A solar self-powered wireless communication power sensor system

Abstract

The application relates to the field of wireless sensing technology 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 abnormality processing module. The solar energy collection module analyzes the matching degree of energy collection efficiency and stability according to environmental 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 a sampling frequency and a signal path distribution proportion based on the parameter set, and generates an electric quantity dynamic measurement result; the communication control module dynamically adjusts a path distribution and a power ratio of a communication frequency band based on the measurement result, and generates a communication regulation and control data set; and the abnormality processing module analyzes a system abnormality state, and generates a system abnormality intervention data table. The system realizes efficient utilization, dynamic regulation and control and stable communication of energy, and improves overall reliability and adaptability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless sensing, in particular to a solar self-powered wireless communication electric quantity sensor system. BACKGROUND

[0002] In the field of modern Internet of Things and intelligent monitoring, the electric quantity sensor system is a key component for data acquisition and state monitoring. Its running stability and energy supply efficiency directly affect the reliability of the overall system. Traditional electric quantity sensor systems mostly rely on wired power supply or disposable batteries, which have problems such as complex wiring, high maintenance cost, and limited endurance. These defects are more prominent in remote areas, outdoor environments, or large-scale deployment scenarios.

[0003] To solve the power supply problem, some systems have begun to use solar power supply technology. However, existing solar power supply schemes have obvious shortcomings in energy management. For example, most systems can only simply convert solar energy into electrical energy storage, lacking dynamic response to environmental light intensity, temperature changes and other factors, resulting in low energy collection efficiency and difficulty in matching the real-time power consumption demand of sensor nodes. At the same time, the lack of energy distribution mechanism makes the battery unit prone to overcharging and overdischarging, which not only shortens the battery life, but also may cause system power failure, data loss and other risks.

[0004] In the communication and measurement link, the existing system has poor coordination. The sampling frequency and signal path of electric quantity measurement are often fixed and cannot be flexibly adjusted according to the energy supply situation, resulting in high power consumption even when energy is tight, further exacerbating the energy supply and demand contradiction. In the process of wireless communication, problems such as signal strength fluctuation and unstable data transmission efficiency are common, and there is a lack of linkage mechanism with the electric quantity state, making it difficult to guarantee the continuity and accuracy of data transmission. In addition, for abnormal states in system operation, such as sensor failure, communication interruption, energy supply anomaly, etc., the existing scheme mostly adopts simple alarm or restart method, lacking precise abnormal detection and intervention strategy, resulting in weak system fault tolerance and easy to be affected by external interference. SUMMARY

[0005] The present application aims to provide a solar self-powered wireless communication electric quantity sensor system to solve the problems raised in the background art.

[0006] To achieve the above object, the application provides a solar self-powered wireless communication electric quantity sensor system, which comprises a solar collection module, an energy management module, an electric quantity measurement module, a communication control module and an abnormality processing module.

[0007] Preferably, the step of analyzing the matching degree of the energy collection efficiency and stability comprises the following steps: according to the environmental light intensity and temperature data, extracting the output power parameter, voltage fluctuation data and temperature change data of the solar panel, setting a time window, selecting time point matching data, obtaining the output power parameter and voltage fluctuation data by comparing the data correlation and performing data filtering; based on the output power parameter and voltage fluctuation data, performing matching verification on the energy path, calculating the difference between the output power and voltage fluctuation, correcting the energy distribution and fluctuation distribution in combination with the temperature change, adjusting the path parameter through the influence of temperature change on the data, and obtaining the output power and voltage fluctuation matching condition; based on the output power and voltage fluctuation matching condition, performing stability analysis, setting a stability analysis standard, evaluating the energy distribution under different temperature conditions in combination with the dynamic change of system operation, comparing the stability index and optimizing the temperature condition, and obtaining the matching degree of the energy collection efficiency and stability.

[0008] Preferably, the obtaining of the energy distribution parameter set comprises: analyzing energy transmission and stability changes of the system under different environmental conditions based on the energy collection efficiency and stability matching degree, and performing weighted calculation on energy distribution of the system to obtain preliminary energy regulation requirements of the system; analyzing energy balance among the systems based on the preliminary energy regulation requirements of the system, identifying energy transmission efficiency and load distribution relationship among the systems, correcting energy regulation parameters of the system, calculating an adjusted energy output value of the equipment, and obtaining an energy regulation data set among the systems; and distributing energy among the systems in combination with the energy regulation data set among the systems and the stability matching result, optimizing and matching required balance and stability requirements, and obtaining the energy distribution parameter set.

[0009] Preferably, the obtaining of the power storage value and gradient change amount of the battery unit comprises: extracting temperature data of the battery unit based on the energy distribution parameter set, screening temperature points in each time period, analyzing temperature fluctuation in combination with temperature change trends of different positions of the battery unit, and obtaining temperature data of the battery unit; calculating each temperature point and corresponding power storage value based on the temperature data of the battery unit, identifying power change amount of each measurement point by analyzing the relationship between temperature and power storage, comparing power change conditions of different positions in combination with system structure parameters, and obtaining power storage distribution and gradient distribution data; analyzing overall power storage distribution of the battery unit based on the power storage distribution and gradient distribution data, optimizing power gradient in combination with temperature data, analyzing the influence of power change on system performance, determining stable power configuration under different operation conditions, and obtaining the power storage value and gradient change amount of the battery unit.

