A method for predicting icing on transmission lines based on weather factors

Abstract

A method for warning of icing on transmission lines based on weather factors, which belongs to the technical field of inspection of transmission lines. The present invention solves the problem that the time advance amount of the existing method for warning of icing on transmission lines is insufficient. The present invention constructs an icing susceptibility description acquisition module and an ultra-long advance amount periodic icing feature calculation module. By using these two modules, typical weather cycle conditions can be described, and then an ultra-long advance amount cycle recognition model can be obtained, and further warning of ultra-long time advance amount in high-incidence areas of icing on transmission lines can be carried out. Through the method of the present invention, it can be found that the weather cycle that will surely enter the high-incidence weather of icing can be established, the indirect correlation between the weather and the occurrence of icing can be established, and the weather state that is likely to enter the icing-related state under the condition of continuous multi-days can be found; the icing situation can be warned in advance for multiple days. The method of the present invention can be applied to the warning of icing on transmission lines.

Description

Technical Field

[0001] The present invention belongs to the technical field of transmission line inspection, and particularly relates to an icing warning method for transmission lines based on weather factors. Background Art

[0002] In some relatively remote areas in the north of China (such as mountain valleys, river valleys, etc.), icing of transmission lines is likely to occur. Once the icing of transmission lines occurs, it takes a relatively long time to prepare for de-icing, such as checking automatic de-icing equipment, pre-powering, and sending de-icing teams to the icing area of the transmission line. However, during the process of preparing for de-icing, the icing of the transmission line will affect power transmission. Therefore, it is very necessary to carry out ultra-long-term advance icing warning for high-incidence areas of transmission line icing, so as to timely and effectively prepare for icing and reduce the possible disaster losses caused by transmission line icing.

[0003] The existing technical means for the icing warning method of transmission lines mainly include:

[0004] First, based on the daily detailed temperature, humidity, rainfall and snowfall conditions, and combined with the data collected by cameras in the area, icing warning is carried out when relevant indicators exceed a certain value and last for a certain period of time. However, the amount of time in advance of this icing warning method is relatively small. When the warning is issued, the relevant transmission lines are basically in the process of icing, and immediate countermeasures need to be taken;

[0005] Second, prediction is carried out based on micro-scale weather conditions, and a joint analysis of certain weather characteristics and icing conditions is carried out to find the weather conditions directly leading to icing. This type of technology can carry out icing warning about 1 day in advance, and relevant inspection units can preheat de-icing equipment in advance and make preliminary preparations for de-icing.

[0006] The above two types of transmission line icing warning technologies are relatively effective in dealing with small areas, urban areas and low-incidence icing situations; however, for some relatively remote mountain valleys, river valleys, leeward slopes, etc. in the northern region, where icing is highly frequent, when icing occurs, it takes a long time to preheat de-icing equipment and send de-icing teams to the icing area in advance. At this time, the advance amount of the existing two types of transmission line icing warning methods is not enough to meet the requirements; in the case of insufficient advance amount, relevant inspection units may, on the one hand, be insufficient in dealing with icing when it appears, resulting in serious accidents such as the collapse of transmission towers; on the other hand, over-response to icing leads to excessive consumption of electricity, equipment and human resources, and a great management cost is paid. Therefore, it is very necessary to carry out ultra-long-term advance warning for high-incidence icing areas. Summary of the Invention

[0007] The objective of the present invention is to solve the problem that the advance time of the existing overhead transmission line icing warning method is insufficient, and a transmission line icing warning method based on weather factors is proposed.

[0008] The technical solution adopted by the present invention to solve the above technical problems is as follows: A transmission line icing warning method based on weather factors, and the method specifically includes the following steps:

[0009] Step S1: Input a historical data list History of the weather and icing occurrence in a region, the number of days YJLengh for early warning, and the length ZQLength of the period to be recognized.

[0010] According to the historical data list History, calculate the average temperature JZWD, average humidity JZSD, average wind speed JZFS, and snowfall correlation JZJX when icing occurs in the region.

