A mine gushing water source identification and early warning system

Abstract

The embodiment of the application discloses a mine gushing water source identification and early warning system, relates to the technical field of mine safety production, and is convenient for realizing closed-loop management from risk discovery to risk elimination. The system comprises: a water source identification module, which is used for identifying the water source type of the water sample to be identified based on water quality index data; an early warning generation module, which is used for matching preset early warning rules according to the identification result and generating hierarchical early warning information; an information pushing module, which is used for pushing the hierarchical early warning information to the terminal equipment of the corresponding person in charge according to the early warning level and a preset pushing strategy; a disposal closed-loop module, which is used for storing the disposal record in association with the corresponding identification result and early warning information to form a complete closed loop from identification to disposal; and a platform interfacing module, which is used for pushing at least one kind of data in the stored identification result, early warning information and disposal record to an external platform to realize data sharing and business cooperation. The application is suitable for the scene of mine gushing water source identification and early warning.

Description

Technical Field

[0001] This application relates to the field of mine safety production technology, and in particular to a mine water inrush source identification and early warning system. Background Technology

[0002] With the increasing depth of mining and the growing risk of water inrush, the rapid and accurate identification of mine water sources is crucial for the prevention and control of water inrush disasters. Existing mine water source identification systems typically collect water chemical indicators from mine water, input the data into a water quality database or table, and then use a fixed algorithm such as Bayesian discrimination, support vector machine, or random forest, or rely on manual experience to select a model for water source identification. Furthermore, the system's functions mainly focus on data storage and displaying the results of a single model.

[0003] However, existing systems typically stop at outputting water source identification results or generating simple reports. Identification results are disconnected from early warning, information push, closed-loop response, and external platform integration. Specifically, existing solutions cannot automatically trigger tiered early warnings based on identification results, cannot proactively push early warning information to relevant responsible persons, lack response feedback and tracking mechanisms, and are difficult to integrate with existing mine data governance platforms, integrated systems, and other external platforms for data exchange and business collaboration. As a result, the system cannot form a closed-loop management system from risk discovery to risk elimination, and it is difficult to meet the urgent needs of mine water inrush risk prevention and control for response timeliness and multi-department collaborative management. Summary of the Invention

[0004] In view of this, embodiments of this application provide a mine water inrush source identification and early warning system, which facilitates the improvement of response timeliness in mine water inrush risk prevention and control.

[0005] In a first aspect, embodiments of this application provide a mine water inrush source identification and early warning system, comprising: a data acquisition module for acquiring water quality index data of a water sample to be identified; a water source identification module for identifying the water source type of the water sample based on the water quality index data; an early warning generation module for generating graded early warning information by matching preset early warning rules with the identification results of the water source identification module; an information push module for pushing the graded early warning information to the terminal device of the corresponding responsible person according to the early warning level and a preset push strategy; a closed-loop handling module for receiving handling records fed back by the responsible person and storing the handling records in association with the corresponding identification results and early warning information to form a complete closed loop from identification to handling; and a platform docking module for pushing at least one of the stored identification results, early warning information, and handling records to an external platform to achieve data sharing and business collaboration.

[0006] According to a specific implementation of an embodiment of this application, the system further includes: a data verification and preprocessing module, used to perform integrity checks, anion and cation balance tests, missing value processing, outlier processing, and standardization processing on the water quality index data acquired by the data acquisition module; a data review module, used to perform a three-level review process on the data processed by the data verification and preprocessing module, including submission by the data entry clerk, review by the reviewer, and confirmation by the approver; and a water quality database module, including a typical water quality database storing samples with labeled water source types and a daily water quality database storing all tested water samples, used to record metadata.

[0007] According to a specific implementation of an embodiment of this application, the water source identification module includes: a data screening and algorithm selection unit, used to perform feature evaluation on the historical data of the mine where the water sample to be identified is located, and calculate the suitability scores of multiple discrimination algorithms based on the feature evaluation results, and select the discrimination algorithm with the highest suitability score, wherein the feature evaluation includes at least one evaluation method among sample quantity evaluation, linear separability evaluation, data distribution evaluation, category balance evaluation, and feature dimension evaluation; and an identification modeling and reasoning unit, which has multiple discrimination algorithms built in, used to call the discrimination model corresponding to the selected discrimination algorithm with the highest suitability score to reason about the water sample to be identified, and output the water source type and confidence level.

[0008] According to a specific implementation of an embodiment of this application, the multiple discrimination algorithms include at least two of the following: the improved Fisher discrimination method, random forest, support vector machine, and Bayesian discrimination method. When the difference in the fitness scores of multiple discrimination algorithms is less than a preset threshold, the data screening and algorithm selection unit enables a multi-model voting or integration strategy to output the final identification result.

[0009] According to a specific implementation of an embodiment of this application, the early warning generation module includes: a rule engine unit, used to configure at least one early warning rule among a high-risk threshold for water source type, a threshold for abnormal changes in water quality, and a threshold for the proportion of mixed water sources; and a graded early warning unit, used to receive the identification result output by the water source identification module, match the identification result with the early warning rule configured by the rule engine unit, and generate at least one early warning level among red early warning, orange early warning, and yellow early warning based on the degree of matching.

[0010] According to a specific implementation of an embodiment of this application, the information push module includes: a push strategy configuration unit, used to configure the push targets and push timing corresponding to different warning levels generated by the warning generation module; a push content generation unit, used to receive the graded warning information generated by the warning generation module, and generate push content based on the graded warning information, wherein the push content includes at least one of water source type, confidence level, warning level, sampling information, and disposal suggestions; and a multi-channel push unit, used to send the push content generated by the push content generation unit to the terminal device of the corresponding responsible person through at least one of the push methods of in-site message, SMS, email, WeChat Work, and DingTalk, according to the push targets and push timing configured by the push strategy configuration unit.

[0011] According to a specific implementation of an embodiment of this application, the closed-loop disposal module includes: a closed-loop status management unit, used to create a corresponding disposal task and initialize the disposal status to pending disposal based on the identification result output by the water source identification module and the graded early warning information generated by the early warning generation module after the information push module completes the early warning information push; a disposal record receiving unit, used to receive at least one disposal record submitted by the responsible person after the responsible person completes the on-site disposal, including disposal measures, disposal results, responsible person information, and disposal time, and transmit the disposal record to the associated storage unit; and an associated storage unit, used to associate the disposal record with the corresponding identification result and early warning information and store them in a database, and notify the closed-loop status management unit to update the disposal status.

[0012] According to a specific implementation of an embodiment of this application, the platform docking module includes: a data governance platform docking unit, used to push the identification results, early warning information and disposal records to an external data governance platform in JSON format or a standardized data interface after the disposal closed-loop module completes the storage of disposal records, so as to realize the data asset output of the identification results and carry traceability information; and a comprehensive integration system docking unit, used to dock with an external comprehensive integration system after the data governance platform docking unit completes the data push, so as to realize at least one integration function among single sign-on integration, to-do task integration, message center docking and application registration, so that early warning information and disposal tasks can flow synchronously in the external comprehensive integration system.

[0013] According to a specific implementation of an embodiment of this application, the system further includes: a result display and reporting module, used to obtain the identification result and generate a detection report or analysis report after the water source identification module completes the water source type identification, and to synchronously display the graded warning information in the detection report or analysis report after the warning generation module generates graded warning information, wherein the identification result includes water source type, confidence level or probability distribution, selected discrimination algorithm and discrimination algorithm selection basis; and an operation and maintenance and version management module, used to perform at least one operation and maintenance management operation on the discrimination model, including version management, model rollback, performance monitoring, alarm threshold setting, and data backup, and to provide the managed available model version for the identification modeling and inference unit to call during the inference stage.

