New energy building integrated energy-saving design analysis method

Abstract

The invention discloses a new energy building integration energy-saving design analysis method, and relates to the technical field of new energy building integration, and the method comprises the following steps: carrying out the time sequence alignment and unit normalization processing of collected heat flow data, temperature rise data and power output data; according to the heat conduction characteristic of the heat conduction structure and the thermal inertia parameters of the building enclosure structure, a heat flux track and a thermal response profile with a unified thermal resistance reference axis are constructed; and based on the heat flux track and the thermal response profile, extracting a reverse heat flux strength parameter and a heat flux continuous influence duration parameter, and combining the parameters with a temperature control target and a heat conduction structure attribute set in the building to construct a reverse heat conduction interference atlas for judging the heat conduction interference degree of the cold load in the building. The problem of cold load misjudgment caused by the fact that photovoltaic heat is transmitted through a high-heat-conduction structure is solved, and cold and heat conduction accurate recognition and energy consumption dynamic regulation and control are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated new energy buildings, and in particular to an integrated energy-saving design and analysis method for new energy buildings. Background Art

[0002] New energy building integration refers to the organic integration of clean and renewable energy systems, such as solar, wind, and geothermal energy, into building structures during the design phase. This integration makes the energy system not only a functional component of the building, but also a structural and aesthetic component. For example, photovoltaic modules can be embedded in roofs, walls, and shading systems, achieving deep synergy between energy production and building use. Energy-saving design analysis for new energy buildings involves quantitatively evaluating the layout, power generation efficiency, energy-saving potential, and overall energy efficiency of new energy systems during the initial design phase, based on the building's location, orientation, structural parameters, and functional requirements. This evaluation is conducted through digital modeling, simulation, and algorithm optimization, thus providing a basis for decision-making and optimization of design solutions. This analysis is essential because, once a building is completed, retrofitting its energy performance is costly. Therefore, accurately evaluating the integration of new energy systems into buildings during the design phase can significantly improve energy efficiency, reduce operational energy consumption, and avoid resource waste. This, in turn, promotes the development of green and low-carbon buildings and fosters the coordinated evolution of the construction industry and new energy technologies.

[0003] Existing integrated energy-saving design and analysis technologies for new energy buildings primarily rely on building information modeling (BIM), energy 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 design drawings and structural parameters. Incorporating inputs such as geographic location, climate data (such as sunshine, wind speed, temperature and humidity), and user functions, it simulates the energy output capacity and load matching efficiency of different new energy systems (such as photovoltaic power generation, wind-assisted power generation, and geothermal heating) under different configurations. Subsequently, energy simulation tools (such as EnergyPlus and DesignBuilder) are used to dynamically simulate the building's cooling and heating loads, lighting requirements, and other factors throughout the year. Optimization algorithms (such as genetic algorithms and multi-objective optimization) are then used to analyze the energy-saving potential and economic feasibility of various system integration solutions and select the optimal configuration. Finally, key indicators such as power generation efficiency, energy saving rate, and payback period are output, providing quantitative and visual decision support for design solutions. The entire analysis process usually includes six major steps: building modeling, climate data collection, energy system configuration simulation, energy consumption simulation, optimization calculation and result evaluation, to achieve full-process prediction and optimization of the integrated energy-saving performance of new energy buildings.

[0004] The existing technology has the following deficiencies: In the integrated energy-saving design and analysis of new energy buildings, photovoltaic (PV) modules are typically installed directly on the building's exterior surface, especially on metal roofs or in structural areas with high thermal conductivity. They are often secured using aluminum rails, metal supports, and other structures. When the modules generate heat during operation, this heat is conducted into the building along the thermally conductive structures, creating a heat flux reverse coupling effect that alters the thermal load on the building's interior surfaces. This effect is particularly pronounced in summer or during high-temperature climates, easily leading to a significant increase in the building's cooling load. However, existing integrated energy-saving design and analysis techniques for new energy buildings typically treat the heating behavior of PV modules as a single external climatic influence, failing to consider the coupled heat transfer paths formed between the PV modules and the building through the highly thermally conductive structures. This results in an inability to accurately predict the true impact of the structural area on the building's cooling load based on the heat flux reverse coupling phenomenon when the PV modules are in direct contact with the highly thermally conductive structures. Consequently, cooling load assessments are underestimated, impacting the reliability of the air conditioning system design and the accuracy of the overall building energy-saving performance analysis.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide an integrated energy-saving design and analysis method for new energy buildings to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a new energy building integrated energy-saving design and analysis method, which specifically includes the following steps: 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 synchronously collected through optical signal transmission to form a data channel covering the photovoltaic heating path and heat conduction path; S02. Perform time-series alignment and unit normalization on the collected heat flow data, temperature rise data, and power output data. Based on the thermal conductivity characteristics of the heat-conducting structure and the thermal inertia parameters of the building envelope, construct a heat flux trajectory and thermal response profile with a unified thermal resistance reference axis; S03. Based on the heat flux trajectory and thermal response profile, extract the reverse heat flux intensity parameter and the heat flux continuous impact duration parameter. Combine these parameters with the temperature control target and thermal conductivity structural properties set in the building to construct a reverse heat conduction interference map to determine the degree of heat conduction interference of the building's internal cooling load; S04. Match the reverse heat conduction interference map with a pre-established thermal risk boundary model to identify high-risk, controllable-risk, and low-risk thermal interference areas, and calculate a 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; S05. Combine the thermal interference classification results with the cooling load correction factor to establish a dynamic feedback control process, continuously analyze the heat flux change trend, and when the thermal interference area continues to meet high-risk conditions, perform thermal conductivity structure insulation adjustment operations or building cooling source allocation operations to achieve synchronous coordination of cooling load prediction and thermal conductivity impact.

