Power transmission line forest fire early warning system and method based on three-dimensional laser radar

Abstract

The invention relates to the technical field of early warning data management, and particularly discloses a power transmission line forest fire early warning system and method based on a three-dimensional laser radar, and the system guarantees the accuracy and timeliness of data through the precise collection and evaluation of meteorological parameters of a target region and the intelligent initialization of data collection process parameters. After the data acquisition result of the target area is obtained, each abnormal environment area can be accurately positioned, and the fire danger dynamic parameters are deeply acquired and analyzed, so that the data acquisition process parameters are flexibly adjusted, the fire danger is finely monitored, in addition, the abnormal risk level of each abnormal environment area is intelligently matched, whether forest fire early warning needs to be given out is judged in time, and the early warning efficiency is improved. The accuracy and timeliness of early warning are effectively improved, meanwhile, the three-dimensional laser radar technology is used for accurately positioning each section of abnormal power transmission line, the operation abnormal index of the abnormal power transmission line in the third period is evaluated, and a scientific basis is provided for protection and early warning of the power transmission line.

Description

Technical Field

[0001] The present invention relates to the technical field of early warning data management, and in particular to a power transmission line forest fire early warning system and method based on three-dimensional laser radar. Background Art

[0002] In the field of safe operation and maintenance of power grids, wildfire disasters on transmission lines have become a core risk threatening the stable operation of power systems due to their strong destructiveness, rapid evolution, and complex disaster-causing mechanisms. Therefore, intelligent analysis is needed to achieve accurate perception of fire conditions, quantitative threat assessment, and active early warning, providing a new generation of technical support for power grid disaster prevention and mitigation.

[0003] For example, the invention patent with publication number CN104268655A discloses a method for warning wildfires on power transmission lines. Based on the historical satellite monitoring fire point data, precipitation data, and industrial and agricultural fire use customs in the target area, it calculates the current wildfire warning level of the power transmission lines in the same period, and creates a warning table based on the warning level.

[0004] For example, the invention patent with publication number CN118822247A discloses a transmission line wildfire disaster warning method that takes into account the physical process of wildfire disasters. A transmission line wildfire disaster physical process model is constructed based on the transmission line wildfire disaster physical process model; wildfire risk disaster assessment indicators are constructed based on the transmission line wildfire disaster physical process model, including: combustible material risk level, wildfire meteorological risk level, ignition source risk level and wildfire spread risk level; the warning area is divided into multiple grids of a fixed size, and each grid is evaluated by the wildfire risk disaster assessment indicator to obtain the wildfire risk disaster assessment index value of each grid; the wildfire risk disaster level of each tower is evaluated by the wildfire risk disaster assessment index values ​​of all grids within a fixed range of each tower in the transmission line, and then the wildfire risk disaster level of the entire transmission line is evaluated based on the wildfire risk disaster level of all towers.

[0005] However, in the process of implementing the embodiments of the present application, it was found that the above-mentioned technology has at least the following technical problems: in the process of warning of wildfires on power transmission lines, the core logic of the existing technology is still anchored in the passive response to the current state, and can only identify the signs of fire that have appeared or trigger alarms based on preset rules, but cannot achieve active warnings through forward-looking deduction of environmental risk chains. It lacks dynamic perception of the cumulative effects of environmental risks over time, often misses the best intervention window to suppress the spread of disasters, and significantly reduces the timeliness of warnings and the initiative of prevention and control. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a power transmission line forest fire warning system and method based on three-dimensional laser radar, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above object, the present invention is realized by the following technical solutions: In the first aspect of the present invention, a transmission line wildfire warning system based on a three-dimensional lidar is provided, including: an environmental analysis module, configured to collect and evaluate meteorological parameters of a target area, thereby initializing the data acquisition process parameters of the target area, obtaining the data acquisition results of the target area, and locating each abnormal environmental area; a wildfire warning module, configured to collect and analyze the fire risk dynamic parameters of each abnormal environmental area, thereby adjusting the data acquisition process parameters of each abnormal environmental area, matching the abnormal risk level of each abnormal environmental area at the same time, and determining whether to issue a wildfire warning for each abnormal environmental area; a line warning module, configured to locate each abnormal transmission line segment based on the three-dimensional lidar, evaluate the operation abnormality index of each abnormal transmission line segment in the third period, and determine whether to issue a protection warning for each abnormal transmission line segment.

[0008] As a further solution, the specific initialization process of the data acquisition process parameters of the target area is as follows: The parameter sets of each data acquisition device corresponding to each environmental anomaly influence coefficient interval are stored in the warning database. The environmental anomaly influence coefficient of the target area in the first period is compared with the environmental anomaly influence coefficient intervals stored in the warning database. If the environmental anomaly influence coefficient of the target area in the first period belongs to a certain environmental anomaly influence coefficient interval stored in the warning database, the parameter sets of each data acquisition device corresponding to the environmental anomaly influence coefficient interval stored in the warning database are initialized and set as the parameter sets of each data acquisition device to which the target area belongs, thereby completing the initialization of the data acquisition process parameters of the target area.

[0009] As a further solution, the specific adjustment process of the data acquisition process parameters of each abnormal environmental area is as follows: By analyzing the fire risk dynamic parameters of each abnormal environmental area, the abnormal risk factors of each abnormal environmental area in the second period are obtained; the parameter sets of each data acquisition device to which each abnormal environmental area belongs are obtained, and according to the abnormal risk factors of each abnormal environmental area in the second period, the parameter adjustment sets of each data acquisition device to which each abnormal environmental area belongs are matched from the warning database, thereby updating the parameter sets of each data acquisition device to which each abnormal environmental area belongs, and completing the adjustment of the data acquisition process parameters of each abnormal environmental area.

[0010] As a further solution, the determination of whether to issue a wildfire warning for each abnormal environmental area is as follows: The specific determination process is: According to the abnormal risk factors of each abnormal environmental area in the second period, match the abnormal levels of each abnormal environmental area in the second period. If the abnormal level of an abnormal environmental area in the second period belongs to the abnormal warning level, it is determined to issue a wildfire warning for this abnormal environmental area; if the abnormal level of an abnormal environmental area in the second period does not belong to the abnormal warning level, it is determined not to issue a wildfire warning for this abnormal environmental area. At the same time, mark the abnormal environmental areas corresponding to the abnormal levels that do not belong to the abnormal warning level as each abnormal monitoring environmental area, and obtain the abnormal risk factors of each abnormal monitoring environmental area in the second period; obtain the environmental anomaly influence coefficient of the target area in the prediction period, perform a difference processing with the environmental anomaly influence coefficient of the target area in the second period, and mark the processing result as the environmental anomaly influence deviation coefficient of the target area. Then, match the abnormal risk factor correction value from the warning database and correct the abnormal risk factors of each abnormal monitoring environmental area in the second period, so as to re-match the abnormal levels of each abnormal monitoring environmental area in the second period. If the abnormal level of an abnormal monitoring environmental area in the second period belongs to the abnormal warning level, it is determined to issue a wildfire prediction warning for this abnormal monitoring environmental area; if the abnormal level of an abnormal monitoring environmental area in the second period does not belong to the abnormal warning level, it is determined not to issue a wildfire prediction warning for this abnormal monitoring environmental area.

[0011] As a further solution, the determination of whether to issue a protection warning for each abnormal transmission line section is as follows: The specific protection process is: According to the operation abnormal index of each abnormal transmission line section in the third period, match the protection plan of each abnormal transmission line section from the warning database. At the same time, obtain the operation abnormal index corresponding to the protection plan of each abnormal transmission line section and mark it as the reference operation abnormal index of each abnormal transmission line section. Perform a difference processing with the operation abnormal index of each abnormal transmission line section in the third period, and mark the processing result as the operation abnormal index deviation value of each abnormal transmission line section. If the operation abnormal index deviation value of an abnormal transmission line section is less than or equal to the reference operation abnormal index deviation value, increase the adjustment of the protection plan for this abnormal transmission line section; if the operation abnormal index deviation value of an abnormal transmission line section is greater than the reference operation abnormal index deviation value, decrease the adjustment of the protection plan for this abnormal transmission line section; obtain the operation abnormal threshold of each abnormal transmission line section and compare it with the operation abnormal index of the corresponding abnormal transmission line section in the third period. If the operation abnormal index of an abnormal transmission line section in the third period is less than or equal to the corresponding operation abnormal threshold, it is determined not to issue a warning for this abnormal transmission line section; if the operation abnormal index of an abnormal transmission line section in the third period is greater than the corresponding operation abnormal threshold, it is determined to issue a warning for this abnormal transmission line section.

