A power grid dispatching visual monitoring system based on multi-source data fusion

Abstract

The present application relates to the technical field of power grid dispatching, and more particularly to a power grid dispatching visual monitoring system based on multi-source data fusion, which comprises an acquisition module, an anomaly determination module, a screening module, a determination module, an identification module, an adjustment module and an output module. The present application quickly identifies the actual threat source by fusing multi-source data and combining lightning density, screens the abnormal coal mine area by constructing the lightning threat value and combining the real-time line load and gas concentration, identifies the abnormal correlation area affected by the common power grid structure in combination with the spatial correlation characteristics of the abnormal mine area, dynamically adjusts the preset threat threshold according to the spatio-temporal evolution of the abnormal correlation area, and converts the multi-dimensional risk information into a visual dispatching diagram after adjustment, so as to support the rapid positioning of the core risk area and effectively solve the problems of coupled risk misjudgment, early warning lag and low dispatching efficiency caused by data isolation, reliance on manual work and static models.

Description

Technical Field

[0001] This invention relates to the field of power grid dispatching technology, and in particular to a power grid dispatching visualization monitoring system based on multi-source data fusion. Background Technology

[0002] With the continuous improvement of coal mine safety production standards and the deepening of intelligent transformation of mining power grids, the reliable operation of power supply systems under severe convective weather has become a key prerequisite for ensuring the safety of underground operations. Lightning activity caused by thunderstorms not only poses a direct threat to the physical infrastructure of the power grid, but the potential power outages can also quickly escalate into secondary disasters such as ventilation disruptions and gas accumulation in the mine. Therefore, how to quickly identify areas where thunderstorms threaten the mining power grid for precise intervention in the context of complex risks has become an urgent problem to be solved in the field of power grid dispatching.

[0003] Chinese Patent Application Publication No. CN117767567A discloses a lightning protection dispatching system for a smart microgrid. This system includes: a lightning prediction module, a data processing module, a human-machine interaction module, and a centralized control module. The lightning prediction module includes a comprehensive information monitoring unit, a Doppler radar, an atmospheric electric field meter, and a lightning early warning and positioning unit. The data processing module includes an equipment switching decision and lightning protection reliability coefficient assessment unit, an optimal power flow calculation unit, and a "source-load-storage" data processing unit. The human-machine interaction module includes a display unit and a strong control unit. The centralized control module includes a distributed generator control unit, an energy storage control unit, and a controllable load control unit.

[0004] Therefore, the existing technology has the following problems: the system relies on discrete point response patterns, which easily ignores the spatial correlation and collaborative diffusion of risks, leading to fragmentation of regional prevention and control measures; the system relies on static empirical coefficients, which easily underestimates the dynamic changes in the operation of complex systems, resulting in inaccurate assessment of lightning protection capabilities; the system relies on fixed rules, which easily fails to adapt and adjust under sudden lightning or complex power grid conditions, affecting system stability. Summary of the Invention

[0005] To address this, the present invention provides a power grid dispatch visualization monitoring system based on multi-source data fusion, which overcomes the problems of missed detection of coupling risks, delayed early warning, and low dispatch efficiency caused by data isolation, reliance on manual methods and static models in the prior art through multi-source data fusion, dynamic risk coupling assessment, and adaptive threshold optimization.

[0006] To achieve the above objectives, the present invention provides a power grid dispatch visualization monitoring system based on multi-source data fusion, comprising:

[0007] The acquisition module is used to acquire the gas concentration of each coal mine to be screened covered by the power supply line, the centroid coordinates and lightning density of the lightning cloud above each coal mine to be screened, and the line load rate of the power supply line of each coal mine to be screened.

[0008] An anomaly detection module is used to determine whether a lightning cloud is threatening based on the threshold comparison result of the lightning density.

[0009] The screening module is used to screen several candidate coal mining areas based on the determination that the lightning cloud cluster is threatening, according to the comparison results of the regional center coordinates of the coal mining area to be screened, the centroid coordinates, the lightning threat value determined by the lightning density, the preset threat threshold, and the threshold of the line load rate.

[0010] The determination module is used to determine several abnormal coal mining areas and several secondary coal mining areas based on the time variation characteristics of the gas concentration in the candidate coal mining areas and the threshold comparison results of the lightning threat value.

[0011] An identification module is used to identify several abnormal associated areas based on the spatial distribution characteristics determined by the regional center coordinates of the abnormal coal mining area and the line load rate.

[0012] An adjustment module is used to adjust the preset threat threshold based on the spatiotemporal evolution characteristics of the abnormal associated region;

[0013] The output module is used to output a visual scheduling map based on the abnormal correlation characteristics in the abnormal correlation area determined after adjusting the preset threat threshold, the gas concentration and the lightning density, and the scheduling priority of the abnormal coal mine area and the secondary coal mine area.

[0014] Furthermore, the determination that the lightning cloud cluster is threatening is based on the anomaly determination module's determination that the lightning density is greater than a preset density threshold.

[0015] Furthermore, the filtering module includes:

[0016] An impact calculation unit is used to determine the lightning threat value based on the attenuation relationship between the distance of the regional cloud cluster determined by the spatial distance between the coordinates of the center of the region and the coordinates of the centroid, and the lightning density;

[0017] A candidate screening unit is used to determine the coal mining area to be screened as the candidate coal mining area when the lightning threat value is greater than the preset threat threshold and the line load rate is greater than the preset load threshold.

[0018] Furthermore, the influence calculation unit includes:

[0019] The distance calculation subunit is used to determine the distance of the cloud cluster in the region based on the spatial distance between the regional plane coordinates and the central plane coordinates determined by the regional center coordinates and the centroid coordinates, respectively.

[0020] The impact calculation subunit is used to determine the lightning threat value based on the distance of the cloud cluster in the region and the lightning density, according to a preset attenuation model.

[0021] Furthermore, the determining module includes:

[0022] A statistical calculation unit is used to determine the average rate of change of concentration and the concentration fluctuation value based on the average instantaneous rate of change and the degree of dispersion of the gas concentration, respectively.

[0023] A concentration determination unit is used to determine whether the concentration risk level is low or high based on a joint comparison result of the average rate of change of concentration and the threshold of the concentration fluctuation value.

[0024] A lightning determination unit is used to determine whether the lightning threat level is high lightning risk or low lightning risk based on the threshold comparison result of the lightning threat value.

[0025] A type determination unit is used to determine whether the candidate coal mining area is the abnormal coal mining area or the secondary coal mining area based on the concentration risk level and the lightning threat level.

[0026] Furthermore, the type determination unit includes:

[0027] An anomaly determination subunit is used to determine the candidate coal mining area as the anomalous coal mining area when the concentration risk level is the high concentration risk and the lightning threat level is the high lightning risk.

[0028] A classification subunit is used to determine the candidate coal mining area as a coal mining area of ​​concern when the concentration risk level is the low concentration risk and the lightning threat level is the high lightning risk, and to determine the candidate coal mining area as a coal mining area of ​​warning when the concentration risk level is the high concentration risk and the lightning threat level is the low lightning risk.

[0029] The secondary determination subunit is used to determine the coal mining area of ​​concern and the coal mining area of ​​warning as the secondary coal mining area.

