Explosion-proof power distribution control system and control method based on internet of things

Abstract

The present application relates to the field of Internet of Things, and relates to an explosion-proof power distribution control method based on Internet of Things, comprising the following steps: collecting current operation characteristic sequence of a field power distribution terminal node through an Internet of Things gateway; extracting time domain and frequency domain characteristics of the current operation characteristic sequence to obtain a characteristic vector reflecting an explosion risk degree; when the characteristic vector exceeds a preset explosion risk decision boundary, performing multi-level explosion risk assessment and response strategy matching on the field power distribution terminal node based on the characteristic vector to obtain an active explosion-proof instruction; and isolating and driving a power distribution circuit breaker supplying power to the field power distribution terminal node based on the active explosion-proof instruction to form a sparkless power-off circuit; and performing residual energy discharge control on the sparkless power-off circuit to form an intrinsically safe power distribution network, thereby solving the technical problem of how to realize active explosion-proof control of a power distribution system in an explosive environment and ensuring that the post-power-off circuit is intrinsically safe.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and in particular to an explosion-proof power distribution control system and method based on IoT. Background Technology

[0002] In explosive atmospheres, the safe operation of power distribution systems is crucial. Traditional explosion-proof technologies mostly rely on passive protection methods such as flameproofing, increased safety, or pressurization, which are insufficient to eliminate ignition risks at the source. In recent years, intrinsically safe (Type i) explosion-proof technology has become the mainstream solution for low-voltage weak current systems and automatic control circuits because it limits the voltage, current, and energy storage elements in the circuit, ensuring that even electrical sparks or thermal effects generated under normal operating or fault conditions are insufficient to ignite the surrounding explosive mixture.

[0003] However, challenges remain in active explosion-proof control and ensuring the intrinsic safety of the circuit after power failure: on the one hand, it is necessary to integrate intelligent sensing, rapid cut-off and energy release mechanisms to achieve active intervention; on the other hand, it is necessary to ensure that after emergency power failure or fault isolation, the residual energy rapidly decays to below the safety threshold to avoid forming a potential ignition source. Summary of the Invention

[0004] The purpose of this invention is to at least partially solve one of the technical problems existing in the prior art.

[0005] To achieve the above objectives, the present invention provides an explosion-proof power distribution control system based on the Internet of Things, comprising:

[0006] The data acquisition module is used to collect the current operating characteristic sequence of the field power distribution terminal nodes through the Internet of Things gateway;

[0007] The extraction module is used to extract time-domain and frequency-domain features from the current running feature sequence to obtain a feature vector reflecting the explosion hazard level;

[0008] The matching module is used to perform multi-level explosion risk assessment and response strategy matching on the field power distribution terminal node based on the feature vector when the feature vector exceeds the preset explosion risk decision boundary, so as to obtain an active explosion-proof command.

[0009] The drive module is used to isolate and drive the circuit breaker of the power distribution circuit that supplies power to the field power distribution terminal node based on the active explosion-proof command, so as to form a spark-free power-off circuit.

[0010] The control module is used to control the residual energy discharge of the sparkless power-off circuit to form an intrinsically safe power distribution network.

[0011] This invention also provides an explosion-proof power distribution control method based on the Internet of Things, comprising:

[0012] The current operating characteristic sequence of the field power distribution terminal nodes is collected through the Internet of Things gateway;

[0013] Time-domain and frequency-domain features are extracted from the current operating feature sequence to obtain a feature vector reflecting the explosion hazard level;

[0014] When the feature vector exceeds the preset explosion risk decision boundary, a multi-level explosion risk assessment and response strategy matching are performed on the field power distribution terminal node based on the feature vector to obtain an active explosion-proof command.

[0015] Based on the active explosion-proof command, the circuit breaker of the power distribution circuit that supplies power to the field power distribution terminal node is isolated and driven to form a spark-free power-off circuit.

[0016] Residual energy discharge control is applied to the sparkless power-off circuit to form an intrinsically safe power distribution network.

[0017] Furthermore, the current operating characteristic sequence of the field power distribution terminal nodes is collected through the IoT gateway, including:

[0018] The intrinsically safe induction probe in the IoT gateway performs transient magnetic field coupling on the physical feeder of the field power distribution terminal node to obtain a continuous analog electrical signal;

[0019] The continuous analog electrical signal is discretized according to a preset sampling frequency to generate a discrete electrical data stream;

[0020] The discrete electrical data stream is divided into multiple time-series data windows by sliding the segment according to a preset time window length.

[0021] Extract the peak value of the envelope line of each data point within the time-series data window, and then concatenate the extracted peak values ​​in chronological order to generate the current running feature sequence.

[0022] Furthermore, time-domain and frequency-domain features are extracted from the current operating feature sequence to obtain a feature vector reflecting the explosion hazard level, including:

[0023] The current running feature sequence is input into a preset high-pass filter for frequency band truncation to separate high-frequency mode components;

[0024] The root mean square value of the high-frequency modal components within a sliding time window is calculated to obtain a time-domain energy sequence, wherein the time-domain energy sequence includes transient energy fluctuations;

[0025] The high-frequency modal components are subjected to discrete Fourier transform to obtain a frequency domain amplitude sequence, wherein the frequency domain amplitude sequence includes harmonic distribution intensity. The logarithm of the energy ratio between adjacent time points in the time domain energy sequence is calculated to obtain the energy mutation rate.

[0026] The frequency domain amplitude sequence is segmented according to a preset frequency band boundary, the normalized power ratio of each frequency band is calculated, and the Shannon entropy is calculated on the normalized power ratio to obtain the frequency band entropy value.

[0027] The energy mutation rate and the frequency band entropy value are concatenated to generate a feature vector reflecting the explosion hazard level.

[0028] Furthermore, based on the feature vector, a multi-level explosion risk assessment and response strategy matching are performed on the field power distribution terminal node to obtain active explosion-proof instructions, including:

[0029] Extract the set of boundary coordinates of the preset explosion risk decision boundary, calculate the spatial distance between the feature vector and each coordinate point in the set of boundary coordinates to obtain a set of distance values, and extract the minimum value from the set of distance values ​​as the danger distance value.

[0030] The danger distance value is input into a preset distance classification interval for numerical comparison, the target distance interval to which the danger distance value belongs is located, and the risk evolution level bound to the target distance interval is extracted;

[0031] The risk evolution level is input as an index key value into a preset explosion-proof strategy matrix, and the segmentation time sequence matching the index key value is extracted;

[0032] The segmented timing sequence is encapsulated into data frames according to a preset underlying communication protocol to generate an active explosion-proof command.

[0033] Furthermore, the risk evolution level is input as an index key into a preset explosion-proof strategy matrix, and the segmented time sequence matching the index key is extracted, including:

[0034] The risk evolution level is parsed using binary encoding to obtain a matrix addressing pointer, and address offset addressing is performed in the preset explosion-proof strategy matrix based on the matrix addressing pointer to obtain the basic control word set;

[0035] The basic control word set is logically ANDed with the preset hardware mask to obtain the isolation driver bit string, and the effective bit segments in the isolation driver bit string are extracted to obtain the action priority code.

[0036] The action priority code is input into a preset delay register table for lookup matching to obtain the delay clock period, and the absolute action delay is obtained by multiplying and adding the delay clock period with the preset reference clock period.

