Ring main unit lightning microclimate on-line monitoring system and method thereof

Abstract

The invention relates to the technical field of meteorological monitoring, in particular to a ring main unit lightning micrometeorological online monitoring system and method, and the system comprises a feature extraction module, a frequency spectrum detection module, a frequency band pool screening module, a path adjustment module and an interference avoidance module. The method comprises the following steps: collecting an electric field intensity extreme value sequence, calculating a change rate difference ratio, identifying thunder and lightning precursor characteristics, combining a frequency spectrum energy density map, analyzing an energy peak frequency band and a drop point of a frequency hopping signal, judging a thunder and lightning frequency signal concentration area, performing frequency spectrum interference filtering, and combining a dynamic time warping algorithm. The time deviation between a lightning stroke time point and frequency hopping scheduling is analyzed; a frequency hopping path is adjusted in real time; the response timeliness is improved; the difference value before and after electric field disturbance is monitored; and support is provided for monitoring stability and response adaptability of the thunder and lightning risk environment.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological monitoring, in particular to a ring main unit lightning micro-meteorological online monitoring system and method thereof. Background Art

[0002] The technical field of meteorological monitoring includes links such as the acquisition, analysis and data transmission of various meteorological elements in the atmospheric environment, and involves the real-time observation of natural phenomena such as air temperature, air pressure, humidity, wind speed, wind direction, precipitation and lightning. The core content is to obtain meteorological data through various sensors and information acquisition devices, record and manage them, and dynamically master climate change and provide disaster warning services. At present, meteorological monitoring systems generally rely on the combination of various physical measurement methods and data communication means to construct a meteorological perception system with multi-point layout and multi-parameter synchronous acquisition, and are widely used in multiple industries such as electric power, transportation, aviation, and agriculture to improve the adaptability to the natural environment and the level of safety guarantee.

[0003] Among them, a ring main unit lightning micro-meteorological online monitoring system refers to a dedicated online monitoring system constructed for the environmental safety monitoring requirements of ring main unit equipment in the power system, based on the acquisition of lightning characteristic parameters and micro-meteorological factors. It mainly synchronously obtains lightning parameters such as lightning current intensity, electromagnetic radiation characteristics, lightning strike occurrence time and location, as well as micro-meteorological elements such as temperature, humidity, air pressure, and wind speed in a local area. The lightning signal is collected and the micro-meteorological data is measured by arranging multi-functional sensor terminals, the original signal is quantized and transformed by combining with an embedded data processing unit, and the monitoring data is uploaded to the remote monitoring platform in real time through a wired communication link or a wireless transmission module, so as to conduct all-weather risk assessment and data support for the operating environment of the ring main unit.

[0004] The existing technologies rely mostly on the direct acquisition of physical quantity sensors and the transmission method of conventional data links in the acquisition and transmission of meteorological perception data. Although the synchronous acquisition of meteorological factors is carried out, there are deficiencies in the early warning of sudden lightning events and interference identification. The current technology is not sensitive to the weak environmental changes before lightning strikes, and cannot predict the lightning strike risk through the trend of electric field changes, resulting in a lag in early warning. At the same time, there is a lack of effective interference identification and avoidance mechanisms in spectrum signal processing, and it is difficult to efficiently model and analyze the aggregation and jump characteristics of lightning precursor frequency signals. For example, in areas with dense frequency jumps, signal aliasing and misjudgment often occur, affecting the traceability ability of the monitoring system for lightning strike events. In addition, due to the lack of prediction of the correlation between lightning disturbance time and frequency hopping scheduling, the frequency modulation strategy is difficult to match the rhythm of sudden events, resulting in untimely interference avoidance, reducing the reliability of system response and increasing the probability of false alarms. The above problems are more obvious in strong thunderstorm and convective weather, seriously affecting the real-time risk assessment ability of the operating state of the ring main unit. Summary of the Invention

[0005] In order to solve the technical problems existing in the prior art, an embodiment of the present invention provides a ring main unit lightning micro-meteorological online monitoring system and method. The technical solution is as follows: On the one hand, a ring main unit lightning micro-meteorological online monitoring system is provided. The system includes: A feature extraction module, which is used to obtain the extreme value sequence of the atmospheric electric field intensity through the electric field sensor of the ring main unit, calculate the difference ratio of the time change rate, and determine whether it exceeds the control limit, generate a lightning precursor mutation result and transmit it to the spectrum detection module; A spectrum detection module, which is used to obtain the corresponding hopping frequency spectrum energy density distribution map through the lightning precursor mutation result, determine the maximum energy peak frequency band on the frequency axis and the two-side drop points in the map through the spectral clustering algorithm, output the spectrum aggregation determination result and transmit it to the frequency band pool screening module; A frequency band pool screening module, which is used to define the prohibited hopping frequency band on the spectrum according to the peak frequency band of the spectrum aggregation determination result and perform screening, generate an updated frequency band pool and transmit it to the path adjustment module; A path adjustment module, which is used to predict the difference between the lightning strike disturbance time point and the next hopping frequency scheduling time point through the dynamic time warping algorithm according to the updated frequency band pool and the lightning precursor mutation result, and construct the next hopping frequency path and transmit it to the interference avoidance module; An interference avoidance module, which is used to obtain the electric field difference before and after hopping corresponding to the target frequency point in the next hopping frequency path for single-cycle interference avoidance judgment, and generate a hopping frequency avoidance judgment result.

[0006] As a further solution of the present invention, the lightning precursor mutation result is the extreme value sequence of the atmospheric electric field intensity, the difference ratio of the time interval change rate, and the control limit. The spectrum aggregation stable determination result includes the maximum energy peak frequency band on the frequency axis, the two-side drop point frequency domain, the spectrum energy density distribution map, and the spectral clustering algorithm determination. The updated frequency band pool includes the frequency peak and the prohibited hopping frequency band. The next hopping frequency path includes the updated frequency band pool, the dynamic time warping algorithm prediction value, the next hopping frequency scheduling time point, and the lightning strike disturbance time point. The hopping frequency avoidance judgment result is the electric field amplitude change data, the electric field difference before and after hopping of the target frequency point, and the single-cycle interference avoidance judgment.

[0007] As a further solution of the present invention, the feature extraction module includes: An electric field acquisition sub-module, which acquires the electric field intensity data of the ring main unit electric field sensor in the environment, obtains the extreme value sequence of the atmospheric electric field intensity, and records the electric field value at each time point by monitoring the electric field change to obtain the electric field intensity data sequence; A difference ratio calculation sub-module, which calculates the time interval between the extreme values of the electric field intensity based on the electric field intensity data sequence, and calculates the ratio of the time interval change rate to generate a difference ratio result; A mutation determination sub-module, based on the difference ratio result, compares it with a set 3-fold standard deviation control limit, and generates a lightning precursor mutation result when the difference ratio exceeds the control limit.

