Ring main unit lightning microclimate online monitoring system and method thereof

Abstract

The application relates to the technical field of meteorological monitoring, in particular to a lightning microclimate online monitoring system for ring network cabinets and a method thereof. The system comprises a feature extraction module, a spectrum detection module, a frequency band pool screening module, a path adjustment module and an interference avoidance module. In the application, the lightning precursor features are identified by collecting the electric field intensity extreme value sequence and calculating the change rate difference ratio, the energy peak frequency band and the falling point of the frequency hopping signal are analyzed in combination with the spectrum energy density diagram, the lightning frequency signal concentrated area is determined, the spectrum interference filtering is carried out, the time deviation between the lightning point and the frequency hopping scheduling is analyzed in combination with the dynamic time warping algorithm, the frequency hopping path is adjusted in real time, the response timeliness is improved, the difference before and after the electric field disturbance is monitored, the single-cycle interference is avoided, the distinguishing ability of the lightning disturbance and the background interference is enhanced, the frequency hopping anti-interference performance of the lightning precursor information is improved, and support is provided for the monitoring stability and response adaptability of the lightning risk environment.

Description

Technical Field

[0001] This invention relates to the field of meteorological monitoring technology, and in particular to a ring network cabinet lightning micro-meteorological online monitoring system and method. Background Technology

[0002] The field of meteorological monitoring technology encompasses the collection, analysis, and data transmission of various meteorological elements in the atmospheric environment, involving real-time observation of natural phenomena such as temperature, air pressure, humidity, wind speed, wind direction, precipitation, and lightning. Its core content lies in acquiring, recording, and managing meteorological data through various sensors and information acquisition equipment to dynamically monitor climate change and provide disaster early warning services. Currently, meteorological monitoring systems generally rely on a combination of multiple physical measurement methods and data communication technologies to construct a multi-point, multi-parameter, synchronously collected meteorological sensing system. This system is widely applied in various industries such as power, transportation, aviation, and agriculture to improve adaptability to the natural environment and enhance safety assurance levels.

[0003] One type of ring main unit lightning micro-meteorological online monitoring system refers to a dedicated online monitoring system built based on the acquisition of lightning characteristic parameters and micro-meteorological factors to meet the environmental safety monitoring needs of ring main unit equipment in power systems. The system focuses on synchronously acquiring lightning parameters such as lightning current intensity, electromagnetic radiation characteristics, lightning strike time and location, as well as micro-meteorological elements such as temperature, humidity, air pressure, and wind speed in the local area. It collects lightning signals and measures micro-meteorological data by deploying multi-functional sensor terminals, quantifies and converts the raw signals using an embedded data processing unit, and uploads the monitoring data to a remote monitoring platform in real time via wired communication links or wireless transmission modules. This enables all-weather risk assessment and data support for the ring main unit's operating environment.

[0004] Current technologies for meteorological data acquisition and transmission largely rely on direct acquisition from physical quantity sensors and conventional data link transmission methods. While they achieve synchronous acquisition of meteorological factors, they fall short in early warning and interference identification for sudden lightning events. Current technologies are insensitive to subtle environmental changes preceding lightning strikes and cannot predict lightning risk based on electric field trends, resulting in delayed warnings. Furthermore, the lack of effective interference identification and avoidance mechanisms in spectrum signal processing makes it difficult to efficiently model and analyze the clustering and abrupt changes in lightning precursor frequency signals. This includes signal aliasing and misjudgments frequently occurring in areas with dense frequency abrupt changes, affecting the monitoring system's ability to trace the source of lightning events. Additionally, the failure to predict the correlation between lightning disturbance time and frequency hopping scheduling makes it difficult to match frequency modulation strategies to the rhythm of sudden events, leading to untimely interference avoidance, reduced system response reliability, and increased false alarm probability. These problems are more pronounced during severe thunderstorms and convective weather, seriously impacting the ability to conduct real-time risk assessments of ring main unit operation. Summary of the Invention

[0005] To address the technical problems existing in the prior art, this invention provides a ring main unit lightning micro-meteorological online monitoring system and method. The technical solution is as follows:

[0006] On the one hand, a ring main unit lightning micro-meteorological online monitoring system is provided, which includes:

[0007] The feature extraction module is used to obtain the extreme value sequence of atmospheric electric field intensity through the electric field sensor of the ring network cabinet, calculate the difference ratio of the time change rate, determine whether it exceeds the control limit, generate the lightning precursor mutation result and transmit it to the spectrum detection module.

[0008] The spectrum detection module is used to obtain the corresponding frequency hopping spectrum energy density distribution map through the lightning precursor mutation results, and to determine the maximum energy peak frequency band and the descent points on both sides of the frequency axis in the map through the spectrum clustering algorithm, output the spectrum clustering determination result and transmit it to the frequency band pool screening module.

[0009] The frequency band pool filtering module is used to define the no-hopping frequency band on the spectrum based on the peak frequency band of the spectrum aggregation determination result and perform filtering to generate an updated frequency band pool and pass it to the path adjustment module.

[0010] The path adjustment module is used to predict the difference between the lightning disturbance time and the next frequency hopping scheduling time based on the updated frequency band pool and lightning precursor mutation results using a dynamic time warping algorithm, and to construct the next frequency hopping path and transmit it to the interference avoidance module.

[0011] The interference avoidance module is used to obtain the electric field difference before and after the frequency hopping corresponding to the target frequency point in the next frequency hopping path, perform single-cycle interference avoidance judgment, and generate frequency hopping avoidance judgment result.

[0012] As a further aspect of the present invention, the lightning precursor mutation result includes the extreme value sequence of atmospheric electric field intensity, the difference ratio of time interval change rate, and control limits; the spectrum aggregation stability determination result includes the frequency band of the maximum energy peak on the frequency axis, the frequency domain of the descent points on both sides, the spectrum energy density distribution map, and the determination by the spectrum clustering algorithm; the updated frequency band pool includes the frequency peak and the no-hopping frequency band; the next frequency hopping path includes the updated frequency band pool, the predicted value of the dynamic time warping algorithm, the next frequency hopping scheduling time point, and the lightning disturbance time point; and the frequency hopping avoidance judgment result includes electric field amplitude change data, the electric field difference before and after the target frequency point frequency hopping, and the single-cycle interference avoidance judgment.

[0013] As a further aspect of the present invention, the feature extraction module includes:

[0014] The electric field acquisition submodule collects electric field intensity data from the electric field sensor of the ring network cabinet in the environment, obtains the extreme value sequence of atmospheric electric field intensity, and records the electric field value at each time point by monitoring the change of electric field to obtain the electric field intensity data sequence.

[0015] The difference ratio calculation submodule calculates the time interval between the extreme values ​​of electric field intensity based on the electric field intensity data sequence, and calculates the ratio of the rate of change of the time interval to generate the difference ratio result.

