Method and system for identifying, early warning and analyzing forest fire of multi-stage power transmission line

Abstract

The invention discloses a multi-stage power transmission line forest fire identification early warning analysis method and system, and relates to the field of power transmission line forest fire early warning. Power transmission line region division information is acquired, a target key patrol region is extracted, and a forest fire risk level of the target key patrol region is calculated; acquiring a forest fire alarm preset threshold value of the target key patrol area; according to the forest fire risk level, determining an adjustment amplitude of a forest fire alarm preset threshold, and obtaining a parameter interval of the forest fire alarm preset threshold; environment information of the target key patrol area is identified, a specific value in a parameter interval is determined according to the environment information, and a preliminary threshold value is obtained; and identifying the forest fire development situation of the target key patrol area, determining an adjustment coefficient according to the forest fire development situation, and endowing the adjustment coefficient with a preliminary threshold to obtain an alarm threshold. Multi-stage identification early warning is realized by adopting a dynamic threshold value mode, real-time adjustment is performed according to environment change, false alarm and missing alarm are greatly reduced, and early warning accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the field of wildfire warning for transmission lines, and more particularly, to a multi-stage wildfire identification and warning analysis method and system for transmission lines. Background Art

[0002] Currently, for the identification and warning of wildfires on transmission lines, a fixed-threshold identification method is mainly used. This method pre-sets a fixed value as the boundary for judging the occurrence of a wildfire. When the monitored relevant indicators (such as temperature, smoke concentration, etc.) exceed this fixed threshold, a warning signal is sent. However, this fixed-threshold identification method has many defects. Under different environmental conditions, such as different seasons, weather conditions (sunny, cloudy, rainy, foggy, etc.) and different geographical regions (mountains, plains, forest areas, etc.), the fluctuation ranges of normal environmental parameters vary greatly. The fixed threshold cannot adapt to these complex and variable environmental factors, and false alarms or missed alarms are likely to occur. In some mountainous areas, the temperature difference between morning and evening is large. In the early morning or evening, the environmental temperature may be close to or even briefly exceed the fixed threshold, resulting in frequent false alarms of the system. In some high-temperature weather, even if there is a wildfire hazard, the system fails to issue a warning in time because the overall environmental temperature is high and the monitored indicators do not exceed the fixed threshold, causing missed alarms.

[0003] In addition, the existing wildfire identification and warning often only make a single-stage judgment, lacking a comprehensive tracking and analysis of the development trend of wildfires. The development of a wildfire from an initial small flame to a large-scale fire is a dynamic process, and the threat levels to transmission lines in different stages are different. The single-stage warning cannot provide accurate warning information for different stages of wildfire development and is difficult to meet the actual protection needs. In view of this, this application is specifically proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide a multi-stage wildfire identification and warning analysis method and system for transmission lines, which uses a dynamic threshold method to achieve multi-stage identification and warning, adjusts in real time according to environmental changes, greatly reduces false alarms and missed alarms, and improves the warning accuracy; through multi-stage identification, the development trend of wildfires is tracked throughout the process, and different thresholds are used for accurate warning in different stages, greatly improving the accuracy of wildfire identification and warning.

[0005] The embodiments of the present invention are implemented as follows: A multi-stage wildfire identification and warning analysis method for transmission lines includes the following steps: S1000: Obtain the regional division information of the transmission line, extract the target key inspection area and calculate the wildfire risk level of the target key inspection area; S2000: Obtain the preset threshold for wildfire alarms in the target key patrol area; wherein, the preset threshold for wildfire alarms is obtained from the configuration parameters during wildfire early warning in the target key patrol area; S3000: Determine the adjustment range of the preset threshold for wildfire alarms according to the wildfire risk level, and obtain the parameter interval of the preset threshold for wildfire alarms; S4000: Identify the environmental information of the target key patrol area, determine the specific value in the parameter interval according to the environmental information, and obtain the preliminary threshold according to the specific value; S5000: Identify the development trend of wildfires in the target key patrol area, determine the adjustment coefficient according to the development trend of wildfires, assign the adjustment coefficient to the preliminary threshold, and obtain the alarm threshold; use the alarm threshold as the early warning parameter configuration for the corresponding wildfire development stage in the target key patrol area.

[0006] Further, the specific steps for determining the adjustment range of the preset threshold for wildfire alarms according to the wildfire risk level are as follows: Obtain the change value of the wildfire risk level, determine the correlation coefficient between the preset threshold for wildfire alarms and the wildfire risk level, assign the correlation coefficient to the change value of the wildfire risk level, determine the adjustment range of the preset threshold for wildfire alarms, and obtain the parameter interval of the preset threshold for wildfire alarms; Among them, when the correlation coefficient is positive, if the wildfire risk level increases, the adjustment range of the preset threshold for wildfire alarms is positive, and the preset threshold for wildfire alarms increases; if the wildfire risk level decreases, the adjustment range of the preset threshold for wildfire alarms is negative, and the preset threshold for wildfire alarms decreases; when the correlation coefficient is negative, if the wildfire risk level increases, the adjustment range of the preset threshold for wildfire alarms is negative, and the preset threshold for wildfire alarms decreases; if the wildfire risk level decreases, the adjustment range of the preset threshold for wildfire alarms is positive, and the preset threshold for wildfire alarms increases.

[0007] Further, determining the correlation coefficient between the preset threshold for wildfire alarms and the wildfire risk level includes the following steps: Obtain the first change data set of the wildfire risk level change in any key patrol area, and then obtain the second change data set of the preset threshold for wildfire alarms change in the same key patrol area during the same historical period; conduct a correlation analysis on the first change data set and the second change data set to obtain the correlation analysis result, and calculate the correlation coefficient based on the correlation analysis result.

[0008] Further, organize the first change data set and the second change data set into a data matrix according to time points; substitute the first change data set and the second change data set into ; Among them, and are the th observations of the variable wildfire risk level and the preset threshold for wildfire alarms respectively, and are the mean values of the variable wildfire risk level and the preset threshold of wildfire alarm respectively, where is the number of observed values; When is close to 1, there is a positive correlation between the change in the wildfire risk level and the change in the preset threshold of wildfire alarm, that is, when the wildfire risk level increases, the preset threshold of wildfire alarm also increases; When is close to -1, there is a negative correlation between the change in the wildfire risk level and the change in the preset threshold of wildfire alarm, that is, when the wildfire risk level increases, the preset threshold of wildfire alarm decreases; Take

[0009] as the correlation coefficient, assign the correlation coefficient to the change value of the wildfire risk level, and determine the adjustment range of the preset threshold of wildfire alarm. Furthermore, identify the environmental information of the target key patrol area, and determine the specific value in the parameter range according to the environmental information. The specific steps are as follows: The environmental information includes meteorological information and vegetation information. Among them, the meteorological information includes: temperature information, wind information, rainfall information and humidity information; the vegetation information includes: plant species, coverage area, vegetation density and vegetation moisture content;

[0010] Obtain the meteorological information in the key patrol area, and determine the first coefficient based on this meteorological information; obtain the vegetation information in the key patrol area, and determine the second coefficient based on this vegetation information. Determine the combined weight, which includes the first coefficient weight and the second coefficient weight. Combine the result of assigning the first coefficient weight to the first coefficient with the result of assigning the second coefficient weight to the second coefficient, and determine the screening method of the specific value in the parameter range according to the combined result. Specifically, the steps for determining the screening method of the specific value in the parameter range according to the combined result are as follows: Obtain the adjustment ranges of the preset thresholds of wildfire alarm under multiple same wildfire risk levels; calculate the mean, median and standard deviation of the adjustment ranges of the preset thresholds of wildfire alarm under the same risk level; determine the boundary values of the parameter range corresponding to the same wildfire risk level, and divide the parameter range into low-risk range, medium-risk range and high-risk range;

[0011] When the combined result is in the low-risk range, select the upper limit value of the threshold parameter range as the preliminary threshold; when the combined result is in the medium-risk range, select the middle value of the threshold parameter range as the preliminary threshold; when the combined result is in the high-risk range, select the lower limit value of the threshold parameter range as the preliminary threshold. Detect the development stage of wildfires in the key inspection areas of the target, and calculate the development trend parameters from this development stage to the next stage. Among them, the development trend parameters include the wildfire spread speed, wildfire intensity, wind speed, and wind direction. Obtain the development trend parameters of wildfires in multiple historical key inspection areas and the preliminary thresholds of these key inspection areas in the same time period, construct a data matrix, calculate the change amount of the preliminary threshold when the wildfire develops from one stage to the next, and obtain the median of multiple change amounts as the adjustment coefficient.

