New Energy Building Integrated Energy-Saving Design Analysis Method

Abstract

This invention discloses an energy-saving design and analysis method for integrated new energy building technologies, comprising the following steps: Time-series alignment and unit normalization processing of collected heat flux data, temperature rise data, and power output data; construction of a heat flux trajectory and thermal response profile with a unified thermal resistance reference axis based on the thermal conductivity characteristics of the heat-conducting structure and the thermal inertia parameters of the building envelope; extraction of reverse heat flux intensity parameters and heat flux duration parameters based on the heat flux trajectory and thermal response profile; and joint construction of a reverse thermal conduction interference map with the set temperature control target and thermal conductivity structure attributes within the building to determine the degree of thermal conduction interference of the building's internal cooling load. This invention solves the problem of misjudgment of cooling load caused by photovoltaic heat transfer through high thermal conductivity structures, achieving accurate identification of heat and cold conduction and dynamic energy consumption control.

Description

Technical Field

[0001] This invention relates to the field of integrated new energy building technology, specifically to an energy-saving design and analysis method for integrated new energy building. Background Technology

[0002] Building integration with new energy sources refers to the organic integration of clean and renewable energy systems such as solar, wind, and geothermal energy with the building structure during the design phase. This makes the energy system not only a functional part of the building but also an integral part of its structure and aesthetics. For example, photovoltaic modules can be embedded in roofs, walls, and shading systems to achieve deep synergy between energy production and building use. Building integration energy-saving design analysis, on the other hand, involves quantitatively evaluating the layout, power generation efficiency, energy-saving potential, and overall energy efficiency of new energy systems in the early stages of building design, based on information such as the building's location, orientation, structural parameters, and functional uses. This evaluation is achieved through digital modeling, simulation, and algorithm optimization, providing a basis for design decision-making and optimization. The necessity of this analysis lies in the fact that retrofitting a building's energy performance after construction is costly. Therefore, accurately assessing the integration effect of new energy systems with the building during the design phase can significantly improve energy utilization efficiency, reduce operating energy consumption, avoid resource waste, promote the development of green and low-carbon buildings, and drive the collaborative evolution of the construction industry and new energy technologies.

[0003] Existing energy-saving design and analysis technologies for integrated new energy buildings primarily rely on Building Information Modeling (BIM), energy consumption simulation software, environmental simulation systems, and multi-source data analysis platforms. Through digital modeling and algorithmic calculations, they systematically evaluate the collaborative design effects of buildings and new energy systems. Specifically, this technology first constructs a three-dimensional building model based on architectural drawings and structural parameters. It then incorporates input information such as geographical location, climate data (e.g., sunshine duration, wind speed, temperature, humidity), and functional requirements to simulate the energy output capacity and load matching efficiency of different new energy systems (e.g., photovoltaic power generation, wind-assisted heating, geothermal heating) under various configuration schemes. Subsequently, energy consumption simulation tools (e.g., EnergyPlus, DesignBuilder) are used to dynamically simulate the building's annual heating and cooling loads and lighting demands. Next, optimization algorithms (e.g., genetic algorithms, multi-objective optimization) are employed to analyze the energy-saving potential and economic viability of various system integration schemes, selecting the optimal configuration. Finally, key indicators including power generation efficiency, energy saving rate, and investment payback period are output, providing quantitative and visual decision support for the design scheme. The entire analysis process typically includes six major stages: building modeling, climate data acquisition, energy system configuration simulation, energy consumption simulation, optimization calculation, and result evaluation, enabling full-process prediction and optimization of the energy-saving performance of integrated new energy buildings.

[0004] The existing technology has the following shortcomings:

[0005] In building-integrated photovoltaics (BIPV) energy-saving design analysis, photovoltaic modules are typically installed directly on the building's exterior surface, especially on metal roofs or in structural areas with high thermal conductivity, often secured using aluminum profile rails and metal supports. When the modules generate heat during operation, this heat is conducted along the heat-conducting structure into the building interior, creating a reverse heat flux coupling effect that alters the heat load on the building's interior surfaces. This effect is particularly pronounced in summer or high-temperature climates, easily leading to a significant increase in the building's cooling load. However, existing BIPV energy-saving design analysis technologies typically treat the heat generation behavior of photovoltaic modules as a uniform external climate influence, failing to consider the coupled heat transfer path formed between the photovoltaic modules and the building structure through high thermal conductivity. This results in an inability to accurately predict the true impact of the structure on the building's cooling load based on the reverse heat flux coupling phenomenon when the photovoltaic modules are in direct contact with the high thermal conductivity structure. Consequently, the cooling load assessment results are underestimated, affecting the reliability of the air conditioning system design and the accuracy of the overall building energy-saving performance analysis.

[0006] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] The purpose of this invention is to provide an integrated energy-saving design and analysis method for new energy buildings to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: an integrated energy-saving design and analysis method for new energy buildings, specifically including the following steps:

[0009] S01. Heat flow sensing elements and temperature sensing materials are installed on the outer surface of photovoltaic modules, the connection points of metal heat-conducting structures and the corresponding walls inside the building. Heat flow data, temperature rise data and power output data are collected synchronously through optical signal transmission to form a data channel covering the photovoltaic heating path and heat conduction path.

[0010] S02. The collected heat flow data, temperature rise data and power output data are time-series aligned and unit-normalized. Based on the thermal conduction characteristics of the heat-conducting structure and the thermal inertia parameters of the building envelope, a heat flux trajectory and thermal response profile with a unified thermal resistance reference axis are constructed.

[0011] S03. Based on the heat flux trajectory and thermal response profile, extract the reverse heat flux intensity parameter and the heat flux duration parameter, and combine them with the temperature control target and thermal conductivity properties set in the building to construct a reverse thermal conduction interference map, which is used to determine the degree of thermal conduction interference of the cooling load in the building.

[0012] S04. Match the reverse thermal interference map with the pre-established thermal risk boundary model to identify high-risk, controllable-risk, and low-risk thermal interference areas, and calculate the cooling load correction factor for energy consumption correction based on the spatial distribution relationship and heat transfer characteristics of each type of thermal interference area.

[0013] S05. Combine the thermal interference classification results with the cooling load correction factor to establish a dynamic feedback control process, continuously analyze the trend of heat flux change, and when the thermal interference area continues to meet the high-risk conditions, execute the thermal insulation adjustment operation of the heat conduction structure or the building cold source allocation operation to achieve synchronous coordination between cooling load prediction and thermal conduction impact.

[0014] Preferably, S02 specifically includes the following steps:

[0015] S021. Align the heat flow data, temperature rise data and power output data collected from the outer surface of the photovoltaic module, the connection point of the metal heat-conducting structure and the corresponding wall surface inside the building according to the time sequence of the light signal acquisition, and complete the cross-channel time synchronization by using the equal interval interpolation reconstruction method.

[0016] S022. Convert the aligned heat flux data into heat flux density per unit area, convert the aligned temperature rise data into absolute temperature difference, convert the aligned power output data into power generation per unit area, and uniformly construct a standardized sequence of physical quantities.

[0017] S023. Based on the thermal conductivity of the material and the structural dimensions of the thermally conductive structure, extract the thermal resistance parameters of the conduction path, and combine the heat flux density per unit area and the absolute temperature difference to establish the heat flux intensity distribution. At the same time, extract the thermal inertia parameters based on the heat capacity and thermal diffusivity of the building envelope as the basic variables for constructing the dynamic thermal response process.

[0018] S024. In a uniformly constructed thermal resistance reference axis coordinate system, the heat flux density per unit area, absolute temperature difference, and power generation per unit area are mapped and embedded according to the path sequence to form a heat flux trajectory and thermal response profile, which are used to express the complete heat conduction path characteristics from photovoltaic to indoor.

[0019] Preferably, S022 is as follows:

[0020] The heat flux data of each aligned heat flux sensing point is normalized according to the actual area of ​​the sampling surface, and the heat flux density per unit area data sequence is output, while keeping the time axis sequence consistent with the heat flux sampling time sequence.

[0021] The relative temperature difference of each aligned temperature sensing point is calculated one by one, with the temperature reading at a fixed initial time point as the reference value, to construct an absolute temperature difference data sequence, and it is kept consistent with the time node of the heat flow data.

[0022] The aligned power output data is normalized and converted according to the power generation area corresponding to the photovoltaic module to obtain a sequence of power generation values ​​per unit area and establish a unified unit expression.

[0023] The heat flux density data sequence, absolute temperature difference data sequence, and power generation value sequence per unit area are arranged in a corresponding manner on the time axis to establish a standardized physical quantity sequence with unified time series and dimensions, which is used for subsequent heat flux trajectory construction.

[0024] Preferably, S023 is as follows:

[0025] Based on the type of material corresponding to the heat-conducting structure, the thermal conductivity value is obtained, and combined with the thickness and cross-sectional area of ​​the structure, the thermal resistance value is calculated according to the one-dimensional steady-state heat transfer theory to quantify the heat flow conduction capacity.

[0026] The heat flux density per unit area and the absolute temperature difference are matched point by point according to the location of the heat flow path, and then combined with the corresponding thermal resistance value to construct a heat flow intensity distribution sequence of the surface heat conduction path, which reflects the degree of heat input concentration in different parts.

[0027] The heat capacity and thermal diffusivity parameters of the filling layer and surface material in the building envelope are extracted to form a set of thermal inertia parameters associated with the structural layout, which are used to support the modeling of heat storage characteristics in the subsequent dynamic thermal response process.

[0028] Preferably, S024 specifically refers to:

[0029] A thermal resistance reference axis coordinate system is established, and the heat conduction path is unfolded in sequence according to the three key hot spots of photovoltaic modules, heat conduction structure and indoor wall surface. The thermal resistance value is arranged along the axis in an cumulative manner to form a two-dimensional mapping domain with continuous thermal resistance coordinates.

[0030] The heat flux density data sequence per unit area is mapped to the location of the photovoltaic module along the thermal resistance coordinate axis, the absolute temperature difference data sequence is mapped to the nodes of the heat-conducting structure, and the power generation value sequence per unit area is synchronously mapped to the initial location of the photovoltaic module. The three types of data form a heat input-conduction-response path relationship.

[0031] Based on the relative distance and physical sequence order of each point on the thermal resistance axis, the three types of standardized data after mapping are connected to form a heat flux trajectory and thermal response profile that reflect the heat flow change trend and temperature rise response law along the thermal resistance change path.

[0032] Preferably, S03 specifically includes the following steps:

[0033] S031. Extract the heat flux direction change data from the photovoltaic module to the building interior path from the heat flux trajectory and thermal response profile, and extract the reverse heat flux intensity parameter and the heat flux duration parameter based on the section where the heat flow direction changes from the outside to the inside.

