Early warning method and system for hidden danger of circuit trees

Abstract

The invention discloses a line tree hidden danger early warning method and system. The method comprises the steps that first historical data are acquired, and a first judgment standard is established according to the first historical data; obtaining a first real-time tree discharge waveform, and performing first preprocessing on the first real-time discharge waveform to obtain a second real-time tree discharge waveform; and performing early warning according to the second real-time tree discharge waveform in combination with the first judgment standard. By establishing a judgment standard, the method can more accurately identify and early warn the hidden danger of the line tree, so that measures are taken in advance to avoid faults, and economic loss and safety risk are reduced. The early warning system can monitor the discharge condition of the line trees in real time, timely discover hidden dangers and give out early warning, and improve the efficiency and safety of line operation and maintenance.

Description

Technical Field

[0001] The present invention relates to the technical field of early warning of hidden dangers of trees on railway lines, and in particular to an early warning method and system for hidden dangers of trees on railway lines. Background Art

[0002] Transmission and distribution lines are prone to hidden dangers due to overly high trees, which can lead to weakened insulation strength between the high-voltage side conductors and the ground, and between the conductors and the ground wires and towers, causing hidden discharges. If not cleared in time, they will develop into faults and eventually cause the line to trip and stop operating, resulting in significant economic losses and safety risks.

[0003] In order to accurately and timely locate the discharge on the line, most of the transmission and distribution lines of various voltage levels have installed distributed traveling wave fault locating devices. By collecting the traveling waves generated and propagated from the discharge point at both ends of the line, and using a high-precision timing system, the time difference of the traveling waves arriving at both ends is calculated, thereby obtaining the location information of the discharge point and notifying the line operation and maintenance personnel to carry out maintenance work.

[0004] However, traditional fault location methods are only effective on high-voltage transmission lines with simple structures. For the identification and classification of tree hidden danger discharge, current similar technologies mainly extract high-frequency discharge waveform fragments, calculate specified features such as amplitude, phase, power frequency accompanying periodicity, etc., and use pattern classification algorithms such as decision trees, support vector machines, neural networks, etc. to finally output the existence or type label of hidden dangers. However, these can only determine whether tree hidden dangers have occurred, but cannot provide early warnings. Summary of the invention

[0005] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0006] In view of the above existing problems, the present invention is proposed.

[0007] Therefore, the present invention provides an early warning method and system for tree hidden dangers on a line, which can solve the problems mentioned in the background technology.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0009] In a first aspect, the present invention provides an early warning method for hidden dangers of trees on a line, comprising:

[0010] Acquire first historical data, and establish a first judgment criterion according to the first historical data;

[0011] Acquire a first real-time tree discharge waveform, and perform a first preprocessing on the first real-time tree discharge waveform to obtain a second real-time tree discharge waveform;

[0012] An early warning is issued according to the second real-time tree discharge waveform in combination with the first judgment criterion.

[0013] As a preferred solution of the early warning method for tree hazards on a line of the present invention, the first judgment criterion includes:

[0014] Selecting several indicators from the first historical data;

[0015] Preset several risk levels;

[0016] The plurality of indicators are first divided according to the plurality of risk levels.

[0017] As a preferred solution of the early warning method for tree hidden dangers on railway lines of the present invention, the first division includes:

[0018] Divide the aforementioned indicators into regional ranges;

[0019] The several indicators include at least tree line spacing;

[0020] The regional ranges of treeline spacing corresponding to different risk levels do not intersect;

[0021] The area ranges of other indicators except tree line spacing corresponding to different risk levels may intersect or not intersect.

[0022] As a preferred solution of the early warning method for tree hazards on a line of the present invention, the regional division of the several indicators includes:

[0023] Prioritizing the several indicators;

[0024] The indicator with the highest priority except the tree line spacing is selected as the first judgment indicator, and the remaining indicators are selected as the second judgment indicators.

[0025] As a preferred solution of the early warning method for tree hazards on railway lines described in the present invention, the first judgment index and the second judgment index both include at least the average amplitude and / or the number of pulses and / or the pulse energy and / or the phase interval.

