A method and system for supplying power to an on-line monitoring device of a surge arrester

Abstract

The application discloses an online monitoring equipment power supply method and system for a lightning arrester, and specifically comprises the following steps: based on the operating state and environmental parameters of the lightning arrester, activating a multi-source energy module to supply power to the online monitoring equipment in a preset priority order; based on the output characteristics of the multi-source energy module and the remaining power of the online monitoring equipment, 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 on the online monitoring equipment; processing the historical leakage current, environmental temperature and humidity and vibration frequency spectrum characteristics of the lightning arrester collected by the online monitoring equipment through an LSTM prediction model, predicting the energy supply trend of the online monitoring equipment in a preset future time, and dynamically adjusting the working parameters of the online monitoring equipment according to the energy supply trend. The application realizes stable power supply of the online monitoring equipment for the lightning arrester in a complex environment, and improves the power supply reliability and equipment availability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of lightning arrester monitoring equipment, and particularly relates to a lightning arrester online monitoring equipment power supply method and a power supply system. BACKGROUND

[0002] In the power system, the lightning arrester is a key device to protect power equipment from lightning overvoltage, and the real-time monitoring of its operating state is crucial for the safe and stable operation of the power system. With the development of smart grid technology, lightning arrester online monitoring equipment has gradually become an indispensable part of the power system. However, the existing lightning arrester online monitoring equipment still has many deficiencies in power supply stability, power consumption management, communication reliability and data utilization, which seriously restricts the further improvement of its performance and the expansion of its application range.

[0003] Firstly, in extreme environmental conditions such as extreme cold, strong electromagnetic interference, etc., the traditional lightning arrester online monitoring equipment often relies on a single energy module for power supply, such as leakage current energy extraction or solar energy. However, these single energy modules are prone to failure in extreme environments, resulting in insufficient power supply and failure of the equipment to work normally. For example, in extremely cold environments, the power generation efficiency of solar panels will decrease significantly, and even stop working; and in strong electromagnetic interference environment, the leakage current energy extraction module may not be able to accurately extract energy due to electromagnetic interference. The lack of power supply stability not only affects the normal operation of the lightning arrester online monitoring equipment, but also may pose a threat to the safe operation of the entire power system.

[0004] Secondly, the traditional lightning arrester online monitoring equipment often adopts a rough way in power consumption management, without predictive power consumption adjustment combined with the aging characteristics of lightning arrester (such as nonlinear change of leakage current caused by valve deterioration). This rough power consumption management not only wastes valuable energy resources and shortens the service life of the equipment, but also may affect the power supply stability due to high power consumption. For example, in the early stage of lightning arrester aging, the leakage current may show a nonlinear growth trend, and if the equipment cannot adjust the power consumption strategy in time, it will lead to energy waste and power supply shortage.

[0005] Moreover, the traditional lightning arrester online monitoring equipment often relies on a single communication link, such as wireless communication or wired communication. However, in extreme situations such as geological disasters (such as earthquakes, forest fires), a single communication link is prone to interruption, resulting in failure to upload monitoring data in time. This not only affects the real-time grasp of the state of lightning arrester, but also may affect the stable operation of the power supply system due to communication interruption. For example, in the event of an earthquake, the wired communication line may be broken due to ground vibration, resulting in data transmission interruption.

[0006] Finally, the conventional lightning arrester online monitoring equipment has a data island problem in data utilization, that is, the monitoring data is not linked with the power grid dynamic topology, meteorological data, etc. This leads to the inability to quickly locate problems and take effective measures when the power system fails, further affecting the reliability of the power supply system. SUMMARY

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

[0008] In a first aspect, the present application provides a lightning arrester online monitoring equipment power supply method, which specifically includes:

[0009] Based on the operating state and environmental parameters of the lightning arrester, activate the multi-source energy module to supply power to the online monitoring equipment in a predetermined priority order;

[0010] Based on the output characteristics of the multi-source energy module and the remaining power of the online monitoring equipment, 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 equipment;

[0011] Process the lightning arrester historical leakage current, environmental temperature and humidity, and vibration frequency spectrum features collected by the online monitoring equipment through the LSTM prediction model, predict the energy supply trend of the online monitoring equipment in a predetermined future time, and dynamically adjust the working parameters of the online monitoring equipment according to the energy supply trend;

[0012] For the data transmission needs of the online monitoring equipment, a bidirectional coupling mechanism of the power supply state and the communication link is established;

[0013] 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 realized through the digital twin.

[0014] In a second aspect, the present application provides a lightning arrester online monitoring equipment power supply system, which specifically includes:

[0015] A first power supply module for activating the multi-source energy module to supply power to the online monitoring equipment in a predetermined priority order based on the operating state and environmental parameters of the lightning arrester;

[0016] A second power supply module for 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 equipment based on the output characteristics of the multi-source energy module and the remaining power of the online monitoring equipment;

[0017] The third power supply module is configured to process the historical leakage current, environmental temperature and humidity, and vibration spectrum characteristics of the lightning arrester collected by the online monitoring device through an LSTM prediction model, predict the energy supply trend of the online monitoring device in a preset future time, and dynamically adjust the working parameters of the online monitoring device according to the energy supply trend.

[0018] The fourth power supply module is configured to establish a bidirectional coupling mechanism of the power supply state and the communication link for the data transmission requirement of the online monitoring device.

[0019] The fifth power supply module is configured to construct a digital twin of the power supply system based on the operation data of the online monitoring device, and realize adaptive optimization of the power supply strategy of the online monitoring device through the digital twin.

[0020] In a third aspect, the present application 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 of the online monitoring device of the lightning arrester is realized.

[0021] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, when the computer program is executed by a processor, the power supply method of the online monitoring device of the lightning arrester is realized.

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

[0023] 1. The present application realizes stable power supply of the online monitoring device of the lightning arrester in a complex environment, and improves the power supply reliability and device availability.

[0024] 2. The present application activates the multi-source energy module to supply power to the online monitoring device according to the preset priority order, which can automatically switch or be used in combination according to different environmental conditions, ensuring that the online monitoring device can obtain stable power supply in various extreme environments, and effectively improving the stability and reliability of the power supply system.

[0025] 3. The present application executes 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 based on the output characteristics of the multi-source energy module and the remaining power of the online monitoring device, realizing fine management of power consumption.

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

[0027] 5、The application establishes a bidirectional coupling mechanism between the power supply state and the communication link according to the data transmission needs of the online monitoring equipment, improving the reliability of the communication system.

[0028] 6、The application intelligently switches energy modules based on the operating state of the lightning arrester and environmental parameters, optimizing energy utilization efficiency and ensuring that the online monitoring equipment can obtain sufficient power support under different working conditions.

[0029] 7、The application dynamically adjusts the power supply strategy by combining the output characteristics of multi-source energy modules and the equipment power state, ensuring the stable operation of the online monitoring equipment under normal and abnormal environments and improving the anti-interference capability of the equipment.

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

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

[0032] 10、The application establishes a bidirectional coupling mechanism between the power supply state and the communication link, ensuring that the online monitoring equipment can still operate stably under abnormal conditions such as communication interruption, improving the reliability and real-time performance of data transmission.

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

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0035] Figure 1 is a flowchart of a lightning arrester online monitoring equipment power supply method provided by an embodiment of the application;

[0036] Figure 2 is a structural diagram of a lightning arrester online monitoring equipment power supply system provided by an embodiment of the application;

[0037] Figure 3 is a structural diagram of a computer device provided by an embodiment of the application. Detailed Implementation

[0038] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

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

[0040] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0041] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0042] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0043] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of 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 "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0044] In the embodiments of the present application, the execution subject of the flow includes a terminal device. The terminal device 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 flowchart of an on-line monitoring device power supply method of a surge arrester disclosed in an embodiment of the present application is shown, and is described in detail as follows:

[0045] S101, based on the operating state of the surge arrester and the environmental parameters, activating the multi-source energy module in a preset priority order to supply power to the on-line monitoring device.

[0046] In the present embodiment, the environmental temperature, humidity, light intensity, electromagnetic interference intensity and other parameters are monitored in real time by sensors, and the leakage current, valve piece temperature, insulation resistance and other parameters of the surge arrester are collected to evaluate the aging degree and operating state thereof.

[0047] When the surge arrester is normally operated and the leakage current is stable, the leakage current energy harvesting module is preferentially used because of its high energy conversion efficiency and no need for additional energy input. When the environmental light intensity is higher than a threshold value (such as ≥ 50000 lux) and the temperature is suitable (such as -20℃~50℃), the solar cell panel module is activated. When the light or leakage current is insufficient, the stored energy of the super capacitor energy storage module is preferentially released to avoid frequent switching of other modules. When the electromagnetic interference intensity exceeds a threshold value (such as ≥ 50 V / m) and other modules fail, the low-power wireless energy harvesting module is activated to harvest energy from the environmental radio waves (such as mobile communication base station signals). Only when all other modules cannot supply power (such as extremely low temperature, continuous rain and strong electromagnetic interference), the standby battery module is activated to ensure the minimum function of the device.

