Solar self-powered wireless communication electric quantity sensor system

Abstract

The invention relates to the technical field of wireless sensing, and discloses a solar self-powered wireless communication electric quantity sensor system. The system comprises a solar energy collection module, an energy management module, an electric quantity measurement module, a communication control module and an exception handling module. The solar energy acquisition module analyzes the energy acquisition efficiency and stability matching degree according to the environment illumination intensity and temperature data, and generates an energy distribution parameter set; the energy management module adjusts energy storage balance based on the parameter set and generates an energy regulation and control optimization parameter set; the electric quantity measurement module adjusts the sampling frequency and the signal path distribution proportion accordingly to generate an electric quantity dynamic measurement result; the communication control module dynamically adjusts the path distribution and the power ratio of the communication frequency band based on the measurement result, and generates a communication regulation and control data set; the exception handling module analyzes the system exception state and generates a system exception intervention data table. According to the system, efficient utilization, dynamic regulation and control and stable communication of energy are realized, and the overall reliability and adaptability are improved.

Description

Technical Field

[0001] This invention relates to the field of wireless sensing technology, specifically to a solar-powered self-powered wireless communication power sensor system. Background Technology

[0002] In the field of modern Internet of Things (IoT) and intelligent monitoring, power sensor systems are a key component for data acquisition and status monitoring. Their operational stability and energy supply efficiency directly affect the reliability of the overall system. Traditional power sensor systems mostly rely on wired power supply or disposable batteries, which have problems such as complex wiring, high maintenance costs, and limited battery life. These shortcomings are particularly prominent in remote areas, outdoor environments, or large-scale deployment scenarios.

[0003] To address power supply challenges, some systems have begun adopting solar power technology. However, existing solar power solutions have significant shortcomings in energy management. For example, most systems can only simply convert solar energy into electrical energy for storage, lacking dynamic responses to factors such as ambient light intensity and temperature changes. This results in low energy harvesting efficiency and difficulty in matching the real-time power consumption requirements of sensor nodes. Furthermore, the lack of an energy distribution mechanism makes battery cells prone to overcharging and over-discharging, which not only shortens battery life but may also lead to system power outages and data loss.

[0004] In the communication and measurement stages, existing systems exhibit poor coordination. The sampling frequency and signal path for power measurement are often fixed, unable to be flexibly adjusted according to energy supply conditions. This leads to continued high-power operation even during energy shortages, further exacerbating the energy supply-demand imbalance. During wireless communication, issues such as signal strength fluctuations and unstable data transmission efficiency are common, and the lack of a linkage mechanism with power status makes it difficult to guarantee the continuity and accuracy of data transmission. Furthermore, for abnormal states during system operation, such as sensor failures, communication interruptions, and abnormal energy supply, existing solutions mostly rely on simple alarms or restarts, lacking precise anomaly detection and intervention strategies. This results in weak system fault tolerance and susceptibility to external interference. Summary of the Invention

[0005] The purpose of this invention is to provide a solar-powered self-powered wireless communication power sensor system to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, this invention provides a solar-powered self-powered wireless communication power sensor system. The system includes: a solar energy acquisition module, which, based on ambient light intensity and temperature data, retrieves the output power parameters and voltage fluctuation data of the solar panel, analyzes the matching degree between energy acquisition efficiency and stability, allocates energy regulation values ​​and balancing parameters, and generates an energy distribution parameter set; an energy management module, which, based on the energy distribution parameter set, extracts the power storage value and gradient change of the battery cells, analyzes the impact of energy allocation on system stability, adjusts the energy storage balance, and generates an energy regulation optimization parameter set; a power measurement module, which, based on the energy regulation optimization parameter set, analyzes the current and voltage fluctuations of the sensor nodes, adjusts the sampling frequency and signal path distribution ratio, redistributes the acquisition trend and dynamic parameter values ​​of power data, and generates dynamic power measurement results; a communication control module, which, based on the dynamic power measurement results, extracts the signal strength fluctuation rate and data offset during wireless transmission, analyzes the impact of the fluctuation range on data transmission efficiency, dynamically adjusts the path distribution and power ratio of the communication frequency band, and generates a communication regulation dataset; and an anomaly handling module, which, based on the communication regulation dataset, analyzes the anomaly ratio and occurrence time of the system's operating status, adjusts the parameters of the target detection range, and generates a system anomaly intervention data table.

[0007] Preferably, the steps for obtaining the matching degree of energy harvesting efficiency and stability are as follows: Based on ambient light intensity and temperature data, extract the output power parameters, voltage fluctuation data, and temperature change data of the solar panel; set a time window, select time points to match the data, and obtain the output power parameters and voltage fluctuation data by comparing data correlation and filtering data; based on the output power parameters and voltage fluctuation data, perform matching verification on the energy path, calculate the difference between output power and voltage fluctuation, and correct the energy distribution and fluctuation distribution in combination with temperature changes; adjust the path parameters based on the influence of temperature changes on the data to obtain the matching degree of output power and voltage fluctuation; based on the matching degree of output power and voltage fluctuation, perform stability analysis, set stability analysis standards, evaluate the energy distribution under different temperature conditions in combination with the dynamic changes of system operation, compare stability indicators and optimize temperature conditions to obtain the matching degree of energy harvesting efficiency and stability.

[0008] Preferably, the step of obtaining the energy distribution parameter set specifically includes: based on the matching degree of energy acquisition efficiency and stability, analyzing the changes in energy transmission and stability of the system under differentiated environmental conditions, and performing weighted calculations on the energy distribution of the system to obtain the preliminary energy regulation requirements of the system; based on the preliminary energy regulation requirements of the system, analyzing the energy balance between systems, identifying the relationship between energy transmission efficiency and load distribution between systems, correcting the system energy regulation parameters, calculating the adjusted energy output value of the equipment, and obtaining the energy regulation dataset between systems; combining the energy regulation dataset between systems with the stability matching results, allocating energy between systems, optimizing and matching the required balance and stability requirements, and obtaining the energy distribution parameter set.

