A multi-dimensional self-sensing based active power consumption scheduling method and system

Abstract

The application provides a kind of active power consumption scheduling method and system based on multidimensional self-perception, belongs to power consumption scheduling technical field, including: obtaining device physical state, including solar power supply data, energy storage unit data and core host unit data;The device physical state is processed to obtain a group of key health indicators, and a device health digital portrait is constructed;According to the state of charge of battery, device health digital portrait and the energy prediction result obtained in the future preset time period, power consumption scheduling decision is generated.Have beneficial effect: through device health digital portrait, solar panel dust accumulation, battery aging and other sub-health hidden dangers can be found in advance;At the same time, through future energy prediction and active power consumption scheduling, when the device faces continuous rain, snow and other bad weather, limited power can be used to maintain the most core heartbeat and low-frequency monitoring task, avoid completely losing contact due to power depletion, maximize the continuity and integrity of monitoring data.

Description

Technical Field

[0001] This invention relates to the field of power consumption scheduling technology, and in particular to an active power consumption scheduling method and system based on multi-dimensional self-awareness. Background Technology

[0002] Overhead transmission lines are a crucial component of the power system, and their safe and stable operation is of paramount importance. In recent years, with the development of technologies such as the Internet of Things and image recognition, installing online image monitoring devices integrating cameras and various sensors on transmission towers has become the mainstream operation and maintenance method. These devices are typically powered by solar panels in conjunction with batteries, and transmit monitoring data back to the monitoring center via wireless network.

[0003] However, with the large-scale deployment of these monitoring devices in the field, their own operation and maintenance management problems have gradually become prominent, becoming a new pain point restricting the improvement of transmission line operation and maintenance efficiency. On the one hand, the perception of equipment health status is a black box, and the operation and maintenance response is lagging behind. Current operation and maintenance methods can usually only determine whether the equipment is "online" or "offline". For the "sub-healthy" state of the equipment, such as the decrease in charging efficiency caused by dust accumulation or bird droppings on the surface of solar panels, the aging, increased internal resistance, and capacity decay of batteries due to frequent charging and discharging and the influence of high and low temperature environments, or the slight and continuous overheating of the core processor due to summer sun exposure and poor heat dissipation, these problems cannot be effectively monitored. These "sub-healthy" states will continue to deteriorate, eventually causing the equipment to suddenly shut down or be damaged without warning. The operation and maintenance personnel can only passively carry out repairs and replacements, which is not only costly, but also leads to the long-term loss of monitoring data.

[0004] On the other hand, power management strategies are crude and unable to adapt to dynamic environments. Existing monitoring devices typically employ fixed power management strategies, such as taking photos, processing data, and uploading data at preset time intervals. This "one-size-fits-all" approach fails to consider the device's own energy consumption. In situations with ample sunlight and fully charged batteries, this can lead to energy waste; however, in extreme weather conditions such as continuous rain or heavy snowfall with severely insufficient sunlight, the device still attempts to maintain a fixed high-power operating mode, thus accelerating the depletion of its already limited power supply, causing the device to be taken offline prematurely and missing critical periods for line status monitoring. Summary of the Invention

[0005] To address the above technical problems, this invention provides an active power consumption scheduling method based on multidimensional self-sensing; on the other hand, it also provides an active power consumption scheduling system based on multidimensional self-sensing, which enables monitoring devices to precisely sense their own "sub-healthy" state and, based on this state and predictions of future environmental changes, intelligently and proactively plan their own power consumption.

[0006] The technical problem solved by this invention can be achieved by the following technical solution: a multi-dimensional self-sensing-based active power consumption scheduling method, comprising: step S1, acquiring the physical state of the device; wherein, the physical state of the device includes solar power supply data, energy storage unit data, and core main control unit data; step S2, processing the physical state of the device to obtain a set of key health indicators, and constructing a digital health profile of the device based on the set of key health indicators; step S3, generating a power consumption scheduling decision based on the battery state of charge, the digital health profile of the device, and the energy prediction results obtained within a future preset time period.

[0007] Preferably, the solar power supply data includes at least one or more combinations of solar panel output voltage, solar panel output current, and ambient light intensity; the energy storage unit data includes at least one or more combinations of battery voltage, charging / discharging current, and battery surface temperature; and the core control unit data includes at least the internal temperature of the main processor or core computing chip.