[0026] As a preferred solution of the early warning method for tree hazards on a line of the present invention, the early warning in combination with the first judgment criterion includes:

[0027] Acquire real-time data corresponding to the first judgment index and the second judgment index in the second real-time tree discharge waveform;

[0028] Classifying the real-time data into risk levels based on the first judgment criterion;

[0029] Issue early warnings based on risk levels.

[0030] As a preferred solution of the early warning method for tree hidden dangers on power lines of the present invention, the first preprocessing at least includes judging the effectiveness and processing the characteristics of the first real-time discharge waveform.

[0031] In a second aspect, the present invention provides an early warning system for hidden dangers of trees on a line, comprising:

[0032] A judgment standard establishing module, used for acquiring first historical data and establishing a first judgment standard according to the first historical data;

[0033] A waveform acquisition module, used for acquiring a first real-time tree discharge waveform, and performing a first preprocessing on the first real-time tree discharge waveform to obtain a second real-time tree discharge waveform;

[0034] An early warning module is used to issue an early warning according to the second real-time tree discharge waveform in combination with the first judgment criterion.

[0035] In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned method when executing the computer program.

[0036] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the method described above when executed by a processor.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention proposes an early warning method and system for tree hidden dangers on a line, obtains first historical data, and establishes a first judgment standard based on the first historical data; obtains a first real-time tree discharge waveform, and performs a first preprocessing on the first real-time discharge waveform to obtain a second real-time tree discharge waveform; and issues an early warning based on the second real-time tree discharge waveform in combination with the first judgment standard. By establishing the judgment standard, the present application can more accurately identify and warn of tree hidden dangers on the line, thereby taking measures in advance to avoid failures, reduce economic losses and safety risks. The early warning system of the present application can monitor the discharge of trees on the line in real time, discover hidden dangers in a timely manner and issue early warnings, and improve the efficiency and safety of line operation and maintenance. The early warning method and system of the present application have broad application prospects, are suitable for transmission and distribution lines of various voltage levels, and can effectively improve the stability and reliability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0039] Figure 1 A method flow chart of a method and system for early warning of tree hazards on a line provided by an embodiment of the present invention;

[0040] Figure 2 A tree-induced line tripping development trend diagram of a line tree hidden danger early warning method and system provided by an embodiment of the present invention;

[0041] Figure 3 A tree risk level assessment flow chart of a method and system for early warning of hidden dangers of trees on a line provided by an embodiment of the present invention;

[0042] Figure 4 An internal structural diagram of a computer device of a method and system for early warning of tree hazards on a line provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0044] Example 1

[0045] Reference Figure 1-Figure 4 , which is the first embodiment of the present invention, provides a method and system for early warning of hidden dangers of trees on a line, including:

[0046] There are some problems in the existing related technologies. For example, trees growing too fast may come into contact with power lines, causing tripping accidents. In addition, due to the lack of effective monitoring methods, the prediction of tree growth trends is not accurate enough, resulting in the inability to take preventive measures in time.

[0047] The present application provides a method that can effectively solve the above-mentioned problems. Next, multiple embodiments will be combined to explain in detail how to implement the early warning method of tree hidden dangers on the line;

[0048] Figure 1 A method flow chart of an early warning method and system for tree hidden dangers on a line is shown, including:

[0049] S101, obtaining first historical data, and establishing a first judgment criterion according to the first historical data;

[0050] In an optional embodiment, the first historical data is generally collected by sensors installed on power lines, which can monitor the distance changes between trees and power lines. The system further analyzes these data to identify tree growth trends and potential risk points.

[0051] In an optional embodiment, the first historical data may include information such as the tree type, growth rate, and initial distance from the power line. With this data, the early warning system can more accurately predict the growth trend of the tree, and combine with the real-time monitoring data to evaluate the possible impact of tree growth on the power line. In addition, the system can also automatically adjust the early warning threshold based on historical data and real-time data to adapt to the impact of different environments and seasonal changes on tree growth.

[0052] In an optional embodiment, the first judgment criterion is a grading criterion based on the first historical data, and is generally divided according to specific parameters of the first historical data. For example, the distance between trees and lines can be divided into three levels: safe, warning, and dangerous. When the distance between trees and lines is less than the safety threshold, the system will issue a warning signal to prompt staff to check and handle it. If the distance is further reduced to the danger threshold, the system will trigger an emergency response to prevent possible power outages or accidents. In this way, the early warning system can effectively guide on-site personnel to take appropriate preventive measures to ensure the stability and safety of power supply.

