Safety early warning method and system based on smart mine and related device

Abstract

The invention provides a safety early warning method and system based on an intelligent mine and a related device, and the method comprises the following steps: collecting basic information of the mine, the basic information comprising geographic information, ecological information and operation and maintenance information; constructing a sample database based on the basic information; building a safety state early warning model based on the sample database; obtaining early warning information based on the operation and maintenance information and a safety state early warning model; according to the safety early warning method and system based on the smart mine and the related device, comprehensive perception and intelligent analysis of coal mine safety production data are achieved, combined research and judgment and collaborative early warning of safety risks of all service links are achieved by building a service collaborative early warning model, and the timeliness and accuracy of early warning are improved; by establishing a data collaborative intelligent early warning platform integrating data processing, model training and early warning publishing, the whole-process closed-loop management of data flow, service flow and alarm flow is realized, and an intelligent management and control means is provided for coal mine safety production.

Description

Technical Field

[0001] The present invention relates to the field of mine safety technology, and in particular to a safety early warning method, system and related devices based on smart mines. Background Art

[0002] Mineral resources are an important material basis for human survival and social development, and have made great contributions to social construction. However, there are still many safety risks in mine development. Therefore, it is crucial to realize early warning analysis of mine safety risks.

[0003] Smart mines are built on the basis of digitization, informatization, virtualization, intelligence, and integration. They comprehensively consider various factors such as production, management, operation, safety, benefits, environment, and resources, and use computers, networks, communications, virtual simulation, automatic control, and monitoring technologies to manage various information resources in mines in a comprehensive, efficient, and orderly manner. The system covers the entire process of production and operation of mining enterprises. The establishment of smart mines can not only realize the digitization of safety management and provide information guarantee for building intrinsically safe mines, but also realize the refinement of production management and provide decision-making means for building high-yield and high-efficiency mines.

[0004] At present, smart mines are still mainly focused on a certain local link in the mining operation, and have not run through the entire process. They are also unable to realize multi-dimensional early warning monitoring of mine safety risk analysis. Therefore, it is impossible to provide advance warning of safety risks in mines, and it is difficult to formulate reasonable early warning control operations in a timely manner, let alone ensure the stable operation of mine operations.

[0005] Therefore, there is a technical problem in the existing technology that it is impossible to realize multi-dimensional early warning monitoring for mine safety risk analysis. Summary of the invention

[0006] The main purpose of the present invention is to provide a safety early warning method, system and related devices based on smart mines to solve the technical problem that the prior art cannot realize multi-dimensional early warning monitoring of mine safety risk analysis.

[0007] In order to achieve the above-mentioned purpose, according to one aspect of the present invention, a safety warning method based on a smart mine is provided, comprising the following steps: collecting basic information of the mine, wherein the basic information includes geographic information, ecological information and operation and maintenance information; constructing a sample database based on the basic information; building a safety status warning model based on the sample database; and obtaining warning information based on the operation and maintenance information and the safety status warning model.

[0008] In some embodiments, the collection of basic mine information includes: using a three-dimensional laser scanner to collect three-dimensional real-life data of the mining area's topography to obtain high-precision three-dimensional point cloud data; using a drone to collect mining area environmental data to obtain ecological information; and collecting operation and maintenance information during underground operations.

[0009] In some embodiments, building a safety status warning model based on the sample database includes the following steps: preprocessing the basic information, the preprocessing at least including cleaning and standardizing the basic information; obtaining a mining feature subset based on the preprocessed basic information; building a safety status warning model based on the mining feature subset, the safety status warning model including a mechanical accident warning model, a working face roof fall warning model, a personnel violation identification model, a gas over-limit warning model and a collaborative warning model.

[0010] In some embodiments, obtaining warning information based on the operation and maintenance information and the safety status warning model includes: setting safety threshold information, inputting the operation and maintenance information and the safety threshold information into the safety status warning model to obtain warning information, and visually displaying the warning information.

[0011] In some embodiments, the safety warning method further includes: constructing a visualization platform to visualize the warning information and basic information.

[0012] In some embodiments, the safety warning method further includes: determining the danger level based on the warning information and constructing an optimal evacuation route for personnel.

