Coal mine well inclination self-adaptive analysis and early warning platform

Abstract

The invention discloses a coal mine shaft inclination adaptive analysis and early warning platform, and relates to the related field of safety early warning, and the platform comprises a roadway feature building module which is used for building roadway features; the penalty construction module is used for evaluating the importance degree of the roadway and generating a roadway early warning penalty factor; the periodic monitoring module is used for performing periodic monitoring on the roadway; the direct early warning module is used for carrying out direct inclination influence analysis and activating a roadway early warning penalty factor to generate a direct inclination early warning result; the hidden danger construction module is used for establishing an inclination hidden danger feature set; the indirect early warning module is used for carrying out indirect inclination influence analysis and activating a roadway early warning penalty factor to generate an indirect inclination early warning result; and the combined early warning module is used for generating a combined early warning result. The technical problems of low early warning comprehensiveness and precision and limited early warning effect of the existing coal mine well inclination early warning are solved, and the technical effects of improving the early warning precision and enhancing the early warning comprehensiveness are achieved.

Description

Technical Field

[0001] The present application relates to fields related to safety warning, and in particular to a coal mine shaft inclination adaptive analysis and early warning platform. Background Art

[0002] In the field of coal mining, with the increase in mining depth and the complexity of geological conditions, safety issues in coal mines have become increasingly prominent, especially the problem of tunnel inclination, which has become a major hidden danger affecting the safe production of mines. Tunnel inclination not only causes equipment damage and reduced transportation efficiency, but may also cause serious safety accidents, such as roof collapse and support failure. Therefore, real-time monitoring and early warning of the inclination state of coal mine tunnels have become an important means to ensure the safe production of coal mines. In the current coal mine early warning system, most of them rely on simple threshold judgments and lack an early warning mechanism that integrates multiple influencing factors, resulting in insufficient comprehensiveness and accuracy of the early warning.

[0003] Among the current related technologies, coal mine shaft inclination warning has technical problems such as low comprehensiveness and accuracy, and limited warning effect. Summary of the invention

[0004] This application provides a coal mine inclination adaptive analysis and early warning platform, integrates multiple modules such as tunnel feature establishment, penalty construction, periodic monitoring, direct early warning, hidden danger construction, indirect early warning and joint early warning, adopts real-time monitoring of tunnel inclination, comprehensively considers multiple factors such as tunnel characteristics, traffic conditions, hidden danger factors, and constructs a multi-level early warning mechanism and other technical means, thereby achieving the technical effect of improving the accuracy of early warning and enhancing the comprehensiveness of early warning.

[0005] This application provides a coal mine inclination adaptive analysis and early warning platform, including:

[0006] The tunnel feature establishment module is used to obtain the design information of the coal mine shaft and establish the tunnel features based on the design information. The tunnel features include tunnel geometry features, tunnel support features, and tunnel inclination features. The penalty construction module is used to monitor the tunnel traffic and generate traffic monitoring results. The traffic monitoring results include the importance of traffic flow and traffic equipment. The tunnel importance is evaluated based on the traffic monitoring results and the tunnel features, and a tunnel early warning penalty factor is generated based on the tunnel importance evaluation results. The periodic monitoring module is used to distribute monitoring sensors according to the tunnel geometry features, perform periodic monitoring of the tunnel with the monitoring sensors, and establish periodic inclination factors. a direct warning module, used to perform direct tilt impact analysis based on the periodic tilt data, activate the tunnel warning penalty factor to generate a direct tilt warning result; a hidden danger construction module, used to establish a tilt hidden danger feature set of the tunnel, the tilt hidden danger feature set includes equipment impact hidden danger features, geological hidden danger features, load impact hidden danger features, and environmental impact features; an indirect warning module, used to perform indirect tilt impact analysis based on the tilt hidden danger feature set, activate the tunnel warning penalty factor to generate an indirect tilt warning result; a joint warning module, used to synchronize the direct tilt warning result and the indirect tilt warning result to a joint warning channel to generate a joint warning result.

[0007] In a possible implementation, the hidden danger construction module is used to:

[0008] Acquire the location distribution data of the equipment, and establish the tunnel distance based on the location distribution data; acquire the equipment vibration data, the equipment vibration data includes amplitude data, angular frequency data, vibration frequency data, and the equipment vibration data has a time stamp; perform vibration transfer and vibration resonance fitting based on the tunnel distance and the equipment vibration data, generate equipment impact hidden danger characteristics based on the fitting results, and generate a tilt hidden danger feature set based on the equipment impact hidden danger characteristics.