[0053] In an optional embodiment, the first judgment criterion is a grading criterion based on the first historical data, and is generally divided according to specific parameters of the first historical data. For example, the growth rate and direction of trees can be used as the second judgment criterion to predict the threat that trees may pose to power lines in the future. For example, if the tree grows too fast, or the growth direction is obviously toward the power line, the system will adjust the warning level according to these parameters. In addition, the system can also integrate meteorological data, such as wind speed and precipitation, because these factors may also affect the growth and stability of trees. By integrating these data, the early warning system can more comprehensively assess risks and provide more accurate protection for the safe operation of power lines.

[0054] In the embodiment of the present application, the process of the tree overheight gradually inducing line tripping is relatively intuitive, as shown in the figure below. When the tree height reaches h1 (the formation of corona discharge at the tip of the tree is mainly affected by the line voltage level, line structure, tree type, air humidity, etc.), corona discharge will occur between the line and the tree, accompanied by a slight sizzling sound. When the tree grows to h2, arc discharge will occur between the tree lines, and obvious brush-like arcs will form on the surface of the tree. When the tree reaches a height of about h3, the distance between the tree line reaches the limit value of the flashover fault, and the line will have a flashover fault, causing the line to trip. For the situation shown in the figure, when the tree height reaches near h2, a strong discharge characteristic signal has been generated, which is the tree discharge characteristic, such as Figure 2 As shown;

[0055] In the embodiment of the present application, the first judgment criterion includes:

[0056] Selecting several indicators from the first historical data;

[0057] Preset several risk levels;

[0058] The plurality of indicators are first divided according to the plurality of risk levels.

[0059] In an optional embodiment, several indicators can be selected as parameters such as current intensity, voltage fluctuation, corona discharge frequency and sound intensity. These parameters can effectively reflect the interaction between trees and transmission lines. The early warning system can accurately determine the hidden dangers that tree growth may cause to transmission lines by monitoring these indicators in real time and combining them with the preset risk level. When the monitored indicators exceed the preset safety threshold, the system will issue a warning signal to remind the operation and maintenance personnel to take timely measures, such as pruning trees or adjusting lines, to prevent line tripping accidents.

[0060] In an optional embodiment, several indicators can also be selected as tree-line spacing, average amplitude segment, number of pulses, pulse energy, and phase interval, etc. These indicators can more comprehensively evaluate the safe distance and discharge activities between trees and transmission lines. The tree-line spacing directly reflects the physical distance between trees and transmission lines, and is an important parameter for determining whether there is a risk of contact. The average amplitude segment and number of pulses can reveal the intensity and frequency of discharge activities, while the pulse energy can provide the energy level of the discharge event. The phase interval helps to analyze the distribution of discharge events within the power system cycle. By combining these indicators, the early warning system can more accurately evaluate the potential threat of trees to transmission lines and issue early warnings in a timely manner, thereby improving the operational safety and reliability of the power system.

[0061] In an optional embodiment, the number of risk levels can be set according to different safety thresholds, and the risk levels can be divided into three levels: low, medium, and high. A low risk level means that the distance between the tree and the transmission line is relatively safe, the discharge activity is not frequent, and the energy level is low; a medium risk level indicates that there is a certain risk of contact or the discharge activity is relatively frequent, and attention needs to be paid; a high risk level means that there is a serious risk of contact or the discharge activity is frequent, the energy level is high, and immediate measures need to be taken. The early warning system can automatically determine the risk level based on real-time monitoring data, and provide intuitive risk prompts to operation and maintenance personnel through different early warning signals, such as color, sound, or SMS notifications.

[0062] In an optional embodiment, the number of risk levels can also be adjusted according to different safety requirements and environmental conditions. For example, in areas with high wind speeds or fast tree growth, an "extremely high risk" level can be added to reflect the more serious threat that trees may pose to transmission lines under these conditions. In addition, the early warning system can also integrate weather forecast data so that it can issue early warnings to operation and maintenance personnel before severe weather arrives, thereby further improving the safety protection level of the power system.

