CN118134270B - Mine safety risk early warning method and system - Google Patents
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Abstract
The invention relates to a mine safety risk early warning method and a system, wherein the method comprises the following steps: collecting monitoring data of a mine operation site through a safety monitoring system; clustering the monitoring data, and dividing the monitoring data into a plurality of clusters; processing the monitoring data and the clusters, and determining the anomaly degree of each monitoring data; determining abnormal data with the abnormality degree larger than the cleaning threshold value based on the cleaning threshold value and the abnormality degree of each monitoring data, and acquiring the capacity of normal data except the abnormal data; predicting a first backup capacity of the safety monitoring system at the next preset time based on the abnormality degree of the monitoring data at the current time and the backup capacity of the safety monitoring system for storing normal data at the current time; if the capacity of the normal data at the current moment is smaller than the first backup capacity, the cleaning threshold value is increased while the safety early warning is sent to the area of the mine operation site. The invention can improve the stability of mine safety monitoring, and the accuracy and timeliness of early warning.
Description
Technical Field
The invention relates to the technical field of mine production safety, in particular to a mine safety risk early warning method, a system, electronic equipment and a non-transitory computer readable storage medium.
Background
At present, the mine safety production monitoring and early warning system can be used for finding out personnel illegal behaviors and field equipment abnormal conditions, alarming timely and pushing alarm information to a management center. The system realizes the automation and unattended control of the coal mine operation scene and promotes the digital transformation of the coal mine industry.
However, the capacity of the cloud backup data of the existing system is too low to cause unstable power supply of the monitoring equipment, and the standard data cleaning threshold is too low to cause cleaning of non-abnormal data, so that stability and monitoring effectiveness of the monitoring terminal are reduced, and accuracy and timeliness of mine safety risk early warning cannot be guaranteed.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a mine safety risk early warning method, a system, electronic equipment and a non-transitory computer readable storage medium, which can improve the stability of mine safety monitoring and the accuracy and timeliness of safety early warning.
The technical scheme for solving the technical problems is as follows:
the invention provides a mine safety risk early warning method, which comprises the following steps:
collecting monitoring data of a mine operation site through a safety monitoring system; the monitoring data is related to the safety risk of the mine operation site;
clustering the monitoring data, and dividing the monitoring data into a plurality of clusters;
processing the monitoring data and the clusters, and determining the abnormality degree of each monitoring data;
Determining abnormal data with the abnormality degree larger than the cleaning threshold value based on the cleaning threshold value and the abnormality degree of each monitoring data, and acquiring the capacity of normal data except the abnormal data;
Predicting a first backup capacity of the safety monitoring system at the next preset time based on the abnormality degree of the monitoring data at the current time and the backup capacity of the safety monitoring system for storing the normal data at the current time;
if the capacity of the normal data at the current moment is smaller than the first backup capacity, the cleaning threshold value is increased while safety early warning is sent to the area of the mine operation site.
Optionally, the processing the monitoring data and the clusters to determine the abnormality degree of each monitoring data includes:
determining a cluster center of each of the clusters; each cluster center corresponds to one monitoring data;
for each piece of monitoring data, determining a first distance from the monitoring data to the Euclidean distance of the corresponding cluster center, a second distance from the monitoring data to the minimum Euclidean distance of all cluster centers, and a third distance from the monitoring data to the maximum Euclidean distance of all cluster centers;
Determining an anomaly of the monitoring data based on the first distance, the second distance, and the third distance.
Optionally, the first distance is expressed as:
；
Wherein i is a number value, For an ith data point in a plurality of the monitored data,For the ith cluster center, i.e. data pointThe cluster center to which the cluster belongs.
Alternatively, the degree of abnormality is expressed as:
；
where i and j are numbered values, k is the total number of clusters, Is the degree of abnormality of the ith monitored data,Is a first distance from the first point of view,Is a second distance from the first distance to the second distance,Is a third distance.
