A risk assessment method, system, device and medium for offshore wind power

Abstract

The present application relates to the technical field of risk assessment, and in particular to a risk assessment method, system, equipment and medium for offshore wind power. The present application first obtains the environmental parameters of the area where the offshore wind farm is to be built, and divides the wind farm area into multiple candidate areas, then performs risk analysis on each candidate area according to the environmental parameters to obtain the risk analysis results, then performs risk assessment on each candidate area according to the risk analysis results to obtain the comprehensive risk assessment value of each candidate area, and finally compares and sorts each comprehensive risk assessment value to obtain the risk sorting result, draws a risk level distribution map and outputs it; it realizes the refined risk assessment of different candidate areas of the offshore wind farm, improves the accuracy of risk assessment, and makes the evaluation results more intuitive, provides support for the site selection decision of the wind farm, improves the scientificity and reliability of offshore wind power development, and reduces the later operation and maintenance costs.

Description

Technical Field

[0001] The present application relates to the technical field of risk assessment, and in particular to a risk assessment method, system, equipment and medium for offshore wind power. Background Art

[0002] As global energy shortages and environmental pollution become increasingly serious, the development of clean and renewable energy has become a consensus among countries around the world. Offshore wind power has become a hot spot for new energy development in recent years due to its abundant wind energy resources, vast development space and fewer environmental constraints.

[0003] At present, the site selection of offshore wind farms mainly relies on manual field surveys and empirical judgments, which have problems such as low efficiency and strong subjectivity. Existing technologies also use geographic information systems and multi-criteria decision-making methods to select offshore wind farm sites, but most of them only consider the distribution of wind energy resources and construction costs, while ignoring the impact of environmental factors. At the same time, there is a lack of refined risk analysis and evaluation for offshore wind farms, resulting in high subsequent operation and maintenance costs. This situation needs to be further improved. Summary of the invention

[0004] In order to solve the problem of high post-operation and maintenance costs of existing offshore wind farms, the present application provides a risk assessment method, system, equipment and medium for offshore wind power, which adopts the following technical solutions:

[0005] In a first aspect, the present application provides a risk assessment method for offshore wind power, comprising the following steps:

[0006] Acquiring environmental parameters of a proposed offshore wind farm area, performing grid processing on the proposed offshore wind farm area, and dividing the proposed offshore wind farm area into a plurality of candidate areas;

[0007] According to the environmental parameters, risk analysis is performed on each candidate area to obtain a risk analysis result;

[0008] According to the risk analysis results, risk assessment is performed on each candidate area to obtain a comprehensive risk assessment value for each candidate area;

[0009] Compare and rank the comprehensive risk assessment values ​​of each candidate area to obtain the risk ranking result, draw a risk level distribution map and output it.

[0010] By adopting the above technical scheme, the present application first obtains the environmental parameters of the proposed offshore wind farm area, and grids the wind farm area, divides the wind farm area into multiple candidate areas, and then performs risk analysis on each candidate area according to the environmental parameters to obtain the risk analysis results, and then performs risk assessment on each candidate area according to the risk analysis results to obtain a comprehensive risk assessment value for each candidate area, and finally compares and sorts the comprehensive risk assessment values ​​to obtain the risk ranking results, and draws and outputs a risk level distribution map; it realizes the refined risk analysis and evaluation of different candidate areas of the offshore wind farm, improves the accuracy of risk assessment, and at the same time, through risk sorting and distribution map drawing, makes the evaluation results more intuitive, provides support for wind farm site selection decisions, improves the scientificity and reliability of offshore wind power development, and reduces the subsequent operation and maintenance costs.

[0011] Optionally, obtaining environmental parameters of a proposed offshore wind farm area, and performing grid processing on the proposed offshore wind farm area according to the environmental parameters to divide the proposed offshore wind farm area into a plurality of candidate areas specifically includes the following steps:

[0012] Acquiring environmental parameters of the proposed offshore wind farm area, wherein the environmental parameters include wind speed parameters and seawater depth parameters;

[0013] Calculating wind speed distribution grid data and seawater depth distribution grid data of the proposed offshore wind farm area according to the wind speed parameters and seawater depth parameters;

[0014] According to the wind speed distribution grid data and the seawater depth distribution grid data, the proposed offshore wind farm area is divided into a plurality of candidate areas, each candidate area corresponding to a wind speed-seawater depth combination type.

[0015] By adopting the above technical scheme, the present application first obtains the environmental parameters of the proposed offshore wind farm area, including wind speed parameters and sea water depth parameters, and then calculates the wind speed distribution raster data and sea water depth distribution raster data of the wind farm area according to the wind speed parameters and the sea water depth parameters, respectively. Finally, according to the wind speed distribution raster data and the sea water depth distribution raster data, the wind farm area is divided into multiple candidate areas, each candidate area corresponds to a specific wind speed-sea water depth combination type, thereby fully considering the two key environmental factors of wind speed and sea water depth that affect the site selection of wind farms, so that the division of candidate areas is more in line with the actual situation of the wind farm area. At the same time, through regional division, more detailed and accurate basic data is provided for subsequent risk analysis and evaluation, thereby improving the reliability and rationality of risk assessment.

[0016] Optionally, the environmental parameters further include meteorological parameters, ocean parameters, geological parameters and ecological parameters. According to the environmental parameters, risk analysis is performed on various candidate areas for the proposed offshore wind farm to obtain risk analysis results, which specifically includes the following steps:

[0017] Determine the meteorological risk, ocean risk, geological risk and ecological risk of each candidate area based on the environmental parameters;

[0018] According to the preset risk assessment index system and scoring standards, the meteorological risk, marine risk, geological risk and ecological risk of each candidate area are quantitatively assessed to obtain the risk measurement value of each risk factor;

[0019] The risk analysis result is obtained according to the risk measurement value of each risk factor in each candidate area.

