CN115688053A - Mine environment dynamic monitoring management method and system based on data fusion - Google Patents
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Abstract
The invention discloses a dynamic mine environment monitoring and 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 a mine to be monitored to generate a mine regional classification tree; acquiring a plurality of groups of environment monitoring indexes and a plurality of groups of environment monitoring index expected values; collecting a plurality of groups of environment monitoring index characteristic values through an environment monitoring sensor array; judging whether the environmental monitoring index expected values of a plurality of groups are met; when the environmental monitoring index is not satisfied, performing data fusion analysis to generate an environmental monitoring index mapping relation set; loading a plurality of groups of time sequence data of the characteristic values of the environmental monitoring indexes within a preset time granularity, and predicting and generating a plurality of groups of abnormal index times; and generating mine environment health identification information. The mine environment monitoring method and the mine environment monitoring system solve the technical problems that in the prior art, the mine environment monitoring accuracy is low, and the abnormal mine environment cannot be fed back in time, and achieve the technical effects of dynamically monitoring the mine environment and improving the monitoring efficiency and accuracy.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a dynamic monitoring and management method and system for mine environment based on data fusion.
Background
With the development of industry, the demand of ore energy sources rises year by year, but the ore serving as a non-renewable resource is mined for many years, and the landform and ecological environment of mines are damaged. In order to ensure safe production and green use of environmental resources, it is necessary to dynamically monitor the environment of the mine.
At present, new equipment and new technology are utilized to collect data of the environment around a mine, so that the obtained data are gathered to professional technicians to carry out deep analysis and mining of the data. However, with the increase of the collection devices, the types and the amount of collected data are large, 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 are only operated and processed from the surface, and the relevance among the data cannot be deeply analyzed, so that the mine environment is subjected to isolated and one-sided data analysis, and the mine environment cannot be comprehensively and accurately analyzed. The technical problems that the mine environment monitoring accuracy is low and the abnormal mine environment cannot be fed back in time exist in the prior art.
Disclosure of Invention
The application provides a dynamic monitoring and management method and system based on data fusion for mine environment, which are used for solving the technical problems that in the prior art, the mine environment monitoring accuracy is low and the abnormal mine environment cannot be fed back in time.
In view of the above problems, the present application provides a mine environment dynamic monitoring management method and system based on data fusion.
In a first aspect of the application, a dynamic monitoring and management method for mine environment based on data fusion is provided, and the method includes:
carrying out regional classification on a mine to be monitored to generate a mine regional classification tree;
traversing the mine area grading 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 multiple groups of environment monitoring indexes, and acquiring multiple groups of environment monitoring index characteristic values through an environment monitoring sensor array;
judging whether the characteristic values of the multiple groups of environmental monitoring indexes meet the expected values of the multiple groups of environmental monitoring indexes;
when the characteristic values of the multiple groups of environmental monitoring indexes do not meet the expected values of the multiple groups of environmental monitoring indexes, performing data fusion analysis on the multiple groups of environmental monitoring indexes to generate an environmental monitoring index mapping relation set;
loading multiple groups of time series data of characteristic values of the environmental monitoring indexes within preset time granularity, predicting based on the mapping relation set of the environmental monitoring indexes, and generating multiple groups of index abnormal time;
and when the abnormal time of the multiple groups of indexes meets an abnormal time threshold, generating mine environment health identification information.
In a second aspect of the present application, a dynamic mine environment monitoring and management system based on data fusion is provided, the system includes:
the regional hierarchical tree generation module is used for carrying out regional classification on the mine to be monitored to generate a mine regional hierarchical tree;
the monitoring index obtaining module is used for traversing the mine area hierarchical tree and obtaining 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 multiple groups of environment monitoring indexes and acquiring multiple 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 multiple groups of environment monitoring index characteristic values meet the multiple groups of environment monitoring index expected values or not;
the mapping relation set generating module is used for carrying out data fusion analysis on the multiple groups of environmental monitoring indexes when the multiple groups of environmental monitoring index characteristic values do not meet the multiple groups of environmental monitoring index expected values to generate an environmental monitoring index mapping relation set;
the abnormal time generation module is used for loading multiple groups of time sequence data of the characteristic values of the environmental monitoring indexes within a preset time granularity, predicting the time sequence data based on the mapping relation set of the environmental monitoring indexes and generating multiple groups of index abnormal time;
and the identification information generation module is used for generating mine environment health identification information when the abnormal time of the multiple groups of indexes meets an abnormal time threshold value.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the method comprises the steps of carrying out regional classification according to the surrounding environment of a mine to be monitored to obtain a mine regional classification tree reflecting the mine regional classification condition, acquiring environmental monitoring indexes and corresponding expected values of each region in the mine regional classification tree to obtain multiple groups of environmental monitoring indexes and multiple groups of environmental monitoring index expected values, wherein the multiple groups of environmental monitoring indexes correspond to the multiple groups of environmental monitoring index expected values one by one, searching the multiple groups of environmental monitoring indexes one by one, acquiring multiple groups of environmental monitoring index characteristic values by utilizing an environmental monitoring sensor array, further judging whether the multiple groups of environmental monitoring index characteristic values meet the multiple groups of environmental monitoring index expected values, carrying out data fusion analysis on the multiple groups of environmental monitoring indexes when the multiple groups of environmental monitoring index characteristic values do not meet the multiple groups of environmental monitoring index expected values to generate an environmental monitoring index mapping relation set, loading multiple groups of environmental monitoring index characteristic value time sequence data in preset time granularity, predicting on the basis of the environmental monitoring index mapping relation set to obtain multiple groups of index abnormal time, and then indicating that the abnormal environment is abnormal when the multiple groups of index abnormal time meets abnormal time threshold values, generating abnormal environment health identification information. The intelligent dynamic monitoring of the mine environment is achieved, data are fused, the mine environment change condition is comprehensively analyzed, and the timeliness and accuracy of monitoring management are improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a dynamic monitoring and management method for mine environment based on data fusion provided by an embodiment of the application;
fig. 2 is a schematic flow chart of generating a mine area hierarchical tree in a dynamic monitoring and management method based on a data fusion mine environment according to an embodiment of the present application;
fig. 3 is a schematic flow chart illustrating setting of multiple sets of environmental monitoring index expected values in a dynamic mine environment monitoring and management method based on data fusion according to an embodiment of the present 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.