[0008] As a further solution of the present invention, the spectrum detection module includes: A spectrum acquisition sub-module, which obtains the lightning precursor mutation result, acquires a hopping frequency spectrum energy distribution map based on an electric field sensor, analyzes the energy density in the spectrum diagram, identifies the frequency value change and the corresponding energy interval, and generates a spectrum energy density distribution map; A spectrum clustering analysis sub-module, according to the spectrum energy density distribution map, identifies and divides the maximum energy peak frequency band on the frequency axis through a spectrum clustering analysis algorithm, extracts the frequency range of the peak frequency band and calculates the frequency domain of the descending points on both sides of the frequency band, obtains the characteristics of each frequency band, and generates spectrum aggregation characteristics; A stability determination sub-module, which calls the maximum energy peak frequency band in the spectrum aggregation characteristics, calculates the spectrum stability, and generates a spectrum stability determination result when the spectrum stability is greater than 0.85.

[0009] As a further solution of the present invention, the maximum energy peak frequency band on the frequency axis is identified and divided by the spectrum clustering analysis algorithm, using the formula: ; where, is the clustering compactness, and the maximum energy peak frequency band on the frequency axis is divided by selecting a close to 1 and larger frequency band, represents the distance between the i-th sample point and the j-th clustering center on the frequency axis, i ∈ [1, n] is the sample index, j ∈ [1, k] is the clustering center index, represents the spectrum energy density value of the i-th sample point, represents the maximum energy density value within the current clustering cluster, represents an adjustment factor based on the frequency band interval and the sample density , , is calculated through the sampling interval of the electric field sensor, and the value range is [0, 1].

[0010] As a further solution of the present invention, the frequency band pool screening module includes: A frequency band definition sub-module, according to the spectrum stability determination result, extracts the interval where the frequency peak is located, determines the upper and lower boundaries of the multi-frequency peaks, and demarcates the prohibited hopping frequency band on the corresponding spectrum, and generates a prohibited hopping frequency band interval; The anti-hopping band screening sub-module screens the spectrum data item by item based on the anti-hopping band interval against the anti-hopping band range, eliminates all frequency data and scheduling time nodes falling within the anti-hopping band interval range, and generates a set of effective frequency bands; The frequency band pool construction sub-module screens and retains all the frequency intervals according to the set of effective frequency bands, reorganizes them in ascending order of frequency, and marks the start and end values of the frequency bands to generate an updated frequency band pool.

[0011] As a further solution of the present invention, the path adjustment module includes: The disturbance time point extraction sub-module obtains the mutation time points in the electric field signal based on the lightning precursor mutation result, screens the time nodes that meet the typical disturbance characteristics, and generates the disturbance start time; The scheduling difference calculation sub-module calculates the time correlation strength between the disturbance event time series and the frequency hopping scheduling time series based on the disturbance start time value and the frequency hopping scheduling time points in the updated frequency band pool by applying the dynamic time warping algorithm, constructs a difference sequence between the disturbance start time and the scheduling time nodes, determines the time correlation relationship between the disturbance nodes and the frequency hopping scheduling nodes, and calculates the frequency hopping time difference; The frequency hopping path generation sub-module screens the frequency band sequences that meet the scheduling window control requirements according to the frequency hopping time difference, reconstructs the frequency hopping path, and marks the start and end values of the frequency and the jump sequence number to generate the next frequency hopping path.

[0012] As a further solution of the present invention, when calculating the time correlation strength between the disturbance event time series and the frequency hopping scheduling time series by applying the dynamic time warping algorithm, the formula is used: ; Wherein, The time correlation strength, represents the start timestamp of the th disturbance node, represents the reference timestamp of the th frequency hopping scheduling node, represents the center frequency value of the frequency band where the th disturbance node is located, represents the average frequency value of all active frequency bands in the current frequency band pool, and are both in MHz and are calibrated by an FFT spectrum analyzer, represents the frequency fluctuation smoothing constant, and its value is the reciprocal of the sensor sampling frequency, represents the time window scaling factor, which is obtained by training lightning strike data, represents the system clock reference offset, and takes the offset between the clock and the UTC standard time, Represents the total number of frequency-hopping nodes within the current scheduling period.

[0013] As a further solution of the present invention, the interference avoidance module includes: An amplitude acquisition sub-module, which acquires the electric field amplitude data sequences detected by the electric field sensing component before and after frequency hopping, extracts the amplitude sampling values corresponding to each frequency point within 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 at the corresponding time positions of the same frequency point between the two groups of data, and generates the frequency point amplitude difference amount; An electric field ratio difference sub-module, which based on the frequency point amplitude difference amount, calls the target frequency point parameters in the next frequency hopping path, extracts the amplitude sample differences before and after the corresponding frequency hopping, calculates the interval span value of the sample differences, and combines the time distribution trend of the difference change sequence to extract the average change rate value and the maximum change value, and generates the frequency point fluctuation trend amount; A path determination sub-module, which sets the frequency hopping judgment condition according to the frequency point fluctuation trend amount, extracts the time series position information of each target frequency point in the frequency hopping path, compares the fluctuation trend amount with the electric field interference change critical value in the frequency hopping judgment condition, and classifies whether it exceeds the interference threshold according to the judgment condition, and generates the frequency hopping avoidance judgment result.

[0014] On the other hand, a method for online monitoring of lightning micro-meteorology in a ring main unit is provided. This method is applied to the online monitoring system of lightning micro-meteorology in a ring main unit. The method includes: S1: Obtain the extreme value sequence of the atmospheric electric field intensity through the electric field sensor of the ring main unit, calculate the difference ratio of the time change rate, and judge whether it exceeds the control limit to generate the lightning precursor mutation result; S2: Obtain the corresponding frequency hopping spectrum energy density distribution map through the lightning precursor mutation result, and judge the maximum energy peak frequency band and the two side descent points on the frequency axis in the map through the spectral clustering algorithm, and output the spectral clustering judgment result; S3: Define the forbidden frequency hopping band on the spectrum according to the peak frequency band of the spectral clustering judgment result and perform screening to generate the updated frequency band pool; S4: According to the updated frequency band pool and the lightning precursor mutation result, predict the difference between the lightning strike disturbance time point and the next frequency hopping scheduling time point through the dynamic time warping algorithm, and construct the next frequency hopping path; S5: Obtain the electric field difference before and after frequency hopping corresponding to the target frequency point in the next frequency hopping path to perform single-cycle interference avoidance judgment, and generate the frequency hopping avoidance judgment result.

[0015] The beneficial effects brought by the technical solutions provided in the embodiments of the present invention at least include: By collecting the extreme value sequence of the atmospheric electric field intensity around the ring main unit and calculating the difference ratio of the time change rate, the mutation characteristics before lightning activities are mined, and the significant changes in the electric field environment are identified at the lightning precursor stage. After the mutation characteristics are identified, combined with the spectrum energy density distribution map, based on the relationship between the energy peak frequency band and the drop point in the frequency hopping signal, the concentrated area of the lightning frequency signal is determined, and the potential interference interval is filtered through spectrum aggregation analysis. Combining the update of the frequency hopping band and the prediction of lightning strike disturbances, the time deviation between the lightning strike time point and the frequency hopping scheduling is analyzed through the dynamic time warping algorithm, and the real-time adjustment of the frequency hopping path is carried out to improve the timeliness and accuracy of the frequency hopping response. During the process, the difference before and after the electric field disturbance is also monitored to avoid single-cycle interference and enhance the discrimination ability between the lightning strike disturbance signal and the background interference. The above strategies introduce quantization calculation, spectral clustering, and dynamic prediction mechanisms at the key nodes of multi-dimensional feature recognition, time correlation mining, and spectrum interference troubleshooting, greatly improving the fusion efficiency of the recognition of lightning precursor information and the frequency hopping anti-interference ability, and providing multi-dimensional support for the monitoring stability and response adaptability in the lightning risk environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 is the system flow chart of the present invention; Figure 2 is the system block diagram of the present invention; Figure 3 is the schematic diagram of the method steps of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The following describes the technical solutions in the present invention with reference to the drawings.

