A building exterior wall insulation and photovoltaic integrated secure data storage system

Abstract

The present invention discloses a safety data storage system for thermal insulation and photovoltaic integration of building exterior walls, and relates to the technical field of photovoltaic analysis for buildings; the system comprises a photovoltaic data acquisition module, a hypothesis analysis module, a fluid prediction module, a safety judgment module and a visualization module; the system obtains photovoltaic data information of the exterior wall of the building, generates an IV characteristic curve to perform performance analysis on the photovoltaic module, then performs a hypothesis simulation on the heat transfer process of the exterior wall photovoltaic, uses the finite volume method to analyze the time period data to obtain the photovoltaic heat transfer performance, analyzes the fluid convection of the photovoltaic panel based on the photovoltaic heat transfer performance, thereby clarifying the monitoring data quality under the flow form, and finally analyzes the safety impact information of the building exterior wall photovoltaic, determines the performance status of the exterior wall photovoltaic during the monitoring process, binds the photovoltaic area that generates abnormal monitoring signals, realizes the rapid determination of the comprehensive performance of the exterior wall photovoltaic module, and improves the accuracy of the building exterior wall photovoltaic integrated data storage.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic analysis for buildings, and more particularly to a building exterior wall thermal insulation and photovoltaic integrated secure data storage system. Background Art

[0002] Integrated exterior wall insulation and photovoltaics is an innovative construction technology that combines solar photovoltaic systems with building exterior insulation materials, achieving efficient energy utilization and an overall aesthetically pleasing building appearance. In traditional buildings, solar photovoltaic systems are typically installed separately on the roof or ground. In this approach, photovoltaic panels are integrated into the exterior wall structure, allowing the entire building surface to fully utilize solar radiation, thereby achieving clean energy generation.

[0003] Deficiencies in existing technologies:

[0004] The integration of solar photovoltaic and exterior wall insulation technology requires highly professional design and engineering implementation. The integration of photovoltaic panels on the exterior walls of buildings increases the risks of maintenance and operation, especially in high-rise buildings or hard-to-reach places. Insufficient safety measures may lead to accidents, affecting the reliability and long-term operation of the building's photovoltaic panels. The amount of data collected by monitoring all exterior wall photovoltaics is too large, which is prone to data redundancy. The lack of analysis and storage of data collected by building exterior wall photovoltaic panels makes it unclear what the power generation of the building's exterior wall photovoltaic system is, how it affects the building's cooling and heating loads, and the energy-saving potential of photovoltaic associated heat for building heating. This reduces the comprehensive performance of the exterior wall photovoltaic components and increases the monitoring cost of the photovoltaic components.

[0005] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an integrated safety data storage system for building exterior wall insulation and photovoltaics. By acquiring the exterior wall photovoltaic data information of the building, an IV characteristic curve is generated to perform performance analysis on the photovoltaic components, and then a hypothetical simulation of the heat transfer process of the exterior wall photovoltaic is performed. The finite volume method is used to analyze the time period data to obtain the photovoltaic heat transfer performance. The fluid convection of the photovoltaic panel is analyzed based on the photovoltaic heat transfer performance, thereby clarifying the monitoring data quality under the flow form. Finally, the safety impact information of the building exterior wall photovoltaic is analyzed, the performance of the exterior wall photovoltaic monitoring process is determined, and the photovoltaic areas that generate abnormal monitoring signals are bound to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A building exterior wall insulation and photovoltaic integrated safety data storage system includes a photovoltaic data acquisition module, a hypothesis analysis module, a fluid prediction module, a safety judgment module, and a visualization module, and the modules are connected by signals;

[0009] The photovoltaic data acquisition module is used to obtain photovoltaic data information of the building's exterior wall, and perform performance analysis on the output power of photovoltaic modules at different voltages and currents based on the IV characteristic curve, and send the performance analysis results to the hypothesis analysis module;

[0010] The hypothesis analysis module receives data sent by the photovoltaic data acquisition module and is used to simulate the heat transfer process of the exterior wall photovoltaic. It uses numerical methods to solve the problem and uses the finite volume method to analyze the corresponding time period data to obtain the photovoltaic heat transfer performance. The photovoltaic heat transfer performance data is then sent to the fluid prediction module.

[0011] The fluid prediction module receives data from the hypothesis analysis module, analyzes the natural convection of the fluid in the exterior wall and interlayer photovoltaic panels based on the photovoltaic heat transfer performance, and uses the Grashof number to predict the flow pattern of natural convection. It uses variance analysis to monitor the quality differences of the collected data under the flow pattern and sends the data quality difference results to the safety judgment module.

[0012] The safety judgment module receives data sent by the fluid prediction module, monitors the photovoltaic status of the building's exterior wall, analyzes the safety impact information, and determines the performance of the exterior wall photovoltaic monitoring process based on the signals in the analysis results. Based on the performance, it binds the exterior wall photovoltaic areas that generate abnormal monitoring signals and sends the binding results to the visualization module.