[0009] In a possible implementation, the hidden danger construction module is used to:

[0010] Establish the vibration transfer fit as follows: Δθ vib =α·A·e -βd ·cos(ωt-kx); where Δθ vib Characterize the fitting results of the slope change caused by vibration, A is the amplitude data, d is the distance between the equipment and the monitoring sensor, α and β are the correlation constants based on geological conditions and tunnel characteristics, ω is the angular frequency data, k is the vibration wave number, t is the time node, and x is the equipment coordinate generated according to the tunnel distance; establish the vibration resonance fitting as follows: Among them, Δθ resCharacterize the slope change caused by resonance, f is the vibration frequency data, f0 is the natural frequency established based on the tunnel support characteristics, γ is the resonance amplification factor, and v is the system damping constant.

[0011] In a possible implementation, the direct warning module is used to:

[0012] A slope threshold is set, and a final value tilt analysis result is generated based on a comparison between the slope threshold and the final value of the tilt of the periodic tilt data; the slope change rate per unit time is calculated based on the periodic tilt data, and a rate tilt analysis result is generated with the slope change rate calculation result; an influence spatial gradient of the position slope change rate is established, and the cumulative gradient change within the influence range is calculated using the influence spatial gradient, and an associated position impact analysis result is generated with the cumulative gradient change; direct tilt impact analysis is completed with the final value tilt analysis result, the rate tilt analysis result, and the associated position impact analysis result.

[0013] In a possible implementation, the direct warning module is used to:

[0014] The impact spatial gradient is as follows: Among them, G(x i ,x j ) represents the position x i With position x j The slope rate changes between the gradients, the rate of change x i is the position x i The rate of change of the tilt rate, the rate of change x j is the position x j The tilt rate changes, d(x i ,x j ) represents the position x i With position x j The cumulative gradient changes as follows: Among them, S(x i ) represents the position x i The cumulative impact of the change in the tilt rate of its neighborhood, where the neighborhood is the position x i All positions x within a predetermined distance of j A collection of is the position x j The weight factor of λ is the stress attenuation coefficient.

[0015] In a possible implementation, the hidden danger construction module is used to:

[0016] The load impact hidden danger characteristics are calculated by the formula as follows: Among them, Δθ total Characterizes the change in slope caused by load, σ loadCharacterizes the stress caused by the load, L is the length of the stress area of ​​the roadway, E is the Young's modulus of the roadway material, I is the moment of inertia of the roadway section, Δh settle is the amount of foundation settlement.

[0017] In a possible implementation, the platform further includes:

[0018] The early warning module is used to establish a multi-level early warning mechanism, through which the trigger identification of the joint early warning results is performed, and the early warning is issued based on the trigger identification results. The early warning output includes sound and light coordinated alarm, SMS notification, and display screen display.

[0019] In a possible implementation, the platform further includes:

[0020] The feedback warning module is used to receive the coal mine operation plan, compensate the tunnel warning penalty factor based on the coal mine operation plan, generate a compensation result, and complete the coal mine inclination warning with the compensation result.

[0021] The coal mine inclination adaptive analysis and early warning platform proposed in this application is intended to obtain the design information of the coal mine through the tunnel feature establishment module, establish tunnel features based on the design information, including tunnel geometry features, tunnel support features, and tunnel inclination features, monitor the tunnel traffic through the penalty construction module, generate traffic monitoring results, including traffic flow and the importance of traffic equipment, evaluate the importance of the tunnel based on the traffic monitoring results and tunnel characteristics, generate a tunnel early warning penalty factor based on the tunnel importance evaluation results, distribute monitoring sensors according to the tunnel geometry features through the periodic monitoring module, and use the monitoring sensors to perform periodic monitoring of the tunnel, and establish periodic inclination data. Through the direct warning module, direct tilt impact analysis is performed based on periodic tilt data, and the tunnel warning penalty factor is activated to generate direct tilt warning results. Through the hidden danger construction module, a tunnel tilt hidden danger feature set is established, including equipment impact hidden danger characteristics, geological hidden danger characteristics, load impact hidden danger characteristics, and environmental impact characteristics. Through the indirect warning module, indirect tilt impact analysis is performed based on the tilt hidden danger feature set, and the tunnel warning penalty factor is activated to generate indirect tilt warning results. Through the joint warning module, the direct tilt warning results and the indirect tilt warning results are synchronized to the joint warning channel to generate a joint warning result, thereby achieving the technical effect of improving the warning accuracy and enhancing the comprehensiveness of the warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention are briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the platform according to the embodiment of the present application. It should be understood that the previous or following operations are not necessarily performed precisely in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0023] Figure 1 A schematic diagram of the structure of a coal mine shaft inclination adaptive analysis and early warning platform provided in an embodiment of the present application.

