A self-cleaning insulator device, a self-cleaning method, a device and a storage medium

Abstract

The present disclosure relates to the technical field of high-voltage power transmission equipment, and particularly relates to a self-cleaning insulator device, a self-cleaning method, equipment and a storage medium, wherein the device comprises: an insulator body; at least one umbrella skirt sheath installed on the insulator body; a plurality of wind blades installed on one side of the umbrella skirt sheath, used for flowing wind to drive the wind blades to drive the umbrella skirt sheath to rotate, so as to achieve centrifugal self-cleaning of the insulator; and a driving motor electrically connected with the umbrella skirt sheath, used for driving the umbrella skirt sheath to rotate to achieve centrifugal self-cleaning of the insulator when the wind is lower than a preset wind level. Efficient automatic cleaning of the insulator is realized.

Description

Technical Field

[0001] This disclosure relates to the field of high-voltage power transmission equipment technology, and in particular to a self-cleaning insulator device, self-cleaning method, equipment and storage medium. Background Technology

[0002] Wind farms and photovoltaic power stations are typically built in heavily polluted areas such as coastal regions and deserts. Insulator surfaces easily accumulate contaminants such as salt, dust, and bird droppings, leading to frequent flashover accidents. Statistics show that such accidents account for over 80% of power grid failures. Traditional manual cleaning methods, such as using helicopters or high-pressure water jets along rail tracks, are costly, and in cold regions, icing problems are difficult to solve effectively using traditional methods.

[0003] Therefore, most current cleaning technologies rely on a single driving method, and their cleaning efficiency is significantly insufficient in low temperature or high humidity environments, and they lack intelligent control mechanisms. Summary of the Invention

[0004] To address the aforementioned technical problems, this disclosure provides a self-cleaning insulator device, a self-cleaning method, an apparatus, and a storage medium.

[0005] This disclosure provides a self-cleaning insulator device, comprising: Insulator body; At least one of the umbrella skirts is mounted on the insulator body; Multiple wind turbine blades are installed on one side of the umbrella skirt sheath, and the wind turbine blades are used to drive the umbrella skirt sheath to rotate so as to achieve centrifugal self-cleaning of the insulator. A drive motor, electrically connected to the umbrella skirt sheath, is used to drive the umbrella skirt sheath to rotate when the wind force is lower than a preset wind force level, so as to achieve centrifugal self-cleaning of the insulator.

[0006] Furthermore, a heating wire is provided inside the umbrella skirt sheath to heat the surface of the umbrella skirt sheath when it is below a preset temperature to prevent icing.

[0007] Furthermore, the umbrella skirt sheath is spiral-shaped, and the surface of the umbrella skirt sheath is covered with a nano-hydrophobic layer.

[0008] Furthermore, it also includes: a sensor module; The sensor module is embedded in the insulator body or the umbrella skirt sheath.

[0009] Furthermore, it also includes: a communication module and a control module; The communication module is connected to the insulator and is used to wirelessly transmit the data collected by the sensor module to the control module.

[0010] This disclosure also provides a self-cleaning method for controlling the self-cleaning insulator device, comprising: The dirt level, humidity, and image data are acquired through the dirt level sensor, temperature and humidity sensor, and image acquisition in the sensor module, respectively. The insulator is self-cleaned based on the comparison between the pollution level, humidity, and image data and a preset threshold.

[0011] Further, the step of comparing the pollution level, humidity, and image data with a preset threshold to obtain a comparison result, and performing self-cleaning of the insulator based on the comparison result, includes: When the level of contamination is greater than a first threshold, or the humidity is greater than a second threshold, or the image data detection determines that it is true, the insulator is self-cleaned.

[0012] Further, after the step of comparing the pollution level, humidity, and image data with a preset threshold to obtain a comparison result, and performing self-cleaning of the insulator based on the comparison result, the method further includes: The comparison results are input into a pre-trained prediction model to predict the service life of the insulator, and the model is maintained based on the prediction results. The training of the prediction model includes using historical comparison results as training samples. The training samples are used to iterate over the prediction model to obtain the performance metrics of the current model training. If the performance metric does not improve continuously during the iteration process, the model parameters of the current model are saved to obtain the pre-trained prediction model.

