Rock burst early warning system of mine intelligent helmet under open source osmon environment

Abstract

The application provides an impact ground pressure early warning system of a mine-used intelligent helmet in an open source Mongolian environment, belongs to the technical field of mine safety monitoring, and comprises a plurality of mine-used intelligent helmet nodes and a coordination processing node; each mine-used helmet node is integrated with a microseismic ground sound composite sensing module, a posture and wearing state acquisition unit, a roadway position acquisition unit, an open source Mongolian lightweight processing unit, a distributed soft bus communication unit, a local cache unit and a risk avoidance prompt output component. The nodes are connected through a distributed soft bus to form a mobile sensing subnetwork, and record event characteristics and node state summaries synchronously; the coordination processing node generates a homologous micro-fracture event cluster based on time window clustering, main frequency band difference, initial phase difference, arrival time sequence and roadway topology propagation constraints, generates a node credible quality weight according to the node state, and further estimates a seismic source position and determines an early warning level. The application improves the timeliness and reliability of impact ground pressure early warning, and is suitable for underground mobile operation scenes.

Description

Technical Field

[0001] This invention relates to the field of mine safety monitoring technology, and in particular to an early warning system for rockburst in a mine smart helmet under the open-source HarmonyOS environment. Background Technology

[0002] Rockbursts are a common dynamic disaster in deep coal mining, typically caused by the sudden fracturing of coal and rock masses under high stress, resulting in the rapid release of energy. They are characterized by their sudden onset, rapid propagation, and difficulty in timely identification of the affected area, easily causing roadway deformation, equipment damage, and personnel injury. Therefore, during mining operations, how to detect micro-fracture signals in the coal and rock mass in advance and provide reliable early warnings to underground workers before the danger develops into a severe rockburst is a crucial issue that needs to be addressed in the field of mine safety monitoring.

[0003] Existing rockburst monitoring and early warning systems mostly rely on fixed microseismic monitoring stations, ground acoustic monitoring points, stress monitoring instruments, or centralized ground monitoring platforms. While these systems can capture some coal and rock fracture signals, they still have shortcomings in underground mobile operation scenarios: First, there is a spatial distance and roadway obstruction between fixed monitoring points and the actual location of workers. Local micro-fracture signals are easily affected by roadway topology, surrounding rock media, equipment noise, and distance attenuation during propagation, making it difficult to reflect the dangerous state near personnel in a timely manner. Second, single microseismic or single ground acoustic monitoring methods are not adaptable to complex underground interference, and are prone to misjudging signals such as personnel walking, equipment periodic vibration, and tool collisions as dangerous events, or missing micro-fracture events with weak energy but a development trend. Third, existing mining smart helmets mostly focus on positioning, communication, voice, or video functions, failing to use multiple helmet nodes that move with personnel as distributed sensing nodes to participate in collaborative identification. Fourth, underground communication links are unstable. When the edge gateway or ground platform is temporarily unavailable, it is difficult to continue to complete event synchronization, time correction, correlation of events from the same source, and early warning output between local mobile nodes, which can easily cause early warning delays or data loss. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a rockburst early warning system for mining smart helmets under the open-source HarmonyOS environment, in order to solve or alleviate the technical problems existing in the prior art.

[0005] The technical solution of this invention is implemented as follows: a rockburst early warning system for mining smart helmets under the open source HarmonyOS environment, including multiple mining smart helmet nodes and coordination processing nodes; each mining smart helmet node includes a micro-seismic-earth sound composite sensing module, an attitude and wearing status acquisition unit, a roadway location acquisition unit, an open source HarmonyOS lightweight processing unit, a distributed soft bus communication unit, a local cache unit, and a hazard avoidance prompt output component; The microseismic-ground sound composite sensing module is used to collect microseismic signals and ground sound signals; The posture and wearing status acquisition unit is used to acquire the helmet posture and wearing status; The tunnel location acquisition unit is used to acquire the tunnel location where the mining smart helmet node is located. The open-source HarmonyOS lightweight processing unit is used to generate event feature records based on microseismic signals, ground sound signals, acquisition timestamps, tunnel locations, helmet posture, and wearing status, and to mark interference events. The distributed soft bus communication unit is used to enable multiple mining smart helmet nodes to establish a mobile sensing subnet through the distributed soft bus, and to synchronize event feature records and node status summaries within the mobile sensing subnet. The coordination processing node is an underground edge gateway or a coordination helmet node determined by the mobile sensing subnet according to a preset coordination rule. It is used to perform time window clustering on multiple event feature records according to the corrected acquisition timestamp, and generate a cluster of micro-fracture events from the same source by combining the main frequency band difference, the initial phase difference, the arrival time sequence and the roadway topology propagation constraints. It generates node confidence quality weights based on the node status summary, and generates source estimation results and early warning levels based on the cluster of micro-fracture events from the same source, the node confidence quality weights and the roadway topology data. The risk avoidance warning output component is used to output risk avoidance warning information according to the warning level; The local cache unit is used to cache event feature records, node status summaries, and early warning processing data generated when the network is offline.

[0006] Compared with the prior art, the present invention has the following advantages: This invention uses multiple smart helmet nodes for mining as sensing nodes that move with the workers. Each smart helmet node integrates a microseismic-ground sound composite sensing module, a posture and wearing status acquisition unit, a roadway location acquisition unit, and an open-source HarmonyOS lightweight processing unit. This allows microseismic signals, ground sound signals, acquisition timestamps, roadway location, helmet posture, and wearing status to form event feature records at the actual working location of the personnel. Compared to methods relying on fixed monitoring points, this invention extends the sensing range of rockburst micro-fracture events to the area where underground workers are located, reducing early warning lag caused by inconsistencies between fixed monitoring points and the actual personnel location, roadway obstruction, and signal distance attenuation. Simultaneously, through bandpass filtering, noise baseline updating, short-time energy calculation, envelope extraction, frequency band energy ratio calculation, and interference event marking, it can reduce interference from personnel walking, equipment periodic vibration, and instantaneous collisions on the identification of effective events.

[0007] This invention establishes a mobile sensing subnet through the open-source HarmonyOS distributed soft bus, enabling multiple mining smart helmet nodes within the same risk-related area to synchronously record event features and node status summaries. The coordinating processing node further generates clusters of co-originating micro-fracture events based on corrected acquisition timestamps, main frequency band differences, initial phase differences, arrival time sequences, and tunnel topology propagation constraints. It also generates node reliability quality weights based on wearing status, posture stability, sensor self-test status, current signal-to-noise ratio, remaining battery power, communication packet loss rate, and the stress zoning of the tunnel. Thus, the system can fuse event feature records scattered across different helmet nodes into a co-originating event evaluation quantity with propagation time sequences and tunnel topology constraints. Furthermore, it reduces the data impact of abnormal wearing, sensor drift, poor communication quality, or nodes that do not meet propagation constraints during source estimation, improving the reliability of source estimation results and early warning level determination.

