A coal mine equipment intelligent linkage control method based on personnel positioning

Abstract

The present application relates to the technical field of self-adaptive equipment linkage control of underground coal mine environment, and particularly relates to a coal mine equipment intelligent linkage control method based on personnel positioning.A coal mine equipment intelligent linkage control method based on personnel positioning, comprising the following steps: S1: dividing the edge node area of the underground coal mine according to the explosion-proof type, determining the environment sensitive area and the environment stable area, performing load testing on the edge computing node in the environment sensitive area to determine the corresponding curve type; S2: determining all adjacent areas of each environment sensitive area in the edge node area of the underground coal mine, obtaining the cross-area coordination task success rate, and determining the edge computing capability of the environment sensitive area according to the cross-area coordination task success rate.The present application adjusts the start-stop strategy through area division, sensitive identification and clustering grouping, improves the positioning linkage adaptability, and guarantees the continuous production of the coal mine.

Description

Technical Field

[0001] This invention relates to the field of adaptive equipment linkage control technology in underground coal mines, and in particular to an intelligent linkage control method for coal mine equipment based on personnel positioning. Background Technology

[0002] With the advancement of intelligent coal mine construction, precise personnel positioning technology is gradually being integrated with the automated control of major equipment such as coal mining machines, conveyors, and ventilation fans. Based on the principle of ultra-wideband positioning, miners' position coordinates are uploaded to the monitoring system in real time, providing spatial basis for equipment start-up, shutdown, and interlocking. Existing coal mine equipment linkage control methods mostly adopt fixed electronic fences and single-point interlocking logic, that is, when personnel enter a preset area, the equipment is powered off or an audible and visual alarm is triggered. This achieves human-machine separation to a certain extent, reduces the probability of collision and entanglement accidents, and improves the inherent safety level of underground operations.

[0003] However, existing methods have significant shortcomings in dealing with the complex and dynamic environment of mining faces. Strong electromagnetic interference from the start-up and shutdown of frequency converter clusters in the roadway, the attenuation of wireless signals by high concentrations of dust and water mist, and the coordinate drift of positioning base stations caused by roof deformation all result in real-time fluctuations in the available computing power of edge computing nodes. Traditional fixed threshold logic cannot adapt to these dynamic changes, leading to frequent erroneous equipment shutdowns or missed alarms. This is especially true during the inertial sliding period of belt conveyor rollers with large rotational inertia, where power-off feedback alone cannot confirm the safety status and can easily cause secondary injuries. Furthermore, there is a fundamental conflict between safety interlocking and production continuity between the reverse coal flow start-up and forward coal flow shutdown of multi-stage transport equipment, necessitating an intelligent linkage control method that can adapt to environmental fluctuations and equipment dynamic characteristics. Summary of the Invention

[0004] To overcome the shortcomings of insufficient edge computing capabilities, this invention provides an intelligent linkage control method for coal mine equipment based on personnel positioning.

[0005] The technical implementation scheme of the present invention is: an intelligent linkage control method for coal mine equipment based on personnel positioning, comprising the following steps: S1: Divide the underground edge node areas of coal mines according to the explosion-proof type and determine the environmentally sensitive area and the environmentally stable area. Perform load tests on the edge computing nodes in the environmentally sensitive area to determine the corresponding curve type. S2: Determine all adjacent regions of each environmentally sensitive area in the underground edge node region of the coal mine, obtain the success rate of cross-regional coordination tasks, and determine the edge computing capability of the environmentally sensitive area based on the success rate of cross-regional coordination tasks; S3: Determine the inertial connection points based on the physical linkage relationship between coal mine equipment, and determine the edge connection points based on the edge computing capability of environmentally sensitive areas. Based on the characteristics of the inertial connection points and edge connection points, cluster the equipment in the linkage equipment group. S4: Determine the countercurrent locking point and the downstream starting point based on the clustering categories of inertial-edge integrated influence characteristics and personnel positioning information, and then execute the linkage control of coal mine equipment after dynamic adjustment.

[0006] Preferably, the step of dividing the underground edge node area of ​​the coal mine according to the explosion-proof type and determining the environmentally sensitive area and the environmentally stable area includes: The spatial area enclosed by the boundary line of each explosion-proof zone is regarded as an independent underground edge node area of ​​the coal mine; Within each underground edge node region of a coal mine, the edge computing capability of the underground edge node region is continuously measured, and environmental parameters are measured simultaneously. The first-order partial derivative of edge computing capability with respect to changes in environmental parameters; If the absolute value of the first-order partial derivative exceeds a preset threshold at any time, the underground edge node area of ​​the coal mine will be identified as an environmentally sensitive area. If the absolute value of the first-order partial derivative never exceeds the preset threshold, then the underground edge node region of the coal mine is determined as an environmentally stable region.

[0007] Preferably, the step of performing load testing on edge computing nodes within environmentally sensitive areas to determine the corresponding curve type includes: Load tests were performed on edge computing nodes in each environmentally sensitive area, and the corresponding curves of edge computing node power consumption and edge computing node performance were recorded. If the performance decreases smoothly with increasing power consumption without sudden jumps, the corresponding curve is determined to be a gradual nonlinearity. If a sudden drop in performance occurs at a certain power consumption point, the corresponding curve is identified as a sudden nonlinearity.