[0034] S032. The extracted reverse heat flux intensity parameters and heat flux duration parameters are matched with the temperature control targets corresponding to the areas within the building, and the thermal resistance, structural thickness and thermal diffusivity of each material in the thermal conductive structure are combined to form a set of building thermal conductive structure attributes.

[0035] S033. Based on the reverse heat flux intensity parameter, the heat flux duration parameter, the temperature control target and the thermal conductivity structure attribute, a coordinate mapping domain is constructed in three-dimensional space. Different thermal interference characteristics are mapped to the coordinate domain to form a partitioned expression, and a reverse thermal conduction interference map is generated to determine the degree of thermal conduction interference of the cold load inside the building.

[0036] Preferably, S031 specifically refers to:

[0037] In the heat flux trajectory, the heat flux segments that are conducted from the outside to the inside are selected according to the path direction, and the reverse heat flux intensity parameter is extracted from the point corresponding to the maximum heat flux value in the time series to represent the maximum disturbance energy density in the heat conduction path.

[0038] Within the same heat flux range, determine the time interval during which the heat flux continuously exceeds the static baseline value, and extract the time span of the continuous heat transfer process as a parameter for the duration of the heat flux's continuous influence.

[0039] By binding the reverse heat flux intensity parameter and the heat flux duration parameter with the original heat flux trajectory sequence, a thermal interference feature data pair with time label and path location identifier is established for subsequent association with building control parameters.

[0040] Preferably, S033 is as follows:

[0041] A two-dimensional coordinate domain is established with the reverse heat flux intensity parameter as the horizontal axis and the heat flux duration parameter as the vertical axis, and the offset range of the building temperature control target value is used as the contour distribution line.

[0042] Based on the thermal resistance, thermal diffusivity and thickness of each structural layer in the thermal conductivity properties, a set of building response correction factors corresponding to each coordinate point is established and integrated into the coordinate domain to form a three-dimensional parameter set.

[0043] The reverse heat flux intensity parameter, heat flux duration parameter, building temperature control target value offset range, thermal resistance value, thermal diffusivity and structural thickness contained in the thermal conductivity properties are combined into a six-dimensional parameter set. The parameters are then mapped to the three-dimensional coordinate domain according to the coordinate axis embedding logic. Each thermal interference level region is identified by superimposing the contour density distribution and thermal response weight, generating a reverse thermal interference map that can be used to identify the thermal conduction interference level of the cold load inside the building.

[0044] Preferably, S04 specifically includes:

[0045] The reverse heat flux intensity parameters, heat flux duration parameters, building temperature control target value offset range and thermal conductivity property coordinates contained in the reverse heat conduction interference map are mapped to a unified parameter space, and then matched with the high-risk, controllable-risk and low-risk intervals defined in the pre-established thermal risk boundary model. Based on the matching results, the corresponding thermal interference level is marked for the thermal interference points in the map.

[0046] Based on the spatial distribution of marked high-risk, controllable-risk, and low-risk thermal interference points, thermal interference areas with continuous boundaries are aggregated, and the spatial distribution characteristics of each type of thermal interference area are statistically analyzed, including the occupied area, distribution density, boundary orientation, and relative positional relationship with the target cooling load area.

[0047] Based on the spatial distribution of each type of thermal interference area and the heat transfer characteristics of each structural layer in the thermal conductivity properties, including thermal resistance, thermal diffusivity and structural thickness, a quantitative model reflecting the influence of the heat conduction path is constructed, and the energy offset affecting the building's cooling load in this area is extracted.

[0048] The energy offset corresponding to each type of thermal interference area is converted into a unit area cooling load impact value. Combining the area coverage and interference duration weights, a cooling load correction factor for energy consumption correction is calculated and summarized to form a cooling load correction factor cluster covering the building space, which serves as the input basis for subsequent air conditioning load allocation and energy efficiency management.

[0049] Preferably, S05 specifically includes:

[0050] A dynamic identification model for thermal risk status is constructed based on the thermal interference classification results and the cooling load correction factor. The model is input with the heat flux intensity change curve of each region and the corresponding cooling load correction value to form a thermal interference monitoring dataset with time dimension and risk level label.

[0051] The heat flux intensity change value of each monitoring point in the heat flux trajectory is continuously acquired, and the heat interference intensity change rate is calculated by combining the time series analysis method. By comparing with the high-risk judgment threshold set in the risk identification model, the heat interference area that meets the high-risk state is identified.

[0052] For the identified high-risk thermal interference areas, the thermal conductivity properties, including thermal resistance, thermal diffusivity and structural thickness, are combined to calculate the parts in the heat conduction path that can be thermally adjusted and their adjustment upper limit, and to determine the thermal conductivity structure thermal adjustment operation strategy.

[0053] At the same time, based on the change range of the cooling load correction factor in the identified high-risk thermal interference area and the current cooling source supply capacity, it is determined whether there is a cooling source allocation margin. If the determination result is that there is a cooling source allocation margin, a cooling source allocation operation strategy including the location of the target area, the load adjustment coefficient and the cooling source path is generated.

[0054] By simultaneously implementing the thermal insulation adjustment strategy of the heat-conducting structure and the cold source allocation strategy, the joint intervention on the impact of thermal interference and the response of cold load is achieved, thereby realizing the coordinated control of cold load prediction and thermal conduction impact in the building thermal management system.

[0055] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0056] 1. This invention achieves closed-loop management of the entire process in integrated energy-saving design of new energy buildings, from heat flux data perception, thermal coupling path modeling, thermal interference spectrum construction to cooling load correction and dynamic control, realizing multi-dimensional performance improvements compared to existing technologies. Firstly, this scheme introduces a heat flux trajectory and thermal response profile modeling method based on a thermal resistance reference axis, accurately revealing the thermal coupling behavior of photovoltaic modules conducting heat to the building interior through metal thermal conductive structures. Furthermore, by extracting reverse heat flux intensity parameters and heat flux duration parameters, the thermal interference effect is quantitatively expressed as a visual spectrum. This method breaks through the traditional coarse approach of equating photovoltaic thermal effects with external climate loads, enabling building heat load analysis to leap from static estimation to dynamic measurement and refined transmission modeling, significantly improving the accuracy of heat load assessment.

[0057] 2. This invention, by constructing a reverse thermal interference spectrum identification mechanism that matches the thermal risk boundary model, combined with cooling load correction factor calculation and dynamic feedback control process, achieves the identification, classification, and coordinated response of high-risk thermal interference areas. The system can not only propose differentiated intervention strategies for different levels of thermal interference areas (such as thermal path adiabatic adjustment and cold source allocation), but also achieve synchronous coordination of cooling load prediction and thermal conduction control based on the rate of change of thermal interference intensity. The overall solution has high engineering practicality, effectively solving the problem of misjudgment of the internal thermal environment of buildings due to photovoltaic module heating under high thermal conductivity structural conditions, improving the reliability and overall energy efficiency of building air conditioning system design, and demonstrating good energy-saving benefits and the advantages of intelligent system synergy. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0059] Figure 1 This is a flowchart illustrating the energy-saving design and analysis method for integrated new energy building according to the present invention. Detailed Implementation

[0060] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0061] This invention provides, for example Figure 1 The energy-saving design and analysis method for integrated new energy buildings shown includes the following steps:

[0062] S01. Heat flow sensing elements and temperature sensing materials are installed on the outer surface of photovoltaic modules, the connection points of metal heat-conducting structures and the corresponding walls inside the building. Heat flow data, temperature rise data and power output data are collected synchronously through optical signal transmission to form a data channel covering the photovoltaic heating path and heat conduction path.

[0063] To address the issue that heat generated by photovoltaic modules may be conducted into the building interior along the metal thermally conductive structure and interfere with the building's cooling load assessment, the following steps can be used to construct a multi-point sensing data channel covering both the photovoltaic heating path and the heat conduction path for subsequent heat flux coupling analysis:

[0064] First, select several representative points on the outer surface of the photovoltaic modules laid near the building skin, and evenly deploy heat flow sensing elements and temperature sensing materials. Preferably, heat flow plates with miniaturized structures and thermal response films with high sensitivity are used. The heat flow sensing elements are used to obtain the heat flux density per unit area of ​​the photovoltaic modules under different operating conditions, and the temperature sensing materials are used to record the surface temperature change curve over time, ensuring continuous tracking of the dynamic changes in the heating behavior of the modules.

[0065] Secondly, at the connection points of the metal heat-conducting structure between the photovoltaic modules and the building structure, heat flow sensing elements and temperature sensing materials of the same type as described above should be installed. These are preferably placed at the bottom of the guide rails, at the joints of the aluminum profiles, or at the support structure adjacent to the backsheet of the photovoltaic modules. This ensures accurate capture of the heat transfer process from the modules to the support structure. The placement of these elements must consider the distribution of structural thermal bridge effects, ensuring that the sensing locations cover typical heat conduction paths to the greatest extent possible, and guaranteeing a good fit between the sensing elements and the structure to improve the accuracy of heat flow sensing.

[0066] Third, in the building's interior wall areas directly connected to the aforementioned metal heat-conducting structure, select corresponding locations to deploy the same type of heat flow sensing elements and temperature sensing materials to monitor the heat flow response and temperature rise changes ultimately conducted into the indoor environment via the heat conduction path. This deployment should focus on areas with strong structural thermal coupling, such as interior ceiling corners, inner roof panels, and beam-column intersections, to capture the terminal response behavior of heat transfer from the outside in, providing a data loop for reverse heat flux analysis.

[0067] Finally, to ensure the synchronization and stability of data collection across all deployment points in the time dimension, the acquisition signals of all heat flow sensing elements and temperature sensing materials are integrated into a set of optical signal transmission channels. A one-to-one data input link is established through optical fiber lines, and each acquisition point is assigned a unique identification code. Combined with a synchronously triggered optical acquisition mechanism, the heat flow data, temperature rise data, and power output data of the three key heat conduction locations—photovoltaic modules, heat-conducting structures, and indoor walls—are collected in a unified manner. Ultimately, a complete data channel covering the entire heat conduction path is formed in the integrated building structure, providing spatiotemporally consistent data support for subsequent unified thermal resistance analysis and reverse coupling strength calculation.