Description of the reference numerals: the system comprises a regional hierarchical tree generation module 11, a monitoring index acquisition module 12, an index characteristic value acquisition module 13, an index characteristic value judgment module 14, a mapping relation set generation module 15, an abnormal time generation module 16 and an identification information generation module 17.
Detailed Description
The application provides a dynamic monitoring and management method based on data fusion mine environment, which is used for solving the technical problems that in the prior art, the mine environment monitoring accuracy is low, and the abnormal mine environment cannot be fed back in time.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the accompanying drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Moreover, 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 explicitly listed, but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
Example one
As shown in fig. 1, the present application provides a dynamic monitoring and management method for mine environment based on data fusion, where the method includes:
step S100: carrying out regional classification on a mine to be monitored to generate a mine regional classification tree;
further, as shown in fig. 2, the mine to be monitored is classified in regions to generate a mine region classification tree, and step S100 in the embodiment of the present application further includes:
step S110: generating a related 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 hierarchical subtrees;
step S130: and transversely fusing the plurality of regional hierarchical subtrees to generate the mine regional hierarchical tree.
Specifically, the mine to be monitored is any mine that needs to dynamically monitor the environment of the mine. And 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 mountain range of the mine. The plurality of regional grading subtrees are obtained by dividing the associated region set step by step according to the administrative membership, and each regional grading subtree corresponds to one region. The mine region hierarchical region tree transversely fuses a plurality of region hierarchical subtrees, and sets the hierarchical subtrees with the same region level at the same layer, so that the associated regions are clearly described in a hierarchical manner.
Specifically, according to the geographic positioning position of the mine to be monitored, the central region of the mine is used as a positioning point, the mountain range trend of the mine is used as a basis, the region related to the mountain range is collected, the associated region set is obtained, and then according to the administrative membership of each region, the urban region is divided into city regions from provinces, the city regions are further divided into regions and counties, and further the city regions are divided from the counties to towns and towns, so that a plurality of grading regions are obtained. And then, obtaining a plurality of regional hierarchical sub-trees according to the division mode of the plurality of hierarchical regions of the associated region set. Each regional hierarchical sub-tree corresponds to one region. And further, transversely fusing the plurality of regional hierarchical subtrees to obtain the mine regional hierarchical tree. The technical effects that clear and hierarchical structural description is carried out on the mine area to be monitored, and a clear monitoring object is provided for follow-up dynamic monitoring management are achieved.
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, traversing the mine area hierarchical tree to obtain multiple sets of environmental monitoring indexes and multiple sets of environmental monitoring index expected values, where the multiple sets of environmental monitoring indexes correspond to the multiple sets of environmental monitoring index expected values one to one, 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 area hierarchical tree according to the environment monitoring initial index set to analyze the cleaning degree, and generating a plurality of groups of index cleaning degree scores;
step 230: traversing the multiple groups of index cleanliness scores, screening out the environment monitoring initial index set meeting the cleanliness score threshold, and acquiring the multiple groups of environment monitoring indexes according to the remaining environment monitoring initial index set;
step S240: and traversing the multiple groups of environment monitoring indexes, and setting the expected values of the multiple groups of environment monitoring indexes.
Further, traversing the mine area hierarchical tree according to the initial index set for environmental monitoring to perform cleaning degree analysis, and generating a plurality of sets of index cleaning degree scores, in the embodiment of the present application, step S220 further includes:
step S221: traversing the mine area hierarchical tree to generate a mine environment state characteristic matrix;
step S222: according to the mine environment state feature matrix, collecting target label vectors by taking the environment monitoring initial index set as a screening condition;
step S223: acquiring an abnormal frequency label and an abnormal duration label according to the target label vector;
step S224: obtaining a data cleaning degree evaluation formula:
wherein,
the ith mine area was characterized,
a target label vector characterizing the jth index of the ith mine area,
the abnormal frequency label of the target label vector of the jth index of the ith mine area is characterized,
the abnormal duration of the target label vector of the jth index representing the ith mine area, and alpha and beta represent
And
and is greater than 0;
step S225: and traversing the abnormal frequency label and the abnormal duration label according to the data cleanliness evaluation formula to generate the multiple groups of index cleanliness scores.
