Optical-storage-diesel integrated power supply, monitoring and power scheduling method

Abstract

The invention relates to the technical field of new energy power supply, and discloses a light-storage-diesel integrated power supply, monitoring and power scheduling method, which comprises a power supply method based on a planar honeycomb structure, a partition dynamic reconstruction monitoring method and a direct power scheduling method. According to the method, a regular hexagonal frame array is laid on the surface of a photovoltaic panel to form a planar honeycomb structure, and a'multi-stage self-adaption-active fault tolerance 'operation and maintenance framework is constructed. According to the power supply method, a plurality of sensor arrays are configured, an equipment-component-module evaluation system is constructed, and adaptive switching of system operation modes is realized based on health indexes. According to the monitoring method, adjacent hexagonal units are combined into microscopic, mesoscopic and macroscopic three-level monitoring units, a hierarchical-progressive data processing model is adopted, and a dynamic reconstruction mechanism is triggered when key parameters exceed a preset threshold value. According to the power scheduling method, a three-level power management architecture is established based on a honeycomb structure, and a direct power supply mode from a power generation unit to an electricity load is realized by using a multi-level real-time matching algorithm.

Description

Technical Field

[0001] The present invention relates to the field of new energy, and particularly to a photovoltaic, energy storage and diesel generator set integrated power supply technology based on a planar honeycomb structure, and more particularly to a method for integrated power supply, monitoring and power dispatch of photovoltaic, energy storage and diesel generator set, which is applicable to intelligent microgrids, distributed energy systems, off-grid power supply systems and scenarios requiring highly reliable power supply. Background Art

[0002] With the deepening of energy transformation and the continuous increase in the proportion of renewable energy, the integrated power supply mode of photovoltaic power generation, energy storage systems and traditional diesel generator sets has become an important development direction of distributed energy systems. This integrated power supply system with multi-energy complementarity can effectively balance the volatility and intermittency of renewable energy, improve the reliability and stability of the power supply system, and is especially suitable for remote areas, islands, communication base stations and industrial scenarios requiring uninterrupted power supply.

[0003] In the prior art, for example, application publication number CN 118432051 A discloses an integrated photovoltaic, energy storage and diesel generator power supply system and its power supply method. This system coordinates and controls the operation strategies of the power grid, photovoltaic, diesel generator and energy storage. When the power grid is powered off, the photovoltaic cannot generate electricity, and the battery power is too low, the diesel generator supplies power to the load and charges the battery; during the process of the diesel generator shutting down for refueling, the energy storage system supplies power, so as to achieve continuous and uninterrupted power supply. This technology makes full use of the high dynamic stability control characteristics of power electronics technology, improves the power quality of power supply through a hybrid inverter, improves the power supply reliability to a certain extent, and avoids the use limitations of photovoltaic and energy storage.

[0004] However, the existing integrated photovoltaic, energy storage and diesel generator power supply systems still have the following technical challenges in terms of intelligent and refined operation and maintenance: In terms of the health assessment mechanism: Although it has basic monitoring functions, it mainly relies on single-index evaluation, lacks intelligent fusion analysis of multi-dimensional features, has insufficient evaluation accuracy for equipment performance degradation, and the accuracy of predictive maintenance needs to be improved; In terms of the monitoring architecture: Existing monitoring systems mostly adopt regular grid or random dot layout, lack systematic and geometric optimization design, resulting in uneven monitoring coverage, and it is difficult to achieve efficient monitoring and accurate anomaly location of large-area photovoltaic arrays; In terms of the energy dispatch strategy: Existing dispatch algorithms can already achieve basic energy balance and uninterrupted power supply, but there is still room for improvement in multi-objective optimization, the adaptive ability to environmental and load changes is limited, and the overall system efficiency can be further improved; In terms of the fault handling mechanism: It already has basic fault detection and protection functions, but the ability to accurately locate and quickly isolate faults needs to be improved, the intelligent level of the system recovery strategy is insufficient, and the collaborative handling ability of multiple faults needs to be strengthened; System adaptability: The existing system has basic mode switching functions, but it lacks adaptability to complex working conditions, lacks a flexible performance tailoring mechanism, and the system reconstruction capability needs to be improved.

[0005] In order to further improve the performance and reliability of the photovoltaic-storage-diesel integrated power supply system, it is necessary to: build an intelligent health assessment system based on multi-dimensional feature fusion, develop an adaptive multi-objective energy optimization scheduling strategy, achieve accurate fault location and rapid isolation, and improve the intelligent operation and maintenance level of the system. Summary of the invention

[0006] The purpose of the present invention is to address the deficiencies of the prior art and to provide a highly reliable adaptive photovoltaic-storage-diesel fusion power supply system and its cellular monitoring and power scheduling method. By constructing a "multi-level adaptive-active fault-tolerant" intelligent operation and maintenance architecture and an innovative cellular monitoring structure, accurate perception of system status, preventive maintenance of faults and efficient coordination of multiple energy sources are achieved, effectively solving the technical problems of traditional power supply systems in monitoring accuracy, fault prevention and energy utilization efficiency.

[0007] In order to achieve one of the above purposes, the present invention adopts the following technical solution: A highly reliable adaptive photovoltaic-storage-diesel fusion power supply method based on a planar honeycomb structure includes: constructing a planar honeycomb monitoring network, laying a regular hexagonal frame array on the surface of the photovoltaic panel to form a planar honeycomb structure, configuring various sensor arrays in the honeycomb structure, and collecting system operation status data in real time; performing health status assessment, calculating the health index of each component through a multi-dimensional feature fusion algorithm; implementing an adaptive degradation control strategy, automatically switching between multiple operation modes according to the health index; optimizing the energy scheduling plan, using a distributed energy management strategy to optimize the energy flow distribution, and realizing multi-energy coordinated and complementary power supply. This method significantly improves the system's state perception and autonomous adjustment capabilities through the combination of a honeycomb monitoring architecture and multi-level adaptive control.

[0008] In order to achieve one of the above purposes, the present invention also adopts the following technical solution: A partitioned dynamic reconstruction monitoring method based on a planar honeycomb structure includes: constructing a honeycomb multi-level monitoring network, laying a regular hexagonal frame array on the surface of the photovoltaic panel to form a planar honeycomb structure, combining multiple adjacent hexagonal units into three-level monitoring units of micro, meso and macro; performing real-time status monitoring and collecting various operating parameters; implementing dynamic reconstruction control, when the key parameters exceed the preset threshold, triggering the reconstruction mechanism, performing local, regional or global reconstruction; performing multi-level data analysis, using a "layered-progressive" data processing model to evaluate the system health status and risk level. This method realizes multi-level adaptive monitoring from micro to macro, greatly improving the accuracy and timeliness of fault diagnosis.

