Coal mine disaster monitoring and early warning method and system based on mine gap operating system

Abstract

The invention relates to a coal mine disaster monitoring and early warning method and system based on a mine gap operating system, and belongs to the technical field of coal mine safety. In order to solve the problems that an existing monitoring system is serious in information island, multi-source heterogeneous data fusion is difficult, and a unified risk evaluation system and mobile terminal active early warning are lacked, a coal mine safety data interaction standard and a multi-source heterogeneous data acquisition specification are established; unified acquisition and sharing of monitoring data of various disasters such as gas, water disaster, fire disaster, roof, dust and the like are realized; a mine safety risk quantitative index system and an anomaly automatic identification model are constructed, and disaster risk accurate analysis and fusion early warning are realized; a mobile terminal application program is developed based on a mine gap operating system, and alarm active pushing is achieved through a standard Web API interface. The problems that disaster data sharing is difficult, risk identification is inaccurate, early warning information transmission lags behind and the like are thoroughly solved, and the comprehensive prevention and control capacity and the intelligent management level of coal mine disasters are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of coal mine safety technology and relates to a coal mine disaster monitoring and early warning method and system based on the HarmonyOS operating system for mines. Background Technology

[0002] In existing technologies, various monitoring systems, such as those for gas, water hazards, fires, roof collapses, and dust, operate independently, resulting in severe information silos. The lack of unified and comprehensive data acquisition standards among these systems makes data sharing extremely difficult. The diverse formats and significant differences in data structure generated by different systems hinder effective data fusion. Furthermore, current technologies have not established a unified disaster risk assessment index system or disaster early warning analysis model, making it impossible to achieve integrated analysis and collaborative early warning of multiple disaster trends.

[0003] Existing disaster monitoring results are mainly presented in the form of traditional reports and curves, lacking in-depth analysis of disaster data and convenient and quick disaster early warning push methods. It is difficult to form a complete disaster management closed loop process of real-time monitoring, disaster analysis, early warning result generation, and message push, and it is difficult to meet the needs of mine managers and emergency rescue personnel to obtain early warning information in a timely manner.

[0004] Existing coal mine disaster early warning systems suffer from the following major problems: a lack of unified standards for collecting multi-source heterogeneous data, leading to difficulties in data fusion and sharing; a lack of a comprehensive safety risk analysis and assessment system and disaster risk analysis module, resulting in difficulties in automatic risk identification and disaster early warning; and a lack of mobile terminal-based disaster early warning applications, making real-time disaster monitoring and proactive message push difficult. These deficiencies severely restrict further improvements in the comprehensive prevention and control capabilities and intelligent level of coal mine disasters.

[0005] Therefore, there is a need to improve the existing technology. Summary of the Invention

[0006] In view of this, the purpose of this invention is to provide a coal mine disaster monitoring and early warning method and system based on the Mining HarmonyOS operating system. This involves establishing a multi-source heterogeneous data acquisition standard to achieve unified acquisition of various monitoring data such as gas, water, fire, roof, and dust; designing a multi-source heterogeneous data interaction interface to facilitate data integration and sharing; establishing a quantitative assessment index system and model for mine safety risks; integrating monitoring data and early warning indicators for coal mine gas disasters, water hazards, dust, and fires; and using rapid extraction and anomaly identification methods based on monitoring data characteristics to achieve coal mine disaster risk analysis and anomaly identification, ultimately generating early warning results. Based on the Mining HarmonyOS operating system architecture, a disaster early warning APP with functions such as environmental monitoring, disaster early warning, safety briefings, SMS notifications, safety situation awareness, and proactive alarm push is developed to realize integrated monitoring and early warning functions for coal mine disasters.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A coal mine disaster monitoring and early warning method based on the HarmonyOS operating system for mining includes the following steps: S1: The data acquisition module collects and standardizes multi-source heterogeneous monitoring data of gas, water damage, fire, roof and dust, and provides a multi-source heterogeneous data interaction interface. S2: Based on the pre-established mine safety risk quantitative assessment index system and safety risk analysis model, the data service module performs feature extraction and anomaly identification on the monitoring data collected by S1, and generates disaster early warning results. S3: The disaster warning results generated by S2 are transmitted to the mobile terminal APP through the data interaction interface module using the standard Web API interface; S4: Receive early warning results through a mobile terminal APP based on the HarmonyOS operating system for mining, and realize real-time disaster monitoring, early warning display and proactive alarm push.

