Distribution line ice melting method, device and equipment and storage medium

Abstract

The invention discloses a power distribution line ice melting method, device and equipment and a storage medium, and relates to the technical field of disaster prevention of power systems, and the method comprises the steps: determining a corresponding target liquid-water factor based on the target icing weight data and target meteorological data of a power distribution line, determining predicted icing weight data corresponding to each distribution line based on the target liquid-water factor and the target icing weight data; the liquid water factor is a parameter for measuring the liquid water content in the ice layer; according to the predicted icing weight data, determining a to-be-ice-melted power distribution line meeting a preset ice melting condition, and based on a preset ice melting scheme optimization model and target icing weight data corresponding to the to-be-ice-melted power distribution line, determining a minimum risk coefficient of the to-be-ice-melted power distribution line; and generating an initial ice melting scheme corresponding to the distribution line to be subjected to ice melting based on each minimum risk coefficient, optimizing the initial ice melting scheme in real time, and performing ice melting treatment on the distribution line to be subjected to ice melting based on the optimized initial ice melting scheme. The ice melting efficiency of the distribution line can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of disaster prevention in power systems, and particularly to a method, device, equipment and storage medium for deicing distribution lines. Background Art

[0002] The power grid distribution lines are vulnerable to the influence of low-temperature rain and snow weather in winter, resulting in ice formation on the surface of the conductors. The ice not only increases the load of the line, but also may cause serious accidents such as wire breakage and tower collapse, posing a serious threat to the safe operation of the power grid. Traditional deicing methods often rely on manual judgment and empirical decision-making, lacking real-time performance and accuracy, and it is difficult to efficiently cope with the ice covering problems under different conditions. Moreover, most traditional deicing methods are for regular inspection and manual deicing, unable to effectively respond to rapidly changing meteorological conditions and complex line conditions. At the same time, the traditional AC short-circuit deicing method will cause a significant drop in the power grid voltage during the deicing process, affecting the normal power consumption of surrounding users.

[0003] In summary, how to improve the deicing efficiency of the power grid distribution lines and ensure the safety of the power grid operation is a technical problem that needs to be solved urgently at present. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method, device, equipment and storage medium for deicing distribution lines, which can improve the deicing efficiency of the power grid distribution lines and ensure the safety of the power grid operation. The specific scheme is as follows:

[0005] In the first aspect, the present application provides a method for deicing distribution lines, including:

[0006] Determining a corresponding target liquid water factor based on the target ice covering weight data and target meteorological data of the distribution lines collected in real time, and determining the predicted ice covering weight data corresponding to each distribution line based on the target liquid water factor and the target ice covering weight data; the liquid water factor is a parameter used to measure the content of liquid water in the ice layer;

[0007] Determining the distribution lines to be deiced that meet the preset deicing conditions according to the predicted ice covering weight data, and determining the minimum risk coefficient of the distribution lines to be deiced based on the preset deicing scheme optimization model and the target ice covering weight data corresponding to the distribution lines to be deiced;

[0008] Generating an initial deicing scheme corresponding to the distribution lines to be deiced based on each minimum risk coefficient, optimizing the initial deicing scheme in real time, and performing deicing treatment on the distribution lines to be deiced based on the optimized initial deicing scheme.

[0009] Optionally, the target meteorological data includes target rainfall intensity, target wind speed, target temperature and target humidity;

[0010] Correspondingly, determining the corresponding target liquid water factor based on the target ice accretion weight data and target meteorological data of the distribution line collected in real time includes:

[0011] Determining a first correction coefficient corresponding to the target temperature and a second correction coefficient corresponding to the target humidity based on a preset machine learning algorithm, historical liquid water factors, the target climate characteristics corresponding to the distribution line, and the target line environment corresponding to the distribution line;

[0012] Determining the target liquid water factor corresponding to the distribution line based on the target ice accretion weight data, the target rainfall intensity, the target wind speed, the target temperature, the target humidity, the first correction coefficient, and the second correction coefficient of the distribution line collected in real time.

[0013] Optionally, determining the predicted ice accretion weight data corresponding to each distribution line based on the target liquid water factor and the target ice accretion weight data includes:

[0014] Determining target model parameters based on a preset regression analysis method, and constructing a corresponding target ice accretion weight prediction model based on the target model parameters;

[0015] Using the target ice accretion weight prediction model to determine the predicted ice accretion weight data corresponding to each distribution line based on the target liquid water factor and the target ice accretion weight data.

[0016] Optionally, determining the distribution lines to be de-iced that meet the preset de-icing conditions according to the predicted ice accretion weight data includes:

[0017] If it is determined that the target ice accretion weight data corresponding to the current distribution line exceeds the preset line safety bearing capacity threshold, then monitor the target change trend corresponding to the predicted ice accretion weight data of the current distribution line;

[0018] If the target change trend meets the preset rising condition, then determine that the current distribution line is the distribution line to be de-iced that meets the preset de-icing conditions.

[0019] Optionally, determining the minimum risk coefficient of the distribution line to be de-iced based on a preset de-icing scheme optimization model and the target ice accretion weight data corresponding to the distribution line to be de-iced includes:

[0020] Determining the target number of users and target load types corresponding to the distribution line to be de-iced, and determining the target importance score corresponding to the distribution line to be de-iced based on preset scoring standard conditions, the target number of users, and the target load types;

[0021] Determine the target distance between the power distribution line to be de-iced and the target de-icing equipment, determine the target de-icing material reserve data corresponding to the power distribution line to be de-iced, and determine the accessible degree of the target de-icing resources corresponding to the power distribution line to be de-iced based on the target distance and the target de-icing material reserve data;

[0022] Use the preset de-icing scheme optimization model to determine the minimum risk coefficient of the power distribution line to be de-iced based on a preset multi-objective optimization algorithm, the target icing weight data corresponding to the power distribution line to be de-iced, the target importance score, the accessible degree of the target de-icing resources, and a preset weight coefficient.