[0048] The environmental parameters and the operating state of the surge arrester are comprehensively evaluated every 5 seconds to determine the current optimal power supply module. For example, if the leakage current is stable and ≥ 1 mA, the leakage current energy harvesting module is immediately switched to; if the light intensity is ≥ 50000 lux and the temperature is within the range of -20℃~50℃, the solar cell panel module is switched to; if the remaining capacity of the super capacitor is ≥ 30%, the super capacitor energy storage module is preferentially used when the light or leakage current is insufficient; if the electromagnetic interference intensity is ≥ 50 V / m and other modules fail, the low-power wireless energy harvesting module is switched to; if all modules cannot supply power and the standby battery capacity is ≥ 20%, the standby battery module is started. When the modules are switched, the DC-DC converter is used to realize the smooth transition of voltage and current to avoid device restart or data loss.

[0049] The output voltage, current and temperature of each energy module are periodically detected, and the faulty module is marked and isolated. When the main module fails, it is automatically downgraded to the next priority module to ensure the continuity of power supply. The 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 device are significantly improved through the dynamic activation scheme of the multi-source energy module based on the operating state of the lightning arrester and the environmental parameters.

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

[0052] In this embodiment, the conventional lightning arrester online monitoring device lacks coordinated optimization of the output characteristics of the multi-source energy module and the device power in the power supply strategy, resulting in the following problems: 1. Rigidity of the conventional power supply mode: fixed voltage / current output is adopted, which cannot adapt to the dynamic fluctuations of multi-source energy modules (such as solar energy and leakage current energy extraction), which is easy to cause power supply interruption or device overload. 2. Poor adaptability to abnormal conditions: in strong electromagnetic interference, extreme temperature and other environments, the anti-interference power supply strategy is not designed for energy module performance degradation, resulting in distorted data acquisition or device downtime. 3. Imbalance between energy efficiency and life: the power supply mode is not dynamically adjusted in combination with the remaining power of the device, resulting in energy waste or battery overcharging / overdischarging, shortening the device life. Therefore, this embodiment realizes the balance of power supply stability, energy efficiency and device life through the coordination of the normal mode (constant voltage-constant current hybrid power supply) and the abnormal mode (anti-interference power supply).

[0053] Specifically, for the leakage current energy extraction module, the solar panel, the super capacitor energy storage module and the low-power wireless energy extraction module, their output characteristic models are established respectively, including: output voltage / current fluctuation range (such as the illumination-temperature correlation of the solar module); dynamic response time (such as the transient output capability of the leakage current energy extraction module under electromagnetic interference); aging attenuation characteristics (such as the decline of the photoelectric conversion efficiency of the solar panel after long-term use).

[0054] According to the remaining power (SOC, State of Charge) of the online monitoring device, the power state is divided into the following levels: high power area (SOC ≥ 80%): the device has sufficient energy reserve and can support high-power tasks (such as high-frequency data acquisition); medium power area (30% ≤ SOC < 80%): the device power is moderate and needs to balance power consumption and endurance; low power area (SOC < 30%): the device power is insufficient and needs to start a low-power mode to extend the running time.

[0055] In the normal mode, the voltage-stable energy module (such as the supercapacitor energy storage module) is used as the main power supply, and a constant voltage output is provided through the DC-DC converter to ensure the stability of the core functions (such as data acquisition) of the device. When the voltage of the main power supply fluctuates, the current-stable energy module (such as the leakage current energy harvesting module) is activated dynamically as an auxiliary power supply to achieve current compensation through parallel power supply, thereby maintaining the constant total power consumption of the device. When the light is sufficient and the leakage current is stable, the solar module and the leakage current energy harvesting module jointly supply power, and the power distributor allocates the load as needed to reduce the loss of the energy storage module.

[0056] When the intensity of electromagnetic interference is detected to exceed the threshold value (such as ≥80 V / m), the leakage current energy harvesting module is automatically isolated, switched to a low-power wireless energy harvesting module (powered by environmental radio waves), and the device sampling frequency is reduced (such as from 1 time / second to 1 time / minute) to reduce power consumption. In extremely cold environments (below -40°C), when the solar module power generation efficiency is less than 10%, the supercapacitor energy storage module and the backup battery module are switched to jointly supply power, and the pulse power supply technology (intermittent activation of the device) is used to extend the endurance. When the device power enters the low power area, the three-level emergency response is started: 1. First-level response (SOC < 30%): non-core functions (such as historical data storage) are turned off, and only the arrester state real-time monitoring and key data uploading are retained; 2. Second-level 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. Third-level response (SOC < 10%): only the arrester overvoltage alarm function is retained, and all other functions are hibernated until the power is restored or manually intervened.

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

[0058] S103, the historical leakage current of the arrester, the environmental temperature and humidity, and the vibration frequency spectrum characteristics collected by the online monitoring device are processed through the LSTM prediction model to predict the energy supply trend of the online monitoring device in the preset future time, and the working parameters of the online monitoring device are dynamically adjusted according to the energy supply trend.

[0059] In this embodiment, the power consumption and working mode of the traditional lightning arrester online monitoring device are statically configured, without dynamic optimization in combination with future energy supply capacity, resulting in the following problems: 1. Energy supply and load imbalance: when the energy supply is insufficient (such as continuous rainy weather causing solar energy failure), the device still maintains a high power consumption mode (such as high-frequency data acquisition), accelerating the depletion of the energy storage module; 2. Resource waste: when the energy supply is excessive (such as sufficient light and the lightning arrester being in a healthy state), the device does not improve the sampling accuracy or increase the frequency of redundant communication, resulting in that the data value is not fully tapped; 3. Emergency response lag: when the energy supply trend changes suddenly (such as sudden strong electromagnetic interference causing leakage current energy failure), the device cannot adjust the power consumption strategy in advance, causing power supply interruption risk. Therefore, this embodiment realizes intelligent matching of device working parameters (such as sampling frequency, communication period, and power consumption mode) through historical data mining and trend prediction, improving system energy efficiency and reliability.

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

[0061] An LSTM prediction model is constructed, which includes an input layer, a hidden layer, and an output layer. In the input layer, a multi-time series feature matrix is constructed, and the lightning arrester historical leakage current (such as past 72 hours of data), environmental temperature and humidity (such as past 24 hours of data), and vibration spectrum (such as past 48 hours of data) are sliced according to a time window (such as 15 minutes) to form a three-dimensional feature tensor (time step x feature dimension x sample number); in the hidden layer, a double-layer LSTM network is used, the first layer extracts time series dependent features (such as the nonlinear growth trend of leakage current), and the second layer fuses multi-modal data (such as the correlation between temperature and humidity and energy supply); in the output layer, the energy supply trend in a preset time window (such as 24 hours) in the future is predicted, such as total energy supply (in Wh), energy supply volatility (such as standard deviation, range), and energy supply interruption risk level (such as high risk / medium risk / low risk).

[0062] The actual operation data of the collection device (covering extreme scenarios such as extreme cold, strong electromagnetic interference, geological disasters, etc.) is collected to construct a labeled data set, wherein the energy supply trend label is generated by the combination of the energy storage module SOC change rate and the energy module output stability. The sliding window method is used to generate training samples, the time step is set to 12 (i.e., every 15 minutes of data to predict the trend for the next 3 hours), and the LSTM network weight is optimized by the back propagation algorithm to make the prediction error (such as the root mean square error RMSE) between the predicted value and the actual energy supply trend less than 5%.

[0063] If it is a high energy supply trend, the leakage current sampling accuracy is increased (such as from 0.1 mA to 0.01 mA), the valve temperature monitoring frequency is increased (from 1 time / hour to 1 time / 10 minutes), the redundant communication link (such as wireless + wired dual channel) is enabled, the data upload interval is shortened (from 15 minutes / time to 5 minutes / time), and the edge computing module is activated to locally preprocess historical data and reduce the amount of data transmitted to the cloud. If it is a medium energy supply trend, the regular sampling accuracy (0.1 mA) is maintained, but the sampling interval is dynamically adjusted according to the leakage current change rate (such as increased to 1 time / 5 minutes when the change rate is greater than 10%); the main communication link (such as wireless) is maintained, the non-critical data upload interval is extended (such as from 15 minutes / time to 30 minutes / time), and the non-core function (such as historical data storage) is turned off, leaving only real-time monitoring and alarm functions. If it is a low energy supply trend, the leakage current sampling accuracy is reduced (such as from 0.1 mA to 1 mA), the valve temperature monitoring interval is extended (from 1 time / hour to 1 time / 2 hours), the low-power communication mode (such as LoRa) is switched to, only critical alarm data (such as overvoltage events) is uploaded, and the pulse power supply technology (intermittent activation of the device) is started to extend the endurance time of the energy storage module.

[0064] The LSTM prediction model is run every 15 minutes to generate a 3-hour energy supply trend and match the corresponding working parameter strategy; through the built-in edge controller of the device, the strategy parameters (such as sampling interval, communication period) are sent to the sensor, communication module and power consumption management unit. After the device runs according to the new strategy, the actual energy supply amount (energy storage module SOC change) and power consumption (voltage / current sensor data) are continuously monitored, the strategy execution deviation (such as the relative error between the predicted energy and the actual energy) is calculated, and if the deviation exceeds the threshold (such as ±15%), the online fine tuning of the model is triggered (such as increasing the weight of recent data and optimizing the LSTM hidden layer parameters).