[0008] Preferably, determining all adjacent areas of each environmentally sensitive area in the underground edge node area of ​​the coal mine and obtaining the success rate of cross-regional coordination tasks includes: Continuously record the state sequence of environmentally sensitive areas on the time axis, and assign each state in the state sequence to either an environmentally sensitive state or an environmentally stable state. The ratio of the length of time an environmentally sensitive area is in an environmentally sensitive state to the total length of the observation period is calculated and denoted as the sensitive state time percentage. For environmentally sensitive areas and all adjacent areas, the success rate of cross-regional coordination tasks was measured under the following two conditions: the first condition is that the environmentally sensitive area is in an environmentally sensitive state and remains in that state; the second condition is that the environmentally sensitive area changes from an environmentally sensitive state to an environmentally stable state and remains in that stable state. The success rate of the cross-regional coordination task is obtained by sending a set of standard coordination command sequences to environmentally sensitive areas and all adjacent areas, and statistically analyzing the proportion of commands that successfully return correct responses.

[0009] Preferably, determining the edge computing capability of environmentally sensitive areas based on the success rate of cross-regional coordination tasks includes: The regional coordination robustness value of environmentally sensitive areas is defined as: the cross-regional coordination task success rate measured under the first condition multiplied by the proportion of sensitive state time, plus the cross-regional coordination task success rate measured under the second condition multiplied by 1 minus the proportion of sensitive state time. For each environmentally sensitive area, the regional coordination robustness value is multiplied by the nominal peak computing power of the regional edge computing node, and the resulting product is taken as the edge computing capability of the environmentally sensitive area.

[0010] Preferably, the step of determining the inertial connection point based on the physical linkage between coal mine equipment and determining the edge connection point based on the edge computing capability of the environmentally sensitive area includes: Based on the mechanical transmission relationship and physical connection structure between equipment in underground coal mines, the fixed connection parts that directly transmit motion or force between equipment are determined, and each of the fixed connection parts is determined as an inertial connection point. Based on the boundaries between the defined environmentally sensitive and environmentally stable areas, and the edge computing capability values ​​of the environmentally sensitive areas, the network communication paths for data exchange between edge computing nodes in different areas are determined, and each of the network communication paths is defined as an edge connection point.

[0011] Preferably, the step of clustering the devices in the linkage device group based on the characteristics of inertial connection points and edge connection points includes: For each device in each linkage device group, measure the physical inertia value corresponding to the inertial connection point of the device. The physical inertia value is obtained by measuring the sliding stop time or rotational inertia of the device after power failure. For each device, the average communication delay and packet loss rate of the edge connection points of the device are measured. The average communication delay is normalized and then weighted and summed with the packet loss rate. The result is used as the edge connection point influence coefficient. For each device, count the number of times the device starts and stops per unit time, and use the number of starts and stops as the frequency of start and stop coefficient; The physical inertia value, edge connection point influence coefficient, and frequent start-stop coefficient of each device are combined into a three-dimensional feature vector.

[0012] Preferably, the step of assembling a three-dimensional feature vector from the physical inertia value, edge connection point influence coefficient, and frequent start-stop coefficient of each device includes: The three-dimensional feature vectors of all devices are normalized so that the numerical range of each dimension is mapped to the interval between zero and one. Hierarchical clustering algorithm is used to cluster the normalized three-dimensional feature vectors. The number of clusters is determined by the silhouette coefficient or elbow rule. The clustering results are used as the category label for each device in the linked device group. Devices in the same category have similar inertial-edge combined influence characteristics.

[0013] Preferably, determining the countercurrent locking point and the downstream initiation point based on the clustering categories of inertial-edge combined influence characteristics and personnel positioning information includes: When the personnel positioning system detects that personnel have entered the danger zone of a device, the device is marked as a trigger device. Starting from the trigger device, upstream devices are checked one by one along the reverse coal flow direction. Based on the inertial-edge combined influence characteristics of each upstream device, the device is clustered into a category, and all upstream devices that need to be shut down to prevent coal pile-up or to protect personnel are added to the initial reverse flow blocking point set. When the system needs to start a group of devices to resume production, the downstream device that needs to be started is identified as the starting device. Starting from the starting device, the upstream devices are checked one by one along the reverse coal flow direction. Based on the inertia-edge combined influence characteristics of each upstream device, the clustering category is determined, and all upstream devices that need to be started to prevent no-load or damage are added to the initial downstream starting point set.

[0014] Preferably, the dynamic adjustment followed by the execution of coal mine equipment linkage control includes: For each device in the set of countercurrent blocking points, the device's coasting stopping progress is continuously monitored, and the ratio of the device's actual coasting time to the maximum coasting time of the corresponding cluster category is used as the removal judgment value; when the removal judgment value is greater than the preset removal threshold, the device is removed from the set of countercurrent blocking points, and its upstream device is added to the set of countercurrent blocking points. For each device in the downstream start point set, the start preparation progress of the device is continuously monitored, and the ratio of the current preparation time of the device to the maximum preparation time of the corresponding cluster category is used as the abandonment judgment value; when the abandonment judgment value is greater than the preset abandonment threshold, the device is removed from the downstream start point set and the start-up of its downstream devices is stopped. For the equipment in the adjusted set of counter-current blocking points, shutdown commands are issued in the order of coal flow, and the equipment is waited for to stop according to the sliding safety waiting time of the corresponding cluster category; for the equipment in the adjusted set of co-current starting points, start commands are issued sequentially in the order of coal flow, and the start-up process is monitored according to the no-load operation allowable time of the corresponding cluster category; dynamic adjustments are continuously made during the control execution until production stops or resumes.