[0014] According to a specific implementation of an embodiment of this application, the identification modeling and inference unit further includes a model training and evaluation subunit. The model training and evaluation subunit is used to: divide a training set, a validation set, and a test set from a typical water quality database of the water quality database module; perform hyperparameter tuning on the various discrimination algorithms based on the training set, and evaluate the performance of the tuned model using cross-validation; perform a final evaluation on the test set using at least one evaluation metric selected from accuracy, precision, recall, F1 score, and confusion matrix; and when the evaluation metric meets a preset engineering metric threshold, release the corresponding model as a usable version for the identification modeling and inference unit to use during the inference phase.

[0015] The mine water inrush source identification and early warning system provided in this application first acquires water quality index data of the water sample to be identified through a data acquisition module, providing a standardized and traceable data source for the entire system and ensuring the basic reliability of subsequent analysis. Second, the water source identification module automatically identifies the water source type based on the water quality index data, enabling rapid and objective output of identification results, avoiding the subjectivity and lag of manual experience-based judgment. Based on this, the early warning generation module matches the identification results according to preset rules and automatically generates graded early warning information, achieving accurate risk classification and rapid response, transforming passive post-event handling into proactive, immediate early warning. Subsequently, the information push module pushes information to the corresponding responsible persons according to the early warning level and preset strategies, ensuring that key information accurately and timely reaches the decision-making and execution levels. Finally, the closed-loop handling module receives and associates the handling records of the responsible persons, forming a closed loop between the identification results and actual actions, effectively avoiding information gaps and lack of accountability. Finally, the platform integration module pushes various stored data to external platforms, breaking down information silos and enabling cross-system data sharing and business collaboration. This provides data support and business linkage capabilities for the digital and intelligent integrated management and control of mines. Overall, this application's embodiment realizes a fully intelligent chain from water source identification, tiered early warning, precise push notifications to handling feedback and platform collaboration, achieving closed-loop management from risk discovery to risk elimination. This improves the efficiency, response speed, and collaborative capabilities of mine water inrush risk prevention and control, providing reliable technical means to ensure safe mine production. Attached Figure Description

[0016] 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 some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A schematic diagram of the structure of a mine water inrush source identification and early warning system provided in an embodiment of this application;

[0018] Figure 2 A detailed structural diagram of the mine water inrush source identification and early warning system provided in this application embodiment;

[0019] Figure 3 This is a schematic diagram of the water source identification module provided in an embodiment of this application;

[0020] Figure 4 This is a schematic diagram of the structure of the information push module provided in the embodiments of this application;

[0021] Figure 5This is a schematic diagram of the processing closed-loop module provided in an embodiment of this application. Detailed Implementation

[0022] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0023] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0024] Current mine water source identification technologies primarily rely on the detection of water chemical indicators, including macro-ions, general indicators, trace components, and specific ions, supplemented by cation and anion balance for quality control. For water source identification modeling, algorithms such as Fisher's linear discriminant analysis, Bayesian discriminant analysis, support vector machines, or random forests are commonly used. However, in practical applications, a single model is often consistently chosen or selection is based on human experience. While browser / server architectures have been implemented at the information system level... The system includes a B / S architecture, database management, report generation, message push, and basic interface with external platforms. However, it still has significant shortcomings. Specifically, due to the large differences in sample size, category balance, data distribution, and feature complexity among different mines, fixed algorithms or manual model selection can easily lead to model mismatch, making it difficult to guarantee the accuracy and generalization ability of the identification. At the same time, the lack of systematic data quality control, standardized collection templates, and automated review mechanisms means that data noise, missing data, and anomalies directly affect the stability and traceability of the identification results. More importantly, the identification results are disconnected from the early warning, information push, and disposal closed loop, as well as the interface with external platforms. It is impossible to achieve a complete business closed loop from water source identification to rapid decision-making, responsibility assignment, and disposal feedback, which makes it difficult to meet the urgent needs of mine water inrush risk prevention and control for response timeliness and multi-department collaborative management.

[0025] Based on this, the embodiments of this application construct a mine water inrush source identification and early warning system to achieve automated identification and rapid response, improved multi-algorithm adaptation and small sample generalization ability, and support for mixed water source risk warning. Furthermore, through hierarchical early warning and multi-channel push, the system significantly improves the efficiency of risk disposal, forming a closed-loop management capability from water source identification to risk elimination.

[0026] In a first aspect, embodiments of this application provide a mine water inrush source identification and early warning system, which facilitates improving the response timeliness of mine water inrush risk prevention and control.

[0027] like Figure 1As shown, an embodiment of this application provides a mine water inrush source identification and early warning system, comprising: a data acquisition module 1, used to acquire water quality index data of a water sample to be identified; a water source identification module 2, used to identify the water source type of the water sample to be identified based on the water quality index data; an early warning generation module 3, used to generate graded early warning information by matching preset early warning rules with the identification results of the water source identification module; an information push module 4, used to push the graded early warning information to the terminal device of the corresponding responsible person according to the early warning level and preset push strategy; a closed-loop handling module 5, used to receive handling records fed back by the responsible person, and associate and store the handling records with the corresponding identification results and early warning information to form a complete closed loop from identification to handling; and a platform docking module 6, used to push at least one of the stored identification results, early warning information, and handling records to an external platform to achieve data sharing and business collaboration.

[0028] In response to the prominent problems in existing technologies, such as delayed response, difficulty in assigning responsibility, information gaps, and low collaboration efficiency caused by the separation of identification results and handling procedures, embodiments of this application provide a mine water inrush source identification and early warning system. This system realizes intelligent and full-process control of mine water inrush risk by constructing a complete business closed loop from data collection to handling feedback.

[0029] Specifically, the system provided in the embodiments of this application includes six core modules: a data acquisition module 1, a water source identification module 2, an early warning generation module 3, an information push module 4, a closed-loop disposal module 5, and a platform docking module 6. The data acquisition module 1 serves as the system's data entry point, used to acquire water quality indicator data of the water sample to be identified. In some examples, the water quality indicator data includes macro-ions, general indicators, trace components, and special ions, with macro-ions being... , , , , , General indicators may include pH, TDS, total hardness, and conductivity; trace components include Fe, Mn, Sr, Ba, Li, and B; special ions include... , , These data provide a standardized and traceable raw data foundation for subsequent water source identification, ensuring the reliability and accuracy of the analysis results. Water source identification module 2 is used to automatically identify the water source type of the water sample based on water quality index data. In some cases, module 2 incorporates multiple discrimination algorithms and can adaptively select the optimal algorithm or enable a multi-model voting integration strategy based on the historical data characteristics of the mine where the water sample is located, such as sample size, linear separability, data distribution, category balance, and feature dimensions. It outputs the water source type and its confidence level. The water source type can be Taihu Lake water, Ordovician Lake water, old workings water, roof sandstone water, aquifer 4, or a mixed water source, effectively solving the problem of fixed algorithm mismatch under different mine data conditions and significantly improving the identification accuracy and generalization ability. The early warning generation module 3 is used to automatically generate graded early warning information by matching the identification results output by the water source identification module with preset early warning rules. In some examples, the early warning generation module 3 has a built-in rule engine that can be configured with various early warning rules such as high-risk thresholds for water source types, thresholds for abnormal changes in water quality, and thresholds for the proportion of mixed water sources. Based on the degree of matching between the identification results and the early warning rules, it generates early warning information of different levels, such as red, orange, and yellow, achieving accurate risk classification and immediate triggering, and providing a basis for decision-making for subsequent rapid response. The information push module 4 is used to proactively push graded early warning information to the terminal devices of the corresponding responsible persons according to the early warning level and preset push strategy, ensuring that key information can reach the decision-making and execution levels accurately, timely, and through multiple channels. The closed-loop handling module 5 receives handling records from responsible personnel and associates and stores these records with corresponding judgment results and early warning information to form a complete closed loop from judgment to handling. In some examples, the closed-loop handling module 5 can create handling tasks and initialize the handling status to pending handling after the information is pushed. It receives handling records such as handling measures, handling results, responsible personnel information, and handling time submitted by responsible personnel, associates the handling records with corresponding judgment results and early warning information, and stores them in the database. The status management unit tracks the handling status in real time, which includes four types: pending handling, handling in progress, handled, and archived. This ensures that every early warning is responded to, every handling is recorded, and every step is traceable, effectively avoiding information gaps and lack of responsibility. The platform integration module 6 pushes at least one of the stored judgment results, early warning information, and handling records to an external platform to achieve data sharing and business collaboration. This breaks down information silos, achieves integration with the mine's existing digital platform, and provides data support and business linkage capabilities for the mine's digital and intelligent integrated management and control.

