Mine ecological environment monitoring system

Abstract

The invention belongs to the technical field of mine monitoring, and particularly relates to a mine ecological environment monitoring system which comprises a monitoring module, an evaluation module and a feedback module. The monitoring module constructs a regional difference monitoring node network by combining a monitoring equipment distribution space, a map grid segmentation algorithm and a graph algorithm, and monitors and obtains a layered ecological index state space of a to-be-monitored mine region in real time; the evaluation module obtains main and auxiliary evaluation indexes by using the regional hierarchical index state space in combination with a factor analysis algorithm, and calculates the ecological health index of each sub-region in combination with a preset distributed node evaluation network and a hierarchical evaluation index library; and finally, real-time monitoring and early warning are realized according to the ecological health index of each sub-region and a layered stepping early warning space preset by a feedback module. According to the method, the Internet of Things technology and layered multi-index evaluation are fully utilized, the data calculation amount is reduced, the real-time ecological environment condition of the mine can be accurately monitored, and potential risks are found in time and early warning is carried out.

Description

Technical Field

[0001] The present invention belongs to the technical field of mine monitoring, and in particular relates to a mine ecological environment monitoring system. Background Art

[0002] Current mine monitoring technologies mostly focus on single safety or environmental indicators, making it difficult to achieve coordinated management of multiple factors in the ecological environment. For example, the existing Chinese patent with authorization publication number CN117514360B discloses a mine monitoring and early warning system that optimizes communication and early warning stability by adjusting cloud backup data capacity. However, it is limited to communication parameter management and lacks dynamic monitoring of ecological indicators such as air and water quality. The Chinese patent with authorization publication number CN108678807B discloses a mine pressure monitoring and early warning method for tunnels. It combines tunnel roof pressure and separation values ​​to assess roof collapse risk, but does not address ecological issues such as soil and water pollution and vegetation restoration. Traditional monitoring systems generally suffer from the following flaws: First, indicators are fragmented, focusing only on local parameters such as geological deformation and equipment status, while ignoring key ecological factors such as soil heavy metals and tailings leachate. Second, data silos are severe, with each subsystem operating independently, making it impossible to integrate multi-source data from communications, environment, and equipment to construct a global risk assessment model. Third, reliance on manual sampling and laboratory analysis results in poor real-time performance and high costs, making it difficult to respond to sudden pollution incidents in a timely manner. Fourth, early warning mechanisms lag behind. Existing technologies mostly rely on threshold-triggered alarms and lack proactive prevention and control capabilities based on time series prediction and spatial correlation. The above problems have led to the fragmentation of mine ecological monitoring, making it difficult to meet the "full-factor, full-process, and intelligent" supervision needs in green mine construction. Summary of the Invention

[0003] In response to the shortcomings of the existing technology, the present invention proposes a mine ecological environment monitoring system, which includes a monitoring module, an evaluation module and a feedback module. The monitoring module uses a regional difference monitoring node network constructed by combining the distribution space of monitoring equipment with a map grid segmentation algorithm and a graph algorithm to monitor and obtain the state space of the hierarchical indicators of the mine area to be monitored in real time; the evaluation module uses a factor analysis algorithm to extract the main and auxiliary evaluation indicators of the sub-region from the state space, and combines the preset distributed node evaluation network and the hierarchical evaluation indicator library to obtain the comprehensive evaluation score of each sub-region; the feedback module realizes real-time monitoring and early warning based on the comprehensive evaluation score and the preset hierarchical indicator early warning space; this application makes full use of the Internet of Things technology and hierarchical multi-indicator evaluation, which not only greatly reduces the amount of data calculation, but also can accurately monitor the real-time ecological environment status of the mine, timely discover potential risks and issue early warnings.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A mine ecological environment monitoring system includes: an evaluation module and a feedback module.

[0006] The evaluation module obtains the regional ecological health index corresponding to each sub-region based on the main evaluation indicators and auxiliary evaluation indicators corresponding to each sub-region in the regional hierarchical ecological indicator state space obtained through real-time monitoring, combined with the distributed node evaluation network and hierarchical evaluation indicator library constructed by the sub-regions and corresponding adjacent relationships; performs real-time monitoring and early warning based on the regional ecological health index corresponding to each sub-region combined with the hierarchical step warning space preset by the feedback module; the hierarchical evaluation indicator library is constructed by a fast index constructed by combining the hierarchical ecological indicator database and the correlation between the hierarchical ecological indicator and the hierarchical evaluation indicator; the main evaluation indicators and auxiliary evaluation indicators corresponding to each sub-region are obtained through the regional hierarchical ecological indicator state space combined with the factor analysis algorithm; the hierarchical step warning space is constructed by the differential hierarchical warning levels corresponding to the main evaluation indicators and auxiliary evaluation indicators and the corresponding rendering colors and the main evaluation indicator scores and auxiliary evaluation scores.

[0007] Specifically, the mine ecological environment monitoring system also includes a monitoring module; the monitoring module includes a monitoring matrix unit, a grid analysis unit, a data acquisition unit and an integrated feature extraction unit.

[0008] The monitoring matrix unit obtains a differential monitoring node network through a graph algorithm based on the monitoring equipment distribution space combined with the map layer feature space of the historical monitoring area of ​​each monitoring equipment.

[0009] The monitoring equipment distribution space is constructed by the monitoring equipment distribution location points, the type of ecological indicators monitored by each equipment and the size of the effective monitoring area, equipment physical property parameters, dynamic monitoring parameters, dynamic operating status parameters and environmental signal compensation parameters.

[0010] The grid analysis unit divides the differential monitoring node network into differential areas according to the characteristic information in the monitoring equipment distribution space corresponding to each node in the differential monitoring node network, the network topology relationship corresponding to all nodes in the differential monitoring node network, the overlapping coverage rate and blind spot compensation coefficient between all nodes and the accuracy of the evaluation index corresponding to each segmented area, and obtains a regional differential monitoring node network.

[0011] The data collection unit obtains a dynamic monitoring data set with monitoring device tags corresponding to all monitoring nodes in each sub-region based on the regional difference monitoring node network and the dynamic monitoring frequency configured for each monitoring node.

[0012] The network topology relationship corresponding to all the nodes is constructed by a topology algorithm based on the monitoring device, the number of relay hops between devices, the parent node-child node connection relationship, and the maximum coverage distance of the device wireless transmission.

[0013] The integrated feature extraction unit obtains the ecological indicator state space corresponding to all monitoring nodes in each sub-region based on the dynamic monitoring data set with monitoring equipment tags corresponding to all sub-regions and a preset federal feature extraction model.

[0014] Specifically, the evaluation module includes an indicator matching unit, a factor analysis unit and a distributed evaluation unit.

[0015] The indicator matching unit obtains the hierarchical evaluation indicator space corresponding to all monitoring nodes in each sub-region based on the ecological indicator state space corresponding to all monitoring nodes in each sub-region combined with the knowledge graph through a matching algorithm and a quick index preset in the hierarchical evaluation indicator library; the hierarchical evaluation indicator space is constructed by all hierarchical evaluation indicator sets used corresponding to each sub-region.

[0016] The factor analysis unit, based on the hierarchical evaluation indicator space corresponding to all monitoring nodes in each sub-region and the contribution rate of the historical evaluation indicators of the current sub-region, obtains the main hierarchical evaluation indicator set corresponding to each sub-region and the effective contribution rate corresponding to each main evaluation indicator, and the auxiliary hierarchical evaluation indicator set and the effective contribution rate corresponding to each auxiliary evaluation indicator through a factor analysis algorithm combined with a preset effective contribution threshold.

[0017] The distributed evaluation unit, based on the hierarchical distributed evaluation model configured for each monitoring node, combines the main hierarchical evaluation indicator set corresponding to each sub-region and the effective contribution rate corresponding to each main evaluation indicator with the auxiliary hierarchical evaluation indicator set and the effective contribution rate corresponding to each auxiliary evaluation indicator to obtain the regional comprehensive ecological health index score, the main evaluation indicator ecological health score and the auxiliary evaluation indicator ecological health index score and the corresponding evaluation accuracy.

[0018] Specifically, the feedback module includes an evaluation and early warning unit and a monitoring and feedback unit.

[0019] The assessment and early warning unit compares the regional comprehensive ecological health index score, the main assessment indicator ecological health score, the auxiliary assessment indicator ecological health index score and the corresponding assessment accuracy with the preset hierarchical step warning space to obtain hierarchical warning information corresponding to the assessment accuracy threshold.

[0020] The hierarchical step warning space is constructed by combining the regional comprehensive ecological health index score, the main evaluation indicator ecological health score, the auxiliary evaluation indicator ecological health index score and the preset warning level with the color-score mapping space.

[0021] The hierarchical warning information corresponding to the evaluation accuracy threshold is fed back to the monitoring center through the monitoring feedback unit, and the monitoring indicator information corresponding to the evaluation accuracy threshold is fed back to the grid analysis unit and the data acquisition unit. The corresponding sub-area division and the monitoring data sampling frequency are cyclically adjusted based on the corresponding effective contribution rate, and the hierarchical warning information corresponding to the evaluation accuracy threshold after adjustment is adjusted and fed back.

