Mine area disaster early warning method and system based on digital twinning

Abstract

The invention relates to the technical field of mine early warning, and discloses a mine area disaster early warning method and system based on digital twinning, and the method comprises the steps: deploying a sensor network in a mine area, collecting the environment data of the mine area in real time, and carrying out the preprocessing of the environment data; constructing a digital twinborn model of the mine area by utilizing a three-dimensional modeling technology, and mapping the preprocessed environment data into the digital twinborn model; based on the historical disaster data and the expert knowledge base, establishing a mine disaster risk assessment index system, and determining the weight of each index; determining a disaster risk value of the mine area according to the weight of each index and a digital twinborn model; and comparing the disaster risk value with a preset threshold value, triggering an early warning mechanism according to a result, and generating early warning information for alarming. The method can accurately reflect the risk in real time, carries out early warning in advance, overcomes the limitation of a traditional method, effectively reduces the disaster loss, and guarantees the safety production of a mine.

Description

Technical Field

[0001] The present invention relates to the technical field of mine warning, and in particular to a mine area disaster warning method and system based on digital twin. Background Art

[0002] As the core area of resource exploitation, the mine area not only provides important support for the country's economic development, but also faces many serious disaster risks. These disasters not only threaten the lives of miners, but also may have a huge impact on mine facilities, the surrounding ecological environment and the lives of local residents. Common mine disasters include collapses, landslides, gas explosions, water inrusions, etc., and their occurrences often have characteristics such as suddenness, concealment and strong destructiveness.

[0003] Traditional warning methods have problems such as limited monitoring means, insufficient data processing and analysis capabilities, and low warning accuracy. Therefore, there is an urgent need for a mine area disaster warning method and system based on digital twin to achieve accurate warning. Summary of the Invention

[0004] The purpose of the present invention is to provide a mine area disaster warning method and system based on digital twin, aiming to solve the above problems.

[0005] The present invention provides a mine area disaster warning method based on digital twin, including: Deploying a sensor network in the mine area to collect environmental data of the mine area in real time and preprocessing the environmental data; Using three-dimensional modeling technology to construct a digital twin model of the mine area and mapping the preprocessed environmental data into the digital twin model; Based on historical disaster data and an expert knowledge base, establishing a mine disaster risk assessment index system and determining the weights of each index in the mine disaster risk assessment index system; Determining the disaster risk value of the mine area according to the weights of each index and the digital twin model; Comparing the disaster risk value with a preset threshold, triggering an early warning mechanism according to the comparison result, and generating early warning information for alarm.

[0006] Preferably, the sensor network includes: a stress sensor for detecting stress changes in the mine area; A gas concentration sensor for detecting the gas concentration in the mine area; A water level sensor for detecting the water level height in the mine area; A displacement sensor for detecting the rock mass displacement in the mine area; A temperature sensor for detecting the temperature value in the mine area.

[0007] Preferably, the preprocessing includes data cleaning, filtering, normalization, and outlier handling.

[0008] Preferably, based on historical disaster data and an expert knowledge base, a mine disaster risk assessment index system is established, including: The historical disaster data includes disaster type, occurrence time, historical environmental data, geological conditions, mining activities, and equipment operation status; Based on the historical disaster data, key risk assessment indicators are determined according to the expert knowledge base; Based on the key risk assessment indicators, a mine disaster risk assessment index system is established; Among them, the key risk assessment indicators include: Hazard-causing factors include: formation lithology, geological structure, meteorological data, hydrogeological conditions, mining depth; Disaster-bearing environments include: ventilation capacity, drainage capacity, ecological environment; Vulnerability of disaster-bearing bodies includes: personnel distribution, equipment reliability, and building disaster resistance ability.

[0009] Preferably, the weights of each indicator in the mine disaster risk assessment index system are determined, including: The analytic hierarchy process is used to determine the subjective weights of each indicator; The coefficient of variation method is used to determine the objective weights of each indicator; According to the subjective weights and objective weights, the weights of each indicator in the mine disaster risk assessment index system are determined, weight = α × subjective weight + β × objective weight, and α + β = 1; Among them, using the analytic hierarchy process to determine the subjective weights of each indicator includes: The 1-9 scale method is used to construct the judgment matrix of the indicators, determine the maximum eigenvalue and eigenvector of the judgment matrix, and determine the weight vector of each criterion layer; Based on the weight vector of the criterion layer, the subjective weights of the indicator layer are determined; Using the coefficient of variation method to determine the objective weights of each indicator includes: Each indicator is normalized to determine the mean and standard deviation of each indicator; According to the mean and standard deviation, the coefficient of variation is calculated, and the objective weights of each indicator are calculated according to the coefficient of variation.