[0012] Furthermore, when the combined result is in the low-risk interval, the development trend parameters remain unchanged, and at this time the preliminary threshold remains unchanged; when the combined result is in the medium-risk interval, twice the development trend parameters are used as the data for calculating the preliminary threshold; when the combined result is in the high-risk interval, three times the development trend parameters are used as the data for calculating the preliminary threshold.

[0013] Furthermore, extracting the target key inspection area and calculating the wildfire risk level of the target key inspection area includes the following steps: Classify multiple key inspection areas to obtain the risk level factor group of each key inspection area, and determine the risk coefficient based on this risk level factor group; use the preliminary prediction model to predict wildfires on the lines in each key inspection area to obtain the corresponding wildfire prediction level; assign the risk coefficient of each key inspection area to this wildfire prediction level to obtain the corrected wildfire risk level. Among them, when classifying the key inspection areas, the main level and the secondary level are determined respectively, and the main level and the secondary level are combined into the preliminary level. The main level is determined by the historical short-circuit data between the poles, and the secondary level is determined by the pole number information and the pole distribution information.

[0014] A multi-stage transmission line wildfire identification and early warning analysis system includes: a risk level unit, which is used to obtain the transmission line area division information, extract the target key inspection area and calculate the wildfire risk level of the target key inspection area. A threshold acquisition unit, which is used to acquire the preset wildfire alarm threshold for the target key patrol area; wherein, the preset wildfire alarm threshold is obtained from the configuration parameters during wildfire early warning in the target key patrol area; a threshold adjustment unit, which is used to determine the adjustment range of the preset wildfire alarm threshold according to the wildfire risk level, and obtain the parameter interval of the preset wildfire alarm threshold; a threshold locking unit, which is used to identify the environmental information of the target key patrol area, determine the specific value in the parameter interval according to the environmental information, and obtain the preliminary threshold according to the specific value; a threshold correction unit, which is used to identify the wildfire development trend in the target key patrol area, determine the adjustment coefficient according to the wildfire development trend, and assign the adjustment coefficient to the preliminary threshold to obtain the alarm threshold; using the alarm threshold as the early warning parameter configuration for the corresponding wildfire development stage in the target key patrol area.

[0015] The beneficial effects of the embodiments of the present invention are: The multi-stage transmission line wildfire identification and early warning analysis method and system provided by the embodiments of the present invention utilize steps such as wildfire risk level division in key patrol areas, determination of the parameter interval for adjusting the preset wildfire alarm threshold, determination of the preliminary threshold, and optimization of the preliminary threshold, to achieve dynamic adjustment of the preset wildfire alarm threshold for transmission lines. In particular, the parameter interval of the preset wildfire alarm threshold is determined according to the wildfire risk level, the threshold is determined according to the environmental information, and considering the influence of the wildfire development trend on the transmission line wildfire, the adjustment coefficient is determined according to the wildfire development trend, and the adjustment coefficient is assigned to the preliminary threshold to obtain the alarm threshold. The key lies in using the wildfire development trend as the basis for threshold adjustment, truly achieving the purpose of multi-stage identification and early warning; by continuously adjusting the threshold (i.e., dynamic threshold) according to different parameters, the reliability and accuracy of wildfire identification and early warning can be greatly improved. Especially in the case of continuously changing environmental information, it provides more accurate early warning services for the safe operation of transmission lines.

[0016] Generally speaking, the multi-stage transmission line wildfire identification and early warning analysis method and system provided by the embodiments of the present invention use the dynamic threshold method to achieve multi-stage identification and early warning, adjust in real time according to environmental changes, greatly reduce false alarms and missed alarms, and improve the early warning accuracy; through multi-stage identification, the wildfire development trend is tracked throughout the process, and different thresholds are used for accurate early warning at different stages, greatly improving the accuracy of wildfire identification and early warning. Description of the Drawings

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for the embodiments will be briefly introduced below. It should be understood that the following accompanying drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related accompanying drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a flowchart of the main steps of the analysis method provided by the embodiments of the present invention; Figure 2 is Figure 1 a flowchart of one of the steps S3000 of the illustrated analysis method; Figure 3 is Figure 1 a flowchart of one of the steps S4000 of the illustrated analysis method; Figure 4 is Figure 1 a flowchart of one of the steps S5000 of the illustrated analysis method; Figure 5 is Figure 1 a flowchart of one of the steps S1000 of the illustrated analysis method; Figure 6 It is a modular schematic diagram of the analysis system provided by the embodiments of the present invention.

[0019] Icons: 6000 - analysis system, 6100 - risk level unit, 6200 - threshold acquisition unit, 6300 - threshold adjustment unit, 6400 - threshold locking unit, 6500 - threshold correction unit. Detailed implementation manners

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations.

[0021] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed present invention, but merely represents the selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0022] Example: Currently, the mainstream approach to identifying and warning transmission line wildfires is a fixed threshold approach. This approach has significant limitations. During system initialization, a fixed threshold is set based on experience and common environmental parameters as the critical value for determining wildfire occurrence. For example, using temperature monitoring as an example, if the ambient temperature or line surface temperature detected by the monitoring equipment exceeds this fixed threshold, and if the smoke concentration monitoring data also exceeds the preset smoke concentration threshold, the system triggers a warning signal.

[0023] However, the real-world environment is complex and diverse. For example, the ambient base temperature varies significantly across seasons. Summer temperatures are generally higher, while winter temperatures are lower. Fixed thresholds are difficult to accurately adapt to these seasonal variations. Furthermore, weather conditions vary. Direct sunlight on sunny days can increase ambient temperatures, while cloudy days can lower them. High humidity on rainy days can affect smoke diffusion, altering smoke concentration monitoring data. Fog can obstruct light transmission, affecting the accuracy of optical monitoring equipment. Furthermore, environmental characteristics vary significantly across geographic regions. Mountainous areas have complex terrain and distinct local microclimates, with morning-to-evening temperature differences reaching 10-20°C. In the early morning or evening, ambient temperatures may approach or even briefly exceed fixed thresholds due to normal temperature fluctuations. This can easily lead to frequent false alarms from the system, consuming significant manpower and resources to verify and resolve them. In hot, arid forested areas, even during the high summer temperatures, even small, early wildfire flames may appear, but the overall ambient temperature far exceeds the fixed threshold. The temperature fluctuations caused by the monitored wildfire are masked by the high ambient temperature. Smoke may also be less noticeable due to dense forest vegetation. If monitoring indicators do not exceed the fixed threshold, the system will be unable to detect potential wildfires in a timely manner, resulting in serious underreporting. A detailed analysis of technical principles shows that fixed thresholds fail to reflect the dynamic changes in environmental parameters and lack effective consideration of the real-time and relevance of data.

