Gridding weak point identification method for power distribution network under typhoon heavy rainfall

Abstract

The invention discloses a power distribution network gridding weak point identification method under typhoon heavy rainfall, and particularly relates to the technical field of power control, which comprises the following steps: carrying out regional parameter acquisition and grid division, obtaining equipment and environment linkage parameters, carrying out meteorological-power parameter threshold and weight calculation to obtain a parameter comprehensive response value, and carrying out power distribution network weak point identification on the basis of the parameter comprehensive response value. And calculating a single-point equipment risk value, and judging a systematic weak point. According to the method for identifying the grid weak points of the power distribution network under the typhoon heavy rainfall, a risk level matrix is constructed through multi-dimensional parameter acquisition and crossover operation, terrain elevation and gradient data are acquired by using an unmanned aerial vehicle carried laser radar system, drainage capacity parameters such as the diameter and the material of a drainage pipeline are analyzed in combination with field survey, and the weak points of the power distribution network are identified. And after grading according to the elevation difference, the gradient and the drainage efficiency, crossover operation is performed, grid units are accurately divided, and the ponding duration is calculated, so that the spatial fine assessment of the power distribution network regional risk under the typhoon heavy rainfall is realized, and the one-sidedness of single parameter assessment is avoided.

Description

Technical Field

[0001] This invention relates to the field of power control technology, and in particular to a method for identifying weak points in a grid-based power distribution network under heavy rainfall during typhoons. Background Technology

[0002] The field of power control technology encompasses research and application of technologies related to power system operation, control, and protection. Its core content is to achieve effective management of the power system through various technical means, ensuring its safe, stable, and efficient operation. Power control requires the application of control theory and information technology to precisely control each link in the power generation, transmission, transformation, distribution, and consumption processes. For example, it involves controlling the output of power generation equipment to ensure a stable power supply; and monitoring the operation of transmission lines to promptly detect and address potential faults, thereby maintaining the optimal operating condition of the entire power system. The method for identifying weak points in a grid-based power distribution network under typhoon-induced heavy rainfall refers to a technical approach that, in response to the impact of severe weather such as typhoons on the power distribution network, divides the network into grids and accurately identifies vulnerable points prone to failure. This method requires collecting a large amount of basic data, including power distribution network line parameters and equipment information, as well as historical meteorological data related to typhoon-induced heavy rainfall. By analyzing this data and utilizing methods such as establishing fault probability models and simulations, the method studies the likelihood of failures in lines, towers, transformers, and other equipment within each grid of the power distribution network under typhoon-induced heavy rainfall conditions due to factors such as strong winds, torrential rain, and flooding, thereby determining the location of weak points in the power distribution network.

[0003] Current technologies for identifying weak points in power distribution networks during typhoon-induced heavy rainfall rely on manually collected data and historical meteorological data to build fault probability models. These methods lack detailed analysis of terrain features and drainage capacity, resulting in coarse grid divisions that fail to accurately reflect the actual impact of water accumulation risks on equipment. Data acquisition uses a fixed frequency without establishing a dynamic correlation mechanism between environmental parameters and acquisition frequency. This may lead to missed early changes in equipment anomalies due to insufficient data density, resulting in delayed fault warnings. Risk assessments do not divide meteorological processes into stages and lack differentiated weight settings, leading to significant discrepancies between assessment results and actual fault probabilities. Single-point equipment risk is judged using static thresholds without dynamically adjusting weights based on real-time meteorological parameters, potentially causing missed or incorrect assessments. Systemic weak point identification only analyzes isolated risks, failing to build equipment connection relationship models and identify risk propagation areas. Fault prevention is limited to single-point maintenance. For example, it may fail to detect the accumulation of risks in feeder areas, leading to regional faults. Summary of the Invention

