Power transmission and transformation construction safety management and control method based on meteorological factors

Abstract

A power transmission and transformation construction safety management and control method based on meteorological factors belongs to the technical field of electric power safety. According to the invention, the meteorological early warning index system is established, and the Hadoop technology is utilized to carry out normalization processing on the meteorological data, so that efficient processing and analysis of a large amount of real-time meteorological data are realized. And an adjustment item epsilon is added on the basis of the entropy weight, so that the accuracy of the objective weight is ensured, the rationality of weighting the early warning index based on the comprehensive weighting method is further ensured, and the early warning result is more scientific and reliable. According to the method, the safety risk of the construction site can be evaluated and early warned in real time, and the construction plan can be adjusted in time before severe weather comes, so that the construction safety is effectively improved. The method also enhances the decision support capability of construction site management, and has wide application value.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power safety, and in particular relates to a method for safety control of power transmission and transformation construction based on meteorological factors. Background Art

[0002] Safety control of power transmission and transformation construction sites is crucial in power engineering construction, and is directly related to the safety of construction workers and the stable operation of the power system. However, in the actual construction process, weather factors have a significant impact on site safety, especially in severe weather conditions such as strong winds, heavy rains, lightning, etc., which can easily cause safety accidents, leading to construction stagnation and even casualties. Most of the existing safety control methods rely on traditional manual monitoring and experience judgment, which makes it difficult to fully and timely respond to complex and changeable meteorological conditions. In addition, the lack of effective data processing and analysis tools makes it difficult for meteorological warning information to accurately reflect the actual situation on site, thus affecting the scientificity and timeliness of decision-making. With the rapid development of information technology, especially the maturity of big data processing technology, how to effectively combine meteorological data with construction site safety management has become an urgent problem to be solved in the current power engineering field. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a power transmission and transformation construction safety management and control method based on meteorological factors to solve the technical problem that most of the existing power transmission and transformation construction safety management and control methods rely on traditional manual monitoring and experience judgment, and are difficult to comprehensively and timely respond to complex and changeable meteorological conditions.

[0004] The safety control method for power transmission and transformation construction based on meteorological factors includes the following steps, and the following steps are performed in sequence:

[0005] S1: Select meteorological warning indicators and set the threshold nodes of each level of warning severity, establish a meteorological warning indicator system and obtain the warning severity level of each meteorological warning indicator;

[0006] S2: Collect real-time meteorological data monitored at different operating points, confirm the warning severity of the meteorological data corresponding to the meteorological warning indicators based on the Hadoop method and perform normalization processing;

[0007] S3: Weighting meteorological warning indicators based on comprehensive weighting method;

[0008] ① Obtain the meteorological warning indicator matrix based on probability time distribution;

[0009] Assuming the total measurement time is T, the meteorological warning indicator monitoring data matrix Z ij It is expressed as:

[0010]

[0011] In the formula, z ij is the monitoring value of the jth meteorological warning indicator at the ith monitoring point;

[0012] Set z ij The statistical duration of the corresponding warning alert level is t rn , we get the meteorological warning index matrix T based on probability time distribution rn for:

[0013]

[0014] Where, t 1n ~t rn They represent the statistical duration of the nth meteorological warning indicator within levels 1 to r;

[0015] ② Use comprehensive weighting method to weight meteorological warning indicators;

[0016] First, the subjective weight ω obtained by subjective weighting based on the hierarchical analysis method n ;

[0017] Secondly, the objective weights obtained by objectively assigning weights based on the improved entropy weight method

[0018] Finally, comprehensive weighting is performed based on the multiplication synthesis method to obtain the comprehensive weight γ n ;

[0019] The subjective weight ω obtained based on the hierarchical analysis method n , and the objective weight obtained based on the improved entropy weight method Multiply and normalize the product to get the comprehensive weight γ n ;

[0020] S4: The comprehensive weight γ n And the meteorological warning indicator matrix T based on probability time distribution rn Multiply them together to form the real-time meteorological warning result W for each operation point at the power transmission and transformation construction site, complete the safety assessment of the power transmission and transformation construction site based on real-time meteorological data, and issue a warning according to the set threshold.

