A power transmission line icing data processing method, device and equipment

Abstract

Embodiments of the present application disclose a power transmission line icing data processing method, device and equipment, comprising: acquiring an icing data sequence, preprocessing the icing data sequence; performing frequency spectrum analysis, determining a screening threshold based on the frequency spectrum analysis result; using the latitude and longitude position of the station to perform data binning on the icing data sequence; acquiring meteorological data of each station, generating a first evaluation result of each station by using a pre-established meteorological multiple regression prediction model, generating a comprehensive evaluation result of each station subsequence according to the first evaluation result; comparing the comprehensive evaluation result of each station subsequence with the screening threshold, determining abnormal data in the multiple subsequences according to the comparison result, and marking. The method can clean the icing thickness observation data based on the actual meteorological conditions without the need for substantial modification of the icing observation instrument, and improves the quality of the icing thickness data after processing.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system data monitoring and analysis, and in particular to a power transmission line icing data processing method, device and equipment. BACKGROUND

[0002] Wire icing is the phenomenon of rime, glaze icing on the conductor or wet snow freezing on the conductor, which usually occurs under three weather conditions of supercooled fog, freezing rain and wet snow. When it is severe, it will cause significant losses to power transmission. Therefore, the power department has developed special instruments to observe the wire icing thickness. The mechanism is to use sensors to obtain the gravity change of the conductor, the inclination angle of the tower insulator, the conductor dancing frequency, and the temperature, humidity, wind speed, wind direction, rainfall and other data information on the line site, and calculate the conductor icing thickness through a mathematical model.

[0003] For the icing monitoring system equipment, due to the interference of ice and snow, dust and other elements, there are abnormal values in the data of the icing monitoring platform. In addition, since the observation area is mostly located in the mountainous area, the detector is insufficient in power supply and the equipment is abnormal, and the maintenance of the monitoring equipment is difficult, and the data quality needs to be further controlled.

[0004] Through analysis and comparison of actual wire icing observation data, it is found that when the meteorological elements do not meet the basic conditions of icing, the icing observation data greater than 0 is often output. In addition, by observing the distribution of the instrument in the multi-dimensional space of meteorological elements, it is found that there is obvious abnormal distribution in some icing thickness. Therefore, the icing thickness data obtained by the existing icing observation instrument and its algorithm has a large deviation from the actual situation. SUMMARY

[0005] Therefore, the present application provides a power transmission line icing data processing method to solve the problem of inaccurate existing icing thickness data and large deviation from the actual situation. In order to achieve one or part or all of the above purposes or other purposes, the present application provides a power transmission line icing data processing method, comprising:

[0006] Obtaining the icing data sequence collected by the icing monitoring device, and preprocessing the icing data sequence;

[0007] Performing frequency spectrum analysis on the preprocessed icing data sequence, and determining a screening threshold based on the frequency spectrum analysis result;

[0008] binning the icing data sequence according to the latitude and longitude position of the stations to obtain a plurality of station subsequences; obtaining meteorological data of each station, generating predicted icing data of each station by using a pre-established meteorological multiple regression prediction model, generating a first evaluation result of the station subsequence based on the predicted icing data, and generating a comprehensive evaluation result of each station subsequence according to the first evaluation result;

[0009] comparing the comprehensive evaluation result of each station subsequence with the screening threshold, determining abnormal data in the plurality of station subsequences according to a comparison result, and marking the abnormal data.

[0010] According to a specific embodiment, the meteorological multiple regression prediction model in the power line icing data processing method is pre-established by the following steps:

[0011] obtaining historical icing data and corresponding meteorological data, performing standardization processing on the meteorological data, and then performing multiple fitting according to the standardization-processed meteorological data and the icing data to obtain the meteorological multiple regression prediction model;

[0012] The meteorological data includes temperature, relative humidity, wind speed, and altitude.

[0013] According to a specific embodiment, the multiple linear regression model in the power line icing data processing method is:

[0014] Pred = a0 + a1TMP + a2RH + a4WS + a3DEM

[0015] wherein Pred is a regression model prediction value, Pred0 is a current station prediction value, a0, a1, a2, a3, and a4 are model constants obtained by fitting; TMP is temperature, RH is relative humidity, WS is maximum wind speed, and DEM is altitude.

