A mine explosion-proof type permanent magnet mechanism vacuum power distribution device and intelligent control system

Abstract

The application discloses a mine explosion-proof type permanent magnet mechanism vacuum power distribution device, comprising: a decision unit arranged in an explosion-proof shell; the decision unit is configured to: acquire an arc-after voltage recovery waveform and device ambient temperature information after a vacuum arc-extinguishing chamber breaks a fault current; calculate a medium recovery safety waiting time according to a ratio of a short-circuit current of the fault breaking to a rated current and the device ambient temperature information; determine whether the medium strength of the vacuum arc-extinguishing chamber meets a safe reclosing condition based on a comparison result of a characteristic parameter of the arc-after voltage recovery waveform and the medium recovery safety waiting time; determine a current ablation state of a vacuum arc-extinguishing chamber contact based on a cumulative breaking joule integral and a cumulative breaking number, and calculate a contact current residual life evaluation result based on the current ablation state; and perform reclosing feasibility judgment based on whether the medium strength meets the safe reclosing condition, the current ablation state and the contact current residual life evaluation result.

Description

Technical Field

[0001] This application relates to the field of mining electrical equipment technology, and in particular to a mining explosion-proof permanent magnet mechanism vacuum power distribution device and intelligent control system. Background Technology

[0002] With the continuous development of power system technology, mining power distribution equipment is also constantly being improved. Some explosion-proof mining power distribution devices have adopted vacuum interrupters and permanent magnet mechanisms. Vacuum interrupters are widely used in high-voltage power distribution due to their excellent breaking performance, while permanent magnet mechanisms improve operating efficiency with their fast response characteristics.

[0003] In related technologies, mine explosion-proof power distribution devices typically interrupt fault current through a vacuum interrupter and rely on a permanent magnet mechanism to achieve reclosing operation.

[0004] However, in actual operation, relying solely on simple opening and reclosing logic may lead to insufficient assessment of the vacuum contact erosion state and medium recovery, resulting in unsafe reclosing decisions. Therefore, improvements are urgently needed. Summary of the Invention

[0005] Based on this, it is necessary to provide a mine-use explosion-proof permanent magnet mechanism vacuum power distribution device and intelligent control system to improve the reclosing safety of the device and address the above-mentioned technical problems.

[0006] In a first aspect, this application provides a mine-use explosion-proof permanent magnet mechanism vacuum power distribution device, comprising: a vacuum interrupter; a permanent magnet mechanism; an explosion-proof enclosure; and a decision unit disposed inside the explosion-proof enclosure; the decision unit is configured to: acquire the post-arc voltage recovery waveform after the vacuum interrupter interrupts the fault current and the device ambient temperature information; calculate the medium recovery safe waiting time based on the ratio of the short-circuit current to the rated current after the fault interruption and the device ambient temperature information; the medium recovery safe waiting time is the shortest time required after the vacuum interrupter interrupts the fault, used to ensure that the medium strength between the vacuum contacts recovers to a safe level; and determine the vacuum interrupter based on the comparison results of the characteristic parameters of the post-arc voltage recovery waveform and the medium recovery safe waiting time. Whether the dielectric strength of the arc chamber meets the safe reclosing conditions; dielectric strength is a physical quantity characterizing the ability of the insulating dielectric between vacuum contacts to resist electrical breakdown; acquire the opening history data of the vacuum interrupter, calculate the cumulative breaking Joule integral and the cumulative breaking count; based on the cumulative breaking Joule integral and the cumulative breaking count, determine the current ablation state of the vacuum interrupter contacts, and calculate the current remaining life assessment result of the contacts based on the current ablation state; the current remaining life assessment result of the contacts is a parameter characterizing the remaining service life of the contacts; based on whether the dielectric strength meets the safe reclosing conditions, the current ablation state, and the current remaining life assessment result of the contacts, make a reclosing feasibility judgment; based on the reclosing feasibility judgment result, generate a reclosing decision command to control the permanent magnet mechanism to perform the reclosing operation.

[0007] In one embodiment, when the decision unit calculates the media recovery safety waiting time based on the ratio of the short-circuit current to the rated current during fault interruption and the ambient temperature information of the device, it is specifically configured to: obtain the difference between the ambient temperature information of the device and a reference temperature; the reference temperature is a preset base temperature value; and calculate the media recovery safety waiting time based on the ratio and difference between the short-circuit current to the rated current during fault interruption.

[0008] In one embodiment, when the decision unit performs a comparison between the characteristic parameters of the post-arc voltage recovery waveform and the dielectric recovery safe waiting time to determine whether the dielectric strength of the vacuum interrupter meets the safe reclosing condition, it is specifically configured to: perform noise reduction processing on the post-arc voltage recovery waveform to obtain a noise-reduced voltage recovery waveform; calculate the recovery voltage slope of the noise-reduced voltage recovery waveform; the recovery voltage slope is a parameter characterizing the rate of change of the recovery voltage over time.

[0009] If the number of sampling points where the recovery voltage slope is continuously lower than the preset threshold reaches the preset number, and the duration for which the denoised voltage recovery waveform reaches the preset voltage ratio is greater than the safety margin time, then the dielectric strength of the vacuum interrupter is determined to meet the safe reclosing condition. The preset threshold is a critical value determined based on the physical characteristics of vacuum interruption. The safety margin time is the time value obtained by multiplying the dielectric recovery safe waiting time by the safety margin coefficient.

[0010] In one embodiment, when the decision unit determines the current ablation state of the vacuum interrupter contacts based on the cumulative Joule integral of the interruption and the cumulative number of interruptions, it is specifically configured to: acquire the moving contact displacement signal during historical closing operations; the moving contact displacement signal is the moving contact trajectory data acquired from a position sensor mounted on the permanent magnet mechanism transmission rod; identify the moment of first contact of the contacts based on the moving contact displacement signal; the moment of first contact of the contacts is the point in time when the moving contact and the stationary contact make physical contact; and extract the bouncing oscillation signal within a preset time window after the moment of first contact of the contacts; the bouncing oscillation signal is the vibration generated after the contacts make contact. The signal is analyzed; the time it takes for the amplitude of the bouncing oscillation signal to decay to a preset proportion of the initial value is calculated to obtain the bouncing decay time; based on the bouncing decay time, the surface roughness of the contact is calculated; a feature vector is constructed based on the surface roughness of the contact, the cumulative breaking Joule integral, and the cumulative number of breaking operations; based on the feature vector, the empirical coefficients of the three-stage ablation assessment model are adjusted; the three-stage ablation assessment model is a mathematical model that characterizes the evolution law of the contact ablation process from the initial stage, the stable stage to the accelerated stage; using the adjusted three-stage ablation assessment model, the current ablation state of the vacuum interrupter contact is determined; the current ablation state is a physical quantity that characterizes the surface morphology and material loss of the contact.

[0011] In one embodiment, after generating the reclosing decision command, the decision unit is further configured to: after determining that reclosing operation is prohibited and confirming that the fault circuit has been physically isolated by the protection device, periodically acquire the residual voltage, weak leakage current, and ambient temperature at both ends of the isolation circuit at preset time intervals; the preset time interval is dynamically adjusted based on the fault type and environmental conditions; compare the residual voltage, weak leakage current, and ambient temperature with the equivalent physical model of the fault circuit and historical fault self-clearing characteristic data; the equivalent physical model is a physical model established based on the electrical parameters of the fault circuit, used to simulate the electrical characteristics of the fault point under different states; based on the comparison results, analyze the trend of electrical characteristic changes of the fault point; the trend of electrical characteristic changes is... The residual voltage and leakage current are characterized by parameters such as phase relationship and amplitude change rate; the instantaneous impedance change rate of the fault circuit is determined based on the trend of electrical characteristic changes; the residual thermal effect of the fault circuit is determined based on the trend of electrical characteristic changes; the residual thermal effect is a physical quantity characterizing the temperature change trend of the fault point, calculated through the ambient temperature change rate and heat conduction model; a fault point state assessment model is established based on the instantaneous impedance change rate and residual thermal effect; the fault point state assessment model is a classification model trained based on historical fault data, used to map electrical and thermal characteristic parameters to the fault point state; using the fault point state assessment model, it is determined whether the fault point has been cleared by itself, physically suppressed, or transformed into a high-resistance state; a high-resistance state is a state in which the resistance of the fault point is greater than a preset threshold.

[0012] In one embodiment, when the decision-making unit performs the reclosing feasibility judgment, it is further configured to: perform state prediction and anomaly detection based on the historical data of the acquired mine production scheduling instructions, gas concentration and local geological microseismic intensity, obtain the predicted trend and risk probability, and generate a dynamic reclosing risk tolerance based on the predicted trend and risk probability; and adjust the judgment method of reclosing feasibility judgment based on the dynamic reclosing risk tolerance.

[0013] In one embodiment, when generating the dynamic reclosing risk tolerance, the decision-making unit is further configured to: perform correlation analysis with historical secondary disaster events based on the acquired data on roof delamination rate, surrounding rock stress abrupt change amplitude, and roadway toxic gas concentration to obtain disaster evolution law characteristic parameters; determine the adjustment parameters of the fuzzy logic membership function and the adjustment coefficients of the rule weights based on the disaster evolution law characteristic parameters, and apply the adjustment parameters and adjustment coefficients to the risk utility function; generate a dynamic penalty function based on the disaster evolution law characteristic parameters, and perform weighted fusion of the risk utility function and the dynamic penalty function to generate the dynamic reclosing risk tolerance.