[0030] Furthermore, the identification module includes:

[0031] An adjacency building unit is used to determine a connection pair based on the threshold screening result of the spatial distance between any one of the abnormal coal mining areas and the other abnormal coal mining areas, combined with the mining area codes of the two abnormal coal mining areas.

[0032] A regional association unit is used to group all the abnormal coal mining areas that have direct or indirect connections into the same abnormal association region according to the connection pairs, so as to obtain several abnormal association regions.

[0033] Furthermore, the adjustment module includes:

[0034] The distance calculation unit is used to determine the distance change rate based on the average instantaneous change of the average distance between the regional center coordinates and the preset reference coordinates of the abnormally associated region within the previous preset adjustment period.

[0035] A quantity calculation unit is used to determine the quantity change rate based on the average instantaneous change of the associated quantity in the abnormal associated region within the preset adjustment period;

[0036] An adjustment unit is used to adjust the preset threat threshold based on the threshold comparison results of the distance change rate and the quantity change rate, and according to the relative deviations of the distance change rate and the quantity change rate from their respective thresholds.

[0037] Furthermore, the output module includes:

[0038] The correlation calculation unit is used to determine the degree of abnormal correlation based on the number of abnormal coal mining areas in the abnormal correlation zone and the coupling relationship between the spatial distance of any two abnormal coal mining areas.

[0039] The priority determination unit is used to determine the abnormal priority of each abnormal coal mine area based on the abnormal correlation degree of the abnormal coal mine area in the abnormal correlation zone and the average change rate of concentration, and to determine the attention priority of each coal mine area of ​​concern based on the lightning density, and to determine the warning priority of each coal mine area of ​​warning based on the gas concentration.

[0040] The scheduling visualization unit is used to mark the abnormal coal mining area or the secondary coal mining area corresponding to the abnormal priority, the attention priority and the warning priority based on different visual features, so as to generate the visualized scheduling map.

[0041] Furthermore, the priority determination unit includes:

[0042] An anomaly priority subunit is used to determine the anomaly priority as high priority, medium priority, or low priority based on the threshold comparison results of the anomaly correlation degree and the threshold comparison results of the average concentration change rate.

[0043] The secondary priority subunit is used to determine the attention priority as high priority, medium priority, or low priority based on the threshold comparison result of the lightning density.

[0044] The alert priority subunit is used to determine the alert priority as high alert priority, medium alert priority, or low alert priority based on the threshold comparison result of the gas concentration.

[0045] Compared with existing technologies, the advantages of this invention lie in its ability to rapidly identify substantial threat sources based on lightning density, construct lightning threat values ​​by fusing spatial distance and lightning density, and filter these values ​​in conjunction with real-time line load to locate candidate coal mining areas simultaneously exposed to lightning threats and high load pressure. Furthermore, by performing multi-dimensional correlation analysis between the dynamic changes in gas concentration and lightning threat levels, precise differentiation of candidate coal mining areas is achieved. In addition, by analyzing the spatial correlation characteristics of abnormal mining areas, abnormally correlated regions affected by the shared power grid structure are identified, thereby revealing the regional diffusion patterns of risks. Simultaneously, the preset threat threshold is dynamically adjusted based on the spatiotemporal evolution of abnormally correlated regions to ensure the robustness of assessments under different thunderstorm scenarios. By fusing multi-dimensional risk information into an intuitive panoramic visualization dispatch map, the cognitive load on dispatchers is reduced, effectively supporting their rapid location and forward-looking decision-making in core risk areas. This effectively solves the problems of coupled risk omissions, delayed early warnings, and low dispatch efficiency caused by data isolation, reliance on manual methods, and static models.

[0046] Furthermore, by using clear quantitative standards, targets with truly high discharge intensity can be quickly identified, directly eliminating a large amount of low-intensity or irrelevant lightning activity data. This ensures that subsequent analysis focuses only on substantial threats, making steps such as lightning threat value calculation and candidate mining area screening based on effective threat sources. As a result, the overall efficiency of the process from risk perception to decision output and the accuracy of resource allocation are optimized.

[0047] Furthermore, by calculating the regional cloud cluster distance between the coal mining area and the thunderstorm cloud cluster, and combining this with lightning density, a lightning threat value was constructed. This transformed the abstract lightning density into a quantitative threat indicator that decays spatially and is specific to the coal mining area, allowing risk assessment to be refined from an area-based meteorological threat to a point-to-point targeted assessment. Based on this, by comparing the lightning threat value with line load rate thresholds, it was ensured that the selected mining areas were potential targets of lightning activity while the power grid itself was operating under high stress, thus narrowing down the scope requiring subsequent complex gas correlation analysis.

[0048] Furthermore, by converting the geographic coordinates of the mining area and the thunderstorm cloud cluster to planar coordinates and calculating the Euclidean distance between them, the impact range of the thunderstorm cloud cluster on the coal mining area can be accurately measured, ensuring the effective location of lightning threats. Based on this, by fusing lightning density with scaled spatial distance to generate a lightning threat value, and incorporating distance attenuation processing, the lightning threat assessment becomes more consistent with reality, ensuring the accuracy of threat value calculation.

[0049] Furthermore, by quantifying changes in gas concentration, the dynamic changes of gas in underground coal mines can be accurately monitored, abnormal fluctuations in gas concentration can be detected in a timely manner, and the risk differences between low-concentration fluctuations and high-concentration fluctuations can be distinguished. By judging the risk level of gas concentration based on preset concentration change thresholds and preset fluctuation thresholds, normal and abnormal gas concentration changes can be clearly distinguished. In addition, potential lightning threat sources can be identified by judging the lightning threat level, and by combining the lightning threat with the load pressure of the mine's power grid, high-risk coal mining areas can be screened out, avoiding over-reaction in areas with low lightning density. On this basis, by comprehensively assessing gas concentration and lightning threat, abnormal coal mining areas or secondary coal mining areas can be identified, thus identifying coal mining areas with real threats.

[0050] Furthermore, by identifying abnormal coal mining areas when both the gas concentration risk level and lightning threat level are high-risk, the most dangerous scenario where external lightning threats and internal gas dynamic anomalies highly overlap is pinpointed. This ensures that limited dispatch resources can be prioritized for extreme coupled risk points that could directly induce gas accidents due to lightning tripping. For situations dominated by a single risk, high lightning risk accompanied by low gas risk is identified as a coal mining area of ​​concern, and high gas risk accompanied by low lightning risk is identified as a coal mining area of ​​warning. This allows for precise differentiation based on the dominant source and nature of the risk. In addition, by unifying these two categories into secondary coal mining areas, together with abnormal coal mining areas, a risk classification system is formed, ultimately resulting in a risk classification map that highlights key points and has distinct levels.

[0051] Furthermore, by setting a preset distance threshold, pairwise spatial relationships are determined for all abnormal coal mining areas, and connection pairs are recorded. This transforms the abstract geographical distribution into concrete graph theory connections, quantifying the spatial density between high-risk points. In addition, based on these connection pairs, by identifying all abnormally associated areas with direct or indirect connections, directly adjacent mining areas and indirectly connected mining areas are clustered together. This transforms the originally isolated point-like information of abnormal coal mining areas into area-like information representing the risk aggregation range and potential impact of a shared power grid structure.