[0011] Step S2: Establish an icing susceptibility description acquisition module MYFVector. The input of the module MYFVector is an icing susceptibility description input variable MYFInput with the same record structure as each record in the historical data list History, and the output is an icing susceptibility description output vector ;

[0012] Step S3: Construct an advance periodic icing feature calculation module MCCFeature. The input of MCCFeature is a periodic list MCCFeatureInput composed of records for consecutive ZQLength days, and the structure of each record in the periodic list MCCFeatureInput is the same as the record structure of the historical data list History;

[0013] The module MCCFeature uses MYFVector to process each record in the periodic list MCCFeatureInput respectively to obtain an advance periodic icing feature output MCCFeatureOutput;

[0014] Step S4: Use MCCFeature and History to construct an advance periodic icing feature table TZTable, and obtain an advance period recognition model SModel based on TZTable;

[0015] Step S5: Input the weather condition data BTest for the most recent ZQLength days in the region where overhead transmission line icing disaster warning is to be carried out, and use SModel to warn whether overhead transmission line icing will occur YJLengh days later in the region where overhead transmission line icing disaster warning is to be carried out.

[0016] The beneficial effects of the present invention are:

[0017] The present invention provides an ultra-long lead-time warning method for high-incidence areas of transmission line icing based on the identification of typical weather cycles. Firstly, an icing susceptibility description acquisition module and an ultra-long lead-time periodic icing feature calculation module are constructed. Using these two modules, the typical weather cycle situation can be described, and then an ultra-long lead-time cycle identification model can be obtained, and further, an ultra-long lead-time warning for high-incidence areas of transmission line icing can be carried out. Through the method of the present invention, it can be found that the weather cycle that will surely enter the high-incidence icing weather can be established, the indirect correlation between the weather and the occurrence of icing can be established, and the weather state related to icing can be found with a high probability in the case of continuous multi-days; the icing situation can be warned in advance for many days, so that the inspection unit can timely and effectively make preparations for dealing with icing, reduce the disaster losses that may be brought by icing, and at the same time prevent over-protection and effectively reduce the management cost of maintenance work. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flowchart of an icing warning method for transmission lines based on weather factors according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] DETAILED DESCRIPTION OF THE EMBODIMENT 1: Combined with Figure 1 This embodiment is described. An icing warning method for transmission lines based on weather factors described in this embodiment specifically includes the following steps:

[0020] Step S1: Input a historical data list History of the weather and the occurrence of icing in a region (the region here is a high-incidence area of icing disasters), the number of days of early warning YJLengh, and the length ZQLength of the period to be identified;

[0021] According to the historical data list History, the average temperature JZWD, the average humidity JZSD, the average wind speed JZFS, and the snowfall correlation JZJX when icing occurs in the region are statistically calculated;

[0022] Step S2: Establish an icing susceptibility description acquisition module MYFVector. The input of the module MYFVector is an icing susceptibility description input variable MYFInput with the same record structure as each record in the historical data list History, and the output is an icing susceptibility description output vector ;

[0023] Step S3: Construct a lead-time periodic icing feature calculation module MCCFeature. The input of MCCFeature is a periodic list MCCFeatureInput composed of records for consecutive ZQLength days. The structure of each record in the periodic list MCCFeatureInput is the same as that of the records in the historical data list History.

[0024] The module MCCFeature processes each record in the periodic list MCCFeatureInput respectively using MYFVector to obtain an ultra-long lead-time periodic icing feature output MCCFeatureOutput.

[0025] Step S4: Use MCCFeature and History to construct a lead-time periodic icing feature table TZTable, and obtain a lead-time period recognition model SModel based on TZTable.

[0026] Step S5: Input the weather condition data BTest for the most recent ZQLength days in the area where transmission line icing disaster warning is to be carried out. The structure of each record in BTest is the same as that of the records in History, and there are a total of ZQLength records. Use SModel to give a warning on whether transmission line icing will occur YJLengh days later in the area where transmission line icing disaster warning is to be carried out.

[0027] The method of the present invention can establish an indirect correlation between weather and icing, and give an ultra-long lead warning for transmission line icing.

[0028] Specific Embodiment 2: The difference between this embodiment and Specific Embodiment 1 is that the specific process of step S1 is as follows:

[0029] Step S101: Input a historical data list History of weather and icing occurrence in a region. Each record in the historical data list History includes the weather condition and icing condition within one day (that is, each record is used to record the weather condition and icing condition in the historical one day in this region); that is, each record includes the following fields:

[0030] H00WD: The temperature value at 0:00 on the day.