[0019] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of the word "example" is intended to present concepts in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two can be selected.

[0020] In the embodiments of the present invention, the terms "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, they have the same meaning. The terms "of", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, they have the same meaning.

[0021] In the embodiments of the present invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference between them is not emphasized, they have the same meaning.

[0022] To make the technical problems, technical solutions, and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0023] The embodiments of the present invention provide a ring main unit lightning micro-meteorological online monitoring system. Please refer to Figures 1 to 2 , the present invention provides a technical solution. A ring main unit lightning micro-meteorological online monitoring system includes: A feature extraction module, configured to obtain an extreme value sequence of the atmospheric electric field intensity through an electric field sensor of the ring main unit, calculate the difference ratio of the time change rate, determine whether it exceeds the control limit, generate a lightning precursor mutation result, and transmit it to the spectrum detection module; A spectrum detection module, configured to obtain a corresponding hopping frequency spectrum energy density distribution diagram through the lightning precursor mutation result, determine the maximum energy peak frequency band on the frequency axis in the diagram and the descending points on both sides through a spectral clustering algorithm, output a spectrum aggregation determination result, and transmit it to the frequency band pool screening module; A frequency band pool screening module, configured to define a forbidden hopping frequency band on the spectrum according to the peak frequency band of the spectrum aggregation determination result and perform screening, generate an updated frequency band pool, and transmit it to the path adjustment module; A path adjustment module, configured to predict the difference between the lightning strike disturbance time point and the next hopping frequency scheduling time point through a dynamic time warping algorithm according to the updated frequency band pool and the lightning precursor mutation result, and construct a next hopping frequency path and transmit it to the interference avoidance module; An interference avoidance module, configured to obtain the electric field difference before and after hopping corresponding to the target frequency point in the next hopping frequency path for single-cycle interference avoidance judgment, and generate a hopping frequency avoidance judgment result.

[0024] The results of lightning precursor mutations are the extreme value sequence of atmospheric electric field intensity, the difference ratio of the time interval change rate, and the control limit. The spectrum aggregation stability determination results include the frequency band with the maximum energy peak on the frequency axis, the frequency domain of the two descending points on both sides, the spectrum energy density distribution diagram, and the determination of the spectral clustering algorithm. The updated frequency band pool includes the frequency peak and the forbidden hopping frequency band. The next hopping path includes the updated frequency band pool, the prediction value of the dynamic time warping algorithm, the next hopping scheduling time point, and the lightning strike disturbance time point. The hopping avoidance judgment result is the electric field amplitude change data, the electric field difference before and after hopping at the target frequency point, and the single-cycle interference avoidance judgment.

[0025] Please refer to Figure 2 , and the feature extraction module includes: An electric field acquisition sub-module that acquires the electric field intensity data of the ring main unit electric field sensor 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 electric field change to obtain the electric field intensity data sequence; Install an EFS-200 type electric field sensor on the surface of the ring main unit and set the sampling interval , and the sensor outputs a voltage signal After AD conversion, a 12-bit digital quantity is obtained , and according to the calibration equation Calculate the electric field intensity value (kV / m), including when , calculate to obtain , continuously collect for 10 minutes to obtain 30,000 data points, and use the sliding window extreme value detection algorithm to define the window width sampling points (corresponding to a 1-second duration), when satisfying and ( ), mark it as an extreme value point. The detection results of a certain time are recorded in Table 1: Table 1 Electric field extreme value monitoring data table As shown in Table 1, after the system detects an extreme value of 2.31 kV / m at 125.4 seconds, it continues to scan the subsequent data points. When it is found that the value at 126.8 seconds is larger than the previous 25 points (124.3 - 126.7 seconds) and the next 25 points (126.9 - 128.3 seconds), mark this point as a valid extreme value to form a sequence .

[0026] A difference ratio calculation sub-module that calculates the time interval between the extreme values of the electric field intensity based on the electric field intensity data sequence and calculates the ratio of the change rate of the time interval to generate the difference ratio result; Based on the electric field intensity data sequence, calculate the adjacent extreme value time interval , represents the time when the th extreme value appears in the electric field intensity data sequence, Represents the time when the th extreme value in the electric field intensity data sequence appears adjacent to each other. For the data in Table 1, , , construct the time interval sequence , calculate the change rate ratio , substitute the data to get . When new is added, calculate . The circular buffer of the system maintenance capacity stores the latest values, calculate the mean value and the standard deviation . When , are measured, set the control limit . When the newly measured difference ratio exceeds the upper limit, trigger an anomaly flag.

[0027] Mutation determination sub-module, according to the difference ratio result, and compare it with the set 3-fold standard deviation control limit. When the difference ratio exceeds the control limit, generate the lightning precursor mutation result; When a new difference ratio is detected, perform the comparison operation , trigger the verification process after the warning is issued, and continuously monitor sampling periods of values. When the measured sequence is obtained, determine that it meets , generate a warning message including: , is the timestamp, is the electric field intensity array, confirm the mutation event, publish the message to the topic lightning / alert through the MQTT protocol, set QoS = 1, and retain the flag retain = true.

[0028] Please refer to Figure 2 . The spectrum detection module includes: Spectrum acquisition sub-module, obtain the lightning precursor mutation result, based on the electric field sensor, obtain the hopping frequency spectrum energy distribution map, analyze the energy density in the spectrum map, identify the change of the frequency value and the corresponding energy interval, and generate the spectrum energy density distribution map; When the lightning precursor mutation result is triggered, the system starts the EFS-300 type sensor to sampling rate to collect time domain signals , obtain discrete points , and obtain through Hamming window weighting processing, The time-domain signal after windowing is subjected to a 1024-point FFT operation: , represents the complex result in the frequency domain, and the energy density (μV² / Hz) is calculated. Table 2 shows a fragment of the experimental data: Table 2 Frequency-domain energy distribution table As shown in Table 2, a peak value of 3340 μV² / Hz is measured at 180 Hz, and a threshold is set to mark all frequency points, and the frequency range of 150 - 210 Hz is identified as the key energy interval.

