Mine gushing water disaster early warning system and method based on multi-parameter fusion identification

Abstract

The application discloses a mine gushing water disaster early warning system and method based on multi-parameter fusion identification, and belongs to the technical field of mining engineering. The system comprises a multi-parameter monitoring module, a data preprocessing module, a data analysis module and an early warning module. The multi-parameter monitoring module is used for collecting multi-source monitoring index data related to mine gushing water disaster risk. The data preprocessing module is used for preprocessing the collected multi-source monitoring index data to obtain multiple early warning index data. The data analysis module is used for calculating a comprehensive early warning level based on the early warning index data. The early warning module is used for outputting an early warning result according to the comprehensive early warning level. The application overcomes the problems of insufficient reliability of single index threshold alarm, insufficient description of multi-parameter coupling relationship and insufficient early warning identification accuracy when approaching the alarm threshold in the prior art, and improves the mine gushing water disaster early warning accuracy.

Description

Technical Field

[0001] This invention relates to the field of mining engineering technology, and in particular to a mine water inrush disaster early warning system and method based on multi-parameter fusion identification. Background Technology

[0002] Existing methods for monitoring and warning of mine water hazards mostly rely on single monitoring indicators or fixed thresholds, such as alarms based solely on changes in water inflow, seepage pressure, or stress. While these methods are simple to implement, they fail to reflect the nonlinear coupling relationship between stress disturbances, seepage pressure changes, and temperature responses during the formation of sudden water inrushes, leading to false alarms, missed alarms, or delayed warnings. Furthermore, on-site monitoring data in mines are significantly affected by noise, sensor drift, construction disturbances, and variations in geological conditions, resulting in insufficient accuracy in warning identification when approaching alarm thresholds.

[0003] Therefore, there is an urgent need to propose a mine water inrush disaster early warning system and method that can simultaneously acquire multi-parameter information such as surrounding rock disturbance stress, strain, temperature and seepage pressure, and comprehensively consider the risk of individual indicators, the probability of joint anomalies of multiple parameters and the uncertainty of early warning level, so as to improve the accuracy, real-time performance and reliability of water inrush disaster early warning.

[0004] Prior art 1 discloses a multi-source, three-dimensional, collaborative prevention and control method for complex disasters in coal mines with thick aquifers. It establishes a multi-field coupled disaster criterion of "stress-seepage-fracture" for rockburst and water inrush disasters, acquires multi-dimensional data at various locations on the surface and in the rock strata, performs three-dimensional pressure-yielding layout and source-based pressure control based on the multi-dimensional data, and calculates the rockburst risk coefficient and water inrush risk coefficient. Using the rockburst risk coefficient and the water inrush risk coefficient, a risk level matrix is ​​established to obtain the current risk level of water inrush and rockburst. Prior art 2 discloses an intelligent early warning system and method for mine water hazards, relating to the field of mine water hazard monitoring technology. The water inrush risk early warning uses a sliding window to process the time-series data of water inflow. Three consecutive abnormalities trigger a level-three water inrush risk early warning, which can be upgraded to predict the water inrush volume. The flooding risk early warning sets three threshold levels based on the ratio of water inflow to drainage capacity and water reservoir volume. Through multi-parameter collaborative monitoring, intelligent analysis, and expert judgment, the accuracy, timeliness, and intelligence level of water hazard early warning are improved.

[0005] Although the aforementioned existing technologies monitor multiple parameters, they mostly analyze them individually without considering the coupling relationship between the multiple parameters. Furthermore, when the results of the multiple parameters contradict each other, false alarms or missed alarms are likely to occur. Moreover, since most monitoring systems use thresholds to make direct judgments, the identification results are either one or the other, lacking continuity and uncertainty. Summary of the Invention