[0024] Figure 2 A schematic diagram of the process of performing direct tilt impact analysis in the coal mine shaft inclination adaptive analysis and early warning platform provided in an embodiment of the present application.

[0025] Explanation of the reference numerals: tunnel feature establishment module 10 , penalty construction module 20 , periodic monitoring module 30 , direct warning module 40 , hidden danger construction module 50 , indirect warning module 60 , joint warning module 70 . DETAILED DESCRIPTION

[0026] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0027] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0028] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0029] The embodiment of the present application provides a coal mine inclination adaptive analysis and early warning platform, such as Figure 1 As shown, the platform includes:

[0030] The tunnel feature establishment module 10 is used to obtain the design information of the coal mine, and establish the tunnel features based on the design information, wherein the tunnel features include tunnel geometry features, tunnel support features, and tunnel inclination features. Specifically, the design information of the coal mine is obtained by consulting documents such as mine design drawings, geological exploration reports, and mining plans. Based on the design information, a three-dimensional model of the tunnel is established using a geographic information system GIS (or computer-aided design CAD software), and the geometric features of the tunnel (parameters describing the shape and size of the tunnel, such as length, width, height, curvature, etc.), support features (parameters describing the support structure and strength of the tunnel, such as support type, support strength, support spacing, etc.) and initial inclination features (parameters describing the inclination state of the tunnel in three-dimensional space, such as initial inclination angle, inclination direction, etc.) are extracted, and the extracted tunnel feature data is stored in a database for use by subsequent modules.

[0031] The penalty construction module 20 is used to monitor the traffic in the tunnel and generate traffic monitoring results. The traffic monitoring results include the traffic flow and the importance of the traffic equipment. The importance of the tunnel is evaluated based on the traffic monitoring results and the tunnel characteristics, and the tunnel early warning penalty factor is generated based on the tunnel importance evaluation results. Specifically, the tunnel traffic is monitored by sensors installed in the tunnel (infrared counters, RFID readers, etc.), and the traffic flow and the importance of the equipment are recorded. Among them, the traffic flow refers to the number of people or equipment passing through the tunnel per unit time; the equipment importance is a weight determined according to the criticality and value of the equipment in mine production. Based on the tunnel characteristics and traffic monitoring results, the expert scoring method is used to evaluate the importance of the tunnel. According to the tunnel importance evaluation results, different levels of early warning penalty factors are set. The early warning penalty factor is a coefficient used to adjust the early warning level. The higher the importance, the larger the penalty factor, indicating a higher early warning level.

[0032] The periodic monitoring module 30 is used to distribute monitoring sensors according to the geometric characteristics of the tunnel, perform periodic monitoring of the tunnel with the monitoring sensors, and establish periodic tilt data. Specifically, the monitoring sensors are distributed according to the geometric characteristics of the tunnel (length, width, height change points, etc.). The monitoring sensors are devices used to measure the tilt state of the tunnel, including inclination sensors, laser rangefinders, etc. When configuring the monitoring sensors, it should be ensured that the monitoring points can fully reflect the tilt state of the tunnel. Set a monitoring period (such as every hour, every day, etc.), use monitoring sensors to regularly collect the tilt data of the tunnel, pre-process the collected tilt data (denoising, interpolation, etc.), and establish a periodic tilt data sequence, that is, the periodic tilt data is a set of tunnel tilt data collected and processed within a specific period.