[0008] Furthermore, S1 establishes a coal mine safety data interaction standard, including a safety monitoring data interaction standard, an early warning analysis data interaction standard, and an electromechanical equipment data interaction standard. The early warning analysis data interaction standard includes a unified regional code, a unified early warning event, a unified early warning level, and a unified interaction format. The unified early warning level is divided into five levels: normal, Level I blue warning, Level II yellow warning, Level III orange warning, and Level IV red warning.

[0009] Furthermore, in S1, a protocol adapter is used to convert data from monitoring systems from different manufacturers into at least one of defined data, real-time data, abnormal data, historical data, and statistical data, and unified collection is achieved through at least one of the OPC DA protocol, OPC UA protocol, FTP protocol, and MQTT protocol.

[0010] Furthermore, the security risk analysis model in S2 includes the following sub-steps: The fluctuation characteristics of the monitoring data are constructed, and the average actual fluctuation amplitude is calculated using the following formula:

[0011] in, For monitoring cycle t The maximum value within, To monitor the minimum value within the period t, For smoothing coefficients, This represents the average actual fluctuation range of the previous period. Construct features of minute variations and trend changes in multi-time-window data; Short-term trend identification is performed based on the MK trend test method, and the Z-score is calculated:

[0012] in, S For order series statistics, for S The variance; By integrating the identification of false data of integrated equipment failure, the identification of monitoring failure, the identification of slow upward trend in the medium and long term, and the identification of abnormal data mutation, disaster early warning results are generated.

[0013] Furthermore, the data mutation anomaly identification includes calculating the average fluctuation range RT of the data over the most recent 30 minutes, identifying an upward trend using the MK trend test method, and calculating the degree of deviation between the current monitored value and the 3-day average. When at least one of the preset threshold conditions is met, it is determined to be a mutation anomaly.

[0014] A coal mine disaster monitoring and early warning system based on the HarmonyOS operating system for mining includes a data acquisition module, a data service module, a data interaction interface module, and a mobile terminal APP module; The data acquisition module is used to uniformly collect and standardize multi-source heterogeneous monitoring data of gas, water damage, fire, roof and dust, and provide a multi-source heterogeneous data interaction interface. The data service module is connected to the data acquisition module and is used to extract features and identify anomalies in the collected monitoring data based on the mine safety risk quantitative assessment index system and safety risk analysis model, and generate disaster early warning results. The data interaction interface module is connected to the data service module and uses a standard Web API interface to transmit disaster early warning results to the mobile terminal APP module. The mobile terminal APP module is based on the mining HarmonyOS operating system and is connected to the data interaction interface module. It is used to receive early warning results and realize real-time disaster monitoring, early warning display and active alarm push.

[0015] Furthermore, the data acquisition module incorporates coal mine safety data interaction standards, including safety monitoring data interaction standards, early warning analysis data interaction standards, and electromechanical equipment data interaction standards. The early warning analysis data interaction standards include unified regional coding, unified early warning events, and unified early warning levels divided into normal, Level I blue warning, Level II yellow warning, Level III orange warning, and Level IV red warning.

[0016] Furthermore, the data service module has a built-in automatic identification model for abnormal monitoring data. This model includes a device fault pseudo data identification unit, a monitoring failure identification unit, a medium-to-long-term trend slow upward identification unit, and a data mutation abnormal identification unit.