[0016] The mutation determination submodule compares the difference ratio result with the set control limit of 3 times the standard deviation. When the difference ratio exceeds the control limit, it generates a lightning precursor mutation result.

[0017] As a further aspect of the present invention, the spectrum detection module includes:

[0018] The spectrum acquisition submodule acquires the lightning precursor abrupt change results, obtains the frequency hopping spectrum energy distribution map based on the electric field sensor, analyzes the energy density in the spectrum map, identifies frequency value changes and corresponding energy intervals, and generates a spectrum energy density distribution map.

[0019] The spectral clustering analysis submodule identifies and divides the maximum energy peak frequency band on the frequency axis based on the spectral energy density distribution map using a spectral clustering analysis algorithm, extracts the frequency range of the peak frequency band, calculates the frequency domain of the descent points on both sides of the frequency band, obtains the characteristics of each frequency band, and generates spectral clustering features.

[0020] The stability determination submodule calls the frequency band with the maximum energy peak in the spectrum aggregation feature to calculate the spectrum stability. When the spectrum stability is greater than 0.85, a spectrum stability determination result is generated.

[0021] As a further aspect of the present invention, the identification and division of the maximum energy peak frequency band on the frequency axis using a spectral clustering analysis algorithm employs the following formula:

[0022] ;

[0023] in, It is the cluster density, which is determined by selecting... Close to 1 and Larger frequency bands are defined on the frequency axis as the frequency bands with the highest energy peak. This represents the distance between the i-th sample point and the j-th cluster center on the frequency axis, where i∈[1,n] is the sample index and j∈[1,k] is the cluster center index. This represents the spectral energy density value of the i-th sample point. This represents the maximum energy density value within the current cluster. Represents frequency band spacing and sample density Adjustment factor , The value is obtained by calculating the sampling interval of the electric field sensor, and the value range is [0, 1].

[0024] As a further aspect of the present invention, the frequency band pooling module includes:

[0025] The frequency band definition submodule extracts the interval where the frequency peak is located based on the spectrum stability determination result, determines the upper and lower boundaries of multiple frequency peaks, and delineates the no-hopping frequency band on the corresponding spectrum to generate the no-hopping frequency band interval.

[0026] The no-hopping frequency band screening submodule, based on the no-hopping frequency band interval, screens the spectrum data item by item against the no-hopping frequency band range, removes all frequency data and scheduling time nodes that fall within the no-hopping frequency band interval range, and generates a set of effective frequency bands;

[0027] The frequency band pool module is constructed by filtering and retaining all frequency intervals based on the set of effective frequency bands, reorganizing them in ascending order of frequency, marking the start and end values ​​of the frequency bands, and generating an updated frequency band pool.

[0028] As a further aspect of the present invention, the path adjustment module includes:

[0029] The disturbance timing extraction submodule, based on the lightning precursor abrupt change results, obtains the abrupt change time points in the electric field signal, filters time nodes that meet typical disturbance characteristics, and generates the disturbance start time.

[0030] The scheduling difference calculation submodule, based on the disturbance start time value and the frequency hopping scheduling time point in the updated frequency band pool, applies a dynamic time warping algorithm to calculate the temporal correlation strength between the disturbance event time series and the frequency hopping scheduling time series, constructs the difference sequence between the disturbance start time and the scheduling time node, determines the temporal correlation relationship between the disturbance node and the frequency hopping scheduling node, and calculates the frequency hopping time difference;

[0031] The frequency hopping path generation submodule filters frequency band sequences that meet the scheduling window control requirements based on the frequency hopping time difference, reconstructs the frequency hopping path, marks the frequency start and end values ​​and hopping sequence number, and generates the next frequency hopping path.

[0032] As a further aspect of the present invention, the application of the dynamic time warping algorithm to calculate the time correlation strength between the disturbance event time series and the frequency hopping scheduling time series uses the following formula:

[0033] ;

[0034] in, Temporal correlation strength Representing the The start timestamp of each disturbed node Representing the The base timestamp of each frequency hopping scheduling node Representing the The center frequency value of the frequency band where each disturbance node is located. This represents the average frequency value of all active frequency bands in the current frequency band pool. and All data are in MHz and were obtained through calibration using an FFT spectrum analyzer. This represents the frequency fluctuation smoothing constant, and its value is the reciprocal of the sensor's sampling frequency. The time window scaling factor is obtained through training with lightning strike data. This represents the system clock reference offset, which is the offset between the clock and UTC standard time. This represents the total number of frequency-hopping nodes in the current scheduling cycle.

[0035] As a further aspect of the present invention, the interference avoidance module includes:

[0036] The amplitude acquisition submodule acquires the electric field amplitude data sequence detected by the electric field sensing component before and after frequency hopping, extracts the amplitude sampling value of the corresponding frequency point in each time period, establishes the electric field amplitude value group before frequency hopping and the electric field amplitude value group after frequency hopping, calculates the amplitude difference between the two sets of data at the same frequency point corresponding to the time position, and generates the frequency point amplitude difference quantity.

[0037] The electric field difference submodule calls the target frequency parameters in the next frequency hopping path based on the frequency amplitude difference, extracts the amplitude sample difference before and after the corresponding frequency hopping, calculates the interval span value of the sample difference, and extracts the average rate of change value and the maximum change value by combining the time distribution trend of the difference change sequence, and generates the frequency fluctuation trend value.

[0038] The path determination submodule sets frequency hopping judgment conditions based on the frequency fluctuation trend, extracts the time series position information of each target frequency point in the frequency hopping path, compares the fluctuation trend with the electric field interference change threshold in the frequency hopping judgment conditions, classifies whether the interference threshold is exceeded according to the judgment conditions, and generates frequency hopping avoidance judgment results.

[0039] On the other hand, a method for online monitoring of lightning micro-meteorological conditions in ring main units is provided. This method is applied to an online monitoring system for lightning micro-meteorological conditions in ring main units, and the method includes:

[0040] S1: Obtain the extreme value sequence of atmospheric electric field intensity through the electric field sensor of the ring network cabinet, calculate the difference ratio of the time change rate, determine whether it exceeds the control limit, and generate the lightning precursor change result;

[0041] S2: Obtain the corresponding frequency hopping spectrum energy density distribution map through the lightning precursor mutation results, and determine the maximum energy peak frequency band and the two descending points on both sides of the frequency axis in the map through the spectrum clustering algorithm, and output the spectrum clustering determination result;

[0042] S3: Define no-hopping frequency bands on the spectrum based on the peak frequency band of the spectrum aggregation determination result and filter them to generate an updated frequency band pool;

[0043] S4: Based on the updated frequency band pool and lightning precursor mutation results, predict the difference between the lightning disturbance time and the next frequency hopping scheduling time using the dynamic time warping algorithm, and construct the next frequency hopping path;

[0044] S5: Obtain the electric field difference before and after the frequency hopping corresponding to the target frequency point in the next frequency hopping path, perform single-cycle interference avoidance judgment, and generate frequency hopping avoidance judgment result.