[0033] The direct warning module 40 is used to perform direct tilt impact analysis based on the periodic tilt data, activate the lane warning penalty factor and generate a direct tilt warning result. Specifically, the periodic tilt data includes indicators such as the tilt angle and tilt rate of the lane at different time points. The tilt angle of the current cycle is compared with the preset safety threshold. If the tilt angle exceeds the threshold, it is considered that there is a direct tilt risk; the changing trend of the tilt angle is analyzed, including the calculation of the tilt rate, the estimation of the tilt acceleration, etc. If the tilt rate or acceleration increases abnormally, it is also considered that there is a direct tilt risk; combined with the tilt state of adjacent lanes or adjacent areas, the chain effect of the current lane tilt on other areas is evaluated through spatial analysis. According to the results of the direct tilt impact analysis, the corresponding lane warning penalty factor is activated, and the direct tilt warning result is generated by combining the results of the direct tilt impact analysis and the activated warning penalty factor. The warning result includes information such as the warning level, warning content, and recommended measures. The direct tilt warning result is synchronized to the joint warning channel through the joint warning module 70.

[0034] like Figure 2 As shown, in a possible implementation, the direct warning module 40 is used to: set a slope threshold, generate a final value tilt analysis result based on the comparison between the slope threshold and the tilt final value of the periodic tilt data; calculate the slope change rate per unit time based on the periodic tilt data, and generate a rate tilt analysis result with the slope change rate calculation result; establish an influence spatial gradient of the position slope change rate, calculate the cumulative gradient change within the influence range through the influence spatial gradient, and generate an associated position impact analysis result with the cumulative gradient change; complete the direct tilt impact analysis with the final value tilt analysis result, the rate tilt analysis result, and the associated position impact analysis result.

[0035] Specifically, according to the safety standards and design requirements of coal mines, a slope threshold is set. The slope threshold refers to the safe upper or lower limit of the inclination angle of the roadway. Exceeding this value may cause safety problems. Therefore, the slope threshold is used to determine whether the inclination of the roadway exceeds the safe range. The final value of the inclination of each monitoring point (i.e., the inclination angle of the current monitoring period) is extracted from the latest periodic inclination data, and these final values ​​are compared with the set slope threshold to determine whether the final value of the inclination of any monitoring point exceeds the threshold. If it exceeds, the final value inclination analysis result is generated, and the position and inclination angle of the monitoring point that exceeds the standard are recorded. The periodic inclination data is further processed, and the slope change rate per unit time of each monitoring point is calculated by comparing the inclination angles of two adjacent monitoring periods. The slope change rate reflects the dynamic change of the stability of the roadway. The calculated slope change rate is compared with the preset rate threshold to determine whether there is an abnormal increase in rate. If so, the rate inclination analysis result is generated, and the position and rate value of the monitoring point where the abnormal rate occurs are recorded. In order to evaluate the chain effect of roadway tilt on other areas, the spatial gradient of the rate of position slope change is established based on the spatial analysis method. The spatial gradient refers to the degree of propagation and diffusion of roadway tilt change in space, reflecting the potential impact of tilt change on the surrounding environment. By calculating the spatial gradient of influence, the cumulative gradient change that the surrounding area may be affected by when a certain monitoring point changes in tilt occurs is evaluated. The cumulative gradient change refers to the cumulative effect of the impact of tilt change on the surrounding environment in space. Based on the cumulative gradient change, the results of the associated position impact analysis are generated to indicate which areas may be affected by the tilt change and to what extent. The results of the final value tilt analysis, the rate tilt analysis and the associated position impact analysis are combined to complete the direct tilt impact analysis. This implementation method comprehensively analyzes multiple aspects of roadway tilt, including whether the final value of the tilt exceeds the standard, whether the tilt change rate is abnormal, and the potential impact of the tilt change on the surrounding environment. This analysis method comprehensively and accurately evaluates the risk level of roadway tilt, providing a scientific basis for subsequent early warning and response measures. At the same time, by analyzing the impact of the final value, rate and associated position respectively, the characteristics and laws of roadway tilt are more carefully portrayed, and the accuracy and pertinence of the early warning are improved.

[0036] In a possible implementation, the direct warning module 40 includes: affecting the spatial gradient as follows:

[0037]

[0038] Among them, G(x i ,x j ) represents the position x i With position x j The slope rate changes between the gradients, the rate of change x i is the position x iThe rate of change of the tilt rate, the rate of change x j is the position x j The tilt rate changes, d(x i ,x j ) represents the position x i With position x j The cumulative gradient changes as follows:

[0039]

[0040] Among them, S(x i ) represents the position x i The cumulative impact of the change in the tilt rate of its neighborhood, where the neighborhood is the position x i All positions x within a predetermined distance of j A collection of is the position x j The weight factor of λ is the stress attenuation coefficient.