[0006] This invention provides a mine water inrush disaster early warning system and method based on multi-parameter fusion identification, in order to solve the technical problems of existing technologies that do not consider the coupling relationship between multiple parameters, are prone to false alarms or missed alarms when the results between multiple parameters are contradictory, and that most monitoring systems use thresholds to make direct judgments, resulting in either one or the other, lacking continuity and uncertainty.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: On one hand, the present invention provides a mine water inrush disaster early warning system based on multi-parameter fusion identification, the mine water inrush disaster early warning system based on multi-parameter fusion identification includes: The multi-parameter monitoring module is used to collect multi-source monitoring indicator data related to the risk of mine water inrush disasters. The data preprocessing module is used to preprocess the collected multi-source monitoring indicator data to obtain multiple early warning indicator data; The data analysis module is used to calculate the comprehensive early warning level based on early warning indicator data; The early warning module is used to output early warning results based on the calculated comprehensive early warning level.

[0008] Furthermore, the multi-parameter monitoring module integrates a temperature sensor, a strain sensor, and a seepage pressure sensor into one unit to achieve spatiotemporal coordinated monitoring of surrounding rock disturbance stress, temperature, and seepage pressure.

[0009] Furthermore, the data preprocessing module is specifically used for: Time alignment is performed on the collected multi-source monitoring index data to ensure that disturbance stress data, temperature data, and seepage pressure data have the same or interpolable corresponding time labels. Data cleaning is performed on the data aligned to the completion time, including handling missing values ​​and removing outliers; For the cleaned data, temperature compensation is performed on the disturbance stress data and osmotic pressure data based on the temperature data. For the data that has undergone temperature compensation, a sliding time window is used to extract multiple early warning indicator data.

[0010] Based on the newly added monitoring indicator data, the verification results of on-site early warning results, and the false alarm and missed alarm records, the parameters of the calculation model for the comprehensive early warning level are updated.

[0011] Furthermore, the early warning indicator data includes: stress change amount, stress change rate, temperature change amount, temperature change rate, seepage pressure change amount, and seepage pressure rise rate.

[0012] Furthermore, the data analysis module is specifically used for: Determine the certainty of each warning indicator corresponding to each warning level; Calculate the joint risk probability when multiple early warning indicators are simultaneously in an abnormal state; The comprehensive warning level is obtained by integrating the certainty of each warning indicator corresponding to each warning level, as well as the joint risk probability when multiple warning indicators are in an abnormal state at the same time.

[0013] Furthermore, determining the certainty of each warning indicator corresponding to each warning level includes: Based on the measured values ​​corresponding to the early warning indicators, a standard cloud model for each early warning level corresponding to each indicator is constructed. Based on the constructed standard cloud model, the certainty of each warning indicator corresponding to each warning level is determined.

[0014] Furthermore, the joint risk probability when multiple early warning indicators are simultaneously in an abnormal state is calculated, specifically as follows: The Copula joint risk assessment method was used to calculate the joint risk probability when multiple early warning indicators are simultaneously in an abnormal state.

[0015] Furthermore, the method of fusing the certainty of each warning indicator corresponding to each warning level and the joint risk probability when multiple warning indicators are simultaneously in an abnormal state to obtain a comprehensive warning level is as follows: The Dempster-Shafer evidence theory is used to fuse the certainty of each warning level corresponding to the warning indicators and the joint risk probability when multiple warning indicators are in an abnormal state at the same time, so as to obtain a comprehensive warning level.

[0016] Furthermore, the step of outputting the early warning result based on the calculated comprehensive early warning level includes: The calculated comprehensive warning level is compared with the preset warning threshold to determine the warning result; the warning result includes safe status, relatively safe status, medium risk and high risk. If the warning result is a safe status, it means that all monitoring indicators are within a safe fluctuation range; If the warning result is a relatively safe state, it indicates that some indicators are abnormal and there is a certain probability of a sudden water inrush accident. At this time, we should pay more attention and increase the frequency of on-site inspections. If the warning result is medium risk, it means that multiple monitoring indicators are abnormal or the probability of combined risk is increased. At this time, on-site emergency preparedness should be activated and drainage inspection and hidden danger investigation should be carried out. If the warning result is high risk, it indicates that the monitoring indicators show a significant abnormal response, suggesting that the risk of sudden water inrush exceeds the preset threshold. In this case, preset emergency measures should be taken immediately. On the other hand, this invention also provides a method for early warning of sudden water inrush disasters in mines based on multi-parameter fusion identification, implemented using the aforementioned mine sudden water inrush disaster early warning system based on multi-parameter fusion identification, comprising: S1, collects multi-parameter monitoring data; S2, Data cleaning, compensation and windowing; S3, extract disturbance stress, temperature and osmotic pressure characteristics; S4, cloud model calculates the certainty of a single indicator; S5, Copula calculates the joint anomaly risk probability; S6, DS evidence fusion forms a comprehensive risk; S7 outputs the warning level and dynamically updates the model.