[0012] The second aspect of the present invention provides a method for warning of forest fires on transmission lines based on 3D lidar, including: Step 1, collecting and evaluating meteorological parameters of the target area, thereby initializing the data acquisition process parameters of the target area, obtaining the data acquisition results of the target area, and locating each abnormal environmental area; Step 2, collecting and analyzing the fire risk dynamic parameters of each abnormal environmental area, thereby adjusting the data acquisition process parameters of each abnormal environmental area, matching the abnormal risk level of each abnormal environmental area at the same time, and determining whether to issue a forest fire warning for each abnormal environmental area; Step 3, locating each section of abnormal transmission line based on 3D lidar, evaluating the operation abnormality index of each section of abnormal transmission line in the third cycle, and determining whether to issue a protection warning for each section of abnormal transmission line.

[0013] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention provides a system and method for warning of forest fires on transmission lines based on 3D lidar. By accurately collecting and evaluating the meteorological parameters of the target area, intelligently initializing the data acquisition process parameters, ensuring the accuracy and timeliness of the data, after obtaining the data acquisition results of the target area, it can accurately locate each abnormal environmental area, deeply collect and analyze its fire risk dynamic parameters, thereby flexibly adjusting the data acquisition process parameters, realizing the fine monitoring of fire risks. In addition, it intelligently matches the abnormal risk levels of each abnormal environmental area, timely determines whether a forest fire warning needs to be issued, effectively improving the accuracy and timeliness of the warning. At the same time, using 3D lidar technology to accurately locate each section of abnormal transmission line and evaluating its operation abnormality index in the third cycle provides a scientific basis for the protection warning of the transmission line.

[0014] (2) The present invention calculates the difference in the environmental anomaly influence coefficient between the prediction cycle and the second cycle of the target area to obtain the environmental anomaly influence deviation coefficient, and uses the correction value of the abnormal risk factor in the warning database to accurately correct the abnormal risk factor in the second cycle, re-match the abnormal level, thereby significantly improving the accuracy and pertinence of the forest fire prediction and warning, effectively avoiding false alarms and missed alarms, and timely warning the abnormal monitoring environmental areas that truly have the risk of forest fires, providing more scientific and reliable decision-making support for forest fire prevention and control.

[0015] (3) The present invention realizes the accurate protection warning of each section of abnormal transmission line by quantifying the degree of operation abnormality, not only improving the accuracy and timeliness of the warning, but also being able to take timely measures to avoid line failures, ensuring the stable operation of the power system. At the same time, the quantitative warning can also provide a scientific basis for line maintenance, optimize the allocation of maintenance resources, and improve the safety and reliability of the overall transmission line. Description of the Drawings

[0016] The present invention will be further described with reference to the accompanying drawings. However, the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the following drawings without creative efforts.

[0017] Figure 1 It is a schematic diagram of the connection of the system modules of the present invention.

[0018] Figure 2 It is a schematic diagram of the flow of the method steps of the present invention. Detailed implementation manners

[0019] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0020] Referring to Figure 1 As shown, the first aspect of the present invention provides a transmission line wildfire warning system based on a three-dimensional lidar, including: an environmental analysis module, a wildfire warning module, a line warning module, and a warning database.

[0021] The warning database is used to store the parameters involved in the transmission line wildfire warning system based on the three-dimensional lidar.

[0022] The environmental analysis module is connected to the wildfire warning module, the wildfire warning module is connected to the line warning module, and the environmental analysis module, the wildfire warning module, and the line warning module are all connected to the warning database.

[0023] The environmental analysis module is used to collect and evaluate the meteorological parameters of the target area, thereby initializing the data acquisition process parameters of the target area, obtaining the data acquisition results of the target area, and locating each abnormal environmental area.

[0024] The above-mentioned locating of each abnormal environmental area means using the three-dimensional lidar scanning technology to quickly scan the transmission lines in the forest area, synchronously collecting the infrared and visible image data on site, and constructing a transmission line model in the forest area through three-dimensional modeling technology (such as geographical information spatial analysis and integration). The transmission line model in the forest area is the data acquisition result of the target area. The environmental areas in the transmission line model in the forest area where the temperature is greater than the preset temperature threshold are automatically marked as abnormal environmental areas, and the abnormal environmental areas are divided according to the area division criteria preset by the data analysis personnel, so as to locate each abnormal environmental area (such as forest areas, etc.); the above-mentioned temperature threshold represents the maximum allowable temperature and is extracted from the warning database.

[0025] Specifically, the meteorological parameters of the target area are collected and evaluated. The specific evaluation process is as follows: The meteorological parameters of the target area include the average air pressure in the first cycle in the target area, the total rainfall in the first cycle in the target area, and the total solar radiation accumulation in the first cycle in the target area. The above-mentioned first cycle refers to the time period for analyzing meteorological data, and the specific duration is determined by meteorological data analysts. The above-mentioned average air pressure represents the average value of the atmospheric pressure in the target area within the first cycle. The above-mentioned total rainfall refers to the total amount of precipitation in the target area within the first cycle, usually measured in millimeters, representing the depth of precipitation accumulated on a horizontal plane. The above-mentioned total solar radiation accumulation represents the total amount of solar radiation energy received in the target area within the first cycle, reflecting the energy input of solar radiation to the target area. The average air pressure, total rainfall, and total solar radiation accumulation can all be obtained from the meteorological station to which the target area belongs.

[0026] By introducing influence coefficients, the influence degree of the proportional relationship between the average air pressure deviation value and the defined average air pressure deviation value on the environmental anomaly influence coefficient, the influence degree of the proportional relationship between the total rainfall and the defined total rainfall on the environmental anomaly influence coefficient, and the influence degree of the proportional relationship between the total solar radiation accumulation and the defined total solar radiation accumulation on the environmental anomaly influence coefficient are quantified respectively. Then, the various influence degrees are coupled to obtain the environmental anomaly influence coefficient of the target area in the first cycle. The environmental anomaly influence coefficient of the target area in the first cycle is used to characterize the degree of increase in the risk of wildfire occurrence due to abnormal environmental conditions in the target area in the first cycle. The specific expression is: ; ; In the formula, is the environmental anomaly influence coefficient of the target area in the first cycle, is the average air pressure deviation value of the target area in the first cycle, is the total rainfall of the target area in the first cycle, is the total solar radiation accumulation of the target area in the first cycle, is the defined average air pressure deviation value preset in the early warning database, is the defined total rainfall preset in the early warning database, is the defined total solar radiation accumulation preset in the early warning database, is the average air pressure of the target area in the first cycle, is the reference average air pressure preset in the early warning database, is the influence coefficient corresponding to the average air pressure deviation value preset in the early warning database, is the influence coefficient corresponding to the total rainfall preset in the early warning database, Is the influence coefficient corresponding to the preset solar radiation accumulation in the early warning database.

[0027] The above-defined average air pressure deviation value represents the maximum allowable value of the average air pressure deviation; the above-defined total rainfall represents the minimum allowable value of the total rainfall; the above-defined solar radiation accumulation represents the maximum allowable value of the solar radiation accumulation; the above reference average air pressure represents the reference value of the average air pressure; the above average air pressure deviation value represents the degree of deviation between the average air pressure and the reference average air pressure.