[0052] Furthermore, by calculating the rate of change of the center of the anomalous associated region relative to distance and the rate of change of the number of anomalous associated regions, the spatial movement speed and direction of high-risk regions and whether the number of high-risk regions tends to stabilize or disperse are quantified, reflecting whether the correlation between risk points is strengthening or weakening. Based on this, a state of distributional instability is determined by comparing thresholds of distance change rate and number change rate, ensuring that the judgment of unstable situations has both spatial movement and scale fluctuation as evidence, thus improving the reliability of state identification. In addition, by automatically raising the threat assessment threshold when a rapid and disordered spatiotemporal evolution of a high-risk region is detected, limited analytical resources can be more concentrated on the core risk points with the most obvious and urgent threat at that moment.

[0053] Furthermore, by generating anomaly correlation by comprehensively considering the number and average spatial distance of abnormal coal mining areas, not only is the spatial clustering scale of high-risk mining areas quantified, but the density of their spatial distribution is also reflected, thus upgrading discrete high-risk points into structured information characterizing the overall threat intensity of the region. Based on this, by implementing differentiated grading strategies according to the nature of the risk, the urgency of handling various risks can be clearly quantified and distinguished. In addition, by encoding and spatially mapping the aforementioned complex multi-category, multi-level priority information using differentiated visual features such as color systems, symbol shapes, and dynamic effects, a panoramic situational map integrating geographical, power grid, and risk information is generated. This reduces the dimensionality of abstract data analysis conclusions into an intuitively perceptible visual language, enabling dispatchers to quickly identify key targets based on visual priorities, effectively focusing on the core of the anomaly and avoiding information overload and unclear focus.

[0054] Furthermore, by employing a dual-condition judgment based on anomaly correlation and average concentration change rate, it ensures that the objects given the highest dispatch urgency are extremely coupled risk points that are spatially highly concentrated and temporally rapidly deteriorating. This allows for the precise allocation of the most limited emergency resources to core hubs that may trigger chain reactions. For secondary coal mining areas, priority is assigned to mining areas based on different lightning activity intensities. This identifies mining areas with high lightning intensity and significant potential impact. Simultaneously, based on gas concentration, gas risk is also classified into high, medium, and low alert priorities, ensuring that the intensity of inspections and handling of gas hazards is strictly matched to their real-time concentration levels. Through a meticulous multi-level assessment system, the risks of various mining areas are accurately classified, ensuring efficient response of the dispatch system and optimal resource allocation. Attached Figure Description

[0055] Figure 1 This is a schematic diagram of the structure of the power grid dispatch visualization monitoring system based on multi-source data fusion in this embodiment;

[0056] Figure 2This is a logic diagram of the anomaly determination module in this embodiment for determining cloud anomalies;

[0057] Figure 3 This embodiment focuses on the logic diagram for determining candidate coal mining areas by the screening unit;

[0058] Figure 4 This embodiment defines the logic diagram for determining the unstable distribution of abnormal associated regions by adjusting the unit. Detailed Implementation

[0059] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0060] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0061] Please see Figure 1 As shown, this is a power grid dispatch visualization monitoring system based on multi-source data fusion in this embodiment. This embodiment provides a power grid dispatch visualization monitoring system based on multi-source data fusion, including:

[0062] The acquisition module is used to acquire the gas concentration of each coal mine to be screened covered by the power supply line, the centroid coordinates and lightning density of the lightning cloud above each coal mine to be screened, and the line load rate of the power supply line of each coal mine to be screened.

[0063] An anomaly detection module, which is connected to the acquisition module, is used to determine whether a lightning cloud cluster is threatening based on the threshold comparison result of the lightning density.

[0064] A screening module, which is connected to the acquisition module and the anomaly determination module respectively, is used to screen several candidate coal mining areas based on the determination result that the lightning cloud cluster is threatening, according to the comparison results of the regional center coordinates of the coal mining area to be screened, the centroid coordinates, the lightning threat value determined by the lightning density, the preset threat threshold, and the threshold of the line load rate.

[0065] A determination module, which is connected to the acquisition module and the screening module respectively, is used to determine several abnormal coal mining areas and several secondary coal mining areas based on the time variation characteristics of the gas concentration in the candidate coal mining areas and the threshold comparison results of the lightning threat value.

[0066] An identification module, which is connected to the acquisition module and the determination module respectively, is used to identify several abnormal associated areas based on the spatial distribution characteristics determined by the regional center coordinates of the abnormal coal mining area and the line load rate.

[0067] An adjustment module, which is connected to the identification module and the filtering module respectively, is used to adjust the preset threat threshold according to the spatiotemporal evolution characteristics of the abnormal association region;

[0068] The output module, which is connected to the acquisition module, the determination module and the adjustment module respectively, is used to output a visual scheduling map based on the abnormal association features in the abnormal association area determined after adjusting the preset threat threshold, the gas concentration and the lightning density, and the scheduling priority of the abnormal coal mine area and the secondary coal mine area.

[0069] In this embodiment, the power grid dispatch visualization monitoring system based on multi-source data fusion is mainly applied to intelligent safety dispatch and risk prevention scenarios for mining power grids that are affected by severe convective weather and have extremely high requirements for power supply continuity and security. Because mining power grids not only need to ensure continuous power supply for coal mine production, they also need to cope with multiple risk factors such as changes in methane concentration, line load fluctuations, and lightning activity in complex geological environments. Especially during the rainy season, power outages caused by lightning strikes not only affect production efficiency but may also lead to methane accumulation due to ventilation system interruptions, creating serious safety hazards. Therefore, the system integrates multi-source data to construct a multi-dimensional risk assessment model, aiming to achieve dynamic perception and intelligent dispatch of the mining power grid's operating status.

[0070] Lightning density refers to the number of lightning strikes per unit area per unit time. It is an important indicator describing the spatial distribution and intensity of lightning activity. The system receives lightning point data streams from a lightning location network in real time via a data interface. By maintaining a 30-minute sliding time window buffer for lightning points, for each lightning cloud with centroid coordinates acquired from the radar system, a spatial area is delineated with the centroid as the center and a preset radius of 20 kilometers. The total number of lightning strikes falling into this area within the time window is counted. By dividing the total number of lightning strikes by the area, the lightning density, characterizing the discharge intensity of the lightning cloud, is calculated. The centroid coordinates of a lightning cloud are geographic coordinates describing the spatial location of the core threat source of the lightning system. Weather radar detects the location, shape, and trajectory of lightning clouds by emitting electromagnetic waves and receiving the echo signals reflected back from them. By analyzing the time delay and intensity of the echo signals, the radar can calculate the reflection area of ​​the lightning cloud in space. Combining radar data such as azimuth, elevation, and detection range, radar can accurately determine the spatial location of lightning clouds. By weighting the data from multiple reflection points and the reciprocal of the distances to each point, the centroid coordinates of the cloud can be obtained. Gas concentration refers to the volume percentage concentration of flammable gases such as methane in the air of underground coal mine roadways and working faces. It is continuously monitored by a network of gas sensors deployed in key underground areas such as coal faces, tunneling faces, return airways, and electromechanical chambers, and transmitted in real-time to the ground control station via an industrial ring network or dedicated safety monitoring system such as the KJ series. Line load rate refers to the ratio of the current actual transmission power of a coal mine's power supply line to its designed rated transmission capacity, usually expressed as a percentage. It reflects the line's real-time operational margin and its ability to withstand disturbances and can be obtained in real-time from the power grid dispatch center's energy management system or wide-area measurement system.