[0020] By adopting the above-mentioned technical scheme, in order to comprehensively assess the risks of the proposed offshore wind farm, it is necessary to comprehensively consider various environmental factors such as meteorology, ocean, geology and ecology. This application conducts a quantitative assessment of the meteorological risk, ocean risk, geological risk and ecological risk of each candidate area based on meteorological, oceanic, geological and ecological environmental parameters, according to the preset risk assessment index system and scoring standards, and obtains the risk measurement value of each risk factor. Then, the risk measurement value of each candidate area in each risk factor is comprehensively considered to obtain a risk analysis result reflecting the overall risk level, which improves the scientific nature and operability of the risk analysis. At the same time, by analyzing the risks of each candidate area one by one, high-risk areas can be identified in a targeted manner, providing an important basis for subsequent risk prevention and control and optimized site selection.

[0021] Optionally, according to the risk analysis result, risk assessment is performed on each candidate area to obtain a comprehensive risk assessment value of each candidate area, which specifically includes the following steps:

[0022] Determine, according to the risk analysis result, a target risk level of each risk factor of a target candidate area, wherein the target candidate area is any one of the candidate areas;

[0023] According to the preset risk level evaluation rules, the target risk level of each risk factor is evaluated to obtain the risk evaluation value of each risk factor;

[0024] The risk evaluation values ​​according to various risk factors are weighted averaged to calculate the comprehensive risk evaluation value of the target candidate area.

[0025] By adopting the above-mentioned technical scheme, the present application first determines the target risk level of each risk factor of the target candidate area according to the risk analysis results, and then performs risk evaluation on the target risk level of each risk factor according to the preset risk level evaluation rules to obtain the risk evaluation value of each risk factor, and then performs weighted average on each risk evaluation value to calculate the comprehensive risk evaluation value of the target candidate area, thereby realizing the quantification of risk evaluation by quantifying the risk level into risk evaluation value, improving the measurability and comparability of the evaluation results, standardizing the standards and processes of risk evaluation, and making the risk evaluation process clearer, simpler and more operational.

[0026] Optionally, after performing risk assessment on each candidate area according to the risk analysis result and obtaining a comprehensive risk assessment value of each candidate area, the method further comprises the following steps:

[0027] Based on the comprehensive risk assessment values ​​of each candidate area, determine the high-risk candidate areas whose risk scores exceed the preset threshold;

[0028] Determine a risk coupling matrix between the high-risk candidate area and other candidate areas according to the location distribution information and historical environmental data of each candidate area of ​​the proposed offshore wind farm;

[0029] According to the risk coupling matrix, the risk coupling correction value of the high-risk candidate area to other candidate areas is calculated, and the comprehensive risk assessment values ​​of other candidate areas are corrected according to the risk coupling correction value.

[0030] By adopting the above technical scheme, in order to further improve the accuracy and reliability of risk assessment of offshore wind farms, it is necessary to consider the risk coupling effect of high-risk alternative areas on other alternative areas. This application sets a risk score threshold, and determines the alternative areas with comprehensive risk assessment values ​​exceeding the threshold as high-risk alternative areas. According to the location distribution information of each alternative area of ​​the proposed offshore wind farm, a risk coupling matrix between high-risk alternative areas and other alternative areas is established. Then, according to the risk coupling matrix, the risk coupling correction value of the high-risk alternative area to other alternative areas is calculated, and the comprehensive risk assessment value of other alternative areas is corrected using the risk coupling correction value to obtain a corrected comprehensive risk assessment value that takes into account the risk coupling effect. By introducing quantitative indicators such as the risk coupling matrix and the risk coupling correction value, a quantitative description and evaluation of the risk coupling effect is achieved, which enhances the scientificity and credibility of the evaluation results. At the same time, through the risk coupling correction, other areas that are greatly affected by the high-risk areas are identified, which provides an important reference for risk prevention and control and optimized site selection.

[0031] Optionally, according to the risk coupling matrix, calculating the risk coupling correction value of the high-risk candidate area to other candidate areas, and correcting the comprehensive risk assessment values ​​of other candidate areas according to the risk coupling correction value, specifically includes the following steps:

[0032] Assume that the i-th high-risk candidate area is HR i , the set of other candidate areas within the risk coupling influence range of the i-th high-risk candidate area is {A j |j=1,2,...,m}, the risk coupling matrix is ​​C=[c ij ]n×m;

[0033] Where n represents the number of high-risk candidate areas, m represents the number of candidate areas within the risk coupling influence range of each high-risk candidate area, and c ij Represents the ith high-risk candidate area HR i With alternative area A j The risk coupling coefficient between Among them, d ij For HR i With A j , λ is the risk coupling coefficient attenuation parameter, d0 is the threshold distance of risk coupling impact, sign(x) is the sign function, which takes 1 when x>0 and takes 0 when x≤0;

[0034] Calculate the HR of the i-th high-risk candidate area i For the jth candidate area A j Risk coupling correction value ΔR ij =c ij *R i *(1-R j / R max ), where R i is the ith high-risk candidate area HR i The comprehensive risk assessment value, R j is the jth candidate area A j The comprehensive risk assessment value, R max It is the maximum value of the comprehensive risk assessment values ​​of all candidate areas;

[0035] For the jth candidate area A j The comprehensive risk assessment value of the jth candidate area A is corrected. j The comprehensive risk assessment value R j’ =R j +∑ω i *ΔR ij , i = 1, 2, 3, ..., n, where ω iis the risk coupling weight of the i-th high-risk candidate area, ∑ω i =1.

[0036] In a second aspect, the present application provides a risk assessment system for offshore wind power, comprising:

[0037] A region division module is used to obtain environmental parameters of the proposed offshore wind farm area, perform grid processing on the proposed offshore wind farm area, and divide the proposed offshore wind farm area into multiple candidate areas;

[0038] A risk analysis module is used to perform risk analysis on each candidate area according to the environmental parameters to obtain risk analysis results; a risk assessment module is used to perform risk assessment on each candidate area according to the risk analysis results to obtain a comprehensive risk assessment value of each candidate area;

[0039] The result output module is used to compare and sort the comprehensive risk assessment values ​​of each candidate area, obtain the risk ranking result, draw a risk level distribution map and output it.

