Early warning analysis system based on icing disaster mechanism

Abstract

The invention relates to the technical field of power transmission line safety monitoring, and discloses an early warning analysis system based on an icing disaster mechanism, which comprises a data acquisition module, a data transmission module, a data processing center, an early warning release module, a user management module and a data visualization module, the data acquisition module is used for acquiring related data of the power transmission line, and the data acquisition module comprises meteorological data acquisition units which are arranged at meteorological monitoring stations along the power transmission line and around the power transmission line, and the meteorological data acquisition units are connected with the data acquisition module through electric signals. According to the early warning analysis system based on the icing disaster mechanism, data acquisition of local meteorological conditions, geographical environments and characteristics of power transmission lines in a line coverage area is added, so that an icing prediction model for the lines in the area is established, and the system is more accurate in prediction of icing thickness, formation time and development trend, and has a good early warning effect. And the situations of false alarm and missing alarm are greatly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of transmission line safety monitoring, and particularly to an early warning analysis system based on the mechanism of icing disasters. Background Art

[0002] In today's power transmission network, transmission lines, as the key infrastructure for power transmission, play a crucial role in the normal operation of society. However, the icing phenomenon of transmission lines poses a serious threat to it. Especially in northern regions such as Shanxi Province, due to its specific geographical and climatic conditions, the icing problem is particularly prominent. With the impact of global climate change, the frequency and intensity of extreme weather events have both increased. In winter or cold weather conditions, weather phenomena such as glaze ice, rime, and wet snow frequently occur. When these forms of precipitation encounter a low-temperature environment, they are extremely likely to freeze on the surface of transmission lines, forming ice. The emergence of the icing phenomenon not only increases the weight of the conductor but also changes the mechanical and electrical properties of the conductor, thus triggering a series of serious problems. However, there are some defects, such as:

[0003] Traditional monitoring methods often can only simply measure basic parameters such as the thickness of ice, and cannot comprehensively consider factors such as local weather conditions and the mechanism of ice formation, resulting in insufficient accuracy and timeliness of early warnings, and it is difficult to effectively improve the ability of power grid lines to defend against icing disasters and the operation and maintenance capabilities of the power grid. Moreover, existing early warning systems often do not fully combine factors such as the local geographical environment, meteorological characteristics, and specific structural parameters of transmission lines, making the early warning information inaccurate and difficult to meet the actual needs of power grid operation and maintenance personnel for early prevention and effective response to icing disasters.

[0004] In view of the above problems, there is an urgent need to innovate and design on the basis of the original line icing early warning analysis system. Summary of the Invention

[0005] The purpose of the present invention is to provide an early warning analysis system based on the mechanism of icing disasters to solve the problems raised in the above background art that existing early warning systems can only simply measure basic parameters such as the thickness of ice, and often do not fully combine factors such as the local geographical environment, meteorological characteristics, and specific structural parameters of transmission lines, making the early warning information inaccurate and difficult to meet the actual needs of power grid operation and maintenance personnel for early prevention and effective response to icing disasters.

[0006] To achieve the above purpose, the present invention provides the following technical solution: An early warning analysis system based on the mechanism of icing disasters, including the following modules:

[0007] Data acquisition module, data transmission module, data processing center, early warning release module, user management module, and data visualization module;

[0008] The data acquisition module is used to acquire data related to the transmission line. The data acquisition module includes:

[0009] A meteorological data acquisition unit: It is set at meteorological monitoring stations along and around the transmission line. The meteorological data acquisition unit is electrically connected to the data acquisition module;

[0010] A line status data acquisition unit: It is installed on the transmission line, and the data of the line status data acquisition unit is acquired by the data acquisition module;

[0011] A wireless communication module unit is set in the data transmission module. The data transmission module is connected to the data acquisition module, and the data collected by the data acquisition module is transmitted to the data processing center through the wireless communication method of the wireless communication module unit. The data transmission module uses an encrypted transmission protocol to ensure the security and integrity of the data;

[0012] The data processing center is used to process and analyze the received data. The data processing center includes:

[0013] A data storage unit, which is used to store the historical data collected by the data acquisition module and the result data after analysis and processing. The data storage unit uses a large-capacity storage device and has data backup and recovery functions;

[0014] An icing disaster mechanism analysis unit, based on the geographical environment information and line structure parameters of the transmission line. The geographical environment information includes terrain and altitude, and the line structure parameters include conductor type, tower height and spacing;

[0015] An early warning model construction unit, which uses machine learning algorithms to construct an icing thickness early warning model according to the analysis results of the icing disaster mechanism analysis unit and the real-time collected data. The machine learning algorithms include but are not limited to neural networks, support vector machines and decision tree algorithms. The early warning model is used to predict the icing thickness and early warning level of the transmission line. The early warning levels are divided into light icing, moderate icing, heavy icing and emergency icing;

[0016] The user management module has functions of user identity authentication and permission verification, and manages the data acquisition module, data transmission module, data processing center, early warning release module and data visualization module;

[0017] The data visualization module is used to visually display the collected data, analysis results and early warning information in the form of charts, maps or 3D models. The data visualization module of the data processing center is connected to the data processing center.

[0018] Adopting the above technical solution, by comprehensively considering geographical environment information, line parameters and natural factors, the early warning information of the occurrence of icing disasters is comprehensively considered and analyzed.

[0019] Preferably, the meteorological sensor unit of the meteorological data acquisition unit is provided with a temperature sensor, a humidity sensor, a wind speed sensor, a wind direction sensor, a precipitation sensor and an atmospheric pressure sensor.

[0020] Adopting the above technical solution, accurate data monitoring is provided for the meteorological factors of the cables within the region.

[0021] Preferably, the measurement accuracy of the temperature sensor reaches ±0.5°C, the measurement accuracy of the humidity sensor reaches ±3%RH, the measurement accuracy of the wind speed sensor reaches ±0.3m / s, the measurement accuracy of the wind direction sensor reaches ±5°, the measurement resolution of the precipitation sensor reaches 0.1mm, and the measurement accuracy of the atmospheric pressure sensor reaches ±0.1hPa.

[0022] Adopting the above technical solution, the subtle changes in meteorological conditions can be accurately captured, and these subtle changes are often the key factors for ice formation.

[0023] Preferably, the line state data acquisition unit includes an icing sensor, a tension sensor, a tower inclination sensor and an insulator leakage current sensor, which are respectively used to collect data on the ice thickness of the conductor, the tension of the conductor, the inclination angle of the tower and the leakage current of the insulator.

[0024] Adopting the above technical solution, through the setting of the instrument, the actual icing condition and the stress condition of the transmission line can be accurately reflected in real time.

[0025] Preferably, the icing sensor adopts an optical measurement principle or a gravity measurement principle, and its measurement accuracy reaches ±1mm. The tension sensor adopts a strain gauge sensor, and the measurement accuracy reaches ±0.5% of the full scale. The tower inclination sensor adopts an electronic gyroscope or an inclinometer, and the measurement accuracy reaches ±0.1°. The insulator leakage current sensor adopts a through-core current transformer, and the measurement accuracy reaches ±1mA.

[0026] Adopting the above technical solution, accurate data monitoring is provided for the icing condition of the line.

[0027] Preferably, the data transmission module is provided with a 4G / 5G network and a dedicated wireless transmission frequency band by the wireless communication module unit, and has an automatic retransmission and data verification function to ensure the stability and accuracy of data transmission.

[0028] Adopting the above technical solution, it is convenient to stably transmit the collected real-time data.

[0029] Preferably, when analyzing the mechanism of icing disasters, the icing disaster mechanism analysis unit adds the analysis of natural factors such as solar radiation, terrain shelter, and atmospheric circulation on the formation of icing, and incorporates the natural factors into the mathematical model of the early warning model construction unit.

[0030] Adopting the above technical solution, adding natural factors such as solar radiation, terrain shelter, and atmospheric circulation for analysis makes the early warning model more in line with the actual situation.

[0031] Preferably, when constructing the early warning model, the early warning model construction unit uses cross-validation and grid search techniques to optimize the parameters of the machine learning algorithm, so as to improve the accuracy and generalization ability of the early warning model.