[0009] To achieve one of the above objects, the present invention also adopts the following technical solutions: A direct power scheduling method based on a planar honeycomb structure, comprising: constructing a honeycomb hierarchical power management system, laying a regular hexagonal frame array on the surface of a photovoltaic panel to form a planar honeycomb structure, and establishing a three-level power management architecture based on this structure; performing real-time power monitoring, and automatically starting a grouping and recombination mechanism when environmental or power parameters change beyond a threshold; implementing dynamic power scheduling, using a multi-level real-time matching algorithm to adjust power distribution according to load requirements; optimizing system operation efficiency and improving energy utilization efficiency. This method realizes a direct power supply mode from a power generation unit to an electrical load through intelligent grouping and dynamic power distribution of a photovoltaic array, avoiding the process of energy storage and reconversion in a traditional system, and significantly improving system efficiency.

[0010] Compared with the prior art, the present invention has the following remarkable beneficial effects: (1) Innovative planar honeycomb monitoring structure: The present invention designs a honeycomb monitoring architecture with a regular hexagonal frame array laid on the surface of a photovoltaic panel. This structure makes full use of geometric characteristics to realize a multi-level monitoring network from micro to macro. Compared with traditional point or grid monitoring methods, the honeycomb structure has better spatial coverage and uniformity, can realize regionalized fine monitoring of energy parameters, and greatly improves monitoring accuracy and fault location ability.

[0011] Health assessment method with multi-dimensional feature fusion: The present invention adopts a health assessment method with multi-dimensional feature fusion such as temperature, vibration, and acoustics. Feature fusion is carried out through an improved Dempster-Shafer evidence theory, and a dynamic weight adjustment strategy is introduced to make the assessment results more objective and accurate. Under complex environments and diverse working conditions, the assessment accuracy of this method is 15%-25% higher than that of traditional single-feature assessments.

[0012] Hierarchical adaptive control strategy: The present invention realizes automatic switching between full-power mode, optimization mode, economic mode, emergency mode, and protection mode based on a health index. The system can adaptively adjust the operation mode according to the health status to achieve smooth degradation of performance, avoiding complete shutdown caused by sudden failures in traditional systems. Practical applications show that this strategy can improve system availability to over 99.95%.

[0013] Partition dynamic reconstruction technology: The partition dynamic reconstruction monitoring method proposed by the present invention can automatically adjust the combination mode of monitoring units according to monitoring data, so as to execute the most suitable reconstruction strategy for different types and ranges of abnormal conditions. This dynamic reconstruction ability enables the system to more accurately locate and analyze the spatial distribution characteristics of performance degradation, and the fault diagnosis time is shortened by 55%-70% compared with traditional methods.

[0014] Direct power dispatch mode: The honeycomb direct power dispatch method of the present invention realizes a direct power supply mode from the power generation unit to the electrical load by establishing a three-level power management architecture at the micro, meso, and macro levels. This method avoids the intermediate link of energy storage and reconversion in the traditional system, improves the overall system efficiency by 5.8%, increases the energy utilization rate by 7.2%, and reduces the operation and maintenance cost by 12.5% at the same time.

[0015] Optimized reflective wall design: The present invention innovatively introduces an adjustable reflective wall design in the honeycomb structure, including various specifications such as standard type, enhanced type, and adjustable type. It can automatically adjust the height and angle of the reflective wall according to the light conditions, optimize the light distribution, reduce the hot spot effect, and effectively improve the power generation efficiency and service life of the photovoltaic system.

[0016] Significant comprehensive benefits: Practical application verification shows that compared with the traditional integrated power supply system of photovoltaic, energy storage, and diesel, the fault prediction accuracy of the technical solution of the present invention has increased by 22%, the system reliability has increased by 15%, the maintenance cost has been reduced by 30%, the energy utilization rate has increased by 12%, and the overall economic benefit has increased by more than 18%.

[0017] In summary, the highly reliable and adaptive integrated power supply system of photovoltaic, energy storage, and diesel based on a planar honeycomb structure and its monitoring and power dispatch method provided by the present invention comprehensively solve the technical bottlenecks of traditional power supply systems in aspects such as monitoring accuracy, fault prevention, and energy coordination through the combination of innovative structural design and intelligent algorithms, providing a new technical path for the efficient and reliable operation of distributed energy systems. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is a partial system operation flow chart of Embodiment 1; Figure 2 It is a partial system operation flow chart of Embodiment 1; Figure 3 It is the system operation flow chart of Embodiment 2; Figure 4 It is the system operation flow chart of Embodiment 3; Figure 5 It is the system operation flow chart of Embodiment 4; Figure 6 It is a schematic diagram of the honeycomb structure for laying photovoltaic panels.

[0020] Reference numerals: health assessment unit 10, adaptive degradation unit 20, fault tolerance control unit 30, mode management unit 40, sound sensor 11, vibration sensor 12, temperature sensor 13, temperature sensor array 13-1, vibration sensor array 12-1, sound sensor array 11-1. Detailed implementation manners

[0021] The following will be combined with Figures 1 - 6 , and the preferred implementation manners of the present invention will be described in detail. It should be noted that the following description is only the preferred embodiments of the present invention, rather than a limitation on the present invention. Those skilled in the art should understand that various modifications and variations can be made to the present invention without departing from the spirit and scope of the present invention. The protection scope of the present invention should be subject to the appended claims.

[0022] Embodiment 1: This embodiment provides a highly reliable adaptive optical storage and diesel hybrid power supply system and its control method. The system realizes the autonomous and reliable operation of the system by constructing an intelligent operation and maintenance architecture of "multi-level adaptive - active fault tolerance". As Figure 1 shown, the power supply system adopts a hierarchical distributed control structure, including four major functional units: health assessment unit 10, adaptive degradation unit 20, fault tolerance control unit 30, and mode management unit 40. Among them, the health assessment unit 10 monitors the states of all components of the system in real time and outputs a health index (Health Index, HI); the adaptive degradation unit 20 dynamically adjusts the operation mode of the system based on the health index, supporting the adaptive switching among full power mode, optimization mode, economic mode, emergency mode, and protection mode; the fault tolerance control unit 30 ensures that a single point of failure does not affect the overall power supply through modular redundant design and intelligent partition isolation technology; the mode management unit 40 is responsible for optimizing the mode switching strategy and multi-path dynamic scheduling of the energy flow, achieving the optimal balance between system performance and reliability.

[0023] Among them, the health assessment unit 10 serves as the state perception center of the system and constructs a three-level health assessment system for equipment-component-module. This unit configures a sound sensor 11, a vibration sensor 12, and a temperature sensor 13 to collect the operation status data of the photovoltaic system 01, the energy storage system 02, and the diesel generator set 03 in real time, and calculates the health index (HI) of each level of equipment through a multi-dimensional feature fusion algorithm. The adaptive degradation unit 20, based on the health index, adopts a modular redundant design and intelligent partition isolation technology to automatically adjust the operation mode when detecting a decrease in the equipment HI, realizing the smooth degradation of the system performance. The fault-tolerant control unit 30 ensures the continuous and stable operation of the power supply system in the case of a single-point fault through a "1+1" redundant configuration and a fast isolation circuit (response time <5ms). The mode management unit 40, based on the distributed energy management strategy, optimizes the energy flow distribution under each operation mode in real time, maximizing the system operation efficiency while ensuring the power supply reliability.

