Power transmission line risk assessment early warning method and system

Abstract

The invention is suitable for the technical field of power risk monitoring, and provides a power transmission line risk assessment early warning method and system, and the method comprises the following steps: collecting meteorological temperature data, equipment state data, remote sensing images and construction data; determining the icing thickness according to the meteorological temperature data, and obtaining a meteorological risk index according to the icing thickness and the real-time wind speed; extracting equipment operation time and a monitoring strain value in the equipment state data, and calculating to obtain an equipment health index; analyzing the remote sensing image to determine a forest fire risk index and a flood risk index, and determining a flow disaster risk index; a construction position, a construction type and a machine type in the construction data are extracted, and a construction risk index is obtained through calculation; and calculating a comprehensive risk index of the power transmission line based on the meteorological risk index, the equipment health index, the mobile disaster risk index and the construction risk index. According to the invention, the risk condition of the power transmission line can be comprehensively evaluated, and the automation and intelligence of risk evaluation and early warning are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power risk monitoring, in particular to a power transmission line risk assessment and early warning method and system. BACKGROUND

[0002] With the rapid development of the power industry, the safe and stable operation of the power transmission line, as an important part of the power system, is of great importance. The power transmission line often spans a vast geographical area and is affected by complex and variable natural environments and human activities. In terms of natural environment, meteorological conditions such as low temperature, strong wind, rainfall, etc. may cause icing on the power transmission line, which may increase the weight and tension of the line and even cause accidents such as wire breakage and tower collapse. Natural disasters such as forest fires and floods may also damage the power transmission line and affect its normal operation. In terms of human activities, various construction activities, especially those near the power transmission line, may pose a threat to the safety of the power transmission line due to improper mechanical operation or poor construction range control. At present, the risk assessment of the power transmission line mainly relies on manual inspection and regular equipment detection, which is not only inefficient but also difficult to discover potential risks in a timely manner. At the same time, the existing risk assessment methods mostly consider only a single factor and lack comprehensive consideration of multiple risk factors, resulting in inaccurate and incomplete assessment results. Therefore, it is necessary to provide a power transmission line risk assessment and early warning method and system to solve the above problems. SUMMARY

[0003] In view of the deficiencies in the prior art, the purpose of the present application is to provide a power transmission line risk assessment and early warning method and system to solve the problems in the background art.

[0004] The present application is implemented as follows: a power transmission line risk assessment and early warning method, the method comprising the following steps:

[0005] Collecting meteorological temperature data, equipment status data, remote sensing images and construction data, the construction data being determined through construction permit information;

[0006] Determining the icing thickness according to the meteorological temperature data, and obtaining the meteorological risk index according to the icing thickness, real-time wind speed and environmental temperature;

[0007] Extracting the equipment running time and monitoring strain value in the equipment status data, and calculating the equipment health index;

[0008] Analyzing the remote sensing images to determine the forest fire risk index and the flood risk index, and determining the flow disaster risk index;

[0009] Extracting the construction position, construction type and mechanical type in the construction data, and calculating the construction risk index;

[0010] The comprehensive risk index of the power transmission line is calculated based on the meteorological risk index, the equipment health index, the mobile disaster risk index and the construction risk index, and the early warning information is generated.

[0011] As a further scheme of the present application, the step of determining the icing thickness according to the meteorological temperature data and obtaining the meteorological risk index according to the icing thickness, the real-time wind speed and the environmental temperature specifically comprises:

[0012] The precipitation rate P, the liquid water content LWC, the real-time wind speed Va and the environmental temperature Ta in the meteorological temperature data are called;

[0013] The accumulated icing thickness I is calculated, η is the collision efficiency, Tf is the freezing threshold temperature, and K is the temperature sensitivity coefficient, and when Ta is higher than Tf, no icing is generated;

[0014] The meteorological risk index M1 is calculated, V0 is the design reference wind speed, I0 is the design reference icing thickness, and α, β and γ are weight coefficients.