[0008] Preferably, 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 points of the metal heat-conducting structure, and the corresponding wall surface inside the building in a unified manner according to the time sequence of optical signal collection, and use equal-interval interpolation and reconstruction to achieve cross-channel time synchronization; S022. Convert the aligned heat flow 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, thereby uniformly constructing a standardized physical quantity sequence; S023. Extract the thermal resistance parameters of the conduction path based on the thermal conductivity and structural dimensions of the heat-conducting structure. Establish the heat flux intensity distribution by combining the heat flux density per unit area and the absolute temperature difference. Simultaneously, 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 the uniformly constructed thermal resistance reference axis coordinate system, the heat flux density per unit area, the absolute temperature difference, and the power generation value per unit area are mapped and embedded in the order of the paths, and combined to form the heat flux trajectory and thermal response profile, which are used to express the complete heat conduction path characteristics from photovoltaic to indoor.

[0009] Preferably, S022 is specifically: The heat flux data of each aligned heat flux sensing point is normalized and calculated according to the actual area of ​​the acquisition surface, and the heat flux density per unit area data sequence is output, while keeping the time axis sequence consistent with the heat flux acquisition time sequence; The temperature data of each aligned temperature sensing point is taken as the reference value of the temperature reading at the fixed initial time point, and the relative temperature difference is calculated one by one to construct the absolute temperature difference data sequence, and keep it consistent with the time node of the heat flow data; Normalize and convert the aligned power output data according to the power generation area corresponding to the photovoltaic modules to obtain a sequence of power generation values ​​per unit area and establish a unified unit expression; The unit area heat flux density data series, absolute temperature difference data series, and unit area power generation value series are arranged correspondingly on the time axis to establish a standardized physical quantity sequence with unified time sequence and dimension for subsequent heat flux trajectory construction.

[0010] Preferably, S023 is specifically: According to the material type corresponding to the heat-conducting structure, the thermal conductivity value is obtained. Combined with the thickness and cross-sectional area of ​​the structure, the thermal resistance value is calculated based on 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 combined with the corresponding thermal resistance value to construct a heat flux intensity distribution sequence facing the heat conduction path, reflecting the degree of heat input concentration at different locations; The heat capacity and thermal diffusivity parameters of the filling layer and surface materials in the building envelope structure are extracted to form a thermal inertia parameter group associated with the structural layout, which is used to support the modeling of heat storage characteristics in the subsequent dynamic thermal response process.

[0011] Preferably, S024 is specifically: A thermal resistance reference axis coordinate system was established, and the heat conduction path was sequentially expanded according to the three key thermal locations: photovoltaic modules, heat conduction structures, and indoor walls. The thermal resistance values ​​were cumulatively arranged along the axis to form a two-dimensional mapping domain with continuous thermal resistance coordinates. The unit area heat flux density data sequence is mapped to the corresponding photovoltaic module position along the thermal resistance coordinate axis, the absolute temperature difference data sequence is mapped to the heat conduction structure node, and the unit area power generation value sequence is synchronously mapped to the initial point of the photovoltaic module. The three types of data form a heat input-conduction-response path relationship; According to the relative distance of each point on the thermal resistance axis and the physical sequence order, the three types of mapped standardized data are connected in trajectory to construct a heat flux trajectory and thermal response profile that reflects the heat flow change trend and temperature rise response law along the thermal resistance change path.

[0012] Preferably, S03 specifically includes the following steps: S031. Extract the heat flux direction change data on the path from the photovoltaic module to the interior of the building from the heat flux trajectory and thermal response profile, and extract the reverse heat flux intensity parameter and the heat flux continuous impact duration parameter based on the section where the heat flow direction changes from outside to inside; S032. Match the extracted reverse heat flux intensity parameter and heat flux continuous impact duration parameter with the temperature control targets corresponding to the areas within the building, and combine the thermal resistance, structural thickness, and thermal diffusivity of each material in the thermal conductive structure to form a set of building thermal conductive structure attributes; S033. Construct a coordinate mapping domain in three-dimensional space based on the reverse heat flux intensity parameter, the heat flux continuous impact duration parameter, the temperature control target, and the heat conduction structural properties. Map different thermal interference characteristics to the coordinate domain to form a partition expression, and generate a reverse heat conduction interference map for determining the degree of heat conduction interference of the cooling load inside the building.

[0013] Preferably, S031 specifically includes: In the heat flux trajectory, the heat flux segment that conducts from outside to inside is 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 interference energy density in the heat conduction path; In the same heat flux section, the time interval in which the heat flux continuously exceeds the static reference value is determined, and the time span of the continuous heat transfer process is extracted as the heat flux continuous impact duration parameter; The reverse heat flux intensity parameter and the heat flux continuous impact duration parameter are bound to the original heat flux trajectory sequence to establish a thermal interference feature data pair with time tags and path position identifiers, which is used for subsequent association construction with building control parameters.