[0023] Optionally, generating the initial de-icing scheme corresponding to the power distribution line to be de-iced based on each of the minimum risk coefficients includes:

[0024] Sort each of the minimum risk coefficients based on a preset order condition, and determine the target de-icing order corresponding to the power distribution line to be de-iced based on the sorted minimum risk coefficients;

[0025] Construct a target de-icing time calculation model based on target de-icing parameters, and use the target de-icing time calculation model to determine the target de-icing time corresponding to the power distribution line to be de-iced based on the target icing weight data corresponding to the power distribution line to be de-iced and the target conductor parameters corresponding to the power distribution line to be de-iced, so as to generate the initial de-icing scheme corresponding to the power distribution line to be de-iced based on the target de-icing order and the target de-icing time.

[0026] Optionally, during the process of real-time optimizing the initial de-icing scheme and de-icing the power distribution line to be de-iced based on the optimized initial de-icing scheme, it further includes:

[0027] Real-time monitor the changes in the target conductor temperature and target current corresponding to the power distribution line to be de-iced, and real-time optimize the initial de-icing scheme based on the target de-icing time, the target conductor temperature, the target current changes, and a preset feedback adjustment strategy;

[0028] Real-time collect the target icing weight data corresponding to the power distribution line to be de-iced, and optimize the target icing weight prediction model and the preset de-icing scheme optimization model based on the target icing weight data;

[0029] Use the optimized target icing weight prediction model, the optimized preset de-icing scheme optimization model, and the target icing weight data to determine the remaining risk coefficient corresponding to the power distribution line to be de-iced, and conduct a risk assessment on the power distribution line to be de-iced based on the remaining risk coefficient.

[0030] Second aspect, the present application provides a distribution line ice melting device, including:

[0031] A predicted icing weight data determination module, configured to determine a corresponding target liquid water factor based on the target icing weight data and target meteorological data of the distribution line collected in real time, and determine the predicted icing weight data corresponding to each distribution line based on the target liquid water factor and the target icing weight data; the liquid water factor is a parameter for measuring the content of liquid water in the ice layer;

[0032] A minimum risk coefficient determination module, configured to determine the distribution lines to be ice melted that meet the preset ice melting conditions according to the predicted icing weight data, and determine the minimum risk coefficient of the distribution lines to be ice melted based on a preset ice melting scheme optimization model and the target icing weight data corresponding to the distribution lines to be ice melted;

[0033] A distribution line ice melting module, configured to generate an initial ice melting scheme corresponding to the distribution lines to be ice melted based on each minimum risk coefficient, optimize the initial ice melting scheme in real time, and perform ice melting treatment on the distribution lines to be ice melted based on the optimized initial ice melting scheme.

[0034] Third aspect, the present application provides an electronic device, including:

[0035] A memory, configured to store a computer program;

[0036] A processor, configured to execute the computer program to implement the foregoing distribution line ice melting method.

[0037] Fourth aspect, the present application provides a computer-readable storage medium, configured to store a computer program; wherein, when the computer program is executed by a processor, the foregoing distribution line ice melting method is implemented.

[0038] In this application, first, the corresponding target liquid water factor is determined based on the target ice - covering weight data and target meteorological data of the power distribution lines collected in real - time, and the predicted ice - covering weight data corresponding to each power distribution line is determined based on the target liquid water factor and the target ice - covering weight data. The liquid water factor is a parameter used to measure the content of liquid water in the ice layer. Then, the power distribution lines to be de -iced that meet the preset de -icing conditions are determined according to the predicted ice - covering weight data, and the minimum risk coefficient of the power distribution lines to be de -iced is determined based on the preset de -icing scheme optimization model and the target ice - covering weight data corresponding to the power distribution lines to be de -iced. Finally, an initial de -icing scheme corresponding to the power distribution lines to be de -iced is generated based on each minimum risk coefficient, the initial de -icing scheme is optimized in real - time, and the power distribution lines to be de -iced are de -iced based on the optimized initial de -icing scheme. As can be seen from the above, in this application, the target ice - covering weight data of the power distribution lines is collected; the predicted ice - covering weight data of the power distribution lines is determined based on the target ice - covering weight data, target liquid water factor, and target meteorological data, and the power distribution lines to be de -iced are determined according to the predicted ice - covering weight data; the minimum risk coefficient of the power distribution lines to be de -iced is solved based on the preset de -icing scheme optimization model, and an initial de -icing scheme is generated; then, the initial de -icing scheme is optimized in real - time during the de -icing process, and the power distribution lines to be de -iced are continuously de -iced based on the optimized initial de -icing scheme. In this way, in this application, the power distribution lines to be de -iced are determined by predicting the ice - covering weight data of the power distribution lines, and the minimum risk coefficient of the power distribution lines to be de -iced is determined based on the preset de -icing scheme optimization model, so as to generate the corresponding de -icing scheme and continuously optimize the de -icing scheme, which can realize the effective monitoring and timely intervention of the ice - covering situation of the power grid distribution lines, improve the de -icing efficiency of the power grid distribution lines, and ensure the safe, stable, and efficient operation of the power grid under extreme weather conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on the provided drawings without creative efforts.

[0040] Figure 1 It is a flowchart of a method for de -icing power distribution lines provided by this application;

[0041] Figure 2 It is a flowchart of a specific method for de -icing power distribution lines provided by this application;

[0042] Figure 3 It is a flowchart of a specific method for de -icing power distribution lines provided by this application;

[0043] Figure 4Structural schematic diagram of an ice melting device for a power distribution line provided by this application

[0044] Figure 5 Structural diagram of an electronic device provided by this application Specific implementation manners

[0045] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying 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 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

[0046] The power grid distribution lines are vulnerable to the influence of low-temperature rain and snow weather in winter, resulting in ice covering on the surface of the conductors. The ice covering not only increases the load of the lines, but also may cause serious accidents such as wire breakage and tower collapse, posing a serious threat to the safe operation of the power grid. Traditional ice melting methods often rely on manual judgment and empirical decision-making, lacking real-time performance and accuracy, and it is difficult to efficiently cope with ice covering problems under different conditions. Moreover, most traditional ice melting methods are for regular inspections and manual ice melting, and they cannot effectively cope with rapidly changing meteorological conditions and complex line conditions. At the same time, traditional AC short-circuit ice melting methods will cause a significant drop in the power grid voltage during the ice melting process, affecting the normal power consumption of surrounding users. For this reason, this application provides an ice melting solution for power grid distribution lines, which can improve the ice melting efficiency of power grid distribution lines and ensure the safety of power grid operation

