Mine ecological environment monitoring system

Abstract

The application 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 obtains a hierarchical ecological index state space of a to-be-monitored mine area in real time; the evaluation module obtains main and auxiliary evaluation indexes by using the hierarchical index state space and combining a factor analysis algorithm, and calculates an ecological health index of each sub-region by combining a preset distributed node evaluation network and a hierarchical evaluation index library; finally, real-time monitoring and early warning are realized according to the ecological health index of each sub-region and a preset hierarchical step-by-step early warning space of the feedback module; the application fully utilizes the Internet of Things technology and hierarchical multi-index evaluation, reduces the data calculation amount, and accurately monitors the real-time ecological environment of the mine, discovers potential risks in time and gives early warnings.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of mine monitoring, and particularly relates to a mine ecological environment monitoring system. BACKGROUND

[0002] Current mine monitoring technologies focus on single safety or environmental indicators, making it difficult to achieve coordinated management and control of multiple ecological environment factors. For example, the Chinese patent with the authorization announcement number CN117514360B discloses a mine monitoring and early warning system, which adjusts the cloud backup data capacity to optimize the communication early warning stability, but it is limited to communication parameter management and lacks dynamic monitoring of ecological indicators such as atmosphere and water quality; the Chinese patent with the authorization announcement number CN108678807B discloses a roadway mine pressure monitoring and early warning method, which evaluates the risk of roof falling by combining the roadway roof pressure and separation value, but does not involve ecological problems such as water and soil pollution and vegetation restoration; traditional monitoring systems generally have the following defects: first, the indicators are fragmented, only targeting local parameters such as geological deformation and equipment status, ignoring key ecological factors such as soil heavy metals and tailing leachate; second, there are serious data silos, with each subsystem operating independently, making it impossible to integrate communication, environmental, and equipment multi-source data to build a global risk assessment model; third, relying on manual sampling and laboratory analysis, real-time performance is poor and costs are high, making it difficult to respond to sudden pollution incidents in a timely manner; fourth, the early warning mechanism is lagging, with existing technologies mostly triggering alarms based on threshold values, lacking proactive prevention and control capabilities based on time series prediction and spatial correlation. The above problems lead to fragmentation of mine ecological monitoring, making it difficult to meet the regulatory needs of "full factors, full process, and intelligentization" in green mine construction. SUMMARY

[0003] In view of the deficiencies of the prior art, the present application provides a mine ecological environment monitoring system, which comprises a monitoring module, an evaluation module, and a feedback module. The monitoring module uses a regional difference monitoring node network constructed by monitoring equipment distributed in space, combined with a map grid segmentation algorithm and a graph algorithm, to real-time monitor and obtain the hierarchical indicator state space of the mine area to be monitored; the evaluation module extracts the main and auxiliary evaluation indicators of the sub-regions from the state space using a factor analysis algorithm, and obtains the comprehensive evaluation score of each sub-region by combining a pre-set distributed node evaluation network and a hierarchical evaluation indicator library; the feedback module realizes real-time monitoring and early warning based on the comprehensive evaluation score and a pre-set hierarchical indicator early warning space; the present application fully utilizes the Internet of Things technology and hierarchical multi-indicator evaluation, not only greatly reducing the data calculation amount, but also accurately monitoring the real-time ecological environment status of the mine, discovering potential risks in a timely manner and giving early warnings.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0005] A mine ecological environment monitoring system comprises 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 index and the 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 constructed based on the sub-regions and the corresponding adjacent relationship and the hierarchical evaluation index library.

[0007] Specifically, the mine ecological environment monitoring system further comprises a monitoring module; the monitoring module comprises 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 a monitoring device distribution space in combination with a historical monitoring region map layer feature space of each monitoring device.

[0009] The monitoring device distribution space is constructed based on monitoring device distribution position points, the monitoring ecological indicator type of each device, the effective monitoring area size, device physical attribute parameters, dynamic monitoring parameters, dynamic operation state parameters and environmental signal compensation parameters.

[0010] The grid analysis unit performs differential regional division on the differential monitoring node network through a grid division algorithm optimized by a particle swarm algorithm based on the feature information in the monitoring device distribution space corresponding to each node in the differential monitoring node network, the network topology relationship between all nodes in the differential monitoring node network, the overlapping coverage rate and blind area compensation coefficient between all nodes and the evaluation index accuracy corresponding to each partition region.

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

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

[0013] The integrated feature extraction unit obtains, according to all sub-regional corresponding dynamic monitoring data sets with monitoring device tags in combination with a preset federal feature extraction model, an ecological index state space corresponding to all monitoring nodes of each sub-region.

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

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

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

[0017] The distributed evaluation unit obtains, based on a hierarchical distributed evaluation model configured by each monitoring node in combination with the main hierarchical evaluation index set corresponding to each sub-region and the effective contribution rate corresponding to each main evaluation index and the auxiliary hierarchical evaluation index set and the effective contribution rate corresponding to each auxiliary evaluation index, a regional comprehensive ecological health index score, a main evaluation index ecological health score, an auxiliary evaluation index ecological health index score, and a corresponding evaluation accuracy.