[0021] The subjective weighting based on the hierarchical analysis method includes the following steps:

[0022] A. Compare the importance of meteorological warning indicators, set different scales according to the relative importance of the two indicators, and form a subjective weight judgment matrix O = (o xy ) n×n , where o xy Indicates the importance of indicator x relative to indicator y;

[0023] B. For the judgment matrix O = (o xy ) n×n Perform normalization processing to obtain the normalized matrix M = (m xy ) n×n , where element m xy for:

[0024]

[0025] Sum the normalized matrix M row by row to obtain the vector c=(c 1 ,c 2 ,c 3 ,...,c n ) T , where element c n for:

[0026]

[0027] For vector c = (c 1 ,c 2 ,c 3 ,...,c n ) T Normalization is performed to obtain the subjective weight ω of the meteorological warning index. n :

[0028]

[0029] Since the judgment matrix involves pairwise comparisons of multiple meteorological warning indicators, contradictions may occur, making its consistency worse. Therefore, a consistency test is performed on the judgment matrix, and the inconsistency degree CI of the judgment matrix is ​​calculated:

[0030]

[0031] In the formula, Λ max is the maximum eigenvalue of the judgment matrix;

[0032] Further, calculate the consistency ratio CR:

[0033]

[0034] In the formula, RI is the average random consistency index, which is the standard value of the average random consistency index;

[0035] CR≤0.1 means that the consistency test has been passed. The smaller the value, the better the consistency. The subjective weight of the meteorological warning indicator ω n If yes, return to step A, readjust the judgment matrix and make another judgment.

[0036] The objective weighting based on the improved entropy weight method includes the following steps:

[0037] A. According to the meteorological warning indicator matrix T based on probability time distribution rn Calculate the entropy value of each meteorological warning indicator, h n is the entropy value of the nth indicator, and its solution formula is:

[0038]

[0039] Among them, T rn =1 or T rn =0, R rn l T rn =0;

[0040] Entropy value h n The smaller it is, the greater the difference in the indicator is, and the more important the indicator is;

[0041] B. Calculate the coefficient of variation d n :

[0042] d n =1-h n ;

[0043] C. Based on the coefficient of variation d n , calculate the objective weight of each meteorological warning indicator

[0044]

[0045] In the formula, The larger it is, the more information the nth meteorological warning indicator contains;

[0046] D. The above traditional entropy weight method has shortcomings. n When h approaches 1, n Small changes in the objective weights can lead to Therefore, an adjustment term ε is added on the basis of entropy weight to solve this problem. The objective weight of the improved index is:

[0047]

[0048] In the formula, the adjustment term ε is taken as 0.1.

[0049] The multiplication synthesis method is to convert the subjective weight ω n Objective weighting of indicators with improvements Multiply and normalize the product to get the comprehensive weight γ n :

[0050]

[0051] Through the above design scheme, the present invention can bring the following beneficial effects:

[0052] The present invention realizes efficient processing and analysis of a large amount of real-time meteorological data by establishing a meteorological early warning indicator system and using Hadoop technology to normalize meteorological data. The adjustment term ε is added on the basis of entropy weight to ensure the accuracy of the objective weight, thereby further ensuring the rationality of weighting the early warning indicators based on the comprehensive weighting method, making the early warning results more scientific and reliable. The method can evaluate and warn the safety risks of the construction site in real time, ensure that the construction plan is adjusted in time before the arrival of bad weather, thereby effectively improving the construction safety. This method also enhances the decision-making support capability of construction site management and has a wide range of application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The present invention is further described below with reference to the accompanying drawings and specific embodiments:

[0054] Figure 1 The present invention is a flowchart of the method for safety control of power transmission and transformation construction based on meteorological factors. DETAILED DESCRIPTION

[0055] like Figure 1 As shown, the technical solution of the present invention to solve the above technical problems is as follows:

[0056] The present invention provides a method for safety control of power transmission and transformation construction based on meteorological factors, comprising:

[0057] S1: Establish a meteorological early warning indicator system;

[0058] S2: Normalize meteorological data based on Hadoop technology;

[0059] S3: Weighting meteorological warning indicators based on comprehensive weighting method;

[0060] S4: Evaluate and warn of on-site operations for power transmission and transformation based on real-time meteorological data.

[0061] Optionally, in step S1, an assessment and early warning of the power transmission and transformation construction site operations are performed based on real-time meteorological data.

[0062] Optionally, in step S2, the meteorological data is normalized based on Hadoop technology.

[0063] Optionally, in step S3, the meteorological warning indicators are weighted based on a comprehensive weighting method.

[0064] Optionally, in step S4, an assessment and early warning of the power transmission and transformation construction site operations are performed based on real-time meteorological data.