[0040] Optionally, the area division module includes:

[0041] A parameter acquisition unit, used to acquire environmental parameters of the proposed offshore wind farm area, wherein the environmental parameters include wind speed parameters and seawater depth parameters;

[0042] A data calculation unit, used to calculate the wind speed distribution grid data and the seawater depth distribution grid data of the proposed offshore wind farm area according to the wind speed parameter and the seawater depth parameter;

[0043] The area division unit is used to divide the proposed offshore wind farm area into a plurality of candidate areas according to the wind speed distribution grid data and the seawater depth distribution grid data, each candidate area corresponding to a wind speed-seawater depth combination type.

[0044] In a third aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned risk assessment method for offshore wind power when executing the computer program.

[0045] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned risk assessment method for offshore wind power are implemented.

[0046] In summary, the present application includes at least one of the following beneficial technical effects:

[0047] 1. This application first obtains the environmental parameters of the proposed offshore wind farm area, and performs grid processing on the wind farm area, divides the wind farm area into multiple candidate areas, and then performs risk analysis on each candidate area according to the environmental parameters to obtain the risk analysis results, and then performs risk assessment on each candidate area according to the risk analysis results to obtain the comprehensive risk assessment value of each candidate area, and finally compares and sorts the comprehensive risk assessment values ​​to obtain the risk sorting results, and draws and outputs the risk level distribution map; it realizes the refined risk analysis and evaluation of different candidate areas of the offshore wind farm, improves the accuracy of risk assessment, and at the same time, through risk sorting and distribution map drawing, makes the evaluation results more intuitive, provides support for wind farm site selection decisions, improves the scientificity and reliability of offshore wind power development, and reduces the later operation and maintenance costs;

[0048] 2. This application first obtains the environmental parameters of the proposed offshore wind farm area, including wind speed parameters and seawater depth parameters, and then calculates the wind speed distribution grid data and seawater depth distribution grid data of the wind farm area according to the wind speed parameters and seawater depth parameters, and finally divides the wind farm area into multiple candidate areas according to the wind speed distribution grid data and seawater depth distribution grid data, each candidate area corresponds to a specific wind speed-seawater depth combination type, thereby fully considering the two key environmental factors of wind speed and seawater depth that affect the site selection of the wind farm, so that the division of the candidate areas is more in line with the actual situation of the wind farm area, and at the same time, through regional division, provides more refined and accurate basic data for subsequent risk analysis and evaluation, thereby improving the reliability and rationality of risk assessment;

[0049] 3. This application sets a risk score threshold, identifies the alternative areas with comprehensive risk assessment values ​​exceeding the threshold as high-risk alternative areas, and establishes a risk coupling matrix between high-risk alternative areas and other alternative areas based on the location distribution information of the alternative areas for the proposed offshore wind farms. Then, based on the risk coupling matrix, the risk coupling correction value of the high-risk alternative areas to other alternative areas is calculated, and the comprehensive risk assessment values ​​of other alternative areas are corrected using the risk coupling correction value to obtain a corrected comprehensive risk assessment value that takes into account the risk coupling effect. By introducing quantitative indicators such as the risk coupling matrix and the risk coupling correction value, a quantitative description and evaluation of the risk coupling effect is achieved, thereby enhancing the scientificity and credibility of the evaluation results. At the same time, through the risk coupling correction, other areas that are greatly affected by the high-risk areas are identified, providing an important reference for risk prevention and control and optimized site selection. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of a risk assessment method for offshore wind power according to an embodiment of the present application;

[0051] Figure 2This is a flow chart of step S10 in a risk assessment method for offshore wind power according to an embodiment of the present application;

[0052] Figure 3 This is a flow chart of step S20 in a risk assessment method for offshore wind power according to an embodiment of the present application;

[0053] Figure 4 This is a flow chart of step S30 in a risk assessment method for offshore wind power according to an embodiment of the present application;

[0054] Figure 5 It is a schematic diagram of a process for correcting a risk assessment value in a risk assessment method for offshore wind power according to an embodiment of the present application;

[0055] Figure 6 This is a flow chart of step S36 in a risk assessment method for offshore wind power according to an embodiment of the present application;

[0056] Figure 7 This is a module schematic diagram of a risk assessment system for offshore wind power according to an embodiment of the present application;

[0057] Figure 8 It is a diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0058] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0059] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0060] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0061] In a first aspect, the present application provides a risk assessment method for offshore wind power, referring to Figure 1The method comprises the following steps: S10, obtaining environmental parameters of the proposed offshore wind farm area, performing grid processing on the proposed offshore wind farm area, and dividing the proposed offshore wind farm area into multiple candidate areas.

[0062] In this embodiment, by obtaining multiple environmental parameters of the proposed offshore wind farm area and using a grid processing method, the entire wind farm area is divided into multiple candidate areas, providing a basic unit for subsequent risk analysis and evaluation. Among them, the type and acquisition method of environmental parameters are flexibly selected according to actual conditions, and may include wind speed, sea depth, geological conditions, ecological environment and other factors.

[0063] Specifically, using different data processing software and algorithms, the wind farm area is divided into regular or irregular grid units according to a certain spatial resolution. Each grid unit contains attribute information of various environmental parameters and represents an independent candidate risk assessment area. The division of the candidate area is optimized and adjusted according to the actual situation of the wind farm and the assessment requirements.

[0064] S20. Perform risk analysis on each candidate area according to environmental parameters to obtain risk analysis results.

[0065] In this embodiment, after the candidate areas are divided, appropriate risk analysis methods are used according to various environmental parameters to quantitatively evaluate the risk level of each candidate area in terms of different risk factors, and risk analysis results are obtained to provide a basis for subsequent risk assessment.

[0066] Specifically, a risk assessment index system is constructed for key risk factors of offshore wind farms, such as typhoons, waves, earthquakes, and bird activities. For example, typhoon risk can be measured by indicators such as the average number of typhoons per year and the maximum wind force; wave risk can be measured by indicators such as the height and period of waves that occur once in a hundred years; earthquake risk can be measured by indicators such as earthquake intensity and rock liquefaction; and bird activity risk can be measured by indicators such as bird species and migration routes. Then, a combination of qualitative and quantitative methods is used to score each indicator of each candidate area to obtain a risk measurement value.

