Intelligent lightning arrester control method and device

Abstract

The present application provides a method and device for controlling an intelligent lightning arrester. The method includes: real-time monitoring of current environmental data, the environmental data including weather conditions, electric field strength and thunderstorm activity; based on the monitored current environmental data, applying a prediction model to obtain a prediction result, the prediction result characterizing the probability of thunderstorm occurrence and the thunderstorm propagation path; based on the prediction result, generating a lightning protection strategy; wherein the lightning protection strategy includes a plurality of sub-strategies corresponding to a plurality of different areas of the thunderstorm propagation path, each sub-strategy is used to indicate at least one of the target lightning arrester contained in the thunderstorm propagation path, the working parameters that the target lightning arrester needs to adjust, the alarm signal indication, and the prevention indication; according to the lightning protection strategy, the target lightning arrester is controlled by wireless communication. The present application provides a control strategy for an intelligent lightning arrester, which can realize thunderstorm prediction and pre-deployment of lightning protection schemes according to actual environmental conditions.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to an intelligent lightning arrester control method and device. Background Art

[0002] With the advancement of science and technology and the development of society, the safe operation of power systems has become increasingly important. Lightning, as a common natural phenomenon, often poses a huge threat to power facilities, especially for key facilities such as high-voltage transmission lines and substations. Lightning strikes may cause serious economic losses or even casualties. Therefore, it is crucial to design an effective lightning protection system.

[0003] Traditional lightning protection measures mostly rely on fixed physical structures, such as lightning rods and lightning conductors. Although these devices can reduce lightning damage to a certain extent, they are usually passive and cannot be flexibly adjusted according to actual conditions. In addition, traditional lightning protection systems often lack the ability to predict lightning activities and cannot issue early warnings, thus limiting their protective effects.

[0004] In recent years, with the development of Internet of Things technology and artificial intelligence, a new generation of lightning arresters has emerged that can actively adapt to environmental changes and make intelligent decisions. This type of intelligent lightning arrester can not only monitor environmental data (such as weather conditions, electric field strength, thunderstorm activity, etc.) in real time, but also use advanced prediction models to analyze current environmental data and combine historical data to estimate the possibility of thunderstorms and propagation paths. Based on such prediction results, the intelligent lightning arrester can generate corresponding lightning protection strategies, such as adjusting its own working parameters, issuing alarm signals, or providing preventive guidance, so as to more effectively protect power facilities from lightning strikes. However, in the existing technology, most intelligent lightning arresters still face many challenges, such as how to efficiently collect and process a large amount of environmental data, how to build a high-precision thunderstorm prediction model, and how to ensure the security and reliability of wireless communication control.

[0005] Therefore, the present application provides an intelligent lightning arrester control method and device to solve one of the above technical problems. Summary of the invention

[0006] The purpose of this application is to provide an intelligent lightning arrester control method and device, which can solve at least one of the above-mentioned technical problems. The specific solution is as follows:

[0007] According to the specific implementation methods of the present application, in the first aspect, the present application provides a control method for an intelligent lightning arrester, comprising: real-time monitoring of current environmental data, wherein the environmental data include weather conditions, electric field strength, and thunderstorm activity; based on the monitored current environmental data, applying a prediction model to obtain a prediction result, wherein the prediction result characterizes the probability of thunderstorm occurrence and the thunderstorm propagation path; based on the prediction result, generating a lightning protection strategy; wherein the lightning protection strategy includes a plurality of sub-strategies corresponding to a plurality of different areas along the thunderstorm propagation path, each of the sub-strategies being used to indicate at least one of a target lightning arrester contained in the thunderstorm propagation path, a working parameter that needs to be adjusted for the target lightning arrester, an alarm signal indication, and a prevention indication; according to the lightning protection strategy, the target lightning arrester is controlled by wireless communication.

[0008] According to a specific implementation of the present application, in a second aspect, the present application provides a control device for an intelligent lightning arrester, comprising:

[0009] An environmental data sensor module is used to monitor the current environmental data in real time, and the environmental data includes at least weather conditions, electric field strength and thunderstorm activities; a processing unit is configured to generate a prediction result based on the current environmental data monitored by the environmental data sensor module and apply a preset prediction model, and the prediction result at least characterizes the probability of thunderstorm occurrence and the thunderstorm propagation path; a strategy generation module is configured to generate a lightning protection strategy based on the prediction result, and the lightning protection strategy includes at least indicating the target lightning arrester included in the thunderstorm propagation path, the working parameters that need to be adjusted for the target lightning arrester, an alarm signal indication, and at least one of a prevention indication; a wireless communication module is configured to control the target lightning arrester through wireless communication according to the lightning protection strategy generated by the strategy generation module.