[0013] According to another aspect of the present invention, the present invention also provides a safety warning device based on a smart mine, including: a collection module for collecting basic information of the mine, wherein the basic information includes geographic information, ecological information and operation and maintenance information; a construction module for constructing a sample database based on the basic information; a building module for constructing a safety status warning model based on the sample database; and an acquisition module for obtaining warning information based on the operation and maintenance information and the safety status warning model.

[0014] According to another aspect of the present invention, the present invention also provides a smart mine safety early warning system, including a central early warning unit, wherein the central early warning unit is communicatively connected to a data acquisition component, an information display platform and a monitoring component, wherein the data acquisition component is used to collect geographic information, ecological information and operation and maintenance information, and send it to the monitoring component; the monitoring component is used to obtain the geographic information, ecological information and operation and maintenance information of the mine in real time, and perform mine risk prediction and analysis, thereby generating a geographic information early warning signal, an ecological information early warning signal or an operation and maintenance information early warning signal, and sending it to the central early warning unit, the central early warning unit receives various types of risk determination signals, and performs comprehensive safety risk prediction and analysis on various types of risk determination signals, generates comprehensive early warning information, and sends the comprehensive early warning information to the information display platform for display and explanation.

[0015] According to another aspect of the present invention, the present invention further provides a storage medium storing a computer program, wherein when the computer program is executed by a processor, the steps of any of the aforementioned safety warning methods based on smart mines are implemented.

[0016] According to another aspect of the present invention, the present invention also provides an electronic device, comprising at least a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program on the memory, the steps of any of the aforementioned safety warning methods based on smart mines are implemented.

[0017] By applying the technical solution of the present invention, basic mine information is collected, wherein the basic information includes geographic information, ecological information and operation and maintenance information; a sample database is constructed based on the basic information; a safety status early warning model is constructed based on the sample database; early warning information is obtained based on the operation and maintenance information and the safety status early warning model; comprehensive perception and intelligent analysis of coal mine production safety data are realized, and by building a mining-digging-transportation-communication business collaborative early warning model, joint assessment and collaborative early warning of safety risks in various business links are realized, greatly improving the timeliness and accuracy of early warnings; by establishing a data collaborative intelligent early warning platform integrating data processing, model training, and early warning release, full-process closed-loop management of data flow, business flow, and alarm flow is realized, providing a new type of intelligent management and control means for coal mine production safety; through the refined management of background data such as production, personnel, and ecological environment, and the real-time loading of corresponding early warning models, a new safety early warning model based on spatiotemporal big data analysis is formed, realizing the transformation from post-event disposal to pre-event early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0019] Figure 1 A flowchart showing the steps of the safety early warning method based on smart mines according to the present invention is shown;

[0020] Figure 2 A flowchart showing the steps of building a safety status early warning model based on a sample database according to the present invention is shown;

[0021] Figure 3 It shows a structural block diagram of a safety warning device based on a smart mine according to the present invention;

[0022] Figure 4 The architecture diagram of the safety early warning platform based on smart mines according to the present invention is shown;

[0023] Figure 5 A schematic structural diagram of an electronic device according to the present invention is shown. DETAILED DESCRIPTION

[0024] The specific embodiments of the present disclosure are described in detail below with reference to the accompanying drawings, but are not intended to limit the present disclosure.

[0025] It should be understood that various modifications may be made to the embodiments disclosed herein. Therefore, the above description should not be considered as limiting, but merely as an example of an embodiment. Other modifications within the scope and spirit of the present disclosure will occur to those skilled in the art.

[0026] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the general description of the present disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the present disclosure.

[0027] These and other characteristics of the present disclosure will become apparent from the following description of a preferred form of embodiment given as a non-limiting example, with reference to the attached drawings.

[0028] It should also be understood that, although the present disclosure has been described with reference to some specific examples, those skilled in the art will be able to realize many other equivalent forms of the present disclosure that have the features described in the claims and are therefore within the scope of protection defined thereby.

[0029] The above and other aspects, features and advantages of the present disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.