[0045] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0046] By collecting extreme value sequences of atmospheric electric field intensity around the ring main unit and calculating the time change rate difference ratio, abrupt changes before lightning activity are identified, allowing for the identification of drastic changes in the electric field environment during the lightning precursor stage. After identifying abrupt changes, the concentration area of ​​lightning frequency signals is determined based on the relationship between the peak frequency band and the descent point in the frequency hopping signal, combined with the spectral energy density distribution map, and potential interference intervals are filtered out through spectral aggregation analysis. Combining frequency hopping band updates and lightning disturbance prediction, the time deviation between the lightning strike time and frequency hopping scheduling is analyzed using a dynamic time warping algorithm, enabling real-time adjustment of the frequency hopping path and improving the timeliness and accuracy of the frequency hopping response. Furthermore, monitoring the difference before and after the electric field disturbance helps avoid single-cycle interference and enhances the ability to distinguish lightning disturbance signals from background interference. This strategy introduces quantitative calculation, spectral clustering, and dynamic prediction mechanisms into key nodes of multi-dimensional feature identification, temporal correlation mining, and spectral interference investigation, significantly improving the recognition accuracy of lightning precursor information and the fusion efficiency of frequency hopping immunity, providing multi-dimensional support for monitoring stability and response adaptability in lightning risk environments. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a system flowchart of the present invention;

[0049] Figure 2This is a system block diagram of the present invention;

[0050] Figure 3 This is a schematic diagram of the method steps of the present invention. Detailed Implementation

[0051] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0052] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0053] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0054] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0055] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0056] This invention provides an online lightning micro-meteorological monitoring system for ring main units. Please refer to [link to relevant documentation]. Figures 1 to 2 This invention provides a technical solution: an online monitoring system for lightning micro-meteorology in a ring main unit, comprising:

[0057] The feature extraction module is used to obtain the extreme value sequence of atmospheric electric field intensity through the electric field sensor of the ring network cabinet, calculate the difference ratio of the time change rate, determine whether it exceeds the control limit, generate the lightning precursor mutation result and transmit it to the spectrum detection module.

[0058] The spectrum detection module is used to obtain the corresponding frequency hopping spectrum energy density distribution map through the lightning precursor mutation results, and to determine the frequency band with the maximum energy peak and the descent points on both sides of the frequency axis in the map through the spectrum clustering algorithm, output the spectrum clustering determination result and transmit it to the frequency band pool screening module.

[0059] The frequency band pool filtering module is used to define no-hopping frequency bands on the spectrum based on the peak frequency band of the spectrum aggregation determination result and to filter them, generate an updated frequency band pool and pass it to the path adjustment module.

[0060] The path adjustment module is used to predict the difference between the time of lightning disturbance and the time of the next frequency hopping scheduling based on the updated frequency band pool and lightning precursor mutation results using a dynamic time warping algorithm, and to construct the next frequency hopping path and pass it to the interference avoidance module.

[0061] The interference avoidance module is used to obtain the electric field difference before and after the frequency hopping at the target frequency point in the next frequency hopping path, perform single-cycle interference avoidance judgment, and generate frequency hopping avoidance judgment results.

[0062] The lightning precursor mutation results include the extreme value sequence of atmospheric electric field intensity, the difference ratio of time interval change rate, and control limits. The spectrum aggregation stability determination results include the frequency band of the maximum energy peak on the frequency axis, the frequency domain of the descent points on both sides, the spectrum energy density distribution map, and the determination by the spectrum clustering algorithm. The updated frequency band pool includes the frequency peak and the no-hopping frequency band. The next frequency hopping path includes the updated frequency band pool, the predicted value of the dynamic time warping algorithm, the next frequency hopping scheduling time point, and the lightning disturbance time point. The frequency hopping avoidance judgment results include electric field amplitude change data, the electric field difference before and after the target frequency point frequency hopping, and the single-cycle interference avoidance judgment.

[0063] Please see Figure 2 The feature extraction module includes:

[0064] The electric field acquisition submodule collects electric field intensity data from the electric field sensor of the ring network cabinet in the environment, obtains the extreme value sequence of atmospheric electric field intensity, and records the electric field value at each time point by monitoring the change of electric field to obtain the electric field intensity data sequence.

[0065] Install an EFS-200 electric field sensor on the surface of the ring main unit and set the sampling interval. Sensor output voltage signal After AD conversion, a 12-bit digital value is obtained. According to the calibration equation Calculate the electric field strength value (kV / m), including when At that time, the calculation yielded 30,000 data points were collected continuously for 10 minutes. A sliding window extreme value detection algorithm was used, with the window width defined. Each sampling point (corresponding to a 1-second duration) is used when the following conditions are met: and ( Points with a maximum value are marked as such, as shown in Table 1, which records the results of a certain test:

[0066] Table 1 Electric Field Extreme Value Monitoring Data

[0067]

[0068] As shown in Table 1, after detecting the extreme value of 2.31 kV / m at 125.4 seconds, the system continued to scan subsequent data points. When it found that the value at 126.8 seconds was larger than both the previous 25 points (124.3-126.7 seconds) and the next 25 points (126.9-128.3 seconds), it marked this point as a valid extreme value, forming a sequence. .

[0069] The difference ratio calculation submodule calculates the time interval between electric field intensity extrema based on the electric field intensity data sequence, and calculates the ratio of the rate of change of the time interval to generate the difference ratio result.

[0070] Calculate the time interval between adjacent extreme values ​​based on the electric field intensity data sequence. , The first data in the electric field intensity data sequence The time when each extreme value occurs The first data in the electric field intensity data sequence The time between the occurrence of consecutive extreme values, based on the data in Table 1, yields... , Construct time interval sequences Calculate the ratio of the rate of change Substituting the data yields When new At that time, calculate System maintenance capacity The circular buffer stores the most recent Value, calculate the mean and standard deviation When measured , When setting control limits When the newly measured difference ratio exceeds the upper limit, an anomaly flag is triggered.

[0071] The mutation determination submodule compares the difference ratio result with the set control limit of 3 times the standard deviation. When the difference ratio exceeds the control limit, it generates the lightning precursor mutation result.

[0072] When a new difference ratio is detected At that time, perform comparison operations. Once an alert is triggered, the verification process is initiated and continuous monitoring is performed. Each sampling period Value, when the measured sequence When, it is determined that the condition is met. The generated early warning message includes: , It's a timestamp. The electric field strength array is used to confirm the sudden change event. The message is published to the topic lightning / alert via the MQTT protocol, QoS=1 is set, and the retain flag is set to retain=true.

[0073] Please see Figure 2 The spectrum detection module includes:

[0074] The spectrum acquisition submodule acquires lightning precursor abrupt changes, obtains frequency hopping spectrum energy distribution map based on electric field sensor, analyzes the energy density in spectrum map, identifies frequency value changes and corresponding energy ranges, and generates spectrum energy density distribution map.