[0004] The main objective of this invention is to provide a method for identifying weak points in the grid-based distribution network under heavy rainfall during typhoons, which can effectively solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for identifying weak points in a power distribution network under heavy typhoon rainfall includes the following steps: S1. Collect regional parameters and divide the grid. Obtain the terrain feature parameters and drainage capacity parameters of each point in the region. After classifying the two, perform cross-calculation to determine the risk level. Divide the grid cells accordingly and calculate the duration of water accumulation in each grid. S2. Collect equipment and environmental parameters in a coordinated manner, collect equipment operating status parameters and environmental parameters, establish association rules, preset the correspondence between environment and collection frequency, and dynamically adjust the collection frequency to obtain the linkage parameters. S3. Perform threshold and weight calculations for meteorological and power parameters, divide the meteorological process into stages, calculate the thresholds of meteorological elements in each stage, establish weight rules, and calculate the comprehensive response value of power parameters of the equipment group. S4. Calculate the risk value of a single point device, call the linkage acquisition parameters to calculate the deviation of the device parameters, adjust the deviation weight according to the meteorological parameters, and obtain the dynamic risk value of the single point device. S5. Identify systemic vulnerabilities. Based on the distribution network topology, analyze the neighboring devices of a single high-risk device, determine the risk propagation area, and mark it as a systemic vulnerability.

[0006] Preferably, the regional parameter acquisition and grid division described in S1 specifically involves using a drone equipped with a lidar system to collect terrain feature parameters, obtaining drainage capacity parameters through field surveys and analysis of pipeline data; classifying the terrain feature parameters according to elevation difference and slope, and classifying the drainage capacity parameters according to drainage efficiency per unit area; constructing a risk level matrix by cross-calculating the classification results, and dividing the grid units according to the risk level; calculating the rainwater flow velocity and direction using the Manning formula based on the terrain feature parameters, and calculating the duration of water accumulation in each grid using the continuity equation and the law of conservation of mass in combination with the drainage capacity parameters.

[0007] Preferably, the linkage acquisition of equipment and environmental parameters in S2 specifically involves real-time acquisition of equipment operating status parameters and environmental parameters through a sensor network; establishing equipment-environment parameter association rules by analyzing historical data using an association rule mining algorithm; pre-setting the correspondence between the gradient of environmental parameter changes and the gradient of the acquisition frequency of equipment operating parameters; and automatically increasing the acquisition frequency of the corresponding equipment operating parameters when the environmental parameters reach a preset proportion of historical fault characteristic values.

[0008] Preferably, the calculation of meteorological-power parameter thresholds and weights in S3 specifically involves dividing the meteorological process into three stages: short-term, medium-term, and long-term. For each stage, based on the magnitude and trend of meteorological element changes, a statistical analysis method is used to calculate the meteorological element threshold. A weighting rule for the combination of meteorological-power parameters is established: a basic weight is assigned when a single meteorological element reaches its threshold, and the weights are superimposed when multiple elements reach their thresholds simultaneously. The collected real-time voltage, current, and temperature parameters are converted into standardized parameters based on the minimum-maximum normalization method. The weights of the meteorological elements are then calculated using a formula based on the power parameters of the equipment group. Calculate the comprehensive response value of the power parameters; where, For the corresponding weighting coefficients, These are the standardized parameters corresponding to the real-time parameter values.

[0009] Preferably, the calculation of the single-point device risk value in S4 specifically involves calling the parameters collected through the linkage between the device and the environment, and then using the formula... The deviation of the real-time operating parameters of the calculated equipment from the historical normal range is used to obtain the parameter deviation index, where x is the real-time parameter value. This is the average of the historical normal range. The standard deviation is the historical normal range; the deviation weight is adjusted based on real-time meteorological parameters according to the equipment-environment association rules and the meteorological-power parameter weighting rules; the formula is used to... Calculate the dynamic risk value of a single point device, where Here, D represents the deviation weight, and D represents the deviation.

[0010] Preferably, the systemic vulnerability determination described in S5 specifically involves constructing a network model based on the distribution network topology, clarifying the equipment connection relationships and electrical distances; identifying the neighboring equipment of the feeder where a single high-risk equipment is located, where the neighboring equipment is directly connected to the high-risk equipment or connected through no more than 3 intermediate equipment; when the number of high-risk equipment in the neighboring equipment exceeds 30%, and the standard deviation of the risk value of each equipment is less than a set threshold, the area is determined to be a systemic vulnerability.