[0010] Preferably, the obtaining of the energy regulation optimization parameter set comprises: determining a time sequence of power change based on the power storage value and gradient change amount of the battery unit, comparing the current power storage value with original power data, analyzing power gradient at each moment, generating a preliminary power change parameter set according to system state partition definition and corresponding threshold value; analyzing the preliminary power change parameter set, analyzing the influence of battery unit power on system power output stability, identifying the relevance between power and power output, and calculating a section power stability influence coefficient; adjusting power storage balance and optimizing power regulation data by analyzing the section power stability influence coefficient in combination with power change parameters of the battery unit, and generating the energy regulation optimization parameter set.

[0011] Preferably, the generating the power dynamic measurement result obtaining step specifically comprises: based on the energy regulation and optimization parameter set, extracting signal collection data of the sensor node, monitoring the collection rate of the signal at the differentiated node, combining external environmental factors of time and temperature to infer the diffusion characteristics of the signal, defining the collection and diffusion rate coefficient, generating the collection and diffusion dynamic parameter set; analyzing the influence of the collection and diffusion dynamic parameter set on the sampling frequency and distribution, optimizing the ratio between the signal path and the sampling frequency according to the demand of the node signal concentration distribution, calculating the adjustment coefficient of the power concentration distribution trend, generating the power concentration regulation result; analyzing the power concentration regulation result, adjusting the proportional relationship between the sampling frequency and the signal path, distributing the power concentration trend, combining the collection and diffusion parameters and the adjustment coefficient, and obtaining the power dynamic measurement result.

[0012] Preferably, the generating the communication regulation data set obtaining step specifically comprises: based on the power dynamic measurement result, monitoring the signal strength fluctuation rate and data offset in the real-time transmission process of the system, identifying the fluctuation range, eliminating system fault abnormal values, analyzing the average fluctuation rate of the data, and obtaining the signal and data fluctuation data; analyzing the influence of the signal and data fluctuation range on the data transmission rate, analyzing the relationship between the signal and the data by using the known transmission rate, calculating the transmission rate under the differentiated fluctuation range, and obtaining the transmission rate influence data; dynamically adjusting the path distribution and power ratio of the communication frequency band according to the transmission rate influence data, adjusting according to the relationship between the transmission rate influence data and the signal and data fluctuation range, distributing the communication frequency band and power control range, and generating the communication regulation data set.

[0013] Preferably, the generating the system abnormal intervention data table obtaining step specifically comprises: based on the communication regulation data set, performing system running state abnormality and occurrence time analysis, collecting abnormal proportion data at differentiated time points, arranging abnormal time distribution, analyzing the proportion change trend and classifying the data, and obtaining system abnormal distribution data; based on the system abnormal distribution data, adjusting the target detection range parameter, analyzing the optimal detection time and proportion distribution of the system abnormality, adjusting the detection threshold, time, and frequency band operation conditions by comparing the proportion change under the differentiated detection conditions, obtaining the target detection range parameter; based on the target detection range parameter, adjusting the detection conditions according to the current operation parameters, controlling the variable relationship of the detection time, threshold, and frequency band, performing real-time detection according to the adjusted parameters, and obtaining the system abnormal intervention data table.

[0014] Preferably, the system further comprises: a data fusion module based on the system abnormal intervention data table, extracting multi-source sensor data, analyzing the correlation between the data, and fusing to generate unified data output; and a system optimization module based on the unified data output, adjusting the system running parameters, and generating the final control instruction.

[0015] Preferably, the data fusion module's acquisition step is specifically: based on the system abnormal intervention data table, extracting the current, voltage and environmental data of the sensor node, analyzing the data consistency, and generating a fusion parameter set; based on the fusion parameter set, comparing the differences between the data, optimizing the data weight distribution, and obtaining unified data output.

[0016] Compared with the prior art, the beneficial effects of the present application are: through the cooperative work of multiple modules, the overall performance and adaptability of the system are significantly improved. The solar energy collection module dynamically calls the output power parameters and voltage fluctuation data of the solar panel according to the environmental light intensity and temperature data, analyzes the matching degree of energy collection efficiency and stability, and then distributes energy regulation values and balance parameters to generate an energy distribution parameter set. This process makes the utilization of solar energy more efficient, can adjust the energy collection strategy in real time according to environmental changes, ensures stable energy input under different light and temperature conditions, and reduces the problem of unstable energy supply caused by environmental fluctuations.

[0017] The energy management module extracts the power storage value and gradient change of the battery unit based on the energy distribution parameter set, analyzes the influence of energy distribution on system stability, adjusts the energy storage balance, and generates an energy regulation optimization parameter set. In this way, the charging and discharging process of the battery unit is finely managed, avoiding overcharging and overdischarging, prolonging the service life of the battery, and ensuring the balance of energy storage, so that the system can maintain stable energy supply in long-term operation, reducing system failures caused by battery problems.

[0018] The power measurement module analyzes the current and voltage fluctuations of the sensor node with the help of the energy regulation optimization parameter set, adjusts the sampling frequency and signal path distribution ratio, redistributes the collection trend and dynamic parameter value of the power data, and generates power dynamic measurement results. This makes the power measurement adapt to the energy supply situation, and can increase the sampling frequency to obtain more detailed power data when the energy is sufficient, and reduce the sampling frequency to save energy when the energy is tight, achieving dynamic balance between measurement accuracy and energy consumption, ensuring the accuracy of power data and avoiding unnecessary energy waste.