[0015] As a further scheme of the present application, the step of calculating the equipment health index specifically comprises:

[0016] The historical failure data is called, and the defect index HDI of each equipment type is obtained according to the historical failure data, and the equipment types include steel core aluminum stranded wire, composite insulator and tower structure;

[0017] The health index of each equipment type is calculated, λ is the aging attenuation coefficient, Tn is the equipment operation time, ∈ is the monitored strain value, ∈1 and ∈2 are respectively the design minimum strain and the material yield limit strain, and w1 and w2 are weight coefficients;

[0018] The maximum one of the health indexes of various equipment types is determined as the equipment health index M2 of the power transmission line.

[0019] As a further scheme of the present application, the step of determining the mobile disaster risk index by analyzing the remote sensing image to determine the mountain fire risk index and the flood risk index specifically comprises:

[0020] The remote sensing image is analyzed to determine the fire line information, the vegetation type and the barrier information, and the hydrological information is called;

[0021] The mountain fire spreading speed Vs is calculated, Vs=Vc×(1+k0×Va), Vc is the reference spreading speed, which is determined by the vegetation type, Va is the real-time wind speed, k0 is a constant coefficient, and the barrier effectiveness Be is determined according to the barrier information;

[0022] Calculation D is the nearest distance of the fire line to the power transmission line, theta is the angle between the moving direction of the fire line and the normal direction of the line, ZA is the relative humidity, and Ta is the ambient temperature;

[0023] The hydrological information is compared with historical flood data to determine a flood risk index, and a mobile disaster risk index M3 is obtained according to the mountain fire risk index and the flood risk index.

[0024] As a further scheme of the present application, the step of comparing the hydrological information with the historical flood data to determine the flood risk index specifically comprises:

[0025] The hydrological information is matched with the historical flood data to determine similar flood data;

[0026] The coverage range and flood depth information in the similar flood data are called to determine the flood risk index.

[0027] As a further scheme of the present application, the step of calculating the construction risk index specifically comprises:

[0028] A position risk factor is determined according to the construction position, a construction risk factor is determined according to the construction type, and a mechanical risk factor is determined according to the mechanical type;

[0029] The construction risk index M4 is obtained according to the position risk factor, the construction risk factor and the mechanical risk factor.

[0030] Another object of the present application is to provide a power transmission line risk assessment and early warning system, which comprises:

[0031] A multi-dimensional data acquisition module is used to acquire meteorological temperature data, equipment state data, remote sensing images and construction data, and the construction data is determined through construction license information;

[0032] A meteorological risk index module is used to determine the ice thickness according to the meteorological temperature data, and obtain the meteorological risk index according to the ice thickness, real-time wind speed and ambient temperature;

[0033] An equipment health index module is used to extract the equipment running time and monitored strain value in the equipment state data, and calculate the equipment health index;

[0034] A disaster risk index module is used to analyze the remote sensing images to determine the mountain fire risk index and the flood risk index, and determine the mobile disaster risk index;

[0035] A construction risk index module is used to extract the construction position, construction type and mechanical type in the construction data, and calculate the construction risk index;

[0036] The comprehensive risk early warning module is configured to calculate a comprehensive risk index of the power transmission line based on the meteorological risk index, the equipment health index, the flow disaster risk index and the construction risk index, and generate early warning information.

[0037] As a further scheme of the present application, the meteorological risk index module comprises:

[0038] The meteorological data calling unit is configured to call a precipitation rate P, a liquid water content LWC, a real-time wind speed Va and an ambient temperature Ta in meteorological temperature data.

[0039] The icing thickness calculation unit is configured to calculate a cumulative icing thickness I, η is a collision efficiency, Tf is a freezing threshold temperature, and K is a temperature sensitivity coefficient, and no icing occurs when Ta is higher than Tf.

[0040] The meteorological risk index unit is configured to calculate a meteorological risk index M1, V0 is a design reference wind speed, I0 is a design reference icing thickness, and α, β and γ are weight coefficients.

[0041] As a further scheme of the present application, the equipment health index module comprises:

[0042] The defect index determination unit is configured to call historical fault data, and obtain a defect index HDI of each equipment type according to the historical fault data, the equipment types including a steel-cored aluminum stranded wire, a composite insulator and a tower structure.

