Power supply method and power supply system for online monitoring equipment of lightning arrester

Abstract

The invention discloses a power supply method and a power supply system for on-line monitoring equipment of a lightning arrester, and the method specifically comprises the steps: activating a multi-source energy module according to a preset priority order based on the operation state and environment parameters of the lightning arrester, and supplying power to the on-line monitoring equipment; based on the output characteristics of the multi-source energy module and the residual electric quantity of the on-line monitoring equipment, a constant voltage-constant current hybrid power supply strategy in a conventional mode or an anti-interference power supply strategy in an abnormal mode is executed on the on-line monitoring equipment; and processing the historical leakage current, environmental temperature and humidity and vibration spectrum characteristics of the lightning arrester acquired by the online monitoring equipment through the LSTM prediction model, predicting the energy supply trend of the online monitoring equipment in the preset future time, and dynamically adjusting the working parameters of the online monitoring equipment according to the energy supply trend. According to the invention, stable power supply of the lightning arrester on-line monitoring equipment in a complex environment is realized, and the power supply reliability and the equipment availability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of arrester monitoring equipment, and in particular to a power supply method and a power supply system for online monitoring equipment of an arrester. Background Art

[0002] In power systems, lightning arresters (SAs) are key devices that protect electrical equipment from lightning overvoltages. Real-time monitoring of their operating status is crucial for the safe and stable operation of power systems. With the development of smart grid technology, online SLA monitoring equipment has become an integral part of power systems. However, existing SLA online monitoring equipment still suffers from numerous deficiencies in power supply stability, power consumption management, communication reliability, and data utilization, severely hindering further performance improvements and expanding its application scope.

[0003] First, in extreme environmental conditions, such as extreme cold and strong electromagnetic interference, traditional online lightning arrester monitoring equipment often relies on a single energy module, such as leakage current energy harvesting or solar energy. However, these single energy modules are prone to failure in extreme environments, causing the equipment to malfunction due to insufficient power supply. For example, in extremely cold environments, the power generation efficiency of solar panels will drop significantly or even cease operation. In environments with strong electromagnetic interference, leakage current energy harvesting modules may be unable to accurately extract energy due to electromagnetic interference. This inadequate power supply not only affects the normal operation of the online lightning arrester monitoring equipment but also poses a threat to the safe operation of the entire power system.

[0004] Secondly, traditional online lightning arrester monitoring equipment often uses a crude approach to power management, failing to incorporate arrester aging characteristics (such as nonlinear changes in leakage current caused by valve plate degradation) to predictively adjust power consumption. This crude approach not only wastes valuable energy resources and shortens equipment lifespan, but can also impact power supply stability due to excessive power consumption. For example, during the early stages of arrester aging, leakage current may exhibit a nonlinear growth trend. Failure to adjust power consumption strategies in a timely manner can lead to energy waste and power shortages.

[0005] Furthermore, traditional online lightning arrester monitoring equipment often relies on a single communication link, either wireless or wired. However, in extreme situations like geological disasters (such as earthquakes and wildfires), this single communication link is easily interrupted, preventing timely upload of monitoring data. This not only hinders real-time monitoring of the arrester's status but can also impact the stable operation of the power supply system. For example, during an earthquake, ground shaking can cause wired communication lines to break, disrupting data transmission.

[0006] Finally, traditional online lightning arrester monitoring equipment suffers from data silos, meaning that monitoring data isn't linked to the grid's dynamic topology or meteorological data. This makes it difficult to quickly locate faults and implement effective countermeasures when power system failures occur, further impacting power supply reliability. Summary of the Invention

[0007] The purpose of the present invention is to provide a power supply method and power supply system for online monitoring equipment of a lightning arrester, which realizes stable power supply of the online monitoring equipment of the lightning arrester in a complex environment, improves power supply reliability and equipment availability, and solves at least one of the above-mentioned prior art problems.

[0008] In a first aspect, the present invention provides a method for powering an online monitoring device of a lightning arrester, the method specifically comprising:

[0009] Based on the operating status of the arrester and environmental parameters, the multi-source energy modules are activated in a preset priority order to power the online monitoring equipment;

[0010] Based on the output characteristics of the multi-source energy module and the remaining power of the online monitoring device, a constant voltage-constant current hybrid power supply strategy in normal mode or an anti-interference power supply strategy in abnormal mode is implemented for the online monitoring device;

[0011] The LSTM prediction model processes the arrester's historical leakage current, ambient temperature and humidity, and vibration spectrum characteristics collected by the online monitoring equipment to predict the energy supply trend of the online monitoring equipment within a preset future time. The operating parameters of the online monitoring equipment are dynamically adjusted based on the energy supply trend.

[0012] In response to the data transmission needs of online monitoring equipment, a bidirectional coupling mechanism between power supply status and communication link is established;

[0013] Based on the operating data of online monitoring equipment, a digital twin of the power supply system is constructed, and the adaptive optimization of the power supply strategy of the online monitoring equipment is achieved through the digital twin.

[0014] In a second aspect, the present invention provides a power supply system for an online monitoring device of a lightning arrester, the system specifically comprising:

[0015] The first power supply module is used to activate the multi-source energy module to supply power to the online monitoring equipment in a preset priority order based on the operating status of the lightning arrester and environmental parameters;

[0016] The second power supply module is used to execute a constant voltage-constant current hybrid power supply strategy in a normal mode or an anti-interference power supply strategy in an abnormal mode for the online monitoring device based on the output characteristics of the multi-source energy module and the remaining power of the online monitoring device;

[0017] The third power supply module is used to process the arrester's historical leakage current, ambient temperature and humidity, and vibration spectrum characteristics collected by the online monitoring equipment through the LSTM prediction model, predict the energy supply trend of the online monitoring equipment in a preset future time, and dynamically adjust the operating parameters of the online monitoring equipment based on the energy supply trend;

[0018] The fourth power supply module is used to establish a bidirectional coupling mechanism between the power supply status and the communication link according to the data transmission requirements of the online monitoring equipment;

[0019] The fifth power supply module is used to build a digital twin of the power supply system based on the operating data of the online monitoring equipment, and realize adaptive optimization of the power supply strategy of the online monitoring equipment through the digital twin.

[0020] In a third aspect, the present invention provides a computer device comprising: a memory and a processor and a computer program stored in the memory. When the computer program is executed on the processor, the power supply method for an online monitoring device of a lightning arrester as described in any one of the above methods is implemented.

[0021] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for powering an online monitoring device of a lightning arrester as described in any one of the above methods is implemented.

[0022] Compared with the prior art, the present invention has at least one of the following technical effects:

[0023] 1. The present invention realizes the stable power supply of the arrester online monitoring equipment in complex environments, thereby improving the power supply reliability and equipment availability.

[0024] 2. The present invention activates multi-source energy modules in a preset priority order to power the online monitoring equipment. It can automatically switch or use them in combination according to different environmental conditions, ensuring that the online monitoring equipment can obtain a stable power supply in various extreme environments, effectively improving the stability and reliability of the power supply system.

[0025] 3. Based on the output characteristics of the multi-source energy module and the remaining power of the online monitoring device, the present invention implements a constant voltage-constant current hybrid power supply strategy in normal mode or an anti-interference power supply strategy in abnormal mode for the online monitoring device, thereby realizing refined management of power consumption.

[0026] 4. The present invention processes the historical leakage current of the lightning arrester, ambient temperature and humidity, and vibration spectrum characteristics collected by the online monitoring equipment through the LSTM prediction model, predicts the energy supply trend of the online monitoring equipment in a preset future time, and dynamically adjusts the working parameters of the online monitoring equipment according to the energy supply trend, further optimizing the power consumption management strategy and improving energy utilization efficiency.

[0027] 5. The present invention improves the reliability of the communication system by establishing a bidirectional coupling mechanism between the power supply status and the communication link in response to the data transmission requirements of the online monitoring equipment.

[0028] 6. The present invention intelligently switches energy modules based on the operating status of the arrester and environmental parameters, optimizes energy utilization efficiency, and ensures that online monitoring equipment can obtain sufficient power support under different working conditions.

[0029] 7. The present invention combines the output characteristics of the multi-source energy module and the power status of the equipment to dynamically adjust the power supply strategy, ensuring the stable operation of the online monitoring equipment in normal and abnormal environments and improving the anti-interference ability of the equipment.

[0030] 8. The present invention uses the LSTM prediction model to accurately predict the energy supply trend, realizes the dynamic optimization of the working parameters of the online monitoring equipment, reduces power consumption, and extends the service life of the equipment.

[0031] 9. The present invention dynamically adjusts the sampling frequency and sensor activation state according to the ratio of predicted power consumption to current power consumption, thereby achieving a balance between power consumption and monitoring accuracy and improving the energy efficiency of online monitoring equipment.

[0032] 10. The present invention establishes a bidirectional coupling mechanism between the power supply status and the communication link, thereby ensuring that the online monitoring equipment can maintain stable operation under abnormal conditions such as communication interruption, thereby improving the reliability and real-time performance of data transmission.

[0033] 11. The present invention realizes adaptive optimization of power supply strategy based on digital twin, improves the adaptability of online monitoring equipment to complex power grid environment, and realizes more accurate energy management and fault warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0035] Figure 1 This is a flow chart of a method for powering an online monitoring device of a lightning arrester provided by one embodiment of the present invention;

[0036] Figure 2 This is a schematic structural diagram of a power supply system for an online monitoring device of a lightning arrester provided by one embodiment of the present invention;

[0037] Figure 3 It is a structural diagram of a computer device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0038] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0039] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0040] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0041] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0042] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0043] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0044] In the embodiments of the present application, the execution subject of the process includes a terminal device, which includes but is not limited to: a server, a computer, a smart phone, a tablet computer, and other devices capable of executing the method disclosed in the present application. Figure 1 A flow chart of a method for powering an online monitoring device for a lightning arrester according to an embodiment of the present invention is shown, and is described in detail as follows:

[0045] S101, based on the operating status of the lightning arrester and environmental parameters, activate the multi-source energy module in a preset priority order to power the online monitoring equipment.

[0046] In this embodiment, sensors are used to monitor parameters such as ambient temperature, humidity, light intensity, and electromagnetic interference intensity in real time, and to collect parameters such as leakage current, valve plate temperature, and insulation resistance of the lightning arrester to evaluate its aging degree and operating status.

[0047] When the lightning arrester is operating normally and the leakage current is stable, the leakage current energy harvesting module is prioritized due to its high energy conversion efficiency and the lack of need for additional energy input. When the ambient light intensity is above a threshold (e.g., ≥50,000 lux) and the temperature is suitable (e.g., -20°C to 50°C), the solar panel module is activated. When the light intensity or leakage current is insufficient, the supercapacitor energy storage module prioritizes energy storage, avoiding frequent switching between other modules. When the electromagnetic interference intensity exceeds a threshold (e.g., ≥50 V / m) and other modules fail, the low-power wireless energy harvesting module is activated, utilizing ambient radio waves (e.g., mobile communication base station signals) for energy harvesting. The backup battery module is activated only when all other modules are unable to provide power (e.g., in extreme low temperatures, continuous rain, and strong electromagnetic interference) to ensure minimum device functionality.