[0013] The visualization module receives data sent by the safety judgment module, visualizes the bound exterior wall photovoltaic area and issues early warning reminders.

[0014] In a preferred embodiment, a method for simulating the heat transfer process of an exterior photovoltaic system using a numerical method is used to solve the problem, and the finite volume method is used to analyze the corresponding time period data to obtain the photovoltaic heat transfer performance, including the following steps:

[0015] A descriptive analysis is conducted on the photovoltaic surface of the exterior wall and the air inside the interlayer, and a density change term caused by temperature change is introduced into the fluid dynamics equation to establish an assumption;

[0016] The finite volume method or finite element method is used to calculate the fluid dynamics equations and obtain the photovoltaic heat transfer performance.

[0017] In a preferred embodiment, the natural convection of the fluid in the exterior wall and the interlayer photovoltaic panel is analyzed based on the photovoltaic heat transfer performance, and the flow pattern of the natural convection is predicted using the Grashof number. The specific steps include:

[0018] The outer surface of the cover of the exterior photovoltaic panel is divided into a large space for natural convection, and the inner layer is divided into a limited space for natural convection;

[0019] The natural convection on the outer surface of the cover and inside the interlayer is analyzed using the Grashof number;

[0020] The flow pattern is predicted based on the Grashof number on the outer surface of the cover and inside the interlayer.

[0021] In a preferred embodiment, the quality difference of the collected data of the monitoring sensor under the flow pattern is analyzed by using variance analysis. The specific process is as follows:

[0022] Collect the monitoring data collected by each sensor, and the sample size of each sensor monitoring data group is equal;

[0023] Calculate the sample mean of each device monitoring data group;

[0024] The hypothesis is established: the null hypothesis is that the means of photovoltaic data of different sensor groups are equal, and the alternative hypothesis is that there are significant differences in the means of photovoltaic data of different sensor groups;

[0025] Use ANOVA to calculate the between-group variance and within-group variance, and compare the two to determine whether there is a significant difference;

[0026] Calculate the intra-group sum of squares of the device monitoring data to obtain the variation within each group.

[0027] In a preferred embodiment, for monitoring the photovoltaic status of a building exterior wall, analyzing the safety impact information, and combining the signals in the analysis results to determine the performance of the exterior wall photovoltaic monitoring process, the specific steps are:

[0028] Acquire safety impact information during exterior wall photovoltaic monitoring, including regional fluctuation information and energy efficiency deviation information;

[0029] Regional fluctuation information includes the heat dissipation fluctuation variation index, and energy efficiency deviation information includes the energy efficiency conversion time sensitivity index;

[0030] The heat dissipation fluctuation variation index in the regional fluctuation information and the energy efficiency conversion time-sensitive index in the energy efficiency deviation information are jointly generated to generate a data traceability storage coefficient;

[0031] The heat dissipation fluctuation variation index is directly proportional to the data traceability storage coefficient, and the energy efficiency conversion time sensitivity index is inversely proportional to the data traceability storage coefficient.

[0032] In a preferred embodiment, binding the exterior wall photovoltaic areas that generate abnormal monitoring signals according to performance conditions includes:

[0033] Compare the data traceability storage coefficient with the storage judgment threshold;

[0034] If the data traceability storage coefficient is greater than or equal to the storage judgment threshold, an abnormal monitoring signal is generated, the external wall photovoltaic area generating the abnormal monitoring signal is bound and marked, and an early warning is issued;

[0035] If the data traceability storage coefficient is less than the storage judgment threshold, a normal monitoring signal is generated.

[0036] The technical effects and advantages of the building exterior wall insulation and photovoltaic integrated safe data storage system of the present invention are as follows:

[0037] The present invention obtains photovoltaic data information of the building's exterior wall, generates an IV characteristic curve, and performs performance analysis on the output power of photovoltaic modules under different voltages and currents, thereby hypothetically simulating the heat transfer process of the exterior wall photovoltaics. The corresponding time period data is analyzed by the finite volume method to obtain the photovoltaic heat transfer performance. The natural convection of the fluid in the exterior wall and the interlayer photovoltaic panel is analyzed based on the photovoltaic heat transfer performance, so as to monitor the quality difference of the collected data of the sensor under the flow form. Finally, the safety impact information of the building's exterior wall photovoltaics is analyzed to determine the performance of the exterior wall photovoltaics during the monitoring process, and the exterior wall photovoltaic areas that generate abnormal monitoring signals are bound, thereby realizing rapid determination of the comprehensive performance of the exterior wall photovoltaic modules, reducing the situation where the monitoring data is too large and data redundancy is generated, and thus improving the accuracy of the integrated data storage of the building's exterior wall photovoltaics. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a structural schematic diagram of a building exterior wall insulation and photovoltaic integrated secure data storage system according to the present invention. DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0040] In order to achieve the above objectives, Figure 1 A structural schematic diagram of a building exterior wall insulation and photovoltaic integrated safety data storage system of the present invention is given, which specifically includes a photovoltaic data acquisition module, a hypothesis analysis module, a fluid prediction module, a safety judgment module and a visualization module, and the modules are connected by signals.