[0029] The spectral clustering analysis sub-module, based on the spectral energy density distribution map, identifies and divides the frequency axis into the maximum energy peak frequency bands through the spectral clustering analysis algorithm, extracts the frequency range of the peak frequency bands, calculates the frequency domain of the descending points on both sides of the frequency bands, obtains the characteristics of each frequency band, and generates spectral clustering characteristics; Construct a frequency sample set and an energy set , calculate the sample spacing (where is the energy weight), where m / n represents the serial number of the elements in the spectral feature set F, and initial clustering centers , , are selected. According to the sampling interval , calculate the density factor to obtain the adjustment factor , and calculate the clustering compactness: , where is the clustering compactness. By selecting close to 1 and relatively large, the frequency axis is divided into the maximum energy peak frequency bands. represents the distance between the i-th sample point and the j-th clustering center on the frequency axis. i ∈ [1, n] is the sample index, and j ∈ [1, k] is the clustering center index. represents the spectral energy density value of the i-th sample point. represents the maximum energy density value within the current clustering cluster. represents the adjustment factor based on the frequency band interval and the sample density . is obtained by calculating the sampling interval of the electric field sensor, and its value range is [0, 1]. , when the ratio of the clustering compactness of adjacent clustering centers When merging the 150 - 180 Hz and 180 - 210 Hz frequency bands, determine that the main peak range is 120 - 230 Hz, and extract the -3 dB drop points (at 120 Hz , at 230 Hz ).

[0030] The stability determination sub - module calls the maximum energy peak frequency band in the spectral aggregation feature, calculates the spectral stability. When the spectral stability is greater than 0.85, generate a spectral stability determination result; Within three consecutive detection cycles, obtain the energy sequence in the main peak area , calculate the mean value , the standard deviation , spectral stability: , when , generate a determination result and trigger a linkage control signal.

[0031] Please refer to Figure 2 , the frequency band pool screening module includes: The frequency band definition sub - module, according to the spectral stability determination result, extracts the interval where the frequency peak is located, determines the upper and lower boundaries of multiple frequency peaks, and demarcates the forbidden frequency hopping bands on the corresponding spectrum to generate the forbidden frequency hopping band interval; Based on the calculated spectral stability determination result, including when receiving a signal to verify spectral stability, the signal includes a stability value and the associated core peak frequency band information, that is, from 120 Hz to 230 Hz, starting from the maximum energy density value recorded in the peak interval, calculate a dynamic threshold , taking as a reference, starting from the lower boundary of 120 Hz in the core peak interval, check the energy density value one by one for each frequency point (including a step of 1 Hz) downwards. When checking the frequency point of 119 Hz, the energy density is , the value is still greater than , continue downwards. At 118 Hz, the energy density is , also greater than , until scanning to 115 Hz, the energy density is , when , so the next higher frequency point of 116 Hz after 115 Hz is defined as the critical point of the lower boundary. Similarly, starting from the upper boundary of 230 Hz in the core peak interval, check each frequency point upwards one by one. When checking 234 Hz, the energy density is , at 235 Hz, the energy density is , the previous lower frequency point of 234 Hz at 235 Hz is defined as the critical point of the upper boundary, thus initially determining that the current energy concentration region is from 116 Hz to 234 Hz. Subsequently, for the reliability of communication and to avoid potential edge interference, the system adds a safety margin with a width of on both sides of the initially determined frequency band. The setting of the margin refers to the typical bandwidth of interference signals in the same type of environment, generally taking 2 to 5 times the frequency resolution of the monitoring system. Therefore, the lower limit of the frequency hopping prohibited band is calculated as , and the upper limit is calculated as , thereby generating the frequency hopping prohibited band interval corresponding to this lightning event as 111 Hz to 239 Hz, which will be independently executed for each lightning precursor mutation event. Including in another event 2023081502, if the core peak is from 98 Hz to 217 Hz and the maximum energy density is , then the boundary threshold is . Through boundary search and estimation, the current energy concentration region is obtained as 95 Hz to 220 Hz. After adding a 5 Hz margin, the frequency hopping prohibited band is 90 Hz to 225 Hz. As shown in Table 3, it records multiple lightning events and the corresponding frequency hopping prohibited band definitions. Table 3 Frequency Hopping Prohibited Band Definition Table As shown in Table 3, it lists the complete process and multiple parameter values of the frequency hopping prohibited band interval generated after starting from the initial core peak information, obtaining the maximum energy density, calculating the boundary threshold, searching for the energy boundary, and applying the safety margin in three multiple lightning events.

[0032] The frequency hopping prohibited band screening sub-module, based on the frequency hopping prohibited band interval, screens the spectrum data item by item against the frequency hopping prohibited band range, and eliminates all frequency data and scheduling time nodes that fall within the frequency hopping prohibited band interval range to generate a set of effective frequency bands; Receive the generated frequency hopping prohibited band interval, including for event 2023081501, the obtained frequency hopping prohibited band is 111 Hz to 239 Hz. Call the preset communication frequency planning table, which contains the list of center frequency points of all available wireless communication channels in the current system. The list is , and the system will perform a screening operation on each frequency point in it to determine whether it falls within the obtained frequency hopping prohibited band interval. The specific judgment logic is: if , then this frequency point is regarded as unavailable. For the frequency point , because , it does not meet the condition. Therefore is an effective frequency. For the frequency point , because , it meets the condition. Therefore Marked as invalid, the system performs this comparison for all frequency points in the list in sequence, including (falling into), (falling into), (falling into), (falling into), while (valid), (valid), (valid). After screening all frequency points, the set of frequency data to be excluded is . At the same time, the current communication scheduling time table is also checked. The table records the frequencies and time nodes planned to be used for communication tasks within a period of time, including. If there is an entry in the schedule "Task A, time node , using frequency 150Hz", since 150Hz has been identified as a frequency within the prohibited hopping frequency band, the frequency 150Hz involved in the schedule entry will be marked as unavailable, and the execution of Task A at the time node will be affected, and it is necessary to reallocate the frequency or delay the execution. If another entry is "Task B, time node , using frequency 85Hz", since 85Hz is a valid frequency, the schedule entry is not affected. After the above item-by-item screening and exclusion of frequency data and scheduling time nodes, a set of frequency points that only do not fall within the current prohibited hopping frequency band range is generated, that is, the effective frequency band set .