[0030] Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it should be understood that the disclosed embodiments are merely examples of the present disclosure, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid obscuring the present disclosure with unnecessary or redundant details. Therefore, the specific structural and functional details disclosed herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present disclosure in a variety of ways with substantially any suitable detailed structure.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0032] This specification may use the phrases "in one embodiment," "in another embodiment," "in a further embodiment," or "in other embodiments," all of which may refer to one or more of the same or different embodiments according to the present disclosure.

[0033] The present disclosure is further described below in conjunction with the accompanying drawings and specific embodiments.

[0034] In order to solve the technical problem in the prior art that multi-dimensional early warning monitoring for mine safety risk analysis cannot be achieved, the present invention provides a safety early warning method, system and related devices based on a smart mine.

[0035] Figure 1 The flowchart of the steps of the safety early warning method based on the smart mine provided by the present invention is shown. As shown in the figure, the safety early warning method based on the smart mine provided by the present invention includes the following steps:

[0036] S101, collecting basic information of the mine, wherein the basic information includes geographic information, ecological information and operation and maintenance information.

[0037] In this step, basic information of the mine is collected, wherein the basic information includes geographic information, ecological information and operation and maintenance information.

[0038] Specifically, basic information during the operation of the mine is collected, and the basic information includes operation and maintenance information related to the operation of the mine and natural environment information related to the operation of the mine, specifically including geographic information and ecological information.

[0039] In the process of surface data collection, high-precision 3D laser scanners such as Leica ScanStation P50 are used to collect 3D real-scene data of the mining area surface. The point cloud density is not less than 1,000 points per square meter, and the coordinate system adopts the CGCS2000 national geodetic coordinate system.

[0040] At the same time, a DJI M300 RTK drone equipped with a Zenmuse P1 multispectral camera and a hyperspectral camera is used to collect data on the mining environment. The multispectral camera has no less than 5 bands, and the hyperspectral camera has a spectral resolution better than 10nm.

[0041] While collecting surface information, underground information is also collected. The Surphaser 100HSX high-precision 3D laser scanner is used to collect 3D real-life data of underground tunnels, with a point cloud density of no less than 5,000 points / square meter. Gas sensors are deployed in mining areas, return air flow and other areas to collect gas concentration data in real time, with a sensor range of 0-5%. At the same time, underground water level and water quality monitoring sensors are deployed to collect mine water data in real time, with a water level sensor range of 0-200m. Water quality sensor detection items include pH value, conductivity, dissolved oxygen, etc.

[0042] Among them, high-precision water pressure, water level, water quality and other sensors are deployed in key areas such as mining areas and excavation working faces to sense the dynamic changes of mine water in real time.

[0043] By adopting multi-source data fusion technology, the collected 3D point cloud data is integrated with the mining area’s geological, hydrological, engineering and other data to build a comprehensive mining area geographic information system.

[0044] In order to understand the operating status of the mine in real time, it is also necessary to deploy intelligent video acquisition equipment in key areas such as coal mining faces and main transportation tunnels, such as smart cameras, to collect data such as equipment status and personnel behavior. The video resolution must be no less than 1080P.

[0045] Through video analysis technology, it is possible to detect the working behavior of underground personnel in real time and build a knowledge base of underground personnel behavior, including categories such as safe behavior and illegal behavior.

[0046] At the same time, UWB positioning technology is used to deploy positioning base stations underground to accurately locate the positioning tags worn by workers and obtain the real-time location distribution and movement trajectory of the personnel.

[0047] By collecting geographic information, ecological information and operation and maintenance information during the operation of the mine, we can understand the operating status and changing trends of equipment, personnel and environment during the operation of the mine in real time, which is conducive to timely prediction of the operating trend of the mine and early detection of possible dangers.

[0048] S102: construct a sample database based on the basic information.

[0049] After completing the above steps, in this step, a sample database is constructed based on the basic information.

[0050] Specifically, after the basic information is collected, high-speed wireless communication technologies such as 5G and WiFi6 are used to build an integrated data transmission network above and below the well, realize real-time data convergence, and transmit through wireless network communication, which is more conducive to forming an integrated network transmission above and below the well. When the underground environment is harsh, a relay base station can be set up to strengthen the network signal to facilitate signal transmission.

