Extreme Weather Vulnerable Feeder State Evaluation Method and Medium Based on Trapezoidal Cloud Model

Abstract

The present invention discloses a method and medium for evaluating the state of vulnerable feeders under extreme weather based on the trapezoidal cloud model. The method incorporates weather indicators into the feeder state evaluation system, and then, in view of the data randomness and ambiguity problems existing in the conversion process from quantitative feeder state scores to qualitative state levels, applies the trapezoidal cloud algorithm in fuzzy theory to solve the problems. By generating the state level cloud model of the overall feeder and then generating the score cloud model of the feeder to be evaluated, calculating the similarity between each level cloud model and the score cloud model, and finally dividing the state level of the feeder to be evaluated after being affected by extreme weather, the evaluation of the state of the distribution network feeder under extreme weather conditions is realized. The method of the present invention improves the traditional cloud model, makes the cloud model more in line with the actual equipment operation and maintenance by using the trapezoidal cloud, and the method takes into account meteorological indicators and is more comprehensive.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network power supply, and particularly relates to a method and medium for evaluating the state of extremely weather-vulnerable feeders based on a trapezoidal cloud model. Background Art

[0002] Urban distribution network feeders are characterized by long distances, large capacities, and hybrid AC / DC operation. Extreme weather such as typhoons and heavy rains is likely to cause lightning strikes and external damage to the grid feeders. In seasons with frequent extreme weather, by means of meteorological forecast indicators, reasonably predicting the impact of weather on the feeder state, positioning in advance the feeders vulnerable to external damage and prone to failures, and then increasing the attention or conducting fine operation and maintenance to eliminate defects according to the corrected state evaluation results is an effective means to strengthen the full-life cycle management ability of numerous equipment components of the feeders and improve the power supply safety and reliability of the distribution network.

[0003] To reflect the impact of external factors on the equipment operation state, it is necessary to recalculate the equipment score in combination with weather indicators and finally solve the problem of which state level the equipment should belong to. In this process, the randomness of the equipment state value, meteorological prediction value, and model prediction value, as well as the fuzziness of the conversion from quantitative scoring to the concept of state level, conform to the characteristics of the cloud model in fuzzy theory. At present, some researchers have used fuzzy evaluation, matter-element method, cloud model method, and grey matter-element method to perform fuzzy transformation on state parameters, and then combined with weight allocation methods such as evidence theory and entropy weight method to comprehensively evaluate the state level.

[0004] The existing feeder state evaluation methods only consider the health degree of the equipment itself. When classifying the state of distribution network feeders considering external environmental factors such as extreme weather, the criteria are still relatively single. Moreover, most of the existing technologies for state evaluation using fuzzy theory adopt the traditional membership function method, which is only suitable for modeling simple phenomena. Summary of the Invention

[0005] The main purpose of the present invention is to overcome the shortcomings and deficiencies of the existing technology, and propose a method and medium for evaluating the state of extremely weather-vulnerable feeders based on a trapezoidal cloud model.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions:

[0007] A method for evaluating the state of extremely weather-vulnerable feeders based on a trapezoidal cloud model, comprising the following steps:

[0008] S1. Taking the feeders vulnerable to failures under extreme weather as samples, obtaining their historical state data sets and corresponding historical weather data sets, calculating the comprehensive scores of each sample feeder, and obtaining a sample feeder comprehensive score data set;

[0009] S2. Evaluate the status to which the sample feeder belongs according to expert opinions and classify it into 4 status levels;

[0010] S3. According to the classified status levels, divide the comprehensive score dataset of the sample feeder into 4 classification-level cloud training sets, and input them into the inverse trapezoidal cloud generator to extract 4 numerical features of the status levels;

[0011] S4. Input the obtained 4 numerical features of the status levels and the specified number of cloud droplets N1 into the forward trapezoidal cloud generator to generate 4 status-level clouds;

[0012] S5. When evaluating the status of a specific feeder, collect its status data and the weather forecast data at the previous five different times before the evaluation, calculate the score for each time data, and form the real-time score dataset of the feeder to be evaluated;

[0013] S6. Input the obtained real-time score dataset into the inverse trapezoidal cloud generator to extract numerical features;

[0014] S7. Input the numerical features obtained in step S6 and the specified number of cloud droplets N2 into the forward trapezoidal cloud generator to generate the score cloud of the device to be evaluated;

[0015] S8. Calculate the similarity between the score cloud of the device to be evaluated obtained in step S7 and the 4 status-level clouds obtained in step S4 respectively;

[0016] S9. Classify the feeder to be evaluated into the status level with the highest similarity to it, and output the status level result.