[0031] H00SD: The humidity value at 0:00 on the day.

[0032] H06WD: The temperature value at 6:00 on the day.

[0033] H06SD: The humidity value at 6:00 on the day.

[0034] H12WD: The temperature value at 12:00 on the day.

[0035] H12SD: Humidity value at 12:00 on the same day;

[0036] H18WD: Temperature value at 18:00 on the same day;

[0037] H18SD: Humidity value at 18:00 on the same day;

[0038] HFS: Average wind speed value on the same day;

[0039] HJX: Whether there is snowfall on the same day, 1 indicates snowfall, 0 indicates no snowfall;

[0040] HFB: Whether there is icing on the transmission line on the same day, 0 indicates no icing, 1 indicates icing;

[0041] Among them, the unit of temperature value is degree Celsius; the unit of humidity value is relative humidity; the unit of wind speed value is meter per second;

[0042] Step S102: Input the early warning days YJLengh, and the default value of YJLengh is 3 days;

[0043] Step S103: Input the period to be recognized ZQLength, and the default value of ZQLength is 5 days;

[0044] Step S104: JZWD = Retrieve all records in the historical data list History where HFB is equal to 1, and calculate the average value of the H00WD field in all retrieved records;

[0045] Step S105: JZSD = Retrieve all records in the historical data list History where HFB is equal to 1, and calculate the average value of the H00SD field in all retrieved records;

[0046] Step S106: JZFS = Retrieve all records in the historical data list History where HFB is equal to 1, and calculate the average value of the HFS field in all retrieved records;

[0047] Step S107: JZJX = Retrieve all records in the historical data list History where HFB is equal to 1, and calculate the average value of the HJX field in all retrieved records.

[0048] Other steps and parameters are the same as those in the first specific implementation manner.

[0049] Specific implementation manner three: The difference between this implementation manner and the first or second specific implementation manner is that the specific process of step S2 is as follows:

[0050] Step S201: Establish an icing susceptibility description acquisition module MYFVector. The input of module MYFVector is an icing susceptibility description input variable MYFInput with the same record structure as each record in the historical data list History.

[0051] Step S202: Establish an icing susceptibility description output vector MYFOutput = a vector containing 10 elements. Initialize all elements in the icing susceptibility description output vector MYFOutput to 0.

[0052] Step S203: Let MYFOutput [1] = (the H00WD field of MYFInput - JZWD) / 5.

[0053] Among them, MYFOutput [1] is the first element in the icing susceptibility description output vector MYFOutput.

[0054] Step S204: Let MYFOutput [3] = (the H06WD field of MYFInput - 3 - JZWD) / 5.

[0055] Among them, MYFOutput [3] is the third element in the icing susceptibility description output vector MYFOutput.

[0056] Step S205: Let MYFOutput [5] = (the H12WD field of MYFInput - 5 - JZWD) / 5.

[0057] Among them, MYFOutput [5] is the fifth element in the icing susceptibility description output vector MYFOutput.

[0058] Step S206: Let MYFOutput [7] = (the H18WD field of MYFInput - 3 - JZWD) / 5.

[0059] Among them, MYFOutput [7] is the seventh element in the icing susceptibility description output vector MYFOutput.

[0060] Step S207: Let MYFOutput [2] = (the H00SD field of MYFInput - JZSD) / 10.

[0061] Among them, MYFOutput [2] is the second element in the icing susceptibility description output vector MYFOutput.

[0062] Step S208: Let MYFOutput[4] = (H06SD field of MYFInput + 10 - JZSD) / 10;

[0063] Among them, MYFOutput[4] is the 4th element in the icing susceptibility description output vector MYFOutput;

[0064] Step S209: Let MYFOutput[6] = (H12SD field of MYFInput + 15 - JZSD) / 10;

[0065] Among them, MYFOutput[6] is the 6th element in the icing susceptibility description output vector MYFOutput;

[0066] Step S210: Let MYFOutput[8] = (H18SD field of MYFInput + 10 - JZSD) / 10;

[0067] Among them, MYFOutput[8] is the 8th element in the icing susceptibility description output vector MYFOutput;

[0068] Step S211: If the HFS field of MYFInput is less than or equal to JZFS, then MYFOutput[9] = 0;