[0014] In one embodiment, the decision unit is further configured to: control the permanent magnet mechanism to perform a standard fault-free tripping action under calibration triggering conditions that meet the following conditions: the grid load rate is lower than the load threshold, there is no fault-free tripping within a continuous time period, and the ambient gas concentration is lower than the safety threshold; acquire transient electromagnetic response and transient temperature data under the standard fault-free tripping action, and compare the transient data with the theoretical physical model output of the permanent magnet mechanism under standard operating conditions; based on the comparison results, determine the measurement error characteristic mode, correct the sensor error parameters based on the measurement error characteristic mode, and apply the corrected error parameters to the subsequently acquired sensor data to perform sensor data error compensation.

[0015] In one embodiment, when the decision unit performs a fault point state assessment model to determine whether the fault point has been cleared, physically suppressed, or transformed into a high-resistivity state, it is further configured to: acquire acoustic emission signals and mechanical noise signals in the isolation circuit through a non-invasive ultrasonic acoustic diagnostic unit, as well as acquire the partial discharge rate monitored by the partial discharge sensor and the fault point temperature rise signal monitored by the temperature sensor; and, based on the acoustic emission signals, mechanical noise signals, partial discharge rate, and fault point temperature rise signal, use a fault point impedance physical model to determine whether the fault point has been cleared, physically suppressed, or transformed into a high-resistivity state.

[0016] Secondly, this application also provides an intelligent control system for a mine explosion-proof permanent magnet mechanism vacuum power distribution device. The intelligent control system is configured in the decision-making unit inside the mine explosion-proof permanent magnet mechanism vacuum power distribution device. The intelligent control system includes: a data acquisition module, used to acquire the post-arc voltage recovery waveform after the vacuum interrupter interrupts the fault current and the device ambient temperature information.

[0017] The safety waiting calculation module is used to calculate the medium recovery safety waiting time based on the ratio of the short-circuit current to the rated current during fault interruption and the ambient temperature information of the device. The medium recovery safety waiting time is the shortest time required after the vacuum interrupter interrupts a fault, and is used to ensure that the medium strength between the vacuum contacts recovers to a safe level.

[0018] The dielectric determination module is used to determine whether the dielectric strength of the vacuum interrupter meets the safe reclosing conditions based on the comparison results of the characteristic parameters of the post-arc voltage recovery waveform and the dielectric recovery safe waiting time. Dielectric strength is a physical quantity that characterizes the ability of the insulating dielectric between vacuum contacts to resist electrical breakdown.

[0019] The history processing module is used to acquire the opening history data of the vacuum interrupter and calculate the cumulative opening Joule integral and the cumulative number of openings.

[0020] The contact evaluation module is used to determine the current ablation state of the vacuum interrupter contacts based on the cumulative Joule integral and the cumulative number of interruptions, and to calculate the current remaining life evaluation result of the contacts based on the current ablation state; the current remaining life evaluation result of the contacts is a parameter characterizing the remaining service life of the contacts.

[0021] The feasibility assessment module is used to assess the feasibility of reclosing based on whether the medium strength meets the safe reclosing conditions, the current ablation status, and the current remaining lifespan of the contacts.

[0022] The decision generation module is used to generate reclosing decision instructions based on the reclosing feasibility assessment results, so as to control the permanent magnet mechanism to perform reclosing operations.

[0023] The aforementioned mine-use explosion-proof permanent magnet vacuum power distribution device and intelligent control system achieves accurate assessment of the vacuum contact medium recovery status by acquiring the post-arc voltage recovery waveform after the vacuum interruptor interrupts the fault current and analyzing its characteristic parameters. Combined with the ratio of the fault interruption current to the rated current and the ambient temperature, the system dynamically calculates the medium recovery safety waiting time. Simultaneously, by acquiring historical interruption data and calculating the cumulative interruption Joule integral and cumulative interruption count, the system quantitatively determines the contact erosion status and predicts the remaining lifespan, thus constructing a complete contact health status monitoring system. Finally, by comprehensively judging the three key factors of medium strength, erosion status, and remaining lifespan from multiple dimensions, a scientific and reliable reclosing feasibility assessment mechanism is formed. This fundamentally solves the problem of unsafe reclosing decisions caused by the inability to accurately assess the vacuum contact medium recovery status and erosion degree in traditional technologies, significantly improving the safety and reliability of mine power distribution devices. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the decision-making unit workflow provided in this embodiment.

[0026] Figure 2 This is a flowchart illustrating the process by which the decision unit calculates the media recovery security waiting time provided in this embodiment.

[0027] Figure 3 This is a schematic diagram of the process by which the decision-making unit performs a safety determination of the medium strength provided in this embodiment.

[0028] Figure 4This is a schematic diagram of the process by which the decision unit in this embodiment determines the contact ablation status.

[0029] Figure 5 This is a schematic diagram of the process by which the decision unit performs fault point status assessment in this embodiment.

[0030] Figure 6 This is a schematic diagram of the process by which the decision unit generates the dynamic reclosing risk tolerance, provided in this embodiment.

[0031] Figure 7 This is a schematic diagram of the process by which the decision unit generates a dynamic reclosing risk tolerance based on the law of disaster evolution, provided in this embodiment.

[0032] Figure 8 This is a flowchart illustrating the process of generating a network-level reclosing decision sequence by the decision unit provided in this embodiment.

[0033] Figure 9 This is a flowchart illustrating the process by which the decision unit dynamically adjusts the reclosing decision threshold based on a Bayesian network, as provided in this embodiment.

[0034] Figure 10 This is a structural block diagram of the intelligent control system of a mine explosion-proof permanent magnet mechanism vacuum power distribution device in one embodiment. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0036] The mine explosion-proof permanent magnet mechanism vacuum power distribution device provided in this embodiment includes a vacuum interrupter, a permanent magnet mechanism, an explosion-proof enclosure, and a decision unit disposed inside the explosion-proof enclosure.

[0037] The vacuum interrupter uses copper-chromium alloy contacts, and the inner cavity maintains a 10°C temperature. -3 ~10 -4 The high vacuum level of Pa is used to interrupt fault current and extinguish electric arcs. In a 6kV power supply system in a mine, the rated breaking current of the vacuum interrupter is 1.25kA, and it can interrupt short-circuit currents up to 8kA.

[0038] The permanent magnet mechanism consists of a permanent magnet, a moving contact transmission rod, and a switching coil, used to drive the moving contact of the vacuum interrupter to perform opening and closing operations. Compared with traditional spring-operated mechanisms, the permanent magnet mechanism has a simpler structure, higher reliability, and is particularly suitable for harsh mining environments. In this embodiment, the opening time of the permanent magnet mechanism is less than 30ms, and the closing time is less than 60ms.

[0039] The explosion-proof enclosure is welded from Q235B steel plate with a thickness of 8mm, meeting the safety requirements for explosion-proof equipment in mining. The width of the explosion-proof joint surface is not less than 25mm, and the gap is not greater than 0.2mm, which can effectively prevent the internal explosion from propagating outward.

[0040] The decision-making unit, the core of this embodiment, is located within an intrinsically safe control cavity inside the explosion-proof enclosure. It includes a high-performance microprocessor, a data acquisition module, a communication interface, and a storage unit. The data acquisition module contains a 16-bit high-precision ADC with a sampling rate of up to 100kHz, used to acquire voltage, current, and temperature signals. The storage unit uses non-volatile memory, capable of storing historical data from at least 1000 on / off events. The decision-making unit communicates with the mine monitoring system via a CAN bus, reporting equipment status in real time and receiving control commands.

[0041] See Figure 1 The decision unit performs the following steps: Step S101: Obtain the post-arc voltage recovery waveform and device ambient temperature information after the vacuum interrupter interrupts the fault current.

[0042] In the event of a short-circuit fault in the mine's power supply system, the vacuum interrupter successfully interrupts the fault current. At this point, the contact gap undergoes a recovery process from a conductive state to an insulating state. The decision unit uses a high-precision voltage sensor to acquire the post-arc voltage recovery waveform after the vacuum interrupter interrupts the fault current.

[0043] The post-arc voltage recovery waveform refers to the process of voltage recovery between contacts over time after the fault current is interrupted in a vacuum interruptor, reflecting the recovery state of the vacuum medium. According to vacuum arc theory, after the contacts separate, the vacuum medium in the contact gap needs a certain amount of time to recover its insulation strength. The voltage change characteristics during this process can accurately characterize the recovery state of the medium.

[0044] In this embodiment, the voltage sensor employs a capacitive voltage divider, with a measurement range of 0-10kV and an accuracy of ±0.5%. The sampling frequency is set to 100kHz to ensure accurate capture of key characteristics during the voltage recovery process. For example, when a 6kA short-circuit fault occurs in a mine, the voltage sensor records that the voltage between the contacts gradually increases from 0V, reaching 80% of the system's rated voltage after approximately 5ms. The waveform of this process is the post-arc voltage recovery waveform.

[0045] Meanwhile, the decision-making unit obtains the ambient temperature information of the device through a PT100 temperature sensor. The measurement location is near the vacuum interrupter to ensure accurate reflection of the operating environment temperature of the interrupter. The PT100 sensor has an accuracy of ±0.1℃ and a measurement range of -20℃ to +85℃. In a certain practical application, the current mine ambient temperature was measured to be 25.3℃.