[0030] The embodiments of this application do not simply piece together modules such as data collection, identification, early warning, push notification, disposal, and docking. Instead, they construct a complete feedback loop from mine water samples to identification results and back to disposal actions through standardized data flow interfaces and state-driven business logic, ensuring the coordinated operation of each module. Specifically, firstly, vertical data is interconnected between modules, from the data collection module to the water source identification module, and then to the early warning generation module. Data transmission does not rely on manual intervention but flows automatically through a pre-set data pipeline, ensuring end-to-end timeliness from raw water samples to decision information. Secondly, horizontal feedback linkage exists between modules. The disposal closed-loop module does not store records independently but transmits the disposal effect back to the data governance platform through the platform docking module, thereby triggering incremental model training or adaptive adjustment of early warning rules, forming a continuously optimized positive feedback mechanism. Thirdly, a state synchronization mechanism exists between modules. The information push module and the disposal closed-loop module are linked through shared disposal task status. After successful push, a disposal task is automatically generated and set to pending disposal. After confirmation by the responsible person, it is set to disposal in progress. The state machine-driven collaborative mechanism, which sets the status of handling records to pending review and marks them as handled upon successful review, ensures seamless temporal connection between early warning information and handling actions. Simultaneously, cross-system collaboration among modules allows the platform interface module to push the system's internal judgment results, early warning information, and handling records to the mine's existing data governance platform and integrated system via standardized data interfaces. This ensures the system is not an isolated information silo but an integral part of the mine's overall digital safety production system. Furthermore, single sign-on integration eliminates the need for repeated logins; task integration allows early warning handling tasks to appear directly in the responsible person's unified task list; and message center integration allows early warning information to be uniformly aggregated and pushed along with other mine safety alarm information. This four-layered collaborative design enables the system to achieve a functional leap from judgment result output to risk handling closed-loop management—something that cannot be achieved by any single module or simple module aggregation.

[0031] In summary, this application embodiment, through the organic synergy of the above six modules, constructs a fully intelligent management mechanism that covers the entire process from water source identification, adaptive algorithm judgment, hierarchical early warning, precise push, disposal feedback to platform collaboration. It realizes closed-loop linkage between judgment results and disposal actions, as well as data sharing and business collaboration between internal and external platforms, significantly improving the prevention and control efficiency, response speed, and collaborative management capabilities of mine water inrush risk, and providing reliable technical means to ensure safe mine production.

[0032] The mine water inrush source identification and early warning system provided in this application first acquires water quality index data of the water sample to be identified through a data acquisition module, providing a standardized and traceable data source for the entire system and ensuring the basic reliability of subsequent analysis. Second, the water source identification module automatically identifies the water source type based on the water quality index data, enabling rapid and objective output of identification results, avoiding the subjectivity and lag of manual experience-based judgment. Based on this, the early warning generation module matches the identification results according to preset rules and automatically generates graded early warning information, achieving accurate risk classification and rapid response, transforming passive post-event handling into proactive, immediate early warning. Subsequently, the information push module pushes information to the corresponding responsible persons according to the early warning level and preset strategies, ensuring that key information accurately and timely reaches the decision-making and execution levels. Finally, the closed-loop handling module receives and associates the handling records of the responsible persons, forming a closed loop between the identification results and actual actions, effectively avoiding information gaps and lack of accountability. Finally, the platform integration module pushes various stored data to external platforms, breaking down information silos and enabling cross-system data sharing and business collaboration. This provides data support and business linkage capabilities for the digital and intelligent integrated management and control of mines. Overall, this application's embodiment realizes a fully intelligent chain from water source identification, tiered early warning, precise push notifications to handling feedback and platform collaboration, achieving closed-loop management from risk discovery to risk elimination. This improves the efficiency, response speed, and collaborative capabilities of mine water inrush risk prevention and control, providing reliable technical means to ensure safe mine production.

[0033] In some embodiments, Figure 2 For a detailed structural diagram of the mine water inrush source identification and early warning system provided in this application embodiment, please refer to... Figure 2 The system further includes: a data verification and preprocessing module 7, used to perform integrity checks, anion and cation balance tests, missing value processing, outlier processing, and standardization processing on the water quality index data acquired by the data acquisition module; a data review module 8, used to perform a three-level review process on the data processed by the data verification and preprocessing module, including submission by the data entry clerk, review by the reviewer, and confirmation by the approver; and a water quality database module 9, including a typical water quality database storing samples with labeled water source types and a daily water quality database storing all tested water samples, used to record metadata.

[0034] To further improve the data quality and traceability of the system, the mine water inrush source identification and early warning system provided in this application also introduces a data quality control and management support module to ensure high reliability and standardization at every stage from the data source to the model application.

[0035] Specifically, the system in this embodiment also includes the following three modules: a data verification and preprocessing module 7, a data auditing module 8, and a water quality database module 9. The data verification and preprocessing module 7 is used to perform integrity checks, anion and cation balance tests, missing value handling, outlier handling, and standardization on the water quality index data acquired by the data acquisition module. In some examples, integrity checks can identify missing fields, anion and cation balance tests can ensure that the error does not exceed a preset threshold, missing value handling uses mean filling or K-Nearest Neighbors Imputation (KNN) interpolation, and outlier handling is based on the 3σ principle or the interquartile range method. Range (IQR) is used to remove or correct outlier data. Standardization is achieved by using Z-score standardization or min-max standardization to eliminate the influence of dimensions, thus providing clean, standardized, and usable high-quality data for subsequent water source identification. The data review module 8 is used to perform a three-level review process on the preprocessed data entering the database, including submission by the data entry clerk, review by the reviewer, and confirmation by the approver. Through multi-level access control and review mechanisms, it is ensured that every piece of data undergoes strict quality control before entering the database, effectively avoiding the risk of data errors, omissions, or tampering. The water quality database module 9 includes a typical water quality database storing samples of labeled water source types and a daily water quality database storing all tested water samples. It is used to record metadata such as analysis methods, testing personnel, review status, entry time, modification time, etc. The typical water quality database provides a standardized sample set for model training, while the daily water quality database provides complete data support for online identification, historical query, and trend analysis.

[0036] In one specific embodiment, when performing anion-cation balance testing, the charge equivalents of total cations and total anions are first calculated:

[0037]

[0038]

[0039] The denominator represents the equivalent weight of the corresponding molecular ion. It is the cation equivalent concentration. The concentration is the anion equivalent concentration, and the unit ion concentration C is expressed in mg / L.