[0063] In the embodiment of the present application, several indicators are selected as tree line spacing, average amplitude segment, pulse number, pulse energy and phase interval.

[0064] In the embodiment of the present application, the first division includes:

[0065] Divide the aforementioned indicators into regional ranges;

[0066] The several indicators include at least tree line spacing;

[0067] The regional ranges of treeline spacing corresponding to different risk levels do not intersect;

[0068] The area ranges of other indicators except tree line spacing corresponding to different risk levels may intersect or not intersect.

[0069] For example, the formation of tree faults is a gradual process. As the tree gradually approaches the high-potential wire, the field strength at the top of the tree gradually increases, and the air gap between the tree and the wire changes from weak to strong discharge until it completely breaks down. The type of hidden danger discharge is identified, such as the tree discharge type, and calculation, assignment, and stage warning are performed according to the following hierarchical warning algorithm steps:

[0070] After using the extreme value interval method to determine that the line has hidden dangers, the time and frequency domain characteristics of the tree discharge hidden danger discharge waveform data are analyzed and calculated according to the existing feature processing algorithm, and the calculation results are compared with the set risk level range. The set risk level range can be based on amplitude, number of pulses, energy, and phase. The critical → flashover discharge process is divided into several development stages. For example, the tree discharge development change data in the table refers to the obvious characteristic parameters that change with the development stage. According to the characteristic performance under different tree line spacing, it is divided into the following primary, secondary,... Several stages, according to the level classification standards in the table, set the risk threshold, so as to correspond to the risk level (development stage) of this discharge, and achieve the purpose of graded warning of tree discharge. The graded warning processing flow is as follows:

[0071] Table 2 Classification of 20kV tree discharge development stages

[0072]

[0073] It should be noted that the above table is only an empirical classification method. Relevant technical personnel can make other types of designs according to actual needs, and this application does not impose any restrictions.

[0074] In an optional embodiment, the first division may also be adjusted according to other parameters, such as the type of tree, the growth rate of the tree species, the ambient humidity, the temperature, etc., to adapt to the specific conditions of different regions.

[0075] It should be noted that, as can be seen from the table, the regional ranges of tree-line spacing corresponding to different risk levels do not intersect, that is, the tree-line spacing is divided very standardly and there is no intersection. The regional ranges of other indicators except tree-line spacing corresponding to different risk levels intersect or do not intersect because the average amplitude segment, number of pulses, pulse energy and phase interval are highly volatile, so there may be intersections.

[0076] In the embodiment of the present application, the regional division of the several indicators includes:

[0077] Prioritizing the several indicators;

[0078] The indicator with the highest priority except the tree line spacing is selected as the first judgment indicator, and the remaining indicators are selected as the second judgment indicators.

[0079] The first judgment index and the second judgment index both include at least an average amplitude and / or a pulse number and / or a pulse energy and / or a phase interval.

[0080] In an optional embodiment, the priority of the several indicators can be divided by a decision-making method based on an expert system, which establishes an expert knowledge base and uses expert experience to rank the importance of different indicators. First, the opinions of experts in related fields are collected to form a preliminary indicator priority list. Then, through testing and verification of actual cases, the weights of the indicators are continuously adjusted and optimized to ensure the accuracy and reliability of the early warning system. Finally, the system will make more accurate assessments and early warnings of tree hazards based on the optimized indicator priorities.

[0081] In an optional embodiment, the prioritization of the several indicators may also be performed using an algorithm based on machine learning, which can learn and identify the degree of influence of different indicators on tree hidden danger warning through historical data. Through the training data set, the algorithm model can automatically adjust the weight of each indicator, thereby realizing intelligent assessment of tree hidden dangers. In addition, this embodiment can also be combined with real-time data updates to adapt to environmental changes and the dynamic characteristics of tree growth, further improving the accuracy and timeliness of warnings.