[0019] The communication control module extracts the signal strength fluctuation rate and data offset in the wireless transmission process based on the power dynamic measurement results, analyzes the influence 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 data set. This mechanism allows wireless communication to adaptively adjust according to the power state and signal conditions, ensuring data transmission quality while reasonably allocating communication power, reducing energy consumption caused by unstable signals or excessive power, and improving the stability and efficiency of data transmission.

[0020] The abnormality processing module analyzes the abnormality proportion and occurrence time of the system running state based on the communication regulation data set, adjusts the parameters of the target detection range, and generates a system abnormality intervention data table. Through accurate analysis and timely intervention of the system abnormal state, problems occurring in the running process can be quickly identified and processed, reducing the influence of the abnormal state on the system, improving the fault tolerance and reliability of the system, and ensuring the continuous and stable operation of the system. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 A timing diagram of the solar self-powered wireless communication power sensor system according to the present application;

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

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

[0024] Figure 4 A flowchart for generating an energy regulation optimization parameter set;

[0025] Figure 5 A flowchart for generating a communication regulation data set. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0027] Please refer to Figure 1 The present application provides a solar self-powered wireless communication power sensor system, which comprises a solar energy collection module, an energy management module, a power measurement module, a communication control module and an abnormality processing module.

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

[0029] Embodiment 1: refer to Figure 2 , relates to the analysis process of the matching degree of energy collection efficiency and stability in the solar energy collection module. The process realizes the conversion of environmental parameters to energy characteristics parameters through multi-stage data processing. The system configures TSL2561 digital light intensity sensor and DS18B20 temperature sensor to form an environmental monitoring unit. The two types of sensors are connected to the host controller through I2C bus and single bus protocol respectively, and the data acquisition period is fixed at 5 seconds. The light intensity monitoring adopts an automatic range switching mechanism, which maintains a measurement accuracy of ±5% in the range of 0-40000 lux. The temperature monitoring points are distributed on the back plate of the solar cell panel and the surrounding environment, forming a temperature gradient monitoring network.

[0030] The solar cell panel output characteristic modeling is based on a single diode equivalent circuit. The host controller calculates the theoretical maximum power point voltage in real time. This calculation process introduces a temperature compensation coefficient to correct the open circuit voltage parameter. The actual output power is obtained through a four-wire measurement method. The voltage sampling uses a 12-bit ADC to collect at a frequency of 1 kHz, and the current sampling uses a 0.1Ω precision shunt in combination with an instrumentation amplifier. The voltage fluctuation analysis uses a sliding window algorithm with a window width of 10 seconds. The standard deviation of the voltage values in the window is calculated as the fluctuation quantization indicator. The temperature influence modeling establishes a three-dimensional parameter space, and generates a temperature-voltage-power mapping table through an interpolation algorithm.

[0031] The stability evaluation mechanism includes a dynamic threshold adjustment function. The power fluctuation threshold is initially set to ±5% of the nominal value, and automatically expanded to ±8% when the environmental light change rate exceeds 10% / minute. The evaluation algorithm uses a state machine model, which defines three states: normal, warning, and abnormal. When the threshold range is exceeded for three consecutive sampling periods, a stability alarm is triggered. The matching degree synthesis unit integrates three evaluation elements: the power output efficiency is calculated by the ratio of actual power to theoretical maximum power, the voltage stability is represented by the inverse of the fluctuation coefficient, and the temperature influence factor is converted according to the degree of deviation from the 25°C reference. The weight distribution of each element uses an adaptive strategy, which increases the temperature factor weight to 0.4 in high temperature environment, and maintains balanced weight of 0.33 for each factor in normal environment.

[0032] The data processing flow includes an outlier filtering link, and the ±3σ range outside the sampling points is removed by using the Laplace criterion. The time point matching algorithm ensures that the light, temperature and electrical parameter data are strictly time-synchronized through timestamp alignment technology. The data correlation analysis calculates the correlation coefficient of light-power and temperature-voltage, and when the correlation coefficient is lower than 0.7, the data reacquisition process is started. The output parameter calibration module periodically executes the self-correction program, compares the measured value with the reference value under standard test conditions, and generates a parameter correction coefficient table.

[0033] The temperature compensation system includes two-stage correction mechanism, the primary correction is based on the linear compensation of the panel temperature coefficient, and the secondary correction introduces a nonlinear compensation term to handle extreme temperature conditions. When the panel temperature is detected to be higher than 60℃, the thermal runaway protection mode is activated, and the matching degree calculation will limit the highest score to not more than 80 points. The stability index optimization algorithm analyzes the historical operation data, and automatically adjusts the evaluation criteria in different temperature intervals, and relaxes the voltage fluctuation tolerance to ±7% in the low temperature interval of-10℃ to 10℃.

[0034] The entire analysis process finally outputs the dual indicators of energy harvesting efficiency score and stability level, the efficiency score range of 0-100 points corresponds to the efficiency value of 0-100%, and the stability level is divided into five levels A-E. The score result and the energy distribution parameters establish a mapping relationship, when the score is lower than 60 points or the stability level is D / E level, the system automatically reduces the energy harvesting priority and switches to the battery power supply mode. The output parameter set includes 12 parameters such as real-time efficiency value, stability coefficient, temperature influence factor, which are transmitted to the energy management module through the serial peripheral interface.