[0071] The preset threat threshold is a benchmark value used to determine whether the lightning threat to a coal mining area reaches the threshold for inclusion in subsequent risk analysis. It depends on the lightning protection design standards of the mining area's power grid, the insulation level of the equipment, and the historical lightning damage probability of the area. It is usually set between 0.15 and 0.35. In this embodiment, it is set to 0.25, which can effectively eliminate non-critical areas with low lightning threat values ​​and focus on coal mining areas that face both significant lightning threats and high line loads.

[0072] By rapidly identifying substantial threat sources based on lightning density and constructing a lightning threat value by fusing spatial distance and lightning density, and then filtering based on real-time line load, candidate coal mining areas simultaneously exposed to lightning threats and high load pressure are located. Furthermore, a multi-dimensional correlation analysis is performed between the dynamic changes in gas concentration and lightning threat levels, enabling precise differentiation of candidate coal mining areas. In addition, by analyzing the spatial correlation characteristics of abnormal mining areas, abnormally correlated regions affected by the shared power grid structure are identified, revealing the regional diffusion patterns of risks. Simultaneously, preset threat thresholds are dynamically adjusted based on the spatiotemporal evolution of abnormally correlated regions to ensure robustness of assessments under different thunderstorm scenarios. By fusing multi-dimensional risk information into an intuitive panoramic visualization dispatch map, the cognitive load on dispatchers is reduced, effectively supporting their rapid location and forward-looking decision-making in core risk areas. This effectively solves the problems of coupled risk omissions, delayed early warnings, and low dispatch efficiency caused by data isolation, reliance on manual methods, and static models.

[0073] Please see Figure 2 As shown, this is the logic diagram for the anomaly determination module to determine cloud anomalies in this embodiment. In this embodiment, the determination result that the lightning cloud is threatening is based on the anomaly determination module determining that the lightning density is greater than a preset density threshold.

[0074] The preset density threshold is a baseline discharge intensity used to determine whether a lightning cloud cluster is threatening. It depends on historical lightning strike statistics for a specific area, the lightning withstand level of key equipment in the mining area's power grid, and the balance between risk perception sensitivity and false alarm rate required by the dispatch system. It is typically set between 0.10 times / km² / 30 minutes and 0.30 times / km² / 30 minutes. In this embodiment, the value is set to 0.20 times / km² / 30 minutes, which effectively distinguishes between ordinary discharge activity and strong thunderstorms with sustained development potential and a high probability of hazard.

[0075] By using clear quantitative standards, targets with truly high discharge intensity can be quickly identified, directly eliminating a large amount of low-intensity or irrelevant lightning activity data. This ensures that subsequent analysis focuses only on substantial threats, making steps such as lightning threat value calculation and candidate mining area screening based on effective threat sources. As a result, the overall efficiency of the process from risk perception to decision output and the accuracy of resource allocation are optimized.

[0076] Please see Figure 3 As shown, this is the decision logic diagram for the screening unit to determine candidate coal mining areas in this embodiment. In this embodiment, the screening module includes:

[0077] The influence calculation unit is used to determine the distance from each coal mine area to be screened to the lightning cloud based on the coordinates of the center of the region and the coordinates of the centroid, so as to obtain the distance of the regional cloud and determine the lightning threat value based on the attenuation relationship between the distance of the regional cloud and the lightning density.

[0078] A candidate screening unit, connected to the impact calculation unit, is used to determine the coal mining area to be screened as the candidate coal mining area when the lightning threat value is greater than the preset threat threshold and the line load rate is greater than the preset load threshold.

[0079] The preset load threshold is a critical percentage value used to determine whether the power supply line in the coal mining area is in a high load operating state and thus significantly increases the risk of lightning strike failure. It depends on the thermal stability limit of the power grid line, the safe operation criteria and the dispatching procedures. It is usually set between 75% and 85%. In this embodiment, it is set to 80%, which can effectively identify critical lines with insufficient operating margin and more prone to failure under external lightning strikes.

[0080] By calculating the regional cloud cluster distance between the coal mining area and the thunderstorm cloud cluster, and combining this with lightning density, a lightning threat value was constructed. This transformed the abstract lightning density into a quantitative threat indicator that decays spatially and is specific to the coal mining area, allowing risk assessment to be refined from a general meteorological threat to a point-to-point targeted assessment. Based on this, by comparing the lightning threat value with line load rate thresholds, it was ensured that the selected mining areas were potential targets of lightning activity, and that the power grid itself was also operating under high stress, thus narrowing down the scope requiring subsequent complex gas correlation analysis.

[0081] Specifically, the influence calculation unit includes:

[0082] The distance calculation subunit is used to convert the coordinates of the center of the region and the coordinates of the centroid into planar coordinates to obtain the planar coordinates of the region and the central planar coordinates, and to calculate the Euclidean distance between the planar coordinates of the region and the central planar coordinates to obtain the distance of the cloud cluster in the region.

[0083] An impact calculation subunit, connected to the distance calculation subunit, is used to determine the lightning threat value based on a preset attenuation model, according to the distance to the cloud cluster in the region and the lightning density. The preset attenuation model is Y = (M / M0) × e （-J / R） Where Y is the lightning threat value, M is the lightning density, M0 is the preset density threshold, J is the distance to the regional cloud cluster, and R is the preset lightning influence radius.

[0084] In this embodiment, the preset attenuation model comprehensively quantifies the potential threat value of lightning to the power grid in the mining area by integrating the lightning density of the lightning cloud, the spatial distance between the lightning cloud and the coal mining area, and the preset lightning influence radius. The calculation of this model is based on the formula: Y=(M / M0)×e（-J / R） The first term, M / M0, is a lightning density normalization factor, reflecting the relative magnitude of the current lightning activity intensity compared to the regional baseline intensity. It is used to identify substantial threat sources where the discharge intensity exceeds the safe threshold. Lightning threat depends not only on intensity but also decreases with distance. This decrease is non-linear and common in physical phenomena such as electromagnetic waves and light. As distance increases, the density of the electric field intensity or energy distribution weakens rapidly; therefore, the impact does not decrease linearly but rather exponentially. Therefore, the model introduces the second term, e. （-J / R） As a spatial attenuation factor, the regional cloud distance is the distance between the coal mining area and the centroid of the lightning cloud, while the preset lightning influence radius is used to characterize the characteristic scale of threat attenuation with distance. As the regional cloud distance increases, the spatial attenuation factor decreases rapidly, significantly reducing the threat to coal mining areas far from the lightning cloud. The preset lightning influence radius further defines the effective spatial range of the lightning threat. Furthermore, under normal circumstances, lightning clouds have a significant impact on surrounding areas within the preset lightning influence radius; beyond this range, the threat drops sharply to a negligible level. The ratio of the regional cloud distance to the preset lightning influence radius quantifies the relationship between distance and influence radius, reflecting the relative position of the coal mining area to the lightning cloud. If the coal mining area is far from the lightning cloud (i.e., a large regional cloud distance) or the lightning cloud's influence range is large (i.e., a large preset lightning influence radius), the threat attenuation rate will accelerate. By calculating the ratio of the regional cloud distance to the preset lightning influence radius, the threat levels of different coal mining areas can be standardized to a single scale. This scale reflects the relative relationship between the coal mining area and the lightning cloud, effectively distinguishing the risks of different mining areas. Therefore, by combining relative discharge intensity determination with exponential spatial decay and constraining its range of influence with the radius of influence, this model can reflect the degree of harm caused by lightning activity of different intensities, while reasonably limiting its spatial influence boundary and avoiding assessment distortion caused by over-considering distant low-risk clouds. Based on this, the model can dynamically calculate the threat value according to real-time lightning density, actual distance, and grid load status, thus adapting to the actual needs of different geographical locations, lightning intensities, and operating conditions. It possesses strong flexibility, adaptability, and engineering practicality, providing a scientific and accurate quantitative basis for lightning protection early warning and risk management in power grid dispatching.