[0015] Beneficial effects: This invention divides underground edge node areas by explosion-proof type, identifies environmentally sensitive areas in real time based on first-order partial derivatives, and distinguishes nonlinear degradation modes by load testing, effectively overcoming the problem of real-time fluctuations in edge computing capabilities caused by strong electromagnetic interference and dust attenuation. Furthermore, it accurately determines the edge computing capabilities of environmentally sensitive areas by quantifying the success rate of cross-regional coordination tasks, and groups equipment by using three-dimensional feature vector clustering based on the inertia-edge comprehensive influence characteristics, realizing dynamic adaptive adjustment of reverse coal flow interlocking and forward coal flow start-up. This invention significantly improves the adaptive capability of personnel positioning and equipment linkage in complex dynamic environments, avoids accidental shutdowns and secondary injuries from inertial sliding, and ensures the continuity and inherent safety of coal mine production. Attached Figure Description

[0016] Figure 1 This is a flowchart of the intelligent linkage control method for coal mine equipment based on personnel positioning according to the present invention; Figure 2 This is a flowchart of the method for determining environmentally sensitive areas and environmentally stable areas according to the present invention; Figure 3 This is a flowchart of the method for determining the countercurrent locking point and the downstream starting point of the present invention; Figure 4 This is a flowchart of the dynamic adjustment and execution linkage control method of the present invention. Detailed Implementation

[0017] The above-described solution will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of this application. The implementation conditions used in the embodiments may be further adjusted according to the conditions of specific manufacturers, and the implementation conditions not specified are generally those in routine experiments.

[0018] A method for intelligent linkage control of coal mine equipment based on personnel positioning, such as Figure 1 , Figure 2 , Figure 3 and Figure 4 As shown, it includes the following steps: S1-1: Based on the explosion-proof type, delineate the edge node areas of underground coal mines and determine environmentally sensitive and environmentally stable areas, including: The spatial area enclosed by the boundary line of each explosion-proof zone is regarded as an independent underground edge node area of ​​the coal mine; Within each underground edge node region of a coal mine, the edge computing capability of the underground edge node region is continuously measured, and environmental parameters are measured simultaneously. The first-order partial derivative of edge computing capability with respect to changes in environmental parameters; If the absolute value of the first-order partial derivative exceeds a preset threshold at any time, the underground edge node area of ​​the coal mine will be identified as an environmentally sensitive area. If the absolute value of the first-order partial derivative never exceeds the preset threshold, then the underground edge node region of the coal mine is determined as an environmentally stable region.

[0019] It should be noted that underground coal mines are classified into explosion-proof types based on gas concentration and ignition source risk, including flameproof, intrinsically safe (ia), intrinsically safe (ib), increased safety, and pressurized. The boundary lines of each type of area on the explosion-proof drawings are spatial boundaries. The enclosed space enclosed by each boundary line is set as an independent underground edge node area for deploying edge computing nodes and collecting local data. Edge computing capability refers to the effective computing power of a node in completing location calculation, status monitoring, and control command output per unit time. Environmental parameters include temperature, vibration acceleration, electromagnetic field strength, and dust concentration. These environmental parameters constitute a multi-dimensional vector, and the first-order partial derivatives need to be calculated for each parameter separately. The maximum absolute value of each partial derivative is used as the basis for judging regional sensitivity. The first-order partial derivatives of edge computing capability with respect to environmental parameters are calculated to measure the instantaneous sensitivity of capability to environmental changes: the larger the absolute value of the partial derivative, the greater the impact of small environmental fluctuations on capability. Through real-time monitoring of partial derivatives, when the absolute value exceeds a certain threshold calibrated on-site, it indicates that the capability of the area is significantly dominated by the environment, and it is classified as an environmentally sensitive area; conversely, if the absolute value does not exceed the threshold, the capability is significantly affected by the environment. The area is designated as an environmentally stable region. The threshold setting method is as follows: select multiple representative areas downhole, continuously collect data for a sufficient period of time, plot the distribution curve of the absolute value of partial derivatives, and use the inflection point value that clearly distinguishes between sensitive and stable areas in the curve as the initial threshold value. Then, fine-tune it through short-term field verification until the classification is stable. It should be noted that the calculation of partial derivatives adopts numerical difference approximation, for example, by dividing the difference in edge computing capability between two adjacent measurements by the difference in environmental parameters. The sampling interval is dynamically set according to the rate of change of environmental parameters, usually 1 to 10 seconds. The initial threshold value is obtained by plotting the distribution curve of the absolute value of partial derivatives, and is subsequently adjusted online adaptively according to the false alarm rate and the missed alarm rate, such as fine-tuning once per shift.

[0020] S1-2: Perform load testing on edge computing nodes within environmentally sensitive areas to determine the corresponding curve type, including: Load tests were performed on edge computing nodes in each environmentally sensitive area, and the corresponding curves of edge computing node power consumption and edge computing node performance were recorded. If the performance decreases smoothly with increasing power consumption without sudden jumps, the corresponding curve is determined to be a gradual nonlinearity. If a sudden drop in performance occurs at a certain power consumption point, the corresponding curve is identified as a sudden nonlinearity.