Specifically, the multiple sets of environmental monitoring indexes refer to multiple sets of monitoring indexes for evaluating mine environmental states in a region, and each set of environmental monitoring indexes corresponds to one region classification subtree in a mine region classification tree. The multiple groups of environment monitoring index expected values are parameter values corresponding to each monitoring index when the mine environment set according to the management requirement meets the requirement, and the multiple groups of environment monitoring index expected values correspond to the multiple groups of environment monitoring indexes one to one. The environment index monitoring initial index set is set according to local mine environment management standards and used for monitoring the whole mine environment, and comprises the following steps: rainfall, underground water level, underground water temperature, rock-soil moisture content and the like. The multiple groups of index cleaning degree scores are scoring results obtained after environment states of different regions of the mine region grading tree are evaluated one by one, and the mine environment states of the different regions are reflected. The washing degree score threshold is a preset minimum washing degree score value capable of meeting the environmental condition requirement, and is set by a worker, and is not limited herein.
Specifically, the mine environment state feature matrix is a matrix describing the features of the environment state of each region in the mine region hierarchical tree. And the target label vector is obtained after the actual state acquisition of the hydrological, soil and geological changes of the mine is carried out according to the mine environment state matrix, and the actual state values of all states of the mine are reflected. The abnormal frequency label is a label describing the number of times of occurrence in unit time of an abnormal state. The abnormal duration label is a label describing the time length of the abnormal state. The data cleaning degree 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 duration label.
Specifically, a mine environment state feature matrix is obtained by evaluating the mine environment state of each region in the mine region classification tree, and then state collection is performed on the feature matrix from three aspects of hydrology, soil and geology by taking the monitoring indexes in the environment monitoring initial index set as screening conditions, so that the target tag vector is obtained. And further, deep mining and analysis statistics are carried out on the target label vector to obtain the abnormal frequency label and the abnormal duration label, the abnormal frequency and abnormal time of different indexes in each region of the mine are counted, and basic data are provided for the follow-up quantitative calculation of index screening. And quantitatively evaluating the abnormal degrees of different indexes in different mine areas according to the data cleanliness evaluation formula to obtain multiple groups of index cleanliness scores.
Specifically, the cleaning degree scores of the multiple groups of indexes are compared and judged with the cleaning degree score threshold, the environment monitoring initial index set with the score higher than the cleaning degree score threshold is removed, namely, the indexes with better states are removed, and the indexes with higher abnormal degrees and urgent need to be processed are left, so that the aims of efficiently utilizing resources and reducing analysis data are fulfilled. And searching the plurality of groups of environmental monitoring indexes one by one to obtain expected corresponding plurality of groups of environmental monitoring index expected values. The technical effects of quantitatively screening dynamically monitored indexes, ensuring the reliability of the indexes, improving the monitoring management efficiency and maximally utilizing management resources are achieved.
Further, according to the mine environment state feature matrix, the environment monitoring initial index set is used as a screening condition to collect target tag vectors, and step S222 in 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, collecting a first sub-label vector by taking the hydrologic monitoring initial index set as a first screening condition;
step S2223: according to the mine environment state feature matrix, collecting a second sub-label vector by taking the soil monitoring initial index set as a second screening condition;
step S2224: according to the mine environment state feature matrix, collecting a third sub-tag vector by taking the geological change monitoring initial index set as a third screening condition;
step S2225: adding the first, second, and third sub-label vectors into the target label vector.
Specifically, three dimensions of hydrology, soil and geology are extracted from the environment monitoring initial index set, and the hydrology monitoring initial index set, the soil monitoring initial index set and the geological change monitoring initial index set are obtained. The hydrologic monitoring initial index set is an index set for monitoring the water quality of mine underground water and comprises indexes such as water temperature, chromaticity, turbidity, visible substances, pH value, total hardness, metal element content and the like. The soil monitoring initial index set is an index set for monitoring the state of mine soil, and comprises indexes such as PH value, cadmium content, mercury content, arsenic content and lead content. The geological change monitoring initial index set is an index set for monitoring the geological change condition of a mine, and comprises indexes such as a landform structure, a permeability coefficient and a dispersion coefficient.
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. And the first sub-label vector is obtained by monitoring the hydrological conditions of each mine area. And according to the mine environment state feature matrix, taking the soil monitoring initial index set as a second screening condition, and screening and extracting the state feature matrix to obtain the second sub-label vector. And the second sub-label vector is obtained by monitoring the soil condition of each mine area. And according to the mine environment state feature matrix, taking the geological change monitoring initial index set as a third screening condition, and screening and extracting the state feature matrix to obtain a third sub-tag vector. And the third sub-tag vector is obtained by monitoring the geological change condition of each mine area. And summarizing according to the first sub-label vector, the second sub-label vector and the third sub-label vector, and adding the summarized sub-label vectors into the target label vector, so as to objectively describe the environmental state condition of the mine area.