[0008] Preferably, the key health indicators include a solar charging efficiency index, a battery health status index, and a device thermal health index; the method for constructing the device health digital profile includes: performing a weighted average of the solar charging efficiency index, the battery health status index, and the device thermal health index to obtain the device health digital profile.

[0009] Preferably, the method for calculating the solar charging efficiency index includes: calculating the output power based on the output voltage and output current of the solar panel; and obtaining the solar charging efficiency index based on the ratio of the output power to the theoretical maximum output power under ambient light intensity.

[0010] Preferably, the method for calculating the battery health status index includes:

[0011]

[0012] in, , These represent the weighting factors; Indicates the battery's internal resistance; Indicates the initial internal resistance of the battery; This represents the internal resistance threshold used to determine battery aging. Indicates the battery's full charge capacity; This indicates the battery's rated capacity at the time of manufacture. This indicates the battery health status index.

[0013] Preferably, the battery internal resistance is the ratio of the sudden change in battery voltage to the sudden change in battery current before and after the device switches between different operating modes.

[0014] The full charge capacity of the battery is the ratio of the total amount of electricity charged in one charging cycle to the change in the battery's state of charge, and the total amount of electricity charged is the time integral of the charging current in the charging cycle.

[0015] Preferably, the method for calculating the thermal health index of the equipment includes:

[0016]

[0017] in, Indicates the surface temperature of the battery; This indicates the internal temperature of the main processor or core computing chip. Indicates the optimal operating temperature; Indicates the overheat warning temperature; This indicates the thermal health index of the device.

[0018] Preferably, the method for obtaining the energy prediction results within the preset future time period includes:

[0019]

[0020] in, This indicates the estimate based on weather data within a preset future time period. Average light intensity per hour; Indicates average light intensity The theoretical maximum output power is as follows; Indicates the solar charging efficiency index; Indicates a time interval; This indicates the energy forecast results for a predetermined time period in the future.

[0021] Preferably, multiple working modes are predefined, each corresponding to different power consumption and task priorities. In step S3, the method for generating the power consumption scheduling decision includes: when the energy prediction result within the future preset time period exceeds a first preset threshold, the device health digital profile exceeds a second preset threshold, and the battery state of charge exceeds a third preset threshold, a power consumption scheduling decision containing the highest-level working mode is generated; when the energy prediction result within the future preset time period exceeds a fourth preset threshold and the battery state of charge exceeds a fifth preset threshold, a power consumption scheduling decision containing the next lower-level working mode or the next-next-lower-level working mode is generated; when the solar charging efficiency index is less than a sixth preset threshold or the battery health state index is less than a seventh preset threshold, a power consumption scheduling decision with a working mode down one level is generated; when the battery state of charge is less than an eighth preset threshold, a power consumption scheduling decision containing the first-level working mode or the second-level working mode is generated.

[0022] On the other hand, a multi-dimensional self-sensing-based active power consumption scheduling system is also provided to implement the multi-dimensional self-sensing-based active power consumption scheduling method described above. The system includes: a multi-dimensional sensing and data acquisition module for acquiring the physical state of the device; wherein the physical state of the device includes solar power supply data, energy storage unit data, and core control unit data; a device health digital profile construction module connected to the multi-dimensional sensing and data acquisition module for processing the physical state of the device to obtain a set of key health indicators, and constructing a device health digital profile based on the set of key health indicators; and an active power consumption scheduling module connected to the device health digital profile construction module for generating power consumption scheduling decisions based on the battery state of charge, the device health digital profile, and the energy prediction results obtained within a preset future time period.

[0023] The advantages or beneficial effects of the technical solution of this invention are as follows: By constructing a digital health profile of the equipment, this invention can detect potential sub-health issues such as dust accumulation on solar panels and battery aging in advance, avoiding emergency repairs caused by sudden equipment failures and significantly reducing the manpower, material resources, and time costs of operation and maintenance. At the same time, through accurate future energy prediction and proactive power consumption scheduling, the equipment can use its limited power to maintain the most critical heartbeat and low-frequency monitoring tasks when facing severe weather such as continuous rain or heavy snow, avoiding complete loss of connection due to power depletion and maximizing the continuity and integrity of monitoring data. In addition, this invention not only focuses on short-term operation but also assesses long-term losses through health profiles. For example, in the later stages of battery aging, it will automatically adopt a more gentle charging and discharging strategy and a lower power consumption mode, thereby extending its service life and improving the overall life-cycle value of the equipment asset. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating the active power consumption scheduling method based on multi-dimensional self-awareness, as a preferred embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram of the principle of a multi-dimensional self-sensing active power scheduling system in a preferred embodiment of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0028] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the scope of the invention.