[0032] Adopting the above technical solution, using cross-validation and grid search techniques to optimize the parameters of the machine learning algorithm further improves the model performance.

[0033] Preferably, when releasing early warning information, the early warning release module sends the early warning information to the communication terminals of relevant personnel according to the responsibilities and authorities of grid operation and maintenance personnel, and records and tracks the sending status of the early warning information to ensure the effective delivery of the early warning information.

[0034] Adopting the above technical solution enables operation and maintenance personnel at different levels to quickly respond to the early warning and take corresponding measures according to the early warning level.

[0035] Preferably, the user management module is used to manage the account information, permission settings, and operation logs of grid operation and maintenance personnel.

[0036] Adopting the above technical solution, through the user identity authentication and permission verification functions, strictly controls the access permissions of different operation and maintenance personnel to each module of the system, and effectively prevents unauthorized operations and data leakage.

[0037] Compared with the prior art, the beneficial effects of the present invention are: The early warning analysis system based on the icing disaster mechanism:

[0038] 1. By increasing the data collection of local meteorological conditions, geographical environment, and the characteristics of transmission lines themselves within the line coverage area, an icing prediction model for the lines in this area is established. Compared with the traditional early warning methods that only rely on single parameters or simple empirical formulas, the prediction of icing thickness, formation time, and development trend by this system is more accurate, greatly reducing the situations of false alarms and missed alarms, and providing reliable decision-making basis for grid operation and maintenance personnel;

[0039] Precisely determine the icing areas that need to be focused on and given priority for treatment, rationally allocate operation and maintenance resources, improve operation and maintenance efficiency, while reducing equipment damage and replacement costs caused by icing disasters, extend the service life of transmission lines, reduce the overall operation and maintenance costs of the power grid, and enhance economic benefits;

[0040] 2. With the accurate early warning information from the icing prediction model, grid operation and maintenance personnel can formulate targeted maintenance plans and response strategies in advance, and take effective anti-icing and de-icing measures in a timely manner. Measures such as arranging manual de-icing in advance, starting the ice melting device, or adjusting the grid operation mode are provided in the plan. Among them, the data visualization module displays data and early warning information in the form of intuitive charts, maps, or 3D models, enabling grid operation and maintenance personnel to quickly and clearly understand the icing situation and potential risks of transmission lines. The visualization display method helps operation and maintenance personnel more intuitively grasp the line conditions, so as to make more accurate operation and maintenance decisions, helps avoid faults such as conductor breakage, tower collapse, and insulator flashover caused by icing, reduce the number of transmission line trips, ensure the safe and stable operation of the power grid, reduce the occurrence frequency and duration of power outages, improve the reliability of power supply, and meet the continuous demand for electricity in social production and life. Brief Description of the Drawings

[0041] Figure 1 It is a schematic flow structure diagram of the early warning analysis system of the present invention;

[0042] Figure 2 It is a schematic structure diagram of the early warning analysis system of the present invention. Detailed Embodiments

[0043] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0044] Please refer to Figure 1 - Figure 2 , the present invention provides a technical solution: an early warning analysis system based on the mechanism of icing disasters, including the following modules:

[0045] A data acquisition module, a data transmission module, a data processing center, an early warning release module, a user management module, and a data visualization module;

[0046] The data acquisition module is used to collect data related to transmission lines. The data acquisition module includes:

[0047] Meteorological data acquisition unit: It is set at meteorological monitoring stations along and around the transmission line. The meteorological data acquisition unit is electrically connected to the data acquisition module;

[0048] The meteorological sensor unit of the meteorological data acquisition unit is equipped with a temperature sensor, a humidity sensor, a wind speed sensor, a wind direction sensor, a precipitation sensor, and an atmospheric pressure sensor. The measurement accuracy of the temperature sensor reaches ±0.5°C, the measurement accuracy of the humidity sensor reaches ±3%RH, the measurement accuracy of the wind speed sensor reaches ±0.3m / s, the measurement accuracy of the wind direction sensor reaches ±5°, the measurement resolution of the precipitation sensor reaches 0.1mm, and the measurement accuracy of the atmospheric pressure sensor reaches ±0.1hPa;