[0028] The influence coefficient corresponding to the above average air pressure deviation value represents the degree of influence of the unit value of the average air pressure deviation on the environmental anomaly influence coefficient; the influence coefficient corresponding to the above total rainfall represents the degree of influence of the unit value of the total rainfall on the environmental anomaly influence coefficient; the influence coefficient corresponding to the above solar radiation accumulation represents the degree of influence of the unit value of the solar radiation accumulation on the environmental anomaly influence coefficient; the corresponding relationship between the average air pressure deviation value, the total rainfall, and the solar radiation accumulation and their corresponding influence coefficients is stored in the early warning database. For example, when the average air pressure deviation value, the total rainfall, and the solar radiation accumulation are input into the early warning database, the early warning database can match the influence coefficient corresponding to the average air pressure deviation value, the influence coefficient corresponding to the total rainfall, and the influence coefficient corresponding to the solar radiation accumulation, and their value ranges are all between 0 and 1.

[0029] It should be explained that a relatively large average air pressure deviation value means that the environmental air pressure in the target area shows a significant abnormal state. This change is often closely related to the adjustment of the atmospheric circulation and the change of air flow movement. Specifically, the prevalence of high-pressure areas is often accompanied by sinking airflows and relatively dry air. Such meteorological conditions are not conducive to the formation and maintenance of clouds, thus inhibiting the rainfall process, resulting in a decrease in total rainfall and the area tending to be dry. At the same time, high-pressure areas are usually accompanied by clear weather, thin clouds, and improved atmospheric transparency, enabling solar radiation to reach the ground more directly and efficiently, thereby promoting the increase in solar radiation accumulation. This change not only intensifies the heating process of the ground but also further reduces the evaporation of surface water, further drying the environment. The interaction of the above meteorological parameters jointly reveals the abnormal state of the environment in the target area. This abnormality is not only reflected in the significant changes in air pressure, rainfall, and solar radiation but may also indicate an increased risk of wildfires. The dry environment, reduced rainfall, and enhanced solar radiation all provide favorable conditions for the occurrence of wildfires. Therefore, through continuous monitoring and analysis of these meteorological parameters, especially using the comprehensive index of the environmental anomaly influence coefficient, the degree of environmental anomaly in the target area can be more accurately evaluated, thereby guiding the adjustment and optimization of data collection equipment, ensuring the accuracy and timeliness of monitoring data, and providing a scientific basis for wildfire early warning and emergency response.

[0030] Further, the process parameters of data acquisition for the target area are initialized as follows: in the early warning database, parameter sets of each data acquisition device corresponding to each environmental anomaly influence coefficient interval are stored. The environmental anomaly influence coefficient of the target area in the first period is compared with the environmental anomaly influence coefficient intervals stored in the early warning database. If the environmental anomaly influence coefficient of the target area in the first period belongs to a certain environmental anomaly influence coefficient interval stored in the early warning database, the parameter sets of each data acquisition device corresponding to the environmental anomaly influence coefficient interval stored in the early warning database are initialized and set as the parameter sets of each data acquisition device to which the target area belongs, thus completing the initialization of the process parameters of data acquisition for the target area. It should be noted that each data acquisition device includes, but is not limited to, three-dimensional lidar scanning devices, infrared thermal imagers, visible light cameras, etc.; the above-mentioned parameter sets of each data acquisition device refer to a set of parameter configurations necessary to ensure that each data acquisition device can accurately and efficiently collect information related to the target area. These parameter sets specify in detail the specific technical specifications, measurement ranges, sampling frequencies, resolutions, accuracy requirements, and data transmission formats that each device needs to follow when performing tasks, jointly constituting the basic framework of the data acquisition task and ensuring that the data collected from the target area is highly accurate, complete, and timely.

[0031] The wildfire early warning module is used to collect and analyze the wildfire risk dynamic parameters of each abnormal environmental area, thereby adjusting the process parameters of data acquisition for each abnormal environmental area, matching the abnormal risk levels of each abnormal environmental area, and determining whether to issue a wildfire early warning for each abnormal environmental area.

[0032] Specifically, the adjustment of the process parameters of data acquisition for each abnormal environmental area is as follows: by analyzing the wildfire risk dynamic parameters of each abnormal environmental area, the abnormal risk factors of each abnormal environmental area in the second period are obtained. The above-mentioned second period refers to the time period set for in-depth data analysis of the identified abnormal environmental areas. The second period not only covers the previous first period but also extends in terms of time span. The specific duration of the second period is determined by data analysis personnel.

[0033] Obtain the parameter sets of each data acquisition device belonging to each abnormal environment area. According to the abnormal risk factors of each abnormal environment area in the second period, match the parameter adjustment sets of each data acquisition device belonging to each abnormal environment area from the early warning database, so as to update the parameter sets of each data acquisition device belonging to each abnormal environment area, and complete the adjustment of the data acquisition process parameters of each abnormal environment area; it should be noted that before adjusting the data acquisition process parameters of each abnormal environment area, the parameter sets of each data acquisition device belonging to each abnormal environment area are consistent with the parameter sets of each data acquisition device deployed in the target area. In other words, given that each abnormal environment area is essentially a part of the target area, the parameter sets of the data acquisition devices they use are exactly the corresponding device parameter sets adopted by the entire target area; the specific matching process of the above-mentioned parameter adjustment sets of each data acquisition device belonging to each abnormal environment area is as follows: the parameter adjustment sets of each data acquisition device corresponding to each abnormal risk factor interval are stored in the early warning database. Compare the abnormal risk factors of each abnormal environment area in the second period with the abnormal risk factor intervals stored in the early warning database. If the abnormal risk factor of an abnormal environment area in the second period belongs to an abnormal risk factor interval stored in the early warning database, then set the parameter adjustment set of each data acquisition device corresponding to the abnormal risk factor interval stored in the early warning database as the parameter adjustment set of each data acquisition device belonging to the abnormal environment area; among them, the parameter adjustment set of each data acquisition device represents a set for adjusting the parameter set of each data acquisition device, covering all parameter modification items required for each data acquisition device, including but not limited to key parameters such as the adjusted sampling frequency, updated measurement accuracy, revised data transmission protocol, and optimized device working mode. These adjustments are aimed at enabling the data acquisition device to more accurately adapt to and meet the urgent needs of monitoring specific abnormal environment areas.

[0034] Specifically, the abnormal risk factors of each abnormal environment area in the second cycle are analyzed as follows: The fire risk dynamic parameters of each abnormal environment area include the vegetation water loss factor of each abnormal environment area in the second cycle, the relationship factor between the thermal radiation field and the average thermal radiation intensity of each abnormal environment area in the second cycle, and the duration of thermal anomaly persistence of each abnormal environment area in the second cycle. The above-mentioned vegetation water loss factor is a numerical value representing the degree of vegetation water loss. By obtaining the canopy temperature and air temperature in the abnormal environment area from the forest area transmission line model and calculating the difference between the two, the temperature difference value of each abnormal environment area in the second cycle can be obtained. The vegetation water loss factor corresponding to the temperature difference value of each abnormal environment area in the second cycle can be obtained by querying the early warning database. The above-mentioned relationship factor between the thermal radiation field and the average thermal radiation intensity represents the weighted aggregation relationship between the change rate of the thermal radiation field and the change rate of the average thermal radiation intensity in the abnormal environment area. It not only considers the overall intensity and distribution of the thermal radiation field, but also considers the change trend of these parameters over time, so as to more comprehensively reflect the fire risk status of the abnormal environment area. The above-mentioned duration of thermal anomaly persistence refers to the duration during which the thermal radiation intensity in the abnormal environment area continuously exceeds the average thermal radiation intensity threshold in the second cycle, which can be extracted from the infrared data in the forest area transmission line model. The average thermal radiation intensity threshold represents the maximum allowable value of the thermal radiation intensity and is extracted from the early warning database.