[0008] This invention, when generating early warning levels, integrates the number of micro-fracture event clusters originating from the same source within a unit time, the event cluster growth rate, the cumulative event energy value, the source location reliability, the event cluster consistency score, the path distance from the source to the workers, the stress level in the area where the source is located, and the current distribution of workers. This allows the early warning level to simultaneously reflect the development trend of micro-fracture events, the reliability of the source, and the risk status of the roadway where the workers are located, enabling the hazard avoidance information to be closer to the actual danger level of underground personnel. Furthermore, when the mobile sensing subnet is disconnected from the surface safety management platform, the local caching unit can cache event feature records, node status summaries, micro-fracture event clusters originating from the same source, source estimation results, early warning levels, and early warning output records. After the connection is restored, the synchronization priority is determined according to the early warning level, location reliability, event cluster consistency score, and early warning generation time, and incremental synchronization is performed, thereby reducing data loss and early warning chain interruption caused by underground communication interruptions. Attached Figure Description

[0009] The accompanying drawings are provided to further understand the present invention and form part of the specification. They are used together with the embodiments of the present invention to explain the invention and do not constitute a limitation thereof.

[0010] Figure 1 This is a schematic diagram of the overall architecture of the present invention; Figure 2 This is a schematic diagram of the structure of the intelligent helmet node for mining according to the present invention; Figure 3 This is a schematic diagram of the process for establishing a mobile sensing subnet and synchronizing event feature recording in this invention. Figure 4 This is a schematic diagram of the process for generating clusters of homologous micro-fracture events, estimating seismic sources, and outputting early warning levels according to the present invention. Detailed Implementation

[0011] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0012] In this invention, a mining smart helmet node refers to a mining smart helmet equipped with sensing, processing, communication, caching, and prompt output capabilities; a coordination processing node refers to a processing node used within the mobile sensing subnet to cluster, associate, weight, locate, and determine the early warning level of event feature records uploaded by multiple mining smart helmet nodes; it can be an underground edge gateway, or a coordination helmet node determined by the mobile sensing subnet according to preset coordination rules when the underground edge gateway is unavailable; the mobile sensing subnet refers to a temporary collaborative network established by multiple mining smart helmet nodes located in the same risk association area through the open-source HarmonyOS distributed soft bus; an event feature record refers to structured event data generated by the mining smart helmet node based on microseismic signals, ground sound signals, acquisition timestamps, roadway location, helmet posture, and wearing status; and a node status summary refers to structured summary data used to describe the wearing status, posture stability, sensor status, communication status, and roadway stress environment of the mining smart helmet node.

[0013] Example 1 like Figures 1 to 4 As shown, this embodiment provides a rockburst early warning system for mining smart helmets under the open-source HarmonyOS environment, including multiple mining smart helmet nodes and a coordination and processing node. Each mining smart helmet node includes a micro-seismic-ground sound composite sensing module, an attitude and wearing status acquisition unit, a roadway location acquisition unit, an open-source HarmonyOS lightweight processing unit, a distributed soft bus communication unit, a local cache unit, and a hazard avoidance prompt output component.

[0014] During system operation, each mining smart helmet node accompanies the workers into the underground roadway area. The microseismic-ground sound composite sensing module continuously collects microseismic and ground sound signals, the posture and wearing status acquisition unit collects the helmet's posture and wearing status, and the roadway location acquisition unit acquires the roadway location where the mining smart helmet node is located. The above data, along with the acquisition timestamp, is input into the open-source HarmonyOS lightweight processing unit.

[0015] The open-source HarmonyOS lightweight processing unit performs local preprocessing on microseismic signals and ground sound signals, and generates event feature records by combining the acquisition timestamp, tunnel location, helmet posture and wearing status. It also marks interference signals that are suspected to be formed by personnel walking, equipment periodic vibration or instantaneous collision.

[0016] The distributed soft bus communication unit uses the open-source HarmonyOS distributed soft bus to perform node discovery, authentication, and task domain establishment among multiple mining smart helmet nodes, forming a mobile sensing subnet. Within the mobile sensing subnet, each mining smart helmet node synchronizes event feature records and node status summaries, enabling the coordination processing node to obtain event data collected by multiple nodes within the same risk-related area.

[0017] The coordination processing node performs time window clustering on multiple event feature records based on the corrected acquisition timestamps, and generates a cluster of micro-fracture events from the same source by combining the main frequency band, starting phase, arrival time sequence and tunnel topology propagation constraints.

[0018] The coordination processing node further generates a node reliability quality weight for each mining smart helmet node based on the node status summary. The node reliability quality weight is used to describe the degree of reliability contribution of the node's data to subsequent source estimation. When a node exhibits abnormal wearing, drastic posture changes, sensor drift, low remaining battery, high communication packet loss rate, or arrival time sequence that does not meet the roadway topology propagation constraints, the coordination processing node reduces the node reliability quality weight of that node.

[0019] The coordinated processing node generates source estimation results based on clusters of micro-rupture events from the same source, node credibility quality weights, and tunnel topology data. It further combines event development trends, source credibility, and personnel distribution to generate early warning levels.

[0020] The coordination and processing node generates an early warning level based on the number of micro-fracture event clusters from the same source per unit time, the event cluster growth rate, the cumulative event energy, the source location reliability, the event cluster consistency score, the path distance from the source to the workers, the stress level in the source area, and the current distribution of workers. The evacuation warning output component outputs evacuation warning information based on the early warning level. This information may include audible and visual alarms, voice prompts, vibration alerts, evacuation direction prompts, and danger zone warnings.

[0021] When the mobile sensing subnet loses connection with the ground safety management platform, the coordination processing node caches event characteristic records, clusters of micro-rupture events from the same source, source estimation results, warning levels, and warning output records in the form of structured logs in its local cache or a local cache unit within the mobile sensing subnet. Upon reconnection, the coordination processing node determines synchronization priorities based on warning level, location reliability, event cluster consistency score, and warning generation time, and incrementally synchronizes the data from the structured logs to the ground safety management platform, or forwards it to the ground safety management platform via the downhole edge gateway.

[0022] Example 2 Based on Example 1, this embodiment describes the local event triggering process of the microseismic-ground sound composite sensing module and the open-source HarmonyOS lightweight processing unit in the mining smart helmet node.

[0023] The microseismic-ground sound composite sensing module includes an accelerometer for acquiring low-frequency microseismic signals and a ground sound probe for acquiring high-frequency ground sound signals. The accelerometer is fixed to the inside of the shell or the buffer layer of the mining smart helmet to acquire the low-frequency vibration response caused by micro-fractures in the coal and rock mass; the ground sound probe is installed on the shell of the mining smart helmet or at a fixed mounting position near the brim to acquire high-frequency ground sound signals propagating into the roadway space during the coal and rock fracturing process.

[0024] In this embodiment, the low-frequency microseismic signal is not limited to the original microseismic signal at the source point of coal and rock mass fracture, but refers to the low-frequency vibration response formed after the micro-fracture of coal and rock mass occurs, propagating through the surrounding rock, roadway support structure, roadway space, or the location of the worker to the node of the mining smart helmet. The high-frequency ground sound signal refers to the high-frequency acoustic vibration response that propagates to the roadway space and is received by the ground sound probe during the process of coal and rock fracturing, fracture expansion, or local energy release. With this limitation, the mining smart helmet node collects the microseismic-ground sound composite response related to the current location of the worker, rather than requiring the helmet node to form a fixed rigid coupling with the coal and rock mass.