[0048] Every 5 seconds, a comprehensive assessment of environmental parameters and lightning arrester operating status is performed to determine the optimal power supply module. For example, if the leakage current is stable and ≥1 mA, the system immediately switches to the leakage current energy harvesting module. If the light intensity is ≥50,000 lux and the temperature is between -20°C and 50°C, the system switches to the solar panel module. If the remaining supercapacitor charge is ≥30%, it is prioritized when light intensity or leakage current is insufficient. If the electromagnetic interference intensity is ≥50 V / m and other modules fail, the system switches to the low-power wireless energy harvesting module. If all modules are unable to provide power and the backup battery charge is ≥20%, the backup battery module is activated. During module switching, a DC-DC converter ensures a smooth transition between voltage and current, preventing device restarts or data loss.

[0049] Regularly monitor the output voltage, current, and temperature of each energy module, flagging and isolating faulty modules. When the primary module fails, power is automatically downgraded to the next-priority module to ensure power continuity. Module switching time, reason, and duration are recorded to provide data support for subsequent maintenance.

[0050] In this embodiment, the power supply stability and energy efficiency of the online monitoring equipment are significantly improved through the dynamic activation scheme of the multi-source energy module based on the operating status of the lightning arrester and environmental parameters.

[0051] S102 , based on the output characteristics of the multi-source energy module and the remaining power of the online monitoring device, executing a constant voltage-constant current hybrid power supply strategy in a normal mode or an anti-interference power supply strategy in an abnormal mode for the online monitoring device.

[0052] In this embodiment, the traditional lightning arrester online monitoring equipment lacks coordinated optimization of the output characteristics of multi-source energy modules and the device's power consumption in its power supply strategy, leading to the following problems: 1. Rigid conventional power supply mode: Utilizing a fixed voltage / current output, it cannot adapt to the dynamic fluctuations of multi-source energy modules (such as solar energy and leakage current energy extraction), easily causing power outages or equipment overloads. 2. Poor adaptability to abnormal operating conditions: In environments with strong electromagnetic interference and extreme temperatures, the equipment fails to design an anti-interference power supply strategy to address energy module performance degradation, resulting in data acquisition distortion or equipment downtime. 3. Imbalance between energy efficiency and lifespan: The power supply mode is not dynamically adjusted based on the device's remaining power, resulting in energy waste or battery overcharge / over-discharge, shortening the device's lifespan. Therefore, this embodiment achieves a balance between power supply stability, energy efficiency, and device lifespan by synergizing the conventional mode (constant voltage and constant current hybrid power supply) with the abnormal mode (anti-interference power supply).

[0053] Specifically, output characteristic models are established for leakage current energy harvesting modules, solar panels, supercapacitor energy storage modules, and low-power wireless energy harvesting modules, including: output voltage / current fluctuation range (such as the light-temperature correlation of solar modules); dynamic response time (such as the transient output capability of leakage current energy harvesting modules under electromagnetic interference); aging attenuation characteristics (such as the decrease in photoelectric conversion efficiency of solar panels after long-term use).

[0054] Based on the remaining power (SOC, State of Charge) of the online monitoring device, the power status is divided into the following levels: High power zone (SOC ≥ 80%): The device has sufficient energy reserves and can support high-power tasks (such as high-frequency data acquisition); Medium power zone (30% ≤ SOC < 80%): The device has a moderate power level and needs to balance power consumption and battery life; Low power zone (SOC < 30%): The device is low on power and needs to start low-power mode to extend operating time.

[0055] In normal mode, energy modules with stable voltage (such as supercapacitor energy storage modules) are prioritized as the primary power source. A DC-DC converter provides a constant voltage output to ensure the stability of core device functions (such as data acquisition). When the primary power source voltage fluctuates, energy modules with stable current (such as leakage current energy harvesting modules) are dynamically activated as auxiliary power sources. By connecting them in parallel, current compensation is achieved, maintaining a constant total power consumption. When sunlight is sufficient and leakage current is stable, the solar module and the leakage current energy harvesting module combine to provide power, distributing the load as needed through a power divider, minimizing losses in the energy storage modules.

[0056] When electromagnetic interference intensity exceeds a threshold (e.g., ≥80 V / m), the system automatically isolates the leakage current energy harvesting module and switches to a low-power wireless energy harvesting module (powered by ambient radio waves). The system also reduces the sampling frequency (e.g., from 1 time / second to 1 time / minute) to minimize power consumption. In extremely cold environments (below -40°C) and when the solar module's efficiency falls below 10%, the system switches to a combined supercapacitor energy storage module and backup battery module for power, using pulsed power technology (intermittently activating the device) to extend battery life. When the device battery enters the low battery zone, a three-level emergency response is initiated: 1. Level 1 response (SOC < 30%): shut down non-core functions (such as historical data storage), retaining only real-time monitoring of the arrester status and uploading of key data; 2. Level 2 response (SOC < 20%): reduce the sensor sampling accuracy (such as reducing the leakage current resolution from 0.1 mA to 1 mA), and extend the data upload interval (from 5 minutes / time to 30 minutes / time); 3. Level 3 response (SOC < 10%): retain only the arrester overvoltage alarm function, and all other functions are dormant until the battery is restored or manual intervention occurs.

[0057] In this embodiment, the power supply strategy is dynamically switched based on the output characteristics of the multi-source energy module and the power of the equipment, which significantly improves the power supply reliability, energy efficiency and life of the lightning arrester online monitoring equipment under complex working conditions.

[0058] S103, using the LSTM prediction model to process the arrester's historical leakage current, ambient temperature and humidity, and vibration spectrum characteristics collected by the online monitoring device, predict the energy supply trend of the online monitoring device within a preset future time, and dynamically adjust the working parameters of the online monitoring device according to the energy supply trend.

[0059] In this embodiment, the power consumption and operating mode of traditional lightning arrester online monitoring equipment are statically configured, without dynamic optimization based on future energy supply capabilities. This leads to the following problems: 1. Imbalance between energy supply and load: When energy supply is insufficient (e.g., continuous rainy weather causes solar power failure), the equipment remains in a high-power consumption mode (e.g., high-frequency data acquisition), accelerating the depletion of the energy storage module; 2. Resource waste: When energy supply is excessive (e.g., sufficient sunlight and the lightning arrester is in a healthy state), the equipment does not improve sampling accuracy or increase redundant communication frequency, resulting in insufficient data value; 3. Delayed emergency response: When energy supply trends suddenly change (e.g., sudden strong electromagnetic interference causes leakage current energy extraction failure), the equipment cannot adjust its power consumption strategy in advance, leading to the risk of power outage. Therefore, this embodiment uses historical data mining and trend prediction to achieve intelligent matching of equipment operating parameters (e.g., sampling frequency, communication cycle, power consumption mode), improving system energy efficiency and reliability.

[0060] Specifically, a multi-sensor array is deployed in the arrester online monitoring equipment to collect the following data: 1. Arrester operating characteristics: leakage current (real-time value, change rate), valve temperature, and number of operations; 2. Environmental dynamic parameters: ambient temperature and humidity (real-time value, day and night fluctuation rate), vibration spectrum (acceleration amplitude, frequency distribution), and light intensity (diurnal change curve); 3. Energy supply characteristics: output voltage / current of each energy module (such as solar energy, leakage current energy acquisition module), energy storage module SOC (State of Charge, remaining power), and charging / discharging rate.

[0061] An LSTM prediction model is constructed, consisting of an input layer, a hidden layer, and an output layer. In the input layer, a multi-time series feature matrix is constructed, slicing the arrester's historical leakage current (e.g., data from the past 72 hours), ambient temperature and humidity (e.g., data from the past 24 hours), and vibration spectrum (e.g., data from the past 48 hours) into time windows (e.g., 15 minutes) to form a three-dimensional feature tensor (time step × feature dimension × number of samples). In the hidden layer, a two-layer LSTM network is used. The first layer extracts time-dependent features (e.g., the nonlinear growth trend of leakage current), and the second layer integrates multimodal data (e.g., the correlation between temperature and humidity and energy supply). The output layer predicts energy supply trends within a preset time window (e.g., 24 hours), including total energy supply (in Wh), energy supply volatility (e.g., standard deviation, range), and energy supply interruption risk level (e.g., high risk, medium risk, low risk).

[0062] Actual equipment operating data (covering extreme scenarios such as extreme cold, strong electromagnetic interference, and geological disasters) was collected to construct a labeled dataset. The energy supply trend label was generated by combining the energy storage module SOC change rate and the energy module output stability. Training samples were generated using a sliding window method with a time step of 12 (predicting trends for the next three hours based on every 15 minutes of data). The LSTM network weights were optimized using a backpropagation algorithm to ensure that the error between the predicted value and the actual energy supply trend (e.g., root mean square error (RMSE)) was less than 5%.

[0063] If the energy supply trend is high, the leakage current sampling accuracy is improved (e.g., from 0.1 mA to 0.01 mA), the valve temperature monitoring frequency is increased (from once per hour to once every 10 minutes), redundant communication links are enabled (e.g., wireless + wired dual channels), and the data upload interval is shortened (from 15 minutes to 5 minutes). The edge computing module is activated to pre-process historical data locally, reducing the amount of data transmitted to the cloud. If the energy supply trend is medium, the standard sampling accuracy (0.1 mA) is maintained, but the sampling interval is dynamically adjusted based on the leakage current change rate (e.g., increasing to once every 5 minutes when the change rate is greater than 10%). The primary communication link (e.g., wireless) is maintained, and the non-critical data upload interval is extended (e.g., from 15 minutes to 30 minutes). Non-core functions (e.g., historical data storage) are disabled, retaining only real-time monitoring and alarm functions. If the trend is low energy supply, reduce the leakage current sampling accuracy (for example, from 0.1 mA to 1 mA), extend the valve temperature monitoring interval (from 1 time / hour to 1 time / 2 hours); switch to low-power communication mode (such as LoRa), and only upload key alarm data (such as overvoltage events); start pulse power supply technology (intermittently activate the device) to extend the battery life of the energy storage module.

[0064] The LSTM prediction model runs every 15 minutes to generate energy supply trends for the next three hours and match them to the corresponding operating parameter strategy. The device's built-in edge controller distributes strategy parameters (such as sampling intervals and communication cycles) to sensors, communication modules, and the power management unit. After the device operates according to the new strategy, it continuously monitors actual energy supply (e.g., changes in the energy storage module's SOC) and power consumption (e.g., voltage / current sensor data), calculating the strategy execution deviation (e.g., the relative error between predicted and actual energy). If the deviation exceeds a threshold (e.g., ±15%), online model fine-tuning is triggered (e.g., increasing the weight of recent data and optimizing LSTM hidden layer parameters).

