CN115688053B - Mine environment dynamic monitoring management method and system based on data fusion - Google Patents

Abstract

The invention discloses a mine environment dynamic monitoring management method and system based on data fusion, and relates to the technical field of data processing, wherein the method comprises the following steps: carrying out regional classification on the mine to be monitored to generate a mine regional classification tree; acquiring a plurality of groups of environment monitoring indexes and expected values of the plurality of groups of environment monitoring indexes; collecting a plurality of groups of environment monitoring index characteristic values through an environment monitoring sensor array; judging whether the expected values of a plurality of groups of environment monitoring indexes are met; when the data is not satisfied, carrying out data fusion analysis to generate an environment monitoring index mapping relation set; loading a plurality of groups of environment monitoring index characteristic value time sequence data within a preset time granularity, and predicting and generating a plurality of groups of index abnormal time; generating mine environment health identification information. The invention solves the technical problems that the mine environment monitoring accuracy is low and the mine abnormal environment cannot be fed back in time in the prior art, and achieves the technical effects of dynamically monitoring the mine environment and improving the monitoring efficiency and accuracy.

Description

Mine environment dynamic monitoring management method and system based on data fusion

Technical Field

The invention relates to the technical field of data processing, in particular to a mine environment dynamic monitoring and management method and system based on data fusion.

Background

With the development of industry, the demand for ore energy increases year by year, but the ore is taken as a non-renewable resource, and the exploitation for many years has damage to the topography and ecological environment of the mine. In order to ensure safe production and green use of environmental resources, it is necessary to dynamically monitor the mine environment.

At present, data acquisition is carried out on the surrounding environment of a mine by utilizing new equipment and new technology, so that the obtained data are summarized to professional technicians, and deep analysis and mining of the data are carried out. However, with the increase of the acquisition devices, the acquired data types and data volumes are numerous, and the data cannot be processed quickly only by manually analyzing the data. Although office software is also used for rapidly summarizing data, the data is only calculated and processed from the surface, and the correlation between the data cannot be deeply analyzed, so that the mine environment is isolated and one-sided data analysis is performed, and the mine environment cannot be comprehensively and accurately analyzed. In the prior art, the mine environment monitoring accuracy is low, and the mine abnormal environment cannot be fed back in time.

Disclosure of Invention

The application provides a mine environment dynamic monitoring management method and system based on data fusion, which are used for solving the technical problems that in the prior art, the mine environment monitoring accuracy is low and the mine abnormal environment cannot be fed back in time.

In view of the problems, the application provides a mine environment dynamic monitoring and management method and system based on data fusion.

The application provides a mining environment dynamic monitoring management method based on data fusion, which comprises the following steps:

carrying out regional classification on the mine to be monitored to generate a mine regional classification tree;

traversing the mine area hierarchical tree to obtain a plurality of groups of environment monitoring indexes and a plurality of groups of environment monitoring index expected values, wherein the plurality of groups of environment monitoring indexes correspond to the plurality of groups of environment monitoring index expected values one by one;

traversing the plurality of groups of environment monitoring indexes, and collecting characteristic values of the plurality of groups of environment monitoring indexes through an environment monitoring sensor array;

judging whether the characteristic values of the environmental monitoring indexes meet expected values of the environmental monitoring indexes;

when the characteristic values of the plurality of groups of environment monitoring indexes do not meet the expected values of the plurality of groups of environment monitoring indexes, carrying out data fusion analysis on the plurality of groups of environment monitoring indexes to generate an environment monitoring index mapping relation set;

Loading a plurality of groups of environment monitoring index characteristic value time sequence data in a preset time granularity, and predicting based on the environment monitoring index mapping relation set to generate a plurality of groups of index abnormal time;

and when the abnormal time of the multiple groups of indexes meets an abnormal time threshold value, generating mine environment health identification information.

In a second aspect of the application, a mining environment dynamic monitoring management system based on data fusion is provided, the system comprises:

the regional classification tree generation module is used for carrying out regional classification on the mine to be monitored and generating a mine regional classification tree;

the monitoring index obtaining module is used for traversing the mine region hierarchical tree to obtain a plurality of groups of environment monitoring indexes and a plurality of groups of environment monitoring index expected values, wherein the plurality of groups of environment monitoring indexes correspond to the plurality of groups of environment monitoring index expected values one by one;

the index characteristic value acquisition module is used for traversing the plurality of groups of environment monitoring indexes and acquiring a plurality of groups of environment monitoring index characteristic values through the environment monitoring sensor array;

the index characteristic value judging module is used for judging whether the plurality of groups of environment monitoring index characteristic values meet the plurality of groups of environment monitoring index expected values or not;

The mapping relation set generation module is used for carrying out data fusion analysis on the plurality of groups of environment monitoring indexes to generate an environment monitoring index mapping relation set when the characteristic values of the plurality of groups of environment monitoring indexes do not meet the expected values of the plurality of groups of environment monitoring indexes;

the abnormal time generation module is used for loading time sequence data of a plurality of groups of environment monitoring index characteristic values in preset time granularity, predicting based on the environment monitoring index mapping relation set and generating a plurality of groups of index abnormal time;

the identification information generation module is used for generating mine environment health identification information when the abnormal time of the plurality of groups of indexes meets an abnormal time threshold.