[0009] Preferably, the steps for extracting the battery cell's energy storage value and gradient change amount specifically include: based on the energy distribution parameter set, extracting the battery cell's temperature data, filtering temperature points within each time period, and analyzing temperature fluctuations by combining the temperature change trends of differentiated locations of the battery cell to obtain the battery cell's temperature data; based on the battery cell's temperature data, calculating the corresponding energy storage value for each temperature point, identifying the energy change amount at each measurement point by analyzing the relationship between temperature and energy storage, comparing the energy change at differentiated locations with system structural parameters to obtain energy storage distribution and gradient distribution data; based on the energy storage distribution and gradient distribution data, analyzing the overall energy storage distribution of the battery cell, optimizing the energy gradient by combining temperature data, analyzing the impact of energy changes on system performance, determining the stable energy configuration under differentiated operating conditions, and obtaining the battery cell's energy storage value and gradient change amount.

[0010] Preferably, the step of obtaining the energy regulation optimization parameter set specifically involves: determining the time series of energy changes based on the battery cell's stored energy value and gradient change amount; comparing the current stored energy value with the original energy data; analyzing the energy gradient at each moment; and defining corresponding thresholds according to system state partitions to generate a preliminary energy change parameter set; analyzing the preliminary energy change parameter set to analyze the impact of battery cell energy on system power output stability; identifying the correlation between energy and power output; and calculating the segment power stability influence coefficient; and by analyzing the segment power stability influence coefficient and combining it with the battery cell energy change parameters, adjusting the energy storage balance, optimizing the energy regulation data, and generating the energy regulation optimization parameter set.

[0011] Preferably, the step of obtaining the dynamic power measurement result is as follows: based on the energy regulation optimization parameter set, extract the signal acquisition data of the sensor nodes, monitor the signal acquisition rate at differentiated nodes, infer the signal diffusion characteristics by combining external environmental factors such as time and temperature, define the acquisition and diffusion rate coefficients, and generate a dynamic parameter set for acquisition and diffusion; analyze the influence of the dynamic parameter set on the sampling frequency and distribution, optimize the ratio between the signal path and the sampling frequency according to the requirements of the node signal concentration distribution, calculate the adjustment coefficient of the power concentration distribution trend, and generate the power concentration regulation result; analyze the power concentration regulation result, adjust the ratio between the sampling frequency and the signal path, allocate the power concentration distribution trend, and combine the acquisition diffusion parameters and the adjustment coefficient to obtain the dynamic power measurement result.

[0012] Preferably, the step of generating the communication control dataset specifically includes: based on the dynamic power measurement results, monitoring the signal strength fluctuation rate and data offset during the real-time transmission process of the system, identifying the fluctuation range, eliminating system fault anomalies, analyzing the average fluctuation rate of the data, and obtaining signal and data fluctuation data; analyzing the impact of the signal and data fluctuation range on the data transmission rate, using the known transmission rate, analyzing the relationship between the signal and data, calculating the transmission rate under the differentiated fluctuation range, and obtaining transmission rate impact data; based on the transmission rate impact data, dynamically adjusting the path distribution and power ratio of the communication frequency band, adjusting according to the relationship between the transmission rate impact data and the signal and data fluctuation range, allocating the communication frequency band and power control range, and generating the communication control dataset.

[0013] Preferably, the steps for obtaining the system anomaly intervention data table are as follows: Based on the communication control dataset, analyze the system operation status anomalies and occurrence times, collect anomaly proportion data at different time points, organize the anomaly time distribution, analyze the proportion change trend and classify the data to obtain system anomaly distribution data; Based on the system anomaly distribution data, adjust the target detection range parameters, analyze the optimal detection time and proportion distribution of system anomalies, and adjust the operation conditions of detection threshold, time, and frequency band by comparing the proportion changes under different detection conditions to obtain target detection range parameters; Based on the target detection range parameters, adjust the detection conditions according to the current operation parameters, control the variable relationship of detection time, threshold, and frequency band, and perform real-time detection according to the adjusted parameters to obtain the system anomaly intervention data table.

[0014] Preferably, the system further includes: a data fusion module that extracts multi-source sensor data based on the system anomaly intervention data table, analyzes the correlation between the data, and fuses them to generate a unified data output; and a system optimization module that adjusts the system operating parameters based on the unified data output and generates the final control command.

[0015] Preferably, the acquisition steps of the data fusion module are as follows: based on the system anomaly intervention data table, extract the current, voltage and environmental data of the sensor nodes, analyze the data consistency, and generate a fusion parameter set; based on the fusion parameter set, compare the differences between the data, optimize the data weight allocation, and obtain unified data output.

[0016] Compared with existing technologies, the beneficial effects of this invention are: through the collaborative work of multiple modules, the overall performance and adaptability of the system are significantly improved. The solar energy acquisition module dynamically calls upon the output power parameters and voltage fluctuation data of the solar panels based on ambient light intensity and temperature data, analyzes the matching degree between energy acquisition efficiency and stability, and then allocates energy regulation values ​​and balancing parameters to generate an energy distribution parameter set. This process makes solar energy utilization more efficient, enabling real-time adjustment of the energy acquisition strategy according to environmental changes, ensuring stable energy input under different light and temperature conditions, and reducing the problem of unstable energy supply caused by environmental fluctuations.

[0017] The energy management module, based on an energy distribution parameter set, extracts the battery cell's stored energy value and gradient change, analyzes the impact of energy distribution on system stability, adjusts energy storage balance, and generates an optimized energy regulation parameter set. In this way, the charging and discharging process of the battery cells is managed with precision, avoiding overcharging and over-discharging, extending battery life, and ensuring energy storage balance. This allows the system to maintain a stable energy supply during long-term operation, reducing system failures caused by battery issues.