[0011] Preferably, the terrain feature parameters include terrain elevation data and slope data; the environmental parameters include rainfall, wind speed, air humidity, and temperature.

[0012] Preferably, the drainage capacity parameters include the diameter, material, slope, and pump station drainage flow rate of the drainage pipe; the equipment operating status parameters include the equipment voltage, current, temperature, and vibration frequency.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention constructs a risk level matrix through multi-dimensional parameter collection and cross-operation. It uses a drone equipped with a lidar system to obtain terrain elevation and slope data, and combines it with on-site surveys and analysis of drainage capacity parameters such as drainage pipe diameter and material. After classification by elevation difference, slope and drainage efficiency, cross-operation is performed to accurately divide grid units and calculate the duration of water accumulation. This enables a spatial and precise assessment of the risk of power distribution network areas under typhoon heavy rainfall, avoiding the one-sidedness of single parameter assessment.

[0014] 2. This invention collects real-time operating status parameters such as equipment voltage and current, as well as environmental parameters such as rainfall and wind speed, through a sensor network. It uses an association rule mining algorithm to establish a dynamic association between the equipment and environmental parameters. Based on the gradient of environmental parameter changes, it presets a rule to increase the collection frequency. When the environmental parameters approach historical fault characteristic values, it automatically increases the collection frequency to ensure the acquisition of high-density and high-timeliness linkage data under extreme weather conditions. This solves the problem of fixed data collection frequency in traditional methods that cannot be dynamically adjusted with environmental changes, and improves the ability of data to capture abnormal equipment states.

[0015] 3. This invention divides meteorological processes into short-term, medium-term, and long-term stages, calculates thresholds for changes in meteorological elements in each stage, establishes basic weights for single elements reaching thresholds and weight rules for multiple elements superimposed, and calculates the comprehensive response value of power parameters through formulas, thereby realizing differentiated assessment of meteorological risks in different time periods and making equipment risk assessment more in line with the characteristics of typhoon development cycles.

[0016] 4. This invention calls upon the linkage acquisition parameters to calculate the deviation between the real-time parameters of the equipment and the historical normal range. Based on meteorological parameters and correlation rules, it dynamically adjusts the deviation weights and derives the dynamic risk value of a single-point equipment through a formula. This achieves real-time dynamic quantitative assessment of equipment risk, overcoming the deficiency of static assessment in reflecting the real-time impact of meteorology. Simultaneously, it constructs a network model based on the distribution network topology, defining neighboring equipment by direct connection or no more than three intermediate connected devices. By determining the proportion of high-risk neighboring equipment and the standard deviation of risk values, it identifies systemic weaknesses, realizing a systematic analysis of the propagation of risk from a single point to a regional risk. This enables early identification of risk diffusion areas and provides a scientific basis for overall vulnerability assessment and fault prevention of the distribution network. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the overall steps of the present invention; Figure 2 This is a flowchart of the region parameter acquisition and grid division process of the present invention; Figure 3 This is a flowchart illustrating the linkage between the device and environmental parameters in this invention. Figure 4 This is a flowchart of the meteorological-power parameter threshold and weight calculation process of the present invention; Figure 5 This is a flowchart illustrating the calculation of single-point equipment risk values ​​according to the present invention. Figure 6 This is a flowchart for determining systemic weaknesses in this invention. Detailed Implementation

[0018] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0019] like Figure 1 As shown, this invention discloses a grid-based method for identifying weak points in a power distribution network under typhoon-induced heavy rainfall. Specifically, it involves steps such as regional parameter collection and grid division, coordinated collection of equipment and environmental parameters, calculation of meteorological and power parameter thresholds and weights, calculation of single-point equipment risk values, and determination of systemic weak points. Combined with multi-dimensional data including terrain, drainage, equipment operation, and meteorology, a risk assessment model is constructed. The aim is to solve the problem of timely and accurate identification of weak points in the power distribution network under typhoon-induced heavy rainfall, achieving dynamic and systematic identification of weak points and improving the operational reliability and fault early warning capabilities of the power distribution network under extreme weather conditions.