[0040] In theory, = However, measurement errors actually exist. To measure the magnitude of these errors, a normalized relative error formula is used: When the absolute value of the relative error E is less than or equal to 5%, the data quality is considered acceptable and can be used in the database. When the absolute value of E is greater than 5% but less than or equal to 10%, a warning is issued, prompting the auditor to review the data for measurement errors or missing ions. When the absolute value of E is greater than 10%, the data is automatically deemed unacceptable, rejected from the database, and requires retesting or a check of the data entry accuracy. For example, if a water sample test yields a total cation equivalent of 12.50 meq / L and a total anion equivalent of 11.90 meq / L, the relative error E = (12.50 - 11.90) / (12.50 + 11.90) × 100% = 2.46%, which is less than 5%, and is automatically deemed acceptable for database use. Another test result showed a total cation equivalent of 15.20 meq / L and a total anion equivalent of 13.40 meq / L, with a relative error E = 6.30%. This was deemed a warning and required review by an auditor. The auditor discovered an error in the data entry of a certain ion concentration. After correction, the error was recalculated to 2.10%, and the data was approved and entered into the database. This application's embodiment, through the aforementioned cation-anion balance testing mechanism, can automatically identify and filter substandard water sample data before data entry, ensuring the accuracy and reliability of the data used for water source identification.

[0041] In one specific embodiment, when handling missing and outlier values, for water samples with missing values, the distance between the water sample and other complete samples in Euclidean space is calculated. Assuming there are n completely observed indicators, the distance between the sample to be imputed x and historical samples is calculated. Distances across these n metrics: ,in, For distance, It is the value of the j-th known index of the sample to be imputed. It is the value of the j-th indicator in the historical sample.

[0042] Based on the calculated distances, sort the historical samples in ascending order, and select the top K historical samples, denoted as [K]. The range of i is from 1 to k. These K samples are considered the most similar cases to the missing sample. The default value of K is 5. Then, a weighted average is calculated using the measurements of these K samples on indicator k. The estimated value of the missing value is then: ,in, For estimated values, weights Take the reciprocal of the distance , It is the measurement of the i-th neighbor on the missing index k. For example, a water sample is missing... Concentration data, calculate the water sample's concentration relative to other intact samples. , , Based on the Euclidean distance of the measured indicators, find the 5 closest complete samples. The concentrations were 320 mg / L, 335 mg / L, 310 mg / L, 340 mg / L, and 325 mg / L, with corresponding distances of 2.1, 2.5, 3.2, 2.8, and 3.5, respectively. A weighted average was calculated to obtain... , used as filler values ​​for missing values.

[0043] Outlier handling employs a dual verification mechanism combining the 3σ principle and the IQR method. First, for indices that follow an approximately normal distribution, the 3σ principle is used to calculate the sample mean μ and standard deviation σ. Outliers exceeding this value are then removed. Data outside the specified range is considered outlier. For example, if the historical total dissolved solids (TDS) data for a certain mine has a mean of 1200 mg / L and a standard deviation of 150 mg / L, then data with TDS below 750 mg / L or above 1650 mg / L are considered outliers. For indicators that do not follow a normal distribution, the IQR method is used to calculate the first quartile Q1 and the third quartile Q3, where IQR = Q3 - Q1. Data outside the range [Q1 - 1.5 × IQR, Q3 + 1.5 × IQR] are considered outliers. For example, a certain mine... If Q1 is 300 mg / L, Q3 is 500 mg / L, and IQR is 200 mg / L, then data below 0 mg / L or above 800 mg / L are considered abnormal. In this embodiment, abnormal data is not directly deleted; instead, it is marked as potentially abnormal and submitted to the auditor for confirmation. After review by the auditor, data confirmed to be truly abnormal is replaced using the mean fill method; data confirmed to be correct retains its original value, and the auditor's comments are recorded.

[0044] This embodiment, through the synergistic effect of the above three modules, constructs a full-process data quality management system from data collection, verification, review to storage, laying a solid data foundation for the accuracy of water source identification, the reliability of model training, and the traceability of disposal decisions.

[0045] In some embodiments, Figure 3 This is a schematic diagram of the water source identification module structure provided in an embodiment of this application. See also... Figure 3The water source identification module 2 includes: a data screening and algorithm selection unit 21, used to perform feature evaluation on the historical data of the mine where the water sample to be identified is located, and calculate the suitability scores of multiple discrimination algorithms based on the feature evaluation results, and select the discrimination algorithm with the highest suitability score. The feature evaluation includes at least one evaluation method among sample quantity evaluation, linear separability evaluation, data distribution evaluation, category balance evaluation, and feature dimension evaluation; and an identification modeling and reasoning unit 22, which has multiple discrimination algorithms built in, used to call the discrimination model corresponding to the selected discrimination algorithm with the highest suitability score to reason about the water sample to be identified, and output the water source type and confidence level.

[0046] In order to achieve adaptive and accurate identification under different mines and different data conditions, the water source identification module of this application further introduces an intelligent model selection and reasoning mechanism, which overcomes the technical defects of mismatch that are easy to occur with fixed algorithms or manual model selection.

[0047] Specifically, the water source identification module 2 includes the following two units: a data screening and algorithm selection unit 21 and an identification modeling and reasoning unit 22. The data screening and algorithm selection unit 21 is used to perform multi-dimensional feature evaluation on the historical data of the mine where the water sample to be identified is located. The feature evaluation specifically includes at least one of the following evaluation methods: sample quantity evaluation, linear separability evaluation, data distribution evaluation, class balance evaluation, and feature dimension evaluation. In some examples, sample quantity evaluation can determine whether the samples of each class are sufficient, linear separability evaluation can use indicators such as Fisher's linear discriminant ratio to measure the degree of linear separability of the data, data distribution evaluation can determine whether the Gaussian distribution assumption is met through normality test, class balance evaluation can calculate the degree of class imbalance, and feature dimension evaluation can analyze the number of effective features and the size of the dimensions. Next, the data screening and algorithm selection unit 21 calculates the suitability scores of various discrimination algorithms based on the above evaluation results, and automatically selects the discrimination algorithm with the highest suitability score, thereby ensuring that the selected algorithm achieves optimal matching with the current mine data conditions; the identification modeling and reasoning unit 22 has multiple built-in discrimination algorithms, which are used to call the discrimination model corresponding to the discrimination algorithm with the highest suitability score selected by the data screening and algorithm selection unit, to quickly reason about the water sample to be identified, output the water source type and the corresponding confidence level, and when the suitability scores of multiple algorithms are close, a multi-model voting or integration strategy is used to output the final identification result.

[0048] This embodiment achieves dynamic matching between the algorithm and data conditions through the coordinated operation of the two units, significantly improving the accuracy, generalization ability, and adaptability to complex data scenarios such as small samples, nonlinearity, and high dimensionality of water source identification.

[0049] In some embodiments, the multiple discrimination algorithms include at least two of the following: the improved Fisher discrimination algorithm, random forest, support vector machine, and Bayesian discrimination algorithm. When the difference in the fitness scores of multiple discrimination algorithms is less than a preset threshold, the data screening and algorithm selection unit enables a multi-model voting or integration strategy to output the final identification result.

[0050] To further improve the robustness and accuracy of water source identification, this application integrates multiple discrimination algorithms in the water source identification module and designs a flexible multi-model collaborative mechanism.