[0085] The preset lightning impact radius is a characteristic length scale used to quantify how quickly lightning density decays with spatial distance. It depends on the typical horizontal scale of thunderstorm clouds, the energy radiation characteristics of ground lightning and cloud lightning, and the influence of regional topography on lightning propagation. As an example, the preset lightning impact radius can be determined by analyzing the positional relationship between historical lightning strikes and thunderstorm clouds. Specifically, the lightning clouds that caused lightning faults in the mining area's power grid equipment can be statistically analyzed, and the distance distribution between their centroids and the fault points can be calculated. The 85th percentile of this distance distribution can be used as the recommended value for the preset lightning impact radius. Through the analysis of data from multiple typical mining areas, the preset lightning impact radius usually falls within the range of 15 to 25 kilometers. The historical data calibration result for the mining area in this embodiment is 20 kilometers.

[0086] By converting the geographic coordinates of the mining area and thunderstorm clouds to planar coordinates and calculating the Euclidean distance between them, the impact range of thunderstorm clouds on the coal mining area can be accurately measured, ensuring the effective location of lightning threats. Based on this, by fusing lightning density with scaled spatial distance to generate lightning threat values ​​and incorporating distance attenuation processing, the lightning threat assessment becomes more realistic, ensuring the accuracy of threat value calculations.

[0087] Specifically, the determining module includes:

[0088] The statistical calculation unit is used to calculate the average value of the relative deviation of the gas concentration between the next moment and the adjacent previous moment within the previous preset risk period, so as to obtain the average rate of change of concentration, and to calculate the standard deviation of the gas concentration within the preset risk period, so as to obtain the concentration fluctuation value.

[0089] A concentration determination unit, connected to the statistical calculation unit, is used to determine the concentration risk level as low concentration risk when the average rate of change of concentration is less than or equal to a preset concentration change threshold and the concentration fluctuation value is less than or equal to a preset fluctuation threshold, and to determine the concentration risk level as high concentration risk when the average rate of change of concentration is greater than the preset concentration change threshold and the concentration fluctuation value is greater than the preset fluctuation threshold.

[0090] A lightning determination unit is used to determine the lightning threat level as high lightning risk when the lightning threat value is greater than a preset lightning threshold, and to determine the lightning threat level as low lightning risk when the lightning threat value is less than or equal to the preset lightning threshold.

[0091] A type determination unit, which is connected to the concentration determination unit and the lightning determination unit respectively, is used to determine the candidate coal mining area as the abnormal coal mining area or the secondary coal mining area based on the concentration risk level and the lightning threat level.

[0092] The preset risk duration is an analytical time window used to calculate the temporal variation characteristics of gas concentration. It depends on the typical development speed of abnormal gas outburst events and the frequency of data updates, and is typically set between 10 and 30 minutes. In this embodiment, it is set to 15 minutes, which can effectively capture rapid concentration increases that are of safety significance. The preset concentration change threshold is a critical rate value used to determine whether the gas concentration is in a rapid increase state. It depends on the limitations on gas outburst rates imposed by coal mine safety regulations and geological conditions, and is typically set between 0.1% and 0.3%. In this embodiment, it is set to 0.2%, which can accurately identify abnormal outburst situations that require high attention. The preset fluctuation... The threshold is a limit on the degree of dispersion used to determine whether the gas concentration data is stable. It depends on the sensor measurement accuracy and the normal concentration fluctuation range downhole, and is usually set between 0.1% and 0.3%. In this embodiment, it is set to 0.2%, which can accurately identify gas concentrations that change drastically in a short period of time. The preset lightning threshold is a critical value used to determine whether the lightning threat has reached a dangerous level that requires special attention. It depends on the meteorological data of a specific area and the occurrence of historical lightning strikes. It is usually set between 0.15 and 0.35. In this embodiment, it is set to 0.3, which can effectively screen out mining areas with potential lightning hazards and focus on those areas with high lightning threats.

[0093] By quantifying changes in methane concentration, the dynamic changes of methane in underground coal mines can be accurately monitored, abnormal fluctuations in methane concentration can be detected in a timely manner, and the risk differences between low-concentration fluctuations and high-concentration fluctuations can be distinguished. By judging the risk level of methane concentration based on preset concentration change thresholds and preset fluctuation thresholds, normal and abnormal methane concentration changes can be clearly distinguished. Furthermore, by judging the lightning threat level, potential lightning threat sources can be identified, and by combining lightning threat with the load pressure of the mine's power grid, high-risk coal mining areas can be screened out, avoiding overreaction in areas with low lightning density. Based on this, by comprehensively assessing methane concentration and lightning threat, abnormal or secondary coal mining areas can be identified, thus identifying coal mining areas with genuine threats.

[0094] Specifically, the type determination unit includes:

[0095] An anomaly determination subunit is used to determine the candidate coal mining area as the anomalous coal mining area when the concentration risk level is the high concentration risk and the lightning threat level is the high lightning risk.

[0096] A classification subunit is used to determine the candidate coal mining area as a coal mining area of ​​concern when the concentration risk level is the low concentration risk and the lightning threat level is the high lightning risk, and to determine the candidate coal mining area as a coal mining area of ​​warning when the concentration risk level is the high concentration risk and the lightning threat level is the low lightning risk.

[0097] The secondary determination subunit, which is connected to the classification subunit, is used to determine the coal mining area of ​​concern and the coal mining area of ​​warning as the secondary coal mining area.

[0098] By identifying anomalous coal mining areas when both gas concentration risk level and lightning threat level are high, the most dangerous scenario where external lightning threat and internal gas dynamic anomalies highly overlap is pinpointed. This ensures that limited dispatch resources can be prioritized for extreme coupled risk points that could directly induce gas accidents due to lightning tripping. For situations dominated by a single risk, high lightning risk accompanied by low gas risk is classified as a coal mining area of ​​concern, and high gas risk accompanied by low lightning risk is classified as a coal mining area of ​​alert. This allows for precise differentiation based on the dominant source and nature of the risk. Furthermore, by unifying these two categories into secondary coal mining areas, together with anomalous coal mining areas, a risk classification system is formed, ultimately resulting in a risk classification map that highlights key areas and has distinct levels.