Optionally, before the determining the anomaly data with the anomaly degree greater than the cleaning threshold value, the method further includes: determining a cleaning threshold for the monitored data, the cleaning threshold determined by:
Determining an average anomaly of a plurality of the monitoring data based on the anomaly of each of the monitoring data and a total number of the plurality of monitoring data;
Determining a corresponding anomaly standard deviation based on the anomalies of the plurality of monitored data and the average anomaly;
a first constant is obtained and the cleaning threshold is determined based on the first constant, the average anomaly degree, and the anomaly degree standard deviation.
Optionally, the cleaning threshold is expressed as:
；
Where T is the flush threshold, n is the total number of monitored data, Is a first constant which,Is the standard deviation of the degree of abnormality.
Optionally, the predicting the first backup capacity of the safety monitoring system at the next preset time includes:
The abnormality degree of the monitoring data at the current moment and the backup capacity of the safety monitoring system for storing the normal data at the current moment;
acquiring the system load condition of the safety monitoring system and the historical backup data quantity of the backup historical data;
and processing the abnormality degree, the backup capacity, the system load condition and the historical backup data amount of the monitoring data at the current moment based on a prediction function, and predicting to obtain the first backup capacity at the next preset moment.
Optionally, the first backup capacity is expressed as:
；
Wherein, Is the backup capacity at the next preset time t +1,And f (), which are the backup capacity, the anomaly degree of the monitored data, the system load condition and the historical backup data quantity at the current time t corresponding to the safety monitoring system, are prediction functions.
Optionally, the collecting monitoring data of the mine operation site includes:
Arranging acquisition equipment on the mine operation site, wherein the acquisition equipment comprises an equipment sensor, an environment monitor, a video monitor, a personnel locator and a data transmission memory;
Receiving initial monitoring data sent by the acquisition equipment;
preprocessing the initial monitoring data, and taking the preprocessed data as the monitoring data.
The invention also provides a mine safety risk early warning system, which comprises:
the data acquisition module is used for acquiring monitoring data of the mine operation site through the safety monitoring system; the monitoring data is related to the safety risk of the mine operation site;
the data clustering module is used for carrying out clustering processing on the monitoring data and dividing the monitoring data into a plurality of clusters;
the anomaly degree calculation module is used for processing the monitoring data and the clusters and determining the anomaly degree of each monitoring data;
The abnormality determining module is used for determining abnormal data with the abnormality degree larger than the cleaning threshold value based on the cleaning threshold value and the abnormality degree of each piece of monitoring data, and acquiring the capacity of normal data except the abnormal data;
The capacity calculation module is used for predicting the first backup capacity of the safety monitoring system at the next preset time based on the abnormality degree of the monitoring data at the current time and the backup capacity of the safety monitoring system at the current time for storing the normal data;
And the safety early warning module is used for increasing the cleaning threshold value while sending safety early warning to the area of the mine operation site if the capacity of the normal data at the current moment is smaller than the first backup capacity.
In addition, to achieve the above object, the present invention also proposes an electronic device including: a memory for storing a computer software program; and the processor is used for reading and executing the computer software program so as to realize the mine safety risk early warning method.
In addition, in order to achieve the above object, the present invention also proposes a non-transitory computer readable storage medium, in which a computer software program is stored, which when executed by a processor, implements a mine safety risk warning method as described above.
The beneficial effects of the invention are as follows:
(1) According to the invention, the backup capacity and the cleaning threshold value of the system are dynamically adjusted by comparing the capacity of normal data at the current moment with the predicted backup capacity of the system, and the backup system is intelligently managed according to the actual demand and the system load condition, so that the more reasonable cleaning threshold value can be obtained, the normal data is prevented from being cleaned, the proportion of the backup data is stabilized, the stability and the effectiveness of mine risk safety monitoring are improved, the timeliness and the accuracy of mine safety early warning are improved, the utilization efficiency of resources is also improved, and the waste of resources is reduced.
(2) By the real-time monitoring and early warning system, abnormal behaviors and equipment anomalies in mine operation can be found in time, the risk of accidents is effectively reduced, and the safety of the mine operation is improved.