[0051] Specifically, the various discrimination algorithms include at least two of the following: the improved Fisher discriminant method (Grey Wolf), random forest, support vector machine, and Bayesian discriminant method. The improved Fisher discriminant method finds the optimal discriminant vector through the Grey Wolf optimization algorithm, which is suitable for data with moderate sample size, good linear separability, and approximately normal distribution. The random forest algorithm can vote by integrating multiple decision trees, which is suitable for data with high feature dimension and complex nonlinear relationships. The support vector machine algorithm can also map the data to a high-dimensional space through kernel functions to find the optimal classification hyperplane, which is suitable for small sample size and linear or nonlinear data. The Bayesian discriminant method can make discrimination based on probability distribution, which is suitable for data with sufficient sample size and satisfying the normal distribution assumption. In some cases, after the data screening and algorithm selection unit evaluates the suitability of the above algorithms, if the difference in suitability scores of multiple discrimination algorithms is less than a preset threshold, such as a difference of less than 5%, it indicates that multiple algorithms have similar applicability under the current data conditions. At this time, the data screening and algorithm selection unit will enable a multi-model voting or integration strategy, and output the final identification result by combining the identification results of multiple algorithms, such as using majority voting, weighted voting or stacked integration.

[0052] In this embodiment, the multi-model collaboration mechanism effectively avoids the risk of misjudgment by a single algorithm in scenarios with ambiguous boundaries, and makes full use of the complementary advantages of different algorithms on different data features, further improving the robustness, accuracy and generalization ability of water source identification results.

[0053] In a specific embodiment, when using the improved Fisher discriminant method, the correlation coefficient between each water quality feature and the inrush type label can be calculated first. The absolute value of the correlation coefficient reflects the degree of linear correlation between the water quality feature and the inrush type. The closer the correlation coefficient is to 1 or -1, the greater the potential contribution of the water quality feature to the inrush type discrimination. A correlation coefficient close to 0 indicates a weaker linear relationship between the water quality feature and the inrush type. Then, all water quality features are arranged according to the correlation coefficient, and water quality features with stronger correlation to the inrush type label are retained first. This ensures that in the subsequent redundant judgment process, features with higher correlation can be preferentially included in the feature subset, thereby maximizing the amount of discriminant information retained while simplifying features. Next, each water quality feature is traversed sequentially according to the arrangement result. The correlation between the currently traversed water quality feature and all water quality features already retained in the feature subset are calculated one by one. The degree of correlation between the two is compared. Based on the comparison result, it is determined whether the current water quality feature is a redundant feature. The redundant feature is removed and not included in the feature subset.

[0054] After obtaining a simplified feature subset through initial correlation screening, this embodiment employs a preset algorithm to automatically search for the optimal feature combination within the initially screened feature subset, using the fitness function as the criterion for evaluating the quality of each feature combination. Specifically, binary encoding is used to encode each water quality feature in the initially screened feature subset. The preset algorithm iteratively updates the feature combination scheme in the search space constructed by the binary encoding by simulating a swarm intelligence optimization mechanism. In each iteration, the fitness function value corresponding to each current feature combination scheme needs to be calculated. In some cases, the fitness function value directly reflects the quality of the feature combination scheme. The design of the fitness function takes into account both classification performance and the number of features. On the one hand, it evaluates the classification accuracy of the Fisher discriminant model trained based on the current feature combination on the training samples. The higher the accuracy, the stronger the discriminative ability of the combination. On the other hand, it counts the number of features contained in the current feature combination. The more features there are, the higher the model complexity and the greater the data collection cost. This embodiment combines classification accuracy with a feature quantity penalty term. The fitness function can encourage the algorithm to choose a more concise feature combination while pursuing high classification accuracy. In each iteration, the preset algorithm sorts all current feature combination schemes according to the fitness function value, determines the current best schemes, and guides other schemes to update in a better direction. After multiple iterations of optimization, the preset algorithm gradually converges to the feature combination scheme with the highest fitness function value. This feature combination scheme is the optimal feature combination obtained by the search.

[0055] After obtaining the optimal feature combination through a pre-defined algorithm, a discriminative model that can be used for practical identification needs to be constructed based on the optimal feature combination. Specifically, the original mine water quality sample data is first used as training samples. The water quality index data corresponding to the optimal feature combination is extracted to form a dimensionality-reduced training dataset, which is then standardized to eliminate dimensional differences. Based on the standardized feature data, the intra-class scatter matrix and inter-class scatter matrix of the training samples are calculated. After adding a regularization term to the intra-class scatter matrix, the generalized eigenvalue problem is solved to obtain the optimal projection vector. The samples are then projected onto a low-dimensional discriminative space to maximize the inter-class distance and minimize the intra-class distance. Subsequently, the centroid position of each class in the projection space is calculated as the classification benchmark, and the optimal feature combination, optimal projection vector, centroid of each class, standardized parameters, and label encoder are saved as a model file. Furthermore, this application embodiment also outputs a classification result file and an optimization history file. The classification result file records the true type and discriminant type of each training sample, which facilitates viewing the recognition effect and analyzing the reasons for misclassification. The optimization history file records the changes in fitness value, classification accuracy, and number of features during the iteration process, which can be used to evaluate the convergence performance and optimization effect of the algorithm. The model file, classification result file, and optimization history file together constitute a complete model output system, providing comprehensive support for the practical application of the model, effect evaluation, and algorithm debugging.

[0056] In this embodiment, the model file solidifies the optimal feature combination and optimal classification parameters obtained during the training phase, so that in practical applications, there is no need to repeat the feature selection and model training process. This allows for rapid and stable type identification of unknown water inrush samples, thus providing reliable technical support for the rapid identification of mine water inrush sources and early warning of water inrush risks.

[0057] In some embodiments, the early warning generation module includes: a rule engine unit, configured to configure at least one early warning rule among a high-risk threshold for water source type, a threshold for abnormal changes in water quality, and a threshold for the proportion of mixed water sources; and a graded early warning unit, configured to receive the identification result output by the water source identification module, match the identification result with the early warning rule configured by the rule engine unit, and generate at least one early warning level among red, orange, and yellow early warnings based on the degree of matching.

[0058] To achieve precise linkage from water source identification to risk response, the early warning generation module in this application embodiment constructs a flexible and configurable rule engine and a hierarchical early warning mechanism to ensure that different risk levels can be identified and triggered in a timely and accurate manner.

[0059] Specifically, the early warning generation module includes two units: a rule engine unit and a hierarchical early warning unit. The rule engine unit can be configured with at least one of the following early warning rules: a high-risk threshold for water source type, a threshold for abnormal water quality changes, and a threshold for the proportion of mixed water sources. This provides a flexible and configurable basis for risk identification. In some examples, when executing the high-risk threshold for water source type early warning, if the identification result is high-risk water sources such as Ordos limestone water or old hollow water, an early warning is directly triggered. When executing the threshold for abnormal water quality changes, if indicators such as conductivity and total dissolved solids fluctuate significantly in a short period of time, an early warning is triggered. The hierarchical early warning unit can also be configured with at least one of the following early warning rules: a high-risk threshold for water source type, a threshold for abnormal water quality changes, and a threshold for the proportion of mixed water sources. When setting early warning rules, if the proportion of high-risk water sources in a mixed water source exceeds a set percentage, an early warning is triggered. The tiered early warning unit receives the identification results output by the water source identification module, then matches the identification results with the early warning rules configured in the rule engine unit item by item, and generates early warning information of corresponding levels based on the degree of matching. The identification results include water source type, confidence level, and proportion of mixed water sources, etc. The early warning information includes red, orange, and yellow warnings. In some cases, a red warning corresponds to a major risk that requires immediate action; an orange warning corresponds to a relatively high risk that requires close attention; and a yellow warning corresponds to a general risk that requires careful observation.