One or more technical schemes provided by the application have at least the following technical effects or advantages:

according to the method, regional classification is carried out according to the surrounding environment of a mine to be monitored to obtain a mine regional classification tree reflecting the regional classification condition of the mine, environmental monitoring indexes of each region in the mine regional classification tree and corresponding expected values of the environmental monitoring indexes are collected to obtain a plurality of groups of environmental monitoring indexes and a plurality of groups of environment monitoring index expected values, the plurality of groups of environment monitoring indexes are in one-to-one correspondence, then the plurality of groups of environment monitoring indexes are searched one by one, the environmental monitoring sensor array is utilized to collect the characteristic values of the plurality of groups of environment monitoring indexes, further whether the characteristic values of the plurality of groups of environment monitoring indexes meet the expected values of the plurality of groups of environment monitoring indexes is judged, when the characteristic values of the plurality of groups of environment monitoring indexes do not meet the expected values of the plurality of groups of environment monitoring indexes, data fusion analysis is carried out on the plurality of groups of environment monitoring indexes to generate an environment monitoring index mapping relation set, then a plurality of groups of environment monitoring index characteristic value time sequence data within a preset time granularity are loaded, prediction is carried out on the basis of the environment monitoring index mapping relation set to obtain a plurality of groups of index abnormal time, and when the abnormal time of the plurality of groups of indexes meet an abnormal time threshold value of the mine at the moment, so that the mine environment is indicated to appear abnormal, and environment health identification information is generated. The intelligent dynamic monitoring of the mine environment is achieved, the data are subjected to fusion treatment, the change condition of the mine environment is comprehensively analyzed, and the technical effects of timeliness and accuracy of monitoring management are improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic flow chart of a dynamic monitoring and managing method based on a data fusion mine environment, which is provided by the embodiment of the application;

fig. 2 is a schematic flow chart of generating a mine regional classification tree in a dynamic monitoring and management method based on a data fusion mine environment according to an embodiment of the application;

fig. 3 is a schematic flow chart of setting expected values of multiple environmental monitoring indexes in a dynamic monitoring management method based on a data fusion mine environment according to an embodiment of the application;

fig. 4 is a schematic structural diagram of a mine environment dynamic monitoring and management system based on data fusion according to an embodiment of the present application.

Reference numerals illustrate: the system comprises a regional classification tree generating module 11, a monitoring index obtaining module 12, an index characteristic value collecting module 13, an index characteristic value judging module 14, a mapping relation set generating module 15, an abnormal time generating module 16 and an identification information generating module 17.

Detailed Description

The application provides a mine environment dynamic monitoring management method based on data fusion, which is used for solving the technical problems that the mine environment monitoring accuracy is low and the mine abnormal environment cannot be fed back in time in the prior art.

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

As shown in fig. 1, the application provides a mine environment dynamic monitoring management method based on data fusion, which comprises the following steps:

step S100: carrying out regional classification on the mine to be monitored to generate a mine regional classification tree;

further, as shown in fig. 2, the step S100 of the embodiment of the present application further includes:

step S110: generating an associated region set according to the geographical positioning information of the mine to be monitored;

step S120: performing longitudinal membership distribution on the associated region set to generate a plurality of regional classification subtrees;

step S130: and transversely fusing the regional classification subtrees to generate the mine regional classification tree.

Specifically, the mine to be monitored is any mine which needs to dynamically monitor the environment of the mine. The associated region set is obtained by positioning according to the spatial position of the mine to be monitored and summarizing the related regions according to the trend of the mine mountain. The regional classification subtrees are obtained by dividing the associated regional collection step by step according to membership relations among administrative departments, and each regional classification subtree corresponds to one region. The mine regional classification regional tree is formed by transversely fusing a plurality of regional classification subtrees, and setting the classification subtrees of regional classes at the same layer, so that the associated region is clearly described in a hierarchical mode.

Specifically, according to the geographical positioning position of the mine to be monitored, the region related to the mountain is acquired by taking the central zone of the mine as a positioning point and the trend of the mountain of the mine as a basis, so as to obtain the associated region set, and then the urban region is divided into regions and counties according to the administrative membership of each region, and the regions are further divided from the counties to the villages and towns to obtain a plurality of classification regions by dividing the urban region into the urban regions from provinces in an exemplary manner. And then, the associated region set is divided into a plurality of region classification subtrees according to the classification modes of the plurality of classification regions. Wherein each regional classification subtree corresponds to a region. And further, carrying out transverse fusion on the plurality of regional classification subtrees to obtain the mine regional classification tree. The method achieves clear and well-defined structural description of the mine area to be monitored, and provides a clear technical effect of monitoring objects for subsequent dynamic monitoring management.

Step S200: traversing the mine area hierarchical tree to obtain a plurality of groups of environment monitoring indexes and a plurality of groups of environment monitoring index expected values, wherein the plurality of groups of environment monitoring indexes correspond to the plurality of groups of environment monitoring index expected values one by one;

Further, as shown in fig. 3, the traversing the mine region hierarchical tree obtains a plurality of sets of environmental monitoring indexes and a plurality of sets of environmental monitoring index expected values, where the plurality of sets of environmental monitoring indexes and the plurality of sets of environmental monitoring index expected values are in one-to-one correspondence, and step S200 in the embodiment of the present application further includes:

step S210: acquiring an environment monitoring initial index set;

step S220: traversing the mine region hierarchical tree according to the environment monitoring initial index set to perform cleaning degree analysis, and generating a plurality of groups of index cleaning degree scores;

step 230: traversing the plurality of groups of index cleanliness scores, screening out the environment monitoring initial index sets meeting a cleanliness score threshold, and acquiring the plurality of groups of environment monitoring indexes according to the rest environment monitoring initial index sets;

step S240: traversing the plurality of groups of environment monitoring indexes, and setting expected values of the plurality of groups of environment monitoring indexes.

Further, the step S220 of the embodiment of the present application further includes:

step S221: traversing the mine regional classification tree to generate a mine environment state feature matrix;

Step S222: according to the mine environment state feature matrix, taking the environment monitoring initial index set as a screening condition, and collecting a target label vector;

step S223: acquiring an abnormal frequency label and an abnormal time length label according to the target label vector;

step S224: acquiring a data cleaning degree evaluation formula:

wherein,,the region of the ith mine is characterized,a target tag vector for a j-th index characterizing an i-th mine region,abnormal frequency tags of the target tag vector of the j-th index representing the i-th mine region,abnormal duration of target label vector of jth index representing ith mine area, alpha and beta are representedAndan offset parameter of greater than 0;

step S225: and traversing the abnormal frequency label and the abnormal time length label according to the data cleaning degree evaluation formula to generate the multiple groups of index cleaning degree scores.