[0017] Further, step S1 is specifically:

[0018] Take multiple feeders that are vulnerable to faults under extreme weather as samples, and take their historical status dataset D s1 and the corresponding historical weather dataset D w1 , and according to the secondary index weight vector, calculate the comprehensive score of each sample feeder through formula (1):

[0019] S level = 0.696W s D s1 + 0.304W w D w1 (1)

[0020] Among them, W s =(0.109 0.039 0.065 0.264 0.085 0.064 0.091 0.022 0.048 0.030.006 0.019 0.038 0.011 0.014) T, which is the weight vector of the secondary indicators of the feeder equipment, where the values are the weights of the three evaluation contents of the cable, overhead line, pole-mounted switch, substation equipment, and distribution room equipment, including the main body, accessory components, and test and detection. W w =(0.096 0.343 0.446 0.115) T , which is the weight vector of the secondary indicators of extreme weather, where the values are the weights of the four contents of average temperature, daily rainfall, maximum hourly wind speed, and extreme weather trip frequency.

[0021] Further, step S2 is specifically as follows:

[0022] Evaluate the status of multiple sample feeders according to expert opinions. The experts classify the status of the sample feeders. The judgment basis is whether the sample feeders in the selected historical status have failed due to extreme weather, and according to the expert operation and maintenance experience, whether the feeders should be given higher attention, and they are classified into 4 levels of "normal", "attention", "abnormal", and "severe".

[0023] Further, step S3 is specifically as follows:

[0024] According to the divided status levels, divide the comprehensive score dataset S of the sample feeders level into 4 levels of cloud training sets S1, S2, S3, and S4, and input them into the reverse trapezoidal cloud generator respectively to extract the expectations, entropies, and hyper-entsropies of the 4 levels of status clouds.

[0025] Further, in step S3, input the reverse trapezoidal cloud generator to extract the numerical features of the 4 levels of status, which specifically includes the following steps:

[0026] S31. Calculate the mean of the input dataset and variance S X ;

[0027] S32. Obtain the left expectation Ex1 and right expectation Ex2 of the cloud model. When the input is the comprehensive score dataset of the sample feeder, the expectation assignment formula is formula (2). When the input is the real-time score dataset of the feeder to be evaluated,

[0028]

[0029] where i = 1, 2, 3, 4 respectively represent the status levels of "normal", "attention", "abnormal", and "severe";

[0030] S33. Calculate the entropy En of the cloud model:

[0031]

[0032] Among them, n represents the data volume of the data set,

[0033] S34. Calculate the hyper entropy He of the cloud model:

[0034]

[0035] S35. Output the numerical characteristics {Ex1, Ex2, En, He} of the cloud model C.

[0036] Further, step S4 is specifically as follows:

[0037] Input the numerical characteristics of the four types of state levels obtained in step S3 and the specified number of cloud droplets N1 into the forward trapezoidal cloud generator to generate the cloud model C of the four types of state levels level_i :{Ex 1i ,Ex 2i ,En i ,He i}, i = 1, 2, 3, 4, respectively representing the four levels of "normal", "attention", "abnormal", and "severe"; specifically:

[0038] Input the numerical characteristics {Ex1, Ex2, En, He} of the cloud model of the data set, and in the forward trapezoidal cloud generator, perform the following steps:

[0039] S41. Generate a normal distribution random number Enn with En as the expectation and He as the variance;

[0040] S42. Generate a normal random number x1 with Ex1 as the expectation and Enn as the variance;

[0041] S43. If x1 ≥ Ex2, set x1 = 0, and then perform S44; if not, directly perform S44;

[0042] S44. Generate a normal random number x2 with Ex2 as the expectation and Enn as the variance;

[0043] S45. If x2 ≤ Ex1, set x2 = 0, and then perform S46; if not, directly perform S46;

[0044] S46. Let be the abscissa of the cloud droplet in the cloud model, and calculate the certainty degree according to formula (5) as the ordinate of the cloud droplet:

[0045]

[0046] S47. Output the cloud model composed of N cloud droplets with coordinates (x, μ(x)).