[0065] In this embodiment, the LSTM prediction model is used to achieve accurate prediction of energy supply trends and dynamic adaptation of equipment operating parameters, which solves problems such as imbalance between energy supply and load, waste of resources, and delayed emergency response in traditional solutions, and provides technical support for the intelligent and efficient operation of lightning arrester online monitoring equipment.

[0066] S104: In response to the data transmission requirements of the online monitoring equipment, a bidirectional coupling mechanism between the power supply status and the communication link is established.

[0067] In this embodiment, traditional lightning arrester online monitoring equipment suffers from the following data transmission issues: 1. Power-communication disconnection: The power supply status (e.g., remaining charge in the energy storage module, output stability of the energy module) and the communication link (e.g., wireless / wired mode) operate independently. This results in high-power communication (e.g., frequent full data uploads) even when the power supply is insufficient, accelerating energy storage depletion. When the communication link is interrupted, the power supply strategy is not adjusted promptly (e.g., continuously activating the high-power wireless module), resulting in inefficient energy consumption. 2. Delayed response to extreme scenarios: When geological disasters (e.g., earthquakes, wildfires) cause communication link failures, the equipment cannot predict link risks based on the power supply status, resulting in the loss of critical data (e.g., overvoltage alarms). 3. Energy waste: The data compression rate and transmission priority are not dynamically optimized based on the communication link quality, resulting in redundant data occupying limited bandwidth and energy resources. Therefore, this embodiment establishes three modules: power supply status awareness, adaptive communication link adjustment, and dynamic data scheduling. This achieves coordinated optimization of power supply and communication, improving data transmission reliability and system energy efficiency.

[0068] Specifically, the online monitoring equipment monitors the status of the energy storage module (such as remaining power, charge and discharge current, and internal resistance change rate), the output of the energy module (such as output voltage / current of solar energy and leakage current energy, and output power fluctuation rate), and the power supply stability (such as voltage ripple coefficient and number of transient overvoltage / undervoltage events).

[0069] Based on the monitoring data, the power supply status is divided into the following levels:

[0070] Level 1 (sufficient power supply): SOC ≥ 80%, stable energy module output (fluctuation rate ≤ 5%), voltage ripple coefficient ≤ 2%;

[0071] Level 2 (normal power supply): 50% ≤ SOC < 80%, energy module output fluctuates slightly (5% < fluctuation rate ≤ 15%), voltage ripple coefficient ≤ 5%;

[0072] Level 3 (power supply warning): 20% ≤ SOC < 50%, energy module output fluctuation is large (15% < fluctuation rate ≤ 30%), voltage ripple coefficient ≤ 10%;

[0073] Level 4 (emergency power supply): SOC < 20%, energy module output is unstable (fluctuation rate > 30%), and voltage ripple coefficient > 10%.

[0074] Evaluation indicators are established for wireless communications (such as LoRa and 4G) and wired communications (such as optical fiber and power line carrier): 1. Link availability: based on the heartbeat packet response rate (if there is no response for three consecutive times, the link is considered disconnected); 2. Transmission latency: the round-trip time (RTT) from the transmission of a data packet to its confirmation; 3. Bit error rate: the ratio of the number of erroneous bits in the received data packet to the total number of bits; 4. Bandwidth utilization: the ratio of the current transmission rate to the theoretical maximum rate of the link.

[0075] When the power supply is sufficient / normal, give priority to high-bandwidth, low-latency communication links (such as 4G or optical fiber), and enable multi-channel redundant transmission (such as wireless + wired parallel); support full data upload (such as high-frequency sampling data, historical logs), and set the data compression rate to low (such as 10%~30%).

[0076] When a power supply warning occurs, switch to a low-power communication link (such as LoRa or power line carrier) and close the redundant channel; only upload key data (such as lightning arrester overvoltage alarm and leakage current mutation events), set the data compression rate to medium (such as 30%~50%); extend the data transmission interval (such as from 15 minutes / time to 30 minutes / time).

[0077] In the event of a power emergency, only the lowest power consumption communication link (such as the intermittently awakened LoRa module) is retained, and all non-essential communications are shut down; only emergency alarm data (such as lightning arrester breakdown events) is uploaded, and the data compression rate is set to high (such as 50%~70%); the "store-and-forward" mechanism is activated to temporarily store the data locally and upload it centrally after power is restored.

[0078] The arrester monitoring data is divided into the following levels:

[0079] Level 1 (emergency data): Arrester overvoltage / breakdown alarm, valve plate temperature exceeding limit event;

[0080] Level 2 (important data): leakage current mutation (change rate > 20%), valve aging characteristics (such as nonlinear impedance change);

[0081] Level 3 (routine data): periodic status data (such as hourly leakage current average, ambient temperature and humidity);

[0082] Level 4 (redundant data): historical logs, debugging information.

[0083] When the power supply is sufficient / normal, level 1 data is uploaded in real time, level 2 data is uploaded on demand (such as when a threshold is triggered), level 3 data is uploaded according to a planned cycle, and level 4 data is only stored locally; it supports multi-link parallel transmission to ensure that high-priority data occupies bandwidth first.

[0084] When a power supply warning is issued, the first-level data is uploaded immediately, the second-level data is uploaded with a delay (such as after the power supply status is restored), the third-level data is suspended from uploading, and the fourth-level data is discarded; data compression and fragmented transmission are enabled to reduce the energy consumption of a single transmission.

[0085] In the event of a power emergency, only level 1 data is uploaded, and all level 2 and below data are discarded. The "last mile" transmission strategy is initiated to send critical alarms through an extremely low-power link (such as LoRa single wake-up).

[0086] The device's built-in edge controller collects power status data every five minutes and triggers a communication link switching command based on the classification results. During a link switch, data to be transmitted is temporarily stored in a buffer queue to prevent data loss. The communication module provides real-time feedback on link health (such as bit error rate and latency) to the edge controller. If link quality continues to deteriorate (e.g., bit error rate > 10%), it proactively reduces communication power consumption (e.g., lowering transmit power, extending sleep cycles), and simultaneously requests a temporary energy boost from the power supply module (e.g., activating a backup energy module). Based on historical data and device aging characteristics, the power status classification thresholds and link switching conditions are dynamically optimized. For example, in the late stages of lightning arrester aging (where decreased valve plate impedance leads to increased leakage current), the SOC threshold for the "power warning" function is lowered (e.g., from 50% to 40%), triggering low-power communication mode in advance.

[0087] In this embodiment, by establishing a bidirectional coupling mechanism between the power supply status and the communication link, the coordinated optimization of power supply resources and communication resources is achieved, which solves the problems of power supply-communication separation, delayed response to extreme scenarios, and energy efficiency waste in traditional solutions, and provides technical guarantee for the reliable operation of lightning arrester online monitoring equipment in complex environments.

[0088] S105: Based on the operating data of the online monitoring equipment, a digital twin of the power supply system is constructed, and adaptive optimization of the power supply strategy of the online monitoring equipment is achieved through the digital twin.

[0089] In this embodiment, the optimization of power supply strategies for traditional lightning arrester online monitoring equipment has the following pain points: 1. Static strategy rigidity: Power supply parameters (such as voltage thresholds and energy allocation weights) are usually preset based on manual experience and cannot be dynamically adjusted according to the actual operating status of the equipment (such as aging or environmental changes), resulting in low power supply efficiency or redundant energy consumption; 2. Multi-source data fragmentation: The power supply system involves multi-source heterogeneous data (such as lightning arrester leakage current, ambient temperature and humidity, energy storage module SOC, and energy module output power), but traditional solutions do not establish data association models, making it difficult to achieve global optimization; 3. Fault response lag: When equipment anomalies occur (such as energy module failure or communication link interruption), traditional solutions rely on manual intervention or preset rules to trigger strategy switching, and cannot autonomously predict risks and optimize in advance; 4. High verification cost: New power supply strategies need to be verified through hardware iteration or field testing, which is long and costly, making it difficult to quickly adapt to complex scenario requirements. Therefore, this embodiment constructs a virtual model that is mapped to the physical equipment in real time, combined with simulation analysis driven by operation data, to achieve dynamic generation, real-time verification, and closed-loop optimization of power supply strategies, thereby improving the reliability and energy efficiency of the power supply system in complex environments.

[0090] Specifically, a digital twin of the power supply system is constructed, which includes the physical layer, data layer, model layer and service layer.

[0091] Among them, the physical layer maps the hardware entity of the lightning arrester online monitoring equipment, which includes a multi-source energy module (solar panel, leakage current energy acquisition module, backup battery), an energy storage module (lithium battery, supercapacitor), a communication module (wireless / wired hybrid communication interface) and a sensor group (leakage current sensor, temperature and humidity sensor, vibration sensor).

[0092] The data layer integrates multi-dimensional operating data including real-time status data (output voltage / current of each energy module, SOC of energy storage module, health of communication link (bit error rate, delay)), historical operating data (lightning arrester leakage current trend, ambient temperature and humidity change curve, equipment power consumption record) and equipment characteristic data (lightning arrester model parameters (rated voltage, valve impedance), energy module output characteristic curve, communication protocol specifications).

[0093] The model layer includes: power supply behavior model: describes the output characteristics of the energy module (such as the correlation between the power generation efficiency of the solar panel and the ambient temperature), the charging and discharging characteristics of the energy storage module (such as the nonlinear relationship between internal resistance and SOC), equipment aging model: based on the nonlinear change characteristics of the lightning arrester leakage current (such as the impedance drop caused by valve degradation), predicts the remaining life of the equipment and the growth trend of power consumption, communication-power supply coupling model: quantifies the mutual influence of communication link quality (such as bandwidth, bit error rate) and power supply strategy (such as transmission power, sleep cycle), environmental interference model: simulates the impact of extreme environments (such as extreme cold, strong electromagnetic interference) on the performance of energy modules and equipment.

[0094] The service layer provides: power supply strategy simulation interface: input strategy parameters (such as multi-source energy module priority and voltage threshold), and output the strategy's energy efficiency and reliability evaluation results; fault diagnosis interface: based on real-time data and model comparison, locate power supply system anomalies (such as energy module failure and energy storage module aging); optimization suggestion generation interface: based on simulation results and diagnosis conclusions, recommend the optimal power supply strategy combination.

[0095] Through the edge computing gateway, the operating data of the physical equipment is synchronized to the digital twin every 10 seconds; the Kalman filter algorithm is used to fuse multi-sensor data to eliminate noise interference and ensure that the state deviation between the virtual model and the physical equipment is less than 1%; based on the equipment aging model and the environmental interference model, the parameters of the digital twin (such as the solar panel power generation efficiency attenuation coefficient and the communication link attenuation factor) are automatically updated every 24 hours.