[0035] It should be noted that when the change rate of the thermal radiation field is large, it means that the thermal radiation field in the abnormal environment area has changed significantly in a short period of time. This change may be caused by the presence of high-temperature objects, the initial stage of a fire, or the influence of other heat sources. The enhancement of the thermal radiation field will increase the possibility of a fire because high-temperature objects or flames can quickly heat the surrounding air and combustibles, thus triggering a fire. The increase in the change rate of the average thermal radiation intensity indicates that the average thermal radiation intensity of the entire abnormal environment area is rising, which may be due to the increase in environmental temperature, the accumulation of combustibles, or the combined effect of other factors. The increase in the average thermal radiation intensity means that the combustibles in the entire area are more likely to be ignited, thus increasing the fire risk. The larger the relationship factor between the thermal radiation field and the average thermal radiation intensity, the stronger the interaction and mutual influence between the thermal radiation field and the average thermal radiation intensity. The strengthening of this comprehensive effect will lead to a significant increase in the fire risk because the enhancement of the thermal radiation field and the increase in the average thermal radiation intensity will promote each other, forming a vicious cycle, making the fire more likely to occur and spread. Therefore, the larger the relationship factor between the thermal radiation field and the average thermal radiation intensity, the higher the fire risk in the abnormal environment area. This indicator provides an important reference basis for fire warning and risk assessment, helping relevant departments to take preventive measures in a timely manner to reduce the probability and harm of fire occurrence.

[0036] Obtain the environmental anomaly influence coefficient of the target area in the second period. By coupling the influence components of the vegetation water loss factor, the influence component of the thermal anomaly duration, and the influence component of the thermal radiation field - average thermal radiation intensity relationship factor in each abnormal environmental area in the second period, and introducing the influence degree of the environmental anomaly influence coefficient of the target area in the second period on the abnormal risk factor, the abnormal risk factors of each abnormal environmental area in the second period are obtained; the environmental anomaly influence coefficient of the above-mentioned target area in the second period is consistent with the acquisition method and the meaning represented by the environmental anomaly influence coefficient of the target area in the first period, only with a time difference.

[0037] The abnormal risk factors of each abnormal environmental area in the second period characterize the abnormal risk degree of each abnormal environmental area in the second period, and the specific expression is: ; ; ; ; ; In the formula, is the abnormal risk factor of the a-th abnormal environmental area in the second period, is the environmental anomaly influence coefficient of the target area in the second period, is the influence component of the vegetation water loss factor of the a-th abnormal environmental area in the second period, is the influence component of the thermal radiation field - average thermal radiation intensity relationship factor of the a-th abnormal environmental area in the second period, is the thermal radiation field - average thermal radiation intensity relationship factor of the a-th abnormal environmental area in the second period, is the influence component of the thermal anomaly duration of the a-th abnormal environmental area in the second period, is the defined thermal radiation field - average thermal radiation intensity relationship factor preset in the early warning database, is the influence value corresponding to the environmental anomaly influence coefficient preset in the early warning database, is the influence factor corresponding to the vegetation water loss factor preset in the early warning database, is the influence factor corresponding to the thermal radiation field - average thermal radiation intensity relationship factor preset in the early warning database, is the influence factor corresponding to the thermal anomaly duration preset in the early warning database, is the vegetation water loss factor of the a-th abnormal environmental area in the second period, is the defined vegetation water loss factor preset in the early warning database, is the thermal radiation field change rate of the a-th abnormal environmental area in the second period, is the average rate of change of the thermal radiation intensity in the second period for the a-th abnormal environment area, is the duration of thermal anomaly persistence in the second period for the a-th abnormal environment area, is the predefined duration for defining thermal anomaly persistence in the warning database, is the influence factor corresponding to the rate of change of the thermal radiation field preset in the warning database, is the influence factor corresponding to the average rate of change of the thermal radiation intensity preset in the warning database, where a is the number of each abnormal environment area, , and b is the total number of abnormal areas.

[0038] The above-mentioned rate of change of the thermal radiation field refers to the rate at which the intensity of the thermal radiation field in the abnormal environment area changes with time in the second period. By using a thermal imager or other relevant equipment to continuously monitor the abnormal environment area, time-series data of the thermal radiation field is obtained. Through data processing software (such as MATLAB) to process the monitored thermal radiation field data, the intensity of the thermal radiation field at each time point is calculated, and using the thermal radiation field data of adjacent time points, the rate of change of the thermal radiation field is calculated; the above-mentioned average rate of change of the thermal radiation intensity refers to the rate at which the average thermal radiation intensity in the abnormal environment area changes with time in the second period. Similarly, using a thermal imager to continuously monitor the abnormal environment area to obtain time-series data of the thermal radiation field, at each time point, the average thermal radiation intensity of the entire area is calculated, which usually involves performing a spatial averaging operation on the thermal radiation field data, and using the average thermal radiation intensity data of adjacent time points to calculate the rate of change of the average thermal radiation intensity.

[0039] Among them, the thermal radiation field intensity focuses on the thermal radiation situation in the entire area or space, and this intensity is spatially distributed. The average thermal radiation intensity focuses more on the heat radiation energy reception situation at a specific point or area, and this intensity is time-accumulated. Therefore, it is usually expressed by the heat radiation energy received per unit area per unit time.

[0040] The above-mentioned defined vegetation water loss factor represents the allowable maximum value of the vegetation water loss factor; the above-mentioned defined thermal radiation field - average thermal radiation intensity relationship factor represents the allowable maximum value of the thermal radiation field - average thermal radiation intensity relationship factor; the above-mentioned defined duration of thermal anomaly persistence represents the allowable maximum value of the duration of thermal anomaly persistence; the above-mentioned influence component of the vegetation water loss factor represents the degree of influence of the vegetation water loss factor on the abnormal risk factor; the above-mentioned influence component of the thermal radiation field - average thermal radiation intensity relationship factor represents the degree of influence of the thermal radiation field - average thermal radiation intensity relationship factor on the abnormal risk factor; the above-mentioned influence component of the duration of thermal anomaly persistence represents the degree of influence of the duration of thermal anomaly persistence on the abnormal risk factor.

[0041] The influence value corresponding to the above environmental anomaly influence coefficient indicates the influence degree of the unit value after de-uniting the environmental anomaly influence coefficient on the anomaly risk factor; the influence factor corresponding to the above vegetation water loss factor indicates the influence degree of the unit value of the vegetation water loss factor on the anomaly risk factor; the influence factor corresponding to the above heat radiation field-average heat radiation intensity relationship factor indicates the influence degree of the unit value of the heat radiation field-average heat radiation intensity relationship factor on the anomaly risk factor; the influence factor corresponding to the above heat anomaly duration indicates the influence degree of the unit value of the heat anomaly duration on the anomaly risk factor; the influence factor corresponding to the above heat radiation field change rate indicates the influence degree of the unit value after de-uniting the heat radiation field change rate on the heat radiation field-average heat radiation intensity relationship factor; the influence factor corresponding to the above average heat radiation intensity change rate indicates the influence degree of the unit value after de-uniting the average heat radiation intensity change rate on the heat radiation field-average heat radiation intensity relationship factor; the warning database stores the corresponding relationships between the environmental anomaly influence coefficient, the vegetation water loss factor, the heat radiation field-average heat radiation intensity relationship factor, the heat anomaly duration, the heat radiation field change rate, and the average heat radiation intensity change rate and their corresponding influence factors. For example, when the environmental anomaly influence coefficient, the vegetation water loss factor, the heat radiation field-average heat radiation intensity relationship factor, the heat anomaly duration, the heat radiation field change rate, and the average heat radiation intensity change rate are input into the warning database, the warning database can match the influence value corresponding to the environmental anomaly influence coefficient, the influence factor corresponding to the vegetation water loss factor, the influence factor corresponding to the heat radiation field-average heat radiation intensity relationship factor, the influence factor corresponding to the heat anomaly duration, the influence factor corresponding to the heat radiation field change rate, and the influence factor corresponding to the average heat radiation intensity change rate, and their value ranges are all between 0 and 1.