[0041] Specifically, the distance between locations refers to the straight-line distance between two monitoring points in space, which is used to evaluate the degree of mutual influence between them. The impact space gradient describes the degree of propagation and diffusion of the change in the tilt rate between different locations in the tunnel, and reflects the continuity and correlation of the tilt change in space. The neighborhood of a location is a circular area with the location as the center and a predetermined distance as the radius. A weight factor is assigned to each location in the neighborhood. This factor is determined according to the distance between the location and the center location, and is used to quantify the relative importance of the influence of different locations in the neighborhood on the center location. The cumulative gradient change refers to the comprehensive influence of a certain location on the change in the tilt rate of all locations in its neighborhood, taking into account the spatial distribution, distance attenuation and weight distribution of the influence. The stress attenuation coefficient is a physical constant used to describe the weakening of the influence as the distance increases. This implementation method finely evaluates the potential impact of tunnel tilt on the surrounding environment by calculating the impact space gradient and cumulative gradient change. By introducing weight factors and stress attenuation coefficients, it reflects the relative importance of the influence between different locations and the actual situation of the influence attenuating with distance, making the analysis results more consistent with the actual physical process and improving the accuracy of the early warning.

[0042] The hidden danger construction module 50 is used to establish a hidden danger feature set of the tilt of the tunnel, and the hidden danger feature set includes hidden danger features of equipment impact, geological hidden danger features, hidden danger features of load impact, and environmental impact features. Specifically, various basic data related to hidden dangers of tunnel tilt are collected through database query, on-site investigation, and equipment monitoring system interface, including but not limited to equipment information, geological exploration report, tunnel load conditions, and environmental factors. Based on the collected basic data, the hidden danger feature set of the tunnel tilt is defined through expert experience and industry standards, and the feature set includes hidden danger features of equipment impact, geological hidden danger features, hidden danger features of load impact, and environmental impact features. Among them, hidden danger features of equipment impact refer to features that may affect the tilt of the tunnel due to reasons such as equipment aging and failure, such as equipment type, service life, maintenance status, etc.; geological hidden danger features refer to features that may affect the tilt of the tunnel due to unstable geological conditions, such as rock formation stability, groundwater hydrology, etc.; hidden danger features of load impact refer to features that may affect the stability due to excessive tunnel load, such as transportation volume, personnel flow, etc.; hidden danger features of environmental impact refer to features that may affect the tilt of the tunnel due to changes in environmental factors, such as temperature, humidity, wind pressure, etc.

[0043] In a possible implementation, the hidden danger construction module 50 is used to: obtain location distribution data of the equipment, and establish the tunnel distance based on the location distribution data; obtain equipment vibration data, the equipment vibration data includes amplitude data, angular frequency data, vibration frequency data, and the equipment vibration data has a time mark; perform vibration transfer and vibration resonance fitting based on the tunnel distance and the equipment vibration data, generate equipment impact hidden danger characteristics based on the fitting results, and generate a tilt hidden danger feature set based on the equipment impact hidden danger characteristics.

[0044] Specifically, it interacts with the equipment management system in the coal mine to obtain the specific location information of all relevant equipment (such as conveyors, coal mining machines, support equipment, etc.) in the tunnel. This information exists in the form of coordinates, indicating the spatial position of the equipment in the tunnel. Using the acquired equipment location distribution data, the distance between any two devices, as well as the distance between the equipment and important locations such as the tunnel wall and tunnel intersection, is calculated to evaluate the propagation distance and attenuation of the vibration wave in the tunnel. Vibration data of the equipment is obtained in real time through the vibration sensor installed on the equipment, including amplitude (indicating the strength of the vibration), angular frequency (indicating the rotation speed of the vibration), vibration frequency (indicating the number of vibrations per unit time), etc., and each data point is marked with a time stamp. Using the tunnel distance information and equipment vibration data, vibration transmission analysis is performed to simulate the propagation path and attenuation of the vibration wave in the tunnel. At the same time, combined with the vibration resonance theory, the possible resonance phenomenon is fitted and predicted. According to the results of vibration transmission and resonance fitting, the degree of influence of equipment vibration on the stability of the roadway is evaluated, and the equipment influence hidden danger characteristics are generated. The equipment influence hidden danger characteristics refer to the potential hidden danger characteristics of the roadway stability reduction or damage caused by equipment vibration, including excessive vibration intensity, resonance risk level, vibration propagation range, etc. The equipment influence hidden danger characteristics are integrated with other hidden danger characteristics to form a complete tilt hidden danger feature set. This implementation method obtains the location distribution data and vibration data of the equipment, simulates the propagation and resonance phenomenon of the vibration wave in the roadway, accurately evaluates the impact of equipment vibration on the stability of the roadway, and improves the accuracy of the equipment influence hidden danger analysis.