[0049] Line status data acquisition unit: It is installed on the transmission line, and the data of the line status data acquisition unit is collected by the data acquisition module;

[0050] The line status data acquisition unit includes an ice coating sensor, a tension sensor, a tower inclination sensor, and an insulator leakage current sensor, which are respectively used to collect data on the ice coating thickness of the conductor, the tension of the conductor, the inclination angle of the tower, and the leakage current of the insulator. The ice coating sensor adopts the optical measurement principle or the gravity measurement principle, and its measurement accuracy reaches ±1mm. The tension sensor adopts a strain gauge sensor, and the measurement accuracy reaches ±0.5% of the full scale. The tower inclination sensor adopts an electronic gyroscope or an inclinometer, and the measurement accuracy reaches ±0.1°. The insulator leakage current sensor adopts a through-core current transformer, and the measurement accuracy reaches ±1mA;

[0051] The data transmission module is provided with a wireless communication module unit, and the data transmission module is connected to the data acquisition module. The data collected by the data acquisition module is transmitted to the data processing center through the wireless communication method of the wireless communication module unit. The data transmission module adopts an encrypted transmission protocol to ensure the security and integrity of the data. The data transmission module is provided with a 4G / 5G network and a dedicated wireless transmission frequency band by the wireless communication module unit, and has an automatic retransmission and data verification function to ensure the stability and accuracy of data transmission;

[0052] Combined with the Figure 1 - Figure 2 shown in the accompanying drawings of the specification, the meteorological data acquisition unit and the line status data acquisition unit set in the data acquisition module are used to collect data within the line area. Among them, the temperature sensor, humidity sensor, wind speed sensor, wind direction sensor, precipitation sensor, and atmospheric pressure sensor in the meteorological data acquisition unit constitute a meteorological monitoring station, which is widely distributed along and around the transmission line;

[0053] By installing high-precision meteorological monitoring equipment to collect long-term time series data that at least cover temperature, humidity, wind speed, wind direction, precipitation, air pressure, solar radiation, etc. during multiple icing seasons, ensuring that the time resolution of the data is fine enough. The sensors of the meteorological data acquisition unit collect data every 15 minutes or 30 minutes to capture the rapid changes in meteorological conditions;

[0054] Conduct quality checks on the collected meteorological data, remove obvious error or abnormal data points. These abnormalities may stem from sensor failures, electromagnetic interference, etc. Use data interpolation methods or statistical outlier handling methods to fill in a small amount of missing data to ensure data continuity;

[0055] Among them, the icing sensors, tension sensors, tower inclination sensors, and insulator leakage current sensors of the line state data acquisition unit are deployed on the transmission line;

[0056] The icing sensor, based on principles such as optics, gravity, capacitance, etc., enables its measurement accuracy to reach the millimeter level;

[0057] The tension sensor is used to accurately reflect the change in wire stress, and the accuracy is a certain proportion of the full scale;

[0058] The tower inclination sensor uses an electronic gyroscope or an inclinometer;

[0059] The insulator leakage current sensor uses a through-core current transformer, and the measurement accuracy is at the milliampere level;

[0060] By obtaining key line state information such as the icing thickness of the wire, tension, tower inclination status, and the degree of influence on the insulation performance of the insulator in real time, also clean the line state data, eliminate unreasonable data caused by reasons such as short-term sensor malfunctions, and accurately match the line state data with the corresponding meteorological data according to the time stamp to form a complete data set;

[0061] After that, collect detailed geographical information of the area where the transmission line is located, including topography, altitude, water system distribution, and land cover type, etc. Digitalize the geographical data through geographical information system software and convert it into a format that can be used by the model, and associate it with meteorological and line state data to provide support for analyzing the geographical background of icing formation;

[0062] Among them, due to the huge differences in the dimensions and numerical ranges of different types of data such as meteorology and line state, in order to make the model training process more stable and efficient, all data is normalized or standardized;