[0010] Compared with the prior art, the above solution of the embodiment of the present application has at least the following beneficial effects:

[0011] By combining real-time monitoring technology and prediction algorithms, the intelligent lightning arrester control method can dynamically evaluate the risk of thunderstorm activities and generate corresponding lightning protection strategies accordingly. This method not only improves the response speed and accuracy of the lightning arrester to thunderstorm events, but also enhances the overall protection efficiency of the system. Real-time monitoring of current environmental data (such as weather conditions, electric field strength, and thunderstorm activities) enables the system to obtain the latest environmental information in real time, thereby quickly identifying potential lightning strike threats. The application prediction model based on these monitoring data can accurately predict the probability of occurrence and propagation path of thunderstorms, and then generate targeted lightning protection strategies, such as adjusting the working parameters of the target lightning arrester, triggering alarm signals, or providing preventive guidance. Remote control of lightning arresters through wireless communication technology not only simplifies the operating process, but also greatly improves the flexibility and response efficiency of the system. The control method provided in this application significantly improves the active defense capability of the power system in the face of lightning disasters, effectively protects power facilities from lightning damage, and also reduces maintenance costs, enhancing the overall safety and stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A flow chart showing a control method of an intelligent lightning arrester according to an embodiment of the present application is shown;

[0013] Figure 2 A flow chart of a method for applying a prediction model to obtain a prediction result based on the monitored current environmental data is shown;

[0014] Figure 3 A flow chart of a method for training a prediction model according to an embodiment of the present application is shown;

[0015] Figure 4 A flow chart of a method for generating a sub-strategy corresponding to a wind farm area is shown;

[0016] Figure 5 A flow chart of a method for generating a sub-strategy corresponding to a high-rise building area is shown;

[0017] Figure 6 A flow chart of a method for generating a sub-strategy corresponding to a smart park is shown;

[0018] Figure 7 A flow chart showing a control method of an intelligent lightning arrester according to an embodiment of the present application is shown;

[0019] Figure 8 A unit block diagram of a control device for an intelligent lightning arrester according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0021] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings, and "multiple" generally includes at least two.

[0022] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0023] It should be understood that although the terms first, second, third, etc. may be used to describe in the embodiments of the present application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0024] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0025] It should also be noted that the term "includes", "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, so that a commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprising a ..." do not exclude the existence of other identical elements in the commodity or device including the elements.

[0026] It should be particularly noted that any symbols and / or numbers in the specification that are not marked in the accompanying drawings are not drawing marks.

[0027] The optional embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0028] The embodiment provided in this application is an embodiment of a control method for an intelligent lightning arrester.

[0029] Combine the following Figure 1 The embodiments of the present application are described in detail.

[0030] Figure 1 A flow chart of a control method for an intelligent lightning arrester according to an embodiment of the present application is shown. Figure 1 As shown, the steps S101 to S104 are included:

[0031] Step S101, real-time monitoring of current environmental data, the environmental data including weather conditions, electric field strength and thunderstorm activity.

[0032] Step S102, based on the monitored current environmental data, a prediction model is applied to obtain a prediction result, where the prediction result represents the probability of thunderstorm occurrence and the thunderstorm propagation path.

[0033] Step S103: generating a lightning protection strategy based on the prediction result.

[0034] The lightning protection strategy includes a plurality of sub-strategies corresponding to a plurality of different areas of the thunderstorm propagation path, each of which is used to indicate at least one of a target lightning arrester included in the thunderstorm propagation path, a working parameter of the target lightning arrester that needs to be adjusted, an alarm signal indication, and a prevention indication;

[0035] Step S104: Control the target lightning arrester by wireless communication according to the lightning protection strategy.

[0036] By combining real-time monitoring technology and prediction algorithms, the intelligent lightning arrester control method can dynamically evaluate the risk of thunderstorm activities and generate corresponding lightning protection strategies accordingly. This method not only improves the response speed and accuracy of the lightning arrester to thunderstorm events, but also enhances the overall protection efficiency of the system. Real-time monitoring of current environmental data (such as weather conditions, electric field strength, and thunderstorm activities) enables the system to obtain the latest environmental information in real time, thereby quickly identifying potential lightning strike threats. The application prediction model based on these monitoring data can accurately predict the probability of occurrence and propagation path of thunderstorms, and then generate targeted lightning protection strategies, such as adjusting the working parameters of the target lightning arrester, triggering alarm signals, or providing preventive guidance. Remote control of lightning arresters through wireless communication technology not only simplifies the operation process, but also greatly improves the flexibility and response efficiency of the system. This intelligent control method significantly improves the active defense capability of the power system in the face of lightning disasters, effectively protects power facilities from lightning damage, and also reduces maintenance costs, enhancing the overall safety and stability of the power system.

