Waste mine geothermal monitoring data management method and system based on time sequence database

Abstract

The invention provides a waste mine geothermal monitoring data management method and system based on a time sequence database, and belongs to the technical field of data management, and the method comprises the steps: obtaining waste mine geothermal monitoring data, and constructing a time sequence database architecture of a multi-stage association table structure; starting time sequence database extension and performing optimal configuration, converting a time sequence data table in the time sequence database into a supertable, and setting a partitioning strategy, index optimization and a data compression strategy; importing the monitoring data into a time sequence database and controlling the quality in real time; intelligent analysis is carried out based on a built-in function of a time sequence database, multi-time granularity aggregation analysis, trend analysis and anomaly detection are realized, a multi-dimensional intelligent early warning mechanism is constructed, and real-time early warning message pushing is realized through a trigger; and storing an analysis result to realize data life cycle management. According to the invention, efficient storage, rapid query and time series data management with intelligent analysis capability are realized.

Description

Technical Field

[0001] This invention belongs to the field of data management technology, and in particular relates to a method and system for managing geothermal monitoring data in abandoned mines based on a time-series database. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Geothermal resources, as an important type of clean energy, play a significant role in promoting the optimization and transformation of the energy structure through large-scale development and utilization. During the exploration and development of geothermal resources, various monitoring devices are deployed to continuously observe key parameters such as well water level, temperature, and flow rate, forming geothermal monitoring datasets with typical temporal characteristics. These data exhibit significant characteristics such as strong temporal sequence, massive data volume, and multi-source heterogeneity, including both numerical monitoring data collected at fixed frequencies and multi-dimensional information such as spatial location information and equipment attribute data. The development and utilization of geothermal resources in abandoned mines faces many unique technical challenges: mine water levels fluctuate dramatically, influenced by various geological and human factors, making accurate prediction of water inflow difficult; mine temperatures tend to decrease after mining activities cease, and the rate of change needs precise monitoring to assess resource potential; complex hydraulic connections exist between multiple aquifers, with significant mutual influence, increasing the difficulty of dynamic system analysis; and the vast scale of historical monitoring data accumulated over a long period is difficult for traditional databases to handle in terms of storage and query efficiency.

[0004] Currently, the geothermal monitoring field generally uses traditional relational databases for data storage and management, which presents many technical bottlenecks in practical applications: At the data storage level, the continuous and rapid influx of time-series data leads to a rapid expansion of database tables, severely affecting data insertion efficiency and query response speed; at the data analysis level, traditional databases lack dedicated time-series data processing functions, making it difficult to support intelligent analysis needs such as multi-time granularity aggregation analysis, trend feature extraction, and anomaly pattern recognition; at the data management level, there is a lack of effective data lifecycle management mechanisms, and maintenance operations such as historical data cleaning and archiving heavily rely on manual intervention, resulting in high management costs; at the system expansion level, the existing architecture is difficult to adapt to the needs of large-scale geothermal monitoring scenarios with high-frequency acquisition and multi-point monitoring. Summary of the Invention

[0005] To overcome the shortcomings of the existing technologies, this invention proposes a method and system for managing geothermal monitoring data in abandoned mines based on a time-series database, which enables efficient storage, rapid querying, and intelligent analysis of time-series data.

[0006] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: In a first aspect, the present invention discloses a method for managing geothermal monitoring data of abandoned mines based on a time-series database, comprising: Acquire geothermal monitoring data from abandoned mines and construct a time-series database architecture with a multi-level relational table structure; Enable time series database extension and optimize its configuration, convert the time series data tables in the time series database into supertables, and set block partitioning strategy, index optimization and data compression strategy; The monitoring data is imported into a time-series database and the quality is controlled in real time. Intelligent analysis is performed based on the built-in functions of the time series database to achieve multi-time granularity aggregation analysis, trend analysis and anomaly detection, to build a multi-dimensional intelligent early warning mechanism, and to push real-time early warning messages through triggers; Save the analysis results to achieve data lifecycle management.