[0063] Normalize the temperature data to the interval [0,1], and the Min-Max normalization method can be used, that is, (original value - minimum value) / (maximum value - minimum value);

[0064] For data with a normal distribution characteristic in part of the meteorological data, Z-score standardization is adopted to transform the data into a standard normal distribution with a mean of 0 and a standard deviation of 1. The calculation formula is: (original value - mean) / standard deviation;

[0065] Derive more representative and physically meaningful features from the original data. Calculate the saturation vapor pressure based on temperature and humidity, which is closely related to the condensation of water vapor on the surface of the wire;

[0066] Calculate the vertical and horizontal components of the wind speed to better reflect the impact of wind on ice formation;

[0067] Extract the change rates of meteorological factors such as temperature and humidity over a period of time to capture the dynamic trend of meteorological conditions, because rapid changes in temperature and humidity may accelerate or inhibit the icing process;

[0068] Specifically, use statistical methods, namely Pearson correlation coefficient analysis, to judge the linear correlation between features and the ice thickness;

[0069] For machine learning algorithms, namely recursive feature elimination, screen features by repeatedly constructing a model to evaluate feature importance, and select the most critical feature subset for ice prediction;

[0070] By removing redundant or highly correlated features, reduce the data dimension, reduce the computational burden of model training, and at the same time avoid the negative impact of multicollinearity between features on the model performance;

[0071] The processed data above is transmitted through the wireless communication module unit in the data transmission module. This unit uses 4G / 5G networks and dedicated wireless transmission frequency bands to quickly and efficiently send the data to the data processing center in a wireless communication manner. To ensure the security and integrity of the data, an encrypted transmission protocol is adopted to prevent the data from being stolen or tampered with during transmission;

[0072] The data processing center is used to process and analyze the received data. The data processing center includes:

[0073] A data storage unit, which is used to store the historical data collected by the data acquisition module and the result data after analysis and processing. The data storage unit uses a large-capacity storage device and has data backup and recovery functions;

[0074] An ice disaster mechanism analysis unit, based on the geographical environment information and line structure parameters of the transmission line. The geographical environment information includes terrain and altitude, and the line structure parameters include conductor type, tower height and spacing;

[0075] When analyzing the mechanism of icing disasters, the icing disaster mechanism analysis unit adds the analysis of natural factors such as solar radiation, terrain shelter, and atmospheric circulation to the impact on icing formation, and incorporates natural factors into the mathematical model of the warning model construction unit;

[0076] The warning model construction unit uses machine learning algorithms to construct an icing thickness warning model based on the analysis results of the icing disaster mechanism analysis unit and the real-time collected data. The machine learning algorithms include but are not limited to neural networks, support vector machines, and decision tree algorithms. The warning model is used to predict the icing thickness and warning levels of transmission lines. The warning levels are divided into light icing, moderate icing, heavy icing, and emergency icing;

[0077] When constructing the warning model, the warning model construction unit optimizes the parameters of the machine learning algorithm by using cross-validation and grid search techniques to improve the accuracy and generalization ability of the warning model. When releasing warning information, the warning release module sends warning information to the communication terminals of relevant personnel according to the responsibilities and authorities of grid operation and maintenance personnel, and records and tracks the sending status of warning information to ensure the effective delivery of warning information;

[0078] Combined with the Figure 1 - Figure 2 shown in the specification drawings, the data storage unit receives and stores a large amount of historical data from the data acquisition module and the subsequent result data after analysis and processing. It uses a large-capacity storage device to ensure that there is enough storage space for data, and has data backup and recovery functions to prevent data loss due to hardware failures, human errors, or other accidents, ensuring the long-term availability and integrity of data, and providing rich data resources for the analysis of icing disaster mechanisms and model construction;

[0079] The icing disaster mechanism analysis unit deeply studies the occurrence mechanism of icing disasters based on the geographical environment information and line structure parameters of the transmission line;

[0080] During this process, natural factors such as solar radiation, terrain shelter, and atmospheric circulation are also included in the analysis scope. When the transmission line appears in the mountainous area, due to terrain shelter, the wind speed, temperature, and humidity in local areas may change, thus affecting the formation and development of icing;