[0069] If the HFS field of MYFInput is greater than JZFS and the HFS field of MYFInput is less than JZFS + 10, then MYFOutput[9] = 0.5, otherwise MYFOutput[9] = 1;

[0070] Among them, MYFOutput[9] is the 9th element in the icing susceptibility description output vector MYFOutput;

[0071] Step S212: Let MYFOutput

[10] = HJX field of MYFInput - JZJX;

[0072] Among them, MYFOutput

[10] is the 10th element in the icing susceptibility description output vector MYFOutput;

[0073] Step S213: Let , where tanh calculates the hyperbolic tangent value for each element of the vector MYFOutput;

[0074] Step S214: Take the calculated in Step S213 as the result output of the icing susceptibility description acquisition module MYFVector.

[0075] Other steps and parameters are the same as those in the first or second specific implementation manner.

[0076] Specific implementation manner four: The difference between this implementation manner and one of the first to third specific implementation manners is that the specific process of step S3 is as follows:

[0077] Step S301: Construct an advance periodic icing feature calculation module MCCFeature. The input of MCCFeature is a periodic list MCCFeatureInput composed of records for consecutive ZQLength days. The structure of each record in the periodic list MCCFeatureInput is the same as that of the History record structure.

[0078] Step S302: Establish an advance periodic icing feature output MCCFeatureOutput = an empty list;

[0079] Step S303: Advance periodic icing feature single feature counter TZDgCounter = 1;

[0080] Step S304: Advance periodic icing feature first temporary vector TZTempVecot1 = a vector containing 10 elements, and initialize the values of all elements in the vector to 0;

[0081] Step S305: Advance periodic icing feature second temporary vector TZTempVecot2 = calculate using MYFVector, and the input of MYFVector = the TZDgCounter-th record in MCCFeatureInput;

[0082] Step S306: Update TZTempVecot1 = TZTempVecot2 - TZTempVecot1;

[0083] Step S307: Append TZTempVecot2 as a vector to MCCFeatureOutput;

[0084] Step S308: Append TZTempVecot1 as a vector to MCCFeatureOutput;

[0085] Step S309: Let TZTempVecot1 = TZTempVecot2;

[0086] Step S310: Let TZDgCounter = TZDgCounter + 1;

[0087] Step S311: If TZDgCounter is less than ZQLength, go to step S305; otherwise, go to step S312.

[0088] Step S312: Output MCCFeatureOutput as the result of MCCFeature.

[0089] Other steps and parameters are the same as those in any one of the first to third specific embodiments.

[0090] Specific Embodiment 5: The difference between this embodiment and any one of the first to fourth specific embodiments is that the specific process of step S4 is as follows:

[0091] Step S401: Construct an advance periodic icing feature table TZTable, where TZTable contains two fields:

[0092] TZInputVector, the input sequence feature of the advance periodic icing feature table;

[0093] TZDecision, the decision feature of the advance periodic icing feature table;

[0094] Step S402: Set the icing feature table construction counter TZCounter = 1;

[0095] Step S403: Set the first temporary variable TZTableTemp1 of the icing feature table temporary variable = calculate using MCCFeature, and the input of MCCFeature = take out all records from the TZCounter-th to the (TZCounter + ZQLength - 1)-th records of History;

[0096] Step S404: Set the second temporary variable TZTableTemp2 of the icing feature table temporary variable = take out the HFB field from the record at the (TZCounter + ZQLength - 1 + YJLengh)-th position of History;

[0097] Step S405: Create a new row of data for TZTable, where the TZInputVector of this row is equal to TZTableTemp1 and the TZDecision is equal to TZTableTemp2;

[0098] Step S406: Let TZCounter = TZCounter + 1;

[0099] Step S407: If (TZCounter + ZQLength - 1 + YJLengh) is less than the number of data entries in History, go to step S403; otherwise, go to step S408;

[0100] Step S408, SModel = Establish a long short-term memory network (i.e., LSTM, Long Short-Term Memory). The input of SModel is a sequence composed of ZQLength vectors, and each vector in the sequence is 10-dimensional. The output of SModel is a decision output. An output of 1 indicates that the prediction result is that an icing disaster will occur after YJLengh days in the future, and an output of 0 indicates that the prediction result is that an icing disaster will not occur after YJLengh days in the future.