[0075] When the lightning precursor mutation result is triggered, the system activates the EFS-300 sensor to... Sampling rate acquisition Time domain signal , obtain discrete points The weighted processing obtained through Hanning window , The windowed time-domain signal is subjected to a 1024-point FFT operation: , Representing the frequency domain complex result, calculate the energy density. (μV² / Hz), as shown in Table 2, is a fragment of the experimental data:

[0076] Table 2 Frequency Domain Energy Distribution Table

[0077]

[0078] As shown in Table 2, a peak value of 3340 μV² / Hz was measured at 180 Hz, and a threshold was set. Mark all The frequency range of 150-210Hz was identified as the key energy range.

[0079] The spectral clustering analysis submodule identifies and divides the frequency band with the maximum energy peak on the frequency axis based on the spectral energy density distribution map using the spectral clustering analysis algorithm, extracts the frequency range of the peak frequency band and calculates the frequency domain of the descent points on both sides of the frequency band, obtains the characteristics of each frequency band, and generates spectral clustering features.

[0080] Constructing a frequency sample set With energy collection Calculate the sample interval (in (where m / n represents the index of the element in the spectral feature set F, and is taken as the energy weight). Initial cluster centers , , According to the sampling interval Calculate density factor Adjustment factor Calculate cluster compactness: ,in, It is the cluster density, which is determined by selecting... Close to 1 and Larger frequency bands are defined on the frequency axis as the frequency bands with the highest energy peak. This represents the distance between the i-th sample point and the j-th cluster center on the frequency axis, where i∈[1,n] is the sample index and j∈[1,k] is the cluster center index. This represents the spectral energy density value of the i-th sample point. This represents the maximum energy density value within the current cluster. Represents frequency band spacing and sample density Adjustment factor , The values ​​are calculated using the sampling interval of an electric field sensor and range from [0, 1]. When the density ratio of adjacent cluster centers At that time, the 150-180Hz and 180-210Hz frequency bands were merged, and the main peak range was determined to be 120-230Hz. The -3dB drop point (at 120Hz) was extracted. At 230Hz ).

[0081] The stability determination submodule calls the frequency band with the maximum energy peak in the spectrum aggregation feature to calculate the spectrum stability. When the spectrum stability is greater than 0.85, it generates the spectrum stability determination result.

[0082] The energy sequence of the main peak region was obtained over three consecutive detection cycles. Calculate the mean Standard deviation Spectral stability: ,when At that time, a judgment result is generated, triggering a linkage control signal.

[0083] Please see Figure 2 The frequency band pool selection module includes:

[0084] The frequency band definition submodule extracts the interval where the frequency peak is located based on the spectrum stability determination result, determines the upper and lower boundaries of multiple frequency peaks, and delineates the no-hopping frequency band on the corresponding spectrum to generate the no-hopping frequency band interval;

[0085] Based on the calculated spectral stability determination results, including when a signal verifying spectral stability is received, the signal includes a stability value. And the associated core peak frequency band information, namely 120Hz to 230Hz, the maximum energy density value recorded from the peak range. Let's begin by calculating a dynamic threshold. ,by Using 120Hz as a baseline, starting from the lower boundary of the core peak range, the energy density value is checked downwards at each frequency point (including in 1Hz steps). When the frequency point of 119Hz is checked, the energy density is... The value is still greater than Continuing downwards, the energy density at 118 Hz is Also greater than Until the scan reaches 115Hz, the energy density is ,hour Therefore, 116Hz, the next high frequency point after 115Hz, is defined as the critical point of the lower boundary. Similarly, starting from the upper boundary of the core peak range at 230Hz, the frequency points are checked upwards one by one. When 234Hz is checked, the energy density is... The energy density at 235 Hz is The preceding lower frequency point of 234Hz was defined as the critical point of the upper boundary, thus initially determining the current energy concentration area as 116Hz to 234Hz. Subsequently, to ensure communication reliability and avoid potential edge interference, the system added a bandwidth of [missing information - likely a value] on both sides of the initially determined frequency band. The safety margin is set with reference to the typical bandwidth of interference signals in similar environments, and is generally taken as 2 to 5 times the frequency resolution of the monitoring system. Therefore, the lower limit of the no-hopping frequency band is calculated as follows: The upper limit is calculated as follows This generates a no-hopping frequency band of 111Hz to 239Hz corresponding to the lightning event. This band will be executed independently for each lightning precursor event, including in another event 2023081502, where if the core peak frequency is 98Hz to 217Hz, the maximum energy density is... Then the boundary threshold is The current energy concentration zone was estimated to be 95Hz to 220Hz through boundary search. After adding a 5Hz margin, the no-hopping frequency band is 90Hz to 225Hz, as shown in Table 3. Table 3 records multiple lightning events and their corresponding no-hopping frequency band definitions.

[0086] Table 3 Definition of No-Hopping Band

[0087]

[0088] Table 3 shows the complete process and multiple parameter values ​​of the no-hopping frequency band generated in three lightning events, starting from the initial core peak information, through the acquisition of maximum energy density, calculation of boundary thresholds, search of energy boundaries, and application of safety margins.

[0089] The no-hopping frequency band screening submodule, based on the no-hopping frequency band interval, screens the spectrum data item by item against the no-hopping frequency band range, removes all frequency data and scheduling time nodes that fall within the no-hopping frequency band interval range, and generates a set of effective frequency bands;

[0090] The system receives the generated no-hopping frequency band, including the no-hopping frequency band from 111Hz to 239Hz obtained for event 2023081501. It then calls a preset communication frequency planning table, which contains a list of all available wireless communication channel center frequency points in the current system. The system will Each frequency point in Perform a screening operation to determine whether it falls within the acquired no-hopping frequency band. The specific judgment logic is as follows: If Then at that frequency point Considered unavailable for frequency points ,because The conditions are not met, therefore For the effective frequency, at the frequency point ,because The conditions are met, therefore If a frequency is marked as invalid, the system performs this comparison sequentially on all frequency points in the list, including... (fall into), (fall into), (fall into), (Falling in), and (efficient), (efficient), (Effective) After screening all frequency points, the set of frequency data points that were removed is obtained. Simultaneously, the current communication scheduling schedule is checked. This schedule records the frequency and time points of planned communication tasks over a period of time, including, if the scheduling table contains an entry "Task A, Time Point..." The entry states, "Using frequency 150Hz". Since 150Hz is identified as a frequency within the no-hopping band, the frequency 150Hz mentioned in the scheduling entry will be marked as unavailable, and task A will be at the specified time node. The execution will be affected, requiring a reallocation of frequency or a delay in execution. If the other entry is "Task B, Time Node",... "Using frequency 85Hz", since 85Hz is an effective frequency, the scheduling entry is unaffected. After screening and eliminating frequency data and scheduling time nodes item by item, a set of frequency points that do not fall within the current no-hopping frequency band is generated, i.e., the effective frequency band set. .