[0047] See Figure 1 As shown, an ice melting method for a power distribution line disclosed in an embodiment of the present invention may include

[0048] Step S11: Determine a corresponding target liquid water factor based on the target ice covering weight data and target meteorological data of the power distribution line collected in real time, and determine the predicted ice covering weight data corresponding to each power distribution line based on the target liquid water factor and the target ice covering weight data; the liquid water factor is a parameter used to measure the content of liquid water in the ice layer

[0049] In this embodiment, an ice covering monitoring device may be installed on the power distribution line conductor to collect the actual ice covering weight of the power distribution line conductor in real time, that is, the target ice covering weight data , so as to ensure that the ice melting operation can be dynamically adjusted according to the real-time situation. At the same time, meteorological data can be collected by using meteorological observation equipment. The meteorological data may include target rainfall intensity, target wind speed, target temperature, target humidity, etc. The collected data can be transmitted to the central monitoring system through a communication network for centralized processing and analysis

[0050] It should be noted that the liquid water factor is an important parameter for measuring the liquid water content in the ice layer and directly affects the efficiency of the ice melting process. Determining the corresponding target liquid water factor based on the target ice coating weight data and target meteorological data of the power distribution line collected in real time as described above may include: First, determining a first correction coefficient corresponding to the target temperature and a second correction coefficient corresponding to the target humidity based on a preset machine learning algorithm, historical liquid water factors, the target climate characteristics corresponding to the power distribution line, and the target line environment corresponding to the power distribution line; then determining the target liquid water factor corresponding to the power distribution line based on the target ice coating weight data, target rainfall intensity, target wind speed, target temperature, target humidity, the first correction coefficient, and the second correction coefficient of the power distribution line collected in real time. Specifically, the calculation formula of the liquid water factor is as follows:

[0051] ;

[0052] wherein, is the liquid water factor; is the increased value of the conductor weight, that is, the actual ice coating weight of the conductor of the power distribution line, with the unit of kg; R is the rainfall intensity, with the unit of mm / h; is the average wind speed, with the unit of m / s; T is the ambient temperature, with the unit of K; h is the relative humidity; and are correction coefficients determined through experimental data or machine learning algorithms, is the first correction coefficient corresponding to the temperature, is the second correction coefficient corresponding to the humidity. According to the climate characteristics and line environment of different regions, and the correction coefficients can be adjusted, enhancing the adaptability of the formula. The ambient temperature affects the ice melting speed, and the relative humidity affects the condensation of water vapor, making the calculation of the liquid water factor more accurate.

[0053] In this embodiment, an ice coating weight prediction model can be constructed based on the collected ice coating weight data to predict the ice coating weight of the power distribution line, and the prediction accuracy can be improved by combining the liquid water factor. Determining the predicted ice coating weight data corresponding to each power distribution line based on the target liquid water factor and the target ice coating weight data as described above may include: First, determining target model parameters based on a preset regression analysis method, and constructing a corresponding target ice coating weight prediction model based on the target model parameters; then using the target ice coating weight prediction model to determine the predicted ice coating weight data corresponding to each power distribution line based on the target liquid water factor and the target ice coating weight data corresponding to each power distribution line. Specifically, this embodiment can construct an ice coating weight prediction model based on a parameter constraint model in combination with the liquid water factor. The ice coating weight prediction model can be expressed by the following formula:

[0054] ;

[0055] Wherein, I is the predicted ice accretion weight corresponding to the distribution line, with the unit of kg; is the actual ice accretion weight of the distribution line obtained by real-time monitoring, with the unit of kg; is a model parameter, which can be obtained by fitting using mathematical methods such as regression analysis. Substituting the predicted meteorological data, the development trend of future ice accretion and the ice accretion weight at a certain future moment can be obtained, and the model parameters can be adjusted according to the actual ice accretion weight data of the distribution line obtained by monitoring, so as to make the ice accretion prediction result more accurate.

[0056] Step S12: Determine the distribution lines to be de-iced that meet the preset de-icing conditions according to the predicted ice accretion weight data, and optimize the model based on the preset de-icing scheme and determine the minimum risk coefficient of the distribution lines to be de-iced corresponding to the target ice accretion weight data of the distribution lines to be de-iced.

[0057] In this embodiment, the predicted ice accretion weight data can be used to know in advance the development trend of ice accretion on the distribution line and assist in the de-icing decision-making. The above-mentioned determining the distribution lines to be de-iced that meet the preset de-icing conditions according to the predicted ice accretion weight data may include: if it is determined that the target ice accretion weight data corresponding to the current distribution line exceeds the preset line safety bearing capacity threshold, then monitor the target change trend corresponding to the predicted ice accretion weight data of the current distribution line; if the target change trend meets the preset rising condition, then determine that the current distribution line is the distribution line to be de-iced that meets the preset de-icing conditions. That is, by monitoring the target change trend corresponding to the predicted ice accretion weight data of the distribution line, the distribution lines to be de-iced can be determined. Specifically, when it is monitored that the actual ice accretion weight data of the distribution line is close to the line design bearing capacity, if the predicted ice accretion weight corresponding to the current distribution line shows an upward trend, the de-icing program needs to be started immediately to prevent failures; if the predicted ice accretion weight corresponding to the current distribution line shows a downward trend, the de-icing program does not need to be started. After determining the distribution lines to be de-iced, the minimum risk coefficient of the distribution lines to be de-iced can be solved by using the de-icing scheme optimization model according to the target ice accretion weight data corresponding to the distribution lines to be de-iced, so as to determine the de-icing sequence of the distribution lines to be de-iced according to the minimum risk coefficient of the distribution lines to be de-iced subsequently and generate the corresponding de-icing scheme. That is, this embodiment can classify and manage the distribution lines through the minimum risk coefficient, and focus on monitoring and protecting the high-risk distribution lines.

