Mining area safety early warning model method based on multi-source time sequence InSAR fusion

Abstract

The invention relates to the technical field of industrial data management, in particular to a multi-source time sequence InSAR fused mining area safety early warning model method, which comprises the following steps: constructing an interference level evaluation model based on dust concentration and meteorological conditions, dynamically planning remote sensing data acquisition time in combination with a mining area operation plan, and improving the management adaptability of data acquisition; multi-band remote sensing data are further fused, interference suppression data are output, and the stability of deformation monitoring in a complex environment is improved; identifying a critical phase change point of the operation state of the key area based on the deformation trend change to form feedback data; based on the structure functional risk assessment model, the functional indexes and feedback data are fused to assess the operation safety level; and finally, calculating a function-structure collaborative redundancy level as a risk bearing capacity index, and driving dynamic adjustment of a safety management strategy and resource priority configuration optimization.

Description

Technical Field

[0001] This invention relates to the field of industrial data management technology, specifically to a method for mine safety early warning model based on multi-source time-series InSAR fusion. Background Technology

[0002] In the production organization and safety management of open-pit mines, slope stability issues have a direct impact on safety risk management, mining path adjustment, and work task arrangement. In recent years, with the improvement of mine intelligence, how to achieve quantitative assessment and management decisions of slope risks based on large-scale, long-term remote sensing observation data has become an important issue in mine operation and management.

[0003] InSAR (Inductively Coupled Aperture Radar Interferometry) technology, with its high spatial resolution and wide coverage, has become a major data source for monitoring surface deformation. However, under the combined influence of high-frequency disturbances and dynamic environmental factors in mining areas, traditional safety management methods struggle to meet the following management needs in practice: First, they lack the ability to discriminate functional slope failures, failing to support scenario-based scheduling optimization and construction adjustments. Second, they lack the ability to identify and model abrupt changes in evolutionary states, leading to management delays or misjudgments. Third, observational data is disconnected from management processes, making it difficult to form a closed-loop decision-making system and integrate into daily management processes such as production planning, inspection scheduling, and safety level classification in mining areas.

[0004] Therefore, the key question is how to integrate multi-source temporal InSAR deformation information and combine it with the functional structure status and dynamic critical point characteristics of slopes to construct a safety management method oriented towards functional degradation and state change, so as to achieve more proactive, dynamic, and adaptive safety decision-making and task optimization in mining area management, and realize the transformation from "passive response" to "proactive management". Summary of the Invention

[0005] The purpose of this invention is to provide a mine safety early warning model method based on multi-source temporal InSAR fusion. By fusing multi-source temporal InSAR deformation information and combining the functional structure status and dynamic critical point characteristics of slopes, a safety management method oriented towards functional degradation and state change is constructed. This enables more proactive, dynamic, and adaptive safety decision-making and task optimization in mine management, and realizes the transformation from "passive response" to "proactive management".

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for mine safety early warning model based on multi-source temporal InSAR fusion includes:

[0008] A dust disturbance level assessment model is constructed based on dust concentration and meteorological conditions to assess the degree of dust disturbance.

[0009] planning a collection time schedule of the remote sensing data based on the operation plan and the dust interference degree, and collecting the remote sensing data according to the collection time;

[0010] analyzing data stability of different remote sensing data under environmental interference, processing the remote sensing data based on the data stability to obtain interference suppression data;

[0011] identifying a critical phase change point of the key area from the first stage to the second stage according to the interference suppression data to obtain feedback data;

[0012] constructing a structure-function risk assessment model, combining a functional index with the feedback data to assess an operation safety level of the key area;

[0013] calculating a function-structure collaborative redundancy level based on the feedback data and the operation safety level, and dynamically adjusting a safety management strategy.

[0014] Preferably, the dust interference level assessment model comprises a dust concentration collection unit, a meteorological condition analysis unit and an interference level calculation unit.

[0015] The dust concentration collection unit monitors real-time dust concentration data of a mining area operation surface and establishes a dust concentration space-time distribution database; the meteorological condition analysis unit collects wind speed, wind direction, humidity and temperature, analyzes the influence law of meteorological conditions on dust diffusion, and obtains meteorological influence factors; and the interference level calculation unit assesses dust interference based on the dust concentration space-time distribution database and the meteorological influence factors to obtain the dust interference degree.

[0016] Preferably, the collection time schedule of the remote sensing data is planned, and the specific process comprises:

[0017] obtaining mining area operation plan information including blasting time, operation area and equipment running state; establishing a dust concentration-time and space position relationship model to predict the space-time distribution evolution of a dust cloud after blasting to obtain space-time evolution prediction results; determining a time window of dust cloud dissipation after blasting based on the space-time evolution prediction results; identifying a bad weather period in combination with weather forecast data; the bad weather includes sandstorm, heavy rain and gale; dynamically adjusting the time interval and priority order of remote sensing data collection according to the mining area operation plan information, the dust interference degree, the time window of dust cloud dissipation after blasting and the bad weather period, and generating a multi-source remote sensing data collection time table; and collecting the remote sensing data based on the collection time table.