[0016] According to a specific embodiment, the comprehensive evaluation result of each station subsequence according to the first evaluation result in the power line icing data processing method includes:

[0017] performing multiple clustering analysis on the icing thickness in the meteorological element multidimensional space by using a clustering algorithm, and generating a second evaluation result of each station subsequence according to a clustering result;

[0018] generating a third evaluation result of the corresponding station subsequence based on icing data within a preset distance range of the station;

[0019] generating a comprehensive evaluation result of the station subsequence based on the first evaluation result, the second evaluation result, and the third evaluation result.

[0020] According to a specific embodiment, in the power transmission line icing data processing method, the clustering algorithm is a K-means algorithm, and the preset distance is 30 km.

[0021] According to a specific embodiment, in the power transmission line icing data processing method, the generating a comprehensive evaluation result of the site sub-sequence based on the first evaluation result, the second evaluation result, and the third evaluation result comprises:

[0022] calculating a weighted average of the first evaluation result, the second evaluation result, and the third evaluation result, and taking the weighted average as the comprehensive evaluation result.

[0023] According to a specific embodiment, in the power transmission line icing data processing method, the preprocessing the icing data sequence comprises:

[0024] filtering the icing data sequence based on a preset icing data range, and removing data points outside the preset icing data range;

[0025] and filtering the icing data sequence based on a preset meteorological condition, and removing data points that do not meet the preset meteorological condition.

[0026] According to a specific embodiment, in the power transmission line icing data processing method, the determining a screening threshold based on the frequency spectrum analysis result comprises:

[0027] finding an abnormally high frequency data segment in the sequence based on the frequency spectrum analysis result, setting a first screening threshold based on the abnormally high frequency data segment, and setting a second screening threshold based on a normal data segment;

[0028] the comparing the comprehensive evaluation result of each site sub-sequence with the screening threshold comprises:

[0029] determining whether there is an abnormally high frequency data segment in the site sub-sequence, if yes, comparing the first screening threshold with the abnormally high frequency data segment, and comparing the second threshold with other data segments, and if not, comparing the second screening threshold with the site sub-sequence.

[0030] In another aspect of the present application, a power transmission line icing data processing device is provided, comprising:

[0031] an acquisition unit configured to acquire an icing data sequence collected by an icing monitoring device, and preprocess the icing data sequence;

[0032] a frequency spectrum analysis unit configured to perform frequency spectrum analysis on the preprocessed icing data sequence, and determine a screening threshold based on the frequency spectrum analysis result;

[0033] The computing unit is used for adopting the latitude and longitude position of the station to carry out data binning on the ice data sequence to obtain a plurality of station subsequences; meteorological data of each station is acquired, a pre-established meteorological multiple regression prediction model is used to generate predicted ice data of each station, a first evaluation result of the station subsequence is generated based on the predicted ice data, and a comprehensive evaluation result of each station subsequence is generated according to the first evaluation result;

[0034] The data cleaning marking unit is used for comparing the comprehensive evaluation result of each station subsequence with the screening threshold, determining abnormal data in the plurality of subsequences according to the comparison result, and marking.

[0035] In another aspect of the present application, an electronic device is provided, comprising at least one processor, and a memory connected in communication with the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the power line icing data processing method.

[0036] The implementation of the embodiment of the present application will have the following beneficial effects:

[0037] The power line icing data processing method provided by the embodiment of the present application, by acquiring the ice data sequence collected by the ice monitoring device, pre-processing and spectrum analysis are performed on the ice data sequence, a screening threshold is determined, and based on the meteorological data of each station, a pre-established meteorological multiple regression prediction model is used to evaluate the meteorological data of each station, abnormal data is determined according to the evaluation result and the screening threshold, and is marked and cleaned; the method can clean the ice thickness observation data based on the actual meteorological conditions without the need of greatly modifying the ice observation instrument, improves the quality of the ice thickness data after processing, and thus provides strong guarantee for power transmission safety through accurate data monitoring technology. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0039] Among them:

[0040] Figure 1 It is a flowchart of the power line icing data processing method in an embodiment;

[0041] Figure 2Fig. 1 is a schematic diagram of rough screening based on preset icing data range in one embodiment of the present application;

[0042] Figure 3 Fig. 2 is a schematic diagram of relative humidity and icing thickness in one embodiment of the present application;

[0043] Figure 4 Fig. 3 is a schematic diagram of minimum temperature and icing thickness in one embodiment of the present application;

[0044] Figure 5 Fig. 4 is a schematic diagram of maximum wind speed and icing thickness in one embodiment of the present application;

[0045] Figure 6 Fig. 5 is a schematic diagram of icing thickness spectrum distribution in one embodiment of the present application;

[0046] Figure 7 Fig. 6 is a schematic diagram of electronic device structure in one embodiment of the present application. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0048] Embodiment 1

[0049] Figure 1 The power line icing data processing method of the exemplary embodiment of the present application is shown, comprising:

[0050] Obtaining the icing data sequence collected by the icing monitoring device, and preprocessing the icing data sequence;

[0051] Performing spectrum analysis on the preprocessed icing data sequence, and determining a screening threshold based on the spectrum analysis result;

[0052] Performing data binning on the icing data sequence by using the latitude and longitude position of the station to obtain a plurality of station subsequences, obtaining the meteorological data of each station, generating the predicted icing data of each station by using a pre-established meteorological multiple regression prediction model, generating the first evaluation result of the station subsequence based on the predicted icing data, and generating the comprehensive evaluation result of each station subsequence according to the first evaluation result;

[0053] Comparing the comprehensive evaluation result of each station subsequence with the screening threshold, determining the abnormal data in the plurality of station subsequences according to the comparison result, and marking the abnormal data.

[0054] In the embodiment, by acquiring the ice-coating data sequence collected by the ice-coating monitoring device, the ice-coating data sequence is preprocessed and spectrum analyzed, the screening threshold is determined, and based on the meteorological data of each site, the meteorological multiple regression prediction model established in advance is used to evaluate the meteorological data of each site, and according to the evaluation result and the screening threshold, the abnormal data is determined and marked and cleaned; the method can clean the ice-coating thickness observation data based on the actual meteorological conditions without the need to greatly modify the ice-coating observation instrument, and improve the quality of the ice-coating thickness data after processing.

[0055] Embodiment 2

[0056] In a possible implementation, in the power line ice-coating data processing method, the preprocessing of the ice-coating data sequence includes:

[0057] The ice-coating data sequence is screened based on a preset ice-coating data range, and data points outside the preset ice-coating data range are removed.

[0058] Specifically, the ice-coating data range is determined by the following method: based on the actual monitoring system sensitivity, error (power line dancing) and actual experience, 0.5 mm is set as the lower limit of the effective ice-coating thickness, so that the data less than 0.5 mm and greater than 0 in the instrument observation data is processed as 0. The upper limit threshold of the ice-coating thickness is set: according to the artificial observation ice-coating thickness data and long-term experience, the upper limit of the ice-coating thickness is set as 50 mm, and all data exceeding the upper limit in the instrument observation data is removed based on this. Accordingly, as shown in Figure 2 The points of the ice-coating data sequence outside the preset data range are removed.

[0059] In the embodiment, by the sensitivity, error (power line dancing) and a large number of observation experience of the ice-coating monitoring device, the ice-coating data range is clearly demarcated, and the ice-coating data is effectively preliminarily screened based on the demarcated ice-coating data range.

[0060] In a possible implementation, in the power line ice-coating data processing method, the preprocessing of the ice-coating data sequence further includes:

[0061] The ice-coating data sequence is filtered based on a preset meteorological condition, and data points that do not meet the preset meteorological condition are filtered out.

[0062] Specifically, the distribution of the ice-coating thickness in the corresponding relative humidity, minimum temperature, wind speed and precipitation dimensions is analyzed, and the ice-coating data range is determined based on the analysis result. Figures 3-5The relative relationship diagram is shown, so that the icing weather condition is obtained from the figure: the minimum temperature is less than 5℃, the minimum relative humidity is greater than 40%, the maximum wind speed is less than 10m / s, and the observed icing thickness data that obviously does not meet the icing requirement is eliminated.