[0043] The health index calculation unit is configured to calculate a health index of each equipment type, λ is an aging attenuation coefficient, Tn is an equipment operation time, ∈ is a monitored strain value, ∈1 and ∈2 are respectively a design minimum strain and a material yield limit strain, and w1 and w2 are weight coefficients.

[0044] The equipment health index unit is configured to determine a maximum one of the health indexes of the various equipment types as the equipment health index M2 of the power transmission line.

[0045] As a further scheme of the present application, the disaster risk index module comprises:

[0046] The feature information extraction unit is configured to perform feature analysis on the remote sensing image, determine fire line information, vegetation types and barrier information, and call hydrological information.

[0047] A spreading speed calculation unit is configured to calculate a wildfire spreading speed Vs, Vs=Vc x (1+k0 x Va), where Vc is a reference spreading speed determined by vegetation type, Va is a real-time wind speed, and k0 is a constant coefficient determined according to barrier information to determine barrier effectiveness Be;

[0048] A wildfire risk index unit is configured to calculate a wildfire risk index M1 according to the barrier effectiveness Be, the vegetation type, and the real-time wind speed. D is the closest distance between the fire line and the power transmission line, theta is the included angle between the fire line moving direction and the normal direction of the line, ZA is the relative humidity, and Ta is the ambient temperature.

[0049] A flood risk index unit is configured to compare hydrological information with historical flood data to determine a flood risk index, and obtain a mobile disaster risk index M3 according to the wildfire risk index and the flood risk index.

[0050] Compared with the prior art, the present application has the following beneficial effects:

[0051] The present application comprehensively considers meteorological risk indexes, equipment health indexes, mobile disaster risk indexes, and construction risk indexes, and obtains a comprehensive risk index of the power transmission line through calculation, so that the risk condition of the power transmission line can be more comprehensively and accurately evaluated, and the limitations of single-factor evaluation are avoided. The collected various data are intelligently processed by using data analysis and calculation models, so that the automation and intelligence of risk assessment and early warning are realized, manual intervention is reduced, work efficiency and evaluation accuracy are improved, and the power transmission line operation environment can be better adapted to complex and changeable conditions. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 It is a flowchart of a power transmission line risk assessment and early warning method.

[0053] Figure 2 It is a flowchart of obtaining a meteorological risk index in a power transmission line risk assessment and early warning method.

[0054] Figure 3 It is a flowchart of obtaining an equipment health index in a power transmission line risk assessment and early warning method.

[0055] Figure 4 It is a flowchart of determining a mobile disaster risk index in a power transmission line risk assessment and early warning method.

[0056] Figure 5 It is a flowchart of obtaining a construction risk index in a power transmission line risk assessment and early warning method.

[0057] Figure 6 It is a structural schematic diagram of a power transmission line risk assessment and early warning system. DETAILED DESCRIPTION

[0058] In order to make the objects, technical solutions and advantages of the present application clearer, the following further describes the present application in detail with reference to the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0059] The specific implementation of the present application is described in detail below in combination with specific embodiments.

[0060] As shown in the accompanying drawings, the embodiment of the present application provides a power transmission line risk assessment and early warning method, which comprises the following steps: Figure 1

[0061] S100, collecting meteorological temperature data, equipment state data, remote sensing images and construction data, wherein the construction data is determined through construction license information;

[0062] S200, determining the ice thickness according to the meteorological temperature data, and obtaining the meteorological risk index according to the ice thickness, real-time wind speed and environmental temperature;

[0063] S300, extracting the equipment running time and monitoring strain value in the equipment state data, and calculating the equipment health index;

[0064] S400, analyzing the remote sensing images to determine the mountain fire risk index and the flood risk index, and determining the mobile disaster risk index;

[0065] S500, extracting the construction position, construction type and mechanical type in the construction data, and calculating the construction risk index;

[0066] S600, calculating the comprehensive risk index of the power transmission line based on the meteorological risk index, the equipment health index, the mobile disaster risk index and the construction risk index, and generating early warning information.