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

[0019] The evaluation and early warning unit compares, based on the regional comprehensive ecological health index score, the main evaluation index ecological health score, the auxiliary evaluation index ecological health index score, and the corresponding evaluation accuracy with a preset hierarchical step-by-step early warning space, to obtain hierarchical early warning information corresponding to an evaluation accuracy threshold.

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

[0021] The hierarchical early warning information meeting the evaluation accuracy threshold is fed back to the monitoring center through the monitoring feedback unit, and the monitoring index information not meeting the evaluation accuracy threshold is fed back to the grid analysis unit and the data acquisition unit, the corresponding sub-regional division and monitoring data sampling frequency are adjusted based on the corresponding effective contribution rate, and the adjusted hierarchical early warning information meeting the evaluation accuracy threshold 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 coverage type map, and ground-underground hydrological distribution thermal map of the to-be-monitored mine area, and construct a three-dimensional map layer of the to-be-monitored mine area.

[0024] Based on the three-dimensional map layer of the to-be-monitored mine area, the monitoring device position distribution information, and the effective monitoring area size of each monitoring device, the effective monitoring influence domain of each monitoring device in the three-dimensional map layer is obtained through the Voronoi graph algorithm.

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

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

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

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

[0029] Based on the initial monitoring node set, the first connection relationship, the second connection relationship, and the third connection relationship, and combining the graph algorithm, the initial monitoring node network is obtained.

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

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

[0032] The environmental information includes a rock density distribution state, a rock density-to-signal intensity mapping distribution, a vegetation density distribution state, and a vegetation density-to-signal intensity mapping distribution.

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

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

[0035] Based on the historical monitoring index type and the corresponding early warning level of each monitoring node and the area size of the historical sub-regions, a level-region mapping function of the corresponding early warning level of each monitoring node and the area division is constructed.

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

[0037] Meanwhile, a regional division optimization constraint function and corresponding constraint conditions are constructed based on the overlapping coverage, the effective monitoring influence domain, the blind area, the unit time signal transmission delay, and the evaluation accuracy, 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 a particle swarm algorithm with a built-in Kriging interpolation function, and combined with the optimized constraint threshold and the training period, a set of regional division parameters of the optimized dynamic monitoring node network is obtained.

[0039] The set of regional division parameters of the optimized dynamic monitoring node network is input into a regional grid algorithm, the dynamic monitoring node network is divided into regions, 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 evaluation index ecological health score, and the auxiliary evaluation index ecological health score includes:

[0041] 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, an evaluation connection relationship between all evaluation nodes in the distributed node evaluation network is constructed.

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

[0043] Based on the regional difference monitoring node network and the configured data acquisition parameters, a real-time dynamic monitoring data set corresponding to each monitoring node in each sub-region is obtained.

[0044] The real-time dynamic monitoring data set is input into a data classification layer in the federated feature extraction model for data type classification, and the data type and classification accuracy in 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 is input into a distributed feature extraction layer according to the corresponding type for corresponding type data feature extraction, and an 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 sub-layers; the M feature extraction sub-layers are the same as and one-to-one corresponding to the data types obtained by the data classification layer.

[0047] Specifically, the hierarchical evaluation index library includes a first index layer and a second index layer; the first index layer includes a cover layer macro index, a soil layer macro index, a hydrological layer index, a geological layer macro index, and an air layer macro index; the second index layer is constructed from the first-level indexes, second-level indexes, and third-level indexes corresponding to the cover layer macro index, soil layer macro index, hydrological layer index, geological layer macro index, and air layer macro index.

[0048] The cover layer macro index includes a first-level land resource monitoring index, a first-level solid waste monitoring index, a first-level soil and water environment monitoring index, and the corresponding second-level index and third-level index for each first-level index; the soil layer macro index includes a first-level soil pollution monitoring index and the corresponding second-level index and third-level index; the hydrological layer index includes a first-level mine surface water pollution monitoring index, a first-level wastewater discharge monitoring index, a first-level groundwater monitoring index, and the corresponding second-level index and third-level index for each first-level index; the geological layer macro index includes a first-level goaf ground subsidence monitoring index, a first-level mountain geological disaster monitoring index, a first-level ground fissure monitoring index, and the corresponding second-level index and third-level index for each first-level index; the air layer macro index includes a first-level air pollutant concentration index and the corresponding second-level index and third-level index; the corresponding macro index level in the first index layer is greater than the first-level index in the second index layer, the corresponding level of the first-level index is greater than the second-level index, and the level of the second-level index is greater than the third-level index; the hierarchical evaluation index library is constructed from the first index layer and the second index layer according to the corresponding index level in combination with a tree database.

[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 main-auxiliary hierarchical evaluation index score in each sub-region, combining the preset hierarchical step warning space, the corresponding warning information of each level and the warning level rendering color of the effective monitoring influence domain of each monitoring node are obtained according to the index level from large to small.