[0022] Specifically, the construction process of the regional difference monitoring node network includes:

[0023] Obtain the digital elevation model, geological structure map, surface cover type map, and ground-underground hydrological distribution thermal map of the mining area to be monitored, and construct a three-dimensional map layer of the mining area to be monitored.

[0024] Based on the three-dimensional map layer of the mining area to be monitored, combined with the location distribution information of the monitoring equipment and the effective monitoring area of ​​each monitoring equipment, the effective monitoring influence domain of each monitoring equipment in the three-dimensional map layer is obtained through the Voronoi diagram algorithm.

[0025] The device location distribution information includes the device location latitude and longitude coordinates and altitude.

[0026] Based on the effective monitoring influence domain of each monitoring device, the overlapping coverage and blind area between all monitoring devices are obtained.

[0027] Based on the distribution of each device location point and the signal base station location point, the initial monitoring node set and transmission node set are obtained.

[0028] Based on the effective monitoring influence domain of the monitoring equipment and the effective radiation radius of the base station signal, the first connection relationship between the monitoring node and the transmission node within the effective radiation radius of each base station signal in the initial monitoring node network, the corresponding second connection relationship between the transmission nodes, and the corresponding third connection relationship between the transmission nodes are constructed.

[0029] An initial monitoring node network is obtained based on the initial monitoring node set in combination with the first connection relationship, the second connection relationship and the third connection relationship in combination with a graph algorithm.

[0030] Specifically, the construction process of the regional difference monitoring node network also includes:

[0031] Based on the corresponding distance and environmental information between the monitoring node and the transmission node and between the transmission nodes within the effective radiation radius of each base station signal, a first signal attenuation compensation coefficient between each monitoring node and the corresponding transmission node and a second signal attenuation compensation coefficient between the transmission nodes are constructed.

[0032] The environmental information includes rock density distribution and mapping distribution of rock density to signal strength, and vegetation density distribution and mapping distribution of vegetation density to signal strength.

[0033] The first signal attenuation compensation coefficient and the second signal attenuation compensation coefficient are configured into the corresponding second connection relationship and third connection relationship to obtain a dynamic monitoring node network.

[0034] Specifically, the construction process of the regional difference monitoring node network also includes:

[0035] Based on the historical monitoring indicator type and warning level corresponding to each monitoring node and the area size of the historically divided sub-region, a level-region mapping function is constructed to obtain the warning level corresponding to each monitoring node and the regional division area.

[0036] An output sequence of the particle swarm optimization algorithm is constructed based on the level-area mapping function, the overlapping coverage and blind area between all monitoring devices in the dynamic monitoring node network, the effective monitoring influence domain corresponding to each monitoring node, the signal transmission delay per unit time of the transmission node in the corresponding divided area, the comprehensive ecological health index score of each divided area, the main evaluation indicator ecological health index score and the auxiliary evaluation indicator ecological health index score and the corresponding evaluation accuracy.

[0037] At the same time, based on the overlapping coverage, effective monitoring influence domain, blind area, signal transmission delay per unit time and evaluation accuracy, the regional division optimization constraint function and corresponding constraint conditions are constructed, and the minimum value of the regional division optimization constraint function is taken.

[0038] The output sequence of the particle swarm optimization algorithm and the regional division optimization constraint function and the corresponding constraint conditions are input into the particle swarm optimization algorithm with a built-in Kriging interpolation function, and combined with the optimization constraint threshold and training cycle of the constraint, the optimized regional division parameter set of the dynamic monitoring node network is obtained.

[0039] The optimized regional division parameter set of the dynamic monitoring node network is input into the regional grid algorithm, the dynamic monitoring node network is regionally divided, a regional difference monitoring node network is obtained, and the divided regions are marked in real time.

[0040] Specifically, the process of obtaining the regional comprehensive ecological health index score, the main assessment indicator ecological health score, and the auxiliary assessment indicator ecological health index score includes:

[0041] Based on the regional difference monitoring node network, each divided sub-region is used as each evaluation node of the distributed node evaluation network, and the adjacent relationship of all divided sub-regions is used to construct the evaluation connection relationship between all evaluation nodes in the distributed node evaluation network.

[0042] Based on the evaluation nodes and evaluation connection relationships corresponding to all divided sub-areas, a distributed node evaluation network is constructed through graph algorithms.

[0043] Based on the regional difference monitoring node network combined with the configured data collection parameters, a real-time dynamic monitoring data set with monitoring device tags and collection timestamps corresponding to all monitoring nodes in each sub-region is obtained.

[0044] The real-time dynamic monitoring data set is input into the data classification layer in the federated feature extraction model to perform data type classification, and the data type and classification accuracy of the real-time dynamic monitoring data set corresponding to each sub-region are obtained.

[0045] The data in the real-time ecological indicator state space of each sub-region are input into the distributed feature extraction layer according to the corresponding type to extract the corresponding type of data features, and the ecological indicator state space corresponding to all monitoring nodes in each sub-region is obtained.

[0046] The distributed feature extraction layer includes M feature extraction sublayers; the M feature extraction sublayers have the same number of data types as those obtained by the data classification layer and have a one-to-one correspondence.

[0047] Specifically, the hierarchical evaluation index library includes a first index layer and a second index layer; the first index layer includes a large index of the cover layer, a large index of the soil layer, an index of the hydrological layer, a large index of the geological layer, and a large index of the air layer; the second index layer is constructed by the first-level indicators, second-level indicators and third-level indicators corresponding to the large index of the cover layer, the large index of the soil layer, the index of the hydrological layer, the large index of the geological layer and the large index of the air layer respectively.

[0048] The major indicators of the covering layer include the first-level land resource monitoring indicator, the first-level solid waste monitoring indicator, the first-level water and soil environment monitoring indicator, and the second-level indicators and third-level indicators corresponding to each first-level indicator; the major indicators of the soil layer include the first-level soil pollution monitoring indicator and the corresponding second-level indicators and third-level indicators; the hydrological layer indicators include the first-level mining area surface water pollution monitoring indicator, the first-level wastewater and waste liquid discharge monitoring indicator, the first-level groundwater monitoring indicator, and the second-level indicators and third-level indicators corresponding to each first-level indicator; the major indicators of the geological layer include the first-level goaf area ground subsidence monitoring indicator, the first-level mountain geological disaster monitoring indicator, the first-level ground fissure monitoring indicator, and the second-level indicators and third-level indicators corresponding to each first-level indicator; the major indicators of the air layer include the first-level air pollutant concentration indicator and the corresponding second-level indicators and third-level indicators; the corresponding major indicator level in the first indicator layer is greater than the first-level indicator in the second indicator layer, the corresponding level of the first-level indicator is greater than the second-level indicator, and the level of the second-level indicator is greater than the third-level indicator; the hierarchical evaluation indicator library is constructed by combining the first indicator layer and the second indicator layer with the tree database according to the corresponding indicator levels.

[0049] Specifically, the process of obtaining the regional comprehensive ecological health index score, the main assessment indicator ecological health score, and the auxiliary assessment indicator ecological health index score also includes:

[0050] Based on the ecological indicator state space corresponding to all monitoring nodes in each sub-region and the hierarchical evaluation indicator library, all hierarchical evaluation indicator sets corresponding to each sub-region are obtained through a matching algorithm and the fast indexing.

[0051] Based on all the hierarchical evaluation indicator sets corresponding to each sub-region, the contribution rate of each major indicator in the first indicator layer to the regional comprehensive ecological health index score and the contribution rate of the third-level indicators in the second indicator layer to the corresponding second-level indicators and the contribution rate of the second-level indicators to the corresponding first-level indicators are obtained through the factor analysis algorithm.

[0052] According to the contribution rate of each major indicator to the regional comprehensive ecological health index score, the major indicator with the largest contribution rate is set as the main evaluation major indicator, and the remaining major indicators are set as the first auxiliary evaluation major indicator, the second auxiliary evaluation major indicator, the third auxiliary evaluation major indicator, and the fourth auxiliary evaluation major indicator according to the corresponding contribution rate.

[0053] Based on the main evaluation indicator, the first auxiliary evaluation indicator, the second auxiliary evaluation indicator, the third auxiliary evaluation indicator, the fourth auxiliary evaluation indicator and the corresponding first-level indicators, second-level indicators and third-level indicators of each sub-region, as well as the contribution rate of each indicator in the first indicator layer to the regional comprehensive ecological health index score, the contribution rate of the third-level indicators in the second indicator layer to the corresponding second-level indicators, the contribution rate of the second-level indicators to the corresponding first-level indicators, and the contribution rate of the first-level indicators to the corresponding major indicators, the main-auxiliary hierarchical evaluation indicator scores and the corresponding evaluation accuracy in each sub-region are obtained through a hierarchical distributed evaluation model.

[0054] Specifically, the scores of the main-auxiliary hierarchical evaluation indicators in each sub-region include the evaluation scores of each first-level indicator corresponding to the main evaluation indicator, the evaluation scores of each first-level indicator corresponding to the first auxiliary evaluation indicator, the second auxiliary evaluation indicator, the third auxiliary evaluation indicator, and the fourth auxiliary evaluation indicator, the main evaluation indicator ecological health score, the first auxiliary evaluation indicator ecological health index score, the second auxiliary evaluation indicator ecological health index score, the third auxiliary evaluation indicator ecological health index score, the fourth auxiliary evaluation indicator ecological health index score, and the regional comprehensive ecological health index score.