[0067] It should be noted that the traditional risk assessment is difficult to obtain real-time and comprehensive data of meteorology, equipment state, natural disasters and construction, and cannot timely grasp various risk information of the power transmission line. The traditional risk assessment and early warning method mainly relies on manual experience, lacks intelligent data analysis and processing capability, and is difficult to cope with the complex and changeable power transmission line operation environment. The embodiment of the present application aims to solve the above problems.

[0068] ​In the embodiment of the present application, the collected multi-dimensional data includes meteorological temperature data, equipment state data, remote sensing images and construction data. The equipment state data is mainly obtained by installing sensors, the remote sensing images can be obtained by aerial photography of a drone, and the construction data is determined by the construction permit information on the relevant engineering approval website. In this way, the overall monitoring of the operation environment of the power transmission line is realized, and the related data of various potential risk factors can be obtained in time to provide a reliable data basis for accurate risk assessment. Then the icing thickness is determined according to the meteorological temperature data, the meteorological risk index M1 is obtained according to the icing thickness, real-time wind speed and environmental temperature, the icing increases the weight of the conductor, changes the conductor sag and damages the insulator string, the wind increases the conductor tension, causes vibration and affects the stability of the tower, and the temperature affects the thermal expansion and contraction of the conductor and the conductor resistance and power loss, so the meteorological risk index is more accurate. Then the equipment running time and the monitoring strain value in the equipment state data are extracted, the equipment health index M2 is calculated, the remote sensing images are analyzed to determine the mountain fire risk index and the flood risk index, and the flowing disaster risk index M3 is determined. Mountain fire and flood have a greater impact on the power transmission line. In addition, the construction position, construction type and mechanical type in the construction data are extracted, and the construction risk index M4 is calculated. If the operation is improper or lacks effective control, the construction machinery such as tower crane, excavator and pile driver is easy to collide with the power transmission line and has risks; even if the construction machinery does not directly collide with the power transmission line, the vibration generated by the operation in the vicinity may also cause damage to the tower foundation and equipment of the power transmission line. Finally, the comprehensive risk index of the power transmission line is calculated according to the meteorological risk index, the equipment health index, the flowing disaster risk index and the construction risk index, the comprehensive risk index = K1*M1+K2*M2+K3*M3+K4*M4, K1, K2, K3 and K4 are constant coefficients, then the warning level is determined according to the specific value of the comprehensive risk index, when it is grade I, power-off, evacuation and emergency treatment are immediately taken; when it is grade II, voltage reduction operation, on-site disposal and emergency preparation are taken; when it is grade III, monitoring is strengthened and warning is issued; when it is grade IV, normal monitoring is performed. The embodiment of the present application comprehensively considers the meteorological risk index, the equipment health index, the flowing disaster risk index and the construction risk index and the like, and obtains the comprehensive risk index of the power transmission line through comprehensive calculation, so that the risk condition of the power transmission line can be more comprehensively and accurately evaluated, and the limitation of single factor evaluation is avoided. The collected various data are intelligently processed by using data analysis and calculation model, the automation and intelligence of risk assessment and warning are realized, manual intervention is reduced, the work efficiency and the accuracy of evaluation are improved, and the power transmission line operation environment can be better adapted to the complex and changeable power transmission line operation environment.

[0069] As Figure 2As shown, in a preferred embodiment of the present invention, the steps of determining the icing thickness based on meteorological temperature data and obtaining the meteorological risk index based on the icing thickness, real-time wind speed, and ambient temperature specifically include:

[0070] S201, retrieve precipitation rate P, liquid water content LWC, real-time wind speed Va, and ambient temperature Ta from meteorological temperature data;

[0071] S202, calculate the cumulative icing thickness I;

[0072] S203, the meteorological risk index M1 is calculated.

[0073] In this embodiment of the invention, the meteorological temperature data includes at least the precipitation rate P (mm / h, which is converted to precipitation rate by dividing by 10 when it is snowfall rate), liquid water content LWC, real-time wind speed Va, and ambient temperature Ta. Then, the icing thickness I can be calculated. η is the collision efficiency, determined by the transmission line diameter; Tf is the freezing threshold temperature, for example, 0 degrees Celsius; K is the temperature sensitivity coefficient, a constant value. It's easy to understand that when Ta is higher than Tf, no icing will occur. Next, the meteorological risk index M1 is calculated. V0 is the design reference wind speed (15m / s), I0 is the design reference icing thickness (10mm), and α, β and γ are weighting coefficients, which can be set to 0.4, 0.4 and 0.2 respectively.

