Multi-parameter intelligent monitoring and early warning system for mine pressure of working face

Abstract

The present application relates to the technical field of working face mine pressure multi-parameter intelligent monitoring and early warning system, and specifically discloses a working face mine pressure multi-parameter intelligent monitoring and early warning system. The system comprises a multi-source sensing data acquisition device, a pulse sequence coding unit, a bionic pulse neural network early warning model, a dynamic attention regulation module and an early warning information output terminal. The multi-source heterogeneous data such as hydraulic support resistance, microseismic energy and acoustic emission frequency are converted into pulse event sequences of biological neural signals, and the bionic model of simulated neuron discharge mechanism and synaptic plasticity is used to identify rock mass instability precursors. In combination with the dynamic attention mechanism, the key signals are adaptively strengthened and the noise is suppressed. The present application can capture multi-parameter coupling characteristics with high sensitivity, improve the early warning accuracy and advance, reduce the false alarm rate, and provide reliable protection for deep coal mine safety.

Description

Technical Field

[0001] This invention belongs to the field of mine safety monitoring and intelligent early warning technology, specifically relating to an intelligent monitoring and early warning system for multiple parameters of mine pressure at the working face. Background Technology

[0002] With the continuous acceleration of smart mine construction, safety monitoring and disaster early warning in fully mechanized mining faces have become core technological pillars for ensuring efficient coal mine production. Accurate monitoring of mine pressure manifestations can effectively prevent roof falls and dynamic disasters such as rock bursts, serving as a safety cornerstone for achieving unmanned or minimally manned mining. Under deep and complex geological conditions, the intensity and unpredictability of rock strata movement significantly increase, posing challenges to the data processing accuracy and early warning sensitivity of monitoring systems.

[0003] Multi-parameter monitoring of mine pressure typically encompasses various dimensions of physical information, including hydraulic support resistance, micro-vibration energy, and acoustic emission frequency. By acquiring and comprehensively evaluating these multi-source heterogeneous sensor data in real time, a dynamic perception system for the surrounding rock condition of the working face can be constructed, providing auxiliary decision support for mine disaster prevention and control.

[0004] Traditional monitoring systems often rely on threshold judgment logic for single physical quantities. When faced with severe electromagnetic interference and equipment wear and tear downhole, they are prone to generating numerous false alarms, impacting the credibility of early warning information. Traditional statistical analysis methods struggle to efficiently extract deep features from massive datasets, resulting in insufficient perception of weak anomalies preceding rock instability and an inability to capture the nonlinear coupling patterns of multiple parameters in the spatiotemporal dimension. Existing systems lack the ability to perform biomimetic-level fusion processing of multi-source information; simple numerical superposition fails to reflect the true stress evolution trajectory, leading to limited early warning lead times and low reliability.

[0005] Therefore, a multi-parameter intelligent monitoring and early warning system for working face mine pressure is needed. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent monitoring and early warning system for multiple parameters of working face mine pressure, which can solve the problems of difficult feature extraction and high false alarm rate in the above-mentioned background technology.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A multi-parameter intelligent monitoring and early warning system for mine pressure at a working face includes a multi-source sensor data acquisition device, a pulse sequence encoding unit, a biomimetic spiking neural network early warning model, a dynamic attention control module, and an early warning information output terminal, wherein:

[0009] The multi-source sensor data acquisition device is configured to acquire the raw physical signals of hydraulic support resistance, micro-vibration energy and acoustic emission frequency of the fully mechanized mining face in real time, and convert the raw physical signals into digital signals and transmit them to the pulse sequence encoding unit.

[0010] The pulse sequence encoding unit is configured to receive digital signals from the multi-source sensor data acquisition device, and according to the rate of change and amplitude characteristics of each physical quantity, map different types of heterogeneous data into pulse event sequences with timestamps, forming a spatiotemporal encoding format similar to biological neural signals.

[0011] The biomimetic spiking neural network early warning model is configured to receive a multi-channel spiking event sequence generated by the spiking sequence encoding unit, and to perform nonlinear integration processing on the spiking event sequence based on the calculation rules that simulate the firing mechanism of biological neurons, so as to identify the multi-parameter coupling mode corresponding to the precursor of rock instability.

[0012] The dynamic attention control module is configured to adaptively adjust the weight allocation of each input channel according to the spatiotemporal distribution characteristics of the current input pulse stream during the operation of the biomimetic spiking neural network early warning model, thereby enhancing the attention to key precursor signals and suppressing the influence of noise interference components.

[0013] The early warning information output terminal is configured to receive the early warning judgment result output by the bionic spiking neural network early warning model, and generate a visual alarm prompt or control command according to the preset risk level for on-site personnel or automated control system to respond and handle.

[0014] Preferably, the pulse sequence encoding unit adopts a dual encoding strategy based on firing rate and timing to transform continuously changing physical quantities into a discrete pulse event stream. High rate of change regions correspond to high-frequency pulse firing, and low rate of change regions correspond to sparse pulse firing, thereby preserving the key dynamic features of the original signal.

[0015] Furthermore, the biomimetic spiking neural network early warning model contains multiple hierarchical clusters of spiking neurons. Each cluster transmits information through a connection pathway with synaptic plasticity. Synaptic plasticity is the ability to dynamically adjust the connection strength based on the pulse arrival time difference, which is used to simulate the learning mechanism of long-term enhancement and long-term inhibition in biological nervous systems.