[0013] This disclosure also provides a computer device including a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the self-cleaning method.

[0014] This disclosure also provides a computer-readable storage medium storing computer-readable instructions that, when executed by a processor, implement the steps of the self-cleaning method.

[0015] The technical solution provided in this disclosure has the following advantages compared with the prior art: The system comprises an insulator body; a skirt sheath, with at least one skirt sheath mounted on the insulator body; multiple wind turbine blades mounted on one side of the skirt sheath, used by flowing air to drive the blades to rotate the skirt sheath, achieving centrifugal self-cleaning of the insulator; and a drive motor electrically connected to the skirt sheath, used to drive the skirt sheath to rotate when the wind force is lower than a preset wind level, thus achieving efficient automatic cleaning of the insulator. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0017] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic diagram of a self-cleaning insulator device provided in an embodiment of this disclosure; Figure 2 A schematic diagram of the self-cleaning method provided in the embodiments of this disclosure; The components include: 1. Insulator body; 2. Drive motor; 3. Umbrella skirt sheath; 4. Wind turbine blades. Detailed Implementation

[0019] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0020] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0021] Figure 1 This is a schematic diagram of a self-cleaning insulator device provided in an embodiment of this disclosure; as shown. Figure 1As shown, a self-cleaning insulator device includes: an insulator body 1; a skirt sheath 3, at least one skirt sheath 3 is installed on the insulator body 1; multiple wind turbine blades 4, multiple wind turbine blades 4 are installed on one side of the skirt sheath 3, used by the flowing wind to drive the wind turbine blades 4 to drive the skirt sheath 3 to rotate, so as to achieve centrifugal self-cleaning of the insulator; and a drive motor 2, the drive motor 2 is electrically connected to the skirt sheath 3, used to drive the skirt sheath 3 to rotate when the wind force is lower than a preset wind force level, so as to achieve centrifugal self-cleaning of the insulator.

[0022] In this embodiment, the insulator body 1 serves as the basic load-bearing structure of the entire device, bearing the mechanical loads of the conductors and equipment while providing stable and reliable electrical insulation performance, thereby ensuring the safety and long-term stability of the insulator under high-voltage operating conditions. The shed sheath 3 installed on the insulator body 1 forms a rotatable external structure, which on the one hand expands the creepage distance of the insulator and improves the electric field distribution, and on the other hand, acts as a direct centrifugal cleaning component during rotation, effectively throwing off dirt attached to the surface under centrifugal force, thereby reducing the risk of flashover. Multiple wind turbine blades 4 set on one side of the shed sheath 3 can convert the kinetic energy of the environment into the rotational kinetic energy of the shed sheath 3 under the action of natural wind, enabling the insulator to achieve passive and continuous self-cleaning when wind conditions are available, reducing dependence on external energy and manual maintenance. The drive motor 2 electrically connected to the shed sheath 3 actively drives the shed sheath 3 to rotate when the ambient wind force is lower than the preset wind force level, compensating for the insufficient drive of the wind turbine blades 4 under windless or light wind conditions, thereby ensuring the continuity and reliability of the centrifugal self-cleaning function. Through the coordinated operation of the insulator body 1, the shed sheath 3, the wind turbine blades 4, and the drive motor 2, this device can maintain a stable centrifugal self-cleaning effect under both windy and windless conditions, achieving a complementarity between passive and active cleaning. This not only significantly reduces the adverse effects of long-term accumulation of dirt on insulation performance, but also reduces the frequency of manual cleaning and operation and maintenance costs, thereby improving the operational safety and adaptability of the insulator in complex environments.

[0023] Specifically, a rotating shaft is provided inside the insulator body 1, and the rotating shaft can be connected to the insulator body 1. The insulator body 1 and the shed sheath 3 are integrally formed. The drive motor 2 drives the rotating shaft, so that the insulator body 1 drives the shed sheath 3 to rotate. Alternatively, the rotating shaft can be set inside the insulator body 1 and connected to the shed sheath 3. The shed sheath 3 is installed on the insulator body 1, and the drive motor 2 drives the rotating shaft, so that the shed sheath 3 rotates outside the insulator body 1.