[0041] The photovoltaic data acquisition module obtains photovoltaic data information of the building's exterior wall and performs performance analysis on the output power of photovoltaic modules at different voltages and currents based on the IV characteristic curve;

[0042] There are three main installation methods for combining photovoltaic modules with buildings: exposed frame, semi-concealed frame and fully concealed. In photovoltaic building integration, there are air flow channels between the photovoltaic modules and the concrete wall, that is, the photovoltaic cover, photovoltaic modules, photovoltaic backsheet, air and the outer surface of the wall from the outside to the inside;

[0043] The heat generated by photovoltaic modules can be mainly divided into the following four parts:

[0044] The absorption by the components causes the temperature of the components to rise. Photovoltaic components generate electricity by absorbing photons in sunlight, but they also absorb part of the energy of light and convert it into heat, which causes the temperature of the photovoltaic components themselves to rise;

[0045] Heat is transferred to the surrounding environment through convection and radiation through photovoltaic covers. The surface of photovoltaic modules is usually covered with photovoltaic covers. These covers absorb solar energy and also cause the temperature to rise. Heat is transferred to the surrounding environment through convection and radiation.

[0046] Heat is transferred to the air space through the photovoltaic backsheet in the form of convection and conduction: There is usually a backsheet on the back of the photovoltaic module, which also absorbs some heat and then transfers it to the air space below the photovoltaic module through convection and conduction;

[0047] The heat generated by the photovoltaic modules is transferred to the outer surface of the wall through the photovoltaic backplane in the form of radiation heat exchange, and is transferred to the indoor space by heat conduction, becoming a cooling load. Part of the heat generated by the photovoltaic modules is transferred to the backplane through radiation, and then to the outer surface of the wall through heat conduction. This heat may eventually become the cooling load of the building and affect the indoor temperature.

[0048] Heat accumulated on the surface of photovoltaic modules will accelerate the aging of the battery modules, thereby reducing the service life of the photovoltaic modules. In addition, excessive temperature increases will reduce the power generation efficiency of the photovoltaic modules. Install photovoltaic modules on the exterior wall and analyze the heat transfer process between the photovoltaic modules, the wall and the air space between them;

[0049] In construction engineering, wind pressure is the pressure exerted by natural wind on the surface of a building. When the direction of the natural wind is fixed, the wind pressure at a certain point on the obstacle can be expressed using the following basic wind pressure expression: Where P is wind pressure, ρ is air density, V is wind speed, C p is the wind pressure coefficient, wind pressure coefficient C pIt is a dimensionless coefficient, which is affected by the shape of the obstacle and the wind direction. For different shapes and directions, the value of the wind pressure coefficient may be different. The wind pressure coefficients of some common building shapes and directions can be obtained from relevant wind tunnel experiments or wind engineering manuals. The above expression is the wind pressure at a certain point on the surface of the building, but if the wind load on the entire building surface needs to be calculated, the building surface needs to be subdivided, the corresponding wind pressure is calculated for each small area, and the sum of the wind pressures in each small area is considered.

[0050] It should be noted that the direction of wind pressure is usually perpendicular to the building surface, so the angle between the direction of wind pressure and the normal of the building surface needs to be considered during calculation. If the angle between the wind direction and the normal of the building surface is θ, then the actual wind pressure can be expressed as Pac = Pcos(θ).

[0051] The area of ​​PV modules and their nominal photoelectric conversion efficiency are factory-calibrated parameters of PV modules. However, the module operating temperature and local solar irradiance change at any time, causing the DC current and voltage values ​​emitted by the modules to change accordingly, and the generated photovoltaic data information also changes. In other words, the module power generation efficiency is affected by the operating temperature and solar irradiance. The IV characteristic curve describes the output power of PV modules at different voltages and currents, and measures the performance of PV modules. The details are as follows:

[0052] As the operating temperature of the photovoltaic module increases, the conductivity of the photovoltaic cell decreases, which affects the output current of the cell. This causes the IV characteristic curve to shift to the left under high temperature conditions, that is, the current decreases. Therefore, the power generation efficiency of the module will decrease with the increase in temperature;

[0053] Changes in solar irradiance will affect the rate at which photovoltaic cells generate photogenerated carriers. When the irradiance is low, the rate of photogenerated carrier generation slows down, thereby affecting the output current of the battery. Therefore, the IV characteristic curve shifts to the left under low irradiance conditions. The reduction in solar irradiance will also affect the output voltage of the photovoltaic cell. A temperature sensor is used to monitor the temperature of the photovoltaic module.