[0016] Furthermore, the dynamic attention control module monitors the local consistency and global correlation of pulse activity in each input channel, dynamically evaluates its contribution to the overall state judgment, and adjusts the activation threshold and response gain of the corresponding input path in the biomimetic spiking neural network early warning model in real time accordingly.

[0017] Preferably, the multi-source sensor data acquisition device integrates an anti-electromagnetic interference filter circuit and a self-calibration function module, which can maintain the stability and accuracy of sensor data under complex downhole conditions and avoid false abnormal signals caused by equipment drift or environmental noise.

[0018] Furthermore, the early warning information output terminal supports multi-level early warning strategies. When the bionic pulse neural network early warning model detects a combination of pulse activities that conforms to a preset precursor pattern, it classifies the early warning level based on the degree of matching between the combination and historical disaster samples, and triggers corresponding audible and visual alarms, remote notifications, or linked shutdown operations.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] 1. The intelligent monitoring and early warning system for multi-parameter mine pressure provided by this invention abandons the traditional single-parameter alarm logic that relies on fixed thresholds, and instead constructs a biomimetic spiking neural network early warning model that integrates neuroscience principles and deep learning architecture.

[0021] 2. This system can convert multi-source heterogeneous sensor data into pulse sequences of biological neural signals, and utilizes algorithms that simulate the brain's attention mechanism and synaptic plasticity to achieve highly sensitive capture of weak multi-parameter coupling characteristics before rock strata fracturing. This invention improves robustness to noise interference and reduces the false alarm rate; simultaneously, through in-depth mining of nonlinear correlation features in the spatiotemporal dimensions, it significantly improves the lead time and accuracy of early warnings, providing reliable technical support for safe coal mine production under deep and complex geological conditions. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the overall technical solution architecture according to the present invention;

[0023] Figure 2 This is a schematic diagram illustrating the core principle framework of the biomimetic spiking neural network early warning model according to the present invention;

[0024] Figure 3 This is a flowchart illustrating the logical process of converting multi-source sensor data into pulse sequence encoding according to the present invention.

[0025] Figure 4 This is a schematic diagram illustrating the interaction and data flow between the dynamic attention control module and the pulse flow input channel according to the present invention.

[0026] Figure 5 This is a flowchart illustrating the logical flow of the early warning information output based on multi-parameter coupled pattern recognition and risk level classification according to the present invention. Detailed Implementation

[0027] Example 1: Please refer to the appendix Figure 1To be continued Figure 5 To make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments.

[0028] A multi-parameter intelligent monitoring and early warning system for working face mine pressure includes a multi-source sensor data acquisition device, a pulse sequence encoding unit, a biomimetic spiking neural network early warning model, a dynamic attention control module, and an early warning information output terminal;

[0029] The multi-source sensor data acquisition device is used to acquire raw physical signals reflecting the evolution characteristics of the surrounding rock stress state in real time through a heterogeneous sensor matrix deployed at various monitoring points in the fully mechanized mining face. These raw physical signals include at least hydraulic support resistance signals, micro-vibration energy signals, and acoustic emission frequency signals. The multi-source sensor data acquisition device integrates signal conditioning circuitry, high-precision analog-to-digital conversion circuitry, and electromagnetic interference filtering circuitry. It is configured to perform noise reduction and gain compensation processing on the raw physical signals after synchronous sampling, thereby converting continuous analog voltage or current signals into a standardized digital signal stream. This digital signal stream is transmitted in real time to the pulse sequence encoding unit via an industrial-grade fieldbus or wireless transmission protocol.

[0030] The pulse sequence encoding unit establishes a communication connection with the multi-source sensor data acquisition device to receive and parse the digital signal stream. The pulse sequence encoding unit is configured to employ a dual encoding strategy based on firing rate and timing, according to the dynamic characteristics of each physical quantity, including the rate of change, amplitude, and temporal fluctuation characteristics. Within this pulse sequence encoding unit, the digital signal is mapped in real time into a series of discrete pulse events with precise timestamps. By performing pulsed processing on physical information of different dimensions in a unified format, the pulse sequence encoding unit can compress and transform high-dimensional, nonlinear heterogeneous data streams into a spatiotemporal encoding format capable of characterizing the dynamic process of rock strata fracturing, forming a multi-channel pulse sequence input, providing a compatible basic data format for subsequent brain-like processing.

[0031] The biomimetic spiking neural network early warning model has its input coupled to the output of the pulse sequence encoding unit to receive the multi-channel pulse sequence. The model consists of multiple hierarchically distributed clusters of spiking neurons, and its internal computational logic completely simulates the membrane potential accumulation and firing mechanism of biological neurons. The model is configured to nonlinearly integrate the pulse events received from each channel along the time axis, simulating synaptic plasticity—that is, dynamically adjusting the connection strength between neuron nodes based on the time difference of pulse arrival. This mechanism allows the model to spontaneously develop the ability to memorize and recognize specific rock strata instability precursor patterns during processing, extracting multi-parameter coupling features characterizing dynamic disaster risks from complex background noise.

[0032] The dynamic attention control module is integrated into the feedback control loop of the biomimetic spiking neural network early warning model. It is used to adjust the resource allocation weights of each input channel in real time based on the spatiotemporal distribution density and consistency characteristics of the current input pulse stream. The dynamic attention control module is configured to automatically identify and enhance the activation gain of key precursor signal channels by calculating the correlation score of pulse activity between different sensor channels, while suppressing isolated noise pulse channels caused by equipment failure or environmental vibration. Through this dynamic threshold adjustment and gain control, the module can improve the system's sensitivity to capturing real risk signals and reduce the false alarm rate caused by interference.