[0065] In this embodiment, the precise prediction of the energy supply trend and the dynamic adaptation of the device working parameters are realized through the LSTM prediction model, solving the problems of energy supply and load imbalance, resource waste, and lagging emergency response in traditional solutions, and providing technical support for the intelligent and efficient operation of the lightning arrester online monitoring device.

[0066] S104, a bidirectional coupling mechanism of power supply state and communication link is established for data transmission requirements of the online monitoring device.

[0067] In this embodiment, the traditional lightning arrester online monitoring device has the following problems in data transmission: 1. Power supply-communication split: the power supply state (such as the remaining capacity of the energy storage module, the output stability of the energy module) and the communication link (such as wireless / wired mode) operate independently, resulting in high power consumption communication (such as frequent uploading of full data) even when the power supply is insufficient, accelerating the depletion of energy storage, and not adjusting the power supply strategy (such as continuously activating high-power wireless modules) when the communication link is interrupted, causing ineffective energy consumption. 2. Extreme scenario response lag: when geological disasters (such as earthquakes, forest fires) cause communication link failure, the device cannot predict link risks through power supply state, resulting in loss of critical data (such as overvoltage alarm). 3. Energy waste: data compression rate and transmission priority are not dynamically optimized according to communication link quality, resulting in redundant data occupying limited bandwidth and energy resources. Therefore, this embodiment establishes three modules of power supply state perception, communication link adaptive adjustment, and dynamic data scheduling to realize the cooperative optimization of power supply and communication, and improve the reliability of data transmission and system energy efficiency.

[0068] Specifically, the state of the energy storage module (such as remaining capacity, charge and discharge current, internal resistance change rate), the output of the energy module (such as solar energy, leakage current energy output voltage / current, output power fluctuation rate) and the power supply stability (such as voltage ripple coefficient, transient overvoltage / undervoltage times) are monitored in the online monitoring device.

[0069] According to the monitoring data, the power supply state is divided into the following levels:

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

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

[0072] Third level (power supply warning): 20%≤SOC<50%, energy module output fluctuates greatly (15%<fluctuation rate≤30%), voltage ripple coefficient≤10%;

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

[0074] For wireless communication (such as LoRa, 4G) and wired communication (such as optical fiber, power line carrier), evaluation indicators are established: 1. Link availability: based on the response rate of heartbeat packets (such as 3 consecutive non-responses to determine link interruption); 2. Transmission delay: round-trip time (RTT) from data packet sending to confirmation; 3. Error rate: the ratio of error bits to total bits of received data packets; 4. Bandwidth utilization: the ratio of current transmission rate to theoretical maximum rate of the link.

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

[0076] When power supply is warning, switch to low-power communication links (such as LoRa or power line carrier), close redundant channels; only upload key data (such as lightning arrester overvoltage alarm, leakage current sudden event), data compression rate set to medium (such as 30%~50%); extend data transmission interval (such as from 15 minutes / time to 30 minutes / time).

[0077] In the case of power supply emergency, only the lowest power communication link (such as intermittent LoRa module) is retained, all unnecessary communication is closed; only upload emergency alarm data (such as lightning arrester breakdown event), data compression rate set to high (such as 50%~70%); start "store-forward" mechanism, store data locally, and upload in bulk after power supply is restored.

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

[0079] Level 1 (emergency data): lightning arrester overvoltage / breakdown alarm, valve temperature overrun 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 state data (such as hourly leakage current average, environmental temperature and humidity);

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

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

[0084] In power supply warning, level 1 data is uploaded immediately, level 2 data is uploaded with delay (e.g. to be uploaded after the power supply state is recovered), level 3 data is suspended from uploading, and level 4 data is discarded; data compression and fragmented transmission are enabled to reduce energy consumption of single transmission.

[0085] In power supply emergency, only level 1 data is uploaded, and level 2 and lower data are all discarded; a “last mile” transmission strategy is started to send critical alarms through an extremely low-power link (e.g. LoRa single wake-up).

[0086] The built-in edge controller of the device collects power supply state data every 5 minutes and triggers communication link switching instructions according to the classification results; when the link is switched, the buffered queue is used to temporarily store the data to be transmitted to avoid data loss. The communication module feeds back the link health (e.g. error rate, latency) to the edge controller in real time; if the link quality continues to deteriorate (e.g. error rate > 10%), the communication power is actively reduced (e.g. reducing the transmission power, extending the sleep cycle), and at the same time, the power supply module is applied for temporary power increase (e.g. activating the standby energy module). Based on historical data and device aging characteristics, the power supply state classification threshold and link switching conditions are dynamically optimized; for example, in the later stage of arrester aging (leakage current increases due to the decrease of valve impedance), the SOC threshold of “power supply warning” is reduced (e.g. from 50% to 40%), and the low-power communication mode is triggered in advance.

[0087] In this embodiment, by establishing a bidirectional coupling mechanism between the power supply state and the communication link, the collaborative optimization of power supply resources and communication resources is realized, and the problems such as power supply-communication separation, lag in extreme scenario response, and energy waste in traditional solutions are solved, which provides technical support for the reliable operation of arrester online monitoring devices in complex environments.

[0088] S105, based on the operation data of the online monitoring device, a digital twin of the power supply system is constructed, and adaptive optimization of the power supply strategy of the online monitoring device is realized through the digital twin.

[0089] In this embodiment, the power supply strategy optimization of the traditional lightning arrester online monitoring device has the following pain points: 1. Static strategy rigidity: The power supply parameters (such as voltage threshold, energy distribution weight) usually rely on manual experience preset and cannot be dynamically adjusted according to the actual operation state of the device (such as aging degree, environmental change), 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, environmental temperature and humidity, energy storage module SOC, energy module output power), but the traditional scheme does not establish a data correlation model, making it difficult to achieve global optimization; 3. Fault response lag: When the device is abnormal (such as energy module failure, communication link interruption), the traditional scheme relies on manual intervention or preset rule to trigger strategy switching, and cannot predict risks and optimize in advance; 4. High verification cost: New power supply strategy needs to be verified through hardware iteration or field test, which is long in cycle and high in cost, and is difficult to quickly adapt to complex scene requirements. Therefore, this embodiment builds a virtual model that is real-time mapped with physical devices, and realizes dynamic generation, real-time verification and closed-loop optimization of power supply strategy through simulation analysis driven by operation data, improving the reliability and energy efficiency of the power supply system in complex environments.

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

[0091] The physical layer maps the hardware entities of the lightning arrester online monitoring device, which includes multi-source energy modules (solar panels, leakage current energy harvesting modules, backup batteries), energy storage modules (lithium batteries, super capacitors), communication modules (wireless / wired hybrid communication interfaces) and sensor groups (leakage current sensors, temperature and humidity sensors, vibration sensors).

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

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

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

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

[0096] The digital twin automatically identifies the current power supply scene (such as "extreme cold + power supply warning" "strong electromagnetic interference + device aging") based on real-time data and historical trends; from the pre-set strategy library, candidate strategies that meet the scene characteristics are selected (such as "high priority activates solar + super capacitor hybrid power supply" "reduce wireless communication transmission power to 17dBm"); taking power supply stability (SOC>30%), energy efficiency (total device power consumption<5W), and communication reliability (error rate<5%) as constraint conditions, the optimal strategy parameters (such as energy module priority order, constant voltage-constant current switching threshold) are generated through simulation deduction; in the later stage of device aging (such as a 20% decrease in valve impedance), the power supply stability constraint is relaxed (the SOC threshold is reduced from 30% to 25%), and key data transmission is prioritized.

[0097] Input the current running data into the digital twin, replay the power supply process of the past 72 hours, evaluate the energy efficiency improvement amplitude of the candidate strategy (such as a 15% reduction in total power consumption), and the number of fault avoidance times (such as avoiding 3 times of over-discharge of the energy storage module); simulate extreme environments (such as an environment temperature of -40℃ and strong electromagnetic interference), and verify the robustness of the candidate strategy (such as a 80% reduction in power supply interruption time).

[0098] Randomly generate 1000 sets of environmental parameters (such as light intensity, electromagnetic interference intensity), and statistically analyze the failure probability of the candidate strategy (such as a power supply interruption probability of less than 0.5%); quantify the influence of key parameters (such as solar panel area, energy storage module capacity) on the strategy effect, and provide a basis for hardware upgrade.

[0099] The candidate strategy parameters (such as energy module priority, voltage threshold) that pass the verification are preloaded to the edge controller of the physical device; a 10-second transition period is set between the original strategy and the new strategy, the energy module output weight is gradually adjusted to avoid power interruption; if the new strategy leads to abnormal power supply (such as SOC being lower than 20% for 3 minutes in a row), automatically roll back to the last stable strategy and trigger an alarm.

[0100] Real-time collection of operation data under the new strategy (such as power supply stability, communication reliability), comparison with the simulation results of the digital twin, calculation of error rate (such as power consumption prediction error <3%), automatic correction of the parameters of the digital twin (such as energy module output characteristic curve) according to the error rate to improve the accuracy of subsequent strategy optimization.