[0021] It should be noted that the reference Figure 2 Load testing was conducted by gradually increasing the input power consumption of the edge computing nodes. The tests were scheduled during downhole equipment maintenance or production breaks to minimize disruption to normal production. Considering the dynamic frequency / voltage adjustment capabilities of the edge computing nodes, local fluctuations in the power consumption-performance curve were observed. When abrupt nonlinearity was identified, an irreversible, significant performance drop (e.g., a decrease exceeding three times the average drop over the gradual transition range) was used as the criterion. The test was repeated at least three times to eliminate transient interference. Simultaneously, the instruction processing throughput per watt of power consumption was recorded to determine the true response characteristics of edge computing capabilities as power consumption changed. The obtained power consumption-performance curves showed the distribution of processing capabilities at different power consumption levels. When performance continuously declined with increasing power consumption without a sudden drop point, the curve was classified as a gradual nonlinearity, indicating that the node could maintain its processing capability even as power consumption increased. The system remains usable even during degradation and will not experience sudden interruptions. Conversely, if performance drops drastically at a specific power consumption value, meaning the performance decline exceeds the threshold for abrupt changes determined through field calibration experiments (e.g., three times the performance decline within a gradual range), it is classified as abrupt nonlinearity, indicating that the node will lose most of its processing capacity once it crosses this power consumption boundary. The criteria for determining a sudden performance drop are: during a continuous increase in power consumption, when the power consumption increases by a small step, the performance decline significantly exceeds the average decline of the node in the adjacent gradual range. This small step is, for example, 0.5 to 1% of the nominal power consumption. The specific threshold for abrupt changes (including the power consumption step size and the performance decline factor) should be determined through field calibration experiments, for example, using three times the performance decline within a gradual range as the threshold, or adjusted according to the actual response characteristics of the device.

[0022] S2-1: Determine all adjacent areas of each environmentally sensitive area within the underground edge node area of ​​the coal mine, and obtain the success rate of cross-regional coordination tasks, including: Continuously record the state sequence of environmentally sensitive areas on the time axis, and assign each state in the state sequence to either an environmentally sensitive state or an environmentally stable state. The ratio of the length of time an environmentally sensitive area is in an environmentally sensitive state to the total length of the observation period is calculated and denoted as the sensitive state time percentage. For environmentally sensitive areas and all adjacent areas, the success rate of cross-regional coordination tasks was measured under the following two conditions: the first condition is that the environmentally sensitive area is in an environmentally sensitive state and remains in that state; the second condition is that the environmentally sensitive area changes from an environmentally sensitive state to an environmentally stable state and remains in that stable state. The success rate of the cross-regional coordination task is obtained by sending a set of standard coordination command sequences to environmentally sensitive areas and all adjacent areas, and statistically analyzing the proportion of commands that successfully return correct responses.

[0023] It should be noted that adjacent areas of the environmentally sensitive region constitute the communication range of the cross-regional coordination task, and these areas are used to define the boundaries of subsequent instruction testing. The state sequence continuously monitors the fluctuation variance of edge computing capabilities. When the variance exceeds a set threshold, it is recorded as an environmentally sensitive state. This threshold is obtained by collecting the fluctuation variance distribution under normal operating conditions, taking the 95th percentile as the initial value, and adjusting it on-site according to the false alarm rate. Conversely, it is recorded as an environmentally stable state. This sequence reveals the change law of the regional state over time. The sensitive state time ratio is the ratio of the cumulative time in the sensitive state to the total observation time. The larger the ratio, the more persistent the region is affected by environmental fluctuations. The success rate of the cross-regional coordination task is defined as the proportion of correct response instructions obtained after sending a set of standard instructions to the target region and all its adjacent regions. The first condition (state unchanged) measures the steady-state coordination capability, and the second condition (state switching) measures the coordination capability during the transition process. The two conditions respectively cover the two typical operating conditions of the region's state remaining stable and changing.

[0024] S2-2: Determine the edge computing capabilities of environmentally sensitive areas based on the success rate of cross-regional coordination tasks, including: The regional coordination robustness value of environmentally sensitive areas is defined as: the cross-regional coordination task success rate measured under the first condition multiplied by the proportion of sensitive state time, plus the cross-regional coordination task success rate measured under the second condition multiplied by 1 minus the proportion of sensitive state time. For each environmentally sensitive area, the regional coordination robustness value is multiplied by the nominal peak computing power of the regional edge computing node, and the resulting product is taken as the edge computing capability of the environmentally sensitive area.

[0025] It should be noted that regional coordination robustness, as a comprehensive weighted coefficient, reflects the overall ability of environmentally sensitive areas to reliably transmit cross-regional commands under both state maintenance and state transition conditions. This coefficient is constructed using a weighted average method: steady-state success rate multiplied by the proportion of time spent in sensitive states, plus transition success rate multiplied by (1 - proportion of time spent in sensitive states). This weighting logic allocates weights based on the time proportions of the two states in actual regional operation; a higher steady-state proportion contributes more to the steady-state success rate, and a higher transition proportion contributes more to the transition success rate. The edge computing capability of environmentally sensitive areas is obtained by multiplying regional coordination robustness by the nominal peak computing power, where the nominal peak computing power is the node... The theoretical maximum processing capacity at the time of manufacture, and the nominal peak computing power, are dynamically updated based on the historical maximum computing power monitored by the node during actual operation, for example, taking the median of the hourly peak over the past 30 days. State switching conditions (from an environmentally sensitive state to an environmentally stable state) do not require active triggering. The system continuously monitors the state sequence, and automatically records the success rate of cross-regional coordination tasks during that period when a state switch occurs naturally, obtaining a stable value through long-term statistics. The product result is the effective available computing power after deducting the impact of environmental fluctuations and state switching. The principle is that regional coordination robustness comprehensively reflects the actual coordination capability of the node in both sensitive and stable states. Multiplying this by the nominal peak computing power is equivalent to adjusting the system's performance based on environmental conditions. The nominal value was weighted and adjusted based on the time percentage of fluctuations and the success rate under different states to obtain a more realistic usable computing power that more closely reflects the dynamic downhole environment. It should be noted that for edge computing nodes with abrupt nonlinearity, since their performance drops sharply after power consumption exceeds a critical value, their regional coordination robustness should be multiplied by a conservative coefficient (e.g., 0.9) to obtain a corrected regional coordination robustness. This corrected value is then multiplied by the nominal peak computing power to obtain the effective usable computing power of the node. The specific value of this conservative coefficient is determined based on the ratio of the power consumption threshold at the performance drop point in the abrupt curve to the node's current operating power consumption: if the current power consumption is close to the drop point, a smaller value, such as 0.7, is used. ~0.8; if the distance from the dropout point is far, take a larger value, such as 0.9~0.95; or through field calibration experiments, plot the performance degradation rate curves under different power consumption ranges, set the conservative coefficient as the inverse proportional function of the performance degradation rate, or use piecewise linear interpolation to obtain it; for gradually changing nodes, since the performance degradation is smooth, there is no need to multiply by the conservative coefficient; or reserve more computing power margin when scheduling instructions; for gradually changing nonlinear nodes, use the converted effective computing power normally; traditional methods directly use nominal values ​​or simple measured values, without considering the loss of coordination ability caused by state changes; this step obtains a more realistic edge computing capability that is closer to the dynamic environment of the downhole through robust weighted conversion.