[0014] Preferably, S033 is specifically: A two-dimensional coordinate domain is established with the reverse heat flux intensity parameter as the horizontal axis and the heat flux continuous impact duration parameter as the vertical axis, and the building temperature control target value offset range as the isovalue distribution line; According to the thermal resistance, thermal diffusivity and thickness of each structural layer in the thermal conductive structural 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 continuous impact duration parameter, building temperature control target value offset range, thermal resistance value included in the thermal conductive structural properties, thermal diffusivity and structure thickness are combined as a six-dimensional parameter set and mapped into the three-dimensional coordinate domain according to the coordinate axis embedding logic. The various thermal interference level areas are identified by superimposing the contour line density distribution and the thermal response weight, and a reverse heat conduction interference map is generated, which can be used to identify the heat conduction interference level of the cooling load inside the building.

[0015] Preferably, S04 specifically includes: The reverse heat flux intensity parameters, heat flux continuous impact duration parameters, building temperature control target value offset range, and thermal conductive structural attribute coordinates contained in the reverse heat conduction interference map are mapped into a unified parameter space. The parameters are then matched with the high-risk, controllable-risk, and low-risk intervals defined in the pre-established thermal risk boundary model. The corresponding thermal interference levels are then marked on the thermal interference points in the map based on the matching results. Based on the spatial distribution of the marked high-risk, controllable-risk, and low-risk thermal interference points, thermal interference areas with continuous boundaries are aggregated. The spatial distribution characteristics of each type of thermal interference area are statistically analyzed, including occupied area, distribution density, boundary direction, and relative position relationship with the cooling load target 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 conductive structure, including thermal resistance, thermal diffusivity, and structural thickness, a quantitative model reflecting the influence of the heat conduction path was constructed to extract the energy offset of the area affecting the building cooling load. The energy offset corresponding to each type of thermal interference area is converted into a cooling load impact value per unit area. Combined with the regional coverage and interference duration weight, the 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 distribution and energy efficiency management.

[0016] Preferably, S05 specifically includes: Based on the thermal interference classification results and cooling load correction factors, a dynamic identification model for thermal risk status was constructed. The heat flux intensity variation curve of each area and the corresponding cooling load correction value were input into the model to form a thermal interference monitoring dataset with time dimension and risk level labels. Continuously obtain the heat flux intensity change value of each monitoring point in the heat flux trajectory, and calculate the thermal interference intensity change rate by combining the time series analysis method. By comparing it with the high-risk judgment threshold set in the risk identification model, the thermal interference area that meets the high-risk status is identified; For the identified high-risk thermal interference areas, the thermal conductivity structural properties, including thermal resistance, thermal diffusivity, and structure thickness, are combined to calculate the locations in the thermal conductivity path where thermal insulation adjustment can be implemented and their upper limit, thereby determining the thermal insulation adjustment operation strategy for the thermal conductivity structure. At the same time, based on the change in the cooling load correction factor of the identified high-risk thermal interference area and the current cooling source supply capacity, it is determined whether there is cooling source allocation margin. If the judgment result is that there is cooling source allocation margin, a cooling source allocation operation strategy is generated, which includes the target area location, load adjustment coefficient and cooling path; The thermal insulation adjustment operation strategy of the heat-conducting structure and the cold source allocation operation strategy are executed simultaneously to complete the joint intervention of the thermal interference effect and the cooling load response, and realize the coordinated regulation of the cooling load prediction and thermal conductivity effect in the building thermal management system.

[0017] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention realizes closed-loop management of the entire process from heat flux data perception, thermal coupling path modeling, thermal interference map construction to cooling load correction and dynamic regulation in the integrated energy-saving design of new energy buildings, achieving multi-dimensional performance improvement compared to existing technologies. First, the scheme introduces a heat flux trajectory and thermal response profile modeling method based on the thermal resistance reference axis, which accurately reveals the thermal coupling behavior of photovoltaic components transmitted to the interior of the building through the metal heat-conducting structure, and quantitatively expresses the thermal interference effect as a visual map by extracting the reverse heat flux intensity parameter and the heat flux continuous impact time parameter. This method breaks through the traditional rough treatment of equating photovoltaic thermal effects with external climate loads, enabling the building heat load analysis to leap from static estimation to dynamic measurement and fine conduction modeling, significantly improving the accuracy of heat load assessment.

[0018] 2. This invention achieves the identification, classification, and coordinated response of high-risk thermal interference areas by constructing a reverse thermal interference map identification mechanism that matches the thermal risk boundary model, combined with cooling load correction factor calculation and dynamic feedback control processes. The system not only proposes differentiated intervention strategies for different levels of thermal interference areas (such as adiabatic adjustment of heat conduction paths and allocation of cooling sources), but also achieves simultaneous coordination of cooling load prediction and thermal conductivity control based on the rate of change of thermal interference intensity. This overall solution is highly practical in engineering, effectively addressing the problem of misjudging the internal thermal environment of a building due to heat generation from photovoltaic modules in high-thermal conductivity structures. It improves the reliability and overall energy efficiency of building air conditioning system design, demonstrating excellent energy-saving benefits and the synergistic advantages of system intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0020] Figure 1 This is a flow chart of the integrated energy-saving design and analysis method for new energy buildings of the present invention. DETAILED DESCRIPTION