[0101] In some embodiments, in step S101, based on the operating state of the lightning arrester and the environmental parameters, the multi-source energy module is activated in a predetermined priority order to supply power to the online monitoring device, specifically including:

[0102] Based on the leakage current amplitude and vibration acceleration of the lightning arrester, the wideband energy extraction circuit extracts electrical energy and supplies it to the power management module of the online monitoring device;

[0103] When the leakage current energy of the lightning arrester is detected to be lower than the preset energy threshold, the piezoelectric vibration energy extraction module is enabled to generate electricity using the mechanical vibration of the lightning arrester installation base and supplement the energy storage unit of the online monitoring device;

[0104] Monitor the solar irradiance around the lightning arrester, and when the solar irradiance meets the preset light conditions, supplement the electrical energy through the photovoltaic conversion circuit to charge the supercapacitor group of the online monitoring device;

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

[0106] In this embodiment, the leakage current wideband energy harvesting module is used to extract power based on the wideband characteristics of the arrester leakage current (0.1 mA~10 mA, frequency 50 Hz~1 MHz) through magnetic core coupling and rectifier circuit, and to preferentially power the power management module of the online monitoring device; the piezoelectric vibration energy harvesting module is used to utilize the mechanical vibration (frequency 5 Hz~500 Hz, acceleration 0.1 g~5 g) of the arrester installation base, and to convert vibration energy into electrical energy through a piezoelectric ceramic sheet to supplement the energy of the energy storage unit (such as a lithium battery); the photovoltaic conversion circuit is used to monitor the solar radiation intensity (0 W / m²~1300 W / m²) around the arrester, and to convert light energy into electrical energy through a monocrystalline silicon photovoltaic panel to charge the supercapacitor group; the three-port energy router includes three input ports (connected to the leakage current energy harvesting module, the piezoelectric vibration energy harvesting module, and the photovoltaic conversion circuit, respectively) and one output port (connected to the online monitoring device), and is used to dynamically allocate the output power of each energy source, to realize power smoothing and merging through an impedance matching network, and to suppress total input power fluctuations.

[0107] The leakage current wideband energy harvesting module is used as the first priority for real-time power supply. For example, when the arrester leakage current amplitude is >0.5 mA (typical value) and the electromagnetic interference intensity is <50 dBμV / m, the leakage current is converted into direct current (voltage range 3 V~15 V) through the rectifier filter unit of the wideband energy harvesting circuit, and the power management module is preferentially powered. When the leakage current amplitude is <0.2 mA or the electromagnetic interference intensity is >70 dBμV / m, the output weight of this module is automatically reduced to 30%, to avoid power interruption caused by interference.

[0108] The photovoltaic conversion circuit is used as the second priority to supplement power when there is sufficient light. For example, when the solar radiation intensity is >200 W / m² (typical sunny day threshold) and the ambient temperature is -30℃~+60℃, the photovoltaic panel output voltage is dynamically adjusted through the MPPT (maximum power point tracking) algorithm to charge the supercapacitor group (target voltage 5 V, allowable fluctuation ±0.2 V). When the radiation intensity is <100 W / m², the module output is automatically turned off to avoid reverse consumption of energy storage energy.

[0109] The piezoelectric vibration energy harvesting module is used as the third priority to supplement energy storage when the leakage current is insufficient. For example, when the leakage current energy harvesting module output power is <0.5 W (for more than 10 seconds) and the energy storage unit SOC is <40%, the vibration energy is converted into electrical energy (voltage range 2 V~8 V) through the parallel resonant circuit of the piezoelectric ceramic sheet, and the lithium battery is charged after the DC-DC boost circuit. According to the arrester installation location (such as mountainous area / city), the vibration acceleration threshold is dynamically adjusted (mountainous area 0.5 g, city 1.2 g) to avoid invalid power generation.

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

[0111] The fuzzy PID control strategy is adopted 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 large" and dΔP / dt is "positive fast", the output weight of the photovoltaic module is reduced (e.g. from 60% to 40%) and the output weight of the vibration module is increased (e.g. from 10% to 20%) at the same time. If ΔP is "negative medium" and dΔP / dt is "zero", the output weight of the leakage current module is fine-tuned (e.g. from 30% to 32%) to keep the total power stable. The output impedance adjustment amount (ΔZ) is divided into five fuzzy sets: "negative large" (ΔZ<-10Ω), "negative medium" (-10Ω≤ΔZ<-5Ω), "zero" (-5Ω≤ΔZ≤5Ω), "positive medium" (5Ω<ΔZ≤10Ω), and "positive large" (ΔZ>10Ω), and the precise value is calculated by the center of gravity method. The fuzzy PID control cycle is executed once every 100ms to dynamically adjust the impedance matching parameters of the three-port energy router, ensuring that the total input power fluctuation range is ≤±5%.

[0112] In this embodiment, the problems of single power supply, large fluctuation, and poor adaptability of the traditional lightning arrester online monitoring device power supply system are solved by the coordinated power supply and dynamic power distribution technology of the multi-source energy module, and the power supply reliability and energy utilization rate are significantly improved.

[0113] In some embodiments, in the step S102, the output characteristics of the multi-source energy module and the remaining power of the online monitoring device are 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, which 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, and the Buck-Boost topology circuit is used to maintain the power supply voltage of the online monitoring device within the preset voltage range;

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

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

[0117] In the abnormal mode, such as when the environmental temperature is lower than -30°C, the output capacity of the lithium battery decreases due to the increase of the internal resistance (capacity attenuation exceeds 30%), which may cause the device to fail 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 is interrupted (packet loss rate >5%); immediately switch to the anti-interference power supply mode, isolate the interference source through filtering and shielding technology, and ensure the continuity of the key functions (such as fault recording and state alarm).

[0118] When the device is in standby, intermittent sampling or low-frequency communication state (such as transformer oil chromatographic monitoring device sampling 3 times a day, each time consuming <0.5 seconds), the Buck-Boost circuit works in constant voltage output mode, and the current is dynamically adjusted according to the load (0.05A~0.3A), which reduces the charge and discharge loss of the energy storage unit by reducing the output current ripple (ripple rate <3%); preferentially use photovoltaic conversion circuit (output power 8W~15W on sunny days) and leakage current energy taking module (1.5W~4W under normal working conditions) for power supply, and only when the multi-source energy is insufficient, the lithium battery is used for supplementary discharge (discharge current <0.3A), which prolongs the service life of the energy storage unit.

[0119] When the load current requirement exceeds 0.6A, the Buck-Boost circuit automatically switches to constant current mode, the output current stabilizes at 1.2A (for 60ms), and the voltage dynamically adjusts with the load resistance (4.5V~5.5V), avoiding instantaneous voltage drop causing communication interruption or sensor sampling distortion; through multi-source energy collaborative output (photovoltaic + leakage current + lithium battery combined power supply), ensuring that the instantaneous power demand (peak power > 6W) is met.

[0120] A multi-stage RC filter circuit is designed, and the circuit structure includes: the first stage (high-frequency noise suppression): an RC filter with a cutoff frequency of 80kHz is used to filter out high-frequency harmonics generated by the photovoltaic panel MPPT circuit or switching power supply (attenuation amplitude > 45dB@1.2MHz); the second stage (pulse interference attenuation): an RC filter with a cutoff frequency of 8kHz is used to suppress the intermediate-frequency pulse caused by lightning impact or power grid fluctuation (attenuation amplitude > 55dB@120kHz); the third stage (low-frequency harmonic elimination): an RC filter with a cutoff frequency of 800Hz is used to attenuate the low-frequency interference coupled by motor start-stop or mechanical vibration (attenuation amplitude > 35dB@12kHz). Each stage of filter selects high-precision capacitor (X7R material, temperature coefficient ±12%) and low ESR inductor (ferrosilicon aluminum core, Q value > 90) to ensure the interference attenuation effect while controlling the output voltage phase delay to <3°, avoiding sensor signal distortion.

[0121] The communication module power supply line, sensor sampling line and microprocessor power supply end are implemented with full-path shielding, using double-layer copper foil wrapping structure (outer copper foil thickness 0.12mm, inner aluminum foil thickness 0.06mm), shielding effectiveness > 85dB@1.2GHz; the shielding layer is connected with the device metal shell through 360° ring connection, and the grounding resistance is <0.8Ω, avoiding shielding failure caused by poor grounding; in a 65dBμV / m strong electromagnetic interference environment, the communication error rate is reduced from 15% to 0.2% after shielding, and the sensor sampling value offset is reduced from ±10% to ±1%.

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

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

[0124] The historical leakage current, environmental temperature and humidity, and vibration spectrum characteristics of the lightning arrester collected by the online monitoring device are fused and preprocessed to generate a standardized feature matrix.

[0125] Based on the standardized feature matrix, the energy supply trend is predicted through a double-layer LSTM network, and an energy supply prediction sequence in the future preset time is output.

[0126] Based on the energy supply prediction sequence, an energy state evaluation function is set, and a parameter adjustment instruction is generated through a fuzzy control strategy.