[0007] Secondly, this invention discloses a geothermal monitoring data management system for abandoned mines based on a time-series database, comprising: The data acquisition module is used to acquire geothermal monitoring data from abandoned mines and construct a time-series database architecture with a multi-level relational table structure. The data conversion module is used to enable time series database extension and optimize configuration, convert time series data tables in the time series database into super tables, and set block partitioning strategy, index optimization and data compression strategy. The data import module is used to import the monitoring data into a time-series database and control the quality in real time. The analysis and early warning module is used to perform intelligent analysis based on the built-in functions of the time series database, realize multi-time granularity aggregation analysis, trend analysis and anomaly detection, build a multi-dimensional intelligent early warning mechanism, and push real-time early warning messages through triggers. The results saving module is used to save analysis results and realize data lifecycle management.

[0008] Thirdly, the present invention discloses an electronic device, including a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, complete the steps of the above-mentioned method for managing geothermal monitoring data of abandoned mines based on a time-series database.

[0009] Fourthly, the present invention discloses a computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the steps of the above-described method for managing geothermal monitoring data of abandoned mines based on a time-series database.

[0010] Compared with the prior art, the beneficial effects of the present invention are as follows: To address the shortcomings of existing technologies in managing geothermal monitoring data from abandoned mines, such as low efficiency in time-series data storage, slow query response, lack of dedicated analysis functions, and reliance on manual intervention for data lifecycle management, this invention effectively combines TimescaleDB time-series database technology, a multi-dimensional intelligent early warning mechanism, and automated data quality control technology. It can efficiently store and quickly retrieve massive amounts of time-series data, achieving a real-time response with a delay of less than 3 seconds from data acquisition to early warning triggering.

[0011] This invention allows for flexible configuration of early warning thresholds and rules, automatic data compression and lifecycle management, batch processing of multi-source heterogeneous monitoring data, and more convenient inter-well correlation analysis and anomaly pattern identification, thereby reducing safety risks in the development and utilization of geothermal resources in abandoned mines.

[0012] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0013] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0014] Figure 1 This is a diagram of the overall architecture of the time-series database described in Embodiment 1 of the present invention.

[0015] Figure 2 This is a diagram showing the four-level relational data structure of the time-series database described in Embodiment 1 of the present invention.

[0016] Figure 3 This is a flowchart of the multi-source monitoring data import and quality control process described in Embodiment 1 of the present invention.

[0017] Figure 4 This refers to the temperature-water level joint anomaly early warning decision tree described in Embodiment 1 of the present invention.

[0018] Figure 5 This is a schematic diagram of the continuous anomaly pattern recognition algorithm described in Embodiment 1 of the present invention.

[0019] Figure 6 This is a flowchart of the intelligent analysis and processing of time-series data as described in Embodiment 1 of the present invention.

[0020] Figure 7 This is a graph showing the change in water level and temperature over time as described in Embodiment 1 of the present invention. Detailed Implementation

[0021] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0022] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0023] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0024] Example 1 In one or more embodiments, a method for managing geothermal monitoring data from abandoned mines based on a time-series database is disclosed, such as... Figure 1 As shown, it includes the following steps: Step S1: Obtain geothermal monitoring data from abandoned mines; construct a time-series database architecture based on the business logic of geothermal monitoring in abandoned mines; and design and create a multi-level relational table structure. Among them, multi-level related table structures, such as Figure 2 As shown, the table includes: a borehole table for storing basic mine information, including fields such as borehole ID, name, coordinates, depth, and drilling time; a well table for storing monitoring well information, including fields such as well ID, associated borehole ID, depth, diameter, and drilling process; a monitoring point table for storing sensor deployment information, including fields such as monitoring point ID, associated well ID, monitoring parameter type, burial depth, sensor model, and sampling frequency; and a time-series data table for storing monitoring time-series data, using timestamps as the primary key, and including fields such as monitoring point ID, parameter value, and data quality identifier.

[0025] Step S2: Enable time-series database extension and optimize its configuration, convert the time-series data table into a supertable, and set the partitioning strategy, index optimization, and data compression strategy.