[0017] Furthermore, the mobile terminal APP module adopts a hybrid development mode of ArkTS native development and WebView+H5 to realize environmental monitoring, disaster early warning, safety briefing, SMS notification, security situation awareness and alarm proactive push functions. The alarm proactive push is implemented by polling in combination with the mobile system NotificationService service. The mobile terminal APP module supports message subscription. Users can subscribe to alarm types and alarm levels to achieve personalized alarm push. The push message includes the alarm device name, installation address, type, status, start time, duration and monitoring value. It also supports alarm cause analysis, handling measures recommendations, on-site video retrieval, nearby personnel viewing and instant messaging functions.

[0018] The beneficial effects of this invention are as follows: This invention establishes a unified standard for coal mine safety data exchange and a multi-source heterogeneous data acquisition specification, enabling standardized and unified acquisition and efficient fusion and sharing of monitoring data for various disasters such as gas, water hazards, fires, roof collapses, and dust. It fundamentally solves the core problems of existing monitoring systems, such as severe information silos, large differences in data structures, and difficulties in sharing and fusion.

[0019] This invention constructs a complete quantitative assessment index system for mine safety risks and a multidimensional safety risk analysis model. Through rapid extraction of monitoring data features and automatic anomaly identification methods, it achieves accurate analysis, automatic anomaly identification, and integrated early warning of various disaster risks in coal mines, significantly improving the timeliness and accuracy of disaster risk identification and overcoming the shortcomings of existing technologies that lack a unified risk assessment system and automatic early warning capabilities.

[0020] This invention develops a fully functional mobile terminal disaster early warning application based on the HarmonyOS operating system for mining. It realizes real-time disaster monitoring, visualization of early warning results, safety situation awareness, and proactive and accurate push of alarm information. It completely solves the problems of the existing technology's single form of disaster information display and the inability to deliver early warning messages to relevant personnel in a timely manner, enabling mine managers and emergency rescue personnel to grasp the dynamics of disasters anytime and anywhere and respond quickly.

[0021] This invention organically combines multi-source data acquisition, in-depth risk analysis, standard interface interaction, and mobile intelligent early warning to form a complete closed-loop system for coal mine disaster integrated monitoring and early warning. This greatly improves the comprehensive prevention and control capabilities and safety management level of coal mine disasters, fully conforms to the development direction of intelligent coal mine construction, and has good compatibility, scalability, and practical application value.

[0022] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0023] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 This is a diagram of the overall system architecture. Figure 2 For a unified data collection architecture; Figure 3 This constitutes the standard data for safety monitoring; Figure 4 Technical architecture for data acquisition services; Figure 5 Design diagram for APP development architecture; Figure 6 Flowchart of message push notification process; Figure 7 This is a system functional structure diagram. Detailed Implementation

[0024] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0025] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0026] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0027] Example 1 This embodiment demonstrates deployment and application in a large-scale modern mine with an annual output of 2 million tons. The mine has established multiple environmental monitoring subsystems, including gas monitoring, hydrological monitoring, roof pressure monitoring, dust concentration monitoring, bundled tube monitoring, fiber optic temperature measurement, and fire monitoring. It also possesses a comprehensive underground wireless communication network and coverage for mobile terminals operating the HarmonyOS mining operating system.

[0028] Figure 1 The overall system architecture diagram of this invention is shown. The system consists of four main parts: a data acquisition module, a data service module, a data interaction interface module, and a mobile terminal application module. The data acquisition module is deployed on an industrial server in the mine's surface central computer room. The data service module is deployed on the same high-performance server as the data acquisition module. The data interaction interface module provides services externally using a standard Web API. The mobile terminal application is installed on tablets or mobile phones carrying mine management personnel, safety inspectors, and frontline team leaders, all equipped with the mine's HarmonyOS operating system. The four main parts achieve real-time data interaction through the mine's gigabit industrial ring network and wireless communication network.

[0029] Figure 2 The diagram illustrates the overall architecture of the unified data acquisition system. The data acquisition module first establishes a coal mine safety data element description specification according to national and industry standards, and formulates unified standards for safety monitoring data interaction, early warning analysis data interaction, and electromechanical equipment data interaction. Then, it uses various protocol adapters to enable access to existing subsystems, and finally converts all heterogeneous data into a unified standard format and stores it in the central database.