[0096] Based on real-time data and historical trends, the digital twin automatically identifies the current power supply scenario (such as "extreme cold + power supply warning" and "strong electromagnetic interference + equipment aging"); selects candidate strategies that meet the scenario characteristics from the preset strategy library (such as "high priority activation of solar energy + supercapacitor hybrid power supply" and "reducing wireless communication transmission power to 17dBm"); uses power supply stability (SOC>30%), energy efficiency (total equipment power consumption <5W), and communication reliability (bit error rate <5%) as constraints, and generates optimal strategy parameters (such as energy module priority order and constant voltage-constant current switching threshold) through simulation and deduction; in the later stage of equipment aging (such as a 20% drop in valve plate impedance), relaxes the power supply stability constraint (SOC threshold reduced from 30% to 25%) to prioritize the transmission of critical data.

[0097] Current operating data is input into the digital twin, and the power supply process of the past 72 hours is replayed to evaluate the energy efficiency improvement of the candidate strategy (such as a 15% reduction in total power consumption) and the number of fault avoidance times (such as avoiding three over-discharges of the energy storage module). Extreme environments (such as an ambient temperature of -40°C and strong electromagnetic interference) are simulated to verify the robustness of the candidate strategy (such as an 80% reduction in power outage duration).

[0098] Randomly generate 1,000 sets of environmental parameters (such as light intensity and electromagnetic interference intensity) and calculate the failure probability of candidate strategies (such as the probability of power outage <0.5%). Quantify the impact of key parameters (such as solar panel area and energy storage module capacity) on strategy effectiveness to provide a basis for hardware upgrades.

[0099] Preload verified candidate policy parameters (such as energy module priority and voltage threshold) into the edge controller of the physical device. Set a 10-second transition period between the original and new policies to gradually adjust the energy module output weights to avoid power interruptions. If the new policy causes power supply anomalies (such as SOC being below 20% for three consecutive minutes), it automatically rolls back to the last stable policy and triggers an alarm.

[0100] Collect operational data (such as power supply stability and communication reliability) under the new strategy in real time, compare it with the simulation results of the digital twin, and calculate the error rate (such as power consumption prediction error <3%). Automatically correct the parameters of the digital twin (such as the energy module output characteristic curve) based on the error rate to improve the accuracy of subsequent strategy optimization.

[0101] In some embodiments, in step S101, based on the operating status of the arrester and environmental parameters, activating the multi-source energy modules in a preset priority order to power the online monitoring device specifically includes:

[0102] Based on the arrester's leakage current amplitude and vibration acceleration, the system extracts electrical energy through a broadband energy extraction circuit and supplies it to the power management module of the online monitoring device.

[0103] When the arrester's leakage current energy is detected to be lower than the preset energy threshold, the piezoelectric vibration energy harvesting module is activated to generate electricity using the mechanical vibration of the arrester's mounting base to supplement the energy storage unit of the online monitoring equipment.

[0104] Monitor the solar radiation intensity around the arrester. When the solar radiation intensity meets the preset lighting conditions, the photovoltaic conversion circuit will be used to supplement the power and charge the supercapacitor bank of the online monitoring equipment.

[0105] The output power of each energy source is dynamically distributed through a three-port energy router, and the fuzzy PID algorithm is used to adjust the impedance matching of each channel of the three-port energy router in real time, so that the total input power fluctuation range of the online monitoring equipment is maintained within the preset fluctuation range.

[0106] In this embodiment, the leakage current broadband energy harvesting module is used to extract electrical energy based on the broadband characteristics of the arrester leakage current (0.1mA-10mA, frequency 50Hz-1MHz) through magnetic core coupling and rectification circuits, preferentially powering the power management module of the online monitoring equipment. The piezoelectric vibration energy harvesting module is used to utilize the mechanical vibration of the arrester mounting base (frequency 5Hz-500Hz, acceleration 0.1g-5g) to convert vibration energy into electrical energy through piezoelectric ceramic sheets to replenish energy storage units (such as lithium batteries). The photovoltaic conversion circuit is used to monitor the solar radiation intensity (0W / m²-1300W / m²) around the arrester and convert solar energy into electrical energy through monocrystalline silicon photovoltaic panels to charge the supercapacitor bank. The three-port energy router includes three input ports (connected to the leakage current energy harvesting module, piezoelectric vibration energy harvesting module, and photovoltaic conversion circuit, respectively) and one output port (connected to the online monitoring equipment). It is used to dynamically distribute the output power of each energy source and achieve power smoothing through an impedance matching network to suppress fluctuations in the total input power.

[0107] The wideband leakage current energy harvesting module provides real-time power supply with top priority. For example, if the arrester leakage current amplitude exceeds 0.5mA (typical) and the electromagnetic interference intensity is less than 50dBμV / m, the wideband energy harvesting circuit's rectifier and filter unit converts the leakage current into DC power (voltage range 3V-15V), which is then preferentially supplied to the power management module. When the leakage current amplitude is less than 0.2mA or the electromagnetic interference intensity is greater than 70dBμV / m, the module's output weight is automatically reduced to 30% to prevent power interruptions caused by interference.

[0108] The photovoltaic conversion circuit provides secondary power during periods of sufficient sunlight. For example, when solar irradiance exceeds 200W / m² (a typical sunny day threshold) and the ambient temperature ranges from -30°C to +60°C, the MPPT (maximum power point tracking) algorithm dynamically adjusts the PV panel output voltage to charge the supercapacitor bank (target voltage 5V, with a tolerance of ±0.2V). When irradiance falls below 100W / m², the module's output is automatically shut down to prevent reverse consumption of stored energy.

[0109] The piezoelectric vibration energy harvesting module, a third-priority module, supplements energy storage when leakage current is insufficient. For example, when the leakage current energy harvesting module output power is less than 0.5W (for more than 10 seconds) and the energy storage unit SOC is less than 40%, the piezoelectric ceramic parallel resonant circuit converts the vibration energy into electrical energy (voltage range 2V to 8V). After passing through the DC-DC boost circuit, it charges the lithium battery. The vibration acceleration threshold is dynamically adjusted based on the lightning arrester installation location (e.g., mountainous areas / urban areas) (0.5g in mountainous areas, 1.2g in urban areas) to avoid ineffective power generation.

[0110] The three-port energy router uses the total input power fluctuation range (for example, when the target value is 10W, the allowed fluctuation is 9.5W~10.5W) and the output weight of each energy source as the power allocation target (for example, on sunny days, the photovoltaic module weight is 60%, the leakage current module weight is 30%, and the vibration module weight is 10%).

[0111] The fuzzy PID control strategy is used to control the impedance matching network of each channel of the three-port energy router (power distribution is achieved by adjusting the inductance and capacitance parameters). For example, the total input power deviation (ΔP) is divided into five fuzzy sets: “negative large” (ΔP<-1W), “negative medium” (-1W≤ΔP<-0.5W), “zero” (-0.5W≤ΔP≤0.5W), “positive medium” (0.5W<ΔP≤1W), and “positive large” (ΔP>1W); the deviation change rate (dΔP / dt) is divided into five fuzzy sets: “negative fast” (dΔP / dt<-0.5W / s), “negative slow” (-0.5W / s≤dΔP / dt<-0.1W / s), “zero” (-0.1W / s≤dΔP / dt≤0.1W / s), “positive slow” (0.1W / s<dΔP / dt≤0.5W / s), and “positive fast” (dΔP / dt>0.5W / s). If ΔP is positive and dΔP / dt is positive and fast, the photovoltaic module output weight is prioritized (e.g., from 60% to 40%), while the vibration module weight is increased (e.g., from 10% to 20%). If ΔP is negative and dΔP / dt is zero, the leakage current module weight is fine-tuned (e.g., from 30% to 32%) to maintain total power stability. The output impedance adjustment (ΔZ) is divided into five fuzzy sets: negative (ΔZ < -10Ω), negative (-10Ω ≤ ΔZ < -5Ω), zero (-5Ω ≤ ΔZ ≤ 5Ω), positive (5Ω < ΔZ ≤ 10Ω), and positive (ΔZ > 10Ω). The precise value is calculated using the center of gravity method. A fuzzy PID control loop is executed every 100ms to dynamically adjust the impedance matching parameters of the three-port energy router to ensure that the total input power fluctuation range is ≤ ±5%.

[0112] In this embodiment, the collaborative power supply and dynamic power distribution technology of multi-source energy modules solves the problems of singleness, large fluctuation, and poor adaptability of the traditional lightning arrester online monitoring equipment power supply system, and significantly improves the power supply reliability and energy utilization.

[0113] In some embodiments, in step S102, based on the output characteristics of the multi-source energy module and the remaining power of the online monitoring device, executing the constant voltage-constant current hybrid power supply strategy in the normal mode or the anti-interference power supply strategy in the abnormal mode for the online monitoring device specifically includes:

[0114] If the remaining power of the online monitoring device is greater than the preset power threshold and the environmental parameters are normal, a constant voltage-constant current hybrid power supply strategy is adopted to maintain the power supply voltage of the online monitoring device within the preset voltage range through a Buck-Boost topology circuit;

[0115] When it is detected that the ambient temperature is lower than the preset temperature threshold or the electromagnetic interference intensity is greater than the preset interference intensity threshold, it switches to the anti-interference power supply mode. The anti-interference power supply mode includes suppressing high-frequency interference through a multi-stage RC filtering circuit and applying an electromagnetic shielding layer to the key power supply line of the online monitoring equipment.

[0116] In this embodiment, in normal mode, such as when the remaining capacity (SOC) of the device is higher than 60% (preset redundancy threshold) and the ambient temperature is between -20°C and +50°C (normal operating range of lithium batteries), and the electromagnetic interference intensity is lower than 50dBμV / m (to avoid sampling noise or communication errors), the hybrid power supply strategy is enabled; through the voltage-current dual-loop control of the topology circuit, the conversion from a wide range of input voltage (5V~24V) to a stable output voltage (5V±0.1V) is achieved, and instantaneous large current output (peak current 3A, lasting 50ms) is supported to adapt to the dynamic switching of the device from standby to high power consumption.

[0117] In abnormal mode, such as when the ambient temperature is below -30°C, the internal resistance of the lithium battery increases, resulting in a decrease in output capacity (capacity attenuation exceeds 30%), which may cause the device to malfunction due to insufficient power supply. When the electromagnetic interference intensity exceeds 70dBμV / m, the sensor signal may be overwhelmed by noise (such as leakage current sampling value offset >15%) or the communication link may be interrupted (packet loss rate >5%). Immediately switch to anti-interference power supply mode, isolate the interference source through filtering and shielding technology, and ensure the continuity of key functions (such as fault recording and status alarms).