[0029] In a preferred embodiment of the present invention, addressing the problems of insufficient awareness of equipment health status and crude power consumption management in the operation and maintenance management of existing monitoring devices, a method and system for proactive power consumption scheduling based on multi-dimensional self-awareness is provided. This method constructs a digital profile of equipment health and combines it with energy prediction to achieve closed-loop intelligent power consumption scheduling, enabling the monitoring device to possess "self-reflection" and "foresight" capabilities, and offering significant advantages in terms of low cost and high reliability. The method and system aim to: achieve accurate perception and quantitative assessment of equipment sub-health status; and monitor and diagnose key "sub-health" indicators affecting the long-term stable operation of equipment in real time, such as solar charging efficiency, battery aging degree, and internal core temperature, transforming the vague "status" into a quantifiable "health profile."

[0030] Achieve accurate prediction of future energy balance: It can combine weather forecasts and its own health status to predict the amount of energy collected in the future, providing a forward-looking basis for power consumption scheduling.

[0031] Implement proactive and intelligent power consumption scheduling strategies: Based on its own health digital profile and future energy predictions, it can dynamically adjust its working mode and power consumption level, maximizing the online survival time of the equipment in harsh environments while ensuring core monitoring tasks, thereby improving the overall reliability and resilience of operation.

[0032] The method and system will be described in detail below, such as Figure 1 and Figure 2 As shown, the method includes:

[0033] Step S1: Obtain the physical status of the equipment. Specifically, in this embodiment of the invention, existing sensors within the monitoring device are utilized, or only a small number of low-cost sensors need to be added, to monitor the health status of the online image monitoring device integrating cameras and various sensors installed on the transmission tower. These sensors periodically (e.g., every 10 minutes) collect raw data reflecting the physical status of the equipment.

[0034] Existing technologies often limit themselves to monitoring battery voltage, and this single-dimensional monitoring method cannot comprehensively and accurately reflect the true health status of the equipment. The raw data collected in this embodiment of the invention covers multiple dimensions, including but not limited to solar power data, energy storage unit data, and core main control unit data, which can more comprehensively present the physical state of the equipment.

[0035] The solar power data includes, but is not limited to, the output voltage of the solar panels. U pvSolar panel output current I pv Ambient light intensity L Among them, the output voltage of the solar panel U pv This reflects the power generation capacity of the solar panel under current sunlight conditions; output current. I pv This reflects the actual electrical energy output of the solar panel. Ambient light intensity. L This directly affects the power generation efficiency of solar panels and can be measured using a low-cost photodiode or photoresistor sensor.

[0036] Energy storage unit data includes, but is not limited to, battery voltage. U bat Charging / discharging current I bat Battery surface temperature T bat Among them, the battery voltage U bat This reflects the battery's remaining charge and health status; charging / discharging current. I bat This reflects the energy flow of the battery; battery surface temperature T bat Excessive temperature may accelerate battery aging and affect its lifespan. A surface-mount NTC thermistor can be used to measure the battery surface temperature. T bat .

[0037] Core control unit data includes, but is not limited to, the internal temperature of the main processor (CPU) or core computing chip. T cpu The temperature can be measured using the chip's built-in temperature sensor, without the need for additional hardware; alternatively, a thermistor can be installed on the printed circuit board (PCB) near the CPU to more accurately measure the chip's actual temperature.

[0038] Furthermore, the physical status of the equipment may also include the current operating mode of the equipment. M current and total power consumption P total Work mode M current This reflects the current operating status of the device; different operating modes may have different impacts on the device's power consumption and performance; total power consumption. P total This reflects the energy consumption of the equipment in its current state.

[0039] Step S2 involves processing the physical state of the equipment to obtain a set of key health indicators (KHIs), and then constructing a digital health profile of the equipment based on these KHIs. Specifically, by processing the collected raw data, a digital health profile is generated that intuitively reflects the current health status of the equipment. This profile consists of a set of key health indicators (KHIs), including the solar charging efficiency index. H pv Battery health status index H bat Equipment thermal health index H thermal .