[0010] Preferably, the disaster risk value is compared with a preset threshold, and according to the comparison result, an early warning mechanism is triggered to generate early warning information for alarm, including: The disaster risk value is compared with the preset threshold. If the disaster risk value is less than the preset threshold, it is determined that the early warning mechanism is not triggered; If the disaster risk value is greater than or equal to the preset threshold, the warning mechanism is determined to be triggered, and the risk difference between the disaster risk value and the preset threshold is determined. The disaster risk level is determined according to the risk difference, and a warning message is generated according to the disaster risk level for alarm.

[0011] Preferably, determining the disaster risk level according to the risk difference includes: A first risk difference and a second risk difference are preset, and the first risk difference is less than the second risk difference; The disaster risk level is set according to the relationship between the risk difference and the first risk difference and the second risk difference; If the risk difference is less than the first risk difference, it is determined that the disaster risk level is a low risk level; If the risk difference is greater than or equal to the first risk difference and the risk difference is less than the second risk difference, it is determined that the disaster risk level is a medium risk level; If the risk difference is greater than or equal to the second risk difference, it is determined that the disaster risk level is a high risk level.

[0012] Preferably, the warning message includes the disaster type, the predicted occurrence location, the predicted occurrence time, and the disaster risk level.

[0013] Preferably, when alarming, an audible and visual alarm, a short message platform, and a mine area broadcast are used for alarming.

[0014] The present invention also discloses a mine area disaster warning system based on digital twin for applying the above-mentioned mine area disaster warning method based on digital twin, including: A data acquisition module, configured to deploy a sensor network in the mine area, collect environmental data of the mine area in real time, and preprocess the environmental data; A model construction module, configured to construct a digital twin model of the mine area by using three-dimensional modeling technology, and map the preprocessed environmental data into the digital twin model; An index establishment module, configured to establish a mine disaster risk assessment index system based on historical disaster data and an expert knowledge base, and determine the weight of each index in the mine disaster risk assessment index system; A disaster determination module, configured to determine the disaster risk value of the mine area according to the weight of each index and the digital twin model; A disaster warning module, configured to compare the disaster risk value with a preset threshold, trigger a warning mechanism according to the comparison result, and generate a warning message for alarm.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows. The present invention collects and preprocesses environmental data in real time by deploying a sensor network to ensure data quality. A digital twin model is constructed using three-dimensional modeling and the data is mapped to it, visually presenting the mine state. A risk assessment index system is established based on historical data and expert knowledge, the weights are scientifically determined, and the risks are comprehensively evaluated. The disaster risk value is calculated by combining the index weights and the model, and compared with the threshold to trigger an early warning, which can accurately reflect the risks in real time, give an early warning in advance, overcome the limitations of traditional methods, reduce disaster losses, and ensure the safe production of mines. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings according to the provided drawings without creative efforts.

[0017] Figure 1 is a schematic flowchart of a method for disaster early warning in a mine area based on digital twin of the present invention; Figure 2 is a functional block diagram of a system for disaster early warning in a mine area based on digital twin of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0019] As Figure 1 shown, the present invention provides a method for disaster early warning in a mine area based on digital twin, including: Deploy a sensor network in the mine area to collect environmental data in the mine area in real time and preprocess the environmental data.

[0020] Use three-dimensional modeling technology to construct a digital twin model of the mine area and map the preprocessed environmental data into the digital twin model.

[0021] Based on historical disaster data and an expert knowledge base, establish a mine disaster risk assessment index system and determine the weights of each index in the mine disaster risk assessment index system.

[0022] Determine the disaster risk value of the mine area according to the weights of each index and the digital twin model.

[0023] Compare the disaster risk value with a preset threshold, trigger an early warning mechanism according to the comparison result, and generate early warning information for alarm.