[0021] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0022] The present invention provides Figure 1 The integrated energy-saving design and analysis method for new energy buildings shown in the figure specifically includes the following steps: 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 synchronously collected through optical signal transmission to form a data channel covering the photovoltaic heating path and heat conduction path; To address the issue of heat generated by PV panels being conducted along metal heat-conducting structures into the building and interfering with building cooling load assessment, the following steps can be used to construct a multi-point sensing data channel covering both the PV heating path and the heat conduction path for subsequent heat flux coupling analysis: First, multiple representative points are selected on the outer surface of the photovoltaic modules laid near the building's exterior, and heat flow sensing elements and temperature sensing materials are evenly distributed. Heat flow sheets with miniaturized structures and highly sensitive thermal response films are preferred. The heat flow sensing elements are used to obtain the heat flux density per unit area of ​​the photovoltaic modules under different working conditions, and the temperature sensing materials are used to record the change curve of its surface temperature over time, ensuring continuous tracking of the dynamic changes in the heating behavior of the modules.

[0023] Secondly, heat flow sensing elements and temperature sensing materials of the same type as those described above should be placed at the connection points of the metal heat-conducting structure connecting the photovoltaic modules to the main structure of the building. These elements should preferably be placed at the bottom of the guide rails, at the seams of the aluminum profiles, or in the support structure adjacent to the photovoltaic module backplane to ensure accurate capture of the transfer of heat from the modules to the support structure. This placement should take into account the distribution of the structural thermal bridge effect, ensuring that the sensing positions cover the typical heat conduction paths to the greatest extent possible and ensuring a close fit between the sensing elements and the structure to improve the accuracy of heat flow sensing.

[0024] Third, within the building's interior wall areas directly connected to the aforementioned metal heat-conducting structure, the same type of heat flux sensing elements and temperature sensing materials should be deployed in corresponding locations to monitor the heat flux response and temperature rise changes ultimately conducted into the indoor environment through the heat conduction path. This deployment should focus on areas with strong structural thermal coupling, such as indoor top corners, the inside of roof panels, and beam-column intersections, to capture the terminal response of heat transfer from outside to inside, providing a closed-loop data loop for reverse heat flux analysis.

[0025] Finally, to ensure the synchronization of data collection and transmission stability in the time dimension between each layout point, the collection 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 the optical fiber line, and each collection point is given a unique identification code. Combined with the synchronously triggered optical collection mechanism, the unified collection of heat flow data, temperature rise data and power output data at three key heat conduction locations: photovoltaic components, thermal conductive structures, and indoor walls is achieved. Ultimately, a complete data channel covering the entire heat conduction path is formed in the integrated structure of the building, providing consistent data support in time and space for the subsequent unified thermal resistance analysis and reverse coupling strength calculation.

[0026] The above-mentioned arrangement of heat flux sensing elements and temperature sensing materials, along with the construction of a complete data channel covering both the photovoltaic heating and heat conduction paths, is driven by the fact that in integrated new energy building structures, photovoltaic modules, acting as energy input units, are directly integrated with the building envelope. The heat released during operation not only impacts the external environment but can also potentially create hidden reverse heat flux pathways through the metal heat-conducting support structure, conducting heat into the interior space and interfering with accurate assessment of the building's cooling load. Without a multi-point continuous data collection mechanism for this path, the temperature rise and energy transfer behavior during heat conduction would be difficult to quantify and model. This would lead to systematic deviations in key parameters such as cooling source configuration, insulation design, and air conditioning load estimation in energy-saving design analysis, reducing the reliability of overall energy efficiency simulations. Therefore, it is essential to establish a high-resolution, continuous, and synchronized data channel between the module heat source, the heat conduction relay structure, and the indoor response end. This allows for real-time capture and restoration of heat flow dynamics at each stage, providing a precise and comprehensive physical foundation for establishing a unified thermal resistance analysis framework, identifying heat flux reverse coupling strength, and refining cooling load predictions. This move not only fills the gap in existing design analysis in neglecting implicit heat transfer paths, but also provides key support for building a true thermal behavior closed loop. It is an indispensable technical prerequisite for achieving high-precision building energy efficiency analysis.

[0027] S02. Perform time-series alignment and unit normalization on the collected heat flow data, temperature rise data, and power output data. Based on the thermal conductivity characteristics of the heat-conducting structure and the thermal inertia parameters of the building envelope, construct a heat flux trajectory and thermal response profile with a unified thermal resistance reference axis; In this embodiment, 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 points of the metal heat-conducting structure, and the corresponding wall surface inside the building in a unified manner according to the time sequence of optical signal collection, and use equal-interval interpolation and reconstruction to achieve cross-channel time synchronization; When multiple sensing devices are deployed simultaneously on the exterior surfaces of photovoltaic modules, at the connection points of metal thermal structures, and on the corresponding walls within buildings, the heat flux, temperature rise, and power output data collected by each sensing point often experience timeline misalignment due to differences in device response speed, signal latency, and trigger frequency. This results in inconsistent data in the temporal dimension, making it difficult to form a unified foundation for thermal behavior analysis. Therefore, all data must be aligned based on the trigger time of optical signal acquisition, and a uniformly spaced interpolation reconstruction method is used to synchronize data from different channels at the same time point. This uniformly spaced interpolation reconstruction method involves linearly or spline interpolating the raw data from each channel according to its timestamp on a timeline with a uniform time step, ensuring that all data at the same time point have valid values, thereby eliminating the impact of data misalignment. This method uses interpolation functions to approximate the values ​​at missing time points, resulting in a standardized input sequence of heat flux, temperature rise, and power output data with the same frequency, time point, and accuracy. This processing not only ensures the physical consistency of subsequent normalization transformation and path mapping operations, but also provides a unified and coherent data basis for constructing heat flux trajectories and thermal response profiles.