[0033] The early warning information output terminal is connected to the decision output terminal of the biomimetic spiking neural network early warning model and is used to receive the early warning judgment result generated by the model. The early warning judgment result includes the predicted probability of risk occurrence, the potential disaster level, and the corresponding spatiotemporal location coordinates. The early warning information output terminal is configured to generate hierarchical visual alarm instructions according to a preset risk response strategy. The instructions include, but are not limited to, on-site audible and visual alarm drive signals, real-time pop-up prompts on the ground dispatch room monitoring interface, and automatic interlocking or deceleration control signals sent to the coal mining machine or support electro-hydraulic control system, realizing closed-loop control from risk perception to emergency response.

[0034] The multi-source sensor data acquisition device includes a support pressure monitoring submodule, a microseismic monitoring submodule, and an acoustic emission monitoring submodule. The support pressure monitoring submodule includes a pressure transmitter mounted on the cylinder of the hydraulic support column, configured to acquire cyclic resistance data of the working face support at a sampling frequency of not less than 10 Hz. The microseismic monitoring submodule includes an array of seismic sensors arranged around the working face level or goaf area to capture low-frequency vibration waveforms generated by large-scale rock fractures. The acoustic emission monitoring submodule includes a high-frequency acoustic sensor specifically designed to capture high-frequency elastic waves generated during the development of micro-fractures in the rock mass. The electromagnetic interference filtering circuit inside the multi-source sensor data acquisition device employs a three-stage inductor-capacitor filtering structure, combined with opto-isolation technology, to ensure a signal-to-noise ratio better than 60 dB in environments with strong interference such as frequency converters and high-power motors. The device also integrates an automatic zero-point calibration circuit, configured to automatically perform benchmark calibration on the pressure sensor during the support depressurization phase to eliminate the impact of sensor temperature drift and zero-point drift on monitoring accuracy.

[0035] The internal logic of the pulse sequence encoding unit is further refined. For the hydraulic support resistance signal, this unit employs a firing rate encoding logic. When the absolute value of the support resistance exceeds a first preset threshold or its rate of change exceeds a second preset threshold, the firing frequency of the corresponding channel increases accordingly, allowing the dynamic characteristic of a sharp increase in resistance to be expressed in the form of a high-frequency pulse stream. For microseismic energy and acoustic emission frequency signals, this unit employs a timing encoding logic. This utilizes the precise arrival time interval of the pulses to encode the signal's energy intensity. The greater the energy, the earlier the initial pulse is fired, thus preserving the transient contrast characteristics of energy release from rock mass fracture in the time dimension. The pulse sequence encoding unit also includes a global synchronization clock to ensure that sensor pulses from different physical locations and of different types can be timestamped under a unified time reference system, with the error range controlled within 1 millisecond.

[0036] The biomimetic spiking neural network early warning model comprises an input encoding layer, a feature mapping layer, a spatiotemporal integration layer, and a decision-making layer. The input encoding layer receives standardized pulse event sequences and distributes them to the corresponding neuronal receptive fields. The feature mapping layer contains multiple clusters of neurons with specific connection patterns. Each cluster is pre-configured to sensitively respond to specific mining pressure manifestation patterns, such as step-like changes in support resistance or micro-seismic bursts. The spatiotemporal integration layer is the core of the model, incorporating leakage current integration triggering logic. Each virtual neuron node possesses a membrane potential attribute. When an input pulse arrives, the membrane potential increases with weights, while during periods without pulse input, the membrane potential decreases exponentially over time according to a preset decay constant. Only when the membrane potential accumulates to exceed a trigger threshold within a very short time window will the neuron output a pulse to the next layer. This mechanism naturally filters out scattered interference pulses while generating a strong resonant response to spatiotemporally correlated precursor signal sequences.

[0037] Furthermore, the biomimetic spiking neural network early warning model possesses synaptic plasticity learning capabilities. The system pre-stores a historical disaster database, containing multi-parameter evolution samples prior to typical accidents such as large-area roof collapse, rockburst, and scaffold collapse. The model automatically optimizes the connection weights between neurons during offline training by simulating long-term enhancement and long-term inhibition learning rules. When a certain set of spiking combinations frequently appears in the accident precursor sequence, the synaptic weights between the corresponding neurons are automatically increased, enabling the system to identify similar risk patterns more quickly and with higher confidence in subsequent operation.

[0038] The dynamic attention control module includes a spatiotemporal correlation assessment subunit and a weight adaptive correction subunit. The spatiotemporal correlation assessment subunit is configured to perform sliding window statistics on the impulse activity of all input channels, calculating the impulse firing density of each channel within the current time window. If the impulse firing of a channel exhibits a random distribution and lacks logical synchronization with other channels, it is determined that the channel contains a high level of equipment noise or environmental interference. The weight adaptive correction subunit then dynamically lowers the synaptic gain of the interfering channel or increases the firing threshold of the corresponding input neuron based on the assessment results, achieving physical isolation of the interference signal. Conversely, if stent pressure, microvibration, and acoustic emission all show a coordinated increase in impulse density within the same time period, the module determines that the current stage is a high-risk evolution phase, automatically lowering the trigger threshold of the model's decision layer and assigning higher weight gains to these associated channels to ensure that weak correlation features are fully transmitted to the decision layer.