[0033] Build a frequency band pool module. According to the effective frequency band set, screen and retain all frequency intervals and reorganize them in ascending order of frequency, mark the start and end values of the frequency of the frequency band, and generate an updated frequency band pool; Based on the generated effective frequency band set , and the known available spectrum resource range of the system, including the total spectrum range authorized for system use is 20Hz to 300Hz, and at the same time referring to the previously defined prohibited hopping frequency band interval as 111Hz to 239Hz, the system first identifies continuous available frequency interval blocks within the total spectrum range. The starting point of the first available interval block is the lower limit 20Hz of the system spectrum, and the end point is before the lower limit 111Hz of the prohibited hopping frequency band, forming the interval [20Hz, 111Hz). The starting point of the second available interval block is after the upper limit 239Hz of the prohibited hopping frequency band, and the end point is the upper limit 300Hz of the system spectrum, forming the interval (239Hz, 300Hz]. Next, the system will integrate and refine the discrete effective frequency points in , the continuous interval is divided, and the sub-bands including known valid frequency points are preferentially retained. All the retained frequency intervals are screened and will be reorganized in ascending order of frequency. For example, for the interval of 20Hz to 111Hz, it can be divided into a sub-band including 85Hz, such as 75Hz, 95Hz with the center at 85Hz, and a sub-band including 100Hz, 90Hz, 110Hz with the center at 100Hz. At the same time, the remaining parts within the large interval, including 20Hz, 75Hz and 110Hz, 111Hz, with a bandwidth greater than or equal to , will also be retained. For the interval (239Hz, 300Hz], which includes the valid frequency points 245Hz and 260Hz, it can also be divided into a sub-band including 245Hz, (235Hz, 255Hz) (note that the start cannot be lower than 239Hz, so it is adjusted to 239Hz, 255Hz with the center at 247Hz, setting 245Hz within the range), a sub-band including 260Hz, including [250Hz, 270Hz), and the remaining parts 255Hz, 250Hz (if any and with sufficient bandwidth) and 270Hz, 300Hz. The system will organize these divided sub-bands, mark the exact start and end frequencies of each sub-band, and remove overlapping or overly narrow (less than the minimum channel bandwidth ) frequency bands. For example, if the valid and non-overlapping frequency bands obtained after the above division and screening are: 20Hz, 70Hz, 75Hz, 95Hz, 95Hz, 110Hz, 240Hz, 255Hz, 255Hz, 270Hz, 270Hz, 300Hz, these frequency bands will be recorded in the frequency band pool. Each entry includes the start frequency, end frequency, and marks its status as "available" to generate an updated frequency band pool, as shown in Table 4.

[0034] Table 4 Example of the updated available frequency band pool As shown in Table 4, it lists the available frequency band pool formed after the hopping band rejection screening and frequency band reorganization. Each frequency band has the start and end frequencies, the calculated bandwidth, and a status flag indicating availability (including 0x01 representing available).

[0035] Please refer to Figure 2 , the path adjustment module includes: The perturbation time point extraction sub-module, based on the lightning precursor mutation result, obtains the mutation time points in the electric field signal, screens the time nodes that meet the typical perturbation characteristics, and generates the perturbation start time; After receiving the generated lightning precursor mutation result, the result includes a rough timestamp indicating the moment of mutation occurrence, including recorded as , the system immediately retrieves and extends 200 milliseconds before and after the time point (i.e., from arrive ) The original high sampling rate electric field strength data sequence within the window The data is continuously recorded by the EFS-300 sensor at a sampling rate of 2kHz. The system analyzes the 400 data points in the window and first calculates the first-order difference value of each sampling point, the instantaneous change of the electric field strength To characterize the rate of change of the electric field, At the point in time The electric field strength value measured at Compare One sampling interval earlier and set a change rate threshold The threshold is based on the 95th percentile value of the electric field change caused by typical lightning discharge in the statistical analysis of 1000 lightning events, and an absolute amplitude change threshold is set , the threshold is set to 15% of the sensor dynamic range. Satisfy at the same time as well as When there are two conditions, the time point It was initially marked as a potential disturbance feature point, including, when the analysis was completed to the time stamp of 1678886401.100 seconds (corresponding to When the electric field value suddenly increases from 0.2kV / m at the previous sampling point to 1.9kV / m, the rate of change is , greater than , and the amplitude changes (Set reference electric field If the time point of the disturbance is greater than 0.1kV / m, 1.8kV / m, or greater than 1.5kV / m, then 1678886401.100 seconds is regarded as a disturbance feature point. If multiple feature points are identified within the 200ms analysis window, including the detection of features that meet the conditions at 1678886401.100 seconds, 1678886401.150 seconds, and 1678886401.220 seconds, the system will select the earliest and most drastic change (including the point with the largest weighted value of amplitude and rate, and set the amplitude weight 0.6 and rate weight 0.4 as the key disturbance start time of this lightning event according to experience. After the screening process, the disturbance start time determined is ,The timestamp output is used as the benchmark input for scheduling impact analysis to generate the disturbance start time.

[0036] The scheduling difference calculation sub-module calculates the time correlation strength between the disturbance event time series and the frequency hopping scheduling time series by applying the dynamic time warping algorithm based on the disturbance start time value and the updated frequency hopping scheduling time points in the frequency band pool, constructs a difference sequence between the disturbance start time and the scheduling time nodes, determines the time correlation relationship between the disturbance nodes and the frequency hopping scheduling nodes, and calculates the frequency hopping time difference; Based on the extracted and verified disturbance start time value, including , and the frequency hopping scheduling time point sequence in the current frequency hopping scheduling plan obtained from Table 5 "Example Table of Updated Available Frequency Band Pool", the system will evaluate the time correlation strength between the disturbance event and the predetermined frequency hopping operation, and set that there is a time series composed of key disturbance feature points , including , whose timestamps are respectively , , . At the same time, there is a corresponding reference timestamp sequence generated by the communication scheduling module, including planned frequency hopping nodes , and the timestamps are respectively , , . The system uses the correlation strength calculation formula of dynamic time warping to quantify this correlation, and the parameter acquisition and assignment are as follows: represents the start timestamp of the th disturbance node, represents the reference timestamp of the th frequency hopping scheduling node, represents the center frequency value (MHz) of the key affected frequency band when the th disturbance node occurs, including obtaining by performing fast Fourier transform (FFT) analysis on the electric field signal within a very short time (5 ms) before and after the disturbance occurs and identifying the frequency band center where the energy is concentrated, and setting to obtain , , . represents the average center frequency value of all active communication frequency bands (including the frequency bands being used or planned to be used in the near future) in the current frequency band pool. Set the current active frequency band pool to include four frequency bands with center frequencies of 0.110 MHz, 0.150 MHz, 0.190 MHz, and 0.230 MHz, then . represents the frequency fluctuation smoothing constant, defined as the reciprocal of the sensor sampling frequency. If the sensor sampling frequency , then . To ensure the dimensional consistency (square frequency term) inside the square root in the formula, in the calculation Take a small positive number with the same dimension as including , , representing the time window scaling factor, the value of which is obtained through statistical analysis and parameter optimization training of 1000 lightning strike event data, adjusting the sensitivity of time difference, including setting to , , representing the reference offset between the system local clock and the UTC standard time, obtained through regular calibration by the NTP service, including , , representing the total number of planned frequency hopping nodes within the currently evaluated scheduling period, including , the item in the denominator is the sum of the timestamps of all planned frequency hopping points (after subtracting the clock offset) within the current scheduling period. Set the timestamps of these planned frequency hopping points to be respectively , , , , . The calculation logic of the formula is that for each pair of perturbations and scheduling nodes , calculate their time difference , and multiply it by a weight factor related to the frequency deviation. The weight factor amplifies the situation where the frequency band affected by the perturbation is significantly different from the system average working frequency band. Then, normalize this product with respect to the time scale of the entire scheduling period (denominator term). Finally, accumulate the calculation results of all paired nodes to obtain the total time correlation intensity , whose dimension is MHz. Calculate the numerator part of the item: frequency-related item . Therefore, the numerator of the item is . Similarly, calculate the item: , , and the numerator is . Calculate the item: , , and the numerator is . So , . The advantage of the formula is that by the deviation between the perturbation occurrence time and the scheduled time, the difference between the frequency band affected by the perturbation and the system normal working frequency band, and combining with the time scale of the current scheduling period for normalization, it can quantify the potential impact degree of lightning perturbation on frequency hopping scheduling. The calculation result Indicates that the temporal correlation strength between the perturbation event sequence and the frequency hopping adjustment time sequence is very low (due to an extremely small order of magnitude), which means that in this example, despite the temporal proximity, after considering the frequency factor and the scheduling cycle scale, the system, according to value (including, if is less than the preset threshold then it is determined to have a strong correlation, otherwise it is weak), determines the strength of the correlation relationship and generates a difference sequence between the perturbation start time and the scheduling time node, including , and the difference will be used as the value of the frequency hopping time difference for path planning.