[0035] Embodiment 2: see Figure 3 , relates to the generation process of the energy distribution parameter set and the battery cell parameter extraction mechanism. The system initialization stage loads the pre-stored battery pack configuration parameters, including the number of series-connected monomers, the number of parallel branches, the rated capacity of monomers and other structural information. The energy transmission modeling is based on the improved node voltage analysis method, and a circuit network model is constructed, which includes wire impedance, contact resistance and internal equivalent resistance. The model describes the energy flow path through the impedance parameter matrix, and the matrix dimension strictly corresponds to the battery pack topology.

[0036] The environmental adaptation analysis unit receives the energy distribution parameter set from the solar energy collection module, analyzes the temperature gradient data, light intensity variation curve and stability score contained therein. The differential environment simulator generates twelve typical working condition combinations, covering a temperature range of-20°C to 60°C and a light intensity interval of 100-1000W / m². For each working condition, the system performs energy transmission simulation, records the branch current distribution and node voltage fluctuation data. The weighted calculation module introduces a time decay factor, gives higher weight to the recent load history data, and solves the optimal energy distribution scheme by using a constrained optimization algorithm.

[0037] The balanced state monitoring system is configured with a high-precision voltage acquisition circuit, and each battery monomer is equipped with an independent sampling channel. A 24-bit ADC synchronously acquires all monomer terminal voltages at a frequency of 10Hz. The differential pressure analysis algorithm calculates the maximum voltage deviation in the battery pack in real time, and activates the active balancing control when a voltage difference of more than 50mV is detected. The balancing strategy adopts an energy transfer type architecture, which transfers charge between adjacent monomers through a bidirectional DC-DC converter. The load prediction module integrates a Kalman filter, which predicts the power demand trend in the next five minutes based on real-time load data collected by the current sensor.

[0038] The battery parameter extraction system includes a distributed temperature sensing network, which is arranged at key positions of the battery pack, including the pole connection, the geometric center point and the edge area. The temperature acquisition uses PT1000 platinum resistance with a constant current source circuit, and the measurement resolution is 0.1°C. The temperature field reconstruction algorithm generates a three-dimensional temperature distribution cloud map of the battery pack through finite element interpolation. The power mapping unit establishes a temperature-capacity relationship database, and adopts differentiated open-circuit voltage-state-of-charge corresponding curves for different temperature intervals.

[0039] The power gradient analysis uses time domain differential technology to calculate the power change rate every minute. The dynamic threshold setting mechanism automatically adjusts according to the battery health state, and the new battery sets a SOC change tolerance band of ±5%, and the aged battery expands to ±8%. The gradient distribution modeling divides the battery pack into sixteen virtual sections, and independently calculates the power change trend of each section. The performance impact evaluation unit analyzes the correlation between the power gradient and the output ripple, and automatically adjusts the load distribution weight of the specific section when detecting a sharp fluctuation in the power of the section.

[0040] The energy regulation optimization process adopts a hierarchical decision structure. The primary optimization adjusts the DC-DC converter operating point based on real-time power data, so that the output current accurately tracks the reference value. The secondary optimization considers the battery aging factor, estimates the capacity attenuation degree through cycle counter and internal resistance measurement data, and dynamically limits the maximum charge and discharge current. The tertiary optimization performs energy redistribution, and automatically transfers energy load to the battery branch with better state when detecting local overheating or overcharging risk.

[0041] The parameter set generation module integrates all optimization results and outputs an energy regulation optimization parameter set containing 36 parameters such as branch current set value, single cell voltage protection threshold, and temperature compensation coefficient. The parameter set is transmitted to the lower module through a dual-redundant CAN bus, with a transmission period of 1 second, and a data frame containing a CRC32 check code and a serial number identifier. The historical parameter storage unit retains the optimization parameter records of the last 24 hours, supporting parameter rollback and comparative analysis functions.

[0042] Embodiment 3: refer to Figure 4 , which relates to the process of dynamic measurement of electric quantity and generation of energy regulation optimization parameter set. The system uses a coulomb measurement chip to monitor the battery charging and discharging process, and the built-in 16-bit delta-sigma ADC realizes high-precision charge measurement with a minimum resolution of 0.05 mAh. The time series analysis module establishes an autoregressive integrated moving average model to process the electric quantity data, and the model input contains the electric quantity sampling sequence of the past 15 minutes, and the output predicts the electric quantity change trend in the next 5 minutes. The gradient calculation uses an improved five-point difference method to calculate the electric quantity change rate within the time window [t-2Δt, t+2Δt], effectively suppressing the influence of measurement noise.

[0043] In the electric quantity change parameter extraction process, the system defines three working state partitions: normal zone, transition zone, and warning zone. The partition threshold is dynamically set according to the battery type. For lithium iron phosphate batteries, the normal zone is set to 20%-80% SOC range, the transition zone is 10%-20% and 80%-90%, and the warning zone is 0%-10% and 90%-100%. The state recognition algorithm monitors the direction and amplitude of the electric quantity gradient in real time, and triggers a warning signal when a sustained and rapid decrease in electric quantity is detected and enters the transition zone. The parameter set generation unit records the duration, number of entries, and gradient change characteristics of each state partition, forming a multi-dimensional electric quantity change feature vector.