[0067] S30. Based on the risk analysis results, risk assessment is performed on each candidate area to obtain a comprehensive risk assessment value for each candidate area.

[0068] In this embodiment, based on the quantitative indicators of each risk factor obtained by risk analysis, a risk assessment model is used to comprehensively evaluate the risk level of each candidate area to obtain a comprehensive risk assessment value reflecting the overall risk level of the candidate area.

[0069] Specifically, the analytic hierarchy process is used to determine the weight of each risk factor, and the comprehensive risk assessment value is calculated by weighted summation. First, experts are invited to compare each risk factor in pairs, build a judgment matrix, and perform consistency tests to calculate the weight coefficient of each factor. Then, the risk measurement value of each candidate area is multiplied by the weight coefficient, and then the sum is accumulated to obtain the comprehensive risk assessment value of the area. The higher the comprehensive evaluation value, the higher the risk level of the area, and vice versa.

[0070] S40, comparing and ranking the comprehensive risk assessment values ​​of the candidate areas to obtain a risk ranking result, and drawing and outputting a risk level distribution map.

[0071] In this embodiment, according to the comprehensive risk evaluation value of each candidate area, an intuitive visualization method is used to clearly display the relative high and low risk levels and spatial distribution characteristics of each area, providing an intuitive reference for the site selection decision of the wind farm.

[0072] Specifically, the comprehensive risk assessment values ​​are sorted to obtain the order of risk levels from high to low. Then, using software such as ArcGIS's symbolization and hierarchical display functions, the candidate areas are divided into three levels of "high risk", "medium risk" and "low risk" according to the sorting results, and marked with different colors such as red, yellow and green to generate a risk level distribution map. At the same time, other relevant layers such as coastlines and isobaths are superimposed to fully display the geographical environment characteristics of the wind farm area. The risk level distribution map can be exported as a picture format, or further edited and analyzed in the GIS platform.

[0073] In one embodiment, referring to Figure 2 In step S10, environmental parameters of the proposed offshore wind farm area are obtained, and according to the environmental parameters, the proposed offshore wind farm area is gridded to divide the proposed offshore wind farm area into multiple candidate areas, which specifically includes the following steps:

[0074] S11. Obtain environmental parameters of the proposed offshore wind farm area, where the environmental parameters include wind speed parameters and seawater depth parameters.

[0075] In this embodiment, the wind speed parameter is obtained by actual measurement by on-site wind towers, buoys, meteorological stations and other equipment, and can also be indirectly obtained by numerical simulation, satellite remote sensing and other methods. The seawater depth parameter can be directly obtained by ship-borne depth sounders, unmanned ship measurements and other methods, and can also be indirectly obtained using existing nautical charts, topographic maps and other materials.

[0076] Specifically, 5-10 wind towers are deployed in the proposed wind farm area to continuously monitor wind speed and direction data, and the average wind speed is taken as the wind speed parameter. Using a single (multi) beam echo sounder, full coverage seawater depth measurement is carried out according to a certain route spacing and measurement point spacing to obtain high-precision water depth point data, and spatial interpolation is performed to generate seawater depth parameters.

[0077] S12. Calculate the wind speed distribution grid data and the sea depth distribution grid data of the proposed offshore wind farm area according to the wind speed parameters and the sea depth parameters.

[0078] In this embodiment, the wind speed distribution grid data reflects the spatial distribution characteristics of wind energy resources in the wind farm area, and the seawater depth distribution grid data reflects the fluctuation of the seabed topography in the wind farm area, which is a key factor in determining the foundation form and construction plan of the wind turbine.

[0079] Specifically, spatial interpolation methods such as inverse distance weighted interpolation are used to convert discrete wind speed and water depth measurement point data into continuous rasterized data layers. During the interpolation process, suspected outliers are identified and processed to improve the interpolation accuracy. The spatial resolution of the wind speed and water depth raster data generated finally matches the scale of the subsequent gridding processing.

[0080] S13. Divide the proposed offshore wind farm area into a plurality of candidate areas according to the wind speed distribution grid data and the seawater depth distribution grid data, each candidate area corresponding to a wind speed-seawater depth combination type.

[0081] In this embodiment, the wind speed-sea depth combination type refers to the combination of wind speed and sea depth within a certain range, which reflects the wind energy resource conditions and construction conditions of each candidate area. When dividing the candidate areas, the combined influence of wind speed and water depth is fully considered to ensure the efficient use of wind energy resources while taking into account the safety and economy of engineering construction.

[0082] Specifically, the raster calculator function of GIS software is used to merge the wind speed distribution raster data and the seawater depth distribution raster data to obtain a two-dimensional attribute table of wind speed-seawater depth. Referring to the power curve of the wind turbine and the applicable water depth range, the wind speed and water depth are divided into several grade intervals to generate a wind speed-seawater depth combination type map. On this basis, the vector boundary of the wind farm area is superimposed, and the 500m×500m fishing net processing is used to divide the wind farm area into regular grid units as alternative evaluation areas. The wind speed-water depth combination attribute of each area is determined by the average or dominant attribute of the grid it covers.

[0083] In one embodiment, referring to Figure 3In step S20, the environmental parameters also include meteorological parameters, ocean parameters, geological parameters and ecological parameters. According to the environmental parameters, risk analysis is performed on each candidate area of ​​the proposed offshore wind farm to obtain risk analysis results, which specifically includes the following steps:

[0084] S21. Determine the meteorological risk, marine risk, geological risk and ecological risk of each candidate area based on environmental parameters.

[0085] In this embodiment, meteorological risk mainly considers the impact of disastrous weather events such as typhoons, thunderstorms, and extreme temperatures on wind farms. Marine risk mainly considers the impact of ocean dynamic processes such as waves, currents, tides, and sea ice on wind turbine foundations and construction operations. Geological risk mainly considers the impact of geological disasters such as earthquakes, liquefaction, and uneven settlement on wind turbine safety and stability. Ecological risk mainly considers the impact of ecological processes such as bird migration, fish migration, and marine mammal activities on wind power projects.