[0118] When the device is in standby, intermittent sampling, or low-frequency communication mode (for example, transformer oil chromatography monitoring equipment samples three times a day, each time taking less than 0.5 seconds), the Buck-Boost circuit operates in constant voltage output mode, and the current is dynamically adjusted with the load (0.05A~0.3A). By reducing the output current ripple (ripple rate <3%), the charging and discharging losses of the energy storage unit are reduced; the photovoltaic conversion circuit (output power 8W~15W on sunny days) and the leakage current energy harvesting module (1.5W~4W under normal operating conditions) are preferentially used for power supply. The lithium battery is only supplemented by discharge (discharge current <0.3A) when multiple sources of energy are insufficient, extending the life of the energy storage unit.

[0119] When the load current demand exceeds 0.6A, the Buck-Boost circuit automatically switches to constant current mode, with the output current stabilized at 1.2A (lasting 60ms). The voltage is dynamically adjusted (4.5V to 5.5V) according to the load resistance to avoid instantaneous voltage drops that may cause communication interruption or sensor sampling distortion. Through the coordinated output of multiple energy sources (photovoltaic + leakage current + lithium battery power supply), the instantaneous power demand (peak power > 6W) is ensured to be met.

[0120] A multi-stage RC filter circuit was designed. The circuit structure includes the following: the first stage (high-frequency noise suppression): an 80kHz cutoff RC filter filters high-frequency harmonics generated by the photovoltaic panel MPPT circuit or switching power supply (attenuation >45dB @ 1.2MHz); the second stage (pulse interference attenuation): an 8kHz cutoff RC filter suppresses medium-frequency pulses caused by lightning strikes or grid fluctuations (attenuation >55dB @ 120kHz); and the third stage (low-frequency harmonic elimination): an 800Hz cutoff RC filter attenuates low-frequency interference from motor startup and shutdown or mechanical vibration coupling (attenuation >35dB @ 12kHz). Each filter stage uses high-precision capacitors (X7R material, temperature coefficient ±12%) and low-ESR inductors (Senduin core, Q value >90) to ensure effective interference attenuation while keeping the output voltage phase delay within <3°, preventing sensor signal distortion.

[0121] Full-path shielding is implemented for the communication module power supply line, sensor sampling line, and microprocessor power supply terminal, using a double-layer copper foil wrapping structure (outer copper foil thickness 0.12mm, inner aluminum foil thickness 0.06mm), with a shielding effectiveness of >85dB@1.2GHz. The shielding layer is connected to the equipment metal casing via a 360-degree loop connection, with a grounding resistance of <0.8Ω to prevent shielding failure due to poor grounding. In a strong electromagnetic interference environment of 65dBμV / m, the communication bit error rate after shielding is reduced from 15% to 0.2%, and the sensor sampling value offset is reduced from ±10% to ±1%.

[0122] In this embodiment, the power supply reliability and energy efficiency of the online monitoring equipment in complex environments are significantly improved through the dynamic switching of power supply strategies, the integrated application of multi-stage filtering and electromagnetic shielding technology.

[0123] In some embodiments, in the above step S103, the arrester historical leakage current, ambient temperature and humidity, and vibration spectrum characteristics collected by the online monitoring device are processed by the LSTM prediction model to predict the energy supply trend of the online monitoring device in a preset future time, and the working parameters of the online monitoring device are dynamically adjusted according to the energy supply trend, specifically including:

[0124] The arrester's historical leakage current, ambient temperature and humidity, and vibration spectrum characteristics collected by the online monitoring equipment are fused and preprocessed to generate a standardized feature matrix;

[0125] Based on the standardized feature matrix, a two-layer LSTM network is used to predict the energy supply trend and output the energy supply forecast sequence within the preset time period in the future.

[0126] Based on the energy supply prediction sequence, an energy state evaluation function is set up, and parameter adjustment instructions are generated through fuzzy control strategy;

[0127] Based on the parameter adjustment instructions, the working parameters of the online monitoring equipment are updated in real time.

[0128] In this embodiment, the full current of the lightning arrester (0.1mA~10mA) and its third harmonic component (accounting for 5%~30%) are collected, and statistical characteristics including mean, variance, range and harmonic distortion rate are calculated through a sliding window (window length 1 hour, step length 15 minutes). Outliers (such as instantaneous pulse current >20mA caused by lightning interference) and noise (high-frequency glitches are eliminated through median filtering, window length 5 minutes) are eliminated to form the historical leakage current characteristics of the lightning arrester.

[0129] Ambient temperature (-40°C to +80°C) and humidity (0% to 100% RH) are collected, and the daily temperature change rate (absolute value <15°C / day) and humidity fluctuation range (daily range <30% RH) are calculated to prevent interference from extreme environmental parameters on the prediction model. Lag compensation is performed on the temperature data (correcting sensor response delay to <3 minutes), and temperature and humidity coupling correction is performed on the humidity data (eliminating sensor measurement deviation in high temperature and high humidity environments) to form environmental temperature and humidity characteristics.

[0130] The vibration signal of the arrester base (frequency range 0.1Hz~1kHz) is collected through a three-axis acceleration sensor, and the time domain characteristics (peak acceleration, root mean square value) and frequency domain characteristics (main frequency, frequency band energy distribution) are extracted. Mechanical vibration interference (such as low-frequency vibration <10Hz caused by the start and stop of nearby equipment) and environmental noise (high-frequency noise >800Hz) are eliminated. The vibration characteristic frequency band related to the internal electric field distribution of the arrester (30Hz~200Hz) is retained to form the vibration spectrum characteristics.

[0131] Leakage current, temperature, humidity, and vibration data were aligned by timestamp to construct a three-dimensional feature tensor (time × feature type × sensor node). Mutual information analysis was used to screen for strongly correlated features (e.g., leakage current with a correlation coefficient greater than 0.7 with temperature and a correlation coefficient greater than 0.6 with the main vibration frequency). Dimensionality reduction was performed on these correlated features (e.g., principal component analysis retained principal components with a cumulative contribution greater than 90%) to reduce the impact of redundant information on the prediction model. Each feature was normalized (linearly mapped to the interval [0, 1]) to eliminate dimensional differences. A standardized feature matrix (dimensions: time step × feature dimension; for example, 24 hours of data corresponds to 96 time steps × 12-dimensional features) was constructed as input to the LSTM model.

[0132] The two-layer LSTM network consists of an input layer, a first LSTM layer, a second LSTM layer, a fully connected layer, and an output layer. The input layer receives a normalized feature matrix. The first LSTM layer uses 128 LSTM units to extract feature dependencies along the time dimension (such as diurnal fluctuations in leakage current and seasonal variations in temperature and humidity). A forget gate bias (initial value 0.5) is set to balance the weight of historical information with the current input to prevent gradient vanishing or exploding. The second LSTM layer receives the hidden state sequence output by the first layer and uses 64 LSTM units to model the coupling relationships between features (such as leakage current baseline drift caused by temperature rise and internal insulation degradation reflected by changes in the vibration spectrum). An attention mechanism is introduced (weights are initialized to a uniform distribution) to dynamically allocate the contribution of different time steps to the prediction results (for example, data before and after thunderstorms are given higher weight). The fully connected layer maps the output of the second LSTM layer to an energy supply prediction sequence (dimension: prediction time step × 1, such as 96 prediction values corresponding to the next 24 hours). The prediction value represents the relative change trend of energy supply (such as photovoltaic output power and the conversion efficiency of the leakage current energy acquisition module). The unit is a standardized energy index (0~1). The output layer outputs the energy supply prediction sequence.

[0133] A two-layer LSTM network model was trained using sliding window cross-validation (window length 7 days, step length 1 day) to ensure robustness in forecasting under scenarios such as seasonal fluctuations (e.g., a 30% increase in photovoltaic output in summer) and sudden disturbances (e.g., sudden changes in vibration characteristics caused by strong winds). An early stopping mechanism (training is terminated if the validation set loss does not decrease for five consecutive rounds) was introduced to prevent overfitting.

[0134] An energy status assessment function was designed. Its multi-dimensional evaluation indicators include: 1. Energy supply adequacy: The ratio of the predicted energy index to the device's energy consumption baseline (e.g., sufficient energy is determined when the combined photovoltaic and leakage current energy consumption is greater than 1.2 times the device's standby power consumption); 2. Energy fluctuation risk: The ratio of the standard deviation to the mean of the predicted series (reflecting the stability of energy supply; for example, a fluctuation risk greater than 0.3 indicates high risk); and 3. Energy gap duration: The number of consecutive time steps during which the predicted energy index falls below the device's minimum power consumption threshold (e.g., a six-hour energy gap triggers an alarm). A comprehensive energy status index (ranging from 0 to 1) was calculated through a weighted summation (e.g., a weight of 0.5 for adequacy, 0.3 for fluctuation risk, and 0.2 for gap duration) to quantify the health of the future energy supply.

[0135] The comprehensive energy status index is divided into five fuzzy subsets (very low, low, medium, high, and very high), with corresponding membership functions adopting a trapezoidal distribution (for example, the threshold range for the "high" subset is [0.7, 0.85, 1, 1]). Equipment operating parameter adjustment requirements are divided into three fuzzy subsets (aggressive, moderate, and conservative), with corresponding adjustment ranges of ±30%, ±15%, and ±5%, respectively. Twenty-five fuzzy rules (for example, "If the energy status index is high and the volatility risk is low, the parameter adjustment requirement is conservative") are developed based on expert experience to cover all possible combinations of energy supply and parameter adjustment. Fuzzy rules are synthesized using the Mamdani inference method, and defuzzified using the center of gravity method to generate precise parameter adjustment instructions (such as sampling frequency adjustment values and communication cycle adjustment values).

[0136] Parameter adjustment instructions generated by fuzzy control strategies optimize device operating parameters in real time, balancing energy supply and functional requirements. For example, when energy is sufficient (comprehensive energy status index > 0.8), the sampling frequency is increased to once every five minutes (enhancing early fault diagnosis capabilities); when energy is scarce (comprehensive energy status index < 0.4), the sampling frequency is reduced to once every two hours (extending device battery life). When energy supply is stable (fluctuation risk < 0.2), the communication cycle is shortened to 15 minutes (improving data real-time performance); when energy supply fluctuates (fluctuation risk > 0.5), the communication cycle is extended to one hour (reducing communication energy consumption). Sensor operating modes are dynamically switched based on energy status (for example, a vibration sensor uses high-frequency sampling mode when energy is sufficient and switches to event-triggered mode when energy is scarce).

[0137] Edge computing nodes (deployed locally on the device) achieve millisecond-level responses to parameter adjustment commands, avoiding control lags caused by cloud communication delays. A parameter adjustment lockout period is set (e.g., 30 minutes after each adjustment) to prevent frequent adjustments from causing system oscillations. Parameter adjustment commands are range-checked (e.g., sampling frequency is no less than once per day, and communication cycle is no longer than 24 hours) to prevent device malfunctions due to abnormal commands. Parameter adjustment logs (timestamp, pre-adjustment parameters, and post-adjustment parameters) are recorded to support fault tracing and model optimization.