[0024] The present invention can realize real-time monitoring and early warning of disasters in the mine area, improving the accuracy and timeliness of disaster early warning. By deploying a sensor network, environmental data in the mine area can be comprehensively collected, including various factors such as geology, meteorology, and hydrology, providing a rich data source for disaster early warning. The digital twin model constructed using three-dimensional modeling technology can visually display the environmental conditions in the mine area, facilitating the analysis and judgment of disaster risks. At the same time, the mine disaster risk assessment index system established by combining historical disaster data and an expert knowledge base can scientifically evaluate the disaster risks in the mine area, providing a reliable basis for triggering the early warning mechanism. In addition, this method can automatically generate early warning information according to the disaster risk value, realizing the automation and intelligence of disaster early warning, and providing a strong guarantee for mine safety production.

[0025] In some embodiments of the present application, the sensor network includes: a stress sensor for detecting stress changes in the mine area; a gas concentration sensor for detecting the gas concentration in the mine area; a water level sensor for detecting the water level height in the mine area; a displacement sensor for detecting the rock mass displacement in the mine area; and a temperature sensor for detecting the temperature value in the mine area.

[0026] In some embodiments of the present application, the preprocessing includes data cleaning, filtering, normalization, and outlier processing.

[0027] It can be understood that by real-time monitoring of key parameters such as stress, gas concentration, water level, rock mass displacement, and temperature, potential disaster risks in the mine area can be discovered in a timely manner. Data cleaning can remove invalid or incorrect data to ensure data accuracy; filtering can smooth data fluctuations and reduce noise interference; normalization converts data with different dimensions into the same scale for subsequent analysis; outlier processing can identify and process abnormal data to avoid its impact on disaster risk assessment. These preprocessing steps together improve the reliability and usability of environmental data, laying a solid foundation for subsequent digital twin model construction and disaster risk assessment.

[0028] In some embodiments of the present application, based on historical disaster data and an expert knowledge base, a mine disaster risk assessment index system is established, including: The historical disaster data includes disaster type, occurrence time, historical environmental data, geological conditions, mining activities, and equipment operation status; based on the historical disaster data, key risk assessment indicators are determined according to the expert knowledge base; a mine disaster risk assessment index system is established based on the key risk assessment indicators. Among them, the key risk assessment indicators include: The disaster-causing factors include: formation lithology, geological structure, meteorological data, hydrogeological conditions, and mining depth. The disaster-bearing environment includes: ventilation capacity, drainage capacity, and ecological environment. The vulnerability of the disaster-bearing body includes: personnel distribution, equipment reliability, and building disaster resistance ability.

[0029] It can be understood that by integrating historical disaster data and an expert knowledge base, the key factors affecting mine safety can be comprehensively and accurately identified. The historical disaster data provides actual cases of disaster occurrences, providing a valuable empirical basis for risk assessment; the expert knowledge base condenses the wisdom and experience of experts in the field, ensuring the scientificity and authority of risk assessment. Based on the combination of the two, the established risk assessment index system not only covers multiple dimensions such as disaster-causing factors, disaster-bearing environments, and the vulnerability of disaster-bearing bodies, but also ensures the comprehensiveness and pertinence of the index system.

[0030] In some embodiments of the present application, determining the weights of each index in the mine disaster risk assessment index system includes: using the analytic hierarchy process to determine the subjective weights of each index; using the coefficient of variation method to determine the objective weights of each index; determining the weights of each index in the mine disaster risk assessment index system according to the subjective weights and objective weights, weight = α × subjective weight + β × objective weight, and α + β = 1.

[0031] Among them, using the analytic hierarchy process to determine the subjective weights of each index includes: using the 1-9 scale method to construct the judgment matrix of the index, determining the maximum eigenvalue and eigenvector of the judgment matrix, and determining the weight vector of each criterion layer; determining the subjective weights of the index layer based on the weight vector of the criterion layer.

[0032] Using the coefficient of variation method to determine the objective weights of each index includes: normalizing each index to determine the mean and standard deviation of each index; calculating the coefficient of variation according to the mean and standard deviation, and calculating the objective weights of each index according to the coefficient of variation.

[0033] In this embodiment, using the analytic hierarchy process to determine the subjective weights includes: Construct the judgment matrix: Organize experts to make pairwise comparisons of each level of indicators. According to levels such as "equally important", "slightly important", "significantly important", "strongly important", and "extremely important", use the 1-9 scale method to construct the judgment matrix. For example, compare the importance of disaster-causing factors, disaster-forming environments, and vulnerability of disaster-bearing bodies in the criterion layer relative to the target layer of mine disaster risk assessment to obtain the judgment matrix A. Calculate the weight vector: By calculating the maximum eigenvalue and the corresponding eigenvector of the judgment matrix A, and after normalization, obtain the weight vector W1 of each criterion layer. Similarly, for the sub-indicators under each criterion layer, construct judgment matrices respectively, and calculate the weight vectors W2, W3, and W4 of each sub-indicator relative to its corresponding criterion layer.