[0068] The reason for employing the aforementioned deployment of heat flux sensing elements and temperature sensing materials, and constructing a complete data channel covering both the photovoltaic heating and heat conduction paths, is that in building-integrated photovoltaic (BIPV) structures, photovoltaic modules, as energy input units, are directly integrated with the building envelope. The heat released during their operation not only impacts the external environment but may also form a hidden reverse heat flux path through the metal thermally conductive support structure, conducting heat to the indoor space and interfering with the accurate assessment of the building's cooling load. Without a multi-point continuous acquisition mechanism for this path, the temperature rise changes and energy transfer behavior during heat conduction are difficult to quantify and model, leading to systematic deviations in key parameters such as cold source configuration, insulation design, and air conditioning load estimation in energy-saving design analysis, thus reducing the credibility of overall energy efficiency simulation. Therefore, it is essential to establish a high-resolution, continuous, and synchronized data channel between the module's heat source, the thermally conductive relay structure, and the indoor response end, ensuring that the heat flux dynamics of each link are captured and reconstructed in real time. This provides a precise and comprehensive physical basis for subsequently establishing a unified thermal resistance analysis framework, identifying the reverse coupling strength of heat flux, and correcting cooling load predictions. This move not only fills the gap in the neglect of implicit heat transfer paths in existing design analysis, but also provides key support for constructing a closed loop of real thermal behavior, and is an indispensable technical prerequisite for achieving high-precision building energy efficiency analysis.

[0069] S02. The collected heat flow data, temperature rise data and power output data are time-series aligned and unit-normalized. Based on the thermal conduction characteristics of the heat-conducting structure and the thermal inertia parameters of the building envelope, a heat flux trajectory and thermal response profile with a unified thermal resistance reference axis are constructed.

[0070] In this embodiment, S02 specifically includes the following steps:

[0071] S021. Align the heat flow data, temperature rise data and power output data collected from the outer surface of the photovoltaic module, the connection point of the metal heat-conducting structure and the corresponding wall surface inside the building according to the time sequence of the light signal acquisition, and complete the cross-channel time synchronization by using the equal interval interpolation reconstruction method.

[0072] When multiple sensing devices are simultaneously deployed on the outer surface of photovoltaic modules, at the connection points of metal thermally conductive structures, and on corresponding walls inside buildings, the heat flow, temperature rise, and power output data collected by each sensing point often exhibit timeline misalignment due to differences in device response speed, signal delay, and trigger frequency. This results in inconsistencies in the temporal dimension of the collected data, making it difficult to establish a unified basis for thermal behavior analysis. Therefore, it is necessary to align all data based on the trigger time of optical signal acquisition and employ an equal-interval interpolation reconstruction method to achieve synchronous comparison of data from different channels at the same time point. The equal-interval interpolation reconstruction method refers to linearly or spline interpolating and filling the original data of each channel according to its timestamp on a timeline with a set uniform time step, ensuring that it has valid values ​​at all unified time points, thereby eliminating the impact of data temporal misalignment. This method approximates the values ​​at missing time points through an interpolation function, making all heat flow data, temperature rise data, and power output data form a standardized input sequence with the same frequency, time point, and accuracy. This process not only ensures the physical consistency of subsequent normalization transformations and path mapping operations, but also provides a unified and coherent data foundation for constructing heat flux trajectories and thermal response profiles.

[0073] S022. Convert the aligned heat flux data into heat flux density per unit area, convert the aligned temperature rise data into absolute temperature difference, and convert the aligned power output data into power generation value per unit area. Construct a standardized sequence of physical quantities to ensure consistency in the expression of subsequent thermal behavior.

[0074] S023. Based on the thermal conductivity of the material and the structural dimensions of the thermally conductive structure, extract the thermal resistance parameters of the conduction path, and combine the heat flux density per unit area and the absolute temperature difference to establish the heat flux intensity distribution. At the same time, extract the thermal inertia parameters based on the heat capacity and thermal diffusivity of the building envelope as the basic variables for constructing the dynamic thermal response process.

[0075] S024. In a uniformly constructed thermal resistance reference axis coordinate system, the heat flux density per unit area, absolute temperature difference, and power generation per unit area are mapped and embedded according to the path sequence to form a heat flux trajectory and thermal response profile, which are used to express the complete heat conduction path characteristics from photovoltaic to indoor.

[0076] The above setup aims to address the issue of the reverse coupling phenomenon of heat flux generated by photovoltaic modules transferring heat into the building interior through thermally conductive structures, which is often overlooked in existing energy-saving design analyses. By first unifying the time sequence of heat flow, temperature rise, and power output data collected from different spatial locations, and then using equal-interval interpolation reconstruction to achieve cross-channel time synchronization, a data foundation with temporal consistency can be established, avoiding thermal behavior distortion caused by data misalignment. Subsequently, the data undergoes unit normalization to form a standardized sequence of physical quantities, making the perceived data of different physical characteristics comparable and facilitating subsequent analysis. Next, thermal resistance information is extracted by combining the thermal conductivity and dimensional parameters of the thermally conductive structure, and thermal inertia characteristics are extracted by combining the heat capacity and thermal diffusivity parameters of the building envelope. This accurately reconstructs the heat storage and transfer characteristics during the heat transfer process, providing a physical basis for dynamic thermal response modeling. Finally, by embedding standardized data into a unified thermal resistance reference axis, a heat flux trajectory and thermal response profile are constructed. This not only accurately represents the complete heat transfer path from photovoltaic to the interior but also provides precise evidence for identifying the strength of reverse coupling and areas of cold load interference, thus providing reliable support for subsequent energy-saving design and control measures.

[0077] In this embodiment, S022 specifically refers to:

[0078] The heat flux data of each aligned heat flux sensing point is normalized according to the actual area of ​​the sampling surface, and the heat flux density per unit area data sequence is output, while keeping the time axis sequence consistent with the heat flux sampling time sequence.

[0079] To ensure consistency in heat flux representation across different heat flux sensing points, the raw measurement values ​​of each heat flux data point need to be normalized based on its actual sampling area to obtain the heat flux density per unit area. This process can be achieved by dividing the total heat flux at each sensing point by its corresponding effective sensing area. Specifically, this involves: first, reading the instantaneous heat flux value collected by each sensing device at a specific time point, in watts (W); second, obtaining the sensing area corresponding to that sensing point, in square meters (m²); and then dividing the heat flux value by the area to obtain the heat flux density per unit area, in W / m². This calculation needs to be performed sequentially at each time point, ultimately outputting a complete sequence of heat flux density per unit area data. For example, if a heat flux sensor collects an instantaneous heat flux of 30 W on an area of ​​0.01 m², its heat flux density per unit area is 30 ÷ 0.01 = 3000 W / m². By repeating this calculation at each time point, a data sequence of heat flux density per unit area for that sensing point throughout the entire sampling period can be constructed, ensuring that its timeline order remains completely consistent with the original acquisition sequence. The purpose of this normalization operation is to eliminate the deviation in heat flux values ​​caused by different sensing areas in different deployment regions, so that subsequent thermal behavior analysis has a unified dimension, facilitating the comparison of thermal coupling characteristics and path modeling across points.

[0080] The relative temperature difference of each aligned temperature sensing point is calculated one by one, with the temperature reading at a fixed initial time point as the reference value, to construct an absolute temperature difference data sequence, and it is kept consistent with the time node of the heat flow data.

[0081] To accurately depict the temperature rise trend at each point in the heat conduction path over time and to standardize the thermal response scale, it is necessary to use the temperature reading at a fixed initial time point as a reference benchmark and calculate the relative difference of the temperature change at each temperature sensing point at subsequent time points. This yields the magnitude of the temperature change relative to the initial state, i.e., the absolute temperature difference. This process can be achieved by subtracting the temperature value at the initial time (e.g., t=0) from the temperature value at each time point, obtaining the relative temperature difference value at each time point. All differences are arranged in chronological order to form the absolute temperature difference data sequence for that point. For example, if the temperature of a temperature sensing material is 28℃ at t=0, and the temperatures at t=1s, 2s, and 3s are 29.1℃, 30.4℃, and 31.0℃ respectively, then the corresponding absolute temperature differences are 1.1℃, 2.4℃, and 3.0℃, and the sequence is [1.1, 2.4, 3.0]. This sequence must be consistent with the corresponding heat flux data time nodes to ensure the comparability of the two types of physical quantities on the same time axis and the consistency of the thermal behavior mapping logic. This calculation method not only eliminates system offsets caused by differences in initial sensor calibration, but also accurately reflects the dynamic response of the heating process at a specific structural location, which helps to accurately construct the coupling relationship between heat conduction flux and temperature rise response in subsequent analysis.

[0082] The aligned power output data is normalized and converted according to the power generation area corresponding to the photovoltaic module to obtain a sequence of power generation values ​​per unit area and establish a unified unit expression.

[0083] In the energy-saving design analysis of new energy buildings, the power generation area of ​​different photovoltaic modules varies. If the raw power output data is directly used for thermal behavior analysis, the energy density will be distorted due to the inconsistency in area, thus affecting the accuracy of the thermal-electric coupling assessment. To solve this problem, the power output data collected by the photovoltaic modules needs to be normalized according to their corresponding actual power generation area, converted into the power generation value per unit area, and a data sequence with a unified scale needs to be constructed. This process can be achieved by dividing the power output value at each time point by the effective power generation area of ​​the photovoltaic module, outputting the power generation value per unit area, and arranging them in chronological order to form a complete power value sequence. For example, if a photovoltaic module has an area of ​​5.0 square meters, and its output power at t=1min, 2min, and 3min is 450W, 500W, and 480W respectively, then the corresponding power generation values ​​per unit area are 90W / m², 100W / m², and 96W / m², respectively, forming a power value sequence of [90, 100, 96]. By using a unified unit of expression, an equal comparison between electrical energy output and heat flux can be achieved, providing standardized input parameters for the subsequent construction of heat flux trajectories. At the same time, it ensures the comparability of energy indicators between different photovoltaic modules or deployment areas, thereby improving the accuracy of quantitative analysis of the heat-electric relationship and the consistency of lateral evaluation between structural locations.

[0084] The heat flux density data sequence, absolute temperature difference data sequence, and power generation value sequence per unit area are arranged in a corresponding manner on the time axis to establish a standardized physical quantity sequence with unified time series and dimensions, which is used for subsequent heat flux trajectory construction.