[0026] Specifically, in relational databases, enable time-series database extensions to convert time-series data tables into hypertables and set up automatic block partitioning by time intervals; create composite indexes to optimize query performance by monitoring points and time ranges; enable automatic data compression, set compression strategies, including segmented compression by monitoring points and reverse time compression; and set data retention strategies to achieve automated management of the entire data lifecycle.

[0027] As one implementation method, the relational database can be PostgreSQL, or other time-series database systems (such as InfluxDB, Prometheus, etc.) can be used to implement the core storage and processing logic of this invention. Only the configuration of data table partitioning, indexing and compression functions need to be adjusted accordingly; the time-series database extension can use TimescaleDB.

[0028] Step S3 involves importing multi-source monitoring data into a time-series database and controlling its quality in real time. It supports batch import and real-time streaming writing. During the data import process, triggers are used to perform automatic quality checks and clean up abnormal data.

[0029] Specifically, two methods are used: batch file import (such as CSV format) and real-time API streaming. During the data import process, triggers are used to implement automatic quality checks, including verification of the reasonableness of numerical ranges, verification of the validity of timestamps, and detection of duplicate data. Abnormal data is automatically marked and cleaned to ensure the reliability of the imported data.

[0030] Furthermore, in addition to supporting batch CSV file import, it can also be extended to stream data writing based on API interface to adapt to monitoring scenarios with higher real-time requirements.

[0031] Step S4: Perform intelligent analysis based on the built-in functions of the time series database to achieve multi-time granularity aggregation analysis, trend analysis and anomaly detection, build a multi-dimensional intelligent early warning mechanism, and push real-time early warning messages through triggers.

[0032] Specifically, by using time-series database-specific functions combined with an adaptive dynamic threshold anomaly detection algorithm, we can achieve multi-time granularity aggregation analysis, moving window analysis (including rate of change calculation, moving average, cumulative statistics and ranking), trend analysis based on exponential weighted moving average (EWMA), automatic anomaly detection, and inter-well time-series correlation analysis to identify the hydraulic connections between aquifers. Specifically, the multi-time-granularity aggregation analysis adopts a hierarchical time bucket mechanism, supporting data aggregation at multiple time granularities such as minutes, hours, days, weeks, and months. Each aggregation level calculates statistical indicators such as mean, maximum, minimum, and standard deviation.

[0033] The automatic anomaly detection employs a two-layer hybrid detection strategy: the first layer uses the Z-Score statistical method for rapid screening, determining the anomaly by calculating the deviation of the monitored value from the mean within an adaptive sliding window. The specific formula is as follows: Z=(x-μ w ) / σ w Where Z is the standardized anomaly score, representing the degree to which the current monitored value deviates from the normal range, x is the current monitored value, and μ w and σw These are the mean and standard deviation of the data within the sliding window, respectively. The window size is adaptively adjusted according to the sampling frequency (a larger window is used for high-frequency sampling to smooth noise, and a smaller window is used for low-frequency sampling to maintain sensitivity). When |Z| exceeds the preset threshold, it is marked as a suspicious outlier.

[0034] The second layer employs an LSTM autoencoder-based temporal reconstruction error method to confirm suspicious anomalies. An LSTM autoencoder network consisting of an encoder and a decoder is constructed. The encoder compresses the input temporal window into a low-dimensional feature vector, and the decoder reconstructs the feature vector into a temporal sequence. The network is trained using historical data from normal operation. During online detection, the mean square error (MSE) between the actual monitored sequence and the reconstructed sequence is calculated. When the reconstruction error exceeds a dynamic threshold θ... recon The final determination is an anomaly, and the threshold is determined based on the 99th percentile of the reconstruction error distribution of the training set.

[0035] This two-layer strategy balances detection efficiency and accuracy. The first layer ensures real-time response capability, while the second layer effectively reduces the false alarm rate.