[0024] Furthermore, existing wildfire identification and early warning systems mostly rely on a single-stage assessment. However, wildfire development is a dynamic process. Relying solely on a fixed assessment standard fails to provide accurate and detailed early warning information tailored to the different stages of a wildfire's development, such as the incipient, developing, and active phases. This makes it difficult to formulate effective response strategies in advance during actual fire prevention efforts, hindering the practical needs of ensuring power transmission line safety. From the perspective of practical protection strategies and risk assessment, a single-stage early warning system cannot provide tiered, phased decision support for subsequent protective measures.

[0025] Based on the above reasons and ideas, please refer to Figure 1 This embodiment provides a multi-stage transmission line wildfire identification and warning analysis method, including the following steps: S1000: Obtain the information on the regional division of the power transmission line, extract the target key inspection areas and calculate the wildfire risk levels of the target key inspection areas; this step indicates that the entire area of the power transmission line is first divided, and the key inspection areas among them are extracted as the key detection objects. When screening the key inspection areas, multiple factors such as the historical number of fires, fire frequency, fire size, and fire range can be considered; then, the risk levels of all key inspection areas are determined. For example, the wildfire risk levels can be divided into low risk, medium risk, and high risk, so as to mark the wildfire risk levels of each key inspection area, and further facilitate the subsequent division, screening, and management of key inspection areas with different wildfire risk levels.

[0026] S2000: Obtain the preset wildfire alarm threshold of the target key inspection area; among them, the preset wildfire alarm threshold is obtained from the configuration parameters during the wildfire early warning of the target key inspection area; this step indicates that a preset wildfire alarm threshold needs to be set in advance when performing wildfire identification and early warning, so as to compare the preset wildfire alarm threshold with the actual data to determine whether to alarm; in conventional technologies, the preset wildfire alarm threshold may include multiple parameters, such as one or more of the temperature threshold, smoke concentration threshold, visible light threshold, spectral image threshold, sound threshold, and airborne particulate matter threshold.

[0027] S3000: Determine the adjustment range of the preset wildfire alarm threshold according to the wildfire risk level to obtain the parameter range of the preset wildfire alarm threshold; this step indicates that on the basis of the original fixed preset wildfire alarm threshold, the fixed wildfire early warning preset threshold is adjusted according to the wildfire risk level, determine the adjustment range of the wildfire early warning preset threshold according to the change of the wildfire risk level, and determine the parameter range of the preset wildfire alarm threshold according to the maximum and minimum values of the wildfire early warning preset threshold; for example, when the wildfire risk level is low risk, the temperature threshold in the preset wildfire alarm threshold is 35°C and the smoke concentration threshold is 50 ppm at this time; when the wildfire risk level changes from low risk to medium risk, the temperature threshold in the preset wildfire alarm threshold is 30°C and the smoke concentration threshold is 35 ppm at this time; when the wildfire risk level changes from medium risk to high risk, the temperature threshold in the preset wildfire alarm threshold is 25°C and the smoke concentration threshold is 20 ppm at this time; thus, it can be determined that for each change in the wildfire risk level, the adjustment range of the temperature threshold in the preset wildfire alarm threshold is 5°C, and the adjustment range of the smoke concentration threshold is 15 ppm. At the same time, the parameter range of the temperature threshold in the preset wildfire alarm threshold is 25°C - 30°C, and the parameter range of the smoke concentration threshold is 20 ppm - 50 ppm.

[0028] It should be noted that during the actual work process, the change ranges of the various parameters in the preset threshold of wildfire warning for the change of wildfire risk level may not be fixed. For example, when the wildfire risk level is low risk, the temperature threshold in the preset threshold of wildfire alarm is 35°C at this time. When the wildfire risk level changes from low risk to medium risk, the temperature threshold in the preset threshold of wildfire alarm is 30°C at this time. When the wildfire risk level changes from medium risk to high risk, the temperature threshold in the preset threshold of wildfire alarm is 26°C. In this case, the average value or median of multiple data can be selected as the adjustment range based on multiple historical data, or the adjustment range can be determined according to other strategies.

[0029] S4000: Identify the environmental information of the key inspection area of the target, determine the specific value in the parameter range according to the environmental information, and obtain the preliminary threshold based on the specific value. This step means that on the premise of determining the adjustment parameter range of the preset threshold of wildfire warning according to the wildfire risk level, further select the specific value of the wildfire preset threshold in the parameter range based on the environmental information of the key inspection area, so as to determine the preliminary threshold. For example, in two key inspection areas with the same wildfire risk level, the moisture content of the vegetation in the two key inspection areas is different. Under this condition, the temperature thresholds for wildfires to occur in the two key inspection areas must be different. Therefore, further selecting a specific value in the parameter range based on the environmental information on the basis of the preset threshold of wildfire alarm as the preliminary threshold can further improve the accuracy of the threshold and achieve further dynamic adjustment of the threshold.

[0030] S5000: Identify the development trend of wildfires in the key inspection area of the target, determine the adjustment coefficient according to the development trend of wildfires, assign the adjustment coefficient to the preliminary threshold, and obtain the alarm threshold. Use the alarm threshold as the warning parameter configuration for the corresponding wildfire development stage in the key inspection area of the target. This step means that the preliminary threshold is further adjusted and optimized through the development trend of wildfires to obtain the alarm threshold, realizing multi-stage identification and warning.

[0031] Through the above technical solutions, through steps such as the classification of the wildfire risk level in the key inspection area, the determination of the parameter range for adjusting the preset threshold of wildfire alarm, the determination of the preliminary threshold, and the optimization of the preliminary threshold, the dynamic adjustment of the preset threshold of wildfire alarm for transmission lines is realized. In particular, the parameter range of the preset threshold of wildfire alarm is determined by the wildfire risk level, the threshold is determined by the environmental information, and considering the impact of the wildfire development trend on the wildfire of transmission lines, the adjustment coefficient is determined by the wildfire development trend, and the adjustment coefficient is given to the preliminary threshold to obtain the alarm threshold. The key lies in using the wildfire development trend as the basis for threshold adjustment, and truly realizing the purpose of multi-stage identification and early warning; by continuously adjusting the threshold (i.e., the dynamic threshold) according to different parameters, the reliability and accuracy of wildfire identification and early warning can be greatly improved. Especially in the case of continuously changing environmental information, it provides more accurate early warning services for the safe operation of transmission lines.

[0032] Generally speaking, the multi-stage wildfire identification and early warning analysis method for transmission lines provided by the embodiments of the present invention realizes multi-stage identification and early warning by using the dynamic threshold method, adjusts in real time according to environmental changes, greatly reduces false alarms and missed alarms, and improves the accuracy of early warning; through multi-stage identification, it tracks the wildfire development trend throughout the process, and uses different thresholds for accurate early warning in different stages, greatly improving the accuracy of wildfire identification and early warning.