[0085] In the integrated energy-saving design and analysis of new energy buildings, heat conduction behavior, temperature rise response characteristics, and changes in power output often involve multiple data sequences with different dimensions and sampling frequencies. If these sequences are not uniformly aligned, accurate correspondence between variables in path analysis will be impossible, affecting the effectiveness of heat flux trajectory construction. To achieve consistency of multidimensional data in terms of time dimension and physical units, the heat flux density data sequence per unit area, the absolute temperature difference data sequence, and the power generation value sequence per unit area need to be arranged correspondingly on the time axis. This ensures a one-to-one correspondence between the three types of data at each time point and is standardized into a unified unit system. Specifically, this can be achieved by uniformly setting equally spaced time sampling points, such as one data unit per minute, and then inserting data from various data sources arranged according to the same time point into a new sequence, constructing a multidimensional array under a unified time index. For example, at t=1 min, the three data sequences are 85 W / m², 3.5°C, and 92 W / m², respectively; at t=2 min, they are 90 W / m², 3.8°C, and 98 W / m², and so on, ultimately forming a standardized physical quantity sequence in the form of [(1 min, 85, 3.5, 92), (2 min, 90, 3.8, 98)]. This provides unified, complete, and synchronous data support for subsequent heat flux trajectory construction, ensuring high consistency matching of key heat conduction variables in path modeling and avoiding distortion of the thermal response spectrum or evaluation bias caused by incoordination of data sources.

[0086] In this embodiment, S023 specifically refers to:

[0087] Based on the type of material corresponding to the heat-conducting structure, the thermal conductivity value is obtained, and combined with the thickness and cross-sectional area of ​​the structure, the thermal resistance value is calculated according to the one-dimensional steady-state heat transfer theory to quantify the heat flow conduction capacity.

[0088] This process can be calculated based on one-dimensional steady-state heat transfer theory, which assumes that heat conduction occurs only in a single direction and that the heat flow remains constant over time. In this case, the thermal resistance value can be derived from the relationship between the structural thickness, cross-sectional area, and the thermal conductivity of the material. Specifically, the thermal resistance value is calculated as R = L / (λ·A), where L is the length of the heat conduction path (i.e., the structural thickness), A is the heat transfer cross-sectional area, and λ is the thermal conductivity of the heat-conducting material. Thermal conductivity values ​​can be obtained from material databases or standard thermal property tables. For example, the thermal conductivity of aluminum profiles can be taken as 230 W / (m·K), and that of steel structures is approximately 50 W / (m·K). For instance, for an aluminum rail with a thickness of 0.01 m and a cross-sectional area of ​​0.005 m², its thermal resistance R = 0.01 / (230 × 0.005) ≈ 0.0087 K / W. This thermal resistance value can be considered as the degree to which the structure inhibits heat conduction. The smaller the thermal resistance, the easier it is for heat to enter the room. Therefore, each segment of the heat-conducting structure must be accurately modeled to accurately reflect the heat distribution when constructing the subsequent thermal response path, avoiding distortion of the cooling load assessment due to ambiguity in the heat conduction path. This process of thermal resistance quantification based on physical feature extraction and combined with a steady-state heat transfer model can improve the accuracy of heat flux assessment in building energy-saving design.

[0089] The heat flux density per unit area and the absolute temperature difference are matched point by point according to the location of the heat flow path, and then combined with the corresponding thermal resistance value to construct a heat flow intensity distribution sequence of the surface heat conduction path, which reflects the degree of heat input concentration in different parts.

[0090] To accurately represent the concentration of heat input at various points along the heat conduction path from photovoltaic modules to the building interior, it is necessary to map the heat flux density and absolute temperature difference data per unit area to the location along the heat flow path, and combine this with the thermal resistance value at each location to construct a heat flux intensity distribution sequence along the surface-to-surface heat conduction path. In terms of implementation, firstly, the heat flux and temperature difference data at key locations such as the photovoltaic outer surface, metal thermally conductive connections, and building interior walls should be spatially mapped to ensure that each data point can find its corresponding physical location along the heat conduction path. Next, the heat flux density is combined with the corresponding thermal resistance value, and the heat flow penetration capacity per unit thermal resistance at each location is calculated to form a heat flux intensity index. This index reflects the concentrated heat conduction area through the change in heat flux density gradient between data points. For example, if the thermal resistance value of a thermally conductive connection point is low while the corresponding heat flux density is significantly high, then this point can be identified as a concentrated heat flow area. Arranging the above heat flux intensity data according to the path sequence constructs a heat flux intensity distribution sequence, which reveals the local accumulation trend and distribution structure of heat during the conduction process. This sequence not only enhances the spatial identification capability of the thermal coupling-affected area, but also provides a key reference for subsequent dynamic response analysis and cooling load adjustment, avoiding misjudgments in energy-saving design due to unclear identification of heat flow concentration points.

[0091] The heat capacity and thermal diffusivity parameters of the filling layer and surface material in the building envelope are extracted to form a set of thermal inertia parameters associated with the structural layout, which are used to support the modeling of heat storage characteristics in the subsequent dynamic thermal response process.

[0092] To accurately simulate the temperature response of a building structure under heat flow disturbance, it is necessary to extract key thermophysical parameters, particularly heat capacity and thermal diffusivity, from the infill layers and surface materials of the building envelope, and construct a thermal inertia parameter set based on the spatial distribution relationship of each structural layer. In practice, this requires first clarifying the material composition and arrangement sequence of each wall, roof, and floor slab according to the architectural design drawings, and determining the location, thickness, and material type of each structural layer. Then, using a material thermal database or actual testing methods, the specific heat capacity and thermal diffusivity of the corresponding materials are extracted. Heat capacity reflects the energy required for a component to absorb heat per unit volume, while thermal diffusivity describes the rate at which heat propagates within the material. Combining the two characterizes the component's ability to accumulate and respond to thermal energy. To reflect the influence of structural layout on heat conduction behavior, the above parameters need to be normalized and combined according to the arrangement sequence and thickness ratio of the structural layers to construct a thermal inertia parameter set, which can reflect the overall heat storage and heat transfer dynamic behavior of the building structure under heat flow disturbance. For example, if an exterior wall consists of a concrete base, a rock wool filling layer, and a metal shell, the thermal inertia parameter set needs to include the heat capacity and thermal diffusivity data of the three materials, weighted according to their thickness, and then used for subsequent thermal response curve modeling and load impact assessment. This process helps to accurately capture the hysteresis and buffering characteristics of thermal coupling effects in the time dimension, and is an important prerequisite for constructing a dynamic energy-saving response analysis model.

[0093] In this embodiment, S024 specifically refers to:

[0094] A thermal resistance reference axis coordinate system is established, and the heat conduction path is unfolded in sequence according to the three key hot spots of photovoltaic modules, heat conduction structure and indoor wall surface. The thermal resistance value is arranged along the axis in an cumulative manner to form a two-dimensional mapping domain with continuous thermal resistance coordinates.

[0095] To model the continuous heat conduction characteristics from the photovoltaic heating path to the indoor heated area, a reference axis coordinate system based on thermal resistance needs to be established, and a two-dimensional mapping domain capable of characterizing the segmented heat conduction process along the path needs to be constructed. This process first requires clarifying the structural sequence of the heat conduction path, which typically includes the photovoltaic module surface, the metal heat-conducting structure connecting the module and the building, and the final contact area with the indoor wall surface. These three heat points are considered key nodes in the heat conduction chain. In spatial coordinate construction, the path direction is used as the main axis, and the thermal resistance values ​​of each structural segment are sequentially superimposed according to the heat conduction sequence to form a reference axis with cumulative thermal resistance characteristics. The thermal resistance values ​​of each segment can be obtained based on the previously extracted material thermal conductivity and structural geometric parameters, calculated using a one-dimensional steady-state heat transfer model. Subsequently, each thermal resistance increment position on the axis is mapped one-to-one with its corresponding heat flow sensing point, temperature rise sensing point, and power output point to establish a two-dimensional coordinate graph, where the horizontal axis represents the cumulative thermal resistance value, and the vertical axis represents the intensity or response state of the standardized thermophysical quantity. The establishment of this thermal resistance reference axis coordinate system helps to unify the spatial correspondence between heat flow, temperature rise and power generation behavior in the structure. On the other hand, it provides a basic framework for subsequent heat flux trajectory connection and thermal response profile construction, so that the thermal coupling relationship between different structural segments can be continuously expressed in a unified thermal resistance dimension, thereby improving the integrity and accuracy of energy-saving design analysis.

[0096] The heat flux density data sequence per unit area is mapped to the location of the photovoltaic module along the thermal resistance coordinate axis, the absolute temperature difference data sequence is mapped to the nodes of the heat-conducting structure, and the power generation value sequence per unit area is synchronously mapped to the initial location of the photovoltaic module. The three types of data form a heat input-conduction-response path relationship.

[0097] To model the complete heat conduction path from photovoltaic (PV) heating to indoor heating in a building-integrated photovoltaic (BIPV) structure, standardized physical quantities from different sources need to be accurately mapped to corresponding positions on the thermal resistance reference axis. The heat flux density per unit area data sequence reflects the heat release intensity of the PV module's outer surface and should therefore be mapped to the PV module's position near the starting point of the thermal resistance coordinate axis. The absolute temperature difference data sequence reflects the temperature difference change along the heat-conducting structure and should be mapped to the heat-conducting structure segment on the thermal resistance axis according to the actual position of each sensing point on the heat conduction path. The power generation per unit area data sequence is essentially directly related to the PV module's operating state and should be synchronously mapped to the PV module's starting point to reflect the dynamic changes in the input energy source. Through this mapping, each type of data occupies a coordinate on the thermal resistance axis that matches its physical source and structural position, achieving a continuous spatial representation of heat energy from PV heating and heat conduction to the building's interior wall response. This process can be accomplished by establishing a unified coordinate system, calibrating the structural geometric thermal resistance distribution, and binding data according to the sensor placement numbers. The core significance of completing this mapping lies in the fact that by constructing an input-conduction-response path chain for the three types of data sequences along the thermal resistance dimension, it is possible not only to quantitatively present the spatial patterns of building thermal coupling effects, but also to provide a data foundation with a clear physical hierarchy and close node correlation for subsequent heat flux trajectory analysis and interference identification.

[0098] Based on the relative distance and physical sequence order of each point on the thermal resistance axis, the three types of standardized data after mapping are connected to form a heat flux trajectory and thermal response profile that reflect the heat flow change trend and temperature rise response law along the thermal resistance change path.

[0099] To accurately characterize the heat transfer path between photovoltaic modules and indoor walls, three types of standardized physical data mapped onto a thermal resistance reference axis need to be connected along their corresponding thermal resistance coordinates. The position of each data point on the thermal resistance axis represents the degree of accumulated heat transfer resistance along the heat conduction path, while the physical sequence reflects the temporal and spatial evolution of heat transfer. Therefore, by connecting the data points corresponding to the heat flux density per unit area, absolute temperature difference, and power generation per unit area in ascending order of thermal resistance coordinates in a two-dimensional thermal resistance-physical quantity coordinate system, a continuous heat flux trajectory and thermal response profile can be constructed. This trajectory connection can be achieved using curve fitting, piecewise linear interpolation, or piecewise polynomial approximation to ensure that the heat transfer characteristics at different locations are fully expressed. In practice, it is necessary to ensure that the mapping intervals of the three types of data on the thermal resistance axis are non-overlapping and in the same order, thus forming a directed data flow graph reflecting the relationship between heat input, conduction, and response. This construction method not only allows for a direct visualization of the heat flow's layer-by-layer attenuation trend and the delayed response mode of temperature rise in the heat conduction path, but also provides precise visualization support for identifying abnormal thermal resistance sections and energy consumption anomalies. It is an important foundation for achieving high-resolution thermal behavior analysis and energy-saving optimization design.