[0024] The insulator features an internal heating wire (3) to heat the surface of the insulator when it falls below a preset temperature, preventing icing. This integrated heating wire allows the insulator to maintain its surface temperature below the preset threshold, actively raising the surface temperature to prevent icing. By directly integrating the anti-icing function into critical contaminated and icy areas of the insulator, heating can be applied directly and quickly to the most icy areas of the insulator surface. Compared to external or overall heating, this method offers advantages such as higher thermal efficiency, faster response, and lower energy consumption. In low-temperature, high-humidity, or rain / snow conditions prone to icing, ice formation on the insulator surface significantly reduces the effective creepage distance and can cause ice to adhere between insulators, affecting normal rotation and even causing mechanical jamming. Timely heating by the heating wire effectively inhibits ice crystal adhesion and ice growth, preventing icing at its source. Furthermore, the heating wire works synergistically with the centrifugal self-cleaning function of the insulator skirts. While heating softens and melts ice or frozen contaminants, the rotation of the skirts generates centrifugal force, quickly dislodging melted water droplets or loose contaminants from the skirt surface, further improving cleaning efficiency and anti-icing effects. This not only significantly enhances the environmental adaptability of insulators in cold regions and high-risk icing areas, but also reduces insulation performance degradation, mechanical failure, and the need for manual de-icing maintenance caused by icing. It is of great significance for ensuring the safe and stable operation of power transmission equipment under complex climatic conditions.

[0025] Furthermore, the umbrella skirt cover 3 is designed in a spiral shape, and its surface is covered with a nano-hydrophobic layer. This synergistically enhances self-cleaning and anti-fouling performance from both structural and surface characteristics perspectives. During rotation, the spiral umbrella skirt cover 3 forms continuous axial flow and radial detachment paths, making it easier for dust, salt spray particles, bird droppings, and other contaminants adhering to the umbrella skirt surface to be guided to the edge and detached from the surface under the combined influence of centrifugal force and spiral guidance. Compared to traditional straight or regular umbrella skirt structures, the spiral structure effectively reduces the probability of contaminants remaining in localized areas, thereby improving overall cleaning uniformity. Simultaneously, the nano-hydrophobic layer covering the umbrella skirt cover 3 reduces surface energy and increases the droplet contact angle, making it difficult for water, mud, and dissolved contaminants to spread and adhere to the umbrella skirt surface, thus weakening the adhesion between contaminants and the umbrella skirt at a microscopic level. This nano-hydrophobic layer, combined with the spiral structure, slows down the rate of contaminant deposition under static conditions and further amplifies the centrifugal cleaning effect under dynamic rotation conditions, making it easier for contaminants to be detached rather than reattached. This not only reduces the rate of contamination accumulation in insulators during long-term operation but also decreases reliance on high-frequency cleaning actions, extending the effective working cycle of the self-cleaning system. Simultaneously, the hydrophobic layer can inhibit water film formation to some extent, reducing the risk of flashover under wet conditions. This complements the electrical insulation function of the insulator body 11, significantly improving the operational safety of insulators in high-humidity and high-pollution environments.

[0026] In some possible implementations, a sensor module is also included; the sensor module is embedded in the insulator body 1 or the skirt sheath 3.

[0027] In this embodiment, by embedding the sensor module inside the insulator body 1 or the shed sheath 3, the insulator can be monitored in real time for its operating status and environmental parameters without affecting the integrity of its external structure and insulation performance. The sensor module may include a contamination sensor, a temperature and humidity sensor, and an image acquisition device, used to acquire intuitive information on the degree of contamination on the shed surface, changes in ambient temperature and humidity, and surface condition. Through this embedded arrangement, the sensors can be placed closer to the monitored object, reducing external interference and measurement errors, and improving the accuracy and reliability of data acquisition. This transforms the traditional passive management method, which relies on manual inspections or periodic experience-based maintenance, into a proactive sensing mode based on real-time data, enabling timely capture and quantification of the insulator's contamination evolution, environmental changes, and potential risks. Simultaneously, the sensor module provides a data foundation for subsequent cleaning strategies, heating strategies, and lifespan assessments, allowing the system to determine whether to perform cleaning or protective actions based on actual operating conditions rather than fixed time intervals, thereby avoiding unnecessary energy consumption and mechanical wear. Furthermore, by accumulating and analyzing historical sensor data, the patterns of insulator contamination and icing in different regions and seasons can be identified, providing a scientific basis for operation and maintenance decisions and equipment selection. This implementation significantly improves the intelligence level of insulators, upgrading them from traditional passive insulation components to intelligent power transmission equipment units with state awareness capabilities.