[0026] S3-1: Determine inertial connection points based on the physical linkage relationships between coal mine equipment, and determine edge connection points based on the edge computing capabilities of environmentally sensitive areas, including: Based on the mechanical transmission relationship and physical connection structure between equipment in underground coal mines, the fixed connection parts that directly transmit motion or force between equipment are determined, and each of the fixed connection parts is determined as an inertial connection point. Based on the boundaries between the defined environmentally sensitive and environmentally stable areas, and the edge computing capability values ​​of the environmentally sensitive areas, the network communication paths for data exchange between edge computing nodes in different areas are determined, and each of the network communication paths is defined as an edge connection point.

[0027] It should be noted that inertial connection points are located at points where motion and force are directly transmitted between devices via mechanical transmission or rigid connections, such as couplings or the contact surfaces of belts and rollers. These points define the propagation path of physical inertia between devices. Edge connection points, on the other hand, correspond to network communication links used for exchanging control data between edge computing nodes in different areas. Their quality directly affects the timeliness of interlocking commands. The purpose of separately labeling these two types of connection points is to incorporate physical inertial risks and network communication risks into the same analytical framework: inertial connection points delineate the dangerous ripple effect of coasting after shutdown, while the communication delay and packet loss rate of edge connection points determine whether interlocking commands can take effect in a timely manner. The connections are marked one by one according to the mechanical drawings of the equipment; for edge connection points, nodes with strong capabilities are selected as communication relays based on the boundary of the divided area and the edge computing capability value of the environmentally sensitive area; it should be noted that the actual communication path of the edge connection point changes dynamically with the network status, such as link interruption and route switching; the system should periodically (e.g., every 30 seconds) detect the reachable path between adjacent nodes and maintain a list of currently available paths; when the preferred path fails, it automatically switches to the alternative path and uses the switched path as a new edge connection point in subsequent calculations; the communication delay and packet loss rate in the edge connection point influence coefficient should be the real-time measurement values ​​of the currently active path.

[0028] S3-2: Based on the characteristics of inertial connection points and edge connection points, cluster the equipment in the linkage equipment group, including: For each device in each linkage device group, measure the physical inertia value corresponding to the inertial connection point of the device. The physical inertia value is obtained by measuring the sliding stop time or rotational inertia of the device after power failure. For each device, the average communication delay and packet loss rate of the edge connection points of the device are measured. The average communication delay is normalized and then weighted and summed with the packet loss rate. The result is used as the edge connection point influence coefficient. For each device, count the number of times the device starts and stops per unit time, and use the number of starts and stops as the frequency of start and stop coefficient; The physical inertia value, edge connection point influence coefficient, and frequent start-stop coefficient of each device are combined into a three-dimensional feature vector; The three-dimensional feature vectors of all devices are normalized so that the numerical range of each dimension is mapped to the interval between zero and one. Hierarchical clustering algorithm is used to cluster the normalized three-dimensional feature vectors. The number of clusters is determined by the silhouette coefficient or elbow rule. The clustering results are used as the category label for each device in the linked device group. Devices in the same category have similar inertial-edge combined influence characteristics.

[0029] It should be noted that the linked equipment group consists of equipment coupled together by coal flow or mechanical transmission, such as a transportation link composed of a coal mining machine, scraper conveyor, transfer conveyor, and multiple belt conveyors; the purpose of clustering is to group equipment with similar inertial-edge combined influence characteristics into the same category, facilitating the adoption of a unified control strategy subsequently; the physical inertia degree numerically quantifies the ease with which equipment can stop gliding after a power outage, with a larger value indicating stronger inertia; the average communication delay and packet loss rate reflect the response speed and transmission reliability of the edge connection point, respectively, and their weighted sum constitutes the edge connection point influence coefficient, while the average communication delay needs to be normalized first. The process involves several steps: First, the delay is divided by a reference delay, such as the average delay under normal operating conditions or the maximum allowable delay set by the system, to make it dimensionless before weighted summation with the packet loss rate. Second, the weighting coefficients are allocated based on the impact of delay and packet loss on the command success rate in actual field measurements. For example, an initial equal-weighting is used, followed by fine-tuning through verification tests. The weighting coefficients are recalibrated every production cycle (e.g., 7 days), and the least squares method is used to fit the contribution of the normalized delay value and the packet loss rate to the command success rate. Alternatively, a sliding window can be used for online updates, with the window size being the record size of the most recent 1000 commands. The frequent start / stop coefficient is calculated based on the single... The number of start-stop cycles within a given time period is used to identify the equipment's sensitivity to the timeliness of control commands; three coefficients form a three-dimensional feature vector, characterizing the comprehensive characteristics of the equipment from three dimensions: physical inertia, network communication, and operating frequency; after normalization to eliminate dimensional differences, a hierarchical clustering algorithm is used to group the equipment based on the feature vector distance, and the number of clusters is determined by the silhouette coefficient; it should be noted that equipment characteristics, such as coasting time increasing due to roller wear and communication delay increasing due to equipment aging, will drift slowly over time; to ensure the timeliness of the clustering results, the system should perform clustering every production cycle (e.g., 30 days) or when a certain equipment is detected. When the feature vector deviates from its category center by more than the cluster center deviation threshold (e.g., Euclidean distance greater than 0.3), re-clustering is triggered. During re-clustering, the feature vectors of all devices are recalculated using historical data from the last 30 days, and the normalization and hierarchical clustering steps are repeated. In addition, after a device is replaced or overhauled, the physical inertia and communication delay parameters of the device should be remeasured immediately, and a local update or full re-clustering should be triggered. Category labels such as high-inertia-high-sensitivity class and low-inertia-stable class indicate that devices within the same category have similar inertia-edge combined influence characteristics, suggesting similar response requirements for linkage control.