[0058] Step S13: Generate the initial de-icing scheme corresponding to the distribution lines to be de-iced based on each minimum risk coefficient, optimize the initial de-icing scheme in real time, and perform de-icing treatment on the distribution lines to be de-iced based on the optimized initial de-icing scheme.

[0059] In this embodiment, in order to de-ice the de-icing distribution lines in a hierarchical manner and ensure the efficiency of the de-icing process, the above-mentioned generation of the initial de-icing plan corresponding to the de-icing distribution lines based on each of the minimum risk coefficients may include: First, sort the minimum risk coefficients corresponding to each de-icing distribution line based on a preset order condition, and determine the target de-icing order corresponding to the de-icing distribution line based on the sorted minimum risk coefficients; then construct a target de-icing time calculation model based on the target de-icing parameters, and use the target de-icing time calculation model to determine the target de-icing time corresponding to the de-icing distribution line based on the target ice-covered weight data corresponding to the de-icing distribution line and the target wire parameters corresponding to the de-icing distribution line. Finally, generate the initial de-icing plan corresponding to the de-icing distribution line based on the target de-icing order and the target de-icing time. Specifically, the minimum risk coefficients can be sorted in descending order, and the de-icing distribution lines with high risk coefficients are preferentially de-iced. At the same time, this embodiment can introduce the heat conduction theory and construct a de-icing time calculation model based on parameters such as the actual ice-covered weight of the distribution line, the heat transfer coefficient h, and the temperature and so on. Calculate the de-icing time required for the de-icing distribution line using the de-icing time calculation model to ensure the efficiency of the de-icing process. The formula of the de-icing time calculation model is as follows:

[0060] ;

[0061] where t is the de-icing time required for the de-icing distribution line, is the specific heat capacity of the ice layer, with the unit of ; is the density of the ice, with the unit of ; h is the heat transfer coefficient, with the unit of ; is the ice melting temperature, with the unit of K; is the ambient temperature, with the unit of K; A is the surface area of the wire, with the unit of . The formula of the de-icing time calculation model is derived based on the principle of conservation of energy, that is, the heat required for de-icing is equal to the heat transferred to the ice through heat exchange. Based on the de-icing time calculation model, calculate the de-icing time required for the de-icing distribution line, and generate the initial de-icing plan corresponding to the de-icing distribution line in combination with the minimum risk coefficient of the de-icing distribution line, which can avoid unnecessary joule heat loss and improve energy utilization efficiency.

[0062] It should be noted that in order to determine the ice melting effect in real time, during the above process of optimizing the initial ice melting plan in real time and performing ice melting treatment on the power distribution line to be de-iced based on the optimized initial ice melting plan, the following steps can also be included: monitoring in real time the target conductor temperature and the change of the target current corresponding to the power distribution line to be de-iced, and optimizing the initial ice melting plan in real time based on the target ice melting time, the target conductor temperature, the change of the target current, and a preset feedback adjustment strategy; collecting in real time the target ice coating weight data corresponding to the power distribution line to be de-iced, and optimizing the target ice coating weight prediction model and the preset ice melting plan optimization model based on the target ice coating weight data; using the optimized target ice coating weight prediction model, the optimized preset ice melting plan optimization model, and the target ice coating weight data to determine the remaining risk coefficient corresponding to the power distribution line to be de-iced, and performing a risk assessment on the power distribution line to be de-iced based on the remaining risk coefficient. Specifically, during the ice melting process, the temperature and current changes of the conductor corresponding to the power distribution line to be de-iced can be monitored in real time to judge the ice melting effect and avoid damage to the conductor caused by over-icing. In this embodiment, image recognition technology and drone inspection can also be used to visually monitor the ice melting effect and intuitively judge the melting situation of the ice layer. According to the monitoring data during the ice melting process, a detailed feedback adjustment strategy can be formulated to dynamically adjust the ice melting plan to ensure the safety and efficiency of the ice melting process and avoid unnecessary energy consumption. At the same time, the actual data monitored during the ice melting process can be fed back to the ice coating weight prediction model and the ice melting plan optimization model to correct the models in real time and improve the accuracy and adaptability of the models. In addition, in this embodiment, the risk during the ice melting process can be evaluated by calculating the remaining risk coefficient in real time, such as the line outage time, the grid voltage fluctuation, the probability of equipment damage, etc. The remaining risk coefficient can be used to evaluate the dynamic impact of the ice melting operation on the safety of the power grid operation.

[0063] It is understandable that the minimum risk coefficient is calculated by optimizing the model through the ice melting scheme, taking into account factors such as line importance, ice accretion severity, and ice melting resource availability. It represents the theoretically achievable lowest risk value obtained through the multi-objective optimization algorithm in an ideal situation. The remaining risk coefficient is a coefficient obtained by evaluating the remaining risk of the distribution line after ice melting operations based on real-time monitoring data and actual ice melting conditions during the ice melting process. The remaining risk coefficient is a dynamically changing value that reflects the risk changes caused by various uncertain factors during the ice melting process, such as sudden changes in meteorological conditions and equipment failures. Generally, before the start of ice melting, since no ice melting operation has been carried out, the remaining risk coefficient is equal to the minimum risk coefficient. As the ice melting process progresses, if the ice melting operation proceeds as expected, the ice accretion on the distribution line decreases and the risk reduces, and the remaining risk coefficient will be less than the minimum risk coefficient; if unexpected situations occur, such as poor ice melting effect or the emergence of new risk factors, for example, strong wind causes the ice accretion on the un-melted distribution line to increase, the remaining risk coefficient may be greater than the minimum risk coefficient. The minimum risk coefficient is the basis for formulating the ice melting scheme, determining the priority and general process of ice melting. The remaining risk coefficient is used to monitor the ice melting process in real time. When the remaining risk coefficient approaches or exceeds the preset safety threshold, even if the ice melting is not completed, it may be necessary to suspend or adjust the ice melting scheme, and take additional measures, such as increasing ice melting equipment and changing the ice melting method, to reduce the risk and ensure the safe and stable operation of the power grid. The minimum risk coefficient and the remaining risk coefficient jointly support the ice melting decision-making to ensure the safety and efficiency of the ice melting process.