[0101] Step S409, Correlate the input of SModel with the TZInputVector field of TZTable, and correlate the output of SModel with the TZDecision field of TZTable.

[0102] Step S410, Use the data in TZTable to train SModel. The trained SModel is used to predict icing disasters.

[0103] Other steps and parameters are the same as those in any one of the specific embodiments one to four.

[0104] The SModel model in this embodiment can be trained using weather and icing condition data from multiple regions, but the input in each set of training data must be the input for ZQLength consecutive days in the same region.

[0105] Specific Embodiment Six: The difference between this embodiment and any one of the specific embodiments one to five is that the specific process of step S5 is as follows:

[0106] Step S501, Input the weather condition data BTest for the region to be warned of transmission line icing disasters in the most recent ZQLength days.

[0107] Step S502, The decision-making periodic icing feature DJCFeature = Calculate using MCCFeature, and the input of MCCFeature = BTest.

[0108] Step S503, Input DJCFeature into SModel, obtain the decision output of SModel, and store the decision output in the decision variable DecParam.

[0109] Step S504, If DecParam is equal to 1, then go to step S505; otherwise, go to step S506.

[0110] Step S505, Discover a typical special period that causes icing disasters. An icing disaster will occur to the transmission lines in the region to be warned of transmission line icing disasters after YJLengh days, and go to step S507.

[0111] Step S506: No special cycle that typically causes icing disasters is found. The area where transmission line icing disaster warning is to be carried out will not have transmission line icing disasters after YJLengh days, and it proceeds to Step S507;

[0112] Step S507: The whole process ends.

[0113] Other steps and parameters are the same as those in any one of the specific embodiments one to five.

[0114] Embodiment

[0115] To test the effectiveness of the method of the present invention, the weather and icing conditions in a certain mountainous area with high incidence of transmission line icing in the north in 2022 and 2023 are introduced; the method of the present invention and some traditional methods are used to predict the icing conditions 3 days in advance, and the results are compared as shown in Table 1:

[0116] Table 1

[0117] Method The number of times of successful prediction of ice coating occurred 17 times in two years Prediction accuracy (%) Number of false alarms The method of the present invention 16 94.12 1 Method for detecting data exceeding threshold 1 5.88 0 Micro-weather + LSTM deep neural network 8 47.06 27 Micro-weather + Transformer deep neural network 10 58.82 47

[0118] It can be seen from the results that the method of the present invention can give early warnings of icing with a very long lead time, while other methods are those that try to find the direct correlation between weather and icing, and they have lower accuracy and higher false alarm rates in terms of very long lead time warnings.

[0119] The above numerical examples of the present invention are only to illustrate in detail the calculation model and calculation process of the present invention, rather than to limit the embodiments of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the embodiments here. Any obvious changes or modifications derived from the technical solutions of the present invention still fall within the protection scope of the present invention.

Claims

1. A transmission line icing warning method based on weather factors, characterized in that: The method specifically comprises the following steps: Step S1, inputting a list of historical data of weather and icing occurrence in a region History, the number of early warning days YJLengh and the length of the period to be identified ZQLength; According to the historical data list History, the mean temperature JZWD, mean humidity JZSD, mean wind speed JZFS and snowfall correlation JZJX in the statistical area under ice cover conditions; Step S2: Establish an icing susceptibility description acquisition module MYFVector. The input of the module MYFVector is an icing susceptibility description input variable MYFInput with the same structure as each record in the history data list History. The output is an icing susceptibility description output vector ; Step S3, constructing an advance periodic icing feature calculation module MCCFeature, the input of MCCFeature is a periodic list MCCFeatureInput consisting of records of consecutive ZQLength days, and the structure of each record in the periodic list MCCFeatureInput is the same as the record structure of the historical data list History; The module MCCFeature uses MYFVector to process each record in the periodic list MCCFeatureInput separately to obtain the advance periodic icing feature output MCCFeatureOutput; Step S4, constructing an advance periodic icing feature table TZTable using MCCFeature and History, and obtaining an advance periodic identification model SModel based on TZTable; Step S5, input the weather data BTest of the recent ZQLength days in the area to be warned of transmission line icing disaster, and use SModel to warn whether transmission line icing will occur in the area to be warned of transmission line icing disaster in YJLengh days.