[0046] Step S102: Calculate the medium recovery safe waiting time based on the ratio of the short-circuit current to the rated current during fault interruption and the ambient temperature information of the device.

[0047] The media recovery safety waiting time is the shortest time required after a vacuum interrupter breaks a fault, ensuring that the medium strength between the vacuum contacts recovers to a safe level. Based on the physical characteristics of a vacuum arc, this waiting time is closely related to the magnitude of the fault current and the ambient temperature; the larger the current, the longer the waiting time; and the higher the temperature, the shorter the waiting time.

[0048] In this embodiment, the decision unit first calculates the ratio of the short-circuit current to the rated current during fault interruption: k = I_fault / I_rated; where I_fault is the fault interruption current and I_rated is the rated current.

[0049] Then, calculate the media recovery safe waiting time using the following empirical formula: Where: k is the ratio of short-circuit current to rated current; α is the current influence coefficient (usually 1.0-1.5); β is the temperature influence coefficient (usually 0.01-0.03). is the ambient temperature; f is an empirical function of the media recovery safety waiting time.

[0050] For example, when k=6.4 (short-circuit current 8kA, rated current 1.25kA) and T_env=25.3℃, T_wait≈15.2ms is calculated. In a mine environment, due to the possible presence of flammable and explosive gases such as methane, the requirements for medium strength are more stringent. Therefore, the calculated waiting time is usually multiplied by a safety margin factor greater than 1 (such as 1.2) to obtain the final safety margin time: T_safety=T_wait×γ; where γ is the safety margin factor (usually 1.1-1.5).

[0051] Step S103: Based on the comparison results of the characteristic parameters of the post-arc voltage recovery waveform and the safe waiting time for dielectric recovery, determine whether the dielectric strength of the vacuum interrupter meets the safe reclosing conditions.

[0052] The decision unit analyzes the obtained post-arc voltage recovery waveform and extracts characteristic parameters, such as the voltage recovery slope and the time to reach a specific voltage ratio. Then, these characteristic parameters are compared with the calculated dielectric recovery safe waiting time to determine whether the dielectric strength of the vacuum interrupter meets the safe reclosing conditions.

[0053] In this embodiment, the determination logic is as follows: the post-arc voltage recovery waveform is analyzed, characteristic parameters are extracted, the characteristic parameters are compared with the dielectric recovery safe waiting time, and the dielectric strength is determined to meet the safe reclosing condition based on the comparison result.

[0054] For example, when a low voltage recovery slope is detected and the time to reach a specific voltage ratio is long, the system determines that the dielectric strength meets the safe reclosing conditions. Dielectric strength is a physical quantity characterizing the ability of the insulating medium between vacuum contacts to resist electrical breakdown; a higher value indicates better insulation performance. In mining environments, due to the potential presence of flammable and explosive gases such as methane, the requirements for dielectric strength are even more stringent.

[0055] Step S104: Obtain the opening history data of the vacuum interrupter, and calculate the cumulative opening Joule integral and the cumulative number of openings.

[0056] The decision unit retrieves historical switching data of the vacuum interrupter from non-volatile memory, including current waveforms and switching times for each switching operation. Based on this data, it calculates the cumulative switching Joule integral and the cumulative number of switching operations. The cumulative switching Joule integral is an important indicator reflecting the degree of contact erosion in the vacuum interrupter, and its calculation formula is as follows: Where: n is the number of interruptions; i(t) is the current during the interruption process; the integration interval is each interruption process.

[0057] In this embodiment, each time the decision unit detects an interruption event, it calculates the Joule integral of that interruption and adds it to the total value. For example, if a vacuum interrupter interrupted a fault 15 times in the past year, by calculating and summing the integral of the square of the current over time during each interruption process, the cumulative interruption Joule integral is obtained as 1.8 × 10⁻⁶. 6 A 2 The cumulative number of interruptions is directly recorded as the total number of interruption events. In this embodiment, a circular queue is used to store the interruption events, retaining the most recent 100 interruption records to ensure data integrity and storage efficiency.

[0058] Step S105: Based on the cumulative Joule integral of interruption and the cumulative number of interruptions, determine the current ablation state of the vacuum interrupter contacts, and calculate the current remaining life assessment result of the contacts based on the current ablation state.

[0059] Contact erosion is caused by the evaporation and transfer of metal materials due to the high temperature of the electric arc, which alters the surface morphology and material properties of the contacts. According to research in the field of vacuum circuit breakers, the contact erosion process can generally be divided into three stages: initial stage: microscopic surface irregularities form, and the erosion rate gradually increases; stable stage: the erosion rate is relatively constant, and a stable morphology is formed on the contact surface; accelerated stage: the erosion rate increases sharply, and the contact performance declines rapidly.

[0060] In this embodiment, the decision-making unit determines the current contact ablation state by comparing the current parameters with the preset thresholds: State = initial stage, if J < J1 and n < n1; State = stable stage, if J1 ≤ J < J2 and n1 ≤ n < n2; State = acceleration stage, if J ≥ J2 or n ≥ n2; where J1, J2, n1, and n2 are preset thresholds. For example, J1 = 1.0×10 6 A 2 s, J2 = 5.0×10 6 A 2 s, n1 = 10, n2 = 50.

[0061] Based on the current ablation state, the decision-making unit further calculates the evaluation result of the current remaining life of the contact. The remaining life evaluation uses the linear extrapolation method: N_remaining = (J_max - J) / J_avg; where: J_max is the maximum cumulative breaking joule integral allowed for the contact (determined by the contact material and structure), J is the current cumulative breaking joule integral, and J_avg is the joule integral per average break.

[0062] For example, when J_max = 3.5×10 6 A 2 s, J = 1.8×10 6 A 2 s, J_avg = 0.1×10 6 A 2 s, the calculated N_remaining = 17 times. The evaluation result of the current remaining life of the contact is a parameter characterizing the remaining service life of the contact, and its calculation takes into account the contact material characteristics, usage environment, and historical breaking data.

[0063] Step S106: Based on whether the dielectric strength meets the safe reclosing condition, the current ablation state, and the evaluation result of the current remaining life of the contact, judge the feasibility of reclosing.

[0064] Among them, the decision-making unit comprehensively considers three key factors: whether the dielectric strength meets the safe reclosing condition, the current ablation state of the contact, and the evaluation result of the current remaining life of the contact, and judges the feasibility of reclosing.

[0065] In this embodiment, the decision-making unit adopts a multi-factor comprehensive judgment method: F = g(M, E, L); where: F is the reclosing feasibility, M is the dielectric strength determination result, E is the contact ablation state, L is the evaluation result of the contact remaining life, and g is the reclosing feasibility judgment function.

[0066] For example, if the medium strength meets the condition (M=1), the contact erosion state is good (E="stable period"), and the remaining life is sufficient (L="sufficient"), then reclosing is deemed feasible; if the medium strength does not meet the condition (M=0), then reclosing is deemed infeasible regardless of other conditions. In a mining environment, due to extremely high safety requirements, the weighting of these three factors differs from that in a general industrial environment, with the weight of medium strength typically set higher.

[0067] Step S107: Based on the reclosing feasibility assessment result, generate a reclosing decision command to control the permanent magnet mechanism to perform the reclosing operation.

[0068] Based on the feasibility assessment of reclosing, the decision-making unit generates a corresponding reclosing decision command. If reclosing is deemed feasible, a "reclosing permitted" command is generated; if it is deemed infeasible, a "reclosing prohibited" command is generated.

[0069] In this embodiment, the instructions use a standard communication protocol and include instruction type, timestamp, and checksum. For example, when reclosing is deemed feasible, the decision unit generates the following instruction: {Command_Type: "ALLOW_RECLOSE",Timestamp: 1625097600123, Checksum: 0x3A7F}. This instruction is sent to the permanent magnet mechanism controller via a high-speed communication interface (such as optical fiber). After receiving the instruction, the permanent magnet mechanism performs the reclosing operation: first, it magnetizes the permanent magnet to generate sufficient electromagnetic force to move the moving contact towards the stationary contact, completing the closing operation. In a mining environment, the reclosing operation must ensure absolute safety; therefore, the decision unit sets a limit on the number of reclosing attempts (e.g., a maximum of 3 times). After 3 consecutive reclosing failures, the system will lock and issue an alarm, requiring manual intervention to recover. Furthermore, the system records detailed data for each reclosing decision, including decision time, input parameters, and judgment results, for subsequent analysis and optimization. This reclosing decision-making method based on multi-factor comprehensive judgment has been reliably applied in coal mine power supply systems, significantly improving power supply reliability and safety.

[0070] See Figure 2 This embodiment describes in detail how the decision unit performs the specific process of calculating the medium recovery safety waiting time based on the ratio of the short-circuit current to the rated current during fault interruption and the ambient temperature information of the device.

[0071] Step S201: Obtain the difference between the ambient temperature information of the device and the reference temperature.

[0072] In the event of a short-circuit fault in the mine's power supply system, which is successfully interrupted by the vacuum interrupter, the decision-making unit first obtains the difference between the ambient temperature and the reference temperature. The ambient temperature is measured in real-time by a PT100 temperature sensor installed near the vacuum interrupter, with a measurement accuracy of ±0.1℃ and a measurement range of -20℃ to +85℃. The reference temperature is a preset baseline value, typically set to 20℃, which represents the ambient temperature under standard testing conditions for electrical equipment.