[0017] In another aspect, the present invention also provides an electronic device comprising a processor and a memory; wherein the memory stores at least one instruction, which is loaded and executed by the processor to implement the above-described method.

[0018] In another aspect, the present invention also provides a computer-readable storage medium storing at least one instruction, which is loaded and executed by a processor to implement the above-described method.

[0019] The beneficial effects of the technical solution provided by this invention include at least the following: 1. This invention achieves spatiotemporal coordinated acquisition of surrounding rock disturbance stress, temperature and seepage pressure through multi-parameter integrated monitoring, reducing the inconsistency of multi-source data in space and time; avoiding the problem of data being difficult to align in space and time due to different sensor installation locations and times, and multiple sensors occupying limited borehole space.

[0020] 2. This invention reduces the impact of random fluctuations on early warning results by using a sliding window and multi-feature extraction.

[0021] 3. This invention uses a cloud model to express the uncertainty of the warning level boundary, making the single-indicator identification results continuous and interpretable.

[0022] 4. This invention uses the Copula function to characterize the nonlinear dependence and tail correlation among multiple monitoring indicators, which can reflect the risk of sudden water inrush caused by the common anomaly of multiple indicators.

[0023] 5. This invention improves the robustness of comprehensive early warning results by integrating multi-source evidence through the Dempster-Shafer evidence theory.

[0024] 6. This invention enhances the model's adaptability to different geological conditions and mining stages through a dynamic update mechanism. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a functional module block diagram of the mine water inrush disaster early warning system based on multi-parameter fusion identification provided in the embodiments of the present invention; Figure 2 This is a schematic diagram of the physical structure of the mine water inrush disaster early warning system based on multi-parameter fusion identification provided in an embodiment of the present invention; Figure 3 This is a structural diagram of the multi-parameter integrated monitoring sensor provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the execution flow of the mine water inrush disaster early warning method based on multi-parameter fusion identification provided in the embodiments of the present invention; Figure 5 This is a system block diagram of the electronic device provided in the embodiments of the present invention; Figure 6 This is a cross-sectional view of the multi-parameter integrated monitoring sensor provided in an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0028] First, it should be noted that in the embodiments of the present invention, the words "exemplarily," "for example," etc., are used to indicate that they are examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the term "exemplarily" is intended to present the concept in a specific manner. Furthermore, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either one or the other.

[0029] First Embodiment

[0030] This embodiment provides a mine water inrush disaster early warning system based on multi-parameter fusion identification. The functional modules of the system are as follows: Figure 1 As shown, the solid structure is as follows Figure 2 As shown, it includes: The multi-parameter monitoring module is used to collect multi-source monitoring indicator data related to the risk of mine water inrush disasters. The data preprocessing module is used to preprocess the collected multi-source monitoring indicator data to obtain multiple early warning indicator data; The data analysis module is used to calculate the comprehensive early warning level based on early warning indicator data; The early warning module is used to output early warning results based on the calculated comprehensive early warning level.