(3) The invention can lighten the burden of manual monitoring, so that the monitoring is more comprehensive and timely, the early warning and response capability can improve the efficiency of mine production, and the production interruption and delay are avoided.
(4) The invention realizes automation and unattended operation control of mine operation scenes, promotes digital transformation of the mine industry and improves the informatization management level.
(5) The invention reduces the loss and the production stagnation cost caused by accidents by preventing the accidents and optimizing the production flow, improves the resource utilization efficiency and reduces the backup and storage cost.
In conclusion, the invention brings comprehensive benefits to mine enterprises, not only can improve the safety and the production efficiency, but also can reduce the cost and promote the digital transformation, and provides powerful support for sustainable development of the mine industry.
Drawings
FIG. 1 is a scene diagram of a mine safety risk early warning method provided by the invention;
FIG. 2 is a flow chart of a mining safety risk early warning method provided by the invention;
FIG. 3 is a schematic diagram of a mine safety risk early warning system according to the present invention;
Fig. 4 is a schematic hardware structure of one possible electronic device according to the present invention;
Fig. 5 is a schematic hardware structure of a possible computer readable storage medium according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In the description of the present invention, the term "for example" is used to mean "serving as an example, instance, or illustration. Any embodiment described as "for example" in this disclosure is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is presented to enable any person skilled in the art to make and use the invention. In the following description, details are set forth for purposes of explanation. It will be apparent to one of ordinary skill in the art that the present invention may be practiced without these specific details. In other instances, well-known structures and processes have not been described in detail so as not to obscure the description of the invention with unnecessary detail. Thus, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.
Referring to fig. 1, fig. 1 is a schematic diagram of a mine safety risk early warning method provided by the invention. As shown in fig. 1, the terminal and the server are connected through a network, for example, a wired or wireless network connection. The terminal may include, but is not limited to, mobile terminals such as mobile phones and tablets, and fixed terminals such as computers, inquiry machines and advertising machines, in which applications of various network platforms are installed. The server provides various business services for the user, including a service push server, a user recommendation server and the like.
It should be noted that, the scene diagram of the mine security risk early warning method shown in fig. 1 is only an example, and the terminal, the server and the application scenario described in the embodiment of the present invention are for more clearly describing the technical solution of the embodiment of the present invention, and do not generate any limitation on the technical solution provided by the embodiment of the present invention, and as a person of ordinary skill in the art can know that, with the evolution of the system and the appearance of a new service scenario, the technical solution provided by the embodiment of the present invention is also applicable to similar technical problems.
Wherein the terminal may be configured to:
collecting monitoring data of a mine operation site through a safety monitoring system; the monitoring data is related to the safety risk of the mine operation site;
clustering the monitoring data, and dividing the monitoring data into a plurality of clusters;
processing the monitoring data and the clusters, and determining the abnormality degree of each monitoring data;
Determining abnormal data with the abnormality degree larger than the cleaning threshold value based on the cleaning threshold value and the abnormality degree of each monitoring data, and acquiring the capacity of normal data except the abnormal data;
Predicting a first backup capacity of the safety monitoring system at the next preset time based on the abnormality degree of the monitoring data at the current time and the backup capacity of the safety monitoring system for storing the normal data at the current time;
if the capacity of the normal data at the current moment is smaller than the first backup capacity, the cleaning threshold value is increased while safety early warning is sent to the area of the mine operation site.
Referring to fig. 2, a flowchart of a mine safety risk early warning method of the present invention is provided, including the following steps:
Step 201, monitoring data of a mine operation site are collected through a safety monitoring system.
Wherein the monitoring data may relate to a safety risk at the mine operation site.
In some embodiments, step 201 may include:
Arranging acquisition equipment on the mine operation site, wherein the acquisition equipment comprises an equipment sensor, an environment monitor, a video monitor, a personnel locator and a data transmission memory;
Receiving initial monitoring data sent by the acquisition equipment;
preprocessing the initial monitoring data, and taking the preprocessed data as the monitoring data.