[0073] In this embodiment, the temperature difference is calculated as follows: ΔT = T_env - T_ref; where T_env is the ambient temperature of the device; and T_ref is the reference temperature (20°C).

[0074] For example, when the PT100 temperature sensor measures the current mine ambient temperature to be 25.3℃, the calculated value is: ΔT = 25.3℃ - 20℃ = 5.3℃. This temperature difference is based on mature temperature measurement technology, which has been applied in various power equipment. The temperature sensor is specially designed to be located near the vacuum interrupter but in an area unaffected by arc heat, ensuring that the measured ambient temperature accurately reflects the working environment of the interrupter.

[0075] Step S202: Calculate the medium recovery safety waiting time based on the ratio and difference between the short-circuit current and the rated current during fault interruption.

[0076] In this embodiment, the medium recovery safety waiting time is the shortest time required after a vacuum interrupter interruption fault, used to ensure that the medium strength between the vacuum contacts recovers to a safe level.

[0077] In this embodiment, the media recovery safety wait time is calculated according to the following empirical formula: T_wait ;in: The baseline waiting time is 5-10ms; k is the ratio of short-circuit current to rated current; α is the current influence coefficient (usually 1.0-1.5); β is the temperature influence coefficient (usually 0.01-0.03); ΔT is the temperature difference (T_env-T_ref).

[0078] In practical applications, α and β were obtained through fitting a large amount of experimental data and were used for calibration of specific models of vacuum interrupters. For example, the calibration parameters for a certain mine vacuum interrupter are: =10ms, α=1.2, β=0.02. Specific calculation example: Short-circuit current I_fault=8kA; Rated current I_rated=1.25kA; Temperature difference ΔT=5.3℃; Calculation process: This calculation method requires only basic mathematical operations and can be efficiently implemented in a microprocessor, with the calculation time typically completed within 1ms.

[0079] See Figure 3 This embodiment describes in detail the specific process by which the decision unit executes the comparison results of the characteristic parameters of the post-arc voltage recovery waveform and the safe waiting time for dielectric recovery to determine whether the dielectric strength of the vacuum interrupter meets the safe reclosing conditions.

[0080] Step S301: Denoise the post-arc voltage recovery waveform to obtain the denoised voltage recovery waveform.

[0081] In this embodiment, considering the electromagnetic interference characteristics of the mine site, a moving average filtering algorithm is adopted. This algorithm is simple to calculate, has low latency, and is suitable for real-time processing. The implementation process of the moving average filtering is as follows: Set the window length W=5 (corresponding to 50μs, when the sampling rate is 100kHz), and for each sampling point i, calculate the average value of its W neighboring points: U_smooth(i)=(U(i-2)+U(i-1)+U(i)+U(i+1)+U(i+2)) / 5; further, replace the original value U(i) with the smoothed value U_smooth(i).

[0082] This denoising method is based on the fundamental principles of signal processing and can effectively remove random noise while preserving the main characteristics of the waveform. In practical applications, the window length W can be adjusted according to the noise characteristics of the environment to achieve the best denoising effect.

[0083] Step S302: Calculate the recovery voltage slope of the denoised voltage recovery waveform.

[0084] The recovery voltage slope characterizes the rate at which the recovery voltage changes over time. In this embodiment, the recovery voltage slope is obtained by calculating the ratio of the voltage difference to the time difference between adjacent sampling points: S(i) = (U(i+1) - U(i)) / Δt; where U(i) is the value of the denoised voltage waveform at the i-th sampling point, and Δt is the sampling time interval (Δt = 10 μs when the sampling rate is 100 kHz). For example, when U(i) = 4.2 kV, U(i+1) = 4.2005 kV, and Δt = 10 μs: S(i) = (4.2005 - 4.2) / 0.00001 = 50 V / ms.

[0085] Step S303: Determine the recovery voltage slope condition.

[0086] In this embodiment: preset threshold The critical value is determined based on the physical characteristics of vacuum arc extinguishing, set to 500V / ms, with a preset quantity. Set to 10 sampling points (corresponding to 100μs, when the sampling rate is 100kHz).

[0087] The determination process is as follows: Initialize the counter count=0, traverse and recover the voltage slope sequence starting from the waveform start point, if S(i) < If count is greater than or equal to 1, then count = count + 1; otherwise, count = 0. If the condition is met, then the condition is satisfied. For example, if the recovery voltage slope of 12 consecutive sampling points is all below 500V / ms, then the condition is considered satisfied.

[0088] Step S304: Determine the voltage recovery duration condition.

[0089] In this embodiment: preset voltage ratio The safety margin time T_safety is set to 80%, and has been determined in the aforementioned embodiments. The determination process is as follows: Determine when the voltage first reaches... Time point ;from Begin by looking for the voltage to first drop below [a certain value]. Time point ; Calculate duration ;Compare With T_safety.

[0090] For example, when T_safety=18ms, and the voltage reaches 80% of the rated voltage and remains there for 20ms, the condition is considered met.

[0091] Step S305: If the number of sampling points where the recovery voltage slope is continuously lower than the preset threshold reaches a preset number, and the duration for which the denoised voltage recovery waveform reaches the preset voltage ratio is greater than the safety margin time, then it is determined that the dielectric strength of the vacuum interrupter meets the safe reclosing condition.

[0092] The decision-making unit, considering the results of the above two conditions, determines whether the medium strength meets the safe reclosing condition: M=1, if( AND ( ), M=0, otherwise; where: This indicates that the condition for the recovery voltage slope is met. This indicates that the voltage recovery duration condition is met. In practical applications, if... and If both conditions are met, then M=1 is determined, and the medium strength satisfies the safe reclosing condition; otherwise, M=0 is determined.

[0093] See Figure 4This embodiment describes in detail the specific process by which the decision unit determines the current ablation state of the vacuum interrupter contacts based on the cumulative Joule integral of the interruption and the cumulative number of interruptions.

[0094] Step S401: Obtain the moving contact displacement signal during the historical closing operation process.

[0095] The moving contact displacement signal is the motion trajectory data of the moving contact obtained from a position sensor mounted on the transmission rod of the permanent magnet mechanism. In this embodiment, the position sensor is a high-precision linear displacement sensor with a measurement range of 0-50mm, a resolution of 0.01mm, and a sampling rate of 1kHz. For example, in a closing operation, the displacement sensor records the process of the moving contact moving from its initial position to contact with the stationary contact, obtaining a displacement-time curve.

[0096] Step S402: Identify the moment of first contact of the contact based on the displacement signal of the moving contact.

[0097] The initial contact moment is the point in time when the moving contact and the stationary contact make physical contact. In this embodiment, the identification process is as follows: Differentiating the displacement signal yields the velocity signal: v(t) = dU(t) / dt; detecting abrupt changes in the velocity signal: when the velocity changes abruptly from a positive value to a negative value or approaches zero, it indicates that the contacts have made contact, thus determining the initial contact moment. For example, when the speed suddenly changes from 10 mm / ms to -1 mm / ms, the system determines that this moment is the moment when the contactor makes its first contact.

[0098] Step S403: Capture the bouncing oscillation signal within a preset time window after the initial contact moment of the contactor.

[0099] The bouncing oscillation signal is the vibration signal generated after the contact point makes contact. In this embodiment, the preset time window is set to 20ms. The extraction process is as follows: Starting from [ , The displacement signal within a time period of +20ms is filtered to remove low-frequency motion components and retain high-frequency oscillation components. For example, the captured bouncing oscillation signal shows that the contactor bounced multiple times after contact, with the amplitude gradually decreasing.

[0100] Step S404: Calculate the time it takes for the amplitude of the bouncing oscillation signal to decay to a preset proportion of the initial value.

[0101] In this embodiment: the initial value is taken as the first peak amplitude A0 of the bouncing oscillation signal, the preset ratio is set to 10%, and the bouncing decay time is... This is the time required for the amplitude to decay to 0.1A0.

[0102] The calculation process is as follows: Determine the first peak amplitude A0, and find the time point when the amplitude first drops to 0.1A0. ,calculate = - For example, when A0 = 0.5 mm, =8ms means the bounce decay time is 8ms.

[0103] Step S405: Calculate the contact surface roughness based on the bounce decay time.

[0104] In this embodiment, the surface roughness of the contact is... With bounce decay time The relationship is: Wherein, k is a proportionality coefficient, which is obtained through experimental calibration. In this embodiment, k = 0.05 μm / ms.

[0105] For example, when =8ms: This calculation method is based on contact mechanics theory. The rougher the contact surface, the longer the bounce decay time. Surface roughness and bounce decay time are positively correlated.

[0106] Step S406: Construct a feature vector based on the contact surface roughness, cumulative breaking Joule integral, and cumulative breaking count.

[0107] In this embodiment, the feature vector X is represented as: ;in: Let J be the contact surface roughness, J be the cumulative breaking Joule integral, and n be the cumulative breaking number. For example, when J = 1.8 × 10 6 A 2 When s and n=15: X=[0.4, 1.8×10 6

[15] . This feature vector construction method is based on the fundamental principle of multi-parameter fusion and can comprehensively reflect the contact state.

[0108] Step S407: Adjust the empirical coefficients of the three-stage ablation assessment model based on the feature vector.

[0109] This model characterizes the evolution of the contact ablation process from the initial stage, the stable stage, to the accelerated stage. In this embodiment, the three-stage ablation assessment model is expressed as follows: Where: J is the cumulative Joule integral of the break, n is the cumulative number of breaks, and a, b, and c are empirical coefficients.