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

[0058] According to the main-auxiliary hierarchical evaluation index score in each sub-region, the warning score threshold under the corresponding level is set according to the evaluation index level from large to small, if the current sub-region corresponding regional comprehensive ecological health index score is less than the corresponding warning score threshold, the main evaluation index ecological health score is compared with the corresponding warning score threshold, when the main evaluation index ecological health score is less than the corresponding warning score threshold and the evaluation score of each first-level index under the main evaluation index is less than the corresponding warning score threshold, the auxiliary evaluation index warning is performed.

[0059] When the regional comprehensive ecological health index score, the main evaluation index ecological health score or the evaluation score of the first-level index does not meet the corresponding warning score threshold, the highest level warning is performed and the effective monitoring influence domain which does not meet the warning score threshold is rendered with the color of the highest warning level.

[0060] When the auxiliary evaluation index warning is performed, according to the warning level interval to which the product result of the current level auxiliary evaluation index score and the corresponding contribution rate belongs, the corresponding auxiliary warning of the warning level and the color rendering of the corresponding auxiliary warning level are performed.

[0061] Compared with the prior art, the beneficial effects of the present application are:

[0062] The present application aims at the deficiencies of the prior art, and realizes multi-dimensional real-time monitoring of the mine ecological environment by constructing a regional difference monitoring node network, uses a Voronoi diagram algorithm and a grid segmentation technology optimized by a particle swarm, combines a three-dimensional map layer and equipment distribution parameters, dynamically divides monitoring sub-regions and optimizes network topology, effectively improves monitoring coverage and data collection accuracy, realizes feature fusion and efficient matching of multi-source heterogeneous ecological data through a federal feature extraction model and a fast indexing mechanism of a hierarchical evaluation index library, dynamically determines main and auxiliary evaluation indexes and their contribution rates by combining a factor analysis algorithm, ensures the adaptability and accuracy of the evaluation model, realizes multi-level real-time early warning based on dynamic threshold determination and color rendering rules of a hierarchical step-by-step early warning space, and automatically adjusts monitoring frequency and grid division parameters through a feedback mechanism to form a closed-loop control system of'monitoring-evaluation-early warning-optimization'. BRIEF DESCRIPTION OF DRAWINGS

[0063] Figure 1 FIG. 1 is a structural diagram of a mine ecological environment monitoring system according to an embodiment of the present application.

[0064] Figure 2 FIG. 2 is a construction process structure diagram of a regional difference monitoring node network according to an embodiment of the present application. DETAILED DESCRIPTION

[0065] Embodiment 1

[0066] Please refer to Figure 1 The present application provides an embodiment: a mine ecological environment monitoring system, comprising: a monitoring module, an evaluation module, and a feedback module.

[0067] The monitoring module, based on a preset regional difference monitoring node network, realizes real-time monitoring and acquisition of a hierarchical ecological index state space of a to-be-monitored mine region.

[0068] The regional difference monitoring node network is obtained by combining a map grid segmentation algorithm and a graph algorithm through a monitoring equipment distribution space.

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

[0070] The hierarchical evaluation index library is constructed by a hierarchical ecological index combined database and a quick index constructed between the hierarchical ecological index and the hierarchical evaluation index.

[0071] The main evaluation index and the auxiliary evaluation index corresponding to each sub-region are obtained by combining a factor analysis algorithm with a state space of a regional ecological index.

[0072] Based on the regional ecological health index corresponding to each sub-region and the hierarchical step early warning space preset by the feedback module, real-time monitoring and early warning are performed.

[0073] The hierarchical step early warning space is constructed by the difference hierarchical early warning level corresponding to the main evaluation index and the auxiliary evaluation index, the corresponding rendering color, and the scores of the main evaluation index and the auxiliary evaluation index.

[0074] Further, 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 difference monitoring node network by a graph algorithm based on a monitoring device distribution space and a historical monitoring region map layer feature space of each monitoring device.

[0076] The monitoring device distribution space is constructed by a monitoring device distribution position point, a monitoring ecological index type of each device, an effective monitoring area size, a device physical attribute parameter, a dynamic monitoring parameter, a dynamic operation state parameter, and an environmental signal compensation parameter.

[0077] Further, in the monitoring matrix unit, first, a multi-mode communication protocol is integrated to ensure data transmission reliability, and physical attributes include sensor types, radio frequency gain, and other basic parameters; second, a programmable sampling frequency and an adaptive adjustment mechanism are configured to support multi-dimensional data acquisition such as particulate matter concentration and fluid mechanics indicators, and Kalman filtering and hierarchical compression algorithms are equipped to optimize signal quality; operation state monitoring tracks battery power, signal strength, and storage resource usage in real time, and a built-in hexadecimal coding fault diagnosis system and multi-level energy efficiency mode switching strategy are provided; third, an environmental compensation mechanism integrates a geological medium attenuation model, a meteorological interference dynamic correction, and a multipath suppression algorithm to realize monitoring accuracy optimization in complex scenes through rock density-signal attenuation mapping, temperature drift compensation coefficient matrix, and time domain equalization control, forming a full-link closed-loop regulation and control capability from data perception to environmental adaptation.