[0028] In some possible implementations, a communication module and a control module are also included; the communication module is connected to the insulator and is used to wirelessly transmit the data collected by the sensor module to the control module.

[0029] In this embodiment, by setting up a communication module and a control module, and connecting the communication module to the insulator, data collected by the sensor module is wirelessly transmitted to the control module, enabling centralized processing and intelligent decision-making regarding the insulator's operating status. The communication module employs low-power wireless communication, allowing insulators to stably upload monitoring data to local or remote control modules even in widely distributed and geographically dispersed wind farms or photovoltaic power plants. The control module then performs analysis, judgment, and control logic based on the received data to determine whether to activate actuators such as skirt rotation, drive motor 2, or heating wires. This achieves closed-loop control of monitoring and execution, eliminating the need for manual intervention in the insulator's self-cleaning and anti-icing functions, enabling automatic responses to environmental changes based on real-time status. Through the communication module, the control module can not only acquire operating information from individual insulators but also manage and coordinate multiple insulators, avoiding energy consumption peaks or operational interference caused by centralized actions. Furthermore, the control module can feed back operating status, anomaly information, and maintenance suggestions to the maintenance terminal, enabling remote monitoring and predictive maintenance, reducing on-site inspection frequency and labor costs. This transforms insulators from simple physical devices into network nodes that can be connected to intelligent operation and maintenance systems, significantly improving the manageability and operational reliability of power transmission equipment and playing a vital supporting role in building intelligent and digital power transmission systems for new energy power plants.

[0030] Figure 2 A schematic diagram of the self-cleaning method provided in the embodiments of this disclosure; as shown Figure 2 As shown, this disclosure also provides a self-cleaning method for controlling a self-cleaning insulator device, comprising: Step S1: Obtain dirt level, humidity and image data through the dirt level sensor, temperature and humidity sensor and image acquisition in the sensor module, respectively; In this embodiment, a sensor module mounted on the insulator utilizes a pollution level sensor, a temperature and humidity sensor, and an image acquisition device to acquire multi-dimensional, real-time data on the insulator's operating status and its surrounding environment. The pollution level sensor quantifies the accumulation of contaminants on the insulator's apron surface by detecting parameters such as surface conductivity, resistance changes, or equivalent leakage current. The temperature and humidity sensor collects temperature and relative humidity information of the environment surrounding the insulator to determine whether it is in a high-humidity, condensation, or wet-pollution flashover condition. The image acquisition device acquires visual images of the apron surface to intuitively record the distribution of contaminants, the type of contaminants, and the presence of icing, condensation, or abnormal deposits. Through the coordinated operation of these three types of sensors, the insulator's state can be comprehensively characterized from three levels: physical quantities, environmental conditions, and visual characteristics. Compared to monitoring methods relying on only a single sensor, this significantly improves the accuracy and reliability of state perception. Furthermore, the collected data can be used for current state assessment or stored and analyzed as historical data for long-term analysis, providing a data foundation for subsequent trend prediction and strategy optimization. This transforms insulators from traditional devices into intelligent units with real-time status sensing capabilities, enabling them to promptly reflect dirt accumulation, environmental changes, and potential risks. This provides an objective and accurate basis for subsequent self-cleaning decisions, reducing misjudgments or delayed responses caused by insufficient information.

[0031] Step S2: Based on the pollution level, humidity and image data, compare with the preset threshold to obtain the comparison results, and perform self-cleaning of the insulator according to the comparison results.