Step S300: traversing the multiple groups of environment monitoring indexes, and acquiring multiple groups of environment monitoring index characteristic values through an environment monitoring sensor array;
step S400: judging whether the characteristic values of the multiple groups of environmental monitoring indexes meet the expected values of the multiple groups of environmental monitoring indexes or not;
specifically, the environment monitoring sensor array is formed by arranging sensors for monitoring the environment in the mine area in real time according to different areas. And the multiple groups of environment monitoring index characteristic values are used for monitoring targets according to the multiple groups of environment monitoring indexes, and acquiring environment information by using the environment monitoring sensor array to obtain regional environment index characteristic values corresponding to the monitoring indexes. And comparing the plurality of groups of environmental monitoring index expected values with the plurality of groups of environmental monitoring index characteristic values to judge whether the requirements are met. The mine environment target acquisition is achieved, and the technical effect of providing analysis data for subsequent analysis of environment change conditions is achieved.
Step S500: when the characteristic values of the multiple groups of environmental monitoring indexes do not meet the expected values of the multiple groups of environmental monitoring indexes, performing data fusion analysis on the multiple groups of environmental monitoring indexes to generate an environmental monitoring index mapping relation set;
further, when the characteristic values of the multiple groups of environmental monitoring indicators do not satisfy the expected values of the multiple groups of environmental monitoring indicators, the multiple groups of environmental monitoring indicators are subjected to data fusion analysis to generate an environmental monitoring indicator mapping relationship set, in which step S500 of the embodiment of the present application further includes:
step S510: traversing the multiple groups of environmental monitoring indexes to obtain multiple index sets of the bottom mine area;
step S520: traversing the index sets, and collecting index abnormity monitoring record data;
step S530: obtaining a confidence evaluation formula:
wherein,
the kth indicator characterizing the l-th grade of the underlying mine area,
the (k + d) th index of the l grade of any mine area is represented,
characterization of
The number of records that occur alone is,
characterization of
The number of records that occur alone is,
characterization of
、
The number of co-occurring recordings,
in the l-th grade
And
the degree of support of (c);
step S540: traversing the index abnormity monitoring record data according to the confidence evaluation formula, and obtaining a plurality of confidence evaluation results;
step S550: comparing the confidence evaluation results with confidence threshold values, and screening multiple groups of associated index sets, wherein the multiple groups of associated index sets correspond to the multiple index sets one by one;
step S560: constructing a plurality of same-level mapping relations according to the plurality of groups of associated index sets;
step S570: establishing a multi-level mapping relation based on the mine area hierarchical tree according to the multiple groups of association index sets and the multiple same-level mapping relations;
step S580: adding the plurality of same-level mapping relationships and the multi-level mapping relationship into the set of environmental monitoring indicator mapping relationships.
Specifically, when the characteristic values of the multiple groups of environmental monitoring indexes do not meet the expected values of the multiple groups of environmental monitoring indexes, it is indicated that the regional environmental state at the moment can not meet the requirements, and at the moment, data fusion analysis is performed on the multiple groups of environmental monitoring indexes, so that the regional environment is subjected to overall analysis. The environment monitoring index mapping relation set is a relation set which describes the relation between the monitoring indexes from two angles of the mapping relation between the same-level indexes and the mapping relation between the multi-level regional indexes.
Specifically, the plurality of index sets are index sets for evaluating an environmental state of a bottom layer mine area, which is an area located at a bottom layer in the mine area hierarchical tree. The index abnormality detection recording data is data for acquiring index data of the index sets one by one and recording abnormality detection conditions in the index data. The confidence evaluation formula is a formula for quantitatively evaluating the reliability of the association between the indexes. And the confidence evaluation results are obtained by analyzing and calculating the index abnormality detection record data according to the confidence evaluation formula, so that the reliable evaluation result of the correlation degree between any two indexes is obtained. The confidence threshold is the lowest support value at which the confidence meets the requirement. The multiple groups of related index sets are index sets with index association degrees meeting requirements in the sets. The multiple same-level mapping relations refer to mapping relations among multiple indexes at the same association level. The multi-level mapping relation is obtained by fusing monitoring data of a low-level area to obtain an index mapping relation of a high-level area according to the area classification condition in the mine area classification tree and a plurality of groups of associated index sets and a plurality of equal-level mapping relations.
Specifically, a plurality of indexes in the bottommost mine area are collected to obtain a plurality of index sets, then index abnormality monitoring recorded data are obtained, quantitative analysis is carried out on the index abnormality monitoring recorded data by using a confidence coefficient evaluation formula, the index sets are quantitatively calculated one by one, the times of independent appearance and appearance of the indexes together when abnormality occurs are analyzed, and when the frequency of appearance of the indexes together is higher, the degree of association between the two indexes is higher. And screening out multiple groups of associated index sets meeting the confidence coefficient threshold value by judging the confidence coefficient evaluation results and the confidence coefficient threshold values.