[0037] The absolute action delay is concatenated with the isolation drive bit string to obtain a timing control message, and a check bit is added to the timing control message to obtain the segmented timing sequence, wherein the segmented timing sequence includes a hardware trigger pulse.

[0038] Furthermore, based on the active explosion-proof command, the circuit breaker of the power distribution circuit supplying power to the field power distribution terminal node is isolated and driven to form a spark-free power-off circuit, including:

[0039] The hardware address in the active explosion-proof command is parsed and extracted, and the corresponding power distribution circuit breaker is addressed based on the hardware address, and the load current sequence of the power distribution circuit breaker is collected.

[0040] The current signs of adjacent sampling periods in the load current sequence are compared cycle by cycle. When a reversal of positive and negative polarity is detected between two adjacent sampling values, a linear interpolation operation is performed based on the two adjacent sampling values ​​and their corresponding timestamps to obtain the current zero-crossing time point.

[0041] Calculate the difference between the current zero-crossing time and the preset solid-state conduction delay, and use the difference as the commutation trigger timestamp;

[0042] Based on the commutation trigger timestamp, a conduction level is injected into the parallel electronic switch in the distribution circuit breaker, and the bypass current value of the parallel electronic switch is collected. The current difference between the current sample value of the load current sequence and the bypass current value is calculated.

[0043] When the current difference is less than the preset current cut-off threshold, a tripping pulse is sent to the mechanical main contact of the power distribution circuit breaker to disconnect the mechanical main contact, extract the physical isolation branch where the mechanical main contact is located, and use the physical isolation branch as a sparkless power-off circuit.

[0044] Furthermore, residual energy discharge control is implemented for the sparkless power-off circuit to form an intrinsically safe power distribution network, including:

[0045] The terminal attenuation voltage sequence and inductive freewheeling sequence of the sparkless power-off circuit are collected, and the terminal attenuation voltage sequence and the inductive freewheeling sequence are multiplied in the time domain to obtain the transient residual power flow.

[0046] The difference between the transient residual power flow and the preset intrinsically safe dissipation power threshold is calculated to obtain the overlimit power flow. The overlimit power flow is then integrated according to the preset discharge time step to obtain the energy value to be discharged.

[0047] Divide the energy value to be discharged by the preset single-stage dissipation energy constant to obtain the number of parallel access stages, and convert the number of parallel access stages into a binary control word;

[0048] The high and low level sequence of the binary control word is extracted as the hardware switching level, and the hardware switching level is injected into the nonlinear clamping branch connected to the sparkless power-off circuit to form an intrinsically safe distribution network.

[0049] This invention provides an explosion-proof power distribution control method based on the Internet of Things (IoT), comprising the following steps: collecting the current operating characteristic sequence of the field power distribution terminal node through an IoT gateway; extracting time-domain and frequency-domain features from the current operating characteristic sequence to obtain a feature vector reflecting the explosion hazard; when the feature vector exceeds a preset explosion risk decision boundary, performing multi-level explosion risk assessment and response strategy matching on the field power distribution terminal node based on the feature vector to obtain an active explosion-proof command; isolating and driving the circuit breaker of the power distribution circuit supplying the field power distribution terminal node based on the active explosion-proof command to form a sparkless power-off circuit; and controlling the residual energy discharge of the sparkless power-off circuit to form an intrinsically safe power distribution network. This method solves the technical problem of how to achieve active explosion-proof control of the power distribution system in an explosive environment and ensure that the circuit is intrinsically safe after power failure. It realizes a paradigm shift from passive explosion-proof to active risk prediction and dynamic intervention, effectively improving the real-time perception capability, multi-level collaborative response accuracy, and intrinsic safety guarantee level of the power distribution system in an explosive environment. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a schematic diagram of an explosion-proof power distribution control system based on the Internet of Things in one embodiment of the present invention;

[0052] Figure 2 This is a schematic diagram of the steps of an explosion-proof power distribution control method based on the Internet of Things in one embodiment of the present invention;

[0053] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0054] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0055] Please see Figure 1 One embodiment of an explosion-proof power distribution control system based on the Internet of Things (IoT) of the present invention includes:

[0056] The acquisition module 21 is used to acquire the current operating characteristic sequence of the field power distribution terminal nodes through the Internet of Things gateway;

[0057] Extraction module 22 is used to extract time-domain and frequency-domain features from the current running feature sequence to obtain a feature vector reflecting the explosion hazard level;

[0058] Matching module 23 is used to perform multi-level explosion risk assessment and response strategy matching on the field power distribution terminal node based on the feature vector when the feature vector exceeds the preset explosion risk decision boundary, so as to obtain an active explosion-proof command.

[0059] Drive module 24 is used to isolate and drive the circuit breaker of the power distribution circuit that supplies power to the field power distribution terminal node based on the active explosion-proof command, so as to form a spark-free power-off circuit.

[0060] Control module 25 is used to control the residual energy discharge of the sparkless power-off circuit to form an intrinsically safe power distribution network.

[0061] In this embodiment, the specific implementation of each module in the above-described device embodiment will not be described in detail here.

[0062] The following is a reference to the appendix. Figure 2 A detailed description of an explosion-proof power distribution control method based on the Internet of Things (IoT) according to embodiments of the present invention will be provided with reference to the appendix. Figure 2 This invention describes an explosion-proof power distribution control method based on the Internet of Things (IoT) according to an embodiment of the present invention.

[0063] Figure 2 This invention provides an IoT-based explosion-proof power distribution control method, comprising:

[0064] Step S1: Collect the current operating characteristic sequence of the field power distribution terminal node through the Internet of Things gateway.

[0065] Specifically, the IoT gateway establishes a stable connection with the field power distribution terminal node through wired or wireless communication interfaces, and collects raw electrical parameters such as voltage, current, power factor, and partial discharge signals in real time, forming a multi-channel operational feature sequence aligned with timestamps. This sequence is synchronously recorded at a sampling frequency of no less than 10 kHz to ensure the capture of microsecond-level transient anomalies. The built-in edge preprocessing unit filters, denoises, and aligns the raw data to eliminate interference from transmission jitter and sensor drift. The collected feature sequence is encapsulated into standardized data frames, encrypted using an industrial-grade security protocol, and uploaded to the local edge computing node for subsequent time-frequency joint analysis. In this embodiment, the above scheme effectively ensures the integrity and timeliness of raw operational data in high-risk environments, providing a high-fidelity input foundation for the early identification of explosion risks.

[0066] Step S2: Extract time-domain and frequency-domain features from the current operating feature sequence to obtain a feature vector reflecting the explosion hazard level.

[0067] Specifically, when extracting features from the current operating characteristic sequence, the root mean square value, peak factor, and rise time of the partial discharge pulse of the current and voltage signals are first calculated in the time domain to characterize equipment load fluctuations and arcing tendency. Then, the original sequence is segmented and windowed, and Fast Fourier Transform or wavelet packet decomposition methods are used to extract energy concentration, harmonic distortion rate, and spectral entropy of a specific frequency band (e.g., 20–50 kHz) within the range of 0.1 kHz to 100 kHz in the frequency domain. This frequency band corresponds to high-frequency oscillations caused by early micro-discharges and poor contact. The above time-domain and frequency-domain indicators are normalized and concatenated into a feature vector of a unified dimension. The weights of each component are dynamically calibrated using principal component analysis based on historical explosion accident data to ensure that high-risk patterns are effectively amplified. In this embodiment, the above scheme achieves multi-dimensional quantitative characterization of potential explosion triggers at power distribution terminal nodes, significantly improving the sensitivity and discriminative power of hazard assessment.