[0064] In a specific embodiment, referring to Figure 2 as shown, the process of the distribution line ice melting method can specifically be: S1. Ice accretion data collection. The collection of ice accretion data is the basis for ice melting decision-making, ensuring that the ice melting operation can be dynamically adjusted according to real-time conditions; S2. Ice accretion weight prediction. Based on the collected data, prediction is carried out through the ice accretion weight prediction model; S3. Ice melting scheme optimization; S4. Determination and calculation of conductor ice melting parameters; S5. Real-time monitoring and feedback adjustment; S6. Prediction result and risk assessment.

[0065] As can be seen from the above, in this embodiment, first, based on the target icing weight data and target meteorological data of the power distribution line collected in real time, the corresponding target liquid water factor is determined, and based on the target liquid water factor and the target icing weight data, the predicted icing weight data corresponding to each power distribution line is determined; the liquid water factor is a parameter used to measure the content of liquid water in the ice layer; then, according to the predicted icing weight data, the power distribution lines to be de-iced that meet the preset de-icing conditions are determined, and based on the preset de-icing scheme optimization model and the target icing weight data corresponding to the power distribution lines to be de-iced, the minimum risk coefficient of the power distribution lines to be de-iced is determined; finally, based on each minimum risk coefficient, an initial de-icing scheme corresponding to the power distribution lines to be de-iced is generated, the initial de-icing scheme is optimized in real time, and the power distribution lines to be de-iced are de-iced based on the optimized initial de-icing scheme. As can be seen from the above, in this embodiment, the target icing weight data of the power distribution line is collected; based on the target icing weight data, target liquid water factor and target meteorological data, the predicted icing weight data of the power distribution line is determined, and the power distribution lines to be de-iced are determined according to the predicted icing weight data; based on the preset de-icing scheme optimization model, the minimum risk coefficient of the power distribution lines to be de-iced is solved, and an initial de-icing scheme is generated; then, during the de-icing process, the initial de-icing scheme is optimized in real time, and the power distribution lines to be de-iced are continuously de-iced based on the optimized initial de-icing scheme. In this way, in this embodiment, by predicting the icing weight data of the power distribution line, the power distribution lines to be de-iced are determined, and based on the preset de-icing scheme optimization model, the minimum risk coefficient of the power distribution lines to be de-iced is determined, so as to generate the corresponding de-icing scheme and continuously optimize the de-icing scheme, which can realize the effective monitoring and timely intervention of the icing condition of the power grid distribution line, improve the de-icing efficiency of the power grid distribution line, and ensure the safe, stable and efficient operation of the power grid under extreme weather.

[0066] See Figure 3 As shown, in order to perform hierarchical de-icing on the power distribution line and improve the de-icing efficiency of the power grid distribution line, an embodiment of the present invention further discloses a power distribution line de-icing method, which may include:

[0067] Step S21: Based on the target icing weight data and target meteorological data of the power distribution line collected in real time, determine the corresponding target liquid water factor, and based on the target liquid water factor and the target icing weight data, determine the predicted icing weight data corresponding to each power distribution line; the liquid water factor is a parameter used to measure the content of liquid water in the ice layer.

[0068] Step S22: According to the predicted icing weight data, determine the power distribution lines to be de-iced that meet the preset de-icing conditions, and based on the preset de-icing scheme optimization model and the target icing weight data corresponding to the power distribution lines to be de-iced, determine the minimum risk coefficient of the power distribution lines to be de-iced.

[0069] In this embodiment, determining the minimum risk coefficient of the power distribution line to be de-iced based on the preset de-icing scheme optimization model and the target ice accretion weight data corresponding to the power distribution line to be de-iced may include: First, determine the target number of users and the target load type corresponding to the power distribution line to be de-iced, and determine the target importance score corresponding to the power distribution line to be de-iced based on the preset scoring standard conditions, the target number of users, and the target load type; Then, determine the target distance between the power distribution line to be de-iced and the target de-icing equipment, determine the target de-icing material reserve data corresponding to the power distribution line to be de-iced, and determine the target de-icing resource availability corresponding to the power distribution line to be de-iced based on the target distance and the target de-icing material reserve data; Finally, use the preset de-icing scheme optimization model to determine the minimum risk coefficient of the power distribution line to be de-iced based on the preset multi-objective optimization algorithm, the target ice accretion weight data corresponding to the power distribution line to be de-iced, the target importance score, the target de-icing resource availability, and the preset weight coefficient. Specifically, the minimum risk coefficient of the power distribution line to be de-iced can be solved using the de-icing scheme optimization model based on a multi-objective optimization algorithm, such as a genetic algorithm or a particle swarm optimization algorithm. Factors such as the importance of the line, the severity of the ice accretion, and the availability of de-icing resources are considered in the de-icing scheme optimization model to ensure that the risk of ice accretion on the power distribution line is minimized to the greatest extent under limited resources. The objective function of the de-icing scheme optimization model is as follows:

[0070] ;

[0071] Among them, represents the minimum risk coefficient, and the minimum risk coefficient comprehensively reflects the risk level faced by the power distribution line de-icing; n represents the total number of lines that need to be de-iced, that is, the total number of power distribution lines to be de-iced; i represents the i-th line; represents the actual value of the ice accretion weight of the i-th power distribution line to be de-iced, that is, the target ice accretion weight, which is a key indicator to measure the severity of ice accretion. The heavier the ice accretion, the higher the risk; is the maximum value of the ice accretion weights of all power distribution lines to be de-iced. By comparing the actual ice accretion weight of each power distribution line to be de-iced with the maximum value, the relative magnitude of the severity of ice accretion of the power distribution lines to be de-iced can be measured on a unified scale; represents the importance score of the i-th line, which can be comprehensively determined based on factors such as the number of users served by the power distribution line to be de-iced, the load type, such as the proportion of industrial load, the proportion of residential load, and the impact on the power grid stability. The higher the importance, the higher the priority in the de-icing decision-making; is the maximum value of the importance scores of all power distribution lines to be de-iced and is used for normalization; represents the accessibility of ice melting resources for the \(i\)-th distribution line to be de-iced, such as the distance from the ice melting equipment to the line, the reserve of materials required for ice melting, etc. The larger the value, the more difficult it is to obtain ice melting resources and the higher the risk. , , are the weight coefficients corresponding to the actual ice coating weight, line importance, and accessibility of ice melting resources respectively. The value range is between 0 and 1. The weight coefficient reflects the relative importance of different factors in risk assessment and can be determined by AHP (Analytic Hierarchy Process), expert experience method, etc. It should be noted that the above weight coefficients satisfy .