Specifically, the multiple groups of environment monitoring indexes refer to multiple groups of monitoring indexes for evaluating the mine environment states in the areas, and each group of environment monitoring indexes corresponds to one regional classification subtree in the mine regional classification tree. The expected values of the environmental monitoring indexes are parameter values corresponding to the monitoring indexes when the mine environment set according to the management requirement meets the requirement, and the expected values of the environmental monitoring indexes are in one-to-one correspondence with the environmental monitoring indexes. The environment index monitoring initial index set is an initial index set for monitoring the whole mine environment according to the local mine environment management specification, and comprises the following components: rainfall, groundwater level, groundwater temperature, rock-soil moisture content, etc. The grading result obtained after the environmental states of different areas of the mine area grading tree are evaluated one by one reflects the mine environmental states of the different areas. The cleanliness score threshold is a preset minimum cleanliness score value capable of meeting environmental condition requirements, and is set by a worker without limitation.

Specifically, the mine environmental state feature matrix is a matrix describing features of environmental states of each region in the mine region hierarchical tree. The target tag vector is obtained by acquiring actual states of hydrologic, soil and geological changes of the mine according to the mine environment state matrix, and reflects actual state values of all states of the mine. The abnormal frequency label refers to a label for describing the occurrence times of the abnormal state in unit time. The abnormal time length label is a label for describing the time length of occurrence of the abnormal state. The data cleanliness evaluation formula is a formula for quantitatively evaluating the environmental state of the area according to the target label vector, the abnormal frequency label and the abnormal time length label.

Specifically, the mine environment state of each area in the mine area grading tree is evaluated to obtain a mine environment state feature matrix, and then the monitoring indexes in the environment monitoring initial index set are used as screening conditions, and the state of the feature matrix is acquired from three aspects of hydrology, soil and geology to obtain the target tag vector. And further, deep mining and analysis statistics are carried out on the target label vector to obtain the abnormal frequency label and the abnormal time length label, statistics is carried out on the frequency and time of abnormal occurrence of different indexes in each area of the mine, and basic data is provided for subsequent quantitative calculation of index screening. And quantitatively evaluating the abnormal degree of different indexes in different mine areas according to the data cleaning degree evaluation formula to obtain a plurality of groups of index cleaning degree scores.

Specifically, comparing and judging the cleaning degree scores of the multiple groups of indexes with the cleaning degree score threshold, removing the initial index set with the environmental monitoring score higher than the cleaning degree score threshold, namely removing the indexes with good states, leaving the indexes with high anomaly degree and urgent need to be processed, realizing the efficient utilization of resources, and reducing the analysis data. And searching the environmental monitoring indexes one by one according to the environmental monitoring indexes to obtain expected values of the environmental monitoring indexes. The method achieves the technical effects of quantitatively screening the dynamically monitored indexes, guaranteeing the reliability of the indexes, improving the monitoring management efficiency and maximizing the utilization of management resources.

Further, according to the mine environmental state feature matrix, the method uses the environmental monitoring initial index set as a screening condition to collect a target tag vector, and step S222 of the embodiment of the present application includes:

step S2221: acquiring a hydrologic monitoring initial index set, a soil monitoring initial index set and a geological change monitoring initial index set according to the environment monitoring initial index set;

step S2222: according to the mine environment state feature matrix, taking the hydrologic monitoring initial index set as a first screening condition, and collecting a first sub-label vector;

Step S2223: according to the mine environment state feature matrix, taking the soil monitoring initial index set as a second screening condition, and collecting a second sub-label vector;

step S2224: according to the mine environment state feature matrix, taking the geological change monitoring initial index set as a third screening condition, and collecting a third sub-label vector;

step S2225: adding the first sub-tag vector, the second sub-tag vector, and the third sub-tag vector to the target tag vector.

Specifically, extracting three dimensions of hydrology, soil and geology from the environment monitoring initial index set to obtain the hydrology monitoring initial index set, the soil monitoring initial index set and the geology change monitoring initial index set. The initial hydrologic monitoring index set is an index set for monitoring the water quality of mine underground water and comprises indexes such as water temperature, chromaticity, turbidity, macroscopic objects, PH value, total hardness, metal element content and the like. The initial index set for monitoring the soil is an index set for monitoring the state of mine soil and comprises indexes such as PH value, cadmium content, mercury content, arsenic content, lead content and the like. The initial geological change monitoring index set is an index set for monitoring geological change conditions of mines and comprises indexes such as a landform structure, a permeability coefficient, a dispersion coefficient and the like.

Specifically, according to the mine environment state feature matrix, the hydrologic monitoring initial index set is used as a first screening condition, and the state feature matrix is screened and extracted to obtain the first sub-tag vector. The first sub-tag vector is a vector obtained after monitoring hydrologic conditions of each mine area. And screening and extracting the state feature matrix by taking the soil monitoring initial index set as a second screening condition according to the mine environment state feature matrix to obtain the second sub-tag vector. The second sub-tag vector is a vector obtained after monitoring the soil conditions of each mine area. And screening and extracting the state feature matrix by taking the geological change monitoring initial index set as a third screening condition according to the mine environment state feature matrix to obtain the third sub-tag vector. The third sub-tag vector is a vector obtained after monitoring geological change conditions of each mine area. And summarizing according to the first sub-tag vector, the second sub-tag vector and the third sub-tag vector, and adding the summarized sub-tag vectors into the target tag vector, so that the environmental state condition of the mine area is objectively described.

Step S300: traversing the plurality of groups of environment monitoring indexes, and collecting characteristic values of the plurality of groups of environment monitoring indexes through an environment monitoring sensor array;

step S400: judging whether the characteristic values of the environmental monitoring indexes meet expected values of the environmental monitoring indexes;

specifically, the environment monitoring sensor array is formed by arranging sensors for monitoring the environment in a mine area in real time according to different areas. And the plurality of groups of environment monitoring index characteristic values are obtained by utilizing the environment monitoring sensor array to acquire environment information according to the plurality of groups of environment monitoring indexes to monitor targets, so as to obtain the regional environment index characteristic values corresponding to the monitoring indexes. And comparing the expected values of the environmental monitoring indexes with the characteristic values of the environmental monitoring indexes, and judging whether the expected values meet the requirements. The method achieves the technical effect of carrying out targeted acquisition on the mine environment and providing analysis data for the subsequent analysis of the environment change condition.