[0068] Step S3: When the feature vector exceeds the preset explosion risk decision boundary, a multi-level explosion risk assessment and response strategy matching are performed on the field power distribution terminal node based on the feature vector to obtain an active explosion-proof command.

[0069] Specifically, when any dimension of the feature vector or its weighted combination exceeds the explosion risk decision boundary set according to the IEC 60079 standard, the system immediately activates a multi-level risk assessment mechanism: First, the risk level is determined based on the relative magnitude of the partial discharge intensity and harmonic distortion rate in the feature vector. If the exceedance is only slight, it is marked as a Level 1 warning and triggers a local audible and visual alert. If it is accompanied by a sudden increase in current and a concentration of high-frequency energy, it is determined to be a Level 2 high-risk system. The system automatically calls the pre-stored response strategy library and matches the corresponding active explosion-proof command, including the circuit breaker action priority, isolation timing, and subsequent discharge path configuration. This matching process is jointly implemented based on a rule engine and a lightweight decision tree, ensuring that the entire process from assessment to command generation is completed within 200 milliseconds. In this embodiment, the above scheme achieves precise coupling between risk level and response measures, ensuring rapid closed-loop response capability in high-risk scenarios while avoiding malfunctions.

[0070] Step S4: Based on the active explosion-proof command, the circuit breaker of the power distribution circuit supplying power to the field power distribution terminal node is isolated and driven to form a spark-free power-off circuit.

[0071] Specifically, upon receiving the active explosion-proof command, the system drives the dedicated circuit breaker corresponding to the field power distribution terminal node to perform an isolation operation. This circuit breaker has a built-in vacuum interrupter and magnetic blowout device. Its opening action is triggered by a solid-state relay, and the current zero-crossing detection circuit is activated simultaneously. The contact separation is completed within 1 millisecond before the AC current naturally crosses zero, ensuring that the arc energy during the disconnection process is below the minimum ignition energy threshold. At the same time, the circuit breaker drive circuit adopts a dual-redundant optocoupler isolation signal channel to prevent control-side interference from entering the main circuit. The entire opening process is completed within 50 milliseconds, and after the contacts are completely disconnected, the auxiliary contact feedback status is sent to the IoT gateway to confirm that a spark-free power-off circuit has been formed. In this embodiment, the above scheme effectively blocks the path of electric spark generation through the synergistic effect of precise timing control and intrinsically safe switching devices, providing a reliable electrical isolation basis for subsequent residual energy discharge.

[0072] Step S5: Perform residual energy discharge control on the sparkless power-off circuit to form an intrinsically safe power distribution network.

[0073] Specifically, after confirming the formation of a sparkless power-off circuit, the system immediately activates the residual energy discharge unit. This unit consists of a discharge resistor array connected in parallel to the input of the power distribution terminal and a high-speed electronic switch. Its conduction sequence is triggered by an isolation feedback signal. The discharge process is executed in a closed-loop control manner, monitoring the circuit voltage in real time. When the residual voltage is detected to be higher than the safety limit of 12 V, the electronic switch is turned on, and the remaining charge in the energy storage element is quickly dissipated through the 50Ω discharge resistor, ensuring that the capacitor energy is reduced to below 0.2 mJ within 200 milliseconds, which is lower than the minimum ignition energy of a methane-air mixture. After the discharge is completed, the voltage monitoring module continuously verifies the circuit potential. If it does not fall back to the intrinsically safe threshold, the redundant discharge channel is activated. In this embodiment, the above scheme, through an active, rapid, and verifiable residual energy removal mechanism, ensures that the entire power distribution terminal node meets the intrinsic safety requirements specified in GB 3836.4, effectively eliminating the risk of secondary detonation after a power outage.

[0074] In a specific embodiment, the current operating characteristic sequence of the field power distribution terminal node is collected through an IoT gateway, including:

[0075] The intrinsically safe induction probe in the IoT gateway performs transient magnetic field coupling on the physical feeder of the field power distribution terminal node to obtain a continuous analog electrical signal;

[0076] The continuous analog electrical signal is discretized according to a preset sampling frequency to generate a discrete electrical data stream;

[0077] The discrete electrical data stream is divided into multiple time-series data windows by sliding the segment according to a preset time window length.

[0078] Extract the peak value of the envelope line of each data point within the time-series data window, and then concatenate the extracted peak values ​​in chronological order to generate the current running feature sequence.

[0079] Specifically, the system collects the current operating characteristic sequence of the field power distribution terminal nodes through an IoT gateway. This is achieved first by using an intrinsically safe induction probe built into the gateway to perform non-contact transient magnetic field coupling on the physical feeder. The probe employs a toroidal ferrite core structure with a high-precision induction coil tightly wound around it. When alternating current exists in the feeder, a continuous analog electrical signal proportional to the rate of change of current is generated at both ends of the coil, based on the principle of electromagnetic induction. To ensure intrinsic safety, the signal amplitude is limited to the range of 0 to 5 volts, and the entire sensing circuit meets the energy limitation requirements specified in GB 3836.4.

[0080] The aforementioned continuous analog electrical signal is fed into a 16-bit high-resolution analog-to-digital converter and discretized according to a preset sampling frequency of 20 kHz. This sampling rate strictly follows the Nyquist sampling theorem and is sufficient to cover the highest effective harmonic frequency that may occur in the power distribution system (usually not exceeding 10 kHz), thereby generating a discrete electrical data stream with precisely aligned timestamps and no aliasing distortion, with each data point carrying microsecond-level time accuracy.

[0081] The system performs sliding window segmentation on the discrete electrical data stream with a fixed window length of 100 milliseconds and an overlap rate of 50%, forming a series of continuous and partially overlapping time-series data windows, each containing 2000 sampling points. For the data within each window, a Hilbert transform is used to construct its analytic signal, and the magnitude of this analytic signal is calculated as the instantaneous envelope curve. Local maxima are identified on this envelope curve, and their peak values ​​are extracted as representative features of that window.

[0082] The peak values ​​of the envelope lines corresponding to all time-series data windows are sequentially spliced ​​together according to the original time order to form a one-dimensional current operating characteristic sequence. This sequence effectively condenses key information such as energy mutations, high-frequency oscillations, and potential discharge pulses of the feeder current at the microsecond scale. In this embodiment, the above scheme achieves high-sensitivity and low-latency perception of early arc and partial discharge anomalies without interrupting power supply through a synergistic mechanism of non-invasive magnetic field coupling, high-fidelity sampling, and envelope feature extraction. This provides a high-quality, structured input data foundation for subsequent multi-level explosion risk assessment.

[0083] In a specific embodiment, time-domain and frequency-domain features are extracted from the current operating feature sequence to obtain a feature vector reflecting the explosion hazard level, including:

[0084] The current running feature sequence is input into a preset high-pass filter for frequency band truncation to separate high-frequency mode components;

[0085] The root mean square value of the high-frequency modal components within a sliding time window is calculated to obtain a time-domain energy sequence, wherein the time-domain energy sequence includes transient energy fluctuations;

[0086] The high-frequency modal components are subjected to discrete Fourier transform to obtain a frequency domain amplitude sequence, wherein the frequency domain amplitude sequence includes harmonic distribution intensity. The logarithm of the energy ratio between adjacent time points in the time domain energy sequence is calculated to obtain the energy mutation rate.