[0040] Solar charging efficiency index H pv This is used to characterize whether solar panels have problems such as dust accumulation, shading, or degradation. The calculation process is as follows:

[0041] First, calculate the theoretical maximum output power. ;

[0042] Ideally, the output power of a solar panel is primarily determined by the intensity of sunlight. By calibrating clean solar panels in a laboratory setting or during initial deployment, their performance under different sunlight intensities can be determined. The theoretical output power curve or lookup table can be used. To simplify calculations, a simplified linear model can be stored internally in the device:

[0043]

[0044] in, This represents the photoelectric conversion coefficient, which is a factory calibration constant. This indicates the currently measured ambient light intensity; Indicates the temperature coefficient of the solar panel (e.g., -0.4% / °C); The ambient temperature can be approximated by the surface temperature of the battery. Alternatively, an additional ambient temperature sensor can be installed; This indicates a reference temperature, typically 25°C. Indicates ambient light intensity The theoretical maximum output power is as follows.

[0045] Next, based on the output voltage of the solar panel U pv and output current I pv The actual output power is calculated. ;

[0046]

[0047] in, Indicates the output voltage of the solar panel; This indicates the output current of the solar panel.

[0048] Next, calculate the output power. Theoretical maximum output power under ambient light intensity The ratio of the two values ​​yields the solar charging efficiency index. ;

[0049]

[0050] Among them, the solar charging efficiency index It is a value between 0 and 1. When its value is close to 1, it indicates good charging efficiency.

[0051] Under high light intensity, the solar charging efficiency index If the value remains below a certain threshold (e.g., 0.7), it can be determined that the solar panel has serious dust accumulation or obstruction, requiring an alarm and prompting maintenance personnel to clean it.

[0052] Battery Health Status Index To quantify the aging degree of a battery, it is mainly achieved by evaluating its internal resistance. and full charge capacity It is achieved through changes.

[0053] First, dynamically estimate the battery's internal resistance. ;

[0054] The battery's internal resistance is calculated by utilizing the instantaneous change in current when the device switches between different operating modes (such as switching from sleep mode to photo upload). Battery internal resistance It is the ratio of the sudden change in battery voltage to the sudden change in battery current before and after the equipment switches between different operating modes.

[0055] When the device switches from one operating mode to another, the current changes from the first current value. The sudden change to the second current value At that time, the battery terminal voltage will change from the first voltage value. The sudden change to the second voltage According to Ohm's law, the internal resistance of a battery... It can be estimated as follows:

[0056]

[0057] This calculation process can be performed before and after each high-power operation of the device (such as 4G communication). The system records and smooths out the battery's internal resistance. Historical data. Through smoothing, fluctuations and noise in the data are eliminated to observe the long-term growth trend of battery internal resistance.

[0058] A continuously increasing battery internal resistance This is a clear sign of battery aging. As batteries are used, their internal chemical composition gradually changes, leading to a gradual increase in internal resistance. When a continuous increase in battery internal resistance is observed, it can be determined that the battery has entered the aging stage, allowing for timely intervention, such as battery replacement, to ensure the normal operation of the equipment.

[0059] Next, estimate the battery's full charge capacity. ;

[0060] The system continuously tracks the battery's state of charge (SOC) using the coulomb method (ampere-hour integration method). In a complete charging cycle (e.g. from During the process of charging from 20% to 100%, the charging current is controlled. By integrating over time, the total amount of electricity charged can be obtained. :

[0061]

[0062] Simultaneously, the battery state of charge during this charging cycle is recorded. Change (e.g., 80%).

[0063] Battery full charge capacity The total amount of electricity charged during this charging cycle. With battery state of charge Change The ratio of the two values ​​indicates the current full charge capacity of the battery. It can be estimated as follows:

[0064]

[0065] The system will estimate the full charge capacity of the battery. With the battery's rated capacity at the time of manufacture Compare them.