[0033] Please refer to Figure 2 , in this embodiment, the specific steps for determining the adjustment range of the preset threshold of wildfire alarm according to the wildfire risk level are as follows: S3100: Obtain the change value of the wildfire risk level, and determine the correlation coefficient between the preset threshold of wildfire alarm and the wildfire risk level; S3200: Assign the correlation coefficient to the change value of the wildfire risk level; S3300: Determine the adjustment range of the preset threshold of wildfire alarm to obtain the parameter range of the preset threshold of wildfire alarm; Among them, when the correlation coefficient is positive, if the wildfire risk level increases, the adjustment range of the preset threshold of wildfire alarm is positive, and the preset threshold of wildfire alarm increases; for example, when large-scale fire prevention isolation belts are built in the area where the transmission line is located or there is continuous monitoring by professional fire fighting forces, although the wildfire risk level increases due to some natural factors in the surrounding environment, due to the strengthening of human prevention and control measures, in order to avoid frequent triggering of early warnings due to some small interference factors, the threshold can be appropriately increased. For example, increase the smoke concentration early warning threshold, because there are more reliable prevention and control means at this time, and the real wildfire risk will not be missed due to the increase of the threshold. If the wildfire risk level decreases, the adjustment range of the preset threshold of wildfire alarm is negative, and the preset threshold of wildfire alarm decreases; for example: the wildfire risk level around the transmission line decreases due to short-term rainfall, but the rainfall duration is short. To prevent the rapid rebound of the wildfire risk, the thresholds of indicators such as temperature and humidity can be appropriately reduced to more closely monitor environmental changes.

[0034] When the correlation coefficient is negative, if the wildfire risk level increases, the adjustment range of the preset wildfire alarm threshold is negative, and the preset wildfire alarm threshold decreases; for example, in terms of meteorological conditions, if the temperature continues to rise, the humidity continues to decrease, and the wind speed increases, all of these increase the likelihood of a wildfire occurring. At this time, the warning thresholds for indicators such as temperature, humidity, and wind speed should be lowered. Suppose the normal temperature warning threshold is 35°C. When the risk level rises, the threshold can be lowered to 32°C to more timely capture the meteorological changes that may trigger a wildfire. In terms of vegetation factors, if the amount of flammable vegetation near the transmission line increases and the moisture content of the vegetation decreases, the thresholds related to vegetation should also be lowered, such as the moisture content threshold of the vegetation being lowered from 50% to 45% to early warn of the wildfire risk. If the wildfire risk level decreases, the adjustment range of the preset wildfire alarm threshold is positive, and the preset wildfire alarm threshold increases. For example, after a rainfall, the air humidity increases, the moisture content of the vegetation rises, and the wildfire risk level decreases. At this time, the humidity warning threshold can be increased from 40% to 50% to avoid frequent triggering of the warning due to normal fluctuations in the environment.

[0035] It should be noted that the change value of the wildfire risk level can be presented in the form of a percentage. For example, the wildfire risk level is 20% or the wildfire risk level is 70%. At this time, the larger the percentage value, the higher the risk level. It can also be presented in the form of regional division, such as low risk, medium risk, and high risk.

[0036] Another thing to note is that in some embodiments, the preset wildfire alarm threshold may include multiple parameters, such as one or more of the temperature threshold, smoke concentration threshold, visible light threshold, spectral image threshold, sound threshold, air particulate matter threshold, etc. When specifically calculating the correlation coefficient based on the wildfire risk level, the correlation coefficient can be calculated separately for each threshold parameter, or of course, the correlation coefficient can be calculated as a whole, or other methods can be used.

[0037] In some embodiments, when determining the correlation coefficient between the preset wildfire alarm threshold and the wildfire risk level, statistical analysis can be performed through multiple historical data. For example, obtain the change values of the wildfire risk levels in multiple key inspection areas, formulate a change curve graph of the wildfire risk level, and at the same time obtain the change curve graph of the preset wildfire alarm threshold in the same key inspection area; perform a correlation analysis on the wildfire risk level change curve graph and the preset wildfire alarm threshold change curve graph to determine the correlation coefficient between the preset wildfire alarm threshold and the wildfire risk level.

[0038] In some other embodiments, when determining the correlation coefficient between the preset wildfire alarm threshold and the wildfire risk level, the first change data set of the wildfire risk level change in any key patrol area can be obtained first, and then the second change data set of the preset wildfire alarm threshold change in the same key patrol area and the same historical period can be obtained; the first change data set and the second change data set are subjected to correlation analysis to obtain the correlation analysis result, and the correlation coefficient is calculated based on the correlation analysis result.

[0039] For example, the first change data set and the second change data set are sorted into a data matrix according to time points; the first change data set and the second change data set are brought into the formula ; Where and are the th observed values of the variable wildfire risk level and the preset wildfire alarm threshold respectively, and are the means of the variable wildfire risk level and the preset wildfire alarm threshold respectively, is the number of observed values; When is close to 1, there is a positive correlation between the change amount of the wildfire risk level and the change amount of the preset wildfire alarm threshold, that is, when the wildfire risk level increases, the preset wildfire alarm threshold increases accordingly; When is close to -1, there is a negative correlation between the change amount of the wildfire risk level and the change amount of the preset wildfire alarm threshold, that is, when the wildfire risk level increases, the preset wildfire alarm threshold decreases; Take as the correlation coefficient, assign the correlation coefficient to the change value of the wildfire risk level, and determine the adjustment range of the preset wildfire alarm threshold.

[0040] It should be noted that the purpose of determining the correlation coefficient between the preset wildfire alarm threshold and the wildfire risk level is to determine the adjustment standard of the preset wildfire alarm threshold when the wildfire risk level changes by one standard unit, so as to determine the overall parameter range of the preset wildfire alarm threshold.

[0041] For example, if the wildfire risk level is divided into four levels: A, B, C, and D, then the corresponding preset wildfire alarm thresholds can also be divided into a, b, c, d (a, b, c, d represent different thresholds respectively, for example, a: the temperature threshold is 35°C, and the smoke concentration threshold is 50 ppm; b: the temperature threshold is 45°C, and the smoke concentration threshold is 60 ppm). At this time, the parameter range of the preset wildfire alarm threshold is a - d.

[0042] Through the above steps, according to the change of the wildfire risk level, the corresponding correlation coefficient can be found, so as to make a corresponding adjustment to the preset threshold of wildfire alarm, change the traditional fixed threshold method to a dynamic threshold, and determine the parameter interval of the preset threshold of wildfire alarm according to the adjustment range of the wildfire risk level. By real-time monitoring the wildfire risk level and adjusting the preset threshold of wildfire alarm, the warning system can more accurately reflect the wildfire risk situation. In the initial stage of wildfire, the risk level is relatively low. Appropriately increasing the preset threshold of wildfire alarm can avoid frequent alarms caused by minor environmental changes and reduce false alarms. When the wildfire risk level rises, timely reducing the preset threshold of wildfire alarm can capture the increase of wildfire risk in advance, issue a warning in time, and gain precious time for prevention and control. In addition, adjusting the preset threshold of wildfire alarm according to the wildfire risk level can provide a basis for precise prevention and control. When the risk level is low, the preset threshold of wildfire alarm is relatively high, and the focus of prevention and control can be placed on daily monitoring and preventive measures. When the risk level rises and the preset threshold of wildfire alarm decreases, after the alarm is triggered, targeted prevention and control measures can be immediately launched, such as deploying professional fire-fighting teams and preparing fire-fighting equipment, to achieve precise prevention and control of wildfires. In addition, by adjusting the preset threshold of wildfire alarm according to the wildfire risk level, resources can be reasonably allocated according to the risk degree. When the risk level is low, resource investment can be reduced to avoid waste of resources. When the risk level rises and the preset threshold of wildfire alarm decreases, more resources can be timely allocated to high-risk areas to improve the utilization efficiency of resources and ensure that there are sufficient resources to deal with wildfires during critical periods.