[0138] In this embodiment, an energy-function collaborative optimization framework for online monitoring equipment is constructed through multi-source data fusion preprocessing, two-layer LSTM prediction, fuzzy control decision-making and dynamic parameter updating, which significantly improves the energy supply prediction accuracy and equipment adaptability in complex environments.

[0139] Furthermore, the real-time updating of the working parameters of the online monitoring equipment based on the parameter adjustment instruction specifically includes:

[0140] When the ratio between the predicted power consumption value and the current power consumption value of the online monitoring device is greater than or equal to a first preset ratio, the sampling frequency of the online monitoring device is increased to the first sampling frequency, and all sensors are activated;

[0141] When the ratio between the predicted power consumption value and the current power consumption value of the online monitoring device is less than a first preset ratio and greater than or equal to a second preset ratio, maintaining the sampling frequency of the online monitoring device at a reference sampling frequency and only starting the core sensor;

[0142] When the ratio between the predicted power consumption value and the current power consumption value of the online monitoring device is less than a second preset ratio, reducing the sampling frequency of the online monitoring device to the second sampling frequency and shutting down all non-essential peripherals;

[0143] The first preset ratio is greater than the second preset ratio, the first sampling frequency is greater than the reference sampling frequency and the second sampling frequency, and the reference sampling frequency is greater than the second sampling frequency.

[0144] In this embodiment, the total power consumption of the device is collected in real time through a hardware power monitoring module (such as a high-precision Hall current sensor + voltage sampling circuit), including the dynamic energy consumption of the sensor module (power consumption accounts for 30% to 50%), the communication module (power consumption accounts for 15% to 30%) and the main control unit (power consumption accounts for 20% to 40%). The sliding window statistical method (window length 5 minutes, step length 1 minute) is used to calculate the current power consumption average to eliminate the interference of instantaneous power consumption fluctuations (such as sensor startup inrush current > 1A) on the judgment result.

[0145] Based on the 24-hour energy supply forecast sequence output by the LSTM model, combined with the device's historical power consumption curve (for example, the average daily power consumption in summer is 25% higher than in winter) and the environmental parameter correction factor (power consumption increases by 5% to 8% for every 10°C increase in temperature), the predicted power consumption value within a preset time period is estimated. The predicted power consumption value is then evaluated for confidence (for example, the confidence interval width is less than 15% of the predicted value). If the confidence level is insufficient, a backup prediction algorithm (such as exponential smoothing) is used for cross-validation.

[0146] The first preset ratio (high energy efficiency threshold) is set to 1.5 (i.e., predicted power consumption ≥ 150% of current power consumption), indicating that the energy supply is sufficient and can support the operation of the equipment's expanded functions; such as during peak photovoltaic power generation (light intensity > 800W / m²) or during grid maintenance (when the output of the leakage current energy acquisition module is stable).

[0147] The second preset ratio (low power consumption threshold) is set to 0.7 (i.e., predicted power consumption < 70% of current power consumption), indicating that the energy supply may be insufficient and that core functions need to be prioritized; for example, in continuous rainy weather (photovoltaic output attenuation of more than 80%) or extreme low temperature environment (lithium battery capacity attenuation of 40%).

[0148] The benchmark power consumption ratio range is set to [0.7, 1.5), indicating that the energy supply basically matches the current demand and maintains the normal operation mode of the equipment.

[0149] The first sampling frequency is set to three times the regular sampling frequency (e.g., increasing the regular sampling frequency from once every 15 minutes to once every 5 minutes) to accommodate high-frequency data collection needs (e.g., monitoring millisecond-level transient changes in lightning arrester leakage current). When the predicted power consumption is significantly higher than the current power consumption, the device's sampling frequency is increased to the first sampling frequency, and all deployed sensor nodes (e.g., leakage current sensors, ambient temperature and humidity sensors, vibration acceleration sensors, and partial discharge sensors) are activated to achieve multi-dimensional state awareness. All sensors are time-synchronized (with a synchronization error of less than 1ms) to ensure temporal consistency of multi-source data and support multi-physics coupled analysis of fault characteristics.

[0150] When the predicted power consumption roughly matches the current power consumption, the system maintains the baseline sampling frequency and activates only core sensors, maintaining a regular sampling frequency (e.g., once every 15 minutes) to meet basic equipment monitoring needs (e.g., tracking daily trends in arrester resistive current). By dynamically adjusting the sampling window (e.g., immediately starting high-frequency sampling after arrester activation, then resuming the baseline frequency after 10 minutes), the system accurately captures key events. By operating only key sensors (e.g., leakage current sensors and ambient temperature sensors) and disabling non-essential sensors (e.g., vibration sensors and partial discharge sensors), the system reduces standby power consumption (reducing total power consumption by approximately 20%). Core sensors undergo health self-checks (e.g., monthly zero drift testing and sensitivity calibration) to ensure the reliability of monitoring data.

[0151] The second sampling frequency is set to 1 / 5 of the regular sampling frequency (e.g., once every two hours) to accommodate survival needs in extremely low-power scenarios (e.g., devices retaining only fault alarm capabilities). When the predicted power consumption is significantly lower than the current power consumption, the device's sampling frequency is reduced to the second sampling frequency. By reducing the sampling frequency and shutting down non-essential peripherals, the system completely shuts down all non-core peripherals (e.g., display screens, storage modules, and GPS positioning functions on communication modules), reducing standby power consumption (reducing total power consumption by approximately 60%). The system also physically isolates the peripherals (e.g., using relays to cut off the power supply circuits for non-core peripherals) to prevent dark current leakage.

[0152] In this embodiment, through the hierarchical judgment of power consumption ratio and differentiated parameter adjustment strategy, the adaptive operation of online monitoring equipment under complex energy supply conditions is realized, which not only ensures the monitoring capability of the equipment when energy is sufficient, but also extends the endurance of the equipment when energy is scarce, providing technical support for the intelligent and long-life operation of the power equipment status monitoring system.

[0153] In some embodiments, in step S104, establishing a bidirectional coupling mechanism between the power supply status and the communication link in response to the data transmission requirements of the online monitoring device specifically includes:

[0154] The power supply status parameters and communication link parameters are collected in real time by multi-source sensors to form a joint parameter set. The power supply status parameters include the remaining power and the power fluctuation rate, and the communication link parameters include the signal strength and the bit error rate.

[0155] Based on the joint parameter set, the power margin index and channel quality index are calculated to build an energy-communication coupling model;

[0156] The bidirectional coupling coefficient matrix is calculated based on the energy-communication coupling model, and a dynamic optimization objective function is established according to the bidirectional coupling coefficient matrix;

[0157] Based on the dynamic optimization objective function, the gradient projection algorithm is used to solve the optimal adjustment strategy of the bidirectional coupling between the power supply status and the communication link.

[0158] In this embodiment, a distributed sensor network deployed across various functional modules of the device collects dynamic parameters of the power supply status and communication link in real time, constructing a multi-dimensional joint parameter set. Specifically, dual-redundant power detection technology is employed, with cross-validation between a coulomb counter (accuracy ±0.5%) and a voltage-to-capacity mapping table (based on the battery's open-circuit voltage-to-remaining-capacity curve) to eliminate errors associated with a single detection method. To address the aging characteristics of lithium batteries (e.g., increased SoC estimation error after a 20% capacity decay), a dynamic calibration factor (updated quarterly based on battery internal resistance test results) is introduced to improve the accuracy of remaining-capacity detection. A high-precision power analysis module is deployed, sampling the device's input / output power (including the photovoltaic module, CT module, and energy storage unit) at a 100ms period, calculating the power fluctuation rate (fluctuation amplitude / average power) per unit time. To address transient power surges (e.g., loads >10W when a lightning arrester operates), a sliding window filtering algorithm (with a window length of 1s, averaging the maximum and minimum values after removing them) is employed to suppress noise interference. A multi-antenna diversity reception system is deployed at the front end of the communication module. The maximum ratio combining (MRC) algorithm is used to combine the received signal strengths of each antenna, improving detection sensitivity in weak signal environments (RSSI detection error is reduced by 30% in weak signal scenarios). A Kalman filter algorithm (with a filter time constant of 0.5s) is used to smooth RSSI signals (with a filter time constant of 0.5s) to address signal fluctuations caused by multipath effects (such as reflections from metal cabinets in substations), eliminating the effects of fast fading. A real-time error detection module is embedded in the communication protocol stack, combining cyclic redundancy check (CRC) and forward error correction (FEC) to calculate bit error rates. To address bit error rate spikes caused by sudden interference (such as electromagnetic pulse interference), a sliding window is used to eliminate outliers (with a window length of 100 frames, eliminating frames with a BER greater than 10⁻³) to prevent erroneous data from affecting link quality assessment. The power supply status parameters (remaining power, power fluctuation rate) and communication link parameters (RSSI, BER) are aligned by timestamp to form a four-dimensional joint parameter set. The joint parameters are normalized (remaining power is mapped to [0, 1], and RSSI is mapped to the interval [-110dBm, -30dBm] for linear normalization) to eliminate the impact of dimensional differences on subsequent modeling.

[0159] Based on a joint parameter set, a dynamic mapping relationship between power reserve and channel quality is constructed to quantify the degree of coupling between power supply status and communication links, providing a basis for decision-making in optimization strategies. Specifically, the energy reserve margin is calculated based on the device's historical power consumption data (e.g., daily average power consumption fluctuation range ±15%) and the current remaining power. The power fluctuation tolerance is calculated by combining the dynamic response capability of the device's power module (e.g., maximum load step response time ≤ 50ms) with the current power fluctuation rate. A link stability index is constructed based on a joint evaluation of the energy reserve margin and power fluctuation tolerance. Communication redundancy is calculated based on the current data transmission volume and the available bandwidth of the communication module. A joint evaluation function for power reserve and channel quality is constructed, and the coupling degree is classified into the following levels: strong coupling (Coupling ≥ 0.8): both power supply and communication are in high-quality conditions, and high-power functions (such as high-frequency sampling) can be expanded; medium coupling (0.5 ≤ Coupling < 0.8): balancing power and communication resources is required, and regular monitoring is maintained; weak coupling (Coupling < 0.5): bottlenecks in power supply or communication exist, and emergency strategies need to be activated.