[0018] The power measurement module optimizes the parameter set through energy regulation, analyzes current and voltage fluctuations at sensor nodes, adjusts the sampling frequency and signal path distribution ratio, and redistributes the power data acquisition trends and dynamic parameter values ​​to generate dynamic power measurement results. This allows power measurement to adapt to energy supply conditions. When energy is sufficient, the sampling frequency can be increased to obtain more detailed power data, while the sampling frequency can be reduced to save energy when energy is scarce. This achieves a dynamic balance between measurement accuracy and energy consumption, ensuring the accuracy of power data while avoiding unnecessary energy waste.

[0019] Based on dynamic power measurement results, the communication control module extracts signal strength fluctuation rate and data offset during wireless transmission, analyzes the impact of fluctuation range on data transmission efficiency, and dynamically adjusts the path distribution and power ratio of the communication frequency band to generate a communication control dataset. This mechanism enables wireless communication to adaptively adjust according to power status and signal conditions, ensuring data transmission quality while rationally allocating communication power. This reduces energy consumption caused by signal instability or excessive power, improving the stability and efficiency of data transmission.

[0020] The anomaly handling module, based on the communication control dataset, analyzes the proportion and timing of system operational anomalies, adjusts the parameters of the target detection range, and generates a system anomaly intervention data table. Through precise analysis and timely intervention of system anomalies, it can quickly identify and address problems arising during operation, reduce the impact of anomalies on the system, improve the system's fault tolerance and reliability, and ensure continuous and stable system operation. Attached Figure Description

[0021] Figure 1 This is a timing diagram of the solar-powered self-powered wireless communication power sensor system described in this invention;

[0022] Figure 2 A flowchart for analyzing the matching degree between energy harvesting efficiency and stability;

[0023] Figure 3 A flowchart for generating the energy distribution parameter set;

[0024] Figure 4 A flowchart for generating an optimized set of energy regulation parameters;

[0025] Figure 5 A flowchart for generating a communication control dataset. 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] Please see Figure 1 The present invention provides a solar-powered self-powered wireless communication power sensor system, the system comprising: a solar energy acquisition module, an energy management module, a power measurement module, a communication control module, and an anomaly handling module.

[0028] The solar energy acquisition module collects ambient light intensity and temperature data in real time through photovoltaic panels, and calculates the current energy harvesting efficiency using a built-in power output model. The energy management module dynamically adjusts the charging and discharging strategies of the battery cells based on the energy distribution parameter set to maintain system energy balance. The power measurement module uses a high-precision ADC sampling circuit to monitor load current and voltage in real time, and optimizes the sampling strategy through digital signal processing algorithms. The communication control module implements wireless data transmission based on the Zigbee protocol stack, dynamically adjusting the transmission power and communication frequency band according to channel quality. The anomaly handling module monitors system operating parameters using a state machine model, classifies abnormal events, and generates intervention strategies.

[0029] Example 1: See Figure 2 This study analyzes the matching degree between energy harvesting efficiency and stability in solar energy harvesting modules. The process involves multi-stage data processing to convert environmental parameters into energy characteristic parameters. The system is configured with a TSL2561 digital light intensity sensor and a DS18B20 temperature sensor to form an environmental monitoring unit. These two types of sensors are connected to the main controller via I2C bus and single-bus protocol, respectively, with a fixed data acquisition cycle of 5 seconds. Light intensity monitoring employs an automatic range switching mechanism, maintaining a measurement accuracy of ±5% within the range of 0-40000 lux. Temperature monitoring points are distributed on the back of the solar panels and in the surrounding environment, forming a temperature gradient monitoring network.

[0030] The output characteristics of the solar panel are modeled based on a single diode equivalent circuit. The main controller calculates the theoretical maximum power point voltage in real time, and a temperature compensation coefficient is introduced to correct the open-circuit voltage parameter in this calculation process. The actual output power is obtained through a four-wire measurement method. Voltage sampling uses a 12-bit ADC at a frequency of 1kHz, and current sampling uses a 0.1Ω precision shunt in conjunction with an instrumentation amplifier. Voltage fluctuation analysis uses a sliding window algorithm with a window width of 10 seconds, and the standard deviation of the voltage values ​​within the window is calculated as a fluctuation quantification index. Temperature effect modeling establishes a three-dimensional parameter space, and an interpolation algorithm is used to generate a temperature-voltage-power mapping table.

[0031] The stability assessment mechanism includes a dynamic threshold adjustment function. The power fluctuation threshold is initially set at ±5% of the nominal value, and automatically expands to ±8% when the ambient light change rate exceeds 10% / minute. The assessment algorithm uses a state machine model, defining three levels of states: normal, warning, and abnormal. A stability alarm is triggered when the threshold range is exceeded for three consecutive sampling periods. The matching degree synthesis unit integrates three dimensions of assessment elements: power output efficiency is calculated by the ratio of actual power to theoretical maximum power; voltage stability is represented by the reciprocal of the fluctuation coefficient; and the temperature influence factor is calculated based on the degree of temperature deviation from the 25℃ benchmark. The weight allocation of each element adopts an adaptive strategy, increasing the weight of the temperature factor to 0.4 in high-temperature environments and maintaining a balanced weight of 0.33 for each factor in normal environments.

[0032] The data processing workflow includes an outlier filtering stage, employing the Laida criterion to remove sampling points outside the ±3σ range. A time-point matching algorithm uses timestamp alignment technology to ensure strict time synchronization of illumination, temperature, and electrical parameter data. Data correlation analysis calculates the correlation coefficients between illumination and power, and temperature and voltage; when the correlation coefficient falls below 0.7, a data re-acquisition process is initiated. The output parameter calibration module periodically executes a self-calibration procedure, comparing measured values ​​with baseline values ​​under standard test conditions to generate a parameter correction coefficient table.