[0087] The frequency domain amplitude sequence is segmented according to a preset frequency band boundary, the normalized power ratio of each frequency band is calculated, and the Shannon entropy is calculated on the normalized power ratio to obtain the frequency band entropy value.

[0088] The energy mutation rate and the frequency band entropy value are concatenated to generate a feature vector reflecting the explosion hazard level.

[0089] Specifically, the current operating feature sequence is subjected to time-domain and frequency-domain feature extraction. First, it is fed into a third-order Butterworth high-pass filter with a cutoff frequency of 2 kHz. This filter is used to suppress steady-state components such as the 50 Hz power frequency and its low-order harmonics, retaining only high-frequency modal components in the range of 5 kHz to 50 kHz. This frequency band highly matches the electromagnetic transient signals excited by dangerous events such as arc discharge and local breakdown in coal mines or chemical environments, thus effectively separating dynamic information strongly correlated with explosion risk.

[0090] After obtaining the high-frequency modal components, the system employs a sliding time window mechanism for time-domain energy analysis. The window length is set to 50 milliseconds, and the sliding step size is 10 milliseconds to balance time resolution and computational stability. Within each window, the root mean square value of the component is calculated, forming a time-domain energy sequence. This sequence can accurately reflect the energy fluctuations caused by microsecond-level transient pulses, and is particularly sensitive to transient energy fluctuations caused by intermittent discharges.

[0091] Simultaneously, a Discrete Fourier Transform is performed on the same high-frequency modal component to generate a frequency domain amplitude sequence. The horizontal axis of this sequence corresponds to frequency points (resolution 10 Hz), and the vertical axis represents the amplitude intensity of each frequency component, clearly showing the concentration or dispersion characteristics of harmonic distribution intensity. Based on this, the spectrum is further divided into five sub-bands according to preset boundaries: 5–10 kHz, 10–20 kHz, 20–30 kHz, 30–40 kHz, and 40–50 kHz. The proportion of signal power to total power within each sub-band is calculated to obtain a normalized power proportion vector.

[0092] For the time-domain energy sequence, the ratio of the root mean square values ​​of adjacent windows is taken, and their natural logarithm is calculated to generate the energy mutation rate, which quantifies the severity of energy growth per unit time. When this value exceeds 0.3, it usually indicates the presence of a rapidly developing discharge process. For the frequency-domain normalized power proportion vector, Shannon entropy is calculated. The lower the resulting frequency band entropy value, the more concentrated the frequency domain energy, and the more likely it corresponds to a dangerous partial discharge mode.

[0093] The energy mutation rate and frequency band entropy value are concatenated in a fixed order to form a two-dimensional feature vector, which serves as the core input reflecting the explosion hazard level. In this embodiment, the above scheme constructs a lightweight feature vector with high discriminative power for early electric sparks or arcs by fusing the dynamic mutation of energy in the time domain and the complexity of energy distribution in the frequency domain. This significantly improves the accuracy and robustness of explosion-proof early warning while ensuring real-time performance.

[0094] In a specific embodiment, a multi-level explosion risk assessment and response strategy matching are performed on the field power distribution terminal node based on the feature vector to obtain active explosion-proof instructions, including:

[0095] Extract the set of boundary coordinates of the preset explosion risk decision boundary, calculate the spatial distance between the feature vector and each coordinate point in the set of boundary coordinates to obtain a set of distance values, and extract the minimum value from the set of distance values ​​as the danger distance value.

[0096] The danger distance value is input into a preset distance classification interval for numerical comparison, the target distance interval to which the danger distance value belongs is located, and the risk evolution level bound to the target distance interval is extracted;

[0097] The risk evolution level is input as an index key value into a preset explosion-proof strategy matrix, and the segmentation time sequence matching the index key value is extracted;

[0098] The segmented timing sequence is encapsulated into data frames according to a preset underlying communication protocol to generate an active explosion-proof command.

[0099] Specifically, when conducting multi-level explosion risk assessments of on-site power distribution terminal nodes based on the aforementioned feature vectors, the first step is to extract a set of boundary coordinates from the system's pre-stored explosion risk decision boundary database. This set consists of closed curves formed by joint calibration of a large amount of measured and simulated data in a two-dimensional feature space, used to define the boundary between the safe operating area and the critical hazardous area. Subsequently, the Euclidean distance between the current feature vector and each coordinate point in this set of boundary coordinates is calculated, forming a set of distance values. The minimum value is selected as the hazard distance value; this value physically represents the "closest approximation" of the current operating state to the known critical explosion state, and the smaller the value, the higher the risk.

[0100] After obtaining the hazard distance value, it is compared with a preset distance classification interval to determine the risk level. Specifically, the system divides the distance space into three continuous and mutually exclusive intervals: 0 to 0.15 is defined as a high-risk zone, corresponding to an extremely high risk of explosion requiring immediate intervention; 0.15 to 0.35 is a warning zone, indicating a potential tendency for discharge development, allowing for a short delay in confirmation; and values ​​greater than 0.35 are classified as a safe zone, requiring only routine monitoring. By determining which interval the hazard distance value falls into, the associated risk evolution level can be uniquely determined, which is "emergency cut-off," "delayed cut-off," or "continuous monitoring."

[0101] After determining the risk evolution level, the system uses it as an index key to query the preset explosion-proof strategy matrix. This matrix is ​​configured offline during the equipment deployment phase based on the environment of the power distribution terminal (e.g., underground coal mine, chemical reaction zone), load type (resistive, inductive, or mixed), and explosion-proof level (e.g., Ex d IIC T6), ensuring that each risk level corresponds to an engineering-verified disconnection sequence. For example, "emergency disconnection" corresponds to the main contactor forcibly tripping and blocking the reclosing function within 10 milliseconds; "delayed disconnection" starts a 200-millisecond countdown, canceling the action if the characteristic vector returns to the safe zone during this period, otherwise executing the disconnection.

[0102] The selected interruption timing sequence is standardized and encapsulated into data frames according to the underlying communication protocol used in the field (such as IEC 61850-9-2 or Modbus RTU). This includes embedding the target device address, operation command code, timestamp, and CRC check field, generating a complete active explosion-proof command that can be directly parsed by the smart circuit breaker. This command is then sent to the execution unit in real time via an IoT gateway. In this embodiment, the above solution maps the geometric distance in the feature space to an executable hierarchical response strategy, achieving a closed-loop linkage from risk quantification to physical action. This effectively suppresses the risks of erroneous operation and failure to operate while ensuring the safety of personnel and equipment.

[0103] In a specific embodiment, the risk evolution level is input as an index key into a preset explosion-proof strategy matrix, and the segmented time sequence matching the index key is extracted, including:

[0104] The risk evolution level is parsed using binary encoding to obtain a matrix addressing pointer, and address offset addressing is performed in the preset explosion-proof strategy matrix based on the matrix addressing pointer to obtain the basic control word set;

[0105] The basic control word set is logically ANDed with the preset hardware mask to obtain the isolation driver bit string, and the effective bit segments in the isolation driver bit string are extracted to obtain the action priority code.

[0106] The action priority code is input into a preset delay register table for lookup matching to obtain the delay clock period, and the absolute action delay is obtained by multiplying and adding the delay clock period with the preset reference clock period.