[0037] The present application exemplifies some feasible lightning protection strategies below.

[0038] Dynamic adjustment of arrester operating parameters: If the forecast model indicates that thunderstorm activity is about to increase, the triggering threshold of the arrester can be adjusted in advance so that it activates at a lower voltage or current level to protect the connected equipment earlier. For arresters with variable parameters, their operating parameters can be dynamically adjusted according to the probability and intensity of thunderstorms, such as reducing reaction time or increasing the ability to absorb energy.

[0039] Alarm and early warning mechanism: When the probability of a thunderstorm exceeds a certain threshold, the sound and light alarm system will be activated immediately to remind nearby personnel to pay attention to safety, and notify relevant managers through SMS or email, etc. Instant notifications within the mobile application will be issued to inform users of potential risk areas and provide safety guidelines.

[0040] Intelligent disconnection of non-critical loads: When a thunderstorm is predicted, power to non-critical electrical equipment is automatically cut off to prevent induced current from damaging these devices. For critical equipment, additional protection measures can be initiated, such as switching to an uninterruptible power supply (UPS).

[0041] Optimize the lightning arrester network configuration: Based on the predicted thunderstorm propagation path, reconfigure the lightning arresters at various nodes in the power grid so that those areas expected to be affected by lightning strikes are given priority protection. Adjust the coordination mode between lightning arresters to ensure coverage of the entire protected area and avoid blind spots.

[0042] Enhanced monitoring and response speed: Strengthen monitoring of areas with frequent thunderstorm activity and increase the frequency of data collection to enable faster response. When extreme thunderstorm events are predicted, a maintenance team will be dispatched to stand by to quickly intervene and inspect and repair after a lightning strike occurs.

[0043] Preventive maintenance: Based on historical data and current trends in thunderstorm activity, establish a regular inspection schedule to ensure that all lightning protection devices are in good working condition. Perform comprehensive preventive maintenance work before the thunderstorm season arrives to replace old or degraded parts.

[0044] Figure 2 A flowchart of a method for applying a prediction model to obtain a prediction result based on the monitored current environmental data is shown, such as Figure 2 As shown, the following steps are included.

[0045] Step S201, collecting historical environmental data, the historical environmental data including historical meteorological data, historical geographical data, historical thunderstorm records and historical working status data of lightning arresters.

[0046] Step S202, taking the current environment data and the historical environment data as input, inputting them into the prediction model to obtain the prediction result.

[0047] In this application, by collecting historical environmental data (including historical meteorological data, historical geographical data, historical thunderstorm records, and historical working status data of lightning arresters), and combining these data with current environmental data and inputting them into the prediction model, the accuracy and reliability of the prediction model can be greatly improved. Among them, the introduction of historical data enables the prediction model to learn based on rich past information, so as to better understand and predict future thunderstorm activity trends. This method enhances the confidence of the prediction results.

[0048] In this application, the prediction model uses the formula P thunderstorm (t)=f(Xt;θ) to calculate the prediction result. thunderstorm (t) is the probability of thunderstorm occurrence at time t, Xt is the input data vector at time t, f is the prediction model function, and θ is the model parameter. By clarifying the mathematical form of the prediction model Pthunderstorm(t)=f(Xt;θ)Pthunderstorm(t)=f(Xt;θ), the calculation method of the model output thunderstorm occurrence probability is defined, which makes the prediction process more transparent and verifiable. In this application, the use of this formal expression can ensure that the output of the prediction model has high interpretability and consistency, which helps to improve the accuracy of lightning protection strategy formulation.

[0049] For example, the input data vector is a data vector generated from current environment data and historical environment data.

[0050] In the present application, all the data in the current environmental data and the historical environmental data can be used to generate an input data vector. However, based on the training value determination, the introduction of all the data cannot make the model reach the optimal convergence. Therefore, based on the combination of the data therein, a method of generating an input data vector with the best convergence is finally determined. That is, the input data vector is generated based on the current ambient temperature T, the current ambient humidity H, the current ambient air pressure P, the current ambient electric field strength E, and the historical thunderstorm record R. In this case, the input data vector is represented by the formula Xt=[T(t), H(t), P(t), E(t), R(t)]. Among them, T(t), H(t), P(t), E(t) respectively represent the temperature, humidity, air pressure and electric field strength values ​​at time t, and R(t) represents the historical thunderstorm event record up to time t. Among them, by specifying the input data vector Xt=[T(t),H(t),P(t),E(t),R(t)], multi-dimensional data such as temperature, humidity, air pressure, electric field strength and historical thunderstorm records are included in the model input, so that the prediction model takes into account several factors with the best model effect, and captures the impact of environmental changes on thunderstorm activities in more detail, thereby improving the accuracy and reliability of the prediction results.