[0031] Specifically, the multi-parameter monitoring module in this embodiment can employ an integrated multi-parameter monitoring sensor. This sensor includes a hollow inclusion strain gauge, a temperature sensor, a pressure sensor, a water-blocking airbag, a device frame, data transmission cables, and grouting and venting pipes. It integrates the hollow inclusion strain gauge, temperature sensor, and pressure sensor into a single unit to achieve spatiotemporal coordinated monitoring of surrounding rock disturbance stress, temperature, and pressure. Its structure is as follows: Figure 3 and Figure 6 As shown. The device frame is a hollow structure made of aluminum alloy, internally used for grouting pipes, venting pipes, data transmission cables, and airbag inflation lines. The hollow-encapsulated strain gauge, the main body of the multi-parameter integrated monitoring device, has three sets of strain gauges evenly spaced circumferentially encapsulated within an epoxy resin layer. These strain gauges acquire the strain information of the surrounding rock around the borehole, and the strain data is exported via strain gauge cables. Later, it is converted into perturbation stress data using the elastic constitutive relationship established in the data preprocessing module. The temperature sensor, a thin-film platinum resistor, is located next to the strain gauges of the hollow-encapsulated strain gauge and is also encapsulated in an epoxy resin layer. It monitors the temperature and its changes at the monitoring point and exports the data via the platinum resistor data cable. The pressure sensor, a piezoresistive piezometer, is located at the top of the multi-parameter integrated monitoring device. The outermost end uses permeable stone to filter groundwater. The filtration pressure of the groundwater causes deformation of the sensitive diaphragm, which is recorded by the piezoresistive sensor and transmitted via the piezometer cable. It is used to monitor the pressure of fracture water or pore water in the surrounding rock. All sensors used output voltage signals. The piezometer cable, strain gauge data cable, and platinum resistance thermometer data cable converge at a multi-parameter data transmission cable, which then transmits the multi-parameter data to the data preprocessing device. The water-blocking airbag is made of rubber. After the device reaches the monitoring point, the airbag is inflated through an inflation pipe to seal the front end of the device before grouting and bonding. This ensures effective bonding between the device and the surrounding rock during seepage, achieving simultaneous, localized, and synchronous acquisition of multiple parameters including stress, strain, temperature, and seepage pressure. The three types of sensors sample simultaneously at the same location, and the sampled data is transmitted to the data preprocessing module via wired connection.

[0032] The data preprocessing module calculates the disturbance stress based on the strain values ​​of strain gauges in different directions, the elastic modulus of the rock mass, and Poisson's ratio, and further calculates the stress increment and stress change rate. When the monitoring area is affected by mining disturbances, fracture propagation, or the formation of water-conducting channels, the surrounding rock stress may change rapidly; seepage pressure may rise continuously or change abruptly; and temperature may be affected by water movement, heat exchange, or changes in the sensor environment, resulting in anomalies. Joint analysis of these characteristics can more accurately reflect the gestation process of sudden water inrush disasters.

[0033] Specifically, the data preprocessing module first aligns the raw data in time to ensure that strain, temperature, and pressure data have the same or interpolable time labels; secondly, it handles missing values ​​and removes outliers; thirdly, it performs temperature compensation on strain and pressure measurements based on the temperature sensor output; and finally, it uses a sliding time window to extract six features (stress change, stress change rate, temperature change, temperature change rate, pressure change, and pressure rise rate). The width of the sliding window can be set to several sampling periods depending on the monitoring object; for example, a shorter window can be used during the rapid advancement phase of the working face, while a longer window can be used during the stable phase. The model parameters are dynamically updated based on newly added monitoring data, on-site verification results, false alarm records, and missed alarm records.

[0034] The data analysis module, acting as a ground-based data processor, first establishes cloud models corresponding to each warning interval. Based on the relationship between monitoring characteristic values ​​and the standard cloud models for each warning level, it calculates the certainty of monitoring indicators belonging to each warning level, ensuring the continuity and interpretability of single-indicator identification results. Then, a Copula joint risk assessment is performed, establishing the marginal distribution of each monitoring indicator, converting the original monitoring characteristic values ​​into pseudo-observations within the (0,1) interval, and establishing a multi-parameter joint distribution model to calculate the joint risk probability when multiple indicators are simultaneously in an abnormal state. Finally, Dempster-Shafer evidence theory is used for fusion to obtain the comprehensive warning level.