[0028] S022. Convert the aligned heat flow 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 per unit area, uniformly constructing a standardized physical quantity sequence to ensure consistency in subsequent thermal behavior expression; S023. Extract the thermal resistance parameters of the conduction path based on the thermal conductivity and structural dimensions of the heat-conducting structure. Establish the heat flux intensity distribution by combining the heat flux density per unit area and the absolute temperature difference. Simultaneously, 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 the uniformly constructed thermal resistance reference axis coordinate system, the heat flux density per unit area, the absolute temperature difference, and the power generation value per unit area are mapped and embedded in the order of the paths, and combined to form the heat flux trajectory and thermal response profile, which are used to express the complete heat conduction path characteristics from photovoltaic to indoor.

[0029] This setup aims to address the neglected issue of heat flux back-coupling caused by heat generated by photovoltaic modules and transferred into the building through the thermally conductive structure in existing energy-saving design analyses. By first aligning the heat flux, temperature rise, and power output data collected at different spatial locations and achieving cross-channel time synchronization through equally spaced interpolation reconstruction, a temporally consistent data foundation is established, preventing thermal behavior distortion caused by data misalignment. Subsequently, the data is normalized to form a standardized physical quantity sequence, making the perceived data of different physical properties 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 allows for accurate reconstruction of the heat storage and transfer characteristics during the heat transfer process, providing a physical basis for dynamic thermal response modeling. Finally, the standardized data is embedded in the unified thermal resistance reference axis to construct heat flux trajectories and thermal response profiles. This not only accurately depicts the complete heat transfer path from photovoltaic modules to the interior, but also provides a precise basis for identifying back-coupling strength and cooling load interference areas, providing reliable support for subsequent energy-saving design and control measures.

[0030] In this embodiment, S022 specifically includes: The heat flux data of each aligned heat flux sensing point is normalized and calculated according to the actual area of ​​the acquisition surface, and the heat flux density per unit area data sequence is output, while keeping the time axis sequence consistent with the heat flux acquisition time sequence; To ensure consistent heat flux representation across different heat flux sensing points, the raw measured values ​​of each heat flux data point must be normalized based on their actual collection area to obtain the heat flux density per unit area. This process is accomplished by dividing the total heat flux at each sensing point by its corresponding effective sensing area. This is accomplished by first reading the instantaneous heat flux value collected by each sensing device at a specific point in time, in watts (W). Next, the sensing area corresponding to that sensing point is calculated, in square meters (m²). This heat flux value is then divided by the area to obtain the heat flux density per unit area, in W / m². This calculation is performed at each time point, ultimately outputting a complete data sequence of heat flux density per unit area. For example, if a heat flux sensor collects an instantaneous heat flux of 30 W over a 0.01 m² area, its heat flux density per unit area is 30 ÷ 0.01 = 3000 W / m². By repeating this calculation at each time point, we can construct a data series of the heat flux density per unit area for that sensing point throughout the entire sampling period, ensuring that its time axis sequence remains completely consistent with the original acquisition time series. The purpose of this normalization operation is to eliminate the thermal flux value deviation caused by the different sensing areas in different layout areas, so that subsequent thermal behavior analysis has a unified dimension, facilitating the comparison of thermal coupling characteristics and path modeling across points.

[0031] The temperature data of each aligned temperature sensing point is taken as the reference value of the temperature reading at the fixed initial time point, and the relative temperature difference is calculated one by one to construct the absolute temperature difference data sequence, and keep it consistent with the time node of the heat flow data; To accurately characterize the temperature rise trend over time at each point in the heat conduction path and standardize the thermal response scale, it is necessary to use the temperature reading at a fixed initial time point as a reference. The relative difference in temperature change at each temperature sensing point at subsequent time points is calculated to determine the temperature change relative to the initial state, or the absolute temperature difference. This is accomplished by subtracting the temperature value at the initial time (e.g., t = 0) from the temperature value at each time point to obtain the relative temperature difference at each time point. All differences are arranged in chronological order to form a sequence of absolute temperature difference data for that point. For example, if the temperature of a temperature sensing material is 28°C at t = 0, and the temperatures at t = 1s, 2s, and 3s are 29.1°C, 30.4°C, and 31.0°C, respectively, the corresponding absolute temperature differences are 1.1°C, 2.4°C, and 3.0°C, resulting in a sequence of [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 initial sensor calibration differences, but also truly reflects the dynamic response of the heating process at specific structural locations, helping to accurately establish the coupling relationship between heat conduction flux and temperature rise response in subsequent analysis.