[0030] S4-1: Determine the countercurrent closure point and the downstream initiation point based on the clustering categories of inertial-edge combined influence characteristics and personnel positioning information, including: When the personnel positioning system detects that personnel have entered the danger zone of a device, the device is marked as a trigger device. Starting from the trigger device, upstream devices are checked one by one along the reverse coal flow direction. Based on the inertial-edge combined influence characteristics of each upstream device, the device is clustered into a category, and all upstream devices that need to be shut down to prevent coal pile-up or to protect personnel are added to the initial reverse flow blocking point set. When the system needs to start a group of devices to resume production, the downstream device that needs to be started is identified as the starting device. Starting from the starting device, the upstream devices are checked one by one along the reverse coal flow direction. Based on the inertia-edge combined influence characteristics of each upstream device, the clustering category is determined, and all upstream devices that need to be started to prevent no-load or damage are added to the initial downstream starting point set.

[0031] It should be noted that the reference Figure 3 The role of the counter-current interlock point is as follows: when the personnel positioning system detects that personnel have entered the danger zone of a certain equipment, it delineates the upstream equipment that needs to be shut down along the counter-current coal flow direction to prevent coal pile-up or entanglement injuries. The downstream start point is used in the production recovery phase, delineating the upstream equipment that needs to be started sequentially along the counter-current coal flow direction to avoid no-load operation or mechanical impact. The triggering equipment is the starting equipment for personnel entry, and the counter-current interlock check proceeds upstream from this equipment: it determines the situation that must be shut down based on the cluster category to which each upstream equipment belongs, such as equipment with large inertia or poor communication at edge connection points being forced to shut down, otherwise it continues to operate. The starting equipment is the downstream equipment that needs to be started, and the downstream start check proceeds upstream from this equipment: it determines which upstream equipment is allowed to start based on the cluster category, with equipment with small inertia and reliable communication being prioritized for start-up. The two sets correspond to safety interlock and production recovery, respectively, both in the direction of counter-current coal flow, but one is used for shutdown and the other for startup. Traditional methods do not handle equipment differentiation, which can easily lead to accidental shutdown or coal pile-up accidents. This step achieves targeted interlock and startup decisions through cluster categories.

[0032] The system pre-determines two Boolean parameters, needBlock (backflow blocking) and needStart (upflow start), for each cluster category. These parameters are set based on the relationship between the average physical inertia and average edge connection point influence coefficient of the device category and the global threshold. Specifically, the physical inertia values ​​of all members of the device category are statistically analyzed. If the 90th percentile of the value is greater than the global coasting time safety threshold (which needs to be calibrated through on-site testing, for example, initially set to 5 seconds and then adjusted according to actual coasting conditions), then needBlock is set to true; otherwise, it is set to false. The upflow start attribute is similar. If the edge connection point influence coefficient of the 90th percentile of the device category is less than the start reliability threshold (which needs to be determined by fitting historical data, for example, initially set to 0.3 and then corrected according to the false start rate), then it is set to prioritize start; otherwise, it starts sequentially.

[0033] S4-2: Dynamically adjust and execute the linkage control of coal mine equipment, including: For each device in the set of countercurrent blocking points, the device's coasting stopping progress is continuously monitored, and the ratio of the device's actual coasting time to the maximum coasting time of the corresponding cluster category is used as the removal judgment value; when the removal judgment value is greater than the preset removal threshold, the device is removed from the set of countercurrent blocking points, and its upstream device is added to the set of countercurrent blocking points. For each device in the downstream start point set, the start preparation progress of the device is continuously monitored, and the ratio of the current preparation time of the device to the maximum preparation time of the corresponding cluster category is used as the abandonment judgment value; when the abandonment judgment value is greater than the preset abandonment threshold, the device is removed from the downstream start point set and the start-up of its downstream devices is stopped. For the equipment in the adjusted set of counter-current blocking points, shutdown commands are issued in the order of coal flow, and the equipment is waited for to stop according to the sliding safety waiting time of the corresponding cluster category; for the equipment in the adjusted set of co-current starting points, start commands are issued sequentially in the order of coal flow, and the start-up process is monitored according to the no-load operation allowable time of the corresponding cluster category; dynamic adjustments are continuously made during the control execution until production stops or resumes.