[0107] The absolute action delay is concatenated with the isolation drive bit string to obtain a timing control message, and a check bit is added to the timing control message to obtain the segmented timing sequence, wherein the segmented timing sequence includes a hardware trigger pulse.

[0108] Specifically, when the risk evolution level is input as an index key into the preset explosion-proof strategy matrix to extract the matching interruption timing sequence, the risk evolution level is first parsed using binary encoding. For example, if the system defines three levels of risk ("continuous monitoring", "delayed interruption", "emergency cut-off"), they correspond to the codes 00, 01, and 10 respectively. This binary code is converted by an address decoder to generate a unique matrix addressing pointer. This pointer is used to perform address offset addressing in the explosion-proof strategy matrix, locate the corresponding row, and read the pre-stored basic control word set. This control word set is an 8-bit or 16-bit wide set of logic instructions, including the initial enable states of the main circuit breaker, isolation relay, and interlocking unit.

[0109] The basic control word set is subjected to a bitwise logical AND operation with a preset hardware mask. The hardware mask is embedded in the firmware according to the actual drive circuit topology of the field power distribution terminal and is used to shield unconnected or disabled output channels, thereby generating an isolated drive bit string containing only valid drive signals. In this bit string, high-level bits represent execution units that need to be activated. The system further extracts the continuous valid bit segments starting from the least significant bit and maps the action priority code according to the bit segment length and position. For example, if the bit string is "00110000", the valid bit segment is the 4th-5th bit, corresponding to a priority code of 2, indicating that the upper-level isolation needs to be triggered before the main circuit disconnection is executed.

[0110] The action priority code is used as an index to perform a lookup in a preset delay register table. This table is calibrated during system initialization based on the equipment's mechanical response characteristics and the arc extinction time constant, with each priority code uniquely corresponding to a delay clock cycle. For example, priority code 2 corresponds to 30 system clock cycles (the system reference clock frequency is 10 MHz, i.e., 100 nanoseconds per cycle). The absolute action delay is obtained by multiplying and adding this delay clock cycle to the preset reference clock cycle; in this case, it is 3 microseconds. This ensures that high-priority actions are precisely triggered before low-priority actions, preventing arc reignition or energy feedback.

[0111] The absolute action delay field is concatenated with the aforementioned isolation drive bit string in a preset format to form a timing control message. This message structure includes a synchronization header, drive bit field, delay parameters, and a reserved check area. Subsequently, a CRC-16 algorithm is used to add check bits to the entire frame of data, generating a complete interruption timing sequence with anti-interference capabilities. This sequence is output through the GPIO interface of an FPGA or dedicated ASIC, directly driving the optocoupler isolation circuit to generate nanosecond-level precision hardware trigger pulses to control the opening coils of vacuum circuit breakers or solid-state power switches. In this embodiment, the above scheme accurately converts risk levels into timing-constrained hardware drive instructions, ensuring coordinated action of multiple execution units while achieving sub-microsecond deterministic response, significantly improving the reliability and operational consistency of the active explosion-proof system.

[0112] In a specific embodiment, the matrix addressing pointer performs address offset addressing within the preset explosion-proof strategy matrix to obtain a basic control word set, including:

[0113] The preset explosion-proof strategy matrix is ​​parsed using memory mapping to obtain the read-only storage area and the storage word length. The matrix addressing pointer and the storage word length are then multiplied in a stepwise manner to obtain the byte offset.

[0114] The byte offset is added to the base address of the read-only memory area using the same units to obtain the absolute physical address segment;

[0115] Based on the absolute physical address segment, a single-cycle memory read is performed on the read-only storage area to obtain the original strategy byte stream;

[0116] The original strategy byte stream is stripped of its check bits and the opcode is extracted to obtain a clean instruction bit string.

[0117] The pure instruction bit string is truncated to a fixed length and concatenated with fields according to the preset hardware register bit width to obtain the basic control word set.

[0118] Specifically, in the process of extracting the basic control word set from the preset explosion-proof strategy matrix based on the matrix addressing pointer, the system first performs memory mapping parsing on the explosion-proof strategy matrix. This matrix is ​​fixed in the on-chip read-only memory (ROM) during the device firmware burning stage, and its memory layout includes a clearly defined read-only storage area start base address and a uniform storage word length; for example, in a typical industrial-grade microcontroller, the storage area base address is 0x8004000, and the storage word length is 4 bytes (32 bits), ensuring that each row of strategy records is aligned to the word boundary to support efficient access. Subsequently, the matrix addressing pointer obtained from the risk evolution level parsing (e.g., the value 2) is multiplied by the storage word length in a step multiplication operation, that is, the pointer value is multiplied by the number of bytes occupied by each row, thereby obtaining the byte offset relative to the start position of the storage area; in this example, 2×4=8 bytes, indicating that the target strategy is located in the 3rd row.

[0119] The byte offset is added to the base address of the read-only memory area using the same units—both are address offsets in bytes. The sum yields the absolute physical address segment, here 0x8004008. This address directly points to the starting position of the target policy record in the physical storage space. The system then initiates an uncached memory read operation within a single processor clock cycle, continuously reading a complete storage word (32 bits) from this absolute physical address segment to obtain the original policy byte stream. This operation relies on the burst read mechanism of the independent instruction / data bus or AXI bus under the Harvard architecture, ensuring completion within sub-microsecond time to meet the real-time requirements of explosion-proof response.

[0120] After acquiring the raw policy byte stream, the system immediately performs data purification processing. First, it strips the data according to the preset check bit positions (e.g., the high 4 bits are CRC-4 checksums), retaining only the low 28 bits of the payload. Then, it performs a bitwise logical AND operation between the remaining data and a predefined opcode mask (e.g., 0x0FFFFFFF) to mask invalid high bits that may be introduced due to storage disturbances, thereby extracting the clean instruction bit string. This bit string encodes the logical state combination of multiple control signals, such as circuit breaker tripping, isolation relay enabling, and interlocking signal output.

[0121] Based on the hardware register width (usually 8 or 16 bits) used in the field power distribution terminal, the clean instruction string is truncated and reassembled according to a fixed-length rule. For example, if the target driver chip is two cascaded 8-bit GPIO expanders, the 28-bit instruction string is split into a high 12-bit padding and a low 16-bit valid string, and further concatenated into two consecutive 8-bit control fields, corresponding to the main control and auxiliary control channels respectively. After this field alignment and encapsulation, a basic control word set that strictly matches the hardware interface is finally formed, which can be directly written into the output register array to drive the power execution unit. In this embodiment, the above scheme achieves deterministic, low-latency mapping from abstract risk levels to physical control signals through precise memory address calculation, single-cycle strategy reading, and hardware-aligned instruction reconstruction. This effectively eliminates malfunctions caused by address misalignment, data pollution, or bit width mismatch, significantly enhancing the execution reliability and engineering deployability of the active explosion-proof system.

[0122] In a specific embodiment, the circuit breaker of the power distribution circuit supplying the field power distribution terminal node is isolated and driven based on the active explosion-proof command to form a spark-free power-off circuit, including:

[0123] The hardware address in the active explosion-proof command is parsed and extracted, and the corresponding power distribution circuit breaker is addressed based on the hardware address, and the load current sequence of the power distribution circuit breaker is collected.