[0035] Specifically, the data analysis module in this embodiment classifies the early warning levels into safe, relatively safe, medium risk, and high risk, displayed using indicator lights with corresponding indicator colors of green, blue, yellow, and red. For the six monitoring indicators, standard cloud models for each level are pre-determined based on historical field data, indoor test data, numerical simulation results, and expert experience. Each standard cloud model consists of the expected E... x Entropy E n and hyperentropy H e Three numerical characteristics describe it. E x E represents the typical value of the indicator corresponding to this level. n H represents the fuzziness and randomness of the boundary of this level of concept. eThis represents the uncertainty of entropy. After real-time feature values ​​are input into the cloud model, the certainty of their belonging to each warning level can be obtained, thus forming a single-index warning level certainty vector. For example, for the seepage pressure rise rate index, different threshold intervals can be divided based on historical safe operation data and precursor data of sudden water inrush, and standard cloud model parameters can be calculated from these threshold intervals. When the real-time seepage pressure rise rate is near the boundary between two warning levels, the cloud model will not simply provide a single level, but rather multiple certainty values ​​for adjacent levels, thus better reflecting the actual situation of uncertain warning level boundaries in engineering sites.

[0036] Copula Joint Risk Assessment is used to reflect the dependencies between the six monitoring indicators. Specifically, for the j-th monitoring indicator x... j Establish the marginal distribution function F j The marginal distribution function can be a normal distribution, log-normal distribution, Gamma distribution, Weibull distribution, kernel density estimation, or empirical distribution function. When the amount of data is insufficient or the distribution shape is uncertain, rank transformation can be used to generate pseudo-observations u. j =r j / (n+1), where r j Let be the rank of the sample in the same index sequence, and n be the total number of samples. This transforms monitoring indicators with different dimensions and distributions into a unified probability scale within the interval (0,1).

[0037] After obtaining pseudo-observations, the Copula joint risk assessment module can select Gaussian Copula, t-Copula, Clayton Copula, Gumbel Copula, Frank Copula, or Vine Copula to construct a joint distribution model. Gaussian Copula is suitable for scenarios with relatively symmetrical correlation structures; t-Copula is suitable for scenarios with two-tailed correlations; Gumbel Copula is suitable for scenarios with significant common anomalies at high quantiles; Clayton Copula is suitable for scenarios with significant common anomalies at low quantiles; and Vine Copula is suitable for high-dimensional complex dependency structures. In mine inrush water early warning, when focusing on high-risk conditions where increased seepage pressure, enhanced disturbance stress, and temperature anomalies occur simultaneously, the fitting effects of t-Copula and Gumbel Copula should be compared first.

[0038] Copula parameters are determined through maximum likelihood estimation. Given a Copula density function c(u1,u2,…,u6,θ), where θ is the parameter to be estimated and u1,u2,…,u6 are pseudo-observations of six monitoring indicators, a log-likelihood function is constructed.

[0039] By finding the parameter θ that maximizes the log-likelihood function through numerical optimization, the estimated Copula parameter can be obtained. The optimal model is determined using a combination of AIC and BIC indices.

[0040] After establishing the joint distribution model, the Copula joint risk assessment module calculates the joint risk probability based on the hazard threshold or quantile threshold of each monitoring indicator. Taking disturbance stress σ, temperature T, and seepage pressure p as examples, P can be calculated. c =P(σ>σ c ,T>T c ,p>p c ), where σ c T c and p c These represent the danger thresholds for stress, temperature, and osmotic pressure, respectively. This joint risk probability characterizes the likelihood of multiple indicators simultaneously entering an abnormal state and serves as input evidence for joint risk into the evidence fusion module.

[0041] The evidence fusion module converts the determination vectors of the stress cloud model, temperature cloud model, and pressure cloud model, as well as the Copula joint risk results, into basic probability allocation functions. Specifically, a cloud model is established for each warning level based on a preset risk probability level interval, and P is calculated. c Certainty of the warning level The formula for calculating the degree of certainty is:

[0042] in The certainty is normalized to obtain the corresponding support.