[0051] In order to facilitate data management, existing big data platforms such as Apache Hadoop are used to store and manage the collected multi-source heterogeneous data, and to build a data sample library covering the entire process, all elements and all aspects of mining operations; and to use ontology construction technology to build a coal mine safety knowledge ontology library to form a coal mine safety knowledge graph.

[0052] Data integration management can select existing integration methods according to actual needs. This patent does not further limit the choice of database, and data classification and integration can be carried out according to needs.

[0053] In another embodiment of the present invention, various geological exploration data such as drilling, geophysical exploration, and measurement can be collected and integrated to construct a three-dimensional geological structure model of the mining area, including strata, structures, coal seams, goafs, etc.

[0054] S103: Building a safety status early warning model based on the sample database.

[0055] After completing the above steps, in this step, a safety status early warning model is built based on the sample database.

[0056] Further, Figure 2 The flowchart of the steps of building a safety status early warning model based on the sample database provided by the present invention is shown as follows: Figure 2 As shown, building a security status early warning model based on the sample database includes the following specific steps:

[0057] S201, preprocessing the basic information, wherein the preprocessing at least includes cleaning and standardizing the basic information.

[0058] In this step, the basic information is preprocessed, and the preprocessing at least includes cleaning and standardizing the basic information.

[0059] Specifically, after data collection, the collected multi-source heterogeneous data are preprocessed, wherein the preprocessing at least includes cleaning and standardizing the data. Cleaning and standardizing the data is helpful to improve the data quality.

[0060] At the same time, signal processing technologies such as wavelet transform are used to perform noise reduction and feature extraction on sensor data to improve the accuracy of sensor detection data.

[0061] S202: Obtain a mining feature subset based on the preprocessed basic information.

[0062] After completing the above step S201, in this step, a mining feature subset is obtained based on the preprocessed basic information.

[0063] Specifically, after obtaining the basic information of the mine, the Pearson correlation coefficient, mutual information and other indicators are used to analyze the historical data of mining, excavation, transportation, and communication business links to find the safety influencing factors of each business link and the correlation between them; feature selection algorithms such as recursive feature elimination (RFE) and LASSO are used to screen out feature subsets that are highly correlated with safety warnings.

[0064] S203, building a safety status warning model based on the mining feature subset, the safety status warning model includes a mechanical accident warning model, a working face roof fall warning model, a personnel violation behavior identification model, a gas over-limit warning model and a collaborative warning model.

[0065] After completing the above step S202, in this step, a safety status warning model is built based on the mining feature subset, and the safety status warning model includes a mechanical accident warning model, a working face roof fall warning model, a personnel violation behavior identification model, a gas over-limit warning model and a collaborative warning model.

[0066] Specifically, the safety status warning model may include multiple sub-models, and each sub-model is established in the following manner, for example:

[0067] The support vector machine (SVM) algorithm was used to construct a coal mining machinery accident warning model. The model hyperparameters were optimized through grid search, and the model performance was evaluated using 5-fold cross validation. The warning accuracy was no less than 80%.

[0068] A gated recurrent unit (GRU) neural network is used to build a roof fall warning model for the working face. The EarlyStopping strategy is used to control network training, and the leave-one-out method is used to evaluate model performance. The warning accuracy is no less than 80%.

[0069] The convolutional neural network (CNN) algorithm is used to build a model for identifying illegal behaviors of underground personnel. The generalization ability of the model is improved through transfer learning. The confusion matrix is ​​used to evaluate the model performance. The accuracy rate of illegal behavior identification is not less than 80%.

[0070] Use ensemble learning techniques, such as AdaBoost and random forest, to build a gas over-limit warning model, optimize the model through feature importance analysis, and use the ROC curve to evaluate the model performance. The AUC value of the warning model is not less than 0.8.

[0071] Petri nets are used to construct a collaborative early warning workflow model for mining, excavation, transportation and communication to achieve information sharing and collaborative decision-making among various business links.