[0051] Furthermore, the prediction model uses the formula P thunderstorm (t)=σ(W1·Xt+b1) to get the prediction result. thunderstorm (t) is the probability of thunderstorm occurrence at time t, W1 is the first-layer weight matrix of the model, b1 is the bias term, and σ is the activation function.

[0052] For example, the activation function may be a Sigmoid function, which is used to map the result of the linear combination to a probability space and then output a probability value.

[0053] In this application, when the probability of a thunderstorm occurring is greater than a defined value, the data before the activation function is input represents the thunderstorm propagation path.

[0054] In this application, the prediction model is trained in the following manner.

[0055] Figure 3 A flow chart of a method for training a prediction model according to an embodiment of the present application is shown. Figure 3 As shown, the following steps are included.

[0056] Step S301, using historical environment data as a training set.

[0057] Step S302: based on preset labels, mark the training set into a training subset and a validation subset.

[0058] Step S303, update the model parameters θ based on the training subset, and evaluate the model performance based on the validation subset.

[0059] Step S304, using the loss function to measure the difference between the predicted result and the actual result, and updating the model parameter θ again based on the back propagation algorithm.

[0060] In this application, by describing the training process of the prediction model, including the division of the data set, the updating and evaluation of the model parameters, and the definition of the loss function, the standardization and scientificity of the model training are ensured. This method can effectively improve the generalization ability of the prediction model, so that the model not only performs well on the training data, but also can accurately predict the probability of thunderstorms in practical applications. When the optimal parameters required by the model, that is, the optimal θ, are updated, the model training is considered to be completed, and the optimal model parameter θ is used as the model parameter value during the use of the model, so that the model can obtain the optimal prediction result.

[0061] In this application, the loss function can be expressed as express.

[0062] Where L(θ) represents the loss function of the model parameter θ, N is the number of training samples, and P thunderstorm (Xi) is the probability of thunderstorm occurrence predicted by the model for the i-th sample Xi, and Yi is the thunderstorm occurrence label of the i-th sample. For example, for the thunderstorm occurrence label, for example, 1 can represent the occurrence of thunderstorm, and 0 can represent the absence of thunderstorm.

[0063] In this application, by defining the loss function L(θ) and using the loss function to optimize the model parameter θ, the prediction results are ensured to be highly consistent with the actual thunderstorm occurrence. This method quantifies the prediction error, allowing the model to continuously adjust itself and improve the prediction accuracy, thereby better guiding the formulation of lightning protection strategies.

[0064] In the present application, the multiple different areas along the thunderstorm propagation path include, for example, a wind farm area. The present application exemplarily describes a lightning protection strategy for a wind farm area.

[0065] Figure 4 A flow chart of a method for generating a sub-strategy corresponding to a wind farm area is shown, such as Figure 4 As shown, the following steps are included.

[0066] Step S401 : in response to the thunderstorm occurrence probability exceeding the response probability threshold of the wind farm area, a map image of the wind farm area is acquired.

[0067] Step S402 , referring to the current environmental data and based on the wind turbine distribution information and landform information in the map image, a thunderstorm prediction hotspot map is generated.

[0068] Among them, the thunderstorm prediction hotspot map contains a set of hotspots that need to be protected from lightning in the target area. The target area is the overlapping area between the wind farm area and the thunderstorm propagation path.

[0069] Step S403: generating a sub-strategy corresponding to the wind farm area based on the prediction result and the thunderstorm prediction hotspot map.

[0070] For example, the sub-strategies corresponding to the wind farm area include the following two aspects.

[0071] Aspect one is to broadcast a thunderstorm prediction hotspot map in the wind farm area, so that the target lightning arrester in the wind farm area receives the thunderstorm prediction hotspot map and adjusts its position according to the hotspot set.

[0072] Aspect 2 is to broadcast the prediction results in the wind farm area so that the target arrester for posture adjustment searches for wind turbines within the specified range and adjusts the operating speed of the searched wind turbines according to the probability of thunderstorms represented by the prediction results. The higher the probability of thunderstorms, the lower the adjusted operating speed.

[0073] In the present application, the above two aspects can be independently used as sub-strategies corresponding to the wind farm area, or can be jointly implemented as sub-strategies corresponding to the wind farm area.

[0074] This application obtains a map image of the wind farm area when the probability of a thunderstorm exceeds the response probability threshold of the wind farm area, and generates a thunderstorm prediction hotspot map in combination with the current environmental data, thereby accurately locating the hotspot collection that needs lightning protection. The sub-strategy generated based on this information can provide accurate lightning protection measures for the overlap between the wind farm area and the thunderstorm propagation path, ensuring the safe operation of the wind farm equipment and improving the response efficiency and protection effectiveness of the system.