[0020] The identification method of the present invention will be further disclosed and explained below with reference to specific data.

[0021] Example 1, see Figure 2 This embodiment mainly implements the regional parameter acquisition and grid division module through the following steps: Specifically, it acquires the terrain feature parameters and drainage capacity parameters of each point in the region, classifies the two and cross-calculates to determine the risk level, divides the grid units accordingly, and calculates the duration of water accumulation in each grid. In the specific implementation process of the regional parameter acquisition and grid division, a drone equipped with a lidar system is used to collect terrain feature parameters, including terrain elevation data and slope data. In a certain coastal area, the terrain elevation data obtained by lidar ranges from 5 meters to 30 meters, and the slope data ranges from 0.5° to 15°. Meanwhile, drainage capacity parameters were obtained through on-site surveys and analysis of pipeline data. These parameters include the diameter, material, slope, and pump station drainage flow rate of the drainage pipes. According to the survey, the diameter of the drainage pipes in the above-mentioned area is 0.8 meters to 2 meters, the material is mainly concrete and cast iron, the pipe slope is 0.3% to 1.2%, and the pump station drainage flow rate is 500 cubic meters / hour to 2000 cubic meters / hour.

[0022] Topographic feature parameters are classified according to elevation difference and slope, with the specific classification criteria as follows: A terrain elevation difference of less than 5 meters and a slope of less than 3 degrees is classified as a low-risk level. A terrain elevation difference of 5 to 10 meters or a slope of 3° to 8° is classified as a medium-risk level. A terrain elevation difference greater than 10 meters or a slope greater than 8° is classified as a high-risk level.

[0023] Drainage capacity parameters are classified according to drainage efficiency per unit area, with a drainage efficiency of more than 5 cubic meters per hour per square meter being classified as a high drainage capacity level. A drainage efficiency of 3 cubic meters per hour per square meter to 5 cubic meters per hour per square meter is considered a medium drainage capacity level. A drainage efficiency of less than 3 cubic meters per hour per square meter is classified as a low drainage capacity level.

[0024] The risk level matrix is ​​constructed by cross-operating the classification results. When the terrain feature parameter is classified as medium risk and the drainage capacity parameter is classified as low drainage capacity, the risk level of the area is determined to be high risk after cross-operation. Grid cells are divided according to risk level, and the entire area is divided into grids of different risk levels.

[0025] Based on topographic feature parameters, the Manning formula is used to calculate rainwater flow velocity and direction. Specifically: Where v is the flow velocity (m / s), n is the Manning coefficient (0.013 for concrete pipes and 0.012 for cast iron pipes), R is the hydraulic radius (m), and S is the slope; Taking a certain grid as an example, the pipes within this grid are made of concrete, with a hydraulic radius of 0.3 meters and a slope of 0.5%. What is the rainwater flow velocity? .

[0026] The duration of water accumulation in each grid is calculated using the continuity equation and the law of conservation of mass, based on drainage capacity parameters. Assuming the grid area is 10,000 square meters, the rainfall is 50 mm / hour, and the drainage capacity is 3,000 cubic meters / hour, the water accumulation volume per unit time is... The volume is cubic meters, where the negative sign indicates that the drainage rate is greater than the rate of water accumulation. If the initial water volume is 1000 cubic meters, then the duration of water accumulation is... Hour.

[0027] Example 2, see Figure 3 This embodiment, based on Embodiment 1, further collects equipment operating status parameters and environmental parameters, establishes association rules, presets the correspondence between the environment and the collection frequency, and dynamically adjusts the collection frequency to obtain linkage parameters. In the specific implementation process, the linkage collection of equipment and environmental parameters is achieved through the following steps: The device's operating status parameters and environmental parameters are collected in real time through a sensor network. The device's operating status parameters include the device's voltage, current, temperature, and vibration frequency. For example, the real-time voltage of a transformer is 10.2kV, the current is 50A, the temperature is 60℃, and the vibration frequency is 10Hz. Environmental parameters include rainfall, wind speed, air humidity, and temperature. During the heavy rainfall of a typhoon, at a certain moment, the rainfall was 30 mm / hour, the wind speed was 20 m / s, the air humidity was 90%, and the temperature was 25℃.