[0055] The process of obtaining the regional comprehensive ecological health index score, the main assessment indicator ecological health score, and the auxiliary assessment indicator ecological health index score also includes:

[0056] Based on the scores of the primary and secondary hierarchical evaluation indicators in each sub-area and the preset hierarchical step warning space, the warning information corresponding to each level and the warning level rendering color of the effective monitoring influence domain corresponding to each monitoring node are obtained according to the indicator level from large to small.

[0057] Specifically, the construction process of the hierarchical step warning space includes:

[0058] According to the scores of the main and auxiliary hierarchical evaluation indicators in each sub-region, the warning score thresholds at the corresponding levels are set from large to small according to the evaluation indicator levels. If the comprehensive ecological health index score of the corresponding area of ​​the current sub-region is less than the corresponding warning score threshold, the ecological health score of the main evaluation indicator is compared with the corresponding warning score threshold. When the ecological health score of the main evaluation indicator is less than the corresponding warning score threshold and the evaluation scores of each first-level indicator corresponding to the main evaluation indicator are less than the corresponding warning score threshold, the auxiliary evaluation indicator warning is issued.

[0059] When one of the regional comprehensive ecological health index score, the main evaluation indicator ecological health score or the first-level indicator evaluation score does not meet the corresponding warning score threshold, the highest level warning will be issued and the effective monitoring impact domain that does not meet the warning score threshold will be rendered in the highest warning level color.

[0060] When issuing an auxiliary evaluation indicator warning, an auxiliary warning of the corresponding warning level and color rendering of the corresponding auxiliary warning level are carried out according to the warning level interval to which the product of the current level auxiliary evaluation indicator score and the corresponding contribution rate belongs.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] The present invention addresses the deficiencies of the existing technology by constructing a regionally differentiated monitoring node network to achieve multi-dimensional real-time monitoring of the mine ecological environment. It utilizes the Voronoi diagram algorithm and particle swarm optimization grid segmentation technology, combined with three-dimensional map layers and equipment distribution parameters, to dynamically divide monitoring sub-areas and optimize the network topology, effectively improving monitoring coverage and data acquisition accuracy. It uses a federated feature extraction model and a fast indexing mechanism of a hierarchical evaluation index library to achieve feature fusion and efficient matching of multi-source heterogeneous ecological data. It combines a factor analysis algorithm to dynamically determine the primary and secondary evaluation indicators and their contribution rates, ensuring the adaptability and accuracy of the evaluation model. It implements multi-level linkage real-time warning based on dynamic threshold determination and color rendering rules in a hierarchical step warning space, and automatically adjusts the monitoring frequency and grid division parameters through a feedback mechanism to form a "monitoring-assessment-warning-optimization" closed-loop control system. Furthermore, the present application overcomes complex terrain interference through a signal attenuation compensation coefficient and an environmental adaptive algorithm, and uses a distributed evaluation network and a knowledge graph algorithm to solve the real-time problem of large-scale data processing. Ultimately, it constructs an intelligent monitoring system with self-learning and self-optimization capabilities to provide precise decision-making support for mine ecological environment management. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a structural diagram of a mine ecological environment monitoring system according to Example 1 of the present invention.

[0064] Figure 2 This is a structural diagram of the construction process of the regional difference monitoring node network in Example 1 of the present invention. DETAILED DESCRIPTION

[0065] Example 1

[0066] See also Figure 1 An embodiment of the present invention provides a mine ecological environment monitoring system, including: a monitoring module, an evaluation module, and a feedback module.

[0067] The monitoring module, based on the preset regional difference monitoring node network, monitors and obtains the state space of stratified ecological indicators of the mining area to be monitored in real time.

[0068] The regional difference monitoring node network is constructed by combining the monitoring equipment distribution space with the map grid segmentation algorithm and graph algorithm.

[0069] The evaluation module obtains the regional ecological health index corresponding to each sub-region based on the main evaluation indicators and auxiliary evaluation indicators corresponding to each sub-region in the regional hierarchical ecological indicator state space, combined with the preset distributed node evaluation network and hierarchical evaluation indicator library.

[0070] The hierarchical evaluation index library is constructed by combining hierarchical ecological indicators with a database and a fast index constructed by the correlation between hierarchical ecological indicators and hierarchical evaluation indicators.

[0071] The main and auxiliary evaluation indicators corresponding to each sub-region are obtained through the regional stratified ecological indicator state space combined with the factor analysis algorithm.

[0072] Real-time monitoring and early warning are carried out based on the regional ecological health index corresponding to each sub-region and the hierarchical step warning space preset by the feedback module.

[0073] The hierarchical step warning space is constructed by the differential hierarchical warning levels corresponding to the main evaluation indicators and the auxiliary evaluation indicators, the corresponding rendering colors, and the scores of the main evaluation indicators and the auxiliary evaluation indicators.

[0074] Furthermore, the monitoring module includes a monitoring matrix unit, a grid analysis unit, a data acquisition unit and an integrated feature extraction unit.

[0075] The monitoring matrix unit obtains a differential monitoring node network through graph algorithms based on the distribution space of monitoring equipment combined with the feature space of the map layer of the historical monitoring area of ​​each monitoring equipment.

[0076] The distribution space of monitoring equipment is constructed by the distribution locations of monitoring equipment, the type of ecological indicators monitored by each equipment and the size of the effective monitoring area, equipment physical property parameters, dynamic monitoring parameters, dynamic operating status parameters and environmental signal compensation parameters.

[0077] Furthermore, the monitoring matrix unit integrates a multi-mode communication protocol to ensure data transmission reliability, and its physical properties cover basic parameters such as sensor type and RF gain. Secondly, it features a programmable sampling frequency and adaptive adjustment mechanism to support multi-dimensional data collection, including particulate matter concentration and fluid dynamics indicators. Kalman filtering and a hierarchical compression algorithm are also included to optimize signal quality. Operational status monitoring tracks battery charge, signal strength, and storage resource utilization in real time, with a built-in hexadecimal-coded fault diagnosis system and a multi-level energy efficiency mode switching strategy. Thirdly, the environmental compensation mechanism integrates a geological medium attenuation model, dynamic correction of meteorological interference, and a multipath suppression algorithm. Through rock density-signal attenuation mapping, a temperature drift compensation coefficient matrix, and time-domain equalization control, it optimizes monitoring accuracy in complex scenarios, forming a full-link closed-loop control capability from data perception to environmental adaptation.

[0078] The grid analysis unit divides the differential monitoring node network into differential areas according to the characteristic information of the monitoring equipment distribution space corresponding to each node in the differential monitoring node network, the network topology relationship corresponding to all nodes in the differential monitoring node network, the overlapping coverage rate and blind spot compensation coefficient between all nodes and the accuracy of the evaluation index corresponding to each segmented area. The grid segmentation algorithm optimized by the particle swarm algorithm is used to divide the differential monitoring node network into differential areas to obtain a regional differential monitoring node network. All monitoring nodes in each sub-area correspond to a monitoring data set with monitoring equipment labels.

[0079] The data collection unit obtains a dynamic monitoring data set with monitoring device tags corresponding to all monitoring nodes in each sub-area based on the regional difference monitoring node network and the dynamic monitoring frequency configured for each monitoring node.

[0080] The corresponding network topology relationship between all nodes is constructed through the topology algorithm by monitoring devices, the number of relay hops between devices, the parent node-child node connection relationship, and the maximum coverage distance of device wireless transmission.

[0081] The integrated feature extraction unit obtains the ecological indicator state space corresponding to all monitoring nodes in each sub-region based on the dynamic monitoring data set with monitoring equipment labels corresponding to all sub-regions and the preset federal feature extraction model.

[0082] Furthermore, the evaluation module includes an indicator matching unit, a factor analysis unit and a distributed evaluation unit.

[0083] The indicator matching unit is based on the hierarchical evaluation indicator library constructed based on the ecological indicator state space corresponding to all monitoring nodes in each sub-area and the knowledge graph. Through the matching algorithm and the preset fast index of the hierarchical evaluation indicator library, the hierarchical evaluation indicator space corresponding to all monitoring nodes in each sub-area is obtained; the hierarchical evaluation indicator space is constructed by all the hierarchical evaluation indicator sets used in each sub-area.

[0084] The factor analysis unit, based on the hierarchical evaluation indicator space corresponding to all monitoring nodes in each sub-region and the contribution rate of the historical evaluation indicators of the current sub-region, obtains the main hierarchical evaluation indicator set corresponding to each sub-region and the effective contribution rate corresponding to each main evaluation indicator, as well as the auxiliary hierarchical evaluation indicator set and the effective contribution rate corresponding to each auxiliary evaluation indicator through the factor analysis algorithm combined with the preset effective contribution threshold.

[0085] The distributed evaluation unit, based on the hierarchical distributed evaluation model configured at each monitoring node, combines the main hierarchical evaluation indicator set corresponding to each sub-region and the effective contribution rate corresponding to each main evaluation indicator with the auxiliary hierarchical evaluation indicator set and the effective contribution rate corresponding to each auxiliary evaluation indicator to obtain the regional comprehensive ecological health index score, the main evaluation indicator ecological health score and the auxiliary evaluation indicator ecological health index score and the corresponding evaluation accuracy.