[0033] The temperature compensation system includes a two-stage correction mechanism. The primary correction uses linear compensation based on the solar panel temperature coefficient, while the secondary correction introduces a nonlinear compensation term to handle extreme temperature conditions. When the solar panel temperature exceeds 60°C, the thermal runaway protection mode is activated, at which point the matching degree calculation is limited to a maximum score of 80 points. The stability index optimization algorithm analyzes historical operating data and automatically adjusts the evaluation criteria for different temperature ranges, relaxing the voltage fluctuation tolerance to ±7% in the low-temperature range of -10°C to 10°C.

[0034] The entire analysis process ultimately outputs two indicators: an energy harvesting efficiency score and a stability level. The efficiency score ranges from 0 to 100, corresponding to an efficiency value of 0-100%, while the stability level is divided into five grades: A, E, and E. A mapping relationship is established between the score and energy distribution parameters. When the score is below 60 or the stability level is D / E, the system automatically reduces the energy harvesting priority and switches to battery power mode. The output parameter set includes 12 parameters such as real-time efficiency value, stability coefficient, and temperature influence factor, which are transmitted to the energy management module via a serial peripheral interface.

[0035] Example 2: See Figure 3 This involves the generation process of energy distribution parameter sets and the battery cell parameter extraction mechanism. During system initialization, pre-stored battery pack configuration parameters are loaded, including structural information such as the number of series-connected cells, the number of parallel branches, and the rated capacity of each cell. Energy transfer modeling is based on an improved nodal voltage analysis method, constructing a circuit network model that includes conductor impedance, contact resistance, and internal equivalent resistance. This model describes the energy flow path through an impedance parameter matrix, with the matrix dimensions strictly corresponding to the battery pack topology.

[0036] The environmental adaptation analysis unit receives the energy distribution parameter set from the solar energy acquisition module and analyzes the temperature gradient data, light intensity variation curves, and stability scores contained therein. A differentiated environment simulator generates twelve typical operating condition combinations, covering a temperature range of -20℃ to 60℃ and a light intensity range of 100-1000 W / m². For each operating condition, the system performs energy transfer simulation, recording the current distribution of each branch and node voltage fluctuation data. The weighted calculation module introduces a time decay factor, assigning higher weight to recent load history data, and uses a constrained optimization algorithm to solve for the optimal energy allocation scheme.

[0037] The equalization status monitoring system is equipped with a high-precision voltage acquisition circuit, with each battery cell having an independent sampling channel. A 24-bit ADC synchronously acquires the terminal voltage of all cells at a frequency of 10Hz. A differential voltage analysis algorithm calculates the maximum voltage deviation within the battery pack in real time. When a differential voltage exceeding 50mV is detected, active equalization control is activated. The equalization strategy employs an energy transfer architecture, transferring charge between adjacent cells via a bidirectional DC-DC converter. The load prediction module integrates a Kalman filter, predicting power demand trends over the next five minutes based on real-time load data acquired by a current sensor.

[0038] The battery parameter extraction system includes a distributed temperature sensing network, with eight temperature monitoring points positioned at key locations within the battery pack, including the terminal connection points, the geometric center, and the edge regions. Temperature acquisition utilizes a PT1000 platinum resistance thermometer in conjunction with a constant current source circuit, achieving a measurement resolution of 0.1℃. The temperature field reconstruction algorithm generates a three-dimensional temperature distribution cloud map of the battery pack through finite element interpolation. A capacity mapping unit establishes a temperature-capacity relationship database, employing differentiated open-circuit voltage-state-of-charge curves for different temperature ranges.

[0039] The charge gradient analysis employs time-domain differential technology to calculate the charge change rate at minute intervals. A dynamic threshold setting mechanism automatically adjusts based on battery health status, setting a ±5% SOC change tolerance band for new batteries and extending it to ±8% for aged batteries. Gradient distribution modeling divides the battery pack into sixteen virtual segments, independently calculating the charge change trend for each segment. The performance impact assessment unit analyzes the correlation between charge gradient and output ripple; when drastic charge fluctuations are detected in a specific segment, the load distribution weight for that segment is automatically adjusted.

[0040] The energy regulation optimization process employs a hierarchical decision-making structure. Primary optimization adjusts the DC-DC converter's operating point based on real-time power data, ensuring the output current accurately tracks the reference value. Secondary optimization considers battery aging factors, estimating capacity degradation through a cycle count counter and internal resistance measurements, and dynamically limiting the maximum charge / discharge current. Tertiary optimization performs energy redistribution; when localized overheating or overcharging risks are detected, the energy load is automatically transferred to the battery branch in better condition.

[0041] The parameter set generation module integrates all optimization results and outputs an energy regulation optimization parameter set containing 36 parameters, including branch current setpoints, individual unit voltage protection thresholds, and temperature compensation coefficients. This parameter set is transmitted to the next-level module via a dual-redundant CAN bus with a transmission cycle of 1 second. The data frame includes a CRC32 checksum and a serial number identifier. The historical parameter storage unit retains the optimization parameter records for the most recent 24 hours, supporting parameter rollback and comparative analysis functions.

[0042] Example 3: See Figure 4 This system involves the generation of a parameter set for dynamic battery measurement and energy regulation optimization. A coulomb metering chip monitors the battery charging and discharging process. The chip incorporates a 16-bit Δ-Σ ADC to achieve high-precision charge measurement with a minimum resolution of 0.05 mAh. A time series analysis module establishes an autoregressive integral moving average model to process the battery data. The model input includes the battery sampling sequence of the past 15 minutes, and the output predicts the battery change trend for the next 5 minutes. Gradient calculation employs an improved five-point difference method, calculating the rate of change of battery charge within the time window [t-2Δt, t+2Δt], effectively suppressing the influence of measurement noise.