[0091] Construct a frequency band pool module, which filters and retains all frequency ranges based on the set of effective frequency bands, reorganizes them in ascending order of frequency, marks the start and end values ​​of the frequency bands, and generates an updated frequency band pool.

[0092] Based on the generated effective frequency band set Given the known available spectrum resources of the system, including the total authorized spectrum range of 20Hz to 300Hz, and referring to the previously defined no-hopping frequency band range of 111Hz to 239Hz, the system first identifies consecutive available frequency band blocks within the total spectrum range. The starting point of the first available band block is the lower limit of the system spectrum at 20Hz, and the ending point is before the lower limit of the no-hopping frequency band at 111Hz, forming the band [20Hz, 111Hz]. The starting point of the second available band block is after the upper limit of the no-hopping frequency band at 239Hz, and the ending point is after the upper limit of the system spectrum at 300Hz, forming the band (239Hz, 300Hz). Next, the system will... The discrete effective frequency points are integrated and refined with these continuous available interval blocks. For the intervals 20Hz and 111Hz, which include effective frequency points 85Hz and 100Hz, the system will determine the frequency based on the standard bandwidth requirements of the communication channel, including the bandwidth of each channel. The continuous interval is divided, and sub-bands containing known effective frequencies are prioritized. All retained frequency intervals are then reorganized in ascending order of frequency. For example, the 20Hz and 111Hz intervals can be divided into sub-bands including 85Hz (e.g., 75Hz, 95Hz, with a center of 85Hz) and sub-bands including 100Hz (e.g., 90Hz, 110Hz, with a center of 100Hz). Meanwhile, the remaining portions within the larger intervals, including 20Hz, 75Hz, 110Hz, and 111Hz, with bandwidth greater than or equal to... The frequency range (239Hz, 300Hz), including the effective frequency points 245Hz and 260Hz, will also be retained. It can be further divided into sub-bands including 245Hz, 235Hz, and 255Hz (note that the starting frequency cannot be lower than 239Hz, so it is adjusted to 239Hz, 255Hz, with a center of 247Hz, setting 245Hz within the range, including the sub-band of 260Hz, including [250Hz, 270Hz), and the remaining 255Hz, 250Hz (if they exist and the bandwidth is sufficient) and 270Hz, 300Hz. The system will organize these sub-bands, marking the precise start and end values ​​of each sub-band, and removing overlapping or excessively narrow (less than the minimum channel bandwidth) sub-bands. The frequency bands, including, if after the above division and filtering, the effective and non-overlapping frequency bands are: 20Hz, 70Hz, 75Hz, 95Hz, 95Hz, 110Hz, 240Hz, 255Hz, 255Hz, 270Hz, 270Hz, and 300Hz, the frequency bands will be recorded in the frequency band pool. Each entry includes the start frequency and the end frequency, and its status is marked as "available". The updated frequency band pool is generated, as shown in Table 4.

[0093] Table 4. Example of the updated available frequency band pool

[0094]

[0095] As shown in Table 4, the pool of available frequency bands formed after the no-hopping frequency band screening and frequency band reorganization is listed. Each frequency band has a start and end frequency, a calculated bandwidth, and a status indicator indicating availability (including 0x01 representing availability).

[0096] Please see Figure 2 The path adjustment module includes:

[0097] The disturbance timing extraction submodule, based on the lightning precursor abrupt change results, obtains the abrupt change time points in the electric field signal, filters the time nodes that meet the typical disturbance characteristics, and generates the disturbance start time;

[0098] After receiving the generated lightning precursor mutation results, the results include a coarse timestamp indicating the time of the mutation, including records as... The system immediately retrieves data with a timeframe extended by 200 milliseconds before and after the specified time point (i.e., from...). arrive The original high-sampling-rate electric field intensity data sequence within the window The data was continuously recorded by an EFS-300 sensor at a sampling rate of 2kHz. The system analyzed 400 data points within the window, first calculating the first-order difference value for each sampling point and the instantaneous change in electric field intensity. To characterize the rate of change of electric field, At the point of time The electric field strength value measured at that location, Compare Set an earlier sampling interval and a rate of change threshold. The threshold is derived from the 95th percentile of the electric field changes caused by typical lightning discharges in a statistical analysis of 1000 lightning events, while an absolute amplitude change threshold is also set. The threshold is set to 15% of the sensor's dynamic range, when at time point Simultaneously satisfy as well as When two conditions are met, the time point It was initially identified as a potential perturbation feature point, including the one analyzed at timestamp 1678886401.100 (corresponding to...). When the electric field value (near the sampling point) suddenly increases from 0.2 kV / m at the previous sampling point to 1.9 kV / m, the rate of change is... greater than And the amplitude changes (Setting a reference electric field) If the amplitude is 0.1 kV / m, 1.8 kV / m, or greater than 1.5 kV / m, then 1678886401.100 seconds is considered a disturbance feature point. If multiple feature points are identified within a 200 ms analysis window, including those at 1678886401.100 seconds, 1678886401.150 seconds, and 1678886401.220 seconds, the system will select the earliest occurrence and the most drastic change (including the point with the largest amplitude and rate weighting). Based on experience, the amplitude weight is set to 0.6 and the rate weight to 0.4 as the key disturbance start time for this lightning event. The disturbance start time determined after the screening process is set as follows: The timestamp is output and used as the baseline input for scheduling impact analysis to generate the disturbance start time.

[0099] The scheduling difference calculation submodule, based on the disturbance start time value and the frequency hopping scheduling time point in the updated frequency band pool, applies the dynamic time warping algorithm to calculate the temporal correlation strength between the disturbance event time series and the frequency hopping scheduling time series, constructs the difference sequence between the disturbance start time and the scheduling time node, determines the temporal correlation between the disturbance node and the frequency hopping scheduling node, and calculates the frequency hopping time difference;