[0066] This invention integrates two dimensions—battery internal resistance and capacity—and uses a weighted or fuzzy logic method to comprehensively calculate the battery health status index. Specifically, based on the battery internal resistance... relative to initial internal resistance Changes in battery aging internal resistance threshold The ratio is used to subtract the ratio from 1 to obtain the first battery health parameter based on the change in internal resistance; based on the battery's full charge capacity... With the battery's rated capacity at the time of manufacture The ratio of the first and second battery health parameters is used to obtain the second battery health parameter based on capacity change; the battery health state index is obtained by weighted summation of the first and second battery health parameters. Its simplified weighted model is as follows:

[0067]

[0068] in, , They represent the weighting factors, and ; Indicates the battery's internal resistance; Indicates the initial internal resistance of the battery; This represents the internal resistance threshold used to determine battery aging. Indicates the battery's full charge capacity; This indicates the battery's rated capacity at the time of manufacture. This indicates the battery health status index.

[0069] Equipment thermal health index This is used to assess whether there is a persistent, minor risk of overheating in the equipment. The calculation process is as follows: First, select the battery surface temperature. With the internal temperature of the main processor or core computing chip The larger of the two values, and based on the larger of the two values ​​and the optimal operating temperature. The first difference is calculated; based on the overheat warning temperature... With optimal operating temperature The second difference is calculated; based on the ratio of the first difference to the second difference, 1 is subtracted from the ratio to obtain the equipment thermal health index. The calculation formula is as follows:

[0070]

[0071] in, Indicates the surface temperature of the battery; This indicates the internal temperature of the main processor or core computing chip. Indicates the optimal operating temperature, such as 25°C; Indicates the overheat warning temperature, such as 65°C; This indicates the thermal health index of the equipment.

[0072] When the equipment's thermal health index A persistently low reading indicates that the device may have a problem with heat dissipation.

[0073] Finally, the weighted average of the above sub-health indices, such as the solar charging efficiency index, battery health status index, and equipment thermal health index, yields a digital profile of the equipment's health. :

[0074]

[0075] in, , , These represent the weights assigned based on the importance of each sub-health index.

[0076] Device Health Digital Profile It is a comprehensive score between 0 and 1, along with the individual health indices. , , Together, they form a complete digital profile of device health.

[0077] Step S3: Based on the battery state of charge, the device health digital profile, and the energy prediction results obtained for the future preset time period. This generates power consumption scheduling decisions.

[0078] Specifically, step S3 is the core of the invention's "smart butler" function. The scheduler no longer uses a fixed working mode, but instead actively decides "how to work today" (power consumption scheduling) based on the "current status of the device" (device health digital profile) and "whether there is enough energy tomorrow" (future energy prediction).

[0079] The methods for obtaining energy prediction results within a predetermined time period include:

[0080] First, acquire weather data; the device accesses public external weather forecast service APIs at regular intervals (such as early morning) every day via wireless communication modules (such as 4G / NB-IoT) to obtain key weather forecast information (such as sunny, cloudy, overcast, rainy, snowy) or more accurate hourly solar irradiance prediction data for the next 24-48 hours.

[0081] Next, based on weather data and its own digital health profile of the equipment, the energy forecast results are predicted for a preset time period (such as the next 24 hours). The calculation formula is as follows:

[0082]

[0083] in, This indicates the estimated number of irradiance values ​​based on weather data within a preset future time period (e.g., "sunny" corresponds to high irradiance, "cloudy" corresponds to low irradiance). Average light intensity per hour; Indicates average light intensity The theoretical maximum output power is as follows; This indicates the current solar charging efficiency index of the device itself, which is used to correct for power generation under ideal conditions and reflects the impact of "sub-healthy" conditions such as dust accumulation. Indicates a time interval, such as 1 hour; This indicates the energy forecast results for a predetermined time period in the future.

[0084] The methods for generating power scheduling decisions include:

[0085] First, define multi-level working modes: decompose and classify the tasks of the device, and predefine multi-level working modes. Each level of working mode corresponds to a power level (PL) with different power consumption and task priority, as shown in Table 1 below.

[0086] Table 1. Device operating modes and corresponding power consumption and functions

[0087]

[0088] During the scheduling decision execution process, the scheduling algorithm is executed periodically (e.g., every hour), based on the current battery state of charge. Digital profile of equipment health Energy forecast for the next 24 hours Choose the optimal power consumption level PL optional .