[0045] In a possible implementation, the hidden danger construction module 50 is used to establish a vibration transfer fit as follows:

[0046] Δθ vib =α·A·e -βd ·cos(ωt-kx);

[0047] Among them, Δθ vib Characterize the fitting results of the slope change caused by vibration, A is the amplitude data, d is the distance between the equipment and the monitoring sensor, α and β are the correlation constants based on geological conditions and tunnel characteristics, ω is the angular frequency data, k is the vibration wave number, t is the time node, and x is the equipment coordinate generated according to the tunnel distance;

[0048] Establish the vibration resonance fitting as follows:

[0049]

[0050] Among them, Δθ resCharacterize the slope change caused by resonance, f is the vibration frequency data, f0 is the natural frequency established based on the tunnel support characteristics, γ is the resonance amplification factor, and δ is the system damping constant. This implementation method uses a mathematical model for quantitative analysis, which is highly scientific and reliable, ensuring the accuracy and effectiveness of the evaluation results.

[0051] In a possible implementation, the hidden danger construction module 50 is used to calculate the load impact hidden danger characteristics by using a formula as follows:

[0052]

[0053] Among them, Δθ total Characterizes the change in slope caused by load, σ load Characterizes the stress caused by the load, L is the length of the stress area of ​​the roadway, E is the Young's modulus of the roadway material, I is the moment of inertia of the roadway section, Δh settle is the amount of foundation settlement.

[0054] Specifically, the change in slope caused by the load indicates the degree to which the roadway is tilted or deformed due to the load acting on the roadway. The stress caused by the load refers to the internal force generated when the load acts on the roadway structure, which may cause the roadway to deform or be damaged. The length of the roadway force area is the length of the area where the load directly acts on the roadway, and this length will affect the distribution and magnitude of the stress. The Young's modulus of the roadway material is a physical quantity that describes the ability of the roadway material to resist deformation when subjected to external forces. The moment of inertia of the roadway section is related to the shape and size of the roadway section and is used to describe the ability of the section to resist bending deformation. The foundation settlement is the vertical displacement of the foundation surface due to compression or consolidation of the foundation soil layer, and the foundation settlement will affect the stability and slope of the roadway. This implementation method is based on mechanical principles and mathematical models. By quantitatively analyzing the impact of load on the roadway slope, it provides a scientific basis for evaluating safety hazards in the roadway.

[0055] The indirect warning module 60 is used to perform indirect tilt impact analysis based on the tilt hidden danger feature set, activate the lane warning penalty factor to generate an indirect tilt warning result. Specifically, real-time data related to the tilt hidden danger feature is collected, including equipment operation status, geological monitoring data, load monitoring data, and environmental parameters (temperature, humidity, wind pressure, etc.). The collected data is preprocessed, including data cleaning, denoising, normalization, etc., to ensure the accuracy and comparability of the data. For each hidden danger feature, a mathematical model is used for quantitative analysis to evaluate its potential impact on the lane tilt, and the analysis results of each hidden danger feature are integrated by a weight distribution method to evaluate the overall level of the lane tilt risk. According to the hidden danger feature analysis results, an early warning threshold is set based on expert judgment, and the hidden danger feature analysis results are compared with the early warning threshold to determine whether the lane has a tilt risk. If it is determined that there is a tilt risk, the corresponding lane early warning penalty factor is activated according to the severity of the risk, and a detailed indirect tilt early warning result is generated, including information such as warning level, risk description, and recommended measures. The indirect tilt early warning result is synchronized to the joint early warning module 70 through the joint early warning channel to generate a comprehensive joint early warning result.