Specifically, the same-level mapping relationship analysis is performed on the multiple groups of correlation index sets to obtain multiple same-level mapping relationships. For example, when the water quality index of the mine area is analyzed, when the turbidity degree of water is higher, the water quality index is related to a plurality of water quality indexes, including indexes such as chromaticity, metal content, visible objects and pH value. The turbidity degree and the plurality of water quality indexes all have correlation relations, so that the same-level mapping relation is constructed between the turbidity degree and the plurality of water quality indexes. And according to the multiple groups of association indexes, collecting the multiple same-level mapping relations, and constructing a multilayer mapping relation by taking the region level in the mine region hierarchical tree as a basis. Illustratively, the monitoring data of the local level of the downtown is obtained by fusing a plurality of county monitoring data within the scope of the downtown, so that a plurality of same-level mapping relationships within the county are fused and summarized to the downtown, and then the fused data of the local level of the downtown is secondarily fused according to the fused data of different downtown to obtain the monitoring data of the local level of the downtown, namely the monitoring data of the local level of the downtown. The technical effects of comprehensive fusion of data in the mine area and clear level processing are achieved.
Further, the step S560 in this embodiment of the application further includes, according to the multiple groups of association index sets, constructing multiple same-level mapping relationships:
step S561: traversing any group of the multiple groups of correlation index sets to obtain representative correlation indexes and common correlation indexes;
step S562: based on the index abnormality monitoring record data, taking the representative associated index as input data and the common associated index as output data, training a common associated index evaluation model;
step S563: and constructing the multiple same-level mapping relations based on the common correlation index evaluation model.
Specifically, any one group of correlation index sets in a plurality of groups of correlation index sets are selected in a traversing mode, and the index sets are analyzed to obtain the representative correlation index and the common correlation index. Wherein, the representative related indexes are indexes which have a relationship with a plurality of indexes in the index set. The common associated index is an index with a single association relation in the index set. And according to the index abnormality monitoring record data, taking the representative correlation index as input data, training the common correlation index evaluation model by using the common correlation index, training the model until the model converges, judging whether the accuracy of the model output meets the requirement, if so, obtaining the trained common correlation index evaluation model, and if not, obtaining more data to carry out incremental learning on the model.
Specifically, the mapping relationship between the indexes of the same level in the index set is intelligently analyzed according to the common associated index evaluation model, so that the mapping relationships of the multiple same levels are obtained. The technical effects of quickly analyzing the mapping relation between indexes and improving the analysis accuracy and efficiency are achieved.
Step S600: loading multiple groups of time series data of characteristic values of the environmental monitoring indexes within preset time granularity, predicting based on the mapping relation set of the environmental monitoring indexes, and generating multiple groups of index abnormal time;
further, the loading multiple groups of time series data of the characteristic values of the environmental monitoring indexes within the preset time granularity, performing prediction based on the mapping relation set of the environmental monitoring indexes, and generating multiple groups of index abnormal time, in step S600 in the embodiment of the present application, further includes:
step S610: carrying out 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 multiple groups of time sequence data of the characteristic values of the representative correlation indexes from the multiple groups of time sequence data of the characteristic values of the environmental monitoring indexes according to the representative correlation indexes;
step S630: traversing the time series data of the characteristic values of the plurality of groups of representative correlation indexes, and constructing a plurality of groups of representative correlation index change curves;
step 640: and generating the multiple groups of index abnormal time based on the multiple groups of representative correlation index change curves and the multiple groups of representative correlation 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, which is not limited herein. The time sequence data of the multiple groups of environmental monitoring index characteristic values are obtained by sequencing the collected multiple groups of environmental monitoring index characteristic values according to a time sequence in a preset time granularity. And calculating the abnormal time of the common index to obtain the multiple groups of index abnormal time after obtaining the change trend of the representative associated index and the time reaching each threshold value according to the index mapping relation in the environment monitoring index mapping relation set. The multiple groups of index abnormal time refers to a time period when multiple groups of indexes are abnormal.
Specifically, according to the expected value size corresponding to each index in the multiple groups of environment monitoring index expected values, the expected value corresponding to the multiple representative associated indexes meeting the environment requirement is determined, so that the multiple groups of representative associated index expected values are obtained. Preferably, the expected values of the multiple common associated indexes associated with the multiple representative associated indexes are corresponding to the expected values of the multiple sets of environmental monitoring indexes. Further, a plurality of expected values corresponding to each representative correlation index can be obtained from expected values of a plurality of general correlation indexes corresponding to each representative correlation index among the plurality of representative correlation indexes and from a correlation between the representative correlation index and the general indexes, wherein each expected value corresponds to one general correlation index.
Specifically, the time series data of the characteristic values of the multiple representative associated indexes are obtained from the time series data of the characteristic values of the multiple representative associated indexes, wherein the time series data of the characteristic values of the multiple representative associated indexes are taken as an extraction basis. And (3) constructing a plurality of groups of representative associated index change curves by taking time as an abscissa and time series data in the plurality of groups of representative associated index characteristic value time series data as an ordinate. The multiple groups of representative associated index change curves respectively reflect the change trend conditions of the representative associated indexes in the preset time granularity, including change values and change time. And then, according to the variation trend of the plurality of groups of representative related index variation curves, with the plurality of groups of representative related index expected values as targets, predicting the time for the representative indexes to reach the expected values, and accordingly obtaining the abnormal time of each common index correspondingly. Therefore, the technical effects of predicting the index abnormal time and providing reliable judgment basis for the follow-up analysis of the abnormal conditions of the mine environment are achieved.
Step S700: and when the abnormal time of the multiple groups of indexes meets an abnormal time threshold, generating mine environment health identification information.