[0075] In the present application, the multiple different areas along the thunderstorm propagation path include, for example, high-rise building areas. The present application exemplarily describes the lightning protection strategy for high-rise building areas.

[0076] Figure 5 A flow chart of a method for generating sub-strategies corresponding to high-rise building areas is shown, such as Figure 5 As shown, the following steps are included.

[0077] Step S501, based on the current environmental data and the thunderstorm propagation path, thunderstorm simulation is performed for each point in the target area to obtain the thunderstorm occurrence height range corresponding to each point.

[0078] Among them, the target area is the overlapping area between the high-rise building area and the thunderstorm propagation path.

[0079] Step S502, obtaining the top building height corresponding to each point in the target area.

[0080] Step S503, for each point in the target area, determine the minimum height difference between the thunderstorm occurrence height range and the top building height.

[0081] Step S504: determining the lightning protection warning level corresponding to each point in the target area based on the minimum height difference of each point in the target area.

[0082] Step S505: generating a sub-strategy corresponding to the high-rise building area based on the prediction result and the lightning protection warning level.

[0083] In this application, the sub-strategy corresponding to the high-rise building area includes broadcasting the minimum height difference of each point in the target area within the high-rise building area, so that each target lightning arrester in the high-rise building area can respectively determine the nearest target point, and adjust the position and absorption power according to the lightning warning level corresponding to the target point.

[0084] For example, each point in the target area refers to each coordinate point in the area.

[0085] This application can accurately calculate the minimum safe distance between the top of each building and the thunderstorm cloud layer by conducting a detailed thunderstorm simulation analysis on the high-rise building area, and then set the corresponding lightning warning level. This allows the area to prepare in advance based on the prediction results, and guide the lightning protection device to adjust to the best protection state by broadcasting the minimum height difference information, effectively improving the safety and response efficiency of high-rise buildings in thunderstorm weather, and reducing potential damage risks.

[0086] In the present application, multiple different areas along the thunderstorm propagation path include, for example, a smart park. The present application exemplarily describes a lightning avoidance strategy for a smart park.

[0087] Figure 6 A flow chart of a method for generating a sub-strategy corresponding to a smart park is shown, such as Figure 6 As shown, the following steps are included.

[0088] Step S601, sending the prediction results to the smart park management system, so that the smart park management system adjusts the environmental data in the park based on the prediction results.

[0089] The adjusted environmental data within the park includes at least one of temperature, humidity, air pressure, and electric field strength.

[0090] Step S602, in response to the intelligent park management system completing the adjustment, obtaining the arrester cooperation mode generated by the intelligent park management system based on the prediction result and the adjusted in-park environment data.

[0091] Step S603: Use the arrester cooperation mode as a sub-strategy corresponding to the smart park.

[0092] In the present application, there may be multiple lightning arrester cooperation modes, for example, including the following three lightning arrester cooperation modes.

[0093] Mode 1: Thunderstorm path priority protection mode, which is used to set the key power facilities in the smart park along the thunderstorm propagation path as high priority, so that the target lightning arrester in the smart park can adjust its position according to the location of the key power facilities and perform high absorption power adjustment.

[0094] Mode 2: Dynamic adjustment collaboration mode, used to dynamically adjust the target lightning arresters in the smart park so that at every moment, the target lightning arresters at the thunderstorm location are in working state, and the target lightning arresters at the non-thunderstorm location are in standby state.

[0095] Mode three: Intelligent disconnection mode, which is selected for execution when the dynamic adjustment cooperation mode is executed, and is used to cut off power to power facilities in thunderstorm-occurring locations, and to maintain power to power facilities in non-thunderstorm-occurring locations.

[0096] In this application, one or more lightning arrester cooperation modes can be used as sub-strategies corresponding to the smart park.

[0097] This application integrates thunderstorm prediction results into the management system of the smart park, dynamically adjusts the environmental parameters in the park, and generates an optimized lightning arrester collaboration mode accordingly. This not only helps to improve the protection level of key power facilities and ensure their safe operation under severe weather conditions, but also saves energy by dynamically adjusting the working state of the lightning arrester, and intelligently cuts off the power supply to the area affected by the thunderstorm when necessary, thereby significantly improving the safety and energy efficiency management of the entire smart park.

[0098] In the present application, the alarm signal indication includes at least one of an audible alarm, a flashing light, a mobile device notification, or an email reminder.

[0099] This application ensures that users can receive thunderstorm warning information in a timely manner in different scenarios through diversified alarm signal indication methods, such as sound alarms, flashing lights, mobile device notifications or email reminders. This method improves the coverage and timeliness of warning information transmission, helps users take necessary precautions and reduce losses caused by thunderstorms.