[0036] Trend analysis uses the EWMA method, and the formula for calculating the predicted value is: S t =α×x t +(1-α)×S t-1 Among them, S t S is the trend prediction value at time t. t-1 x is the trend prediction value from the previous moment. t α is the actual monitored value at time t, where α is a smoothing factor that is automatically adjusted based on the volatility of historical data.

[0037] A multi-dimensional early warning mechanism is built based on time-series data, including: temperature drop rate warning, which calculates the temperature change rate in real time and sets a threshold; water level change warning; temperature-water level joint anomaly warning, which uses a multi-parameter joint judgment method based on decision trees to identify multiple composite anomaly patterns; continuous anomaly pattern identification warning; an automatic warning level escalation mechanism, where three warnings accumulated at the same location within 24 hours are automatically escalated to a severe level; and automatic recording of warning events through database triggers, which are then pushed to management personnel in real time via message notification interfaces. Warning information is also synchronously stored in the warning log table, supporting subsequent traceability and statistical analysis.

[0038] Among them, the water level sudden change early warning adopts a method based on a combination of change rate and statistical threshold to identify short-term drastic changes in water level and issue graded alarms. Specifically, it calculates the water level change rate ΔH / Δt between adjacent sampling points, and at the same time calculates the standard deviation of the change rate σ of historical data. rate The criterion for determining short-term drastic changes is: when |ΔH / Δt|>k×σrate An anomaly is determined by the time specified, where k is the sensitivity coefficient; the grading mechanism adopts a three-level early warning system: Level 1 early warning (advance warning level): the rate of change exceeds 2 times the standard deviation (2σ). rate A minor anomaly is indicated by a log entry; Level 2 warning (alert level): the rate of change exceeds 3 times the standard deviation (3σ). rate If the water level change exceeds 0.5 meters in a single instance, a notification will be sent within the system; Level 3 warning (severe): The rate of change exceeds 4 times the standard deviation (4σ). rate If the water level changes by more than 1 meter in a single instance, the information should be immediately sent to the management personnel and an emergency response should be triggered.

[0039] A multi-parameter joint decision-making method based on decision trees is used to identify various composite anomaly patterns, specifically: within the same time window T w Inside, the rate of temperature change and the rate of water level change are calculated separately. When the rate of temperature decrease exceeds the threshold θ, temp (Default setting is 0.5℃ / day) when an abnormal temperature drop is detected, and when the rate of water level change exceeds the threshold θ. level (Default setting is 2m / hour) when it is determined to be an abnormal change in water level; according to the combination of temperature and water level status, the composite abnormal mode includes but is not limited to the following situations: (1) when the temperature drops abnormally and the water level rises abnormally, it is determined to be a serious level, indicating the risk of cold water intrusion or aquifer penetration. The response measures include immediately stopping pumping, sealing the environment, activating the emergency plan, and notifying experts; (2) when the temperature drops abnormally and the water level is normal or drops, it is determined to be a warning level, indicating the risk of heat source attenuation. The response measures include increasing the monitoring frequency, analyzing the cause of the temperature drop, and checking the equipment operation; (3) when the temperature is normal and the water level rises abnormally, it is determined to be a warning level, indicating the risk of abnormal water level fluctuation. The response measures include checking the pump operation status, detecting water quality changes, and checking the surrounding geological conditions; (4) when the temperature is normal and the water level is normal or drops, it is determined to be a normal state, the detection parameters are normal, and routine monitoring continues.

[0040] The continuous anomaly pattern recognition adopts a time-series grouping detection algorithm based on window functions: First, the monitoring data within the last 24 hours are filtered and abnormal data points are marked; then, the monitoring points are grouped by window function and sorted by timestamp, and the continuous anomaly group identifier is calculated using the row number difference; next, data groups with more than a set threshold of continuous anomalies are filtered out, and the monitoring point ID, anomaly start time, end time and number of continuous anomalies are output; when the number of continuous anomalies exceeds the threshold, an early warning is triggered, thereby effectively reducing the false alarm rate caused by single-point noise.