[0043] For each source of evidence, the basic probability assignment function represents the degree to which the evidence supports each warning level. Subsequently, the Dempster-Shafer evidence theory is used to calculate the evidence conflict coefficient and fuse the results to obtain the fused confidence level of the overall warning level. When a source of evidence significantly conflicts with other sources, the weight of that source can be reduced based on the conflict coefficient, or a manual review mechanism can be triggered to avoid a single anomalous data point having an excessive impact on the overall warning result.

[0044] The early warning module outputs early warning information based on the comprehensive early warning level. The early warning level set is Θ={H1, H2, H3, H4}, which are green, blue, yellow, and red, respectively. Green indicates safety, with all monitoring indicators within a safe fluctuation range; blue indicates a relatively safe state, but some indicators show slight abnormalities, with a low probability of a sudden water inrush accident, requiring increased attention and more frequent on-site inspections; yellow indicates medium risk, with multiple indicators showing abnormalities or an increased probability of combined risk, indicating a high probability of a sudden water inrush accident, requiring on-site emergency preparedness, drainage checks, and hazard investigations; red indicates high risk, with significant abnormal responses from indicators, indicating an extremely high risk of a sudden water inrush, requiring immediate emergency measures such as evacuation, shutdown of mining, drainage, and sealing. Based on the above, this embodiment also provides a mine sudden water inrush disaster early warning method implemented using the above-mentioned mine sudden water inrush disaster early warning system based on multi-parameter fusion identification, such as... Figure 4 As shown, it includes: S1, collects multi-parameter monitoring data; The strain, temperature and osmotic pressure data of the hollow inclusions are collected synchronously by a multi-parameter integrated monitoring sensor.

[0045] S2, Data cleaning, compensation and windowing; The collected data is cleaned, outliers are removed, temperature compensation is applied, and a sliding window is created.

[0046] S3, extract disturbance stress, temperature and osmotic pressure characteristics; The disturbance stress is calculated and the stress, temperature and seepage pressure related features are extracted.

[0047] S4, cloud model calculates the certainty of a single indicator; By inputting the feature values ​​into the cloud model, the certainty of the early warning level for a single indicator can be obtained.

[0048] S5, Copula calculates the joint anomaly risk probability; The multi-indicator features are converted into pseudo-observations and input into the Copula model to calculate the joint risk probability.

[0049] S6, DS evidence fusion forms a comprehensive risk; This is based on the Dempster-Shafer evidence theory, which integrates various sources of evidence.

[0050] S7 outputs the warning level and dynamically updates the model; Output a comprehensive early warning level and use new samples and on-site feedback for dynamic model updates.

[0051] In summary, this embodiment provides a mine water inrush hazard early warning system based on multi-parameter fusion identification. This system achieves spatiotemporal collaborative acquisition of surrounding rock disturbance stress, temperature, and seepage pressure through integrated multi-parameter monitoring, reducing spatial and temporal inconsistencies in multi-source data. It avoids the problems of data misalignment and multiple sensors crowding out limited borehole space caused by different sensor installation locations and times. The impact of random fluctuations on early warning results is reduced through sliding windows and multi-feature extraction. The uncertainty of early warning level boundaries is expressed using a cloud model, ensuring the continuity and interpretability of single-indicator identification results. The nonlinear dependencies and tail correlations among multiple monitoring indicators are characterized by a Copula function, reflecting the risk of water inrush caused by common anomalies in multiple indicators. The robustness of the comprehensive early warning results is improved by fusing multi-source evidence using Dempster-Shafer evidence theory. A dynamic update mechanism enhances the model's adaptability to different geological conditions and mining stages. This overcomes the problems of insufficient reliability of single-index threshold alarms, inadequate characterization of multi-parameter coupling relationships, and insufficient accuracy of early warning identification when approaching the alarm threshold in existing technologies, thereby improving the accuracy of early warning for mine water inrush disasters.