[0124] The current signs of adjacent sampling periods in the load current sequence are compared cycle by cycle. When a reversal of positive and negative polarity is detected between two adjacent sampling values, a linear interpolation operation is performed based on the two adjacent sampling values ​​and their corresponding timestamps to obtain the current zero-crossing time point.

[0125] Calculate the difference between the current zero-crossing time and the preset solid-state conduction delay, and use the difference as the commutation trigger timestamp;

[0126] Based on the commutation trigger timestamp, a conduction level is injected into the parallel electronic switch in the distribution circuit breaker, and the bypass current value of the parallel electronic switch is collected. The current difference between the current sample value of the load current sequence and the bypass current value is calculated.

[0127] When the current difference is less than the preset current cut-off threshold, a tripping pulse is sent to the mechanical main contact of the power distribution circuit breaker to disconnect the mechanical main contact, extract the physical isolation branch where the mechanical main contact is located, and use the physical isolation branch as a sparkless power-off circuit.

[0128] Specifically, when executing a sparkless power-off operation on the power supply circuit of the field power distribution terminal node based on an active explosion-proof command, the system first parses the hardware address field embedded in the command. This field uses IEEE EUI-64 format encoding to uniquely identify the target power distribution circuit breaker. Subsequently, it initiates directional addressing communication via CAN bus or RS-485 physical layer to establish a data link with the intelligent controller inside the circuit breaker, and simultaneously starts a high-precision current sampling module to continuously acquire its load current sequence at a sampling frequency of 10 kHz. This current sequence is output by a Hall effect sensor, converted by a 16-bit ADC to form a timestamped digital quantity for subsequent zero-crossing detection.

[0129] The system then compares the current signs of adjacent sampling periods in the load current sequence period by period. When two consecutive sampled values ​​are detected to be positive and negative (or vice versa), it is determined that a current polarity reversal has occurred, indicating that the AC current is crossing zero. At this time, the system retrieves these two sampled values ​​and their corresponding timestamps (e.g., ...). =10.05 ms =+12 A; =10.15 ms =-8 A), the precise zero-crossing time of the current is calculated based on the principle of linear interpolation: assuming the current changes linearly within this small interval, then the zero-crossing time... satisfy ,in =0, solving for gives ≈10.09 ms. This method can control the zero-crossing positioning error within ±50 μs in a 50 Hz power frequency system.

[0130] The system reads the preset solid-state turn-on delay parameter from the firmware configuration table. This parameter is calibrated according to the gate drive circuit characteristics of the IGBT or SiC MOSFET used, with a typical value of 20 μs. It is used to compensate for the delay between receiving the turn-on command and the actual turn-on of the electronic switch. This delay is subtracted from the current zero-crossing time point, and the resulting difference is the commutation trigger timestamp (e.g., 10.07 ms), ensuring that the parallel electronic switch completes the turn-on preparation before the current naturally crosses zero, achieving seamless current transfer.

[0131] When the commutation trigger timest is reached, the system injects a high-level gate drive signal into the parallel electronic switch (usually composed of anti-parallel IGBT modules) built into the distribution circuit breaker, causing it to enter a low-resistance conduction state. Simultaneously, the bypass current value is acquired in real time through an independent shunt and the difference is calculated with the current sampled value of the load current sequence to obtain the residual current carried by the main contacts. When this current difference falls below a preset current-cutting threshold (set to 3% of the rated current according to IEC 60947-2 standard, for example, 3 A in a 100 A circuit), the system immediately sends a 5ms-wide opening pulse to the opening electromagnet of the mechanical main contacts, driving the contacts to separate. Since the main circuit current has essentially shifted to the electronic bypass branch at this time, the mechanical contacts open under near-zero current conditions, effectively suppressing arc generation.

[0132] After confirming complete separation of the mechanical main contacts, the system marks the physically isolated branch containing them as a spark-free power-off circuit. This branch, possessing a visible break point and lacking continuous arc energy, meets the explosion-proof requirements of Ex e (increased safety) or Ex i (intrinsically safe). In this embodiment, the above solution achieves zero-arc, highly reliable disconnection of the power distribution circuit in an explosive environment through precise current zero-crossing prediction, solid-state-mechanical hybrid switch collaborative control, and dynamic current threshold judgment. This fundamentally eliminates the risk of secondary combustion and explosion caused by arcing during the disconnection process of traditional circuit breakers.

[0133] In a specific embodiment, residual energy discharge control is applied to the sparkless power-off circuit to form an intrinsically safe power distribution network, including:

[0134] The terminal attenuation voltage sequence and inductive freewheeling sequence of the sparkless power-off circuit are collected, and the terminal attenuation voltage sequence and the inductive freewheeling sequence are multiplied in the time domain to obtain the transient residual power flow.

[0135] The difference between the transient residual power flow and the preset intrinsically safe dissipation power threshold is calculated to obtain the overlimit power flow. The overlimit power flow is then integrated according to the preset discharge time step to obtain the energy value to be discharged.

[0136] Divide the energy value to be discharged by the preset single-stage dissipation energy constant to obtain the number of parallel access stages, and convert the number of parallel access stages into a binary control word;

[0137] The high and low level sequence of the binary control word is extracted as the hardware switching level, and the hardware switching level is injected into the nonlinear clamping branch connected to the sparkless power-off circuit to form an intrinsically safe distribution network.

[0138] Specifically, after completing the sparkless power-off operation, to ensure that the distribution terminal node completely enters an intrinsically safe state, the system immediately initiates the residual energy discharge control process. First, a high-impedance differential probe and a high-speed isolation amplifier synchronously acquire the terminal attenuation voltage sequence of the sparkless power-off circuit output terminals. This sequence reflects the natural voltage decay process caused by the line-to-ground capacitance and distributed inductance after the circuit breaker trips. Simultaneously, a Rogowski coil or integrated current sensor is used to acquire the inductive freewheeling current sequence, which characterizes the reverse freewheeling current generated by inductive loads such as motor windings and transformer magnetizing inductance during sudden current changes. Both sequences are time-aligned and acquired at a sampling rate of at least 50 kHz and then sent to the digital signal processing unit.

[0139] The system multiplies the terminal decay voltage sequence and the inductive freewheeling current sequence point-by-point in the time domain to obtain the transient residual power flow. This power flow physically represents the instantaneous electromagnetic energy exchange rate that still exists in the circuit after power failure. Its peak value typically appears within several hundred microseconds after the circuit is opened and exhibits an exponential decay trend. The system calculates in real time the difference between this transient residual power flow and a preset intrinsically safe dissipation power threshold—this threshold is set according to GB 3836.4 or IEC 60079-11 standards, for example, 1.3 W in a Class IIC gas environment—as the maximum allowable continuous dissipation power limit. When the transient power exceeds this limit, the excess portion is defined as the over-limit power flow.

[0140] To quantify the total energy that needs to be actively released, the system performs numerical integration on the excess power flow according to a preset release time step (e.g., 100 μs), and uses the trapezoidal rule to accumulate the power-time area within each time step to obtain the energy value to be released, in joules. This energy value directly reflects the dangerous energy level that could ignite if left unattended. Next, the system divides this energy value by a preset single-stage dissipation energy constant—this constant is determined by the maximum energy that each release unit (e.g., a series-parallel combination of a metal oxide varistor (MOV) and a release resistor) in the parallel-connected nonlinear clamping branch can safely absorb, with a typical value of 0.5 J / stage. The resulting quotient is rounded up to obtain the required number of parallel stages; for example, if 2.3 stages are calculated, then 3 stages are chosen to ensure that the engineering margin meets explosion-proof safety requirements.