[0025] To reduce the impact of head movement, helmet impact, and equipment vibration on the acquisition results, the accelerometer is mounted on the inside of the helmet shell or on the buffer layer using vibration damping pads, elastic mounting bases, or a fixed mounting structure that fits snugly against the inside of the helmet shell. The ground sound probe is mounted on the helmet shell or near the brim using vibration isolation fasteners, ensuring stable contact between the ground sound probe and the helmet shell while minimizing the impact of instantaneous impacts on the high-frequency ground sound channel. The posture and wearing status acquisition unit synchronously acquires the helmet posture angle and wearing status, which are used to identify interference signals caused by walking, helmet movement, helmet removal, and impacts during subsequent processing.

[0026] The open-source HarmonyOS lightweight processing unit reads the low-frequency micro-vibration signal output by the accelerometer and the high-frequency ground sound signal output by the ground sound probe at a preset sampling period. Let the... Each mining smart helmet node is at any time The collected low-frequency microseismic signals are High-frequency ground sound signal is The open-source HarmonyOS lightweight processing unit respectively... and Bandpass filtering is performed to obtain a low-frequency micro-vibration filtered signal. and high-frequency ground sound filtering signal .

[0027] The sampling frequencies of the low-frequency microseismic channel and the high-frequency ground sound channel are determined according to their respective analysis frequency band upper limits, and meet the sampling accuracy required for effective signal analysis, envelope extraction and main frequency band identification; the bandpass range of the two is determined based on the statistical results of historical microseismic events, historical ground sound events, equipment periodic vibration frequencies and personnel behavior interference frequencies in the mine.

[0028] In practical implementation, the bandpass range, sampling period, and short-time analysis window length can be configured by the underground edge gateway or the surface safety management platform based on historical microseismic-earthquake event samples from the mine, and then distributed to the mining smart helmet node along with the task domain identifier. The mining smart helmet node only stores the parameter configuration for the current working area locally to reduce the computational burden on the open-source HarmonyOS lightweight processing unit.

[0029] In this embodiment, short-time energy can be calculated using a sliding time window, as shown in the following formula:

[0030] In the formula, For the first The mining smart helmet node is at the... The short-time energy value corresponding to each sampling point; This is the length of the short-time analysis window; For the low-frequency micro-vibration filter signal in the first... The amplitude of each sampling point; For the high-frequency ground sound filtering signal in the first The amplitude of each sampling point; This is the energy weighting coefficient for the ground sound signal.

[0031] The open-source HarmonyOS lightweight processing unit updates the noise baseline based on the short-term average energy value during the most recent period of no-trigger state. Let the... The mining smart helmet node is at the... The noise baseline corresponding to each sampling point is Its update formula is:

[0032] In the formula, For the first The mining smart helmet node is at the... The noise baseline updated for each sampling point; This is the noise baseline corresponding to the previous sampling point; This is the baseline smoothing coefficient, with a value ranging from 0 to 1.

[0033] The open-source HarmonyOS lightweight processing unit further extracts the envelopes of the filtered low-frequency micro-vibration signal and high-frequency ground sound signal, and calculates the envelope abrupt change value based on the envelope amplitude changes of adjacent sampling points. Let the... The mining smart helmet node is at the... The envelope amplitude corresponding to each sampling point is The envelope mutation value is then expressed as:

[0034] In the formula, For the first The mining smart helmet node is at the... The envelope mutation value corresponding to each sampling point; The envelope amplitude of the current sampling point; This represents the envelope amplitude of the previous sampling point. The envelope abrupt change value is used to characterize the degree to which the microseismic-ground sound composite signal changes from background noise to anomalous pulse signal within a short period of time.

[0035] Meanwhile, the open-source HarmonyOS lightweight processing unit calculates the frequency band energy ratio within the event window. The frequency band energy corresponding to the low-frequency microseismic signal is denoted as... The frequency band energy corresponding to the high-frequency ground sound signal is denoted as The frequency band energy ratio is expressed as:

[0036] In the formula, For the first The frequency band energy ratio of each mining smart helmet node within the event window; This refers to the frequency band energy corresponding to the high-frequency ground sound signal; This represents the frequency band energy corresponding to the low-frequency microseismic signal. To avoid constants with a denominator of zero, the frequency band energy ratio is used to reflect the energy distribution relationship between high-frequency ground sound components and low-frequency microseismic components within the same event window.

[0037] When short-time energy Above the noise baseline The corresponding energy trigger threshold and envelope mutation value Above the envelope mutation threshold, the bandwidth energy ratio When the signal segment is within the preset effective frequency band ratio range and the duration is within the preset effective duration range, the open-source HarmonyOS lightweight processing unit determines the signal segment as a valid event and triggers the generation of the corresponding event feature record.

[0038] The determination of a valid event trigger can be expressed as:

[0039] In the formula, Energy trigger threshold; This represents the envelope mutation threshold. This is the lower limit of the band energy ratio; This represents the upper limit of the band energy ratio; The duration of the signal; This is the lower limit of the effective duration; This represents the maximum effective duration.

[0040] The energy trigger threshold, envelope mutation threshold, frequency band energy ratio range, and effective duration range are determined based on historical event-free state data, historical effective micro-fracture event data, and historical interference event data of the current operating area. The energy trigger threshold can be determined according to... set up, and These represent the short-time energy mean and standard deviation under historical event-free conditions, respectively. This is the energy threshold adjustment coefficient; the envelope mutation threshold can be adjusted according to... set up, and These represent the mean and standard deviation of the envelope mutation value under historical event-free conditions. Envelope mutation threshold adjustment coefficient. The frequency band energy ratio range is determined by the energy distribution range of high-frequency ground sound components and low-frequency microseismic components in historical effective micro-rupture events, and the effective duration range is determined by the duration distribution of historical micro-rupture events.

[0041] When a mining smart helmet node enters a new roadway area or the stress level of the area changes, the coordination processing node can update the above threshold based on the historical sample statistics of the corresponding roadway area, and synchronize the updated threshold parameters to the relevant mining smart helmet nodes through the mobile sensing subnet.

[0042] After meeting the above-mentioned valid event triggering conditions, the open-source HarmonyOS lightweight processing unit extracts the peak value of the microseismic channel, the peak value of the ground sound channel, the main frequency band, the initial phase, the duration, the signal-to-noise ratio, the short-time energy value, and the frequency band energy ratio from the signal segment. Combined with the acquisition timestamp, tunnel location, helmet posture, and wearing status, it generates corresponding event feature records. These event feature records serve as the basic input data for subsequent mobile sensing subnet synchronization, generation of co-origin micro-fracture event clusters, calculation of node reliability quality weights, and determination of early warning levels.

[0043] Through the above processing, the low-frequency microseismic signal and the high-frequency ground sound signal are not triggered independently, but are combined within the same mining smart helmet node after bandpass filtering, noise baseline update, short-time energy calculation, envelope extraction, frequency band energy ratio calculation and abnormal pulse detection to form an effective event triggering basis, so that the local event triggering result can simultaneously reflect the correlation between the low-frequency microseismic response and the high-frequency ground sound response.

[0044] Example 3 This embodiment, based on Embodiment 2, explains the field composition, generation process, and interference event marking method of the event feature record.

[0045] Upon successful event triggering, the open-source HarmonyOS lightweight processing unit writes signal characteristics, time information, location status information, and time correction information into the event characteristic record. Signal characteristics include peak values ​​of the microseismic channel and ground sound channel, dominant frequency band, initial phase, duration, signal-to-noise ratio, short-time energy value, and frequency band energy ratio. Location status information includes the coordinates of the tunnel, helmet posture angle, and wearing status. Time information includes the acquisition timestamp and time correction parameters.