[0082] In an optional embodiment, the priority of the several indicators can also be divided by a preset standard division method, and the priority of the indicators is divided by a preset standard. This method is usually based on widely accepted industry standards or historical data statistical analysis results. For example, it is possible to determine which indicators are more critical to the early warning system based on factors such as tree growth rate, past tree collapse records, and the distance between trees and power lines. In this way, the early warning system can respond quickly without the need for complex algorithms or expert knowledge, thereby simplifying the system's operating procedures while ensuring the efficiency and effectiveness of the early warning.

[0083] For example, the present application selects the average discharge amplitude of tree hidden danger as the first judgment index, and the other indexes as the second judgment index, such as Figure 3 As shown:

[0084] Among them, I m To monitor the average discharge amplitude of tree hazards, I m1 In order to monitor the maximum amplitude of corona discharge, X (corona discharge is a weak discharge, and its amplitude generally does not exceed 20mA for 110kV voltage level, and it is recommended that X be 1.5 to 2 times), I m2 In order to detect the minimum amplitude of tree discharge, I m3 is the maximum value of tree discharge detected; then I m1 m2 m m3 .f m ​​​f1 is the center frequency value of tree hidden dangers, f2 is the maximum value of tree discharge frequency, f3 is the minimum value of tree discharge frequency, f1>f2>f m >f3. Assign values ​​to the variables defined in the above formula, such as (I m1 =50mA)<(I m2 =100mA)<(I m =150mA)<(I m3 =250mA), then the amplitude weight and frequency weight of each risk level can be calculated according to the set value using the following formula.

[0085] k1 is the first-level risk level amplitude weight, and its value is a range, that is, I m1 / I m <k1<I m2 / I m .

[0086] l1 is the frequency weight of the first-level risk level, and its value is a range, namely f1 / f m >l1>f2 / f m .

[0087] k2 is the secondary risk level amplitude weight, and its value is a range, that is, I m2 / I m <k2<I m3 / I m .

[0088] l2 is the frequency weight of the secondary risk level, and its value is a range, namely f2 / f m >l2>f3 / f m .

[0089] In the embodiment of the present application, the threshold amplitude of tree hidden danger is I mi and threshold frequency f i As a benchmark, then use the range values ​​of k1, l1, k2, l2 above to calculate the formula a1=k1I mi +α / (l1 f i ), a2=k2I mi +α / (l2 f i ) is used for calculation, and we can get two range values ​​of a and c. Take the lower limit value a of a1 and the upper limit value c of a2. If the final calculation results a1 and a2 have an intersection, then the middle value of the intersection range of a1 and a2 is taken as b. If a1 and a2 do not intersect, then the middle value of the difference between the lower limit value of a2 and the upper limit value of a1 is taken as b, and then three range limit values ​​a, b, c can be obtained. Because the numerical dimensions and value ranges of the parts before and after the plus sign of a1 and a2 are different, the correction coefficient α is taken, and its value is calculated according to k1Imi and k2I mi To select appropriately, the effect is to convert 1 / (l1 f i ) to enlarge or reduce the frequency part and amplitude part of a1 and a2, so as to make the frequency part and amplitude part of a1 and a2 equal in value, so as to avoid excessive contribution of either amplitude or frequency to the values ​​of a1 and a2.

[0090] First, the waveform is processed to obtain the amplitude I and frequency f of the discharge pulse;

[0091] Furthermore, for waveform validity judgment, when the monitored waveform amplitude Imeasure exceeds Imi and this section of the discharge waveform has periodic repeatability compared with the power frequency, it is judged to be a valid waveform, and the Imeasure and fmeasure of the valid waveform are entered into the history library.

[0092] Furthermore, the intermediate frequency weight k is calculated N =I m2 / I m , the intermediate amplitude weight l N =f2 / f m According to the formula a = k N I m测 +1 / (l N f 测 ) calculating the amplitude I test and the frequency f test of the effective pulse to obtain a calculation result a test;

[0093] Furthermore, for range judgment, if the calculated result a is less than a, it is judged as the first risk level; if it is within the range of a and b, it is judged as the second risk level; if it is within the range of b and c, it is judged as the third risk level; if it exceeds the range of c, it is judged as the fourth risk level.