[0141] The number of parallel access stages is then converted into a fixed-width binary control word, such as a 4-bit code representing a maximum of 15 switching stages. The system extracts the high and low level states of each bit in the binary control word to form a hardware switching level sequence corresponding one-to-one with the physical discharge unit. These level signals, after being optocoupled and isolated, drive a solid-state relay array to connect a corresponding number of nonlinear clamping branches in parallel to the output terminal of the sparkless power-off circuit. The nonlinear clamping branches are composed of varistors, transient suppression diodes, and power resistors, possessing fast response and self-recovery characteristics, capable of clamping residual voltage and dissipating stored electromagnetic energy within microseconds.

[0142] In this embodiment, the above scheme achieves accurate, graded, and rapid discharge of residual energy after power failure by real-time sensing of the coupling power of residual voltage and freewheeling current, dynamic calculation of over-limit energy, and activation of multi-level nonlinear discharge units as needed. This ensures that the entire power distribution circuit remains below the intrinsically safe energy limit in an explosive environment, effectively preventing the risk of secondary ignition caused by the release of inductor energy storage or capacitor residual voltage, thereby constructing a truly intrinsically safe power distribution network.

[0143] In a specific embodiment, the hardware switching level is injected into the nonlinear clamping branch connected to the sparkless power-off circuit to form an intrinsically safe distribution network, including:

[0144] The hardware switching level is injected into the power switch gate in the nonlinear clamping branch connected to the sparkless power-off circuit, thereby turning on the nonlinear clamping branch and generating an energy discharge circuit.

[0145] The real-time decay voltage across the energy discharge circuit is collected, and the envelope peak value of the real-time decay voltage is extracted to obtain the transient peak voltage.

[0146] The residual voltage difference is obtained by subtracting the transient peak voltage from the preset intrinsically safe reference voltage.

[0147] The residual voltage difference is input to a preset voltage-controlled constant current source for linear conversion to obtain an analog feedback current;

[0148] The simulated feedback current is injected into the status indicator port of the sparkless power-off circuit, and the final physical network formed after the status indicator port is closed is extracted. The final physical network is used as the intrinsically safe distribution network.

[0149] Specifically, in the process of injecting hardware switching levels into the nonlinear clamping branch to construct an intrinsically safe distribution network, the system first applies the high and low level signals corresponding to the aforementioned generated binary control word to the gate of the power switch inside the nonlinear clamping branch, which is connected to the output terminal of the sparkless power-off circuit, after high-speed optocoupler isolation. This power switch typically uses an enhancement-mode SiC MOSFET or a high-voltage IGBT, with a nonlinear energy dissipation network composed of a metal oxide varistor (MOV) and a bleeder resistor connected in parallel between its source and drain. When the high level is active, the power switch quickly turns on, allowing the nonlinear clamping branch to connect at low resistance, forming an independent energy discharge circuit from the load side through the clamping branch back to the power supply ground, used to absorb the back electromotive force released by the inductive load and the residual charge stored in the line distributed capacitance.

[0150] The system uses isolated differential voltage sensors to acquire the voltage waveforms across the energy discharge loop in real time, with a sampling frequency of at least 100 kHz to capture microsecond-level transient processes. A sliding window peak detection algorithm is used to process the acquired real-time decaying voltage, extracting the local maximum value on its envelope, i.e., the transient peak voltage. This voltage reflects the highest potential that the residual energy can reach in the initial stage of discharge and is a key parameter for determining whether the intrinsically safe voltage limit is met. For example, in a Class IIC explosive gas environment, according to the IEC 60079-11 standard, the upper limit of the intrinsically safe reference voltage is 28 V. Based on this, the system sets a preset intrinsically safe reference voltage of 25 V, reserving a 3 V engineering margin to cope with measurement errors and environmental disturbances.

[0151] The system further algebraically subtracts the transient peak voltage from the intrinsically safe reference voltage to obtain the residual voltage difference. If the transient peak voltage is 32 V, the residual voltage difference is +7 V, indicating that the current discharge capability is insufficient to suppress the voltage below the safe threshold. This difference signal is then fed into a preset voltage-controlled constant current source module, which consists of an operational amplifier, a precision reference source, and a current output transistor, possessing good linearity and temperature stability. In this module, the residual voltage difference is linearly converted into a proportional analog feedback current; for example, each volt difference corresponds to 1 mA of output current, thereby generating a 7 mA feedback current signal.

[0152] The simulated feedback current is injected into the status indication port of the sparkless power-off circuit, which is electrically connected in series with the coil circuit of a normally open safety relay. When the injected current exceeds the relay's operating threshold (e.g., 5mA), the coil is energized, causing the contacts to close, thereby physically connecting a final physical network protected by multiple redundant isolation barriers. This network not only includes the distribution terminal nodes where the main power supply has been cut off, but also integrates all branches where residual energy has been discharged and the voltage has been clamped below the intrinsically safe limit, and is completely isolated from non-intrinsically safe areas by safety barriers. Once the status indication port is closed, the system determines that the entire distribution area has entered an intrinsically safe operating state.

[0153] In this embodiment, the above scheme converts transient voltage overshoot into a precise analog current signal through a closed-loop feedback mechanism, drives the safety relay to complete physical verification, realizes a complete safety link from "energy leakage to state verification", ensures that the distribution network is declared safe only when the residual voltage is truly below the intrinsic safety limit, eliminates the risk of misjudgment that may be caused by relying solely on open-loop control, and significantly improves the functional safety level and on-site deployment reliability of the explosion-proof system.

[0154] The above describes an explosion-proof power distribution control method based on the Internet of Things (IoT) in an embodiment of the present invention. The following describes an explosion-proof power distribution control system based on the IoT in an embodiment of the present invention. Please refer to [link / reference]. Figure 2 One embodiment of an explosion-proof power distribution control system based on the Internet of Things (IoT) of the present invention includes:

[0155] The acquisition module 21 is used to acquire the current operating characteristic sequence of the field power distribution terminal nodes through the Internet of Things gateway;

[0156] Extraction module 22 is used to extract time-domain and frequency-domain features from the current running feature sequence to obtain a feature vector reflecting the explosion hazard level;

[0157] Matching module 23 is used to perform multi-level explosion risk assessment and response strategy matching on the field power distribution terminal node based on the feature vector when the feature vector exceeds the preset explosion risk decision boundary, so as to obtain an active explosion-proof command.

[0158] Drive module 24 is used to isolate and drive the circuit breaker of the power distribution circuit that supplies power to the field power distribution terminal node based on the active explosion-proof command, so as to form a spark-free power-off circuit.

[0159] Control module 25 is used to control the residual energy discharge of the sparkless power-off circuit to form an intrinsically safe power distribution network.

[0160] In this embodiment, the specific implementation of each module in the above device embodiment is described in the above method embodiment, and will not be repeated here.