[0034] Consistency test: To ensure the rationality of the judgment matrix, a consistency test is required. Calculate the consistency index CI = (λmax - n) / (n - 1), where λmax is the maximum eigenvalue of the judgment matrix and n is the order of the matrix. Look up the corresponding average random consistency index RI (which can be queried through a table). When CR = CI / RI < 0.1, it is considered that the judgment matrix has satisfactory consistency; otherwise, the judgment matrix needs to be adjusted again.

[0035] Use the coefficient of variation method to determine the objective weights, including: Data processing: Normalize each indicator to make the data comparable. For positive indicators (the larger the value, the better), use the formula Xij = (Xij - min(Xj)) / (max(Xj) - min(Xj)); for negative indicators (the smaller the value, the better), use the formula Xij = (max(Xj) - Xij) / (max(Xj) - min(Xj)). Where Xij is the value of the jth indicator of the ith sample, and max(Xj) and min(Xj) are the maximum and minimum values of the jth indicator respectively. Calculate the mean and standard deviation: Calculate the mean μj and standard deviation σj of each indicator. Calculate the coefficient of variation: The coefficient of variation Cj = σj / μj, which reflects the degree of dispersion of the data.

[0036] Determine the objective weights: Calculate the objective weights Pj of each indicator according to the coefficient of variation, where Pj = Cj / ∑Cj. Finally, fuse the subjective weights and objective weights according to a certain ratio (such as the weighted average method) to obtain the comprehensive weights of each indicator. Comprehensive weight = α × subjective weight + β × objective weight (α + β = 1, and the values of α and β can be determined according to specific situations).

[0037] It can be understood that by combining the analytic hierarchy process and the coefficient of variation method, the present application realizes the scientific determination of the weights of various indicators in the mine disaster risk assessment index system. The analytic hierarchy process constructs a judgment matrix and calculates the eigenvector, fully considering the subjective judgment of experts on the importance of indicators and reflecting the relative importance degree among indicators. The coefficient of variation method, on the other hand, reflects the objective fluctuation of indicator data by calculating the coefficient of variation of each indicator, thereby determining the objective weight of the indicator. The combination of the two takes into account both the subjective experience of experts and the objective characteristics of the data, making the determined weight more reasonable and reliable.

[0038] In some embodiments of the present application, the disaster risk value is compared with a preset threshold, and according to the comparison result, an early warning mechanism is triggered to generate an early warning message for alarm, including: comparing the disaster risk value with the preset threshold. If the disaster risk value is less than the preset threshold, it is determined that the early warning mechanism is not triggered; if the disaster risk value is greater than or equal to the preset threshold, it is determined that the early warning mechanism is triggered, and the risk difference between the disaster risk value and the preset threshold is determined. According to the risk difference, the disaster risk level is determined, and an early warning message is generated for alarm according to the disaster risk level.

[0039] It can be understood that by setting a preset threshold, the present application realizes the automatic judgment and early warning of mine disasters. When the disaster risk value reaches or exceeds the preset threshold, the early warning mechanism is triggered, and the system can respond quickly, generate an early warning message and give an alarm. At the same time, according to the risk difference between the disaster risk value and the preset threshold, the system can further determine the disaster risk level, providing more specific guidance for disaster prevention and control work.

[0040] In some embodiments of the present application, determining the disaster risk level according to the risk difference includes: presetting a first risk difference and a second risk difference, where the first risk difference is less than the second risk difference; setting the disaster risk level according to the relationship between the risk difference and the first risk difference and the second risk difference; if the risk difference is less than the first risk difference, it is determined that the disaster risk level is a low risk level; if the risk difference is greater than or equal to the first risk difference and less than the second risk difference, it is determined that the disaster risk level is a medium risk level; if the risk difference is greater than or equal to the second risk difference, it is determined that the disaster risk level is a high risk level.