[0089] Decision logic can be based on utility functions The optimization aims to maximize the monitoring value of the equipment while ensuring it remains offline. The utility function is expressed as follows:

[0090]

[0091]

[0092] in, Indicates the working mode PL The value of monitoring data that can be obtained at each level, such as Its value is the highest;

[0093] Indicates working mode PL Penalties under different levels, when the device health digital profile At lower power levels, high-power operation accelerates device aging and increases penalties. For example... ; express;

[0094] This represents the predicted minimum state of battery charge within a preset future time period, with the corresponding constraints as follows: ;

[0095] This indicates the minimum safe state of battery charge that the device must maintain, such as 15%.

[0096] A simplified example of a decision rule is as follows: When the energy forecast results for a predetermined future time period... Exceeding the first preset threshold, device health digital profile Exceeding the second preset threshold and the battery state of charge If the third preset threshold is exceeded, a power scheduling decision including the highest-level operating mode is generated; that is, if... and and ,but ,in Indicates power consumption level The corresponding power consumption value. At this point, the device's energy and health are both ideal, allowing it to operate at full capacity.

[0097] When the energy forecast results for a predetermined time period in the future Exceeding the fourth preset threshold and the battery state of charge When the threshold is exceeded (the fifth preset threshold), a power scheduling decision is generated that includes either the next lower level operating mode or the next lower level operating mode; that is, if... and ,but or ,in Indicates power consumption level The corresponding power consumption value. At this point, it is predicted that future energy revenue will be less than the consumption in energy-saving mode, and the current power supply is insufficient. The equipment must immediately take throttling measures and rationally plan the use of resources to ensure normal operation in the future.

[0098] When solar charging efficiency index Less than the sixth preset threshold or battery health status index If the value is less than the seventh preset threshold, a power scheduling decision is generated to reduce the operating mode by one level; that is, if or ,but Downgrade the power consumption level and report a specific sub-health alarm. In other words, if the current power consumption level is... If a power scheduling decision is received, a power level of 100% will be executed. The corresponding operating mode. At this time, if the equipment itself malfunctions, it will proactively reduce its workload and request assistance, while simultaneously sending out a distress signal to remind relevant personnel to pay attention and perform maintenance.

[0099] When the battery state of charge is less than the eighth preset threshold, a power scheduling decision is generated that includes either the first-level operating mode or the second-level operating mode; that is, if ,but or At this point, the device's battery is about to run out, and the device will enter its most basic operating mode to maintain the operation of core functions.

[0100] By applying these decision-making rules, the equipment can accurately combine its own health status, current energy reserves, and future energy harvesting conditions to dynamically and proactively adjust its operating mode, thereby ensuring stable and reliable operation over a long period.

[0101] This invention also provides a multi-dimensional self-aware active power scheduling system for implementing the multi-dimensional self-aware active power scheduling method described above. Figure 2 As shown, it includes: a multi-dimensional sensing and data acquisition module 1, used to acquire the physical state of the equipment; wherein, the physical state of the equipment includes solar power supply data, energy storage unit data, and core main control unit data; an equipment health digital profile construction module 2, connected to the multi-dimensional sensing and data acquisition module 1, used to process the physical state of the equipment, obtain a set of key health indicators, and construct an equipment health digital profile based on the set of key health indicators; and an active power consumption scheduling module 3 based on the profile and energy prediction, connected to the equipment health digital profile construction module 2, used to generate power consumption scheduling decisions based on the battery state of charge, the equipment health digital profile, and the energy prediction results obtained in the future preset time period.

[0102] The specific embodiments of the present invention will be described in detail below.

[0103] Imagine an online monitoring device deployed on a power transmission tower in a mountainous area. This device is equipped with a 50W solar panel, which converts solar energy into electricity under sunlight conditions to power its operation. It also carries a 12V / 40Ah lithium iron phosphate battery to store energy, ensuring normal operation even in low-light conditions or other special circumstances. Furthermore, the device incorporates a 4G communication module and a main processor with AI processing capabilities. The 4G module enables data transmission between the device and the outside world, while the main processor intelligently analyzes and processes the collected raw data.

[0104] Step 1: Initialization and Parameter Calibration; The device undergoes initialization and calibration after leaving the factory or after new installation. For clean solar panels and brand-new batteries, record their photoelectric conversion coefficients under standard illumination. Initial internal resistance of the battery and rated capacity Preset power consumption level The corresponding power consumption value, and the setting of the minimum safe battery state of charge. Key thresholds such as (15%).