[0072] In the actual production process, a hydrogeological database for the mining area can be constructed according to the geographical environment, including data such as aquifer distribution, water-conducting faults, and water-gushing points, to form a three-dimensional hydrogeological model, and load a water hazard early warning model; a soil environment database for the mining area can be constructed, including data such as soil type and physical and chemical properties, and a soil environment quality evaluation and early warning model can be formed through data mining and analysis; a vegetation ecology database for the mining area can be constructed, including data such as vegetation type and coverage, and a vegetation ecology quality evaluation and early warning model can be formed through multi-temporal remote sensing image analysis;

[0073] For personnel behavior, attention mechanism models such as Transformer can also be used to build an underground personnel behavior analysis and early warning model to analyze personnel behavior safety risks in real time; build an underground personnel behavior database, identify personnel work behaviors through video analysis technology, and combine with the expert knowledge base to load the violation behavior identification and early warning model.

[0074] For key equipment such as coal mining machines and hydraulic supports, an equipment health record database is built and the equipment failure warning model is loaded.

[0075] S104, obtaining warning information based on the operation and maintenance information and the safety status warning model.

[0076] After the model is built, in this step, warning information is obtained based on the operation and maintenance information and the safety status warning model.

[0077] Specifically, after the model is built, the safety threshold information can be set according to the mine operation safety requirements and environmental requirements, and the operation and maintenance information and the safety threshold information can be input into the safety status warning model. The safety status warning model judges the operation and maintenance information and the safety threshold to obtain warning information. Among them, the safety threshold can also be dynamically adjusted in combination with expert experience and machine learning methods, so as to overcome the limitations of traditional fixed threshold warnings and improve the sensitivity and accuracy of warnings.

[0078] It is also possible to use data mining algorithms such as association rules and decision trees to analyze massive historical data, discover the precursor characteristics and patterns of safety accidents, and build a multi-level and multi-dimensional safety warning model.

[0079] For example, in the process of personnel management, electronic fences of key areas can be superimposed on the three-dimensional scene to carry out refined management and control of personnel, vehicles, equipment, etc. in the area; and regional early warning rules can be set, such as the length of time people stay in the area, the speed of vehicles, etc., to provide timely warnings for violations and prevent regional safety risks.

[0080] In addition, computer vision technology can be used to perform intelligent analysis of video surveillance images, identify targets such as people, vehicles, and equipment, track their movement trajectories, and detect abnormal behavior.

[0081] In another embodiment of the present patent, the security warning method further includes: constructing a visualization platform to visualize the warning information and basic information.

[0082] Specifically, the Vue3-Vite framework is used to build the system front-end architecture, and visual component libraries such as DataV and ECharts are used to design a large-screen monitoring interface with a unified style and reasonable layout. The large-screen monitoring integrates and displays the geographic information, ecological information, and operation and maintenance information during the operation of the mine, as well as the status information representing the operation of the mine and the warning information pushed by the warning model, and uses bar charts, pie charts, radar charts, etc. for display, and uses visual elements such as color and flashing to intuitively display the safety status of equipment and personnel.

[0083] In order to further ensure the personal safety of the operating personnel, the safety warning method also includes determining the danger level based on the warning information and constructing the best risk avoidance route for the personnel.

[0084] Specifically, the early warning system uses UWB positioning technology to deploy positioning base stations underground, accurately locate the positioning tags worn by each employee, and track the location distribution and movement trajectory of personnel in real time. Based on the Cesium engine, a three-dimensional underground tunnel model is constructed and associated with the personnel positioning information to intuitively display the spatial position relationship between personnel and risk sources. The distance between personnel trajectories and major risk sources (such as goafs, fault fracture zones, etc.) is analyzed to predict the degree of danger and provide the best risk avoidance route.

[0085] The safety warning method based on smart mines provided by the present invention is applied to collect basic mine information, wherein the basic information includes geographic information, ecological information and operation and maintenance information; construct a sample database based on the basic information; build a safety status warning model based on the sample database; obtain warning information based on the operation and maintenance information and the safety status warning model; realize comprehensive perception and intelligent analysis of coal mine production safety data, and realize joint assessment and collaborative warning of safety risks in various business links by building a mining-digging-transportation-communication business collaborative warning model, thereby greatly improving the timeliness and accuracy of the warning; realize full-process closed-loop management of data flow, business flow and alarm flow by establishing a data collaborative intelligent warning platform integrating data processing, model training and warning release, thereby providing a new type of intelligent management and control means for coal mine production safety; through the refined management of background data such as production, personnel, and ecological environment, and the real-time loading of corresponding warning models, a new safety warning model based on spatiotemporal big data analysis is formed, realizing the transformation from post-event disposal to pre-event warning.