[0024] Referring to Figure 2 , in some specific embodiments, the monitoring configuration of the health assessment unit 10 for the photovoltaic system 01, the energy storage system 02, and the diesel generator set 03 is as follows: The monitoring configuration of the photovoltaic system 01 adopts a multi-module adaptive architecture, including a temperature sensor array 13-1, a vibration sensor array 12-1, and a sound sensor array 11-1. Among them, the temperature sensor array 13-1 adopts a "three-level adaptive monitoring architecture": intelligent infrared thermal imagers (scanning range 120°, resolution 640×480 pixels) are arranged at the four corners and the center of the photovoltaic array, supporting the adaptive sampling frequency adjustment based on the health index, and real-time monitoring of the surface temperature distribution of the photovoltaic panel components to detect the hot spot effect in a timely manner; a "1+1" redundant design is adopted on the backplane of the photovoltaic panel components, and dual-mode PT100 temperature sensors are arranged every 1 meter, improving the monitoring reliability through cross-validation between sensors; an intelligent temperature display coating with a thickness of 0.3 mm is coated on the surfaces of key equipment such as the busbar box and the inverter, and a backup temperature sensor is configured to achieve fault-tolerant monitoring of key nodes. The vibration sensor array 12-1 adopts a master-slave monitoring architecture: two sets of three-axis acceleration sensors (master sensor and backup sensor) are set at the foundation of the photovoltaic support, and by comparing the data differences between the master and backup sensors in real time, when the difference exceeds a preset threshold (such as 10%), cross-validation is triggered to improve the data reliability; a "2+1" redundant arrangement of the vibration sensor array 12-1 is adopted at the top and bottom of the inverter cabinet, and any sensor failure can ensure the monitoring function through the redundancy design. The sound sensor array 11-1 adopts an intelligent redundant arrangement method, and 3 high-precision microphones are staggered at a distance of 1 meter around the inverter, and the acquisition accuracy of sound characteristics is improved through the adaptive beamforming technology.

[0025] The health assessment unit 10 calculates the health index (HI) of each component of the system using a multi-dimensional feature fusion algorithm. The specific implementation method is as follows: 1. Calculation method of the health index (HI): The value range of the health index is [0, 1], where 1 indicates that the device is in the best working state, 0 indicates that the device has completely failed, and intermediate values indicate that the device is in a state of performance degradation to varying degrees. This index is calculated through multi-dimensional feature fusion and is used to characterize the overall operating state of the device. The specific calculation process includes two main steps: basic feature extraction and feature fusion calculation: Step 1. The basic feature extraction part includes: (1) Extraction and calculation of the temperature feature (FT): Temperature sensors are arranged at key points of the photovoltaic array to collect real-time temperature data; Calculate the temperature mean Tm: The arithmetic mean of the temperature values of N measurement points is calculated, that is, Tm = ∑Ti / N, where Ti is the temperature value of the i-th measurement point; Calculate the temperature variance σt: The temperature variance (σt) characterizes the degree of dispersion of the temperature distribution through the standard variance calculation formula σt = √[∑(Ti - Tm)² / N]. Here: Ti represents the specific temperature value of the i-th measurement point (in °C), Tm is the arithmetic mean temperature of all measurement points, calculated through Tm = ∑Ti / N; (Ti - Tm)² in the formula represents the square of the deviation of each measurement point from the average temperature. This design can not only eliminate the influence of the cancellation of positive and negative deviations but also increase the weight of outliers; ∑(Ti - Tm)² / N represents the average squared deviation, which is standardized by dividing by the total number of measurement points N, making the results of different numbers of measurement points comparable; finally, taking the square root √ is to return the unit to the original temperature unit and reflect the actual average deviation degree. For example, for the five measurement points [42, 44, 43, 45, 41] °C on the photovoltaic panel, first calculate the average temperature of 43 °C, then calculate the sum of squared deviations (42 - 43)² + (44 - 43)² + (43 - 43)² + (45 - 43)² + (41 - 43)² = 10, divide by the number of measurement points 5 to get 2, and finally take the square root to get the standard deviation of about 1.4 °C, which indicates that the temperature distribution is relatively uniform. This index is of great significance in the photovoltaic system and can be used to detect hot spot effects in a timely manner, evaluate the heat dissipation performance, and warn of potential component failures; Calculate the temperature gradient Gt: Calculate the temperature change rate between adjacent measurement points through the formula Gt = ΔT / ΔL, where ΔT represents the temperature difference between adjacent measurement points, and ΔL represents the actual distance between these two points.

[0026] To make the temperature characteristics comparable under different working conditions, the system adopts standardization processing: the standardized value of the temperature mean Tm_norm is calculated by (Tm - Tm_min) / (Tm_max - Tm_min), where Tm_min and Tm_max are the lowest and highest operating temperatures allowed by the system respectively; the standardized value of the temperature variance σt_norm is calculated by 1 - σt / σt_max, where σt_max is the maximum temperature variance allowed by the system; the standardized value of the temperature gradient Gt_norm is calculated by 1 - Gt / Gt_max, where Gt_max is the maximum temperature gradient allowed by the system. The final temperature characteristic FT is calculated by weighted fusion: FT = 0.4×Tm_norm + 0.3×σt_norm + 0.3×Gt_norm. This weight configuration reflects the different degrees of influence of the mean, variance, and gradient on the health state of the device. Taking a certain photovoltaic panel as an example, when the temperatures at 5 temperature measurement points are [42, 44, 43, 45, 41] °C, the calculated temperature mean is 43 °C, the temperature variance is 1.4 °C, and the temperature gradient is 2 °C / m; after standardizing these parameters with respect to the system standard values (standard operating temperature 40 °C, allowed maximum variance and maximum gradient), Tm_norm = 0.92, σt_norm = 0.88, Gt_norm = 0.85 are obtained; finally, the calculated temperature characteristic FT = 0.4×0.92 + 0.3×0.88 + 0.3×0.85 = 0.887. This value indicates that the temperature characteristics of this photovoltaic panel are within the healthy state range.

[0027] (2) Extraction and calculation of vibration characteristics (FV): The acquisition of vibration characteristics (FV) is based on the data acquisition of a three-axis acceleration sensor, and the sampling frequency is set to 1 kHz to ensure data accuracy. Its core parameters include: the vibration amplitude A is calculated by the root mean square value method, that is, A = √(∑xi² / N), where xi represents the time-domain sampling value. This calculation method can effectively reflect the average energy level of vibration; the system obtains the frequency spectrum distribution S through fast Fourier transform (FFT) analysis to evaluate the frequency characteristics of vibration; at the same time, the maximum absolute value within the sampling period is recorded as the acceleration peak P to monitor instantaneous shock conditions.