[0018] Preferably, the specific process of obtaining the interference suppression data comprises:

[0019] A dust electromagnetic scattering physical model is established to evaluate the interference degree of dust on remote sensing data of different frequency bands and obtain dust sensitivity; a remote sensing data quality evaluation system is constructed to quantitatively score the data quality of multi-source remote sensing data under different environmental conditions by using coherence coefficients, phase residuals and intensity stability; the relative stability of remote sensing data of different frequency bands under the current dust interference level is comprehensively analyzed, and the data stability is calculated by combining the dust sensitivity and the data quality; a remote sensing data fusion strategy based on dust response weight is constructed according to the data stability, and multi-source remote sensing data is processed according to the remote sensing data fusion strategy: for remote sensing data with data stability higher than a data stability threshold, reserved remote sensing data is obtained by preferentially reserving; for remote sensing data with data stability lower than the data stability threshold, reconstructed data is obtained by interpolating the deformation trend of adjacent time phases and reconstructing the missing data based on a historical deformation database; the reserved remote sensing data and the reconstructed data are fused to output interference suppression data.

[0020] Preferably, the specific process of obtaining the feedback data comprises:

[0021] Based on the interference suppression data, the dynamic indicators of each monitoring point in the key area are calculated, including deformation rate, acceleration and acceleration second derivative, the change trend of the dynamic indicators in the time series is extracted, and a rate-acceleration joint mutation criterion is constructed; based on the rate-acceleration joint mutation criterion, the critical phase change point of the key area from the first stage to the second stage is identified; the first stage is a stable stage; the second stage is a nonlinear acceleration stage; through a confidence evaluation mechanism, the identification result of the critical phase change point is verified for spatial consistency and error denoising, the critical phase change point is taken as an important event of deformation stage conversion, and the feedback data with time mark and spatial position is generated by combining the regional function attribute label.

[0022] Preferably, the structure-function risk assessment model comprises: a functional index construction unit, a deformation-function degradation modeling unit, a function failure determination unit and a functional risk level evaluation unit.

[0023] The functional index construction unit constructs a functional index reflecting the operation capacity of the region according to the functional attribute of the key region, including a transportation channel index, a work platform index and a drainage facility index; the deformation-function degradation modeling unit constructs a relationship between remote sensing data and the degradation degree of the functional index based on the feedback data, forming a ''deformation-function'' mapping model; the function failure determination unit sets a degradation threshold corresponding to each functional index, and determines whether the functional index in the current key region reaches a failure state according to the ''deformation-function'' mapping model and the degradation threshold; and the functional risk level evaluation unit is used to combine the feedback data and the failure state determination result of the functional index, divide the operation state of the key region into levels, and output the operation safety level of the key region.

[0024] Preferably, the specific process of calculating the ''function-structure'' cooperative redundancy level includes:

[0025] According to the operation safety level of the key region, the influence weight of the functional index on the overall operation of the mining area is evaluated, and a functional influence factor matrix is formed; the structure state of the key region is evaluated in real time according to the functional influence factor matrix and combined with the feedback data, and the remaining structure bearing capacity for function support is identified; a ''function-structure'' coupling model is constructed based on the mapping relationship between the functional influence factor matrix and the remaining structure bearing capacity, and the redundancy level under the current state is measured; and a risk response suggestion is output according to the redundancy level, and the dynamic adjustment of the safety management strategy of the mining area and the priority allocation of resources are carried out.

[0026] Compared with the prior art, the present application has the following advantages:

[0027] 1. The present application proposes a remote sensing data acquisition time planning method, which introduces dust concentration monitoring and meteorological factor modeling to quantify the degree of dust interference, and combines dynamic information such as blasting plan and operation timing to construct a remote sensing acquisition time optimization scheduling strategy, realizes active avoidance of high interference period and selection of acquisition window, thereby ensuring data continuity while improving data quality and practicality, effectively supporting subsequent interference suppression and deformation analysis work.

[0028] 2. The present application proposes a critical phase change point identification method based on rate-acceleration joint mutation criterion, which can accurately capture the conversion time of the key region of the mining area from the stable stage to the nonlinear acceleration stage. By calculating dynamic indexes such as deformation rate, acceleration and acceleration second derivative, a multi-dimensional mutation identification system is constructed, and spatial consistency verification is carried out combined with confidence evaluation mechanism, which greatly improves the accuracy and reliability of phase change point identification. At the same time, according to the dust sensitivity difference of different frequency band remote sensing data, a data fusion strategy based on dust response weight is established, and low quality data is intelligently reconstructed, ensuring the continuity and integrity of the interference suppression data, providing a high-quality data basis for subsequent risk assessment.

[0029] 3, The application proposes a "function-structure" cooperative redundancy level, constructs a structural functional risk assessment model, and realizes the transformation of the evaluation mode from pure structural safety to functional safety. By constructing a functional index system covering key functional elements such as transportation channels, operation platforms, and drainage facilities, a "deformation-function" mapping model is established to associate structural deformation with functional degradation. Based on the coupling analysis of the functional influence factor matrix and the residual structural bearing capacity, the redundancy level under the current state is quantitatively evaluated, providing a scientific basis for the dynamic adjustment of safety management strategies. This cooperative evaluation mechanism can identify functional risks in advance, realize the transformation from passive response to active prevention, and significantly improve the forward-looking and effectiveness of mine safety management. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 A flowchart of a mine safety early warning model method of multi-source time sequence InSAR fusion provided by an embodiment of the application is shown.