[0074] like Figure 3 As shown, in a preferred embodiment of the present invention, the step of calculating the equipment health index specifically includes:

[0075] S301, retrieve historical fault data, and obtain the Defect Index (HDI) for each type of equipment based on the historical fault data;

[0076] S302, calculate the health index for each device type.

[0077] S303, determine the largest of the health indices for various equipment types as the equipment health index M2 for transmission lines.

[0078] In this embodiment of the invention, the Defect Index (HDI) for each equipment type needs to be obtained based on historical fault data. The equipment types specifically include steel-cored aluminum stranded wire, composite insulators, and tower structures. The HDI is calculated from the number of defects and the threshold number over the past two years. Then, the health index for each equipment type is calculated. Lambda is an aging attenuation coefficient, the lambda of the steel-cored aluminum stranded wire is 0.08, the lambda of the composite insulator is 0.12, and the lambda of the tower structure is 0.05, Tn is the equipment operation time, epsilon is a monitoring strain value obtained by a sensor, epsilon 1 and epsilon 2 are respectively a design minimum strain and a material yield limit strain, and w1 and w2 are weight coefficients.

[0079] As shown in Figure 4 As a preferred embodiment of the present application, the step of determining the flowing disaster risk index by analyzing the remote sensing image to determine the wildfire risk index and the flood risk index, specifically comprises:

[0080] S401, performing feature analysis on the remote sensing image to determine the fire line information, the vegetation type and the barrier information; and calling hydrological information;

[0081] S402, calculating the wildfire spread speed Vs, Vs=Vc x (1+k0 x Va);

[0082] S403, calculating

[0083] S404, comparing the hydrological information with historical flood data to determine the flood risk index, and obtaining the flowing disaster risk index M3 according to the wildfire risk index and the flood risk index.

[0084] In the embodiment of the present application, the remote sensing image needs to be analyzed, and the analyzed features include fire body features, vegetation features, water body features, bare land features and building features, the fire line information, the vegetation type and the barrier information can be determined according to the identified features, the barrier information includes barrier type and size, and the barrier type is water body, bare land and building. Then, the wildfire spread speed Vs needs to be calculated, Vs=Vc x (1+k0 x Va), Vc is a reference spread speed, which is determined by the vegetation type, Va is a real-time wind speed, k0 is a constant coefficient, the barrier effectiveness Be is determined according to the barrier information, the barrier effectiveness is determined by the barrier type and size, then, the D is the nearest distance of the fire line to the power transmission line, theta is the included angle between the fire line moving direction and the normal direction of the power transmission line, ZA is the relative humidity, Ta is the environmental temperature, and the fire line moving direction can be determined according to multiple remote sensing images. Finally, the larger one of the wildfire risk index and the flood risk index is taken as the flowing disaster risk index M3.

[0085] The step of comparing the hydrological information with the historical flood data to determine the flood risk index comprises: matching the hydrological information with the historical flood data to determine the most similar flood data, then calling the coverage range and flood depth information in the most similar flood data in the historical data to determine the flood risk index, when the power transmission line is not in the coverage range, the flood risk index is 0; when the power transmission line is in the coverage range, the maximum depth along the power transmission line is determined according to the flood depth information, and the flood risk index is calculated according to the maximum depth.

[0086] As shown in Figure 5 , as a preferred embodiment of the present application, the step of calculating the construction risk index comprises:

[0087] S501, determining a position risk factor according to the construction position, a construction risk factor according to the construction type, and a mechanical risk factor according to the mechanical type;

[0088] S502, obtaining the construction risk index M4 according to the position risk factor, the construction risk factor and the mechanical risk factor.