2. The method for early warning of ice coating on transmission lines based on weather factors according to claim 1, characterized in that: The specific process of step S1 is as follows: Step S101: input a history data list History of weather and ice occurrence in a region. Each record in the history data list History includes weather conditions and ice conditions in one day; that is, each record includes the following fields: H00WD: temperature value at 0 o'clock on the day; H00SD: humidity value at 0 o'clock on the day; H06WD: temperature value at 6 o'clock on the day; H06SD: humidity value at 6 o'clock on the day; H12WD: temperature value at 12 o'clock on the day; H12SD: humidity value at 12 o'clock on the day; H18WD: temperature value at 18:00 on the day; H18SD: humidity value at 18:00 on the day; HFS: average wind speed value of the day; HJX: whether there is snowfall on that day, 1 means there is snowfall, 0 means there is no snowfall; HFB: Whether the transmission line is covered with ice on that day, 0 means no ice, 1 means ice; The unit of temperature is Celsius, the unit of humidity is relative humidity, and the unit of wind speed is meter per second. Step S102, input the number of advance warning days YJLengh; Step S103, input the period ZQLength to be identified; Step S104, JZWD=fetch all records in the history data list History where HFB is equal to 1, and calculate the mean value of the H00WD field in all the fetched records; Step S105, JZSD=fetch all records in the history data list History where HFB is equal to 1, and calculate the mean value of the H00SD field in all the fetched records; Step S106, JZFS=fetch all records in the history data list History where HFB is equal to 1, and calculate the mean value of the HFS field in all the retrieved records; Step S107, JZJX= fetches all records in the history data list History where HFB is equal to 1, and calculates the average value of the HJX field in all the fetched records.

3. The method for early warning of ice coating on transmission lines based on weather factors according to claim 2 is characterized in that: The specific process of step S2 is: Step S201, establish an icing susceptibility description acquisition module MYFVector, the input of the module MYFVector is an icing susceptibility description input variable MYFInput with the same structure as each record in the history data list History; Step S202, establish an icing susceptibility description output vector MYFOutput = a vector containing 10 elements, and initialize the values ​​of all elements in the icing susceptibility description output vector MYFOutput to be 0; Step S203, let MYFOutput [1] = (H00WD field of MYFInput - JZWD) / 5; Where MYFOutput [1] is the first element in the icing susceptibility description output vector MYFOutput; Step S204, set MYFOutput [3] = (H06WD field of MYFInput - 3 - JZWD) / 5; Where MYFOutput [3] is the third element in the icing susceptibility description output vector MYFOutput; Step S205, let MYFOutput [5] = (H12WD field of MYFInput - 5 - JZWD) / 5; Where MYFOutput [5] is the fifth element in the icing susceptibility description output vector MYFOutput; Step S206, set MYFOutput [7] = (H18WD field of MYFInput - 3 - JZWD) / 5; Among them, MYFOutput [7] is the 7th element in the output vector MYFOutput describing the icing susceptibility; Step S207, let MYFOutput [2] = (H00SD field of MYFInput - JZSD) / 10; Where MYFOutput [2] is the second element in the icing susceptibility description output vector MYFOutput; Step S208, set MYFOutput [4] = (H06SD field of MYFInput + 10 - JZSD) / 10; Where MYFOutput [4] is the fourth element in the icing susceptibility description output vector MYFOutput; Step S209, set MYFOutput [6] = (H12SD field of MYFInput + 15 - JZSD) / 10; Where MYFOutput [6] is the sixth element in the icing susceptibility description output vector MYFOutput; Step S210, let MYFOutput [8] = (H18SD field of MYFInput + 10 - JZSD) / 10; Where MYFOutput [8] is the 8th element in the output vector MYFOutput describing the icing susceptibility; Step S211, if the HFS field of MYFInput is less than or equal to JZFS, then MYFOutput [9] = 0; If the HFS field of MYFInput is greater than JZFS and the HFS field of MYFInput is less than JZFS+10, then MYFOutput[9]=0.5, otherwise MYFOutput[9]=1; Where MYFOutput [9] is the 9th element in the icing susceptibility description output vector MYFOutput; Step S212, let MYFOutput [10] = HJX field of MYFInput - JZJX; Where MYFOutput [10] is the 10th element in the icing susceptibility description output vector MYFOutput; Step S213: , where tanh is the hyperbolic tangent value calculated for each element of the vector MYFOutput; Step S214: The calculated value in step S213 As the result output of the icing susceptibility description acquisition module MYFVector.