[0094] The amplitude and frequency values ​​of tree hazard waveforms determined to be valid waveforms are stored in the historical database, which can further increase the database base, making the amplitude and frequency values ​​involved in the risk level calculation richer, and the average amplitude, center frequency value, etc. gradually becoming reasonable and stable, and thus the limit values ​​a, b, and c of each risk level range are also more reasonable and stable.

[0095] It should be noted that obtaining the first historical data and establishing the first judgment standard based on the first historical data can more accurately identify and predict the hidden dangers of trees on the line. By analyzing the historical data, the normal range of the discharge pulse amplitude and frequency can be determined, so that abnormal waveforms can be quickly identified in actual monitoring. In addition, the establishment of the first judgment standard helps to reduce false alarms and missed alarms, and improve the accuracy and reliability of the early warning system.

[0096] S102, acquiring a first real-time tree discharge waveform, and performing a first preprocessing on the first real-time tree discharge waveform to obtain a second real-time tree discharge waveform;

[0097] In an embodiment of the present application, the first preprocessing at least includes determining the validity and performing feature processing on the first real-time discharge waveform.

[0098] In an optional embodiment, the first preprocessing may be to remove noise through a filter to ensure the purity of the waveform. Then, the amplitude and frequency of the processed waveform are extracted to obtain more accurate measurement results. In addition, digital signal processing techniques, such as fast Fourier transform (FFT), can be applied to analyze the spectral characteristics of the waveform to further improve the accuracy of waveform analysis. Through these preprocessing steps, useful information can be effectively extracted from complex signals, providing solid data support for subsequent risk assessment and early warning.

[0099] In an optional embodiment, the validity judgment of the first preprocessing and the feature processing can be achieved by setting thresholds. Specifically, an amplitude threshold and a frequency threshold can be set, and only when the amplitude and frequency of the discharge waveform exceed these thresholds, the waveform is considered to be a valid discharge signal. Such processing can effectively exclude signals generated by environmental interference or other non-discharge factors, ensuring the accuracy of subsequent analysis.

[0100] In an optional embodiment, feature processing may include extracting specific features of the discharge waveform, such as the rising and falling edge times of the waveform, the duration of the waveform, etc. These features are crucial for subsequent discharge type identification and risk assessment. Through these methods, more accurate and reliable input data can be provided for the early warning system.

[0101] In the embodiment of the present application, there is no restriction on the first preprocessing, and relevant technical personnel can flexibly select the preprocessing method according to the actual application requirements and signal characteristics. For example, digital filtering technology can be used to remove noise, or wavelet transform can be applied to analyze the local characteristics of the signal. In addition, it is also possible to combine the machine learning algorithm to obtain a model through training to automatically identify and extract features related to discharge, thereby further improving the intelligence level of the early warning system. Through these comprehensive processing methods, it is possible to ensure that the discharge signal is accurately extracted from the complex signal, providing more accurate data support for subsequent risk assessment and early warning.

[0102] It should be noted that obtaining the first real-time tree discharge waveform and performing the first preprocessing on the first real-time discharge waveform to obtain the second real-time tree discharge waveform can effectively remove the noise and interference in the signal, thereby improving the clarity and accuracy of the discharge waveform. This step is crucial for subsequent feature extraction and analysis because it directly affects the judgment accuracy and response speed of the early warning system. Through preprocessing, it can ensure that the key features in the discharge waveform, such as the rising and falling edge time, duration, etc., are accurately identified and quantified, providing a solid data foundation for subsequent risk assessment and early warning decisions.

[0103] S103: issuing an early warning according to the second real-time tree discharge waveform and the first judgment criterion.

[0104] In the embodiment of the present application, the step of providing an early warning in combination with the first judgment criterion includes:

[0105] Acquire real-time data corresponding to the first judgment index and the second judgment index in the second real-time tree discharge waveform;

[0106] Classifying the real-time data into risk levels based on the first judgment criterion;

[0107] Issue early warnings based on risk levels.

[0108] In an optional embodiment, early warning according to the risk level can be achieved by setting different early warning thresholds. For example, when the risk level is classified as high risk, the system will immediately send out an alarm signal and initiate an emergency response procedure to prevent possible power outages or equipment damage. Medium risk levels may trigger routine inspections and maintenance, while low risk levels are recorded as a reference for future analysis and preventive measures. In this way, the early warning system can take appropriate measures based on the analysis results of real-time data to ensure the stable operation of the power system.