[0041] Inter-well temporal correlation analysis was performed using the Pearson correlation coefficient calculation method within a sliding time window to identify the degree of hydraulic connection between different monitoring wells. When the correlation coefficient was greater than 0.7, it was determined that there was a significant hydraulic connection. At the same time, cross-correlation analysis was used to determine the response lag time between different wells, which was used to analyze the direction and velocity of groundwater flow.

[0042] Furthermore, the multi-parameter joint anomaly determination method is scalable, supporting the dynamic addition of new anomaly pattern types, adjustment of determination threshold parameters, and the introduction of more monitoring parameters (such as flow rate, water quality, etc.) to participate in joint determination based on the continuous accumulation of actual monitoring data and a deeper understanding of geological laws, thus forming a more complete multi-dimensional anomaly identification system.

[0043] Step S5: Save the intelligent analysis results, optimize performance and manage the data lifecycle, create materialized views to accelerate queries, configure automatic refresh and backup strategies, provide standardized interfaces to support multi-terminal visualization, and save the analysis results.

[0044] Specifically, it creates materialized views to accelerate frequently used queries; configures automatic refresh strategies and scheduled backup tasks to ensure long-term, efficient, and stable system operation; provides standardized RESTful API interfaces for visualization systems to call; supports dashboard integration (such as Grafana) to display time-series curves and alert dashboards in real time; and supports GeoJSON format export to integrate spatial location information for multi-dimensional visualization.

[0045] The method of the present invention will be further illustrated below using geothermal monitoring data from an abandoned mine as an example.

[0046] Step 1: Connect to the PostgreSQL server using pgAdmin4, create a database named `geothermal_monitoring`, and then create the tables `BOREHOLE`, `WELL`, `MONITORING_POINT`, and `TIME_SERIES_DATA` to construct a four-level time-series database structure. The `BOREHOLE` table contains information on three abandoned mine boreholes, and the `WELL` table contains basic information on three monitoring wells. Figure 2 The diagram shown is a four-level relational data structure diagram of a time-series database. Step 2: Enable TimescaleDB extension, convert the TIME_SERIES_DATA table to a hypertable, set the block interval to 7 days, create a composite index (monitoring_point_id, timestamp DESC), enable the automatic compression strategy, set the compression delay to 30 days, and configure the data retention strategy to automatically clean up expired data every 365 days. Step 3: Import 31 days of water level and temperature monitoring data from three monitoring wells in the abandoned mine, totaling 93 records. During the import process, automatic quality checks are performed, including water level range verification, temperature range verification, timestamp validity verification, and duplicate data detection. Abnormal data exceeding the threshold is automatically marked and recorded in the cleaning log, such as... Figure 3 The diagram shown is a flowchart of multi-source monitoring data import and quality control. Step 4, Execute Intelligent Analysis and Early Warning: Use the `time_bucket` function to aggregate and analyze water level change trends daily; employ a dual-layer hybrid detection strategy to automatically detect abnormal data points. The first layer uses the Z-Score method (with a threshold of 2 standard deviations) to quickly screen suspicious data, while the second layer uses an LSTM autoencoder to calculate reconstruction errors and confirm anomalies; use the CORR function to calculate the correlation coefficient of water level changes between different wells, which is 0.87, identifying a strong hydraulic connection between two wells; trigger joint anomaly early warnings using a decision tree method based on temperature and water level time-series data. Multi-mode identification is performed based on the combined state of temperature and water level change rates, identifying a composite anomaly event where the daily average temperature decreases by 1℃ (exceeding the threshold of 0.5℃ / day) and the water level change rate exceeds 2m / hour, conforming to the "abnormal temperature decrease and abnormal water level increase" pattern. This is determined to be a severity level by the decision tree, automatically generating a high-level early warning and pushing it to management personnel via a message interface. Figure 4 As shown, this is a decision tree for joint temperature-water level anomaly early warning. Figure 5 The diagram shown is a schematic of a continuous anomaly pattern recognition algorithm. Figure 6 The diagram shown is a flowchart of intelligent analysis and processing of time series data. Step 5: Create a materialized view named `daily_statistics` to save the daily statistical analysis results, configure an hourly automatic refresh strategy, and set up a scheduled backup task every Sunday at 2 AM; connect to the Grafana dashboard via a RESTful API interface to display the water level and temperature time-series curves and early warning status dashboards of the three monitoring wells in real time, such as... Figure 7 The graph shows the water level-temperature change over time, and supports exporting monitoring well spatial distribution maps in GeoJSON format.