[0039] The early warning information output terminal includes a multi-level risk assessor and a response execution matrix. The multi-level risk assessor maps the model's output pulse density to four risk levels: green (safe), blue (attention), yellow (warning), and red (emergency). The response execution matrix performs differentiated actions based on the risk level. In the green state, the terminal only performs routine data storage and display; in the blue state, the system sends early warning information to the explosion-proof mobile terminals of the personnel following the workface, reminding them to pay attention to changes in the surrounding rock; in the yellow state, the system automatically activates the audible and visual alarms in the roadway and suggests that the dispatch room slow down the workface advance; in the red state, the system directly issues an emergency command to the fully mechanized mining automation control system, forcibly executing a shutdown or emergency pressure relief operation, and highlights the stress distribution heat map of the risk area on the large screen in the ground command center.

[0040] To ensure high availability of the entire system in complex downhole environments, the multi-source sensor data acquisition device is encapsulated in an explosion-proof housing with an IP65 protection rating, and its internal circuit boards are coated with a moisture-proof and salt-spray-proof coating. The communication links between devices employ a redundant ring network structure. When a single point of failure occurs in the main optical cable, data can automatically reroute through redundant paths, with a switching time of no more than 50 milliseconds. The biomimetic pulse neural network early warning model is deployed in a high-performance industrial server equipped with a dedicated artificial intelligence acceleration board, supporting parallel processing of massive pulse streams. This ensures that the end-to-end latency from sensor data acquisition to the generation of early warning information is less than 500 milliseconds, fully meeting the real-time requirements of mine pressure prevention and control.

[0041] In practical engineering applications, the system provided in this embodiment can uncover the nonlinear stress evolution patterns hidden beneath massive amounts of noise data through a "brain-like" fusion of multi-source heterogeneous parameters. For example, before a large-area roof pressure event, the system can identify an abnormally high-frequency jump in the acoustic emission pulse frequency, followed by a delayed high-energy clustering of micro-vibration energy pulses, and finally a step-like increase in the support resistance pulse. This spatiotemporal-based logical correlation analysis increases the lead time for early warning compared to traditional systems based on a single threshold, thereby improving the level of safety assurance in underground coal mines.

[0042] Example 2: A multi-parameter intelligent monitoring and early warning system for mine pressure at a working face. Based on Example 1, this example further optimizes system performance through a distributed edge computing architecture to meet the monitoring needs of ultra-long-distance fully mechanized mining faces. In this example, the system includes an edge-side processing node cluster, a central cloud decision server, and a distributed fiber optic sensor network.

[0043] The edge-side processing node cluster is deployed at each roadway and transfer point in the fully mechanized mining face. Each edge-side processing node integrates a hardware implementation of the pulse sequence encoding unit. Specifically, the edge-side processing node includes an embedded system-on-a-chip (SoC) with a high-speed pulse generation algorithm embedded within it. The edge-side processing node is directly connected to surrounding pressure sensors and acoustic emission probes, configured to instantly convert physical signals into pulse events at the forefront of physical sensing. The advantage of this distributed architecture is that massive amounts of analog or high-frequency digital sampling data do not need to be uploaded to the central server in full; only encoded discrete pulse data packets need to be transmitted. The data volume of the pulse data packets is only 10% to 1% of the original sampling data, alleviating the pressure on the mine's main communication bandwidth and reducing the risk of signal jitter caused by long-distance transmission.

[0044] The central cloud-based decision server, located in the coal mine's ground monitoring center, maintains real-time connectivity with all edge processing nodes via gigabit Ethernet. This central cloud-based decision server carries a high-level version of the biomimetic spiking neural network early warning model. Unlike Embodiment 1, the model in this embodiment employs a deep recurrent spiking neural network architecture. The model includes recurrent neuron layers with feedback connections, enabling it to memorize the temporal evolution trend of mine pressure over hours or even days. The cloud-based decision server is also equipped with a large-scale historical case knowledge base. Whenever the model generates a new early warning judgment, the system automatically uses the key feature vectors extracted by the dynamic attention control module to perform rapid retrieval and similarity comparison in the knowledge base. If the current pulse sequence features are highly similar to those of a known historical accident, the system will automatically invoke the expert handling plan for that accident as an attachment to the early warning information and push it to the early warning information output terminal, providing more instructive decision support.

[0045] The distributed fiber optic sensing network, serving as a supplement and enhancement to the multi-source sensing data acquisition device, is laid inside the roof of the working face and the sidewalls of the tunnel. This network employs Brillouin optical time-domain reflectometry (BDR) technology, enabling continuous strain and temperature monitoring over several kilometers using a single optical fiber. The fiber optic sensor host is configured to convert optical echo signals into corresponding equivalent pressure pulse flows and connect them to the pulse sequence encoding unit. By introducing distributed fiber optic sensing, the system can obtain surrounding rock strain field information with higher spatial resolution than discrete sensor points. When processing this data, the dynamic attention control module is configured to perform spatial convolution operations on the pulse activity of adjacent fiber optic sensing points based on spatial topological relationships, thereby more accurately locating the position and evolution trajectory of stress concentration zones.

[0046] In Example 2, the dynamic attention control module also incorporates environmental condition perception factors. The system accesses production status data from the fully mechanized mining automation system, such as the coal mining machine position, cutting speed, and support step distance, using these parameters as external input references for attention adjustment. The module is configured to automatically increase the attention weight of the surrounding sensor channels when the coal mining machine is in high-speed cutting mode, and perform precise background ripple offsetting at the pulse decoding layer based on the mechanical vibration frequency characteristics generated by the coal mining machine, filtering out false interference pulses not caused by rock strata movement. This adaptive adjustment mechanism, deeply coupled with the production process, enables the system to maintain extremely high recognition accuracy even under complex dynamic load environments.