[0031] Figure 2 A flowchart of the collection time arrangement of planning remote sensing data provided by an embodiment of the application is shown.

[0032] Figure 3 A structural diagram of a structural functional risk assessment model provided by an embodiment of the application is shown. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0034] The application proposes a mine safety early warning model method of multi-source time sequence InSAR fusion, which can fuse multi-source time sequence InSAR deformation information, and jointly construct a safety management method for functional degradation and state mutation by combining the function structure state and dynamic critical point characteristics of the slope, so as to realize more forward, dynamic, and adaptive safety early warning in mine management, and realize the transformation from "passive response" to "active management". In order to illustrate that the method of the application can realize more forward, dynamic, and adaptive safety early warning in mine management, the effectiveness of the application will be illustrated from two embodiments below.

[0035] Embodiment one

[0036] In the embodiments of the present application, the method proposed in the present application is used for safety early warning management of the main slope area of a large open pit iron mine. The mine area includes multiple main transport channels, several operation platforms and complete drainage facilities, and daily blasting operation is frequent. Figure 1 The method of the present application is a specific flowchart, which includes: constructing a dust disturbance level evaluation model according to dust concentration and meteorological conditions to evaluate the degree of dust disturbance; based on the operation plan and the degree of dust disturbance, planning the collection time arrangement of remote sensing data, and collecting remote sensing data according to the collection time; analyzing the data stability of different remote sensing data under environmental disturbance, processing the remote sensing data based on the data stability to obtain interference suppression data; identifying the critical phase change point from the first stage to the second stage of the key area according to the interference suppression data to obtain feedback data; constructing a structure-function risk evaluation model, combining the functional index with the feedback data to evaluate the operation safety level of the key area; based on the feedback data and the operation safety level, calculating the "function-structure" collaborative redundancy level, and dynamically adjusting the safety management strategy. The following is described according to the content of Figure 1 :

[0037] A dust disturbance level evaluation model is constructed according to dust concentration and meteorological conditions to evaluate the degree of dust disturbance.

[0038] The dust disturbance level evaluation model includes: a dust concentration collection unit, a meteorological condition analysis unit and a disturbance level calculation unit.

[0039] The dust concentration collection unit monitors the dust concentration data of the mine area operation surface in real time, and establishes a dust concentration spatio-temporal distribution database; the meteorological condition analysis unit collects wind speed, wind direction, humidity and temperature, analyzes the influence law of meteorological conditions on dust diffusion, and obtains meteorological influence factors; the disturbance level calculation unit evaluates the dust disturbance based on the dust concentration spatio-temporal distribution database and the meteorological influence factors, and obtains the degree of dust disturbance.

[0040] Specifically, a plurality of dust monitoring points are arranged in the mine area, and the inhalable particulate matter concentration is monitored in real time by using a laser scattering method, and the data collection frequency is set to high frequency continuous monitoring; a dust concentration spatio-temporal distribution database is established, and the record format contains time stamp, spatial coordinates and particulate matter concentration and other key information.

[0041] The meteorological influence factors comprehensively consider the dominant role of wind speed on dust transmission, the influence of wind direction on diffusion path, the promoting effect of humidity on dust deposition and the influence of temperature on atmospheric stability.

[0042] The interference level calculation unit multiplies the normalized fine dust concentration data with meteorological influence factors and spatial influence factors, wherein the spatial influence factors reflect the diffusion range of the dust cloud. According to the calculation result, the interference level is divided into three levels of low interference, medium interference and high interference.

[0043] The embodiment realizes intelligent perception and quantitative description of the complex mine environment by constructing a dust interference level evaluation model, including a dust concentration collection unit, a meteorological condition analysis unit and an interference level calculation unit. The dust concentration spatio-temporal distribution database is established to provide a basis for data quality evaluation. The meteorological influence factor modeling significantly improves the technical adaptability of the system in complex environments. Based on historical data and real-time monitoring, the dust interference degree prediction capability is provided, which provides environmental basis for subsequent collection time planning.

[0044] Further, based on the operation plan and the dust interference degree, the collection time of the remote sensing data is planned, and the remote sensing data is collected according to the collection time; referring to Figure 2 ;

[0045] The collection time of the remote sensing data is planned, and the specific process includes:

[0046] Obtain mine operation plan information, including blasting time, operation area and equipment running state; establish a relationship model of dust concentration and time, spatial position, predict the spatio-temporal distribution evolution of dust cloud after blasting, and obtain the spatio-temporal evolution prediction result; based on the spatio-temporal evolution prediction result, determine the time window of dust cloud dissipation after blasting; identify the adverse weather period in combination with weather forecast data; the adverse weather includes sandstorm, heavy rain and strong wind; according to the mine operation plan information, the dust interference degree, the time window of dust cloud dissipation after blasting and the adverse weather period, dynamically adjust the time interval and priority order of remote sensing data collection, and generate a multi-source remote sensing data collection time table; collect the remote sensing data based on the collection time table.