In particular implementations, device sensors may be mounted on critical devices for monitoring the operating conditions and operating parameters of the devices, such as temperature, pressure, vibration, etc. The environmental monitor can be placed around a mine operation scene for monitoring environmental parameters such as air quality, noise level, dust concentration, etc. The video monitor can be arranged at each corner of the mine operation site and used for monitoring the working condition of workers, the running state of equipment and the change of operation scenes in real time. The personnel locator can track the position and movement track of the workers and their safety states in real time through the locating device worn by the workers. The data transmission memory can be used for transmitting the collected monitoring data to a data center and storing and managing the data.
In some embodiments, the security monitoring system may receive raw monitoring data sent from various acquisition devices, including, for example, real-time data collected by sensors, images and video captured by video monitors, worker location information tracked by personnel positioners, and so forth.
In some embodiments, the received raw monitoring data may be pre-processed, including, for example, data cleansing, noise removal, and outliers removal; data correction, namely ensuring the accuracy and consistency of data; data integration, integrating data from different sources into a unified data format, and the like.
In some embodiments, the preprocessed data is considered as final monitoring data, which can reflect real-time status and conditions of the mine work site. The monitoring data may include a variety of information including equipment operating parameters, environmental indicators, worker position trajectories, video surveillance frames, and the like.
By the mode, the safety monitoring system can comprehensively and timely collect and record monitoring data of the mine operation site. Such data is critical to identify potential safety hazards, to improve operating efficiency, and to ensure worker safety.
Step 202, clustering the monitoring data, and dividing the monitoring data into a plurality of clusters.
In some embodiments, the collected monitoring data may beDividing the monitoring data into different clustersThe cluster center isK is the total number of clusters.
It will be appreciated that in the clustering process, each clusterAll have a cluster centerRepresenting the average or center point of all data points in the cluster, the cluster centerIs typically determined by calculating the average of all data points in the cluster.
By dividing the monitoring data into a plurality of clusters, the distribution situation and the characteristics of the data can be better understood, different data modes and behavior rules can be identified, potential abnormal conditions or differences among different clusters can be found, and therefore more accurate security risk early warning and management advice can be provided.
And 203, processing the monitoring data and the clusters, and determining the anomaly degree of each monitoring data.
In some embodiments, step 203 may comprise:
determining a cluster center of each of the clusters; each cluster center corresponds to one monitoring data;
for each piece of monitoring data, determining a first distance from the monitoring data to the Euclidean distance of the corresponding cluster center, a second distance from the monitoring data to the minimum Euclidean distance of all cluster centers, and a third distance from the monitoring data to the maximum Euclidean distance of all cluster centers;
Determining an anomaly of the monitoring data based on the first distance, the second distance, and the third distance.
In some embodiments, the first distance is expressed as:
；
Wherein i is a number value, For an ith data point in a plurality of the monitored data,For the ith cluster center, i.e. data pointThe cluster center to which the cluster belongs.
In some embodiments, the degree of anomaly is expressed as:
；
where i and j are numbered values, k is the total number of clusters, Is the degree of abnormality of the ith monitored data,Is a first distance from the first point of view,Is a second distance from the first distance to the second distance,Is a third distance.
And 204, determining abnormal data with the abnormality degree larger than the cleaning threshold value based on the cleaning threshold value and the abnormality degree of each piece of monitoring data, and acquiring the capacity of normal data except the abnormal data.
In a specific implementation of the present invention,Is a data point of the monitoring dataWith the cluster center to which it belongsEuclidean distance between them. Euclidean distance is a measure of the distance between two points and is mathematically defined as the linear distance between the two points, i.e., the shortest path length between the two points.
Is a data pointThe minimum of distances from the center of all clusters. In the formula, data points are foundThe minimum distance from the center of all clusters, i.eDistance to nearest cluster center.
Is a data pointThe maximum of the distances from the center of all clusters. In the formula, data points are foundThe maximum distance from the center of all clusters, i.eDistance to the center of the furthest cluster.
Is the degree of abnormality after normalization processing, and represents the data pointRelative to the degree of anomaly of the cluster to which it belongs.The larger the value is between 0 and 1, the more abnormal the data point is.