[0099] Specifically, the identification module includes:

[0100] An adjacency construction unit is used to number all the abnormal coal mining areas to obtain the mining area code of each abnormal coal mining area, and when the adjacency distance determined based on the Euclidean distance between any one of the abnormal coal mining areas and the regional plane coordinates of the other abnormal coal mining areas is less than a preset distance threshold, the two abnormal coal mining areas are recorded as a connection pair according to the mining area codes of the two abnormal coal mining areas.

[0101] A regional association unit, which is connected to the adjacency construction unit, is used to classify all the abnormal coal mining areas that have direct or indirect connections into the same abnormal association region according to the connection pairs, so as to obtain several abnormal association regions.

[0102] In this embodiment, the numbering of all the abnormal coal mining areas adopts a spatial location-based numbering rule. First, the latitude and longitude coordinates of all abnormal coal mining areas are obtained. Then, they are sorted according to a spatial order from north to south and from west to east, prioritizing descending order by latitude, and if the latitudes are the same, then ascending order by longitude. Finally, based on this sorting result, a sequential number is assigned to each abnormal coal mining area. For example, the first-ranked coal mining area is numbered 1, the second-ranked is numbered 2, and so on. This method yields a unique code for each abnormal coal mining area.

[0103] In this embodiment, during the process of grouping all the abnormal coal mining areas with direct or indirect connections into the same abnormal association region based on the connection pairs, firstly, each abnormal coal mining area is initially considered as an independent set; subsequently, based on the connection pairs established by the adjacency relationship construction unit (i.e., the distance between two coal mining areas is less than the adjacency threshold), the sets containing coal mining areas with direct connections are merged; finally, through a recursive merging process, if two coal mining areas are not directly connected but are indirectly connected through other coal mining areas (i.e., there exists a path composed of multiple connection pairs), they will also be grouped into the same set. This ensures that spatially adjacent coal mining areas can be reasonably clustered. Even if the distance between individual coal mining areas may exceed the adjacency threshold, as long as there exists a path composed of multiple short-distance connections, they will still be considered as the same abnormal association region.

[0104] The preset distance threshold is a standard distance used to determine whether the adjacent distance between coal mining areas is close enough to be merged into an abnormal associated area. It depends on the actual distance between coal mining areas and the risk assessment needs in power grid dispatch. It is usually set between 5 kilometers and 15 kilometers. In this embodiment, it is set to 10 kilometers, which can effectively cluster abnormal coal mining areas that are close to each other and ensure that the risks of adjacent coal mining areas are fully associated.

[0105] By setting a preset distance threshold, all abnormal coal mining areas are spatially matched and connected, transforming abstract geographical distributions into concrete graph-theoretic connections and quantifying the spatial density between high-risk points. Furthermore, based on these connections, by identifying all anomalous associated areas with direct or indirect connections, directly adjacent and indirectly connected mining areas are grouped together. This elevates the previously isolated point-like information of abnormal coal mining areas into isomorphic information representing the risk aggregation range and potential impact of a shared power grid structure.

[0106] Please see Figure 4 As shown, this is the logic diagram for determining the unstable distribution of abnormally associated regions by the adjustment unit in this embodiment. In this embodiment, the adjustment module includes:

[0107] The distance calculation unit is used to calculate the average distance between the center coordinates of each abnormal associated region and the preset reference coordinates at each time point within the previous preset adjustment period, so as to obtain the center average distance; and to calculate the average relative deviation of the center average distance between two adjacent time points within the preset adjustment period, so as to obtain the distance change rate.

[0108] The quantity calculation unit is used to calculate the average value of the relative deviation of the number of associations in the abnormal association area at each time point within the preset adjustment period, so as to obtain the quantity change rate.

[0109] An adjustment unit, connected to both the distance calculation unit and the quantity calculation unit, is used to determine that the distribution of the abnormal associated region is unstable when the distance change rate is greater than a preset distance change threshold and the quantity change rate is greater than a preset quantity change threshold. Based on the result of the unstable distribution of the abnormal associated region, the unit increases the preset threat threshold according to the relative deviations between the distance change rate and the preset distance change threshold, and between the quantity change rate and the preset quantity change threshold. Here, D' = D + c × (f1 - f2) / (f1 + f2) + b × (t1 - t2) / (t1 + t2), where D' is the adjusted preset threat threshold, D is the original preset threat threshold, c is the preset distance change adjustment coefficient, f1 is the distance change rate, f2 is the preset distance change threshold, b is the preset quantity change adjustment coefficient, t1 is the quantity change rate, and t2 is the preset quantity change threshold.

[0110] In this embodiment, the preset reference coordinates are fixed geographical reference points used by the entire system to measure the spatial movement trend of abnormally related areas. Their setting depends on the actual geographical location of the power grid dispatch center or the mine safety monitoring center, and is typically the geographical coordinates of that center, facilitating a direct assessment of the location and distance changes of the risk area relative to the command core. In this embodiment, the coordinates are set as the geographical center or main dispatch center of the entire monitored mine power grid coverage area, serving as the unified spatial reference origin for calculating the overall movement trend of all abnormally related areas.

[0111] The preset adjustment duration refers to the time window used to assess the spatiotemporal evolution of anomaly-related areas. It depends on the real-time dynamic response speed of the mining power grid and the update frequency of the monitoring system, and is typically set between 10 and 30 minutes. In this embodiment, it is set to 15 minutes, which effectively captures potential risks brought about by changes in the mining power grid and meteorological environment. The preset distance change threshold is a standard used to judge the stability of the distribution changes in anomaly-related areas. It depends on the geographical environment, the frequency of lightning activity, and the specific risk management requirements of the mining area, and is typically set between 0.01 and 0.05. In this embodiment, it is set to 0.03, which accurately reflects the spatiotemporal changes of anomaly-related areas and ensures the timely response of the dispatch system to risk evolution. The preset quantitative change threshold is the critical rate of change used to determine whether the aomaly-related areas have experienced drastic fluctuations in quantity. It depends on the load changes of the mining power grid and... The device response capability is typically set between 0.02 and 0.08; in this embodiment, it is set to 0.05. This allows the device to accurately capture the changing trends of the associated area under high-risk conditions, ensuring the effectiveness of the early warning system. The preset distance change adjustment coefficient is used to control the influence weight of the distance change rate on the threat threshold adjustment amount. It depends on the system's design requirements for the sensitivity of spatial movement in the risk area and is typically set between 0.05 and 0.15. In this embodiment, it is set to 0.10, enabling the threshold adjustment to respond appropriately to spatial movement information. The preset quantity change adjustment coefficient is used to control the influence weight of the quantity change rate on the threat threshold adjustment amount. It depends on the system's design requirements for the sensitivity of the risk area's scale change and is typically set between 0.05 and 0.15. In this embodiment, it is set to 0.10, enabling the threshold adjustment to respond appropriately to scale change information.

[0112] By calculating the rate of change of the center of anomaly-linked regions relative to distance and the rate of change of the number of anomaly-linked regions, the spatial movement speed and direction of high-risk regions are quantified, as well as whether the number of high-risk regions is stabilizing or dispersing, reflecting whether the correlation between risk points is strengthening or weakening. Based on this, a threshold comparison of the rate of change of distance and the rate of change of number is used to determine an unstable distribution state, ensuring that the judgment of unstable situations is based on both spatial movement and scale fluctuations, thus improving the reliability of state identification. Furthermore, by automatically raising the threat assessment threshold when rapid and disordered spatiotemporal evolution of high-risk regions is detected, limited analytical resources can be concentrated on the core risk points with the most explicit and urgent threat at that moment.