[0037] The frequency hopping path generation sub-module, based on the frequency hopping time difference, filters the frequency band sequence that meets the scheduling window control requirements, reconstructs the frequency hopping path, and marks the start and end frequency values and the hopping sequence number to generate the next frequency hopping path; After obtaining the calculated frequency hopping time difference sequence, including three differences , and the temporal correlation strength , the next frequency hopping path will be filtered and constructed from the available frequency bands listed in Table 5 "Updated Example Table of Available Frequency Band Pools" according to the information. The core of the filtering is to meet the "scheduling window control requirements", which is defined as a time threshold . If any frequency hopping time difference is less than and the corresponding temporal correlation strength is higher than the warning level (including , in the example low, indicating a weak correlation), then the original planned frequency hopping point and the used frequency band need to be adjusted or avoided. In this example, , which means that the third perturbation point is very close in time to the paired scheduling point. Although the overall value is low, the system will still preferentially refer to adjusting the frequency hopping plan associated with , traverse the updated frequency band pool, including the frequency bands F1 20Hz, 70Hz), F2 75Hz, 95Hz, F3 95Hz, 110Hz, F4 240Hz, 255Hz, F5 255Hz, 270Hz, F6 270Hz, 300Hz, and combine with the current communication task bandwidth requirement (including the need for at least 15Hz bandwidth) and the longest allowed channel occupancy time (including ), as well as the shortest frequency hopping interval (including ), and Reconstruct the frequency hopping path, and preferentially select those frequency bands that are shown to be safe in the frequency hopping time difference analysis, that is, the frequency bands far from the scheduled time slots affected by disturbances. If the originally planned The frequency band to be used around (i.e., 1678886401.200s) is F3. Since is small, it is estimated that a safer time point after will be selected (including ) to select a frequency band with sufficient bandwidth from the frequency band pool, including F4, as the jump target. The reconstructed frequency hopping path will list the selected frequency band sequence, and each frequency band is marked with the starting frequency, ending frequency, and the jump sequence number in the path. For example, an estimated path is: first use F1 for 50 ms, then jump to F2 for 30 ms, and then avoid the original sensitive period, and jump to F4 at the current safe time for 40 ms. This new path is formatted and stored, as shown in Table 5. Table 5 Next Frequency Hopping Path Planning Table As shown in Table 5, it shows the next frequency hopping path filtered and reconstructed according to the frequency hopping time difference and scheduling window requirements, including the sequence of each jump step, the frequency band used (starting and ending frequencies), the estimated start time, and the residence duration in the frequency band, to generate the next frequency hopping path.

[0038] Please refer to Figure 2 , the interference avoidance module includes: An amplitude acquisition sub-module that obtains the electric field amplitude data sequence detected by the electric field sensing component before and after frequency hopping, extracts the amplitude sampling values corresponding to each 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, and calculates the amplitude difference between the two groups of data at the corresponding time positions of the same frequency point to generate the frequency point amplitude difference amount; Obtain the electric field amplitude data stream detected and recorded by the EFS-300 type electric field sensing component immediately before and after the execution of the frequency hopping action. The data stream continuously captures the electric field intensity information at the target frequency point at a sampling rate of . The system executes the amplitude extraction process for each predetermined target frequency point in the next frequency hopping path (planned in Table 6). For the action of planning to jump from the current frequency to the target frequency , within the time window before the current execution of the jump instruction, the system commands the electric field sensing component to tune to , and collects the background electric field amplitude at the target frequency point to obtain a sequence including sampling points, and the sequence constitutes the "electric field amplitude value group before frequency hopping" , including, if the target frequency point The center frequency of the frequency band with jump sequence number 1 in Table 6 is 45 Hz. An amplitude sequence is collected within 20 ms before the scheduled jump to 45 Hz. kV / m. Subsequently, within a very short time after the system completes the frequency hopping operation to and stabilizes, including the moment when the frequency hopping is completed After Starting from, it also lasts for a time window, and again, the electric field amplitude of the currently activated target frequency point is collected to obtain another sequence consisting of 40 sampling points. The sequence constitutes the "electric field amplitude value group after frequency hopping". , including, for the 45 Hz frequency point, the value collected after frequency hopping is kV / m. The system then processes these two value groups respectively, calculates the representative amplitude, using the mean value calculation, that is, the electric field strength data set before processing and the electric field strength data set after processing . Taking the data of the 45 Hz frequency point in this example, the calculated and are set. This 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 collected and preliminarily calculated for the first two target frequency points (45 Hz and 85 Hz) in the path are recorded. Table 6 Average Amplitude Collection Table of Target Frequency Points Before and After Frequency Hopping As shown in Table 6, this table lists the average electric field amplitudes collected before and after the frequency hopping operation according to the target frequency points planned in the next frequency hopping path. For the target frequency point 85 Hz with jump sequence number 2, the average amplitude before frequency hopping is 0.0110 kV / m, and the average amplitude after frequency hopping is 0.0140 kV / m. Finally, the difference between these two sets of data, that is, the average amplitudes before and after frequency hopping at the same target frequency point is calculated and defined as . For the 45 Hz frequency point, the amplitude difference is . For the 85 Hz frequency point, its amplitude difference is . The amplitude differences calculated for each target frequency point in the path form a sequence, including kV / m (where the last two values can be calculated according to the data in Table 6 and ). The difference sequence is the generated frequency point amplitude difference.

[0039] The electric field ratio difference sub-module calls the target frequency point parameters in the next hop frequency path based on the frequency point amplitude difference quantity, 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 change rate value and the maximum change value in combination with the time distribution trend of the difference change sequence to generate the frequency point fluctuation trend quantity; Based on the generated sequence of frequency point amplitude difference quantities, the sequence includes the electric field amplitude change amounts that occur before and after the frequency hopping operation for each target frequency point along the next hop frequency path (as planned in Table 6), and the obtained sequence is , first extract the amplitude difference sample values, that is, the numerical sequence kV / m, and then calculate the interval span value of this group of sample differences. The span value is defined as the difference between the maximum value and the minimum value in the sample sequence, and the calculation process is , in combination with the distribution trend of the difference ( ) in the time series, arranged in the time order of the frequency hopping path, extract the average change rate value and the maximum change value. The average change rate value is calculated as follows. First, calculate the change rate of the amplitude difference between every two consecutive frequency hopping targets, , the amplitude difference between the th frequency point and the previous frequency point, directly represents the amplitude measurement value of the th frequency point, represents the th frequency point's start timestamp in frequency hopping communication, where is the th target frequency point's expected start timestamp in the frequency hopping path (from Table 6), including, , similarly, , and , then calculate the arithmetic mean of the change rates as the average change rate value, , the maximum change value is the largest among all samples in the original sequence of frequency point amplitude difference quantities , that is . These three calculated indicators, namely the interval span value 0.0060 kV / m, the average change rate value -0.0143 kV / m / s, and the maximum change value 0.0080 kV / m, together constitute the generated frequency point fluctuation trend quantity.