[0047] Specifically, the relationship model of dust concentration and time, spatial position considers source release, atmospheric diffusion parameters, wind speed influence and natural attenuation, and describes the distribution law of dust cloud in space and time after blasting by an exponential decay function.

[0048] The multi-source remote sensing data collection time table specifically includes: long time delay collection strategy is adopted in high interference period, medium delay strategy is adopted in medium interference period, and normal collection frequency is maintained in low interference period.

[0049] The remote sensing data includes multi-source InSAR data such as C-band, L-band and X-band.

[0050] The embodiment realizes collection efficiency optimization and resource configuration optimization through intelligent planning method of remote sensing data collection time arrangement, combination of operation plan, dust evolution prediction and bad weather identification. The effectiveness of data collection is significantly improved by avoiding high interference period through intelligent time planning. The optimal configuration of multi-source remote sensing resources is realized based on priority sorting and time interval adjustment, and the data acquisition cost is reduced by reducing invalid collection and repeated collection. The continuity and integrity of time series data are ensured by dynamically adjusting the collection strategy. High-quality raw data input is provided for subsequent data stability analysis. The technical scheme is organically combined with the actual production by deeply integrating with the mine operation plan, thereby ensuring the quality basis of subsequent data processing and analysis.

[0051] Further, analyzing data stability of different remote sensing data under environmental interference, processing the remote sensing data based on the data stability to obtain interference suppression data;

[0052] The specific process of obtaining the interference suppression data includes:

[0053] A dust electromagnetic scattering physical model is established to evaluate the interference degree of dust on different frequency band remote sensing data and obtain dust sensitivity. A remote sensing data quality evaluation system is constructed to quantitatively score the data quality of multi-source remote sensing data under different environmental conditions by using coherence coefficient, phase residual and intensity stability. The relative stability of remote sensing data of different frequency bands under the current dust interference level is comprehensively analyzed, and the data stability is calculated by combining the dust sensitivity and the data quality. A remote sensing data fusion strategy based on dust response weight is constructed according to the data stability, and multi-source remote sensing data is processed according to the remote sensing data fusion strategy. Remote sensing data with data stability higher than a data stability threshold is preferentially retained to obtain retained remote sensing data. Remote sensing data with data stability lower than the data stability threshold is reconstructed by interpolation based on the deformation trend of adjacent time phases and the historical deformation database to obtain reconstructed data. The retained remote sensing data and the reconstructed data are fused to output interference suppression data.

[0054] Specifically, the dust sensitivity is calculated by Rayleigh scattering theory, which is inversely proportional to the fourth power of wavelength, and also considers the influence of particle number density and scattering cross section.

[0055] The data quality is quantitatively scored by weighted average method by comprehensively considering coherence coefficient, phase residual and intensity stability, wherein the weight of coherence coefficient is the highest, the weight of phase residual is the second, and the weight of intensity stability is the lowest.

[0056] The data stability is the product of data quality score, anti-interference ability factor and relative stability, wherein the anti-interference ability factor is equal to 1 minus dust sensitivity.

[0057] The embodiment is based on the interference suppression data processing method of data stability, including dust sensitivity evaluation, data quality evaluation and intelligent fusion strategy, which greatly improves the data availability in the interference environment through multi-band adaptive fusion, establishes a dust sensitivity evaluation system based on a physical model to enhance the anti-interference ability of the system, intelligently reconstructs the low-quality data to ensure the continuity and integrity of the data, and first proposes a fusion strategy based on dust response weight to realize the optimal combination of multi-source data. The subsequent phase change point identification provides high-quality, continuous deformation data, guarantees the data quality basis of the entire technical chain, and provides a connection for data acquisition and feature recognition.

[0058] Further, according to the interference suppression data, a critical phase change point of a key area from a first stage to a second stage is identified to obtain feedback data; the specific process of obtaining the feedback data includes:

[0059] Based on the interference suppression data, dynamic indicators of each monitoring point in the key area are calculated, including deformation rate, acceleration and acceleration second derivative, the change trend of the dynamic indicators in the time sequence is extracted, and a rate-acceleration joint mutation criterion is constructed; based on the rate-acceleration joint mutation criterion, a critical phase change point of the key area from the first stage to the second stage is identified; the first stage is a stable stage; the second stage is a nonlinear acceleration stage; through a confidence evaluation mechanism, the identification result of the critical phase change point is verified for spatial consistency and error denoising, the critical phase change point is taken as an important event of deformation stage conversion, and feedback data with time mark and spatial position is generated in combination with the regional function attribute label.

[0060] Specifically, the rate-acceleration joint mutation criterion comprehensively evaluates the mutation degree by weighted combination of rate relative change, acceleration relative change and acceleration second derivative relative change, wherein the rate weight is the highest, the acceleration weight is the second, and the acceleration second derivative weight is the lowest.

[0061] The criterion threshold setting is divided into three levels: the high threshold corresponds to the detection of the critical phase change point, the medium threshold corresponds to the warning state, and the low threshold corresponds to the stable state.

[0062] The spatial consistency verification requires that multiple monitoring points in a certain radius range detect abnormal signals at the same time.