[0047] Compared with existing technologies, the method for managing and providing early warning of geothermal monitoring data in abandoned mines based on time-series databases provided by this invention is accurate, efficient, flexible, simple, and low-cost.

[0048] Example 2 In one or more embodiments, a geothermal monitoring data management system for abandoned mines based on a time-series database is disclosed, specifically including: The data acquisition module is used to acquire geothermal monitoring data from abandoned mines and construct a time-series database architecture with a multi-level relational table structure. The data conversion module is used to enable time series database extension and optimize configuration, convert time series data tables in the time series database into super tables, and set block partitioning strategy, index optimization and data compression strategy. The data import module is used to import the monitoring data into a time-series database and control the quality in real time. The analysis and early warning module is used to perform intelligent analysis based on the built-in functions of the time series database, realize multi-time granularity aggregation analysis, trend analysis and anomaly detection, build a multi-dimensional intelligent early warning mechanism, and push real-time early warning messages through triggers. The results saving module is used to save analysis results and realize data lifecycle management.

[0049] Example 3 This embodiment provides an electronic device, including a memory and a processor, as well as computer instructions stored in the memory and running on the processor. When the computer instructions are executed by the processor, they complete the steps of the above-described method for managing geothermal monitoring data of abandoned mines based on a time-series database.

[0050] Example 4 This embodiment provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the steps of the above-described method for managing geothermal monitoring data in abandoned mines based on a time-series database.

[0051] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0052] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0053] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment, whereby a series of operational steps are performed to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0054] The descriptions of each embodiment in the above embodiments have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0055] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for managing geothermal monitoring data in abandoned mines based on time-series databases, characterized in that, include: Acquire geothermal monitoring data from abandoned mines and construct a time-series database architecture with a multi-level relational table structure; Enable time series database extension and optimize its configuration, convert the time series data tables in the time series database into supertables, and set block partitioning strategy, index optimization and data compression strategy; The monitoring data is imported into a time-series database and the quality is controlled in real time. Intelligent analysis is performed based on the built-in functions of the time series database to realize multi-time granularity aggregation analysis, trend analysis, anomaly detection, and inter-well time series correlation analysis. A multi-dimensional intelligent early warning mechanism is constructed, and real-time early warning messages are pushed through triggers. Save the analysis results to achieve data lifecycle management.

2. The method for managing geothermal monitoring data of abandoned mines based on a time-series database as described in claim 1, characterized in that, The multi-level association table structure includes: a borehole table for storing basic mine information, including borehole ID, name, coordinates, borehole depth, and drilling time; a well table for storing monitoring well information, including well ID, associated borehole ID, well depth, well diameter, and drilling process; a monitoring point table for storing sensor deployment information, including monitoring point ID, associated well ID, monitoring parameter type, burial depth, sensor model, and sampling frequency; and a time-series data table for storing monitoring time-series data, using timestamps as the primary key, and including monitoring point ID, parameter values, and data quality identifiers.

3. The method for managing geothermal monitoring data of abandoned mines based on a time-series database as described in claim 1, characterized in that, The step of enabling time-series database extension and optimizing configuration involves converting the time-series data tables in the time-series database into supertables and setting block partitioning, index optimization, and data compression strategies, specifically: Enable time-series database extensions in relational databases to convert time-series data tables into supertables and set up automatic block partitioning by time intervals; create composite indexes to optimize query performance by monitoring points and time ranges; enable automatic data compression, set compression strategies, and perform segmented compression by monitoring points and time-reverse compression; set data retention strategies to achieve automated management of the entire data lifecycle.