[0056] The joint warning module 70 is used to synchronize the direct tilt warning result and the indirect tilt warning result to the joint warning channel to generate a joint warning result. Specifically, the direct tilt warning result and the indirect tilt warning result are synchronized to the joint warning channel, and the weighted sum method is used to combine the two warning results to generate the final joint warning result, and the joint warning result is notified to relevant personnel. The embodiment of the present application integrates multiple modules such as lane feature establishment, penalty construction, periodic monitoring, direct warning, hidden danger construction, indirect warning and joint warning, adopts real-time monitoring of lane inclination, comprehensively considers multiple factors such as lane characteristics, traffic conditions, hidden danger factors, etc., and constructs a multi-level warning mechanism and other technical means, thereby achieving the technical effect of improving the accuracy of warning and enhancing the comprehensiveness of warning.

[0057] In a possible implementation, the platform further includes:

[0058] The early warning module is used to establish a multi-level early warning mechanism, through which the trigger identification of the joint early warning results is performed, and the early warning is issued based on the trigger identification results. The early warning output includes sound and light coordinated alarm, SMS notification, and display screen display.

[0059] Specifically, multiple warning levels are defined, such as level 1 warning (emergency), level 2 warning (important), level 3 warning (general), etc. Each level corresponds to a different risk level and response measures. Based on the specific parameters in the joint warning results output by the joint warning module, trigger conditions and thresholds are set for each level of warning, the joint warning results output by the joint warning module are received, and key information therein, such as risk description, is parsed. According to the setting of the multi-level warning mechanism, the joint warning results are compared with the trigger conditions and thresholds of each level to identify the current warning level that should be triggered. According to the identified warning level, the corresponding warning output method is selected. For example, the level 1 warning simultaneously triggers the sound and light coordinated alarm, SMS notification and display screen display, while the level 2 or level 3 warning only selects some of these methods. This implementation method ensures that warnings are issued in a timely manner when risks actually occur through a multi-level warning mechanism and trigger identification, and the warning level matches the severity of the risk, thereby improving the accuracy and timeliness of the warning.

[0060] In a possible implementation, the platform further includes:

[0061] The feedback warning module is used to receive the coal mine operation plan, compensate the tunnel warning penalty factor based on the coal mine operation plan, generate a compensation result, and complete the coal mine inclination warning with the compensation result.

[0062] Specifically, an operation plan is received from the coal mine management system, including the mining sequence of the tunnel, support scheme, equipment use plan, etc. The received operation plan is analyzed to evaluate its impact on the geometric characteristics, support characteristics and potential tilt risks of the tunnel. Based on the analysis results of the impact of the operation plan on the tunnel, the existing tunnel warning penalty factor is compensated to more accurately reflect the actual risk level of the tunnel caused by the implementation of the operation plan. The compensation method is to adjust the weight of the penalty factor to consider the additional risks brought by the operation plan. After the compensation calculation, a new tunnel warning penalty factor is generated. The compensation results are applied to the coal mine inclination warning system, combined with the direct warning module and the indirect warning module, to comprehensively evaluate the inclination risk of the tunnel and generate the final warning information. This implementation method analyzes the impact of the operation plan on the tunnel, compensates the warning penalty factor, more accurately reflects the actual risk level of the tunnel during the operation process, and improves the accuracy of the warning system.

[0063] Although the present application makes various references to certain modules in the platform according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be implemented; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0064] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. Coal mine inclination adaptive analysis and early warning platform, characterized by: The platform includes: A tunnel feature establishment module is used to obtain design information of a coal mine shaft and establish tunnel features based on the design information. The tunnel features include tunnel geometry features, tunnel support features, and tunnel inclination features. A penalty construction module is used to monitor the traffic of the lanes and generate traffic monitoring results, wherein the traffic monitoring results include the traffic flow and the importance of traffic equipment, evaluate the importance of the lanes according to the traffic monitoring results and the lane characteristics, and generate a lane warning penalty factor based on the lane importance evaluation results; A periodic monitoring module, used to distribute monitoring sensors according to the geometric characteristics of the lane, perform periodic monitoring of the lane with the monitoring sensors, and establish periodic tilt data; A direct warning module, used for performing direct tilt impact analysis based on the periodic tilt data, activating a lane warning penalty factor to generate a direct tilt warning result; A hidden danger construction module is used to establish a hidden danger feature set of the tunnel tilt, wherein the hidden danger feature set includes equipment impact hidden danger features, geological hidden danger features, load impact hidden danger features, and environmental impact features; An indirect warning module, used for performing indirect tilt impact analysis based on the tilt hidden danger feature set, activating the lane warning penalty factor to generate an indirect tilt warning result; The joint warning module is used to synchronize the direct tilt warning result and the indirect tilt warning result to a joint warning channel to generate a joint warning result.