[0110] The adjustment method for the empirical coefficients is as follows: Based on the eigenvector X, determine the initial estimate for the current ablation stage; fit the empirical coefficients using historical data via the least squares method; and adjust based on the contact surface roughness. Correct the empirical coefficients: .

[0111] For example, when the original coefficient a = 0.1 × 10 6 b=1.0, c=0.5×10 6 , Time: a'=0.104×10 6 b'=0.98, c'=0.508×10 6 This adjustment method is based on the fundamental principle of parameter identification and can optimize model parameters based on real-time measurement data.

[0112] Step S408: Use the adjusted three-stage ablation assessment model to determine the current ablation status.

[0113] In this embodiment, the determination process is as follows: the theoretically accumulated open-ended Joule integral is calculated using the adjusted model. .

[0114] Calculate the deviation between the actual value and the theoretical value: .

[0115] The ablation state is determined based on the magnitude of the deviation: if ΔJ < 0.1, it is determined to be in the initial stage; if 0.1 ≤ ΔJ < 0.3, it is determined to be in the stable stage; if ΔJ ≥ 0.3, it is determined to be in the accelerated stage.

[0116] For example, when J = 1.8 × 10 6 A 2 s, =1.75×10 6 A 2 At time s: ΔJ = |1.8 - 1.75| / 1.75 = 0.029 < 0.1, therefore it is determined to be in the initial stage.

[0117] This determination method is based on the physical characteristics of the ablation process and can accurately reflect the actual state of the contact.

[0118] See Figure 5 This embodiment describes in detail the specific process by which the decision unit evaluates the status of the isolated fault point after generating the reclosing decision command.

[0119] Step S501: Periodically acquire isolation loop parameters.

[0120] In this process, after determining that reclosing is prohibited and confirming that the fault circuit has been physically isolated by the protection device, the decision unit periodically acquires the residual voltage, weak leakage current and ambient temperature at the two ends of the isolation circuit at preset intervals.

[0121] In this embodiment, the measurement period Dynamically adjust based on fault type and environmental conditions: Where: T0 is the reference measurement period (usually 30 seconds), τ is the fault type coefficient (short circuit fault τ=1.0, ground fault τ=0.8), and θ is the environmental condition coefficient (humid environment θ=0.7, dry environment θ=1.0). For example, when a short circuit fault occurs and the environment is humid: =30×1.0×0.7=21 seconds.

[0122] The measurement process is as follows: Residual voltage is measured using a high-precision voltage sensor (range 0-10V, resolution 0.1mV); weak leakage current is measured using a micro-current sensor (range 0-1mA, resolution 0.1μA); and ambient temperature is measured using a temperature sensor (range -20℃ to +85℃, resolution 0.1℃). For example, after a fault isolation, the system measured every 21 seconds, recording that the residual voltage gradually decreased from 8.5V to 0.3V, the leakage current decreased from 850μA to 30μA, and the ambient temperature remained at 25.3℃. This measurement method is based on the fundamental principles of power system monitoring and can accurately capture the residual characteristics of the fault point.

[0123] Step S502: Compare the residual voltage, weak leakage current, and ambient temperature with the equivalent physical model of the fault circuit and historical fault self-clearing characteristic data; based on the comparison results, analyze the trend of electrical characteristic changes at the fault point.

[0124] In this embodiment, the equivalent physical model is expressed as: V = I × Z + V0; where V is the residual voltage, I is the leakage current, Z is the fault point impedance, and V0 is the residual potential.

[0125] The trend of electrical characteristic changes is characterized by the following parameter: phase difference ; Amplitude change rate .

[0126] The comparison and analysis process is as follows: Calculate the phase difference φ and amplitude change rate of the current measurement point. Compare with the predicted values ​​of the equivalent physical model; analyze the trend of change: if and This indicates that the fault is self-clearing.

[0127] For example, when φ=75° is measured, At this time, the system determines that the fault point is in the self-clearing process. This analysis method is based on the basic principles of circuit theory and signal processing and can effectively identify changes in the state of the fault point.

[0128] Step S503: Based on the comparison results, analyze the trend of electrical characteristic changes at the fault point.

[0129] In this embodiment, the instantaneous impedance Z(t) is calculated as: Z(t) = V(t) / I(t).

[0130] The instantaneous impedance change rate Γ is calculated as follows: Γ=dZ / dt≈[Z(t+Δt)-Z(t)] / Δt; where V(t) and I(t) are the residual voltage and leakage current at time t, respectively, and Δt is the time interval between the two measurements.

[0131] For example, when Z(t) = 12kΩ at time t, Z(t + Δt) = 15kΩ at time t + Δt, and Δt = 21 seconds: Γ = (15 - 12) / 21 = 0.143kΩ / s. This calculation method is based on the fundamental principles of Ohm's law and numerical differentiation, and can accurately reflect the rate of change of impedance at the fault point.

[0132] Step S504: Determine the residual thermal effect of the faulty circuit based on the trend of electrical characteristic changes.

[0133] In this embodiment, the residual heat effect H(t) is calculated using the rate of change of ambient temperature and a heat conduction model: Where: C is the heat capacity coefficient, K is the thermal conductivity coefficient, and T is the measurement temperature. To balance the temperature of the environment.

[0134] The rate of change of residual heat effect Λ is calculated as: Λ=dH / dt≈[H(t+Δt)-H(t)] / Δt; for example, when C=0.5J / ℃, K=0.1W / ℃, T=25.3℃, At time: H(t) = 0.5 × 0.01 + 0.1 × (25.3 - 20) = 0.535 W. This calculation method is based on the fundamental principles of thermodynamics and can effectively assess the thermal state of the fault point.

[0135] Step S505: Based on the instantaneous impedance change rate and residual thermal effect, establish a fault point state assessment model.

[0136] In this embodiment, the state assessment model adopts the linear discriminant analysis (LDA) method: D = w1 × Γ + w2 × Λ; where: D is the discriminant function value, w1 and w2 are weight coefficients, which are obtained by training with historical data.

[0137] The training process is as follows: collect historical fault data, including the Γ and Λ values ​​of fault points in known states, calculate the mean vector and covariance matrix of each category (self-clearing, physical suppression, high-resistivity state), and solve for the optimal weight vector w=[w1,w2].

[0138] For example, w1=0.7 and w2=0.3 were obtained through training with historical data. This model building method is based on the basic principles of pattern recognition and has been widely used in the field of fault diagnosis.

[0139] Step S506: Use the fault point status evaluation model to determine whether the fault point has been self-cleared, physically inhibited, or converted to a high-resistance state.

[0140] In this embodiment, the determination rules are as follows: If D > D1, it is determined to be self-cleared; if D2 ≤ D ≤ D1, it is determined to be physically inhibited; if D < D2, it is determined to be in a high-resistance state.

[0141] Among them, D1 and D2 are thresholds determined through historical data.

[0142] For example, when D1 = 0.5, D2 = 0.2, and D = 0.65 is calculated, the system determines that the fault point has been self-cleared. This determination method is based on the physical characteristics of fault evolution and can accurately evaluate the current state of the fault point, providing a basis for subsequent operations.

[0143] See Figure 6 , this embodiment details the specific process of how the decision-making unit performs state prediction and anomaly detection based on historical data of mine production scheduling instructions, gas concentration, and local geological microseismic intensity, and generates a dynamic reclosing risk tolerance based on this.

[0144] Step S601: Based on the obtained historical data of mine production scheduling instructions, gas concentration, and local geological microseismic intensity, perform state prediction and anomaly detection to obtain a prediction trend and a risk probability, and generate a dynamic reclosing risk tolerance according to the prediction trend and the risk probability.

[0145] Among them, the decision-making unit performs state prediction and anomaly detection based on the obtained historical data of mine production scheduling instructions, gas concentration, and local geological microseismic intensity.

[0146] In this embodiment, the data acquisition and processing process is as follows: Obtain historical data of the past 24 hours from the mine monitoring system: Mine production scheduling instructions: including the working state of the coal cutter, the operating state of the transportation equipment, etc.; Gas concentration: CH4 concentration data from each monitoring point, with a sampling frequency of 1 Hz; Local geological microseismic intensity: vibration amplitude data from the microseismic monitoring system, with a sampling frequency of 100 Hz.

[0147] Perform data preprocessing: Smooth the gas concentration data to eliminate measurement noise; Perform spectral analysis on the microseismic intensity data to extract characteristic frequency components; Convert the production scheduling instructions into a numerical feature vector.

[0148] State prediction uses the autoregressive integrated moving average model (ARIMA): For the gas concentration data: ARIMA(2,1,1) model; For the microseismic intensity data: ARIMA(3,1,2) model; Predict the change trend in the next 15 minutes.

[0149] Anomaly detection uses the Mahalanobis distance method: calculating the Mahalanobis distance in the multidimensional feature space. ;when When this occurs, it is determined to be an abnormal state; risk probability. For example, when an abnormal increase in gas concentration and a sudden increase in microseismic intensity are detected, the system calculates the risk probability. Forecast trends indicate that the risks will continue to rise.

[0150] Step S602: Generate dynamic reclosing risk tolerance based on predicted trends and risk probabilities.

[0151] In this embodiment, the decision-making unit generates a dynamic reclosing risk tolerance based on predicted trends and risk probabilities. The calculation is as follows: ;in: For risk probability, Let w1 be the absolute value of the rate of change of the risk probability, and w2 be the weighting coefficients, satisfying w1 + w2 = 1.