[0028] To make these parameters comparable, the system uses standardization processing: the standardized value A_norm of the vibration amplitude is calculated by 1 - A / A_max, where A_max is the maximum vibration amplitude allowed by the system; the standardized value S_norm of the spectral characteristics represents the matching degree between the current spectrum and the spectrum under the standard operating state; the standardized value P_norm of the acceleration peak is calculated by 1 - P / P_max, where P_max is the maximum acceleration value allowed by the system. The final calculation of the vibration characteristics uses a weighted fusion method: FV = 0.35×A_norm + 0.35×S_norm + 0.3×P_norm, and this weight configuration reflects the relative importance of the amplitude, spectrum, and peak value in the evaluation of the equipment state.

[0029] Illustrated by an actual case: the vibration data collected on a certain occasion shows that the vibration amplitude is 0.5g (relative to the standard range of 0 - 1g), the matching degree between the spectral distribution and the standard mode reaches 95%, and the acceleration peak is 1.2g (the maximum value allowed by the system is 2g); after standardization processing, A_norm = 0.90, S_norm = 0.95, and P_norm = 0.90 are obtained; the final calculated vibration characteristic value FV = 0.35×0.90 + 0.35×0.95 + 0.3×0.90 = 0.92, and this value indicates that the vibration characteristics of the equipment are within the good state range.

[0030] (3) Extraction and calculation of acoustic characteristics (FS): The acquisition of acoustic characteristics (FS) is achieved through a high-precision microphone array, and the sampling frequency is set to 44.1kHz to ensure the integrity of the sound signal. Its core parameters include: the sound pressure level L is calculated by the formula L = 20×log10(P / P0), where P represents the measured sound pressure and P0 is the reference sound pressure (the standard value is 20μPa), and this logarithmic calculation method is consistent with the human ear perception characteristics; the system simultaneously extracts the frequency characteristics F, including the identification of the main frequency and the analysis of the full spectrum, for evaluating the frequency composition of the sound; in addition, the waveform of the sound wave W needs to be analyzed, and the total harmonic distortion is calculated to evaluate the quality of the sound signal.

[0031] To achieve data comparability under different working conditions, the system adopts standardization processing: the standardized value L_norm of the sound pressure level is calculated by 1 - |L - L_std| / L_max, where L_std is the sound pressure level under the standard operating state and L_max is the maximum allowable deviation; the standardized value F_norm of the frequency characteristics represents the matching degree between the current frequency characteristics and the standard operating state; the standardized value W_norm of the sound wave form is calculated by 1 - the distortion degree, which directly reflects the purity of the sound wave. The final calculation of the acoustic characteristics adopts a weighted fusion method: FS = 0.4×L_norm + 0.3×F_norm + 0.3×W_norm. This weight configuration reflects the relative importance of the sound pressure level, frequency characteristics, and waveform quality in the evaluation of the equipment state.

[0032] Illustrated with an actual case: When a certain equipment is running, the measured sound pressure level is 65 dB (relative to the standard operating value of 60 dB), the matching degree of the frequency characteristics with the standard mode reaches 85%, and the total harmonic distortion is 2% (the system requirement is less than 3%); after standardization processing, L_norm = 0.90 (indicating that the sound pressure level is close to the standard value), F_norm = 0.85 (indicating that the frequency characteristics basically meet the expectations), and W_norm = 0.90 (indicating good waveform quality); finally, the calculated acoustic characteristic value FS = 0.4×0.90 + 0.3×0.85 + 0.3×0.90 = 0.88. This value indicates that the acoustic characteristics of the equipment are within the normal operating range.

[0033] Step 2. In the feature fusion calculation part, the improved Dempster - Shafer evidence theory is used for feature fusion: (1) Basic calculation formula for the health index: HI = w1·FT + w2·FV + w3·FS; Where: w1 is the weight coefficient of the temperature feature (FT); w2 is the weight coefficient of the vibration feature (FV); w3 is the weight coefficient of the acoustic feature (FS).

[0034] (2) Weight coefficient configuration rules: The weight coefficients satisfy the normalization constraint: w1 + w2 + w3 = 1; the value range of each weight coefficient is [0,1]; to maintain feature diversity, the minimum value of a single weight is not less than 0.2; to avoid a single feature dominating, the maximum value of a single weight does not exceed 0.5.

[0035] (3) Dynamic adjustment strategy for weight coefficients: a) Adjustment in case of sensor abnormality: When abnormal data of a certain type of sensor is detected, automatically reduce the weight of the corresponding feature by 20% - 50%; The weights of other features are proportionally increased to ensure that the sum of the weights remains 1; Example: When the temperature sensor is abnormal, w1 is reduced from 0.4 to 0.2, and w2 and w3 are increased from 0.3 to 0.4 respectively; b) Adjustment based on historical data: Regularly analyze the operation data of the past 1000 hours; adjust the weights according to the prediction accuracy of each feature; for every 10% increase in accuracy, the corresponding weight can be increased by 0.05.

[0036] Example: When the accuracy of the vibration feature reaches 95%, w2 can be increased to 0.4, and at the same time, w1 and w3 are reduced proportionally; c) Environmental adaptability adjustment: Dynamically adjust the weight configuration according to the working conditions; increase the weight of the temperature feature in a high - temperature environment; increase the weight of the vibration feature in a high - vibration environment; the weight adjustment step does not exceed 0.1 to ensure the system stability; Evaluation criteria for the health index: According to the numerical range of the health index, the equipment status is divided into the following levels: [0.9, 1.0]: The equipment status is excellent, and all indicators are within the best range; [0.75, 0.9): The equipment status is good, and the indicators deviate slightly from the best values; [0.6, 0.75): The equipment status is average, and the change trend needs to be closely monitored; [0.3, 0.6): The equipment status is poor, and it is recommended to carry out maintenance and repair; [0, 0.3): The equipment status is dangerous, and immediate handling is required; 0: The equipment is completely failed and needs to be shut down for maintenance.

[0037] Through the above - mentioned method, the system can evaluate the health status of the equipment in real - time and accurately, providing a reliable basis for preventive maintenance and operation optimization. This method has the characteristics of strong self - adaptability and high reliability, and can adapt to different operating environments and working conditions.

[0038] 2. Mode - switching strategy of the adaptive degradation unit 20: (1) Definition of operating modes: Full - power mode: The system operates at the maximum efficiency, and all devices work normally; Optimization mode: The performance of some devices shows slight degradation, and high - efficiency operation is maintained through optimized control; Economic mode: The device performance has significantly declined, and the system reliability is given priority; Emergency Mode: In case of a failure of the core equipment, standby resources are activated to ensure basic power supply. Protection Mode: When there is a serious risk of system failure, the system operates at a minimum level.

[0039] (2) Mode Switching Criteria: HI ≥ 0.9 represents the full-power mode; 0.75 ≤ HI < 0.9 represents the optimized mode; 0.6 ≤ HI < 0.75 represents the economic mode; 0.3 ≤ HI < 0.6 represents the emergency mode; HI < 0.3 represents the protection mode.

[0040] 3. Specific Implementation of the Fault Tolerant Control Unit 30: (1) "1+1" Redundancy Configuration: The main controller and the standby controller operate in parallel; a dual-bus architecture is adopted to ensure communication redundancy; key sensors adopt a dual operating mode to support cross-verification.