[0113] Specifically, the output module includes:

[0114] The association calculation unit is used to calculate the product of the association quantity determined based on the number of abnormal coal mining areas in the abnormal association area, the density factor determined based on the ratio of the average of the Euclidean distance between any two abnormal coal mining areas in the abnormal association area to a preset average distance threshold, and the scale factor determined based on the logarithm of the association quantity, so as to obtain the abnormal association degree.

[0115] A priority determination unit, connected to the association calculation unit, is used to determine the abnormal priority of each abnormal coal mine area based on the abnormal association degree of the abnormal coal mine area in the abnormal association area and the average change rate of concentration, and to determine the attention priority of each coal mine area of ​​concern based on the lightning density, and to determine the warning priority of each coal mine area of ​​warning based on the gas concentration.

[0116] The scheduling visualization unit, which is connected to the associated computing unit, is used to mark the abnormal coal mining area or the secondary coal mining area corresponding to the abnormal priority, the attention priority and the warning priority based on different visual features, so as to generate the visualized scheduling map.

[0117] In this embodiment, the scheduling visualization unit, based on the classification and priority results output by the priority determination unit, visually marks different types of coal mining areas on the visualized scheduling map, thereby intuitively presenting the spatial distribution and level differences of multi-dimensional risks, assisting dispatchers in global situation assessment and decision-making. For abnormal coal mining areas, based on their abnormality priority, they are marked with a red color scheme with strong warning connotations: high-priority abnormal mining areas are marked with a dark red, rapidly flashing icon; medium-priority abnormal mining areas are marked with a red, constantly lit or slowly flashing icon; low-priority abnormal mining areas are marked with a light red, static icon, and the icon shape can be a warning symbol such as an inverted triangle or exclamation mark. For coal mining areas of concern belonging to the secondary coal mining areas, based on their concern priority, they are marked with a yellow color scheme to represent their main lightning threat attributes: high-priority concern mining areas are marked with an orange-yellow icon with a lightning bolt symbol; medium-priority concern mining areas are marked with a yellow icon; low-priority concern mining areas are marked with a light yellow icon, and their dynamic effect can be set to a constantly lit or slowly breathing light effect. For warning coal mining areas belonging to the same secondary coal mining area, they are marked with a purple color scheme according to their warning priority to represent their gas risk attributes: high-priority warning areas are marked with a dark purple icon with a gas symbol; medium-priority warning areas are marked with a purple icon; and low-priority warning areas are marked with a light purple icon. All markers are associated with their corresponding mining area coordinates and are overlaid on a base map containing geographic information and power grid topology. When the dispatcher hovers the cursor over any marker, a detailed data card for that mining area can be displayed, including real-time gas concentration, concentration change rate, lightning density, line load rate, and calculated risk score.

[0118] The preset average distance threshold is a distance benchmark value used to normalize the average Euclidean distance between abnormal coal mining areas when calculating the correlation degree of anomalies. It depends on the average distance of the typical power supply radius of the mining area and the grid topology connection. It is usually set between 5 kilometers and 15 kilometers. In this embodiment, it is set to 8 kilometers, which can eliminate the dimensional influence caused by the geographical span and reflect the degree of spatial clustering between risk points.

[0119] By generating anomaly correlation by comprehensively considering the number and average spatial distance of abnormal coal mining areas, not only is the spatial clustering scale of high-risk mining areas quantified, but the density of their spatial distribution is also reflected, thus upgrading discrete high-risk points into structured information characterizing the overall threat intensity of the region. Based on this, by implementing differentiated grading strategies according to the nature of the risk, the urgency of handling various risks can be clearly quantified and distinguished. Furthermore, by encoding and spatially mapping the aforementioned complex multi-category, multi-level priority information using differentiated visual features such as color systems, symbol shapes, and dynamic effects, a panoramic situational map integrating geographical, power grid, and risk information is generated. This reduces the dimensionality of abstract data analysis conclusions into an intuitively perceptible visual language, enabling dispatchers to quickly identify key targets based on visual priorities, effectively focusing on the core of the anomaly and avoiding information overload and unclear focus.

[0120] Specifically, the priority determination unit includes:

[0121] An anomaly priority subunit is configured to determine the anomaly priority as high priority when the anomaly correlation degree is greater than a preset high-level anomaly threshold and the average concentration change rate is greater than a preset high-level concentration threshold, and to determine the anomaly priority as medium priority when the anomaly correlation degree is greater than a preset high-level anomaly threshold and the average concentration change rate is less than or equal to a preset high-level concentration threshold; otherwise, the anomaly priority is determined as low priority.

[0122] The secondary priority subunit is used to determine the attention priority as high priority when the lightning density is greater than the upper limit of the preset lightning threshold, and to determine the attention priority as medium priority when the lightning density is greater than the lower limit of the preset lightning threshold and less than the upper limit of the preset lightning threshold; otherwise, the attention priority is determined as low priority.

[0123] The warning priority subunit is used to determine the warning priority as high-priority warning when the gas concentration is greater than the upper limit of the preset high-priority gas threshold, and to determine the warning priority as medium-priority warning when the gas concentration is less than the upper limit of the preset high-priority gas threshold and greater than the lower limit of the preset high-priority gas threshold; otherwise, the warning priority is determined as low-priority warning.

[0124] The preset advanced anomaly threshold is a standard used to determine the correlation degree of anomalies in coal mining areas. It depends on the needs of power grid risk management and dispatching in the mining area and is typically set between 0.60 and 0.80. In this embodiment, it is set to 0.70, which can accurately distinguish high-risk anomaly coal mining areas. The preset lightning threshold is a critical value range used to classify the intensity of lightning threats to coal mining areas of concern. It depends on the regional lightning protection level and historical lightning strike density distribution. Typically, its lower limit is set between 0.25 strikes / km² / 30 minutes and 0.35 strikes / km² / 30 minutes, and its upper limit is set between 0.35 strikes / km² / 30 minutes and 0.45 strikes / km² / 30 minutes. In this embodiment, the lower limit is set to 0.28 times / km² / 30 minutes and the upper limit is set to 0.38 times / km² / 30 minutes, which can finely classify the lightning density into high, medium, and low levels. The preset high-level gas threshold is a critical value range used to classify the risk of gas exceeding the limit in the warning coal mine area. It depends on the gas warning concentration and alarm concentration specified in the coal mine safety regulations. Usually, its lower limit is set between 0.8% and 1.0%, and its upper limit is set between 1.0% and 1.2%. In this embodiment, its lower limit is set to 0.85% and its upper limit is set to 1.05%, which can clearly classify the risk into high, medium, and low levels based on the absolute gas concentration value.