[0063] It can be understood that, and in some meteorological environment, it is not enough to cause the wire to freeze, and the weather in different meteorological environments, the corresponding wire icing degree will exist obvious difference. Therefore, in the embodiment, by analyzing a large amount of icing data and meteorological data, the corresponding meteorological range is drawn to effectively coarsely screen the icing data.

[0064] In a possible implementation, in the power transmission line icing data processing method, the meteorological multiple regression prediction model is established in advance by the following steps:

[0065] The historical icing data and the corresponding meteorological data are obtained, the meteorological data is standardized, and then the meteorological data and the icing data after the standardization are fitted to obtain the meteorological multiple regression prediction model;

[0066] The meteorological data includes temperature, relative humidity, wind speed, and altitude.

[0067] Specifically, the temperature factor, the altitude and other factors (Table 1) are standardized to the historical icing thickness data, and then a multiple linear regression model (Formula 2) is constructed. After modeling, the current time meteorological factor and altitude of each station are put into the model to obtain the predicted icing thickness, and the difference percentage between the actual observation and the predicted value (Formula 3) is calculated.

[0068] Table 1 Multiple linear regression factor table

[0069] Sequence Factor 1 Air temperature 2 Relative humidity 3 Wind speed 4 0-Elevation

[0070] Pred=a0+a1TMP+a2RH++a4WS+a3DEM (2)

[0071]

[0072] Wherein Pred is the regression model prediction value, Pred0 is the current station prediction value, a0, a1, a2, a3, a4 are model constants. TMP is temperature, RH is relative humidity, WS is maximum wind speed, DEM is altitude, and the factors are standardized data. Dist2 is the difference percentage between the actual observation and the predicted value (i.e. the first evaluation result).

[0073] In this embodiment, the historical icing data is subjected to multivariate regression fitting with respect to meteorological factors to obtain a corresponding meteorological multivariate regression prediction model, and the icing prediction value is generated according to the actual meteorological data by using the meteorological multivariate regression prediction model, so as to effectively evaluate the actual icing observation value by using the icing prediction value, and provide a basis for subsequent data cleaning.

[0074] In a possible implementation, in the power line icing data processing method, the generating of the comprehensive evaluation result of each station subsequence according to the first evaluation result comprises:

[0075] The clustering algorithm is used to perform multivariate clustering analysis on the icing thickness in the meteorological element multidimensional space, and the second evaluation result of each station subsequence is generated according to the clustering result;

[0076] The third evaluation result of the corresponding station subsequence is generated based on the icing data within a preset distance range of the station;

[0077] The comprehensive evaluation result of the station subsequence is generated based on the first evaluation result, the second evaluation result and the third evaluation result.

[0078] Specifically, the multivariate clustering analysis on the icing thickness in the meteorological element multidimensional space by using the clustering algorithm comprises:

[0079] The multivariate clustering analysis is performed on the icing thickness in the meteorological element multidimensional space, and the multivariate factors are shown in Table 2:

[0080] Table 2 K-means clustering analysis factor table

[0081] Sequence Factor 1 Air temperature 2 Relative humidity 3 Wind speed 4 Elevation 5 Longitude 6 Latitude

[0082] In this embodiment, the clustering algorithm uses the K-means technology, and the elbow rule is used to determine the number of clustering categories. The outlying distance of each data from its own category is calculated, and the relative outlying distance (formula 4) is calculated.

[0083]

[0084] Wherein abs_dist is the absolute distance output in the K-means technology, Medain(abs_dist) is the absolute distance median value of the point belonging to the group, and Dist3 is the outlying relative distance of the outlying point, that is, the second evaluation result.