[0041] (2) Fast Isolation Circuit Design: A solid-state switch is used to quickly isolate the fault point, with a response time < 5ms; an electrical isolation design based on opto-coupling is adopted, with an isolation voltage > 2500V; overcurrent and overvoltage protection functions are integrated, and the protection threshold is programmable.

[0042] 4. Distributed Energy Management Strategy: (1) Energy Scheduling Optimization Objective: min J = α·Cost + β·Loss + γ·Risk; Where: Cost is the system operation cost, Cost = Σ(PiEi + Si), Pi is the operating power of the i-th device, Ei is the unit energy cost of the i-th device, and Si is the start-stop cost of the i-th device; Loss is the energy transmission loss, Loss = Σ(Ri·Ii²), Ri is the equivalent resistance of the i-th transmission line, and Ii is the transmission current of the i-th line; Risk is the system reliability risk degree, Risk = 1 - Π(1 - ri·hi), Π represents the product of all terms, i is the inherent failure rate of the i-th device, and hi is the health index impact factor of the i-th device; α: Cost weight coefficient, reflecting the importance of economic requirements; β: Loss weight coefficient, reflecting the importance of energy efficiency requirements; γ: Risk weight coefficient, reflecting the importance of reliability requirements. α + β + γ = 1. For example, the system state at a certain moment is as follows: Cost = 100 yuan / hour (equipment operation cost); Loss = 5kW (transmission loss); Risk = 0.2 (system risk degree); α = 0.5, β = 0.3, γ = 0.2; Then the objective function value J = 0.5×100 + 0.3×5 + 0.2×0.2 = 51.54; the system will attempt to minimize this value by adjusting the device operating parameters.

[0043] (2) Constraints: Power balance constraint: Ppv + Pbat + Pdie = Pload + Ploss, where: Ppv is the output power of the photovoltaic system; Pbat is the charge and discharge power of the energy storage system; Pdie is the output power of the diesel generator set; Pload is the load demand power; Ploss is the system loss power; Device operation constraint: Pmin,i ≤ Pi ≤ Pmax,i, where Pmin,i and Pmax,i are the minimum and maximum operating powers of the i-th device respectively; Energy storage capacity constraint: SOCmin ≤ SOC(t) ≤ SOCmax, where SOC(t) is the state of charge of the energy storage system at time t, and SOCmin and SOCmax are the allowed minimum and maximum states of charge respectively; Reliability index constraint: R(t) ≥ Rmin, where R(t) is the reliability index of the system at time t; Rmin is the minimum reliability level required by the system.

[0044] (3) Solution method: The system uses an improved particle swarm optimization algorithm (IPSO) to solve the energy scheduling optimization problem. The main steps are as follows: Step 1. Initialization phase: Determine the solution dimension: n = number of devices × number of scheduling periods; Set the initial population: with a size of 50; Set the initial particle positions: representing the power allocation schemes of each device; Set the initial velocity: randomly generated within the allowed range; Step 2. Iterative optimization phase: Calculate the fitness: Evaluate the quality of the solution according to the objective function value; Update the optimal solution: Track the individual historical optimum and the global optimum of the group; Update the particle state: Adjust the velocity and position of the particle based on the inertia weight and acceleration factor; Step 3. Termination conditions: Reach the maximum number of iterations of 200 times; No obvious improvement in 50 consecutive iterations (improvement amplitude < 10⁻ 6 ); Meet all constraint conditions and the objective function meets the requirements; Through the above optimization process, the system can obtain the optimal energy scheduling scheme that meets the constraint conditions. This method has the characteristics of fast convergence speed and high calculation efficiency, and is suitable for real-time energy scheduling optimization.

[0045] Example 2 Based on Example 1, the health assessment unit 10 proposed a "honeycomb monitoring structure for photovoltaic panels" for the photovoltaic system 01. This structure forms a planar honeycomb structure by laying a regular hexagonal border array on the surface of the photovoltaic panel, and based on this, an adaptive evaluation method for multi-dimensional feature fusion is constructed. This design that combines structural innovation with the evaluation method breaks through the limitations of traditional single-index evaluation and constructs a multi-mode evaluation system that supports performance tailoring.

[0046] See Figure 2 , on the photovoltaic panel surface, the system uses regular hexagon frames 300 (inner circle diameter 0.5 m) arranged closely to form a continuous honeycomb structure. The width of each border is 10 - 15 mm, and the height is 15 - 20 mm. An installation groove for installing sensors is provided on the inner side of each border, and the installation groove can adopt a dovetail groove structure to ensure firm installation of the sensors. A connection card slot is provided on the upper plane of each border. The width of the connection card slot is 12 mm, and the depth is 8 mm. It adopts a double-layer stepped structure. The upper layer is used for the preliminary positioning of the reflective wall, and the lower layer is provided with an elastic locking mechanism to ensure the firm installation of the reflective wall. The reflective surface of the reflective wall faces the central area of the hexagon frame and is used to adjust the light distribution in this area.

[0047] A reflective wall that is fixed after being inserted is provided in the connection card slot. The reflective wall has various specifications, including: Standard type: height 50 mm, inner inclination angle 15°, made of high-reflectivity aluminum alloy material (reflectivity > 92%), with a chrome-plated surface treatment; Enhanced type: height 80 mm, inner inclination angle 30°, made of a composite material structure, with a directional reflective surface on the front and a scattering surface on the back; Adjustable type: height adjustable from 60 - 100 mm, inclination angle adjustable from 0 - 45°, designed with a telescopic structure.

[0048] In a preferred implementation, the reflective wall adopts an intelligent drive structure, including: Bottom drive mechanism: Adopts a bimetallic strip temperature difference drive device. When the detected local temperature difference exceeds the preset threshold (default is 8 °C), it automatically triggers the stretching action of the reflective wall; Angle adjustment mechanism: Adopts a micro stepping motor, and cooperates with a reducer to achieve precise angle adjustment of 0.5 °; Position sensor: Used to monitor the working state of the reflective wall in real time, including the stretching height and tilt angle.

[0049] The system sets three types of sensors in each honeycomb unit: Temperature sensor: Adopts a PT100 type temperature sensor, with a measurement accuracy of ±0.1 °C and a range of -40 °C to 120 °C; Light intensity sensor: An optoelectronic diode is used, with a response time < 1 ms and a sensitivity of 10 mV / lux; Voltage and current sensor: Used to monitor the local power generation performance, with a sampling rate of 1 kHz.

[0050] When a significant decrease in temperature and converted electrical energy is detected within a certain honeycomb cell (the temperature increases by more than 10 °C or the power generation efficiency decreases by more than 15%), the system will automatically compare the data of the six adjacent cells around it to accurately determine the cause of the abnormality. The specific judgment logic is as follows: If the data of the surrounding cells is normal while the target cell is abnormal, it is determined as a local problem; If the adjacent area shows a gradient decrease in performance, it is determined as a regional problem; If the performance of the entire area decreases uniformly, it is determined as a systematic problem.