[0109] In an optional embodiment, the early warning according to the first judgment standard also includes real-time updating of data in the history library. When new valid waveforms are detected, the system will store the amplitude and frequency data of these waveforms in the history library. In this way, the data in the history library will be continuously enriched over time, making the calculation of risk levels more accurate and stable. At the same time, the update of the history library also helps the system learn and adapt to environmental changes, and improves the adaptability and accuracy of the early warning system.

[0110] In an optional embodiment, the early warning system can also automatically adjust the early warning threshold according to the analysis results of real-time data. For example, if the system detects that the frequency of tree hazards of a certain risk level increases, the early warning system can automatically increase the early warning threshold of that level to reduce false alarms. Conversely, if the frequency of tree hazards of a certain risk level decreases, the early warning system can lower the early warning threshold of that level to increase the sensitivity of the early warning.

[0111] In an optional embodiment, the early warning system can also combine the Geographic Information System (GIS) technology to combine the early warning information of tree hazards with geographic coordinates to provide accurate geographic location information for power line inspection and maintenance. In this way, power line maintenance personnel can quickly locate specific risk points and conduct targeted inspections and treatments, thereby improving the operational safety and reliability of the power system.

[0112] In summary, the present invention proposes an early warning method for hidden dangers of trees on power lines, which obtains first historical data and establishes a first judgment standard based on the first historical data; obtains a first real-time tree discharge waveform and performs a first preprocessing on the first real-time discharge waveform to obtain a second real-time tree discharge waveform; and issues an early warning based on the second real-time tree discharge waveform in combination with the first judgment standard. By establishing judgment standards, the present application can more accurately identify and warn of hidden dangers of trees on power lines, thereby taking measures in advance to avoid failures and reduce economic losses and safety risks. The early warning system of the present application can monitor the discharge of trees on power lines in real time, discover hidden dangers in a timely manner and issue early warnings, thereby improving the efficiency and safety of line operation and maintenance. The early warning method and system of the present application have broad application prospects, are applicable to transmission and distribution lines of various voltage levels, and can effectively improve the stability and reliability of the power system.

[0113] Example 2

[0114] In a preferred embodiment, Figure 3 As shown, the threshold amplitude of tree hidden danger I mi and threshold frequency f i As a benchmark, then use the range values ​​of k1, l1, k2, l2 above to calculate the formula a1=k1I mi +α / (l1 f i ), a2=k2I mi +α / (l2 f i) is used for calculation, and we can get two range values ​​of a and c. Take the lower limit value a of a1 and the upper limit value c of a2. If the final calculation results a1 and a2 have an intersection, then the middle value of the intersection range of a1 and a2 is taken as b. If a1 and a2 do not intersect, then the middle value of the difference between the lower limit value of a2 and the upper limit value of a1 is taken as b, and then three range limit values ​​a, b, c can be obtained. Because the numerical dimensions and value ranges of the parts before and after the plus sign of a1 and a2 are different, the correction coefficient α is taken, and its value is calculated according to k1I mi and k2I mi To select appropriately, the effect is to convert 1 / (l1f i ) to enlarge or reduce the frequency part and amplitude part of a1 and a2, so as to make the frequency part and amplitude part of a1 and a2 equal in value, so as to avoid excessive contribution of either amplitude or frequency to the values ​​of a1 and a2.

[0115] (1) Waveform processing to obtain the amplitude I and frequency f of the discharge pulse;

[0116] (2) Waveform validity judgment: when the monitored waveform amplitude Imeasure exceeds Imi and the discharge waveform has periodic repeatability compared with the power frequency, it is judged to be a valid waveform. The Imeasure and fmeasure of the valid waveform are entered into the history library.

[0117] (3) Calculate and take the intermediate frequency weight k N =I m2 / I m , the intermediate amplitude weight l N =f2 / f m According to the formula a = k N I m测 +1 / (l N f 测 ) calculating the amplitude I test and the frequency f test of the effective pulse to obtain a calculation result a test;

[0118] (4) Range determination: If the calculated value a is less than a, it is determined to be a level 1 risk. If it is within the range of a and b, it is determined to be a level 2 risk. If it is within the range of b and c, it is determined to be a level 3 risk. If it exceeds the range of c, it is determined to be a level 4 risk.