[0046] Event feature records can be represented using structured fields, including event number, helmet node identifier, acquisition timestamp, time correction parameters, roadway coordinates, helmet attitude angle, wearing status, microseismic channel peak value, ground sound channel peak value, main frequency band, starting phase, duration, signal-to-noise ratio, short-time energy value, frequency band energy ratio, and local preliminary event type.

[0047] Among them, the data collection timestamp is the event trigger time recorded by the local clock of the mining smart helmet node; the time correction parameter is the time offset correction amount issued by the reference node in the mobile sensing subnet; the roadway coordinates may include roadway number, mileage position, roadway branch identifier and height level; the helmet attitude angle may include pitch angle, roll angle and heading angle; the wearing status is used to indicate whether the helmet is in a normal wearing state; the local preliminary event type includes suspected micro-fracture event, personnel walking interference, equipment periodic vibration interference, instantaneous collision interference or unknown event.

[0048] The open-source HarmonyOS lightweight processing unit categorizes interference events based on posture and wearing status, signal periodicity, frequency band distribution, and duration. For personnel walking interference, an event is categorized as such when the helmet posture angle exhibits a periodic change consistent with the walking frequency, and the low-frequency micro-vibration signal displays continuous periodic impact characteristics. For equipment periodic vibration interference, an event is categorized as such when the event's main frequency band is stably concentrated near the equipment's rotation frequency, and the duration exceeds the upper limit of the typical duration of coal and rock micro-fracture events. For transient collision interference, an event is categorized as such when the helmet posture undergoes a sudden change, and the peak values ​​of the micro-vibration channel and the ground sound channel only exhibit single short pulses, while lacking a propagation relationship consistent with the arrival time of adjacent helmet nodes.

[0049] The results of the interference event marking are written to the local preliminary event type field of the event feature record. For event feature records marked as interference events, the open-source HarmonyOS lightweight processing unit excludes them from subsequent association with similar events, or only generates event feature records with the lowest synchronization priority. Event feature records with the lowest synchronization priority are only used for subsequent log tracing, false alarm analysis, or sensor status assessment, and are not used as the main input for source estimation and early warning level determination.

[0050] In this way, the system completes primary interference elimination locally at the mining smart helmet node, reducing the occupation of mobile sensing subnet communication resources by invalid events, and lowering the probability that the coordination processing node will mistakenly associate personnel behavior or equipment vibration as micro-fracture events of the same origin.

[0051] Example 4 This embodiment, based on Embodiment 1, describes the process of establishing a mobile sensing subnet by a distributed soft bus communication unit.

[0052] After each mining smart helmet node is activated, the distributed soft bus communication unit broadcasts node discovery information via the open-source HarmonyOS distributed soft bus. The node discovery information includes the helmet node identifier, node communication address, roadway number, work team identifier, current battery level, communication quality summary, and equipment capability summary.

[0053] After node discovery is complete, two-way authentication is performed between adjacent mining smart helmet nodes. Two-way authentication can be completed based on node certificates, pre-configured keys, random challenge values, and session keys. Authentication-enabled mining smart helmet nodes enter the candidate node set, while unauthenticated nodes are not allowed to join the mobile sensing subnet.

[0054] After authentication, each mining smart helmet node reports its device capabilities to the coordination processing node. This reported capability information includes node computing power, remaining battery power, sensor status, communication quality, cache capacity, and current location source. Node computing power indicates the ability of the open-source HarmonyOS lightweight processing unit to perform local or coordinated computation; remaining battery power determines whether the node can handle coordination tasks; sensor status indicates whether the microseismic-earth sound composite sensing module, attitude and wearing status acquisition unit, and roadway location acquisition unit are functioning normally; communication quality indicates the link quality between the node and adjacent nodes or underground edge gateways; cache capacity indicates the amount of structured log data that the local cache unit can store; and current location source indicates that the roadway location was obtained through one or more methods, including UWB positioning, Bluetooth beacons, inertial trajectory estimation, roadway identification, or manual area registration.

[0055] The coordination and processing nodes determine which mining smart helmet nodes are located within the same risk-related area based on roadway number, work team, adjacent distance, communication quality, local stress level, and remaining battery power. A risk-related area refers to a set of nodes that are correlated in terms of roadway topology path distance, work area, stress zone, and communication accessibility. For example, multiple mining smart helmet nodes within the same mining face, adjacent roadway intersections, or the same stress zone in a longwall roadway can be grouped into the same mobile sensing subnet.

[0056] The coordination processing node generates a task domain identifier and distributes it to the mining smart helmet nodes that have joined the mobile sensing subnet. The task domain identifier is bound to the node set, roadway area, task start time, reference time source, and data synchronization strategy within the mobile sensing subnet. The mining smart helmet nodes subsequently carry the task domain identifier when synchronizing event feature records and node status summaries to ensure that the coordination processing node can distinguish data within different risk-related areas.

[0057] Through the above process, the system can organize multiple mining smart helmet nodes into a mobile sensing subnet for identifying micro-fracture events caused by rockburst, based on the open-source HarmonyOS distributed soft bus, and form a unified task domain within the mobile sensing subnet.

[0058] Example 5 This embodiment, based on Embodiment 4, explains the process of selecting the time reference and correcting the event feature recording time within the mobile sensing subnet.

[0059] When the underground edge gateway is online, the mobile sensing subnet uses the underground edge gateway as its time reference. The underground edge gateway periodically sends time synchronization messages to the mining smart helmet nodes within the mobile sensing subnet. The mining smart helmet nodes estimate their local clock offset based on the local time received from the time synchronization message, the gateway's transmission time, and the round-trip communication delay, and generate corresponding time correction parameters.

[0060] When the underground edge gateway is unavailable, the mobile sensing subnet determines a coordinating helmet node based on at least two of the following: node computing power, remaining battery power, communication quality, and clock stability, according to preset coordination rules. This coordinating helmet node is then used as a temporary reference node. Preset coordination rules may include: prioritizing mining smart helmet nodes with remaining battery power exceeding a preset battery power threshold, communication quality exceeding a preset communication quality threshold, clock drift rate below a preset drift threshold, and computing power sufficient for coordination processing. If multiple nodes meet these conditions, the node with the highest overall score is selected as the coordinating helmet node.

[0061] After the coordination helmet node is determined, time correction parameters are sent to other mining smart helmet nodes within the mobile sensing subnet. Let the first... The local data collection timestamp of each mining smart helmet node is: The time offset is The corrected collection timestamp is:

[0062] In the formula, For the first The corrected collection timestamp of the event feature record corresponding to each mining smart helmet node; Collect the timestamp locally for this node; This is the time correction parameter for this node relative to the reference node.

[0063] Each smart helmet node in the mine writes a local acquisition timestamp and time correction parameters when generating event feature records. After receiving the event feature records, the coordination processing node obtains the corrected acquisition timestamp according to the above formula, and divides each event feature record into the corresponding time window according to the corrected acquisition timestamp. The width of the time window can be determined based on the range of coal and rock medium propagation speed, the maximum roadway topology path distance of the nodes in the mobile sensing subnet, and the allowable time error.

[0064] In this way, even if there is a deviation in the local clock of each mining smart helmet node in the mobile sensing subnet, the coordination processing node can still classify the collection records of the same micro-fracture event on different nodes into the same time window, providing a time basis for the subsequent generation of clusters of micro-fracture events from the same source.