[0054] The hypothesis analysis module is used to simulate the heat transfer process of the exterior wall photovoltaic system, solve it using numerical methods, and analyze the corresponding time period data through the finite volume method to obtain the photovoltaic heat transfer performance;

[0055] Photovoltaic exterior wall refers to the outer surface of the photovoltaic cover, and the interlayer directly uses the photovoltaic back panel, air, and the outer surface of the wall as the interlayer;

[0056] There are multiple heat transfer modes and fluid-solid coupling in photovoltaic facades. During the heat transfer simulation of photovoltaic facades, the changes in heat transfer are not very obvious within a certain time period. That is, the simulation is assumed to be steady-state within this time period. The corresponding time period data is collected and analyzed using the finite volume method.

[0057] Segregated solvers are a method that decomposes the fluid dynamics equations into different physical fields and solves each field separately. This solution is often used for pressure-velocity coupled problems. At each time step, the velocity field is first solved, and then the pressure field is calculated based on the obtained velocity field.

[0058] The coupled solver considers different physical fields in the fluid dynamics equations simultaneously and solves the coupled equations through iteration. In this method, physical fields such as velocity and pressure are considered simultaneously and data coupling is achieved through iterative solution.

[0059] The explicit solver directly solves the fluid dynamics equations as time advances. It has a fast calculation speed but has a limit on the time step. It is suitable for problems with shorter time scales.

[0060] Implicit solvers use iterative methods to solve fluid dynamics equations during the time advancement process. The calculation speed is relatively slow, but it has good numerical stability and is suitable for problems with larger time scales.

[0061] Select the appropriate solver based on the requirements of different time periods. When the numerical models of convection, heat conduction, and radiation need to be coupled, consider multiple aspects such as the impact of solar radiation on the wall, radiation between wall surfaces, natural convection within the air interlayer, and natural convection on the cover surface. Perform fine meshing and appropriate physical models to capture the interaction of various heat transfer modes.

[0062] The density change of the fluid is linear with respect to the density change of the fluid, except in the buoyancy term, which describes the situation of the photovoltaic surface of the exterior wall and the air in the interlayer. The assumption is established by introducing a density change term caused by temperature change in the fluid dynamics equation, which is expressed as follows: ρ1 = ρ0·(1-β·(T-T0)), ρ1 is the density, ρ0 is the density at the reference temperature T0, and β is the thermal expansion coefficient;

[0063] Considering the assumptions, the finite volume method or finite element method is used to calculate the fluid dynamics equation to obtain the photovoltaic heat transfer performance, which is expressed as: represents the partial derivative of density ρ with respect to time t, which represents the rate of change of density with time, that is, the speed at which density changes with time at a given point. u is the velocity field, which describes the velocity vector of each point in the fluid. The change of the velocity field describes the motion state of the fluid. represents the gradient operator;

[0064] It should be noted that during the simulation process, initial conditions and boundary conditions need to be set according to the actual problem, and numerical methods need to be used to solve it. Solving the equations usually requires professional computational fluid dynamics (CFD) software, and the corresponding software and parameters should be selected according to actual needs.

[0065] The fluid prediction module receives data from the hypothesis analysis module, analyzes the natural convection of the fluid in the exterior wall and interlayer photovoltaic panels based on the photovoltaic heat transfer performance, and uses the Grashof number to predict the flow pattern of natural convection. It also uses variance analysis to monitor the quality differences of the collected data of the sensors under the flow pattern.

[0066] In the analysis of photovoltaic panels on exterior walls, the Grashof number can provide information about the natural convection behavior of the fluid in the vertical plane. This value reflects the relative strength between buoyancy and viscous forces;

[0067] The Grashof number can be used to determine whether the fluid tends to undergo natural convection. When the Grashof number reaches a certain critical value, the photovoltaic outside the wall undergoes a transition to natural convection. The flow pattern of natural convection is predicted, such as whether there is stable convection or unstable convection, and the relevant convection data is stored and recorded.

[0068] In the exterior wall photovoltaic panels, the outer surface of the cover plate and the inner layer can be regarded as the natural convection of the large space and the limited space respectively. The Grashof number of the outer surface of the cover plate is used to describe the natural convection from the outer surface of the cover plate to the large space. The calculation formula is: Gr 外 =g*β*(T s -T B )*L 3 / v 2 , g is the acceleration due to gravity, β is the coefficient of thermal expansion, T s is the cover surface temperature, T B is the bulk temperature, L is the characteristic length of the cover plate's outer surface, and v is the dynamic viscosity of the fluid;

[0069] The Grashof number in the interlayer is used to describe the natural convection in the limited space inside the interlayer. The calculation formula is: Gr 夹 =g*β*(T 内 -T 外 )*d 3 / v 2 , T 内 is the temperature inside the interlayer, T 外 is the temperature outside the interlayer, d represents the characteristic dimension of the interlayer, such as the height of the interlayer;

[0070] If Gr 外 With Gr 夹When a certain critical value is reached, the natural convection situation will become significant, that is, according to the size of the Grashof number, the flow pattern of natural convection can be predicted, such as stable convection or unstable convection.