[0028] Association rule mining algorithms are used to analyze historical data and establish association rules between equipment and environmental parameters. For example, through the analysis of historical data, it was found that when the rainfall exceeds 20 mm / hour and the wind speed exceeds 15 m / s, the probability of the transformer temperature rising by more than 5°C reaches 80%. Thus, association rules between rainfall, wind speed and transformer temperature are established.

[0029] The preset relationship between the gradient of environmental parameter changes and the gradient of the frequency of equipment operating condition parameter acquisition is as follows: when the rainfall increases by 10 mm / hour, the frequency of equipment operating condition parameter acquisition increases to 1.5 times the original frequency; when the wind speed increases by 5 m / s, the acquisition frequency increases to 1.2 times the original frequency.

[0030] When environmental parameters reach a preset proportion of historical fault characteristic values, the frequency of collecting operating parameters of the corresponding equipment is automatically increased. For example, if the threshold for rainfall in historical fault characteristic values ​​is 50 mm / hour, when the real-time rainfall reaches 40 mm / hour (i.e., reaches the preset proportion of 80%), the collection frequency of the corresponding equipment is automatically increased from once per minute to 1.5 times per minute.

[0031] Example 3, see Figure 4 This embodiment calculates the thresholds and weights of meteorological and power parameters based on embodiment one, divides the meteorological process into stages, calculates the thresholds of meteorological elements in each stage, establishes weighting rules, and calculates the comprehensive response value of power parameters of the equipment group. Specifically, the meteorological process is divided into three stages according to time: short-term (0 to 6 hours), medium-term (6 to 24 hours), and long-term (more than 24 hours). For each stage, the threshold of meteorological elements is calculated by statistical analysis based on the magnitude and trend of changes in meteorological elements. Taking a certain typhoon as an example, the threshold for rainfall in the short-term stage is 30 mm / hour and the threshold for wind speed is 25 m / s; the threshold for rainfall in the medium-term stage is 20 mm / hour and the threshold for wind speed is 20 m / s; and the threshold for rainfall in the long-term stage is 15 mm / hour and the threshold for wind speed is 15 m / s.

[0032] Establish a weighting rule for the combination of meteorological and power parameters. When a single meteorological element reaches a threshold, it is assigned a basic weight. When multiple elements reach the threshold simultaneously, the weights are superimposed. For example, when rainfall reaches the threshold, it is assigned a basic weight of 0.3. When wind speed reaches the threshold, it is assigned a basic weight of 0.4. If both reach the threshold simultaneously, the weights are superimposed as 0.3 + 0.4 = 0.7.

[0033] Taking a certain equipment group as an example, it includes three electrical parameters: voltage, current, and temperature, with corresponding weights of 0.2, 0.3, and 0.5, respectively. The real-time parameter values ​​are 10.1kV, 55A, and 65℃. For ease of calculation, the minimum-maximum normalization method needs to be used to map the parameters to the [0,1] interval, based on the standardized value formula. Calculate, within the historical normal range of a certain parameter, let... To be the minimum value, The maximum value, For real-time parameter values, based on the above rules, the standardized parameters are set to 0.9, 0.8, and 0.7 respectively. Then the comprehensive response value .

[0034] Example 4, see Figure 5 This implementation further builds upon Embodiments 2 and 3 by calling the linkage acquisition parameters to calculate the deviation of the equipment parameters, adjusting the deviation weight according to the meteorological parameters, and obtaining the dynamic risk value of a single-point equipment. In the specific implementation process, parameters are collected by linking the equipment with the environment, and then processed through formulas. The deviation of the real-time operating parameters of the calculated equipment from the historical normal range is used to obtain the parameter deviation index, where x is the real-time parameter value. This is the average of the historical normal range. This represents the standard deviation of the historical normal range. Taking the current parameters of a certain device as an example, the historical average value within the normal range Standard deviation Real-time current value Then the deviation degree .