[0086] Specifically, collect 20-30 years of meteorological observation data in the wind farm area, such as wind speed, air pressure, temperature, etc., and use extreme value statistical methods to estimate meteorological risk indicators such as extreme wind speed and typhoon recurrence period in each candidate area. Collect 1-3 years of ocean observation data in the wind farm area, such as waves, tide level, flow rate, etc., and use numerical simulation methods to analyze ocean risk indicators such as wave conditions and tidal characteristics in each candidate area. Collect geological survey data in the wind farm area, such as drilling, geophysical exploration, geotechnical testing, etc., and refer to relevant national standards to evaluate geological risk indicators such as earthquake intensity and soil properties in each candidate area. Collect ecological monitoring data in the wind farm area, such as bird surveys and fishery resource censuses, and consult experts in related fields to determine ecological risk indicators such as ecological sensitivity and vulnerability in each candidate area.

[0087] S22. Based on the preset risk assessment indicator system and scoring standards, a quantitative assessment is conducted on the meteorological risk, marine risk, geological risk and ecological risk of each candidate area to obtain the risk measurement value of each risk factor.

[0088] In this embodiment, the risk assessment index system refers to a quantitative index framework composed of various risk factors and their sub-factors, which is used to comprehensively and systematically measure various risks of wind farms. The scoring criteria are the evaluation rules that correspond the quantitative values ​​of each risk indicator to the risk level, which can be divided into 3-5 risk levels. Through the index system and scoring criteria, various risks of each candidate area can be converted into a unified risk measurement value, providing a basis for comprehensive evaluation and comparative analysis.

[0089] Specifically, with reference to international standards, a risk assessment index system including four indicators, namely meteorological, marine, geological and ecological, is constructed. For example, meteorological risks may include extreme wind speed, typhoon recurrence period, typhoon prevention level, etc.; marine risks may include tidal changes, seabed scouring, etc.; geological risks may include basic earthquake intensity, liquefaction level, pile side resistance, etc.; ecological risks may include migratory bird species, fish egg distribution density, marine protected areas, etc. On the basis of the index system, further formulate the scoring standards for each indicator, and use the 5-point system, 100-point system and other assignment methods to quantitatively describe the risk level corresponding to each indicator value. Finally, the indicator values ​​of each candidate area are matched with the scoring standards, and the risk measurement values ​​of the four types of risks, namely meteorological, marine, geological and ecological, can be obtained.

[0090] S23. Obtain risk analysis results based on the risk measurement values ​​of each risk factor in each candidate area.

[0091] In this embodiment, the risk analysis results are expressed using a risk matrix, which reflects both the risk level of each candidate area in different risk types and the relative size of different risk types in the same candidate area. A risk matrix is ​​a rectangular array that divides risks into different levels according to the probability of risk occurrence and the consequences of impact.

[0092] Specifically, a risk matrix of common specifications such as 4×4 and 5×5 can be used, with the vertical axis representing the risk consequence level and the horizontal axis representing the risk probability level, and the various risk measurement values ​​of each candidate area can be mapped to the corresponding position of the risk matrix. The high-risk and low-risk distribution of each candidate area and the proportion of each risk can be intuitively judged based on the position and aggregation of each risk point in the matrix.

[0093] In one embodiment, referring to Figure 4 In step S30, risk assessment is performed on each candidate area according to the risk analysis result to obtain a comprehensive risk assessment value of each candidate area, which specifically includes the following steps:

[0094] S31. Determine the target risk level of each risk factor of the target candidate area according to the risk analysis result. The target candidate area is any one of the candidate areas.

[0095] In this embodiment, the target risk level refers to the risk level that is reclassified for each risk factor of the target area based on risk analysis and according to risk assessment requirements.

[0096] Specifically, each risk factor is divided into four risk levels from low to high according to the risk measurement value: extremely low risk, low risk, medium risk, and high risk. Taking meteorological risk factors as an example, based on the recurrence period of extreme wind speed exceeding 25m / s, areas with a return period of less than 50 years can be classified as extremely low risk, areas with a return period of 50-100 years can be classified as low risk, areas with a return period of 100-200 years can be classified as medium risk, and areas with a return period of more than 200 years can be classified as high risk.

[0097] S32. According to a preset risk level evaluation rule, a risk evaluation is performed on the target risk level of each risk factor to obtain a risk evaluation value of each risk factor.

[0098] In this embodiment, the risk level evaluation rule refers to the mapping relationship of converting the target risk level into a quantitative evaluation value, which can be in different forms such as scoring system, percentage system, probability value, etc. The risk evaluation value is a quantitative expression of the risk level. By reasonably setting the span and weight of the evaluation value, the relative differences of different risk levels are reflected.

[0099] Specifically, for example, the target risk level is quantified using a percentage system, that is, the four levels of extremely low risk, low risk, medium risk, and high risk are assigned 25 points, 50 points, 75 points, and 100 points respectively, forming a linear correspondence between risk level and evaluation value. Taking marine risk factors as an example, if the wave risk in the target area is low risk, the tidal risk is medium risk, and the sea ice risk is extremely low risk, then the risk evaluation values ​​of the three risk factors of waves, tidal currents, and sea ice are 50 points, 75 points, and 25 points respectively. Furthermore, the formulation of risk assessment rules can be adjusted in a targeted manner according to the risk preferences and focus of different projects, such as using an exponential function to increase the evaluation value weight of high risk levels, highlighting the avoidance and control of high risk factors.

[0100] S33. Take a weighted average of the risk assessment values ​​of various risk factors to calculate a comprehensive risk assessment value of the target candidate area.

[0101] Specifically, weighted average means that on the basis of risk assessment value, the importance of each risk factor is taken into account, the weight coefficient of each factor is calculated, and the comprehensive average value of multiple factors is calculated according to the weight coefficient of each factor. The comprehensive risk assessment value quantitatively expresses the overall risk level of the target area. By comparing the assessment values ​​of different areas horizontally, the relative level of risk between regions is revealed, and the area with the lowest risk is selected as the site.