[0089] In the embodiment of the present application, the position risk factor G1 will be determined according to the construction position, for example, when the minimum distance between the construction position and the power transmission line is less than 50 meters, G1 takes 1, when the minimum distance is between 50-100 meters, G1 takes 0.7, when the minimum distance is between 100-200 meters, G1 takes 0.4, and when the minimum distance is greater than 200 meters, G1 takes 0. Then the construction risk factor G2 will be determined according to the construction type, and the G2 corresponding to the foundation construction (such as pile foundation, foundation pit), high-altitude operation (such as tower crane, scaffold), blasting operation and ordinary ground construction are all different, and the G2 corresponding to various construction types needs to be set in advance. Then, the mechanical risk factor G3 will be determined according to the mechanical type, and the G3 corresponding to large hoisting machinery (such as tower crane, crawler crane), excavator, bulldozer, transport vehicle and small manual tool are also all different. Finally, the construction risk index M4 is obtained according to the position risk factor, the construction risk factor and the mechanical risk factor, M4=G1 x (k5 x G2+k6 x G3), k5 and k6 are constant coefficients.

[0090] As shown in Figure 6 , the embodiment of the present application further provides a power transmission line risk assessment and early warning system, which comprises:

[0091] A multi-dimensional data acquisition module 100 is used to acquire meteorological temperature data, equipment state data, remote sensing images and construction data, and the construction data is determined through construction license information;

[0092] The meteorological risk index module 200 is configured to determine an icing thickness according to the meteorological temperature data, and obtain a meteorological risk index according to the icing thickness, a real-time wind speed and an ambient temperature;

[0093] The equipment health index module 300 is configured to extract an equipment running time and a monitored strain value in the equipment state data, and calculate an equipment health index;

[0094] The disaster risk index module 400 is configured to analyze the remote sensing image to determine a forest fire risk index and a flood risk index, and determine a mobile disaster risk index;

[0095] The construction risk index module 500 is configured to extract a construction position, a construction type and a mechanical type in the construction data, and calculate a construction risk index;

[0096] The comprehensive risk early warning module 600 is configured to calculate a comprehensive risk index of the power transmission line based on the meteorological risk index, the equipment health index, the mobile disaster risk index and the construction risk index, and generate early warning information.

[0097] As a preferred embodiment of the present application, the meteorological risk index module 200 comprises:

[0098] The meteorological data calling unit is configured to call a precipitation rate P, a liquid water content LWC, a real-time wind speed Va and an ambient temperature Ta in the meteorological temperature data;

[0099] The icing thickness calculation unit is configured to calculate a cumulative icing thickness I, η is a collision efficiency, Tf is a freezing threshold temperature, and K is a temperature sensitivity coefficient, and no icing is generated when the Ta is higher than the Tf;

[0100] The meteorological risk index unit is configured to calculate a meteorological risk index M1, V0 is a design reference wind speed, I0 is a design reference icing thickness, and α, β and γ are weight coefficients.

[0101] As a preferred embodiment of the present application, the equipment health index module 300 comprises:

[0102] The defect index determination unit is configured to call historical fault data, and obtain a defect index HDI of each equipment type according to the historical fault data, the equipment types including a steel-cored aluminum stranded wire, a composite insulator and a tower structure;

[0103] The health index calculation unit is configured to calculate a health index of each equipment type, λ is an aging attenuation coefficient, Tn is the equipment running time, ∈ is the monitoring strain value, ∈1 and ∈2 are the design minimum strain and the material yield limit strain respectively, w1 and w2 are weight coefficients;

[0104] The device health index unit is configured to determine the maximum one of the health indexes of various device types as the device health index M2 of the power transmission line.

[0105] As a preferred embodiment of the present application, the disaster risk index module 400 comprises:

[0106] The feature information extraction unit is configured to perform feature analysis on the remote sensing image, determine fire line information, vegetation type and barrier information, and retrieve hydrological information.

[0107] The spread speed calculation unit is configured to calculate the spread speed Vs of the wildfire, Vs = Vc x (1 + k0 x Va), where Vc is a reference spread speed determined by the vegetation type, Va is the real-time wind speed, and k0 is a constant coefficient used to determine barrier effectiveness Be according to the barrier information.