[0100] S03. Based on the heat flux trajectory and thermal response profile, extract the reverse heat flux intensity parameter and the heat flux duration parameter, and combine them with the temperature control target and thermal conductivity properties set in the building to construct a reverse thermal conduction interference map, which is used to determine the degree of thermal conduction interference of the cooling load in the building.

[0101] In this embodiment, S03 specifically includes the following steps:

[0102] S031. Extract the heat flux direction change data from the photovoltaic module to the building interior path from the heat flux trajectory and thermal response profile, and extract the reverse heat flux intensity parameter and the heat flux duration parameter based on the section where the heat flow direction changes from the outside to the inside.

[0103] S032. The extracted reverse heat flux intensity parameters and heat flux duration parameters are matched with the temperature control targets corresponding to the areas within the building, and the thermal resistance, structural thickness and thermal diffusivity of each material in the thermal conductive structure are combined to form a set of building thermal conductive structure attributes.

[0104] When matching the extracted reverse heat flux intensity parameters and heat flux duration parameters with the corresponding temperature control targets for different areas within the building, the first step is to map the endpoint of each heat flux response path to a specific indoor zone, such as a living room, bedroom, or equipment room, based on the functional zoning of the building space. Then, the corresponding temperature control parameters are extracted according to the target temperature control value set for that zone in the building's operation strategy. Subsequently, for the reverse heat flux intensity parameter in each heat flux path, the energy coupling degree of the thermal interference is determined based on the offset between its peak heat flux density and the target temperature control range of that zone. As for the heat flux duration parameter, it is compared with the start-up and shutdown time of the zone's air conditioning or the thermal capacity response cycle to determine whether the heat input has a significant impact. To more accurately determine the impact of heat conduction paths on indoor cooling loads, the two parameters mentioned above need to be integrated with the material properties of the thermally conductive structures along the path. Specifically, this includes the thermal resistance (calculated from thermal conductivity and thickness), thermal diffusivity (derived from thermal conductivity, density, and specific heat capacity), and geometric thickness of each structural layer. This establishes a set of building thermally conductive structural properties that are consistent with path continuity and have clearly defined spatial locations. This set not only expresses the heat conduction capacity through each layer of the building envelope but also provides a multi-dimensional quantitative reference for subsequently constructing a thermal interference level determination model, serving as essential support for path energy determination and regional heat load response analysis.

[0105] S033. Based on the reverse heat flux intensity parameter, the heat flux duration parameter, the temperature control target and the thermal conductivity structure attribute, a coordinate mapping domain is constructed in three-dimensional space. Different thermal interference characteristics are mapped to the coordinate domain to form a partitioned expression, and a reverse thermal conduction interference map is generated to determine the degree of thermal conduction interference of the cold load inside the building.

[0106] The fundamental purpose of the above analysis is to accurately determine the abnormal changes in cooling load that may be caused by the heat transfer of photovoltaic modules into the building interior through high thermal conductivity structures. In practical applications of building-integrated photovoltaics (BIPV), photovoltaic modules are not only energy input units but also potential sources of localized thermal interference, especially when there is a direct thermal coupling path between them and the building structure, making it easier to form reverse heat flux from the outside in. By extracting physically directional reverse heat transfer characteristics from the heat flux trajectory and thermal response profile, and further extracting the two key parameters of reverse heat flux intensity and duration of impact, the intensity and timeliness of thermal coupling behavior can be quantified. Furthermore, by matching these thermal interference characteristics with the temperature control targets set for each functional area within the building, and by establishing a set of thermal conductivity attributes based on the physical characteristics of specific thermally conductive structures, the occurrence of thermal interference can be further closely linked to the spatial structure and thermal inertia mechanism. Finally, a coordinate mapping domain was constructed based on the combination of multiple parameters, and a reverse thermal interference map was generated. This not only enabled the visualization of different thermal interference levels, but also provided a scientific, quantitative, and spatially resolution foundation for subsequent cooling load prediction models and thermal impact control strategies, thereby improving the accuracy and pertinence of the entire energy-saving design analysis.

[0107] In this embodiment, S031 specifically refers to:

[0108] In the heat flux trajectory, the heat flux segments that are conducted from the outside to the inside are selected according to the path direction, and the reverse heat flux intensity parameter is extracted from the point corresponding to the maximum heat flux value in the time series to represent the maximum disturbance energy density in the heat conduction path.

[0109] The goal of this study is to identify heat flux segments that conduct heat from the outside inwards along the path of the photovoltaic (PV) modules, aiming to determine whether there is a trend of heat transfer from external modules to the building interior via thermally conductive structures during PV module operation. This identification process involves establishing a unified thermal resistance reference axis coordinate system, mapping the heat flux density per unit area to each node location according to the heat conduction path direction, and then combining this with the sign of the heat flow direction at each moment to identify path segments with negative or significantly lower heat flux density values ​​than the forward conduction benchmark, thus classifying them as reverse heat flux segments. Subsequently, within the identified reverse conduction segments, the point with the maximum absolute value of heat flux density in the time series is extracted, and the corresponding heat flux density value is used as the reverse heat flux intensity parameter to quantify the maximum instantaneous heat input density during the thermal interference process from PV to the interior. This parameter reflects the peak energy input intensity in the thermal coupling path, helping to identify which time periods and path nodes have a stronger risk of thermal disturbance. The reverse heat flux intensity parameters extracted in this way not only have spatiotemporal positioning characteristics, but also provide basic data support for the identification of strong interference areas in the subsequent heat conduction interference spectrum and the regulation of cold load sensitive areas, ensuring that the assessment of thermal behavior has structural correlation and quantifiable interference intensity.

[0110] Within the same heat flux range, determine the time interval during which the heat flux continuously exceeds the static baseline value, and extract the time span of the continuous heat transfer process as a parameter for the duration of the heat flux's continuous influence.

[0111] Determining the time interval within the same heat flux segment where the heat flux continuously exceeds the static baseline value is to quantify the duration of the sustained impact of heat energy on the building interior along the reverse conduction path, and then extract the time characteristic parameter reflecting the persistence of thermal disturbance, the "heat flux duration parameter." The "static baseline value" refers to the average steady-state heat flux density at each node of the heat conduction path under conditions of no external thermal disturbance. It is typically obtained by statistically averaging heat flow data collected at night, during non-power generation periods, or when components are not operating, reflecting the background heat flow level of the structure under natural thermal equilibrium conditions. In practice, a time window can be defined for the reverse conduction segment in the heat flux trajectory, and the heat flow data for this segment can be compared hourly to identify segments where the heat flux density value exceeds the static baseline value at consecutive time points. The start and end times are marked, and the span time is calculated as the duration of the reverse thermal disturbance in that segment. The duration of the heat flux effect parameter helps reveal that thermal disturbances are not instantaneous pulses, but rather have a time-cumulative nature, posing a potential heat storage risk to the building's internal cooling load. Especially under high heat capacity building envelope conditions, its effects can extend beyond the system's steady-state adjustment cycle. Therefore, this parameter is an indispensable dynamic thermal behavior indicator in constructing a reverse thermal disturbance map.

[0112] By binding the reverse heat flux intensity parameter and the heat flux duration parameter with the original heat flux trajectory sequence, a thermal interference feature data pair with time label and path location identifier is established for subsequent association with building control parameters.

[0113] Binding the reverse heat flux intensity parameter and the duration of heat flux impact parameter to the original heat flux trajectory sequence is to form an information structure with complete temporal dimension and spatial path positioning. This allows for the construction of feature data pairs that reflect the thermal interference characteristics of specific locations within a building, facilitating subsequent organic association with parameters such as temperature control targets and response strategies set in the building control system. This supports the judgment of thermal interference risk levels and control decisions. The original heat flux trajectory sequence refers to a complete heat conduction path data set mapped and connected on the thermal resistance coordinate axis according to the path sequence of photovoltaic modules—thermal conductive structure—indoor wall, based on three standardized physical quantities: heat flux density per unit area, absolute temperature difference, and power generation per unit area. It typically includes the heat flux intensity, temperature rise level, and evolution trajectory of each key node on the time axis. In specific implementation, a structure record can be constructed for each reverse thermal disturbance segment, with fields including: start and end timestamps, path start and end coordinates, reverse heat flux intensity value, duration length, and the original heat flux trajectory index position of that segment. This generates thermal interference feature data pairs with spatiotemporal positioning attributes. This binding process can be implemented using hash indexes or time-path double-key mapping to ensure that each thermal disturbance event can be accurately mapped to the original heat conduction behavior. This structured data combination not only facilitates the classification and quantification of thermal disturbances when constructing subsequent thermal conduction disturbance maps, but also lays the data foundation for forming a closed-loop control logic with the dynamic feedback mechanism of the building control system in the future.

[0114] In this embodiment, S033 specifically refers to:

[0115] A two-dimensional coordinate domain is established with the reverse heat flux intensity parameter as the horizontal axis and the heat flux duration parameter as the vertical axis, and the offset range of the building temperature control target value is used as the contour distribution line.

[0116] A two-dimensional coordinate domain is established with the reverse heat flux intensity parameter as the horizontal axis and the heat flux duration parameter as the vertical axis. The aim is to construct a mapping space that can visually express the degree of thermal disturbance, allowing for clear classification of different heat conduction disturbance events along the two key dimensions of intensity and duration. This facilitates quantitative matching with the building's internal cooling load capacity. Specifically, the reverse heat flux intensity value extracted for each thermal disturbance event is first used as the horizontal coordinate input. This value is typically expressed in W / m², reflecting the maximum heat density during the heat flow from the outside to the inside. Then, the corresponding heat flux duration, expressed in seconds or minutes, is used as the vertical coordinate input to reflect the duration of the heat flux disturbance. Subsequently, the offset range of the building's temperature control target value is superimposed on the two-dimensional coordinate system as a contour line. This means that the difference between the upper and lower limits of the allowable fluctuation of the temperature control setpoint for each functional area or temperature-controlled space is defined as the control bandwidth (e.g., if the target is 25℃ and the allowable deviation is ±1℃, then the offset range is 2℃). Based on this, the temperature control impact of different combinations of thermal interference (intensity + duration) is graded and labeled in the coordinate system, such as low-risk areas, medium-interference areas, and high-risk areas. This contour line can be used to establish the influence curve of heat flux on indoor temperature rise through simulation calculations and to infer the isothermal offset boundary, or it can be formed by fitting a function model with measured data, thus creating a response threshold benchmark for the temperature control target on the two-dimensional map. The two-dimensional coordinate domain constructed in this way not only makes the distribution characteristics of thermal interference impact more intuitive but also provides a standardized underlying structure for subsequently superimposing thermal conductivity attributes to form a multi-dimensional interference identification map.