[0102] In one embodiment, referring to Figure 5 In step S30, after performing risk assessment on each candidate area according to the risk analysis result and obtaining the comprehensive risk assessment value of each candidate area, the method further comprises the following steps:

[0103] S34. According to the comprehensive risk evaluation value of each candidate area, determine the high-risk candidate area whose risk score exceeds the preset threshold.

[0104] Specifically, the preset threshold refers to the upper limit of the risk assessment value set artificially based on the actual project and risk control needs. The risk level of the candidate area exceeding this upper limit has exceeded the acceptable range of the project and needs to be marked and controlled, that is, defined as a high-risk candidate area. The existence of a high-risk candidate area will not only affect its own development and construction, but may also amplify the risk level of neighboring areas through the coupling transmission effect of risks. Therefore, more detailed risk assessment and dynamic management are needed.

[0105] S35. Determine the risk coupling matrix between high-risk alternative areas and other alternative areas based on the location distribution information and historical environmental data of each alternative area for the proposed offshore wind farm.

[0106] In this embodiment, risk coupling refers to the mutual influence and transmission effect of risks between different regions, which is usually generated through spatial proximity and environmental similarity. The risk coupling matrix is ​​a quantitative expression of the risk coupling relationship between different regions, and is used to characterize the amplification or reduction effect of high-risk regions on the risk levels of other regions.

[0107] Specifically, based on the geographic information system platform, the spatial location of the candidate areas is analyzed for proximity, the spatial weight matrix of each area is calculated, and the geographical proximity relationship between the areas is characterized. The spatial weight can be expressed in the form of adjacency matrix, distance matrix, K nearest neighbor matrix, etc. At the same time, the spatiotemporal correlation analysis of the meteorological, marine, geological and other environmental monitoring data of the candidate areas for many years is carried out, the spatiotemporal autocorrelation coefficients of environmental elements are calculated, and the environmental similarity relationship between the areas is characterized. On this basis, the coupling coordination degree model is used to perform weighted superposition of the spatial proximity matrix and the spatiotemporal autocorrelation matrix to construct a risk coupling matrix between high-risk areas and other areas. The larger the element value in the risk coupling matrix, the stronger the risk correlation between the regions, and the more significant the risk amplification effect of the high-risk area on the region.

[0108] S36. Calculate the risk coupling correction value of the high-risk candidate area to other candidate areas according to the risk coupling correction value, and correct the comprehensive risk assessment value of other candidate areas according to the risk coupling correction value.

[0109] In this embodiment, the risk coupling correction value indicates the extent to which the original comprehensive risk assessment value is adjusted on the basis of considering the risk transmission effect between regions. The risk coupling correction adopts the correction idea of ​​"basic risk + risk coupling". The basic risk is the original comprehensive risk assessment value of the candidate area, and the risk coupling is the additional risk value imposed on the area by the high-risk area through the spatial proximity effect or environmental convergence effect. In the actual evaluation, the sensitivity and stability of regional risks can be judged according to the changes in risk levels before and after the coupling correction, and then the regional development timing and priority can be adjusted.

[0110] Specifically, the weighted sum method is used to calculate the risk coupling correction value. After the coupling correction, the risk level of the alternative area may increase, and the area needs to be re-risk-rated and zoned.

[0111] In one embodiment, referring to Figure 6 In step S36, according to the risk coupling matrix, the risk coupling correction value of the high-risk candidate area to other candidate areas is calculated, and the comprehensive risk assessment value of other candidate areas is corrected according to the risk coupling correction value, which specifically includes the following steps:

[0112] S361. Obtain the risk coupling matrix.

[0113] Specifically, let the i-th high-risk candidate area be HR i , the set of other candidate areas within the risk coupling influence range of the i-th high-risk candidate area is {A j |j=1,2,...,m}, the risk coupling matrix is ​​C=[c ij ]n×m;

[0114] Where n represents the number of high-risk candidate areas, m represents the number of candidate areas within the risk coupling influence range of each high-risk candidate area, and c ij Represents the ith high-risk candidate area HR i With alternative area A j The risk coupling coefficient between Among them, d ij For HR i With A j , λ is the risk coupling coefficient attenuation parameter, d0 is the threshold distance of risk coupling impact, sign(x) is the sign function, which takes 1 when x>0 and takes 0 when x≤0;

[0115] S362. Calculate the risk coupling correction value of the high-risk candidate area to the candidate area.

[0116] Specifically, calculate the HR of the i-th high-risk candidate area i For the jth candidate area A jRisk coupling correction value ΔR ij =c ij *R i *(1-R j / R max ), where R i is the ith high-risk candidate area HR i The comprehensive risk assessment value, R j is the jth candidate area A j The comprehensive risk assessment value, R max It is the maximum value of the comprehensive risk assessment values ​​of all candidate areas;

[0117] S363. Modify the comprehensive risk assessment value of the alternative area.

[0118] Specifically, for the jth candidate area A j The comprehensive risk assessment value of the jth candidate area A is corrected. j The comprehensive risk assessment value R j’ =R j +∑ω i *ΔR ij , i = 1, 2, 3, ..., n, where ω i is the risk coupling weight of the i-th high-risk candidate area, ∑ω i =1.

[0119] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0120] In a second aspect, the present application provides a risk assessment system for offshore wind power. The risk assessment system for offshore wind power of the present application is described below in combination with the above-mentioned risk assessment method for offshore wind power.

[0121] Reference Figure 7 , a risk assessment system for offshore wind power, comprising:

[0122] A regional division module is used to obtain environmental parameters of the proposed offshore wind farm area, perform grid processing on the proposed offshore wind farm area, and divide the proposed offshore wind farm area into multiple candidate areas;

[0123] The risk analysis module is used to perform risk analysis on each candidate area according to environmental parameters and obtain risk analysis results;

[0124] The risk assessment module is used to conduct risk assessment on each candidate area according to the risk analysis results to obtain the comprehensive risk assessment value of each candidate area;

[0125] The result output module is used to compare and sort the comprehensive risk assessment values ​​of each candidate area, obtain the risk ranking result, draw a risk level distribution map and output it.