[0100] In the present application, the preventive instructions include at least one of a prompt to shut down non-critical electronic equipment, a prompt to evacuate personnel, and an instruction to start a backup power supply.

[0101] This application enhances the user's response capabilities and the overall safety of the system by providing preventive instructions, such as recommending the shutdown of non-critical electronic equipment, recommending personnel evacuation measures, or recommending the activation of backup power systems. This approach not only helps protect personal safety, but also reduces unnecessary property losses and ensures the continuous and stable operation of the power system.

[0102] In the present application, the operating parameters of the target lightning arrester that need to be adjusted include at least one of the opening time, the closing time, the triggering threshold, and the maximum power limit.

[0103] In this application, by allowing the operating parameters of the target arrester to be adjusted, such as the opening time, closing time, trigger threshold or maximum power limit, the arrester can dynamically adjust its working state according to the prediction results to adapt to different thunderstorm environments. This method improves the flexibility and response speed of the arrester and enhances its protection effect in complex environments.

[0104] Figure 7 A flow chart of a control method for an intelligent lightning arrester according to an embodiment of the present application is shown. Figure 7 As shown, the process includes the following steps S701 to S704b.

[0105] Step S701, real-time monitoring of current environmental data, the environmental data including weather conditions, electric field strength and thunderstorm activity.

[0106] Step S702: Based on the monitored current environmental data, a prediction model is applied to obtain a prediction result, where the prediction result represents the probability of thunderstorm occurrence and the thunderstorm propagation path.

[0107] Step S703: Generate a lightning protection strategy based on the prediction result.

[0108] Among them, the lightning protection strategy includes multiple sub-strategies corresponding to multiple different areas of the thunderstorm propagation path, each sub-strategy is used to indicate at least one of the target lightning arrester contained in the thunderstorm propagation path, the working parameters that need to be adjusted of the target lightning arrester, the alarm signal indication, and the prevention indication.

[0109] Step S704a: According to the lightning protection strategy, the target lightning arrester is controlled by wireless communication.

[0110] Step S704b: in response to the prediction result indicating that the probability of thunderstorm occurrence is lower than a preset probability threshold, adjusting the target lightning arrester to enter an energy-saving mode.

[0111] In the present application, when the prediction result does not indicate that the probability of thunderstorm occurrence is lower than the preset probability threshold, only step S704a is executed; when the prediction result indicates that the probability of thunderstorm occurrence is lower than the preset probability threshold, step S704a and step S704b are executed simultaneously.

[0112] In this application, by adjusting the arrester to enter the energy-saving mode when the probability of thunderstorm occurrence is lower than the preset threshold, the safety of the system is ensured and the goal of energy saving and emission reduction is achieved. This method helps to reduce operating costs and improve the economic benefits of the system.

[0113] The present application also provides an apparatus embodiment that is consistent with the above embodiment, which is used to implement the method steps of the above embodiment. The explanation based on the same name meaning is the same as the above embodiment, and has the same technical effect as the above embodiment, which will not be repeated here.

[0114] Figure 8 A unit block diagram of a control device for an intelligent lightning arrester according to an embodiment of the present application is shown.

[0115] like Figure 8 As shown, the present application provides a control device 800 for an intelligent lightning arrester, comprising:

[0116] The environmental data sensor module 801 is used to monitor the current environmental data in real time, and the environmental data includes at least weather conditions, electric field strength and thunderstorm activity. The processing unit 802 is configured to generate a prediction result based on the current environmental data monitored by the environmental data sensor module, and the prediction result at least characterizes the probability of thunderstorm occurrence and the thunderstorm propagation path. The strategy generation module 803 is configured to generate a lightning protection strategy based on the prediction result, and the lightning protection strategy includes a plurality of sub-strategies corresponding to a plurality of different areas of the thunderstorm propagation path, each sub-strategy is used to indicate the target lightning arrester contained in the thunderstorm propagation path, the working parameters that the target lightning arrester needs to adjust, the alarm signal indication, and at least one of the prevention indications. The wireless communication module 804 is configured to control the target lightning arrester through wireless communication according to the lightning protection strategy generated by the strategy generation module.

[0117] Although operations are described in a particular order in the drawings, this should not be understood as requiring that the operations be performed in the particular order shown or in serial order, or that all shown operations be performed to achieve desired results. In certain circumstances, multitasking and parallel processing may be advantageous.

[0118] The methods and devices of the present application can be implemented using standard programming techniques, using rule-based logic or other logic to implement various method steps. It should also be noted that the words "device" and "module" used herein and in the claims are intended to include implementations using one or more lines of software code and / or hardware implementations and / or devices for receiving input.