[0030] Figure 3 The data structure of the safety monitoring standard is shown. All accessed monitoring data is uniformly converted into five categories: definition data, real-time data, abnormal data, historical data, and statistical data. Real-time data is updated once per second, abnormal data is immediately marked when a sensor exceeds its limit or a communication failure occurs, and historical data is compressed and stored at the minute level.

[0031] The specific workflow of Example 1 is as follows: 1. The data acquisition module collects gas sensor data in real time via OPC UA protocol, roof pressure monitoring data via OPC DA protocol, dust concentration data via MQTT protocol, and retrieves historical report files of bundle tube monitoring and fiber optic temperature measurement daily via FTP protocol. All data is immediately converted into a unified format according to predefined data element specifications after entering the protocol adapter.

[0032] 2. The data service module performs a complete security risk analysis on the latest collected real-time data every minute. First, it calculates the fluctuation characteristics, minor change characteristics, and trend characteristics of each monitoring point. Then, it sequentially executes four sub-models: equipment fault pseudo-data identification, monitoring failure identification, identification of slow upward trend in the medium to long term, and identification of data mutation anomalies. When any sub-model outputs an abnormal risk value greater than a set threshold, a corresponding level of early warning event is immediately generated.

[0033] 3. The generated early warning events are pushed to all online mobile terminal applications in real time via the standard Web API of the data interaction interface module.

[0034] 4. After receiving an alert, the mobile terminal application filters the alerts according to the user's pre-set message subscription rules. For alerts that meet the criteria, the notification service of the mining HarmonyOS will immediately pop up with sound, vibration and text reminders. After the user clicks on the notification, they can enter the detailed information interface, where they can view a 3D display of the alarm location, real-time video, distribution of nearby personnel, historical trend curves and automatic recommended handling measures by the system.

[0035] Example 2 This embodiment is applied in depth in another high-gas outburst mine, focusing on verifying the feature extraction of monitoring data, the automatic anomaly identification model, the data mutation anomaly identification model, and the regional collaborative early warning function.

[0036] Figure 4 The diagram illustrates the technical architecture of the data acquisition service. The data acquisition-driven service first backs up the raw data to the local disk, and then passes it through to the data exchange service. The data exchange service is responsible for writing standard-format data into the time-series database, while simultaneously triggering the risk analysis process of the data service module.

[0037] Figure 5 The diagram illustrates the development architecture design of a mobile terminal application. The application employs a hybrid development model. Modules with high performance requirements, such as push notifications, disaster warnings, and real-time video, are developed natively using the ArkTS language. Modules with frequent updates, such as historical data queries and statistical reports, utilize WebView to load H5 pages, enabling rapid iteration.

[0038] Figure 6The message push flowchart is shown. The specific process is as follows: After the application starts, it polls for the latest alarm data every 10 seconds in the foreground or every 30 seconds in the background via the Web API; after the server returns the incremental alarm list, the application filters it according to the local subscription configuration; for alarms that need to be alerted, the Mining HarmonyOS NotificationService service is called to pop up a notification message; after the user clicks on the message, the application pulls the detailed alarm information and marks it as read.

[0039] Figure 7 The system functional structure diagram is shown. The main interface of the mobile terminal application is divided into five modules: environmental monitoring, disaster early warning, safety briefing, SMS notification, and security situation awareness.

[0040] The detailed workflow and core algorithm of Example 2 are explained below: At 23:46 on November 15, 2025, a coal mining face carried out blasting operations. Within 30 seconds after the blasting, the gas sensors showed a significant surge, with the concentration of gas on multiple sensors rapidly increasing from 0.3% to 0.95%.