[0063] The critical phase transition point recognition method based on the rate-acceleration combined mutation criterion of the embodiment includes dynamic index calculation and confidence evaluation, significantly improves the accuracy of critical phase transition point recognition through multi-dimensional dynamic index combined criterion, can capture early signals from stable to nonlinear acceleration conversion, and greatly extends the early warning time. Through confidence evaluation and spatial consistency verification, the reliability of the recognition result is ensured, and feedback data with time and space identification is generated to provide rich information for subsequent risk assessment. It provides key phase transition information input for subsequent functional risk assessment, directly affects the early warning effect, and is the key technology to realize early warning and active management.

[0064] Further, a structural functional risk assessment model is constructed, which combines the functional index and the feedback data to assess the operation safety level of the key area;

[0065] Reference Figure 3 The structural functional risk assessment model includes: a functional index construction unit, a deformation-function degradation modeling unit, a function failure determination unit, and a functional risk level assessment unit;

[0066] The functional index construction unit constructs a functional index reflecting the operation capacity of the region according to the functional properties of the key area, including: a transportation channel index, a work platform index, and a drainage facility index; The deformation-function degradation modeling unit constructs the relationship between remote sensing data and the degradation degree of the functional index based on the feedback data to form a "deformation-function" mapping model; The function failure determination unit sets the degradation threshold corresponding to each functional index, and determines whether the functional index in the current key area reaches the failure state according to the "deformation-function" mapping model and the degradation threshold; The functional risk level assessment unit is used to combine the feedback data and the failure state determination result of the functional index to divide the operation state of the key area into levels, and output the operation safety level of the key area.

[0067] Specifically, the transportation channel index includes road width safety margin (ratio of current width to minimum safety width), maximum slope (set safety and dangerous slope threshold), and bearing capacity (remaining bearing capacity based on deformation variable);

[0068] The work platform index includes effective work area (proportion of available area affected by deformation), and equipment safety space (minimum safety distance required for equipment operation);

[0069] The drainage facility index includes trench integrity (trench deformation degree based on deformation variable), and flood discharge capacity (flow rate influence evaluation of cross-section change);

[0070] The deformation-function degradation modeling unit uses a negative exponential decay function to describe the function degradation process, wherein the degradation degree is related to the ratio of the measured deformation variable and the function failure threshold.

[0071] The function failure determination unit sets a degradation threshold corresponding to each functional index, including:

[0072] Transportation channel index: slope change threshold;

[0073] Work platform index: inclination threshold;

[0074] Drainage facility index: cross-section reduction ratio threshold.

[0075] The embodiment constructs a structure-function risk assessment model, including functional index construction, deformation-function degradation modeling, function failure determination and risk level assessment, realizes the evaluation mode innovation from pure structure safety assessment to function safety assessment, and covers the overall assessment of key functional elements such as transportation channel, work platform and drainage facility. By creating a "deformation-function" quantitative mapping model, the structure change and function degradation are associated. Different risk conditions are provided by multi-level risk level division to provide differentiated management strategies. The operation safety level input is provided for subsequent collaborative redundancy level calculation, the technical analysis results are converted into management decision basis, and the transformation from technical monitoring to management application is realized.

[0076] Further, based on the feedback data and the operation safety level, the "function-structure" collaborative redundancy level is calculated, and the safety management strategy is dynamically adjusted.

[0077] The specific process of calculating the "function-structure" collaborative redundancy level includes:

[0078] According to the influence weight of the operation safety level of the key area on the overall operation of the mine area, a function influence factor matrix is formed; according to the function influence factor matrix and combined with the feedback data, the structure state of the key area is evaluated in real time, and the remaining structure bearing capacity for function support is identified; based on the mapping relationship between the function influence factor matrix and the remaining structure bearing capacity, a "function-structure" coupling model is constructed to measure the redundancy level under the current state; according to the redundancy level, a risk response suggestion is output, and the dynamic adjustment of the mine safety management strategy and the resource priority allocation are carried out.

[0079] Specifically, the function influence factor matrix comprehensively considers the importance of each functional element of the transportation channel, the work platform and the drainage facility to the overall operation;

[0080] The remaining structure bearing capacity is the original bearing capacity multiplied by the structure integrity and the safety factor, wherein the structure integrity is represented by 1 minus the relative deformation variable;

[0081] The redundancy level is calculated by the ratio of the weighted remaining bearing capacity and the weighted demand bearing capacity, and the weight is determined by the function influence factor.

[0082] According to the redundancy level output risk response suggestion, including: maintaining normal operation and regular monitoring at high redundancy level, strengthening monitoring and preparing emergency plan at medium redundancy level, limiting operation and implementing reinforcement measures at low redundancy level, and stopping operation and emergency treatment at very low redundancy level.

[0083] Meanwhile, all thresholds of the present application are obtained by experience.

[0084] The embodiment realizes multi-dimensional comprehensive evaluation through the "function-structure" synergistic redundancy level calculation method, including function influence factor matrix construction, structure state evaluation and coupling model establishment. The intelligent management is realized by dynamically adjusting the safety management strategy based on the real-time redundancy level. The management efficiency is improved by guiding the resource priority configuration through the function influence factor matrix, and the intelligent decision support is realized by automatically generating the risk response suggestion according to the redundancy level. Through the comprehensive feedback data and the operation safety level, the final decision output of the entire technical scheme is completed, a complete technical closed loop is formed, and the whole process from data acquisition to management decision is realized.