[0085] The third evaluation result of the corresponding station subsequence is generated based on the icing data within a preset distance range of the station;

[0086] The difference percentage of the current station and the average value of the nearby stations within 30 km is extracted. The calculation is shown in formula (1)

[0087]

[0088] Where DP0 is the ice thickness record of the observation station, ME30 is the average ice thickness record of all stations within a 30km radius, and Dist1 represents the percentage difference between the observation station and the average value of stations within a 30km radius, thus obtaining the third evaluation result.

[0089] Finally, the percentage differences obtained from the three methods (first evaluation result, second evaluation result, and third evaluation result) are standardized and averaged to obtain the comprehensive difference Distint, which is then sorted according to its numerical value.

[0090] Distint= (Dist1+Dist2+Dist3) / 3 (5)

[0091] Distint is the ensemble distance, which is the overall evaluation result.

[0092] In this embodiment, three noise filtering algorithms are integrated for quality control, avoiding the shortcomings of a single method; ultimately, a complete icing data quality control system is formed, which is of practical significance for the study of icing patterns and the prediction of icing, and ultimately for the formulation and assessment of power disaster prevention and mitigation measures.

[0093] In one possible implementation, the above-mentioned spectral analysis of the preprocessed icing data sequence, and the determination of a screening threshold based on the spectral analysis results, includes:

[0094] Icing data spectral analysis and classification: The spectral distribution of protective gear with icing thickness was analyzed and classified into two categories: abnormal high-frequency data and normal frequency data. For example... Figure 6 As shown, (currently, the example icing data shows an abnormally high frequency around 10, 8, and 6 mm, especially around 10 mm), the filtering ratio for the abnormally high frequency data range (taking the example data as an example, i.e., the icing data in the range of (9.8 10], (7.88], and (5.8 6]) is set to a high filtering ratio, while the filtering ratio for the normal frequency data is set to a low filtering ratio. The filtering ratio conforms to the nine-point smoothing curve of the frequency counting curve with a 0.2 numerical interval. The formula for calculating the high-frequency data filtering ratio is as follows.

[0095] Rtio=(Bin-smth9) / Bin

[0096] Where Rtio is the ratio of abnormal high-frequency data to be filtered, Bin is the historical statistical count of the abnormal high-frequency data range, and smth9 is the value in this range after smoothing the historical statistical histogram count curve at 9 points.

[0097] Normal frequency data were filtered using an empirical ratio of 0.003.

[0098] It can be understood that the frequency distribution of the ice thickness theory should also generally satisfy a distribution similar to an e exponential base function; therefore, in the embodiment, by drawing a histogram of the ice thickness, if the frequency of occurrence in certain value segments is much higher than that of the surrounding value segments, and also greatly deviates from the overall distribution curve, it means that a large amount of data is unreasonably in the value range of this value, which may be caused by instrument or other problems, and the value segment belongs to an abnormally high frequency data segment, and thus in the data screening process, different screening ratios are formulated for the part of the abnormally high frequency value range in the ice data, and the false data in the abnormally high frequency value range is greatly reduced.

[0099] Further, in the power transmission line icing data processing method, the comprehensive evaluation result of each station sub-sequence is compared with the screening threshold, and the abnormal data in the multiple sub-sequences is determined according to the comparison result, and specifically comprising:

[0100] The specific standard is according to the setting of the previous two screening thresholds. Rtio is the abnormal high frequency data screening ratio, and the normal frequency data is screened according to the experience value 0.003 ratio. The data reaching the screening ratio is marked as error, the data less than the screening ratio and the same number of data are marked as suspicious, and the other data are marked as normal; according to this, the abnormal data is screened.

[0101] In another aspect of the application, a power transmission line icing data processing device is provided, comprising:

[0102] An acquisition unit is configured to acquire an icing data sequence collected by an icing monitoring device, and pre-process the icing data sequence.

[0103] A spectrum analysis unit is configured to perform spectrum analysis on the pre-processed icing data sequence, and determine a screening threshold based on the spectrum analysis result.

[0104] A calculation unit is configured to perform data binning on the icing data sequence by using the latitude and longitude positions of stations, to obtain multiple station sub-sequences; acquire meteorological data of each station, generate predicted icing data of each station by using a pre-established meteorological multiple regression prediction model, generate a first evaluation result of the station sub-sequence based on the predicted icing data, and generate a comprehensive evaluation result of each station sub-sequence according to the first evaluation result.