[0078] The grid analysis unit divides the difference monitoring node network into difference regions according to the feature information in the space where the monitoring device of each node in the difference monitoring node network is distributed, the network topology relationship between all nodes in the difference monitoring node network, the overlapping coverage rate between all nodes, and the evaluation accuracy of each partition region corresponding to the evaluation index, and obtains the regional difference monitoring node network through the grid partition algorithm optimized by the particle swarm algorithm.

[0079] The data acquisition unit obtains the dynamic monitoring data set of all monitoring nodes in each sub-region corresponding to the monitoring device label based on the regional difference monitoring node network and the dynamic monitoring frequency configured for each monitoring node.

[0080] The network topology relationship between all nodes is obtained by a topology algorithm based on the monitoring device, the relay hop number between devices, the parent-child node connection relationship, and the maximum coverage distance of device wireless transmission.

[0081] The integrated feature extraction unit obtains the ecological index state space of all monitoring nodes in each sub-region based on the dynamic monitoring data set of all sub-regions corresponding to the monitoring device label and a preset federated feature extraction model.

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

[0083] The index matching unit obtains the hierarchical evaluation index space corresponding to all monitoring nodes in each sub-region based on the ecological index state space of all monitoring nodes in each sub-region and a hierarchical evaluation index library constructed based on a knowledge graph through a matching algorithm and a fast index preset in the hierarchical evaluation index library.

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

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

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

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

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

[0089] The hierarchical early warning information corresponding to the evaluation accuracy threshold is fed back to the monitoring center through the monitoring feedback unit, and the monitoring index information corresponding to the evaluation accuracy threshold is fed back to the grid analysis unit and the data acquisition unit, the corresponding sub-regional division and the monitoring data sampling frequency are cyclically adjusted based on the corresponding effective contribution rate, and the adjusted hierarchical early warning information corresponding to the evaluation accuracy threshold is adjusted and fed back.

[0090] This process realizes the precision and intelligence of mine ecological monitoring through multi-module cooperation and technology fusion; in the monitoring module, the difference monitoring node network is constructed by using graph algorithm in combination with multi-dimensional parameters of equipment, and then the grid segmentation algorithm optimized by particle swarm is used to divide the region according to node characteristics, topological relationship and the like, so as to ensure that the monitoring covers no blind area; 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, so as to improve the comprehensiveness of data acquisition; the data acquisition unit acquires data according to dynamic sampling frequency, the integrated feature extraction unit excavates ecological index features by means of the federal model, and data redundancy is avoided; in the evaluation module, the index matching unit locates the evaluation index by means of the knowledge graph and the fast index, the factor analysis unit screens the main and auxiliary indexes in combination with the historical contribution rate, and the distributed evaluation unit calculates the health index by means of the hierarchical model; for example, in the analysis of soil pollution, the weight is allocated according to the historical influence degree of different indexes, so as to improve the evaluation accuracy; the feedback module compares the evaluation result with the early warning space, and drives the grid and sampling adjustment for the data that does not meet the standard, such as when the monitoring accuracy of a certain region is low, the grid analysis unit re-divides the region, and the data acquisition unit adjusts the sampling frequency, so as to form a closed-loop optimization of monitoring-evaluation-feedback, and finally realize efficient dynamic monitoring and accurate early warning of mine ecology.

[0091] Further, please refer to Figure 2The construction process of the regional difference monitoring node network comprises:

[0092] A digital elevation model, a geological structure map, a surface coverage type map, and a ground-underground hydrological distribution thermal map of the to-be-monitored mine area are acquired to construct a three-dimensional map layer of the to-be-monitored mine area.

[0093] Based on the three-dimensional map layer of the to-be-monitored mine area and the monitoring device position distribution information and the effective monitoring area size of each monitoring device, the effective monitoring influence domain of each monitoring device in the three-dimensional map layer is obtained through a Voronoi diagram algorithm.

[0094] The device position distribution information comprises device position latitude and longitude coordinates and an altitude.

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

[0096] Based on the device position point distribution and the signal base station position point, an initial monitoring node set and a transmission node set are obtained.

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

[0098] Based on the initial monitoring node set, the first connection relationship, the second connection relationship, and the third connection relationship, and combining a graph algorithm, an initial monitoring node network is obtained.

[0099] Based on the distance and the environmental information between the monitoring nodes and the transmission nodes within the effective signal radiation radius of each base station and between the transmission nodes, 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 comprises a rock density distribution state and a mapping distribution of the rock density on the signal strength and a vegetation density distribution state and a mapping distribution of the vegetation density on the 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 index type corresponding to each monitoring node and the corresponding early warning level and the area size of the historical sub-regions, a level-region mapping function of the corresponding early warning level of each monitoring node and the area division size is constructed.

[0103] According to the level-area mapping function, dynamically monitor the overlapping coverage and blind area between all monitoring devices in the 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 index ecological health score and the auxiliary evaluation index ecological health index score and the corresponding evaluation accuracy, the output sequence of the particle swarm optimization algorithm is constructed.

[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 area division optimization constraint function and the corresponding constraint condition are constructed, and the minimum value of the area division optimization constraint function is obtained.