Step S500: when the characteristic values of the plurality of groups of environment monitoring indexes do not meet the expected values of the plurality of groups of environment monitoring indexes, carrying out data fusion analysis on the plurality of groups of environment monitoring indexes to generate an environment monitoring index mapping relation set;

Further, when the characteristic values of the plurality of environmental monitoring indexes do not meet the expected values of the plurality of environmental monitoring indexes, performing data fusion analysis on the plurality of environmental monitoring indexes to generate an environmental monitoring index mapping relation set, and step S500 of the embodiment of the present application further includes:

step S510: traversing the plurality of groups of environment monitoring indexes to obtain a plurality of index sets of the bottom mine area;

step S520: traversing the index sets, and collecting index anomaly monitoring record data;

step S530: obtaining a confidence evaluation formula:

wherein,,a kth indicator characterizing a first grade of the underlying mine region,a k+d index representing a first grade of any mine area,characterization ofThe number of records that occur individually,characterization ofThe number of records that occur individually,characterization of、The number of records that co-occur,representing in the first levelAndis a support degree of (2);

step S540: traversing the index anomaly monitoring record data according to the confidence coefficient evaluation formula, and solving a plurality of confidence coefficient evaluation results;

step S550: comparing the multiple confidence evaluation results with a confidence threshold value, and screening multiple groups of associated index sets, wherein the multiple groups of associated index sets are in one-to-one correspondence with the multiple index sets;

Step S560: constructing a plurality of same-level mapping relations according to the plurality of groups of associated index sets;

step S570: constructing a multi-level mapping relation based on the mine regional classification tree according to the multiple groups of associated index sets and the multiple same-level mapping relations;

step S580: and adding the plurality of same-level mapping relations and the multi-level mapping relation into the environment monitoring index mapping relation set.

Specifically, when the characteristic values of the plurality of groups of environment monitoring indexes do not meet the expected values of the plurality of groups of environment monitoring indexes, the condition of the regional environment at the moment is indicated to be unable to meet the requirement, and at the moment, data fusion analysis is performed on the plurality of groups of environment monitoring indexes, so that the regional environment is subjected to overall analysis. The environment monitoring index mapping relation set is a relation set for describing the relation between monitoring indexes from two angles of the mapping relation between the same-level indexes and the mapping relation between the multi-level area indexes.

Specifically, the plurality of index sets are index sets for evaluating the environmental conditions of the mine area at the bottom layer, which is an area located at the bottom layer in the mine area hierarchical tree. The index anomaly detection record data is data obtained by collecting index data of the plurality of index sets one by one and recording anomaly detection conditions in the index data. The confidence evaluation formula is a formula for quantitatively evaluating the correlation reliability degree between indexes. The confidence evaluation results are obtained by analyzing and calculating the index anomaly detection record data according to the confidence evaluation formula, and the reliable evaluation results of the association degree between any two indexes are obtained. The confidence threshold is the lowest support value when the confidence meets the requirements. The multiple groups of associated index sets are index sets with index association degrees meeting requirements in the sets. The plurality of same-level mapping relations refer to mapping relations among a plurality of indexes in the same association level. The multi-level mapping relation is obtained by fusing the monitoring data of the low-level area to obtain the index mapping relation of the high-level area according to the area grading condition in the mine area grading tree.

Specifically, a plurality of index sets are acquired through collecting a plurality of indexes of a mine area at the bottommost layer, index anomaly monitoring record data are acquired, quantitative analysis is carried out on the index anomaly monitoring record data through a confidence evaluation formula, quantitative calculation is carried out on the plurality of index sets one by one, the number of independent occurrence and the number of occurrence of the indexes are analyzed when the anomalies occur, and when the occurrence frequency is high, the association degree between the two indexes is high. And judging the multiple confidence evaluation results and the confidence threshold value, and screening out multiple groups of associated index sets meeting the confidence threshold value.

Specifically, the multiple groups of associated index sets are subjected to equal-level mapping relation analysis to obtain multiple equal-level mapping relations. Illustratively, when analyzing water quality metrics in a mine area, when the turbidity of the water is high, the water quality metrics are correlated to a plurality of water quality metrics, including chromaticity, metal content, macroscopic matter, and PH. The turbidity and the water quality indexes have an association relation, so that a level mapping relation is constructed between the turbidity and the water quality indexes. And according to the plurality of the same-level mapping relations, constructing a multi-level mapping relation based on the area level in the mine area hierarchical tree. The monitoring data of the regional level of the urban area is obtained by fusing a plurality of county monitoring data within the scope of the urban area, so that the monitoring data of the regional level of the urban area, namely province, is obtained by fusing a plurality of same-level mapping relations in the county, summarizing the same-level mapping relations to the urban area, and then secondarily fusing the fused data of the regional level of the urban area according to the fused data of different urban areas. The technical effect of comprehensive fusion and clear treatment of the mine area data with clear levels is achieved.

Further, the step S560 of the embodiment of the present application further includes:

step S561: traversing any group of the multiple groups of association index sets to obtain a representative association index and a common association index;

step S562: based on the index anomaly monitoring record data, the representative associated index is used as input data, the common associated index is used as output data, and a common associated index evaluation model is trained;

step S563: and constructing the plurality of same-level mapping relations based on the common association index evaluation model.

Specifically, any one group of associated index sets in a plurality of groups of associated index sets is selected through traversal, and the index sets are analyzed to obtain the representative associated index and the common associated index. Wherein the representative associated index is an index which has an associated relation with a plurality of indexes in the index set. The common association index is an index with a single association relation in the index set. And training the common associated index evaluation model by using the common associated index as input data according to the index anomaly monitoring record data, training the common associated index evaluation model until convergence, judging whether the accuracy of model output meets the requirement, if so, obtaining the trained common associated index evaluation model, and if not, obtaining more data to perform incremental learning on the model.