[0126] In an optional embodiment, the region division module includes:

[0127] A parameter acquisition unit, used to acquire environmental parameters of the proposed offshore wind farm area, the environmental parameters including wind speed parameters and seawater depth parameters;

[0128] A data calculation unit, used to calculate wind speed distribution grid data and seawater depth distribution grid data of the proposed offshore wind farm area according to wind speed parameters and seawater depth parameters;

[0129] The area division unit is used to divide the proposed offshore wind farm area into multiple candidate areas according to the wind speed distribution grid data and the sea water depth distribution grid data, and each candidate area corresponds to a wind speed-sea water depth combination type.

[0130] In an optional embodiment, the risk analysis module includes:

[0131] The risk determination unit is used to determine the meteorological risk, marine risk, geological risk and ecological risk of each candidate area based on environmental parameters;

[0132] The risk measurement unit is used to quantitatively evaluate the meteorological risk, marine risk, geological risk and ecological risk of each candidate area according to the preset risk assessment indicator system and scoring criteria, and obtain the risk measurement value of each risk factor;

[0133] The risk analysis unit is used to obtain risk analysis results according to the risk measurement values ​​of each risk factor of each candidate area.

[0134] In an optional embodiment, the risk analysis module includes:

[0135] A level determination unit, used to determine the target risk level of each risk factor of the target candidate area according to the risk analysis result, and the target candidate area is any one of the candidate areas;

[0136] The risk assessment unit is used to perform risk assessment on the target risk level of each risk factor according to a preset risk level assessment rule to obtain a risk assessment value of each risk factor;

[0137] The evaluation value calculation unit is used to perform weighted average of the risk evaluation values ​​according to various risk factors to calculate the comprehensive risk evaluation value of the target candidate area.

[0138] In an optional embodiment, the system further includes:

[0139] A high-risk candidate area determination module is used to determine high-risk candidate areas whose risk scores exceed a preset threshold based on the comprehensive risk evaluation values ​​of each candidate area;

[0140] A risk coupling matrix determination unit, used to determine the risk coupling matrix between the high-risk candidate area and other candidate areas according to the location distribution information and historical environmental data of each candidate area of ​​the proposed offshore wind farm;

[0141] The comprehensive risk assessment value correction module is used to calculate the risk coupling correction value of the high-risk alternative area to other alternative areas according to the risk coupling correction value, and to correct the comprehensive risk assessment values ​​of other alternative areas according to the risk coupling correction value.

[0142] In an optional embodiment, the comprehensive risk assessment value correction module includes:

[0143] The risk coupling matrix determination unit is used to set the i-th high-risk candidate area as HR i , the set of other candidate areas within the risk coupling influence range of the i-th high-risk candidate area is {A j |j=1,2,...,m}, the risk coupling matrix is ​​C=[c ij ]n×m; where n represents the number of high-risk candidate areas, m represents the number of candidate areas within the risk coupling influence range of each high-risk candidate area, and c ij Represents the ith high-risk candidate area HR i With alternative area A j The risk coupling coefficient between Among them, d ij For HR i With A j , / is the risk coupling coefficient attenuation parameter, d0 is the threshold distance of risk coupling impact, sign(x) is the sign function, which takes 1 when x>0 and takes 0 when x≤0;

[0144] Risk coupling correction value calculation unit, used to calculate the HR of the i-th high-risk candidate area i For the jth candidate area A j Risk coupling correction value ΔR ij =c ij *R i *(1-R j / R max ), where R i is the ith high-risk candidate area HR i The comprehensive risk assessment value, R j is the jth candidate area A j The comprehensive risk assessment value, Rmax It is the maximum value of the comprehensive risk assessment values ​​of all candidate areas;

[0145] The risk assessment value correction unit is used to modify the jth candidate area A j The comprehensive risk assessment value of the jth candidate area A is corrected. j The comprehensive risk assessment value R j’ =R j +∑ω i *ΔR ij , i = 1, 2, 3, ..., n, where ω i is the risk coupling weight of the i-th high-risk candidate area, ∑ω i =1.

[0146] In one embodiment, the present application provides an electronic device, which may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The electronic device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a risk assessment method for offshore wind power is implemented.

[0147] Those skilled in the art will understand that Figure 8 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0148] In one embodiment, an electronic device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0149] Those of ordinary skill 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, and the above-mentioned computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0150] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A risk assessment method for offshore wind power, characterized in that: The steps include: Acquiring environmental parameters of a proposed offshore wind farm area, performing grid processing on the proposed offshore wind farm area, and dividing the proposed offshore wind farm area into a plurality of candidate areas; According to the environmental parameters, risk analysis is performed on each candidate area to obtain a risk analysis result; According to the risk analysis results, risk assessment is performed on each candidate area to obtain a comprehensive risk assessment value for each candidate area; Compare and sort the comprehensive risk assessment values ​​of each candidate area to obtain the risk ranking result, draw a risk level distribution map and output it; The environmental parameters also include meteorological parameters, ocean parameters, geological parameters and ecological parameters. Based on the environmental parameters, risk analysis is performed on various candidate areas for the proposed offshore wind farm to obtain risk analysis results, which specifically includes the following steps: Determine the meteorological risk, ocean risk, geological risk and ecological risk of each candidate area based on the environmental parameters; According to the preset risk assessment index system and scoring standards, the meteorological risk, marine risk, geological risk and ecological risk of each candidate area are quantitatively assessed to obtain the risk measurement value of each risk factor; According to the risk measurement value of each risk factor in each candidate area, the risk analysis result is obtained; According to the risk analysis results, risk assessment is performed on each candidate area to obtain a comprehensive risk assessment value of each candidate area, which specifically includes the following steps: Determine, according to the risk analysis result, a target risk level of each risk factor of a target candidate area, wherein the target candidate area is any one of the candidate areas; According to the preset risk level evaluation rules, the target risk level of each risk factor is evaluated to obtain the risk evaluation value of each risk factor; Taking a weighted average of the risk evaluation values ​​according to various risk factors, calculating a comprehensive risk evaluation value of the target candidate area; Based on the comprehensive risk assessment values ​​of each candidate area, determine the high-risk candidate areas whose risk scores exceed the preset threshold; Determine a risk coupling matrix between the high-risk candidate area and other candidate areas according to the location distribution information and historical environmental data of each candidate area of ​​the proposed offshore wind farm; According to the risk coupling matrix, the risk coupling correction value of the high-risk candidate area to other candidate areas is calculated, and the comprehensive risk assessment values ​​of other candidate areas are corrected according to the risk coupling correction value.