[0043] In addition, during long-term work and research, we found that in addition to the influence of the wildfire risk level on the preset threshold of wildfire alarm, environmental factors will also affect the preset threshold of wildfire alarm. For example, in two key patrol areas A and B with the same wildfire risk level, if the environmental humidity in area A is relatively high and the environmental humidity in area B is relatively low, then the temperatures at which fires are triggered in areas A and B are different. If the preset threshold of wildfire alarm is adjusted unilaterally according to the above wildfire risk level, there will still be a certain amount of error, and these errors will undoubtedly affect the accuracy of overhead transmission line wildfire identification and warning.

[0044] Based on this factor, in order to further improve the accuracy of overhead transmission line wildfire identification and warning and reduce errors, and analyze the influence of environmental factors on the preset threshold of wildfire alarm, please refer to Figure 3 , in this embodiment, the environmental information includes meteorological information and vegetation information. Among them, the meteorological information includes: temperature information, wind information, rainfall information and humidity information; the vegetation information includes: plant species, coverage area, vegetation density and vegetation moisture content; S4100: Obtain the meteorological information within the key patrol area, and determine the first coefficient based on this meteorological information; S4200: Obtain the vegetation information within the key patrol area, and determine the second coefficient based on this vegetation information; S4300: Determine the merging weight, which includes the first coefficient weight and the second coefficient weight; S4400: Merge the result of multiplying the first coefficient by the first coefficient weight and the result of multiplying the second coefficient by the second coefficient weight, and determine the screening method for the specific value within the parameter range according to the merged result.

[0045] It should be noted that since the meteorological information contains multiple parameter indicators, in some embodiments, when determining the first coefficient, the values of multiple parameter indicators can be obtained first, and the first coefficient can be determined according to the value with the highest risk index. For example, in the conventional indicators, the temperature information is 35°C, the wind speed information is 2 m / s, the rainfall information is 0, and the humidity information is 35%. In the current actual indicators, the temperature information is 55°C, the wind speed information is 1 m / s, the rainfall information is 0, and the humidity information is 30%. Referring to the conventional information, it can be seen that the current riskiest indicator is the temperature indicator. Therefore, the first coefficient can be determined by the change range of the temperature information. For example, the first coefficient can be divided into 0 - 10, and the probability of the impact of each 1°C change in temperature on the fire can be analyzed through the historical database, so as to determine the specific value of the first coefficient.

[0046] In other embodiments, the parameters in the meteorological information can also be statistically analyzed as a whole. For example, in the conventional indicators, the temperature information is 35°C, the wind speed information is 2 m / s, the rainfall information is 0, and the humidity information is 35%. In the current actual indicators, the temperature information is 55°C, the wind speed information is 10 m / s, the rainfall information is 0, and the humidity information is 20%. The change values of the temperature information, wind speed information, and humidity information can be statistically analyzed, and then the overall risk degree of the meteorological information can be determined through statistical analysis, so as to determine the first coefficient. In addition, other conventional statistical methods can also be used for statistics, and the purpose is to statistically analyze the influence coefficient of the meteorological information on the wildfire threshold.

[0047] Similarly, the same statistical analysis methods as those for meteorological information can be used for the parameters in the vegetation information, and no further elaboration will be made here.

[0048] It should be noted here that the first coefficient and the second coefficient are used to calculate the influence degrees of meteorological information and vegetation information on the occurrence of fire. For example, assume that in scenario 1, in the meteorological information, the temperature and wind speed increase, and the rainfall and humidity remain unchanged; assume that in scenario 2, in the meteorological information, the wind speed, rainfall, and humidity increase, and the temperature decreases. Then it can be judged that the possibility of triggering a fire in scenario 1 is significantly greater than that in scenario 2. Then it also means that the first coefficient in scenario 1 is greater than that in scenario 2.

[0049] In addition, when determining the combined weights of the first coefficient and the second coefficient, in some embodiments, fixed combined weights can be used, such as the first coefficient * 50% + the second coefficient * 50%; variable combined weights can also be used. For example, both the first coefficient and the second coefficient are divided into intervals of 0 - 10. After determining the specific values of the first coefficient and the second coefficient, the combined weights are determined according to the magnitudes of their respective values. For instance, when the first coefficient is 4 and the second coefficient is 6, the combined weight of the first coefficient and the combined weight of the second coefficient can be determined as 40% and 60% respectively at this time. In addition, other distribution methods of combined weights can also be used and set according to actual requirements, which will not be elaborated here.

[0050] In addition, it is found in actual work that in historical data, although the risk levels of two key patrol areas are the same, there are still some differences in the adjustment ranges of their preset wildfire alarm thresholds. For example, when the wildfire risk level is classified as A, the corresponding preset wildfire alarm threshold can also be classified as a. However, at this time, the temperature threshold in the preset wildfire alarm threshold a is 32°C - 36°C, and the smoke concentration threshold is 45 - 55 ppm; this indicates that the preset wildfire alarm threshold determined by the wildfire risk level is an interval value.

[0051] Therefore, specifically, the specific steps of the screening method for determining the specific value in the parameter interval according to the combined result are as follows: Obtain the adjustment ranges of the preset wildfire alarm thresholds under multiple same wildfire risk levels; calculate the mean, median, and standard deviation of the adjustment ranges of the preset wildfire alarm thresholds under the same risk level; determine the boundary values of the parameter interval corresponding to the same wildfire risk level, and divide the parameter interval into a low - risk interval, a medium - risk interval, and a high - risk interval; Divide the combined result into percentages from low to high according to the low - risk interval, medium - risk interval, and high - risk interval. For example, low - risk interval: the combined result is 1 - 3; medium - risk interval: the combined result is 4 - 7; high - risk interval: the combined result is 8 - 10.

[0052] When the combined result is in the low - risk interval, select the upper limit value of the threshold parameter interval as the preliminary threshold; for example, in the preset wildfire alarm threshold at this time, when the temperature threshold interval is 45 - 50°C and the smoke concentration threshold interval is 60 - 65 ppm, then select the upper limit value (i.e., temperature threshold 50°C, smoke concentration threshold 65 ppm) as the preliminary threshold.

[0053] When the combined result is in the medium - risk interval, select the middle value of the threshold parameter interval as the preliminary threshold; for example, in the preset wildfire alarm threshold at this time, when the temperature threshold interval is 39 - 44°C and the smoke concentration threshold interval is 54 - 60 ppm, then select the middle value (i.e., temperature threshold 42°C, smoke concentration threshold 57 ppm) as the preliminary threshold.

[0054] When the combined result is in the high-risk range, select the lower limit value of the threshold parameter range as the preliminary threshold. When the temperature threshold range is 35 - 39 °C and the smoke concentration threshold range is 50 - 54 ppm, select the lower limit values (i.e., temperature threshold 35 °C and smoke concentration threshold 50 ppm) as the preliminary threshold.

[0055] Through the above design, on the premise of initially adjusting the preset wildfire alarm threshold according to the wildfire risk level for dynamic adjustment, environmental information can be introduced to further optimize the preset wildfire alarm threshold, improve the accuracy of the preset wildfire alarm threshold, and take into account the factors that may affect the preset wildfire alarm threshold, thereby making the overall recognition range more extensive. On the one hand, when the temperature rises, the humidity drops, the wind speed increases, and the vegetation is of a flammable type with a decreasing moisture content, the likelihood of a wildfire occurring increases. At this time, lowering the wildfire threshold can issue an early warning to avoid delaying the warning time due to improper threshold setting. Conversely, when the meteorological conditions are stable, the vegetation is not flammable and has a high moisture content, appropriately increasing the threshold can reduce unnecessary warnings and make the warning system more accurately reflect the wildfire risk. On the other hand, reasonably using environmental information to adjust the threshold helps to reasonably allocate forest fire prevention resources according to the actual risk level. In high-risk areas and periods, such as areas with high temperature, low humidity, strong wind, and dense flammable vegetation, after lowering the threshold, more human and material resources, such as professional fire-fighting teams and fire-fighting equipment, can be promptly allocated to ensure a rapid response when a wildfire occurs. In low-risk areas, increasing the threshold can reduce resource investment, avoid resource waste, and achieve efficient use of resources.