[0072] In a specific embodiment, first, the actual ice coating weight data corresponding to the distribution line to be de-iced can be collected through the ice coating monitoring device installed on the distribution line conductor . For the target importance score corresponding to the distribution line to be de-iced, information such as the number of users served by the distribution line to be de-iced and the load type needs to be collected, and then scored according to the pre-established scoring criteria. The target accessibility of ice melting resources corresponding to the distribution line to be de-iced requires statistics of data such as the target distance between the distribution line to be de-iced and the target ice melting equipment, and the reserve of ice melting materials. Then, according to the determined weight coefficients , , , substitute them into the objective function of the ice melting scheme optimization model to calculate the minimum risk coefficient of each distribution line to be de-iced. According to value, the distribution lines to be de-iced are sorted to give priority to de-icing the distribution lines to be de-iced with high risk coefficients.

[0073] Step S23: Generate the initial ice melting scheme corresponding to the distribution line to be de-iced based on each of the minimum risk coefficients, and optimize the initial ice melting scheme in real time, and perform ice melting treatment on the distribution line to be de-iced based on the optimized initial ice melting scheme.

[0074] Among them, for the more specific processing procedures of the above steps S21 and S23, reference can be made to the corresponding content disclosed in the foregoing embodiments, and details will not be elaborated here.

[0075] As can be seen from the above, in this embodiment, the target importance score corresponding to the power distribution line to be de-iced and the target availability degree of de-icing resources corresponding to the power distribution line to be de-iced are determined. Then, based on the preset multi-objective optimization algorithm, the target ice-covered weight data, the target importance score, the target availability degree of de-icing resources, and the preset weight coefficient of the power distribution line to be de-iced, the minimum risk coefficient of the power distribution line to be de-iced is determined by using the de-icing scheme optimization model, so as to perform hierarchical de-icing on the power distribution line according to the minimum risk coefficient, thereby improving the de-icing efficiency of the power grid distribution line. Moreover, in this embodiment, the power grid voltage fluctuation can be controlled within a small range to improve the stability of the power grid operation.

[0076] Correspondingly, as shown in Figure 4 this application embodiment also provides a power distribution line de-icing device, which may include:

[0077] A predicted ice-covered weight data determination module 11, configured to determine a corresponding target liquid water factor based on the target ice-covered weight data and target meteorological data of the power distribution line collected in real time, and determine the predicted ice-covered weight data corresponding to each power distribution line based on the target liquid water factor and the target ice-covered weight data; the liquid water factor is a parameter for measuring the content of liquid water in the ice layer;

[0078] A minimum risk coefficient determination module 12, configured to determine the power distribution line to be de-iced that meets the preset de-icing conditions according to the predicted ice-covered weight data, and determine the minimum risk coefficient of the power distribution line to be de-iced based on the preset de-icing scheme optimization model and the target ice-covered weight data corresponding to the power distribution line to be de-iced;

[0079] A power distribution line de-icing module 13, configured to generate an initial de-icing scheme corresponding to the power distribution line to be de-iced based on each minimum risk coefficient, optimize the initial de-icing scheme in real time, and perform de-icing treatment on the power distribution line to be de-iced based on the optimized initial de-icing scheme.

[0080] As can be seen from the above, in the present application, first, the corresponding target liquid water factor is determined based on the target ice coating weight data and target meteorological data of the power distribution line collected in real time, and the predicted ice coating weight data corresponding to each power distribution line is determined based on the target liquid water factor and the target ice coating weight data; the liquid water factor is a parameter used to measure the content of liquid water in the ice layer; then, the power distribution lines to be de-iced that meet the preset de-icing conditions are determined according to the predicted ice coating weight data, and the minimum risk coefficient of the power distribution lines to be de-iced is determined based on the preset de-icing scheme optimization model and the target ice coating weight data corresponding to the power distribution lines to be de-iced; finally, an initial de-icing scheme corresponding to the power distribution lines to be de-iced is generated based on each minimum risk coefficient, the initial de-icing scheme is optimized in real time, and the power distribution lines to be de-iced are de-iced based on the optimized initial de-icing scheme. As can be seen from the above, in the present application, the target ice coating weight data of the power distribution line is collected; the predicted ice coating weight data of the power distribution line is determined based on the target ice coating weight data, target liquid water factor and target meteorological data, and the power distribution lines to be de-iced are determined according to the predicted ice coating weight data; the minimum risk coefficient of the power distribution lines to be de-iced is solved based on the preset de-icing scheme optimization model, and an initial de-icing scheme is generated; then, the initial de-icing scheme is optimized in real time during the de-icing process, and the power distribution lines to be de-iced are continuously de-iced based on the optimized initial de-icing scheme. In this way, in the present application, the power distribution lines to be de-iced are determined by predicting the ice coating weight data of the power distribution line, and the minimum risk coefficient of the power distribution lines to be de-iced is determined based on the preset de-icing scheme optimization model, so as to generate the corresponding de-icing scheme and continuously optimize the de-icing scheme, which can realize the effective monitoring and timely intervention of the ice coating condition of the power grid distribution line, improve the de-icing efficiency of the power grid distribution line, and ensure the safe, stable and efficient operation of the power grid under extreme weather conditions.

[0081] In some specific embodiments, the target meteorological data may include target rainfall intensity, target wind speed, target temperature and target humidity;

[0082] Correspondingly, the predicted ice coating weight data determination module 11 may include:

[0083] A correction coefficient determination unit, configured to determine a first correction coefficient corresponding to the target temperature and a second correction coefficient corresponding to the target humidity based on a preset machine learning algorithm, historical liquid water factors, the target climate characteristics corresponding to the power distribution line, and the target line environment corresponding to the power distribution line;

[0084] A target liquid water factor determination unit, configured to determine the target liquid water factor corresponding to the power distribution line based on the target ice coating weight data, the target rainfall intensity, the target wind speed, the target temperature, the target humidity, the first correction coefficient, and the second correction coefficient of the power distribution line collected in real time.