[0051] Based on the above judgment results, the system can accurately identify the following fault types: Surface contamination: Characterized by a local increase in temperature and a decrease in power generation efficiency; Material aging: Characterized by a uniform attenuation of performance and a slowdown in response characteristics; Abnormal reflective wall: Characterized by uneven local light intensity distribution and abnormal temperature distribution.

[0052] Embodiment 3 Based on Embodiment 2, this embodiment proposes a honeycomb monitoring strategy of "zoned dynamic reconstruction". This strategy makes full use of the geometric characteristics of the honeycomb structure, combines multiple adjacent hexagonal cells into monitoring areas of different scales, and realizes multi-level adaptive monitoring from micro to macro.

[0053] See Figure 3 , the system defines three basic combinations of monitoring units: micro monitoring unit (7-cell combination), meso monitoring unit (19-cell combination), and macro monitoring unit (37-cell combination). Among them, the micro monitoring unit consists of the central honeycomb and its 6 adjacent honeycombs around it, and is used to accurately locate local abnormalities; the meso monitoring unit consists of 3 micro monitoring units arranged in a triangle, and is used to analyze the performance changes in the area; the macro monitoring unit consists of 3 meso monitoring units, and is used to evaluate the overall state of the system. The monitoring data of each unit is transmitted to the central controller in real time through the fiber optic bus, and the sampling frequency is 100 Hz.

[0054] The dynamic reconstruction process of the system is controlled by an intelligent decision-making algorithm. When the temperature gradient exceeds 5 °C / m, or the power generation efficiency fluctuates by more than 10%, or an abnormal light intensity distribution is detected (relative standard deviation > 15%), the reconstruction mechanism is triggered. The reconstruction process is divided into three levels: local reconstruction (within a 7-unit range), regional reconstruction (within a 19-unit range), and global reconstruction (within a 37-unit range). The reconstruction time is less than 100 ms to ensure the continuity of monitoring.

[0055] This embodiment adopts a "layered - progressive" data processing model. At the micro level, the average temperature (Tavg), standard deviation (σT), and light intensity distribution correlation coefficient (R) of 7 units are calculated; at the meso level, a performance heat map of 19 units is constructed, and the performance gradient (∇P) and directionality index (D) are calculated; at the macro level, a state evaluation matrix of 37 units is generated, including the health index (HI) and risk warning value (RW). The specific calculation formulas are as follows: Micro-level indicators: 1. Temperature uniformity: UI = 1 - σT / Tavg; where: UI is the temperature uniformity index, with a value range of [0,1]; σT is the temperature standard deviation, in °C; Tavg is the average temperature, in °C.

[0056] 2. Light intensity correlation: R = Σ(Ii·Ij) / (σi·σj); where: R is the light intensity correlation coefficient, with a value range of [-1,1]; Ii and Ij are the light intensity values at the i-th and j-th measurement points, in lux; σi and σj are the corresponding standard deviations, in lux.

[0057] 3. Local abnormality: LA = 1 - min(UI, R); where: LA is the local abnormality, with a value range of [0,1]; min(UI,R) represents taking the smaller value of UI and R.

[0058] Meso-level indicators: 1. Performance gradient: ∇P = √[(∂P / ∂x)² + (∂P / ∂y)²]; where: ∇P is the performance gradient, in kW / m²; ∂P / ∂x is the power change rate in the x direction; ∂P / ∂y is the power change rate in the y direction.

[0059] 2. Directionality index: D = arctan(∂P / ∂y) / (∂P / ∂x); where: D is the directionality index, in rad; representing the main direction of performance change.

[0060] 3. Regional uniformity: RA = exp(-k·∇P); where: RA is the regional uniformity index, with a value range of [0,1]; k is the attenuation coefficient, with a default value of 0.5 m² / kW; exp represents the natural exponential function.

[0061] Macro-level indicators: 1. Health Index: HI = w1·UI + w2·RA + w3·F; where: HI is the health index, with a value range of [0,1]; w1, w2, w3 are weight coefficients, and w1 + w2 + w3 = 1; F is the power factor, representing the ratio of the actual power to the rated power. Initial default values: w1 = 0.4, w2 = 0.3, w3 = 0.3.

[0062] 2. Risk Warning Value: RW = α·LA + β·∇P + γ·(1 - HI); where: RW is the risk warning value, with a value range of [0,1]; α, β, γ are risk factors, and α + β + γ = 1; The default value of α is 0.4, the default value of β is 0.3, and the default value of γ is 0.3.

[0063] The system adopts a dynamic risk factor adjustment strategy and makes corresponding adjustments when the following situations occur: When the performance degradation rate > 1% / min: α is increased to 0.6; When an obvious gradient is detected (∇P > 0.5kW / m²): β is increased to 0.5; When the running time > 5000h: γ is increased to 0.4, and for every additional 5000h, γ is increased by 0.1, with a maximum not exceeding 0.6.

[0064] Based on the above calculation results, the system automatically adjusts the monitoring strategy: When LA > 0.2, local reconstruction is initiated; When RA < 0.8, it is extended to regional reconstruction; When HI < 0.6, global reconstruction is triggered. The reconstructed monitoring units are interconnected through an optical fiber ring network to achieve real-time data sharing and cross-verification.

[0065] The monitoring effect evaluation of this embodiment adopts three key indicators: Response Time: Less than 200ms from the occurrence of an anomaly to the completion of reconstruction; Location Accuracy: The location error of the anomaly source is less than the size of a basic unit; Early Warning Accuracy: Not less than 95% (verified based on 1000 hours of operation data).

[0066] Through this partition dynamic reconstruction strategy, the system realizes the all-round and multi-level monitoring of the photovoltaic array, significantly improves the accuracy and timeliness of fault diagnosis, and provides reliable data support for preventive maintenance. Experimental verification shows that this strategy can effectively reduce the fault diagnosis time and improve the operational reliability of the system.

[0067] For the "honeycomb monitoring structure" in Embodiment 2 and Embodiment 3, the connection between the frame and the photovoltaic panel surface adopts a flexible point contact method. 3 - 4 evenly distributed contact points are arranged at the bottom of each frame, and are bonded and fixed with weather-resistant UV curable glue, and the thickness of the glue layer is controlled within 0.8 - 1.2 mm. The main body of the frame is made of PMMA material with a light transmittance greater than 92%, the thickness is 1.5 - 2.0 mm, and the surface is treated with anti-glare.

[0068] To prevent rainwater from converging, a gap of 2 - 3 mm is maintained between the frame and the photovoltaic panel surface, and the edge is designed with a 45° inclination angle to effectively prevent water accumulation. The overall design ensures that the shading rate of the frame is controlled below 3%, minimizing the impact on the photovoltaic power generation efficiency to the greatest extent.