[0105] The output sequence of the particle swarm optimization algorithm and the area division optimization constraint function and the corresponding constraint condition are input into the particle swarm algorithm with the Kriging interpolation function built-in, and the optimized area division parameter set of the dynamic monitoring node network is obtained by combining the optimization constraint threshold and the training period.

[0106] It should be noted that in the particle swarm algorithm with the Kriging interpolation function built-in in the present embodiment, the Kriging interpolation function is used to predict and complete the ecological health index data of the uncovered area during the algorithm iteration process, the feasibility of the particle scheme is judged according to the optimization constraint threshold, the particle position is constantly adjusted to optimize the area division scheme through the speed and position update formula of the particle swarm algorithm, and after multiple iterations, when the preset training period is reached, the particle position parameter with the optimal fitness is selected, thereby obtaining the optimized area division parameter set of the dynamic monitoring node network.

[0107] The optimized area division parameter set of the dynamic monitoring node network is input into the area grid algorithm, the dynamic monitoring node network is divided into areas, and the area difference monitoring node network is obtained and the divided areas are marked in real time.

[0108] The embodiment realizes dynamic optimization and spatial coverage of the mine monitoring network through multi-dimensional coordination; in the spatial modeling layer, a three-dimensional geographic base is constructed by fusing a digital elevation model and geological structure feature data, a standardized spatial reference system is formed by using rock stratum interface parameters to correct equipment elevation positioning; the coverage domain calculation adopts a three-dimensional extension algorithm based on Voronoi diagram, and a device scope model under geometric constraints is established through weighted Delaunay triangulation to realize the topological adaptive reconstruction capability when the device fails. The signal transmission optimization integrates geological medium parameters and surface vegetation characteristics to construct a two-factor attenuation compensation model, and deduces the dynamic signal correction parameters in different lithology regions; the network configuration method combines the Kriging interpolation gradient and the swarm intelligence search mechanism through a hybrid optimization algorithm embedded with a spatial autocorrelation function, and guides the optimal convergence of the device deployment path; the system defines a multi-objective optimization function of coverage overlap and monitoring blind area, and uses a vector space analysis method to deduce the balanced solution set of parameter configuration; the environmental adaptation evaluation adopts a principal component projection technology to construct a hierarchical evaluation model, realizes dimension compression and weight fusion of multiple ecological indicators; and finally a dynamic parameter deployment architecture based on a quadtree index is established, and the spatial index efficiency in complex terrain scenes is improved through a hierarchical grid algorithm.

[0109] Further, the acquisition process of the regional comprehensive ecological health index score, the main evaluation index ecological health score, and the auxiliary evaluation index ecological health index score includes:

[0110] Each of the regional difference monitoring node networks is divided into a sub-region as an evaluation node of the distributed node evaluation network, and the adjacent relationship of all the divided sub-regions is used to construct the evaluation connection relationship between all the evaluation nodes in the distributed node evaluation network.

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

[0112] Based on the regional difference monitoring node network combined with the configured data acquisition parameters, real-time dynamic monitoring data sets with monitoring equipment tags and acquisition time stamps corresponding to all the monitoring nodes in each sub-region are obtained.

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

[0114] The data in the real-time ecological index state space of each sub-region are input to the distributed feature extraction layer according to the corresponding types for corresponding type data feature extraction, and the ecological index state space corresponding to all the monitoring nodes in each sub-region is obtained.

[0115] The distributed feature extraction layer comprises M feature extraction sub-layers; the M feature extraction sub-layers are the same as and one-to-one corresponding to the data types obtained by the data classification layer.

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

[0117] Further, the data processing model adopted in the M feature extraction sub-layers in the embodiment is fine-tuned by a person skilled in the art according to the specific type of data to be processed, combined with the existing pre-trained large model, and built into the corresponding feature extraction sub-layer for specific type data processing.

[0118] Further, the hierarchical evaluation index library comprises a first index layer and a second index layer; the first index layer comprises a cover layer index, a soil layer index, a hydrological layer index, a geological layer index, and an air layer index; the cover layer index comprises a first land resource monitoring index, a first solid waste monitoring index, a first soil and water environment monitoring index, and corresponding second and third indexes of each first index.

[0119] The first land resource monitoring index comprises second indexes of land occupation and destruction monitoring and land restoration monitoring.

[0120] The second land occupation and destruction monitoring index comprises third indexes of land occupation type, land destruction area, land destruction method, vegetation destruction type, and vegetation destruction area; the second land restoration monitoring index comprises third indexes of reclamation land area, reclaimed land area, and reclamation vegetation coverage.

[0121] The first solid waste monitoring index comprises second indexes of solid waste generation and emission monitoring and solid waste comprehensive utilization monitoring; the second solid waste generation and emission monitoring index comprises third indexes of waste type, annual emission amount, cumulative storage amount, source, land occupation area, and main hidden danger; the second solid waste comprehensive utilization monitoring index comprises third indexes of annual comprehensive utilization amount and comprehensive utilization rate.

[0122] The first soil and water environment monitoring index comprises second indexes of soil erosion monitoring and land desertification monitoring; the second soil erosion monitoring index comprises third indexes of soil erosion area and soil erosion modulus; the second land desertification monitoring index comprises third indexes of desertification area and desertification control rate.