[0085] In some specific embodiments, the predicted icing weight data determination module 11 may include:

[0086] A target icing weight prediction model construction unit, configured to determine target model parameters based on a preset regression analysis method, and construct a corresponding target icing weight prediction model based on the target model parameters;

[0087] A predicted icing weight data determination unit, configured to use the target icing weight prediction model to determine the predicted icing weight data corresponding to each of the distribution lines based on the target liquid water factor and the target icing weight data.

[0088] In some specific embodiments, the minimum risk coefficient determination module 12 may include:

[0089] A predicted icing weight data monitoring unit, configured to monitor a target change trend corresponding to the predicted icing weight data of the current distribution line when it is determined that the target icing weight data corresponding to the current distribution line exceeds a preset line safety bearing capacity threshold;

[0090] A power distribution line to be de-iced determination unit, configured to determine the current distribution line as the power distribution line to be de-iced that meets the preset de-icing conditions when the target change trend meets a preset rising condition.

[0091] In some specific embodiments, the minimum risk coefficient determination module 12 may include:

[0092] A target importance score determination unit, configured to determine a target user number and a target load type corresponding to the power distribution line to be de-iced, and determine a target importance score corresponding to the power distribution line to be de-iced based on a preset scoring standard condition, the target user number, and the target load type;

[0093] A target de-icing resource availability determination unit, configured to determine a target distance between the power distribution line to be de-iced and a target de-icing device, determine target de-icing material reserve data corresponding to the power distribution line to be de-iced, and determine a target de-icing resource availability corresponding to the power distribution line to be de-iced based on the target distance and the target de-icing material reserve data;

[0094] A minimum risk coefficient determination unit, configured to use the preset de-icing scheme optimization model to determine the minimum risk coefficient of the power distribution line to be de-iced based on a preset multi-objective optimization algorithm, the target icing weight data corresponding to the power distribution line to be de-iced, the target importance score, the target de-icing resource availability, and a preset weight coefficient.

[0095] In some specific embodiments, the power distribution line ice melting module 13 may include:

[0096] A target ice melting sequence determination unit, configured to sort each of the minimum risk coefficients based on a preset sequence condition, and determine a target ice melting sequence corresponding to the power distribution line to be ice melted based on the sorted minimum risk coefficients;

[0097] An initial ice melting scheme generation unit, configured to construct a target ice melting time calculation model based on target ice melting parameters, and use the target ice melting time calculation model to determine a target ice melting time corresponding to the power distribution line to be ice melted based on the target ice coating weight data corresponding to the power distribution line to be ice melted and the target conductor parameters corresponding to the power distribution line to be ice melted, so as to generate the initial ice melting scheme corresponding to the power distribution line to be ice melted based on the target ice melting sequence and the target ice melting time.

[0098] In some specific embodiments, the power distribution line ice melting module 13 may further include:

[0099] An initial ice melting scheme optimization unit, configured to monitor in real time the target conductor temperature and target current change corresponding to the power distribution line to be ice melted, and optimize the initial ice melting scheme in real time based on the target ice melting time, the target conductor temperature, the target current change, and a preset feedback adjustment strategy;

[0100] A model optimization unit, configured to collect in real time the target ice coating weight data corresponding to the power distribution line to be ice melted, and optimize the target ice coating weight prediction model and the preset ice melting scheme optimization model based on the target ice coating weight data;

[0101] A remaining risk coefficient determination unit, configured to use the optimized target ice coating weight prediction model, the optimized preset ice melting scheme optimization model, and the target ice coating weight data to determine a remaining risk coefficient corresponding to the power distribution line to be ice melted, and perform a risk assessment on the power distribution line to be ice melted based on the remaining risk coefficient.

[0102] Furthermore, an embodiment of the present application also discloses an electronic device, Figure 5 which is a structural diagram of an electronic device 20 shown according to an exemplary embodiment. The content in the figure cannot be considered as any limitation to the scope of use of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. Among them, the memory 22 is used to store a computer program, and the computer program is loaded and executed by the processor 21 to implement the relevant steps in the power distribution line ice melting method disclosed in any of the foregoing embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0103] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and specific limitations are not imposed here; the input / output interface 25 is used to obtain external input data or output data to the outside, and its specific interface type can be selected according to specific application requirements, and specific limitations are not imposed here.

[0104] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, a random access memory, a magnetic disk, an optical disk, etc., and the resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be transient storage or permanent storage.

[0105] Among them, the operating system 221 is used to manage and control each hardware device and the computer program 222 on the electronic device 20, and it can be Windows Server, Netware, Unix, Linux, etc. In addition to the computer program that can be used to complete the power distribution line deicing method executed by the electronic device 20 disclosed in any of the foregoing embodiments, the computer program 222 can further include computer programs that can be used to complete other specific tasks.

[0106] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the power distribution line deicing method disclosed above is implemented. For the specific steps of this method, reference can be made to the corresponding content disclosed in the foregoing embodiments, and details are not repeated here.

[0107] In this specification, the various embodiments are described in a progressive manner, and the key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0108] Those skilled in the art can further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0109] The steps of the methods or algorithms described in connection with the embodiments disclosed herein may be implemented directly in hardware, in software modules executed by a processor, or in a combination thereof. The software modules may be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0110] Finally, it should also be noted that in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0111] The technical solutions provided in this application have been introduced in detail above. Specific examples are used herein to illustrate the principles and implementation manners of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A method for de-icing a distribution line, characterized in that, Including: Determining a corresponding target liquid water factor based on the target ice coating weight data and target meteorological data of the distribution line collected in real time, and determining the predicted ice coating weight data corresponding to each distribution line based on the target liquid water factor and the target ice coating weight data; The liquid water factor is a parameter used to measure the content of liquid water in the ice layer; Determining the distribution lines to be de-iced that meet the preset de-icing conditions according to the predicted ice coating weight data, and determining the minimum risk coefficient of the distribution lines to be de-iced based on the preset de-icing scheme optimization model and the target ice coating weight data corresponding to the distribution lines to be de-iced; Generating an initial de-icing scheme corresponding to the distribution lines to be de-iced based on each minimum risk coefficient, optimizing the initial de-icing scheme in real time, and performing de-icing treatment on the distribution lines to be de-iced based on the optimized initial de-icing scheme.