[0081] Therefore, during the analysis of the icing disaster mechanism, through the statistical analysis of a large amount of historical data, the meteorological data acquisition unit and the line status data acquisition unit further collect the real-time data of the transmission line, and compare the historical data with the real-time data;

[0082] Among them, meteorological factors are used to compare data. Combining with the geographical environment, the topographic and geomorphic effects are further analyzed. At the same time, the impact of altitude on transmission lines is determined. Finally, the materials and structures of the transmission lines themselves are further analyzed, and the actual height of the poles of the transmission lines is used to estimate the conductor state, so that all data are further quantified, thereby determining the internal relationship between the said factors and icing, and providing accurate data correlation for the construction of the warning model;

[0083] When the warning model construction unit constructs and uses the warning model, it obtains the data processed above. Then, according to the characteristics of the icing problem, that is, the time series characteristics, non-linearity degree, sample size, etc. of the data and business requirements, a preliminary evaluation is carried out on a variety of potential algorithm models;

[0084] When the data shows obvious time series patterns and long-term dependence relationships need to be captured, recurrent neural network models such as long short-term memory networks or gated recurrent units are used. By comparing historical data, the working environment of the transmission cable can be quickly determined. Through the collection of a large amount of data over a long time, the comparable data is made richer, making the prediction of icing disasters in the region more accurate;

[0085] When the amount of data is relatively small or large, but there are requirements for the interpretability of the model, a deep neural network is selected, and the architecture of the model is further designed by determining the number of layers of the network, the number of neurons in each layer, and the type of activation function;

[0086] The ReLU function is used in the architecture to solve the problem of gradient disappearance. The tanh function is suitable for the case where the output is in the interval [-1, 1]. A Dropout layer is added to prevent overfitting by randomly discarding a certain proportion of neuron connections. After passing through the Adam optimizer and combining the adaptive learning rate adjustment strategy, the model convergence is accelerated;

[0087] When using, the dataset is divided into a training set, a validation set, and a test set according to a certain proportion. The data in the training set is used to train the selected model. By minimizing the loss function, the mean squared error loss is used for the regression task of predicting the icing thickness, and the cross-entropy loss is used for the classification task of icing category judgment to adjust the weight and bias parameters of the model;

[0088] During the training process, stochastic gradient descent and its variants such as Adagrad, Adadelta, and Adam are used. By iteratively updating the model parameters, the loss value is gradually reduced. Set an appropriate number of training epochs, and at the same time use the early stopping method, that is, when the performance of the model on the validation set no longer improves, the training is terminated in advance to avoid overfitting caused by overtraining;

[0089] For hyperparameters such as the number of neurons in the hidden layer, learning rate, and batch size of the model neural network, grid search, random search, or methods based on Bayesian optimization are used for tuning;

[0090] Grid search exhaustively enumerates all possible combinations of hyperparameters, evaluates the model performance of each combination on the validation set, and finds the optimal settings, but the computational cost is relatively high;

[0091] Random search randomly samples combinations in the hyperparameter space for evaluation, and the efficiency is relatively high;

[0092] Bayesian optimization builds a probability model between hyperparameters and model performance, guides the subsequent search direction based on the existing evaluation results, and finds a better solution with fewer trial times;

[0093] Use the test set data to comprehensively evaluate the trained model, and select appropriate evaluation metrics according to the nature of the icing prediction task;

[0094] For regression tasks such as icing thickness prediction, metrics such as root mean square error, mean absolute error, and coefficient of determination are commonly used;

[0095] Among them, the root mean square error is more sensitive to larger errors, the mean absolute error intuitively reflects the average deviation degree between the predicted value and the true value, and metrics such as the coefficient of determination measure the goodness of fit of the model to the data;

[0096] For the classification task of icing disaster level judgment, that is, mild, moderate, and severe icing, metrics such as accuracy, recall rate, and F1 value are used;

[0097] Accuracy represents the proportion of correctly predicted samples, recall rate reflects the coverage of the model for positive samples, and the F1 value comprehensively considers accuracy and recall rate and balances the weights of the two;