[0047] The early warning information output terminal in this embodiment also supports augmented reality display functionality. Early warning information is no longer presented merely as a flat table or chart; instead, it uses 3D modeling technology to overlay a heat map of mine pressure distribution and the early warning level onto a digital twin model of the working face in real time. On-site management personnel, wearing explosion-proof augmented reality glasses, can intuitively observe the stress accumulation areas within the surrounding rock and the propagation paths of rock fractures calculated by a biomimetic spiking neural network early warning model. This intuitive interactive method shortens reaction time in emergency situations.

[0048] To address the long-term evolution of big data, the system also includes a self-evolution module. This module is configured to periodically and automatically evaluate the early warning accuracy over the past month. If a deviation is found in the model's identification of a specific geological structure (such as a collapse column or fault), the self-evolution module triggers a model retraining process. Using collected labeled false alarm or missed alarm pulse sequences, a reinforcement learning algorithm automatically corrects the synaptic connection thresholds and time constants within the neural network. This self-evolution mechanism ensures that the system can maintain optimal monitoring performance as mining depth increases and geological conditions change, achieving long-term self-adaptation of the monitoring and early warning system.

[0049] Example 3: A multi-parameter intelligent monitoring and early warning system for working face mine pressure. This example aims to provide a hardware implementation scheme with extremely high fault tolerance and self-healing function, which is particularly suitable for fully mechanized mining faces with extremely thin or ultra-thick coal seams that have serious geological disturbance and mechanical collision risks.

[0050] In this embodiment, the multi-source sensor data acquisition device employs a redundant array design. Each monitoring point is equipped with at least two functionally identical backup sensors. The multi-source sensor data acquisition device integrates a health assessment unit for real-time monitoring of the electrical characteristics of each sensor, including output impedance, insulation resistance, and signal baseline stability. The health assessment unit is configured to immediately switch to the backup sensor via an internal analog switch matrix upon detecting a non-physical step change or a continuous smoothing fault in the main sensor's data, and synchronize this fault state to the pulse sequence encoding unit.

[0051] Upon receiving a sensor switching signal, the pulse sequence encoding unit automatically inserts a specific "health status flag" into the generated pulse data stream. The input layer neurons in the biomimetic spiking neural network early warning model are configured to parse this flag. When the health of a certain input channel is low, the model automatically reduces the contribution weight of the failed channel to the global early warning judgment result through the dynamic attention control module and activates the "spatiotemporal information completion algorithm." This algorithm utilizes the spatial correlation between adjacent measuring points and the stationarity of the same measuring point in historical time sequences. Through the interpolation logic of the spiking neural network, it synthesizes a simulated pulse sequence to replace the lost real data, ensuring the continuity of the monitoring logic and avoiding interruption of the early warning function due to the failure of a single sensor.

[0052] In Embodiment 3, the dynamic attention control module is equipped with a more advanced group collaboration strategy. This module divides all sensor channels across the entire working surface into several "bio-collaborative groups." Sensors within each group (such as the left and right column pressures on a support frame, the top beam load, and nearby acoustic emission probes) are considered as a functional whole. The module assesses the data confidence level of the area by calculating the consistency entropy value of pulse emission from each channel within the group. If the entropy value exceeds a preset upper limit, it indicates that the data in that area exhibits high disorder, typically caused by localized mechanical failures or severe electromagnetic surges. The dynamic attention control module temporarily blocks the transmission of input pulses from that group to the decision-making layer and instead uses trend data from adjacent healthy groups for cross-regional risk estimation.

[0053] The biomimetic spiking neural network early warning model employs an asynchronous logic circuit design at the hardware level. Unlike traditional synchronous clock circuits, asynchronous logic circuits only generate computational power consumption when a pulse signal arrives, which is highly consistent with the low-power operation mechanism of the biological brain. This design enables the entire system to maintain an extremely long working time in battery-powered emergency mode. In the event of extreme disasters such as unexpected power outages in the mine, the system can continue to operate for no less than 72 hours using its intrinsically safe backup power supply, providing continuous monitoring data on the stability of the surrounding rock for subsequent rescue operations.

[0054] In this embodiment, the early warning information output terminal also integrates a low-power wide-area network remote communication module. Besides transmission within the local mining area network, the system can also directly send core early warning status to the group company's remote expert consultation platform via satellite link or dedicated long-distance wireless channel. Upon identifying precursors to major dynamic disasters, ground experts can remotely access the pulse flow evolution details within the system and utilize a larger-scale supercomputing center to conduct secondary simulations and verifications of the on-site data, providing higher-level authoritative decision-making recommendations for on-site production shutdowns and personnel evacuation.

[0055] In the system architecture of this embodiment, all critical logic operations are implemented in a custom FPGA-based processor. This processor internally houses tens of thousands of parallel neural computation units. Compared to traditional CPU architectures, this architecture offers zero queuing delay for processing pulse sequences. Each newly generated sensing pulse immediately triggers an update of the neuron's membrane potential. This extremely high temporal resolution enables the system to capture nanosecond-level stress wave characteristics generated at the moment of rock rupture, providing better detection capabilities for sudden disasters such as rockbursts.

[0056] Furthermore, to enhance the system's environmental adaptability, the connection cables of the multi-source sensor data acquisition device employ a carbon fiber-reinforced tensile armor structure, coupled with anti-detachment rotary self-locking connectors. Even under the enormous impact load generated by a roof collapse, the cables maintain the integrity of their physical connection. Simultaneously, the system's internal firmware supports remote, non-destructive upgrades. When new biomimetic algorithms or mining pressure evaluation standards are released, administrators can update the processing logic of all edge nodes with a single click via the network, without disassembling the equipment, thus reducing the system's subsequent maintenance costs.