[0085] The embodiment establishes a complete technical system of multi-source time sequence InSAR fusion mine safety early warning, and proposes a systematic solution covering the whole chain of data acquisition, processing, analysis, evaluation and application, which changes the problem of fragmentation of traditional methods at each link. Through multi-stage progressive processing, the fundamental change from "passive response" to "active management" is realized, and an intelligent decision-making closed loop based on multi-source information fusion and a self-adaptive safety management mechanism capable of dynamic adjustment according to real-time conditions are constructed. According to the logical order of "environment perception→data acquisition→data processing→feature recognition→risk assessment→decision support", a complete technical chain is constructed, a complete information transmission chain and feedback regulation mechanism are established, the system is self-adaptive and self-optimizing, and a progressive technical architecture is provided for the whole mine safety early warning system. The feasibility of engineering implementation is fully considered, and the organic unity of technical advancement and practicality is realized.

[0086] Embodiment two

[0087] In embodiment one, the method proposed by the present application successfully realizes more front-end, dynamic and adaptive safety early warning in mine management, and realizes the change from "passive response" to "active management". In order to further verify the effectiveness of the present application, the main slope area of another large open-pit iron mine is also managed for safety early warning in the embodiment of the present application.

[0088] According to the dust concentration and meteorological conditions, a dust disturbance level evaluation model is constructed to evaluate the degree of dust disturbance;

[0089] The dust disturbance level evaluation model comprises a dust concentration acquisition unit, a meteorological condition analysis unit and a disturbance level calculation unit.

[0090] The dust concentration collection unit monitors the dust concentration data of the working face of the mining area in real time and establishes a dust concentration space-time distribution database; the meteorological condition analysis unit collects wind speed, wind direction, humidity and temperature, analyzes the influence law of meteorological conditions on dust diffusion, and obtains meteorological influence factors; the interference level calculation unit evaluates the dust interference based on the dust concentration space-time distribution database and the meteorological influence factors, and obtains the dust interference degree.

[0091] Further, based on the operation plan and the dust interference degree, the collection time arrangement of remote sensing data is planned, and remote sensing data is collected according to the collection time;

[0092] The specific process of planning the collection time arrangement of remote sensing data includes:

[0093] Obtain the mining area operation plan information, including blasting time, operation area and equipment running state; establish a relationship model of dust concentration and time, space position, predict the space-time distribution evolution of dust cloud after blasting, and obtain the space-time evolution prediction result; determine the time window of dust cloud dissipation after blasting based on the space-time evolution prediction result; identify the adverse weather period in combination with weather forecast data; the adverse weather includes sandstorm, heavy rain and gale; according to the mining area operation plan information, the dust interference degree, the time window of dust cloud dissipation after blasting and the adverse weather period, the time interval and priority order of remote sensing data collection are dynamically adjusted, and a multi-source remote sensing data collection time table is generated; collect the remote sensing data based on the collection time table.

[0094] In the traditional deformation monitoring of mining area, the collection of conventional InSAR data often ignores the influence of dust concentration and operation period on imaging quality, resulting in missing of key timing data or discontinuity of deformation sequence, which restricts the timeliness and accuracy of mining area safety monitoring. The present application constructs a dust interference level evaluation model, integrates dust concentration monitoring, meteorological factor evaluation and operation plan analysis, predicts the space-time diffusion trend of dust cloud after blasting through the dust interference level evaluation model, and plans the "low disturbance window" of remote sensing imaging. Then, the collection time table of multi-source InSAR data is dynamically adjusted, the optimal configuration of source InSAR data in space and time is realized, the continuity, anti-interference and high dynamic adaptability of timing InSAR data are effectively improved, and the data basis for constructing the whole process perception deformation monitoring is provided.

[0095] Further, analyze the data stability of different remote sensing data under environmental interference, process the remote sensing data based on the data stability, and obtain interference suppression data;

[0096] The specific process of obtaining the interference suppression data includes:

[0097] A dust electromagnetic scattering physical model is established to evaluate the interference degree of dust on remote sensing data of different frequency bands and obtain dust sensitivity; a remote sensing data quality evaluation system is constructed to quantitatively score the data quality of multi-source remote sensing data under different environmental conditions by using coherence coefficients, phase residuals and intensity stability; the relative stability of remote sensing data of different frequency bands under the current dust interference level is comprehensively analyzed, and the data stability is calculated by combining the dust sensitivity and the data quality; a remote sensing data fusion strategy based on dust response weight is constructed according to the data stability, and multi-source remote sensing data is processed according to the remote sensing data fusion strategy: for remote sensing data with data stability higher than a data stability threshold, the reserved remote sensing data is obtained by preferentially reserving; for remote sensing data with data stability lower than the data stability threshold, the missing data is reconstructed based on the deformation trend interpolation of adjacent time phases and the historical deformation database to obtain reconstructed data; the reserved remote sensing data and the reconstructed data are fused to output interference suppression data.