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

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

[0129] The environmental temperature (-40℃~+80℃) and humidity (0%~100%RH) are collected, and the temperature daily change rate (absolute value <15℃ / day) and humidity fluctuation amplitude (daily range <30%RH) are calculated to avoid the interference of extreme environmental parameters on the prediction model. The temperature data is lag compensated (sensor response delay <3 minutes is corrected), and the humidity data is temperature and humidity coupled (sensor measurement deviation in high temperature and high humidity environment is eliminated) to form the environmental temperature and humidity characteristics.

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

[0131] The leakage current, temperature and humidity, and vibration data are aligned by timestamp to construct a three-dimensional feature tensor (time x feature type x sensor node), and strong correlation features (such as the correlation coefficient of leakage current and temperature > 0.7, and the correlation coefficient of vibration main frequency > 0.6) are screened out through mutual information analysis; the correlation features are processed by dimension reduction (such as principal component analysis to retain the principal components with cumulative contribution rate > 90%), to reduce the interference of redundant information on the prediction model. The features are normalized (linearly mapped to the [0, 1] interval) to eliminate dimensional differences; a standardized feature matrix (dimension: time step x feature dimension, such as 96 time steps x 12 features for 24-hour data) is constructed as the input of the LSTM model.

[0132] The structure of the double-layer LSTM network includes an input layer, a first-layer LSTM, a second-layer LSTM, a fully connected layer, and an output layer. The input layer receives the standardized feature matrix, the first-layer LSTM extracts the feature dependence relationship in the time dimension (such as the daily periodic fluctuation of leakage current and the seasonal change of temperature and humidity) through 128 LSTM units, sets the forgetting gate bias (initial value 0.5) to balance the weight of historical information and current input, and avoids gradient disappearance or explosion. The second-layer LSTM receives the hidden state sequence output by the first layer, models the coupling relationship between features (such as the baseline drift of leakage current caused by temperature rise and the internal insulation deterioration reflected by vibration spectrum change) through 64 LSTM units, and introduces an attention mechanism (weight initialization is uniformly distributed) to dynamically allocate the contribution of different time steps to the prediction result (such as giving higher weight to data before and after a thunderstorm). The fully connected layer maps the output of the second-layer LSTM to the energy supply prediction sequence (dimension: prediction time step x 1, such as 96 prediction values for the next 24 hours), and the prediction value represents the relative change trend of energy supply (such as photovoltaic output power and conversion efficiency of leakage current power module), with the unit being a standardized energy index (0-1). The output layer outputs the energy supply prediction sequence.

[0133] The double-layer LSTM network model is trained by sliding window cross-validation (window length 7 days, step length 1 day) to ensure the robustness of the prediction result in scenarios such as seasonal change (such as 30% increase in photovoltaic output in summer) and sudden disturbance (such as vibration feature mutation caused by strong wind weather). Early stopping mechanism is introduced (stop training when the validation set loss does not decrease for 5 consecutive rounds), to avoid overfitting.

[0134] An energy state evaluation function is designed, and the multi-dimensional evaluation indexes of the energy state evaluation function include: 1. Energy supply sufficiency rate: the ratio of the predicted energy index to the device energy consumption benchmark value (such as photovoltaic + leakage current energy > 1.2 times the device standby power consumption, which is determined to be sufficient); 2. Energy fluctuation risk degree: the ratio of the standard deviation to the mean of the predicted sequence (reflecting the stability of energy supply, such as fluctuation risk degree > 0.3, which is determined to be high risk); 3. Energy gap duration: the continuous time step when the predicted energy index is lower than the device minimum power consumption threshold (such as 6 hours of energy gap triggering alarm). The comprehensive energy state index (value range 0~1) is calculated by weighted summation (such as sufficiency rate weight 0.5, fluctuation risk degree weight 0.3, and gap duration weight 0.2), which quantifies the health degree of future energy supply.

[0135] The comprehensive energy state index is divided into 5 fuzzy subsets (extremely low, low, medium, high, and extremely high), and the corresponding membership function is trapezoidal distribution (such as the threshold range of the "high" subset is [0.7, 0.85, 1, 1]); The device operating parameter adjustment requirement is divided into 3 fuzzy subsets (aggressive, moderate, and conservative), and the corresponding adjustment amplitude is ±30%, ±15%, and ±5%, respectively. Based on expert experience, 25 fuzzy rules are formulated (such as "if the energy state index is high and the fluctuation risk degree is low, then the parameter adjustment requirement is conservative"), covering all possible combinations of energy supply and parameter adjustment; The Mamdani reasoning method is used to synthesize fuzzy rules, and the barycentric method is used to solve the fuzzification to generate accurate parameter adjustment instructions (such as sampling frequency adjustment value, communication cycle adjustment value).

[0136] The parameter adjustment instructions generated based on the fuzzy control strategy optimize the device operating parameters in real time, and balance the energy supply and functional requirements. For example, when the energy is sufficient (comprehensive energy state index > 0.8), the sampling frequency is increased to 1 / 5 minutes (to enhance the early fault diagnosis capability); when the energy is scarce (comprehensive energy state index < 0.4), the sampling frequency is reduced to 1 / 2 hours (to prolong the device endurance time). When the energy supply is stable (fluctuation risk degree < 0.2), the communication cycle is shortened to 15 minutes (to improve the real-time data); when the energy supply fluctuates (fluctuation risk degree > 0.5), the communication cycle is extended to 1 hour (to reduce the communication energy consumption). According to the energy state, the sensor working mode is dynamically switched (such as the vibration sensor is enabled in high-frequency sampling mode when the energy is sufficient, and is switched to event-triggered mode when the energy is scarce).

[0137] The parameter adjustment instruction is realized by the edge computing node (deployed locally to the device) to achieve millisecond-level response, avoiding the regulation lag caused by the communication delay of the cloud; the parameter adjustment locking period (such as keeping unchanged for 30 minutes after each adjustment) is set to prevent system oscillation caused by frequent adjustment. The parameter adjustment instruction is range-verified (such as the sampling frequency being not lower than 1 time / day and the communication period being not longer than 24 hours) to avoid device function failure caused by abnormal instruction; the parameter adjustment log (time stamp, pre-adjustment parameter, post-adjustment parameter) is recorded to support fault backtracking and model optimization.

[0138] In this embodiment, by multi-source data fusion preprocessing, double-layer LSTM prediction, fuzzy control decision and parameter dynamic updating, an energy-function collaborative optimization framework of the online monitoring device is constructed, which significantly improves the energy supply prediction accuracy and device self-adaptive ability in complex environment.

[0139] Further, the working parameters of the online monitoring device are updated in real time based on the parameter adjustment instruction, specifically including:

[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 a first sampling frequency, and the full-quantity sensor is 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 the first preset ratio and greater than or equal to a second preset ratio, the sampling frequency of the online monitoring device is maintained at a reference sampling frequency, and only the core sensor is started;

[0142] When the ratio between the predicted power consumption value and the current power consumption value of the online monitoring device is less than the second preset ratio, the sampling frequency of the online monitoring device is reduced to a second sampling frequency, and all unnecessary peripherals are turned off;

[0143] Wherein, 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 by 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 ratio 30%~50%), the communication module (power consumption ratio 15%~30%) and the main control unit (power consumption ratio 20%~40%); the current power consumption average is calculated by using the sliding window statistical method (window length 5 minutes, step 1 minute) to eliminate the interference of transient power fluctuation (such as sensor start-up impact current >1A) on the determination result.

[0145] Based on the future 24-hour energy supply prediction sequence output by the LSTM model, combined with the device historical power consumption curve (such as the daily average power consumption in summer is 25% higher than in winter) and the environmental parameter correction coefficient (the power consumption increases by 5%~8% for every 10℃ increase in temperature), the predicted power consumption value in the future preset time is calculated; the confidence of the predicted power consumption value is evaluated (such as the confidence interval width < 15% of the predicted value), and if the confidence is insufficient, a backup prediction algorithm (such as the exponential smoothing method) is enabled 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), representing sufficient energy supply and supporting device expansion function operation; such as photovoltaic power generation peak period (light intensity > 800W / m²) or power grid maintenance period (leakage current energy module output 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), representing possible insufficient energy supply, which needs to prioritize core functions; such as consecutive rainy weather (photovoltaic output attenuation of more than 80%) or extreme low temperature environment (lithium battery capacity attenuation of 40%).

[0148] The reference power consumption ratio interval is set to [0.7, 1.5), representing that the energy supply basically matches the current demand, maintaining the normal operation mode of the device.

[0149] The first sampling frequency is set to 3 times the normal sampling frequency (such as 1 time / 15 minutes to 1 time / 5 minutes), which adapts to the high-frequency data acquisition requirement (such as millisecond-level transient change monitoring of lightning arrester leakage current). When the predicted power consumption is significantly higher than the current power consumption, the sampling frequency of the device is increased to the first sampling frequency, and all deployed sensor nodes (such as leakage current sensor, environmental temperature and humidity sensor, vibration acceleration sensor, partial discharge sensor) are started, realizing multi-dimensional state perception; time synchronization is performed on all sensors (synchronization error < 1ms), ensuring the time consistency of multi-source data, supporting multi-physical field coupling analysis of fault features.