Specifically, the mapping relation among the level indexes in the index set is intelligently analyzed according to the common associated index evaluation model, so that the multiple level mapping relations are obtained. The technical effects of rapidly analyzing the mapping relation among indexes and improving analysis accuracy and efficiency are achieved.

Step S600: loading a plurality of groups of environment monitoring index characteristic value time sequence data in a preset time granularity, and predicting based on the environment monitoring index mapping relation set to generate a plurality of groups of index abnormal time;

further, the loading of the plurality of sets of environment monitoring index feature value time sequence data within the preset time granularity predicts based on the environment monitoring index mapping relation set to generate a plurality of sets of index abnormal time, and the step S600 of the embodiment of the present application further includes:

step S610: performing expected value adjustment on a plurality of representative associated indexes according to the plurality of groups of environment monitoring index expected values to generate a plurality of groups of representative associated index expected values;

step S620: screening out a plurality of groups of time sequence data representing the characteristic values of the associated indexes from the plurality of groups of time sequence data representing the characteristic values of the environment monitoring indexes according to a plurality of groups of time sequence data representing the associated indexes;

Step S630: traversing the plurality of groups of time sequence data representing the characteristic values of the associated indexes to construct a plurality of groups of change curves representing the associated indexes;

step S640: and generating the plurality of groups of index abnormal time based on the plurality of groups of representative associated index change curves and the plurality of groups of representative associated index expected values.

Specifically, the preset time granularity is a preset time period for acquiring index data of the mine area environment, and is set by a worker by himself, and the preset time granularity is not limited herein. The time sequence data of the plurality of groups of environment monitoring index characteristic values are data obtained by sequencing the collected plurality of groups of environment monitoring index characteristic values according to a time sequence within a preset time granularity. And calculating abnormal time of the common index after obtaining variation trend representing the associated index and time reaching each threshold according to the index mapping relation in the environment monitoring index mapping relation set, so as to obtain the abnormal time of the multiple groups of indexes. The abnormal time of the multiple groups of indexes refers to a time period when the multiple groups of indexes are abnormal.

Specifically, according to the expected values corresponding to the various indexes in the plurality of groups of environment monitoring index expected values, the expected values corresponding to the plurality of groups of representative associated index expected values under the condition that the plurality of representative associated indexes meet the environment requirements are determined, so that the plurality of groups of representative associated index expected values are obtained. Preferably, the expected values corresponding to the common association indexes associated with the representative association indexes are compared according to the expected values of the environmental monitoring indexes. Further, according to the expected values of the plurality of common associated indexes corresponding to each of the plurality of representative associated indexes, the plurality of expected values corresponding to each of the representative associated indexes can be obtained according to the association relationship between the representative associated indexes and the common indexes, wherein each expected value corresponds to one common associated index.

Specifically, from a plurality of groups of environment monitoring index characteristic value time sequence data, the plurality of groups of representative associated index characteristic value time sequence data are obtained by taking the plurality of representative associated indexes as extraction basis. And (3) taking time as an abscissa and respectively taking time sequence data in the time sequence data of the characteristic values of the multiple groups of representing associated indexes as an ordinate to construct multiple groups of representing associated index change curves. The multiple groups of representative associated index change curves respectively reflect the change trend conditions of the representative associated indexes of each group in a preset time granularity, and the change trend conditions comprise a change value and change time. And predicting the time when the representative index reaches each expected value by taking the expected values of the plurality of groups of representative associated indexes as targets according to the change trend of the plurality of groups of representative associated index change curves, so as to correspondingly obtain the abnormal time of each common index. Therefore, the technical effect of predicting the index abnormal time and providing reliable judgment basis for the subsequent analysis of the abnormal condition of the mine environment is achieved.

Step S700: and when the abnormal time of the multiple groups of indexes meets an abnormal time threshold value, generating mine environment health identification information.

Specifically, the abnormal time threshold is a time period in which an abnormality occurs in a preset index without affecting the mine environment. When the abnormal time of the multiple groups of indexes meets the abnormal time threshold, the mine environment is in a healthy state, and when the abnormal time of the multiple groups of indexes is not met, an environment abnormal mark is generated and sent to a manager for timely adjustment. The technical effects of dynamically monitoring the mine environment, shortening the monitoring feedback time, improving the feedback efficiency and monitoring management quality are achieved.

In summary, the embodiment of the application has at least the following technical effects:

1. according to the method, the mining to be monitored is subjected to regional classification according to the geographical position of the mining, each region is subjected to traversal searching according to the formed hierarchical mining region classification tree to obtain the monitoring index and index expected value corresponding to each region, the purpose of providing monitoring targets for subsequent environmental monitoring management of the mining is achieved, then multiple groups of environmental monitoring indexes are data acquisition targets, multiple groups of environmental monitoring index characteristic values are acquired through the environmental monitoring sensor array distributed in the mining region, the purpose of providing analysis data for subsequent analysis of the mining environmental state is achieved, further, according to the fact that whether the environmental states corresponding to the multiple groups of environmental monitoring index characteristic values meet the requirements or not is judged according to the multiple groups of environmental monitoring index expected values, when the requirements are not met, the environmental monitoring indexes are subjected to same-grade and multi-layer data fusion, environmental monitoring index mapping relation is built, environmental data in preset time are acquired, the multiple groups of index abnormal time are obtained through prediction according to the time sequence, and the mine abnormal time is obtained through combination of the index trend prediction, and further, the multiple groups of index abnormal time meets the abnormal time threshold value of the mine health identification information is obtained. The technical effects of carrying out data fusion on mine environment monitoring data, comprehensively and objectively carrying out dynamic monitoring management on mine environment and improving management efficiency are achieved.

2. According to the embodiment of the application, the mountain trend of the mine is analyzed according to the geographical positioning information of the mine to be monitored, the area related to the mine is obtained, the membership of each area is distributed according to the administrative division condition, a plurality of area grading subtrees are obtained, and the plurality of area grading subtrees are further subjected to transverse fusion to obtain the mine area grading tree. The technical effect of clearly and clearly dividing the mine area and improving the accuracy of subsequent management is achieved.