[0117] Based on the thermal resistance, thermal diffusivity and thickness of each structural layer in the thermal conductivity properties, a set of building response correction factors corresponding to each coordinate point is established and integrated into the coordinate domain to form a three-dimensional parameter set.

[0118] To further improve the accuracy of thermal disturbance assessment, it is necessary to introduce the building's own thermal response characteristic parameters into the established two-dimensional coordinate domain, thereby constructing a three-dimensional parameter set with structural adaptability. This process uses the thermal resistance, thermal diffusivity, and structural thickness of each structural layer as basic variables in the thermal conductivity properties to construct a set of building response correction factors corresponding to each thermal disturbance coordinate point (a combination of reverse heat flux intensity parameters and heat flux duration parameters). Specifically, firstly, the thermal resistance of each structural layer is calculated using the thermal resistance formula R=L / λ, where L is the material thickness and λ is the material thermal conductivity; then, the thermal diffusivity α=λ / (ρ*c), where ρ is density and c is specific heat capacity, is used to assess the speed of heat propagation in the material. Subsequently, a response time correction factor and a buffer coefficient are established for each structural layer, and these factors are combined to form a thermal response vector bound to the thermal disturbance parameter points, reflecting the building's actual response speed and heat storage capacity to thermal disturbances. This vector is embedded at each point in the two-dimensional thermal disturbance coordinate domain, forming a three-dimensional parameter set with reverse heat flux intensity, heat flux duration, and structural thermal response capability as coordinate axes. In this three-dimensional coordinate system, different thermal disturbance events are not only distinguished by intensity and duration, but also take into account the thermal inertia properties of the building structure, enabling more precise zoning of heat conduction disturbance levels and the construction of dynamic control models. Integrating material thermal properties into the spectral analysis framework in this way helps improve the adaptability and generalization ability of thermal disturbance assessment across different building configurations.

[0119] The reverse heat flux intensity parameter, heat flux duration parameter, building temperature control target value offset range, thermal resistance value, thermal diffusivity and structural thickness contained in the thermal conductivity properties are combined into a six-dimensional parameter set. The parameters are then mapped to the three-dimensional coordinate domain according to the coordinate axis embedding logic. Each thermal interference level region is identified by superimposing the contour density distribution and thermal response weight, generating a reverse thermal interference map that can be used to identify the thermal conduction interference level of the cold load inside the building.

[0120] To accurately identify the level of thermal conduction interference from cooling loads within buildings, a multi-dimensional parameter fusion mechanism needs to be introduced into the thermal interference assessment model. By constructing a complete six-dimensional parameter set from six core variables—reverse heat flux intensity, duration of heat flux impact, building temperature control target value offset range, thermal resistance, thermal diffusivity, and structural thickness—included in the thermal conductivity attributes, this set comprehensively covers the energy intensity, duration of effect, temperature control deviation, and structural thermal response capability along the heat conduction path. In the mapping process, a principal coordinate system is first constructed using reverse heat flux intensity, duration of heat flux impact, and temperature control target value offset range as the three-dimensional spatial coordinate axes. Then, the thermal resistance, thermal diffusivity, and structural thickness of each structural layer in the thermal conductivity attributes are converted into correction coefficients reflecting the structural thermal inertia and mapped as additional dimensions embedded in the logic to each coordinate point in the three-dimensional principal coordinate system, completing the fusion projection of the six-dimensional data in three-dimensional space. This mapping process transforms structural parameters into weighted expressions through embedding rules, enabling thermal interference parameters to acquire dynamic response characteristics in space. This results in the construction of a set of visualized reverse-guided thermal interference maps, which are used to express the distribution of the interference levels of heat conduction on building cooling load under different locations and different thermal structural conditions.

[0121] In the identification of thermal interference levels, the "overlay of contour density distribution and thermal response weights" is a region division method based on spatial statistics and parameter weighting. Contour density distribution refers to fitting the range of thermal interference parameters in a three-dimensional coordinate domain as a continuous function to create boundary lines with the same numerical distribution, used to delineate regional levels with different thermal interference intensities. Thermal response weights, on the other hand, are correction factors reflecting the building's response speed and heat storage capacity, constructed based on thermal resistance, thermal diffusivity, and thickness parameters extracted from thermal conductivity attributes. In practice, contour layers of thermal interference intensity and duration are first generated through data fitting and curve interpolation. Then, based on the structural correction weights corresponding to each coordinate point, the contour density spacing and coverage are adjusted to dynamically reflect the structural response capability's influence on the interference level boundary. Ultimately, the map not only clearly shows high-intensity or long-term thermally affected areas but also assigns differentiated level labels to different regions based on the building structure's buffering capacity against thermal disturbances, providing a visualized and quantitative basis for building thermal management strategies.

[0122] S04. Match the reverse thermal interference map with the pre-established thermal risk boundary model to identify high-risk, controllable-risk, and low-risk thermal interference areas, and calculate the cooling load correction factor for energy consumption correction based on the spatial distribution relationship and heat transfer characteristics of each type of thermal interference area.

[0123] In this embodiment, S04 specifically includes:

[0124] The reverse heat flux intensity parameters, heat flux duration parameters, building temperature control target value offset range and thermal conductivity property coordinates contained in the reverse heat conduction interference map are mapped to a unified parameter space, and then matched with the high-risk, controllable-risk and low-risk intervals defined in the pre-established thermal risk boundary model. Based on the matching results, the corresponding thermal interference level is marked for the thermal interference points in the map.

[0125] When mapping the reverse heat flux intensity parameters, heat flux duration parameters, building temperature control target value offset range, and thermal conductivity attributes contained in the reverse heat conduction interference map into a unified parameter space, these multidimensional physical parameters first need to be normalized to ensure they have consistent dimensions and data scales for easy representation in the same coordinate system. This coordinate space can be constructed using a multidimensional numerical space or a tensor grid model, where each dimension corresponds to the aforementioned key heat interference parameters. Subsequently, the parameter vector of each heat interference point in the map is embedded into this unified parameter space to form a specific location point. Then, by mapping and matching with the risk level intervals in the pre-established thermal risk boundary model, it is identified whether the point falls within the high-risk, controllable-risk, or low-risk boundary range. This matching can be implemented using Euclidean distance classification, clustering algorithms, or support vector boundary models. After matching, the heat interference points are labeled according to the thermal risk boundary label definition, and their heat interference level information is written back to the corresponding points in the map, forming a data structure with risk level classification, providing a basic zoning basis for the subsequent calculation of the cooling load correction factor. This process ensures the accuracy of classification of different thermal interference points under multi-factor conditions and the physical rationality of map partitioning.

[0126] Based on the spatial distribution of marked high-risk, controllable-risk, and low-risk thermal interference points, thermal interference areas with continuous boundaries are aggregated, and the spatial distribution characteristics of each type of thermal interference area are statistically analyzed, including the occupied area, distribution density, boundary orientation, and relative positional relationship with the target cooling load area.

[0127] When constructing thermal interference zones based on the spatial distribution of marked high-risk, controllable-risk, and low-risk thermal interference points, it is necessary to first spatially locate the classified thermal interference points using geographic coordinates or building construction coordinates. Density-based clustering algorithms (such as DBSCAN) or spatial connectivity analysis methods can be used to aggregate adjacent thermal interference points of the same category, forming thermal interference zones with continuous boundaries and consistent categories. After aggregation, boundary tracking algorithms (such as alpha shape method or boundary scanning) are used to identify the geometric contour of each thermal interference zone, and then its occupied area is calculated. Raster statistics or point density functions are used to analyze the distribution density of thermal interference points within each zone. Boundary fitting algorithms (such as minimum bounding rectangle or principal direction analysis) are used to determine the main orientation of the zone boundaries. Furthermore, combining architectural design drawings or coordinate data of the target cooling load area, spatial analysis techniques (such as nearest neighbor calculation or overlap measurement) are used to clarify the relative positional relationship between the thermal interference zone and the target cooling load area, thus providing accurate support for subsequent quantitative analysis of cooling load correction factors and regional policy implementation. The entire process relies on a clear spatial parameter extraction logic to ensure that the thermal interference zone division is consistent with the actual physical space.

[0128] Based on the spatial distribution of each type of thermal interference area and the heat transfer characteristics of each structural layer in the thermal conductivity properties, including thermal resistance, thermal diffusivity and structural thickness, a quantitative model reflecting the influence of the heat conduction path is constructed, and the energy offset affecting the building's cooling load in this area is extracted.

[0129] When constructing a quantitative model of the influence capacity of heat conduction paths based on the spatial distribution relationship of each type of thermal interference area and the heat transfer characteristics of each structural layer in the thermal conductivity properties, it is first necessary to map the spatial location of the thermal interference area to the building envelope it covers, and extract key thermal parameters such as thermal resistance, thermal diffusivity, and structural thickness of structural units such as walls, roofs, and glass curtain walls within the area. Building Information Modeling (BIM) or a structural hierarchy database can be used for structural hierarchy identification and parameter retrieval. Subsequently, a multi-layer series thermal resistance network model based on the heat transfer path is constructed, superimposing the thermal resistance of each structural layer to reflect the overall heat transfer capacity. Combined with thermal diffusivity modeling of transient thermal response capacity, a one-dimensional unsteady-state thermal conductivity equation is used to solve for the temperature rise distribution and heat accumulation under a specific interference duration. Furthermore, based on the distribution density, area, and path direction of the thermal interference area, the intensity of the reverse heat input from this area to the interior is quantified using numerical integration or finite difference methods. This is then compared with the energy demand of the building's target temperature zone for cooling load, extracting the energy offset caused by the thermal interference area to the cooling load, which serves as the core reference value for subsequent energy consumption correction. This modeling process not only considers the thermal properties of materials but also integrates regional spatial morphology, enabling a quantitative expression of thermal conduction interference capability.

[0130] The energy offset corresponding to each type of thermal interference area is converted into a unit area cooling load impact value. Combining the area coverage and interference duration weights, a cooling load correction factor for energy consumption correction is calculated and summarized to form a cooling load correction factor cluster covering the building space, which serves as the input basis for subsequent air conditioning load allocation and energy efficiency management.