[0108] The wildfire risk index unit is configured to calculate D is the nearest distance of the fire line to the power transmission line, θ is the included angle between the fire line movement direction and the normal direction of the line, ZA is the relative humidity, and Ta is the environmental temperature.

[0109] The flood risk index unit is configured to compare the hydrological information with historical flood data to determine a flood risk index, and obtain the mobile disaster risk index M3 according to the wildfire risk index and the flood risk index.

[0110] The above only describes the preferred embodiments of the present application in detail, and is not used to limit the present application, and any modification, equivalent replacement and improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

[0111] It should be understood that although each step in the flowchart of each embodiment of the present application is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless explicitly stated herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in each embodiment can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these sub-steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or sub-steps or stages of other steps.

[0112] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer readable storage medium, and when the program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, storage, databases, or other media in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchl ink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0113] Other embodiments of the present disclosure will be apparent to those skilled in the art with the disclosure in the specification and the examples. The present application is intended to cover any variations, uses, or adaptive changes of the present disclosure that follow the general principles of the present disclosure and include common knowledge or conventional technical means in the art that are not disclosed by the present disclosure. The specification and examples are only considered as exemplary, and the true scope and spirit of the present disclosure are indicated by the claims.

Claims

1. A power transmission line risk assessment and early warning method, characterized in that, The method comprises the following steps: Collecting meteorological temperature data, equipment state data, remote sensing images and construction data, wherein the construction data is determined through construction license information; Determining icing thickness according to the meteorological temperature data, and obtaining a meteorological risk index according to the icing thickness, real-time wind speed and environmental temperature; Extracting equipment running time and monitored strain value in the equipment state data, and calculating an equipment health index; Analyzing the remote sensing images to determine a forest fire risk index and a flood risk index, and determining a mobile disaster risk index; Extracting construction position, construction type and mechanical type in the construction data, and calculating a construction risk index; Calculating a comprehensive risk index of the power transmission line based on the meteorological risk index, the equipment health index, the mobile disaster risk index and the construction risk index, and generating early warning information.

2. The power line risk assessment and warning method of claim 1, wherein The step of determining the icing thickness according to the meteorological temperature data, and obtaining the meteorological risk index according to the icing thickness, the real-time wind speed and the environmental temperature comprises the following steps: Retrieving precipitation rate P, liquid water content LWC, real-time wind speed Va and environmental temperature Ta in the meteorological temperature data; The accumulated ice thickness I is calculated, η is the collision efficiency, Tf is the freezing threshold temperature, and K is the temperature sensitivity coefficient. No ice is generated when Ta is higher than Tf. The meteorological risk index M1 is calculated, V0 is the design reference wind speed, I0 is the design reference icing thickness, and α, β and γ are weight coefficients.

3. The power line risk assessment and warning method of claim 1, wherein The step of calculating the equipment health index comprises the following steps: Retrieving historical failure data, and obtaining a defect index HDI of each equipment type according to the historical failure data, wherein the equipment types include steel core aluminum stranded wire, composite insulator and tower structure; calculating a health index for each device type, λ is the aging attenuation coefficient, Tn is the device running time, ∈ is the monitored strain value, ∈1 and ∈2 are the design minimum strain and material yield limit strain, respectively, and w1 and w2 are weight coefficients; Determining the maximum one of the health indexes of various equipment types as the equipment health index M2 of the power transmission line.

4. The power line risk assessment and warning method of claim 1, wherein The step of analyzing the remote sensing images to determine the forest fire risk index and the flood risk index, and determining the mobile disaster risk index comprises the following steps: Performing feature analysis on the remote sensing images to determine fire line information, vegetation type and barrier information; retrieving hydrological information; Calculating a forest fire spread speed Vs, Vs = Vc x (1 + k0 x Va), wherein Vc is a reference spread speed determined by the vegetation type, Va is the real-time wind speed, and k0 is a constant coefficient; determining barrier effectiveness Be according to the barrier information; Calculate the bushfire risk index = Vs / (D+0.1) x cos θ x (1-Be) x e Va×(1-ZA)×Ta / 30 , D is the nearest distance from the fire line to the power transmission line, θ is the angle between the fire line moving direction and the normal direction of the line, ZA is the relative humidity, and Ta is the ambient temperature. Comparing the hydrological information with historical flood data to determine a flood risk index, and obtaining the mobile disaster risk index M3 according to the forest fire risk index and the flood risk index.