[0047] Further, step S5 is specifically as follows:

[0048] When evaluating the status of a specific feeder, collect its status data D s2 and the weather forecast data D at the previous five different times before evaluation w2 . Calculate the score for the data at each time to form the real-time score dataset of the feeder to be evaluated. The score calculation formula is as follows:

[0049] S state = 0.696W s D s2 + 0.304W w D w2 (6).

[0050] Furthermore, step S7 is specifically as follows:

[0051] Input the numerical features obtained in step S6 and the specified number of cloud droplets N2 into the forward trapezoidal cloud generator to generate the score cloud model C of the device to be evaluated state :{Ex, En, He}.

[0052] Furthermore, step S8 is specifically as follows:

[0053] Calculate the similarity between the score cloud model C of the device to be evaluated obtained in step S7 state :{Ex, En, He} and the 4-class status level cloud model C obtained in step S4 level_i :{Ex 1i , Ex 2i , En i , He i} respectively. The similarity calculation steps are as follows:

[0054] S81. Calculate the boundary determination degree value of the two cloud models according to formula (7):

[0055] α = μ(Ex - 3En) (7)

[0056] S82. Calculate the two intersection points of y = α and the expected curve of the cloud model y = exp[-(x - Ex) 2 / 2(En) 2 . The abscissa of the left intersection point is used as the lower infimum C of the cloud inf , and the abscissa of the right intersection point is used as the upper supremum C sup ; if C 1,inf < C 2,inf and C 1,sup < C 2,sup , then the relationship between the two cloud models is called C1 < C2. If C 1,inf ≤ C 2,inf and C 1,sup ≥ C 2,sup , then it is called

[0057] S83. Calculate the overlap degree of the two cloud models according to formula (8):

[0058]

[0059] S84. Calculate the abscissas x1 and x2 of the intersection points of the expected curves of the two cloud models according to formula (9), and determine the corresponding certainty degrees μ(x1) and μ(x2) at the intersection points according to formula (5):

[0060]

[0061] S85. Calculate the similarity degree of the two cloud models according to formula (10):

[0062]

[0063] The present invention also includes a computer-readable storage medium storing a computer program, which when executed by a processor, implements the evaluation method provided by the present invention.

[0064] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0065] 1. The method of the present invention applies the trapezoidal cloud algorithm in fuzzy theory to the state evaluation of feeders vulnerable to extreme weather in the distribution network to solve the comprehensive evaluation problem, improves the traditional cloud model, makes the cloud model more in line with the actual equipment operation and maintenance using the trapezoidal cloud, thereby increasing the scientificity and reliability of the algorithm; the method takes into account meteorological indicators, is more comprehensive, and also has obvious advantages in retaining the fuzziness and randomness of data. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 is a schematic flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0067] The present invention will be further described in detail below in conjunction with the embodiments and the drawings, but the embodiments of the present invention are not limited thereto.

[0068] Embodiment

[0069] As Figure 1 shown, the present invention, an extreme weather vulnerable feeder state evaluation method based on a trapezoidal cloud model, includes the following steps:

[0070] S1. In this embodiment, 200 feeders vulnerable to faults under extreme weather are used as samples, and their historical state data set D s1 and the corresponding historical weather data set D w1, according to the secondary index weight vectors in Table 1, Table 2, and Table 3 below, calculate the comprehensive score of each sample feeder through the following formula:

[0071] S level = 0.696W s D s1 + 0.304W w D w1 (1)

[0072] Among them, W s is the weight vector composed of the secondary index weights of the feeder equipment, and W w is the weight vector composed of the extreme weather headphone indicators. The specific values of the two vectors are shown in Table 1, Table 2, and Table 3 below.

[0073]

[0074] Table 1

[0075]

[0076] Table 2

[0077]

[0078] Table 3

[0079] S2. Evaluate the status that 200 sample feeders should belong to according to expert opinions. The experts divide the status that the sample feeders should belong to. The judgment basis is whether the sample feeders in the selected historical status have failed due to extreme weather, and according to the expert operation and maintenance experience, whether the feeders should be given higher attention, and it is divided into 4 levels: "normal", "attention", "abnormal", and "severe".