[0060] It is important to note that the rule engine unit is not a simple threshold comparator, but a multi-dimensional composite rule matching engine specifically designed for mine water inrush identification scenarios. Unlike general rule engines, the rule engine unit performs attenuation processing on the confidence level of water source types. In some embodiments, when the confidence level output by the water source identification module is lower than 0.7, it does not directly trigger an alert based on a single water source type. Instead, it enters the mixed water source analysis subprocess, extracts all water source types with a probability greater than 0.15 from the probability distribution, and calculates a weighted risk score according to the preset weights in the water source risk weight table: Ordos limestone water (1.0 weight), old workings water (0.9 weight), Taihu limestone water (0.7 weight), roof sandstone water (0.5 weight), and No. 4 water-bearing water (0.3 weight). If the weighted risk score exceeds 0.6, an orange alert is triggered. This solves the technical problem of traditional single threshold rules being prone to missed or false alarms in mixed water source scenarios. Secondly, the rule engine unit dynamically adapts to the threshold for abnormal water quality changes. Traditional systems use fixed thresholds, but background values ​​vary significantly across different mines and seasons. This embodiment uses an operation and maintenance and version management module to periodically perform statistical analysis on normal water sample indicators in the daily water quality database, automatically updating the baseline mean and standard deviation of each indicator. The trigger threshold for the warning rule is dynamically adjusted to the baseline mean ± 3 times the standard deviation. When a new water sample indicator exceeds this dynamic range, even if the absolute value is small, a yellow warning will be triggered. For example, if the normal TDS baseline for a mine is 800 ± 100 mg / L, when the TDS rises to 1150 mg / L, although it does not reach the common 1500 mg / L alarm line, it is still considered an abnormal change and a warning is triggered, thus achieving early risk identification. Simultaneously, the rule engine unit supports association rules for the mixed water source ratio threshold, such as supporting AND, OR, and NOT logical combinations. Even if a red warning is not triggered when a single water source type is identified, an orange warning can be triggered when the proportions of two medium-to-high-risk water sources reach a certain level.

[0061] This embodiment achieves adaptive mapping of the identification results to the warning level through the coordinated operation of the two units mentioned above. This ensures that high-risk water sources can trigger the highest level warning and that boundary risks can receive appropriate attention, thereby providing a clear decision-making basis for subsequent accurate push and rapid response.

[0062] In some examples, Figure 4 This is a schematic diagram of the information push module provided in an embodiment of this application. See also... Figure 4The information push module 4 includes: a push strategy configuration unit 41, used to configure the push targets and push timing corresponding to different warning levels generated by the warning generation module 3; a push content generation unit 42, used to receive the graded warning information generated by the warning generation module and generate push content based on the graded warning information, wherein the push content includes at least one of the following: water source type, confidence level, warning level, sampling information, and disposal suggestions; and a multi-channel push unit 43, used to send the push content generated by the push content generation unit to the terminal device of the corresponding responsible person through at least one of the following push methods: in-site message, SMS, email, WeChat Work, and DingTalk, according to the push targets and push timing configured by the push strategy configuration unit.

[0063] To ensure that tiered early warning information can reach the decision-making and execution levels accurately, promptly, and through multiple channels, the information push module in this application embodiment constructs a precise push mechanism that coordinates strategy configuration, content generation, and multi-channel execution.

[0064] Specifically, the information push module comprises three units: a push strategy configuration unit, a push content generation unit, and a multi-channel push unit. The push strategy configuration unit configures the push recipients and timing for different warning levels generated by the warning generation module 3. In some examples, a red warning corresponds to the mine's main responsible person and safety director, an orange warning to the shift supervisor and technical supervisor, and a yellow warning to the on-site safety officer and team leader. Push timing can include real-time push, scheduled summary push, or push by shift, ensuring accurate matching between risk level and response level. The push content generation unit receives the tiered warning information generated by the warning generation module and automatically generates structured push content based on this information. In some examples, the push content includes at least one of the following: water source type, confidence level, warning level, sampling information, and disposal suggestions. The confidence level reflects the reliability of the identification results, and the disposal suggestions include immediate evacuation, enhanced monitoring, and investigation of water inrush points, ensuring that the recipient can fully understand the risk situation and obtain clear action guidance. The multi-channel push unit is used to send the push content generated by the push content generation unit to the terminal devices of the corresponding responsible persons through at least one of the following push methods: in-site messages, SMS, email, WeChat Work, and DingTalk. This ensures that the push is sent redundantly through multiple channels in critical scenarios, avoiding information omissions due to the failure of a single channel.

[0065] This embodiment, through the coordinated operation of the three units mentioned above, achieves the transition from risk identification to accountability, providing a solid information guarantee for rapid response and effective handling.

[0066] In some examples, Figure 5This is a schematic diagram of the processing closed-loop module provided in an embodiment of this application. See also... Figure 5 The closed-loop disposal module 5 includes: a closed-loop status management unit 51, used to create a corresponding disposal task and initialize the disposal status to pending disposal after the information push module completes the push of early warning information, based on the identification result output by the water source identification module and the graded early warning information generated by the early warning generation module; a disposal record receiving unit 52, used to receive at least one disposal record submitted by the responsible person after the responsible person completes the on-site disposal, including disposal measures, disposal results, responsible person information, and disposal time, and transmit the disposal record to the associated storage unit; and an associated storage unit 53, used to associate the disposal record with the corresponding identification result and early warning information and store it in the database, and notify the closed-loop status management unit to update the disposal status.

[0067] To ensure that every warning is responded to, every action is recorded, and every step is traceable, the closed-loop management module of this application embodiment constructs a closed-loop management mechanism covering the entire process from task creation, record reception, and associated storage.

[0068] Specifically, the closed-loop disposal module comprises three units: a closed-loop status management unit, a disposal record receiving unit, and an associated storage unit. The closed-loop status management unit, after the information push module completes the early warning information push, automatically creates a corresponding disposal task based on the identification results output by the water source identification module and the hierarchical early warning information generated by the early warning generation module, and initializes the disposal status of the task to "pending disposal," laying the foundation for subsequent disposal tracking. The disposal record receiving unit, after the responsible person receives the early warning information and completes on-site disposal, receives the disposal record submitted by the responsible person through the system interface or mobile terminal. In some examples, the disposal record includes at least one of the following: disposal measures, disposal results, responsible person information, and disposal time. The disposal measures can be... The process involves sealing the water inrush point, starting the drainage pump, and evacuating personnel. The outcome is that the danger has been controlled, the water inrush has decreased, and personnel have been safely evacuated. The responsible person information includes the name, position, and department of the responsible person. Then, the disposal record receiving unit transmits the received disposal records to the associated storage unit to ensure that key information in the disposal process is fully captured. The associated storage unit is used to associate the disposal records transmitted by the disposal record receiving unit with the corresponding judgment results and early warning information, and store them uniformly in the database. At the same time, it notifies the closed-loop status management unit to update the disposal status, such as updating from pending disposal to disposal in progress, updating from disposal to disposal completed, and finally archiving to archived, thus forming a complete business closed loop from risk identification to on-site disposal and from disposal records to status tracking.

[0069] This embodiment achieves seamless connection between identification results and disposal actions, precise binding between disposal process and responsibility implementation, and full controllability of disposal status and traceability management through the coordinated cooperation of the above three units. It effectively solves the problems of risk delay and inadequate disposal caused by information gaps and lack of responsibility in the prior art.