[0098] Due to the complex operation environment of the mining area, which is usually high dust and multiple blasting, the conventional InSAR deformation data has problems of poor coherence, low signal-to-noise ratio and missing, and it is difficult to accurately identify early abnormal deformation signals. The present application proposes a data fusion mechanism for interference suppression, by constructing a dust electromagnetic scattering model and a multi-frequency remote sensing interference sensitivity database, and combining InSAR image quality indicators (coherence coefficients, phase residuals, etc.) to quantify data stability; then, according to the data stability score, the remote sensing data is processed by weight fusion: the data with high stability is directly reserved, and the data with low stability is compensated by adjacent time phase trend interpolation and historical database reconstruction, to finally form complete interference suppression data. Finally, the rate-acceleration mutation joint criterion is used to identify the critical phase change point in the deformation sequence, which improves the sensitivity and accuracy of early risk identification, and breaks through the limitation of traditional single-frequency InSAR that cannot penetrate dust.

[0099] Further, according to the interference suppression data, the critical phase change point of the key area from the first stage to the second stage is identified to obtain feedback data; the specific process of obtaining the feedback data includes:

[0100] calculating dynamic indexes of each monitoring point in the key area based on the interference suppression data, including deformation rate, acceleration, and second derivative of acceleration, extracting the change trend of the dynamic indexes in the time sequence, and constructing a rate-acceleration joint mutation criterion; identifying a critical phase change point of the key area from a first stage to a second stage based on the rate-acceleration joint mutation criterion; the first stage is a stable stage; the second stage is a nonlinear acceleration stage; through a confidence evaluation mechanism, the identification result of the critical phase change point is verified for spatial consistency and error denoising, the critical phase change point is taken as an important event of deformation stage conversion, and feedback data with time identification and spatial position are generated in combination with a regional function attribute label.

[0101] Further, a structural functional risk assessment model is constructed, and the feedback data are combined with the functional indexes to assess the operation safety level of the key area.

[0102] The structural functional risk assessment model includes a functional index construction unit, a deformation-function degradation modeling unit, a function failure determination unit, and a functional risk level assessment unit.

[0103] The functional index construction unit constructs functional indexes reflecting the operation capacity of the key area according to the function attribute of the key area, including transportation channel indexes, operation platform indexes, and drainage facility indexes; the deformation-function degradation modeling unit constructs the relationship between remote sensing data and the degradation degree of the functional indexes based on the feedback data to form a “deformation-function” mapping model; the function failure determination unit sets the degradation threshold corresponding to each functional index, and determines whether the functional index in the current key area reaches a failure state according to the “deformation-function” mapping model and the degradation threshold; the functional risk level assessment unit is used to combine the feedback data and the failure state determination result of the functional indexes to divide the operation state of the key area into levels, and output the operation safety level of the key area.

[0104] Further, based on the feedback data and the operation safety level, the “function-structure” collaborative redundancy level is calculated, and the safety management strategy is dynamically adjusted.

[0105] The specific process of calculating the “function-structure” collaborative redundancy level includes:

[0106] According to the influence weight of the functional index on the overall operation of the mining area according to the operation safety level evaluation function of the key area, a functional influence factor matrix is formed; according to the functional influence factor matrix and combined with the feedback data, the structural state of the key area is evaluated in real time, and the remaining structural bearing capacity for functional support is identified; based on the mapping relationship between the functional influence factor matrix and the remaining structural bearing capacity, a 'function-structure' coupling model is constructed to measure the redundancy level under the current state; according to the redundancy level, a risk response suggestion is output, and the dynamic adjustment of the safety management strategy of the mining area and the priority allocation of resources are carried out.

[0107] The existing mine slope monitoring method mainly focuses on the structural deformation amplitude, lacks the evaluation of 'functional failure' and'system redundancy capability', and the risk response is still lagging behind the evolution process. The present application establishes a structural functional risk assessment model, constructs a multi-dimensional functional index combined with the regional functional index, and quantifies the influence degree of structural deformation on functional degradation through a 'deformation-function' mapping model. Further, the 'function-structure' collaborative redundancy model is introduced, the relationship between the importance weight of the functional index and the remaining structural bearing capacity is evaluated through the feedback data, and the collaborative redundancy level of the current system is dynamically calculated. This 'function-structure' collaborative redundancy model not only reflects the current operation safety level, but also outputs the resource allocation suggestion and management strategy based on risk, promotes the evolution of the mining area from'static response' to 'dynamic self-adaptation', and has strong practical value and popularization potential.

[0108] Although embodiments of the present application have been shown and described, it will be understood by those having ordinary skill in the art that various changes, modifications, substitutions and alterations can be made thereto without departing from the principles and spirit of the present application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-source time-series InSAR fusion mine safety early warning model method, characterized in that, The method comprises the following steps: A dust disturbance level evaluation model is constructed according to dust concentration and meteorological conditions to evaluate the degree of dust disturbance; Based on the operation plan and the degree of dust disturbance, the collection time of remote sensing data is planned, and remote sensing data is collected according to the collection time; The data stability of different remote sensing data under environmental disturbance is analyzed, and the remote sensing data is processed based on the data stability to obtain interference suppression data; According to the interference suppression data, the critical phase change point of the key area from the first stage to the second stage is identified to obtain feedback data; A structure-function risk evaluation model is constructed, and the operation safety level of the key area is evaluated by combining the functional index and the feedback data; Based on the feedback data and the operation safety level, the function-structure collaborative redundancy level is calculated, and the safety management strategy is dynamically adjusted.