[0152] Specific calculation process: Extracting risk probability sequences from historical data. Calculate the rate of change of risk probability: Substitute into the formula to calculate For example, when , When w1=0.7 and w2=0.3, =1-(0.7×0.75+0.3×0.05)=0.46. This dynamic reclosing risk tolerance generation method is based on the fundamental principles of risk assessment and can dynamically reflect the risk level of the mine environment.

[0153] Step S603: Adjust the judgment method for reclosing feasibility based on the dynamic reclosing risk tolerance.

[0154] In this embodiment, the decision-making unit adjusts the method for judging the feasibility of reclosing based on the dynamic reclosing risk tolerance. The adjustment method is as follows: adjusting the feasibility scoring threshold. Where F0 is the baseline threshold (0.7) and k is the adjustment coefficient (1.5).

[0155] Adjust weighting coefficients: Medium strength weight: Ablation state weights: Remaining lifetime weight: Normalized weights: w1''=w1' / (w1'+w2'+w3'); w2''=w2' / (w1'+w2'+w3'); w3''=w3' / (w1'+w2'+w3').

[0156] For example, when When the original weights w1=0.5, w2=0.3, and w3=0.2: w1'=0.5×(1+0.5×0.46)=0.615; w2'=0.3×(1-0.3×0.46)=0.259; w3'=0.2×(1-0.2×0.46)=0.182.

[0157] After normalization: w1''=0.583, w2''=0.245, w3''=0.172; .

[0158] The adjusted feasibility assessment formula is: F=w1''×M+w2''×E+w3''×L; where: F is the feasibility score, M is the medium strength score, E is the ablation state score, and L is the remaining lifetime score.

[0159] This judgment adjustment method is based on the basic principle of risk-adaptive control. It can dynamically adjust the strictness of the reclosing decision according to the level of environmental risk, and ensure that a more conservative reclosing strategy is adopted in high-risk environments.

[0160] See Figure 7 This embodiment describes the specific implementation process for generating dynamic reclosing risk tolerance.

[0161] Step S701: Based on the obtained data on roof delamination rate, surrounding rock stress abrupt change amplitude, and toxic gas concentration in the roadway, conduct correlation analysis with historical secondary disaster events to obtain characteristic parameters of disaster evolution patterns.

[0162] In this embodiment, the correlation analysis process is as follows: Data acquisition: Roof delamination rate: acquired by a roof delamination monitor, sampling frequency 0.1Hz, unit mm / h; Surrounding rock stress change amplitude: acquired by a stress sensor, sampling frequency 1Hz, unit MPa; Concentration of toxic gases in the roadway: acquired by a gas sensor, including CO, H2S, etc., unit ppm.

[0163] Historical secondary disaster event database: contains records of secondary disaster events such as roof collapse accidents and gas outbursts over the past 5 years; each event record includes: time of occurrence, location, disaster type, severity, and precursor characteristics.

[0164] Association analysis employed Pearson correlation coefficient and mutual information methods: Pearson correlation coefficient: Mutual information: .

[0165] Extraction of characteristic parameters for disaster evolution: Roof disaster characteristic parameters: (Separation rate, roof accident) × I(Separation rate, roof accident); Gas hazard characteristic parameters: (Toxic gas concentration, gas outburst) × I (Toxic gas concentration, gas outburst); Composite disaster characteristic parameters: ,in and For weights.

[0166] For example, when analyzing the current roadway data, the system calculates: (High roof disaster risk) (Medium gas disaster risk) (Comprehensive Disaster Risk). This correlation analysis method, based on the fundamental principles of statistics and information theory, can effectively identify the correlation patterns between disaster precursor characteristics and historical disaster events.

[0167] Step S702: Based on the characteristic parameters of disaster evolution, determine the adjustment parameters of the fuzzy logic membership function and the adjustment coefficients of the rule weights, and apply the adjustment parameters and adjustment coefficients to the risk utility function.

[0168] Among them, the decision-making unit determines the adjustment parameters of the fuzzy logic membership function and the adjustment coefficients of the rule weights based on the characteristic parameters of the disaster evolution law.

[0169] In this embodiment, the specific implementation process is as follows: Fuzzy logic system setup: Input variable: Roof disaster risk Gas disaster risk Output variable: Risk utility value U; Membership function: Gaussian function μ(x;σ,c)=e^(-(xc)) 2 / (2σ 2 )).

[0170] Adjustment parameters determined: Membership function width adjustment parameter: δ=1-0.5×λc; Membership function center adjustment parameter: Δc=0.2×λc.

[0171] Rule weight adjustment coefficient: Roof disaster rule weight: Gas disaster rule weight: Normalization:

[0172] Adjusted membership function for risk utility function: μ'(x;σ',c')=e^(-(x-c')) 2 / (2σ' 2 )); where σ'=σ×δ, c'=c+Δc.

[0173] Adjusted risk utility value: .

[0174] For example, when , , hour: ; This fuzzy logic adjustment method is based on the fundamental principles of fuzzy system theory and can dynamically adjust the risk assessment model according to the evolution of disasters.

[0175] Step S703: Generate a dynamic penalty function based on the characteristic parameters of disaster evolution law, and weight and fuse the risk utility function with the dynamic penalty function to generate a dynamic reclosing risk tolerance.

[0176] In this embodiment, the specific implementation process is as follows: Dynamic penalty function generation: Basic penalty function: P0=k×λc, where k is the penalty coefficient (k=0.8); Time decay factor: α=e^(-t / τ), where τ is the decay time constant (τ=60min); Dynamic penalty function: P=P0×(1+β×dλc / dt); where β is the rate of change sensitivity coefficient (β=0.5).

[0177] Weighted fusion process: Determine the fusion weight: ω = 0.3 + 0.4 × λc.

[0178] Dynamic reclosing risk tolerance: =ω×(1-U)+(1-ω)×P.

[0179] Boundary condition handling: when When >1.0, take =1.0; when When <0.0, take =0.0; For example, when U=0.65, P=0.72, λc=0.68: ω=0.3+0.4×0.68=0.572, =0.572×(1-0.65)+(1-0.572)×0.72=0.518.

[0180] This dynamic reclosing risk tolerance generation method is based on the fundamental principles of risk assessment and decision-making theory. It can comprehensively consider the impact of disaster evolution on reclosing decisions and ensure that a more conservative reclosing strategy is adopted in high-risk environments.

[0181] See Figure 8 This embodiment describes in detail the specific process by which the decision-making unit performs error compensation for sensor data.

[0182] Step S801: Calculate the redundancy index of the power supply network based on the current power supply network topology and load distribution status.

[0183] In this embodiment, the calculation process is as follows: Network topology acquisition: The connection relationships of all power distribution nodes in the network are obtained through the communication network, and the adjacency matrix A of the power supply network is constructed, where This indicates that nodes i and j are directly connected. This indicates that they are not connected.

[0184] Load distribution status acquisition: Collect real-time load data from each distribution node and calculate the load factor. ,in This is the actual load. This represents the maximum allowable load.

[0185] Redundancy metric calculation: Node-level redundancy: ,in Let i be the degree (number of connected edges) of node i.

[0186] Network-level redundancy: Where N is the total number of nodes. This represents the importance weight of the nodes.

[0187] Importance weighting: Key production areas (e.g., coal mining face), secondary production area (e.g., transport tunnels), auxiliary areas (e.g., office area).

[0188] For example, in a 6kV power supply network of a certain mine: the total number of nodes N=15, the node degree of the coal mining face d=3, the load factor ρ=0.85, the importance weight w=1.0, and the node-level redundancy R=(3-1) / 3×(1-0.85)=0.10. The network-level redundancy is calculated as follows. This redundancy index calculation method is based on the fundamental principles of graph theory and power system analysis, and can effectively reflect the structural reliability and load margin of the power supply network.

[0189] Step S802: Generate a network-level reclosing decision sequence based on the redundancy index and the reclosing feasibility judgment results.

[0190] In this embodiment, the generation process is as follows: Construct a decision matrix: Rows: each distribution node; Columns: reclosing feasibility F, node redundancy R, node importance w; Decision vector for each node: .

[0191] Decision sequence priority calculation: Priority score: ; where a1, a2, and a3 are weighting coefficients, satisfying a1+a2+a3=1.

[0192] Priority adjustment rules: When network-level redundancy When: a1=0.6, a2=0.2, a3=0.2; when When: a1=0.4, a2=0.3, a3=0.3; when At time: a1=0.2, a2=0.4, a3=0.4.

[0193] Generate decision sequences: score by priority Sort from highest to lowest; when When they are the same, prioritize by node importance. Sort; for example, when At time: a1=0.4, a2=0.3, a3=0.3, Node A: F=0.9, R=0.25, w=1.0→S=0.4×0.9+0.3×0.25+0.3×1.0=0.735, Node B: F=0.7, R=0.4, w=0.7→S=0.4×0.7+0.3×0.4+0.3×0.7=0.61, Decision sequence: Node A→Node B.

[0194] This network-level reclosing decision sequence generation method is based on the fundamental principles of multi-attribute decision theory. It can comprehensively consider reclosing feasibility, network redundancy, and node importance to optimize the network-wide recovery sequence.

[0195] Step S803: Based on the network-level reclosing decision sequence, determine the reclosing priority and timing constraints of each distribution node.