[0034] It should be noted that the reference Figure 4Continuous monitoring of the coasting stopping progress is used to obtain the actual remaining inertia time after the equipment is powered off. The removal judgment value is defined as the ratio of the actual coasting time to the maximum coasting time of the corresponding cluster category. The higher the ratio, the closer the equipment is to a standstill, and the greater the tendency to remove it from the lockout set. The preset removal threshold is calibrated through field tests and historical operating data statistics to ensure that the lockout is removed only after the equipment has come to a complete stop. It is set differently according to the inertia characteristics of equipment in different cluster categories. When the ratio is greater than the preset removal threshold (e.g., 0.9), the equipment is removed from the lockout set and its upper limit is set. The upstream equipment (if the upstream equipment needs to be shut down) is added to the set of countercurrent blocking points. The technical basis for this is as follows: when the ratio is close to 1, the equipment has essentially stopped, and upstream takeover avoids command congestion caused by simultaneous switching of multiple equipment; the maximum coasting time is taken as the 99th percentile of the historical coasting time of this type of equipment, and the maximum preparation time is similarly calculated; continuous monitoring of the startup preparation progress is used to collect the actual time taken for the equipment to go from ready to operational; the abandonment judgment value is equal to the ratio of the current preparation time to the maximum preparation time of the corresponding cluster category; the larger the ratio, the slower the equipment startup; the preset abandonment threshold is determined through field tests and historical data. The system initiates data statistical calibration to avoid unnecessary waiting and ensure efficient production recovery. This calibration is differentiated based on the communication and response characteristics of devices in different cluster categories. When this ratio exceeds a preset abandonment threshold (e.g., 0.9), the startup is abandoned and downstream devices are stopped. It should be noted that for abruptly nonlinear edge computing nodes, whose performance degrades sharply after power consumption exceeds a critical value, the safe waiting time for device gliding in their region should be appropriately shortened to ensure critical instructions are issued before node failure. For example, the waiting time can be multiplied by 0.8 to avoid node failure. The risk of premature failure is mitigated; for gradually nonlinear nodes, the normal calibrated sliding safety waiting time is used; shutdown commands are issued in the order of coal flow and the sliding safety waiting time is waited for to ensure that upstream equipment stops first, and the risk of coal pile-up is eliminated by using inertial time difference; the equipment is started in the order of reverse coal flow and the allowable time of no-load operation is monitored to ensure that the downstream equipment starts first to form a coal flow channel, and then the upstream equipment is started step by step to avoid mechanical impact; dynamic adjustment and repeated execution are performed until production stops or resumes, so that the control strategy matches the changes in equipment status in real time, overcoming the limitation that traditional fixed timing cannot adapt to dynamic fluctuations.

[0035] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent linkage control of coal mine equipment based on personnel positioning, characterized in that, Includes the following steps: S1: Divide the underground edge node areas of coal mines according to the explosion-proof type and determine the environmentally sensitive area and the environmentally stable area. Perform load tests on the edge computing nodes in the environmentally sensitive area to determine the corresponding curve type. S2: Determine all adjacent regions of each environmentally sensitive area in the underground edge node region of the coal mine, obtain the success rate of cross-regional coordination tasks, and determine the edge computing capability of the environmentally sensitive area based on the success rate of cross-regional coordination tasks; S3: Determine the inertial connection points based on the physical linkage relationship between coal mine equipment, and determine the edge connection points based on the edge computing capability of environmentally sensitive areas. Based on the characteristics of the inertial connection points and edge connection points, cluster the equipment in the linkage equipment group. S4: Determine the countercurrent locking point and the downstream starting point based on the clustering categories of inertial-edge integrated influence characteristics and personnel positioning information, and then execute the linkage control of coal mine equipment after dynamic adjustment.

2. The intelligent linkage control method for coal mine equipment based on personnel positioning according to claim 1, characterized in that, The process of dividing the underground edge node areas of coal mines according to explosion-proof type and determining environmentally sensitive areas and environmentally stable areas includes: The spatial area enclosed by the boundary line of each explosion-proof zone is regarded as an independent underground edge node area of ​​the coal mine; Within each underground edge node region of a coal mine, the edge computing capability of the underground edge node region is continuously measured, and environmental parameters are measured simultaneously. The first-order partial derivative of edge computing capability with respect to changes in environmental parameters; If the absolute value of the first-order partial derivative exceeds a preset threshold at any time, the underground edge node area of ​​the coal mine will be identified as an environmentally sensitive area. If the absolute value of the first-order partial derivative never exceeds the preset threshold, then the underground edge node region of the coal mine is determined as an environmentally stable region.

3. The intelligent linkage control method for coal mine equipment based on personnel positioning according to claim 1, characterized in that, The process of determining the corresponding curve type by performing load testing on edge computing nodes in environmentally sensitive areas includes: Load tests were performed on edge computing nodes in each environmentally sensitive area, and the corresponding curves of edge computing node power consumption and edge computing node performance were recorded. If the performance decreases smoothly with increasing power consumption without sudden jumps, the corresponding curve is determined to be a gradual nonlinearity. If a sudden drop in performance occurs at a certain power consumption point, the corresponding curve is identified as a sudden nonlinearity.

4. The intelligent linkage control method for coal mine equipment based on personnel positioning according to claim 1, characterized in that, The process of determining all adjacent areas of each environmentally sensitive area within the underground edge node area of ​​the coal mine and obtaining the success rate of cross-regional coordination tasks includes: Continuously record the state sequence of environmentally sensitive areas on the time axis, and assign each state in the state sequence to either an environmentally sensitive state or an environmentally stable state. The ratio of the length of time an environmentally sensitive area is in an environmentally sensitive state to the total length of the observation period is calculated and denoted as the sensitive state time percentage. For environmentally sensitive areas and all adjacent areas, the success rate of cross-regional coordination tasks was measured under the following two conditions: the first condition is that the environmentally sensitive area is in an environmentally sensitive state and remains in that state; the second condition is that the environmentally sensitive area changes from an environmentally sensitive state to an environmentally stable state and remains in that stable state. The success rate of the cross-regional coordination task is obtained by sending a set of standard coordination command sequences to environmentally sensitive areas and all adjacent areas, and statistically analyzing the proportion of commands that successfully return correct responses.