[0098] In addition to using the test set for evaluation, cross-validation methods are also used to further verify the stability and reliability of the model;

[0099] The dataset is divided into different combinations of training sets and test sets multiple times, k-fold cross-validation is adopted, usually k takes values of 5 or 10, the model is trained and tested multiple times, the evaluation metrics for each time are calculated, and the average value and standard deviation are taken as the final results;

[0100] Thus, a more comprehensive understanding of the performance fluctuations of the model on different data subsets can be obtained, and the contingency of single test set evaluation can be avoided;

[0101] Deploy the model that has passed strict evaluation and verification and meets the performance requirements into the actual icing disaster warning analysis system, ensure the integration of the model with other modules of the system, achieve smooth input of real-time data and timely output of prediction results, guarantee the stability and efficiency of the model in the actual operating environment, enable it to continuously process newly incoming meteorological and line status data, and provide real-time support for the icing warning of transmission lines;

[0102] At the same time, as time goes by, the characteristics of icing disasters, meteorological conditions, and the status of transmission lines may change. Regularly collect new historical data after each icing season ends, merge it with the original dataset, and re-perform data preprocessing, model training, evaluation, and verification steps to adjust and optimize the model parameters and architecture to adapt to the new situation, maintain the high prediction accuracy and reliability of the model, and continuously improve the performance of the icing disaster warning system;

[0103] The user management module has functions of user identity authentication and permission verification, manages the data acquisition module, data transmission module, data processing center, warning release module, and data visualization module. The user management module is used to manage the account information, permission settings, and operation logs of grid operation and maintenance personnel;

[0104] The data visualization module is used to visually display the collected data, analysis results, and warning information in the form of charts, maps, or 3D models. The data visualization module of the data processing center is connected to the data processing center;

[0105] Combined with the Figure 1 - Figure 2 shown in the accompanying drawings of the specification, the user management module is responsible for the unified management of the account information of grid operation and maintenance personnel, including operations such as account creation, modification, and deletion;

[0106] At the same time, the strict permission verification function ensures that operation and maintenance personnel with different responsibilities can only access and operate the system functions and data within their permission scope, including: certain parameter settings of the data acquisition module, the permission to view analysis results of the data processing center, etc., effectively guaranteeing the security of the system and the confidentiality of data;

[0107] In addition, detailed records of the operation logs of operation and maintenance personnel are made so that problems can be traced and audited when they occur, further strengthening the security and stability management of the system;

[0108] The data visualization module is connected to the data processing center and displays the collected meteorological data, line status data, analysis results, and warning information in the form of intuitive charts, maps, or 3D models. The meteorological conditions and the distribution of icing conditions along the transmission line are displayed in the form of a map, and the change trend of icing thickness over time is presented in the form of a chart;

[0109] Enable power grid operation and maintenance personnel to more intuitively and clearly understand the icing situation and potential risks of transmission lines, provide more intuitive and convenient support for their decision-making, help improve the efficiency and accuracy of operation and maintenance work, and timely discover and solve potential icing disaster problems.

[0110] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention.

Claims

1. An early warning analysis system based on the mechanism of ice disaster, characterized in that: Includes the following modules: Data collection module, data transmission module, data processing center, warning release module, user management module and data visualization module; The data acquisition module is used to collect data related to the power transmission line, and the data acquisition module includes: Meteorological data collection unit: It is set up at meteorological monitoring stations along the transmission line and surrounding areas. The meteorological data collection unit is connected to the data collection module through electrical signals; Line status data acquisition unit: it is installed on the transmission line, and the data of the line status data acquisition unit is collected by the data acquisition module; A wireless communication module unit is provided in the data transmission module, and the data transmission module is connected to the data acquisition module, and the data collected by the data acquisition module is transmitted to the data processing center through the wireless communication mode of the wireless communication module unit, and the data transmission module adopts an encrypted transmission protocol to ensure the security and integrity of the data; The data processing center is used to process and analyze the received data, and the data processing center includes: A data storage unit, used to store the historical data collected by the data acquisition module and the result data after analysis and processing, the data storage unit adopts a large-capacity storage device and has data backup and recovery functions; An ice disaster mechanism analysis unit is based on the geographical environment information of the transmission line and the line structure parameters, wherein the geographical environment information includes topography and altitude, and the line structure parameters include conductor type, tower height and spacing; An early warning model building unit, using a machine learning algorithm to build an ice thickness early warning model according to the analysis results of the ice disaster mechanism analysis unit and the real-time collected data, wherein the machine learning algorithm includes but is not limited to a neural network, a support vector machine and a decision tree algorithm, and the early warning model is used to predict the ice thickness and early warning level of the transmission line, wherein the early warning level is divided into light ice, moderate ice, heavy ice and emergency ice; The user management module has the functions of user identity authentication and authority verification, and manages the data acquisition module, data transmission module, data processing center, warning release module and data visualization module; The data visualization module is used to visualize the collected data, analysis results and warning information in the form of charts, maps or three-dimensional models. The data visualization module of the data processing center is connected to the data processing center.