[0069] Embodiment 4 This embodiment proposes a direct power scheduling system based on a honeycomb monitoring structure. The system realizes a direct power supply mode from the power generation unit to the electrical load through intelligent grouping and dynamic power distribution of the photovoltaic array. This power supply method avoids the process of energy storage and reconversion in traditional photovoltaic systems and can significantly improve the system efficiency in specific application scenarios. The core of the system lies in the flexible combination of honeycomb monitoring units to build a multi-level power supply system and achieve precise matching of loads through intelligent scheduling algorithms.

[0070] Based on Embodiments 2 and 3, the scheduling system constructs a complete power scheduling solution. In the organization of the power supply system, a three-level grouping architecture of "micro - meso - macro" is adopted. The micro group consists of 7 honeycomb units (1 central unit plus 6 peripheral units), with a nominal power of about 2 kW; the meso group consists of 3 micro groups, with a nominal power of about 5.3 kW; the macro group consists of 3 meso groups, with a nominal power reaching about 10.4 kW. The nominal power of each honeycomb unit is 280 W ± 5%. Through precise power monitoring and adjustment, the stability of the grouped output is ensured.

[0071] The core implementation of the scheduling system includes two key links: dynamic grouping control and direct power scheduling. In terms of dynamic grouping control, the system sets three types of trigger conditions: when the change in light intensity exceeds 50 W / m² / min, the temperature gradient exceeds 3℃ / m, or the power output fluctuation exceeds 5% / min, the grouping reorganization mechanism will be automatically activated. The grouping reorganization process follows the principle of "nearest first, balanced distribution" to ensure that the power output after reorganization meets the load requirements.

[0072] The direct power scheduling system adopts a multi-level real-time matching algorithm, combined with load characteristic identification, grouped power optimization and dynamic adjustment strategies, and the specific implementation is as follows: 1. Load power characteristic modeling Basic model: P_load(t) = P_base + ΔP(t); Where: P_load is the real-time power demand of the load; P_base is the base power (static power demand of the load); ΔP(t) is the dynamic power fluctuation amount; Power prediction: Based on historical data, construct a feature sequence {P(t-n),..., P(t-1)}, and use the sliding window method to predict P_load(t+1); Analysis of fluctuation characteristics: Calculate the standard deviation σ = √[Σ(P(t) - P_mean)² / n] to evaluate the load stability.

[0073] 2. Optimal configuration of grouped power Total output calculation: P_group(i) = Σ(p_j × η_j); Where: p_j is the real-time output power of the jth photovoltaic unit; η_j is the corresponding power conversion efficiency; Efficiency correction: η_j = η_base × k_temp × k_irr; Where: η_base is the reference conversion efficiency; k_temp is the temperature correction coefficient; k_irr is the light intensity correction coefficient; Grouped power balance: Ensure |p_j - p_mean| ≤ 10%, where p_mean is the average power within the group.

[0074] 3. Evaluation of real-time matching degree Calculation of matching degree: M = 1 - |P_load - P_group| / P_load; Evaluation period: The basic evaluation period T = 100ms. When ΔP(t) / Δt > 10W / s, a fast evaluation is triggered (T = 20ms); Matching threshold: Set M_threshold = 0.95. When M ≥ M_threshold, it is determined as the optimal match; 4. Multi-level dynamic adjustment strategy When power mismatch (M < 0.95) occurs, the system adjusts according to the following process: (1) Calculate the real-time power difference: ΔP = P_load - P_group; The system adopts a three - level adjustment strategy according to the magnitude of the power difference |ΔP|: When |ΔP| does not exceed 100W, the first - level adjustment is adopted, maintaining the existing grouping structure, evenly distributing the power difference to each unit within the group, and the adjustment period is 50ms; When |ΔP| is between 100W and 500W, the second - level adjustment is started, activating the adjacent grouping cooperation mechanism, and achieving power balance by calculating the minimum power adjustment amount (i.e., |ΔP| divided by the total number of units participating in the cooperation), and the adjustment period is extended to 100ms; When |ΔP| exceeds 500W, the third - level adjustment is triggered, and the system will reconstruct the grouping based on the principle of minimum power redundancy, and the adjustment period for this process is 200ms. After each adjustment, the system calculates a new matching degree M_new for verification. If the matching degree decreases, it will roll back to the state before adjustment; If the system still cannot achieve the expected effect after three consecutive adjustments, it will report an abnormal state to the superior control system.

[0075] The scheduling system uses mature power - electronic technologies to achieve power regulation. For example, a combination of a standard MPPT controller and a DC - DC converter is used to achieve the power regulation of photovoltaic units. Among them, the MPPT controller uses the perturbation - observation method, and the DC - DC converter selects the bidirectional Buck - Boost topology; Power sampling uses a combination scheme of a Hall - effect current sensor and a high - precision voltage - dividing network to ensure sampling accuracy; The control system uses the PWM modulation technology commonly used in the industry, with a switching frequency of 20kHz, to ensure fast response and stable control.

[0076] After being verified through trial operation in an industrial park, the system has shown good effects in application scenarios such as data centers, industrial air conditioners, and small industrial equipment. The overall efficiency of the system has increased by 5.8% compared with the traditional scheme, the energy utilization rate has increased by 7.2%, and at the same time, the operation and maintenance cost has been reduced by 12.5%. However, it should be noted that the system has relatively high requirements for load characteristics and environmental conditions, and it is recommended to carry out targeted optimization design according to specific scenario requirements in actual applications.

[0077] The basic principles of the present disclosure have been described above in combination with specific embodiments. However, it should be pointed out that the advantages, advantages, effects, etc. mentioned in the present disclosure are only examples and not limitations. It cannot be considered that these advantages, advantages, effects, etc. are essential for each embodiment of the present disclosure. In addition, the above - disclosed specific details are only for the purpose of illustration and easy understanding, rather than limitations. The above details do not limit the present disclosure to necessarily adopt the above - specific details to implement.

Claims

1. A highly reliable adaptive integrated power supply method for optical storage and diesel based on a planar honeycomb structure, characterized in that, include: Step 1: Construct a planar honeycomb monitoring network: lay a regular hexagonal frame array on the surface of the photovoltaic panel to form a planar honeycomb structure; Configure sound sensor arrays, vibration sensor arrays and temperature sensor arrays in the honeycomb structure to collect real-time operating status data of photovoltaic systems, energy storage systems and diesel generator sets; build a three-level health assessment system of equipment-component-module; and achieve all-round perception of system status; Step 2: Perform health status assessment: Collect and analyze multi-dimensional operation data; calculate the health index of each component through a multi-dimensional feature fusion algorithm, where the health index ranges from [0,1], 1 indicates that the device is in the best working state, and 0 indicates that the device is completely inoperable; evaluate the overall operation status of the system based on the health index; Step 3: Implement an adaptive degradation control strategy: automatically switch between full power mode, optimization mode, economy mode, emergency mode and protection mode according to the health index; Achieve smooth degradation of system performance; Ensure safe and stable operation of the system; Step 4: Optimize the energy scheduling scheme: Use distributed energy management strategy to optimize energy flow distribution; the optimization objectives include the weighted combination of system operation cost, energy transmission loss and system reliability risk; solve the energy scheduling optimization problem by improving the particle swarm algorithm; Realize multi-energy coordinated and complementary power supply.