[0044] The power stability analysis uses a frequency domain analysis method to perform a 4096-point fast Fourier transform on the output current signal. The frequency spectrum feature extraction module focuses on the 1 Hz-1 kHz frequency band, and calculates the proportion of each harmonic component in the total energy. The stability influence coefficient η is calculated by the following formula: ; in the formula: represents the amplitude of the th harmonic component, is the weight coefficient of the corresponding frequency point, represents the electric quantity change rate of the th battery cell, is the upper limit of the harmonic number analyzed, is the total number of battery cells. The coefficient reflects the correlation between electric quantity fluctuation and output harmonics, with a value range of 0-1, and the larger the value, the more significant the influence.

[0045] The power regulation optimization process adopts a hierarchical progressive strategy. The primary regulation is based on real-time power gradient adjustment of charging and discharging current. When a positive gradient is detected beyond a set threshold, the charging current is gradually reduced. The intermediate regulation considers the internal imbalance of the battery pack and calculates the power difference that each cell needs to compensate through a voltage consistency algorithm. The advanced regulation combines historical operating data to learn the optimal power distribution pattern under different working conditions and establishes a case-based decision library. The optimization parameter set includes 28 parameters such as the maximum allowed current of each cell, voltage protection threshold, temperature compensation coefficient, etc., each parameter is labeled with a valid timestamp and a confidence score.

[0046] The sampling frequency adjustment system monitors the signal characteristics of the sensor nodes, configures a programmable anti-aliasing filter, and dynamically adjusts the cutoff frequency with the sampling rate. The signal path optimization uses an adaptive routing algorithm to dynamically select the best 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 signal strength and distance relationship curve. The dynamic parameter set generation unit integrates factors such as sampling rate, path loss, and environmental interference to output an optimization scheme containing 16 adjustment parameters.

[0047] The power concentration distribution calculation introduces a spatial interpolation algorithm to reconstruct the continuous distribution field from discrete node measurement data. The trend adjustment coefficient calculation module analyzes the spatio-temporal variation characteristics of the concentration gradient and identifies abnormal aggregation areas. The distribution optimization algorithm adjusts the weights of sampling points and transmission paths through iterative calculation to make the measurement results closer to the true distribution. The final output of dynamic measurement results includes original sampling values, reconstructed distribution field, confidence assessment, and other data layers, 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 through a high-speed serial bus, and the data acquisition period can be configured from 100ms to 10s. The communication protocol design supports data priority marking, with critical parameter transmission enjoying the highest priority. The exception handling mechanism includes data verification, timeout retransmission, channel switching, and other functions to ensure the integrity and timeliness of measurement data. Historical data storage uses a combination of circular buffering and compressed archiving, with raw data retained for 24 hours and feature data saved for 30 days.

[0049] The parameter calibration system performs an automatic calibration procedure periodically to verify the accuracy of each measurement channel under standard load conditions. The calibration data is stored in non-volatile memory, including calibration time, environmental conditions, correction coefficients, and other information. The self-diagnosis function continuously monitors the sensor state, and when it detects that the drift exceeds the allowed range, it automatically starts the recalibration program. The measurement result output interface supports both analog and digital forms. The analog output provides a linear voltage signal of 0-5V, and the digital interface uses an isolated RS485 bus with a transmission rate configurable from 9600 to 115200bps.

[0050] Example 4: see Figure 5 , relates to communication regulation data set generation and system abnormal intervention mechanism. The wireless communication quality monitoring system configures a dual-channel receiver, the main channel works at 2.4GHz frequency band, and the standby channel uses 868MHz frequency band. The signal strength acquisition uses RSSI detection circuit, and the sampling frequency is set to 200Hz, and the dynamic range covers-90dBm to-20dBm. The data offset detection module is realized by 16-bit CRC check and serial number comparison, and a sliding detection window containing 128 samples is established.

[0051] The communication channel quality evaluation process scans 16 communication channels in real time, records the background noise level and signal conflict times of each channel. The channel selection algorithm is based on a quality score matrix, and the preferred channel list is updated every 30 seconds.

[0052] Time stamp 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 The volatility analysis uses an adaptive window mechanism, using a 60-second time window in stable environments and shortening to 15 seconds in high interference environments. The data filtering link is configured with a three-level processing procedure: first, apply median filtering to remove impulse interference, second, perform mean filtering to smooth random fluctuations, and finally, suppress high-frequency noise through a FIR filter. The transmission rate influence model establishes a two-dimensional relationship matrix, dividing the signal fluctuation amplitude into ten levels, each level corresponding to different transmission efficiency correction coefficients.

[0053] The frequency band switching control uses a state machine model, defining three switching trigger conditions: 5 consecutive data packet losses, average signal strength below-75dBm for 10 seconds, and bit error rate exceeding 2% for 30 seconds. The switching process includes channel scanning, quality evaluation, and link reconstruction, with a total switching time controlled within 300 milliseconds. The power regulation 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 of the received signal strength from the target value.