2. The early warning analysis system based on the ice disaster mechanism according to claim 1 is characterized by: The meteorological sensor unit of the meteorological data acquisition unit is provided with a temperature sensor, a humidity sensor, a wind speed sensor, a wind direction sensor, a precipitation sensor and an atmospheric pressure sensor.

3. The early warning analysis system based on the ice disaster mechanism according to claim 2 is characterized in that: The measurement accuracy of the temperature sensor reaches ±0.5°C, the measurement accuracy of the humidity sensor reaches ±3%RH, the measurement accuracy of the wind speed sensor reaches ±0.3m / s, the measurement accuracy of the wind direction sensor reaches ±5°, the measurement resolution of the precipitation sensor reaches 0.1mm, and the measurement accuracy of the atmospheric pressure sensor reaches ±0.1hPa.

4. The early warning analysis system based on the ice disaster mechanism according to claim 1 is characterized in that: The line status data acquisition unit comprises an ice sensor, a tension sensor, a pole tower tilt sensor and an insulator leakage current sensor, which are respectively used to collect conductor ice thickness, conductor tension, pole tower tilt angle and insulator leakage current data.

5. The early warning analysis system based on the ice disaster mechanism according to claim 4 is characterized in that: The ice sensor adopts optical measurement principle or gravity measurement principle, and its measurement accuracy reaches ±1mm. The tension sensor adopts strain gauge sensor, and its measurement accuracy reaches ±0.5% of the full scale. The tower tilt sensor adopts electronic gyroscope or inclinometer, and its measurement accuracy reaches ±0.1°. The insulator leakage current sensor adopts through-type current transformer, and its measurement accuracy reaches ±1mA.

6. The early warning analysis system based on the ice disaster mechanism according to claim 1 is characterized by: The data transmission module is provided with 4G / 5G network and dedicated wireless transmission frequency band by the wireless communication module unit, and has automatic retransmission and data verification functions to ensure the stability and accuracy of data transmission.

7. The early warning analysis system based on the ice disaster mechanism according to claim 1 is characterized by: When analyzing the occurrence mechanism of icing disasters, the icing disaster mechanism analysis unit adds natural factors such as solar radiation, terrain shielding and atmospheric circulation to analyze the impact on icing formation, and incorporates natural factors into the mathematical model of the early warning model construction unit.

8. The early warning analysis system based on the ice disaster mechanism according to claim 1 is characterized by: When constructing the early warning model, the early warning model construction unit uses cross-validation and grid search technology to optimize the parameters of the machine learning algorithm to improve the accuracy and generalization ability of the early warning model.

9. The early warning analysis system based on the ice disaster mechanism according to claim 1 is characterized by: When issuing warning information, the warning issuing module sends the warning information to the communication terminal of relevant personnel according to the responsibilities and authority of the power grid operation and maintenance personnel, and records and tracks the sending status of the warning information to ensure the effective delivery of the warning information.

10. The early warning analysis system based on the ice disaster mechanism according to claim 1 is characterized by: The user management module is used to manage the account information, authority settings and operation logs of power grid operation and maintenance personnel.

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