[0160] By quantifying the degree of mutual influence between power supply and communication, a bidirectional coupling coefficient matrix is constructed. Based on this matrix, a dynamic optimization objective function is designed to achieve power supply-communication coordinated optimization. Specifically, through experimental testing of communication performance under different power supply conditions, a power supply-communication impact mapping table is established. For example, when the remaining battery power is less than 20%, the communication module's transmit power is limited, γ_S→C = 0.6 (communication capability is reduced by 40%). When the power fluctuation rate is greater than 15%, power supply noise causes an increase in the bit error rate, γ_S→C = 0.7 (communication quality is reduced by 30%). The pressure of communication load on the power supply system is analyzed, and a communication-power supply impact mapping table is established. For example, when the communication module load rate is greater than 80%, the increased power consumption leads to an increase in the discharge rate of the energy storage unit, γ_C→S = 0.5 (power supply endurance is shortened by 50%). When communication interruption triggers the retransmission mechanism, power consumption increases by an additional 20%, γ_C→S = 0.8 (power supply pressure increases by 80%). Based on the power supply-communication impact mapping table and the communication-power supply impact mapping table, a bidirectional coupling coefficient matrix is constructed to characterize the mutual constraints between power supply and communication. Based on the bidirectional coupling coefficient matrix, a dynamic optimization objective function is constructed with maximizing the comprehensive benefits of power supply and communication and minimizing the power supply pressure and communication overhead as the optimization goals, and the maximum output power of the power supply module and the power consumption budget of the communication module as constraints.

[0161] A gradient projection algorithm is used to iteratively optimize power supply and communication parameters, achieving rapid convergence to a global optimal solution while satisfying constraints and ensuring adaptive operation of the device in complex environments. Specifically, an initial power supply strategy (e.g., energy storage unit discharge current of 0.5A) and a communication strategy (e.g., LoRa communication cycle of 15 minutes) are set. The initial objective function value and constraint violations are calculated. The finite difference method is then used to approximate the gradient of the objective function with respect to the power supply parameters (e.g., discharge current) and communication parameters (e.g., communication cycle) . The parameter adjustment direction is projected into the feasible region (e.g., discharge current range [0.1A, 2A], communication cycle range [5 minutes, 60 minutes]) to avoid parameter out-of-bounds. A dynamic step size adjustment strategy (with the step size positively correlated with the gradient norm and the maximum step size not exceeding 10% of the parameter range) is employed to accelerate convergence. The gradient calculation and projection operations are repeated until the objective function converges (F_total change < 0.01 after three consecutive iterations) or the maximum number of iterations (e.g., 50) is reached. The optimal parameter combination (e.g., discharge current 0.8A, communication cycle 10 minutes) is recorded and used as the adjustment strategy for the current scenario.

[0162] In this embodiment, through bidirectional coupling modeling and dynamic optimization of power supply status and communication link, the isolated decision-making problem of traditional equipment in energy-communication resource allocation is solved, and the adaptability and operating efficiency of the equipment in complex environments are significantly improved.

[0163] In some embodiments, in step S105, the process of constructing a digital twin of the power supply system based on the operating data of the online monitoring device and implementing adaptive optimization of the power supply strategy of the online monitoring device through the digital twin specifically includes:

[0164] Based on the energy collection efficiency, device temperature distribution, and electromagnetic interference intensity of the online monitoring equipment, a digital twin of the power supply system is constructed. The digital twin includes a three-dimensional electromagnetic-thermal-mechanical coupling model and an energy conversion efficiency model.

[0165] Redistribute the weights of each energy source in the power supply system based on the digital twin, adjust the usage ratio of high-heat modules, and form power supply strategy adjustment instructions;

[0166] The power supply strategy adjustment instruction is injected into the edge computing unit of the online monitoring device to drive the online monitoring device to adjust the energy allocation strategy and power consumption control logic.

[0167] In this embodiment, based on the multi-source operating data of online monitoring equipment, electromagnetic, thermodynamic and mechanical properties are integrated to construct a digital twin with physical-logical dual mapping capabilities, providing a simulation verification environment for power supply strategy optimization.

[0168] Specifically, a three-dimensional electromagnetic sensor array is deployed at key locations of the equipment (such as power modules and communication antennas) to collect spatial electromagnetic field intensity (E / H field components) and spectrum characteristics (0-3GHz frequency band) with a period of 100ms. The electromagnetic field distribution model is constructed using the finite difference time domain (FDTD) method, and the electromagnetic parameters of the equipment shell material (such as the relative dielectric constant ε_r=1 and the conductivity σ=3.5×10 7 S / m) to simulate the propagation path and attenuation characteristics of electromagnetic interference within the device. The electromagnetic field-induced heat source distribution is calculated based on the electromagnetic loss density (P_loss = 0.5×σ×|E|²), and the electromagnetic loss is used as a boundary condition input for the thermodynamic model. For high-frequency switching power supply modules (such as DC-DC converters), a skin effect correction factor (a 20% increase in copper conductor resistivity at frequencies > 1MHz) is introduced to improve the accuracy of electromagnetic loss calculations. Distributed temperature sensors (with an accuracy of ±0.5°C) are placed at key nodes within the device (such as the CPU, power chip, and sensors), and an infrared thermal imager (with a spatial resolution of 1mm) is used to construct a three-dimensional temperature field of the device. A coupled conduction-convection-radiation model is constructed using the finite element method (FEM), with settings for the convection heat transfer coefficient (5-25 W / (m²·K) for natural convection and 50-200 W / (m²·K) for forced convection) and the radiation emissivity (ε = 0.8 for an alumina surface) to simulate the device's heat dissipation characteristics in both closed cabinets and open environments. The mechanical stress caused by thermal expansion is calculated based on the temperature gradient (σ=E×α×ΔT, where E is the elastic modulus and α is the thermal expansion coefficient). For multi-layer PCB boards (FR4 material α=17×10⁻ 6 / °C) to assess solder joint fatigue risk; a thermal cycle acceleration factor (Coffin-Manson model) was introduced to predict equipment life degradation trends under high-temperature conditions (for example, at 60°C, life degradation would be 60% of normal temperature). Triaxial accelerometers (±10g range, 0.5-1kHz bandwidth) were deployed in equipment cabinets to collect vibration acceleration data and extract frequency domain features (such as 100Hz fundamental frequency harmonics). Combined with electromagnetic force simulation (Maxwell stress tensor method), the impact of vibration on power transformer winding loosening was analyzed and a vibration-electromagnetic interference cross-influence model was established.

[0169] Light intensity sensors (range 0-1500W / m²) and temperature sensors were deployed to establish a three-dimensional mapping table of photovoltaic cell output power, light intensity, and temperature (for example, at 25°C, for every 100W / m² increase in light intensity, the output power increases by 0.3W). To address dust accumulation on photovoltaic panels (output power drops by 20% when transmittance decreases to 80%), a cleanliness correction factor was introduced (updated monthly, based on the dust accumulation area percentage assessed by a visual inspection system).

[0170] The primary current (range 0-1000A) and secondary output voltage are collected through a current transformer (CT) to establish a CT energy extraction efficiency curve (for example, the conversion efficiency is 65% at a primary current of 50A and 85% at 500A). For harmonic currents (efficiency drops by 10% when THD>5%), a Fourier transform is used to extract the fundamental and harmonic components, and the energy extraction efficiency calculation results are corrected.

[0171] Based on the battery management system (BMS), the charge and discharge current, voltage and temperature are collected to establish a lithium battery charge and discharge efficiency model (for example, the charge and discharge efficiency at a rate of 0.5C at 25°C is 92%, and the efficiency drops to 75% at -10°C). In response to battery aging (capacity decays by 20% after cycles >500), a health state (SOH) correction factor is introduced to dynamically adjust the charge and discharge strategy.

[0172] Power consumption monitoring chips (with an accuracy of ±1%) are deployed in each functional module of the device (such as sensors, communication units, and edge computing units) to collect real-time power consumption in a 1s cycle. For sleep-wake-up switching scenarios (for example, sensor wake-up power consumption is 30 times higher than sleep power consumption), dynamic power consumption curves are established to distinguish the proportions of static and dynamic power consumption.

[0173] Combined with temperature field distribution data, a power consumption-temperature mapping relationship is established (for example, for every 10°C increase in CPU temperature, leakage current power consumption increases by 15%), and the power consumption calculation results are corrected. To address performance degradation caused by high temperatures (for example, the CPU main frequency is reduced to 80% at 70°C), a performance compensation factor is introduced to optimize the power consumption allocation strategy.

[0174] By using digital twins to simulate device responses under different power supply strategies and combining real-time operating data, the system dynamically adjusts energy source weights and module utilization ratios to generate an adaptive power supply strategy. Specifically, based on energy harvesting efficiency and reliability assessments, an energy source priority matrix is constructed: High priority: photovoltaic energy (efficiency > 20% and light intensity > 500W / m²) and CT energy (primary current > 100A); Medium priority: energy storage units (SOH > 80% and remaining charge > 30%); Low priority: backup batteries (activated only in the event of a main power failure). The instantaneous conversion efficiency of each energy source is calculated every five minutes (e.g., photovoltaic efficiency = output power / (light intensity × area)), and the weight is dynamically adjusted (for every 5% increase in efficiency, the weight increases by 10%). For CT energy modules, their weight is reduced to below 50% based on the primary current fluctuation rate (e.g., output instability when fluctuation > 30%). When the energy storage unit temperature exceeds 50°C, its discharge weight is reduced (by 15% for every 5°C increase) to avoid the risk of thermal runaway. If the backup battery has not been used for more than three months, a low-current cyclic charge and discharge cycle is forced to start (once a week, at a 10% depth of charge and discharge) to prevent battery sulfation. The thermal stress index (TSI) of each module is calculated based on temperature field distribution data. When the module TSI exceeds 50, the edge computing unit dynamically adjusts its operating frequency (for example, reducing the CPU main frequency from 1GHz to 500MHz) to reduce power consumption and heat generation. For the image sensor module, if the TSI exceeds 60, the sampling frame rate is reduced (for example, from 30fps to 10fps) to reduce data processing. Non-real-time tasks (such as historical data storage) are migrated to low-TSI modules (such as idle edge computing nodes) to achieve load balancing. When the communication module TSI is less than 30, data transmission tasks are prioritized (for example, image data compression tasks are migrated from the CPU to the communication module's built-in DSP).

[0175] In this embodiment, by constructing a digital twin of the power supply system, real-time perception and adaptive optimization of the power supply strategy of the online monitoring equipment are achieved, which solves the problem of power supply decision lag of traditional equipment in complex environments. It can significantly improve the operating stability of the equipment under extreme conditions such as high temperature and strong electromagnetic interference, extend the service life of the equipment, and reduce operation and maintenance costs.