[0032] Normalize and convert the aligned power output data according to the power generation area corresponding to the photovoltaic modules to obtain a sequence of power generation values ​​per unit area and establish a unified unit expression; In the integrated energy-saving design analysis of new energy buildings, the power generation areas of different photovoltaic modules vary. Directly using raw power output data for thermal behavior analysis will distort energy density due to the inconsistent areas, thus affecting the accuracy of the thermal-electrical coupling assessment. To address this issue, the power output data collected from the photovoltaic modules must be normalized according to their corresponding actual power generation areas, converted into power generation per unit area, and a unified data series constructed. This process is 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 per unit area and arranging it in chronological order to form a complete power value series. For example, if a photovoltaic module has an area of ​​5.0 square meters and its output power at t = 1 min, 2 min, and 3 min is 450 W, 500 W, and 480 W, respectively, then the corresponding power generation per unit area values ​​are 90 W / m², 100 W / m², and 96 W / m², respectively, resulting in a power value series of [90, 100, 96]. By expressing in unified units, 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 layout areas, thereby improving the quantitative analysis accuracy of the thermal-electrical relationship and the consistency of horizontal assessment between structural positions.

[0033] The unit area heat flux density data series, absolute temperature difference data series, and unit area power generation value series are arranged correspondingly on the time axis to establish a standardized physical quantity sequence with unified time sequence and dimension for subsequent heat flux trajectory construction.

[0034] In the integrated energy-saving design and analysis process of new energy buildings, heat flow conduction behavior, temperature rise response characteristics, and changes in power output often involve multiple data sequences with different dimensions and different sampling frequencies. If they are not uniformly aligned, the variables in the path analysis cannot be accurately corresponded, affecting the effectiveness of the heat flux trajectory construction. In order to achieve consistency in the time dimension and physical units of multidimensional data, the unit area heat flux density data sequence, the absolute temperature difference data sequence, and the unit area power generation value sequence need to be arranged correspondingly on the time axis to ensure that the three types of data at each time node have a one-to-one correspondence and are normalized to a standard unit system. The specific implementation can be achieved by uniformly setting equally spaced time sampling points, such as one data unit per minute, and then inserting the data arranged at the same time point in each data source into the new sequence to construct a multidimensional array under a unified time index. For example, at t = 1 minute, the three data series are 85 W / m², 3.5°C, and 92 W / m²; at t = 2 minutes, the corresponding series are 90 W / m², 3.8°C, and 98 W / m², and so on, ultimately forming a standardized physical quantity series of the form [(1 minute, 85, 3.5, 92), (2 minutes, 90, 3.8, 98)]. This provides unified, complete, and synchronized data support for subsequent heat flux trajectory construction, ensuring highly consistent matching of key heat conduction variables in path modeling and avoiding distortion of thermal response maps or evaluation bias caused by inconsistent data sources.

[0035] In this embodiment, S023 is specifically: According to the material type corresponding to the heat-conducting structure, the thermal conductivity value is obtained. Combined with the thickness and cross-sectional area of ​​the structure, the thermal resistance value is calculated based on the one-dimensional steady-state heat transfer theory to quantify the heat flow conduction capacity. This process can be calculated based on one-dimensional steady-state heat transfer theory, assuming that heat conduction occurs only in a single direction and that the heat flow remains stable over time. In this case, the thermal resistance value can be derived from the relationship between the structure's thickness and cross-sectional area, as well as the material's thermal conductivity. 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 structure's thickness), A is the heat transfer cross-sectional area, and λ is the thermal conductivity of the heat-conducting material. To obtain thermal conductivity values, refer to material databases or standard thermal property tables. For example, the thermal conductivity of aluminum profiles can be 230 W / (m·K), and that of steel structures is approximately 50 W / (m·K). For example, for a section of aluminum rail with a thickness of 0.01 m and a cross-sectional area of ​​0.005 m², its thermal resistance is R = 0.01 / (230 × 0.005) ≈ 0.0087 K / W. This thermal resistance value can be considered the degree to which the structure inhibits heat flow conduction. The lower the thermal resistance, the easier it is for heat to be transferred into the room. Therefore, it is necessary to accurately model each section of the heat conduction structure to accurately reflect the heat distribution when constructing the thermal response path. This prevents distortion in cooling load assessment due to fuzzy heat conduction paths. This thermal resistance quantification process, 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.

[0036] 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 combined with the corresponding thermal resistance value to construct a heat flux intensity distribution sequence facing the heat conduction path, reflecting the degree of heat input concentration at different locations; To accurately represent the concentration of heat input at various locations along the heat conduction path from photovoltaic panels to the building interior, it is necessary to map the unit area heat flux density and absolute temperature difference data point by point along the heat flow path. This data is then combined with the thermal resistance value at each location to construct a heat flux intensity distribution sequence for the heat conduction path. To achieve this, the heat flux and temperature difference data at key locations, such as the photovoltaic exterior surface, metal thermal connections, and the building's interior walls, are spatially mapped to ensure that each data point has a corresponding physical location within the heat conduction path. Next, the heat flux density is combined with the thermal resistance value at the corresponding point. The heat flow penetration capacity per unit thermal resistance is calculated at each location to form a heat flux intensity index. This index identifies areas of concentrated heat conduction by measuring the gradient of heat flux density between data points. For example, if a thermal connection point has a low thermal resistance but a significantly high corresponding heat flux density, this point is identified as a heat flow concentration zone. By arranging these heat flux intensity data along the path, a heat flux intensity distribution sequence can be constructed, revealing the localized concentration trends and distribution structure of heat during conduction. This sequence not only enhances the spatial identification capability of thermal coupling-affected areas, but also provides a key reference for subsequent dynamic response analysis and cooling load adjustment, thus avoiding misjudgment of energy-saving design due to unclear identification of heat flux concentration areas.