[0131] When converting the energy offset corresponding to each type of thermal interference area into a unit area cooling load impact value, the energy offset of the thermal interference area extracted in the previous stage (in joules or kilowatt-hours) is first used to calculate the energy density based on the actual projected area of ​​the area, thus obtaining the unit area cooling load impact value (e.g., W / m²). Next, weighting factors are set according to the coverage of the area in the overall building space (i.e., the ratio of the area area to the total controlled area) and the duration of its thermal interference effect. For example, the coverage weight can be set as a linear ratio between 0.2 and 1.0, and the interference duration weight can be linearly superimposed based on the number of hours exceeding a set threshold. Finally, the unit area cooling load impact value is combined with the weighting factors through a weighted product model to calculate the cooling load correction factor for each type of thermal interference area (e.g., +15 W / m²). Subsequently, the cooling load correction factors for each type of area are mapped to the functional areas of the building according to spatial coordinates, forming a cooling load correction factor cluster covering the building space. For example, if a west-facing office area experiences high heat disturbance for three consecutive hours during periods of intense sunlight, with an energy offset of 900Wh and an area of ​​30m², the cooling load impact per unit area is 30Wh / m². Combining a coverage weight of 0.8 and a disturbance duration weight of 1.2, the cooling load correction factor is calculated as: 30 × 0.8 × 1.2 = 28.8Wh / m². This result is incorporated into the overall building cooling load correction map, providing a quantitative input basis for localized load scheduling and energy efficiency optimization of the air conditioning system.

[0132] S05. Combine the thermal interference classification results with the cooling load correction factor to establish a dynamic feedback control process, continuously analyze the trend of heat flux change, and when the thermal interference area continues to meet the high-risk conditions, execute the thermal insulation adjustment operation of the heat conduction structure or the building cold source allocation operation to achieve synchronous coordination between cooling load prediction and thermal conduction impact.

[0133] In this embodiment, S05 specifically includes:

[0134] A dynamic identification model for thermal risk status is constructed based on the thermal interference classification results and the cooling load correction factor. The model is input with the heat flux intensity change curve of each region and the corresponding cooling load correction value to form a thermal interference monitoring dataset with time dimension and risk level label.

[0135] This process forms the foundation for building a dynamic control mechanism. Its core lies in establishing a dynamic identification model for thermal risk status by utilizing the correlation between thermal disturbance classification results and cooling load correction factors. This model continuously monitors the heat flux intensity variation curves of each thermal disturbance area within the building, using these curves as input variables and linking them to the cooling load correction values ​​obtained from previous assessments to generate time-series data pairs. Specifically, multidimensional time series analysis can be used to extract the instantaneous gradient, fluctuation amplitude, and trend slope of heat flux changes in each area, and then, combined with the respective cooling load correction factor weights, perform labeling and level classification. By setting dynamic risk identification criteria in this model, data points at each monitored moment are matched to "high risk," "controllable risk," or "low risk" level labels based on their heat flux change characteristics and energy consumption sensitivity. This ultimately forms a thermal disturbance monitoring dataset containing time labels, spatial locations, and risk levels, providing a precise foundation for subsequent control strategies.

[0136] The heat flux intensity change value of each monitoring point in the heat flux trajectory is continuously acquired, and the heat interference intensity change rate is calculated by combining the time series analysis method. By comparing with the high-risk judgment threshold set in the risk identification model, the heat interference area that meets the high-risk state is identified.

[0137] This process aims to identify areas in buildings with potential thermal risks in real time. Its core lies in using time series analysis to extract trends in heat flux changes to determine whether a high-risk thermal disturbance state has been entered. Specifically, the system continuously collects heat flux intensity changes at each monitoring point along the heat flux trajectory, constructing a continuous sequence of heat flux changes over time. By applying time series analysis methods, such as moving average analysis, finite difference method, sliding window slope calculation, or exponential weighted smoothing, the rate of change, fluctuation amplitude, and abnormal acceleration of heat flux intensity are extracted. For example, the sliding window slope method can calculate the heat flux change trend value at each monitoring point over the past 10 minutes and compare it with a preset high-risk threshold in the thermal risk identification model (e.g., a change rate exceeding 5 W / m²·min). If the threshold is exceeded for several consecutive time periods, the system can identify the current area as a high-risk thermal disturbance area. This method not only reflects the heat flux intensity itself but also reveals its dynamic characteristics, helping to predict areas of sudden increases in cooling load pressure and providing early warning signals for subsequent control measures.

[0138] For the identified high-risk thermal interference areas, the thermal conductivity properties, including thermal resistance, thermal diffusivity and structural thickness, are combined to calculate the parts in the heat conduction path that can be thermally adjusted and their adjustment upper limit, and to determine the thermal conductivity structure thermal adjustment operation strategy.

[0139] To develop effective intervention measures for identified high-risk thermal interference areas, it is necessary to assess their thermal conductivity and adjustment potential by considering the corresponding thermal conductivity properties, including thermal resistance, thermal diffusivity, and structural thickness. Specifically, the system first quantifies and models the thermal properties of each structural layer in the thermal interference path, calculating the total thermal resistance layer by layer based on the one-dimensional steady-state heat transfer formula R=L / λ (where R is thermal resistance, L is layer thickness, and λ is thermal conductivity). Then, based on a building material properties database, it analyzes whether the path contains materials with variable thermal conductivity (such as phase change insulation coatings or adjustable density filler layers) to determine the specific locations where insulation adjustments can be made. For example, if a thermal path contains a 10mm thick metal plate with a thermal conductivity of 0.8W / m·K, its thermal resistance is low. It can be assessed whether adding a surface insulation layer (such as adding an insulating coating with a thermal conductivity of 0.03W / m·K) can increase its thermal resistance to the target value. Simultaneously, by combining thermal diffusivity and thermal inertia analysis, an upper limit for the adjustment response is set to avoid disrupting the overall thermal balance. The final output of the adiabatic adjustment operation strategy clearly includes the adjustment location, the amount of material parameter change, and the corresponding target thermal resistance value, providing an executable guide for physical intervention.

[0140] At the same time, based on the change range of the cooling load correction factor in the identified high-risk thermal interference area and the current cooling source supply capacity, it is determined whether there is a cooling source allocation margin. If the determination result is that there is a cooling source allocation margin, a cooling source allocation operation strategy including the location of the target area, the load adjustment coefficient and the cooling source path is generated.

[0141] After identifying high-risk thermal interference areas, it is necessary to assess whether effective cooling control can be achieved by allocating cooling sources to these areas. Specifically, the system first reads the change in the cooling load correction factor of the thermal interference area over time, calculating its cooling capacity deficit trend per unit area. Simultaneously, it retrieves the current operating status of the cooling source system, including parameters such as the main chiller load rate, regional fan coil unit capacity, and water system return water temperature and flow margin, constructing a model of the current cooling source supply capacity. Then, the peak change rate of the cooling load correction factor is compared with the dispatchable cooling capacity in the cooling source supply capacity. If the condition of "dispatchable cooling capacity ≥ cooling capacity deficit in the thermal interference area" is met, it is determined that there is sufficient cooling source allocation capacity. Under this condition, the cooling source allocation path is further determined, such as supplying cooling through nearby cooling source branches or adjusting the zonal control of the intelligent air supply system. Taking an office building area as an example, if the cooling load correction factor corresponding to the thermal interference in the area increases to +35W / m², and the adjacent branch fans in the central air conditioning system still have 50W / m² of available cooling capacity, a cooling source allocation operation strategy can be generated. This strategy includes control parameters such as the spatial location of the target area, the set load adjustment coefficient (e.g., lowering the target temperature by 2°C), and the cooling capacity allocation path (e.g., activating the north branch fan and increasing the chilled water flow rate). This strategy guides the system to implement energy reconfiguration and coordinated allocation of cooling loads.

[0142] By simultaneously implementing the thermal insulation adjustment strategy of the heat-conducting structure and the cold source allocation strategy, the joint intervention on the impact of thermal interference and the response of cold load is achieved, thereby realizing the coordinated control of cold load prediction and thermal conduction impact in the building thermal management system.

[0143] After constructing the thermal insulation adjustment strategy for the heat-conducting structure and the cold source allocation strategy, executing them simultaneously aims to achieve bidirectional control of both the heat conduction source and the indoor temperature control terminal when thermal interference continues to intensify, thereby improving the response speed and control accuracy of the building thermal management system. Specifically, the system first executes insulation adjustment operations along the heat conduction path based on the spatial location of high-risk thermal interference areas. This includes activating the thermal resistance enhancement function of the variable thermal resistance material on the exterior wall, or switching the reflective coating state of the photovoltaic backsheet to suppress heat input. Simultaneously, the intelligent air conditioning system injects additional cold source into the area, adjusting the fan speed and chilled water temperature to compensate for the rapid accumulation of indoor heat. The entire process maintains synchronized control logic through a linkage mechanism between thermal interference level labels and cooling load correction factors. For example, if a sudden increase in reverse heat flux intensity occurs in a conference room area and the duration of the heat flux impact exceeds the limit, the system simultaneously triggers the switching of the insulation state of the exterior metal heat-conducting panels (reducing the thermal conductivity from 15 W / m·K to 3 W / m·K) and activates the south-facing cold source branch to inject an additional 10 kWh of cooling capacity per hour. This ensures that the thermal interference level in the area is reduced from high risk to controllable within ten minutes, ultimately achieving coordinated stability between thermal behavior suppression and indoor cooling load response. This approach avoids delayed or ineffective responses from individual measures, ensuring a dynamic balance between energy efficiency optimization and indoor comfort.

[0144] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions according to the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means (e.g., infrared, wireless, microwave, etc.). A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0145] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0146] Those skilled in the art will recognize that the units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art 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.

[0147] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0148] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0149] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0150] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for energy-saving design and analysis of new energy buildings, characterized in that, Specifically, the following steps are included: S01. Heat flow sensing elements and temperature sensing materials are installed on the outer surface of photovoltaic modules, the connection points of metal heat-conducting structures, and the corresponding walls inside the building. Heat flow data, temperature rise data, and power output data are collected synchronously through optical signal transmission to form a data channel covering the photovoltaic heating path and the heat conduction path. S02. The collected heat flow data, temperature rise data, and power output data are time-series aligned and unit-normalized. Based on the thermal conduction characteristics of the heat-conducting structure and the thermal inertia parameters of the building envelope, a heat flux trajectory and thermal response profile with a unified thermal resistance reference axis are constructed. S03. Based on the heat flux trajectory and thermal response profile, extract the reverse heat flux intensity parameter and the heat flux duration parameter, and combine them with the temperature control target and thermal conductivity properties set in the building to construct a reverse thermal conduction interference map, which is used to determine the degree of thermal conduction interference of the cooling load in the building. S04. Match the reverse thermal interference map with the pre-established thermal risk boundary model to identify high-risk, controllable-risk, and low-risk thermal interference areas. Calculate the cooling load correction factor for energy consumption correction based on the spatial distribution and heat transfer characteristics of each type of thermal interference area. S05. Establish a dynamic feedback control process by combining the thermal interference classification results and the cooling load correction factor. Continuously analyze the trend of heat flux changes. When the thermal interference area continuously meets the high-risk conditions, execute thermal insulation adjustment operations on the heat-conducting structure or building cold source allocation operations to achieve synchronous coordination between cooling load prediction and thermal influence.