2. The mine safety early warning model method of multi-source time-series InSAR fusion according to claim 1, characterized in that: The dust disturbance level evaluation model comprises a dust concentration collection unit, a meteorological condition analysis unit and a disturbance level calculation unit; the dust concentration collection unit monitors the dust concentration data of the mining area operation surface in real time, and establishes a dust concentration space-time distribution database; the meteorological condition analysis unit collects wind speed, wind direction, humidity and temperature, analyzes the influence law of meteorological conditions on dust diffusion, and obtains meteorological influence factors; the disturbance level calculation unit evaluates the dust disturbance based on the dust concentration space-time distribution database and the meteorological influence factors, and obtains the degree of dust disturbance.

3. The mine safety early warning model method of multi-source time-series InSAR fusion according to claim 1, characterized in that: The collection time of remote sensing data is planned, and the specific process comprises the following steps: obtaining the mining area operation plan information, including blasting time, operation area and equipment running state; establishing a dust concentration and time, space position relationship model, predicting the space-time distribution evolution of dust cloud after blasting, and obtaining space-time evolution prediction result; based on the space-time evolution prediction result, determining the time window of dust cloud dissipation after blasting; combining weather forecast data, identifying adverse weather period; the adverse weather includes sandstorm, heavy rain and strong wind; according to the mining area operation plan information, the degree of dust disturbance, the time window of dust cloud dissipation after blasting and the adverse weather period, the time interval and priority order of remote sensing data collection are dynamically adjusted, and the collection time table of multi-source remote sensing data is generated; the remote sensing data is collected based on the collection time table.

4. The mine safety early warning model method of multi-source time-series InSAR fusion according to claim 1, characterized in that: The specific process of obtaining the interference suppression data comprises the following steps: A dust electromagnetic scattering physical model is established to evaluate the interference degree of dust on remote sensing data of different frequency bands and obtain dust sensitivity; a remote sensing data quality evaluation system is constructed to quantitatively score the data quality of multi-source remote sensing data under different environmental conditions by using coherence coefficients, phase residuals and intensity stability; the relative stability of remote sensing data of different frequency bands under the current dust interference level is comprehensively analyzed, and the data stability is calculated in combination with the dust sensitivity and the data quality; a remote sensing data fusion strategy based on dust response weight is constructed according to the data stability, and multi-source remote sensing data is processed according to the remote sensing data fusion strategy; remote sensing data with a data stability higher than a data stability threshold is preferentially retained to obtain retained remote sensing data; remote sensing data with a data stability lower than a data stability threshold is reconstructed based on the deformation trend interpolation of adjacent time phases and a historical deformation database to obtain reconstructed data; and the retained remote sensing data and the reconstructed data are fused to output interference suppression data.

5. The mine safety early warning model method of multi-source time-series InSAR fusion according to claim 1, characterized in that: The specific process of obtaining the feedback data includes: Based on the dynamic indicators of each monitoring point in the key area calculated from the interference suppression data, including deformation rate, acceleration and acceleration second derivative, the change trend of the dynamic indicators in the time sequence is extracted, and a rate-acceleration joint mutation criterion is constructed; based on the rate-acceleration joint mutation criterion, the critical phase change point of the key area from the first stage to the second stage is identified; the first stage is a stable stage; the second stage is a nonlinear acceleration stage; through a confidence evaluation mechanism, the identification result of the critical phase change point is verified for spatial consistency and error denoising, and combined with the regional function attribute label, feedback data with time label and spatial position are generated.

6. The mine safety early warning model method of multi-source time-series InSAR fusion according to claim 1, characterized in that: The structure-function risk assessment model includes: a functional index construction unit, a deformation-function degradation modeling unit, a function failure determination unit and a functional risk level evaluation unit; The functional index construction unit constructs a functional index reflecting the operation capacity of the region according to the functional attributes of the key area, including: transportation channel index, operation platform index and drainage facility index; the deformation-function degradation modeling unit constructs the relationship between remote sensing data and the degradation degree of functional index based on the feedback data to form a "deformation-function" mapping model; the function failure determination unit sets the degradation threshold corresponding to each functional index, and determines whether the functional index in the current key area reaches the failure state according to the "deformation-function" mapping model and the degradation threshold; the functional risk level evaluation unit is used to combine the feedback data and the failure state determination result of the functional index to divide the operation state of the key area into levels and output the operation safety level of the key area.

7. The mine safety early warning model method of multi-source time-series InSAR fusion according to claim 1, characterized in that: The specific process of calculating the "function-structure" collaborative redundancy level includes: According to the operation safety level evaluation function of the key area Influence weight of functional index on the overall operation of the mine area, a functional influence factor matrix is formed; according to the functional influence factor matrix and combined with the feedback data, the structural state of the key area is evaluated in real time, and the remaining structural bearing capacity for functional support is identified; based on the mapping relationship between the functional influence factor matrix and the remaining structural bearing capacity, a "function-structure" coupling model is constructed to measure the redundancy level under the current state; according to the redundancy level, risk response suggestions are output, and the dynamic adjustment of the mine safety management strategy and the resource priority configuration are carried out.

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