[0052] Second Embodiment

[0053] This embodiment provides an electronic device, such as... Figure 5 As shown, the electronic device includes a processor and a memory; wherein the processor and the memory can be connected via a communication bus; the memory stores at least one instruction, which is loaded and executed by the processor to implement the method of the first embodiment described above. Furthermore, the electronic device may also include a transceiver, the processor and the transceiver can be connected via a communication bus, and the transceiver is used to communicate with other devices.

[0054] Below, in conjunction with Figure 5 A detailed introduction to each component of this electronic device is provided below: The processor is the control center of the electronic device. The electronic device may include multiple processors, each of which can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The term "processor" can refer to a single processor or a collective term for multiple processing elements. For example, a processor can be one or more central processing units (CPUs), other general-purpose processors, application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), one or more field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor can perform various functions of the electronic device by running or executing software programs stored in memory and by calling data stored in memory.

[0055] In a specific implementation, as one example, the processor may include one or more CPUs, for example... Figure 5 CPU0 and CPU1 shown are, of course, merely illustrative examples.

[0056] The memory is used to store the software program that executes the solution of the present invention, and the processor controls its execution. For specific implementation methods, please refer to the above method embodiments, which will not be repeated here.

[0057] Optionally, the memory may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory may be integrated with the processor or exist independently, and may be accessed through the interface circuit of the electronic device ( Figure 5 (Not shown in the image) is coupled to the processor; however, this embodiment of the invention does not impose specific limitations on this.

[0058] The transceiver may include a receiver and a transmitter. Figure 5 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function. The transceiver can be integrated with the processor or exist independently, and can be connected through the interface circuit of the electronic device (…). Figure 5 (Not shown in the image) is coupled to the processor, and this embodiment of the invention does not specifically limit this.

[0059] In addition, it should be noted that, Figure 5 The structure of the electronic device shown is not intended to limit the device. Actual devices may include more or fewer components than shown, or combine certain components, or have different component arrangements. Furthermore, the technical effects achieved by this electronic device when performing the method of the first embodiment described above can be referenced to the technical effects described in the first embodiment; therefore, they will not be repeated here.

[0060] Third Embodiment

[0061] This embodiment provides a computer-readable storage medium storing at least one instruction, which is loaded and executed by a processor to implement the method of the first embodiment described above. The computer-readable storage medium may be a ROM, random access memory, CD-ROM, magnetic tape, floppy disk, or optical data storage device, etc. The instruction stored therein can be loaded and executed by a processor in a terminal.

[0062] Furthermore, it should be noted that the present invention can be provided as a method, apparatus, or computer program product. Therefore, embodiments of the present invention can take the form of a completely or partially hardware embodiment, a completely or partially software embodiment, or an embodiment combining software and hardware aspects. Moreover, when implemented in software, embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any usable medium accessible to a computer or a data storage device such as a server or data center containing one or more sets of usable media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive (SSD).

[0063] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0064] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0065] It should also 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. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device 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 terminal device. 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 terminal device that includes said element. Furthermore, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Additionally, the character " / " in this text generally indicates an "or" relationship between the preceding and following objects, but it can also indicate an "AND / OR" relationship. Please refer to the context for specific interpretations. "At least one" refers to one or more items, while "more than" refers to two or more items. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can be represented as: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0066] Furthermore, it is understood that in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0067] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0068] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of functional modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms. Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs. Additionally, the functional units in the various embodiments of this invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0069] If the method is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0070] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention. It should be pointed out that although preferred embodiments of the present invention have been described, those skilled in the art, once they understand the basic inventive concept of the present invention, can make several improvements and modifications without departing from the principles described herein. These improvements and modifications should also be considered within the scope of protection of the present invention. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

Claims

1. A mine water inrush disaster early warning system based on multi-parameter fusion identification, characterized in that, include: The multi-parameter monitoring module is used to collect multi-source monitoring indicator data related to the risk of mine water inrush disasters. The data preprocessing module is used to preprocess the collected multi-source monitoring indicator data to obtain multiple early warning indicator data; The data analysis module is used to calculate the comprehensive early warning level based on early warning indicator data; The early warning module is used to output early warning results based on the calculated comprehensive early warning level.