[0054] The anomaly detection system adopts a distributed architecture, and the detection units are deployed in the communication module and the main controller respectively. The time feature analysis module counts the duration, interval time, and occurrence frequency of abnormal events. The proportion calculation unit aggregates the abnormal type distribution in 15-minute time slices to generate a time-type two-dimensional distribution heat map. The threshold adaptive mechanism has two adjustment dimensions: adjusting the temperature-related threshold based on seasonal factors and adjusting the signal strength threshold based on the day-night mode.

[0055] The detection range optimization adopts a parameter linkage strategy. When abnormal events are found to occur concentratedly in a specific time period, the detection frequency of that time period is automatically expanded. The frequency band adjustment parameter sets three sensitivity levels, which is raised to the highest sensitivity in high interference areas. The threshold dynamic adjustment algorithm analyzes historical false positive data. When the same type of false positive is detected for 20 consecutive times, the judgment threshold of this abnormal type is automatically increased.

[0056] The intervention strategy generation module establishes a mapping relationship between abnormal types and processing measures, including a four-level response mechanism: the first-level response adjusts the communication parameters, the second-level response switches the energy supply mode, the third-level response reduces the sampling frequency, and the fourth-level response starts the system reset. The data table structure is designed in a row-column matrix form, recording the status every 5 minutes along the time axis, and horizontally containing 17 parameter fields: time identifier, abnormal type code, occurrence location, duration, impact range marker, processing measure code, parameter adjustment amount, execution status flag, etc.

[0057] The real-time intervention system adopts a double-thread architecture, with the monitoring thread continuously collecting system status and the execution thread responding to abnormal instructions. The control parameter transmission adopts differential encoding technology, transmitting only the change amount data each time. The feedback verification mechanism continuously monitors for three cycles after parameter adjustment, and records the processing effect score after confirming that the anomaly is eliminated. The historical data storage adopts a hierarchical structure, with the last 24 hours of data retaining detailed records and the historical data being stored in compressed form with key feature values.

[0058] In terms of system implementation, the communication control module adopts the SDR software-defined radio architecture, supporting dynamic reconfiguration of communication protocols. The hardware platform integrates two RF chips, with the main chip handling the 2.4 GHz frequency band and the auxiliary chip responsible for the Sub-1 GHz frequency band. The baseband processing unit is equipped with a dual-core processor, responsible for signal processing and control logic respectively. The watchdog circuit sets a three-level reset mechanism: local reset triggered by communication timeout, module reset triggered by data verification error, and global reset triggered by system crash.

[0059] The parameter configuration interface supports remote update, and the configuration data contains eight types of parameter groups such as frequency band allocation table, power level table, and switching threshold set. The debugging interface outputs real-time state stream, containing 12 parameters such as channel quality index, error code statistics, and power level. The security mechanism uses AES-128 encryption to transmit key instructions, and uses a two-way challenge-response protocol for identity authentication. The system clock synchronization uses the NTP network time protocol, and the time error is controlled within ±10 milliseconds.

[0060] Embodiment 5: A complete process involving multi-source data fusion and system operation optimization. The data fusion module receives the system abnormal intervention data table from the abnormality processing module, while simultaneously collecting real-time data streams from current sensors, voltage sensors, temperature sensors, and humidity sensors. The sensor data synchronization mechanism uses a hardware trigger method, and the main controller sends a synchronization pulse signal. All sensors perform synchronous sampling at the rising edge. The data preprocessing unit performs three-stage filtering on the original signal: first, a digital notch filter is used to eliminate power frequency interference, second, a sliding average filter is used to suppress random noise, and finally, an amplitude limiter is used to eliminate abnormal mutation values.

[0061] The data consistency analysis establishes a multi-dimensional feature space, defining analysis dimensions such as current-voltage phase plane and temperature-time variation curve. The correlation detection algorithm calculates the correlation coefficient matrix between dimensions, and triggers data reacquisition when the current-voltage correlation coefficient is less than 0.85. The fusion parameter generation uses a hierarchical weighting strategy. The base layer assigns fixed weights based on sensor factory precision indicators, and the dynamic layer adjusts weight coefficients based on real-time signal-to-noise ratio. The initial weight of the current sensor is set to 0.45, the voltage sensor is 0.35, and the remaining weight is shared by environmental sensors.

[0062] The weight optimization process introduces a fuzzy decision mechanism, defining five linguistic variables for data quality evaluation: very 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 increase the current weight by 0.05". Defuzzification uses the barycenter method to calculate the optimal weight distribution, outputting a fusion parameter set containing the weight values of each sensor. The unified data output interface is designed as a structure, containing fields such as timestamp, fusion data value, source data value, weight distribution table, and confidence score.

[0063] After receiving the unified data output, the system optimization module first performs operating mode recognition. The mode classifier is based on support vector machines, and the input features include 12 parameters such as average power, fluctuation coefficient, and temperature gradient. Seven operating states are identified, 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 operation effect scores.

[0064] The genetic algorithm optimization unit is activated in the parameter adjustment phase, and the chromosome code contains 16 parameters such as PWM frequency, duty cycle, sampling period, and communication interval. The fitness function considers system efficiency, stability, and life span, and sets a constraint condition to limit the maximum temperature rise to no more than 15°C. The evolution operation adopts a tournament selection strategy, with a crossover probability of 0.85 and a mutation probability of 0.01. The optimization process lasts for eight iterations, with a population size of fifty individuals per generation, and the final output is the parameter combination with the highest fitness.