[0086] In order to better implement the above method, the second aspect of the present disclosure further provides a safety warning device based on a smart mine, which can be integrated on an electronic device.

[0087] For example, Figure 3 As shown, the early warning device 200 may include: a collection module 210, a construction module 220, a building module 230 and an acquisition module 240, which are specifically as follows:

[0088] A collection module 210, the collection module 210 is used to collect basic mine information, wherein the basic information includes geographic information, ecological information and operation and maintenance information;

[0089] A construction module 220, wherein the construction module 220 is used to construct a sample database based on the basic information;

[0090] A building module 230, wherein the building module 230 is used to build a safety status early warning model based on the sample database;

[0091] The acquisition module 240 is used to obtain warning information based on the operation and maintenance information and the safety status warning model.

[0092] Furthermore, the collection of basic mine information includes using a three-dimensional laser scanner to collect three-dimensional real-scene data of the mining area's topography and geomorphology to obtain high-precision three-dimensional point cloud data; using a drone to collect mining area environmental data to obtain ecological information; and collecting operation and maintenance information during underground operations.

[0093] Furthermore, the building module 230 includes a preprocessing unit, an acquisition unit and a building unit, wherein the preprocessing unit is used to preprocess the basic information, and the preprocessing at least includes cleaning and standardizing the basic information; the acquisition unit is used to obtain a mining feature subset based on the preprocessed basic information; the building unit is used to build a safety status warning model based on the mining feature subset, and the safety status warning model includes a mechanical accident warning model, a working face roof fall warning model, a personnel violation behavior identification model, a gas over-limit warning model and a collaborative warning model.

[0094] Furthermore, obtaining warning information based on the operation and maintenance information and the safety status warning model includes setting safety threshold information, inputting the operation and maintenance information and the safety threshold information into the safety status warning model to obtain warning information, and visually displaying the warning information.

[0095] By applying the safety warning device based on smart mines provided by the present invention, warning information is obtained based on the operation and maintenance information and the safety status warning model; comprehensive perception and intelligent analysis of coal mine production safety data are realized, and by building a mining-digging-transportation-communication business collaborative warning model, joint analysis and collaborative warning of safety risks in various business links are realized, which greatly improves the timeliness and accuracy of the warning; by establishing a data collaborative intelligent warning platform integrating data processing, model training, and warning release, full-process closed-loop management of data flow, business flow, and alarm flow is realized, providing a new type of intelligent management and control means for coal mine production safety; through the refined management of background data such as production, personnel, and ecological environment, and the real-time loading of corresponding warning models, a new safety warning model based on spatiotemporal big data analysis is formed, realizing the transformation from post-event disposal to pre-event warning.

[0096] The third aspect of the present disclosure also provides a smart mine safety early warning system, including a central early warning unit, which is communicatively connected to a data acquisition component, an information display platform and a monitoring component, wherein the data acquisition component is used to collect geographic information, ecological information and operation and maintenance information, and send it to the monitoring component; the monitoring component is used to obtain the geographic information, ecological information and operation and maintenance information of the mine in real time, and perform mine risk prediction and analysis, thereby generating a geographic information early warning signal, an ecological information early warning signal or an operation and maintenance information early warning signal, and sending it to the central early warning unit, the central early warning unit receives various types of risk determination signals, and performs comprehensive safety risk prediction and analysis on various types of risk determination signals, generates comprehensive early warning information, and sends the comprehensive early warning information to the information display platform for display and explanation.

[0097] like Figure 4As shown in the figure, the early warning system can also analyze the data as needed, thereby realizing closed-loop management of the entire process including inspection planning, on-site inspection, problem rectification, and statistical analysis. Using big data analysis technology, the inherent correlation and rules of historical accident and hidden danger data can be explored to intelligently generate inspection plans and inspection priorities. Statistical analysis of inspection results can be conducted to identify weak links, evaluate rectification results, and optimize and improve safety management strategies.