2. The ice melting method for a power distribution line according to claim 1, characterized in that, The target meteorological data includes target rainfall intensity, target wind speed, target temperature, and target humidity; Correspondingly, the determining the corresponding target liquid water factor based on the target ice coating weight data and target meteorological data of the distribution line collected in real time includes: Determining a first correction coefficient corresponding to the target temperature and a second correction coefficient corresponding to the target humidity based on a preset machine learning algorithm, historical liquid water factors, the target climate characteristics corresponding to the distribution line, and the target line environment corresponding to the distribution line; Determining the target liquid water factor corresponding to the distribution line based on the target ice coating weight data, the target rainfall intensity, the target wind speed, the target temperature, the target humidity, the first correction coefficient, and the second correction coefficient of the distribution line collected in real time.

3. The ice melting method for a power distribution line according to claim 1, wherein The determining the predicted ice coating weight data corresponding to each distribution line based on the target liquid water factor and the target ice coating weight data includes: Determining target model parameters based on a preset regression analysis method, and constructing a corresponding target ice coating weight prediction model based on the target model parameters; Using the target ice coating weight prediction model to determine the predicted ice coating weight data corresponding to each distribution line based on the target liquid water factor and the target ice coating weight data.

4. The ice melting method for a power distribution line according to claim 1, wherein The determining the distribution lines to be de-iced that meet the preset de-icing conditions according to the predicted ice coating weight data includes: If it is determined that the target ice coating weight data corresponding to the current distribution line exceeds the preset line safety bearing capacity threshold, then monitoring the target change trend corresponding to the predicted ice coating weight data of the current distribution line; If the target change trend meets the preset rising condition, then determining the current distribution line as the distribution line to be de-iced that meets the preset de-icing conditions.

5. The ice melting method for a power distribution line according to claim 1, wherein The determining the minimum risk coefficient of the distribution lines to be de-iced based on the preset de-icing scheme optimization model and the target ice coating weight data corresponding to the distribution lines to be de-iced includes: Determining the target number of users and target load types corresponding to the distribution lines to be de-iced, and determining the target importance score corresponding to the distribution lines to be de-iced based on the preset scoring standard conditions, the target number of users, and the target load types; Determine the target distance between the power distribution line to be de-iced and the target de-icing equipment, determine the target de-icing material reserve data corresponding to the power distribution line to be de-iced, and determine the target de-icing resource availability corresponding to the power distribution line to be de-iced based on the target distance and the target de-icing material reserve data; Use the preset de-icing scheme optimization model to determine the minimum risk coefficient of the power distribution line to be de-iced based on the preset multi-objective optimization algorithm, the target ice coating weight data corresponding to the power distribution line to be de-iced, the target importance score, the target de-icing resource availability, and the preset weight coefficient.

6. The ice melting method for a distribution line according to any one of claims 1 to 5, characterized in that The generating an initial de-icing scheme corresponding to the power distribution line to be de-iced based on each of the minimum risk coefficients includes: Sort the minimum risk coefficients according to a preset order condition, and determine the target de-icing order corresponding to the power distribution line to be de-iced based on the sorted minimum risk coefficients; Construct a target de-icing time calculation model based on the target de-icing parameters, and use the target de-icing time calculation model to determine the target de-icing time corresponding to the power distribution line to be de-iced based on the target ice coating weight data corresponding to the power distribution line to be de-iced and the target conductor parameters corresponding to the power distribution line to be de-iced, so as to generate the initial de-icing scheme corresponding to the power distribution line to be de-iced based on the target de-icing order and the target de-icing time.

7. The ice melting method for a power distribution line according to claim 6, characterized in that, During the process of real-time optimizing the initial de-icing scheme and de-icing the power distribution line to be de-iced based on the optimized initial de-icing scheme, it further includes: Real-time monitor the changes in the target conductor temperature and target current corresponding to the power distribution line to be de-iced, and real-time optimize the initial de-icing scheme based on the target de-icing time, the target conductor temperature, the target current change, and the preset feedback adjustment strategy; Real-time collect the target ice coating weight data corresponding to the power distribution line to be de-iced, and optimize the target ice coating weight prediction model and the preset de-icing scheme optimization model based on the target ice coating weight data; Use the optimized target ice coating weight prediction model, the optimized preset de-icing scheme optimization model, and the target ice coating weight data to determine the remaining risk coefficient corresponding to the power distribution line to be de-iced, and conduct a risk assessment on the power distribution line to be de-iced based on the remaining risk coefficient.

8. An ice melting device for a distribution line, characterized in that, It includes: A predicted ice coating weight data determination module, configured to determine the corresponding target liquid water factor based on the target ice coating weight data and target meteorological data of the power distribution line collected in real time, and determine the predicted ice coating weight data corresponding to each power distribution line based on the target liquid water factor and the target ice coating weight data; The liquid water factor is a parameter used to measure the content of liquid water in the ice layer; A minimum risk coefficient determination module, configured to determine the power distribution line to be de-iced that meets the preset de-icing conditions according to the predicted ice coating weight data, and determine the minimum risk coefficient of the power distribution line to be de-iced based on the preset de-icing scheme optimization model and the target ice coating weight data corresponding to the power distribution line to be de-iced; The power distribution line de-icing module is used to generate an initial de-icing plan corresponding to the power distribution line to be de-iced based on each of the minimum risk factors, optimize the initial de-icing plan in real time, and perform de-icing treatment on the power distribution line to be de-iced based on the optimized initial de-icing plan.

9. An electronic device, characterized in that, The electronic device includes a processor and a memory; wherein, the memory is used to store a computer program, and the computer program is loaded and executed by the processor to implement the power distribution line de-icing method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, For saving a computer program, the computer program realizes the power distribution line de-icing method according to any one of claims 1 to 7 when executed by a processor.

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