[0043] During the extraction of power change parameters, the system defines three working state zones: normal zone, transition zone, and warning zone. The zone thresholds are dynamically set according to the battery type. For lithium iron phosphate batteries, the normal zone is set to 20%-80% SOC, the transition zone to 10%-20% and 80%-90%, and the warning zone to 0%-10% and 90%-100%. The state recognition algorithm monitors the direction and magnitude of the power gradient in real time. When a continuous and rapid decrease in power is detected and the battery enters the transition zone, a warning signal is triggered. The parameter set generation unit records the duration, number of entries, and gradient change characteristics of each state zone, forming a multi-dimensional power change feature vector.

[0044] Power stability analysis employs a frequency domain analysis method, performing a 4096-point Fast Fourier Transform on the output current signal. The spectral feature extraction module focuses on the 1Hz-1kHz frequency band, calculating the proportion of each harmonic component in the total energy. The stability influence coefficient η is calculated using the following formula: In the formula: Indicates the first amplitude of second harmonic components These are the weighting coefficients for the corresponding frequency points. Representing the The rate of change of charge of each individual battery cell This is the upper limit of the harmonic order for analysis. This represents the total number of battery cells. This coefficient reflects the strength of the correlation between power fluctuations and output harmonics, ranging from 0 to 1. A larger value indicates a more significant impact.

[0045] The power regulation optimization process adopts a hierarchical and progressive strategy. Primary regulation adjusts the charging and discharging current based on real-time power gradients; when a positive gradient exceeds a set threshold, the charging current is gradually reduced. Intermediate regulation considers the imbalance within the battery pack and calculates the power difference that each cell needs to compensate for using a voltage consistency algorithm. Advanced regulation combines historical operating data to learn the optimal power allocation pattern under different operating conditions and establishes a case-based decision base. The optimization parameter set includes 28 parameters, such as the maximum allowable current for each cell, voltage protection threshold, and temperature compensation coefficient; each parameter is labeled with a valid timestamp and confidence score.

[0046] The sampling frequency adjustment system monitors the signal characteristics of sensor nodes and configures a programmable anti-aliasing filter, with the cutoff frequency dynamically adjusted according to the sampling rate. Signal path optimization employs an adaptive routing algorithm, dynamically selecting the optimal transmission path based on signal strength and quality indicators between nodes. The diffusion characteristic analysis module establishes a signal attenuation model, considering the impact of temperature on the transmission medium, and corrects the relationship curve between signal strength and distance. The dynamic parameter set generation unit comprehensively considers factors such as acquisition rate, path loss, and environmental interference, outputting an optimized scheme containing 16 adjustment parameters.

[0047] The calculation of charge concentration distribution incorporates a spatial interpolation algorithm to reconstruct a continuous distribution field from discrete node measurement data. The trend adjustment coefficient calculation module analyzes the spatiotemporal variation characteristics of the concentration gradient and identifies anomalous clustering areas. The distribution optimization algorithm iteratively adjusts the weights of sampling points and transmission paths to make the measurement results closer to the true distribution. The final dynamic measurement output includes data layers such as original sampled values, reconstructed distribution field, and confidence level assessment, supporting multiple data fusion methods.

[0048] In terms of system implementation, the power measurement module adopts a distributed architecture, with each battery branch configured with an independent monitoring unit. The main controller polls each monitoring unit via a high-speed serial bus, and the data acquisition cycle can be configured from 100ms to 10s. The communication protocol design supports data priority marking, with key parameter transmissions receiving the highest priority. The anomaly handling mechanism includes functions such as data verification, timeout retransmission, and channel switching to ensure the integrity and timeliness of the measurement data. Historical data storage adopts a combination of circular buffering and compressed archiving, retaining raw data for 24 hours and characteristic data for 30 days.

[0049] The parameter calibration system periodically executes an automatic calibration process to verify the accuracy of each measurement channel under standard load conditions. Calibration data is stored in non-volatile memory, including calibration time, environmental conditions, and correction factors. A self-diagnostic function continuously monitors the sensor status; when drift exceeds the allowable range, it automatically initiates a recalibration procedure. The measurement result output interface supports both analog and digital signals. The analog output provides a linear voltage signal from 0-5V, while the digital interface uses an isolated RS485 bus with a configurable transmission rate of 9600-115200bps.

[0050] Example 4: See Figure 5 This involves the generation of communication control datasets and system anomaly intervention mechanisms. The wireless communication quality monitoring system is equipped with a dual-channel receiver, with the main channel operating in the 2.4GHz band and the backup channel using the 868MHz band. Signal strength acquisition uses an RSSI detection circuit, with a sampling frequency set to 200Hz and a dynamic range covering -90dBm to -20dBm. The data offset detection module achieves this through 16-bit CRC checksum verification and sequence number comparison, establishing a sliding detection window containing 128 samples.

[0051] The communication channel quality assessment process scans 16 communication channels in real time, recording the background noise level and signal collision count for each channel. The channel selection algorithm is based on a quality scoring matrix and updates the preferred channel list every 30 seconds.

[0052] Timestamp Channel number Signal strength (dBm) Noise level (dBm) Bit error rate (%) 202308191030 11 -45.2 -92.1 0.12 202308191030 12 -67.8 -89.7 1.85 202308191030 13 -51.6 -91.3 0.31 202308191030 14 -42.3 -90.5 0.08 Volatility analysis employs an adaptive window mechanism, using a 60-second time window in stable environments and shortening it to 15 seconds in high-interference environments. The data filtering process utilizes a three-stage workflow: first, mean filtering removes impulse interference; second, mean filtering smooths random fluctuations; and finally, an FIR filter suppresses high-frequency noise. The transmission rate impact model establishes a two-dimensional relationship matrix, dividing signal fluctuation amplitude into ten levels, each corresponding to a different transmission efficiency correction coefficient.