[0065] Example 6 This embodiment, based on Embodiment 5, describes the process by which the coordinated processing node generates clusters of homologous micro-fracture events.

[0066] After receiving event feature records synchronized from multiple mining smart helmet nodes, the coordination processing node first removes interfering events marked as excluded from association with the same source event. For the event feature records that are not excluded, they are clustered into candidate event groups according to the corrected collection timestamps. The event feature records within the candidate event groups must be collected within the same time window.

[0067] For each candidate event group, the coordination processing node calculates the main frequency band difference, initial phase difference, peak energy change direction, duration difference, and arrival time sequence relationship between any two event feature records within the group. Let the first... The main frequency center frequency of each event feature record is , No. The main frequency center frequency of each event feature record is The difference in the main frequency band is:

[0068] In the formula, For the first The event characteristic record and the first The difference in the main frequency band between event feature records.

[0069] Let the first The starting phase of each event feature record is , No. The starting phase of each event feature record is The initial phase difference is:

[0070] In the formula, For the first The event characteristic record and the first The initial phase difference between event feature records.

[0071] The coordination processing node reads the tunnel topology data and obtains the first... The mining smart helmet node and the first Path distance between nodes of a mining smart helmet The tunnel topology data includes tunnel nodes, tunnel edges, tunnel length, intersection locations, branch directions, and stress zones. Path distance. It is not the straight-line distance, but the travel or propagation path distance calculated along the topological path of the alleyway.

[0072] The coordination processing node is selected based on the propagation speed within the range of coal and rock media propagation speed. Determine whether the event feature records collected by different mining smart helmet nodes satisfy the propagation constraints: ; In the formula, For the first The corrected collection timestamp of the event feature record corresponding to each mining smart helmet node; For the first The corrected collection timestamp of the event feature record corresponding to each mining smart helmet node; The first in the tunnel topology data The mining smart helmet node and the first The path distance between nodes of the smart helmet for mining; The propagation speed is selected within the range of propagation speeds in coal and rock media; Allowable error for arrival time difference.

[0073] When a candidate event group simultaneously satisfies the requirements of time window consistency, frequency band consistency, phase correlation, reasonable arrival timing, and propagation constraints, the coordinating processing node confirms the candidate event group as a cluster of micro-fracture events originating from the same source. A cluster of micro-fracture events originating from the same source includes the event cluster number, the set of participating helmet nodes, the arrival time of each node, the spatial coordinates of each node, the energy value of each node, the dominant frequency band of the event, the duration of the event, the event cluster consistency score, and the candidate propagation direction.

[0074] The coordination node further calculates the event cluster consistency score:

[0075] In the formula, Assign a consistency score to the event cluster; Main frequency band consistency score; The initial phase correlation degree; To achieve the desired time-series matching degree; The matching degree is used for path propagation constraints; , , , These are the corresponding weight coefficients, and they satisfy:

[0076] Among them, the main frequency band consistency score can be generated based on the relationship between the main frequency band difference within the candidate event group and the preset frequency band difference threshold; the initial phase correlation degree can be generated based on the relationship between the initial phase difference and the preset phase difference threshold; the arrival time sequence matching degree can be generated based on the degree of consistency between the actual arrival time sequence and the theoretical arrival time sequence corresponding to the roadway topology path distance; and the path propagation constraint matching degree can be generated based on the error magnitude of the propagation constraint formula.

[0077] In one implementation, the main frequency band consistency score, initial phase correlation, arrival timing matching, and path propagation constraint matching are all normalized to values ​​between 0 and 1. For any consistency evaluation item, let its corresponding score be... The corresponding error value is The first error threshold is The second error threshold is ,and The score for this consistency evaluation item is determined according to the following rules: when hour,

[0078] when hour,

[0079] when hour,

[0080] In the formula, Indicates the type of consistency evaluation item, and ;when hour, Main frequency band consistency score , Main frequency band difference; when hour, Initial phase correlation , The initial phase difference; when hour, To achieve time-series matching degree , This represents the error between the measured arrival time difference and the theoretical arrival time difference; when hour, Matching degree for path propagation constraints , To propagate the constraint residuals. The first and second error thresholds are preset based on historical micro-fracture event samples, historical interference event samples, the propagation speed range of the current roadway area, and the roadway topology path distance between nodes in the mobile sensing subnet, or are updated by the coordination processing node based on historical statistical results.

[0081] Through the above processing, the coordinated processing node can convert the main frequency band difference, initial phase difference, arrival timing error, and propagation constraint residual into evaluation quantities within the same value range, and then generate an event cluster consistency score according to the corresponding weight coefficients. The event cluster consistency score serves as an input parameter for subsequent source estimation and warning level determination; the higher the event cluster consistency score, the more likely the candidate event group originates from the same micro-rupture event, and the higher the credibility contribution of the event cluster in subsequent source estimation and warning level determination.

[0082] Example 7 This embodiment, based on embodiments one through six, explains the process of generating node state summaries and node trusted quality weights.

[0083] Each smart helmet node in the mine generates a node status summary according to a preset cycle. The node status summary includes wearing status, posture stability, sensor self-test status, current signal-to-noise ratio, remaining battery power, communication packet loss rate, and stress zoning of the roadway.

[0084] Wearing status is acquired by the posture and wearing status acquisition unit to determine whether the helmet is in a normal wearing state. Posture stability is determined based on the change in helmet posture angle over a period of time to determine whether the helmet has experienced severe shaking, falls, or abnormal collisions. Sensor self-test status describes whether the accelerometer, ground sound probe, posture and wearing status acquisition unit, and tunnel location acquisition unit are functioning properly. The current signal-to-noise ratio is calculated by the open-source HarmonyOS lightweight processing unit based on the signal amplitude and noise baseline within the effective event window. The communication packet loss rate is obtained by the distributed soft bus communication unit based on the number of packets sent and acknowledged during the synchronization process, recorded according to event characteristics. The local tunnel stress zoning is determined by the tunnel location acquisition unit in conjunction with tunnel topology data and a preset stress zoning table.

[0085] The coordination processing node generates node confidence quality weights based on the node status summary. These weights are used to adjust the contribution ratio of each node's data during the association of micro-fracture event clusters and source estimation. The node confidence quality weights are updated according to the following formula:

[0086] In the formula, For the first The updated node trust quality weight for each mining smart helmet node; This is the initial weight of the node; The attenuation coefficient is the factor used when the garment is worn. This is the sensor state attenuation coefficient; The remaining power attenuation coefficient; This is the packet loss rate attenuation coefficient for communication. The attenuation coefficients are the propagation constraints of the tunnel topology; each attenuation coefficient ranges from 0 to 1.

[0087] Each attenuation coefficient takes a value between 0 and 1 based on the node status: when the wearing status is normal, the sensor self-test is normal, the signal-to-noise ratio meets the requirements, the remaining power is not lower than the preset power threshold, the communication packet loss rate is not higher than the preset packet loss rate threshold, and the arrival time meets the roadway topology propagation constraints, the corresponding attenuation coefficient takes a value close to 1; when there are abnormal wearing conditions, hat removal, sensor drift, ground sound probe abnormalities, low signal-to-noise ratio, low power, high packet loss rate, or propagation constraints not being met, the corresponding attenuation coefficient is reduced according to the preset rules.