[0105] Step Two: Periodically perform multi-dimensional self-sensing and device health digital profile construction; using various sensors installed on the device, periodically collect the output voltage of the solar panel at a frequency of once every 10 minutes. U pv Solar panel output current I pv Ambient light intensity L Battery voltage U bat Charging / discharging current I bat Battery surface temperature T bat The internal temperature of the main processor (CPU) or core computing chip T cpu Various types of data.

[0106] Device health digital profile calculation: Assume that at noon on a certain day, the external sunlight is extremely strong, i.e., ambient light intensity. The calculated charging efficiency of the solar panels is very high, however. The value was only 0.65, far below the normal value of 0.95. This indicates that the solar panels may be suffering from problems such as being covered by bird droppings or thick dust, which significantly reduces their ability to receive sunlight and convert it into electrical energy.

[0107] During a 4G communication mission, the device's current suddenly increased from 0.1A to 1.5A, and the battery voltage dropped from 13.2V to 13.0V. Calculate the battery's internal resistance. The initial internal resistance of the battery is... This indicates that the battery has aged to some extent. The system will update. The moving average.

[0108] The system will use the charging current from the previous charging cycle as a reference. The points data are used to determine the current full charge capacity of the battery. Make an estimate. For example, estimate that the current remaining battery capacity is approximately 35Ah, which is the battery's rated capacity. If it is 40Ah, then .

[0109] The battery health status index is obtained through comprehensive calculation. It is approximately 0.85.

[0110] On a summer afternoon, the internal temperature of the device's main processor (CPU) or core computing chip... The battery surface temperature reaches 60°C. When the temperature reaches 50°C, the thermal health index of the equipment is calculated. It is relatively low, at 0.75.

[0111] The final weighted calculation yields the device health digital profile score. It is approximately 0.78, and together with the various sub-indices, it forms a digital profile of health at the current moment.

[0112] Step 3: Perform energy prediction and power consumption scheduling daily (e.g., at 3 a.m.); Obtain weather data: At 3 a.m. every day, the device initiates a network request to the weather forecast service API through the 4G communication module to obtain the weather data for the next 48 hours as "continuous moderate rain".

[0113] Predicted Energy: The system uses the extremely low sunlight intensity corresponding to "moderate rain" in weather data, combined with the current low charging efficiency of solar panels. =0.65, calculate the energy forecast for the next 24 hours. At only 5Wh, far below the device's daily energy consumption in normal mode (approximately 30Wh), this suggests that the device may face serious energy shortages in the future.

[0114] Scheduling decision: After the scheduler starts, it reads the current battery state of charge. The percentage is 55%, and the device health digital profile is divided into... The value is 0.78, representing the energy forecast for the next 24 hours. It is 5Wh.

[0115] The algorithm performs a comprehensive evaluation of this data and finds that if it continues to maintain... (Normal mode) Battery state of charge for the next 24 hours It will fall below the set minimum safe state of battery charge. .

[0116] Based on this evaluation result, the system makes the optimal choice, which is to immediately change the operating mode of the device from the normal mode. Downgraded to survival mode .

[0117] In survival mode In this state, the device will cease all image acquisition and AI analysis tasks, retaining only the core system to minimize energy consumption. Simultaneously, the device will be programmed to report a heartbeat packet every 6 hours via the 4G network. This heartbeat packet contains crucial device information, such as device ID, operating mode, and battery state of charge. Digital profile of equipment health Solar panel charging efficiency Battery health status index And alarm information, etc.

[0118] For example, the heartbeat packet content is: "Device ID: XXX, Operating Mode: " , 53%, 0.78, 0.65, 0.85, Warning: Charging efficiency is severely low, battery is slightly aging, and there is a risk of continued disconnection in the next 24 hours.

[0119] Step 4: Operation and Maintenance Response; After receiving the heartbeat packet, the monitoring center automatically parses the alarm information, marks the device as "sub-healthy - requires attention" on the map, and generates a low-priority maintenance work order: "Due to the solar panel blocking the device, the charging capacity is seriously insufficient. Based on the weather forecast, there is a risk of disconnection. Please arrange personnel to clean it after the weather improves."

[0120] Through the above implementation method, the monitoring device successfully survived two consecutive days of moderate rain using its keep-alive mode. It maintains an online status and continuously reports its own status. Meanwhile, neighboring devices using existing technology, unable to anticipate energy crises and adjust power consumption, went offline on the first afternoon due to power depletion. The monitoring device using the method and system of this invention automatically returned to normal mode after the weather cleared. The system continued operating while awaiting maintenance personnel to clean the solar panels, thus averting an unplanned downtime and ensuring the continuity of monitoring services.