[0065] The control instruction converter maps the optimized parameters to executable commands, and the PWM parameters are implemented through timer reload values, while the communication parameters are written to the protocol stack configuration registers. The instruction distribution adopts a combination of broadcast and point-to-point transmission, and the key control instructions are attached with a triple verification mechanism. The execution state monitoring unit tracks the instruction reception confirmation signal, and the timeout unresponsive node triggers the retransmission process, and three consecutive failures mark the node as faulty.

[0066] The unified data output channel is configured with a double-buffer structure, with the front-end buffer storing the original fused data and the back-end buffer storing the transmission data after format conversion. The Modbus-TCP standard is selected for the communication protocol, and the data frame contains the start symbol, address code, function code, data field, and check code. 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. The error recovery mechanism includes automatic reconnection, protocol reset, and data retransmission functions, and automatically stores unsent data when the network is interrupted.

[0067] At the system implementation level, the data fusion module uses a multi-core processor architecture, with dedicated cores handling sensor data acquisition and independent cores performing fusion calculations. The memory management unit divides the system into a secure area and a non-secure area, with critical parameters stored in the secure area with ECC verification. The watchdog system has two levels of monitoring, with the application layer watchdog monitoring algorithm execution progress and the hardware watchdog preventing system deadlock.

[0068] The calibration and maintenance interface supports both local and remote modes, with local calibration through a USB interface and remote calibration through an encrypted channel. The self-test program is automatically executed at midnight every day, with twenty test items including sensor zero drift, ADC linearity, and memory integrity. The log system records all operation events, classified into four levels of debug, information, warning, and error, with error-level events triggering immediate alarm notifications.

[0069] The historical data storage adopts a segmented compression strategy, the recent data is stored in the original format, the data exceeding three days is converted into a differential encoding format, and the data exceeding thirty days is executed Huffman compression. The storage medium is selected as FRAM ferroelectric memory, the erase-write life is up to 1 billion times, and the data retention period is more than ten years. The data retrieval supports time range query, abnormal type filtering, parameter trend analysis and other functions, and the query result can be exported as a CSV format file.

[0070] The system clock synchronization adopts GPS and network dual-source time service, preferentially uses the GPS second pulse signal, and backs up the NTP server synchronization. The timestamp precision reaches the microsecond level, and the clock deviation of each node is controlled within 100 microseconds. The running mode switching includes two mechanisms of soft switching and hard switching, the normal state conversion adopts gradual dynamic control, the fault switching executes millisecond-level fast switching. The final control instruction is output through optical coupling isolation, and drives the power device to perform corresponding operation.

[0071] It should be noted that, in this text, relational terms such as first and second are used merely to distinguish one entity or action from another, without necessarily requiring or implying any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof are intended to cover non-exclusive inclusions, so that a process, method, article, or apparatus including a series of elements includes not only those elements, but also other elements not explicitly listed, or inherent to such a process, method, article, or apparatus.

[0072] Although the embodiments of the present application have been shown and described, it can be understood by those of ordinary skill in the art that various changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and spirits of the present application, and the scope of the present application 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-contained wireless communication power sensor system includes: The solar energy acquisition module, based on ambient light intensity and temperature data, calls upon 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 a set of energy distribution parameters; The energy management module, based on the energy distribution parameter set, extracts the energy storage value and gradient change of the battery cells, analyzes the impact of energy distribution on system stability, adjusts the energy storage balance, and generates an energy regulation optimization parameter set. The power measurement module, 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 power data acquisition trend and dynamic parameter values, and generates dynamic power measurement results. The communication control module, 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 control dataset. The anomaly handling module, based on the communication control dataset, analyzes the anomaly ratio and occurrence time of the system operation status, adjusts the parameters of the target detection range, and generates a system anomaly intervention data table. The specific steps for obtaining the matching degree between the energy harvesting efficiency and stability are as follows: Based on ambient light intensity and temperature data, the output power parameters, voltage fluctuation data, and temperature change data of the solar panels are extracted. A time window is set, and time points are selected to match the data. By comparing the data correlation and filtering the data, the output power parameters and voltage fluctuation data are obtained. Based on the output power parameters and voltage fluctuation data, the energy path is matched and verified, the difference between output power and voltage fluctuation is calculated, the energy distribution and fluctuation distribution are corrected by combining temperature changes, and the path parameters are adjusted by the influence of temperature changes on the data to obtain the matching status of output power and voltage fluctuation. Based on the matching of output power and voltage fluctuation, a stability analysis is performed, a stability analysis standard is set, and the energy distribution under different temperature conditions is evaluated in combination with the dynamic changes of system operation. The stability indexes are compared and the temperature conditions are optimized to obtain the degree of matching between energy harvesting efficiency and stability. The specific steps for obtaining the energy distribution parameter set are as follows: Based on the matching degree between the energy harvesting efficiency and stability, the energy transmission and stability changes of the system under different environmental conditions are analyzed, and the energy distribution of the system is weighted and calculated to obtain the preliminary energy regulation requirements of the system. Based on the initial energy regulation requirements of the system, the energy balance between systems is analyzed, the relationship between energy transmission efficiency and load distribution between systems is identified, the system energy regulation parameters are corrected, the energy output value of the equipment after adjustment is calculated, and the energy regulation dataset between systems is obtained. By combining the energy regulation dataset between systems with the stability matching results, the energy between systems is allocated, optimized, and matched to the required equilibrium and stability requirements, resulting in a set of energy distribution parameters.