Example two

Based on the same inventive concept as the dynamic monitoring management method based on the data fusion mine environment in the foregoing embodiments, as shown in fig. 4, the present application provides a dynamic monitoring management system based on the data fusion mine environment, and the system and method embodiments in the embodiments of the present application are based on the same inventive concept. Wherein the system comprises:

the regional classification tree generation module 11 is used for carrying out regional classification on the mine to be monitored by the regional classification tree generation module 11 to generate a mine regional classification tree;

the monitoring index obtaining module 12 is configured to traverse the mine area hierarchical tree to obtain a plurality of sets of environmental monitoring indexes and a plurality of sets of environmental monitoring index expected values, where the plurality of sets of environmental monitoring indexes and the plurality of sets of environmental monitoring index expected values are in one-to-one correspondence;

The index characteristic value acquisition module 13 is used for traversing the plurality of groups of environment monitoring indexes, and acquiring a plurality of groups of environment monitoring index characteristic values through the environment monitoring sensor array;

the index feature value judging module 14 is configured to judge whether the plurality of sets of environment monitoring index feature values meet the plurality of sets of environment monitoring index expected values, where the index feature value judging module 14 is configured to judge whether the plurality of sets of environment monitoring index feature values meet the plurality of sets of environment monitoring index expected values;

the mapping relation set generating module 15 is configured to perform data fusion analysis on the multiple sets of environmental monitoring indexes to generate an environmental monitoring index mapping relation set when the characteristic values of the multiple sets of environmental monitoring indexes do not meet the expected values of the multiple sets of environmental monitoring indexes;

the abnormal time generation module 16, wherein the abnormal time generation module 16 is used for loading a plurality of groups of environment monitoring index characteristic value time sequence data within a preset time granularity, and predicting based on the environment monitoring index mapping relation set to generate a plurality of groups of index abnormal time;

the identification information generation module 17 is used for generating mine environment health identification information when the abnormal time of the plurality of groups of indexes meets an abnormal time threshold value.

Further, the system further comprises:

the associated region set generating unit is used for generating an associated region set according to the geographical positioning information of the mine to be monitored;

the hierarchical subtree generation unit is used for carrying out longitudinal membership distribution on the associated region set to generate a plurality of regional hierarchical subtrees;

and the transverse fusion unit is used for carrying out transverse fusion on the plurality of regional classification subtrees to generate the mine regional classification tree.

Further, the system further comprises:

the system comprises an initial index set obtaining unit, a monitoring unit and a monitoring unit, wherein the initial index set obtaining unit is used for obtaining an environment monitoring initial index set;

the cleaning degree score generating unit is used for traversing the mine region grading tree according to the environment monitoring initial index set to carry out cleaning degree analysis and generating a plurality of groups of index cleaning degree scores;

the initial index set screening unit is used for traversing the plurality of groups of index cleaning degree scores, screening the environment monitoring initial index sets meeting the cleaning degree scoring threshold, and acquiring the plurality of groups of environment monitoring indexes according to the rest environment monitoring initial index sets;

And the index expected value setting unit is used for traversing the plurality of groups of environment monitoring indexes and setting the plurality of groups of environment monitoring index expected values.

Further, the system further comprises:

the characteristic matrix generation unit is used for traversing the mine regional classification tree to generate a mine environment state characteristic matrix;

the label vector acquisition unit is used for acquiring a target label vector by taking the environment monitoring initial index set as a screening condition according to the mine environment state feature matrix;

the abnormal time length label obtaining unit is used for obtaining an abnormal frequency label and an abnormal time length label according to the target label vector;

a cleanliness evaluation formula obtaining unit for obtaining a data cleanliness evaluation formula:

wherein,,the region of the ith mine is characterized,a target tag vector for a j-th index characterizing an i-th mine region,abnormal frequency tags of the target tag vector of the j-th index representing the i-th mine region,abnormal duration of target label vector of jth index representing ith mine area, alpha and beta are represented Andan offset parameter of greater than 0;

and the plurality of groups of cleaning degree score generating units are used for traversing the abnormal frequency labels and the abnormal duration labels according to the data cleaning degree evaluation formula to generate the plurality of groups of index cleaning degree scores.

Further, the system further comprises:

the monitoring initial index set obtaining unit is used for obtaining a hydrologic monitoring initial index set, a soil monitoring initial index set and a geological change monitoring initial index set according to the environment monitoring initial index set;

the first sub-tag vector acquisition unit is used for acquiring a first sub-tag vector by taking the hydrologic monitoring initial index set as a first screening condition according to the mine environment state feature matrix;

the second sub-tag vector acquisition unit is used for acquiring a second sub-tag vector by taking the soil monitoring initial index set as a second screening condition according to the mine environment state feature matrix;

the third sub-tag vector acquisition unit is used for acquiring a third sub-tag vector by taking the geological change monitoring initial index set as a third screening condition according to the mine environment state feature matrix;

And the target tag vector adding unit is used for adding the first sub-tag vector, the second sub-tag vector and the third sub-tag vector into the target tag vector.

Further, the system further comprises:

the plurality of index set obtaining units are used for traversing the plurality of groups of environment monitoring indexes to obtain a plurality of index sets of the bottom mine area;

the abnormal record data acquisition unit is used for traversing the index sets and acquiring index abnormal monitoring record data;

a confidence evaluation formula obtaining unit for obtaining a confidence evaluation formula:

wherein,,a kth indicator characterizing a first grade of the underlying mine region,a k+d index representing a first grade of any mine area,characterization ofThe number of records that occur individually,characterization ofThe number of records that occur individually,characterization of、The number of records that co-occur,representing in the first levelAndis a support degree of (2);

the confidence evaluation result obtaining units are used for traversing the index anomaly monitoring record data according to the confidence evaluation formula and obtaining a plurality of confidence evaluation results;

The correlation index set screening unit is used for comparing the plurality of confidence evaluation results with a confidence threshold value and screening a plurality of groups of correlation index sets, wherein the plurality of groups of correlation index sets are in one-to-one correspondence with the plurality of index sets;

the same-level mapping relation construction unit is used for constructing a plurality of same-level mapping relations according to the plurality of groups of association index sets;

the multi-level mapping relation construction unit is used for constructing a multi-level mapping relation based on the mine regional hierarchical tree according to the multi-group association index sets and the same-level mapping relations;

and the mapping relation set adding unit is used for adding the plurality of same-level mapping relations and the multi-level mapping relation into the environment monitoring index mapping relation set.