[0121] The advantages or beneficial effects of adopting the above technical solution are as follows:

[0122] (1) By constructing a digital health profile of the equipment, potential "sub-health" hazards such as dust accumulation on solar panels and battery aging can be detected weeks or even months in advance and reported as early warnings. The maintenance department can then formulate planned maintenance based on this information, avoiding emergency repairs due to sudden equipment failures and significantly reducing the manpower, material resources, and time costs of maintenance.

[0123] (2) Through accurate future energy prediction and active power consumption scheduling, the device can automatically enter the "saving and thrift" mode when facing severe weather such as continuous rain and heavy snow. It uses the limited power to maintain the most critical heartbeat and low-frequency monitoring tasks, avoiding complete "disconnection" due to power exhaustion, and maximizing the continuity and integrity of monitoring data.

[0124] (3) This method not only focuses on short-term operation, but also assesses long-term degradation through health profiling. For example, in the later stages of battery aging, a more "gentle" charging and discharging strategy and a lower power consumption mode will be automatically adopted to extend its service life. This improves the overall lifecycle value of the equipment asset.

[0125] (4) This invention mainly relies on the innovation of algorithms and models. The required hardware (such as thermistors and photodiodes) is extremely low in cost. It can be calculated using the main control chip of existing monitoring devices without significantly increasing hardware costs. It is easy to upgrade and transform existing equipment and promote its application in new equipment, and has strong practicality.

[0126] The above are merely preferred embodiments of the present invention and are not intended to limit the implementation methods and protection scope of the present invention. Those skilled in the art should recognize that any equivalent substitutions and obvious changes made using the content of this specification and illustrations should be included within the protection scope of the present invention.

Claims

1. A method for active power consumption scheduling based on multi-dimensional self-awareness, characterized in that, The application relates to a device health digital portrait construction method and device. The application relates to a device health digital portrait construction method and device. The application relates to a device health digital portrait construction method and device. The application relates to a device health digital portrait construction method and device. The application relates to a device health digital portrait construction method and device. ； wherein, represents the battery surface temperature; represents the internal temperature of the main processor or core computing chip; represents the optimal working temperature; represents the overheat warning temperature; represents the device thermal health index.

2. The method of claim 1, wherein, The application relates to a device health digital portrait construction method and device. The application relates to a device health digital portrait construction method and device.

3. The method of claim 1, wherein, The application relates to a device health digital portrait construction method and device. The application relates to a device health digital portrait construction method and device.

4. The method of claim 3, wherein, The application relates to a device health digital portrait construction method and device. The application relates to a device health digital portrait construction method and device. The application relates to a device health digital portrait construction method and device.

5. The method of claim 3, wherein, The application relates to a device health digital portrait construction method and device. ； wherein, , represent a weight factor, respectively; represents a battery internal resistance; represents a battery initial internal resistance; represents an internal resistance threshold value for judging battery aging; represents a battery full charge capacity; represents a battery rated capacity at factory shipment; represents the battery state of health index.

6. The method of claim 5, wherein, The application relates to a device health digital portrait construction method and device. The application relates to a device health digital portrait construction method and device.

7. The method of claim 1, wherein, The application relates to a device health digital portrait construction method and device. ； wherein, represents the average light intensity of the first hour estimated according to the weather data in the future preset time period; represents the average light intensity under the theoretical maximum output power; represents the solar charging efficiency index; represents the time interval; represents the energy prediction result in the future preset time period.

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9. A multi-dimensional self-aware based active power management system, characterized in that, The application discloses a multi-dimensional self-perception-based active power consumption scheduling method. A multi-dimensional perception and data acquisition module is configured to acquire device physical states, wherein the device physical states include solar power supply data, energy storage unit data and core host unit data. A device health digital portrait construction module is connected to the multi-dimensional perception and data acquisition module and configured to process the device physical states to obtain a group of key health indexes and construct a device health digital portrait according to the group of key health indexes. An active power consumption scheduling module is connected to the device health digital portrait construction module and configured to generate a power consumption scheduling decision according to a battery state of charge, the device health digital portrait and an energy prediction result obtained in a future preset time period.

Images