[0037] The heat capacity and thermal diffusivity parameters of the filling layer and surface materials in the building envelope structure are extracted to form a thermal inertia parameter group associated with the structural layout, which is used to support the modeling of heat storage characteristics in the subsequent dynamic thermal response process.

[0038] To accurately simulate the temperature response of a building structure under heat flow disturbances, it is necessary to extract key thermophysical properties, particularly heat capacity and thermal diffusivity, from the infill and surface materials of the building envelope. This is then used to construct a thermal inertia parameter set based on the spatial distribution of each structural layer. To achieve this, the material composition and layout of each wall, roof, and floor slab must be clearly defined based on the architectural design drawings, and the location, thickness, and material type of each structural layer must be determined. The mass specific heat capacity and thermal diffusivity of the corresponding materials are then extracted using a material thermal database or actual testing. Heat capacity reflects the energy required per unit volume of a component to absorb heat, while thermal diffusivity describes the rate of heat transfer within the material. Combining these two parameters characterizes a component's ability to store and respond to thermal energy. To reflect the influence of structural layout on thermal conduction behavior, these parameters are normalized and combined based on the structural layer order and thickness ratio to construct a thermal inertia parameter set that reflects the dynamic heat storage and transfer behavior of the entire building structure under heat flow disturbances. For example, if an exterior wall consists of a concrete base, rock wool infill, and metal shell, the thermal inertia parameter set must include the heat capacity and thermal diffusivity data for these three materials, weighted and integrated according to their thickness. This is then used in subsequent thermal response curve modeling and load impact assessment. This process helps accurately capture the hysteresis and buffering characteristics of thermal coupling effects in the time dimension and is a key prerequisite for building dynamic energy-saving response analysis models.

[0039] In this embodiment, S024 specifically includes: A thermal resistance reference axis coordinate system was established, and the heat conduction path was sequentially expanded according to the three key thermal locations: photovoltaic modules, heat conduction structures, and indoor walls. The thermal resistance values ​​were cumulatively arranged along the axis to form a two-dimensional mapping domain with continuous thermal resistance coordinates. 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 is established, and a two-dimensional mapping domain is constructed to characterize the heat energy conduction process along the path step by step. This process first requires defining the structural sequence of the heat conduction path, which typically includes the photovoltaic module surface, the metal heat-conducting structure connecting the module to the building, and the final contact area on the indoor wall. These three heat sites are considered key nodes in the heat conduction chain. In the spatial coordinate construction, the path direction is used as the main axis, and the thermal resistance values ​​of each structure segment are superimposed in the order of heat conduction to form a reference axis with cumulative thermal resistance characteristics. The thermal resistance values ​​of each segment are calculated using a one-dimensional steady-state heat transfer model based on the previously extracted material thermal conductivity and structural geometric parameters. Subsequently, each thermal resistance increment position on the axis is mapped to its corresponding heat flow sensing point, temperature rise sensing point, and power output point, creating a two-dimensional coordinate map, where the horizontal axis represents the cumulative thermal resistance value and the vertical axis represents the intensity or response state of the standardized thermal physical quantity. The establishment of this thermal resistance reference axis coordinate system, on the one hand, helps to unify the spatial correspondence between heat flow, temperature rise and power generation behavior in the structure, and on the other hand, it can provide a basic framework for the subsequent connection of heat flux trajectories and the construction of thermal response profiles, 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.

[0040] The unit area heat flux density data sequence is mapped to the corresponding photovoltaic module position along the thermal resistance coordinate axis, the absolute temperature difference data sequence is mapped to the heat conduction structure node, and the unit area power generation value sequence is synchronously mapped to the initial point of the photovoltaic module. The three types of data form a heat input-conduction-response path relationship; To model the complete heat conduction path from photovoltaic (PV) heat generation to indoor heating in integrated new energy building structures, standardized physical quantities from various sources must be accurately mapped to corresponding positions on the thermal resistance reference axis. The unit area heat flux density data series reflects the intensity of heat release from the PV module's external surface and should therefore be mapped to the PV module's location near the starting point of the thermal resistance axis. The absolute temperature difference data series reflects the temperature variation along the thermally conductive structure and should be mapped to the thermally conductive structure segment on the thermal resistance axis, based on the actual location of each sensing point along the thermal path. The unit area power generation data series is inherently directly related to the PV module's operating status 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 relationship, each data type occupies a coordinate on the thermal resistance axis that matches its physical source and structural location, achieving a spatially continuous representation of the heat energy transfer from PV heat generation, thermally conductive structure conduction, and internal building wall response. This process is accomplished by establishing a unified coordinate system, calibrating the geometric thermal resistance distribution of the structure, and binding the data based on the sensor point numbering. The core significance of completing this mapping lies in: by forming the three types of data sequences into an input-conduction-response path chain in the thermal resistance dimension, it can not only quantitatively present the spatial laws of the building thermal coupling effect, but also provide a data foundation with clear physical hierarchy and close node association for subsequent heat flux trajectory analysis and interference identification.

[0041] According to the relative distance of each point on the thermal resistance axis and the physical sequence order, the three types of mapped standardized data are connected in trajectory to construct a heat flux trajectory and thermal response profile that reflects the heat flow change trend and temperature rise response law along the thermal resistance change path.