[0071] It should be noted that the volume expansion coefficient describes the density change of the fluid caused by temperature change, which can be obtained by looking up the fluid property table or literature, or using the fluid property calculation tool. The characteristic length of the outer surface of the cover can be the vertical height, or other length dimensions related to natural convection. The specific value is determined according to the geometry and conditions of the system; the dynamic viscosity of the fluid under the conditions of natural convection is usually the dynamic viscosity of gas (such as air) or liquid (such as water). In the off-wall photovoltaic system, air is used as the main body.

[0072] By predicting the flow pattern of natural convection, we can better understand the temperature distribution and heat transfer mechanism around the exterior wall photovoltaic modules, thereby accurately analyzing the heat dissipation design of the photovoltaic modules and ensuring that the exterior wall photovoltaic operating temperature under different flow patterns can be analyzed under various weather conditions, thereby improving the efficiency and stability of the photovoltaic system. Understanding the flow pattern of natural convection can be used to optimize the angle and direction of the photovoltaic panels to maximize the use of sunlight.

[0073] After predicting the flow pattern of natural convection by Grashof number, the photovoltaic data of the exterior wall are monitored;

[0074] During the process of building exterior wall insulation and photovoltaic integration safety, data monitoring is continuously carried out and the data is stored. Due to the large amount of data generated by the sensor equipment in the photovoltaic integration of the building exterior wall, the monitoring process is often interfered by various external factors, such as weather conditions, temperature fluctuations, etc., resulting in errors in the monitoring data and incomplete observations. That is, by using variance analysis to monitor the collected data of the sensors, when monitoring the exterior wall photovoltaic data collected by the sensors, variance analysis can be used to analyze whether there are significant differences between different sensor groups (which may be located in different positions or have different characteristics), as follows:

[0075] Collect the monitoring data collected by each sensor, ensure that the sample size of each sensor monitoring data group is equal or close, and calculate the sample mean of each device monitoring data group Calculate the sum of sample means

[0076] The null hypothesis is that the means of photovoltaic data of different sensor groups are equal, and the alternative hypothesis is that there are significant differences in the means of photovoltaic data of different sensor groups;

[0077] 0.05 was selected as the significance level, indicating that the test was conducted at a 95% confidence level, and exterior wall photovoltaic data from different sensor groups were collected;

[0078] The collected data is input into the ANOVA model for analysis. ANOVA will calculate the between-group variance (differences between different sensor groups) and the within-group variance (differences within the same sensor group), and compare the two to determine whether there is a significant difference.

[0079] Calculate the sum of squares within the device monitoring data group to measure the variation within each group. That is, calculate the sum of the squares of the difference between each data point in each device monitoring data group and its group mean. The calculation expression is: Where k is the number of sensor monitoring data groups, n i is the number of samples in group i, X ij represents the data point j in the i-sensor monitoring data set, Represents the mean of the i-th group of data, calculates the sum of squares between the data groups collected and monitored by the sensor, and obtains the degrees of freedom within the device monitoring data group. Between-group degrees of freedom: Where N represents the total number of all sample points, and the mean value is calculated to obtain the statistic. The calculation expression is: F = MS 组 / MS 组内 .

[0080] At a given significance level, the calculated statistics are compared with the set critical value. If the results of the variance analysis show that the variance between groups is significantly greater than the variance within groups, then the null hypothesis can be rejected and it is believed that there is a significant difference in the mean values ​​of the photovoltaic data of different sensor groups.

[0081] If the results of the variance analysis show that there is no significant difference between the between-group variance and the within-group variance, then the null hypothesis is that the means of the photovoltaic data of different sensor groups are basically equal.

[0082] During the monitoring of exterior wall photovoltaics, photovoltaic cells are relatively close to each other. Usually, the photovoltaic monitoring area is divided, and the photovoltaic cells monitored in the area are analyzed based on the monitoring situation of the area, thereby reducing the situation where each photovoltaic cell generates the same redundant data. However, in some cases, it is necessary to collect data for each photovoltaic cell in the area. When screening and storing photovoltaic data, a security strategy is adopted to improve the cost-effectiveness of storage data, store data that is meaningful for research or monitoring, and reduce redundant data. The specific process is as follows:

[0083] The safety judgment module is used to monitor the photovoltaic status of the building's exterior wall and obtain the safety impact information it generates, including regional fluctuation information and energy efficiency deviation information;

[0084] Regional fluctuation information includes the heat dissipation fluctuation variation index and is calibrated as SRB, and energy efficiency deviation information includes the energy efficiency conversion time sensitivity index and is calibrated as NXZ;

[0085] The heat dissipation fluctuation variation index in regional fluctuation information refers to the temperature changes in the heat dissipation of the building's exterior photovoltaic system. It is used to evaluate the stability and variability of the heat dissipation performance of the exterior photovoltaic panels. That is, in the process of monitoring exterior photovoltaic data, the regional heat dissipation fluctuations are obtained by evaluating the monitored photovoltaic data according to the monitoring area. The heat dissipation fluctuation variation index will have an impact on the following aspects:

[0086] Performance evaluation: The heat dissipation fluctuation variation index can be used to evaluate the stability and variability of the heat dissipation performance of photovoltaic panels on building exterior walls. By analyzing the heat dissipation fluctuation, we can understand the stability of the heat dissipation effect of photovoltaic panels under different conditions, thereby evaluating the overall performance of the system;

[0087] Equipment health monitoring: Large heat dissipation fluctuations indicate instability or potential problems in the heat dissipation process of regional exterior wall photovoltaic systems. Monitoring the heat dissipation fluctuation variation index helps to detect signs of failure or performance degradation early, improve the reliability and stability of the exterior wall photovoltaic system, and help the maintenance team develop targeted maintenance strategies. Large fluctuations may indicate the need to clean dirt on the surface of the photovoltaic panels, improve the heat dissipation effect, or perform other maintenance operations.

[0088] The logic for obtaining the heat dissipation fluctuation variation index is:

[0089] Determine the monitoring area, select the analysis time window through the temperature sensor installed on the photovoltaic wall of the building, determine the time period for evaluating the heat dissipation fluctuation, collect the monitoring data of the photovoltaic panel surface temperature in real time and establish the temperature value set GT i ={GT1, GT2, ..., GT N , N is a positive integer. In the selected time window, the surface temperature of the photovoltaic panels in the monitoring area is calculated to obtain the temperature fluctuation value. The calculation expression is: Obtain the voltage and current data information corresponding to the photovoltaic under the light intensity, draw the IV curve, and obtain the expected power point under the light intensity corresponding to the expected power P on the IV curve 预期 , actual power P 实际 With the historical power P 历史 , the heat dissipation fluctuation variation index is calculated based on the expected power, the actual power, the historical power, and the temperature fluctuation value, and the calculation expression is:

[0090] It should be noted that a light sensor should be installed at an appropriate location near the photovoltaic panel to ensure that the light received by the sensor is similar to that on the surface of the photovoltaic panel, so as to accurately reflect the light conditions received by the photovoltaic panel. As the light intensity increases, the current generated by the photovoltaic panel will also increase. More light means more photons excite electrons, increasing the flow of current. Within a certain range, an increase in light intensity will also cause a slight increase in the voltage of the photovoltaic panel. An increase in light intensity can increase the speed of electron movement in the photovoltaic panel, thereby increasing the voltage. The expected power point under light intensity represents the average expected power within the analyzed time window, and the same applies to the actual power.

[0091] The energy efficiency conversion time sensitivity index in the energy efficiency deviation information is the response speed or sensitivity to changes in light. The energy efficiency conversion time sensitivity index can be used to measure the adjustment speed of the energy efficiency conversion efficiency of the photovoltaic system when the light conditions change. The energy efficiency conversion time sensitivity index can describe the system's adaptability to changes in light intensity in an instant or short period of time. The energy efficiency conversion time sensitivity index will affect the following aspects:

[0092] Conversion energy efficiency and power stability: When the photovoltaic system changes in light intensity, a high energy efficiency conversion time-sensitive index helps maintain system stability. The photovoltaic system can quickly adjust to maintain efficient operation, reduce energy efficiency fluctuations, and reduce instantaneous fluctuations in system power output. This reduces the impact of conversion on the grid when the exterior wall photovoltaic system is connected to the grid.

[0093] Responding to shadow effects: When some photovoltaic modules are shaded, the energy efficiency conversion time-sensitive index improves the photovoltaic ability to adapt to shadow changes and reduces the decline in overall photovoltaic performance. In applications that need to quickly adapt to lighting conditions, the high energy efficiency conversion time-sensitive index makes the system more real-time and can more effectively optimize energy conversion efficiency.

[0094] The method for obtaining the energy efficiency conversion time-sensitive index is as follows:

[0095] The monitoring system records the real-time data of energy efficiency and light intensity, analyzes the light data, detects the time or time period when the light intensity changes, records the corresponding energy efficiency data at the time of light change, calculates the energy efficiency change NX, and establishes the energy efficiency change set NX r ={NX1, NX2, ..., NX m}, m is a positive integer, record the corresponding time change SJ, and establish the change duration set SJ r ={sJ1, sJ2, ..., sJ m}, m is a positive integer, and the energy efficiency conversion time sensitivity index is calculated. The calculation expression is:

[0096] It should be noted that the unit time represents a specific duration, such as the length of a time period of 1 hour, 24 hours, etc. The unit time can also be the number of training iterations or batches, which is set according to the specific situation of the event.

[0097] Combine regional fluctuation information and energy efficiency deviation information to generate data traceability storage coefficients;

[0098] The heat dissipation fluctuation variation index SRB and energy efficiency conversion time-sensitive index NXZ are obtained and normalized to generate data traceability storage coefficients, which are calibrated as SY x , the expression is: Where, SY x is the data traceability storage coefficient, z1 and z2 are the preset proportional coefficients of the heat dissipation fluctuation variation index SRB and the energy efficiency conversion time sensitivity index NXZ, and both z1 and z2 are greater than 0.