[0119] Any steps, operations or procedures described herein may be performed or implemented using one or more hardware or software modules, either alone or in combination with other devices. In one embodiment, the software module is implemented using a computer program product including a computer-readable medium containing computer program code, which can be executed by a computer processor to perform any or all of the described steps, operations or procedures.

[0120] The foregoing description of the implementation of the present application has been given for the purpose of illustration and description. The foregoing description is not exhaustive nor is it intended to limit the present application to the exact form disclosed, and various variations and modifications may exist according to the above teachings or may be obtained from the practice of the present application. These embodiments are selected and described in order to illustrate the principles of the present application and its practical application, so that those skilled in the art can utilize the present application in various embodiments and various modifications suitable for the specific purpose contemplated.

[0121] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0122] It can be further understood that, unless otherwise specified, “connection” includes a direct connection without other components between the two, and also includes an indirect connection with other components between the two.

[0123] It is further understood that, although the operations are described in a specific order in the drawings in the embodiments of the present application, it should not be understood as requiring the operations to be performed in the specific order or serial order shown, or requiring the execution of all the operations shown to obtain the desired results. In certain environments, multitasking and parallel processing may be advantageous.

[0124] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to encompass any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the field of the present application that are not disclosed in the present application. The specification and embodiments are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following scope of rights.

[0125] It should be understood that the present application is not limited to the precise structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the scope of the appended claims.

[0126] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A control method for an intelligent lightning arrester, characterized in that: The method comprises: Real-time monitoring of current environmental data, including weather conditions, electric field strength, and thunderstorm activity; Based on the monitored current environmental data, a prediction model is applied to obtain a prediction result, wherein the prediction result represents the probability of thunderstorm occurrence and the thunderstorm propagation path; Based on the prediction result, a lightning protection strategy is generated; wherein the lightning protection strategy includes a plurality of sub-strategies corresponding to a plurality of different areas along the thunderstorm propagation path, each of which is used to indicate at least one of a target lightning arrester included in the thunderstorm propagation path, a working parameter of the target lightning arrester that needs to be adjusted, an alarm signal indication, and a prevention indication; According to the lightning protection strategy, the target lightning arrester is controlled by wireless communication; The plurality of different areas include a wind farm area; The sub-strategy corresponding to the wind farm area is generated in the following manner: In response to the thunderstorm occurrence probability exceeding a response probability threshold of the wind farm area, acquiring a map image of the wind farm area; Referring to the current environmental data and based on the wind turbine distribution information and topographic information in the map image, a thunderstorm prediction hotspot map is generated; wherein the thunderstorm prediction hotspot map includes a set of hotspots that need to be protected from lightning in a target area, and the target area is an overlapping area between the wind farm area and the thunderstorm propagation path; generating a sub-strategy corresponding to the wind farm area based on the prediction result and the thunderstorm prediction hotspot map; The plurality of different areas include a high-rise building area; The sub-strategy corresponding to the high-rise building area is generated in the following manner: Based on the current environmental data and the thunderstorm propagation path, thunderstorm simulation is performed for each point in the target area to obtain the thunderstorm occurrence height range corresponding to each point; wherein the target area is the overlapping area between the high-rise building area and the thunderstorm propagation path; Obtain the top building height corresponding to each point in the target area; For each point in the target area, determine the minimum height difference between the thunderstorm occurrence height range and the top building height; Determine the lightning protection warning level corresponding to each point in the target area based on the minimum height difference of each point in the target area; Based on the prediction result and the lightning protection warning level, generating a sub-strategy corresponding to the high-rise building area; The sub-strategy corresponding to the high-rise building area includes: The minimum height difference of each point in the target area is broadcasted in the high-rise building area, so that each target lightning arrester in the high-rise building area can respectively determine the nearest target point, and adjust the position and absorption power according to the lightning warning level corresponding to the target point.

2. The control method according to claim 1, characterized in that: The step of applying a prediction model based on the monitored current environmental data to obtain a prediction result includes: Collecting historical environmental data, the historical environmental data including historical meteorological data, historical geographical data, historical thunderstorm records and historical working status data of lightning arresters; The current environmental data and the historical environmental data are used as inputs to the prediction model to obtain a prediction result.

3. The control method according to claim 1, characterized in that: The sub-strategy corresponding to the wind farm area includes: broadcasting the thunderstorm prediction hot spot map in the wind farm area, so that target lightning arresters in the wind farm area receive the thunderstorm prediction hot spot map and adjust their positions according to the hot spot set; and The prediction result is broadcasted in the wind farm area so that the target lightning arrester for posture adjustment searches for wind turbines within a specified range, and adjusts the operating speed of the searched wind turbines according to the probability of thunderstorm occurrence represented by the prediction result; wherein, the higher the probability of thunderstorm occurrence, the lower the adjusted operating speed.