[0086] Furthermore, the feedback module includes an evaluation and early warning unit and a monitoring and feedback unit.

[0087] The assessment and early warning unit compares the regional comprehensive ecological health index score, the main assessment indicator ecological health score, the auxiliary assessment indicator ecological health index score and the corresponding assessment accuracy with the preset hierarchical step warning space to obtain the hierarchical warning information corresponding to the assessment accuracy threshold.

[0088] The hierarchical step warning space is constructed by combining the regional comprehensive ecological health index score, the main evaluation indicator ecological health score, the auxiliary evaluation indicator ecological health index score, and the preset warning level with the color-score mapping space; the color-score mapping space is constructed by fitting the preset HSV color space with the main evaluation indicator ecological health score and the auxiliary evaluation indicator ecological health index score through support vector machine.

[0089] The hierarchical warning information corresponding to the evaluation accuracy threshold is fed back to the monitoring center through the monitoring feedback unit. At the same time, the monitoring indicator information corresponding to the evaluation accuracy threshold is fed back to the grid analysis unit and the data collection unit. The corresponding sub-area division and monitoring data sampling frequency are cyclically adjusted based on the corresponding effective contribution rate, and the hierarchical warning information corresponding to the evaluation accuracy threshold after adjustment is adjusted and fed back.

[0090] This process achieves precision and intelligence in mine ecological monitoring through multi-module collaboration and technology integration. In the monitoring module, graph algorithms are used in combination with multi-dimensional parameters of equipment to build a differential monitoring node network, and then the grid segmentation algorithm optimized by particle swarm optimization is used to divide the area according to node characteristics, topological relationships, etc., to ensure that there are no blind spots in the monitoring coverage. For example, in mountainous mines, the equipment signal is adjusted according to environmental parameters such as terrain attenuation compensation, and the monitoring layout is optimized in combination with grid division to improve the comprehensiveness of data collection. The data acquisition unit obtains data at a dynamic sampling frequency, and the integrated feature extraction unit uses a federated model to mine the characteristics of ecological indicators to avoid data redundancy. In the evaluation module, The indicator matching unit locates the evaluation indicators through knowledge graph and fast indexing, the factor analysis unit selects the main and auxiliary indicators based on the historical contribution rate, and the distributed evaluation unit calculates the health index using a hierarchical model. For example, when analyzing soil pollution, weights are assigned according to the historical impact of different indicators to improve the accuracy of the evaluation; the feedback module compares the evaluation results with the warning space, and drives the grid and sampling adjustments for substandard data. For example, when the monitoring accuracy of a certain area is low, the grid analysis unit re-divides the area, and the data collection unit adjusts the sampling frequency to form a closed-loop optimization of monitoring-evaluation-feedback, ultimately achieving efficient dynamic monitoring and accurate early warning of mine ecology.

[0091] Further, see Figure 2,The construction process of regional difference monitoring node network includes:

[0092] Obtain the digital elevation model, geological structure map, surface cover type map, and ground-underground hydrological distribution thermal map of the mining area to be monitored, and construct a three-dimensional map layer of the mining area to be monitored.

[0093] Based on the three-dimensional map layer of the mining area to be monitored, combined with the location distribution information of the monitoring equipment and the effective monitoring area of ​​each monitoring equipment, the effective monitoring influence domain of each monitoring equipment in the three-dimensional map layer is obtained through the Voronoi diagram algorithm.

[0094] The device location distribution information includes the device location latitude and longitude coordinates and altitude.

[0095] Based on the effective monitoring influence domain of each monitoring device, the overlapping coverage and blind area between all monitoring devices are obtained.

[0096] Based on the distribution of each device location point and the signal base station location point, the initial monitoring node set and transmission node set are obtained.

[0097] Based on the effective monitoring influence domain of the monitoring equipment and the effective radiation radius of the base station signal, the first connection relationship between the monitoring node and the transmission node within the effective radiation radius of each base station signal in the initial monitoring node network, the corresponding second connection relationship between the transmission nodes, and the corresponding third connection relationship between the transmission nodes are constructed.

[0098] An initial monitoring node network is obtained based on the initial monitoring node set in combination with the first connection relationship, the second connection relationship and the third connection relationship in combination with a graph algorithm.

[0099] Based on the corresponding distance and environmental information between the monitoring node and the transmission node and between the transmission nodes within the effective radiation radius of each base station signal, a first signal attenuation compensation coefficient between each monitoring node and the corresponding transmission node and a second signal attenuation compensation coefficient between the transmission nodes are constructed.

[0100] The environmental information includes the rock density distribution state and the mapping distribution of rock density to signal strength, and the vegetation density distribution state and the mapping distribution of vegetation density to signal strength.

[0101] The first signal attenuation compensation coefficient and the second signal attenuation compensation coefficient are configured into the corresponding second connection relationship and the third connection relationship to obtain a dynamic monitoring node network.

[0102] Based on the historical monitoring indicator type and warning level corresponding to each monitoring node and the area size of the historically divided sub-region, a level-region mapping function is constructed to obtain the warning level corresponding to each monitoring node and the regional division area.

[0103] The output sequence of the particle swarm optimization algorithm is constructed based on the level-area mapping function, the overlapping coverage and blind area between all monitoring devices in the dynamic monitoring node network, the effective monitoring influence domain corresponding to each monitoring node, the signal transmission delay per unit time of the transmission node in the corresponding divided area, the comprehensive ecological health index score of each divided area, the ecological health score of the main evaluation indicator and the ecological health index score of the auxiliary evaluation indicator and the corresponding evaluation accuracy.

[0104] At the same time, based on the overlapping coverage, effective monitoring influence domain, blind area, signal transmission delay per unit time and evaluation accuracy, the regional division optimization constraint function and corresponding constraint conditions are constructed, and the minimum value of the regional division optimization constraint function is taken.

[0105] The output sequence of the particle swarm optimization algorithm and the regional division optimization constraint function and the corresponding constraint conditions are input into the particle swarm optimization algorithm with a built-in Kriging interpolation function, and combined with the optimization constraint threshold and training cycle of the constraint, the optimized regional division parameter set of the dynamic monitoring node network is obtained.

[0106] It should be noted that in this embodiment, the particle swarm algorithm with a built-in Kriging interpolation function uses the Kriging interpolation function to predict and complete data such as the ecological health index of the uncovered area during the algorithm iteration process. The feasibility of the particle scheme is judged based on the optimization constraint threshold. The particle position is continuously adjusted to optimize the regional division scheme through the speed and position update formula of the particle swarm algorithm. After multiple rounds of iteration, when the preset training cycle is reached, the particle position parameters with the best fitness are selected to obtain the optimized dynamic monitoring node network regional division parameter set.

[0107] The optimized regional division parameter set of the dynamic monitoring node network is input into the regional grid algorithm to divide the dynamic monitoring node network into regions, obtain the regional difference monitoring node network and mark the divided regions in real time.

[0108] This embodiment achieves dynamic optimization and spatial coverage of the mine monitoring network through multi-dimensional collaboration. At the spatial modeling level, the digital elevation model and geological structure feature data are integrated to construct a three-dimensional geographic base, and the equipment elevation positioning is corrected using rock layer interface parameters to form a standardized spatial reference system. The coverage domain calculation adopts a three-dimensional expansion algorithm based on the Voronoi diagram, and the equipment scope model under geometric constraints is established through weighted Delaunay triangulation to achieve topological adaptive reconstruction capabilities in the event of equipment failure. Signal transmission optimization integrates geological medium parameters and surface vegetation characteristics, constructs a dual-factor attenuation compensation model, and derives dynamic signal correction parameters for different lithologic areas; the networking configuration method combines the Kriging interpolation gradient with the swarm intelligent search mechanism through a hybrid optimization algorithm embedded in the spatial autocorrelation function to guide the optimal convergence of the equipment deployment path; the system defines a multi-objective optimization function for coverage overlap and monitoring blind spots, and uses vector space analytical methods to derive the equilibrium solution set of parameter configuration; environmental adaptation assessment uses principal component projection technology to construct a hierarchical evaluation model to achieve dimensional compression and weight fusion of multiple ecological indicators; finally, a dynamic parameter deployment architecture based on quadtree indexing is established, and the spatial indexing efficiency in complex terrain scenarios is improved through a hierarchical grid algorithm.

[0109] Furthermore, the process of obtaining the regional comprehensive ecological health index score, the main evaluation indicator ecological health score, and the auxiliary evaluation indicator ecological health index score includes:

[0110] Based on the regional difference monitoring node network, each divided sub-region is used as each evaluation node of the distributed node evaluation network, and the evaluation connection relationship between all evaluation nodes in the distributed node evaluation network is constructed based on the adjacent relationship of all divided sub-regions.

[0111] Based on the evaluation nodes and evaluation connection relationships corresponding to all divided sub-areas, a distributed node evaluation network is constructed through graph algorithms.

[0112] Based on the regional difference monitoring node network and the configured data collection parameters, a real-time dynamic monitoring data set with monitoring device labels and collection timestamps corresponding to all monitoring nodes in each sub-region is obtained.