By way of example, assume that there is a cluster centerAnd a data pointThe distance between them is 10. Assume that the minimum value of the distance of the data point to the center of all other clusters is 5 and the maximum value is 20. Then, calculate according to the formulaThat is, the data pointThe anomaly of (2) is 1/3, assuming that the cleaning threshold T is 0.1, the anomaly is greater than the cleaning threshold T, i.e., is a data point of comparative anomaly with respect to the cluster to which it belongs.
In some embodiments, prior to step 204, further comprising:
determining a cleaning threshold for the monitored data, the cleaning threshold determined by:
Determining an average anomaly of a plurality of the monitoring data based on the anomaly of each of the monitoring data and a total number of the plurality of monitoring data;
Determining a corresponding anomaly standard deviation based on the anomalies of the plurality of monitored data and the average anomaly;
a first constant is obtained and the cleaning threshold is determined based on the first constant, the average anomaly degree, and the anomaly degree standard deviation.
In some embodiments, the cleaning threshold is expressed as:
；
Where T is the flush threshold, n is the total number of monitored data, Is a first constant which,Is the standard deviation of the degree of abnormality.
In a specific implementation of the present invention,Is the degree of abnormality of all data pointsTo sum the anomalies of each data point to calculate their average.
N is the total number of data points, i.e., the number of samples in the monitored data; is the average of the outliers for all the data points, and the average outliers for the data points is obtained by dividing the sum of outliers by the total number of data points.
Sigma is the standard deviation of the outlier throughout the monitored data set, which measures the degree of variance of the outlier cluster of data points, the greater the standard deviation, the more outlier cluster of data points is dispersed.
A is a constant for adjusting the influence of the standard deviation of the anomaly degree on the cleaning threshold value, and the sensitivity of the cleaning threshold value can be controlled by adjusting a, so that the cleaning threshold value is more suitable for different data distribution and system environments.
Thus, the calculation method of T is to average the degree of abnormality of all data pointsCombined with the standard deviation sigma of the anomaly to determine the cleaning threshold. The aim is to enable the cleaning threshold to take into account both the average anomaly degree of the data points and the degree of dispersion of the anomaly degree, thereby more accurately identifying the anomaly data.
Step 205, predicting a first backup capacity of the safety monitoring system at a next preset time based on the anomaly degree of the monitoring data at the current time and the backup capacity of the safety monitoring system for storing the normal data at the current time.
In some embodiments, step 205 may comprise:
The abnormality degree of the monitoring data at the current moment and the backup capacity of the safety monitoring system for storing the normal data at the current moment;
acquiring the system load condition of the safety monitoring system and the historical backup data quantity of the backup historical data;
and processing the abnormality degree, the backup capacity, the system load condition and the historical backup data amount of the monitoring data at the current moment based on a prediction function, and predicting to obtain the first backup capacity at the next preset moment.
In some embodiments, the first backup capacity is expressed as:
；
Wherein, Is the backup capacity at the next preset time t +1,And f (), which are the backup capacity, the anomaly degree of the monitored data, the system load condition and the historical backup data quantity at the current time t corresponding to the safety monitoring system, are prediction functions.
In a specific implementation of the present invention,Is at the time ofThe capacity value at the moment represents the backup capacity of the system at the next time step. By adjustingThe system environment and data characteristics that are constantly changing can be adapted to ensure efficient operation and data integrity of the backup system.
The capacity value at the time t represents the current backup capacity of the system, and the capacity value at the last time step.
The degree of abnormality at time t represents the degree of abnormality detected by the system in the monitoring data of the current time step. Degree of abnormalityThe method can be used for evaluating the current running state of the system and the abnormal condition of the data according to the characteristics and modes of the monitoring data.
The system load condition at the time t represents the current workload and resource utilization of the system. The system load can be evaluated according to the performance index, the resource utilization rate, the workload and other factors of the system, and is used for judging whether the system is in a busy state or in a condition of insufficient resources.
Is the historical backup data quantity at the moment of time t, which represents the data quantity of the system which has been backed up in the past period of timeMay be used to predict future backup needs and capacity needs to adjust the capacity of the backup system to meet future data storage needs.