[0057] Example 4: A multi-parameter intelligent monitoring and early warning system for mine pressure at a working face. During the normal advancement of the working face, the coal cutting operation of the coal mining machine and the coal release action of the support frame will generate strong, regular vibrations and pressure fluctuations. In traditional monitoring systems, these normal production disturbances are often confused with potential disaster precursor signals, forcing the sensitivity of the early warning system to be reduced. In this example, the biomimetic spiking neural network early warning model introduces a "reference association learning" module.

[0058] The multi-source sensor data acquisition device is configured to additionally acquire production parameters such as the coal mining machine motor current, the vibration frequency of the cutting section, and the load of the scraper conveyor. The pulse sequence encoding unit also converts these production parameters into pulse sequences. The biomimetic spiking neural network early warning model learns the time-domain response function between the production pulse sequence and the mine pressure monitoring pulse sequence through a "background cancellation layer." Since production disturbances usually have periodicity and logical correlation (e.g., the support movement is inevitably accompanied by a decrease followed by an increase in support resistance), the model can automatically cancel the pulse components caused by normal production operations through internal inhibitory neuron synapses.

[0059] The dynamic attention control module adjusts the detection gain of the effective signal in real time based on the output of the "background cancellation layer." When production disturbances are significant, the module automatically increases the detection threshold, but maintains high sensitivity to non-periodic, non-correlated "abnormal pulse clusters" through "attention focusing" technology. These abnormal pulse clusters typically correspond to the uncontrolled propagation of fractures within the rock mass and are a true early warning indicator of dynamic disasters. The system achieves "stethoscope"-like monitoring in noisy production environments, accurately locating abnormal stress concentration points outside the mining-affected zone.

[0060] In this embodiment, the early warning information output terminal integrates a three-dimensional geomechanical simulation feedback system. Whenever the early warning model identifies a risk point, the system automatically extracts the spatiotemporal characteristics of the surrounding pulses and converts them into mechanical parameters, inputting them in real-time into the numerical simulation software at the ground level. The software quickly solves the problem, predicting the evolution trend of the risk point within the next 15 minutes, and compares the "expected evolution trajectory" obtained from the numerical simulation with the "real evolution trajectory" monitored in real time. If there is a significant deviation between the two, the dynamic attention control module immediately recognizes that the current working condition may exceed the cognitive range of the existing model, automatically triggers the highest level of early warning, and suggests that technicians intervene for manual verification.

[0061] The system in this embodiment also possesses the capability of "semantic fusion" of multi-source sensor data. During the encoding process, the pulse sequence encoding unit assigns specific spatial semantics to the pulse stream based on the sensor's installation location. For example, the support resistance pulse located at the end of the working face is assigned the semantics of "roadway surrounding rock control," while the micro-vibration pulse located in the middle of the working face is assigned the semantics of "basic roof activity." When integrating the data, the biomimetic pulse neural network early warning model considers not only the pulse intensity and frequency but also performs logical reasoning based on the semantic labels. If the pulse stream with the semantic "basic roof activity" increases, but the pulse stream with the semantic "roadway surrounding rock control" remains stable, the model will determine that the current pressure increase is a normal periodic pressure increase and the risk is controllable; conversely, if both are synchronously abnormal, it will determine that there may be a dynamic disaster risk across the entire working face. This semantic-based logical judgment makes the early warning conclusion more in-depth than professional mine pressure analysis.

[0062] The system is also equipped with an emergency search and rescue auxiliary module based on a wireless ad hoc network. Even if the main communication network fails after an extreme disaster, the sensor nodes distributed throughout the working face can utilize their remaining power to form a low-power positioning network. Because these nodes accurately recorded the fracture sequence of the surrounding rock before the disaster, they can provide rescue personnel with a predicted map of the post-disaster tunnel collapse distribution and act as wireless relay points to assist in locating the mobile terminal signals of trapped personnel, achieving a comprehensive functional extension from pre-disaster early warning to post-disaster emergency response.

[0063] In summary, this invention, by constructing a biomimetic architecture that integrates neuroscience and deep learning, completely changes the limitations of traditional mine pressure monitoring systems in terms of planar data processing. Its efficient coding and decision-making mechanisms, simulating those of biological nervous systems, enable the system to possess, when processing massive amounts of heterogeneous mine data, akin to the keen intuition and rigorous logical reasoning abilities of human experts, thus building a solid technical barrier for the safe mining of deep coal mines.

[0064] The above embodiments are merely preferred embodiments of the present invention. Those skilled in the art should understand that any equivalent modifications or substitutions to hardware selection, communication protocols, or the textual implementation of specific neuron calculation formulas, without departing from the system architecture logic and core principles of biomimetic processing of the present invention, should be included within the scope of protection of the present invention. For example, replacing the hydraulic support resistance sensor with a roof subsidence sensor, or changing the structure of the biomimetic spiking neural network from hierarchical to fully connected, as long as its core logic still follows the principle of pulsed heterogeneous signals and using spatiotemporal correlation for early warning judgment, all fall within the technical scope of the present invention.

[0065] In the above embodiments, all numerical comparisons, logical operations, and parameter adjustments involved have been fully described in textual form. For example, describing "potential accumulation" means arithmetically summing the weight increment carried by each received pulse with the currently stored potential value; describing "exponential decay" means that the potential value continuously decreases over time according to a preset proportional coefficient. These textual descriptions are sufficient to guide those skilled in the art to implement the corresponding algorithm logic without relying on specific mathematical formulas.