[0098] Through the visualization system, we can also develop a mine safety culture propaganda platform to popularize safety regulations and standards, accident case warnings, emergency risk avoidance knowledge, etc., to create a strong safety culture atmosphere. We can carry out online learning, examinations, skills competitions, etc. to encourage employees to actively learn safety knowledge and master safe operation skills. We should pay attention to the physical and mental health of employees, provide online psychological counseling services, establish a caring and mutual assistance mechanism for employees, and enhance their sense of safety and happiness.

[0099] A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0100] To this end, a fourth embodiment of the present disclosure provides a storage medium, which is a computer-readable medium and stores a computer program. When the computer program is executed by a processor, the method provided by the embodiment of the present disclosure is implemented, including the following steps S11 to S14:

[0101] S11, collecting basic mine information, wherein the basic information includes geographic information, ecological information and operation and maintenance information;

[0102] S12, constructing a sample database based on the basic information;

[0103] S13, building a safety status early warning model based on the sample database;

[0104] S14, obtaining warning information based on the operation and maintenance information and the safety status warning model.

[0105] Furthermore, when the computer program is executed by a processor, other methods provided by any of the above embodiments of the present disclosure are implemented.

[0106] The safety warning method based on smart mines provided by the present invention is applied to collect basic mine information, wherein the basic information includes geographic information, ecological information and operation and maintenance information; construct a sample database based on the basic information; build a safety status warning model based on the sample database; obtain warning information based on the operation and maintenance information and the safety status warning model; realize comprehensive perception and intelligent analysis of coal mine production safety data, and realize joint assessment and collaborative warning of safety risks in various business links by building a mining-digging-transportation-communication business collaborative warning model, thereby greatly improving the timeliness and accuracy of the warning; realize full-process closed-loop management of data flow, business flow and alarm flow by establishing a data collaborative intelligent warning platform integrating data processing, model training and warning release, thereby providing a new type of intelligent management and control means for coal mine production safety; through the refined management of background data such as production, personnel, and ecological environment, and the real-time loading of corresponding warning models, a new safety warning model based on spatiotemporal big data analysis is formed, realizing the transformation from post-event disposal to pre-event warning.

[0107] A fifth embodiment of the present disclosure provides an electronic device, such as Figure 5 As shown, the electronic device at least includes a memory 310 and a processor 320. The memory 310 stores a computer program. The processor 320 implements the method provided by any embodiment of the present disclosure when executing the computer program on the memory 310. Exemplarily, the method executed by the electronic device computer program is as follows:

[0108] S21, collecting basic information of the mine, wherein the basic information includes geographic information, ecological information and operation and maintenance information;

[0109] S22, constructing a sample database based on the basic information;

[0110] S23, building a safety status early warning model based on the sample database;

[0111] S24, obtaining warning information based on the operation and maintenance information and the safety status warning model.

[0112] In specific implementation, the above-mentioned: collection module 210, construction module 220, construction module 230 and acquisition module 240 are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to implement corresponding functions.

[0113] The safety warning method based on smart mines provided by the present invention is applied to collect basic mine information, wherein the basic information includes geographic information, ecological information and operation and maintenance information; construct a sample database based on the basic information; build a safety status warning model based on the sample database; obtain warning information based on the operation and maintenance information and the safety status warning model; realize comprehensive perception and intelligent analysis of coal mine production safety data, and realize joint assessment and collaborative warning of safety risks in various business links by building a mining-digging-transportation-communication business collaborative warning model, thereby greatly improving the timeliness and accuracy of the warning; realize full-process closed-loop management of data flow, business flow and alarm flow by establishing a data collaborative intelligent warning platform integrating data processing, model training and warning release, thereby providing a new type of intelligent management and control means for coal mine production safety; through the refined management of background data such as production, personnel, and ecological environment, and the real-time loading of corresponding warning models, a new safety warning model based on spatiotemporal big data analysis is formed, realizing the transformation from post-event disposal to pre-event warning.

[0114] The storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0115] The storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains at least two Internet Protocol addresses; sends a node evaluation request including at least two Internet Protocol addresses to a node evaluation device, wherein the node evaluation device selects an Internet Protocol address from the at least two Internet Protocol addresses and returns it; receives the Internet Protocol address returned by the node evaluation device; wherein the obtained Internet Protocol address indicates an edge node in a content distribution network.