[0040] The path determination sub-module sets the frequency hopping judgment conditions according to the frequency point fluctuation trend quantity, extracts the time series position information of each target frequency point in the frequency hopping path, compares the fluctuation trend quantity with the electric field interference change critical value in the frequency hopping judgment conditions, and classifies whether it exceeds the interference threshold according to the judgment conditions to generate the frequency hopping avoidance judgment result; According to the calculated and generated frequency point fluctuation trend quantity, the trend quantity includes three key indicators: interval span value , average change rate value , and maximum change value . First, set a group of predefined frequency hopping judgment conditions. The conditions are the critical values of electric field interference changes for the above-mentioned trend quantity indicators. The setting basis of the critical values is as follows: maximum change value critical value , which is the upper limit of the amplitude difference of the target frequency point in 90% of the successful communication scenarios after more than 5000 frequency hopping operations in the reference data, and identifies the frequency points that deteriorate after a single frequency hopping. Interval span critical value , this value is set based on 3 times the standard deviation of the amplitude difference of multiple frequency points after 10 consecutive frequency hoppings during stable communication, and is used to measure the consistency of the interference level on the frequency hopping path. Absolute average change rate critical value , which is set according to 70% of the change rate required for the electric field strength to reach an unacceptable level (including the signal-to-noise ratio being lower than 5 dB) within 1 second under the simulated environment of the known interference source diffusion speed, and is used to judge whether there is a trend of continuous increase or decrease in interference. Then, extract the time series position information of each unexecuted target frequency point in the next frequency hopping path (as shown in Table 6), including, if the current evaluation has reached the second frequency point in the path, then the third and fourth frequency points and the planned start timestamp and occupancy duration are the objects. Then, the system compares the calculated frequency point fluctuation trend quantity with the critical values in the set frequency hopping judgment conditions item by item. The judgment logic is: if ( ) or ( ) or ( ), 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, is true, is false, is false. Since the first condition is satisfied, the overall judgment is that the fluctuation trend of the current path has exceeded the acceptable interference threshold. According to the judgment conditions, the path is classified as "there is a potential interference risk", and a frequency hopping avoidance judgment result is generated. The result is a status flag, including "PATH-RISK-DETECTED", and is accompanied by the exceeded index that causes the judgment ("MAX-DELTA-EXCEEDED"), as well as an instruction to re-evaluate the unexecuted frequency hopping points (including the jump sequence numbers 3 and 4 in Table 6) or select an alternative path.

[0041] Please refer to Figure 3 , the method includes: S1: Obtain the extreme value sequence of the atmospheric electric field intensity through the electric field sensor of the ring main unit, calculate the difference ratio of the time change rate, and judge whether it exceeds the control limit to generate a lightning precursor mutation result; S2: Obtain the corresponding hopping frequency spectrum energy density distribution map based on the lightning precursor mutation result, determine the maximum energy peak frequency band on the frequency axis in the map and the descending points on both sides through the spectral clustering algorithm, and output the spectral clustering determination result; S3: Define the prohibited hopping frequency band on the spectrum according to the peak frequency band of the spectral clustering determination result and perform screening to generate an updated frequency band pool; S4: According to the updated frequency band pool and the lightning precursor mutation result, predict the difference between the lightning strike disturbance time point and the next hopping frequency scheduling time point through the dynamic time warping algorithm, and construct the next hopping frequency path; S5: Obtain the electric field difference before and after hopping corresponding to the target frequency point in the next hopping frequency path to perform single-cycle interference avoidance judgment, and generate the hopping frequency avoidance judgment result.

[0042] It should be understood that the term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. These three situations, where A and B can be singular or plural. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship. Specifically, it can be understood by referring to the context before and after.

[0043] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following items (pieces)" or similar expressions refer to any combination of these items, including any combination of single items (pieces) or plural items (pieces). For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0044] It should be understood that in various embodiments of the present invention, the magnitude of the serial numbers of the above processes does not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0045] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0046] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the devices, apparatuses, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0047] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0048] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0049] In addition, in each embodiment of the present invention, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0050] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or a part of this 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 for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present invention. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, and other various media that can store program codes.

[0051] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention and should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A lightning micro-meteorological online monitoring system for a ring main unit, characterized in that, The system includes: A feature extraction module, which is used to obtain the extreme value sequence of the atmospheric electric field intensity through the electric field sensor of the ring main unit, calculate the difference ratio of the time change rate, judge whether it exceeds the control limit, generate the lightning precursor mutation result and transmit it to the spectrum detection module; A spectrum detection module, which is used to obtain the corresponding hopping frequency spectrum energy density distribution map through the lightning precursor mutation result, judge the maximum energy peak frequency band and the two-sided drop points on the frequency axis in the map through the spectral clustering algorithm, output the spectrum aggregation judgment result and transmit it to the frequency band pool screening module; A frequency band pool screening module, which is used to define the forbidden hopping frequency band on the spectrum according to the peak frequency band of the spectrum aggregation judgment result and perform screening, generate the updated frequency band pool and transmit it to the path adjustment module; A path adjustment module, which is used to predict the difference between the lightning strike disturbance time point and the next hopping frequency scheduling time point through the dynamic time warping algorithm according to the updated frequency band pool and the lightning precursor mutation result, and construct the next hopping frequency path and transmit it to the interference avoidance module; An interference avoidance module, which is used to obtain the electric field difference before and after hopping corresponding to the target frequency point in the next hopping frequency path to perform single-period interference avoidance judgment, and generate the hopping frequency avoidance judgment result.

2. The online monitoring system for lightning micro-meteorology of the ring main unit according to claim 1, wherein The lightning precursor mutation result is the extreme value sequence of the atmospheric electric field intensity, the difference ratio of the time interval change rate, and the control limit. The spectrum aggregation stability judgment result includes the maximum energy peak frequency band on the frequency axis, the two-sided drop point frequency domain, the spectrum energy density distribution map, and the spectral clustering algorithm judgment. The updated frequency band pool includes the frequency peak and the forbidden hopping frequency band. The next hopping frequency path includes the updated frequency band pool, the prediction value of the dynamic time warping algorithm, the next hopping frequency scheduling time point, and the lightning strike disturbance time point. The hopping frequency avoidance judgment result is the electric field amplitude change data, the electric field difference before and after hopping of the target frequency point, and the single-period interference avoidance judgment.