[0056] Please refer to Figure 4 , in this embodiment, the specific steps to identify the development trend of wildfires in the target key patrol area and determine the adjustment coefficient according to the wildfire development trend are as follows: S5100: Detect the development stage of wildfires in the target key patrol area, S5200: Calculate the development trend parameters from this wildfire development stage to the next stage, where the development trend parameters include the fire spread speed, fire intensity, wind speed, and wind direction; S5300: Obtain the development trend parameters of wildfires in multiple historical key patrol areas and the preliminary threshold of this key patrol area during the same time period, S5400: Construct a data matrix and calculate the change amount of the preliminary threshold from the wildfire development stage to the next stage, S5500: Obtain the median of multiple change amounts as the adjustment coefficient.

[0057] It should be noted that the wildfire development stage is determined by the historical wildfire development trend, and the wildfires are divided through different stages of historical wildfires; for example, level I, level II, level III,... level N, etc. The fire spread speed, fire intensity, and wind speed corresponding to each level are different, showing an increasing trend from level I to level N.

[0058] In some embodiments, the median of the change amount of the initial threshold brought about by the change in the wildfire development trend can be directly used as the adjustment coefficient, and this adjustment coefficient is assigned to the initial threshold to obtain the alarm threshold; the way of assigning the adjustment coefficient to the initial threshold can also adopt the way of direct addition. Of course, this way is only one of the multiple implementation ways and does not mean that there is only this one implementation way.

[0059] Through the above design, the wildfire development situation can be associated with the initial threshold, so as to adjust the initial threshold according to the wildfire development situation, making the initial threshold more accurate. Adjusting the threshold according to the wildfire development situation can enable the early warning system to accurately match the risks at each stage. In the initial stage, the threshold is appropriately increased to avoid frequent triggering of early warnings due to minor environmental changes; when entering the rapid spread stage, the threshold is decreased to timely capture risk signals and give early warnings, so as to gain precious time for prevention and control and improve the accuracy and timeliness of early warnings. In addition, adjusting the threshold according to the wildfire development situation can reasonably allocate resources according to the risk levels at different stages. In the initial stage of low risk, the resource input is reduced to avoid waste of resources; when the risk level increases with the development of the fire, after the threshold is decreased to trigger an early warning, more high-investment resources such as professional fire-fighting teams and large fire-fighting equipment are timely allocated to ensure the effective use of resources at critical stages, improve the resource utilization efficiency, and realize the optimal allocation of resources. In addition, optimizing and adjusting the initial threshold through the wildfire development situation can truly realize the multi-stage identification and early warning of wildfires, form a complete, coherent and effective multi-stage prevention and control system, and improve the overall effect of wildfire prevention and control.

[0060] In some other embodiments, considering the correlation between the wildfire risk level and the wildfire development situation, for example, when the wildfire risk level in the key patrol area is low risk, the wildfire development situation after its ignition is also at a low level.

[0061] Therefore, in this case, when the combined result is in the low-risk interval, the development trend parameter remains unchanged, and at this time the initial threshold remains unchanged; when the combined result is in the medium-risk interval, twice the development trend parameter is used as the data for calculating the initial threshold; when the combined result is in the high-risk interval, three times the development trend parameter is used as the data for calculating the initial threshold. By further limiting the development trend parameter in this form, the effect of limiting the initial threshold is achieved, and the initial threshold is further associated with the wildfire risk level.

[0062] It should be noted that the wildfire risk level can adopt conventional calculation methods, such as judging the wildfire risk through a prediction model, etc. In this embodiment, please refer to Figure 5, in order to effectively obtain the risk level of the key inspection area, extracting the target key inspection area and calculating the wildfire risk level of the target key inspection area includes the following steps: S1100: Divide the area according to the transmission line to obtain multiple sub-observation areas, identify the tower information for each sub-observation area, and obtain multiple key inspection areas; wherein, the tower information includes: the number of towers information, the tower distribution information, and the line state information between towers. This step means that the transmission lines within the prediction range are recorded as the lines to be monitored. The area width is delimited one kilometer to the left and right of the transmission line to obtain multiple sub-observation areas, thereby determining the range of the transmission lines that need to be detected currently. In addition, according to the tower information of the transmission line, the sub-observation areas are further divided, that is, through the number of towers information, the tower distribution information, and the line state information between towers, the key inspection areas are divided within multiple sub-observation areas, thereby narrowing the key detection range, facilitating in-depth monitoring and analysis of wildfire faults caused by transmission lines, and providing a basic guarantee for improving the prediction accuracy.

[0063] S1200: Classify the multiple key inspection areas to obtain a risk level factor group for each key inspection area, and determine the risk coefficient based on this risk level factor group; this step means that the risk level of the key inspection areas that have been divided is first represented, that is, classified, and each corresponding level covers different risk situations.

[0064] Considering that each key inspection area can be divided into different levels (with different risk situations), it is necessary to clarify the different risk situations or degrees covered by different levels during the division. Therefore, it is necessary to represent the data of different situations for different levels. On the one hand, it is convenient to grasp the different degrees of risk situations, and on the other hand, it is convenient for subsequent traceability analysis. Specifically, when classifying the key inspection areas, the main level and the secondary level are determined respectively, and the main level and the secondary level are combined into a preliminary level. The main level is determined by the historical short-circuit data between towers, and the secondary level is determined by the number of towers information and the tower distribution information.

[0065] This step means that the combination of the main level and the secondary level is used to represent different (risk) levels, which means that the main level determines the main risk degree representation, and the secondary level determines the auxiliary representation of the key risk degree.

[0066] S1300: Use the preliminary prediction model to predict wildfires for the lines in each key inspection area to obtain the corresponding wildfire prediction level; wherein, the preliminary prediction model refers to the existing transmission line wildfire prediction model, which predicts transmission line wildfires through real-time analysis of meteorology and vegetation.

[0067] In this embodiment, the wildfire prediction level can be displayed in the form of a percentage, i.e., 0% - 100%. The higher the percentage, the higher the probability of a power transmission line catching fire. The probability of fire obtained by the preliminary prediction model is classified according to the wildfire prediction level classification to obtain the wildfire prediction level.

[0068] S1400: Assign the risk coefficient of each key inspection area to the wildfire prediction level to obtain the corrected wildfire risk level. This step means that the risk coefficient determined through the analysis of risk factors is assigned to the wildfire prediction level predicted by the preliminary model of the key inspection area, and then the wildfire prediction level is further corrected and optimized to determine the final wildfire risk level.

[0069] Through the above technical solutions, by using steps such as refined area division, level division of key inspection areas, extraction of risk factor groups, determination of risk coefficients, preliminary prediction of wildfire risk levels, and correction and optimization of wildfire risk levels, high-precision prediction of wildfire fault information of transmission lines is achieved. Especially by determining key inspection areas based on tower information, dividing the levels of key inspection areas, further improving the screening conditions for prediction, and considering the impact of risk factors on wildfire faults of transmission lines, by obtaining the risk level factor group of each key inspection area, finally determining the risk coefficient, and assigning the risk coefficient to the wildfire prediction level predicted by the preliminary prediction model, the corrected wildfire risk level significantly improves the reliability and accuracy of wildfire fault prediction of transmission lines, especially in areas with different tower information.