[0042] It should be noted that the larger the relationship factor between the thermal radiation field and the average thermal radiation intensity, the stronger the interaction between the thermal radiation field and the average thermal radiation intensity in the abnormal environment area, thus increasing the risk of abnormal events such as fires. The environmental anomaly influence coefficient takes into account the influence degree of meteorological factors on the abnormal environment area. Its change will directly lead to the fluctuations of the thermal radiation field and the average thermal radiation intensity, and then affect the value of the relationship factor between the thermal radiation field and the average thermal radiation intensity. The prolongation of the duration of thermal anomaly will increase the possibility of abnormal events such as fires, because the long-term high-temperature environment will accelerate the drying and combustion processes of combustibles. At the same time, the duration of thermal anomaly will also indirectly lead to the change of the vegetation water loss factor by affecting the growth and water status of vegetation. The larger the vegetation water loss factor, the lower the water content of the vegetation and the higher the flammability. The change of the vegetation water loss factor is not only affected by the duration of thermal anomaly, but also closely related to the environmental anomaly influence coefficient and the relationship factor between the thermal radiation field and the average thermal radiation intensity. For example, the increase of the environmental anomaly influence coefficient may lead to the occurrence of extreme climate conditions such as drought, further exacerbating the water loss process of vegetation; while the increase of the relationship factor between the thermal radiation field and the average thermal radiation intensity will accelerate the heat accumulation and water evaporation on the vegetation surface, resulting in a further increase in the vegetation water loss factor. The changes of these parameters will directly affect the degree of abnormal risk in the abnormal environment area in the second cycle, thus increasing the risk of abnormal events such as fires.

[0043] Furthermore, the determination of whether to issue a wildfire warning for each abnormal environmental area is as follows. The specific determination process is as follows: Based on the abnormal risk factors of each abnormal environmental area in the second period, match the abnormal levels of each abnormal environmental area in the second period. If the abnormal level of a certain abnormal environmental area in the second period belongs to the abnormal warning level, it is determined to issue a wildfire warning for this abnormal environmental area. The process of matching the abnormal levels of each abnormal environmental area in the second period is as follows: The abnormal levels corresponding to each abnormal risk factor interval are stored in the warning database. Query the abnormal risk factor intervals stored in the warning database to which the abnormal risk factors of each abnormal environmental area in the second period belong. Then, the abnormal level corresponding to the corresponding abnormal risk factor interval is the abnormal level of each abnormal environmental area in the second period. The above-mentioned abnormal warning level refers to the highest level among the abnormal levels, which indicates that the abnormal environmental area has developed to a critical level where the warning mechanism must be activated. The appearance of this level means that the environmental abnormality in this area has reached a situation that cannot be ignored, and it is urgent to take corresponding measures to prevent potential risks or hazards. In an exemplary embodiment, the specific warning content for issuing a wildfire warning for this abnormal environmental area is as follows: Specific longitude and latitude range: from XX°XX'XX'' east longitude to XX°XX'XX'' east longitude, and from XX°XX'XX'' north latitude to XX°XX'XX'' north latitude. It is determined that the current abnormal level of this area is the abnormal warning level (the highest level), indicating that there is a very high risk of wildfire occurrence in this area. The vegetation drought stress index has increased significantly, the temperature difference between the canopy and the air exceeds 5°C, indicating that the transpiration of the vegetation has weakened, the water loss is serious, the thermal inertia ratio is lower than 0.6, the vegetation moisture content is low, the flammability has increased, the recent meteorological conditions are dry, the wind is strong, and the relative humidity is low, which is conducive to the rapid spread of the fire. Relevant departments should immediately notify emergency response units at all levels and activate the wildfire emergency plan.

[0044] If the abnormal level of a certain abnormal environmental area in the second period does not belong to the abnormal warning level, it is determined not to issue a wildfire warning for this abnormal environmental area. At the same time, mark the abnormal environmental areas corresponding to the abnormal levels that do not belong to the abnormal warning level as each abnormal monitoring environmental area, and obtain the abnormal risk factors of each abnormal monitoring environmental area in the second period.

[0045] Obtain the environmental anomaly impact coefficient of the target area during the prediction period, perform a difference process with the environmental anomaly impact coefficient of the target area during the second period, and mark the processing result as the environmental anomaly impact deviation coefficient of the target area. Then match the anomaly risk factor correction value from the early warning database, and correct the anomaly risk factors of each anomaly monitoring environmental area during the second period, so as to rematch the anomaly levels of each anomaly monitoring environmental area during the second period. If the anomaly level of a certain anomaly monitoring environmental area during the second period belongs to the anomaly early warning level, it is determined to conduct a wildfire prediction early warning for this anomaly monitoring environmental area. If the anomaly level of a certain anomaly monitoring environmental area during the second period does not belong to the anomaly early warning level, it is determined not to conduct a wildfire prediction early warning for this anomaly monitoring environmental area; the above prediction period refers to the time period for predicting and analyzing the target area, and the specific duration is formulated by meteorological data analysts; the acquisition method and meaning representation of the environmental anomaly impact coefficient of the target area during the prediction period are the same as those of the environmental anomaly impact coefficient of the target area during the first period, only with a time difference; the environmental anomaly impact deviation coefficient of the target area refers to the difference between the environmental anomaly impact coefficient of the target area during the prediction period and the environmental anomaly impact coefficient of the target area during the second period; the anomaly risk factor correction value represents the value for correcting the anomaly risk factor, and the specific matching process is as follows: the early warning database stores the anomaly risk factor correction values corresponding to each environmental anomaly impact deviation coefficient interval. Query the environmental anomaly impact deviation coefficient interval stored in the early warning database to which the environmental anomaly impact deviation coefficient of the target area belongs, and the anomaly risk factor correction value corresponding to this environmental anomaly impact deviation coefficient interval is the matched anomaly risk factor correction value; the correction of the anomaly risk factors of each anomaly monitoring environmental area during the second period means multiplying the anomaly risk factor correction value by the anomaly risk factors of each anomaly monitoring environmental area during the second period, thereby updating the anomaly risk factors of each anomaly monitoring environmental area during the second period.

[0046] It should be explained that if the environmental anomaly impact deviation coefficient of the target area is greater than or equal to 0, the anomaly risk factor correction value is a value greater than 1. If the environmental anomaly impact deviation coefficient of the target area is less than 0, the anomaly risk factor correction value is a value less than 1.

[0047] The above-mentioned wildfire prediction and early warning refer to the process of comprehensively considering and analyzing future environmental factors to determine whether the abnormally monitored environmental area is about to reach the level where the early warning mechanism needs to be activated, and taking early prediction and early warning measures accordingly. This process aims to provide sufficient preparation time for relevant departments and residents through scientific prediction and timely early warning, so that effective prevention and response measures can be taken to reduce the likelihood of wildfires or mitigate the potential hazards they may bring. In an exemplary embodiment, the wildfire prediction and early warning include the specific geographical location (such as longitude and latitude range, administrative division, etc.), the expected time period when wildfires may occur, a detailed description of the environmental anomalies in the early warning area, including changes in key indicators such as the vegetation drought stress index, the temperature difference between the canopy and the air, the ratio of thermal inertia, and the vegetation moisture content. These indicators reflect the drought degree and flammability of the vegetation and are important bases for judging wildfire risks. It also describes in detail the meteorological conditions during the early warning period, including temperature, humidity, wind speed, wind direction, etc. These meteorological factors directly affect the occurrence and spread speed of wildfires, so they must be closely monitored and corresponding response measures must be taken. At the same time, according to the early warning level and environmental anomalies, specific prevention and response measures are given, including restricting fire source activities, strengthening monitoring and patrols, establishing temporary fire isolation belts, strengthening meteorological monitoring and early warning, organizing emergency drills, and preparing emergency supplies and equipment.

[0048] In a specific embodiment, the present invention calculates the difference in the environmental anomaly impact coefficient between the target area in the prediction period and the second period to obtain the environmental anomaly impact deviation coefficient, and uses the corrected value of the abnormal risk factor in the early warning database to accurately correct the abnormal risk factor in the second period and re-match the abnormal level, thereby significantly improving the accuracy and pertinence of wildfire prediction and early warning, effectively avoiding false alarms and missed alarms, and timely warning the abnormally monitored environmental area that truly has a wildfire risk, providing more scientific and reliable decision-making support for wildfire prevention and control.

[0049] The line early warning module is used to locate each abnormal transmission line based on 3D lidar and evaluate the operation abnormality index of each abnormal transmission line in the third period to determine whether to issue a protection early warning for each abnormal transmission line.

[0050] In a specific embodiment, the present invention realizes precise protection early warning for each abnormal transmission line by quantifying the degree of operation abnormality, which not only improves the accuracy and timeliness of early warning, but also can take timely measures to avoid line failures and ensure the stable operation of the power system. At the same time, the quantified early warning can also provide a scientific basis for line maintenance, optimize the allocation of maintenance resources, and improve the safety and reliability of the overall transmission line.