[0113] The real-time dynamic monitoring dataset is input into the data classification layer of the federated feature extraction model for data type classification, and the data type and classification accuracy of the real-time dynamic monitoring dataset corresponding to each sub-region are obtained.

[0114] The data in the real-time ecological indicator state space of each sub-region is input into the distributed feature extraction layer according to the corresponding type to extract the corresponding type of data features, and the ecological indicator state space corresponding to all monitoring nodes in each sub-region is obtained;

[0115] The distributed feature extraction layer includes M feature extraction sub-layers; the number of data types obtained by the M feature extraction sub-layers is the same as that of the data classification layer and they correspond one to one.

[0116] Furthermore, the data types in this embodiment include numerical sequences, image data, text data, etc.;

[0117] Furthermore, the data processing model used in the M feature extraction sublayers in this embodiment is fine-tuned by technical personnel in this field based on the specific type of data to be processed, integrating the existing pre-trained large model with the collected mine monitoring data and built into the corresponding feature extraction sublayer to perform specific type of data processing.

[0118] Furthermore, the hierarchical evaluation index library includes a first indicator layer and a second indicator layer; the first indicator layer includes major indicators of the cover layer, major indicators of the soil layer, major indicators of the hydrological layer, major indicators of the geological layer, and major indicators of the air layer; the major indicators of the cover layer include first-level land resource monitoring indicators, first-level solid waste monitoring indicators, first-level water and soil environment monitoring indicators, and the second-level indicators and third-level indicators corresponding to each first-level indicator.

[0119] The first-level land resource monitoring indicators include second-level indicators such as land encroachment and destruction monitoring and land restoration monitoring.

[0120] The secondary land occupation and destruction monitoring indicators include three-level indicators: the type of occupied land, the area of ​​destroyed land, the method of land destruction, the type of destroyed vegetation and the area of ​​destroyed vegetation; the secondary land restoration monitoring indicators include three-level indicators: the area of ​​reclaimable land, the area of ​​reclaimed land and the coverage rate of reclaimed vegetation.

[0121] The first-level solid waste monitoring indicators include second-level indicators such as solid waste generation and emission monitoring and solid waste comprehensive utilization monitoring; the second-level solid waste generation and emission monitoring indicators include third-level indicators such as waste type, annual emission volume, cumulative accumulation volume, source, occupied land area, and major hidden dangers; the second-level solid waste comprehensive utilization monitoring indicators include third-level indicators such as annual comprehensive utilization volume and comprehensive utilization rate.

[0122] The first-level soil and water environment monitoring indicators include second-level indicators such as soil erosion monitoring and land desertification monitoring; the second-level soil erosion monitoring indicators include third-level indicators such as soil erosion area and soil erosion modulus; the second-level land desertification monitoring indicators include third-level indicators such as desertification area and desertification control rate.

[0123] The major indicators of the soil layer include the first-level soil pollution monitoring indicators and the corresponding second-level indicators and third-level indicators.

[0124] The first-level soil pollution monitoring indicators include second-level indicators such as soil pollution monitoring and pollution hazard monitoring.

[0125] Secondary soil pollution monitoring indicators include three-level indicators such as pollution sources and major pollutants; secondary pollution hazard monitoring indicators include three-level indicators such as pollution degree and hazard scope.

[0126] The hydrological layer indicators include the first-level mining area surface water pollution monitoring indicators, the first-level wastewater and waste liquid discharge monitoring indicators, the first-level groundwater monitoring indicators, and the second-level and third-level indicators corresponding to each first-level indicator.

[0127] The first-level mining area surface water pollution monitoring indicators include secondary indicators such as wastewater discharge monitoring and pollution characteristic monitoring; the second-level wastewater discharge monitoring indicators include third-level indicators such as wastewater type, annual output, annual discharge volume and discharge destination; the second-level pollution characteristic monitoring indicators include third-level indicators such as major pollutants, pollution degree and annual recycling volume.

[0128] The first-level wastewater and waste liquid discharge monitoring indicators include second-level indicators such as discharge volume monitoring and pollution control monitoring; the second-level discharge volume monitoring indicators include third-level indicators such as annual wastewater discharge volume and standard discharge volume; the second-level pollution control monitoring indicators include third-level indicators such as major harmful substances, annual treatment volume and comprehensive utilization volume.

[0129] The first-level groundwater monitoring indicators include second-level indicators such as groundwater equilibrium destruction monitoring and groundwater water quality pollution monitoring.

[0130] The secondary groundwater balance destruction monitoring indicators include three-level indicators such as groundwater level, annual mine drainage volume, aquifer drainage area and groundwater drop funnel area.

[0131] The water quality pollution monitoring indicators of secondary groundwater include three-level indicators such as pH value, ammonia nitrogen concentration, heavy metal concentration and organic pollutants.

[0132] The major geological layer indicators include the first-level mining area ground subsidence monitoring indicators, the first-level mountain geological disaster monitoring indicators, the first-level ground fissure monitoring indicators and the second-level and third-level indicators corresponding to each first-level indicator.

[0133] The first-level ground subsidence monitoring indicators of mined-out areas include second-level indicators such as collapse characteristic monitoring and collapse hazard monitoring; collapse characteristic monitoring includes third-level indicators such as the number of collapse areas, collapse area, maximum depth of collapse pit and depth of accumulated water; collapse hazard monitoring includes third-level indicators such as the degree of collapse damage.

[0134] The first-level mountain geological disaster monitoring indicators include second-level indicators such as geological disaster event monitoring and geological disaster hidden danger monitoring.

[0135] The monitoring of geological disaster events includes three-level indicators, such as the number of occurrences in the year and the damage caused; the monitoring of geological disaster hazards includes three-level indicators, such as the number of hazard points and the number of hazard points that have been managed.

[0136] The first-level ground fissure monitoring indicators include second-level indicators such as ground fissure morphology monitoring and ground fissure hazard monitoring; ground fissure morphology monitoring includes the number of ground fissures, maximum length, maximum width and maximum depth; ground fissure hazard monitoring includes third-level indicators such as direction and degree of damage.

[0137] The major indicators of the air layer include the first-level air pollutant concentration index and the corresponding second-level and third-level indicators.

[0138] The primary air pollutant concentration indicators include secondary indicators such as particulate matter pollution monitoring and gaseous pollutant monitoring.

[0139] Particulate matter pollution monitoring includes three-level indicators such as PM2.5 concentration, PM10 concentration and TSP concentration; gaseous pollutant monitoring includes three-level indicators such as sulfur dioxide concentration and nitrogen oxide concentration.

[0140] The level of the corresponding major indicator in the first indicator layer is greater than the first-level indicator in the second indicator layer, the level of the first-level indicator is greater than the second-level indicator, and the level of the second-level indicator is greater than the third-level indicator; the second indicator layer is constructed by the first-level indicators, second-level indicators and third-level indicators corresponding to the major indicators of the cover layer, soil layer, hydrological layer, geological layer and air layer respectively.

[0141] The hierarchical evaluation index library is constructed by combining the first index layer and the second index layer with the tree database according to the corresponding index levels.

[0142] Furthermore, the detailed construction scheme of the hierarchical evaluation index library in this embodiment includes:

[0143] Based on the tree structure, the cover layer, soil layer, hydrological layer, geological layer, and air layer indicators of the first indicator layer are taken as root nodes; each root node is mounted with the corresponding first-level indicator as a child node, such as the cover layer indicator is connected to the first-level indicators such as land resource monitoring and solid waste monitoring; the first-level indicators are further extended downward to the second-level indicators, such as the land resource monitoring indicator is connected to land encroachment and destruction monitoring, land restoration monitoring, etc.; finally, the second-level indicators are mounted with the third-level indicators to form a complete tree-like hierarchical structure; in this way, the hierarchical storage and management of indicator data are realized, which facilitates data query and call.

[0144] Use relational or non-relational databases to store data. In relational databases, create corresponding data tables for each indicator level, and link the upper and lower indicator data through foreign keys. Non-relational databases use nested document structures to organize indicator data at all levels in one document. At the same time, add unique identifiers, data types, units and other attribute information to each indicator to ensure the standardization and consistency of the data.

[0145] The tree-structured database is combined with knowledge graph technology, and semantic web technology is used to add semantic descriptions to each indicator. For example, for the "soil pollution monitoring" indicator, not only its pollution sources, main pollutants and other data are stored, but also related pollution mechanisms, prevention and control technologies and other knowledge nodes are associated through the knowledge graph. The semantic relationship between indicators is defined using ontology modeling tools. For example, there is a causal relationship between "soil erosion modulus" and "soil and water loss area". Through semantic annotation, intelligent association and reasoning of data are achieved, which facilitates users to conduct knowledge retrieval and in-depth analysis.

[0146] A dynamic weight calculation module has been introduced for each level of indicators. Based on historical and real-time monitoring data, a machine learning algorithm is used to dynamically adjust indicator weights. For example, if a region experiences frequent landslides recently, the weights of the relevant third-level indicators under the mountain geological hazard monitoring index will be automatically increased, making the assessment results more consistent with the actual risk situation. Furthermore, thresholds and trigger conditions for weight adjustment are set to ensure the scientific and stable nature of the weight adjustment.