Specifically, the abnormal time threshold is a time interval in which a preset index is abnormal but does not affect the mine environment. And 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 does not meet the abnormal time threshold, an environment abnormal identifier is generated and sent to a manager for timely adjustment. The technical effects of dynamically monitoring the mine environment, shortening the monitoring feedback time, and improving the feedback efficiency and monitoring management quality are achieved.
In summary, the embodiment of the present application has at least the following technical effects:
1. according to the method, the mine to be monitored is subjected to regional classification according to the geographic position of the mine, the purpose of providing a monitoring target for subsequent environmental monitoring management of the mine is achieved according to a formed hierarchical mine regional classification tree, then multiple groups of environmental monitoring indexes are used as data acquisition targets, multiple groups of environmental monitoring index characteristic values are acquired through an environmental monitoring sensor array distributed in the mine region, the purpose of providing analysis data for subsequent analysis of the mine environmental state is achieved, further, whether the environmental states corresponding to the multiple groups of environmental monitoring index characteristic values meet requirements or not is judged according to the multiple groups of environmental monitoring index expected values serving as judgment bases, when the requirements are not met, the monitoring indexes are subjected to same-grade and multi-layer-grade data fusion, an environmental monitoring index mapping relation is built, the environmental data within preset time are acquired and are arranged according to a time sequence, index development trend prediction is carried out according to the mapping relation, multiple groups of abnormal time is obtained, the purpose of analyzing the multiple groups of abnormal indexes is achieved, and further, when the multiple groups of abnormal time meet abnormal time threshold values, mine environmental health identification information is obtained. The technical effects of carrying out data fusion on the mine environment monitoring data, comprehensively and objectively carrying out dynamic monitoring management on the mine environment and improving the management efficiency are achieved.
2. According to the method and the device for monitoring the mine regional hierarchical tree, the mountain range trend of the mine is analyzed according to the geographical positioning information of the mine to be monitored, the region related to the mine is obtained, the membership of each region is distributed according to administrative division conditions, a plurality of regional hierarchical subtrees are obtained, and then the plurality of regional hierarchical subtrees are transversely fused to obtain the mine regional hierarchical tree. The technical effects of clear hierarchical division of the mine area and improvement of accuracy of follow-up management are achieved.
Example two
Based on the same inventive concept as the dynamic monitoring and management method based on the data fusion mine environment in the foregoing embodiment, as shown in fig. 4, the present application provides a dynamic monitoring and management system based on the data fusion mine environment, and the system and method in the embodiment of the present application are based on the same inventive concept. Wherein the system comprises:
the regional hierarchical tree generation module 11 is used for performing regional classification on the mine to be monitored to generate a mine regional hierarchical tree;
a monitoring index obtaining module 12, where the monitoring index obtaining module 12 is configured to traverse the mine area hierarchical tree to obtain multiple groups of environmental monitoring indexes and multiple groups of environmental monitoring index expected values, where the multiple groups of environmental monitoring indexes correspond to the multiple groups of environmental monitoring index expected values one by one;
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 characteristic value judging module 14, where the index characteristic value judging module 14 is configured to judge whether the multiple sets of environmental monitoring index characteristic values meet the multiple sets of environmental monitoring index expected values;
a mapping relationship set generating module 15, where the mapping relationship set generating module 15 is configured to perform data fusion analysis on the multiple groups of environmental monitoring indicators to generate an environmental monitoring indicator mapping relationship set when the multiple groups of environmental monitoring indicator characteristic values do not satisfy the multiple groups of environmental monitoring indicator expected values;
the abnormal time generation module 16 is configured to load multiple sets of time series data of the environmental monitoring index feature values within a preset time granularity, perform prediction based on the environmental monitoring index mapping relation set, and generate multiple sets of index abnormal times;
and the identification information generating module 17, wherein the identification information generating module 17 is configured to generate mine environment health identification information when the multiple sets of index abnormal time meet the abnormal time threshold.
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 sub-tree generating unit is used for carrying out longitudinal membership distribution on the associated region set to generate a plurality of regional hierarchical sub-trees;
and the transverse fusion unit is used for transversely fusing the plurality of regional hierarchical subtrees to generate the mine regional hierarchical tree.
Further, the system further comprises:
an initial index set obtaining unit, configured to obtain an environment monitoring initial index set;
the washing degree score generation unit is used for traversing the mine area hierarchical tree according to the environment monitoring initial index set to analyze the washing degree and generate a plurality of groups of index washing degree scores;
an initial index set screening unit, configured to traverse the multiple sets of index cleanliness scores, screen out the environmental monitoring initial index set that meets a cleanliness score threshold, and obtain the multiple sets of environmental monitoring indexes according to the remaining environmental monitoring initial index sets;
and the index expected value setting unit is used for traversing the multiple groups of environment monitoring indexes and setting the multiple groups of environment monitoring index expected values.