[0035] Based on real-time meteorological parameters, the deviation weight is adjusted according to the equipment-environment association rules and the meteorological-power parameter weight rules. For example, when the real-time rainfall reaches the threshold, the deviation weight adjustment coefficient is assigned as 1.2 according to the weight rules.

[0036] Through formula Calculate the dynamic risk value of a single point of equipment, assuming the adjusted deviation weight. The dynamic risk value of a single point device .

[0037] Example 5, see Figure 6 In this embodiment, based on the distribution network topology, the neighboring devices of a single high-risk device are analyzed to determine the risk propagation area and mark it as a systemic weak point.

[0038] Specifically, the determination of systemic weak points involves constructing a network model based on the distribution network topology, clarifying the equipment connection relationships and electrical distances; identifying the neighboring equipment of the feeder where a single high-risk equipment is located, where neighboring equipment is directly connected to the high-risk equipment or connected through no more than 3 intermediate equipment; when the number of high-risk equipment in the neighboring equipment exceeds 30% and the standard deviation of the risk value of each equipment is less than a set threshold, the area is determined to be a systemic weak point.

[0039] In the specific implementation process, the identification of systemic weaknesses is achieved through the following steps: A network model is constructed based on the distribution network topology to clarify the device connection relationships and electrical distances. Taking a certain distribution network as an example, the constructed network model shows that each device is connected through feeders, and the connection relationship and electrical distance between each device and other devices are clearly defined.

[0040] Identify the neighboring devices on the feeder line containing a single high-risk device. Neighboring devices are those directly connected to the high-risk device or connected through no more than three intermediate devices. For example, if a high-risk device A is directly connected to device B, connected to device C through one intermediate device, connected to device D through two intermediate devices, and connected to device E through three intermediate devices, then devices B, C, D, and E are all neighboring devices of device A.

[0041] When the number of high-risk devices in a neighboring area exceeds 30%, and the standard deviation of the risk value of each device is less than a set threshold, the area is determined to be a systemic vulnerability. Suppose there are 10 devices in a certain neighborhood, of which 4 are high-risk devices (more than 30%), and the risk values ​​of each device are 1.6, 1.5, 1.7, 1.5, 1.8, 1.5, 1.6, 1.7, 1.5, and 1.6 respectively. Calculate their mean value. Based on the standard deviation formula, the standard deviation can be calculated as follows: ; If the threshold is set to 0.2, since 0.1 is less than 0.2, the area is determined to be a systemic weak point.

[0042] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for identifying weak points in a power distribution network under typhoon-induced heavy rainfall, characterized in that, Includes the following steps: S1. Collect regional parameters and divide the grid. Obtain the terrain feature parameters and drainage capacity parameters of each point in the region. After classifying the two, perform cross-calculation to determine the risk level. Divide the grid cells accordingly and calculate the duration of water accumulation in each grid. S2. Collect equipment and environmental parameters in a coordinated manner, collect equipment operating status parameters and environmental parameters, establish association rules, preset the correspondence between environment and collection frequency, and dynamically adjust the collection frequency to obtain the linkage parameters. S3. Perform threshold and weight calculations for meteorological and power parameters, divide the meteorological process into stages, calculate the thresholds of meteorological elements in each stage, establish weight rules, and calculate the comprehensive response value of power parameters of the equipment group. S4. Calculate the risk value of a single point device, call the linkage acquisition parameters to calculate the deviation of the device parameters, adjust the deviation weight according to the meteorological parameters, and obtain the dynamic risk value of the single point device. S5. Identify systemic vulnerabilities. Based on the distribution network topology, analyze the neighboring devices of a single high-risk device, determine the risk propagation area, and mark it as a systemic vulnerability.