2. The highly reliable adaptive optical storage and diesel integrated power supply method based on a planar honeycomb structure according to claim 1, characterized in that The calculation of the health index includes: Basic feature extraction, including: (1) Temperature feature extraction: Calculate the temperature mean, temperature variance, and temperature gradient, and calculate the temperature features through weighted fusion; (2) Vibration feature extraction: Calculate the vibration amplitude, spectrum distribution, and acceleration peak, and calculate the vibration features through weighted fusion; (3) Acoustic feature extraction: Calculate the sound pressure level, frequency characteristics and sound wave morphology, and calculate the acoustic features through weighted fusion; and Feature fusion calculation: The improved evidence theory is used for feature fusion, and the health index is calculated by weighted fusion, where the weight coefficient satisfies the normalization constraint.

3. The highly reliable adaptive optical storage and diesel integrated power supply method based on a planar honeycomb structure according to claim 2, wherein The dynamic adjustment strategy of the weight coefficient includes: (a) Adjustment in case of sensor anomalies: When a certain type of sensor data anomaly is detected, the weight of the corresponding feature is automatically reduced, and the weights of other features are increased proportionally to ensure that the sum of the weights remains 1; (b) Adjustment based on historical data: Regularly analyze past operating data and adjust the weights based on the prediction accuracy of each feature; (c) Environmental adaptability adjustment: Dynamically adjust the weight configuration according to the working conditions to ensure system stability.

4. The highly reliable adaptive optical storage and diesel integrated power supply method based on a planar honeycomb structure according to claim 1, wherein The specific design of the planar honeycomb structure includes: Regular hexagonal frames are closely arranged on the photovoltaic panel surface to form a continuous honeycomb structure; The upper plane of each frame is provided with a connection slot, which adopts a double-layer step structure; The connection slot is provided with a reflective wall which is fixed after plugging in. The reflective wall has standard, enhanced and adjustable specifications; A temperature sensor, a light intensity sensor, and a voltage and current sensor are arranged in each honeycomb unit.

5. A partition dynamic reconstruction monitoring method based on a planar honeycomb structure, characterized in that include: Construct a honeycomb-shaped multi-level monitoring network: lay a regular hexagonal frame array on the surface of the photovoltaic panel to form a planar honeycomb structure; combine multiple adjacent hexagonal units into micro-monitoring units, meso-monitoring units and macro-monitoring units; the micro-monitoring unit consists of a central honeycomb and its surrounding adjacent honeycombs, which are used to accurately locate local anomalies; The meso-monitoring unit is composed of multiple micro-monitoring units and is used to analyze regional performance changes; the macro-monitoring unit is composed of multiple meso-monitoring units and is used to evaluate the overall status of the system; Perform real-time status monitoring: collect temperature distribution and light intensity parameters; Analyze power generation efficiency and performance trends; Evaluate system operation status; Implement dynamic reconstruction control: When the temperature gradient, power generation efficiency fluctuation or light intensity distribution abnormality exceeds the preset threshold, the reconstruction mechanism is triggered; the type and scope of the abnormality are determined based on the monitoring data; local reconstruction, regional reconstruction or global reconstruction is performed; and system operation performance is optimized; Conduct multi-level data analysis: Use "layered-progressive" data processing to calculate the performance indicators of each monitoring unit; analyze the spatial distribution characteristics of performance degradation; Assess system health and risk levels.

6. The partition dynamic reconstruction monitoring method based on a planar honeycomb structure according to claim 5, wherein The "layered-progressive" data processing model includes: Calculate the temperature mean, standard deviation and correlation coefficient of light intensity distribution at the micro level to evaluate temperature uniformity, light intensity correlation and local anomaly; Construct performance heatmaps at the mesoscopic level, calculate performance gradients, directional indicators, and regional uniformity; Generate a status assessment matrix at the macro level to evaluate the health index and risk warning value.

7. The partition dynamic reconstruction monitoring method based on a planar honeycomb structure according to claim 5, wherein The specific design of the honeycomb monitoring structure includes: The inner side of each frame is provided with a mounting groove for mounting a sensor; The upper plane of each frame is provided with a connection slot, and a reflective wall is provided in the connection slot for fixing after insertion. The reflective wall has the following specifications: Standard type: Made of high reflectivity aluminum alloy, with chrome plating on the surface; Enhanced type: It adopts composite material structure, with the front side as directional reflection surface and the back side as scattering surface; Adjustable type: height and inclination are adjustable, adopting telescopic structure design; The frame and the photovoltaic panel surface are connected by flexible point contact. Evenly distributed contact points are set at the bottom of each frame, and weather-resistant UV curing glue is used for bonding and fixing.

8. A direct power scheduling method based on a planar honeycomb structure, characterized in that, include: Construct a honeycomb hierarchical power management system: lay a regular hexagonal frame array on the surface of the photovoltaic panel to form a planar honeycomb structure; Based on this structure, a three-level power management architecture of micro-group, meso-group and macro-group is established; Realize power monitoring and control at different scales; ensure the hierarchical dispatching capability of the system; Perform real-time power monitoring: collect power output data at all levels; analyze power fluctuation characteristics; Evaluate system operating status; automatically initiate group reorganization when changes in light intensity, temperature gradients, or power output fluctuations exceed preset thresholds; Implement dynamic power scheduling: Use a multi-level real-time matching algorithm, combined with load characteristic identification, group power optimization and dynamic adjustment strategy; adjust power distribution according to load demand; optimize power balance at all levels; ensure power supply stability; Optimize system operation efficiency: analyze power loss distribution; calculate optimal scheduling plan; improve energy utilization efficiency.

9. The direct power scheduling method based on a planar honeycomb structure according to claim 8, wherein The multi-level real-time matching algorithm includes: (a) Modeling of load power characteristics: Construct a basic model, construct a feature sequence based on historical data for power prediction; calculate the standard deviation to evaluate the load stability; (b) Group power optimization configuration: Calculate the total output power, perform efficiency correction, and ensure power balance within the group; (c) Real-time matching degree evaluation: Calculate the matching degree between the load demand and the group output power, and trigger a quick evaluation when the power change rate exceeds the preset threshold; (d) Multi-level dynamic adjustment strategy: When the matching degree is lower than the preset threshold, adopt a hierarchical adjustment strategy according to the magnitude of the power difference.

10. The direct power scheduling method based on a planar honeycomb structure according to claim 8, characterized in that The specific design of the honeycomb structure includes: On the photovoltaic panel surface, regular hexagon frames are closely arranged to form a continuous honeycomb structure; An appropriate gap is maintained between the frame body and the photovoltaic panel surface, and the edge is designed with an inclination angle to effectively prevent water accumulation; The main body of the frame is made of a material with high light transmittance, and the surface is treated with anti-glare; The overall design ensures that the shading rate of the frame is controlled at a low level, minimizing the impact on the photovoltaic power generation efficiency to the greatest extent.

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