[0070] In some examples, the platform docking module includes: a data governance platform docking unit, used to push the identification results, early warning information and disposal records to an external data governance platform in JSON format or a standardized data interface after the disposal closed-loop module completes the storage of disposal records, so as to realize the data asset output of the identification results and carry traceability information; and a comprehensive integration system docking unit, used to dock with an external comprehensive integration system after the data governance platform docking unit completes the data push, so as to realize at least one integration function among single sign-on integration, to-do task integration, message center docking and application registration, so that early warning information and disposal tasks can flow synchronously in the external comprehensive integration system.

[0071] In order to break down information silos and achieve seamless integration and business collaboration with the mine's existing digital platform, the platform integration module in this application embodiment constructs a two-way collaborative mechanism that combines data asset output with business process integration.

[0072] Specifically, platform integration module 6 comprises two units: a data governance platform integration unit and a comprehensive integration system integration unit. The data governance platform integration unit, after the closed-loop processing module completes the storage of processing records, pushes the identification results, early warning information, and processing records to the external data governance platform in JSON format or a standardized data interface. This enables the data asset output of the identification results, carrying traceability information including data source, processing procedure, review status, and version information, providing high-quality data asset accumulation for the mine's data governance, trend analysis, and decision support. The comprehensive integration system integration unit, after the data governance platform integration unit completes the data push, integrates with external comprehensive integration systems, such as the mine's unified portal and digital comprehensive integration platform. The system integrates with external systems to achieve at least one of the following integration functions: single sign-on integration, task integration, message center integration, and application registration. This allows early warning information and response tasks to flow synchronously within the external integrated system. In some examples, single sign-on integration allows users to access the system after unified authentication through the external system, eliminating the need for repeated logins. Task integration pushes early warning response tasks generated by the system to the unified task center of the external system, facilitating centralized handling of cross-system tasks by responsible personnel. Message center integration pushes tiered early warning information generated by the system to the unified message platform of the external system, enabling unified distribution of messages across multiple channels. Application registration registers the system with the application management platform of the external system, achieving unified authorization and access control for applications.

[0073] This embodiment achieves efficient output of internal business data to the external data governance platform and deep integration of internal business processes with the external integrated system through the coordinated operation of the two units. This ensures that data assets are accumulative, traceable, and reusable, and enables cross-system, cross-level, and cross-departmental collaboration, providing solid technical support for the overall digital and intelligent integrated management and control of the mine.

[0074] In some examples, the system further includes: a result display and reporting module, used to obtain the identification results and generate a detection report or analysis report after the water source identification module completes the water source type identification, and to synchronously display the graded early warning information in the detection report or analysis report after the early warning generation module generates graded early warning information, wherein the identification results include water source type, confidence level or probability distribution, selected discrimination algorithm and discrimination algorithm selection basis; and an operation and maintenance and version management module, used to perform at least one operation and maintenance management operation on the discrimination model, including version management, model rollback, performance monitoring, alarm threshold setting, and data backup, and to provide the managed available model version for the identification modeling and inference unit to call during the inference phase.

[0075] To achieve a visual presentation of the identification results and stable operation and maintenance throughout the system's entire lifecycle, this application embodiment also introduces two supporting modules: result display and operation and maintenance management, ensuring the system's continuous and efficient operation while guaranteeing user experience.

[0076] Specifically, this embodiment of the system also includes the following two modules: a result display and reporting module and an operation and maintenance and version management module. The result display and reporting module automatically acquires the identification results and generates a detection report or analysis report after the water source identification module completes the water source type identification. After the early warning generation module generates tiered early warning information, the module synchronously displays the tiered early warning information in the detection report or analysis report, providing users with a comprehensive visual report including identification conclusions, risk levels, and decision-making basis. This facilitates rapid understanding of the risk situation and supports scientific decision-making. In some examples, the identification results include water source type, confidence level or probability distribution, selected discrimination algorithm, and the selection basis for the discrimination algorithm. The probability distribution reflects the reliability of the identification results, and the selection basis for the discrimination algorithm can be a selection criterion based on the evaluation score of features such as sample size, linear separability, and data distribution. The operation and maintenance and version management module is used to perform at least one of the following operation and maintenance management operations throughout the entire lifecycle of model training and deployment: version management, model rollback, performance monitoring, alarm threshold setting, and data backup. It also provides the managed, usable model versions for the identification modeling and inference unit to use during the online identification phase. Version management records model version number, training data range, parameter configuration, evaluation results, etc.; model rollback switches the currently used model to a historically stable version when model performance degrades or anomalies occur; performance monitoring monitors real-time performance metrics such as model identification accuracy, response time, and number of calls; alarm threshold setting provides a configuration interface for operation and maintenance personnel to set alarm trigger thresholds for performance monitoring metrics; and data backup periodically backs up the water quality database, model configuration, and identification records, and supports data recovery.

[0077] This embodiment, through the coordinated operation of the two modules mentioned above, provides users with transparent, interpretable, and visualized identification results, while also providing system maintenance personnel with a full lifecycle model management and maintenance support mechanism to ensure that the system maintains a stable, reliable, and traceable high-performance state during long-term operation.

[0078] In some examples, the identification modeling and inference unit further includes a model training and evaluation subunit, which is used to: divide the typical water quality database of the water quality database module into training, validation, and test sets; perform hyperparameter tuning on the various discrimination algorithms based on the training set, and evaluate the performance of the tuned model using cross-validation; perform a final evaluation on the test set using at least one evaluation metric selected from accuracy, precision, recall, F1 score, and confusion matrix; and when the evaluation metric meets a preset engineering metric threshold, release the corresponding model as a usable version for the identification modeling and inference unit to use during the inference phase.

[0079] To ensure that the water source identification model has high accuracy, strong generalization and reliable engineering deployment, the embodiments of this application integrate a complete model training and evaluation sub-unit in the identification modeling and inference unit, and construct a standardized training mechanism for the entire process from data partitioning, parameter tuning, performance verification to version release.

[0080] Specifically, the identification modeling and inference unit also includes a model training and evaluation subunit. This subunit operates during the offline training phase. First, it divides the typical water quality database from the water quality database module into training, validation, and test sets to ensure that the data used for model training, tuning, and final evaluation are independent and do not interfere with each other, laying the foundation for objectively evaluating model performance. Second, it performs hyperparameter tuning on the various discrimination algorithms based on the training set, such as using grid search or random search to find the optimal parameter combination. Cross-validation is then used to evaluate the tuned model's performance on the validation set, effectively avoiding overfitting and ensuring stable and reliable model performance across different data subsets. Then, at least one evaluation metric from accuracy, precision, recall, F1 score, and confusion matrix is ​​used to perform a final evaluation on the test set, objectively measuring the model's true generalization ability through independent test data. Finally, when the evaluation metrics meet preset engineering thresholds, such as accuracy not less than 85% and F1 score not less than 0.8, the corresponding model is released as a usable version for the identification modeling and inference unit to use during the online identification phase.

[0081] This embodiment, through the standardized training and evaluation process described above, ensures that the released model meets the engineering deployment requirements in terms of accuracy, stability, and generalization ability, providing high-quality and highly reliable model support for online water source identification.