[0041] 1. The data service module detected this change at 23:46:50 and first performed a data fluctuation characteristic calculation. The average actual fluctuation amplitude was calculated using a modified form of the exponential moving average, and the calculation formula is as follows:

[0042] in, For monitoring cycle t The maximum value within, For monitoring cycle t Minimum value within, This is a smoothing coefficient, with a value of 0.3. This represents the average true volatility of the previous period. For the first average true volatility, a simple averaging method is used to begin the calculation:

[0043] in N This represents the number of monitoring periods. The calculated average true fluctuation range (RT) for the most recent 30 minutes is 0.78, which is more than 6 times the historical 3-day average.

[0044] Short-term trend identification was performed using the Mann-Kendall (MK) trend test. For recent... t Time stamp n Data ( n >10), construct the ordered column:

[0045] calculate Svariance If the data is unique, then:

[0046] If duplicate data exists, it is corrected for duplicate groups. The Z-score is calculated as follows:

[0047] This calculation yielded Z The value is 5.67, which is much greater than 1.96, indicating a significant upward trend.

[0048] 3. The deviation of the current monitored value from the 3-day average reaches 320%. All three indicators mentioned above have triggered preset thresholds, and the system determines this as an abnormal data mutation. Simultaneously, due to the simultaneous anomalies of 8 sensors within the same working area, a regional coordinated early warning is triggered, generating a red alert (Level IV).

[0049] 4. Within 0.8 seconds of the warning event being generated, it was pushed to the terminals of all personnel subscribed to that workface via Web API. The dispatch room director, mine manager, ventilation section manager, and 12 other personnel simultaneously received vibration, sound, and text alerts. After clicking the notification, the mine manager accessed the application, directly retrieved four video feeds from the workface to confirm the situation, and used the one-click call function to issue a voice broadcast evacuation order to six workers nearby, while simultaneously issuing a mobilization order to the ground rescue team. The entire process, from the gas outburst to the evacuation of all personnel to a safe location, took less than 4 minutes.

[0050] Table 1 lists the possible values ​​for the data types used in this invention.

[0051] Table 1

[0052] Table 2 lists the data metadata format and its meaning.

[0053] Table 2

[0054] Table 3 lists the subject classification table of basic mining data elements.

[0055] Table 3

[0056] Table 4 lists the assessment levels for monitoring anomalies.

[0057] Table 4

[0058] The above embodiments demonstrate that the present invention can operate stably in different types of mines, successfully realizing a closed-loop management process of unified collection of multi-source heterogeneous data, accurate identification of disaster risks, and second-level push of early warning information, significantly improving the comprehensive disaster prevention and control capabilities of mines.

[0059] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A coal mine disaster monitoring and early warning method based on the HarmonyOS operating system for mining, characterized in that: Includes the following steps: S1: The data acquisition module collects and standardizes multi-source heterogeneous monitoring data of gas, water damage, fire, roof and dust, and provides a multi-source heterogeneous data interaction interface. S2: Based on the pre-established mine safety risk quantitative assessment index system and safety risk analysis model, the data service module performs feature extraction and anomaly identification on the monitoring data collected by S1, and generates disaster early warning results. S3: The disaster warning results generated by S2 are transmitted to the mobile terminal APP through the data interaction interface module using the standard Web API interface; S4: Receive early warning results through a mobile terminal APP based on the HarmonyOS operating system for mining, and realize real-time disaster monitoring, early warning display and proactive alarm push.

2. The coal mine disaster monitoring and early warning method based on the HarmonyOS operating system for mining, as described in claim 1, is characterized in that: The S1 establishes a coal mine safety data interaction standard, including a safety monitoring data interaction standard, an early warning analysis data interaction standard, and an electromechanical equipment data interaction standard. The early warning analysis data interaction standard includes a unified regional code, a unified early warning event, a unified early warning level, and a unified interaction format. The unified early warning level is divided into five levels: normal, Level I blue warning, Level II yellow warning, Level III orange warning, and Level IV red warning.