[0196] In this embodiment, the determination process is as follows:

[0197] Priority assignment: Assigning priority to the node at position i in the decision sequence. Priority range: 1 (highest) to N (lowest).

[0198] Timing constraint calculation: Basic time interval: T0 = 500ms, adjustment factor: k = 1 + 0.2 × (Ni), actual time interval: .

[0199] Reclosing time point determination: First node: t1=t0, subsequent nodes: , where t0 is the reference time when the decision-making unit issues the first reclosing command.

[0200] Special constraints: Critical area nodes: minimum time interval not less than 300ms; same feeder nodes: time interval not less than 800ms; important load nodes: priority not less than 3.

[0201] For example, for a decision sequence of 5 nodes: Node 1 (priority 5): t1=t0; Node 2 (priority 4): Δt1=500×(1+0.2×4)=900ms, t2=t0+900ms; Node 3 (priority 3): Δt2=500×(1+0.2×3)=800ms, t3=t0+1700ms; Node 4 (priority 2): Δt3=500×(1+0.2×2)=700ms, t4=t0+2400ms; Node 5 (priority 1): Δt4=500×(1+0.2×1)=600ms, t5=t0+3000ms.

[0202] The method for determining reclosing priority and timing constraints is based on the fundamental principles of power system recovery control. It can ensure the safety and effectiveness of network-level reclosing operations and avoid system impact caused by simultaneous reclosing of multiple nodes.

[0203] See Figure 9 This embodiment describes the specific process of determining whether a fault point has been automatically cleared, physically suppressed, or transformed into a high-resistivity state.

[0204] Step S901: Construct a Bayesian network model based on historical reclosing event data.

[0205] In this embodiment, the Bayesian network model construction process is as follows: Node definition: Root node: Environmental factors X1: Gas concentration; X2: Ambient temperature; X3: Humidity.

[0206] Intermediate Node: Device Status Y1: Contact erosion state; Y2: Insulation medium recovery state.

[0207] Leaf node: Reclosing result Z={Z}; Z: Reclosing success / failure.

[0208] Network structure determination: The K2 algorithm is used to learn the network structure; mutual information between nodes is calculated. ;when (When threshold θ=0.15) establish directed edges .

[0209] Conditional probability table construction: For the root node: ;in for The number of times it appears, where N is the total number of samples.

[0210] For intermediate nodes: ;in for The set of parent nodes.

[0211] For leaf nodes: P(Z|Pa(Z))=N(Z,Pa(Z)) / N(Pa(Z)).

[0212] For example, based on data from 1000 reclosing events over the past two years: the mutual information I(X1;Y1) between gas concentration and contact erosion state is 0.23 > 0.15, establishing an edge X1→Y1; the mutual information I(X2;Y2) between ambient temperature and insulation recovery state is 0.18 > 0.15, establishing an edge X2→Y2; and the mutual information I(Y1;Z) between contact erosion state and reclosing result is 0.31 > 0.15, establishing an edge Y1→Z. This Bayesian network model construction method is based on the fundamental principles of probabilistic graphical models and can effectively represent the causal relationships and uncertainties in reclosing decisions.

[0213] Step S902: Update the node probability distribution of the Bayesian network model by real-time monitoring data.

[0214] In this embodiment, the update process is as follows: Data acquisition: real-time acquisition of environmental factor measurement values: x={x1,x2,x3}, real-time acquisition of equipment status measurement values: y={y1,y2}.

[0215] Evidence propagation: Approximate reasoning is performed using a likelihood weighting algorithm. For each sample i: starting from the root node, samples are generated in topological order. When an evidence node is encountered... When setting weights .

[0216] Node probability distribution update: For non-evidence node Z: Where e is evidence, Let I be the weight of sample i, and let I(·) be the indicator function.

[0217] Parameter update: Using online learning methods. , where η is the learning rate (η=0.05). This is an estimate based on current observations.

[0218] For example, when real-time monitoring shows: x1=0.5% (gas concentration), x2=28.3℃ (ambient temperature), y1="stable period" (contact erosion state).

[0219] After the system update, we get: P(Z=success|e)=0.87, P(Z=failure|e)=0.13.

[0220] This node probability distribution update method is based on the fundamental principles of Bayesian inference and can dynamically adjust the probability distribution in the network according to real-time monitoring data.

[0221] Step S903: Calculate the posterior probability of the reclosing operation based on the updated node probability distribution, and dynamically adjust the reclosing decision threshold based on the posterior probability.

[0222] In this embodiment, the calculation and adjustment process is as follows: Posterior probability calculation: Success posterior probability: P s =P(Z=success|e); Posterior probability of failure: Pf=P(Z=failure|e)=1-P s .

[0223] Risk Assessment: Success Benefits: Where α is the power restoration value coefficient (α=1.0). Weighting of critical production areas; failure loss: , where β is the failure loss coefficient (β=5.0). Risk tolerance.

[0224] Expected utility: .

[0225] Decision threshold dynamic adjustment: Baseline threshold: Adjustment formula: Where k is the sensitivity coefficient (k=0.5). This is a reference value for expected utility. To maximize the expected utility.

[0226] For example, when hour: ;Rf=5.0×(1-0.45)=2.75; EU=0.87×1.0-0.13×2.75=0.5125; This posterior probability calculation and decision threshold adjustment method is based on the fundamental principles of decision theory and risk assessment. It can dynamically optimize the reclosing decision threshold based on historical data and real-time monitoring, thereby improving the accuracy and adaptability of decision-making.

[0227] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0228] Based on the same inventive concept, this application also provides an intelligent control system for a mine explosion-proof permanent magnet mechanism vacuum power distribution device. The intelligent control system is configured within a decision-making unit of the mine explosion-proof permanent magnet mechanism vacuum power distribution device, such as... Figure 10 As shown, the intelligent control system includes: a data acquisition module 10, used to acquire the post-arc voltage recovery waveform and ambient temperature information of the device after the vacuum interrupter interrupts the fault current.

[0229] The safety waiting calculation module 20 is used to calculate the medium recovery safety waiting time based on the ratio of the short-circuit current to the rated current during fault interruption and the ambient temperature information of the device. The medium recovery safety waiting time is the shortest time required after the vacuum interrupter interrupts a fault, and is used to ensure that the medium strength between the vacuum contacts recovers to a safe level.

[0230] The dielectric determination module 300 is used to determine whether the dielectric strength of the vacuum interrupter meets the safe reclosing conditions based on the comparison results of the characteristic parameters of the post-arc voltage recovery waveform and the dielectric recovery safe waiting time. Dielectric strength is a physical quantity that characterizes the ability of the insulating dielectric between vacuum contacts to resist electrical breakdown.

[0231] The historical processing module 40 is used to acquire the historical data of the vacuum interrupter and calculate the cumulative Joule integral and the cumulative number of interruptions.

[0232] The contact evaluation module 50 is used to determine the current ablation state of the vacuum interrupter contact based on the cumulative breaking Joule integral and the cumulative breaking number, and to calculate the current remaining life evaluation result of the contact based on the current ablation state; the current remaining life evaluation result of the contact is a parameter characterizing the remaining service life of the contact.

[0233] The feasibility assessment module 60 is used to assess the feasibility of reclosing based on whether the medium strength meets the safe reclosing conditions, the current ablation state, and the current remaining life assessment results of the contacts.

[0234] The decision generation module 70 is used to generate a reclosing decision command based on the reclosing feasibility judgment result, so as to control the permanent magnet mechanism to perform the reclosing operation.

[0235] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0236] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0237] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A mine-used explosion-proof permanent magnet mechanism vacuum power distribution device, characterized in that, The mine explosion-proof permanent magnet mechanism vacuum power distribution device comprises: a vacuum interrupter; a permanent magnet mechanism; an explosion-proof housing; and a decision unit arranged inside the explosion-proof housing; The decision unit is configured to: obtain the post-arc voltage recovery waveform of the vacuum interrupter after breaking the fault current and device ambient temperature information; calculate the medium recovery safety waiting time according to the ratio of the short-circuit current to the rated current of the fault breaking and the device ambient temperature information; the medium recovery safety waiting time is the shortest time required after the vacuum interrupter breaks down, which is used to ensure that the medium strength between the vacuum contacts is restored to a safe level; determine whether the medium strength of the vacuum interrupter meets the safe reclosing condition based on the comparison result of the characteristic parameters of the post-arc voltage recovery waveform and the medium recovery safety waiting time; the medium strength is a physical quantity representing the insulation medium resistance to breakdown between the vacuum contacts; obtain the breaking history data of the vacuum interrupter, calculate the cumulative breaking Joule integral and the cumulative breaking times; based on the cumulative breaking Joule integral and the cumulative breaking times, determine the current ablation state of the vacuum interrupter contact, and calculate the current remaining life assessment result of the contact based on the current ablation state of the vacuum interrupter contact; the current remaining life assessment result of the contact is a parameter representing the remaining service life of the contact; based on whether the medium strength meets the safe reclosing condition, the current ablation state of the vacuum interrupter contact and the current remaining life assessment result of the contact, make a reclosing feasibility judgment; generate a reclosing decision instruction according to the reclosing feasibility judgment result to control the permanent magnet mechanism to perform a reclosing operation; wherein, when the decision unit calculates the medium recovery safety waiting time according to the ratio of the short-circuit current to the rated current of the fault breaking and the device ambient temperature information, it is specifically configured to: obtain the difference between the device ambient temperature information and the reference temperature; the reference temperature is a preset reference temperature value; calculate the medium recovery safety waiting time according to the ratio of the short-circuit current to the rated current of the fault breaking and the difference; wherein, when the decision unit determines whether the medium strength of the vacuum interrupter meets the safe reclosing condition based on the comparison result of the characteristic parameters of the post-arc voltage recovery waveform and the medium recovery safety waiting time, it is specifically configured to: perform denoising processing on the post-arc voltage recovery waveform to obtain a denoised voltage recovery waveform; calculate the recovery voltage slope of the denoised voltage recovery waveform; the recovery voltage slope is a parameter representing the rate of change of the recovery voltage with time; if the number of sampling points where the recovery voltage slope continuously falls below the preset threshold value reaches a preset number, and the duration of the denoised voltage recovery waveform reaching a preset voltage ratio is greater than a safety margin time, it is determined that the medium strength of the vacuum interrupter meets the safe reclosing condition; wherein, the preset threshold value is a critical value determined based on the physical characteristics of the vacuum interrupter; the safety margin time is a time value obtained by multiplying the medium recovery safety waiting time by a safety margin coefficient.