[0051] Specifically, the process of locating each abnormal transmission line is as follows: Obtain each abnormal environmental area included in the risk unit to which each section of the transmission line belongs, obtain the abnormal risk factors of the corresponding abnormal environmental areas in the third period, and accumulate them. The accumulated result is marked as the accumulated value of the abnormal risk factors of each section of the transmission line in the third period. The above-mentioned third period refers to the time period for analyzing abnormal transmission lines. The third period not only covers the previous second period but also extends in terms of time span. The specific duration of the third period is determined by data analysts. Each section of the transmission line mentioned above refers to each independent part obtained by dividing the entire transmission network according to a specific division method. For example, each section of the line is defined with the two transmission towers at both ends as the boundaries, that is, the part of the transmission line between one transmission tower and the adjacent other transmission tower is regarded as one section. The risk unit to which each section of the transmission line belongs refers to a specific geographical area delimited based on each section of the line. The environmental conditions within this area may affect the safe and stable operation of the transmission line. For example, for overhead transmission lines, the area within the two parallel planes formed by horizontally extending a certain distance outward from the outer edge of the line and perpendicular to the ground. For example, for a 110 kV line, it extends 10 meters on each side, and for a 220 kV line, it extends 15 meters on each side (the extension distances are different for different voltage levels). This area can be regarded as a risk unit as a whole. The above-mentioned obtaining each abnormal environmental area included in the risk unit to which each section of the transmission line belongs refers to each abnormal environmental area within the range of the risk unit delimited based on each section of the transmission line or having an intersection with the range of the risk unit.

[0052] Perform weighted aggregation on the accumulated value of the abnormal risk factors of each section of the transmission line in the third period and the operation abnormal factors of each section of the transmission line in the third period to evaluate the operation abnormal index of each section of the transmission line in the third period; that is .

[0053] Compare the operation abnormal index of each section of the transmission line in the third period with the operation abnormal definition value. If the operation abnormal index of a certain section of the transmission line in the third period is less than or equal to the operation abnormal definition value, then this section of the transmission line is not marked as an abnormal transmission line. If the operation abnormal index of a certain section of the transmission line in the third period is greater than the operation abnormal definition value, then this section of the transmission line is marked as an abnormal transmission line, thereby locating each section of the abnormal transmission line. The above-mentioned operation abnormal definition value represents the maximum value within the reasonable range of the operation abnormal index and is extracted from the early warning database.

[0054] Specifically, for determining whether to issue a protection warning for each abnormal transmission line, the specific protection process is as follows: Based on the operation abnormality index of each abnormal transmission line in the third period, match the protection plan for each abnormal transmission line from the warning database. At the same time, obtain the operation abnormality index corresponding to the protection plan of each abnormal transmission line, mark it as the reference operation abnormality index of each abnormal transmission line, and perform a difference processing with the operation abnormality index of each abnormal transmission line in the third period. The processing result is marked as the operation abnormality index deviation value of each abnormal transmission line. If the operation abnormality index deviation value of a certain abnormal transmission line is less than or equal to the reference operation abnormality index deviation value, increase and adjust the protection plan for this abnormal transmission line. If the operation abnormality index deviation value of a certain abnormal transmission line is greater than the reference operation abnormality index deviation value, decrease and adjust the protection plan for this abnormal transmission line. The protection plans for the above-mentioned abnormal transmission lines refer to a series of protection measures designed to keep the transmission line operating normally when it appears abnormal or fails, such as current protection, etc. The operation abnormality index of a certain abnormal transmission line in the third period, the specific matching process is as follows: The warning database stores the protection plans corresponding to each operation abnormality index. Perform a difference processing between the operation abnormality index of this abnormal transmission line in the third period and each operation abnormality index stored in the warning database, and query the minimum value of the difference processing. Then, the protection plan corresponding to the operation abnormality index stored in the warning database corresponding to this minimum value is the protection plan for this abnormal transmission line. The above-mentioned reference operation abnormality index deviation value represents the reference value of the operation abnormality index deviation value, which is extracted from the warning database. If the operation abnormality index deviation value is relatively large, greater than the reference operation abnormality index deviation value, it indicates that the actual operation situation is better than the situation corresponding to the matched protection plan. If the protection plan is not adjusted, it may lead to overly sensitive protection, frequent operation in some situations that are not truly faulty or severely abnormal, causing unnecessary power outages or equipment malfunctions, and affecting the normal operation of the transmission line. By decreasing and adjusting the protection plan, the protection action threshold can be made more in line with the actual operation situation and overprotection can be avoided. In an exemplary embodiment, the decrease and adjustment can be: extending the delay time of the overcurrent protection to twice the existing data, so that the protection device operates after the current continuously exceeds the setting value for a period of time, avoiding misoperation of the protection caused by instantaneous current fluctuations. Similarly, increasing and adjusting can also make the protection action threshold more in line with the actual operation situation.

[0055] Obtain the operation anomaly thresholds for each section of the abnormal power transmission line, and compare them with the operation anomaly indices of the corresponding sections of the abnormal power transmission line in the third period. If the operation anomaly index of a certain section of the abnormal power transmission line in the third period is less than or equal to the corresponding operation anomaly threshold, it is determined that no early warning is given to this section of the abnormal power transmission line. If the operation anomaly index of a certain section of the abnormal power transmission line in the third period is greater than the corresponding operation anomaly threshold, it is determined that an early warning is given to this section of the abnormal power transmission line; the operation anomaly thresholds of the above-mentioned sections of the abnormal power transmission line represent the maximum allowable values of the operation anomaly indices of the sections of the abnormal power transmission line in the third period, and are extracted from the early warning database; in an exemplary embodiment, the above-mentioned early warning for this section of the abnormal power transmission line is specifically: immediately send an audible and visual alarm to the monitoring platform of the power operation and maintenance center, and at the same time mark the line number and location information of this section of the abnormal power transmission line with a prominent red flash on the monitoring interface. For example, the transmission line numbered L005 is located between a certain substation in the urban suburb and a certain industrial park. When it is detected that its operation anomaly index in the third period is 85% (the corresponding operation anomaly threshold is 70%), after exceeding the threshold, accurately locate the position of this line on the electronic map and flash a red warning, and at the same time push a text message containing detailed information such as the line number, location, and anomaly index to the mobile phone of the operation and maintenance personnel responsible for this area to remind the operation and maintenance personnel to go to the site as soon as possible to check for potential fault hazards.

[0056] Further, the specific evaluation process for evaluating the operation anomaly indices of each section of the power transmission line in the third period is as follows: Obtain the average thermal radiation intensity of each section of the power transmission line in the third period and the thermal spot area of each section of the power transmission line in the third period; the above-mentioned average thermal radiation intensity, which is the thermal radiation energy received per unit area per unit time, can be extracted from the infrared data in the forest area power transmission line model; the above-mentioned thermal spot area refers to the maximum area occupied by the thermal spots (usually local overheating phenomena caused by current overload, poor contact, line aging, etc.) on each section of the line when the power transmission line is monitored by thermal imaging in the third period, and can be extracted from the thermal imaging data in the forest area power transmission line model. The thermal spot temperature range is formulated by electrical engineers.

[0057] Obtain the environmental anomaly influence coefficient of the target area in the third cycle. Introduce influence weights to quantify the influence degree of the proportional relationship between the average heat radiation intensity and the defined average heat radiation intensity on the operation anomaly factor, and the influence degree of the proportional relationship between the hot spot area and the defined hot spot area on the operation anomaly factor. At the same time, summarize the influence degree of the environmental anomaly influence coefficient of the target area in the third cycle on the operation anomaly factor, so as to obtain the operation anomaly factor. At the same time, perform weighted aggregation on the accumulated value of the anomaly risk factor and the operation anomaly factor, so as to obtain the operation anomaly index. The above operation anomaly factor is used to quantify the total influence of the environmental anomaly influence coefficient, the average heat radiation intensity, and the hot spot area on the operation anomaly degree. The environmental anomaly influence coefficient of the target area in the third cycle is the same as the acquisition method and meaning of the environmental anomaly influence coefficient of the target area in the first cycle, only with a time difference.