2. The coal mine inclination adaptive analysis and early warning platform according to claim 1, characterized in that: The hidden danger construction module is used to: Acquire location distribution data of the equipment, and establish a lane distance based on the location distribution data; Acquire device vibration data, the device vibration data including amplitude data, angular frequency data, and vibration frequency data, and the device vibration data carries a time stamp; Vibration transfer and vibration resonance fitting are performed based on the tunnel distance and the equipment vibration data, equipment impact hidden danger features are generated based on the fitting results, and a tilt hidden danger feature set is generated based on the equipment impact hidden danger features.

3. The coal mine inclination adaptive analysis and early warning platform according to claim 2, characterized in that: The hidden danger construction module is used to: Establish the vibration transfer fit as follows: Dth vib =α·A·e -βd ·cos(ωt-kx); Among them, Δθ vib Characterize the fitting results of the slope change caused by vibration, A is the amplitude data, d is the distance between the equipment and the monitoring sensor, α and β are the correlation constants based on geological conditions and tunnel characteristics, ω is the angular frequency data, k is the vibration wave number, t is the time node, and x is the equipment coordinate generated according to the tunnel distance; Establish the vibration resonance fitting as follows: Among them, Δθ res Characterize the slope change caused by resonance, f is the vibration frequency data, f0 is the natural frequency established based on the tunnel support characteristics, γ is the resonance amplification factor, and δ is the system damping constant.

4. The coal mine inclination adaptive analysis and early warning platform according to claim 1, characterized in that: The direct warning module is used for: Setting a slope threshold, and generating a final value tilt analysis result based on a comparison between the slope threshold and a final value of tilt of periodic tilt data; Calculating the slope change rate per unit time based on the periodic tilt data, and generating a rate tilt analysis result based on the slope change rate calculation result; Establishing an influence spatial gradient of the position slope change rate, calculating the cumulative gradient change within the influence range through the influence spatial gradient, and generating an associated position influence analysis result with the cumulative gradient change; The direct tilt impact analysis is completed based on the final value tilt analysis results, rate tilt analysis results, and associated position impact analysis results.

5. The coal mine inclination adaptive analysis and early warning platform according to claim 4, characterized in that: The direct warning module is used for: The impact spatial gradient is as follows: Among them, G(x i ,x j ) represents the position x i With position x j The slope rate changes between the gradients, the rate of change x i is the position x i The rate of change of the tilt rate, the rate of change x j is the position x j The tilt rate changes, d(x i ,x j ) represents the position x i With position x j distance; The cumulative gradient changes are as follows: Among them, S(x i ) represents the position x i The cumulative impact of the change in the tilt rate of its neighborhood, where the neighborhood is the position x i All positions x within a predetermined distance of j A collection of is the position x j The weight factor of λ is the stress attenuation coefficient.

6. The coal mine inclination adaptive analysis and early warning platform according to claim 1, characterized in that: The hidden danger construction module is used to: The load impact hidden danger characteristics are calculated by the formula as follows: Among them, Δθ total Characterizes the change in slope caused by load, σ load Characterizes the stress caused by the load, L is the length of the stress area of ​​the roadway, E is the Young's modulus of the roadway material, I is the moment of inertia of the roadway section, Δh settle is the amount of foundation settlement.

7. The coal mine inclination adaptive analysis and early warning platform according to claim 1, characterized in that: The platform also includes: The early warning module is used to establish a multi-level early warning mechanism, through which the trigger identification of the joint early warning results is performed, and the early warning is issued based on the trigger identification results. The early warning output includes sound and light coordinated alarm, SMS notification, and display screen display.

8. The coal mine inclination adaptive analysis and early warning platform according to claim 1, characterized in that: The platform also includes: The feedback warning module is used to receive the coal mine operation plan, compensate the tunnel warning penalty factor based on the coal mine operation plan, generate a compensation result, and complete the coal mine inclination warning with the compensation result.

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