[0066] In this invention, "configured for" or "configured to" refers to a hardware device or software module having a physical structural basis, circuit connection relationship, or preset code logic for performing the stated function. In actual manufacturing, the aforementioned functional modules can be implemented by programming a specific netlist file into a programmable logic device or storing a specific instruction sequence in a processor. Unless otherwise specified, all connections between modules refer to electrical, optical, or logical connections capable of data exchange.

[0067] The above description is merely a specific embodiment of the present invention, and is not intended to limit the invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-parameter intelligent monitoring and early warning system for working face mine pressure, characterized in that, include: A multi-source sensor data acquisition device is configured to acquire the raw physical signals of the fully mechanized mining face through a heterogeneous sensor matrix and convert the raw physical signals into a standardized digital signal stream; A pulse sequence encoding unit, connected to the multi-source sensor data acquisition device, is configured to map the digital signal stream into a pulse event sequence with timestamps based on the rate of change and amplitude characteristics of each physical quantity, thereby forming a spatiotemporal encoding format characterizing the dynamic process of rock strata fracturing. A biomimetic spiking neural network early warning model is connected to the spiking sequence encoding unit and configured to receive multi-channel spiking event sequences. Based on the calculation rules that simulate the firing mechanism of biological neurons, the spiking event sequences are nonlinearly integrated to identify the multi-parameter coupling modes corresponding to the precursors of rock instability. The biomimetic spiking neural network early warning model includes an input encoding layer, a feature mapping layer, a spatiotemporal integration layer, and a decision-making layer; The dynamic attention control module is integrated into the feedback control loop of the biomimetic spiking neural network early warning model. It is configured to adaptively adjust the weight allocation of each input channel according to the spatiotemporal distribution characteristics of the current input pulse stream, thereby enhancing the attention to key precursor signals. The early warning information output terminal is connected to the biomimetic spiking neural network early warning model and is configured to receive early warning judgment results and generate visual alarm prompts or linkage control commands based on the risk level. The biomimetic spiking neural network early warning model possesses synaptic plasticity learning capabilities and is connected to a historical disaster database storing typical accident evolution samples. The model is configured to automatically optimize the connection weights between neurons during the training phase by simulating long-term enhancement and long-term inhibition learning rules. When pulse combinations frequently appear in accident precursor sequences, the model is configured to automatically increase the synaptic weights between corresponding neurons to improve the recognition speed and confidence of the mine pressure instability precursors. The model also includes a reference association learning module configured to acquire production parameters including coal mining machine motor current, cutting section vibration frequency, and scraper conveyor load, and convert these production parameters into pulse sequences. A background cancellation layer learns the time-domain response function between the production pulse sequence and the mine pressure monitoring pulse sequence, utilizing inhibitory neuron synapses to cancel pulse components caused by normal production operations. The dynamic attention control module includes a spatiotemporal correlation assessment subunit and a weight adaptive correction subunit. The spatiotemporal correlation assessment subunit is configured to perform sliding window statistics on the pulse activity of all input channels, calculate the pulse firing density of each channel within the current time window, and calculate the correlation score of pulse activity between different sensor channels. The weight adaptive correction subunit is configured to, based on the evaluation results, reduce the synaptic gain of a channel determined to contain device noise or environmental interference, or increase the firing threshold of the corresponding input neuron of that channel. The dynamic attention control module is configured to determine that the current stage is a high-risk evolution phase when the pulse density of the stent pressure, microvibration and acoustic emission increases in tandem within the same time period. In this case, the trigger threshold of the decision-making layer is automatically lowered and the associated channels are given higher weight gain.

2. The intelligent monitoring and early warning system for multi-parameter mine pressure at the working face according to claim 1, characterized in that: The multi-source sensing data acquisition device includes a support pressure monitoring submodule, a micro-vibration monitoring submodule, and an acoustic emission monitoring submodule; The support pressure monitoring submodule includes a pressure transmitter installed on the cylinder body of the hydraulic support column, configured to acquire the cyclic resistance data of the working face support; The microseismic monitoring submodule includes an array of seismic sensors arranged around the working face roadway or goaf area, configured to capture vibration waveform signals generated by rock fractures. The acoustic emission monitoring submodule includes a high-frequency acoustic sensor configured to capture elastic waves during the development of microfractures in the rock mass. The multi-source sensor data acquisition device integrates a signal conditioning circuit, an analog-to-digital conversion circuit, and an anti-electromagnetic interference filter circuit with a three-stage inductor-capacitor filter structure. The multi-source sensor data acquisition device also integrates a zero-point automatic calibration circuit, configured to perform benchmark calibration on the pressure sensor during the support depressurization stage, so as to eliminate sensor temperature drift and zero-point drift. The multi-source sensor data acquisition device adopts a redundant array design, with at least two backup sensors deployed at each monitoring point, and the electrical characteristics of the sensors are monitored by an internal health assessment unit.