3. A coal mine disaster monitoring and early warning method based on the HarmonyOS operating system for mining, as described in claim 1 or 2, characterized in that: In S1, a protocol adapter is used to convert data from monitoring systems from different manufacturers into at least one of defined data, real-time data, abnormal data, historical data, and statistical data, and unified collection is achieved through at least one of the OPC DA protocol, OPC UA protocol, FTP protocol, and MQTT protocol.

4. The coal mine disaster monitoring and early warning method based on the HarmonyOS operating system for mining, as described in claim 1, is characterized in that: The security risk analysis model in S2 includes the following sub-steps: The fluctuation characteristics of the monitoring data are constructed, and the average actual fluctuation amplitude is calculated using the following formula: in, For monitoring cycle t The maximum value within, To monitor the minimum value within the period t, For smoothing coefficients, This represents the average actual fluctuation range of the previous period. Construct features of minute changes and trend changes in multi-time-window data; Short-term trend identification is performed based on the MK trend test, and the Z-score is calculated: in, S For order series statistics, for S The variance; By integrating the identification of false data of integrated equipment failure, the identification of monitoring failure, the identification of slow upward trend in the medium and long term, and the identification of abnormal data mutation, disaster early warning results are generated.

5. A coal mine disaster monitoring and early warning method based on the HarmonyOS operating system for mining, as described in claim 4, is characterized in that: The data mutation anomaly identification includes calculating the average fluctuation range RT of the data over the most recent 30 minutes, using the MK trend test to identify an upward trend, and calculating the degree of deviation between the current monitored value and the 3-day average. When at least one of the preset threshold conditions is met, it is determined to be a mutation anomaly.

6. A coal mine disaster monitoring and early warning system based on the HarmonyOS operating system for mining, characterized in that: It includes a data acquisition module, a data service module, a data interaction interface module, and a mobile terminal APP module; The data acquisition module is used to uniformly collect and standardize multi-source heterogeneous monitoring data of gas, water damage, fire, roof and dust, and provide a multi-source heterogeneous data interaction interface. The data service module is connected to the data acquisition module and is used to extract features and identify anomalies in the collected monitoring data based on the mine safety risk quantitative assessment index system and safety risk analysis model, and generate disaster early warning results. The data interaction interface module is connected to the data service module and uses a standard Web API interface to transmit disaster early warning results to the mobile terminal APP module. The mobile terminal APP module is based on the mining HarmonyOS operating system and is connected to the data interaction interface module. It is used to receive early warning results and realize real-time disaster monitoring, early warning display and active alarm push.

7. A coal mine disaster monitoring and early warning system based on the HarmonyOS operating system for mining, as described in claim 6, is characterized in that: The data acquisition module incorporates coal mine safety data interaction standards, including safety monitoring data interaction standards, early warning analysis data interaction standards, and electromechanical equipment data interaction standards. The early warning analysis data interaction standards include unified regional coding, unified early warning events, and unified early warning levels divided into normal, Level I blue warning, Level II yellow warning, Level III orange warning, and Level IV red warning.

8. A coal mine disaster monitoring and early warning system based on the HarmonyOS operating system for mining, as described in claim 6 or 7, characterized in that: The data service module has a built-in automatic identification model for abnormal monitoring data. This model includes a device fault pseudo data identification unit, a monitoring failure identification unit, a slow upward trend identification unit for medium and long term data, and a data mutation anomaly identification unit.

9. A coal mine disaster monitoring and early warning system based on the HarmonyOS operating system for mining, as described in claim 6, is characterized in that: The mobile terminal APP module adopts a hybrid development mode of ArkTS native development and WebView+H5 to realize environmental monitoring, disaster early warning, safety briefing, SMS notification, security situation awareness and alarm proactive push functions. The alarm proactive push is implemented by polling in combination with the mobile system NotificationService service. The mobile terminal APP module supports message subscription. Users can subscribe to alarm types and alarm levels to achieve personalized alarm push. The push message includes the alarm device name, installation address, type, status, start time, duration and monitoring value. It also supports alarm cause analysis, handling measures recommendations, on-site video retrieval, nearby personnel viewing and instant messaging functions.

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