[0123] The soil layer index comprises a first soil pollution monitoring index and corresponding second and third indexes.

[0124] The first soil pollution monitoring index comprises second indexes of soil pollution monitoring and pollution hazard monitoring.

[0125] The secondary soil pollution monitoring indexes include the pollution source and the main pollutants, etc. The secondary pollution hazard monitoring indexes include the pollution degree and the hazard range, etc.

[0126] The hydrological layer indexes include the first mine surface water pollution monitoring index, the first wastewater discharge monitoring index, the first groundwater monitoring index, and the secondary and tertiary indexes corresponding to each first index.

[0127] The first mine surface water pollution monitoring index includes the wastewater discharge monitoring and the pollution characteristic monitoring, etc. The secondary wastewater discharge monitoring index includes the wastewater type, the annual output, the annual discharge, and the discharge destination, etc. The secondary pollution characteristic monitoring index includes the main pollutants, the pollution degree, and the annual recycling amount, etc.

[0128] The first wastewater discharge monitoring index includes the discharge monitoring and the pollution control monitoring, etc. The secondary discharge monitoring index includes the annual wastewater discharge and the discharge standard discharge, etc. The secondary pollution control monitoring index includes the main harmful substances, the annual treatment amount, and the comprehensive utilization amount, etc.

[0129] The first groundwater monitoring index includes the groundwater balance destruction monitoring and the groundwater water quality pollution monitoring, etc.

[0130] The secondary groundwater balance destruction monitoring index includes the groundwater level, the mine annual water discharge, the aquifer dewatering area, and the groundwater depression cone area, etc.

[0131] The secondary groundwater water quality pollution monitoring index includes the pH value, the ammonia nitrogen concentration, the heavy metal concentration, and the organic pollutants, etc.

[0132] The geological layer index includes the first goaf ground subsidence monitoring index, the first mountain geological disaster monitoring index, the first ground fissure monitoring index, and the secondary and tertiary indexes corresponding to each first index.

[0133] The first goaf ground subsidence monitoring index includes the subsidence characteristic monitoring and the subsidence hazard monitoring, etc. The subsidence characteristic monitoring includes the subsidence area number, the subsidence area, the subsidence pit maximum depth, and the water depth, etc. The subsidence hazard monitoring includes the subsidence damage degree.

[0134] The first mountain geological disaster monitoring index includes the geological disaster event monitoring and the geological disaster hidden danger monitoring, etc.

[0135] The geological disaster event monitoring includes the annual occurrence number and the caused hazard, etc. The geological disaster hidden danger monitoring includes the hidden danger point number and the treated hidden danger point number, etc.

[0136] The first-level ground fissure monitoring index includes a second-level index of ground fissure morphology monitoring and ground fissure hazard monitoring, and the ground fissure morphology monitoring includes a third-level index of ground fissure quantity, maximum length, maximum width and maximum depth, and the ground fissure hazard monitoring includes a third-level index of strike and damage degree.

[0137] The air layer index includes a first-level air pollutant concentration index and corresponding second-level and third-level indexes.

[0138] The first-level air pollutant concentration index includes a second-level index of particulate matter pollution monitoring and gaseous pollutant monitoring.

[0139] The particulate matter pollution monitoring includes a third-level index of PM2.5 concentration, PM10 concentration and TSP concentration, and the gaseous pollutant monitoring includes a third-level index of sulfur dioxide concentration and nitrogen oxide concentration.

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

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

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

[0143] Based on the tree structure, the cover layer, the soil layer, the hydrological layer, the geological layer and the air layer index of the first index layer are taken as root nodes; each root node is hung with corresponding first-level indexes as child nodes, such as the cover layer index connected with the first-level indexes of land resource monitoring and solid waste monitoring; the first-level indexes extend to the second-level indexes, such as the land resource monitoring index connected with the second-level indexes of land occupation and damage monitoring and land restoration monitoring; finally, the second-level indexes are hung with the third-level indexes to form a complete tree hierarchical structure; in this way, hierarchical storage and management of index data are realized, and data query and calling are facilitated.

[0144] The relational database or the non-relational database is used to store data; in the relational database, a corresponding data table is created for each index level, and the upper and lower index data is associated through the foreign key; the non-relational database uses the nested document structure to organize the index data at each level in a document; at the same time, a unique identifier, a data type, a unit and other attribute information are added to each index to ensure the standardization and consistency of the data.

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

[0146] A dynamic weight calculation module is introduced for each level of indicators. Based on historical data and real-time monitoring data, machine learning algorithms are used to dynamically adjust the weights of indicators. For example, when a landslide event occurs frequently in a certain area recently, the weights of related three-level indicators under the mountain geological disaster monitoring indicator are automatically increased, so that the evaluation results are more in line with the actual risk situation. At the same time, weight adjustment thresholds and trigger conditions are set to ensure the scientificity and stability of weight adjustment.