[0150] When the predicted power consumption substantially matches the current power consumption, the system maintains the baseline sampling frequency and only starts the core sensors, keeping the regular sampling frequency (e.g., 1 time / 15 minutes) to meet the basic monitoring needs of the device (e.g., tracking the daily variation trend of the lightning arrester resistive current). By dynamically adjusting the sampling window (e.g., starting high-frequency sampling immediately after the lightning arrester acts, and restoring the baseline frequency after 10 minutes), the precise capture of key events is achieved. Only running the key sensors (e.g., leakage current sensor, ambient temperature sensor), and turning off unnecessary sensors (e.g., vibration sensor, partial discharge sensor), the standby power consumption is reduced (total power consumption is reduced by about 20%). The health status of the core sensors is self-checked (e.g., zero drift test once a month, sensitivity calibration), ensuring the reliability of the monitoring data.

[0151] The second sampling frequency is set to 1 / 5 of the regular sampling frequency (e.g., 1 time / 2 hours), which is suitable for the survival needs in extremely low power consumption scenarios (e.g., the device only retains fault alarm capability). When the predicted power consumption is significantly lower than the current power consumption, the sampling frequency of the device is reduced to the second sampling frequency. The system completely turns off all non-core peripherals (e.g., display screen, storage module, GPS positioning function of communication module) by reducing the sampling frequency and turning off unnecessary peripherals, reducing standby power consumption (total power consumption is reduced by about 60%). The peripherals are physically isolated (e.g., the power supply circuit of non-core peripherals is cut off by a relay), preventing dark current leakage.

[0152] In this embodiment, through the hierarchical determination of power consumption ratio and the differential parameter adjustment strategy, the adaptive operation of online monitoring devices under complex energy supply conditions is realized, which not only guarantees the monitoring ability of the device when the energy is sufficient, but also prolongs the endurance time of the device when the energy is scarce, providing technical support for the intelligentization and long-life operation of the power equipment state monitoring system.

[0153] In some embodiments, in step S104, the data transmission demand of the online monitoring device establishes a bidirectional coupling mechanism for the power supply state and the communication link, specifically including:

[0154] The power supply state parameters include residual power and power fluctuation rate, and the communication link parameters include signal strength and bit error rate.

[0155] Based on the joint parameter set, the power supply margin index and the channel quality index are calculated, and the energy-communication coupling degree model is constructed;

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

[0157] Based on the dynamic optimization objective function, the two-way coupling optimal adjustment strategy between the power supply state and the communication link is solved by the gradient projection algorithm.

[0158] In this embodiment, the dynamic parameters of the power supply state and the communication link are collected in real time through the distributed sensor network deployed in each functional module of the device, and a multi-dimensional joint parameter set is constructed. Specifically, a double-redundancy power detection technology is used, and the Coulomb meter (accuracy ±0.5%) and the voltage-power mapping table (based on the battery open circuit voltage-remaining power curve) are cross-verified to eliminate the error of a single detection method; in view of the aging characteristics of lithium batteries (such as the increase of SoC estimation error after 20% capacity attenuation), a dynamic calibration factor (updated once every quarter, based on the battery resistance test results) is introduced to improve the accuracy of the remaining power detection. A high-precision power analysis module is deployed to sample the input / output power of the device (including photovoltaic power module, CT power module and energy storage unit) at a period of 100ms, and the power fluctuation rate (fluctuation amplitude / average power) in unit time is calculated; in view of the transient power impact (such as >10W transient load when the lightning arrester acts), a sliding window filtering algorithm (window length 1s, taking the mean value after removing the maximum / minimum value) is used to suppress noise interference. A multi-antenna diversity receiving system is deployed in the front end of the communication module, and the received signal strength of each antenna is fused by the maximum ratio combining (MRC) algorithm to improve the detection sensitivity in weak signal environment (RSSI detection error reduced by 30% in weak signal scenario); in view of the signal fluctuation caused by multipath effect (such as the reflection of metal cabinet in the transformer substation), Kalman filtering algorithm is used to smooth the RSSI (filtering time constant 0.5s) to eliminate the influence of fast fading. A real-time error detection module is embedded in the communication protocol stack, and the error rate is calculated by the combination of cyclic redundancy check (CRC) and forward error correction coding (FEC); in view of the error rate peak caused by burst interference (such as electromagnetic pulse interference), a sliding window outlier elimination algorithm (window length 100 frames, removing abnormal frames with BER>10⁻³) is used to avoid the influence of error data on link quality evaluation. The power supply state parameters (remaining power, power fluctuation rate) and the 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], RSSI is mapped to [-110dBm, -30dBm] interval linear normalization) to eliminate the influence of dimension difference on subsequent modeling.

[0159] Based on the joint parameter set, a dynamic mapping relationship between power supply margin and channel quality is constructed, the coupling degree between power supply state and communication link is quantified, and decision basis is provided for optimization strategy. Specifically, according to the historical power consumption data (such as daily average power consumption fluctuation range ± 15%) of the device and the current remaining power, the energy reserve margin is calculated. Combined with the dynamic response ability of the device power module (such as the maximum load step response time ≤ 50 ms) and the current power fluctuation rate, the power fluctuation tolerance is calculated. Based on the joint evaluation of energy reserve margin and power fluctuation tolerance, the link stability index is constructed. According to the current data transmission volume and the available bandwidth of the communication module, the communication redundancy is calculated. The joint evaluation function of power supply margin and channel quality is constructed, and the coupling degree level is divided: strong coupling (Coupling ≥ 0.8): both power supply and communication are in good condition, and high-power functions (such as high-frequency sampling) can be expanded; medium coupling (0.5 ≤ Coupling < 0.8): balance power supply and communication resources to maintain regular monitoring; weak coupling (Coupling < 0.5): there is a bottleneck in power supply or communication, and emergency strategy needs to be started.

[0160] By quantifying the mutual influence degree between power supply and communication, a two-way coupling coefficient matrix is constructed, and a dynamic optimization objective function is designed based on the matrix to realize power supply-communication collaborative optimization. Specifically, the communication performance under different power supply states is tested through experiments, and a power supply-communication influence mapping table is established, for example, when the remaining power is < 20%, the communication module transmission power is limited, γ_S→C=0.6 (communication capability attenuation 40%); when the power fluctuation rate is > 15%, the power supply noise causes the bit error rate to rise, γ_S→C=0.7 (communication quality decreases by 30%). Analyze the pressure of communication load on the power supply system, and establish a communication-power influence mapping table, for example, when the communication module load rate is > 80%, the increased power consumption causes the discharge rate of the energy storage unit to increase, γ_C→S=0.5 (power endurance shortens by 50%); when the communication interruption triggers the retransmission mechanism, the power consumption increases by 20%, γ_C→S=0.8 (power stress increases by 80%). According to the power supply-communication influence mapping table and the communication-power influence mapping table, a two-way coupling coefficient matrix is constructed to represent the mutual restriction relationship between power supply and communication. Based on the two-way coupling coefficient matrix, the maximum power supply-communication comprehensive benefit and the minimum power supply pressure and communication overhead are taken as the optimization objectives, and the maximum output power of the power supply module and the power consumption budget of the communication module are taken as the constraint conditions to construct a dynamic optimization objective function.

[0161] The power supply and communication parameters are iteratively optimized by the gradient projection algorithm to realize the fast convergence of the global optimal solution under the constraint condition, and to ensure the adaptive operation of the device in a complex environment. Specifically, set the initial power supply strategy (such as the discharge current of the energy storage unit 0.5A) and the communication strategy (such as the LoRa communication period 15 minutes), calculate the initial objective function value and the constraint violation amount, and approximate calculate the gradient of the objective function with respect to the power supply parameters (such as the discharge current) and the communication parameters (such as the communication period) by the finite difference method. The parameter adjustment direction is projected into the feasible region (such as the discharge current range [0.1A, 2A] and the communication period range [5 minutes, 60 minutes]) to avoid parameter out-of-bounds; a dynamic step adjustment strategy (the step length is positively correlated with the gradient module length, and the maximum step length is not more than 10% of the parameter range) is adopted to accelerate the convergence. Repeat the gradient calculation and projection operation until the objective function converges (the change amount of F_total is less than 0.01 for 3 consecutive iterations) or the maximum iteration number (such as 50 times) is reached; record the optimal parameter combination (such as the discharge current 0.8A and the communication period 10 minutes) as the adjustment strategy in the current scenario.

[0162] In this embodiment, through the modeling and dynamic optimization of the bidirectional coupling of the power supply state and the communication link, the isolated decision problem of traditional devices in energy-communication resource allocation is solved, and the adaptive ability and operation efficiency of the device in a complex environment are significantly improved.

[0163] In some embodiments, in the step S105, based on the operation data of the online monitoring device, a digital twin of the power supply system is constructed, and adaptive optimization of the power supply strategy of the online monitoring device is realized through the digital twin, specifically including:

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

[0165] According to the digital twin, the weights of each energy source of the power supply system are redistributed, the use proportion of the high-heat module is adjusted, and a power supply strategy adjustment instruction is formed;

[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 distribution strategy and the power consumption control logic.