[0082] In summary, this application's embodiments establish an adaptive algorithm selection mechanism in terms of algorithm selection. This mechanism screens multiple algorithms based on factors such as sample size, linear separability, normality of distribution, class balance, and feature dimensions, calculates multi-algorithm suitability scores, and automatically selects or recommends the optimal algorithm. When scores are close, a multi-model voting or integration strategy is employed. Regarding system architecture, a complete closed-loop system is constructed, including a dual-database structure (typical and daily databases), three-level auditing, data traceability and quality grading, report generation, early warning grading and object configuration, as well as single sign-on, to-do lists, messaging, and data governance platforms. The system includes platform integration functionality; for adaptability to mining scenarios, a generalization and robustness process for small samples is designed, covering missing and outlier handling, cross-validation, model version management and rollback, and engineering deployment under response time and concurrency performance constraints; for result presentation, interpretable output of identification results is achieved, including confidence level or probability distribution, selected algorithm and its selection criteria for association with screening indicators; for risk warning, a warning rule system is constructed, including high-risk thresholds for water source types, thresholds for abnormal water quality changes, thresholds for mixed water source proportions, and multi-channel push strategies. Therefore, this application embodiment constructs a complete system including water sample database management, intelligent analysis, adaptive algorithm selection and identification, result display and reporting, tiered warning push, handling and integration, and backend management. It can achieve automated identification and rapid response, multi-algorithm adaptation and improved small-sample generalization ability, output confidence level and algorithm selection criteria, and support for mixed water source risk warnings. Furthermore, through tiered warnings and multi-channel push, it significantly improves risk handling efficiency.

[0083] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0084] The various embodiments in this specification are described in a related manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0085] In particular, the device embodiment is basically similar to the method embodiment, so the description is relatively simple. For relevant details, please refer to the description of the method embodiment.

[0086] For ease of description, the above apparatus is described by dividing it into various functional units / modules. Of course, in implementing this application, the functions of each unit / module can be implemented in one or more software and / or hardware.

[0087] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0088] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A mine water inrush source identification and early warning system, characterized in that, include: The data acquisition module is used to acquire water quality index data of the water sample to be identified; The water source identification module is used to identify the water source type of the water sample to be identified based on the water quality index data; The early warning generation module is used to generate graded early warning information by matching the identification results of the water source identification module with preset early warning rules. The information push module is used to push the graded warning information to the terminal device of the corresponding responsible person according to the warning level and the preset push strategy; The closed-loop handling module is used to receive handling records from the responsible person and associate and store the handling records with the corresponding identification results and early warning information to form a complete closed loop from identification to handling; The platform integration module is used to push at least one of the stored identification results, early warning information, and handling records to an external platform to achieve data sharing and business collaboration.

2. The mine water inrush source identification and early warning system according to claim 1, characterized in that, The system also includes: The data verification and preprocessing module is used to perform integrity checks, anion and cation balance tests, missing value handling, outlier handling, and standardization processing on the water quality index data acquired by the data acquisition module. The data review module is used to perform a three-level review process on the data entering the database after it has been processed by the data verification and preprocessing module, including submission by the data entry clerk, review by the reviewer, and confirmation by the approver. The water quality database module includes a typical water quality database that stores samples with labeled water source types and a daily water quality database that stores all tested water samples, used to record metadata.

3. The mine water inrush source identification and early warning system according to claim 1, characterized in that, The water source identification module includes: The data screening and algorithm selection unit is used to perform feature evaluation on the historical data of the mine where the water sample to be identified is located, and calculate the suitability score of multiple discrimination algorithms based on the feature evaluation results, and select the discrimination algorithm with the highest suitability score. The feature evaluation includes at least one evaluation method among sample quantity evaluation, linear separability evaluation, data distribution evaluation, category balance evaluation, and feature dimension evaluation. The identification modeling and reasoning unit has multiple built-in discrimination algorithms. It is used to call the discrimination model corresponding to the discrimination algorithm with the highest suitability score to reason about the water sample to be identified and output the water source type and confidence level.

4. The mine water inrush source identification and early warning system according to claim 3, characterized in that, The multiple discrimination algorithms include at least two of the following: the improved Fisher discrimination method, random forest, support vector machine, and Bayesian discrimination method. When the difference in the fitness scores of multiple discrimination algorithms is less than a preset threshold, the data screening and algorithm selection unit enables a multi-model voting or integration strategy to output the final identification result.

5. The mine water inrush source identification and early warning system according to claim 1, characterized in that, The early warning generation module includes: The rule engine unit is used to configure at least one of the following early warning rules: high-risk threshold for water source type, abnormal change threshold for water quality, and mixed water source ratio threshold. The graded early warning unit is used to receive the identification results output by the water source identification module, match the identification results with the early warning rules configured by the rule engine unit, and generate at least one early warning level among red, orange and yellow early warnings based on the degree of matching.

6. The mine water inrush source identification and early warning system according to claim 1, characterized in that, The information push module includes: The push strategy configuration unit is used to configure the push objects and push timing corresponding to different warning levels generated by the warning generation module; The push content generation unit is used to receive the graded early warning information generated by the early warning generation module, and generate push content based on the graded early warning information. The push content includes at least one of the following: water source type, confidence level, early warning level, sampling information, and disposal suggestions. The multi-channel push unit is used to send the push content generated by the push content generation unit to the terminal device of the corresponding responsible person through at least one of the following push methods: in-site message, SMS, email, WeChat Work, and DingTalk, according to the push target and push timing configured by the push strategy configuration unit.

7. The mine water inrush source identification and early warning system according to claim 1, characterized in that, The closed-loop processing module includes: The closed-loop status management unit is used to create a corresponding disposal task and initialize the disposal status to pending disposal after the information push module completes the push of the early warning information, based on the identification result output by the water source identification module and the hierarchical early warning information generated by the early warning generation module. The disposal record receiving unit is used to receive at least one disposal record submitted by the responsible person after the responsible person has completed the on-site disposal, including disposal measures, disposal results, responsible person information, and disposal time, and to transmit the disposal record to the associated storage unit. The associated storage unit is used to associate the handling record with the corresponding judgment result and early warning information and store them in the database, and to notify the closed-loop status management unit to update the handling status.

8. The mine water inrush source identification and early warning system according to claim 1, characterized in that, The platform integration module includes: The data governance platform docking unit is used to push the identification results, early warning information and disposal records to the external data governance platform in JSON format or standardized data interface after the disposal closed-loop module completes the storage of disposal records, so as to realize the data asset output of the identification results and carry traceability information. The integrated system docking unit is used to dock with the external integrated system after the data governance platform docking unit completes the data push, so as to realize at least one of the following integration functions: single sign-on integration, task-to-do integration, message center docking, and application registration, so that early warning information and handling tasks can be synchronously transferred in the external integrated system.

9. The mine water inrush source identification and early warning system according to claim 1, characterized in that, The system also includes: The results display and reporting module is used to obtain the identification results and generate a detection report or analysis report after the water source identification module completes the water source type identification, and to synchronously display the graded warning information in the detection report or analysis report after the warning generation module generates graded warning information. The identification results include water source type, confidence level or probability distribution, selected discrimination algorithm and discrimination algorithm selection basis. The operation and maintenance and version management module is used to perform at least one of the following operation and maintenance management operations on the discrimination model: version management, model rollback, performance monitoring, alarm threshold setting, and data backup, and to provide the managed available model version to the discrimination modeling and inference unit for use in the inference phase.

10. The mine water inrush source identification and early warning system according to claim 3, characterized in that, The identification modeling and reasoning unit further includes a model training and evaluation subunit, which is used for: The training set, validation set, and test set are divided from typical water quality databases in the water quality database module. Based on the training set, the hyperparameters of the various discrimination algorithms were tuned, and the performance of the tuned models was evaluated using cross-validation. The optimized model is finally evaluated on the test set using at least one of the following evaluation metrics: accuracy, precision, recall, F1 score, and confusion matrix. When the evaluation index meets the preset engineering index threshold, the corresponding model is released as a usable version for the identification modeling and reasoning unit to call during the reasoning stage.

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