[0147] Design a unified data access interface to support access from multiple data sources, and use ETL (Extract, Transform, Load) technology to clean, convert, and load data of different formats and frequencies; for example, integrate the real-time groundwater level data collected by sensors with the land desertification area data obtained by satellite remote sensing, unify the data format and timestamp, and enable the database to comprehensively process multi-source data, thereby improving data integrity and availability.

[0148] A web-based visual analysis interface was developed, using visualization libraries such as D3.js and Echarts to display the tree-like database structure and indicator data in intuitive graphics. Users can explore the hierarchical relationship of indicators through operations such as dragging and zooming, and customized queries and analysis are also supported. For example, users can select specific areas and time periods to view the changing trends of the concentrations of various pollutants under the large air layer indicators, and compare the impact of different indicators through interactive charts to assist in decision-making.

[0149] Based on the ecological indicator state space corresponding to all monitoring nodes in each sub-region and the hierarchical evaluation indicator library, all hierarchical evaluation indicator sets corresponding to each sub-region are obtained through matching algorithm and fast indexing.

[0150] Based on all the hierarchical evaluation indicator sets corresponding to each sub-region, the contribution rate of each major indicator in the first indicator layer to the regional comprehensive ecological health index score, the contribution rate of the third-level indicators in the second indicator layer to the corresponding second-level indicators, and the contribution rate of the second-level indicators to the corresponding first-level indicators were obtained through the factor analysis algorithm.

[0151] According to the contribution rate of each major indicator to the regional comprehensive ecological health index score, the major indicator with the largest contribution rate is set as the main evaluation major indicator, and the remaining major indicators are set as the first auxiliary evaluation major indicator, the second auxiliary evaluation major indicator, the third auxiliary evaluation major indicator, and the fourth auxiliary evaluation major indicator according to the corresponding contribution rate.

[0152] Based on the main evaluation indicator, the first auxiliary evaluation indicator, the second auxiliary evaluation indicator, the third auxiliary evaluation indicator, the fourth auxiliary evaluation indicator and the corresponding first-level indicators, second-level indicators and third-level indicators of each sub-region, as well as the contribution rate of each indicator in the first indicator layer to the regional comprehensive ecological health index score, the contribution rate of the third-level indicators in the second indicator layer to the corresponding second-level indicators, the contribution rate of the second-level indicators to the corresponding first-level indicators, and the contribution rate of the first-level indicators to the corresponding major indicators, the main-auxiliary hierarchical evaluation indicator scores and the corresponding evaluation accuracy in each sub-region are obtained through a hierarchical distributed evaluation model.

[0153] Furthermore, the scores of the main-auxiliary hierarchical evaluation indicators in each sub-region include the evaluation scores of each first-level indicator corresponding to the main evaluation indicator, the evaluation scores of each first-level indicator corresponding to the first auxiliary evaluation indicator, the second auxiliary evaluation indicator, the third auxiliary evaluation indicator, and the fourth auxiliary evaluation indicator, the main evaluation indicator ecological health score, the first auxiliary evaluation indicator ecological health index score, the second auxiliary evaluation indicator ecological health index score, the third auxiliary evaluation indicator ecological health index score, the fourth auxiliary evaluation indicator ecological health index score, and the regional comprehensive ecological health index score.

[0154] Based on the scores of the primary and secondary hierarchical evaluation indicators in each sub-area and the preset hierarchical step warning space, the warning information corresponding to each level and the warning level rendering color of the effective monitoring influence domain corresponding to each monitoring node are obtained according to the indicator level from large to small.

[0155] Through multi-level technology integration, the mine ecological health assessment is refined and dynamically adaptable; at the assessment network architecture level, a distributed node assessment network is constructed based on the topological relationship of adjacent sub-regions, and the node connection status is stored in the adjacency matrix, combined with the breadth-first search algorithm to achieve cross-regional data transmission path analysis; the data processing link deploys a federal feature extraction framework, and the feature decoupling of numerical sequence and image data is achieved through the parallel processing mechanism of multimodal classifiers, and an independent feature extraction channel is established; the hierarchical assessment indicator library adopts the semantic fusion architecture of tree database and knowledge graph, and constructs the indicator causal reasoning chain based on RDF triples to achieve semantic association modeling of multi-dimensional indicators; the dynamic weight adaptive mechanism is set A sliding window update strategy and trend prediction model are formed to establish dynamic adjustment rules for the importance of indicators; at the evaluation model level, the factor analysis algorithm is combined with the varimax rotation method to extract the principal component features, and the hierarchical distributed evaluation model adopts a dual-channel attention mechanism to capture the macro-indicator features through the main evaluation channel and extract the fine-grained change features through the auxiliary evaluation channel, and a gated fusion unit is designed to realize dynamic feature integration; the early warning rendering algorithm integrates spatial interpolation technology to transform discrete monitoring data into a continuous early warning field distribution model, and constructs a gradient visualization expression framework based on the semi-variogram function; the system constructs a flexible evaluation framework through feature dimension configuration, tree-like hierarchical architecture and weight update cycle setting to realize multi-source parameter coupling analysis.

[0156] Furthermore, the construction process of the hierarchical step warning space includes:

[0157] According to the scores of the main and auxiliary hierarchical evaluation indicators in each sub-region, the warning score thresholds at the corresponding levels are set from large to small according to the evaluation indicator levels. If the comprehensive ecological health index score of the current sub-region is less than the corresponding warning score threshold, the ecological health score of the main evaluation indicator is compared with the corresponding warning score threshold. When the ecological health score of the main evaluation indicator is less than the corresponding warning score threshold and the evaluation scores of each first-level indicator under the main evaluation indicator are less than the corresponding warning score threshold, the auxiliary evaluation indicator warning is issued.

[0158] When one of the regional comprehensive ecological health index score, the main evaluation indicator ecological health score or the first-level indicator evaluation score does not meet the corresponding warning score threshold, the highest level warning will be issued and the corresponding effective monitoring impact domain that does not meet the warning score threshold will be rendered in the highest warning level color.

[0159] When issuing an auxiliary evaluation indicator warning, an auxiliary warning of the corresponding level and the corresponding auxiliary warning level color rendering are carried out according to the warning level interval corresponding to the product of the auxiliary evaluation indicator score of the corresponding level and the corresponding contribution rate.

[0160] Furthermore, in this embodiment, the principles and examples of constructing the hierarchical step warning space include:

[0161] First, for the primary and secondary hierarchical assessment indicators in each sub-region, corresponding warning score thresholds are set for different levels according to the assessment indicator levels from high to low; these thresholds are the key basis for subsequent judgment of warning levels, which are equivalent to the "thresholds" of different warning levels and are used to measure the risk level of the regional ecological health status.

[0162] If the regional comprehensive ecological health index score of the current sub-region is less than the corresponding warning score threshold, the next judgment process will be entered; this step is a preliminary screening of the overall ecological health status of the region. If the overall score does not meet the standard, further analysis of specific indicators is required.

[0163] Judgment of main indicators and first-level indicators: If the regional comprehensive ecological health index score does not meet the standard, the ecological health score of the main evaluation indicator is then compared with the corresponding early warning score threshold; when the ecological health score of the main evaluation indicator is less than the corresponding early warning score threshold, and the evaluation score of each first-level indicator under the main evaluation indicator is less than the corresponding early warning score threshold, the auxiliary evaluation indicator early warning is triggered; this step goes deep into the main indicator and first-level indicator level. Only when the main indicator and all the first-level indicators under it are at a low level will the auxiliary evaluation indicator be considered.

[0164] Furthermore, in this embodiment, the highest level of warning is determined as follows: if any of the regional comprehensive ecological health index score, the primary assessment indicator ecological health score, or the primary indicator assessment score does not meet the corresponding warning score threshold, a maximum level warning is issued, and the effective monitoring impact area that does not meet the warning score threshold is rendered in the highest warning level color. This setting ensures that if any key indicator shows a serious problem, the most serious warning signal will be issued, so as to attract sufficient attention.

[0165] Furthermore, in this embodiment, the secondary assessment indicator warning process involves issuing a secondary assessment indicator warning based on the warning level range to which the product of the current secondary assessment indicator score and the corresponding contribution rate falls. This process then generates a secondary warning and color rendering for the corresponding secondary warning level. By considering the secondary assessment indicator scores and their contribution rates, a more comprehensive assessment of the regional ecological health status is achieved, resulting in a more refined warning level.