4. The method for early warning of ice coating on transmission lines based on weather factors according to claim 3 is characterized in that: The specific process of step S3 is: Step S301, constructing an advance periodic icing feature calculation module MCCFeature, the input of MCCFeature is a periodic list MCCFeatureInput consisting of records of consecutive ZQLength days, and each record structure of the periodic list MCCFeatureInput is the same as the History record structure; Step S302, establish the advance periodic icing feature output MCCFeatureOutput = an empty list; Step S303, advance periodic icing feature single feature counter TZDgCounter=1; Step S304, the first temporary vector of the periodic icing characteristics of the advance amount TZTempVecot1=a vector containing 10 elements, and the values ​​of all elements in the initialization vector are 0; Step S305, the second temporary vector TZTempVecot2 of the periodic icing feature of the advance amount is calculated by using MYFVector, and the input of MYFVector is the TZDgCounterth record in MCCFeatureInput; Step S306, update TZTempVecot1=TZTempVecot2-TZTempVecot1; Step S307, append TZTempVecot2 as a vector to MCCFeatureOutput; Step S308, append TZTempVecot1 as a vector to MCCFeatureOutput; Step S309, set TZTempVecot1=TZTempVecot2; Step S310, set TZDgCounter=TZDgCounter+1; Step S311, if TZDgCounter is less than ZQLength, go to step S305, otherwise go to step S312; Step S312: Output MCCFeatureOutput as the result of MCCFeature.

5. The method for early warning of ice coating on transmission lines based on weather factors according to claim 4 is characterized in that: The specific process of step S4 is as follows: Step S401: construct an advance periodic icing feature table TZTable, which contains two fields: TZInputVector, input sequence characteristics of the advance periodic icing characteristic table; TZDecision, decision characteristics of the advance periodic icing characteristic table; Step S402, ice feature table construction counter TZCounter=1; Step S403, the first temporary variable TZTableTemp1 of the ice feature table temporary variable = calculation is performed using MCCFeature, the input of MCCFeature = all records from the TZCounterth to the TZCounter+ZQLength-1th of History are retrieved; Step S404, the second temporary variable TZTableTemp2 of the ice feature table is stored = taking out the HFB field in the TZCounter+ZQLength-1+YJLenghth record of History; Step S405, create a new row of data for TZTable, the TZInputVector of the row of data is equal to TZTableTemp1, and TZDecision is equal to TZTableTemp2; Step S406, set TZCounter=TZCounter+1; Step S407, if TZCounter+ZQLength-1+YJLengh is less than the number of data entries in History, go to step S403, otherwise go to step S408; Step S408, SModel=establishes a long short-term memory network, the input of SModel is a sequence of ZQLength vectors, each vector of the sequence is 10-dimensional; the output of SModel is a decision output, output 1 indicates that the prediction result is that an ice disaster will occur after YJLengh days in the future, and output 0 indicates that the prediction result is that an ice disaster will not occur after YJLengh days in the future; Step S409, correspond the input of SModel to the TZInputVector field of TZTable, and correspond the output of SModel to the TZDecision field of TZTable; Step S410: Use the data in TZTable to train SModel, and the trained SModel is used to predict icing disasters.

6. The method for early warning of ice coating on transmission lines based on weather factors according to claim 5, characterized in that: The specific process of step S5 is as follows: Step S501, inputting the weather data BTest of the recent ZQLength days in the area to be warned of the icing disaster of the transmission line; Step S502, the periodic ice feature to be decided DJCFeature=calculated using MCCFeature, the input of MCCFeature=BTest; Step S503, input DJCFeature into SModel, obtain the decision output of SModel and store the decision output into the decision variable DecParam; Step S504: If DecParam is equal to 1, go to step S505; otherwise, go to step S506; Step S505: In the area to be warned of the icing disaster of the transmission line, the icing disaster of the transmission line will occur in YJLengh days, and the process goes to step S507; Step S506: If the area to be warned of the icing disaster of the transmission line will not have the icing disaster of the transmission line after YJLengh days, then go to step S507; Step S507: The whole process ends.

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