F (), which is a predictive function, is used to determine the degree of abnormality based on the current degree of abnormalitySystem load conditionAnd historical backup data volumeTo determine the capacity of the next time step. The function can be designed according to actual conditions and requirements to ensure that the backup system can effectively run and adapt to data changes in different environments. Typically, the function will combine historical data analysis, predictive models, and policy optimization methods to determine an optimal capacity adjustment policy.
In view of the above-mentioned, it is desirable,The calculation formula of (1) describes the backup system at time stepHow to dynamically adjust capacity to accommodate changing system environments and data characteristics based on current anomalies, system load conditions, and historical backup data volumes.
And 206, if the capacity of the normal data at the current moment is smaller than the first backup capacity, sending a safety early warning to the area of the mine operation site, and increasing the cleaning threshold value.
It will be appreciated that if the normal data capacity at the present time is less than the first backup capacityIndicating that the capacity of the backup system may be larger, and that there is a waste of resources. In this case, corresponding measures are required to adjust the operating parameters of the backup system, so as to improve the resource utilization efficiency and the stability of the system.
In some embodiments, when it is detected that the normal data capacity at the current time is less than the backup capacityWhen the system is used, the system should immediately send a safety early warning signal to the relevant area of the mine operation site. This helps to alert the relevant staff to the safety risk present and take timely action to reduce the risk.
Meanwhile, in order to reduce the influence of abnormal data on the system and reduce unnecessary data backup, the value of the data cleansing threshold T may be increased. By increasing the value of T, the criterion for determining that the data is abnormal can be increased, so that normal data is retained more and the backup amount of abnormal data is reduced.
By the mode, the backup capacity and the data cleaning strategy can be effectively adjusted by the system, and the resource utilization efficiency and the stability of the backup system are improved. Meanwhile, the safety early warning is sent out timely, so that the safety and management level of the mine operation site can be improved, and the potential safety risk is reduced.
In some embodiments, the monitoring system may monitor in real-time various data of the mine work site, such as equipment operating status, environmental parameters, worker activity, etc., in order to discover any abnormal conditions or potential safety risks in time.
In some embodiments, the monitoring data may be analyzed and compared according to preset safety precaution criteria and thresholds. If abnormal conditions which are consistent with the safety early warning standard exist in the monitoring data, the system triggers a safety early warning signal.
In some embodiments, once the monitoring system detects an abnormal condition and meets the safety precaution standard, the system immediately sends a safety precaution signal to the relevant area of the mine operation site, for example, the safety precaution signal can comprise a sound alarm, a flash lamp alarm, a mobile phone short message notice and the like, so as to quickly draw the attention of relevant personnel.
In some embodiments, after receiving the safety precaution signal, relevant personnel at the mine work site should take corresponding countermeasures, such as stopping work, evacuating dangerous areas, checking equipment status, enhancing safety protection, etc., to ensure the safety of personnel and equipment.
Through the mode, the safety early warning is sent out to the area of the mine operation site, and the monitoring system can help to improve the alertness and the coping capacity of staff to the potential safety risk, so that accidents can be prevented in time, and the safety and the stability of the mine operation can be guaranteed.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a mine safety risk early warning system provided by the invention.
As shown in fig. 3, a mine safety risk early warning system provided by an embodiment of the present invention includes:
The data acquisition module 301 is used for acquiring monitoring data of a mine operation site through the safety monitoring system; the monitoring data is related to the safety risk of the mine operation site;
the data clustering module 302 is configured to perform clustering processing on the monitoring data, and divide the monitoring data into a plurality of clusters;
An anomaly degree calculation module 303, configured to process the monitoring data and the clusters, and determine an anomaly degree of each monitoring data;
An anomaly determination module 304, configured to determine anomaly data with an anomaly degree greater than a cleaning threshold value based on the cleaning threshold value and the anomaly degree of each of the monitoring data, and acquire a capacity of normal data other than the anomaly data;
A capacity calculation module 305, configured to predict a first backup capacity of the safety monitoring system at a next preset time based on an anomaly degree of the monitoring data at a current time and a backup capacity of the safety monitoring system at the current time for storing the normal data;
and the safety early warning module 306 is configured to increase the cleaning threshold while sending a safety early warning to the area of the mine operation site if the capacity of the normal data at the current moment is smaller than the first backup capacity.