[0088] Through the above processing, the coordination node can transform the node's own state, sensor reliability, communication quality, and propagation constraint consistency into a unified node credibility quality weight. In subsequent source estimation, nodes with higher credibility quality contribute more to the overall positioning error, while nodes with lower credibility quality contribute less, thus reducing the impact of anomalous nodes on the source estimation results.

[0089] Example 8 This embodiment, based on Embodiments 6 and 7, describes the process by which the coordinated processing node generates source estimation results based on clusters of micro-fracture events from the same source, node confidence quality weights, and tunnel topology data.

[0090] The coordination processing node uses the arrival time difference of each mining smart helmet node in the same micro-fracture event cluster as a constraint, and combines it with roadway topology data to generate candidate source regions. The candidate source regions can be composed of roadway segments whose path distance from the participating helmet nodes meets a preset range, or they can be jointly defined by historical high-stress areas, areas adjacent to the mining face, and the direction of event propagation.

[0091] For any candidate source location within the candidate source region The coordination processing node calculates the candidate earthquake source location based on the tunnel topology path. To the The path distance between the locations of the nodes in the mining smart helmet is calculated based on the theoretical time difference of arrival, using the propagation speed of coal and rock media. Meanwhile, based on the location of the candidate epicenter... To the The theoretical energy attenuation value is calculated based on the path distance between individual nodes of the mining smart helmet, the attenuation coefficient of roadway branches, and the attenuation parameters of coal and rock media. Roadway topology path constraint error Used to indicate the location of candidate seismic sources The degree of inconsistency between the corresponding theoretical propagation path and the actual arrival time and candidate propagation direction.

[0092] In one implementation, the candidate seismic source location is set. To the The topological path distance of the tunnel where each mining smart helmet node is located is The propagation speed of coal and rock media is Then the first The theoretical arrival time for each mining smart helmet node can be expressed as:

[0093] When the earliest arriving helmet node or a preset reference node in the cluster of micro-fracture events from the same origin is used as the reference node, the theoretical arrival time difference can be expressed as:

[0094] In the formula, Candidate epicenter locations Corresponding to the The theoretical arrival time difference of each mining smart helmet node Candidate epicenter locations To the The theoretical propagation time of a single smart helmet node for mining applications. Candidate epicenter locations The theoretical propagation time to the reference node.

[0095] The theoretical energy attenuation value is determined based on the roadway topological path distance, roadway branch attenuation coefficient, and coal-rock medium attenuation parameters. The candidate seismic source location is assumed. The corresponding estimated source energy is The coal and rock media attenuation parameters are Candidate epicenter location To the The product of the attenuation coefficients of the roadway branches traversed by the nodes of the mining smart helmet is: Then the first The theoretical energy attenuation value corresponding to a single node in a mining smart helmet can be expressed as:

[0096] In the formula, Candidate epicenter locations Corresponding to the The theoretical energy decay value of a single smart helmet node for mining applications. Candidate epicenter locations The corresponding estimated source energy, It is the product of the attenuation coefficients of the roadway branches. For the attenuation parameters of coal and rock media, Candidate epicenter locations To the The distance of the tunnel topology path for each mining smart helmet node.

[0097] The coal and rock medium attenuation parameters The attenuation coefficient of the roadway branch can be obtained by fitting historical microseismic events, historical ground sound events, or historical data from fixed monitoring points in the mine. The attenuation coefficient of the roadway branch is pre-calibrated based on the number of roadway branches, the roadway turning angle, the type of support structure, and the changes in the roadway cross-section. For roadway areas lacking historical calibration data, the attenuation parameters of the same stress zone or adjacent roadway areas can be used as initial parameters, and corrected based on manual confirmation results or fixed monitoring point records after subsequent early warning log feedback.

[0098] The overall positioning error is calculated using the following formula:

[0099] In the formula, Candidate epicenter locations The corresponding overall positioning error, The number of nodes in the mining smart helmet that participate in the cluster of homogeneous micro-fracture events. For the first The node trust quality weight of each mining smart helmet node. For the first The measured arrival time difference corresponding to each mining smart helmet node Candidate epicenter locations Corresponding to the The theoretical arrival time difference of each mining smart helmet node For the first The signal energy value collected by a mining smart helmet node Candidate epicenter locations Corresponding to the The theoretical energy attenuation value of a single smart helmet node for mining applications. Candidate epicenter locations Corresponding to the The tunnel topology path constraint error of a single mining smart helmet node. , , These are the weighting coefficients for arrival time difference error, signal energy attenuation error, and roadway topology path constraint error, respectively.

[0100] The coordination processing node traverses the candidate source locations within the candidate source region, calculates the comprehensive positioning error corresponding to each candidate source location, and determines the location with the smallest comprehensive positioning error that satisfies the tunnel topology propagation constraint as the source estimation result. The source estimation result includes source coordinates, event energy level, and location confidence.

[0101] Location reliability can be generated based on the comprehensive location error, the number of participating nodes, the average node credibility quality weight, and the event cluster consistency score. For example, the smaller the comprehensive location error, the more participating nodes, the higher the node credibility quality weight, and the higher the event cluster consistency score, the higher the location reliability. The generated source estimation results are then used in the subsequent warning level determination step.

[0102] Therefore, the source estimation result is determined by the arrival time difference of multiple nodes, energy attenuation, tunnel topology path and node credibility quality weight.

[0103] Example 9 This embodiment, based on embodiment eight, explains the process of generating warning levels, outputting risk avoidance prompts, and synchronizing network outage cache.

[0104] The coordination and processing node calculates the number of micro-fracture event clusters from the same source, the growth rate of the event clusters, and the cumulative energy value of the events per unit time according to a preset statistical period. The number of micro-fracture event clusters from the same source per unit time reflects the frequency of micro-fracture events; the growth rate of the event clusters reflects the increasing trend of micro-fracture events over time; and the cumulative energy value of the events reflects the cumulative degree of energy released by coal and rock mass fracturing. The coordination and processing node also reads the source location reliability, event cluster consistency score, path distance from the source to the workers, stress level in the area where the source is located, and the current distribution of workers.

[0105] The warning judgment value corresponding to the warning level is generated according to the following formula:

[0106] In the formula, This is the warning judgment value; This is the normalized value corresponding to the number of homologous microfracture event clusters per unit time; This is the normalized value corresponding to the event cluster growth rate. This is the normalized value corresponding to the accumulated event energy. The fusion value of source location confidence and event cluster consistency score; The risk value corresponds to the path distance from the earthquake source to the workers. The risk value corresponds to the stress level in the area where the earthquake source is located and the current distribution of workers. , , , , , These are the corresponding weighting coefficients.

[0107] Among them, the normalized value corresponding to the number of homologous microfracture event clusters per unit time It can be obtained by normalizing the number of event clusters relative to a preset threshold within the current statistical period; the normalized value corresponding to the event cluster growth rate. It can be obtained from the growth ratio between the number of event clusters in the current statistical period and the number of event clusters in the previous statistical period; the normalized value corresponding to the cumulative event energy value. It can be obtained by weighted accumulation of the energy values ​​of each node in a cluster of micro-rupture events from the same source; the fusion value of the source location confidence and the event cluster consistency score. This reflects the credibility of the event on which the current risk assessment is based; the risk value corresponding to the path distance from the epicenter to the workers. Used to reflect the proximity of the earthquake source and workers in the roadway topology; risk values ​​corresponding to the stress level in the earthquake source area and the current distribution of workers. It is used to reflect the ground pressure risk of the hazardous area itself and the degree of personnel exposure.