2. The energy-saving design and analysis method for integrated new energy buildings according to claim 1, characterized in that, S02 specifically includes the following steps: S021. Align the heat flow data, temperature rise data and power output data collected from the outer surface of the photovoltaic module, the connection point of the metal heat-conducting structure and the corresponding wall surface inside the building according to the time sequence of the light signal acquisition, and complete the cross-channel time synchronization by using the equal interval interpolation reconstruction method. S022. Convert the aligned heat flux data into heat flux density per unit area, convert the aligned temperature rise data into absolute temperature difference, convert the aligned power output data into power generation per unit area, and uniformly construct a standardized sequence of physical quantities. S023. Based on the thermal conductivity of the material and the structural dimensions of the thermally conductive structure, extract the thermal resistance parameters of the conduction path, and combine the heat flux density per unit area and the absolute temperature difference to establish the heat flux intensity distribution. At the same time, extract the thermal inertia parameters based on the heat capacity and thermal diffusivity of the building envelope as the basic variables for constructing the dynamic thermal response process. S024. In a uniformly constructed thermal resistance reference axis coordinate system, the heat flux density per unit area, absolute temperature difference, and power generation per unit area are mapped and embedded according to the path sequence to form a heat flux trajectory and thermal response profile, which are used to express the complete heat conduction path characteristics from photovoltaic to indoor.

3. The energy-saving design and analysis method for integrated new energy buildings according to claim 2, characterized in that, S022 specifically refers to: The heat flux data of each aligned heat flux sensing point is normalized according to the actual area of ​​the sampling surface, and the heat flux density per unit area data sequence is output, while keeping the time axis sequence consistent with the heat flux sampling time sequence. The relative temperature difference of each aligned temperature sensing point is calculated one by one, with the temperature reading at a fixed initial time point as the reference value, to construct an absolute temperature difference data sequence, and it is kept consistent with the time node of the heat flow data. The aligned power output data is normalized and converted according to the power generation area corresponding to the photovoltaic module to obtain a sequence of power generation values ​​per unit area and establish a unified unit expression. The heat flux density data sequence, absolute temperature difference data sequence, and power generation value sequence per unit area are arranged in a corresponding manner on the time axis to establish a standardized physical quantity sequence with unified time series and dimensions, which is used for subsequent heat flux trajectory construction.

4. The energy-saving design and analysis method for integrated new energy buildings according to claim 3, characterized in that, S023 specifically refers to: Based on the type of material corresponding to the heat-conducting structure, the thermal conductivity value is obtained, and combined with the thickness and cross-sectional area of ​​the structure, the thermal resistance value is calculated according to the one-dimensional steady-state heat transfer theory to quantify the heat flow conduction capacity. The heat flux density per unit area and the absolute temperature difference are matched point by point according to the location of the heat flow path, and then combined with the corresponding thermal resistance value to construct a heat flow intensity distribution sequence of the surface heat conduction path, which reflects the degree of heat input concentration in different parts. The heat capacity and thermal diffusivity parameters of the filling layer and surface material in the building envelope are extracted to form a set of thermal inertia parameters associated with the structural layout, which are used to support the modeling of heat storage characteristics in the subsequent dynamic thermal response process.

5. The energy-saving design and analysis method for integrated new energy buildings according to claim 4, characterized in that, S024 specifically refers to: A thermal resistance reference axis coordinate system is established, and the heat conduction path is unfolded in sequence according to the three key hot spots of photovoltaic modules, heat conduction structure and indoor wall surface. The thermal resistance value is arranged along the axis in an cumulative manner to form a two-dimensional mapping domain with continuous thermal resistance coordinates. The heat flux density data sequence per unit area is mapped to the location of the photovoltaic module along the thermal resistance coordinate axis, the absolute temperature difference data sequence is mapped to the nodes of the heat-conducting structure, and the power generation value sequence per unit area is synchronously mapped to the initial location of the photovoltaic module. The three types of data form a heat input-conduction-response path relationship. Based on the relative distance and physical sequence order of each point on the thermal resistance axis, the three types of standardized data after mapping are connected to form a heat flux trajectory and thermal response profile that reflect the heat flow change trend and temperature rise response law along the thermal resistance change path.

6. The energy-saving design and analysis method for integrated new energy buildings according to claim 1, characterized in that, S03 specifically includes the following steps: S031. Extract the heat flux direction change data from the photovoltaic module to the building interior path from the heat flux trajectory and thermal response profile, and extract the reverse heat flux intensity parameter and the heat flux duration parameter based on the section where the heat flow direction changes from the outside to the inside. S032. The extracted reverse heat flux intensity parameters and heat flux duration parameters are matched with the temperature control targets corresponding to the areas within the building, and the thermal resistance, structural thickness and thermal diffusivity of each material in the thermal conductive structure are combined to form a set of building thermal conductive structure attributes. S033. Based on the reverse heat flux intensity parameter, the heat flux duration parameter, the temperature control target and the thermal conductivity structure attribute, a coordinate mapping domain is constructed in three-dimensional space. Different thermal interference characteristics are mapped to the coordinate domain to form a partitioned expression, and a reverse thermal conduction interference map is generated to determine the degree of thermal conduction interference of the cold load inside the building.

7. The energy-saving design and analysis method for integrated new energy buildings according to claim 6, characterized in that, S031 specifically refers to: In the heat flux trajectory, the heat flux segments that are conducted from the outside to the inside are selected according to the path direction, and the reverse heat flux intensity parameter is extracted from the point corresponding to the maximum heat flux value in the time series to represent the maximum disturbance energy density in the heat conduction path. Within the same heat flux range, determine the time interval during which the heat flux continuously exceeds the static baseline value, and extract the time span of the continuous heat transfer process as a parameter for the duration of the heat flux's continuous influence. By binding the reverse heat flux intensity parameter and the heat flux duration parameter with the original heat flux trajectory sequence, a thermal interference feature data pair with time label and path location identifier is established for subsequent association with building control parameters.

8. The energy-saving design and analysis method for integrated new energy buildings according to claim 7, characterized in that, S033 specifically refers to: A two-dimensional coordinate domain is established with the reverse heat flux intensity parameter as the horizontal axis and the heat flux duration parameter as the vertical axis, and the offset range of the building temperature control target value is used as the contour distribution line. Based on the thermal resistance, thermal diffusivity and thickness of each structural layer in the thermal conductivity properties, a set of building response correction factors corresponding to each coordinate point is established and integrated into the coordinate domain to form a three-dimensional parameter set. The reverse heat flux intensity parameter, heat flux duration parameter, building temperature control target value offset range, thermal resistance value, thermal diffusivity and structural thickness contained in the thermal conductivity properties are combined into a six-dimensional parameter set. The parameters are then mapped to the three-dimensional coordinate domain according to the coordinate axis embedding logic. Each thermal interference level region is identified by superimposing the contour density distribution and thermal response weight, generating a reverse thermal interference map that can be used to identify the thermal conduction interference level of the cold load inside the building.

9. The energy-saving design and analysis method for integrated new energy buildings according to claim 1, characterized in that, S04 specifically includes: The reverse heat flux intensity parameters, heat flux duration parameters, building temperature control target value offset range and thermal conductivity property coordinates contained in the reverse heat conduction interference map are mapped to a unified parameter space, and then matched with the high-risk, controllable-risk and low-risk intervals defined in the pre-established thermal risk boundary model. Based on the matching results, the corresponding thermal interference level is marked for the thermal interference points in the map. Based on the spatial distribution of marked high-risk, controllable-risk, and low-risk thermal interference points, thermal interference areas with continuous boundaries are aggregated, and the spatial distribution characteristics of each type of thermal interference area are statistically analyzed, including the occupied area, distribution density, boundary orientation, and relative positional relationship with the target cooling load area. Based on the spatial distribution of each type of thermal interference area and the heat transfer characteristics of each structural layer in the thermal conductivity properties, including thermal resistance, thermal diffusivity and structural thickness, a quantitative model reflecting the influence of the heat conduction path is constructed, and the energy offset affecting the building's cooling load in this area is extracted. The energy offset corresponding to each type of thermal interference area is converted into a unit area cooling load impact value. Combining the area coverage and interference duration weights, a cooling load correction factor for energy consumption correction is calculated and summarized to form a cooling load correction factor cluster covering the building space, which serves as the input basis for subsequent air conditioning load allocation and energy efficiency management.

10. The energy-saving design and analysis method for integrated new energy building as described in claim 1, characterized in that, S05 specifically includes: A dynamic identification model for thermal risk status is constructed based on the thermal interference classification results and the cooling load correction factor. The model is input with the heat flux intensity change curve of each region and the corresponding cooling load correction value to form a thermal interference monitoring dataset with time dimension and risk level label. The heat flux intensity change value of each monitoring point in the heat flux trajectory is continuously acquired, and the heat interference intensity change rate is calculated by combining the time series analysis method. By comparing with the high-risk judgment threshold set in the risk identification model, the heat interference area that meets the high-risk state is identified. For the identified high-risk thermal interference areas, the thermal conductivity properties, including thermal resistance, thermal diffusivity and structural thickness, are combined to calculate the parts in the heat conduction path that can be thermally adjusted and their adjustment upper limit, and to determine the thermal conductivity structure thermal adjustment operation strategy. At the same time, based on the change range of the cooling load correction factor in the identified high-risk thermal interference area and the current cooling source supply capacity, it is determined whether there is a cooling source allocation margin. If the determination result is that there is a cooling source allocation margin, a cooling source allocation operation strategy including the location of the target area, the load adjustment coefficient and the cooling source path is generated. By simultaneously implementing the thermal insulation adjustment strategy of the heat-conducting structure and the cold source allocation strategy, the joint intervention on the impact of thermal interference and the response of cold load is achieved, thereby realizing the coordinated control of cold load prediction and thermal conduction impact in the building thermal management system.

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