[0053] The frequency band switching control employs a state machine model, defining three switching trigger conditions: loss of five consecutive data packets, average signal strength below -75dBm for 10 seconds, and bit error rate exceeding 2% for 30 seconds. The switching process includes three stages: channel scanning, quality assessment, and link reconstruction, with the total switching time controlled within 300 milliseconds. The power conditioning system is configured with 16 power output levels, with a minimum step of 0.5dBm, and the adjustment amplitude is calculated based on the deviation between the received signal strength and the target value.

[0054] The anomaly detection system employs a distributed architecture, deploying detection units in both the communication module and the main controller. The time feature analysis module statistically analyzes parameters such as the duration, interval, and frequency of anomaly events. The proportional calculation unit summarizes the anomaly type distribution in 15-minute time slices, generating a two-dimensional time-type distribution heatmap. The threshold adaptive mechanism includes two adjustment dimensions: adjusting temperature-related thresholds based on seasonal factors, and adjusting signal strength thresholds based on day / night cycles.

[0055] The detection range optimization employs a parameter linkage strategy, automatically expanding the detection frequency for a specific time period when anomalies are detected concentratedly. The frequency band adjustment parameter has three sensitivity levels, increasing to the highest sensitivity in high-interference areas. A dynamic threshold adjustment algorithm analyzes historical false alarm data; when the same type of false alarm is detected 20 times consecutively, the threshold for determining that anomaly type is automatically increased.

[0056] The intervention strategy generation module establishes a mapping relationship between anomaly types and handling measures, including a four-level response mechanism: Level 1 response adjusts communication parameters, Level 2 response switches energy supply mode, Level 3 response reduces sampling frequency, and Level 4 response initiates system reset. The data table structure is designed as a row-column matrix, with the vertical axis recording the status every 5 minutes according to the time axis, and the horizontal axis containing 17 parameter fields: time identifier, anomaly type code, occurrence location, duration, impact range marker, handling measure code, parameter adjustment amount, execution status flag, etc.

[0057] The real-time intervention system employs a dual-thread architecture: the monitoring thread continuously collects system status data, while the execution thread responds to exceptions. Control parameter transmission utilizes differential encoding, transmitting only the change amount data at a time. A feedback verification mechanism continuously monitors for three cycles after parameter adjustments, recording a processing effectiveness score once the anomaly is confirmed to have been eliminated. Historical data storage uses a hierarchical structure, retaining detailed records for the most recent 24 hours, while compressing and storing key feature values ​​from historical data.

[0058] In terms of system implementation, the communication control module adopts an SDR (Software Defined Radio) architecture, supporting dynamic reconfiguration of the communication protocol. The hardware platform integrates two RF chips: the main chip handles the 2.4GHz band, and the auxiliary chip handles the Sub-1GHz band. The baseband processing unit is equipped with a dual-core processor, responsible for signal processing and control logic respectively. The watchdog circuit features a three-level reset mechanism: a partial reset triggered by communication timeout, a module reset triggered by data verification errors, and a global reset triggered by system crash.

[0059] The parameter configuration interface supports remote updates, and the configuration data includes eight parameter groups such as frequency band allocation tables, power level tables, and handover threshold sets. The debugging interface outputs a real-time status stream, including 12 parameters such as channel quality indicators, bit error statistics, and power levels. The security mechanism uses AES-128 encryption to transmit critical commands, and authentication uses a two-way challenge-response protocol. System clock synchronization uses the NTP network time protocol, with time errors controlled within ±10 milliseconds.

[0060] Example 5: A complete process involving multi-source data fusion and system operation optimization. The data fusion module receives system anomaly intervention data tables from the anomaly handling module, and simultaneously acquires real-time data streams from current sensors, voltage sensors, temperature sensors, and humidity sensors in parallel. The sensor data synchronization mechanism adopts a hardware triggering method; the main controller sends a synchronization pulse signal, and all sensors perform synchronous sampling at the rising edge. The data preprocessing unit performs three-stage filtering on the raw signal: first, a digital notch filter is used to eliminate power frequency interference; second, a moving average filter is applied to suppress random noise; and finally, an amplitude limiter is used to remove abnormal abrupt values.

[0061] Data consistency analysis establishes a multi-dimensional feature space, defining analytical dimensions such as the current-voltage phase plane and temperature-time variation curves. A correlation detection algorithm calculates the correlation coefficient matrix between data from each dimension; when the current-voltage correlation coefficient falls below 0.85, a data re-acquisition process is triggered. The fusion parameter generation employs a hierarchical weighted strategy: the base layer assigns fixed weights based on the sensor's factory accuracy specifications, while the dynamic layer adjusts the weight coefficients according to the real-time signal-to-noise ratio. The initial weight for the current sensor is set to 0.45, the voltage sensor to 0.35, and the environmental sensor shares the remaining weights.

[0062] The weight optimization process introduces a fuzzy decision-making mechanism, defining five linguistic variables for data quality evaluation: extremely poor, poor, average, good, and excellent. The rule base contains 32 fuzzy inference rules, such as "if the current data quality is excellent and the voltage data quality is good, then the current weight increases by 0.05." Defuzzification uses the centroid method to calculate the final weight allocation, outputting a fusion parameter set containing the weight values ​​of each sensor. The unified data output interface is designed as a structure, including fields such as timestamp, fused data value, source data values, weight allocation table, and confidence score.

[0063] After receiving unified data output, the system optimization module first performs operating condition mode recognition. The mode classifier is built based on support vector machines, and the input features include 12-dimensional parameters such as average power, fluctuation coefficient, and temperature gradient. It identifies seven operating states, including charging mode, discharging mode, standby mode, and fault mode. The parameter adjustment strategy library presets three optimization schemes for each state, and the scheme selection is automatically updated based on historical operating performance scores.