[0119] The amplitude and frequency values ​​of tree hazard waveforms determined to be valid waveforms are stored in the historical database, which can further increase the database base, making the amplitude and frequency values ​​involved in the risk level calculation richer, and the average amplitude, center frequency value, etc. gradually becoming reasonable and stable, and thus the limit values ​​a, b, and c of each risk level range are also more reasonable and stable.

[0120] Example 3

[0121] This embodiment also provides an early warning system for hidden dangers of trees on a line, including:

[0122] A judgment standard establishing module, used for acquiring first historical data and establishing a first judgment standard according to the first historical data;

[0123] A waveform acquisition module, used for acquiring a first real-time tree discharge waveform, and performing a first preprocessing on the first real-time tree discharge waveform to obtain a second real-time tree discharge waveform;

[0124] An early warning module is used to issue an early warning according to the second real-time tree discharge waveform in combination with the first judgment criterion.

[0125] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0126] This embodiment also provides a computer device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 4 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, an early warning method for hidden dangers of trees on the line is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0127] This embodiment further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0128] Acquire first historical data, and establish a first judgment criterion according to the first historical data;

[0129] Acquire a first real-time tree discharge waveform, and perform a first preprocessing on the first real-time tree discharge waveform to obtain a second real-time tree discharge waveform;

[0130] An early warning is issued according to the second real-time tree discharge waveform in combination with the first judgment criterion.

[0131] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0132] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0133] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0134] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0135] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1The steps for the functions specified in one or more boxes.

[0136] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0137] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for early warning of hidden dangers of trees on a line, characterized in that: include: Acquire first historical data, and establish a first judgment criterion according to the first historical data; Acquire a first real-time tree discharge waveform, and perform a first preprocessing on the first real-time tree discharge waveform to obtain a second real-time tree discharge waveform; An early warning is issued according to the second real-time tree discharge waveform in combination with the first judgment criterion.

2. The early warning method for hidden dangers of trees on a line according to claim 1, characterized in that: The first judgment criteria include: Selecting several indicators from the first historical data; Preset several risk levels; The plurality of indicators are first divided according to the plurality of risk levels.

3. The early warning method for hidden dangers of trees on a line as claimed in claim 2, characterized in that: The first division includes: Divide the aforementioned indicators into regional ranges; The several indicators include at least tree line spacing; The regional ranges of treeline spacing corresponding to different risk levels do not intersect; The area ranges of other indicators except tree line spacing corresponding to different risk levels may intersect or not intersect.

4. The early warning method for hidden dangers of trees on a line as claimed in claim 3, characterized in that: The regional division of the several indicators includes: Prioritizing the several indicators; The indicator with the highest priority except the tree line spacing is selected as the first judgment indicator, and the remaining indicators are selected as the second judgment indicators.

5. The early warning method for hidden dangers of trees on a line as claimed in claim 4, characterized in that: The first judgment index and the second judgment index both include at least an average amplitude and / or a pulse number and / or a pulse energy and / or a phase interval.

6. The early warning method for hidden dangers of trees on a line as claimed in claim 5, characterized in that: The step of providing an early warning in combination with the first judgment criterion includes: Acquire real-time data corresponding to the first judgment index and the second judgment index in the second real-time tree discharge waveform; Classifying the real-time data into risk levels based on the first judgment criterion; Issue early warnings based on risk levels.

7. The early warning method for hidden dangers of trees on a line as claimed in claim 6, characterized in that: The first preprocessing at least includes determining the effectiveness and processing the characteristics of the first real-time discharge waveform.

8. An early warning system for hidden dangers of trees on a line, characterized in that: include: A judgment standard establishing module, used for acquiring first historical data and establishing a first judgment standard according to the first historical data; A waveform acquisition module, used for acquiring a first real-time tree discharge waveform, and performing a first preprocessing on the first real-time tree discharge waveform to obtain a second real-time tree discharge waveform; An early warning module is used to issue an early warning according to the second real-time tree discharge waveform in combination with the first judgment criterion.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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