5. The intelligent linkage control method for coal mine equipment based on personnel positioning according to claim 1, characterized in that, The method of determining the edge computing capabilities of environmentally sensitive areas based on the success rate of cross-regional coordination tasks includes: The regional coordination robustness value of environmentally sensitive areas is defined as: the cross-regional coordination task success rate measured under the first condition multiplied by the proportion of sensitive state time, plus the cross-regional coordination task success rate measured under the second condition multiplied by 1 minus the proportion of sensitive state time. For each environmentally sensitive area, the regional coordination robustness value is multiplied by the nominal peak computing power of the regional edge computing node, and the resulting product is taken as the edge computing capability of the environmentally sensitive area.

6. The intelligent linkage control method for coal mine equipment based on personnel positioning according to claim 1, characterized in that, The process of determining inertial connection points based on the physical linkage between coal mine equipment and determining edge connection points based on the edge computing capabilities of environmentally sensitive areas includes: Based on the mechanical transmission relationship and physical connection structure between equipment in underground coal mines, the fixed connection parts that directly transmit motion or force between equipment are determined, and each of the fixed connection parts is determined as an inertial connection point. Based on the boundaries between the defined environmentally sensitive and environmentally stable areas, and the edge computing capability values ​​of the environmentally sensitive areas, the network communication paths for data exchange between edge computing nodes in different areas are determined, and each of the network communication paths is defined as an edge connection point.

7. The intelligent linkage control method for coal mine equipment based on personnel positioning according to claim 1, characterized in that, The clustering of devices in the linkage equipment group based on the characteristics of inertial connection points and edge connection points includes: For each device in each linkage device group, measure the physical inertia value corresponding to the inertial connection point of the device. The physical inertia value is obtained by measuring the sliding stop time or rotational inertia of the device after power failure. For each device, the average communication delay and packet loss rate of the edge connection points of the device are measured. The average communication delay is normalized and then weighted and summed with the packet loss rate. The result is used as the edge connection point influence coefficient. For each device, count the number of times the device starts and stops per unit time, and use the number of starts and stops as the frequency of start and stop coefficient; The physical inertia value, edge connection point influence coefficient, and frequent start-stop coefficient of each device are combined into a three-dimensional feature vector.

8. The intelligent linkage control method for coal mine equipment based on personnel positioning according to claim 7, characterized in that, The process involves assembling a three-dimensional feature vector from the physical inertia value, edge connection point influence coefficient, and frequent start-stop coefficient of each device, including: The three-dimensional feature vectors of all devices are normalized so that the numerical range of each dimension is mapped to the interval between zero and one. Hierarchical clustering algorithm is used to cluster the normalized three-dimensional feature vectors. The number of clusters is determined by the silhouette coefficient or elbow rule. The clustering results are used as the category label for each device in the linked device group. Devices in the same category have similar inertial-edge combined influence characteristics.

9. The intelligent linkage control method for coal mine equipment based on personnel positioning according to claim 1, characterized in that, The determination of the countercurrent closure point and the downstream initiation point based on the clustering categories of inertial-edge combined influence characteristics and personnel positioning information includes: When the personnel positioning system detects that personnel have entered the danger zone of a device, the device is marked as a trigger device. Starting from the trigger device, upstream devices are checked one by one along the reverse coal flow direction. Based on the inertial-edge combined influence characteristics of each upstream device, the device is clustered into a category, and all upstream devices that need to be shut down to prevent coal pile-up or to protect personnel are added to the initial reverse flow blocking point set. When the system needs to start a group of devices to resume production, the downstream device that needs to be started is identified as the starting device. Starting from the starting device, the upstream devices are checked one by one along the reverse coal flow direction. Based on the inertia-edge combined influence characteristics of each upstream device, the clustering category is determined, and all upstream devices that need to be started to prevent no-load or damage are added to the initial downstream starting point set.

10. The intelligent linkage control method for coal mine equipment based on personnel positioning according to claim 1, characterized in that, The dynamic adjustment followed by the execution of coal mine equipment linkage control includes: For each device in the set of countercurrent blocking points, the device's coasting stopping progress is continuously monitored, and the ratio of the device's actual coasting time to the maximum coasting time of the corresponding cluster category is used as the removal judgment value; when the removal judgment value is greater than the preset removal threshold, the device is removed from the set of countercurrent blocking points, and its upstream device is added to the set of countercurrent blocking points. For each device in the downstream start point set, the start preparation progress of the device is continuously monitored, and the ratio of the current preparation time of the device to the maximum preparation time of the corresponding cluster category is used as the abandonment judgment value; when the abandonment judgment value is greater than the preset abandonment threshold, the device is removed from the downstream start point set and the start-up of its downstream devices is stopped. For the equipment in the adjusted set of counter-current blocking points, shutdown commands are issued in the order of coal flow, and the equipment is waited for to stop according to the sliding safety waiting time of the corresponding cluster category; for the equipment in the adjusted set of co-current starting points, start commands are issued sequentially in the order of coal flow, and the start-up process is monitored according to the no-load operation allowable time of the corresponding cluster category; dynamic adjustments are continuously made during the control execution until production stops or resumes.

Images