[0041] It can be understood that by refining the range of risk differences, the present application can more precisely divide the disaster risk levels. Different risk levels correspond to different degrees of disaster threats, which provides a clearer basis for disaster prevention and control for the mine management department. At the low risk level, the management department can take conventional monitoring measures; at the medium risk level, it is necessary to increase the monitoring frequency and prepare corresponding emergency supplies; at the high risk level, it is necessary to immediately activate the emergency plan and organize emergency measures such as personnel evacuation.

[0042] In some embodiments of the present application, the warning information includes the disaster type, predicted occurrence location, predicted occurrence time, and disaster risk level.

[0043] In some embodiments of the present application, when giving an alarm, it is carried out through an audible and visual alarm, a short message platform, and a mine area broadcast.

[0044] It can be understood that by providing detailed warning information, including the disaster type, predicted occurrence location, predicted occurrence time, and disaster risk level, the present application enables the mine management department to quickly understand the specific situation of the disaster, so as to make an accurate emergency response. The clarity of the disaster type helps the management department to take corresponding countermeasures; the provision of the predicted occurrence location and time makes the allocation of emergency resources more accurate and efficient; and the division of the disaster risk level provides a basis for the priority of the emergency response. In addition, using multiple alarm methods such as audible and visual alarms, short message platforms, and mine area broadcasts can ensure that the warning information is conveyed to relevant personnel in the first time, improving the speed and effect of the emergency response.

[0045] As Figure 2 shown, the present invention discloses a mine area disaster warning system based on digital twin for applying the above-mentioned mine area disaster warning method based on digital twin, including: a data acquisition module configured to deploy a sensor network in the mine area, collect environmental data of the mine area in real time, and preprocess the environmental data.

[0046] A model construction module configured to use three-dimensional modeling technology to construct a digital twin model of the mine area and map the preprocessed environmental data into the digital twin model.

[0047] An index establishment module configured to establish a mine disaster risk assessment index system based on historical disaster data and an expert knowledge base and determine the weights of each index in the mine disaster risk assessment index system.

[0048] A disaster determination module configured to determine the disaster risk value of the mine area according to the weights of each index and the digital twin model.

[0049] The disaster warning module is configured to compare the disaster risk value with a preset threshold, trigger a warning mechanism according to the comparison result, and generate warning information for alarm.

[0050] The present invention realizes the real-time monitoring and warning of the disaster risk in the mine area, and improves the accuracy and timeliness of disaster warning. The data acquisition module collects the environmental data of the mine area in real time, ensuring the timeliness and accuracy of the data; the model construction module constructs a digital twin model of the mine area by using three-dimensional modeling technology, realizing the virtual reproduction of the mine area environment; the index establishment module establishes a mine disaster risk assessment index system based on historical disaster data and expert knowledge base, providing a scientific basis for the quantitative assessment of disaster risk; the disaster determination module determines the disaster risk value of the mine area according to the weight of each index and the digital twin model, providing reliable data support for disaster warning; the disaster warning module compares the disaster risk value with a preset threshold, triggers a warning mechanism according to the comparison result, generates warning information for alarm, and realizes the timely warning and response to the disaster risk in the mine area.

[0051] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0052] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0053] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device realizes the functions specified in Figure 1 one or more flows and / or blocks Figure 1The functions specified in one or more boxes.

[0054] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide for implementing the steps of the functions specified in one or more processes and / or boxes Figure 1 One process or more processes and / or boxes Figure 1 The steps of the functions specified in one box or more boxes.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific implementation manners of the present invention. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A mine area disaster early warning method based on digital twins, characterized in that: include: Deploy a sensor network in the mining area to collect environmental data of the mining area in real time and pre-process the environmental data; Using 3D modeling technology to build a digital twin model of the mine area, and mapping the preprocessed environmental data into the digital twin model; Based on historical disaster data and expert knowledge base, a mine disaster risk assessment index system is established, and the weight of each index in the mine disaster risk assessment index system is determined; Determine the disaster risk value of the mining area according to the weights of various indicators and the digital twin model; The disaster risk value is compared with a preset threshold value, and an early warning mechanism is triggered according to the comparison result to generate early warning information for alarm.

2. The digital twin-based mine area disaster early warning method according to claim 1 is characterized in that: The sensor network includes: a stress sensor for detecting stress changes in the mining area; Gas concentration sensor, used to detect gas concentration in mining areas; Water level sensor, used to detect the water level in the mining area; Displacement sensors are used to detect rock displacement in mining areas; Temperature sensor, used to detect the temperature value in the mining area.