2. The mine water inrush disaster early warning system based on multi-parameter fusion identification as described in claim 1, characterized in that, The multi-parameter monitoring module integrates a temperature sensor, a strain sensor, and a pressure sensor into one unit to achieve spatiotemporal coordinated monitoring of surrounding rock disturbance stress, temperature, and pressure.

3. The mine water inrush disaster early warning system based on multi-parameter fusion identification as described in claim 2, characterized in that, The data preprocessing module is specifically used for: Time alignment is performed on the collected multi-source monitoring index data to ensure that disturbance stress data, temperature data, and seepage pressure data have the same or interpolable corresponding time labels. Data cleaning is performed on the data aligned to the completion time, including handling missing values ​​and removing outliers; For the cleaned data, temperature compensation is performed on the disturbance stress data and osmotic pressure data based on the temperature data. For the data that has undergone temperature compensation, a sliding time window is used to extract multiple early warning indicator data; Based on the newly added monitoring indicator data, the verification results of on-site early warning results, and the false alarm and missed alarm records, the parameters of the calculation model for the comprehensive early warning level are updated.

4. The mine water inrush disaster early warning system based on multi-parameter fusion identification as described in claim 3, characterized in that, The early warning indicator data includes: stress change, stress change rate, temperature change, temperature change rate, seepage pressure change, and seepage pressure rise rate.

5. The mine water inrush disaster early warning system based on multi-parameter fusion identification as described in claim 1, characterized in that, The data analysis module is specifically used for: Determine the certainty of each warning indicator corresponding to each warning level; Calculate the joint risk probability when multiple early warning indicators are simultaneously in an abnormal state; The comprehensive warning level is obtained by integrating the certainty of each warning indicator corresponding to each warning level, as well as the joint risk probability when multiple warning indicators are in an abnormal state at the same time.

6. The mine water inrush disaster early warning system based on multi-parameter fusion identification as described in claim 5, characterized in that, The determination of the certainty of each warning indicator corresponding to each warning level includes: Based on the measured values ​​corresponding to the early warning indicators, a standard cloud model for each early warning level corresponding to each indicator is constructed. Based on the constructed standard cloud model, the certainty of each warning indicator corresponding to each warning level is determined.

7. The mine water inrush disaster early warning system based on multi-parameter fusion identification as described in claim 5, characterized in that, The calculation of the joint risk probability when multiple early warning indicators are simultaneously in an abnormal state is as follows: The Copula joint risk assessment method was used to calculate the joint risk probability when multiple early warning indicators are simultaneously in an abnormal state.

8. The mine water inrush disaster early warning system based on multi-parameter fusion identification as described in claim 5, characterized in that, The method of fusing the certainty of each warning indicator corresponding to each warning level and the joint risk probability when multiple warning indicators are simultaneously in an abnormal state to obtain a comprehensive warning level is as follows: The Dempster-Shafer evidence theory is used to fuse the certainty of each warning level corresponding to the warning indicators and the joint risk probability when multiple warning indicators are in an abnormal state at the same time, so as to obtain a comprehensive warning level.

9. The mine water inrush disaster early warning system based on multi-parameter fusion identification as described in claim 1, characterized in that, The step of outputting early warning results based on the calculated comprehensive early warning level includes: The calculated comprehensive warning level is compared with the preset warning threshold to determine the warning result; the warning result includes safe status, relatively safe status, medium risk and high risk. If the warning result is a safe status, it means that all monitoring indicators are within a safe fluctuation range; If the warning result is a relatively safe state, it indicates that some indicators are abnormal and there is a certain probability of a sudden water inrush accident. At this time, we should pay more attention and increase the frequency of on-site inspections. If the warning result is medium risk, it means that multiple monitoring indicators are abnormal or the probability of combined risk is increased. At this time, on-site emergency preparedness should be activated and drainage inspection and hidden danger investigation should be carried out. If the warning result is high risk, it means that the monitoring indicators have a significant abnormal response, indicating that the risk of sudden water inrush exceeds the preset threshold. At this time, the preset emergency measures should be taken immediately.

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