4. The control method according to claim 1, characterized in that: The plurality of different areas include a smart park; The sub-strategy corresponding to the smart park is generated in the following manner: The prediction result is sent to the intelligent park management system, so that the intelligent park management system adjusts the environmental data in the park based on the prediction result; wherein the adjusted environmental data in the park includes at least one of temperature, humidity, air pressure, and electric field strength; In response to the intelligent park management system completing the adjustment, obtaining a lightning arrester cooperation mode generated by the intelligent park management system based on the prediction result and the adjusted in-park environment data; Using the arrester cooperation mode as a sub-strategy corresponding to the smart park; The sub-strategy corresponding to the smart park includes at least one of the following arrester cooperation modes: The thunderstorm path priority protection mode is used to set the key power facilities in the smart park along the thunderstorm propagation path as high priority, so that the target lightning arrester in the smart park can adjust its position according to the location of the key power facilities and perform high absorption power adjustment; Dynamically adjust the cooperation mode, for dynamically adjusting the target lightning arrester in the smart park, so that at every moment, the target lightning arrester at the thunderstorm location is in working state, and the target lightning arrester at the non-thunderstorm location is in standby state; The intelligent disconnection mode is selected to be executed when the dynamic adjustment cooperation mode is executed, and is used to cut off the power of the power facilities at the location where the thunderstorm occurs, and to maintain the power of the power facilities other than the location where the thunderstorm occurs.

5. The control method according to any one of claims 1 to 4, characterized in that: The alarm signal indication includes at least one of an audible alarm, a flashing light, a mobile device notification, or an email reminder.

6. The control method according to any one of claims 1 to 4, characterized in that: The preventive instructions include at least one of a prompt to shut down non-critical electronic equipment, a prompt to evacuate personnel, and an instruction to start a backup power supply.

7. The control method according to any one of claims 1 to 4, characterized in that: The operating parameters of the target lightning arrester that need to be adjusted include at least one of a start time, a close time, a trigger threshold, and a maximum power limit.

8. An intelligent lightning arrester control device, characterized in that: The device comprises: An environmental data sensor module, used for real-time monitoring of current environmental data, wherein the environmental data at least includes weather conditions, electric field strength, and thunderstorm activity; A processing unit configured to generate a prediction result based on the current environmental data monitored by the environmental data sensor module by applying a preset prediction model, wherein the prediction result at least represents the probability of thunderstorm occurrence and the thunderstorm propagation path; A strategy generation module, configured to generate a lightning protection strategy based on the prediction result, wherein the lightning protection strategy includes a plurality of sub-strategies corresponding to a plurality of different areas along the thunderstorm propagation path, each of which is used to indicate at least one of a target lightning arrester included in the thunderstorm propagation path, a working parameter of the target lightning arrester that needs to be adjusted, an alarm signal indication, and a prevention indication; A wireless communication module configured to control the target lightning arrester by wireless communication according to the lightning protection strategy generated by the strategy generation module; The plurality of different areas include a wind farm area; The sub-strategy corresponding to the wind farm area is generated in the following manner: In response to the thunderstorm occurrence probability exceeding a response probability threshold of the wind farm area, acquiring a map image of the wind farm area; Referring to the current environmental data and based on the wind turbine distribution information and topographic information in the map image, a thunderstorm prediction hotspot map is generated; wherein the thunderstorm prediction hotspot map includes a set of hotspots that need to be protected from lightning in a target area, and the target area is an overlapping area between the wind farm area and the thunderstorm propagation path; generating a sub-strategy corresponding to the wind farm area based on the prediction result and the thunderstorm prediction hotspot map; The plurality of different areas include a high-rise building area; The sub-strategy corresponding to the high-rise building area is generated in the following manner: Based on the current environmental data and the thunderstorm propagation path, thunderstorm simulation is performed for each point in the target area to obtain the thunderstorm occurrence height range corresponding to each point; wherein the target area is the overlapping area between the high-rise building area and the thunderstorm propagation path; Obtain the top building height corresponding to each point in the target area; For each point in the target area, determine the minimum height difference between the thunderstorm occurrence height range and the top building height; Determine the lightning protection warning level corresponding to each point in the target area based on the minimum height difference of each point in the target area; Based on the prediction result and the lightning protection warning level, generating a sub-strategy corresponding to the high-rise building area; The sub-strategy corresponding to the high-rise building area includes: The minimum height difference of each point in the target area is broadcasted in the high-rise building area, so that each target lightning arrester in the high-rise building area can respectively determine the nearest target point, and adjust the position and absorption power according to the lightning warning level corresponding to the target point.

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