[0116] Alternatively, the storage medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device: receives a node evaluation request including at least two Internet Protocol addresses; selects an Internet Protocol address from the at least two Internet Protocol addresses; and returns the selected Internet Protocol address; wherein the received Internet Protocol address indicates an edge node in a content distribution network.

[0117] Computer program code for performing operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the passenger computer, partially on the passenger computer, as a separate software package, partially on the passenger computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the passenger computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0118] It should be noted that the storage medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. Computer readable signal media may also be any storage medium other than computer readable storage media that can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), or any suitable combination of the above.

[0119] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0120] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0121] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs).

[0122] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0123] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0124] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0125] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

[0126] Multiple embodiments of the present disclosure are described in detail above, but the present disclosure is not limited to these specific embodiments. Those skilled in the art can make various variations and modifications to the embodiments based on the concepts of the present disclosure, and these variations and modifications should all fall within the scope of protection required by the present disclosure.

Claims

1. A safety early warning method based on smart mines, characterized in that: The steps include: Collecting basic mine information, including geographic information, ecological information and operation and maintenance information; Building a sample database based on the basic information; Building a safety status early warning model based on the sample database; Early warning information is obtained based on the operation and maintenance information and the safety status early warning model.

2. The safety early warning method based on smart mine according to claim 1 is characterized in that: The collection of basic mine information includes: Use 3D laser scanners to collect 3D real-life data of the mining area's topography and obtain high-precision 3D point cloud data; Use drones to collect environmental data in mining areas and obtain ecological information; Collect operation and maintenance information during underground operations.

3. The safety early warning method based on smart mine according to claim 1 is characterized in that: The step of building a security status early warning model based on the sample database includes the following steps: Preprocessing the basic information, wherein the preprocessing at least includes cleaning and standardizing the basic information; Obtaining a mining feature subset based on the preprocessed basic information; A safety status warning model is built based on the mining feature subset, and the safety status warning model includes a mechanical accident warning model, a working face roof fall warning model, a personnel violation behavior identification model, a gas over-limit warning model and a collaborative warning model.

4. The safety early warning method based on smart mine according to claim 1 is characterized in that: The obtaining of warning information based on the operation and maintenance information and the safety status warning model includes: The safety threshold information is set, and the operation and maintenance information and the safety threshold information are input into the safety status warning model to obtain the warning information, and the warning information is visualized.

5. The safety early warning method based on smart mine according to claim 1 is characterized in that: The safety early warning method also includes: Build a visualization platform to visualize the warning information and basic information.

6. The safety early warning method based on smart mine according to claim 5 is characterized in that: The method further includes: determining the danger level based on the early warning information, and constructing an optimal evacuation route for personnel.

7. A safety warning device based on smart mines, characterized in that: include: A collection module is used to collect basic mine information, wherein the basic information includes geographic information, ecological information and operation and maintenance information; A construction module, used to construct a sample database based on the basic information; A building module, used to build a safety status early warning model based on the sample database; The acquisition module is used to obtain warning information based on the operation and maintenance information and the safety status warning model.

8. A smart mine safety early warning system, characterized in that: It includes a central early warning unit, which is communicatively connected to a data acquisition component, an information display platform and a monitoring component. The data acquisition component is used to collect geographic information, ecological information and operation and maintenance information, and send it to the monitoring component; the monitoring component is used to obtain the geographic information, ecological information and operation and maintenance information of the mine in real time, and conduct mine risk prediction and analysis, and generate geographic information early warning signals, ecological information early warning signals or operation and maintenance information early warning signals based on them, and send them to the central early warning unit. The central early warning unit receives various types of risk determination signals, and performs comprehensive safety risk prediction and analysis on various types of risk determination signals to generate comprehensive early warning information, and sends the comprehensive early warning information to the information display platform for display and explanation.

9. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the safety early warning method based on a smart mine described in any one of claims 1 to 6 are implemented.

10. An electronic device, comprising at least a memory and a processor, wherein a computer program is stored in the memory, wherein: The processor implements the steps of the safety warning method based on smart mine according to any one of claims 1 to 6 when executing the computer program on the memory.

Images