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

[0168] Specifically, a three-dimensional electromagnetic sensor array is deployed at critical locations of the device (e.g., power module, communication antenna) to collect spatial electromagnetic field intensity (E / H field components) and spectral characteristics (0-3 GHz frequency band) at a period of 100 ms; a finite difference time domain (FDTD) method is used to construct an electromagnetic field distribution model, combined with electromagnetic parameters of the device shell material (e.g., relative permittivity of aluminum case ε_r=1, conductivity σ=3.5×10 7 S / m), to simulate the propagation path and attenuation characteristics of electromagnetic interference in the device. Based on the electromagnetic loss density (P_loss=0.5×σ×|E|²), the heat source distribution caused by the electromagnetic field is calculated, and the electromagnetic loss is input as the boundary condition of the thermodynamic model; for high-frequency switching power modules (e.g., DC-DC converter), a skin effect correction factor is introduced (copper conductor resistivity increases by 20% when the frequency is greater than 1 MHz), which improves the calculation accuracy of electromagnetic loss. Distributed temperature sensors (accuracy ±0.5°C) are placed at critical nodes inside the device (e.g., CPU, power chip, sensor), combined with an infrared thermal imager (spatial resolution 1 mm) to construct a three-dimensional temperature field of the device; a finite element method (FEM) is used to establish a heat conduction-convection-radiation coupling model, with convective heat transfer coefficients (natural convection 5-25 W / (m²·K), forced convection 50-200 W / (m²·K)) and radiation emissivity (alumina surface ε=0.8) set to simulate the heat dissipation characteristics of the device in a closed cabinet and an open environment. Based on the temperature gradient, the mechanical stress caused by thermal expansion (σ=E×α×ΔT, where E is the elastic modulus and α is the thermal expansion coefficient) is calculated, and the solder fatigue risk is evaluated for multi-layer PCBs (FR4 material α=17×10⁻ 6 / ℃). A thermal cycle acceleration factor (Coffin-Manson model) is introduced to predict the device life attenuation trend under high temperature conditions (e.g., 60% of normal temperature life at 60°C ambient temperature). A three-axis acceleration sensor (range ±10g, bandwidth 0.5-1 kHz) is deployed in the device cabinet to collect vibration acceleration data and extract frequency domain features (e.g., 100 Hz fundamental harmonic); combined with electromagnetic force simulation (Maxwell stress tensor method), the influence of vibration on power transformer winding looseness is analyzed, and a cross-influence model of vibration-electromagnetic interference is established.

[0169] A light intensity sensor (range 0-1500 W / m²) and a temperature sensor are deployed to establish a photovoltaic cell output power-illumination-temperature three-dimensional mapping table (e.g., at 25°C, the output power increases by 0.3 W for every 100 W / m² increase in illumination); for the dust accumulation problem of photovoltaic panels (output power decreases by 20% when the light transmittance decays to 80%), a cleanliness correction factor is introduced (updated once a month based on a visual detection system to evaluate the dust area ratio).

[0170] The primary side current (range 0-1000A) and the secondary side output voltage are collected by the current transformer (CT) to establish the CT energy efficiency curve (e.g. 65% conversion efficiency at 50A and 85% at 500A); for harmonic current (THD>5% efficiency decreases by 10%), Fourier transform is used to extract the fundamental and harmonic components to correct the energy efficiency calculation results.

[0171] Based on the battery management system (BMS) to collect charging and discharging current, voltage and temperature, a lithium battery charging and discharging efficiency model is established (e.g. 0.5C rate charging and discharging efficiency is 92% at 25℃, and the efficiency decreases to 75% at -10℃); for battery aging (capacity attenuation 20% after 500 cycles), a state of health (SOH) correction factor is introduced to dynamically adjust the charging and discharging strategy.

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

[0173] Combined with temperature field distribution data, a power consumption-temperature mapping relationship is established (e.g. CPU temperature increases by 10℃, leakage current power consumption increases by 15%), the power consumption calculation results are corrected; for performance degradation caused by high temperature (e.g. CPU frequency reduces to 80% at 70℃), a performance compensation factor is introduced to optimize the power consumption distribution strategy.

[0174] By simulating the response of the equipment under different power supply strategies through the digital twin, the weight of the energy source and the usage ratio of the module are dynamically adjusted combined with real-time operation data to generate an adaptive power supply strategy. Specifically, based on the energy collection efficiency and reliability evaluation, an energy source priority matrix is constructed: high priority: photovoltaic power supply (efficiency > 20% and light intensity > 500W / m²), CT power supply (primary side current > 100A); medium priority: energy storage unit (SOH > 80% and remaining power > 30%); low priority: backup battery (only activated when the main power supply fails). The instantaneous conversion efficiency of each energy source is calculated every 5 minutes (such as photovoltaic efficiency = output power / (light intensity x area)), and the weight is dynamically adjusted (efficiency increases by 5%, weight increases by 10%); for the CT power supply module, according to the primary side current fluctuation rate (such as fluctuation rate > 30%, output is unstable), the weight is reduced to below 50%; when the temperature of the energy storage unit is > 50℃, the discharge weight is reduced (every 5℃ increase, the weight is reduced by 15%), to avoid the risk of thermal runaway; if the backup battery has not been used for > 3 months, a small current cycle charging and discharging is forced to start (once a week, charging and discharging depth 10%), to prevent battery sulfuration. Based on the temperature field distribution data, the thermal stress index (TSI) of each module is calculated. When the module TSI > 50, the working frequency of the edge computing unit is dynamically adjusted (such as CPU main frequency from 1GHz to 500MHz), to reduce power consumption and heat generation; for the image sensor module, if TSI > 60, the sampling frame rate is reduced (such as from 30fps to 10fps), to reduce data processing amount. 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 < 30, data transmission tasks are preferentially allocated (such as migrating image data compression tasks from CPU to communication module built-in DSP).

[0175] In this embodiment, by constructing a power supply system digital twin, real-time perception and adaptive optimization of the online monitoring equipment power supply strategy are realized, solving the lagging problem of traditional equipment in complex environment power supply decision-making, which can significantly improve the operation stability of the equipment in extreme conditions such as high temperature and strong electromagnetic interference, prolong the service life of the equipment, and reduce the operation and maintenance cost.

[0176] Reference Figure 2 An embodiment of the present application provides an online monitoring equipment power supply system 2 of a lightning arrester, and the system 2 specifically comprises:

[0177] The first power supply module 201 is used for activating multiple source energy modules to supply power for the online monitoring equipment in a preset priority order based on the running state of the lightning arrester and environmental parameters;

[0178] The second power supply module 202 is used to implement 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 equipment based on the output characteristics of the multi-source energy module and the remaining power of the online monitoring equipment.

[0179] The third power supply module 203 is used to process the historical leakage current of the surge arrester, 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 within a preset future time, and dynamically adjust the working parameters of the online monitoring equipment according to the energy supply trend.

[0180] The fourth power supply module 204 is used to establish a two-way coupling mechanism between power supply status and communication link to meet the data transmission needs of online monitoring equipment.

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

[0182] It is understandable that, such as Figure 1 The content of the power supply method embodiment for the online monitoring equipment of the surge arrester shown is applicable to the power supply system embodiment for the online monitoring equipment of this surge arrester. The specific functions implemented by the power supply system embodiment for the online monitoring equipment of this surge arrester are the same as those shown in the figure. Figure 1 The power supply method for the online monitoring equipment of the surge arrester shown is the same as that in the embodiment, and the beneficial effects achieved are the same as those described above. Figure 1 The beneficial effects achieved by the online monitoring equipment power supply method embodiment of the surge arrester shown are also the same.

[0183] It should be noted that the information interaction and execution process between the above systems are based on the same concept as the method embodiments of the present invention. For details on their specific functions and technical effects, please refer to the method embodiments section, which 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 functional units and modules is taken as an example, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit. In addition, the specific names of the functional units and modules are only for the convenience of mutual distinction, and do not limit the protection scope of the present application. The specific working process of the units and modules in the system can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0185] With reference to Figure 3 The embodiment of the present application also provides a computer device 3, which comprises a memory 302, a processor 301 and a computer program 303 stored in the memory 302, and when the computer program 303 is executed on the processor 301, the on-line monitoring device power supply method of the lightning arrester is realized.

[0186] The computer device 3 can be a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The computer device 3 can comprise, but is not limited to, a processor 301 and a memory 302. Those skilled in the art can understand that, Figure 3 The computer device 3 is only an example and does not constitute a limitation on the computer device 3, and can comprise more or fewer components than those shown, or combine certain components, or different components, for example, can also comprise an input / output device, a network access device and the like.

[0187] The processor 301 can be a central processing unit (CPU), and the processor 301 can also be 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, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0188] The memory 302 may, in some embodiments, be an internal storage unit of the computer device 3, such as a hard disk or a memory of the computer device 3. The memory 302 may, in other embodiments, also be an external storage device of the computer device 3, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, and the like equipped on the computer device 3. Further, the memory 302 may, in addition, include both an internal storage unit and an external storage device of the computer device 3. The memory 302 is used to store an operating system, application programs, a Boot Loader, data, and other programs, such as program codes of the computer program, and the like. The memory 302 may, in addition, be used to temporarily store data that has been output or is to be output.