[0099] It should be noted that the size of the preset proportional coefficient is a specific numerical value obtained by quantifying each parameter. In order to facilitate subsequent comparison, the size of the coefficient depends on the amount of sample data and the preliminary setting of the corresponding preset proportional coefficient for each set of sample data by technical personnel in this field. It is not unique, as long as it does not affect the proportional relationship between the parameter and the quantized value, such as the heat dissipation fluctuation variation index is proportional to the data traceability storage coefficient.

[0100] The larger the heat dissipation fluctuation variation index and the smaller the energy efficiency conversion time-sensitive index, that is, the larger the performance value of the data traceability storage coefficient, indicates that during the exterior wall photovoltaic monitoring process, the heat dissipation does not meet expectations and more time is needed to adjust the energy efficiency. The monitored exterior wall photovoltaic area is more likely to have safety risks. The smaller the heat dissipation fluctuation variation index and the larger the energy efficiency conversion time-sensitive index, that is, the smaller the performance value of the data traceability storage coefficient, indicates that the exterior wall photovoltaic monitoring process has better performance, is more in line with expectations, and performs better in heat dissipation and energy efficiency adjustment, and the probability of exterior wall photovoltaic integrated safety problems is small.

[0101] Compare the generated data traceability storage coefficient with the storage judgment threshold to generate classification identification stability signals and abnormal monitoring signals;

[0102] After obtaining the data traceability storage coefficient, compare the data traceability storage coefficient with the storage judgment threshold, and bind and mark the exterior wall photovoltaic area that generates abnormal monitoring signals according to the performance;

[0103] If the data traceability storage coefficient is greater than or equal to the storage judgment threshold, an abnormal monitoring signal is generated, indicating that the data collected from the external wall photovoltaic monitoring is inconsistent with the actual expectations, and more data needs to be collected from the external wall photovoltaic area, and corresponding analysis and storage of abnormal data are required;

[0104] If the data traceability storage coefficient is less than the storage judgment threshold, a normal monitoring signal is generated, indicating that the data collected by monitoring the exterior wall photovoltaics is consistent with expectations and the exterior wall photovoltaics in this area are in a normal state.

[0105] The visualization module receives data from the safety judgment module, displays the bound exterior wall photovoltaic area visually and issues early warning reminders. Based on the early warning reminders, the exterior wall photovoltaic situation in this area is analyzed and adjusted;

[0106] For exterior photovoltaic areas with abnormal monitoring signals, administrators receive early warnings, adjust strategies, and select high-precision sensors for data collection again. These sensors have higher measurement accuracy and sensitivity, providing more accurate monitoring data and enabling analysis of the exterior photovoltaic areas.

[0107] For the installed monitoring sensors, they are optimized to bring out their full performance. Through calibration, adjustment of sensor parameters, and increase of sampling frequency, they are ensured to provide the best performance when monitoring the exterior wall photovoltaic area. This includes analysis of the working status, power generation efficiency, angle and direction of the components to identify whether there are problems such as component damage, shadow obstruction or inappropriate lighting angle. Based on the analysis results, energy efficiency adjustment strategies are formulated, including adjusting the angle of the photovoltaic panels, optimizing the component layout, improving the heat dissipation system, etc., to improve the overall performance of the exterior wall photovoltaic system, and to perform maintenance on the exterior wall photovoltaic components with problems.

[0108] During the maintenance process of problematic exterior wall photovoltaic modules, relevant data such as weather, temperature, humidity, wind speed, voltage, current, etc. when abnormal monitoring signals are generated are recorded and stored as reference data. When similar situations occur in the future, they can be used as data reference for analyzing and identifying the causes.

[0109] It should be noted that the threshold information in this embodiment is pre-set by professionals and will not be explained in detail here. Some parameters in the embodiments have the same English letters, but have different meanings when used, and will not be explained one by one here.

[0110] The present invention obtains photovoltaic data information of the building's exterior wall, generates an IV characteristic curve, and performs performance analysis on the output power of photovoltaic modules under different voltages and currents, thereby hypothetically simulating the heat transfer process of the exterior wall photovoltaics. The corresponding time period data is analyzed by the finite volume method to obtain the photovoltaic heat transfer performance. The natural convection of the fluid in the exterior wall and the interlayer photovoltaic panel is analyzed based on the photovoltaic heat transfer performance, so as to monitor the quality difference of the collected data of the sensor under the flow form. Finally, the safety impact information of the building's exterior wall photovoltaics is analyzed to determine the performance of the exterior wall photovoltaics during the monitoring process, and the exterior wall photovoltaic areas that generate abnormal monitoring signals are bound, thereby realizing rapid determination of the comprehensive performance of the exterior wall photovoltaic modules, reducing the situation where the monitoring data is too large and data redundancy is generated, and thus improving the accuracy of the integrated data storage of the building's exterior wall photovoltaics.