[0058] The operation anomaly index of each transmission line segment in the third cycle characterizes the operation anomaly degree of each transmission line segment in the third cycle, and the specific expression is: ; ; In the formula, is the operation anomaly index of the c-th abnormal transmission line segment in the third cycle, is the accumulated value of the anomaly risk factor of the c-th transmission line segment in the third cycle, is the operation anomaly factor of the c-th transmission line segment in the third cycle, is the influence weight corresponding to the accumulated value of the anomaly risk factor preset in the early warning database, is the influence weight corresponding to the operation anomaly factor preset in the early warning database. c is the number of each abnormal transmission line segment, , y is the total number of abnormal transmission line segments, is the environmental anomaly influence coefficient of the target area in the third cycle, is the average heat radiation intensity of the c-th transmission line segment in the third cycle, is the hot spot area of the c-th transmission line segment in the third cycle, is the defined average heat radiation intensity preset in the early warning database, is the defined hot spot area preset in the early warning database, is the influence weight corresponding to the environmental anomaly influence coefficient preset in the early warning database, is the influence weight corresponding to the average heat radiation intensity preset in the early warning database, is the influence weight corresponding to the hot spot area preset in the early warning database.

[0059] The above-defined average heat radiation intensity represents the maximum allowable value of the average heat radiation intensity; the above-defined hot spot area represents the maximum allowable value of the hot spot area.

[0060] The influence weight corresponding to the cumulative value of the above abnormal risk factors represents the degree of influence of the unit value after de-unifying the cumulative value of the abnormal risk factors on the operation abnormality index; the influence weight corresponding to the above operation abnormality factor represents the degree of influence of the unit value after de-unifying the operation abnormality factor on the operation abnormality index; the influence weight corresponding to the above environmental abnormality influence coefficient represents the degree of influence of the unit value after de-unifying the environmental abnormality influence coefficient on the operation abnormality factor; the influence weight corresponding to the above average heat radiation intensity represents the degree of influence of the unit value of the average heat radiation intensity on the operation abnormality factor; the influence weight corresponding to the above hot spot area represents the degree of influence of the unit value of the hot spot area on the operation abnormality factor; the warning database stores the corresponding relationships between the cumulative value of the abnormal risk factors, the operation abnormality factors, the environmental abnormality influence coefficient, the average heat radiation intensity, and the hot spot area and their corresponding influence weights. For example, when the cumulative value of the abnormal risk factors, the operation abnormality factors, the environmental abnormality influence coefficient, the average heat radiation intensity, and the hot spot area are input into the warning database, the warning database can match the influence weights corresponding to the cumulative value of the abnormal risk factors, the influence weights corresponding to the operation abnormality factors, the influence weights corresponding to the environmental abnormality influence coefficient, the influence weights corresponding to the average heat radiation intensity, and the influence weights corresponding to the hot spot area, and their value ranges are all between 0 and 1.

[0061] It should be explained that when the abnormality degree of the surrounding environment area of the transmission line (such as woods, etc.) is relatively high, it poses a significant threat to the safe and stable operation of the transmission line. At the same time, abnormal meteorological factors can exacerbate the physical stress and electrical load of the transmission line, thereby triggering or exacerbating the abnormal phenomena on the line. In this environment, the transmission line is more likely to show the characteristics of an enlarged hot spot area and an increased average heat radiation intensity. The formation of hot spots is usually due to local overheating of the line, which may be caused by factors such as current overload, poor contact, or line aging. When environmental factors are abnormal, these potential problems are more likely to be triggered or amplified, making the hot spot phenomenon more significant. The increase in the hot spot area and the increase in the average heat radiation intensity directly reflect the exacerbation of the abnormal degree of the transmission line. These abnormal phenomena may not only damage the insulation performance and mechanical strength of the line itself, but also pose potential safety risks to the surrounding environment, such as further triggering fires, etc. Therefore, continuous monitoring is required for predictive protection and warning.

[0062] In a specific embodiment, the present invention provides a transmission line wildfire warning system and method based on 3D lidar. By accurately collecting and evaluating meteorological parameters of the target area, the data acquisition process parameters are intelligently initialized to ensure the accuracy and timeliness of the data. After obtaining the data acquisition results of the target area, each abnormal environment area can be accurately located, and its fire risk dynamic parameters are deeply collected and analyzed, so as to flexibly adjust the data acquisition process parameters to achieve fine monitoring of the fire risk. In addition, the abnormal risk levels of each abnormal environment area are intelligently matched, and it is timely determined whether a wildfire warning needs to be issued, effectively improving the accuracy and timeliness of the warning. At the same time, the 3D lidar technology is used to accurately locate each section of the abnormal transmission line and evaluate its operation abnormality index in the third cycle, providing a scientific basis for the protection warning of the transmission line.

[0063] Referring to Figure 2 As shown, the second aspect of the present invention provides a transmission line wildfire warning method based on 3D lidar, including: Step 1: Collect and evaluate the meteorological parameters of the target area, thereby initializing the data acquisition process parameters of the target area, obtaining the data acquisition results of the target area, and locating each abnormal environment area; Step 2: Collect and analyze the fire risk dynamic parameters of each abnormal environment area, thereby adjusting the data acquisition process parameters of each abnormal environment area, and at the same time matching the abnormal risk levels of each abnormal environment area, and determining whether to issue a wildfire warning for each abnormal environment area; Step 3: Locate each section of the abnormal transmission line based on 3D lidar, and evaluate the operation abnormality index of each section of the abnormal transmission line in the third cycle, and determine whether to issue a protection warning for each section of the abnormal transmission line.

[0064] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of the present technology make various modifications or supplements or use similar methods to replace the specific embodiments described, as long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all belong to the protection scope of the present invention.

Claims

1. A power transmission line forest fire warning system based on three-dimensional laser radar, characterized in that: include: The environmental analysis module is used to collect and evaluate the meteorological parameters of the target area, so as to initialize the data collection process parameters of the target area, obtain the data collection results of the target area, and locate each abnormal environmental area; The wildfire warning module is used to collect and analyze the dynamic parameters of fire risk in each abnormal environment area, so as to adjust the data collection process parameters of each abnormal environment area, match the abnormal risk level of each abnormal environment area, and determine whether to issue a wildfire warning for each abnormal environment area; The line warning module is used to locate each section of abnormal transmission line based on the three-dimensional laser radar, and evaluate the operation abnormality index of each section of abnormal transmission line in the third cycle, and determine whether to issue a protection warning for each section of abnormal transmission line.

2. The power transmission line forest fire early warning system based on three-dimensional laser radar according to claim 1 is characterized in that: The meteorological parameters of the target area are collected and evaluated, and the specific evaluation process is as follows: The meteorological parameters of the target area include the average air pressure of the target area in the first period, the total rainfall of the target area in the first period, and the accumulated solar radiation of the target area in the first period; By introducing the influence coefficient, the influence degree of the proportional relationship between the mean air pressure deviation value and the defined mean air pressure deviation value on the environmental anomaly influence coefficient, the influence degree of the proportional relationship between the total rainfall and the defined total rainfall on the environmental anomaly influence coefficient, and the influence degree of the proportional relationship between the solar radiation accumulation and the defined solar radiation accumulation on the environmental anomaly influence coefficient are quantified respectively, and each influence degree is coupled to obtain the environmental anomaly influence coefficient of the target area in the first period. The environmental anomaly impact coefficient of the target area in the first period is used to characterize the degree to which the abnormal environmental conditions of the target area in the first period increase the risk of wildfires.

3. The power transmission line forest fire early warning system based on three-dimensional laser radar according to claim 1 is characterized in that: The data acquisition process parameters of the initialization target area are specifically initialized as follows: The early warning database stores parameter sets for each data acquisition device corresponding to each environmental anomaly impact coefficient interval. The environmental anomaly impact coefficient of the target area in the first period is compared with the environmental anomaly impact coefficient intervals stored in the early warning database. If the environmental anomaly impact coefficient of the target area in the first period belongs to a certain environmental anomaly impact coefficient interval stored in the early warning database, the parameter set of each data acquisition device corresponding to the environmental anomaly impact coefficient interval stored in the early warning database is initialized and set as the parameter set of each data acquisition device belonging to the target area, thereby completing the initialization of the data acquisition process parameters of the target area.