4. The method for managing geothermal monitoring data of abandoned mines based on a time-series database as described in claim 1, characterized in that, Importing the monitoring data into a time-series database and controlling its quality in real time includes: using two methods, batch file import and real-time API streaming; implementing automatic quality checks during the data import process through triggers, including verification of the reasonableness of numerical ranges, verification of the validity of timestamps, and detection of duplicate data; and automatically marking and cleaning abnormal data.

5. The method for managing geothermal monitoring data of abandoned mines based on a time-series database as described in claim 1, characterized in that, A multi-dimensional intelligent early warning mechanism is constructed based on time-series data, including: temperature drop rate warning, which calculates the temperature change rate in real time and sets a threshold; water level change warning, which monitors short-term drastic changes in water level and issues graded alarms; temperature-water level joint anomaly warning, which uses a multi-parameter joint judgment method based on decision trees to identify multiple composite anomaly patterns based on the combination of temperature change and water level change states and issues graded warnings. The composite anomaly patterns include: abnormal temperature drop and abnormal water level rise, which are judged as severe level; abnormal temperature drop and normal water level, which are judged as warning level; normal temperature and abnormal water level rise, which are judged as warning level; continuous anomaly pattern recognition warning, which detects multiple consecutive sampling anomalies based on window functions to reduce the false alarm rate; and an automatic warning level escalation mechanism, which automatically escalates the warning level to severe level when the same location receives 3 warnings within 24 hours.

6. The method for managing geothermal monitoring data of abandoned mines based on a time-series database as described in claim 1, characterized in that, The multi-time granularity aggregation analysis, trend analysis, and anomaly detection are as follows: The multi-time granularity aggregation analysis adopts a hierarchical time bucket mechanism, which supports data aggregation at multiple time granularities such as minutes, hours, days, weeks, and months. Each aggregation level calculates the mean, maximum value, minimum value, and standard deviation statistical indicators. Trend analysis employs an exponentially weighted moving average algorithm, with the smoothing factor adaptively adjusted based on data volatility. Anomaly detection utilizes a two-layer hybrid detection strategy. The first layer uses the Z-Score statistical method for rapid screening to obtain standardized anomaly scores, and when the standardized anomaly score exceeds a preset threshold, it is marked as a suspicious anomaly. The second layer uses a temporal reconstruction error method based on an LSTM autoencoder to confirm the suspicious anomaly, and when the reconstruction error exceeds a dynamic threshold, it is finally determined to be an anomaly.

7. The method for managing geothermal monitoring data of abandoned mines based on a time-series database as described in claim 1, characterized in that, The inter-well temporal correlation analysis uses the Pearson correlation coefficient calculation method within a sliding time window to identify the degree of hydraulic connection between different monitoring wells. When the correlation coefficient is greater than 0.7, it is determined that there is a significant hydraulic connection. At the same time, the cross-correlation analysis method is used to determine the response lag time between different wells, which is used to analyze the direction and velocity of groundwater flow.

8. A time-series database-based geothermal monitoring data management system for abandoned mines, characterized in that, include: The data acquisition module is used to acquire geothermal monitoring data from abandoned mines and construct a time-series database architecture with a multi-level relational table structure. The data conversion module is used to enable time series database extension and optimize configuration, convert time series data tables in the time series database into super tables, and set block partitioning strategy, index optimization and data compression strategy. The data import module is used to import the monitoring data into a time-series database and control the quality in real time. The analysis and early warning module is used to perform intelligent analysis based on the built-in functions of the time series database, realize multi-time granularity aggregation analysis, trend analysis and anomaly detection, build a multi-dimensional intelligent early warning mechanism, and push real-time early warning messages through triggers. The results saving module is used to save analysis results and realize data lifecycle management.

9. An electronic device, characterized in that, The method includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor. When the processor executes the computer instructions, it performs the abandoned mine geothermal monitoring data management method based on a time-series database as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, complete the method for managing geothermal monitoring data of abandoned mines based on a time-series database as described in any one of claims 1-7.

Images