[0125] By employing a dual-condition judgment based on anomaly correlation and average concentration change rate, the system ensures that the objects given the highest dispatch urgency are extremely coupled risk points that are spatially highly concentrated and temporally rapidly deteriorating. This allows for the precise allocation of the most limited emergency resources to core hubs that may trigger chain reactions. For secondary coal mining areas, the system prioritizes areas of concern based on different lightning activity intensities, identifying areas with high lightning intensity and significant potential impact. Simultaneously, based on gas concentration, gas risk is also classified into high, medium, and low alert priorities, ensuring that the intensity of inspections and handling of gas hazards is strictly matched to their real-time concentration levels. Through a meticulous multi-level assessment system, the risks of various mining areas are accurately classified, ensuring efficient response of the dispatch system and optimal resource allocation.

[0126] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. 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 power grid dispatch visualization monitoring system based on multi-source data fusion, characterized in that, include: The acquisition module is used to acquire the gas concentration of each coal mine to be screened covered by the power supply line, the centroid coordinates and lightning density of the lightning cloud above each coal mine to be screened, and the line load rate of the power supply line of each coal mine to be screened. An anomaly detection module is used to determine whether a lightning cloud is threatening based on the threshold comparison result of the lightning density. The screening module is used to screen several candidate coal mining areas based on the determination that the lightning cloud cluster is threatening, according to the comparison results of the regional center coordinates of the coal mining area to be screened, the centroid coordinates, the lightning threat value determined by the lightning density, the preset threat threshold, and the threshold of the line load rate. The determination module is used to determine several abnormal coal mining areas and several secondary coal mining areas based on the time variation characteristics of the gas concentration in the candidate coal mining areas and the threshold comparison results of the lightning threat value. An identification module is used to identify several abnormal associated areas based on the spatial distribution characteristics determined by the regional center coordinates of the abnormal coal mining area and the line load rate. An adjustment module is used to adjust the preset threat threshold based on the spatiotemporal evolution characteristics of the abnormal associated region; The output module is used to output a visual scheduling map based on the abnormal association characteristics in the abnormal association area determined after adjusting the preset threat threshold, the gas concentration and the lightning density, and the scheduling priority of the abnormal coal mine area and the secondary coal mine area. The filtering module includes: An impact calculation unit is used to determine the lightning threat value based on the attenuation relationship between the distance of the regional cloud cluster determined by the spatial distance between the coordinates of the center of the region and the coordinates of the centroid, and the lightning density; A candidate screening unit is used to determine the coal mining area to be screened as the candidate coal mining area when the lightning threat value is greater than the preset threat threshold and the line load rate is greater than the preset load threshold. The influence calculation unit includes: The distance calculation subunit is used to determine the distance of the cloud cluster in the region based on the spatial distance between the regional plane coordinates and the central plane coordinates determined by the regional center coordinates and the centroid coordinates, respectively. The impact calculation subunit is used to determine the lightning threat value based on the distance of the cloud cluster in the region and the lightning density, according to a preset attenuation model. The preset attenuation model is Y = (M / M0) × e （-J / R） Where Y is the lightning threat value, M is the lightning density, M0 is the preset density threshold, J is the distance to the regional cloud cluster, and R is the preset lightning influence radius.

2. The power grid dispatch visualization monitoring system based on multi-source data fusion according to claim 1, characterized in that, The determination that the lightning cloud cluster is threatening is based on the anomaly determination module's determination that the lightning density is greater than a preset density threshold.

3. The power grid dispatch visualization monitoring system based on multi-source data fusion according to claim 2, characterized in that, The determining module includes: A statistical calculation unit is used to determine the average rate of change of concentration and the concentration fluctuation value based on the average instantaneous rate of change and the degree of dispersion of the gas concentration, respectively. A concentration determination unit is used to determine whether the concentration risk level is low or high based on a joint comparison result of the average rate of change of concentration and the threshold of the concentration fluctuation value. A lightning determination unit is used to determine whether the lightning threat level is high lightning risk or low lightning risk based on the threshold comparison result of the lightning threat value. A type determination unit is used to determine whether the candidate coal mining area is the abnormal coal mining area or the secondary coal mining area based on the concentration risk level and the lightning threat level.

4. The power grid dispatch visualization monitoring system based on multi-source data fusion according to claim 3, characterized in that, The type determination unit includes: An anomaly determination subunit is used to determine the candidate coal mining area as the anomalous coal mining area when the concentration risk level is the high concentration risk and the lightning threat level is the high lightning risk. A classification subunit is used to determine the candidate coal mining area as a coal mining area of ​​concern when the concentration risk level is the low concentration risk and the lightning threat level is the high lightning risk, and to determine the candidate coal mining area as a coal mining area of ​​warning when the concentration risk level is the high concentration risk and the lightning threat level is the low lightning risk. The secondary determination subunit is used to determine the coal mining area of ​​concern and the coal mining area of ​​warning as the secondary coal mining area.

5. The power grid dispatch visualization monitoring system based on multi-source data fusion according to claim 4, characterized in that, The identification module includes: An adjacency building unit is used to determine a connection pair based on the threshold screening result of the spatial distance between any one of the abnormal coal mining areas and the other abnormal coal mining areas, combined with the mining area codes of the two abnormal coal mining areas. A regional association unit is used to group all the abnormal coal mining areas that have direct or indirect connections into the same abnormal association region according to the connection pairs, so as to obtain several abnormal association regions.

6. The power grid dispatch visualization monitoring system based on multi-source data fusion according to claim 5, characterized in that, The adjustment module includes: The distance calculation unit is used to determine the distance change rate based on the average instantaneous change of the average distance between the regional center coordinates and the preset reference coordinates of the abnormally associated region within the previous preset adjustment period. A quantity calculation unit is used to determine the quantity change rate based on the average instantaneous change of the associated quantity in the abnormal associated region within the preset adjustment period; An adjustment unit is used to adjust the preset threat threshold based on the threshold comparison results of the distance change rate and the quantity change rate, and according to the relative deviations of the distance change rate and the quantity change rate from their respective thresholds.

7. The power grid dispatch visualization monitoring system based on multi-source data fusion according to claim 6, characterized in that, The output module includes: The correlation calculation unit is used to determine the degree of abnormal correlation based on the number of abnormal coal mining areas in the abnormal correlation zone and the coupling relationship between the spatial distance of any two abnormal coal mining areas. The priority determination unit is used to determine the abnormal priority of each abnormal coal mine area based on the abnormal correlation degree of the abnormal coal mine area in the abnormal correlation zone and the average change rate of concentration, and to determine the attention priority of each coal mine area of ​​concern based on the lightning density, and to determine the warning priority of each coal mine area of ​​warning based on the gas concentration. The scheduling visualization unit is used to mark the abnormal coal mining area or the secondary coal mining area corresponding to the abnormal priority, the attention priority and the warning priority based on different visual features, so as to generate the visualized scheduling map.

8. The power grid dispatch visualization monitoring system based on multi-source data fusion according to claim 7, characterized in that, The priority determination unit includes: An anomaly priority subunit is used to determine the anomaly priority as high priority, medium priority, or low priority based on the threshold comparison results of the anomaly correlation degree and the threshold comparison results of the average concentration change rate. The secondary priority subunit is used to determine the attention priority as high priority, medium priority, or low priority based on the threshold comparison result of the lightning density. The alert priority subunit is used to determine the alert priority as high alert priority, medium alert priority, or low alert priority based on the threshold comparison result of the gas concentration.

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