4. The power transmission line forest fire early warning system based on three-dimensional laser radar according to claim 1 is characterized in that: The data collection process parameters of each abnormal environment area are adjusted, and the specific adjustment process is: By analyzing the dynamic parameters of fire risk in each abnormal environment area, the abnormal risk factor of each abnormal environment area in the second period is obtained; Obtain the parameter set of each data acquisition device belonging to each abnormal environmental area, and match the parameter adjustment set of each data acquisition device belonging to each abnormal environmental area from the early warning database according to the abnormal risk factor of each abnormal environmental area in the second cycle, thereby updating the parameter set of each data acquisition device belonging to each abnormal environmental area and completing the adjustment of the data acquisition process parameters of each abnormal environmental area.

5. The power transmission line forest fire early warning system based on three-dimensional laser radar according to claim 1 is characterized in that: The specific process of determining whether to issue a wildfire warning for each abnormal environment area is as follows: According to the abnormal risk factors of each abnormal environmental area in the second period, the abnormal level of each abnormal environmental area in the second period is matched. If the abnormal level of an abnormal environmental area in the second period belongs to the abnormal warning level, it is determined that a wildfire warning is issued for the abnormal environmental area; If the abnormal level of a certain abnormal environment area in the second cycle does not belong to the abnormal warning level, it is determined that no wildfire warning will be issued for the abnormal environment area. At the same time, each abnormal environment area whose abnormal level does not belong to the abnormal warning level is marked as each abnormal monitoring environment area, and the abnormal risk factor of each abnormal monitoring environment area in the second cycle is obtained; The environmental anomaly impact coefficient of the target area within the prediction period is obtained, and the difference processing is performed on the environmental anomaly impact coefficient of the target area within the second period. The processing result is marked as the environmental anomaly impact deviation coefficient of the target area, and the abnormal risk factor correction value is matched from the early warning database. The abnormal risk factor of each abnormal monitoring environment area in the second period is corrected, so as to re-match the abnormal level of each abnormal monitoring environment area in the second period. If the abnormal level of an abnormal monitoring environment area in the second period belongs to the abnormal warning level, it is determined that a wildfire prediction and warning is performed for the abnormal monitoring environment area. If the abnormal level of an abnormal monitoring environment area in the second period does not belong to the abnormal warning level, it is determined that a wildfire prediction and warning is not performed for the abnormal monitoring environment area.

6. The power transmission line forest fire early warning system based on three-dimensional laser radar according to claim 5 is characterized by: The specific analysis process of the abnormal risk factors of each abnormal environment area in the second period is as follows: The fire risk dynamic parameters of each abnormal environmental area include the vegetation dehydration factor of each abnormal environmental area in the second period, the thermal radiation field-average thermal radiation intensity relationship factor of each abnormal environmental area in the second period, and the duration of thermal anomaly in each abnormal environmental area in the second period; Obtain the environmental anomaly impact coefficient of the target area in the second period, and obtain the abnormal risk factor of each abnormal environmental area in the second period by coupling the vegetation dehydration factor influencing component, the thermal anomaly duration influencing component, and the thermal radiation field-average thermal radiation intensity relationship factor influencing component of each abnormal environmental area in the second period, and introduce the influence of the environmental anomaly impact coefficient of the target area in the second period on the abnormal risk factor; The abnormal risk factor of each abnormal environmental area in the second period represents the abnormal risk degree of each abnormal environmental area in the second period.

7. The power transmission line forest fire early warning system based on three-dimensional laser radar according to claim 1 is characterized by: The specific analysis process of locating each section of abnormal transmission line is as follows: Obtain each abnormal environment area contained in the risk unit to which each section of the transmission line belongs, obtain the abnormal risk factor of each corresponding abnormal environment area in the third period, and accumulate them, and the accumulated result is marked as the accumulated value of the abnormal risk factor of each section of the transmission line in the third period; The accumulated value of the abnormal risk factor of each section of the transmission line in the third period and the operation abnormality factor of each section of the transmission line in the third period are weighted and summarized to evaluate the operation abnormality index of each section of the transmission line in the third period; The operation abnormality index of each section of the transmission line in the third cycle is compared with the operation abnormality limit value. If the operation abnormality index of a section of the transmission line in the third cycle is less than or equal to the operation abnormality limit value, the section of the transmission line will not be marked as an abnormal transmission line. If the operation abnormality index of a section of the transmission line in the third cycle is greater than the operation abnormality limit value, the section of the transmission line will be marked as an abnormal transmission line, thereby locating each section of the abnormal transmission line.

8. The power transmission line forest fire early warning system based on three-dimensional laser radar according to claim 7 is characterized by: The operation abnormality index of each section of the transmission line in the third cycle is evaluated, and the specific evaluation process is as follows: Obtaining the average thermal radiation intensity of each section of the transmission line in the third period and the hot spot area of ​​each section of the transmission line in the third period; Influence weights are introduced to quantify the influence of the proportional relationship between the average thermal radiation intensity and the defined average thermal radiation intensity on the operation abnormality factor, as well as the influence of the proportional relationship between the hot spot area and the defined hot spot area on the operation abnormality factor. At the same time, the influence of the environmental abnormality influence coefficient on the operation abnormality factor is summarized. At the same time, the accumulated value of the abnormal risk factor and the operation abnormality factor are weighted and summarized to obtain the operation abnormality index. The operation abnormality index of each section of the transmission line in the third period represents the degree of operation abnormality of each section of the transmission line in the third period.

9. The power transmission line forest fire warning system based on three-dimensional laser radar according to claim 1, characterized in that: The specific protection process of determining whether to provide protection warning for each section of abnormal power transmission line is as follows: According to the abnormal operation index of each section of abnormal transmission line in the third cycle, the protection scheme of each section of abnormal transmission line is matched from the early warning database, and the abnormal operation index corresponding to the protection scheme of each section of abnormal transmission line is obtained, marked as the reference abnormal operation index of each section of abnormal transmission line, and the difference processing is performed with the abnormal operation index of each section of abnormal transmission line in the third cycle. The processing result is marked as the abnormal operation index deviation value of each section of abnormal transmission line. If the abnormal operation index deviation value of a section of abnormal transmission line is less than or equal to the reference abnormal operation index deviation value, the protection scheme of the section of abnormal transmission line is increased and adjusted. If the abnormal operation index deviation value of a section of abnormal transmission line is greater than the reference abnormal operation index deviation value, the protection scheme of the section of abnormal transmission line is decreased and adjusted; The operation abnormality threshold of each section of abnormal transmission line is obtained, and compared with the operation abnormality index of each section of the corresponding abnormal transmission line in the third cycle. If the operation abnormality index of a section of abnormal transmission line in the third cycle is less than or equal to the corresponding operation abnormality threshold, it is determined that no warning is issued for the section of abnormal transmission line. If the operation abnormality index of a section of abnormal transmission line in the third cycle is greater than the corresponding operation abnormality threshold, it is determined that a warning is issued for the section of abnormal transmission line.

10. A method for a power transmission line wildfire early warning system based on three-dimensional laser radar as claimed in any one of claims 1 to 9, characterized in that: include: Step 1: Collect and evaluate the meteorological parameters of the target area, thereby initializing the data collection process parameters of the target area, obtaining the data collection results of the target area, and locating each abnormal environmental area; Step 2: Collect and analyze the dynamic parameters of fire risk in each abnormal environment area, so as to adjust the data collection process parameters of each abnormal environment area, match the abnormal risk level of each abnormal environment area, and determine whether to issue a wildfire warning for each abnormal environment area; Step 3: locate each section of abnormal transmission line based on the three-dimensional laser radar, evaluate the operation abnormality index of each section of abnormal transmission line in the third cycle, and determine whether to issue a protection warning for each section of abnormal transmission line.

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