[0147] A unified data access interface is designed to support the access of multiple data sources. ETL (Extract, Transform, Load) technology is used to clean, convert and load data of different formats and frequencies. For example, real-time data of underground water level collected by sensors and land desertification area data obtained by satellite remote sensing are fused and processed to unify data format and timestamp, so that the database can comprehensively process multi-source data and improve data integrity and availability.

[0148] A Web-based visual analysis interface is developed to visually display the tree database structure and indicator data using D3.js, Echarts and other visualization libraries. Users can explore the hierarchical relationship of indicators through drag-and-drop, zoom and other operations, and also support custom queries and analysis. For example, users can select a specific region and time period to view the trend of pollutant concentration under the air layer index, and compare the impact of different indicators through interactive charts to assist decision-making.

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

[0150] Based on all the hierarchical evaluation index 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 three-level indicators in the second indicator layer to the corresponding two-level indicators and the contribution rate of the two-level indicators to the corresponding one-level indicators are obtained through factor analysis algorithms.

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

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

[0153] Further, the main-auxiliary hierarchical evaluation indicator score in each sub-region includes the evaluation score of each first-level indicator under the main evaluation macro indicator, the evaluation score of each first-level indicator under the first auxiliary evaluation macro indicator, the second auxiliary evaluation macro indicator, the third auxiliary evaluation macro indicator, and the fourth auxiliary evaluation macro indicator, respectively, 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 main-auxiliary hierarchical evaluation indicator score in each sub-region and the preset hierarchical step warning space, the corresponding warning information of each level and the warning level rendering color of the corresponding effective monitoring influence domain of each monitoring node are obtained according to the indicator level from large to small.

[0155] The fine and dynamic adaptability of the mine ecological health assessment is realized by multi-level technology fusion; at the evaluation network architecture level, a distributed node evaluation network is constructed based on the topological relationship of adjacent sub-regions, the connection state of the nodes is stored using an adjacency matrix, and the data transmission path across regions is analyzed using a breadth-first search algorithm; a federal feature extraction framework is deployed in the data processing link, the feature decoupling of numerical sequences and image data is realized through a multi-modal classifier parallel processing mechanism, and an independent feature extraction channel is established; the hierarchical evaluation index library adopts a semantic fusion architecture of tree database and knowledge graph, an index causal reasoning chain is constructed based on RDF triples, and the semantic association modeling of multi-dimensional indexes is realized; a dynamic weight self-adaptive mechanism integrates a sliding window update strategy and a trend prediction model, and a dynamic adjustment rule for the importance of indexes is established; at the evaluation model level, the principal component features are extracted using the factor analysis algorithm combined with the maximum variance rotation method, the hierarchical distributed evaluation model adopts a dual-channel attention mechanism, the macro-index features are captured through the main evaluation channel, the fine-grained change features are extracted through the auxiliary evaluation channel, and a gating fusion unit is designed to realize dynamic integration of features; the warning rendering algorithm integrates spatial interpolation technology to convert discrete monitoring data into a continuous warning field distribution model, and a gradient visualization expression framework based on semi-variogram is constructed; the system constructs an elastic evaluation framework through feature dimension configuration, tree level architecture and weight update period setting, and realizes multi-source parameter coupling analysis.

[0156] Further, the construction process of the hierarchical step-by-step warning space includes:

[0157] According to the main-auxiliary hierarchical evaluation index score in each sub-region, the warning score threshold under the corresponding level is set from large to small according to the evaluation index level, if the current sub-region corresponding regional comprehensive ecological health index score is less than the corresponding warning score threshold, the main evaluation index ecological health score is compared with the corresponding warning score threshold, when the main evaluation index ecological health score is less than the corresponding warning score threshold and the evaluation score of each primary index under the main evaluation index is less than the corresponding warning score threshold, the auxiliary evaluation index warning is performed.

[0158] When the regional comprehensive ecological health index score, the main evaluation index ecological health score or the evaluation score of the primary index does not meet the corresponding warning score threshold, the highest level warning is performed and the effective monitoring influence domain that does not meet the warning score threshold is rendered in the highest warning level color.

[0159] When the auxiliary evaluation index warning is performed, the corresponding warning level interval of the product of the corresponding level auxiliary evaluation index score and the corresponding contribution rate is used to perform the corresponding level auxiliary warning and the corresponding auxiliary warning level color rendering.

[0160] Further, in the present embodiment, the construction principle and examples of the hierarchical step-by-step warning space include:

[0161] First, for each main-aid hierarchical evaluation index in each sub-region, the corresponding early warning score threshold is set for different levels according to the evaluation index level from high to low; these thresholds are the key basis for subsequent judgment of early warning level, which is equivalent to the "threshold" of different early warning levels, used to measure the risk degree 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 early warning score threshold, proceed to the next judgment process; this step is a preliminary screening of the overall ecological health status of the region, if the overall score is not up to standard, further analysis of the specific index situation is needed.