2. The risk assessment method for offshore wind power according to claim 1, characterized in that: Acquiring environmental parameters of a proposed offshore wind farm area, and performing grid processing on the proposed offshore wind farm area according to the environmental parameters, and dividing the proposed offshore wind farm area into a plurality of candidate areas, specifically comprising the following steps: Acquiring environmental parameters of the proposed offshore wind farm area, wherein the environmental parameters include wind speed parameters and seawater depth parameters; Calculating wind speed distribution grid data and seawater depth distribution grid data of the proposed offshore wind farm area according to the wind speed parameters and seawater depth parameters; According to the wind speed distribution grid data and the seawater depth distribution grid data, the proposed offshore wind farm area is divided into a plurality of candidate areas, each candidate area corresponding to a wind speed-seawater depth combination type.

3. The risk assessment method for offshore wind power according to claim 1, characterized in that: According to the risk coupling matrix, the risk coupling correction value of the high-risk candidate area to other candidate areas is calculated, and the comprehensive risk assessment values ​​of other candidate areas are corrected according to the risk coupling correction value, which specifically includes the following steps: Assume that the i-th high-risk candidate area is HR i , the set of other candidate areas within the risk coupling influence range of the i-th high-risk candidate area is {A j |j=1,2,...,m}, the risk coupling matrix is ​​C=[c ij ]n×m; Where n represents the number of high-risk candidate areas, m represents the number of candidate areas within the risk coupling influence range of each high-risk candidate area, and c ij Represents the ith high-risk candidate area HR i With alternative area A j The risk coupling coefficient between Among them, d ij For HR i With A j , λ is the risk coupling coefficient attenuation parameter, d0 is the threshold distance of risk coupling impact, sign(x) is the sign function, which takes 1 when x>0 and takes 0 when x≤0; Calculate the HR of the i-th high-risk candidate area i For the jth candidate area A j Risk coupling correction value ΔR ij =c ij *R i *(1-R j / R max ), where R i is the ith high-risk candidate region HR i The comprehensive risk assessment value, R j is the jth candidate area A j The comprehensive risk assessment value, R max It is the maximum value of the comprehensive risk assessment values ​​of all candidate areas; For the jth candidate area A j The comprehensive risk assessment value of the jth candidate area A is corrected. j The comprehensive risk assessment value R j’ =R j +∑ω i *ΔR ij , i = 1, 2, 3, ..., n, where ω i is the risk coupling weight of the i-th high-risk candidate area, ∑ω i =1.

4. A risk assessment system for offshore wind power, characterized in that: include: A region division module is used to obtain environmental parameters of the proposed offshore wind farm area, perform grid processing on the proposed offshore wind farm area, and divide the proposed offshore wind farm area into multiple candidate areas; A risk analysis module, used to perform risk analysis on each candidate area according to the environmental parameters to obtain risk analysis results; A risk assessment module, used to perform risk assessment on each candidate area according to the risk analysis result, and obtain a comprehensive risk assessment value of each candidate area; The result output module is used to compare and sort the comprehensive risk assessment values ​​of each candidate area, obtain the risk sorting result, draw a risk level distribution map and output it; The environmental parameters also include meteorological parameters, ocean parameters, geological parameters and ecological parameters. The risk analysis module includes: The risk determination unit is used to determine the meteorological risk, marine risk, geological risk and ecological risk of each candidate area based on environmental parameters; The risk measurement unit is used to quantitatively evaluate the meteorological risk, marine risk, geological risk and ecological risk of each candidate area according to the preset risk assessment indicator system and scoring criteria, and obtain the risk measurement value of each risk factor; A risk analysis unit, used to obtain risk analysis results according to the risk measurement values ​​of each risk factor of each candidate area; A level determination unit, used to determine the target risk level of each risk factor of the target candidate area according to the risk analysis result, and the target candidate area is any one of the candidate areas; The risk assessment unit is used to perform risk assessment on the target risk level of each risk factor according to a preset risk level assessment rule to obtain a risk assessment value of each risk factor; An evaluation value calculation unit, used to perform a weighted average of the risk evaluation values ​​according to various risk factors to calculate a comprehensive risk evaluation value of the target candidate area; The system also includes: A high-risk candidate area determination module is used to determine high-risk candidate areas whose risk scores exceed a preset threshold based on the comprehensive risk evaluation values ​​of each candidate area; A risk coupling matrix determination unit, used to determine the risk coupling matrix between the high-risk candidate area and other candidate areas according to the location distribution information and historical environmental data of each candidate area of ​​the proposed offshore wind farm; The comprehensive risk assessment value correction module is used to calculate the risk coupling correction value of the high-risk alternative area to other alternative areas according to the risk coupling correction value, and to correct the comprehensive risk assessment values ​​of other alternative areas according to the risk coupling correction value.

5. The risk assessment system for offshore wind power according to claim 4, characterized in that: The area division module comprises: A parameter acquisition unit, used to acquire environmental parameters of the proposed offshore wind farm area, wherein the environmental parameters include wind speed parameters and seawater depth parameters; A data calculation unit, used to calculate the wind speed distribution grid data and the seawater depth distribution grid data of the proposed offshore wind farm area according to the wind speed parameter and the seawater depth parameter; The area division unit is used to divide the proposed offshore wind farm area into a plurality of candidate areas according to the wind speed distribution grid data and the seawater depth distribution grid data, each candidate area corresponding to a wind speed-seawater depth combination type.

6. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the risk assessment method for offshore wind power described in any one of claims 1 to 3 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the risk assessment method for offshore wind power described in any one of claims 1 to 3 are implemented.

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