[0032] In this embodiment, the acquired contamination level, humidity, and image data are compared and analyzed with preset thresholds to obtain comparison results. Based on these results, the insulator undergoes a corresponding self-cleaning operation. The preset thresholds can be set according to the insulator model, voltage level, installation environment, and historical operating experience. Examples include contamination level thresholds, humidity thresholds, and pollution level thresholds based on image features. When the contamination level exceeds the preset threshold, or when significant dirt, condensation, or icing risk is detected on the surface in a high-humidity environment, the system determines that the insulator requires cleaning and triggers a centrifugal self-cleaning mechanism, such as starting wind-driven or motor-driven skirt rotation to remove dirt adhering to the surface. When the detection results are within a safe range, the system remains in standby mode to avoid unnecessary actions and energy consumption. By comprehensively comparing multi-source sensor data with thresholds, rather than triggering cleaning actions with a single indicator, false triggering or missed triggering can be effectively avoided, improving the scientific rigor and accuracy of self-cleaning decisions. This system enables on-demand cleaning based on actual operating conditions, transforming the insulator's self-cleaning behavior from timed execution to state-driven. This reduces energy consumption, minimizes mechanical wear, and extends equipment lifespan while ensuring insulation performance safety. Furthermore, the threshold comparison mechanism is clearly structured and simple to implement, facilitating rapid response within the control module. Threshold parameters can also be flexibly adjusted according to different application scenarios, demonstrating good engineering applicability and scalability. It can promptly initiate self-cleaning in the early stages of pollution intensification or environmental degradation, effectively suppressing the adverse effects of long-term pollution accumulation on insulation performance, significantly reducing the risk of flashover, and improving the operational reliability of insulators in complex environments.

[0033] In some possible implementations, step S2 involves comparing the contamination level, humidity, and image data with preset thresholds to obtain comparison results, and performing self-cleaning on the insulator based on the comparison results, including: performing self-cleaning on the insulator when the contamination level is greater than a first threshold, or the humidity is greater than a second threshold, or the image data detection determines that it is positive.

[0034] In some possible implementations, after comparing the insulator with a preset threshold based on the degree of contamination, humidity, and image data to obtain the comparison results, and performing self-cleaning of the insulator based on the comparison results, the method further includes: inputting the comparison results into a pre-trained prediction model to predict the service life of the insulator, and maintaining the model based on the prediction results; training the prediction model includes: using historical comparison results as training samples; iterating the prediction model with the training samples to obtain the performance index of the current model training; when the performance index does not improve continuously during the iteration process, saving the model parameters of the current model to obtain the pre-trained prediction model.

[0035] In this embodiment, at the model input layer, the historical comparison results are expressed in a structured manner. The comparison results not only include the deviation of pollution level, humidity, and image features from their respective preset thresholds, but also further include derived features such as deviation duration, rate of change, and frequency of occurrence to form a multi-dimensional feature vector. The image data is preferably preprocessed through an image feature extraction network to extract high-level semantic features characterizing the distribution of pollution, cracks, discharge traces, and icing features on the umbrella skirt surface. These features are then concatenated or weighted with the numerical features from the pollution level sensor and the temperature and humidity sensor at the feature fusion layer to construct a unified model input. In the main model structure, a deep learning regression model can be used to characterize the evolution of the insulator's operating state over time, enabling the model to learn the long-term impact of pollution accumulation, humidity changes, and surface condition degradation on insulation performance degradation. During this process, the input features are nonlinearly mapped through hidden layers, extracting the high-order features most sensitive to the prediction of service life layer by layer. A regularization term is introduced into the network to prevent the model from overfitting individual historical samples. During the model training phase, historical comparison results are input into the model as training samples. The remaining safe service life or health index is set as the target output, and the prediction error is measured using mean squared error or a weighted loss function. The model parameters are iteratively updated using a backpropagation algorithm. After each iteration, performance metrics such as prediction error, goodness of fit, or stability evaluation metrics are calculated based on validation samples to assess the model's accuracy and generalization ability in predicting insulator life. When performance metrics do not show significant improvement over multiple iterations, the model is considered to have converged or entered a performance plateau. At this point, the current model parameters are saved as a pre-trained prediction model to avoid wasting computational resources on ineffective iterations. In the practical application phase, the real-time acquired and calculated comparison results are input into the pre-trained prediction model. Based on the learned degradation patterns, the model outputs corresponding service life prediction results or maintenance risk levels. The control module then generates targeted maintenance decisions, such as pre-arranging cleaning, repair, or replacement plans.

