterized in that, In S4, the hydrogeological association rules include: upstream and downstream association rules, same aquifer association rules, aquitard barrier rules, fracture water conduction association rules, disturbance source proximity association rules, and adjacent monitoring cycle continuation rules. Accordingly, the fusion discrimination includes: determining the spatial transmission relationship of hydrochemical changes based on the upstream and downstream association rules and the same aquifer association rules; determining the degree of hydraulic connection between different monitoring units based on the aquitard barrier rule and the fracture water conduction association rule; and determining the response relationship between hydrochemical changes and mining disturbance events based on the disturbance source proximity association rule and the adjacent monitoring cycle continuation rule.

6. The method for predicting the chemical evolution trend of groundwater in mining areas according to claim 1, characterized in that, In S4, the groundwater chemical evolution state includes: background stable state, seasonal recharge and dilution state, evaporation and concentration state, enhanced water-rock interaction state, mining disturbance response state, and abnormal diffusion risk state. Specifically, when the water level rises, mineralization decreases, and the overall major ions decrease, corresponding to periods of precipitation, snowmelt, or freeze-thaw transition, it is identified as a seasonal recharge and dilution state. When evaporation increases, the water level drops, and mineralization or conductivity increases, but characteristic pollutants do not increase synchronously, it is identified as an evaporation and concentration state. When a mining disturbance event occurs, and the corresponding monitoring unit shows a drop in water level, an increase in characteristic components, or a shift in acidity / alkalinity, and adjacent monitoring units show related changes in subsequent monitoring cycles, it is identified as a mining disturbance response state or an abnormal diffusion risk state.

7. The method for predicting the chemical evolution trend of groundwater in mining areas according to claim 1, characterized in that, S5 includes: The chemical evolution status of groundwater obtained in the current monitoring period is connected with the historical evolution status of the same monitoring unit in chronological order to form the historical evolution stage sequence; Based on the historical evolution stage sequence, identify the state continuation trend, state recovery trend, or state diffusion trend of the corresponding monitoring unit; Based on the state continuation trend, state recovery trend, or state diffusion trend, and combined with the spatial location of the corresponding monitoring unit, the aquifer connectivity, and changes in adjacent monitoring units, the main change direction, risk level, impact range, and credibility indicator in the groundwater chemical evolution trend results are determined. Based on the main direction of change, risk level, scope of impact, and credibility indicator, corresponding monitoring or governance recommendations are output.

8. A device for predicting the chemical evolution trend of groundwater in mining areas based on multi-source heterogeneous data fusion, characterized in that, include: The monitoring data acquisition module is used to acquire multi-source heterogeneous monitoring data of the target mining area; wherein, the multi-source heterogeneous monitoring data includes: groundwater hydrochemical detection data, groundwater dynamic state data, monitoring well spatial attribute data, hydrogeological background data, mining disturbance data, and environmental recharge data; The standardization processing module is used to standardize the multi-source heterogeneous monitoring data based on preset monitoring cycle division rules, spatial attribution division rules, and aquifer zoning rules to obtain standardized groundwater state data corresponding to each monitoring unit; wherein, the monitoring unit is determined by the monitoring well, aquifer attributes, and spatial location. The chemical feature acquisition module is used to extract the groundwater chemical evolution features of each monitoring unit based on the standardized groundwater state data to obtain a water chemical feature set; wherein, the water chemical feature set is used to characterize the groundwater chemical composition, water chemical type and its state of change with the monitoring period; The rule discrimination module is used to fuse and discriminate the hydrochemical feature set with the groundwater dynamic changes, environmental recharge changes, mining disturbance events, hydrogeological background, and changes of adjacent monitoring units of the corresponding monitoring unit based on preset hydrogeological association rules, so as to obtain the groundwater chemical evolution state of the corresponding monitoring unit. The trend evolution module is used to predict the groundwater chemical evolution trend of the corresponding monitoring unit in the next monitoring cycle or a set prediction cycle based on the groundwater chemical evolution state and the historical evolution stage sequence of the corresponding monitoring unit, and output the groundwater chemical evolution trend result.

9. A prediction device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the method for predicting the chemical evolution trend of groundwater in mining areas as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method for predicting the chemical evolution trend of groundwater in mining areas as described in any one of claims 1 to 7.

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