Further, the system further comprises:

the association index obtaining unit is used for traversing any group of multiple groups of association index sets to obtain a representative association index and a common association index;

the index evaluation model training unit is used for training a common association index evaluation model by taking the representative association index as input data and the common association index as output data based on the index anomaly monitoring record data;

And the mapping relation construction unit is used for constructing the plurality of same-level mapping relations based on the common association index evaluation model.

Further, the system further comprises:

the expected value adjusting unit is used for adjusting expected values of a plurality of representative associated indexes according to the plurality of groups of environment monitoring index expected values to generate a plurality of groups of representative associated index expected values;

the time sequence data screening unit is used for screening out a plurality of groups of time sequence data representing the characteristic values of the associated indexes from the plurality of groups of time sequence data representing the characteristic values of the environment monitoring indexes according to a plurality of groups of time sequence data representing the associated indexes;

the index change curve construction unit is used for traversing the plurality of groups of time sequence data representing the characteristic values of the associated indexes to construct a plurality of groups of associated index change curves;

and the abnormal time generation unit is used for generating the plurality of groups of index abnormal time based on the plurality of groups of representative associated index change curves and the plurality of groups of representative associated index expected values.

It should be noted that the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

The foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the application are intended to be included within the scope of the application.

The specification and figures are merely exemplary illustrations of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope of the application. Thus, the present application is intended to include such modifications and alterations insofar as they come within the scope of the application or the equivalents thereof.

Claims (8)

1. The mining environment dynamic monitoring management method based on data fusion is characterized by comprising the following steps of:

carrying out regional classification on the mine to be monitored to generate a mine regional classification tree;

traversing the mine area hierarchical tree to obtain a plurality of groups of environment monitoring indexes and a plurality of groups of environment monitoring index expected values, wherein the plurality of groups of environment monitoring indexes correspond to the plurality of groups of environment monitoring index expected values one by one;

traversing the plurality of groups of environment monitoring indexes, and collecting characteristic values of the plurality of groups of environment monitoring indexes through an environment monitoring sensor array;

Judging whether the characteristic values of the environmental monitoring indexes meet expected values of the environmental monitoring indexes;

when the characteristic values of the plurality of groups of environment monitoring indexes do not meet the expected values of the plurality of groups of environment monitoring indexes, carrying out data fusion analysis on the plurality of groups of environment monitoring indexes to generate an environment monitoring index mapping relation set;

loading a plurality of groups of environment monitoring index characteristic value time sequence data in a preset time granularity, and predicting based on the environment monitoring index mapping relation set to generate a plurality of groups of index abnormal time;

when the abnormal time of the multiple groups of indexes meets an abnormal time threshold, generating mine environment health identification information;

when the characteristic values of the plurality of groups of environment monitoring indexes do not meet the expected values of the plurality of groups of environment monitoring indexes, performing data fusion analysis on the plurality of groups of environment monitoring indexes to generate an environment monitoring index mapping relation set, wherein the method comprises the following steps:

traversing the plurality of groups of environment monitoring indexes to obtain a plurality of index sets of the bottom mine area;

traversing the index sets, and collecting index anomaly monitoring record data;

obtaining a confidence evaluation formula:wherein->The kth index, which characterizes the first class of the mine area of the bottom layer,/- >A k+d index representing a first grade of any mine area,characterization->Number of records presented singly, ">Characterization->Number of records presented singly, ">Characterization->、/>Number of records co-occurring, < >>Indicating +.>And->Is a support degree of (2);

traversing the index anomaly monitoring record data according to the confidence coefficient evaluation formula, and solving a plurality of confidence coefficient evaluation results;

comparing the multiple confidence evaluation results with a confidence threshold value, and screening multiple groups of associated index sets, wherein the multiple groups of associated index sets are in one-to-one correspondence with the multiple index sets;

constructing a plurality of same-level mapping relations according to the plurality of groups of associated index sets;

constructing a multi-level mapping relation based on the mine regional classification tree according to the multiple groups of associated index sets and the multiple same-level mapping relations;

and adding the plurality of same-level mapping relations and the multi-level mapping relation into the environment monitoring index mapping relation set.

2. The method for dynamically monitoring and managing the mine environment based on the data fusion according to claim 1, wherein the step of performing regional classification on the mine to be monitored to generate a mine regional classification tree comprises the steps of:

generating an associated region set according to the geographical positioning information of the mine to be monitored;

Performing longitudinal membership distribution on the associated region set to generate a plurality of regional classification subtrees;

and transversely fusing the regional classification subtrees to generate the mine regional classification tree.

3. The method for dynamically monitoring and managing mine environment based on data fusion according to claim 1, wherein traversing the mine region hierarchical tree to obtain a plurality of sets of environment monitoring indexes and a plurality of sets of environment monitoring index expected values, wherein the plurality of sets of environment monitoring indexes and the plurality of sets of environment monitoring index expected values are in one-to-one correspondence, comprises:

acquiring an environment monitoring initial index set;

traversing the mine region hierarchical tree according to the environment monitoring initial index set to perform cleaning degree analysis, and generating a plurality of groups of index cleaning degree scores;

traversing the plurality of groups of index cleanliness scores, screening out the environment monitoring initial index sets meeting a cleanliness score threshold, and acquiring the plurality of groups of environment monitoring indexes according to the rest environment monitoring initial index sets;

traversing the plurality of groups of environment monitoring indexes, and setting expected values of the plurality of groups of environment monitoring indexes.