[0064] The genetic algorithm optimization unit is activated during the parameter tuning phase. Chromosome encoding includes 16 parameters such as PWM frequency, duty cycle, sampling period, and communication interval. The fitness function comprehensively considers system efficiency, stability, and lifetime, with constraints limiting the maximum temperature rise to no more than 15℃. Evolutionary operations employ a tournament selection strategy, with a crossover probability of 0.85 and a mutation probability of 0.01. The optimization process continues for eight generations, maintaining a population size of fifty individuals per generation, ultimately outputting the parameter combination with the highest fitness.

[0065] The control instruction converter maps optimized parameters to executable commands. PWM parameters are implemented through timer reload values, and communication parameters are written to the protocol stack configuration register. Instruction distribution uses a combination of broadcast and fixed-point transmission, with critical control instructions subject to a triple verification mechanism. The execution status monitoring unit tracks instruction reception confirmation signals; nodes that fail to respond within a timeout trigger a retransmission process, and three consecutive failures mark the node as faulty.

[0066] The unified data output channel is configured with a dual-buffered structure. The front-end buffer stores the raw, fused data, while the back-end buffer stores the format-converted transmission data. The communication protocol chosen is the Modbus-TCP standard, and data frames include a start character, address code, function code, data field, and checksum. The transmission period is dynamically adjusted according to the system state, set to 1 second in steady state and shortened to 200 milliseconds in transient state. Error recovery mechanisms include automatic reconnection, protocol reset, and data retransmission; unsent data is automatically stored in the event of a network interruption.

[0067] At the system implementation level, the data fusion module adopts a multi-core processor architecture, allocating dedicated cores to process sensor data acquisition and independent cores to perform fusion calculations. The memory management unit divides the memory into secure and insecure areas, with key parameters stored in the secure area with ECC verification. The watchdog system has two levels of monitoring: an application-layer watchdog monitors the algorithm execution progress, and a hardware watchdog prevents system deadlock.

[0068] The calibration and maintenance interface supports both local and remote modes. Locally, it connects to the calibration device via a USB interface, while remotely, it receives calibration commands through an encrypted channel. The self-test program executes automatically at midnight daily, checking twenty items including sensor zero-point drift, ADC linearity, and memory integrity. The log system records all operational events, categorized by severity into four levels: debugging, information, warning, and error. Error-level events trigger immediate alarm notifications.

[0069] Historical data storage employs a segmented compression strategy. Recent data is stored in its original format, data older than three days is converted to differential encoding, and data older than thirty days undergoes Huffman compression. The storage medium is FRAM ferroelectric memory, with a write / erase cycle life of 1 billion cycles and a data retention period exceeding ten years. Data retrieval supports functions such as time range queries, anomaly type filtering, and parameter trend analysis. Query results can be exported as CSV files.

[0070] The system clock synchronization employs dual-source timing from GPS and the network, prioritizing the use of the GPS second pulse signal and using an NTP server as a backup. Timestamp accuracy reaches the microsecond level, with clock deviations at each node controlled within 100 microseconds. Operating mode switching includes both soft and hard switching mechanisms; normal state transitions utilize gradual effect control, while fault switching executes millisecond-level rapid switching. Final control commands are output via optocoupler isolation, driving power devices to perform corresponding operations.

[0071] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0072] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A solar-powered self-contained wireless communication power sensor system, characterized in that, The solar-powered self-powered wireless communication power sensor system includes: a solar energy acquisition module, which, based on ambient light intensity and temperature data, retrieves the output power parameters and voltage fluctuation data of the solar panels, analyzes the matching degree between energy acquisition efficiency and stability, allocates energy regulation values ​​and balancing parameters, and generates an energy distribution parameter set; an energy management module, which, based on the energy distribution parameter set, extracts the power storage value and gradient change of the battery cells, analyzes the impact of energy allocation on system stability, adjusts the energy storage balance, and generates an energy regulation optimization parameter set; a power measurement module, which, based on the energy regulation optimization parameter set, analyzes the current and voltage fluctuations of the sensor nodes, adjusts the sampling frequency and signal path distribution ratio, redistributes the acquisition trend and dynamic parameter values ​​of power data, and generates dynamic power measurement results; a communication control module, which, based on the dynamic power measurement results, extracts the signal strength fluctuation rate and data offset during wireless transmission, analyzes the impact of fluctuation range on data transmission efficiency, dynamically adjusts the path distribution and power ratio of the communication frequency band, and generates a communication regulation dataset; and an anomaly handling module, which, based on the communication regulation dataset, analyzes the anomaly ratio and occurrence time of the system's operating status, adjusts the parameters of the target detection range, and generates a system anomaly intervention data table.

2. The solar-powered self-supplied wireless communication power sensor system according to claim 1, characterized in that, The specific steps for obtaining the matching degree between energy harvesting efficiency and stability are as follows: Based on ambient light intensity and temperature data, extract the output power parameters, voltage fluctuation data, and temperature change data of the solar panel; set a time window, select time points to match the data, and obtain the output power parameters and voltage fluctuation data by comparing data correlation and filtering data; based on the output power parameters and voltage fluctuation data, perform matching verification on the energy path, calculate the difference between output power and voltage fluctuation, and correct the energy distribution and fluctuation distribution in combination with temperature changes; adjust the path parameters based on the influence of temperature changes on the data to obtain the matching degree between output power and voltage fluctuation; based on the matching degree between output power and voltage fluctuation, perform stability analysis, set stability analysis standards, evaluate the energy distribution under different temperature conditions in combination with the dynamic changes of system operation, compare stability indicators and optimize temperature conditions to obtain the matching degree between energy harvesting efficiency and stability.