[0176] Reference Figure 2 An embodiment of the present invention provides a power supply system 2 for an online monitoring device of a lightning arrester, the system 2 specifically comprising:

[0177] The first power supply module 201 is used to activate the multi-source energy modules in a preset priority order based on the operating status of the lightning arrester and environmental parameters to supply power to the online monitoring equipment;

[0178] The second power supply module 202 is configured to implement a constant voltage-constant current hybrid power supply strategy in a normal mode or an anti-interference power supply strategy in an abnormal mode for the online monitoring device based on the output characteristics of the multi-source energy module and the remaining power of the online monitoring device;

[0179] The third power supply module 203 is used to process the historical leakage current of the lightning arrester, the ambient temperature and humidity, and the vibration spectrum characteristics collected by the online monitoring device through the LSTM prediction model, predict the energy supply trend of the online monitoring device in a preset future time, and dynamically adjust the operating parameters of the online monitoring device according to the energy supply trend;

[0180] The fourth power supply module 204 is used to establish a bidirectional coupling mechanism between the power supply status and the communication link according to the data transmission requirements of the online monitoring equipment;

[0181] The fifth power supply module 205 is used to build a digital twin of the power supply system based on the operating data of the online monitoring equipment, and realize adaptive optimization of the power supply strategy of the online monitoring equipment through the digital twin.

[0182] It is understandable that if Figure 1 The contents of the embodiment of the on-line monitoring device power supply method of the arrester shown in the figure are applicable to the embodiment of the on-line monitoring device power supply system of the arrester. The functions specifically implemented by the embodiment of the on-line monitoring device power supply system of the arrester are similar to those in the embodiment of the on-line monitoring device power supply system of the arrester. Figure 1 The embodiment of the power supply method for the online monitoring device of the lightning arrester shown is the same as that of the embodiment of the power supply method of the online monitoring device of the lightning arrester shown in FIG. Figure 1 The beneficial effects achieved by the embodiment of the power supply method for online monitoring equipment of a lightning arrester shown are also the same.

[0183] It should be noted that the information interaction, execution process and other contents between the above-mentioned systems are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0184] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0185] Reference Figure 3 An embodiment of the present invention further provides a computer device 3, comprising: a memory 302 and a processor 301 and a computer program 303 stored on the memory 302. When the computer program 303 is executed on the processor 301, the power supply method for the online monitoring device of the lightning arrester as described in any one of the above methods is implemented.

[0186] The computer device 3 may be a desktop computer, a notebook computer, a PDA, a cloud server or other computing devices. The computer device 3 may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will understand that Figure 3 This is merely an example of the computer device 3 and does not constitute a limitation on the computer device 3 . The computer device 3 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device 3 may also include input and output devices, network access devices, etc.

[0187] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor.

[0188] In some embodiments, the memory 302 may be an internal storage unit of the computer device 3, such as a hard drive or memory of the computer device 3. In other embodiments, the memory 302 may also be an external storage device of the computer device 3, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped on the computer device 3. Furthermore, the memory 302 may include both an internal storage unit of the computer device 3 and an external storage device. The memory 302 is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program. The memory 302 may also be used to temporarily store data that has been output or is about to be output.

[0189] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for powering an online monitoring device of a lightning arrester as described in any one of the above methods is implemented.

[0190] In this embodiment, if the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a camera / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, removable hard drives, magnetic disks, or optical disks. In some jurisdictions, based on legislation and patent practice, computer-readable media cannot be electric carrier signals or telecommunication signals.

[0191] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0192] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0193] In the embodiments disclosed in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely schematic. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0194] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

Claims

1. A method for powering an online monitoring device of a lightning arrester, characterized in that: The method specifically includes: Based on the operating status of the arrester and environmental parameters, the multi-source energy modules are activated in a preset priority order to power the online monitoring equipment; Based on the output characteristics of the multi-source energy module and the remaining power of the online monitoring device, a constant voltage-constant current hybrid power supply strategy in normal mode or an anti-interference power supply strategy in abnormal mode is implemented for the online monitoring device; The LSTM prediction model processes the arrester's historical leakage current, ambient temperature and humidity, and vibration spectrum characteristics collected by the online monitoring equipment to predict the energy supply trend of the online monitoring equipment within a preset future time. The operating parameters of the online monitoring equipment are dynamically adjusted based on the energy supply trend. In response to the data transmission needs of online monitoring equipment, a bidirectional coupling mechanism between power supply status and communication link is established; Based on the operating data of online monitoring equipment, a digital twin of the power supply system is constructed, and the adaptive optimization of the power supply strategy of the online monitoring equipment is achieved through the digital twin.

2. The method according to claim 1, characterized in that The method of activating the multi-source energy modules to supply power to the online monitoring equipment in a preset priority order based on the operating status of the arrester and environmental parameters specifically includes: Based on the arrester's leakage current amplitude and vibration acceleration, the system extracts electrical energy through a broadband energy extraction circuit and supplies it to the power management module of the online monitoring device. When the arrester's leakage current energy is detected to be lower than the preset energy threshold, the piezoelectric vibration energy harvesting module is activated to generate electricity using the mechanical vibration of the arrester's mounting base to supplement the energy storage unit of the online monitoring equipment. Monitor the solar radiation intensity around the arrester. When the solar radiation intensity meets the preset lighting conditions, the photovoltaic conversion circuit will be used to supplement the power and charge the supercapacitor bank of the online monitoring equipment. The output power of each energy source is dynamically distributed through a three-port energy router, and the fuzzy PID algorithm is used to adjust the impedance matching of each channel of the three-port energy router in real time, so that the total input power fluctuation range of the online monitoring equipment is maintained within the preset fluctuation range.

3. The method according to claim 1, characterized in that The method of executing a constant voltage-constant current hybrid power supply strategy in a normal mode or an anti-interference power supply strategy in an abnormal mode for the online monitoring device based on the output characteristics of the multi-source energy module and the remaining power of the online monitoring device specifically includes: If the remaining power of the online monitoring device is greater than the preset power threshold and the environmental parameters are normal, a constant voltage-constant current hybrid power supply strategy is adopted to maintain the power supply voltage of the online monitoring device within the preset voltage range through a Buck-Boost topology circuit; When it is detected that the ambient temperature is lower than the preset temperature threshold or the electromagnetic interference intensity is greater than the preset interference intensity threshold, it switches to the anti-interference power supply mode. The anti-interference power supply mode includes suppressing high-frequency interference through a multi-stage RC filtering circuit and applying an electromagnetic shielding layer to the key power supply line of the online monitoring equipment.

4. The method according to claim 1, wherein The LSTM prediction model is used to process the arrester's historical leakage current, ambient temperature and humidity, and vibration spectrum characteristics collected by the online monitoring device, predict the energy supply trend of the online monitoring device within a preset future time, and dynamically adjust the working parameters of the online monitoring device according to the energy supply trend, specifically including: The arrester's historical leakage current, ambient temperature and humidity, and vibration spectrum characteristics collected by the online monitoring equipment are fused and preprocessed to generate a standardized feature matrix; Based on the standardized feature matrix, a two-layer LSTM network is used to predict the energy supply trend and output the energy supply forecast sequence within the preset time period in the future. Based on the energy supply prediction sequence, an energy state evaluation function is set up, and parameter adjustment instructions are generated through fuzzy control strategy; Based on the parameter adjustment instructions, the working parameters of the online monitoring equipment are updated in real time.

5. The method according to claim 4, characterized in that The real-time updating of the working parameters of the online monitoring equipment based on the parameter adjustment instruction specifically includes: When the ratio between the predicted power consumption value and the current power consumption value of the online monitoring device is greater than or equal to a first preset ratio, the sampling frequency of the online monitoring device is increased to the first sampling frequency, and all sensors are activated; When the ratio between the predicted power consumption value and the current power consumption value of the online monitoring device is less than a first preset ratio and greater than or equal to a second preset ratio, maintaining the sampling frequency of the online monitoring device at a reference sampling frequency and only starting the core sensor; When the ratio between the predicted power consumption value and the current power consumption value of the online monitoring device is less than a second preset ratio, reducing the sampling frequency of the online monitoring device to the second sampling frequency and shutting down all non-essential peripherals; The first preset ratio is greater than the second preset ratio, the first sampling frequency is greater than the reference sampling frequency and the second sampling frequency, and the reference sampling frequency is greater than the second sampling frequency.

6. The method according to claim 1, wherein The bidirectional coupling mechanism between the power supply status and the communication link is established to meet the data transmission requirements of the online monitoring equipment, specifically including: The power supply status parameters and communication link parameters are collected in real time by multi-source sensors to form a joint parameter set. The power supply status parameters include the remaining power and the power fluctuation rate, and the communication link parameters include the signal strength and the bit error rate. Based on the joint parameter set, the power margin index and channel quality index are calculated to build an energy-communication coupling model; The bidirectional coupling coefficient matrix is calculated based on the energy-communication coupling model, and a dynamic optimization objective function is established according to the bidirectional coupling coefficient matrix; Based on the dynamic optimization objective function, the gradient projection algorithm is used to solve the optimal adjustment strategy of the bidirectional coupling between the power supply status and the communication link.

7. The method according to claim 1, characterized in that The digital twin of the power supply system is constructed based on the operating data of the online monitoring equipment, and the adaptive optimization of the power supply strategy of the online monitoring equipment is achieved through the digital twin, which specifically includes: Based on the energy collection efficiency, device temperature distribution, and electromagnetic interference intensity of the online monitoring equipment, a digital twin of the power supply system is constructed. The digital twin includes a three-dimensional electromagnetic-thermal-mechanical coupling model and an energy conversion efficiency model. Redistribute the weights of each energy source in the power supply system based on the digital twin, adjust the usage ratio of high-heat modules, and form power supply strategy adjustment instructions; The power supply strategy adjustment instruction is injected into the edge computing unit of the online monitoring device to drive the online monitoring device to adjust the energy allocation strategy and power consumption control logic.

8. A power supply system for online monitoring equipment of a lightning arrester, characterized in that: The system specifically includes: The first power supply module is used to activate the multi-source energy module to supply power to the online monitoring equipment in a preset priority order based on the operating status of the lightning arrester and environmental parameters; The second power supply module is used to execute a constant voltage-constant current hybrid power supply strategy in a normal mode or an anti-interference power supply strategy in an abnormal mode for the online monitoring device based on the output characteristics of the multi-source energy module and the remaining power of the online monitoring device; The third power supply module is used to process the arrester's historical leakage current, ambient temperature and humidity, and vibration spectrum characteristics collected by the online monitoring equipment through the LSTM prediction model, predict the energy supply trend of the online monitoring equipment in a preset future time, and dynamically adjust the operating parameters of the online monitoring equipment based on the energy supply trend; The fourth power supply module is used to establish a bidirectional coupling mechanism between the power supply status and the communication link according to the data transmission requirements of the online monitoring equipment; The fifth power supply module is used to build a digital twin of the power supply system based on the operating data of the online monitoring equipment, and realize adaptive optimization of the power supply strategy of the online monitoring equipment through the digital twin.

9. A computer device, characterized in that: include: A memory, a processor and a computer program stored in the memory, which, when executed on the processor, implements the power supply method for online monitoring equipment of a lightning arrester according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the power supply method for online monitoring equipment of a lightning arrester according to any one of claims 1 to 7 is implemented.

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