[0100] Based on the extracted and verified perturbation start time values, including Based on the frequency hopping schedule time sequence obtained from Table 5, "Example Table of Updated Available Frequency Band Pools," the system will evaluate the temporal correlation strength between the disturbance event and the scheduled frequency hopping operation, and set a condition where a frequency hopping event occurs. A time series composed of key perturbation feature points ,include Their timestamps are respectively , , At the same time, there exists a corresponding one generated by the communication scheduling module, including... Reference timestamp sequence of planned frequency hopping nodes The timestamps are respectively , , The system uses a dynamic time warping correlation strength calculation formula. To quantify this correlation, the parameters are obtained and assigned values ​​as follows: Representing the The start timestamp of each disturbed node Representing the The base timestamp of each frequency hopping scheduling node Representing the The key impact frequency center value (MHz) of each disturbance node is determined by analyzing the electric field signal within a very short time (5ms) before and after the disturbance occurs, identifying the center of the energy-concentrated frequency band, and setting the parameters accordingly. , , , This represents the average center frequency value of all active communication frequency bands (including those currently in use or planned for near-term use) in the current frequency band pool. If the current active frequency band pool includes four frequency bands with center frequencies of 0.110MHz, 0.150MHz, 0.190MHz, and 0.230MHz, then... , The frequency fluctuation smoothing constant is defined as the reciprocal of the sensor sampling frequency. If the sensor sampling frequency... ,but To ensure dimensional consistency within the square root term (square frequency term) in the formula, the calculation will... Take as a and Small positive numbers with the same dimensions, including , This represents the time window scaling factor, the value of which was obtained through statistical analysis and parameter optimization training of 1000 lightning strike events. It adjusts the sensitivity to time differences, including setting it to... , The reference offset representing the system's local clock from UTC standard time is obtained through periodic calibration via the NTP service, including... , This represents the total number of frequency hopping nodes planned within the current assessment scheduling period, including In the denominator This item is the sum of the frequency hopping point timestamps (after subtracting the clock offset) of all plans within the current scheduling period, and is set to... The frequency hopping timestamps for each plan are as follows: , , , , The calculation logic of the formula lies in the fact that for each pair of disturbances and scheduling nodes... Calculate its time difference And multiplied by a weighting factor related to frequency deviation. The weighting factor amplifies situations where the frequency band affected by the disturbance differs significantly from the system's average operating frequency band. Then, this product is normalized relative to the time scale of the entire scheduling cycle (denominator). Finally, all... The total temporal correlation strength is obtained by summing the calculation results of each paired node. Its dimension is MHz, calculate the first The numerator of the term: Frequency-related terms, , so the first The numerator of the term is Similarly, calculate the first item: , molecule is Calculate the first item: , molecule is ,so, , The advantage of the formula lies in its ability to quantify the potential impact of lightning disturbances on frequency hopping scheduling by normalizing the deviation between the disturbance occurrence time and the predetermined scheduling time, the difference between the disturbance's affected frequency band and the system's normal operating frequency band, and by combining this with the time scale of the current scheduling cycle. The calculation results... This indicates that the temporal correlation between the disturbance event sequence and the frequency hopping adjustment time sequence is very low (due to its extremely small magnitude). This means that in this example, despite the temporal proximity, the system, after considering the frequency factor and scheduling cycle scale, will... Values ​​(including, if) Less than the preset threshold (If the correlation is strong, it is considered strong; otherwise, it is weak.) The strength of the correlation is determined, and a difference sequence between the disturbance start time and the scheduling time node is generated, including... The difference will be used as the frequency hopping time difference value for path planning.

[0101] The frequency hopping path generation submodule filters frequency band sequences that meet the scheduling window control requirements based on the frequency hopping time difference, reconstructs the frequency hopping path, marks the frequency start and end values ​​and hopping sequence number, and generates the next frequency hopping path.

[0102] The calculated frequency hopping time difference sequence is obtained, including three differences. and the strength of time correlation Next, based on the information, the next frequency hopping path will be constructed by selecting from the available frequency bands listed in Table 5, "Example Table of Updated Available Frequency Band Pool". The core of the selection is to meet the "scheduling window control requirement", which is defined as a time threshold. If any frequency hopping time difference Less than And the corresponding time correlation strength Higher than the warning level (including In the example Low (indicating a weak correlation), then with The originally planned frequency hopping points and frequency bands need to be adjusted or avoided. In this example, This means that the third disturbance point is very close to the time of the paired scheduling point, although overall... The value is low, but the system will still prioritize adjustments based on it. The associated frequency hopping plan iterates through the updated frequency band pool, including frequency band F1 within the pool. 20Hz, 70Hz), F2 75Hz, 95Hz, F3 95Hz, 110Hz, F4 240Hz, 255Hz, F5 255Hz, 270Hz, F6 270Hz, 300Hz, and combined with the current communication task bandwidth requirements (including the need for at least 15Hz bandwidth) and the longest allowed channel occupancy time (including...) ), and the shortest frequency hopping interval (including Reconstruct the frequency hopping path, prioritizing frequency bands that appear safe in the frequency hopping time difference analysis, i.e., frequency bands far from the scheduling slots affected by disturbances. If the original plan was to... The frequency band used around 1678886401.200s is F3, because... Small, estimated to be chosen in A safer time later (including) Select a frequency band with sufficient bandwidth from the frequency band pool, including F4, as the hop target. The reconstructed frequency hop path will list the selected frequency band sequence. Each frequency band is marked with its start frequency, end frequency, and hop sequence number in the path. For example, an estimated path is: first use F1 for 50ms, then hop to F2 for 30ms, and then avoid the original... During sensitive periods, the system jumps to F4 for 40ms during the current safe period. This new path is formatted and stored, as shown in Table 5.

[0103] Table 5 Next Frequency Hopping Path Planning Table

[0104]

[0105] As shown in Table 5, the next frequency hopping path is filtered and reconstructed based on the frequency hopping time difference and scheduling window requirements. This includes the order of each hopping step, the frequency band used (start and end frequencies), the expected start time, and the dwell time in the frequency band, thus generating the next frequency hopping path.

[0106] Please see Figure 2 The interference avoidance module includes:

[0107] The amplitude acquisition submodule acquires the electric field amplitude data sequence detected by the electric field sensing component before and after frequency hopping, extracts the amplitude sampling value of the corresponding frequency point in each time period, establishes the electric field amplitude value group before frequency hopping and the electric field amplitude value group after frequency hopping, calculates the amplitude difference between the two sets of data at the same frequency point corresponding to the time position, and generates the frequency point amplitude difference quantity.

[0108] The data stream of electric field amplitude detected and recorded by the EFS-300 electric field sensing component before and after the frequency hopping operation is acquired. The system continuously captures electric field intensity information at the target frequency using a sampling rate. For each predetermined target frequency in the next frequency hopping path (planned in Table 6), the system performs an amplitude extraction process. Jump to target frequency The action performed before the current jump instruction is executed. Within the time window, the system commands the electric field sensing component to tune to... And collect the background electric field amplitude at the target frequency point to obtain a range including The sequence of sampling points constitutes a "group of electric field amplitude values ​​before frequency hopping". Including, if the target frequency The center frequency of the frequency band with jump sequence number 1 in Table 6 is 45Hz. The amplitude sequence is collected within 20ms before the scheduled jump to 45Hz. kV / m, subsequently, after the system completes the transfer... The frequency hopping action and the very short time after stabilization, including the moment the frequency hopping is completed. After At the beginning, it also lasted for one Within the time window, re-target the currently activated frequency points. Electric field amplitude was collected to obtain another sequence containing 40 sampling points. This sequence constitutes the "frequency-hopping electric field amplitude value group". This includes data collected after frequency hopping at the 45Hz frequency point. The system then processes these two sets of values ​​separately, calculating representative amplitudes using the mean value, i.e., the electric field strength dataset before processing. and the processed electric field intensity dataset Using the data at the 45Hz frequency point in this example, the calculation was performed as follows: and This process will be repeated for each target frequency point planned in Table 6. As shown in Table 6, the average amplitude data before and after frequency hopping, obtained from the collection and preliminary calculation for the first two target frequencies (45Hz and 85Hz) in the path, are recorded.