4. The method of claim 3, wherein traversing the mine regional classification tree according to the initial set of environmental monitoring indicators for cleaning degree analysis, generating a plurality of sets of indicator cleaning degree scores comprises:

Traversing the mine regional classification tree to generate a mine environment state feature matrix;

according to the mine environment state feature matrix, taking the environment monitoring initial index set as a screening condition, and collecting a target label vector;

acquiring an abnormal frequency label and an abnormal time length label according to the target label vector;

acquiring a data cleaning degree evaluation formula:wherein->Characterization of the ith mine region,/->Target tag vector for the j index characterizing the i mine region,/th index, and a method for determining the target tag vector>Abnormal frequency tags of the target tag vector of the j-th index representing the i-th mine region,abnormal length of target tag vector of jth index characterizing ith mine area, ++>And->Characterization ofAnd +.>An offset parameter of greater than 0;

and traversing the abnormal frequency label and the abnormal time length label according to the data cleaning degree evaluation formula to generate the multiple groups of index cleaning degree scores.

5. The method for dynamically monitoring and managing the mine environment based on the data fusion according to claim 4, wherein the step of collecting the target tag vector by taking the initial index set of the environment monitoring as a screening condition according to the characteristic matrix of the mine environment state comprises the following steps:

Acquiring a hydrologic monitoring initial index set, a soil monitoring initial index set and a geological change monitoring initial index set according to the environment monitoring initial index set;

according to the mine environment state feature matrix, taking the hydrologic monitoring initial index set as a first screening condition, and collecting a first sub-label vector;

according to the mine environment state feature matrix, taking the soil monitoring initial index set as a second screening condition, and collecting a second sub-label vector;

according to the mine environment state feature matrix, taking the geological change monitoring initial index set as a third screening condition, and collecting a third sub-label vector;

adding the first sub-tag vector, the second sub-tag vector, and the third sub-tag vector to the target tag vector.

6. The method for dynamically monitoring and managing the mine environment based on the data fusion according to claim 5, wherein the constructing a plurality of same-level mapping relations according to the plurality of sets of associated index sets comprises:

traversing any group of the multiple groups of association index sets to obtain a representative association index and a common association index;

based on the index anomaly monitoring record data, the representative associated index is used as input data, the common associated index is used as output data, and a common associated index evaluation model is trained;

And constructing the plurality of same-level mapping relations based on the common association index evaluation model.

7. The method for dynamically monitoring and managing the mine environment based on the data fusion according to claim 1, wherein the loading of the plurality of sets of environment monitoring index feature value time sequence data within the preset time granularity, the predicting based on the environment monitoring index mapping relation set, and the generating of the plurality of sets of index abnormal time comprise:

performing expected value adjustment on a plurality of representative associated indexes according to the plurality of groups of environment monitoring index expected values to generate a plurality of groups of representative associated index expected values;

screening out a plurality of groups of time sequence data representing the characteristic values of the associated indexes from the plurality of groups of time sequence data representing the characteristic values of the environment monitoring indexes according to a plurality of groups of time sequence data representing the associated indexes;

traversing the plurality of groups of time sequence data representing the characteristic values of the associated indexes to construct a plurality of groups of change curves representing the associated indexes;

and generating the plurality of groups of index abnormal time based on the plurality of groups of representative associated index change curves and the plurality of groups of representative associated index expected values.

8. The mining environment dynamic monitoring management system based on data fusion is characterized by comprising:

the regional classification tree generation module is used for carrying out regional classification on the mine to be monitored and generating a mine regional classification tree;

The monitoring index obtaining module is used for traversing the mine region hierarchical tree to obtain a plurality of groups of environment monitoring indexes and a plurality of groups of environment monitoring index expected values, wherein the plurality of groups of environment monitoring indexes correspond to the plurality of groups of environment monitoring index expected values one by one;

the index characteristic value acquisition module is used for traversing the plurality of groups of environment monitoring indexes and acquiring a plurality of groups of environment monitoring index characteristic values through the environment monitoring sensor array;

the index characteristic value judging module is used for judging whether the plurality of groups of environment monitoring index characteristic values meet the plurality of groups of environment monitoring index expected values or not;

the mapping relation set generation module is used for carrying out data fusion analysis on the plurality of groups of environment monitoring indexes to generate an environment monitoring index mapping relation set when the characteristic values of the plurality of groups of environment monitoring indexes do not meet the expected values of the plurality of groups of environment monitoring indexes;

the abnormal time generation module is used for loading time sequence data of a plurality of groups of environment monitoring index characteristic values in preset time granularity, predicting based on the environment monitoring index mapping relation set and generating a plurality of groups of index abnormal time;

The identification information generation module is used for generating mine environment health identification information when the abnormal time of the plurality of groups of indexes meets an abnormal time threshold;

the system further comprises:

the plurality of index set obtaining units are used for traversing the plurality of groups of environment monitoring indexes to obtain a plurality of index sets of the bottom mine area;

the abnormal record data acquisition unit is used for traversing the index sets and acquiring index abnormal monitoring record data;

a confidence evaluation formula obtaining unit for obtaining a confidence evaluation formula:wherein->The kth index, which characterizes the first class of the mine area of the bottom layer,/->A k+d index representing the first grade of any mine area,/for the first grade>Characterization->Number of records presented singly, ">Characterization->Number of records presented singly, ">Characterization->、/>Number of records co-occurring, < >>Indicating +.>And->Is a support degree of (2);

the confidence evaluation result obtaining units are used for traversing the index anomaly monitoring record data according to the confidence evaluation formula and obtaining a plurality of confidence evaluation results;

The correlation index set screening unit is used for comparing the plurality of confidence evaluation results with a confidence threshold value and screening a plurality of groups of correlation index sets, wherein the plurality of groups of correlation index sets are in one-to-one correspondence with the plurality of index sets;

the same-level mapping relation construction unit is used for constructing a plurality of same-level mapping relations according to the plurality of groups of association index sets;

the multi-level mapping relation construction unit is used for constructing a multi-level mapping relation based on the mine regional hierarchical tree according to the multi-group association index sets and the same-level mapping relations;

and the mapping relation set adding unit is used for adding the plurality of same-level mapping relations and the multi-level mapping relation into the environment monitoring index mapping relation set.