[0036] In some possible implementations, the insulator body 1 consists of an insulating core rod, a rotatable shed sheath 3, drive blades and a motor. The surface of the shed sheath 3 is provided with spiral guide grooves to enhance the effect of centrifugal force.

[0037] The sensor module is embedded inside the core rod and communicates with the central control system via wireless transmission technology to achieve real-time data transmission and processing.

[0038] Routine cleaning: When the wind speed is greater than 3m / s, the wind drives the umbrella skirt to rotate, performing routine cleaning.

[0039] Emergency mode: When the sensor detects that the level of dirt exceeds the standard or the humidity is greater than 80%, the motor will be started to perform auxiliary cleaning in order to deal with special situations.

[0040] Maintenance early warning: Machine learning models predict the lifespan of insulators and push replacement suggestions in advance to achieve predictive maintenance.

[0041] Application in wind farms: Deploying insulator clusters at the top of the tower can effectively reduce downtime losses caused by pollution flashover and ensure the stable operation of the wind farm.

[0042] Application in photovoltaic power plants: When installed in conjunction with the support structure, it can not only clean the insulators, but also clean the photovoltaic panels and transmission equipment at the same time.

[0043] This disclosure also provides a computer device including a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of a self-cleaning method.

[0044] This disclosure also provides a computer-readable storage medium storing computer-readable instructions that, when executed by a processor, implement the steps of a self-cleaning method.

[0045] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0046] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A self-cleaning insulator device, characterized in that, include: Insulator body; At least one of the umbrella skirts is mounted on the insulator body; Multiple wind turbine blades are installed on one side of the umbrella skirt sheath, and the wind turbine blades are used to drive the umbrella skirt sheath to rotate so as to achieve centrifugal self-cleaning of the insulator. A drive motor, electrically connected to the umbrella skirt sheath, is used to drive the umbrella skirt sheath to rotate when the wind force is lower than a preset wind force level, so as to achieve centrifugal self-cleaning of the insulator.

2. The self-cleaning insulator device according to claim 1, characterized in that, The umbrella skirt sheath is equipped with a heating wire inside, which is used to heat the surface temperature of the umbrella skirt sheath when it is lower than a preset temperature to prevent icing.

3. The self-cleaning insulator device according to claim 1, characterized in that, The umbrella skirt sheath is spiral-shaped, and the surface of the umbrella skirt sheath is covered with a nano-hydrophobic layer.

4. The self-cleaning insulator device according to any one of claims 1 to 3, characterized in that, Also includes: Sensor module; The sensor module is embedded in the insulator body or the umbrella skirt sheath.

5. The self-cleaning insulator device according to claim 4, characterized in that, Also includes: Communication module and control module; The communication module is connected to the insulator and is used to wirelessly transmit the data collected by the sensor module to the control module.

6. A self-cleaning method, characterized in that, For controlling the self-cleaning insulator device as described in any one of claims 1 to 5, comprising: The dirt level, humidity, and image data are acquired through the dirt level sensor, temperature and humidity sensor, and image acquisition in the sensor module, respectively. The insulator is self-cleaned based on the comparison between the pollution level, humidity, and image data and a preset threshold.

7. The self-cleaning method according to claim 6, characterized in that, The process of comparing the pollution level, humidity, and image data with preset thresholds to obtain comparison results, and then performing self-cleaning of the insulators based on the comparison results, includes: When the level of contamination is greater than a first threshold, or the humidity is greater than a second threshold, or the image data detection determines that it is true, the insulator is self-cleaned.

8. The self-cleaning method according to claim 6, characterized in that, After the step of comparing the pollution level, humidity, and image data with a preset threshold to obtain a comparison result, and performing self-cleaning of the insulator based on the comparison result, the method further includes: The comparison results are input into a pre-trained prediction model to predict the service life of the insulator, and the model is maintained based on the prediction results. The training of the prediction model includes using historical comparison results as training samples. The training samples are used to iterate over the prediction model to obtain the performance metrics of the current model training. If the performance metric does not improve continuously during the iteration process, the model parameters of the current model are saved to obtain the pre-trained prediction model.

9. A computer device, characterized in that, The device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the self-cleaning method as described in any one of claims 6 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions that, when executed by a processor, implement the steps of the self-cleaning method as described in any one of claims 6 to 8.

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