[0080] S3. According to the divided status levels, divide the sample feeder comprehensive score dataset S level into 4 levels of cloud training sets: S1, S2, S3, and S4, and input them into the reverse trapezoidal cloud generator respectively to extract the expectations, entropies, and hyper entropies of the 4 levels of status clouds. In this embodiment, the reverse trapezoidal cloud generator specifically performs the following steps:

[0081] S31. Calculate the mean value and variance S X of the input dataset;

[0082] S32. Obtain the left expected value Ex1 and right expected value Ex2 of the cloud model. When the input is the comprehensive score dataset of the sample feeder, the expected value assignment formula is formula (2). When the input is the real-time scoring dataset of the feeder to be evaluated,

[0083]

[0084] Among them, i = 1, 2, 3, 4 represent the state levels of "normal", "attention", "abnormal", and "severe" respectively;

[0085] S33. Calculate the entropy En of the cloud model:

[0086]

[0087] Among them, n represents the amount of data in the data set,

[0088] S34. Calculate the hyperentropy He of the cloud model:

[0089]

[0090] S35. Output the numerical characteristics {Ex1, Ex2, En, He} of the cloud model C.

[0091] S4. Input the numerical characteristics of the 4 types of state levels obtained in step S3 and the specified number of cloud droplets N1 into the forward trapezoidal cloud generator to generate 4 types of state level cloud models C level_i :{Ex 1i , Ex 2i , En i , He i}, i = 1, 2, 3, 4, respectively representing the 4 levels of "normal", "attention", "abnormal", and "severe";

[0092] In this embodiment, input the numerical characteristics {Ex1, Ex2, En, He} of the cloud model of the input data set, and in the forward trapezoidal cloud generator, perform the following steps:

[0093] S41. Generate a normal distribution random number Enn with En as the expectation and He as the variance;

[0094] S42. Generate a normal random number x1 with Ex1 as the expectation and Enn as the variance;

[0095] S43. If x1 ≥ Ex2, let x1 = 0, and then perform S44. If not, directly perform S44;

[0096] S44. Generate a normal random number x2 with Ex2 as the expectation and Enn as the variance;

[0097] S45. If x2 ≤ Ex1, let x2 = 0, and then perform S46. If not, directly perform S46;

[0098] S46. Let be the abscissa of the cloud droplet in the cloud model, and calculate the certainty degree according to formula (5) as the ordinate of the cloud droplet:

[0099]

[0100] S47. A cloud model composed of N cloud droplets with output coordinates (x, μ(x)).

[0101] S5. When evaluating the status of a specific feeder, collect its status data D s2 and the weather forecast data D at the previous five different times before evaluation w2 , calculate the score for the data at each time to form the real-time score data set of the feeder to be evaluated. The score calculation formula is:

[0102] S state = 0.696W s D s2 + 0.304W w D w2 (6).

[0103] S6. Input the obtained real-time score data set into the inverse trapezoidal cloud generator to extract numerical features;

[0104] S7. Input the numerical features obtained in step S6 and the specified number of cloud droplets N2 into the forward trapezoidal cloud generator to generate the score cloud model C of the device to be evaluated state :{Ex, En, He}.

[0105] S8. Calculate the score cloud of the device to be evaluated obtained in step S7 and the 4 types of status level clouds obtained in step S4 respectively; In this embodiment, specifically:

[0106] Calculate the score cloud model C of the device to be evaluated obtained in step S7 state :{Ex, En, He} and the four types of status level cloud models C obtained in step S4 level_i :{Ex 1i , Ex 2i , En i , He i} respectively. The similarity calculation steps are as follows:

[0107] S81. Calculate the boundary determination degree value of the two cloud models according to formula (7):

[0108] α = μ(Ex - 3En) (7)

[0109] S82. Calculate the two intersection points of y = α and the cloud model expectation curve y = exp[-(x - Ex) 2 / 2(En) 2 . The abscissa of the left intersection point is used as the lower infimum C of the cloud inf , and the abscissa of the right intersection point is used as the upper supremum C of the cloud sup ; If C1,inf < C 2,inf and C 1,sup < C 2,sup , it is said that the relationship between the two cloud models is C1 < C2. If C 1,inf ≤ C 2,inf and C 1,sup ≥ C 2,sup , it is said that

[0110] S83. Calculate the overlap degree of the two cloud models:

[0111]

[0112] S84. Calculate the abscissas x1 and x2 of the intersection points of the expected curves of the two cloud models according to formula (9), and determine the corresponding certainty degrees μ(x1) and μ(x2) at the intersection points according to formula (5):

[0113]

[0114] S85. Calculate the similarity degree of the two cloud models:

[0115]

[0116] S9. Classify the feeder to be evaluated into the state level with the highest similarity degree, and output the state level result.