Referring to fig. 4, fig. 4 is a schematic diagram of an embodiment of an electronic device according to an embodiment of the invention. As shown in fig. 4, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420, wherein the processor 420 executes the computer program 411 to implement the following steps:
collecting monitoring data of a mine operation site through a safety monitoring system; the monitoring data is related to the safety risk of the mine operation site;
clustering the monitoring data, and dividing the monitoring data into a plurality of clusters;
processing the monitoring data and the clusters, and determining the abnormality degree of each monitoring data;
Determining abnormal data with the abnormality degree larger than the cleaning threshold value based on the cleaning threshold value and the abnormality degree of each monitoring data, and acquiring the capacity of normal data except the abnormal data;
Predicting a first backup capacity of the safety monitoring system at the next preset time based on the abnormality degree of the monitoring data at the current time and the backup capacity of the safety monitoring system for storing the normal data at the current time;
if the capacity of the normal data at the current moment is smaller than the first backup capacity, the cleaning threshold value is increased while safety early warning is sent to the area of the mine operation site.
Referring to fig. 5, fig. 5 is a schematic diagram of an embodiment of a computer readable storage medium according to an embodiment of the invention. As shown in fig. 5, the present embodiment provides a computer-readable storage medium 500 having stored thereon a computer program 411, which computer program 411, when executed by a processor, performs the steps of:
collecting monitoring data of a mine operation site through a safety monitoring system; the monitoring data is related to the safety risk of the mine operation site;
clustering the monitoring data, and dividing the monitoring data into a plurality of clusters;
processing the monitoring data and the clusters, and determining the abnormality degree of each monitoring data;
Determining abnormal data with the abnormality degree larger than the cleaning threshold value based on the cleaning threshold value and the abnormality degree of each monitoring data, and acquiring the capacity of normal data except the abnormal data;
Predicting a first backup capacity of the safety monitoring system at the next preset time based on the abnormality degree of the monitoring data at the current time and the backup capacity of the safety monitoring system for storing the normal data at the current time;
if the capacity of the normal data at the current moment is smaller than the first backup capacity, the cleaning threshold value is increased while safety early warning is sent to the area of the mine operation site.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and for those portions of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (3)
1. A mine safety risk early warning method, the method comprising:
collecting monitoring data of a mine operation site through a safety monitoring system; the monitoring data is related to the safety risk of the mine operation site;
clustering the monitoring data, and dividing the monitoring data into a plurality of clusters;
processing the monitoring data and the clusters, and determining the abnormality degree of each monitoring data;
Determining abnormal data with the abnormality degree larger than the cleaning threshold value based on the cleaning threshold value and the abnormality degree of each monitoring data, and acquiring the capacity of normal data except the abnormal data;
Predicting a first backup capacity of the safety monitoring system at the next preset time based on the abnormality degree of the monitoring data at the current time and the backup capacity of the safety monitoring system for storing the normal data at the current time;
If the capacity of the normal data at the current moment is smaller than the first backup capacity, the cleaning threshold value is increased while safety early warning is sent to the area of the mine operation site;
The processing the monitoring data and the clusters to determine the abnormality degree of each monitoring data comprises the following steps:
determining a cluster center of each of the clusters; each cluster center corresponds to one monitoring data;
for each piece of monitoring data, determining a first distance from the monitoring data to the Euclidean distance of the corresponding cluster center, a second distance from the monitoring data to the minimum Euclidean distance of all cluster centers, and a third distance from the monitoring data to the maximum Euclidean distance of all cluster centers;
Determining an anomaly of the monitored data based on the first distance, the second distance, and the third distance;
The first distance is expressed as:
d(xi,μi)＝||xi-μi||；
Wherein i is a number value, x i is the ith data point in the plurality of monitoring data, and μ i is the ith cluster center, i.e., the cluster center to which the data point x i belongs;