[0109] Table 6. Average Amplitude Before and After Frequency Hopping at the Target Frequency Point

[0110]

[0111] As shown in Table 6, this table lists the average electric field amplitudes collected before and after the frequency hopping operation, based on the target frequency point planned in the next frequency hopping path. For the target frequency point 85Hz with hopping sequence number 2, the average amplitude before hopping is 0.0110 kV / m, and the average amplitude after hopping is 0.0140 kV / m. Finally, the values ​​between these two sets of data, i.e., at the same target frequency point, are calculated. The difference between the average amplitude before and after frequency hopping is defined as follows: For the 45Hz frequency point, the amplitude difference is For the 85Hz frequency point, the amplitude difference is The amplitude differences calculated for each target frequency point in the path are used to form a sequence, including kV / m (where the last two values ​​can be calculated from the data in Table 6) and The difference sequence is the frequency amplitude difference generated.

[0112] The electric field difference submodule calls the target frequency parameters in the next frequency hopping path based on the frequency amplitude difference, extracts the amplitude sample difference before and after the corresponding frequency hopping, calculates the interval span of the sample difference, and extracts the average rate of change and the maximum change value by combining the time distribution trend of the difference change sequence, and generates the frequency fluctuation trend.

[0113] Based on the generated frequency amplitude difference sequence, the sequence includes the change in electric field amplitude at each target frequency point before and after the frequency hopping operation along the next frequency hopping path (as planned in Table 6), including the obtained sequence. First, the amplitude difference sample values ​​are extracted, i.e., the numerical sequence. The value is calculated as kV / m. Then, the interval span of this set of sample differences is calculated. The span is defined as the difference between the maximum and minimum values ​​in the sample sequence. The calculation process is as follows: Combined with the difference ( The distribution trend in the time series is arranged according to the time sequence of the frequency hopping path. The average rate of change and the maximum rate of change are extracted. The calculation method is to first calculate the rate of change of the amplitude difference between every two consecutive frequency-hopping targets. , No. The amplitude difference between each frequency point and the previous frequency point Directly representing the first Amplitude measurements at each frequency point Representing the The timestamp at which each frequency point begins to be used in frequency hopping communication, among which It is the first The expected start timestamps of the target frequency points in the frequency hopping path (from Table 6) include, Similarly, ,as well as Then, the arithmetic mean of the rates of change is calculated as the average rate of change value. Maximum change value This refers to the original frequency amplitude difference sequence. The largest among all samples, i.e. These three calculated indicators—the interval span value of 0.0060 kV / m, the average rate of change value of -0.0143 kV / m / s, and the maximum change value of 0.0080 kV / m—together constitute the generated frequency fluctuation trend.

[0114] The path determination submodule sets frequency hopping judgment conditions based on the frequency fluctuation trend, extracts the time series position information of each target frequency point in the frequency hopping path, compares the fluctuation trend with the electric field interference change threshold in the frequency hopping judgment conditions, classifies whether the interference threshold is exceeded according to the judgment conditions, and generates frequency hopping avoidance judgment results.

[0115] Based on the calculated and generated frequency fluctuation trend, the trend includes three key indicators: interval span value. Average rate of change and the maximum change value First, a set of predefined frequency hopping judgment conditions are set. The conditions are based on the critical value of electric field interference change for the above trend indicators. The critical value is set according to the following criteria: maximum change value, critical value. It is the upper limit of the amplitude difference of the target frequency point in a 90% successful communication scenario after more than 5000 frequency hopping operations in the reference data, and it identifies the frequency points that deteriorate after a single frequency hopping, and the critical value of the interval span. This value is set based on three times the standard deviation of the amplitude difference at multiple frequency points after 10 consecutive frequency hops during stable communication. It is used to measure the consistency of interference levels on the frequency hopping path and is the critical value of the absolute average rate of change. Based on the simulation of an environment where the spread rate of the known interference source is known, the electric field strength is set to 70% of the rate of change required to reach an unacceptable level (including a signal-to-noise ratio below 5dB) within 1 second. This is used to determine whether the interference has a trend of continuous enhancement or weakening. Then, the time series location information of each unexecuted target frequency point in the next frequency hopping path (as shown in Table 6) is extracted, including, if the second frequency point in the path has been evaluated, the third and fourth frequency points, as well as the planned start timestamp and duration, as objects. Then, the system compares the calculated frequency fluctuation trend with the critical values ​​in the set frequency hopping judgment conditions item by item. The judgment logic is: if ( )or( )or( If the frequency hopping path (or the trend shown by the executed part) exceeds the preset interference threshold, then it is determined that the current frequency hopping path (or the trend shown by the executed part) has exceeded the preset interference threshold. In this example, If true, It is fake. It is false because of the first condition. If the condition is met, the overall judgment is that the fluctuation trend of the current path has exceeded the acceptable interference threshold. Based on the judgment conditions, the path is classified as "potential interference risk exists" and a frequency hopping avoidance judgment result is generated. The result is a status flag, including "PATH-RISK-DETECTED", and is accompanied by the over-limit indicator that caused the judgment ("MAX-DELTA-EXCEEDED"), as well as instructions to re-evaluate or select an alternative path for the frequency hopping points that were not executed (including hopping sequence numbers 3 and 4 in Table 6).

[0116] Please see Figure 3 The methods include:

[0117] S1: Obtain the extreme value sequence of atmospheric electric field intensity through the electric field sensor of the ring network cabinet, calculate the difference ratio of the time change rate, determine whether it exceeds the control limit, and generate the lightning precursor change result;

[0118] S2: Obtain the corresponding frequency hopping spectrum energy density distribution map through the lightning precursor mutation results, and use the spectrum clustering algorithm to determine the frequency band with the maximum energy peak and the falling points on both sides of the frequency axis in the map, and output the spectrum clustering determination result;

[0119] S3: Define no-hop frequency bands on the spectrum based on the peak frequency bands of the spectrum aggregation determination results and filter them to generate an updated frequency band pool;

[0120] S4: Based on the updated frequency band pool and lightning precursor mutation results, predict the difference between the lightning disturbance time and the next frequency hopping scheduling time using the dynamic time warping algorithm, and construct the next frequency hopping path;

[0121] S5: Obtain the electric field difference before and after the frequency hopping at the target frequency point in the next frequency hopping path, perform single-cycle interference avoidance judgment, and generate frequency hopping avoidance judgment result.