2. The mine explosion-proof permanent magnet mechanism vacuum power distribution device according to claim 1, characterized in that, The decision unit is specifically configured to: Obtain a moving contact displacement signal in a historical closing operation process; the moving contact displacement signal is moving contact trajectory data obtained from a position sensor installed on a transmission rod of a permanent magnet mechanism; Identify a first contact time of the contacts based on the moving contact displacement signal; the first contact time of the contacts is a time point at which the moving contact physically contacts the stationary contact; Capture a bounce oscillation signal within a preset time window after the first contact time of the contacts; the bounce oscillation signal is a vibration signal generated after the contacts contact each other; Calculate a bounce decay time of the bounce oscillation signal, wherein the bounce decay time is a time at which an amplitude of the bounce oscillation signal decays to a preset proportion of an initial value; Calculate a surface roughness of the contacts based on the bounce decay time; Construct a feature vector based on the surface roughness of the contacts, the cumulative opening joule integral, and the cumulative opening number; Adjust an experience coefficient of a three-stage ablation evaluation model based on the feature vector; the three-stage ablation evaluation model is a mathematical model representing evolution laws of an ablation process of the contacts from an initial stage, a stable stage, to an acceleration stage; Determine a current ablation state of the contacts of the vacuum interrupter based on the adjusted three-stage ablation evaluation model; the current ablation state is a physical quantity representing a surface morphology of the contacts and a degree of material loss.

3. The mine explosion-proof permanent magnet mechanism vacuum power distribution device of claim 1, wherein, The decision unit is further configured to: Periodically obtain residual voltage, weak leakage current, and ambient temperature at both ends of the isolated circuit at a preset time interval after determining that the reclosing operation is prohibited and confirming that the fault loop has been physically isolated by the protection device; the preset time interval is dynamically adjusted based on the fault type and the environmental conditions; Compare the residual voltage, the weak leakage current, and the ambient temperature with an equivalent physical model of the fault loop and historical fault self-clearing characteristic data to obtain a comparison result; the equivalent physical model is a physical model established based on electrical parameters of the fault loop and used to simulate electrical characteristics of the fault point in different states; Analyze an electrical characteristic change trend of the fault point based on the comparison result; the electrical characteristic change trend is represented by parameters such as a phase relationship between the residual voltage and the leakage current, and an amplitude change rate; Determine an instantaneous impedance change rate of the fault loop according to the electrical characteristic change trend; Determine a residual thermal effect of the fault loop according to the electrical characteristic change trend; The residual thermal effect is a physical quantity representing a temperature change trend of the fault point and is calculated by an ambient temperature change rate and a heat conduction model; Establish a fault point state evaluation model based on the instantaneous impedance change rate and the residual thermal effect; the fault point state evaluation model is a classification model trained based on historical fault data and used to map electrical and thermal characteristic parameters to a fault point state; Determine whether the fault point has been self-cleared, physically suppressed, or converted into a high resistance state by using the fault point state evaluation model; the high resistance state is a state in which a resistance of the fault point is greater than a preset threshold.

4. The mine explosion-proof permanent magnet mechanism vacuum power distribution device of claim 1, wherein, The decision unit is further configured to: based on the obtained mine production scheduling instructions, gas concentration and historical data of local geological microseismic intensity, perform state prediction and anomaly detection to obtain a prediction trend and a risk probability, and generate a dynamic reclosing risk tolerance according to the prediction trend and the risk probability; and adjust the judgment mode of the reclosing feasibility judgment according to the dynamic reclosing risk tolerance.

5. The mine explosion-proof permanent magnet mechanism vacuum power distribution device of claim 4, characterized in that, The decision unit is further configured to: based on the obtained roof separation rate, surrounding rock stress mutation amplitude and roadway toxic gas concentration data, perform correlation analysis with historical secondary disaster events to obtain disaster evolution law characteristic parameters; based on the disaster evolution law characteristic parameters, determine adjustment parameters of a fuzzy logic membership function and an adjustment coefficient of a rule weight, and apply the adjustment parameters and the adjustment coefficient to a risk utility function; based on the disaster evolution law characteristic parameters, generate a dynamic penalty function, weight and fuse the risk utility function and the dynamic penalty function to generate a dynamic reclosing risk tolerance.

6. The explosion-proof permanent magnet mechanism vacuum power distribution device for mine as claimed in claim 1, characterized in that, The decision unit is further configured to: under the calibration trigger condition that the power grid load rate is lower than the load threshold, there is no fault trip in the continuous time period, and the environmental gas concentration is lower than the safety threshold, control the permanent magnet mechanism to perform a standard fault-free opening action; obtain transient electromagnetic response and transient temperature data under the standard fault-free opening action, and compare the transient electromagnetic response and transient temperature data with the theoretical physical model output of the permanent magnet mechanism under standard working conditions; based on the comparison result, determine a measurement error characteristic mode, and correct the sensor error parameters based on the measurement error characteristic mode, and apply the corrected error parameters to the subsequently collected sensor data to perform error compensation of the sensor data.

7. The mine explosion-proof permanent magnet mechanism vacuum power distribution device of claim 3, wherein, The decision unit is further configured to: by a non-invasive ultrasonic acoustic diagnosis unit, obtain acoustic emission signals and mechanical abnormal sound signals in the isolation loop, and obtain local discharge rates monitored by a local discharge sensor and fault point temperature rise signals monitored by a temperature sensor; according to the acoustic emission signals, the mechanical abnormal sound signals, the local discharge rates and the fault point temperature rise signals, utilize the fault point impedance physical model to determine whether the fault point has been self-cleared, physically suppressed or converted into a high resistance state.

8. An intelligent control system of a mine-used explosion-proof permanent magnet mechanism vacuum power distribution device, characterized in that, The intelligent control system is configured in a decision unit inside a mine explosion-proof permanent magnet mechanism vacuum power distribution device, and the intelligent control system comprises: an acquisition module configured to obtain an arc-after voltage recovery waveform after the vacuum arc chamber opens a fault current and device environmental temperature information; The security waiting calculation module is configured to calculate a medium recovery security waiting time according to a ratio of a short-circuit current to a rated current in a fault breaking and the device ambient temperature information; the medium recovery security waiting time is the shortest time that needs to be waited after the vacuum interrupter breaks down a fault, and is used to ensure that the medium strength between the vacuum contacts is recovered to a security level; The medium judging module is configured to judge whether the medium strength of the vacuum interrupter meets a security reclosing condition based on a comparison result of a characteristic parameter of the post-arc voltage recovery waveform and the medium recovery security waiting time; the medium strength is a physical quantity representing the resistance of the insulating medium between the vacuum contacts to electric breakdown; The history processing module is configured to acquire breaking history data of the vacuum interrupter, and calculate a cumulative breaking Joule integral and a cumulative breaking number; The contact evaluating module is configured to judge a current ablation state of the vacuum interrupter contact based on the cumulative breaking Joule integral and the cumulative breaking number, and calculate a contact current residual life evaluation result based on the current ablation state; the contact current residual life evaluation result is a parameter representing the residual service life of the contact; The feasibility judging module is configured to judge the reclosing feasibility based on whether the medium strength meets the security reclosing condition, the current ablation state and the contact current residual life evaluation result; The decision generating module is configured to generate a reclosing decision instruction according to the reclosing feasibility judging result, so as to control the permanent magnet mechanism to perform a reclosing operation. The security waiting calculation module is configured to: acquire a difference between the device ambient temperature information and a reference temperature; the reference temperature is a preset reference temperature value; calculate the medium recovery security waiting time according to the ratio of the short-circuit current to the rated current in the fault breaking and the difference; The medium judging module is configured to: perform denoising processing on the post-arc voltage recovery waveform to obtain a denoised voltage recovery waveform; calculate a recovery voltage slope of the denoised voltage recovery waveform; the recovery voltage slope is a parameter representing the rate of change of the recovery voltage with time; if the number of sampling points, in which the recovery voltage slope continuously is lower than a preset threshold value, reaches a preset number, and the duration in which the denoised voltage recovery waveform reaches a preset voltage ratio is greater than a security margin time, it is judged that the medium strength of the vacuum interrupter meets the security reclosing condition; the preset threshold value is a critical value determined based on the physical characteristics of the vacuum interrupter; and the security margin time is a time value obtained by multiplying the medium recovery security waiting time by a security margin coefficient.

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