[0070] Considering that the main inducing factor of wildfire faults in transmission lines is caused by short circuits in the transmission lines, when determining the main level, the main level is determined by the historical short circuit data of the lines between towers; the secondary level is determined by the tower quantity information and tower distribution information, and the specific steps are as follows: determine the main level based on the short circuit point and short circuit frequency; determine the first additional number of the secondary level based on the tower quantity information associated with the short circuit; then determine the second additional number of the secondary level based on the tower distribution information within the key inspection area where the short circuit occurs, and assign the first additional number and the second additional number to the main level to obtain the preliminary level.

[0071] Through the above steps, the main level can be determined based on the short circuit point and short circuit frequency, the first additional number of the secondary level can be determined based on the tower quantity information associated with the short circuit, and the second additional number of the secondary level can be determined based on the tower distribution information within the key inspection area where the short circuit occurs. Thus, the key factors and secondary factors can be effectively combined, and the key factors play a dominant role, followed by the secondary factors, ensuring the accuracy of area division; and when dividing the secondary level, the influence of multiple secondary factors is considered, so the multiple secondary factors are separately counted by setting additional numbers, thus ensuring the breadth and accuracy of the divided data.

[0072] In this embodiment, in order to further improve the accuracy of the predicted data, a risk level factor group for each key inspection area is obtained, a risk coefficient is determined based on this risk level factor group, and the risk coefficient of each key inspection area is assigned to the forest fire prediction level to obtain a corrected forest fire risk level, and the forest fire fault of the transmission line is predicted based on this forest fire risk level; in this process, first, a risk level factor group for each key inspection area needs to be obtained, which specifically includes the following steps: Determine the risk level factors in the key inspection area, screen the risk level factors based on the preliminary level, and use the screened risk level factors to construct the risk level factor group of this key inspection area. Among them, when screening the risk level factors, the following steps are carried out: Trace the main risk factors based on the short-circuit point and the short-circuit frequency, and conduct a preliminary screening of the risk level factors based on this main risk factor to obtain a preliminary screening factor group; determine the first secondary risk factor based on the number of poles associated with the short circuit, and conduct a screening of the preliminary screening factor group based on this first secondary risk factor to obtain a first screening factor group; determine the second secondary risk factor based on the specific distribution information of the poles in the key inspection area where the short circuit occurs, and conduct a screening of the first screening factor group based on this second secondary risk factor to obtain a second screening factor group; construct a risk level factor group based on the risk level factors in the second screening factor group.

[0073] The purpose of this design is to be able to quickly screen the data, and through hierarchical screening, the way of screening layer by layer can screen the data more deeply, thus further ensuring the accuracy of the final data, and further providing refined support for improving the accuracy of the prediction level.

[0074] Among them, when tracing the main risk factors based on the short-circuit point, it is mainly based on the specific coordinate position of the short-circuit point. By judging the position of the short-circuit point, the influence range of the forest fire, the development trend of the forest fire, and the difficulty of fire fighting can be further analyzed. In addition, based on the short-circuit point, it can also be further analyzed what causes different short-circuit points, so as to analyze what the main risk factors are, and thus make it into a data-based statistic to provide data-based guarantee for the subsequent analysis of the main short-circuit risk factors.

[0075] In the main risk analysis based on the short-circuit frequency, it is mainly based on the number of short circuits in a certain key inspection area; trace the main risk factors that cause the short circuit through the number of short circuits.

[0076] Specifically, the main risk factors are mainly divided into: meteorological factors, vegetation factors, line factors, and special external factors. By collecting data on short-circuit points and short-circuit frequencies, a main risk database is established. By analyzing the main risk database, the relationship between the corresponding short-circuit points, short-circuit frequencies, and short-circuit risk degrees is obtained, thereby forming a main risk data correspondence table. In the main risk data correspondence table, the short-circuit points, short-circuit frequencies, and main risk factors are corresponded. Different main risk factors are corresponded through different short-circuit points and / or short-circuit frequencies, and thus different main risk coefficients are also corresponded; furthermore, different main risk coefficients can be corresponded through different main risk factors.

[0077] In this embodiment, when determining the first secondary risk factor based on the number information of the poles and towers associated with the short circuit, it includes: marking the poles and towers where the short circuit occurs as risk poles and towers, obtaining the number of all risk poles and towers in this key inspection area, calculating the proportion of risk poles and towers in this key inspection area, and obtaining the first secondary risk factor; determining the second secondary risk factor based on the specific distribution information of the poles and towers in the key inspection area where the short circuit occurs includes: analyzing the distribution information of all risk poles and towers, obtaining the distribution shape of the risk poles and towers in this key inspection area, and obtaining the second secondary risk factor according to the distribution pattern of the risk poles and towers.

[0078] By collecting data related to short circuits, including information such as the short-circuit occurrence time, the number of associated poles and towers, the positions of the poles and towers, the surrounding environment, and the short-circuit type, a first secondary risk database is established. Through the long-term accumulation and analysis of these data, the above information is combined with the number of associated poles and towers, so as to correspond the risk degree of different short circuits through the information of the associated poles and towers.

[0079] In this embodiment, a power transmission line wildfire identification and early warning analysis system 6000 is also provided. Please refer to Figure 6 the modular schematic diagram of the power transmission line wildfire identification and early warning analysis system in

[0080] Specifically, the power transmission line wildfire fault dynamic prediction analysis system includes: a risk level unit 6100, a threshold acquisition unit 6200, a threshold adjustment unit 6300, a threshold locking unit 6400, and a threshold correction unit 6500; among them, the risk level unit 6100 is used to obtain the power transmission line area division information, extract the target key inspection area, and calculate the wildfire risk level of the target key inspection area. The threshold acquisition unit 6200 is used to acquire the preset wildfire alarm threshold for the target key patrol area; wherein, the preset wildfire alarm threshold is obtained from the configuration parameters during the wildfire early warning in the target key patrol area; the threshold adjustment unit 6300 is used to determine the adjustment range of the preset wildfire alarm threshold according to the wildfire risk level, so as to obtain the parameter interval of the preset wildfire alarm threshold; the threshold locking unit 6400 is used to identify the environmental information of the target key patrol area, determine the specific value in the parameter interval according to the environmental information, and obtain the preliminary threshold according to the specific value; the threshold correction unit 6500 is used to identify the development trend of the wildfire in the target key patrol area, determine the adjustment coefficient according to the development trend of the wildfire, assign the adjustment coefficient to the preliminary threshold, and obtain the alarm threshold; the alarm threshold is used as the warning parameter configuration for the corresponding wildfire development stage in the target key patrol area.

[0081] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A multi-stage method for identifying and warning of wildfires on transmission lines, characterized in that, It includes the following steps: S1000: Obtain the information on the regional division of the transmission line, extract the target key inspection area and calculate the wildfire risk level of the target key inspection area; S2000: Obtain the preset threshold for wildfire alarm in the target key inspection area; wherein, the preset threshold for wildfire alarm is obtained from the configuration parameters during the wildfire early warning in the target key inspection area; S3000: Determine the adjustment range of the preset threshold for wildfire alarm according to the wildfire risk level, and obtain the parameter interval of the preset threshold for wildfire alarm; S4000: Identify the environmental information of the target key inspection area, determine the specific value in the parameter interval according to the environmental information, and obtain the preliminary threshold according to the specific value; S5000: Identify the development trend of wildfire in the target key inspection area, determine the adjustment coefficient according to the development trend of wildfire, assign the adjustment coefficient to the preliminary threshold to obtain the alarm threshold; use the alarm threshold as the early warning parameter configuration for the corresponding wildfire development stage in the target key inspection area.