[0117] In another embodiment, a computer-readable storage medium is provided, storing a computer program, which realizes the method of the above embodiment when executed by a processor.

[0118] It should also be noted that in this specification, terms such as "including", "comprising" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0119] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An extreme weather vulnerable feeder status evaluation method based on the trapezoidal cloud model, characterized in that It includes the following steps: S1. Take the feeders that are vulnerable to failure under extreme weather as samples, obtain their historical state datasets and corresponding historical weather datasets, calculate the comprehensive scores of each sample feeder, and obtain the sample feeder comprehensive score dataset. Specifically: Taking multiple feeders that are vulnerable to faults under extreme weather as samples, and obtaining their historical state dataset D s1 and the corresponding historical weather dataset D w1 , according to the weight vector of secondary indicators, calculate the comprehensive score of each sample feeder through a preset formula; S2. Evaluate and classify the states that the sample feeders should belong to according to expert opinions into 4 state levels. Specifically: Evaluate the states that multiple sample feeders should belong to according to expert opinions. Let the experts classify the states that the sample feeders should belong to. The judgment basis is whether the sample feeders under the selected historical states have failed due to extreme weather, and according to the expert operation and maintenance experience, whether the feeders should be given higher attention, and classify them into 4 levels: "Normal", "Attention", "Abnormal", and "Severe". S3. According to the classified state levels, divide the sample feeder comprehensive score dataset into 4-level cloud training sets, and input them into the reverse trapezoidal cloud generator to extract the numerical features of the 4 state levels respectively. Specifically: According to the divided state levels, the comprehensive score dataset S of the sample feeder level is divided into four types of level cloud training sets, namely S1, S2, S3, and S4. They are respectively input into the reverse trapezoidal cloud generator to extract the expectation, entropy, and hyperentropy of the four types of state level clouds; S4. Input the obtained numerical features of the 4 state levels and the specified number of cloud droplets N1 into the forward trapezoidal cloud generator to generate the 4 state level clouds. S5. When evaluating the state of a specific feeder, collect its state data and the weather forecast data of the previous five different times before the evaluation, calculate the scores for the data of each time, and form the real-time score dataset of the feeder to be evaluated. S6. Input the obtained real-time score dataset into the reverse trapezoidal cloud generator to extract numerical features. S7. Input the numerical features obtained in step S6 and the specified number of cloud droplets N2 into the forward trapezoidal cloud generator to generate the score cloud of the device to be evaluated. S8. Calculate the similarities between the score cloud of the device to be evaluated obtained in step S7 and the 4 state level clouds obtained in step S4 respectively. S9. Divide the feeder to be evaluated into the state level with the highest similarity to it, and output the state level result.

2. The method for evaluating the state of extremely weather-vulnerable feeders based on the trapezoidal cloud model according to claim 1, wherein Calculate the comprehensive score of each sample feeder through a preset formula. The formula is specifically: S level = 0.696W s D s1 + 0.304W w D w1 (1) Among them, W s =(0.109 0.039 0.065 0.264 0.085 0.064 0.091 0.022 0.048 0.03 0.006 0.019 0.038 0.011 0.014) T , is the weight vector of the secondary indicators of the feeder equipment. The values therein are the weights of the three evaluation contents of the main body, accessory components and test and detection of the cable, overhead line, pole-mounted switch, platform equipment and distribution room equipment respectively. W w =(0.096 0.343 0.446 0.115) T , is the weight vector of the secondary indicators of extreme weather. The values therein are the weights of the four contents of average temperature, daily rainfall, maximum hourly wind speed and extreme weather tripping frequency respectively.