[0111] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0112] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0113] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0114] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0115] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0116] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A building exterior wall insulation and photovoltaic integrated secure data storage system, characterized by: It includes photovoltaic data acquisition module, hypothesis analysis module, fluid prediction module, safety judgment module and visualization module, and the modules are connected through signals; The photovoltaic data acquisition module is used to obtain photovoltaic data information of the building's exterior wall, and perform performance analysis on the output power of photovoltaic modules at different voltages and currents based on the IV characteristic curve, and send the performance analysis results to the hypothesis analysis module; The hypothesis analysis module receives the data sent by the photovoltaic data acquisition module and is used to simulate the heat transfer process of the exterior wall photovoltaic. It uses numerical methods to solve the problem and uses the finite volume method to analyze the corresponding time period data to obtain the photovoltaic heat transfer performance. The photovoltaic heat transfer performance data is then sent to the fluid prediction module. The fluid prediction module receives data from the hypothesis analysis module, analyzes the fluid convection of the exterior wall and interlayer photovoltaic panels based on the photovoltaic heat transfer performance, and uses the Grashof number to predict the flow pattern of natural convection. It uses variance analysis to monitor the quality differences of the collected data under the flow pattern and sends the data quality difference results to the safety judgment module. The safety judgment module receives data sent by the fluid prediction module, is used to monitor the photovoltaic status of the building's exterior wall, analyze the safety impact information, determine the performance of the exterior wall photovoltaic monitoring process based on the signals in the analysis results, bind the exterior wall photovoltaic areas that generate abnormal monitoring signals according to the performance, and send the binding results to the visualization module; The visualization module receives data from the safety judgment module, displays the bound exterior wall photovoltaic area visually, and issues early warning reminders; It is used to monitor the photovoltaic status of building exterior walls, analyze safety impact information, and determine the performance of the exterior wall photovoltaic monitoring process based on the signals in the analysis results. The specific steps are as follows: Acquire safety impact information during exterior wall photovoltaic monitoring, including regional fluctuation information and energy efficiency deviation information; Regional fluctuation information includes the heat dissipation fluctuation variation index, and energy efficiency deviation information includes the energy efficiency conversion time sensitivity index; The heat dissipation fluctuation variation index in the regional fluctuation information and the energy efficiency conversion time-sensitive index in the energy efficiency deviation information are jointly generated to generate a data traceability storage coefficient; Bind the exterior wall photovoltaic areas that generate abnormal monitoring signals based on performance, including: Compare the data traceability storage coefficient with the storage judgment threshold; If the data traceability storage coefficient is greater than or equal to the storage judgment threshold, an abnormal monitoring signal is generated, the external wall photovoltaic area generating the abnormal monitoring signal is bound and marked, and an early warning is issued; If the data traceability storage coefficient is less than the storage judgment threshold, a normal monitoring signal is generated.

2. The building exterior wall insulation and photovoltaic integrated secure data storage system according to claim 1, characterized in that: The hypothesis analysis module is used to simulate the heat transfer process of the exterior wall photovoltaic system, solve it using numerical methods, and analyze the corresponding time period data using the finite volume method to obtain the photovoltaic heat transfer performance. The following steps are included: A descriptive analysis is conducted on the photovoltaic surface of the exterior wall and the air inside the interlayer, and a density change term caused by temperature change is introduced into the fluid dynamics equation to establish an assumption; The finite volume method or finite element method is used to calculate the fluid dynamics equations and obtain the photovoltaic heat transfer performance.

3. The building exterior wall insulation and photovoltaic integrated secure data storage system according to claim 2, characterized in that: The fluid convection of the exterior wall and interlayer photovoltaic panels is analyzed based on the photovoltaic heat transfer performance, and the flow pattern of natural convection is predicted using the Grashof number. The specific steps include: The outer surface of the cover of the exterior photovoltaic panel is divided into a large space for natural convection, and the inner layer is divided into a limited space for natural convection; The natural convection on the outer surface of the cover and inside the interlayer is analyzed using the Grashof number; The flow pattern is predicted based on the Grashof number on the outer surface of the cover and inside the interlayer.

4. The building exterior wall insulation and photovoltaic integrated secure data storage system according to claim 3, characterized in that: The variance analysis is used to analyze the quality of the collected data of the monitoring sensors under the flow pattern. The specific process is as follows: Collect the monitoring data collected by each sensor, and the sample size of each sensor monitoring data group is equal; Calculate the sample mean of each device monitoring data group; The hypothesis is established: the null hypothesis is that the means of photovoltaic data of different sensor groups are equal, and the alternative hypothesis is that there are significant differences in the means of photovoltaic data of different sensor groups; Use ANOVA to calculate the between-group variance and within-group variance, and compare the two to determine whether there is a significant difference; Calculate the intra-group sum of squares of the device monitoring data to obtain the variation within each group.

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