[0166] This embodiment achieves comprehensive monitoring and early warning of the ecological health status of mining areas by constructing a multi-level, intelligent mining ecological environment monitoring and assessment system. Technically, the system establishes a dynamic monitoring node network based on regional differences. It uses a grid partitioning method that combines the Voronoi diagram algorithm with swarm intelligence optimization, combined with three-dimensional modeling that includes equipment physical properties, dynamic monitoring parameters, and environmental compensation parameters to achieve adaptive spatial partitioning and coverage optimization of the monitoring area. The synergistic effect of equipment physical property parameters and environmental signal compensation parameters ensures data reliability in complex terrain, while the real-time interaction between dynamic monitoring parameters and operating status parameters enhances network robustness. A federated feature extraction framework is deployed in the data processing phase. After classifying multi-source heterogeneous data into types such as numerical sequences and image data, it is distributedly processed through a specialized feature extraction sublayer. The spatiotemporal sequence processing sublayer utilizes temporal decomposition and deep encoding algorithms, while the geospatial processing sublayer applies spatial interpolation and convolutional neural networks to achieve multimodal feature decoupling and fusion. Feature extraction results are shared under privacy protection through a federated learning mechanism. A hierarchical tree-like indicator library is constructed in the assessment module, and ecological indicators are divided into multi-dimensional categories such as cover layer and soil layer. Each category has multiple levels of sub-indicators to form a hierarchical system; the indicator contribution rate is dynamically calculated through the factor analysis algorithm, and a weighted health index is generated in combination with the hierarchical distributed assessment model to achieve overall ecological status assessment and accurate positioning of specific problem levels; a hierarchical step-by-step warning mechanism is adopted in the early warning system, and progressive warning upgrades are achieved based on multi-level threshold judgment; when the comprehensive score is lower than the initial threshold, the system checks the main indicator and the first-level indicator scores layer by layer to trigger a graded early warning response strategy; the collaborative judgment mechanism of the main and auxiliary indicators avoids false alarms caused by a single threshold and ensures the accurate triggering of early warning instructions; in addition, the system's self-optimization capability is achieved through the closed-loop control of the feedback module, and the monitoring grid division parameters and data collection frequency are dynamically adjusted based on the historical warning level and real-time network status. Monitoring resources are adaptively allocated according to regional risk levels to form a risk-sensitive dynamic monitoring strategy. Finally, at the technical collaboration level, graph algorithms are used to construct a distributed assessment network, and data linkage and anomaly transmission are achieved through the connection relationship between adjacent sub-regional nodes. A continuous assessment field model is constructed in combination with spatial interpolation functions to solve the fragmentation problem of traditional point-based monitoring and achieve efficient identification and response to cross-regional correlated events.

[0167] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also change, modify, replace and modify the above-mentioned embodiments without departing from the scope of protection of the purpose of the present invention and the claims, and all of these are protected by the present invention.

Claims

1. A mine ecological environment monitoring system, characterized in that: include: Evaluation module, feedback module; The evaluation module obtains the regional ecological health index corresponding to each sub-region based on the main evaluation index and auxiliary evaluation index corresponding to each sub-region in the regional hierarchical ecological indicator state space obtained through real-time monitoring, in combination with the distributed node evaluation network and hierarchical evaluation index library constructed based on the sub-regions and their corresponding adjacent relationships; and performs real-time monitoring and early warning based on the regional ecological health index corresponding to each sub-region and the hierarchical step warning space preset by the feedback module; The hierarchical evaluation index library is constructed by combining the hierarchical ecological indicators with the database and constructing a fast index based on the correlation between the hierarchical ecological indicators and the hierarchical evaluation indicators; the main evaluation indicators and auxiliary evaluation indicators corresponding to each sub-region are obtained by combining the regional hierarchical ecological indicator state space with the factor analysis algorithm; the hierarchical step warning space is constructed by the differential hierarchical warning levels corresponding to the main evaluation indicators and the auxiliary evaluation indicators, the corresponding rendering colors, and the main evaluation indicator scores and the auxiliary evaluation scores.

2. A mine ecological environment monitoring system according to claim 1, characterized in that: The mine ecological environment monitoring system also includes a monitoring module; the monitoring module includes a monitoring matrix unit, a grid analysis unit, a data acquisition unit, and an integrated feature extraction unit; the monitoring matrix unit obtains a differential monitoring node network through a graph algorithm based on the distribution space of the monitoring equipment and the feature space of the map layer of the historical monitoring area of ​​each monitoring equipment; The monitoring equipment distribution space is constructed by the monitoring equipment distribution points, the type of ecological indicators monitored by each equipment and the size of the effective monitoring area, the equipment physical property parameters, dynamic monitoring parameters, dynamic operating status parameters and environmental signal compensation parameters; The grid analysis unit divides the differential monitoring node network into differential regions using a grid segmentation algorithm optimized by a particle swarm algorithm, based on characteristic information within the monitoring device distribution space corresponding to each node in the differential monitoring node network, the network topology relationship corresponding to all nodes in the differential monitoring node network, the overlapping coverage rate and blind spot compensation coefficient between all nodes, and the accuracy of the evaluation index corresponding to each segmentation area, to obtain a regional differential monitoring node network; The data acquisition unit obtains a dynamic monitoring data set with monitoring device tags corresponding to all monitoring nodes in each sub-region based on the regional difference monitoring node network and the dynamic monitoring frequency configured for each monitoring node; The network topology relationship between all nodes is constructed by the monitoring device, the number of relay hops between devices, the parent node-child node connection relationship, and the maximum coverage distance of the device wireless transmission through the topology algorithm; The integrated feature extraction unit obtains the ecological indicator state space corresponding to all monitoring nodes in each sub-region based on the dynamic monitoring data set with monitoring equipment tags corresponding to all sub-regions and a preset federal feature extraction model.

3. A mine ecological environment monitoring system according to claim 2, characterized in that: The evaluation module includes an indicator matching unit, a factor analysis unit and a distributed evaluation unit; The indicator matching unit obtains the hierarchical evaluation indicator space corresponding to all monitoring nodes in each sub-region based on the ecological indicator state space corresponding to all monitoring nodes in each sub-region combined with the hierarchical evaluation indicator library constructed by the knowledge graph through a matching algorithm and a preset fast index of the hierarchical evaluation indicator library; the hierarchical evaluation indicator space is constructed by all hierarchical evaluation indicator sets used corresponding to each sub-region; The factor analysis unit obtains the main hierarchical evaluation indicator set corresponding to each sub-region and the effective contribution rate corresponding to each main evaluation indicator, and the auxiliary hierarchical evaluation indicator set and the effective contribution rate corresponding to each auxiliary evaluation indicator, based on the hierarchical evaluation indicator space corresponding to all monitoring nodes in each sub-region and the contribution rate of the historical evaluation indicators of the current sub-region through a factor analysis algorithm combined with a preset effective contribution threshold; The distributed evaluation unit, based on the hierarchical distributed evaluation model configured for each monitoring node, combines the main hierarchical evaluation indicator set corresponding to each sub-region and the effective contribution rate corresponding to each main evaluation indicator with the auxiliary hierarchical evaluation indicator set and the effective contribution rate corresponding to each auxiliary evaluation indicator to obtain the regional comprehensive ecological health index score, the main evaluation indicator ecological health score and the auxiliary evaluation indicator ecological health index score and the corresponding evaluation accuracy.

4. A mine ecological environment monitoring system according to claim 3, characterized in that: The feedback module includes an evaluation and early warning unit and a monitoring and feedback unit; The evaluation and early warning unit compares the regional comprehensive ecological health index score, the main evaluation indicator ecological health score, the auxiliary evaluation indicator ecological health index score and the corresponding evaluation accuracy with the preset hierarchical step warning space to obtain hierarchical early warning information corresponding to the evaluation accuracy threshold; The hierarchical step warning space is constructed by combining the regional comprehensive ecological health index score, the main assessment indicator ecological health score, the auxiliary assessment indicator ecological health index score and the preset warning level with the color-score mapping space; The hierarchical warning information corresponding to the evaluation accuracy threshold is fed back to the monitoring center through the monitoring feedback unit, and the monitoring indicator information corresponding to the evaluation accuracy threshold is fed back to the grid analysis unit and the data acquisition unit. The corresponding sub-area division and the monitoring data sampling frequency are cyclically adjusted based on the corresponding effective contribution rate, and the hierarchical warning information corresponding to the evaluation accuracy threshold after adjustment is adjusted and fed back.

5. A mine ecological environment monitoring system according to claim 4, characterized in that: The process of constructing the regional difference monitoring node network includes: Obtain the digital elevation model, geological structure map, surface cover type map, and ground-underground hydrological distribution thermal map of the mining area to be monitored, and construct a three-dimensional map layer of the mining area to be monitored; Based on the three-dimensional map layer of the mining area to be monitored, combined with the location distribution information of the monitoring equipment and the effective monitoring area of ​​each monitoring equipment, the effective monitoring influence domain of each monitoring equipment in the three-dimensional map layer is obtained through the Voronoi diagram algorithm; Based on the effective monitoring influence domain of each monitoring device, the overlapping coverage and blind area between all monitoring devices are obtained; Based on the distribution of each device location point and the signal base station location point, the initial monitoring node set and transmission node set are obtained; Based on the effective monitoring influence domain of the monitoring equipment and the effective radiation radius of the base station signal, a first connection relationship between the monitoring node and the transmission node within the effective radiation radius of each base station signal in the initial monitoring node network, a corresponding second connection relationship between the transmission nodes, and a corresponding third connection relationship between the transmission nodes are established; An initial monitoring node network is obtained based on the initial monitoring node set in combination with the first connection relationship, the second connection relationship and the third connection relationship in combination with a graph algorithm.

6. A mine ecological environment monitoring system according to claim 5, characterized in that: The process of constructing the regional difference monitoring node network also includes: Based on the corresponding distance and environmental information between the monitoring node and the transmission node and between the transmission nodes within the effective radiation radius of each base station signal, a first signal attenuation compensation coefficient between each monitoring node and the corresponding transmission node and a second signal attenuation compensation coefficient between the transmission nodes are constructed; The environmental information includes rock density distribution and mapping of rock density to signal strength, and vegetation density distribution and mapping of vegetation density to signal strength. The first signal attenuation compensation coefficient and the second signal attenuation compensation coefficient are configured into the corresponding second connection relationship and third connection relationship to obtain a dynamic monitoring node network.