[0108] In one implementation, the warning judgment value Normalized to a value between 0 and 1. Coordination processing nodes are based on the first-level threshold. Second-level threshold and the third-level threshold The warning levels are divided into four levels: normal, attention, warning, and emergency. :when This is normal; when Attention is always on our minds; when This serves as an early warning; when In an emergency, the hazard warning output component outputs corresponding prompts based on the warning level: a low-intensity prompt is output for a concern level, an audible and visual alarm, voice prompts, and suggested evacuation directions are output for a warning level, and a high-intensity audible and visual alarm, continuous vibration prompts, and emergency evacuation prompts are output for an emergency level. The warning output records are also written to the local cache unit.

[0109] The first, second, and third level thresholds are determined based on historical rockburst event data, historical micro-fracture event cluster statistics, regional stress zoning levels, personnel density, and mine safety management strategies. For high-stress zones, areas with severe mining disturbances, or areas with frequent historical rockburst events, the coordination node can lower the corresponding level threshold, resulting in a higher risk level output for the same warning judgment value; for low-stress zones or areas with fewer personnel, the default level threshold can be maintained.

[0110] The coordination and processing node can also adjust the alert intensity based on the roadway topology path distance from the earthquake source to each mining smart helmet node: when the path distance is less than the preset near-field distance threshold, the alert intensity of the corresponding node is increased and an evacuation direction alert is output; when the path distance is greater than the preset near-field distance threshold but still within the same risk-related area, a attention alert or warning alert is output.

[0111] The early warning output record includes the early warning output time, early warning output method, and receiving node identifier. Early warning output methods include one or more of the following: audible and visual alarms, voice prompts, vibration alerts, screen display prompts, and communication broadcast prompts. The receiving node identifier is used to record the mining smart helmet node that actually receives the hazard avoidance warning information.

[0112] When the mobile sensing subnet is connected to the ground safety management platform, the coordination processing node synchronizes the early warning processing data. When the connection is lost, the coordination processing node writes the event characteristic record, the cluster of micro-rupture events from the same source, the source estimation result, the early warning level and the early warning output record into the structured log, and caches it in the local cache of the coordination processing node or the local cache unit of each mining smart helmet node in the mobile sensing subnet.

[0113] The structured log includes at least the task domain identifier, event number, event cluster number, helmet node identifier, corrected acquisition timestamp, event feature summary, node status summary, node reliability quality weight, source estimation result, warning level, warning output record, and log generation time. After connection is restored, the coordination processing node determines the synchronization priority according to the warning level, location reliability, event cluster consistency score, and warning generation time, and synchronizes the incremental data in the structured log to the ground safety management platform, or forwards it to the ground safety management platform via the downhole edge gateway.

[0114] Through the above processing, the system can still complete local event processing and early warning output by relying on the mobile sensing subnet even when the underground communication link is unstable. After the communication is restored, it can supplement the synchronous early warning process data to ensure the continuity of the rockburst early warning chain.

[0115] During system operation, the microseismic-sound composite sensing module, attitude and wearing status acquisition unit, and tunnel location acquisition unit output microseismic signals, sound signals, helmet attitude, wearing status, and tunnel location, respectively, and input them into the open-source HarmonyOS lightweight processing unit. The open-source HarmonyOS lightweight processing unit generates event feature records and interference event labeling results, which are synchronized to the mobile sensing subnet via the distributed soft bus communication unit. The coordination processing nodes sequentially complete time correction, time window clustering, generation of homogeneous micro-fracture event clusters, calculation of node credibility mass weights, source estimation, and warning level determination. The hazard warning output component outputs hazard warning information according to the warning level. The local cache unit saves event feature records, node status summaries, source estimation results, warning levels, and warning output records when the network is down, and performs incremental synchronization according to synchronization priority after the connection is restored.

[0116] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in the present invention, and these should all be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A pre-warning system for rockburst in a mining smart helmet under the open-source HarmonyOS environment, characterized in that, It includes multiple mining smart helmet nodes and coordination processing nodes; each mining smart helmet node includes a micro-seismic-ground sound composite sensing module, an attitude and wearing status acquisition unit, a roadway location acquisition unit, an open-source HarmonyOS lightweight processing unit, a distributed soft bus communication unit, a local cache unit, and a hazard avoidance prompt output component; The microseismic-ground sound composite sensing module is used to collect microseismic signals and ground sound signals; The posture and wearing status acquisition unit is used to acquire the helmet posture and wearing status; The tunnel location acquisition unit is used to acquire the tunnel location where the mining smart helmet node is located. The open-source HarmonyOS lightweight processing unit is used to generate event feature records based on microseismic signals, ground sound signals, acquisition timestamps, tunnel locations, helmet posture, and wearing status, and to mark interference events. The distributed soft bus communication unit is used to enable multiple mining smart helmet nodes to establish a mobile sensing subnet through the distributed soft bus, and to synchronize event feature records and node status summaries within the mobile sensing subnet. The coordination processing node is an underground edge gateway or a coordination helmet node determined by the mobile sensing subnet according to a preset coordination rule. It is used to perform time window clustering on multiple event feature records according to the corrected acquisition timestamp, and generate a cluster of micro-fracture events from the same source by combining the main frequency band difference, the initial phase difference, the arrival time sequence and the roadway topology propagation constraints. It generates node confidence quality weights based on the node status summary, and generates source estimation results and early warning levels based on the cluster of micro-fracture events from the same source, the node confidence quality weights and the roadway topology data. The risk avoidance warning output component is used to output risk avoidance warning information according to the warning level; The local cache unit is used to cache event feature records, node status summaries, and early warning processing data generated when the network is offline.

2. The rockburst early warning system for a mining smart helmet under the open-source HarmonyOS environment as described in claim 1, characterized in that, The microseismic-ground sound composite sensing module includes an accelerometer for acquiring low-frequency microseismic signals and a ground sound probe for acquiring high-frequency ground sound signals. The open-source HarmonyOS lightweight processing unit performs bandpass filtering, noise baseline updating, short-time energy calculation, envelope extraction, frequency band energy ratio calculation, and abnormal pulse detection on the low-frequency microseismic signals and high-frequency ground sound signals. When the short-time energy, envelope mutation value, frequency band energy ratio, and duration meet the valid event triggering conditions, a valid event is determined, and the corresponding event feature record is generated.

3. The rockburst early warning system for a mining smart helmet under the open-source HarmonyOS environment as described in claim 2, characterized in that, The event feature record includes event number, helmet node identifier, acquisition timestamp, time correction parameters, roadway coordinates, helmet attitude angle, wearing status, micro-vibration channel peak value, ground sound channel peak value, main frequency band, initial phase, duration, signal-to-noise ratio, short-time energy value, frequency band energy ratio, and local preliminary event type. The open-source HarmonyOS lightweight processing unit marks signals with characteristics of personnel walking, equipment periodic vibration, or instantaneous collision as interference events, and excludes these interference events from the association with the same source event, or only generates event feature records with the lowest synchronization priority.