3. The intelligent monitoring and early warning system for multi-parameter mine pressure at the working face according to claim 2, characterized in that: The pulse sequence encoding unit is configured to employ a dual encoding strategy of firing rate and timing. For hydraulic support resistance signals, the pulse sequence encoding unit uses firing rate encoding logic. When the absolute value of the support resistance exceeds a first preset threshold or its rate of change exceeds a second preset threshold, the pulse firing frequency of the corresponding channel is increased. For micro-vibration energy and acoustic emission frequency signals, the pulse sequence encoding unit uses timing encoding logic, utilizing the precise arrival time interval of the pulses to encode the energy intensity of the signal. The greater the energy, the earlier the initial pulse is fired. The pulse sequence encoding unit has an internal global synchronization clock, configured to ensure that sensing pulses from different physical locations are timestamped under a unified time reference system, and the timestamp error range is less than or equal to 1 millisecond. The pulse sequence encoding unit is also configured to insert a health status flag bit into the generated pulse data stream when a sensor fails and switches over.

4. The intelligent monitoring and early warning system for multi-parameter mine pressure at the working face according to claim 3, characterized in that: The input encoding layer is configured to receive the pulse event sequence and distribute it to the corresponding neuron receptive fields; the feature mapping layer contains multiple groups of neuron clusters, each group of clusters is configured to generate a sensitive response to the mining pressure manifestation pattern; the spatiotemporal integration layer introduces leakage current integration triggering model logic, and each virtual neuron node has a membrane potential attribute; the model is configured to increase the membrane potential according to weight when the input pulse arrives, and to make the membrane potential decrease exponentially over time according to a preset decay constant during the period without pulse input; the model is configured to control the neuron to send an output pulse to the next layer only when the membrane potential accumulates to exceed the trigger threshold within a preset time window, filtering out scattered interference pulses and generating a resonant response to signals with spatiotemporal correlation.

5. The intelligent monitoring and early warning system for multi-parameter mine pressure at the working face according to claim 4, characterized in that: The early warning information output terminal includes a multi-level risk determiner and a response execution matrix; the multi-level risk determiner is configured to map the output pulse density of the biomimetic spiking neural network early warning model to four risk levels, namely green safe state, blue attention state, yellow warning state and red emergency state; The response execution matrix configuration is used to execute differentiated actions based on the risk level, including: In blue status, a warning message is sent to the explosion-proof mobile terminal; The tunnel's audible and visual alarm is activated when the yellow status is active. In a red state, an emergency command is issued to the fully automated mining control system to force a shutdown or emergency pressure relief operation. The early warning information output terminal also integrates a three-dimensional geomechanical simulation feedback system, which is configured to extract the surrounding spatiotemporal characteristics of risk points when they are identified and convert them into mechanical parameters, and input them into numerical simulation software to predict the risk evolution trend within the next 15 minutes.

6. The intelligent monitoring and early warning system for multi-parameter mine pressure at the working face according to claim 5, characterized in that: The system adopts a distributed edge computing architecture, including an edge-side processing node cluster deployed at the longwall mining face roadway and transfer point, and a central cloud decision server located in the ground monitoring center. The edge-side processing node cluster contains an embedded system-on-a-chip, configured to convert physical signals into pulse events at the physical sensing front end, so that the amount of pulse data packets transmitted to the central cloud decision server is less than 1 / 100 of the original sampled data. The central cloud decision server carries the biomimetic spiking neural network early warning model, which adopts a deep recurrent spiking neural network architecture, containing recurrent neuron layers with feedback connections, configured to perform temporal memorization of mine pressure evolution trends. The central cloud decision server also includes a self-evolution module, configured to periodically evaluate the early warning accuracy and automatically correct the synaptic connection thresholds inside the neural network using a reinforcement learning algorithm based on labeled false alarm or missed alarm pulse sequences.

7. The intelligent monitoring and early warning system for multi-parameter mine pressure at the working face according to claim 6, characterized in that: The system also includes a distributed optical fiber sensor network, which is laid inside the roof of the working face and the sidewalls of the tunnel. The distributed optical fiber sensor network adopts Brillouin optical time domain reflection technology and is configured by the optical fiber sensor host to convert the optical echo signal into an equivalent pressure pulse stream and connect it to the pulse sequence encoding unit. The dynamic attention control module is configured to perform spatial convolution operations on the pulse activities of adjacent fiber optic sensing points based on spatial topology relationships to locate the position and evolution trajectory of stress concentration areas; the early warning information output terminal supports augmented reality display function and is configured to use three-dimensional modeling technology to overlay the mine pressure distribution heat map and early warning level on the digital twin model of the working face in real time, and display the stress concentration area inside the surrounding rock and the propagation path of rock fracture cracks through explosion-proof augmented reality glasses.

8. The intelligent monitoring and early warning system for multi-parameter mine pressure at the working face according to claim 7, characterized in that: The multi-source sensor data acquisition device is encapsulated in an explosion-proof housing with explosion-proof capability. The internal circuit board is coated with moisture-proof and salt spray-proof paint, and the connecting cable adopts a carbon fiber reinforced tensile armor structure and a rotary self-locking connector. The communication link between the devices adopts a redundant ring network structure, which is configured to allow data to automatically detour through the redundant path when a single point of breakage occurs in the trunk optical cable, with a switching time of no more than 50 milliseconds. The biomimetic spiking neural network early warning model is deployed in an industrial server equipped with an artificial intelligence acceleration board. It adopts an asynchronous logic circuit design at the hardware level and is configured to generate computing power only when a pulse signal arrives. The system is equipped with an intrinsically safe backup power supply, configured to maintain monitoring data operation for no less than 72 hours in the event of a power outage; The system also includes a wireless self-organizing network emergency search and rescue auxiliary module, configured to use residual power to form a low-power positioning network when the main communication network fails, and to provide a post-disaster tunnel collapse distribution prediction map based on the surrounding rock fracture sequence recorded before the disaster.

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