2. The multi-stage transmission line wildfire identification and early warning analysis method according to claim 1, characterized in that The specific steps for determining the adjustment range of the preset threshold for wildfire alarm according to the wildfire risk level are as follows: Obtain the change value of the wildfire risk level, determine the correlation coefficient between the preset threshold for wildfire alarm and the wildfire risk level, assign the correlation coefficient to the change value of the wildfire risk level, determine the adjustment range of the preset threshold for wildfire alarm, and obtain the parameter interval of the preset threshold for wildfire alarm; Wherein, when the correlation coefficient is positive, if the wildfire risk level increases, the adjustment range of the preset threshold for wildfire alarm is positive, and the preset threshold for wildfire alarm increases; if the wildfire risk level decreases, the adjustment range of the preset threshold for wildfire alarm is negative, and the preset threshold for wildfire alarm decreases; When the correlation coefficient is negative, if the wildfire risk level increases, the adjustment range of the preset threshold for wildfire alarm is negative, and the preset threshold for wildfire alarm decreases; if the wildfire risk level decreases, the adjustment range of the preset threshold for wildfire alarm is positive, and the preset threshold for wildfire alarm increases.

3. The multi-stage transmission line wildfire identification and early warning analysis method according to claim 2, wherein, Determining the correlation coefficient between the preset threshold for wildfire alarm and the wildfire risk level includes the following steps: Obtain the first change data set of the wildfire risk level change in any key inspection area, and then obtain the second change data set of the change in the preset threshold for wildfire alarm in the same key inspection area during the same historical period; perform correlation analysis on the first change data set and the second change data set to obtain the correlation analysis result, and calculate the correlation coefficient based on the correlation analysis result.

4. The multi-stage transmission line wildfire identification and early warning analysis method according to claim 3, characterized in that Organize the first change data set and the second change data set into a data matrix according to the time point; bring the first change data set and the second change data set into ； Among them, and are the th observed values of the variable wildfire risk level and the preset threshold of wildfire alarm respectively, and are the means of the variable wildfire risk level and the preset threshold of wildfire alarm respectively, is the number of observed values; When When it is close to 1, there is a positive correlation between the change in the wildfire risk level and the change in the preset wildfire alarm threshold, that is, as the wildfire risk level increases, the preset wildfire alarm threshold increases accordingly; When When approaching -1, there is a negative correlation between the change in the wildfire risk level and the change in the preset threshold for wildfire alarms, that is, as the wildfire risk level increases, the preset threshold for wildfire alarms decreases; Taking as the correlation coefficient, assigning the correlation coefficient to the change value of the wildfire risk level, and determining the adjustment range of the wildfire alarm preset threshold.

5. The multi-stage transmission line wildfire identification and early warning analysis method according to claim 1 or 4, characterized in that, Identify the environmental information of the target key inspection area, and determine the specific value in the parameter interval according to the environmental information. The specific steps are as follows: The environmental information includes meteorological information and vegetation information. Among them, the meteorological information includes: temperature information, wind information, rainfall information and humidity information; the vegetation information includes: plant species, coverage area, vegetation density and vegetation moisture content; Obtain the meteorological information within the key inspection area, and determine the first coefficient based on this meteorological information; obtain the vegetation information within the key inspection area, and determine the second coefficient based on this vegetation information, and determine the combined weight, where the combined weight includes the first coefficient weight and the second coefficient weight. Combine the result of assigning the first coefficient weight to the first coefficient with the result of assigning the second coefficient weight to the second coefficient, and determine the screening method for specific values in the parameter interval according to the combined result.

6. The multi-stage transmission line wildfire identification and early warning analysis method according to claim 5, characterized in that The specific steps for determining the screening method for specific values in the parameter interval according to the combined result are as follows: Obtain the adjustment range of the preset wildfire alarm threshold under multiple same wildfire risk levels; Calculate the mean, median, and standard deviation of the adjustment range of the preset wildfire alarm threshold under the same risk level; Determine the boundary values of the parameter interval corresponding to the same wildfire risk level, and divide the parameter interval into a low-risk interval, a medium-risk interval, and a high-risk interval; When the combined result is in the low-risk interval, select the upper limit value of the threshold parameter interval as the preliminary threshold; When the combined result is in the medium-risk interval, select the middle value of the threshold parameter interval as the preliminary threshold; When the combined result is in the high-risk interval, select the lower limit value of the threshold parameter interval as the preliminary threshold.

7. The multi-stage transmission line wildfire identification and early warning analysis method according to claim 6, characterized in that, Identify the development trend of wildfires in the target key inspection area. The specific steps for determining the adjustment coefficient according to the development trend of wildfires are as follows: Detect the development stage of wildfires in the target key inspection area, and calculate the development trend parameters from this development stage to the next stage. Among them, the development trend parameters include the wildfire spread speed, wildfire intensity, wind speed, and wind direction; Obtain the development trend parameters of wildfires in multiple historical key inspection areas and the preliminary threshold of this key inspection area in the same time period, construct a data matrix, calculate the change amount of the preliminary threshold when the wildfire develops from one stage to the next stage, and obtain the median of multiple change amounts as the adjustment coefficient.

8. The multi-stage transmission line wildfire identification and early warning analysis method according to claim 7, characterized in that When the combined result is in the low-risk interval, the development trend parameters remain unchanged, and at this time the preliminary threshold remains unchanged; When the combined result is in the medium-risk interval, use twice the development trend parameters as the data for calculating the preliminary threshold; When the combined result is in the high-risk interval, use three times the development trend parameters as the data for calculating the preliminary threshold.

9. The multi-stage transmission line wildfire identification and early warning analysis method according to claim 1, characterized in that Extract the target key inspection area and calculate the wildfire risk level of this target key inspection area, including the following steps: Classify multiple said key inspection areas to obtain a risk level factor group for each said key inspection area, and determine the risk coefficient based on this risk level factor group; Use the preliminary prediction model to predict wildfires for the lines within each said key inspection area to obtain the corresponding wildfire prediction levels; Assign the risk coefficient of each key inspection area to the said wildfire prediction level to obtain the corrected wildfire risk level; Among them, when classifying the key inspection areas, the main level and the secondary level are determined respectively, and the main level and the secondary level are combined into a preliminary level. The main level is determined by the historical short-circuit data between the poles and towers, and the secondary level is determined by the pole and tower quantity information and the pole and tower distribution information.

10. A multi-stage transmission line wildfire identification and early warning analysis system, characterized in that, Including: A risk level unit, which is used to obtain the transmission line area division information, extract the target key inspection area and calculate the wildfire risk level of the target key inspection area; A threshold acquisition unit, which is used to obtain the preset wildfire alarm threshold of the target key inspection area; among them, the preset wildfire alarm threshold is obtained from the configuration parameters during the wildfire early warning of the target key inspection area; A threshold adjustment unit, which is used to determine the adjustment range of the preset wildfire alarm threshold according to the wildfire risk level, and obtain the parameter interval of the preset wildfire alarm threshold; A threshold locking unit, which is used to identify the environmental information of the target key inspection area, determine the specific value in the parameter interval according to the environmental information, and obtain a preliminary threshold according to the specific value; and A threshold correction unit, which is used to identify the wildfire development trend of the target key inspection area, determine an adjustment coefficient according to the wildfire development trend, and assign the adjustment coefficient to the preliminary threshold to obtain an alarm threshold; Using the alarm threshold as the warning parameter configuration for the corresponding wildfire development stage of the target key inspection area.

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