2. The method for identifying weak points in a power distribution network under heavy typhoon rainfall as described in claim 1, characterized in that: The regional parameter acquisition and grid division described in S1 specifically involves using a drone equipped with a lidar system to collect terrain feature parameters, obtaining drainage capacity parameters through field surveys and analysis of pipeline data; classifying terrain feature parameters according to elevation difference and slope, and classifying drainage capacity parameters according to drainage efficiency per unit area; constructing a risk level matrix by cross-calculating the classification results, and dividing grid units according to risk level; calculating rainwater velocity and direction using the Manning formula based on terrain feature parameters, and calculating the duration of water accumulation in each grid using the continuity equation and the law of conservation of mass in conjunction with drainage capacity parameters.

3. The method for identifying weak points in a power distribution network under heavy typhoon rainfall as described in claim 1, characterized in that: The device and environmental parameter linkage acquisition described in S2 specifically involves real-time acquisition of device operating status parameters and environmental parameters through a sensor network; establishing device-environment parameter association rules by analyzing historical data using an association rule mining algorithm; pre-setting the correspondence between the gradient of environmental parameter changes and the gradient of the frequency of device operating parameter acquisition; and automatically increasing the frequency of corresponding device operating parameter acquisition when the environmental parameters reach a preset proportion of historical fault characteristic values.

4. The method for identifying weak points in a power distribution network under heavy typhoon rainfall as described in claim 1, characterized in that: The calculation of meteorological-power parameter thresholds and weights in S3 specifically involves dividing the meteorological process into three stages: short-term, medium-term, and long-term. For each stage, the thresholds of meteorological elements are calculated using statistical analysis methods based on the magnitude and trend of changes in meteorological elements. A weighting rule for meteorological and power parameters is established. A single meteorological element reaching a threshold is assigned a basic weight, while multiple elements reaching the threshold simultaneously have their weights superimposed. Real-time voltage, current, and temperature parameters are converted into standardized parameters using a minimum-maximum normalization method. Based on the power parameters of the equipment group combined with the meteorological element weights, a formula is used to... Calculate the comprehensive response value of the power parameters; where, For the corresponding weighting coefficients, These are the standardized parameters corresponding to the real-time parameter values.

5. The method for identifying weak points in a power distribution network under heavy typhoon rainfall as described in claim 1, characterized in that: The calculation of the single-point device risk value described in S4 specifically involves calling up parameters collected through the linkage between the device and the environment, and then applying the formula... The deviation of the real-time operating parameters of the calculated equipment from the historical normal range is used to obtain the parameter deviation index, where x is the real-time parameter value. This is the average of the historical normal range. The standard deviation is the historical normal range; the deviation weight is adjusted based on real-time meteorological parameters according to the equipment-environment association rules and the meteorological-power parameter weighting rules; the formula is used to... Calculate the dynamic risk value of a single point of equipment, where Here, D represents the deviation weight, and D represents the deviation.

6. The method for identifying weak points in a power distribution network under heavy typhoon rainfall as described in claim 1, characterized in that: The systemic vulnerability identification described in S5 specifically involves constructing a network model based on the distribution network topology, clarifying the equipment connection relationships and electrical distances; identifying the neighboring equipment of the feeder where a single high-risk equipment is located, where neighboring equipment is directly connected to the high-risk equipment or connected through no more than 3 intermediate equipment; when the number of high-risk equipment in the neighboring equipment exceeds 30% and the standard deviation of the risk value of each equipment is less than a set threshold, the area is identified as a systemic vulnerability.

7. The method for identifying weak points in a power distribution network under heavy typhoon rainfall as described in claim 1, characterized in that: The terrain feature parameters include terrain elevation data and slope data; the environmental parameters include rainfall, wind speed, air humidity, and temperature.

8. The method for identifying weak points in a power distribution network under heavy typhoon rainfall as described in claim 1, characterized in that: The drainage capacity parameters include the diameter, material, slope, and pump station drainage flow rate of the drainage pipe; the equipment operating status parameters include the equipment voltage, current, temperature, and vibration frequency.

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