3. The method for evaluating the state of extremely weather-vulnerable feeders based on the trapezoidal cloud model according to claim 1, characterized in that In step S3, input the reverse trapezoidal cloud generator to extract the numerical features of the 4 state levels, specifically including the following steps: S31. Calculate the mean of the input data set and variance S X ; S32. Obtain the left expected value Ex1 and the right expected value Ex2 of the cloud model. When the input is the comprehensive score data set of the sample feeder, the expected value assignment formula is formula (2). When the input is the real-time scoring data set of the feeder to be evaluated, Among them, i = 1, 2, 3, 4 represent the state levels of "Normal", "Attention", "Abnormal", and "Severe" respectively. S33. Calculate the entropy En of the cloud model: where n represents the amount of data in the dataset, S34. Calculate the hyperentropy He of the cloud model: S35. Output the numerical features {Ex1, Ex2, En, He} of the cloud model C.

4. The method for evaluating the state of extremely weather-vulnerable feeders based on the trapezoidal cloud model according to claim 3, wherein Step S4 is specifically: Input the four types of state level numerical features and the specified cloud droplet number N1 obtained in step S3 into the forward trapezoidal cloud generator to generate four types of state level cloud models C level_i :{Ex 1i ,Ex 2i ,En i ,He i}, where i = 1, 2, 3, 4, representing the four levels of "normal", "attention", "abnormal", and "severe" respectively; specifically: Input the numerical features {Ex1, Ex2, En, He} of the cloud model of the dataset, and in the forward trapezoidal cloud generator, perform the following steps: S41. Generate a normal distribution random number Enn with En as the expectation and He as the variance. S42. Generate a normal random number x1 with Ex1 as the expectation and Enn as the variance. S43. If x1 ≥ Ex2, let x1 = 0, and then perform S44. If not, directly perform S44. S44. Generate a normal random number x2 with Ex2 as the expectation and Enn as the variance. S45. If x2 ≤ Ex1, let x2 = 0, and then perform S46. If not, directly perform S46. S46. Let be the abscissa of the cloud droplets in the cloud model, and calculate the certainty degree as the ordinate of the cloud droplets according to formula (5): S47. Output a cloud model composed of N cloud droplets with output coordinates of (x, μ(x)).

5. The method for evaluating the state of extremely weather-vulnerable feeders based on the trapezoidal cloud model according to claim 1, wherein Step S5 is specifically as follows: When evaluating the status of a specific feeder, collect its status data D s2 and the weather forecast data D at the previous five different times before evaluation w2 , calculate the score for the data at each time to form the real-time score dataset of the feeder to be evaluated. The score calculation formula is as follows: S state = 0.696W s D s2 + 0.304W w D w2 (6).

6. The method for evaluating the state of extremely weather-vulnerable feeders based on the trapezoidal cloud model according to claim 4, wherein Step S7 is specifically as follows: Input the numerical features obtained in step S6 and the specified number of cloud droplets N2 into the forward trapezoidal cloud generator to generate the scoring cloud model C of the device to be evaluated state :{Ex, En, He} 7. The method for evaluating the state of extremely weather-vulnerable feeders based on the trapezoidal cloud model according to claim 6, wherein Step S8 is specifically as follows: Calculate the similarity between the evaluation cloud model C of the device to be evaluated obtained in step S7 state :{Ex, En, He} and the four-category status level cloud model C level_i :{Ex 1i , Ex 2i , En i , He i} obtained in step S4. The similarity calculation steps are as follows: S81. Calculate the boundary determination degree values of two cloud models according to formula (7): α = μ(Ex - 3En) (7) S82. Calculate the two intersection points of y = α and the cloud model's expected curve y = exp[-(x - Ex) 2 / 2(En) 2 . Take the abscissa of the left intersection point as the lower bound C inf of the cloud, and the abscissa of the right intersection point as the upper bound C sup ; if C 1,inf < C 2,inf and C 1,sup < C 2,sup , then the relationship between the two cloud models is called C1 < C2. If C 1,inf ≤ C 2,inf and C 1,sup ≥ C 2,sup , then it is called S83. Calculate the overlap degree of two cloud models according to formula (8): S84. Calculate the abscissas x1 and x2 of the intersection points of the expected curves of two cloud models according to formula (9), and determine the corresponding determination degrees μ(x1) and μ(x2) at the intersection points according to formula (5): S85. Calculate the similarity degree of two cloud models according to formula (10):

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, the method described in any one of claims 1-7 is implemented.

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