[0163] Main index and first-level index judgment: if the regional comprehensive ecological health index score is not up to standard, then compare the ecological health score of the main evaluation index with the corresponding early warning score threshold; when the ecological health score of the main evaluation index is less than the corresponding early warning score threshold, and the evaluation score of each first-level index under the main evaluation index is less than the corresponding early warning score threshold, the aid evaluation index early warning is triggered; this step goes deep into the main index and first-level index level, only when the main index and all first-level indexes under it are at a low level, the aid evaluation index will be considered.

[0164] Further, in this embodiment, the highest level early warning judgment is: as long as any one of the regional comprehensive ecological health index score, the main evaluation index ecological health score or the evaluation score of the first-level index does not meet the corresponding early warning score threshold, the highest level early warning is carried out, and the effective monitoring influence domain that does not meet the early warning score threshold is rendered with the highest early warning level color. This setting ensures that as long as one key indicator has serious problems, the most serious early warning signal will be sent to attract enough attention.

[0165] Further, in this embodiment, the aid evaluation index early warning processing process is: when the aid evaluation index early warning is carried out, according to the early warning level interval to which the product result of the current level aid evaluation index score and the corresponding contribution rate belongs, the corresponding aid early warning of the early warning level and the color rendering of the corresponding aid early warning level are carried out. By considering the aid evaluation index score and its contribution rate, the regional ecological health status is more comprehensively evaluated, and a more detailed early warning level is given.

[0166] The embodiment realizes all-around monitoring and early warning of the ecological health condition of the mine area by constructing a multi-level intelligent mine ecological environment monitoring and evaluation system; at the technical level, the system establishes a dynamic monitoring node network based on regional differences, adopts a grid division method combining Voronoi graph algorithm and swarm intelligence optimization, and combines three-dimensional modeling including device physical properties, dynamic monitoring parameters and environmental compensation parameters to realize adaptive spatial division and coverage optimization of the monitoring area. The synergistic effect of the device physical property parameters and the environmental signal compensation parameters guarantees the data reliability under complex terrain, and the real-time interaction of the dynamic monitoring parameters and the operating state parameters improves the network robustness; a federal feature extraction framework is deployed at the data processing link, after the multi-source heterogeneous data is classified into types such as numerical sequence and image data, distributed processing is performed through a specialized feature extraction sublayer. The time-space sequence processing sublayer adopts time series decomposition and deep coding algorithm, and the geographic space processing sublayer applies spatial interpolation and convolutional neural network to realize multi-modal feature decoupling and fusion; the feature extraction results are shared through the federal learning mechanism under the privacy protection. A hierarchical tree-shaped index library is constructed in the evaluation module, the ecological indexes are divided into multiple dimensions such as the cover layer and the soil layer, and each category is provided with multiple levels of sub-indexes to form a hierarchical system; the index contribution rate is dynamically calculated through the factor analysis algorithm, and the weighted health index is generated through the hierarchical distributed evaluation model to realize the overall ecological condition evaluation and accurate positioning of specific problem levels; a hierarchical step-by-step early warning mechanism is adopted in the early warning system, and the gradual early warning escalation is realized based on multi-level threshold determination; when the comprehensive score is lower than the initial threshold, the system checks the main index and the first-level index score layer by layer, and triggers the hierarchical early warning response strategy; the main and auxiliary index collaborative judgment mechanism avoids false positives caused by a single threshold, and ensures the accurate triggering of the early warning instruction; in addition, the self-optimization capability of the system is realized through the closed-loop control of the feedback module, and the monitoring grid division parameters and data acquisition frequency are dynamically adjusted based on the historical early warning level and the real-time network state. The monitoring resources are adaptively allocated according to the regional risk level to form a risk-sensitive dynamic monitoring strategy; finally, a distributed evaluation network is constructed through graph algorithm at the technical collaboration level, data linkage and abnormal conduction are realized through the connection relationship of adjacent sub-regional nodes; a continuous evaluation field model is constructed by combining the spatial interpolation function to solve the fragmentation problem of traditional point monitoring, and efficient identification and response of cross-regional related events are realized.

[0167] The embodiments of the application are described above with reference to the drawings, but the application is not limited to the specific embodiments described above, which are only illustrative rather than limiting, and any person of ordinary skill in the art can make changes, modifications, replacements and variations to the above embodiments without departing from the purpose of the application and the scope protected by the claims.

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 hierarchical ecological indicators with a database and a fast index constructed by the correlation between hierarchical ecological indicators and 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 and the corresponding rendering colors and the scores of the main evaluation indicators and the auxiliary evaluations; 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, based on the monitoring equipment distribution space combined with the historical monitoring area map layer feature space of each monitoring equipment, obtains a differential monitoring node network through a graph algorithm; 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.

2. A mine ecological environment monitoring system according to claim 1, 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.

3. A mine ecological environment monitoring system according to claim 2, 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.

4. A mine ecological environment monitoring system according to claim 3, 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.

5. A mine ecological environment monitoring system according to claim 4, 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.

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 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.

7. A mine ecological environment monitoring system according to claim 6, 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.

8. A mine ecological environment monitoring system according to claim 7, 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.

9. A mine ecological environment monitoring system according to claim 8, 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.

10. A mine ecological environment monitoring system according to claim 9, 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.

11. A mine ecological environment monitoring system according to claim 10, 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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