[0122] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0123] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0124] It should be understood that, in various embodiments of the present invention, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0125] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0126] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0127] In the embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0128] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0129] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0130] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0131] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A ring main unit lightning micro-meteorological online monitoring system, characterized in that, The system includes: The feature extraction module obtains the maximum value sequence of atmospheric electric field intensity through the electric field sensor of the ring network cabinet, calculates the time change rate, i.e., the difference ratio, and determines whether it exceeds the control limit. It then generates the lightning precursor mutation result and transmits it to the spectrum detection module. The spectrum detection module includes: The spectrum acquisition submodule acquires the lightning precursor abrupt change results, obtains the spectrum energy distribution map based on the electric field sensor, analyzes the energy density in the spectrum map, identifies frequency value changes and corresponding energy ranges, and generates a spectrum energy density distribution map. The spectral clustering analysis submodule identifies and divides the maximum energy peak frequency band on the frequency axis based on the spectral energy density distribution map using a spectral clustering analysis algorithm, extracts the frequency range of the peak frequency band, calculates the frequency domain of the descent points on both sides of the frequency band, obtains the characteristics of each frequency band, and generates spectral clustering features. The stability determination submodule calls the frequency band with the maximum energy peak in the spectrum aggregation feature to calculate the spectrum stability. When the spectrum stability is greater than 0.85, a spectrum stability determination result is generated. The frequency band pool filtering module is used to define the no-hopping frequency band on the spectrum based on the peak frequency band of the spectrum stability determination result and perform filtering to generate an updated frequency band pool and transmit it to the path adjustment module. The route adjustment module includes: The disturbance timing extraction submodule, based on the lightning precursor abrupt change results, obtains the abrupt change time points in the electric field signal, filters time nodes that meet typical disturbance characteristics, and generates the disturbance start time. The scheduling difference calculation submodule, based on the disturbance start time and the frequency hopping scheduling time point in the updated frequency band pool, applies a formula to calculate the temporal correlation strength between the disturbance event time series and the frequency hopping scheduling time series, and constructs the difference sequence between the disturbance start time and the scheduling time point, i.e., the frequency hopping time difference; The frequency hopping path generation submodule filters the frequency band sequence that meets the scheduling window control requirements based on the frequency hopping time difference, reconstructs the frequency hopping path, marks the frequency start and end values ​​and hopping sequence number, and generates the next frequency hopping path. The formula: ； in, Temporal correlation strength Representing the The start timestamp of each disturbed node Representing the The base timestamp of each frequency hopping scheduling node Representing the The center frequency value of the frequency band where each disturbance node is located. This represents the average frequency value of all active frequency bands in the current frequency band pool. and All data are in MHz and were obtained through calibration using an FFT spectrum analyzer. This represents the frequency fluctuation smoothing constant, and its value is the reciprocal of the sensor's sampling frequency. The time window scaling factor is obtained through training with lightning strike data. This represents the system clock reference offset, which is the offset between the clock and UTC standard time. This represents the total number of frequency-hopping nodes within the current scheduling period; The interference avoidance module is used to obtain the electric field difference before and after the frequency hopping corresponding to the target frequency point in the next frequency hopping path, perform single-cycle interference avoidance judgment, and generate frequency hopping avoidance judgment result.

2. The ring main unit lightning micro-meteorological online monitoring system according to claim 1, characterized in that, The feature extraction module includes: The electric field acquisition submodule collects electric field intensity data from the electric field sensor of the ring network cabinet in the environment, obtains the maximum value sequence of atmospheric electric field intensity, and records the electric field value at each time point by monitoring the change of electric field to obtain the electric field intensity data sequence. The difference ratio calculation submodule calculates the time interval between the maximum electric field intensity values ​​based on the electric field intensity data sequence, calculates the rate of change of the time interval, and generates the difference ratio result. The mutation determination submodule compares the difference ratio result with the set control limit of 3 times the standard deviation. When the difference ratio exceeds the control limit, it generates a lightning precursor mutation result.

3. The ring main unit lightning micro-meteorological online monitoring system according to claim 1, characterized in that, The frequency band pool screening module includes: The frequency band definition submodule extracts the interval where the frequency peak is located based on the spectrum stability determination result, determines the upper and lower boundaries of multiple frequency peaks, and delineates the no-hopping frequency band on the corresponding spectrum to generate the no-hopping frequency band interval. The no-hopping frequency band screening submodule, based on the no-hopping frequency band interval, screens the spectrum data item by item against the no-hopping frequency band range, removes all frequency data and scheduling time nodes that fall within the no-hopping frequency band interval range, and generates a set of effective frequency bands; The frequency band pool module is constructed by filtering and retaining all frequency intervals based on the set of effective frequency bands, reorganizing them in ascending order of frequency, marking the start and end values ​​of the frequency bands, and generating an updated frequency band pool.

4. The ring main unit lightning micro-meteorological online monitoring system according to claim 1, characterized in that, The interference avoidance module includes: The amplitude acquisition submodule acquires the electric field amplitude data sequence detected by the electric field sensing component before and after frequency hopping, extracts the amplitude sampling value of the corresponding frequency point in each time period, establishes the electric field amplitude value group before frequency hopping and the electric field amplitude value group after frequency hopping, calculates the amplitude difference between the two sets of data at the same frequency point corresponding to the time position, and generates the frequency point amplitude difference quantity. The electric field difference submodule calls the target frequency parameters in the next frequency hopping path based on the frequency amplitude difference, extracts the amplitude sample difference before and after the corresponding frequency hopping, calculates the interval span value of the sample difference, and extracts the average rate of change value and the maximum change value by combining the time distribution trend of the difference change sequence, and generates the frequency fluctuation trend value. The path determination submodule sets frequency hopping judgment conditions based on the frequency fluctuation trend, extracts the time series position information of each target frequency point in the frequency hopping path, compares the fluctuation trend with the electric field interference change threshold in the frequency hopping judgment conditions, classifies whether the interference threshold is exceeded according to the judgment conditions, and generates frequency hopping avoidance judgment results.

5. A method for online monitoring of lightning micro-meteorological conditions in a ring main unit, characterized in that, The method is used to implement the ring main unit lightning micro-meteorological online monitoring system according to any one of claims 1-4, and the method includes: S1: Obtain the maximum value sequence of atmospheric electric field intensity through the electric field sensor of the ring network cabinet, calculate the time change rate (i.e., the difference ratio), determine whether it exceeds the control limit, and generate the lightning precursor mutation result; S2: Obtain the corresponding frequency hopping spectrum energy density distribution map through the lightning precursor mutation results, and determine the maximum energy peak frequency band and the two descending points on both sides of the frequency axis in the map through the spectrum clustering algorithm, and output the spectrum stability determination result; S3: Define no-hopping frequency bands on the spectrum based on the peak frequency band of the spectrum stability determination result and filter them to generate an updated frequency band pool; S4: Based on the updated frequency band pool and lightning precursor mutation results, predict the difference between the lightning disturbance time and the next frequency hopping scheduling time, and construct the next frequency hopping path; S5: Obtain the electric field difference before and after the frequency hopping corresponding to the target frequency point in the next frequency hopping path, perform single-cycle interference avoidance judgment, and generate frequency hopping avoidance judgment result.

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