[0189] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program. When the computer program is run by a processor, the method for power supply of an on-line monitoring device of a lightning arrester is implemented.

[0190] In the embodiment, the integrated unit, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the processes of the above-mentioned embodiment methods, which can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer readable storage medium, and the computer program, when executed by a processor, can implement the steps of the above-mentioned various method embodiments. The computer program includes computer program codes, which can be in the form of source codes, object codes, executable files, or some intermediate forms, etc. The computer readable medium at least includes any entity or device capable of carrying the computer program codes to a photographing device / terminal device, a recording medium, a computer memory, a Read-Only Memory (ROM), a Random Access Memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. For example, a U disk, a mobile hard disk, a magnetic disk or an optical disk, etc. In some jurisdictions, according to legislation and patent practice, the computer readable medium may not be an electrical carrier signal and a telecommunication signal.

[0191] In the above-mentioned embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0192] Those skilled in the art can understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0193] In the embodiments disclosed in the present application, it should be understood that the disclosed apparatus / terminal device and method can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0194] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, i.e. they can be located in one place or distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

Claims

1. A method of supplying power to an on-line monitoring device of a surge arrester, characterized by, The method specifically comprises: Based on the operating state and environmental parameters of the surge arrester, activate the multi-source energy module to supply power to the online monitoring equipment according to a preset priority order; Based on the output characteristics of the multi-source energy module and the remaining power of the online monitoring equipment, 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 equipment; Through an LSTM prediction model, process the historical leakage current, environmental temperature and humidity, and vibration frequency spectrum features of the surge arrester collected by the online monitoring equipment, predict the energy supply trend of the online monitoring equipment in a preset future time, and dynamically adjust the working parameters of the online monitoring equipment according to the energy supply trend; For the data transmission requirements of the online monitoring equipment, establish a bidirectional coupling mechanism between the power supply state and the communication link; Based on the operating data of the online monitoring equipment, construct a digital twin of the power supply system, and realize adaptive optimization of the power supply strategy of the online monitoring equipment through the digital twin; For the data transmission requirements of the online monitoring equipment, establish a bidirectional coupling mechanism between the power supply state and the communication link, specifically comprising: Real-time acquisition of power supply state parameters and communication link parameters through multi-source sensors to form a joint parameter set, wherein the power supply state parameters include remaining power and power fluctuation rate, and the communication link parameters include signal strength and bit error rate; Based on the joint parameter set, calculate the power supply margin index and the channel quality index, and construct an energy-communication coupling degree model; Through quantifying the mutual influence degree between power supply and communication, based on the energy-communication coupling degree model, calculate a bidirectional coupling coefficient matrix, and according to the bidirectional coupling coefficient matrix, take maximizing the comprehensive benefits of power supply-communication and minimizing the power supply pressure and communication overhead as the optimization objectives, take the maximum output power of the power supply module and the power consumption budget of the communication module as the constraint conditions, and establish a dynamic optimization objective function; Based on the dynamic optimization objective function, solve the bidirectional coupling optimal adjustment strategy between the power supply state and the communication link through a gradient projection algorithm; Based on the operating data of the online monitoring equipment, construct a digital twin of the power supply system, and realize adaptive optimization of the power supply strategy of the online monitoring equipment through the digital twin, specifically comprising: Based on the energy collection efficiency, device temperature distribution, and electromagnetic interference intensity of the online monitoring equipment, construct a digital twin of the power supply system, wherein the digital twin includes a three-dimensional electromagnetic-thermal-mechanical coupling model and an energy conversion efficiency model; adopt a finite difference time domain method to construct an electromagnetic field distribution model, calculate the heat source distribution caused by the electromagnetic field based on the electromagnetic loss density, input the electromagnetic loss as the boundary condition of the thermodynamic model, combine electromagnetic force simulation, analyze the influence of vibration on the loosening of the power transformer winding, and establish a cross-influence model of vibration-electromagnetic interference; According to the digital twin, redistribute the weights of each energy source of the power supply system, adjust the use proportion of high-heat modules, and form a power supply strategy adjustment instruction; Inject the power supply strategy adjustment instruction into the edge computing unit of the online monitoring equipment to drive the online monitoring equipment to adjust the energy distribution strategy and power consumption regulation logic.

2. The method of claim 1, wherein, The operating state and environmental parameters of the surge arrester activate the multi-source energy module to supply power to the online monitoring equipment in a preset priority order, specifically including: Based on the leakage current amplitude and vibration acceleration of the surge arrester, the wideband energy extraction circuit extracts electrical energy, and supplies the power management module of the online monitoring equipment with electrical energy; When the leakage current energy of the surge arrester is detected to be lower than a preset energy threshold, the piezoelectric vibration energy extraction module is enabled to generate electricity using the mechanical vibration of the surge arrester installation base, and to supplement the energy storage unit of the online monitoring equipment; The solar radiation intensity around the surge arrester is monitored, and when the solar radiation intensity meets the preset illumination conditions, the photovoltaic conversion circuit is used to supplement electrical energy to charge the supercapacitor group of the online monitoring equipment; The output power of each energy source is dynamically allocated through the 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 of the online monitoring equipment is maintained within a preset fluctuation range.

3. The method of claim 1, wherein, The output characteristics of the multi-source energy module and the remaining power of the online monitoring equipment 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, specifically including: If the remaining power of the online monitoring equipment is greater than a preset power threshold and the environmental parameters are normal, a constant-voltage constant-current hybrid power supply strategy is adopted, and the Buck-Boost topology circuit is used to maintain the supply voltage of the online monitoring equipment within a preset voltage range; When the environmental temperature is detected to be lower than a preset temperature threshold or the electromagnetic interference intensity is greater than a preset interference intensity threshold, the anti-interference power supply mode is switched to, which includes suppressing high-frequency interference through a multi-stage RC filter circuit and applying an electromagnetic shielding layer to the key power supply circuit of the online monitoring equipment.

4. The method of claim 1, wherein, The historical leakage current, environmental temperature and humidity, and vibration frequency spectrum characteristics of the surge arrester collected by the online monitoring equipment are processed through the LSTM prediction model to predict the energy supply trend of the online monitoring equipment in a preset future time, and the working parameters of the online monitoring equipment are dynamically adjusted according to the energy supply trend, specifically including: The historical leakage current, environmental temperature and humidity, and vibration frequency spectrum characteristics of the surge arrester collected by the online monitoring equipment are fused and preprocessed to generate a standardized feature matrix; Based on the standardized feature matrix, the energy supply trend is predicted through a double-layer LSTM network, and an energy supply prediction sequence in a future preset time is output; Based on the energy supply prediction sequence, an energy state evaluation function is set, and a parameter adjustment instruction is generated through a fuzzy control strategy; Based on the parameter adjustment instruction, the working parameters of the online monitoring equipment are updated in real time.

5. The method of claim 4, wherein, The working parameters of the online monitoring equipment are updated in real time based on the parameter adjustment instruction, specifically including: When the ratio between the predicted power consumption value and the current power consumption value of the online monitoring equipment is greater than or equal to a first preset ratio, the sampling frequency of the online monitoring equipment is increased to a first sampling frequency, and the full-sensor is activated. When the ratio between the predicted power consumption value and the current power consumption value of the online monitoring device is less than the first preset ratio and greater than or equal to the second preset ratio, the sampling frequency of the online monitoring device is maintained at the reference sampling frequency, and only the core sensor is started; When the ratio between the predicted power consumption value and the current power consumption value of the online monitoring device is less than the second preset ratio, the sampling frequency of the online monitoring device is reduced to the second sampling frequency, and all unnecessary peripherals are turned off; Wherein, 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. A system for implementing an on-line monitoring device power supply method for a surge arrester as claimed in claim 1, characterized in that, The system specifically comprises: The first power supply module is configured to activate the multi-source energy module to supply power to the online monitoring device according to a preset priority order based on the operating state of the lightning arrester and the environmental parameters; The second power supply module is configured 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 configured to process the historical leakage current, environmental temperature and humidity, and vibration frequency spectrum characteristics of the lightning arrester collected by the online monitoring device through an LSTM prediction model, predict the energy supply trend of the online monitoring device in a preset future time, and dynamically adjust the working parameters of the online monitoring device according to the energy supply trend; The fourth power supply module is configured to establish a bidirectional coupling mechanism of the power supply state and the communication link for the data transmission requirement of the online monitoring device; The fifth power supply module is configured to construct a digital twin of the power supply system based on the operating data of the online monitoring device, and realize adaptive optimization of the power supply strategy of the online monitoring device through the digital twin.

7. A computer device, comprising: It comprises: A memory and a processor, and a computer program stored in the memory, when the computer program is executed on the processor, the method for supplying power to the online monitoring device of the lightning arrester is realized as claimed in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, A computer program is stored thereon, and when the computer program is run by a processor, the method for supplying power to the online monitoring device of the lightning arrester is realized as claimed in any one of claims 1 to 5.

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