[0065] Example:

[0066] This embodiment provides a method for safety control of power transmission and transformation construction based on meteorological factors. Figure 1 As shown, including:

[0067] S1: Establish a meteorological early warning indicator system;

[0068] The present invention can select multiple meteorological indicators according to the meteorological requirements of different construction conditions. This embodiment selects wind speed, rainfall, temperature, lightning activity, visibility, and relative humidity as meteorological warning indicators.

[0069] According to the classification of risks in the "State Grid Corporation of China Power Grid Engineering Construction Safety Risk Identification, Assessment and Control Measures", the risk warning alert level is divided into five levels: slight risk, general risk, significant risk, high risk and extremely high risk. The meaning of each level of alert and the warning model are shown in Table 1.

[0070] Table 1 Classification of warning severity

[0071]

[0072] S2: Normalize meteorological data based on Hadoop technology;

[0073] In order to eliminate the differences in dimensions and magnitudes of various indicators, the meteorological data were normalized based on Hadoop technology.

[0074] Normalize the wind speed data:

[0075]

[0076] Among them, z 1 is the normalized wind speed data; χ1 is the original wind speed detection data; E′ is the total number of meteorological data samples; γ 1 is the real-time response parameter of wind speed data; w 1 Retrieve features for wind speed data.

[0077] Normalize the rainfall data:

[0078] z 2 = x 2 |E′|-γ 2 w 2 ;

[0079] Among them, z 2 is the normalized rainfall data; χ2 is the original rainfall detection data; E′ is the total number of meteorological data samples; γ 2 is the real-time response parameter of rainfall data; w 2Retrieve features for rainfall data.

[0080] Normalize the temperature data:

[0081]

[0082] Among them, z 3 is the normalized temperature data; χ3 is the original temperature detection data; E′ is the total number of meteorological data samples; γ 3 is the real-time response parameter of temperature data; w 3 Retrieve features for temperature data.

[0083] Normalize lightning activity data:

[0084]

[0085] Among them, z 4 is the normalized lightning activity data; χ4 is the original lightning activity detection data; E′ is the total number of meteorological data samples; γ 4 Real-time response parameters for lightning activity data; 4 Retrieve features for lightning activity data.

[0086] Normalize the visibility data:

[0087]

[0088] Among them, z 5 is the normalized visibility data; χ5 is the original visibility detection data; E′ is the total number of meteorological data samples; γ 5 is the real-time response parameter of visibility data; 5 Retrieve features for visibility data.

[0089] Normalize the relative humidity data:

[0090]

[0091] Among them, z 6 is the normalized relative humidity data; 6 is the original relative humidity detection data; E′ is the total number of all meteorological data samples; γ 6 is the real-time response parameter of relative humidity data; 6 Retrieve features for relative humidity data.

[0092] S3: Weighting meteorological warning indicators based on comprehensive weighting method;

[0093] A comprehensive weighting method is used to weight meteorological warning indicators. First, subjective weighting is performed based on the hierarchical analysis method, then objective weighting is performed based on the improved entropy weight method, and finally a comprehensive weighting method is performed based on the multiplication synthesis method.

[0094] (1) Based on the analytic hierarchy process, subjective weights are assigned to obtain the subjective weights of meteorological warning indicators ω n :

[0095]

[0096] They represent the subjective weights of wind speed, rainfall, temperature, lightning activity, visibility, and relative humidity respectively; The vector elements represent wind speed, rainfall, temperature, lightning activity, visibility, and relative humidity respectively.

[0097] (2) Objective weighting based on improved entropy weight method:

[0098]

[0099] Represent the objective weights of the improvements in wind speed, rainfall, temperature, lightning activity, visibility, and relative humidity respectively; Represents the entropy value of wind speed, rainfall, temperature, lightning activity, visibility, and relative humidity.

[0100] (3) Subjective and objective comprehensive weighting method based on multiplication synthesis method

[0101] The subjective weight ω obtained based on the hierarchical analysis method n , and the improved objective weight of indicators based on the improved entropy weight method Multiply them, normalize the product, and get the comprehensive weight γ of each meteorological warning indicator n :

[0102]

[0103] S4: Evaluate and warn of on-site operations for power transmission and transformation based on real-time meteorological data.

[0104] The comprehensive weight γ n And the meteorological warning indicator matrix T based on probability time distribution rn Multiply them together to obtain the real-time weather warning result W for the power transmission and transformation construction site:

[0105] W = γ n ·T rn .

[0106] The real-time weather warning result W is used to complete the safety assessment of power transmission and transformation construction site operations based on real-time meteorological data, and to issue corresponding warnings according to the set warning thresholds of different levels.