Further, the system further comprises:
the characteristic matrix generating unit is used for traversing the mine area hierarchical tree and generating a mine environment state characteristic matrix;
the tag vector acquisition unit is used for acquiring a target tag vector by taking the environment monitoring initial index set as a screening condition according to the mine environment state feature matrix;
an abnormal duration label obtaining unit, configured to obtain an abnormal frequency label and an abnormal duration label according to the target label vector;
a cleanliness evaluation formula obtaining unit for obtaining a data cleanliness evaluation formula:
wherein,
the ith mine area was characterized,
a target label vector characterizing the jth index of the ith mine area,
an abnormal frequency tag of a target tag vector of a jth index characterizing an ith mine area,
the abnormal duration of the target label vector of the jth index representing the ith mine area, and alpha and beta represent
And
and is greater than 0;
and the multiple groups of cleaning degree score generating units are used for traversing the abnormal frequency labels and the abnormal time length labels according to the data cleaning degree evaluation formula and generating the multiple groups of index cleaning degree scores.
Further, the system further comprises:
a monitoring initial index set obtaining unit, configured to obtain a hydrologic monitoring initial index set, a soil monitoring initial index set and a geological change monitoring initial index set according to the environmental 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;
a target label vector adding unit to add the first sub-label vector, the second sub-label vector, and the third sub-label vector into the target label vector.
Further, the system further comprises:
a plurality of index set obtaining units, configured to traverse the plurality of sets of environmental monitoring indexes to obtain a plurality of index sets of a 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 indicator characterizing the l-th grade of the underlying mine area,
the (k + d) th index of the l grade of any mine area is represented,
characterization of
The number of recordings that occur individually,
characterization of
The number of recordings that occur individually,
characterization of
、
The number of co-occurring recordings,
in the l-th grade
And
the degree of support of (c);
the confidence evaluation result solving units are used for traversing the index abnormity monitoring record data according to the confidence evaluation formula and solving a plurality of confidence evaluation results;
the relevant index set screening unit is used for comparing the confidence evaluation results with confidence threshold values and screening a plurality of groups of relevant index sets, wherein the plurality of groups of relevant index sets correspond to the index sets one by one;
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 area hierarchical tree according to the multiple groups of association index sets and the multiple same-level mapping relations;
a mapping relationship set adding unit for adding the plurality of same-level mapping relationships and the multi-level mapping relationships into the environmental monitoring index mapping relationship set.
Further, the system further comprises:
the correlation index obtaining unit is used for traversing any group of the multiple groups of correlation index sets to obtain representative correlation indexes and common correlation indexes;
the index evaluation model training unit is used for training a common associated index evaluation model by taking the representative associated index as input data and the common associated index as output data based on the index abnormality monitoring record data;
and the mapping relation construction unit is used for constructing the multiple 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 the expected values of the plurality of representative associated indexes according to the plurality of groups of environmental 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 of the characteristic values of the representative associated indexes from the plurality of groups of time sequence data of the characteristic values of the environmental monitoring indexes according to the representative associated indexes;
the index change curve construction unit is used for traversing the time series data of the characteristic values of the plurality of groups of representative associated indexes and constructing a plurality of groups of representative associated index change curves;
an abnormal time generation unit configured to generate the plurality of sets of index abnormal times based on the plurality of sets of representative associated index variation curves and the plurality of sets of representative associated index expected values.
It should be noted that the order of the above embodiments of the present application is only for description, and does not represent the merits of the embodiments. And that specific embodiments have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may 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 may also be possible or may be advantageous.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.
The specification and figures are merely exemplary of the application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the application. It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and its equivalent technology, it is intended that the present application include such modifications and variations.
Claims (9)
1. A mine environment dynamic monitoring management method based on data fusion is characterized by comprising the following steps:
carrying out regional classification on a mine to be monitored to generate a mine regional classification tree;
traversing the mine area grading 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 multiple groups of environment monitoring indexes, and acquiring characteristic values of the multiple groups of environment monitoring indexes through an environment monitoring sensor array;
judging whether the characteristic values of the multiple groups of environmental monitoring indexes meet the expected values of the multiple groups of environmental monitoring indexes or not;
when the characteristic values of the multiple groups of environmental monitoring indexes do not meet the expected values of the multiple groups of environmental monitoring indexes, performing data fusion analysis on the multiple groups of environmental monitoring indexes to generate an environmental monitoring index mapping relation set;
loading multiple groups of time sequence data of characteristic values of the environmental monitoring indexes within preset time granularity, predicting based on the mapping relation set of the environmental monitoring indexes, and generating multiple groups of index abnormal time;
and when the abnormal time of the multiple groups of indexes meets an abnormal time threshold, generating mine environment health identification information.
2. The dynamic mine environment monitoring and management method based on data fusion as claimed in 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 a related 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 hierarchical subtrees;
and transversely fusing the plurality of regional hierarchical subtrees to generate the mine regional hierarchical tree.
3. The dynamic mine environment monitoring and management method based on data fusion of claim 1, wherein the step of traversing the hierarchical tree of the mine area 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 correspond to one another one by one, comprises the steps of:
acquiring an environment monitoring initial index set;
traversing the mine area hierarchical tree according to the environment monitoring initial index set to analyze the cleaning degree, and generating a plurality of groups of index cleaning degree scores;
traversing the multiple groups of index cleanliness scores, screening out the environment monitoring initial index sets meeting the cleanliness score threshold values, and acquiring the multiple groups of environment monitoring indexes according to the remaining environment monitoring initial index sets;
and traversing the multiple groups of environment monitoring indexes, and setting the expected values of the multiple groups of environment monitoring indexes.