[0105] A data cleaning marking unit is configured to compare the comprehensive evaluation result of each station sub-sequence with the screening threshold, determine abnormal data in the multiple sub-sequences according to the comparison result, and mark the abnormal data.

[0106] The icing data processing device provided by the embodiment can clean icing thickness observation data based on actual meteorological conditions without the need of greatly modifying the icing observation instrument, and improve the quality of the icing thickness data after processing.

[0107] Another aspect of the present application, as Figure 7 Also provided is an electronic device including a processor, a network interface and a memory, which are connected to each other, wherein the memory is configured to store a computer program including program instructions, and the processor is configured to invoke the program instructions to execute the power transmission line icing data processing method.

[0108] Another aspect of the present application also provides a computer storage medium having program instructions stored therein, wherein the program instructions, when executed by at least one processor, are configured to implement the power transmission line icing data processing method.

[0109] In the embodiments of the present application, the processor can be an integrated circuit chip having a processing capability of signals. The processor can be a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0110] The disclosed methods, steps and logic block diagrams in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as a hardware code processor to execute, or be executed by a combination of hardware and software modules in the code processor. The software module can be located in a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register or other mature storage medium in the art. The processor reads the information in the storage medium and combines the hardware to complete the steps of the above method.

[0111] The storage medium can be a memory, for example, can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories.

[0112] The non-volatile memory can be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically EPROM (EEPROM), or a flash memory.

[0113] The volatile memory can be a Random Access Memory (RAM) used as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM).

[0114] The storage media described in the embodiments of the present application are intended to include, but are not limited to these and any other suitable types of memory.

[0115] It should be understood that the system disclosed in the present application can be implemented in other manners. For example, the division of the modules is merely logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the communication connection between the modules can be indirect connection or communication connection through some interfaces, servers or units, and can be electrical or other forms.

[0116] In addition, each function module in each embodiment of the present application can be integrated in a processing unit, or each module can exist alone physically, or two or more modules can be integrated in a processing unit. The integrated unit can be implemented in a form of hardware, or in a form of software functional unit.

[0117] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in various embodiments of the present application. The aforementioned storage medium includes: a U disk, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0118] The above disclosure is only the preferred embodiment of the present application, and of course cannot limit the scope of the right of the present application, so the equivalent changes made according to the claims of the present application still fall within the scope of the present application.

Claims

1. A method for processing icing data on transmission lines, characterized in that: include: Acquire the icing data sequence collected by the icing monitoring device, and preprocess the icing data sequence; Spectral analysis was performed on the preprocessed icing data sequence, and the screening threshold was determined based on the spectral analysis results; The icing data sequence is binned according to the latitude and longitude of the stations to obtain multiple station subsequences; meteorological data of each station is acquired, and predicted icing data of each station is generated using a pre-established meteorological multivariate regression prediction model; a first evaluation result of the station subsequence is generated based on the predicted icing data; and a comprehensive evaluation result of each station subsequence is generated based on the first evaluation result. The comprehensive evaluation result of each of the subsequences of the stations is compared with the screening threshold. Based on the comparison result, abnormal data in multiple subsequences are identified and marked. in, The meteorological multiple regression prediction model is pre-established through the following steps: Historical icing data and its corresponding meteorological data are acquired, the meteorological data is standardized, and then a multivariate fitting is performed between the standardized meteorological data and the icing data to obtain the meteorological multivariate regression prediction model. The meteorological data includes: temperature, relative humidity, wind speed, and altitude; The multiple linear regression model is as follows: Where Pred is the predicted value of the regression model, Pred0 is the predicted value of the current site, a0, a1, a2, a3, and a4 are the model constants obtained from the fitting; TMP is temperature, RH is relative humidity, WS is maximum wind speed, and DEM is altitude. The step of generating a comprehensive evaluation result for each of the site sub-sequences based on the first evaluation result includes: A clustering algorithm was used to perform multivariate clustering analysis on the ice thickness in the multidimensional space of meteorological elements, and a second evaluation result for each subsequence of the station was generated based on the clustering results. The third evaluation result of the corresponding station subsequence is generated based on the icing data within the preset distance range of the station; A comprehensive evaluation result for the site subsequence is generated based on the first evaluation result, the second evaluation result, and the third evaluation result; The comprehensive evaluation result generated based on the first evaluation result, the second evaluation result, and the third evaluation result for the site subsequence includes: Calculate the weighted average of the first evaluation result, the second evaluation result, and the third evaluation result, and use the weighted average as the comprehensive evaluation result.