5. The method of power line risk assessment and early warning according to claim 4, characterized in that, The step of comparing the hydrological information with the historical flood data to determine the flood risk index comprises the following steps: Matching the hydrological information with similar flood data to determine similar flood data; Retrieving coverage range and flood depth information in the similar flood data to determine the flood risk index.

6. The method of power line risk assessment and early warning according to claim 1, characterized in that, The step of calculating the construction risk index comprises the following steps: Determining a position risk factor according to the construction position, a construction risk factor according to the construction type, and a mechanical risk factor according to the mechanical type; Obtaining the construction risk index M4 according to the position risk factor, the construction risk factor and the mechanical risk factor.

7. A power line risk assessment and warning system, characterized by, The system comprises: A multi-dimensional data collection module for collecting meteorological temperature data, equipment state data, remote sensing images and construction data, wherein the construction data is determined through construction license information; A meteorological risk index module for determining icing thickness according to the meteorological temperature data, and obtaining a meteorological risk index according to the icing thickness, real-time wind speed and environmental temperature; The device health index module is configured to extract device running time and monitored strain values in the device state data and calculate a device health index; The disaster risk index module is configured to analyze remote sensing images to determine a forest fire risk index and a flood risk index and determine a mobile disaster risk index; The construction risk index module is configured to extract construction location, construction type and mechanical type in the construction data and calculate a construction risk index; The comprehensive risk early warning module is configured to calculate a comprehensive risk index of the power transmission line based on the meteorological risk index, the device health index, the mobile disaster risk index and the construction risk index and generate early warning information.

8. The transmission line risk assessment warning system of claim 7, wherein, The meteorological risk index module includes: A meteorological data calling unit configured to call precipitation rate P, liquid water content LWC, real-time wind speed Va and environmental temperature Ta in meteorological temperature data; an ice thickness calculation unit configured to calculate an accumulated ice thickness I, η is the collision efficiency, Tf is the freezing threshold temperature, and K is the temperature sensitivity coefficient. No ice is generated when Ta is higher than Tf. a meteorological risk index unit for calculating a meteorological risk index M1, V0 is a design reference wind speed, I0 is a design reference icing thickness, and α, β, and γ are weight coefficients.

9. The transmission line risk assessment warning system of claim 7, wherein, The device health index module includes: A defect index determination unit configured to call historical failure data and obtain a defect index HDI of each device type according to the historical failure data, the device types including steel-cored aluminum stranded wire, composite insulator and tower structure; a health index calculation unit configured to calculate a health index of each device type, λ is an aging attenuation coefficient, Tn is the device running time, ∈ is the monitored strain value, ∈1 and ∈2 are the design minimum strain and the material yield limit strain, respectively, and w1 and w2 are weight coefficients. A device health index unit configured to determine the maximum one of health indexes of various device types as a device health index M2 of the power transmission line.

10. The transmission line risk assessment warning system of claim 7, wherein, The disaster risk index module includes: A feature information extraction unit configured to perform feature analysis on the remote sensing images to determine fire line information, vegetation type and barrier information and call hydrological information; A spread speed calculation unit configured to calculate a forest fire spread speed Vs, Vs = Vc x (1+k0 x Va), Vc being a reference spread speed determined by the vegetation type, Va being the real-time wind speed, k0 being a constant coefficient and the barrier effectiveness Be being determined according to the barrier information; A mountain fire risk index unit for calculating a mountain fire risk index = Vs / (D+0.1)×cosθ×(1-Be)×e Va ×(1-ZA)×Ta / 30 D is the nearest distance from the fire line to the power transmission line, θ is the included angle between the moving direction of the fire line and the normal direction of the line, ZA is the relative humidity, and Ta is the ambient temperature. A flood risk index unit configured to compare the hydrological information with historical flood data to determine a flood risk index and obtain a mobile disaster risk index M3 according to the forest fire risk index and the flood risk index.

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