4. The rockburst early warning system for a mining smart helmet under the open-source HarmonyOS environment as described in claim 1, characterized in that, The distributed soft bus communication unit uses the open-source HarmonyOS distributed soft bus for node discovery, two-way authentication, device capability reporting, and task domain establishment. The device capability reporting information includes node computing power, remaining power, sensor status, communication quality, cache capacity, and current location source. The coordination processing node determines that mining smart helmet nodes in the same risk-related area should jointly join a mobile sensing subnet based on the roadway number, work group, adjacent distance, communication quality, stress level of the area, and remaining power, and generates a task domain identifier.

5. The rockburst early warning system for a mining smart helmet under the open-source HarmonyOS environment as described in claim 1, characterized in that, When the underground edge gateway is online, the mobile sensing subnet uses the underground edge gateway as the time reference; when the underground edge gateway is unavailable, the mobile sensing subnet determines the coordinating helmet node according to at least two of the following: node computing power, remaining power, communication quality, and clock stability, and uses the coordinating helmet node as a temporary reference node. The temporary reference node then sends time correction parameters to other mining smart helmet nodes, so that each event feature record enters the same time window according to the corrected collection timestamp.

6. The rockburst early warning system for a mining smart helmet under the open-source HarmonyOS environment as described in claim 1, characterized in that, The generation of the homogeneous micro-fracture event cluster includes: performing time window clustering on multiple event feature records according to the corrected acquisition timestamps to form candidate event groups; calculating the main frequency band difference, initial phase difference, peak energy change direction, duration difference, and arrival time sequence relationship among the event feature records in the candidate event group; combining the path distance between each mining smart helmet node in the roadway topology data and the range of coal and rock medium propagation speed, determining whether the arrival time difference meets the propagation constraints, and confirming the candidate event group that meets the time window consistency, frequency band consistency, phase correlation, reasonable arrival time sequence, and propagation constraints as the homogeneous micro-fracture event cluster; The propagation constraint is determined according to the following formula: ； In the formula, For the first The corrected collection timestamp of the event feature record corresponding to each mining smart helmet node. For the first The corrected collection timestamp of the event feature record corresponding to each mining smart helmet node. The first in the tunnel topology data The mining smart helmet node and the first The path distance between nodes of the smart helmet for mining. The propagation velocity selected within the range of propagation velocities in coal and rock media. Allowable error for arrival time difference.

7. The rockburst early warning system for a mining smart helmet under the open-source HarmonyOS environment as described in claim 6, characterized in that: The co-originating micro-fracture event cluster includes the event cluster number, the set of participating helmet nodes, the arrival time of each node, the spatial coordinates of each node, the energy value of each node, the dominant frequency band of the event, the duration of the event, the event cluster consistency score, and the candidate propagation direction; the event cluster consistency score is jointly determined by the dominant frequency band consistency, the initial phase correlation, the arrival time sequence matching degree, and the path propagation constraint matching degree, and is used as the input parameter for generating the source estimation results and the warning level; The event cluster consistency score is generated according to the following formula: ； In the formula, Assess the consistency score for event clusters. Main frequency band consistency score. For the initial phase correlation degree, To achieve time-series matching degree, For path propagation constraint matching degree, , , , These are the corresponding weight coefficients, and they satisfy: ； This formula is used to merge event feature records collected by multiple mining smart helmet nodes into a co-source event evaluation quantity that can be used for source estimation and early warning level determination.

8. The rockburst early warning system for a mining smart helmet under the open-source HarmonyOS environment as described in claim 6, characterized in that, The node status summary includes wearing status, posture stability, sensor self-test status, current signal-to-noise ratio, remaining battery power, communication packet loss rate, and stress zoning of the roadway. The coordination processing node generates a node reliability quality weight based on the node status summary. When a mining smart helmet node exhibits abnormal wearing, drastic posture changes, sensor drift, remaining battery power below a preset battery power threshold, communication packet loss rate above a preset packet loss rate threshold, or arrival time sequence that does not meet the roadway topology propagation constraints, the node reliability quality weight corresponding to that node is multiplied by a preset attenuation coefficient. The node's trusted quality weight is updated according to the following formula: ； In the formula, For the first The updated node trust quality weight for each mining smart helmet node. This is the initial weight of the node. The attenuation coefficient is the factor used when the device is worn. The sensor state attenuation coefficient, The remaining power attenuation coefficient is... This is the packet loss rate attenuation coefficient for communication. The attenuation coefficients are the propagation constraints of the tunnel topology, and each attenuation coefficient ranges from 0 to 1.

9. The rockburst early warning system for a mining smart helmet under the open-source HarmonyOS environment according to claim 1, characterized in that, The coordination processing node uses the arrival time difference of each mining smart helmet node in the same micro-fracture event cluster as a constraint, and generates candidate source regions by combining roadway topology data; according to the node confidence quality weight of each node, the arrival time difference error, signal energy attenuation error, and propagation error introduced by the roadway topology path distance of the candidate source regions are weighted and summed to calculate the comprehensive positioning error; the position with the smallest comprehensive positioning error and that satisfies the roadway topology propagation constraint is determined as the source estimation result, and the source estimation result includes source coordinates, event energy level, and location confidence. The overall positioning error is calculated according to the following formula: ； In the formula, Candidate epicenter locations The corresponding overall positioning error, The number of nodes in the mining smart helmet that participate in the cluster of homogeneous micro-fracture events. For the first The node trust quality weight of each mining smart helmet node. For the first The measured arrival time difference corresponding to each mining smart helmet node Candidate epicenter locations Corresponding to the The theoretical arrival time difference of each mining smart helmet node For the first The signal energy value collected by a mining smart helmet node Candidate epicenter locations Corresponding to the The theoretical energy attenuation value of a single smart helmet node for mining applications. Candidate epicenter locations Corresponding to the The tunnel topology path constraint error of a single mining smart helmet node. , , These are the weighting coefficients for arrival time difference error, signal energy attenuation error, and roadway topology path constraint error, respectively.

10. The rockburst early warning system for a mining smart helmet under the open-source HarmonyOS environment according to claim 9, characterized in that, The coordination and processing node generates an early warning level based on the number of micro-fracture event clusters from the same source per unit time, the event cluster growth rate, the cumulative energy value of the event, the source location reliability, the event cluster consistency score, the path distance from the source to the workers, the stress level in the area where the source is located, and the current distribution of the workers. When the mobile sensing subnet is disconnected from the ground safety management platform, the coordination processing node caches the event characteristic records, clusters of micro-rupture events from the same source, source estimation results, warning levels and warning output records in the form of structured logs in the local cache of the coordination processing node or the local cache unit in the mobile sensing subnet. The early warning output record includes the early warning output time, early warning output method, and receiving node identifier; Once the connection with the ground safety management platform is restored, the synchronization priority is determined according to the warning level, location reliability, event cluster consistency score, and warning generation time. The incremental data in the structured log is then synchronized to the ground safety management platform, or forwarded to the ground safety management platform via the downhole edge gateway. The warning judgment value corresponding to the warning level is generated according to the following formula: ； In the formula, This is the warning judgment value; This is the normalized value corresponding to the number of homologous microfracture event clusters per unit time; This is the normalized value corresponding to the growth rate of the event cluster. This is the normalized value corresponding to the accumulated event energy. The fusion value of source location confidence and event cluster consistency score; The risk value corresponds to the path distance from the earthquake source to the workers. The risk value corresponds to the stress level in the area where the earthquake source is located and the current distribution of workers. , , , , , These are the corresponding weighting coefficients.

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