4. The dynamic mine environment monitoring and management method based on data fusion as claimed in claim 3, wherein the traversing the hierarchical tree of mine areas according to the initial index set of environment monitoring for cleanliness analysis to generate a plurality of sets of index cleanliness scores comprises:
traversing the mine area hierarchical tree to generate a mine environment state characteristic matrix;
according to the mine environment state feature matrix, collecting target label vectors by taking the environment monitoring initial index set as a screening condition;
acquiring an abnormal frequency label and an abnormal duration label according to the target label vector;
acquiring a data cleaning degree evaluation formula:
wherein,
the ith mine area is characterized by the characteristics of,
a target label vector characterizing the jth index of the ith mine area,
the abnormal frequency label of the target label vector of the jth index of the ith mine area is characterized,
the abnormal duration of the target label vector of the jth index representing the ith mine area, and alpha and beta represent
And
and is greater than 0;
and traversing the abnormal frequency label and the abnormal duration label according to the data cleanliness evaluation formula to generate the multiple groups of index cleanliness scores.
5. The dynamic mine environment monitoring and management method based on data fusion as claimed in claim 4, wherein the collecting target label vectors by using the initial environmental monitoring index set as a screening condition according to the mine environment state feature matrix comprises:
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, collecting a first sub-label vector by taking the hydrologic monitoring initial index set as a first screening condition;
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-tag vector;
adding the first, second, and third sub-label vectors into the target label vector.
6. The dynamic monitoring and management method for mine environment based on data fusion of claim 5, wherein when the characteristic values of the multiple sets of environmental monitoring indicators do not meet the expected values of the multiple sets of environmental monitoring indicators, performing data fusion analysis on the multiple sets of environmental monitoring indicators to generate a mapping set of environmental monitoring indicators, includes:
traversing the multiple groups of environment monitoring indexes to obtain multiple index sets of the bottom layer mine area;
traversing the index sets, and collecting the monitoring record data of index abnormity;
obtaining a confidence evaluation formula:
wherein,
the kth index characterizing the l-th grade of the underlying mine area,
the (k + d) th index of the l level of any mine area is represented,
characterization of
The number of records that occur alone is,
characterization of
The number of recordings that occur individually,
characterization of
、
The number of co-occurring recordings,
in the l-th grade
And
the degree of support of (c);
traversing the index anomaly monitoring record data according to the confidence coefficient evaluation formula, and obtaining a plurality of confidence coefficient evaluation results;
comparing the confidence evaluation results with confidence threshold values, and screening multiple groups of associated index sets, wherein the multiple groups of associated index sets correspond to the multiple index sets one by one;
constructing a plurality of same-level mapping relations according to the plurality of groups of associated index sets;
establishing a multi-level mapping relation based on the mine area hierarchical tree according to the multiple groups of association index sets and the multiple same-level mapping relations;
adding the plurality of same-level mapping relationships and the multi-level mapping relationship into the set of environmental monitoring indicator mapping relationships.
7. The dynamic monitoring and management method based on the data fusion mine environment as claimed in claim 6, wherein the constructing a plurality of same-level mapping relations according to the plurality of groups of association index sets comprises:
traversing any group of the multiple groups of associated index sets to obtain representative associated indexes and common associated indexes;
based on the index abnormality monitoring record data, taking the representative associated index as input data and the common associated index as output data, training a common associated index evaluation model;
and constructing the multiple same-level mapping relations based on the common correlation index evaluation model.
8. The dynamic mine environment monitoring and management method based on data fusion as claimed in claim 1, wherein the loading of multiple sets of time series data of characteristic values of environment monitoring indexes within a preset time granularity, the prediction based on the mapping relation set of environment monitoring indexes, and the generation of multiple sets of abnormal index times, comprises:
carrying out 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 multiple groups of time sequence data of the characteristic values of the representative correlation indexes from the multiple groups of time sequence data of the characteristic values of the environmental monitoring indexes according to the representative correlation indexes;
traversing the time series data of the characteristic values of the plurality of groups of representative correlation indexes, and constructing a plurality of groups of representative correlation index change curves;
and generating the multiple groups of index abnormal time based on the multiple groups of representative associated index change curves and the multiple groups of representative associated index expected values.
9. The utility model provides a mine environment dynamic monitoring management system based on data fusion which characterized in that includes:
the regional hierarchical tree generation module is used for carrying out regional classification on the mine to be monitored to generate a mine regional hierarchical tree;
the monitoring index obtaining module is used for traversing the mine area hierarchical tree and obtaining 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 multiple groups of environment monitoring index characteristic values meet the multiple groups of environment monitoring index expected values or not;
the mapping relation set generating module is used for carrying out data fusion analysis on the multiple groups of environmental monitoring indexes when the multiple groups of environmental monitoring index characteristic values do not meet the multiple groups of environmental monitoring index expected values to generate an environmental monitoring index mapping relation set;
the abnormal time generation module is used for loading multiple groups of time sequence data of the characteristic values of the environmental monitoring indexes within a preset time granularity, predicting based on the mapping relation set of the environmental monitoring indexes and generating multiple groups of index abnormal time;
and the identification information generation module is used for generating mine environment health identification information when the abnormal time of the multiple groups of indexes meets an abnormal time threshold value.
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