The degree of abnormality is expressed as:
Where i and j are number values, k is the total number of clusters, Y i is the anomaly of the ith monitored data, d (x i,μi) is the first distance, Is a second distance from the first distance to the second distance,Is a third distance;
before the determining the anomaly data with the anomaly degree greater than the cleaning threshold value, further comprising: determining a cleaning threshold for the monitored data, the cleaning threshold determined by:
Determining an average anomaly of a plurality of the monitoring data based on the anomaly of each of the monitoring data and a total number of the plurality of monitoring data;
determining a corresponding anomaly standard deviation based on the anomalies of the plurality of monitored data and the average anomaly:
Acquiring a first constant, and determining the cleaning threshold based on the first constant, the average anomaly degree and the anomaly degree standard deviation;
the cleaning threshold is expressed as:
wherein T is a cleaning threshold, n is the total number of monitored data, a is a first constant, σ is an anomaly standard deviation;
The predicting the first backup capacity of the safety monitoring system at the next preset time comprises the following steps:
The abnormality degree of the monitoring data at the current moment and the backup capacity of the safety monitoring system for storing the normal data at the current moment;
acquiring the system load condition of the safety monitoring system and the historical backup data quantity of the backup historical data;
Processing the abnormality degree, the backup capacity, the system load condition and the historical backup data volume of the monitoring data at the current moment based on a prediction function, and predicting to obtain the first backup capacity at the next preset moment;
the first backup capacity is expressed as:
Vt+1＝f(Vt,Yt,Lt,Dt)；
V t+1 is the backup capacity of the next preset time t+1, V t,Yt,Lt,Dt is the backup capacity of the current time t corresponding to the safety monitoring system, the anomaly degree of the monitored data, the system load condition and the historical backup data quantity, and f (·) is a prediction function.
2. The mine safety risk early warning method according to claim 1, wherein the collecting monitoring data of the mine operation site comprises:
Arranging acquisition equipment on the mine operation site, wherein the acquisition equipment comprises an equipment sensor, an environment monitor, a video monitor, a personnel locator and a data transmission memory;
Receiving initial monitoring data sent by the acquisition equipment;
preprocessing the initial monitoring data, and taking the preprocessed data as the monitoring data.
3. A mine safety risk early warning system applied to the mine safety risk early warning method of claim 1, characterized in that the system comprises:
the data acquisition module is used for acquiring monitoring data of the mine operation site through the safety monitoring system; the monitoring data is related to the safety risk of the mine operation site;
the data clustering module is used for carrying out clustering processing on the monitoring data and dividing the monitoring data into a plurality of clusters;
the anomaly degree calculation module is used for processing the monitoring data and the clusters and determining the anomaly degree of each monitoring data;
The abnormality determining module is used for determining abnormal data with the abnormality degree larger than the cleaning threshold value based on the cleaning threshold value and the abnormality degree of each piece of monitoring data, and acquiring the capacity of normal data except the abnormal data;
The capacity calculation module is used for predicting the first backup capacity of the safety monitoring system at the next preset time based on the abnormality degree of the monitoring data at the current time and the backup capacity of the safety monitoring system at the current time for storing the normal data;
And the safety early warning module is used for increasing the cleaning threshold value while sending safety early warning to the area of the mine operation site if the capacity of the normal data at the current moment is smaller than the first backup capacity.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113297186A (en) * | 2020-02-24 | 2021-08-24 | 华为技术有限公司 | Data processing method, data acquisition equipment and data calculation equipment |
| CN113688169A (en) * | 2021-08-11 | 2021-11-23 | 北京科技大学 | Mine potential safety hazard identification and early warning system based on big data analysis |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113297186A (en) * | 2020-02-24 | 2021-08-24 | 华为技术有限公司 | Data processing method, data acquisition equipment and data calculation equipment |
| CN113688169A (en) * | 2021-08-11 | 2021-11-23 | 北京科技大学 | Mine potential safety hazard identification and early warning system based on big data analysis |
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