3. The digital twin-based mine area disaster early warning method according to claim 1 is characterized in that: The preprocessing includes data cleaning, filtering, normalization and outlier processing.

4. The digital twin-based mine area disaster early warning method according to claim 1 is characterized in that: Based on historical disaster data and expert knowledge base, a mine disaster risk assessment indicator system is established, including: The historical disaster data include disaster type, occurrence time, historical environmental data, geological conditions, mining activities and equipment operating status; Based on the historical disaster data, determining key risk assessment indicators according to the expert knowledge base; Establishing a mine disaster risk assessment indicator system based on the key risk assessment indicators; Among them, the key risk assessment indicators include: Disaster-causing factors include: stratum lithology, geological structure, meteorological data, hydrological conditions, and mining depth; Disaster-prone environments include: ventilation capacity, drainage capacity, and ecological environment; The vulnerability of disaster-prone objects includes: personnel distribution, equipment reliability and building disaster resistance.

5. The digital twin-based mine area disaster early warning method according to claim 1 is characterized in that: Determine the weight of each indicator in the mine disaster risk assessment indicator system, including: The analytic hierarchy process was used to determine the subjective weight of each indicator; The coefficient of variation method is used to determine the objective weight of each indicator; Determine the weight of each indicator in the mine disaster risk assessment index system according to the subjective weight and the objective weight, weight=α×subjective weight+β×objective weight, and α+β=1; Among them, the hierarchical analysis method is used to determine the subjective weight of each indicator, including: The 1-9 scaling method is used to construct the judgment matrix of the indicators, determine the maximum eigenvalue and eigenvector of the judgment matrix, and determine the weight vector of each criterion layer; Determine the subjective weight of the indicator layer based on the weight vector of the criterion layer; The coefficient of variation method is used to determine the objective weight of each indicator, including: Normalize each indicator and determine the mean and standard deviation of each indicator; The coefficient of variation is calculated based on the mean and the standard deviation, and the objective weight of each indicator is calculated based on the coefficient of variation.

6. The digital twin-based mine area disaster early warning method according to claim 1 is characterized in that: Compare the disaster risk value with the preset threshold, trigger the early warning mechanism according to the comparison result, generate early warning information for alarm, including: Comparing the disaster risk value with the preset threshold, and if the disaster risk value is less than the preset threshold, determining not to trigger the early warning mechanism; If the disaster risk value is greater than or equal to the preset threshold, the early warning mechanism is triggered, and the risk difference between the disaster risk value and the preset threshold is determined, the disaster risk level is determined according to the risk difference, and early warning information is generated according to the disaster risk level for alarm.

7. The digital twin-based mine area disaster early warning method according to claim 6 is characterized in that: The disaster risk level is determined based on the risk difference, including: Preset a first risk difference and a second risk difference, wherein the first risk difference is smaller than the second risk difference; setting a disaster risk level according to a relationship between the risk difference and the first risk difference and the second risk difference; If the risk difference is less than the first risk difference, the disaster risk level is determined to be a low risk level; If the risk difference is greater than or equal to the first risk difference, and the risk difference is less than the second risk difference, the disaster risk level is determined to be a medium risk level; If the risk difference is greater than or equal to the second risk difference, the disaster risk level is determined to be a high risk level.

8. The digital twin-based mine area disaster early warning method according to claim 7 is characterized in that: The warning information includes the disaster type, predicted location, predicted time and disaster risk level.

9. The method for early warning of mine area disasters based on digital twins according to claim 1 is characterized in that: When an alarm is issued, it is made through sound and light alarms, SMS platforms and mine area broadcasts.

10. A digital twin-based mine area disaster early warning system, used to apply the digital twin-based mine area disaster early warning method according to any one of claims 1 to 9, characterized in that: include: A data acquisition module is configured to deploy a sensor network in the mine area, collect environmental data of the mine area in real time, and pre-process the environmental data; a model building module configured to build a digital twin model of the mine area using three-dimensional modeling technology and map the preprocessed environmental data into the digital twin model; An indicator establishment module is configured to establish a mine disaster risk assessment indicator system based on historical disaster data and an expert knowledge base, and determine the weight of each indicator in the mine disaster risk assessment indicator system; A disaster determination module is configured to determine a disaster risk value of a mine area according to weights of various indicators and the digital twin model; The disaster warning module is configured to compare the disaster risk value with a preset threshold, trigger a warning mechanism according to the comparison result, and generate warning information for alarm.

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