2. The method for processing transmission line icing data as described in claim 1, characterized in that: The clustering algorithm is as follows: K- means The algorithm specifies a preset distance of 30km.

3. The method for processing transmission line icing data as described in claim 1, characterized in that: Preprocessing the icing data sequence includes: The icing data sequence is filtered based on a preset icing data range, and data points outside the preset icing data range are removed. Furthermore, the icing data sequence is filtered based on preset meteorological conditions to remove data points that do not meet the preset meteorological conditions.

4. The method for processing transmission line icing data as described in any one of claims 1-3, characterized in that: The process of determining the screening threshold based on spectral analysis results includes: Based on the spectral analysis results, abnormal high-frequency data segments are found in the sequence. A first screening threshold is set based on the abnormal high-frequency data segments, and a second screening threshold is set based on the normal data segments. The step of comparing the comprehensive evaluation result of each of the site sub-sequences with the screening threshold includes: Determine whether there are abnormally high-frequency data segments in the subsequence of the station. If so, compare the abnormally high-frequency data segments with the first screening threshold and compare them with other data segments based on the second threshold. If not, compare the subsequence of the station with the second screening threshold.

5. A data processing device for icing on transmission lines, characterized in that: include: The acquisition unit is used to acquire the icing data sequence collected by the icing monitoring device and to preprocess the icing data sequence. The spectrum analysis unit is used to perform spectrum analysis on the preprocessed icing data sequence and determine the screening threshold based on the spectrum analysis results. The calculation unit is used to bin the data in the icing data sequence according to the latitude and longitude of the station to obtain multiple station subsequences; acquire meteorological data of each station; generate predicted icing data for each station using a pre-established meteorological multiple regression prediction model; generate a first evaluation result of the station subsequence based on the predicted icing data; and generate a comprehensive evaluation result for each station subsequence based on the first evaluation result. A data cleaning and labeling unit is used to compare the comprehensive evaluation result of each of the subsequences of the site with the screening threshold, identify abnormal data in multiple subsequences based on the comparison result, and label them. in, The meteorological multiple regression prediction model is pre-established through the following steps: Historical icing data and its corresponding meteorological data are acquired, the meteorological data is standardized, and then a multivariate fitting is performed between the standardized meteorological data and the icing data to obtain the meteorological multivariate regression prediction model. The meteorological data includes: temperature, relative humidity, wind speed, and altitude; The multiple linear regression model is as follows: Where Pred is the predicted value of the regression model, Pred0 is the predicted value of the current site, a0, a1, a2, a3, and a4 are the model constants obtained from the fitting; TMP is temperature, RH is relative humidity, WS is maximum wind speed, and DEM is altitude. The step of generating a comprehensive evaluation result for each of the site sub-sequences based on the first evaluation result includes: A clustering algorithm was used to perform multivariate clustering analysis on the ice thickness in the multidimensional space of meteorological elements, and a second evaluation result for each subsequence of the station was generated based on the clustering results. The third evaluation result of the corresponding station subsequence is generated based on the icing data within the preset distance range of the station; A comprehensive evaluation result for the site subsequence is generated based on the first evaluation result, the second evaluation result, and the third evaluation result; The comprehensive evaluation result generated based on the first evaluation result, the second evaluation result, and the third evaluation result for the site subsequence includes: Calculate the weighted average of the first evaluation result, the second evaluation result, and the third evaluation result, and use the weighted average as the comprehensive evaluation result.

6. An electronic device, characterized in that: The method includes at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to perform the transmission line icing data processing method according to any one of claims 1 to 4.

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