Claims

1. An explosion-proof power distribution control system based on the Internet of Things, characterized in that, include: The data acquisition module is used to collect the current operating characteristic sequence of the field power distribution terminal nodes through the Internet of Things gateway; The extraction module is used to extract time-domain and frequency-domain features from the current running feature sequence to obtain a feature vector reflecting the explosion hazard level; The matching module is used to perform multi-level explosion risk assessment and response strategy matching on the field power distribution terminal node based on the feature vector when the feature vector exceeds the preset explosion risk decision boundary, so as to obtain an active explosion-proof command. The drive module is used to isolate and drive the circuit breaker of the power distribution circuit that supplies power to the field power distribution terminal node based on the active explosion-proof command, so as to form a spark-free power-off circuit. The control module is used to control the residual energy discharge of the sparkless power-off circuit to form an intrinsically safe power distribution network.

2. An explosion-proof power distribution control method based on the Internet of Things, characterized in that, include: The current operating characteristic sequence of the field power distribution terminal nodes is collected through the Internet of Things gateway; Time-domain and frequency-domain features are extracted from the current operating feature sequence to obtain a feature vector reflecting the explosion hazard level; When the feature vector exceeds the preset explosion risk decision boundary, a multi-level explosion risk assessment and response strategy matching are performed on the field power distribution terminal node based on the feature vector to obtain an active explosion-proof command. Based on the active explosion-proof command, the circuit breaker of the power distribution circuit that supplies power to the field power distribution terminal node is isolated and driven to form a spark-free power-off circuit. Residual energy discharge control is applied to the sparkless power-off circuit to form an intrinsically safe power distribution network.

3. The explosion-proof power distribution control method based on the Internet of Things according to claim 1, characterized in that, The current operating characteristic sequence of the field power distribution terminal nodes is collected through the IoT gateway, including: The intrinsically safe induction probe in the IoT gateway performs transient magnetic field coupling on the physical feeder of the field power distribution terminal node to obtain a continuous analog electrical signal; The continuous analog electrical signal is discretized according to a preset sampling frequency to generate a discrete electrical data stream; The discrete electrical data stream is divided into multiple time-series data windows by sliding the segment according to a preset time window length. Extract the peak value of the envelope line of each data point within the time-series data window, and then concatenate the extracted peak values ​​in chronological order to generate the current running feature sequence.

4. The explosion-proof power distribution control method based on the Internet of Things according to claim 1, characterized in that, The current operating feature sequence is subjected to time-domain and frequency-domain feature extraction to obtain a feature vector reflecting the explosion hazard level, including: The current running feature sequence is input into a preset high-pass filter for frequency band truncation to separate high-frequency mode components; The root mean square value of the high-frequency modal components within a sliding time window is calculated to obtain a time-domain energy sequence, wherein the time-domain energy sequence includes transient energy fluctuations; The high-frequency modal components are subjected to discrete Fourier transform to obtain a frequency domain amplitude sequence, wherein the frequency domain amplitude sequence includes harmonic distribution intensity. The logarithm of the energy ratio between adjacent time points in the time domain energy sequence is calculated to obtain the energy mutation rate. The frequency domain amplitude sequence is segmented according to a preset frequency band boundary, the normalized power ratio of each frequency band is calculated, and the Shannon entropy is calculated on the normalized power ratio to obtain the frequency band entropy value. The energy mutation rate and the frequency band entropy value are concatenated to generate a feature vector reflecting the explosion hazard level.

5. The explosion-proof power distribution control method based on the Internet of Things according to any one of claims 2-4, characterized in that, Based on the feature vector, a multi-level explosion risk assessment and response strategy matching are performed on the field power distribution terminal node to obtain active explosion-proof instructions, including: Extract the set of boundary coordinates of the preset explosion risk decision boundary, calculate the spatial distance between the feature vector and each coordinate point in the set of boundary coordinates to obtain a set of distance values, and extract the minimum value from the set of distance values ​​as the danger distance value. The danger distance value is input into a preset distance classification interval for numerical comparison, the target distance interval to which the danger distance value belongs is located, and the risk evolution level bound to the target distance interval is extracted; The risk evolution level is input as an index key value into a preset explosion-proof strategy matrix, and the segmentation time sequence matching the index key value is extracted; The segmented timing sequence is encapsulated into data frames according to a preset underlying communication protocol to generate an active explosion-proof command.

6. The explosion-proof power distribution control method based on the Internet of Things according to claim 5, characterized in that, The risk evolution level is input as an index key into a preset explosion-proof strategy matrix, and the segmented time sequence matching the index key is extracted, including: The risk evolution level is parsed using binary encoding to obtain a matrix addressing pointer, and address offset addressing is performed in the preset explosion-proof strategy matrix based on the matrix addressing pointer to obtain the basic control word set; The basic control word set is logically ANDed with the preset hardware mask to obtain the isolation driver bit string, and the effective bit segments in the isolation driver bit string are extracted to obtain the action priority code. The action priority code is input into a preset delay register table for lookup matching to obtain the delay clock period, and the absolute action delay is obtained by multiplying and adding the delay clock period with the preset reference clock period. The absolute action delay is concatenated with the isolation drive bit string to obtain a timing control message, and a check bit is added to the timing control message to obtain the segmented timing sequence, wherein the segmented timing sequence includes a hardware trigger pulse.

7. The explosion-proof power distribution control method based on the Internet of Things according to claim 1, characterized in that, Based on the active explosion-proof command, the circuit breaker of the power distribution circuit supplying power to the field power distribution terminal node is isolated and driven to form a spark-free power-off circuit, including: The hardware address in the active explosion-proof command is parsed and extracted, and the corresponding power distribution circuit breaker is addressed based on the hardware address, and the load current sequence of the power distribution circuit breaker is collected. The current signs of adjacent sampling periods in the load current sequence are compared cycle by cycle. When a reversal of positive and negative polarity is detected between two adjacent sampling values, a linear interpolation operation is performed based on the two adjacent sampling values ​​and their corresponding timestamps to obtain the current zero-crossing time point. Calculate the difference between the current zero-crossing time and the preset solid-state conduction delay, and use the difference as the commutation trigger timestamp; Based on the commutation trigger timestamp, a conduction level is injected into the parallel electronic switch in the distribution circuit breaker, and the bypass current value of the parallel electronic switch is collected. The current difference between the current sample value of the load current sequence and the bypass current value is calculated. When the current difference is less than the preset current cut-off threshold, a tripping pulse is sent to the mechanical main contact of the power distribution circuit breaker to disconnect the mechanical main contact, extract the physical isolation branch where the mechanical main contact is located, and use the physical isolation branch as a sparkless power-off circuit.

8. The explosion-proof power distribution control method based on the Internet of Things according to claim 1, characterized in that, Residual energy discharge control is applied to the sparkless power-off circuit to form an intrinsically safe power distribution network, including: The terminal attenuation voltage sequence and inductive freewheeling sequence of the sparkless power-off circuit are collected, and the terminal attenuation voltage sequence and the inductive freewheeling sequence are multiplied in the time domain to obtain the transient residual power flow. The difference between the transient residual power flow and the preset intrinsically safe dissipation power threshold is calculated to obtain the overlimit power flow. The overlimit power flow is then integrated according to the preset discharge time step to obtain the energy value to be discharged. Divide the energy value to be discharged by the preset single-stage dissipation energy constant to obtain the number of parallel access stages, and convert the number of parallel access stages into a binary control word; The high and low level sequence of the binary control word is extracted as the hardware switching level, and the hardware switching level is injected into the nonlinear clamping branch connected to the sparkless power-off circuit to form an intrinsically safe distribution network.

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