3. The on-line monitoring system for lightning micro-meteorology of the ring main unit according to claim 1, characterized in that The feature extraction module includes: An electric field acquisition sub-module, which acquires the electric field intensity data of the ring main unit electric field sensor in the environment, obtains the extreme value sequence of the atmospheric electric field intensity, monitors the electric field change, records the electric field value at each time point, and obtains the electric field intensity data sequence; A difference ratio calculation sub-module, which calculates the time interval between the extreme values of the electric field intensity based on the electric field intensity data sequence, and calculates the ratio of the time interval change rate to generate the difference ratio result; A mutation judgment sub-module, which compares the difference ratio result with the set 3-fold standard deviation control limit, and generates a lightning precursor mutation result when the difference ratio exceeds the control limit.

4. The online monitoring system for lightning micro-meteorology of the ring main unit according to claim 1, characterized in that The spectrum detection module includes: A spectrum acquisition sub-module, which obtains the lightning precursor mutation result, obtains the hopping frequency spectrum energy distribution map based on the electric field sensor, analyzes the energy density in the spectrum map, identifies the frequency value change and the corresponding energy interval, and generates the spectrum energy density distribution map; A spectral clustering analysis sub-module, which identifies and divides the maximum energy peak frequency band on the frequency axis through the spectral clustering analysis algorithm according to the spectrum energy density distribution map, extracts the frequency range of the peak frequency band and calculates the two-sided drop point frequency domain of the frequency band, obtains the characteristics of each frequency band, and generates the spectrum aggregation characteristics; The stability determination sub-module calls the maximum energy peak frequency band 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.

5. The online monitoring system for lightning micro-meteorology of the ring main unit according to claim 4, characterized in that, The maximum energy peak frequency band on the frequency axis is identified and divided by the spectral clustering analysis algorithm, using the formula: ； Among them, is the clustering compactness, by selecting close to 1 and the maximum energy peak frequency band on the frequency axis divided by a relatively large frequency band division, 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, represents the spectral energy density value of the i-th sample point, represents the maximum energy density value within the current cluster, represents the adjustment factor based on the frequency band interval and the sample density , which is obtained by calculating the sampling interval of the electric field sensor and has a value range of [0, 1].​ 6. The online monitoring system for lightning micro-meteorology of the ring main unit according to claim 1, characterized in that The frequency band pool screening module includes: The frequency band definition sub-module extracts the interval where the frequency peak is located according to the spectrum stability determination result, determines the upper and lower boundaries of multiple frequency peaks, and demarcates the forbidden frequency hopping band on the corresponding spectrum to generate a forbidden frequency hopping band interval. The forbidden frequency hopping band screening sub-module screens the spectrum data item by item based on the forbidden frequency hopping band interval, excluding all frequency data and scheduling time nodes that fall within the range of the forbidden frequency hopping band interval, and generates a set of effective frequency bands. The frequency band pool construction sub-module screens and retains all frequency intervals according to the set of effective frequency bands, reorganizes them in ascending order of frequency, and marks the start and end values of the frequency of the frequency band to generate an updated frequency band pool.

7. The on-line monitoring system for lightning micro-meteorology of the ring main unit according to claim 1, characterized in that, The path adjustment module includes: The perturbation time point extraction sub-module obtains the mutation time point in the electric field signal based on the lightning precursor mutation result, screens the time nodes that meet the typical perturbation characteristics, and generates the perturbation start time. The scheduling difference calculation sub-module calculates the time correlation strength between the perturbation event time series and the frequency hopping scheduling time series based on the perturbation start time value and the frequency hopping scheduling time points in the updated frequency band pool by applying the dynamic time warping algorithm, constructs a difference sequence between the perturbation start time and the scheduling time nodes, determines the time correlation relationship between the perturbation nodes and the frequency hopping scheduling nodes, and calculates the frequency hopping time difference. The frequency hopping path generation sub-module screens the frequency band sequence that meets the scheduling window control requirements according to the frequency hopping time difference, reconstructs the frequency hopping path, and marks the start and end values of the frequency and the hopping sequence number to generate the next frequency hopping path.

8. The online monitoring system for the micro-meteorology of lightning in a ring main unit according to claim 7, characterized in that When calculating the time correlation strength between the perturbation event time series and the frequency hopping scheduling time series by applying the dynamic time warping algorithm, the formula is used: ； Among them, Time correlation strength, represents the start timestamp of the th perturbation node, represents the reference timestamp of the th frequency hopping scheduling node, represents the center frequency value of the frequency band where the th perturbation node is located, represents the average frequency value of all active frequency bands in the current frequency band pool, and are both in MHz and obtained by calibration with an FFT spectrum analyzer. represents the frequency fluctuation smoothing constant, and its value is the reciprocal of the sensor sampling frequency. represents the time window scaling factor, which is obtained by training with lightning strike data. represents the system clock reference offset, which takes the offset between the clock and the UTC standard time. represents the total number of frequency hopping nodes in the current scheduling period.

9. The online monitoring system for lightning micro-meteorology of the ring main unit according to claim 1, characterized in that, The interference avoidance module includes: The amplitude acquisition sub-module obtains the electric field amplitude data sequence detected by the electric field sensing component before and after frequency hopping, extracts the amplitude sampling values corresponding to the frequency points in each time period, establishes the electric field amplitude value group before frequency hopping and the electric field amplitude value group after frequency hopping, and calculates the amplitude difference at the corresponding time positions of the same frequency point between the two sets of data to generate the frequency point amplitude difference. The electric field ratio difference sub-module calls the target frequency point parameters in the next frequency hopping path based on the frequency point amplitude difference, extracts the amplitude sample differences before and after the corresponding frequency hopping, calculates the interval span value of the sample differences, and extracts the average change rate value and the maximum change value in combination with the time distribution trend of the difference change sequence to generate the frequency point fluctuation trend. The path determination sub-module sets the frequency hopping judgment condition according to the frequency point fluctuation trend quantity, extracts the time series position information of each target frequency point in the frequency hopping path, compares the fluctuation trend quantity with the critical value of the electric field interference change in the frequency hopping judgment condition, and classifies whether it exceeds the interference threshold according to the judgment condition to generate the frequency hopping avoidance judgment result.

10. An on-line monitoring method for lightning micro-meteorology of a ring main unit, characterized in that, The method is used to implement the on-line monitoring system for lightning micro-meteorology of the ring main unit described in any one of claims 1-9, and the method includes: S1: Obtain the extreme value sequence of the atmospheric electric field intensity through the electric field sensor of the ring main unit, calculate the difference ratio of the time change rate, and judge whether it exceeds the control limit to generate the lightning precursor mutation result; S2: Obtain the corresponding frequency hopping spectrum energy density distribution map through the lightning precursor mutation result, and judge the maximum energy peak frequency band and the two descending points on both sides on the frequency axis in the map through the spectral clustering algorithm to output the spectral aggregation judgment result; S3: Define the forbidden frequency hopping band on the spectrum according to the peak frequency band of the spectral aggregation judgment result and perform screening to generate the updated frequency band pool; S4: According to the updated frequency band pool and the lightning precursor mutation result, predict the difference between the lightning strike disturbance time point and the next frequency hopping scheduling time point through the dynamic time warping algorithm, and construct the next frequency hopping path; S5: Obtain the electric field difference before and after frequency hopping corresponding to the target frequency point in the next frequency hopping path to perform single-cycle interference avoidance judgment, and generate the frequency hopping avoidance judgment result.

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