2. The solar-powered self-supplied wireless communication power sensor system according to claim 1, characterized in that, The specific steps for extracting the battery cell's stored energy value and gradient change amount are as follows: Based on the energy distribution parameter set, the temperature data of the battery cells are extracted, the temperature points in each time period are filtered, and the temperature fluctuation is analyzed by combining the temperature change trend of the battery cells at different locations to obtain the temperature data of the battery cells. Based on the temperature data of the battery cell, the corresponding energy storage value is calculated for each temperature point. By analyzing the relationship between temperature and energy storage, the energy change at each measurement point is identified. Combined with the system structure parameters, the energy change at different locations is compared to obtain energy storage distribution and gradient distribution data. Based on the energy storage distribution and gradient distribution data, the overall energy storage distribution of the battery cell is analyzed, the energy gradient is optimized by combining temperature data, the impact of energy changes on system performance is analyzed, the stable energy configuration under differentiated operating conditions is determined, and the energy storage value and gradient change of the battery cell are obtained.

3. The solar-powered self-powered wireless communication power sensor system according to claim 2, characterized in that, The specific steps for obtaining the energy regulation optimization parameter set are as follows: Based on the battery cell's stored power value and gradient change, the time series of power change is determined, the current stored power value is compared with the original power data, the power gradient at each moment is analyzed, and corresponding thresholds are defined according to the system state partition to generate a preliminary power change parameter set. The preliminary set of power change parameters is analyzed to determine the impact of battery cell power on system power output stability, identify the correlation between power and power output, and calculate the influence coefficient of power stability in the section. By analyzing the power stability influence coefficient of the aforementioned section and combining it with the battery cell charge change parameters, the balance of charge storage is adjusted, the charge regulation data is optimized, and an energy regulation optimization parameter set is generated.

4. The solar-powered self-powered wireless communication power sensor system according to claim 3, characterized in that, The specific steps for obtaining the dynamic measurement results of the generated electricity are as follows: Based on the energy regulation optimization parameter set, the signal acquisition data of the sensor nodes are extracted, the acquisition rate of the signal at the differentiated nodes is monitored, the diffusion characteristics of the signal are inferred by combining the external environmental factors of time and temperature, the acquisition and diffusion rate coefficients are defined, and the acquisition and diffusion dynamic parameter set is generated. The influence of the acquisition and diffusion dynamic parameter set on the sampling frequency and distribution is analyzed. Based on the requirements of the node signal concentration distribution, the ratio between the signal path and the sampling frequency is optimized, the adjustment coefficient of the power concentration distribution trend is calculated, and the power concentration control result is generated. 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.

5. The solar-powered self-powered wireless communication power sensor system according to claim 4, characterized in that, The specific steps for obtaining the communication control dataset are as follows: Based on the power dynamic measurement results, the signal strength fluctuation rate and data offset during the real-time transmission process of the monitoring system are identified, the fluctuation range is identified, system fault abnormal values ​​are eliminated, the average fluctuation rate of the data is analyzed, and signal and data fluctuation data are obtained. The influence of the fluctuation range of the signal and data on the data transmission rate is analyzed. Using the known transmission rate, the relationship between the signal and data is analyzed, the transmission rate under the different fluctuation range is calculated, and the data on the influence of the transmission rate are obtained. Based on the transmission rate impact data, the path distribution and power ratio of the communication frequency band are dynamically adjusted. Adjustments are made according to the relationship between the transmission rate impact data and the signal and data fluctuation range. Communication frequency bands and power control ranges are allocated, and a communication regulation dataset is generated.

6. The solar-powered self-powered wireless communication power sensor system according to claim 5, characterized in that, The specific steps for obtaining the system anomaly intervention data table are as follows: Based on the aforementioned communication control dataset, an analysis of system operation status anomalies and their occurrence times is performed. Anomaly proportion data at different time points is collected, the distribution of anomaly times is organized, the trend of proportion changes is analyzed, and the data is classified to obtain system anomaly distribution data. Based on the system anomaly distribution data, the target detection range parameters are adjusted, the optimal detection time and proportion distribution of system anomalies are analyzed, and the operation conditions of detection threshold, time and frequency band are adjusted by comparing the proportion changes under differentiated detection conditions to obtain the target detection range parameters. Based on the target detection range parameters, the detection conditions are adjusted according to the current operating parameters, the variable relationship between detection time, threshold, and frequency band is controlled, and real-time detection is performed according to the adjusted parameters to obtain a system anomaly intervention data table.

7. The solar-powered self-powered wireless communication power sensor system according to claim 6, characterized in that, The system also includes: The data fusion module extracts multi-source sensor data based on the system anomaly intervention data table, analyzes the correlation between the data, and fuses them to generate unified data output; Based on the unified data output, the system optimization module adjusts the system operating parameters and generates the final control command.

8. The solar-powered self-supplied wireless communication power sensor system according to claim 7, characterized in that, The specific steps for obtaining the data fusion module are as follows: Based on the system anomaly intervention data table, current, voltage and environmental data of sensor nodes are extracted, data consistency is analyzed, and a fusion parameter set is generated; Based on the fusion parameter set, the differences between data are compared, the data weight allocation is optimized, and a unified data output is obtained.

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