[0107] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. The safety control method of power transmission and transformation construction based on meteorological factors is characterized by: The method comprises the following steps, and the following steps are performed in sequence: S1: Select meteorological warning indicators and set the threshold nodes of each level of warning severity, establish a meteorological warning indicator system and obtain the warning severity level of each meteorological warning indicator; S2: Collect real-time meteorological data monitored at different operating points, confirm the warning severity of the meteorological data corresponding to the meteorological warning indicators based on the Hadoop method and perform normalization processing; S3: Weighting meteorological warning indicators based on comprehensive weighting method; ① Obtain the meteorological warning indicator matrix based on probability time distribution; Assuming the total measurement time is T, the meteorological warning indicator monitoring data matrix Z ij It is expressed as: In the formula, z ij is the monitoring value of the jth meteorological warning indicator at the ith monitoring point; Set z ij The statistical duration of the corresponding warning alert level is t rn , we get the meteorological warning index matrix T based on probability time distribution rn for: Where, t 1n ~t rn They represent the statistical duration of the nth meteorological warning indicator within levels 1 to r; ② Use comprehensive weighting method to assign weights to each meteorological warning indicator; First, the subjective weight ω obtained by subjective weighting based on the hierarchical analysis method n ; Secondly, the objective weights obtained by objectively assigning weights based on the improved entropy weight method Finally, comprehensive weighting is performed based on the multiplication synthesis method to obtain the comprehensive weight γ n ; The subjective weight ω obtained based on the hierarchical analysis method n , and the objective weight obtained based on the improved entropy weight method Multiply and normalize the product to get the comprehensive weight γ n ; S4: The comprehensive weight γ n And the meteorological warning indicator matrix T based on probability time distribution rn The real-time meteorological warning result W of each operation point at the power transmission and transformation construction site is multiplied to complete the safety assessment of the power transmission and transformation construction site operation based on real-time meteorological data, and an early warning is issued according to the set threshold.

2. The method for safety control of power transmission and transformation construction based on meteorological factors according to claim 1 is characterized in that: The subjective weighting based on the hierarchical analysis method includes the following steps: A. Compare the importance of meteorological warning indicators, set different scales according to the relative importance of the two indicators, and form a subjective weight judgment matrix O = (o xy ) n×n , where o xy Indicates the importance of indicator x relative to indicator y; B. For the judgment matrix O = (o xy ) n×n Perform normalization processing to obtain the normalized matrix M = (m xy ) n×n , where element m xy for: The normalized matrix M is summed row by row to obtain a vector c=(c1, c2, c3, ..., c n ) T , where element c n for: For vector c = (c1, c2, c3, ..., c n ) T Normalization is performed to obtain the subjective weight ω of the meteorological warning index. n : Since the judgment matrix involves pairwise comparisons of multiple meteorological warning indicators, contradictions may occur, making its consistency worse. Therefore, a consistency test is performed on the judgment matrix, and the inconsistency degree CI of the judgment matrix is ​​calculated: In the formula, Λ max is the maximum eigenvalue of the judgment matrix; Further, calculate the consistency ratio CR: In the formula, RI is the average random consistency index; CR≤0.1 means that the consistency test has been passed. The smaller the value, the better the consistency. The subjective weight of the meteorological warning indicator ω n If yes, return to step A, readjust the judgment matrix and make another judgment.

3. The method for safety control of power transmission and transformation construction based on meteorological factors according to claim 1 is characterized in that: The objective weighting based on the improved entropy weight method includes the following steps: A. According to the meteorological warning indicator matrix T based on probability time distribution rn Calculate the entropy value of each meteorological warning indicator, h n is the entropy value of the nth indicator, and its solution formula is: Among them, T rn =1 or T rn =0, T rn l T rn =0; Entropy value h n The smaller it is, the greater the difference in the indicator is, and the more important the indicator is; B. Calculate the coefficient of variation d n : d n =1-h n ; C. Based on the coefficient of variation d n , calculate the objective weight of each meteorological warning indicator In the formula, The larger it is, the more information the nth meteorological warning indicator contains; D. The above traditional entropy weight method has shortcomings. n When h approaches 1, n Small changes in the objective weights can lead to Therefore, an adjustment term ε is added on the basis of entropy weight to solve this problem. The objective weight of the improved index is: In the formula, the adjustment term ε is taken as 0.

1.

4. The method for safety control of power transmission and transformation construction based on meteorological factors according to claim 1 is characterized in that: The multiplication synthesis method is to convert the subjective weight ω n Objective weighting of indicators with improvements Multiply and normalize the product to get the comprehensive weight γ n :

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