7. A mine ecological environment monitoring system according to claim 6, characterized in that: The process of constructing the regional difference monitoring node network also includes: Based on the historical monitoring indicator type and corresponding warning level of each monitoring node and the size of the area of ​​the historically divided sub-region, a level-region mapping function is constructed to obtain the warning level corresponding to each monitoring node and the area of ​​the regional division; Constructing an output sequence of a particle swarm optimization algorithm based on the level-area mapping function, the overlapping coverage and blind area between all monitoring devices in the dynamic monitoring node network, the effective monitoring influence domain corresponding to each monitoring node, the signal transmission delay per unit time of the transmission node in the corresponding divided area, the comprehensive ecological health index score of each divided area, the ecological health score of the main evaluation indicator, the ecological health index score of the auxiliary evaluation indicator, and the corresponding evaluation accuracy; At the same time, based on the overlapping coverage, effective monitoring influence domain, blind area, signal transmission delay per unit time and evaluation accuracy, the regional division optimization constraint function and corresponding constraint conditions are constructed, and the minimum value of the regional division optimization constraint function is taken; The output sequence of the particle swarm optimization algorithm and the regional division optimization constraint function and the corresponding constraint conditions are input into the particle swarm optimization algorithm with a built-in Kriging interpolation function, and combined with the constraint optimization constraint threshold and training cycle, the optimized regional division parameter set of the dynamic monitoring node network is obtained; The optimized regional division parameter set of the dynamic monitoring node network is input into the regional grid algorithm, the dynamic monitoring node network is regionally divided, a regional difference monitoring node network is obtained, and the divided regions are marked in real time.

8. A mine ecological environment monitoring system according to claim 7, characterized in that: The process of obtaining the regional comprehensive ecological health index score, the main assessment indicator ecological health score, and the auxiliary assessment indicator ecological health index score includes: Based on each divided sub-region in the regional difference monitoring node network as each evaluation node of the distributed node evaluation network, and based on the adjacent relationship of all divided sub-regions, constructing the evaluation connection relationship between all evaluation nodes in the distributed node evaluation network; Based on the evaluation nodes and evaluation connection relationships corresponding to all divided sub-areas, a distributed node evaluation network is constructed through graph algorithms; Based on the regional differential monitoring node network and the configured data collection parameters, a real-time dynamic monitoring data set with monitoring device tags and collection timestamps corresponding to all monitoring nodes in each sub-region is obtained; Inputting the real-time dynamic monitoring data set into the data classification layer of the federated feature extraction model to perform data type classification, and obtaining the data type and classification accuracy of the real-time dynamic monitoring data set corresponding to each sub-region; The data in the real-time ecological indicator state space of each sub-region is input into the distributed feature extraction layer according to the corresponding type to extract the corresponding type of data features, and the ecological indicator state space corresponding to all monitoring nodes in each sub-region is obtained; The distributed feature extraction layer includes M feature extraction sublayers; the M feature extraction sublayers have the same number of data types as those obtained by the data classification layer and have a one-to-one correspondence.

9. A mine ecological environment monitoring system according to claim 8, characterized in that: The hierarchical evaluation index library includes a first index layer and a second index layer; the first index layer includes a large index of the cover layer, a large index of the soil layer, an index of the hydrological layer, a large index of the geological layer, and a large index of the air layer; the second index layer is constructed by the first-level index, the second-level index, and the third-level index corresponding to the large index of the cover layer, the large index of the soil layer, the index of the hydrological layer, the large index of the geological layer, and the large index of the air layer; the large index of the cover layer includes a first-level land resource monitoring index, a first-level solid waste monitoring index, a first-level water and soil environment monitoring index, and the second-level index and the third-level index corresponding to each first-level index; the large index of the soil layer includes a first-level soil pollution monitoring index and the corresponding second-level index and the third-level index The hydrological layer indicators include the first-level mining area surface water pollution monitoring indicators, the first-level wastewater and waste liquid discharge monitoring indicators, the first-level groundwater monitoring indicators and the second-level indicators and third-level indicators corresponding to each first-level indicator; the geological layer indicators include the first-level goaf area ground subsidence monitoring indicators, the first-level mountain geological disaster monitoring indicators, the first-level ground fissure monitoring indicators and the second-level indicators and third-level indicators corresponding to each first-level indicator; the air layer indicators include the first-level air pollutant concentration indicators and the corresponding second-level indicators and third-level indicators; the corresponding large indicator level in the first indicator layer is greater than the first-level indicator in the second indicator layer, the corresponding level of the first-level indicator is greater than the second-level indicator, and the level of the second-level indicator is greater than the third-level indicator; The hierarchical evaluation index library is constructed by combining the first index layer and the second index layer with a tree database according to corresponding index levels.

10. A mine ecological environment monitoring system according to claim 9, characterized in that: The process of obtaining the regional comprehensive ecological health index score, the main assessment indicator ecological health score, and the auxiliary assessment indicator ecological health index score also includes: Based on the ecological indicator state space corresponding to all monitoring nodes in each sub-region and the hierarchical evaluation indicator library, all hierarchical evaluation indicator sets corresponding to each sub-region are obtained through a matching algorithm and the fast indexing; Based on all the hierarchical evaluation indicator sets corresponding to each sub-region, the contribution rate of each major indicator in the first indicator layer to the regional comprehensive ecological health index score, as well as the contribution rate of the third-level indicators in the second indicator layer to the corresponding second-level indicators and the contribution rate of the second-level indicators to the corresponding first-level indicators were obtained through the factor analysis algorithm. According to the contribution rate of each major indicator to the regional comprehensive ecological health index score, the major indicator with the largest contribution rate is set as the main evaluation major indicator, and the remaining major indicators are set as the first auxiliary evaluation major indicator, the second auxiliary evaluation major indicator, the third auxiliary evaluation major indicator, and the fourth auxiliary evaluation major indicator according to the corresponding contribution rate; Based on the main evaluation indicator, the first auxiliary evaluation indicator, the second auxiliary evaluation indicator, the third auxiliary evaluation indicator, the fourth auxiliary evaluation indicator and the corresponding first-level indicators, second-level indicators and third-level indicators of each sub-region, as well as the contribution rate of each indicator in the first indicator layer to the regional comprehensive ecological health index score, the contribution rate of the third-level indicators in the second indicator layer to the corresponding second-level indicators, the contribution rate of the second-level indicators to the corresponding first-level indicators, and the contribution rate of the first-level indicators to the corresponding major indicators, the main-auxiliary hierarchical evaluation indicator scores and the corresponding evaluation accuracy in each sub-region are obtained through a hierarchical distributed evaluation model.

11. A mine ecological environment monitoring system according to claim 10, characterized in that: The main-auxiliary hierarchical evaluation index scores in each sub-region include the evaluation scores of each first-level indicator corresponding to the main evaluation indicator, the evaluation scores of each first-level indicator corresponding to the first auxiliary evaluation indicator, the second auxiliary evaluation indicator, the third auxiliary evaluation indicator, and the fourth auxiliary evaluation indicator, the main evaluation indicator ecological health score, the first auxiliary evaluation indicator ecological health index score, the second auxiliary evaluation indicator ecological health index score, the third auxiliary evaluation indicator ecological health index score, the fourth auxiliary evaluation indicator ecological health index score, and the regional comprehensive ecological health index score; The process of obtaining the regional comprehensive ecological health index score, the main assessment indicator ecological health score, and the auxiliary assessment indicator ecological health index score also includes: Based on the scores of the primary and secondary hierarchical evaluation indicators in each sub-area and the preset hierarchical step warning space, the warning information corresponding to each level and the warning level rendering color of the effective monitoring influence domain corresponding to each monitoring node are obtained according to the indicator level from large to small.

12. A mine ecological environment monitoring system according to claim 11, characterized in that: The construction process of the hierarchical step warning space includes: According to the scores of the primary and secondary hierarchical evaluation indicators in each sub-region, the warning score thresholds at the corresponding levels are set according to the evaluation indicator levels from large to small. If the comprehensive ecological health index score of the current sub-region is less than the corresponding warning score threshold, the ecological health score of the primary evaluation indicator is compared with the corresponding warning score threshold. When the ecological health score of the primary evaluation indicator is less than the corresponding warning score threshold and the evaluation scores of each first-level indicator under the primary evaluation indicator are less than the corresponding warning score threshold, a warning of the secondary evaluation indicator is issued; When any of the regional comprehensive ecological health index score, the main assessment indicator ecological health score, or the first-level indicator assessment score does not meet the corresponding warning score threshold, the highest level warning will be issued and the effective monitoring impact area that does not meet the warning score threshold will be rendered in the highest warning level color; When issuing an auxiliary evaluation indicator warning, an auxiliary warning of the corresponding warning level and color rendering of the corresponding auxiliary warning level are carried out according to the warning level interval to which the product of the current level auxiliary evaluation indicator score and the corresponding contribution rate belongs.

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