3. The solar-powered self-powered wireless communication power sensor system according to claim 2, characterized in that, The steps for obtaining the energy distribution parameter set are as follows: Based on the matching degree of energy acquisition efficiency and stability, analyze the changes in energy transmission and stability of the system under differentiated environmental conditions, and perform weighted calculation on the energy distribution of the system to obtain the preliminary energy regulation requirements of the system; based on the preliminary energy regulation requirements of the system, analyze the energy balance between systems, identify the relationship between energy transmission efficiency and load distribution between systems, correct the system energy regulation parameters, calculate the adjusted energy output value of the equipment, and obtain the energy regulation dataset between systems; combining the energy regulation dataset between systems with the stability matching results, allocate energy between systems, optimize and match the required balance and stability requirements, and obtain the energy distribution parameter set.

4. The solar-powered self-powered wireless communication power sensor system according to claim 3, characterized in that, The specific steps for extracting the battery cell's energy storage value and gradient change are as follows: Based on the energy distribution parameter set, extract the battery cell's temperature data, filter temperature points within each time period, analyze temperature fluctuations by combining the temperature change trends of differentiated locations of the battery cell, and obtain the battery cell's temperature data; Based on the battery cell's temperature data, calculate the corresponding energy storage value for each temperature point, identify the energy change at each measurement point by analyzing the relationship between temperature and energy storage, compare the energy changes at differentiated locations by combining system structural parameters, and obtain energy storage distribution and gradient distribution data; Based on the energy storage distribution and gradient distribution data, analyze the overall energy storage distribution of the battery cell, optimize the energy gradient by combining temperature data, analyze the impact of energy changes on system performance, determine the stable energy configuration under differentiated operating conditions, and obtain the battery cell's energy storage value and gradient change.

5. The solar-powered self-powered wireless communication power sensor system according to claim 4, characterized in that, The steps for obtaining the energy regulation optimization parameter set are as follows: Based on the battery cell's stored energy value and gradient change, determine the time series of energy change, compare the current stored energy value with the original energy data, analyze the energy gradient at each moment, and define corresponding thresholds according to the system state partition to generate a preliminary energy change parameter set; analyze the preliminary energy change parameter set to analyze the impact of battery cell energy on the stability of system power output, identify the correlation between energy and power output, and calculate the segment power stability influence coefficient; by analyzing the segment power stability influence coefficient and combining it with the battery cell energy change parameters, adjust the energy storage balance, optimize the energy regulation data, and generate the energy regulation optimization parameter set.

6. The solar-powered self-powered wireless communication power sensor system according to claim 5, characterized in that, The specific steps for obtaining the dynamic measurement results of the generated power are as follows: Based on the energy regulation optimization parameter set, extract the signal acquisition data of the sensor nodes, monitor the acquisition rate of the signal at the differentiated nodes, infer the diffusion characteristics of the signal by combining external environmental factors such as time and temperature, define the acquisition and diffusion rate coefficients, and generate the acquisition and diffusion dynamic parameter set; analyze the influence of the acquisition and diffusion dynamic parameter set on the sampling frequency and distribution, optimize the ratio between the signal path and the sampling frequency according to the requirements of the node signal concentration distribution, calculate the adjustment coefficient of the power concentration distribution trend, and generate the power concentration regulation result; Analyze the power concentration control results, adjust the ratio between sampling frequency and signal path, allocate the power concentration distribution trend, and combine the acquisition diffusion parameters and adjustment coefficients to obtain the dynamic power measurement results.

7. The solar-powered self-powered wireless communication power sensor system according to claim 6, characterized in that, The specific steps for generating the communication control dataset are as follows: Based on the dynamic power measurement results, monitor the signal strength fluctuation rate and data offset during the real-time transmission process of the system, identify the fluctuation range, eliminate system fault anomalies, analyze the average fluctuation rate of the data, and obtain signal and data fluctuation data; analyze the impact of the signal and data fluctuation range on the data transmission rate, use the known transmission rate to analyze the relationship between the signal and data, calculate the transmission rate under the differentiated fluctuation range, and obtain the transmission rate impact data; based on the transmission rate impact data, dynamically adjust the path distribution and power ratio of the communication frequency band, adjust according to the relationship between the transmission rate impact data and the signal and data fluctuation range, allocate the communication frequency band and power control range, and generate the communication control dataset.

8. The solar-powered self-supplied wireless communication power sensor system according to claim 7, characterized in that, The specific steps for obtaining the system anomaly intervention data table are as follows: Based on the communication control dataset, analyze the system operation status anomalies and their occurrence times, collect anomaly proportion data at differentiated time points, organize the anomaly time distribution, analyze the proportion change trend, and classify the data to obtain system anomaly distribution data; Based on the system anomaly distribution data, adjust the target detection range parameters, analyze the optimal detection time and proportion distribution of system anomalies, and adjust the operation conditions of detection threshold, time, and frequency band by comparing the proportion changes under differentiated detection conditions to obtain target detection range parameters; Based on the target detection range parameters, adjust the detection conditions according to the current operation parameters, control the variable relationship of detection time, threshold, and frequency band, and perform real-time detection based on the adjusted parameters to obtain the system anomaly intervention data table.

9. The solar-powered self-sufficient wireless communication power sensor system according to claim 8, characterized in that, The system further includes: a data fusion module that extracts multi-source sensor data based on the system anomaly intervention data table, analyzes the correlation between the data, and fuses them to generate a unified data output; and a system optimization module that adjusts the system operating parameters based on the unified data output and generates the final control command.

10. The solar-powered self-powered wireless communication power sensor system according to claim 9, characterized in that, The specific steps for acquiring data in the data fusion module are as follows: based on the system anomaly intervention data table, extract current, voltage, and environmental data from sensor nodes, analyze data consistency, and generate a fusion parameter set; based on the fusion parameter set, compare the differences between data, optimize data weight allocation, and obtain unified data output.

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