[0042] To accurately characterize the heat conduction path between the photovoltaic module and the indoor wall, the three types of standardized physical data mapped onto the thermal resistance reference axis must be linked together according to the thermal resistance coordinates of their corresponding points. The position of each data point on the thermal resistance axis represents the accumulated degree of heat transfer impedance in the heat conduction path, while the physical sequence reflects the temporal and spatial evolution of the heat conduction process. Therefore, by linking 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 the 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 linking can be accomplished using curve fitting, piecewise linear interpolation, or piecewise polynomial approximation to ensure that the heat conduction characteristics at different locations are fully represented. 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 consistent in order, thereby forming a directed data flow graph reflecting the relationship between heat input, conduction, and response. Through this construction method, not only can the layer-by-layer attenuation trend of heat flow in the heat conduction path and the delayed response pattern of temperature rise be intuitively presented, but it can also provide accurate visualization support for identifying abnormal thermal resistance sections and energy consumption anomalies. It is an important basis for achieving high-resolution thermal behavior analysis and energy-saving optimization design.

[0043] S03. Based on the heat flux trajectory and thermal response profile, extract the reverse heat flux intensity parameter and the heat flux continuous impact duration parameter. Combine these parameters with the temperature control target and thermal conductivity structural properties set in the building to construct a reverse heat conduction interference map to determine the degree of heat conduction interference of the building's internal cooling load; In this embodiment, S03 specifically includes the following steps: S031. Extract the heat flux direction change data on the path from the photovoltaic module to the interior of the building from the heat flux trajectory and thermal response profile, and extract the reverse heat flux intensity parameter and the heat flux continuous impact duration parameter based on the section where the heat flow direction changes from outside to inside; S032. Match the extracted reverse heat flux intensity parameter and heat flux continuous impact duration parameter with the temperature control targets corresponding to the areas within the building, and combine the thermal resistance, structural thickness, and thermal diffusivity of each material in the thermal conductive structure to form a set of building thermal conductive structure attributes; When matching the extracted reverse heat flux intensity parameters and heat flux duration parameters with the temperature control targets corresponding to the building's internal zones, the first step is to map each heat flux response path endpoint to a specific indoor partition, such as a living room, bedroom, or equipment room, based on the functional zoning of the building space. The corresponding temperature control parameters are then extracted based on the target temperature control value set for that partition in the building's operation strategy. Subsequently, for each heat flux path, the degree of energy coupling of the thermal interference is determined based on the offset between its peak heat flux density and the target temperature control range for that partition. For the heat flux duration parameter, it is compared with the time after the partition's air conditioning starts and stops, or the thermal capacity response period, to determine whether the heat input has a significant impact. To more accurately assess the impact of a heat conduction path on indoor cooling loads, it's necessary to integrate the two aforementioned parameters with the material properties of the thermally conductive structures within that path. Specifically, these include the thermal resistance (calculated from thermal conductivity and thickness), thermal diffusivity (derived from a combination of thermal conductivity, density, and specific heat capacity), and geometric thickness of each structural layer. This establishes a set of building thermal conductivity structural properties that aligns with the continuity of the path and clearly defines their spatial location. This set not only expresses the ability of heat to conduct through each layer of the building envelope but also provides a multidimensional quantitative reference for the subsequent construction of a thermal interference level determination model, providing essential support for path energy determination and regional heat load response analysis.

[0044] S033. Construct a coordinate mapping domain in three-dimensional space based on the reverse heat flux intensity parameter, the heat flux continuous impact duration parameter, the temperature control target, and the heat conduction structural properties. Map different thermal interference characteristics to the coordinate domain to form a partition expression, and generate a reverse heat conduction interference map for determining the degree of heat conduction interference of the cooling load inside the building.

[0045] The fundamental purpose of the above analysis is to accurately determine the abnormal changes in cooling load that may be caused by the heat from photovoltaic modules being transferred to the interior of the building through high thermal conductivity structures. In actual integrated applications of new energy buildings, photovoltaic modules are not only energy input units, but also potential sources of local thermal interference. Especially when there is a direct thermal coupling path between them and the building structure, it is more likely to form a reverse heat flux from the outside to the inside. By extracting the reverse heat transfer characteristics with physical directionality from the heat flux trajectory and thermal response profile, and further extracting the two key parameters of reverse heat flux intensity and continuous impact duration, the intensity and timeliness of thermal coupling behavior can be quantified; then, such thermal interference characteristics are matched with the temperature control targets set for each functional area inside the building, and a set of thermal conductivity properties is established in combination with the physical characteristics of the specific thermal conductive structure, which can further closely link the occurrence of thermal interference with the spatial structure and thermal inertia mechanism. Finally, a coordinate mapping domain was constructed based on the multi-parameter combination, and a reverse heat conduction interference map was generated. This not only achieved the visual expression of different thermal interference levels, but also provided a scientific, quantitative and spatially resolved basic support for the subsequent cooling load prediction model and thermal impact control strategy, thereby improving the accuracy and pertinence of the entire energy-saving design analysis.

[0046] In this embodiment, S031 specifically includes: In the heat flux trajectory, the heat flux segment that conducts from outside to inside is 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 interference energy density in the heat conduction path; In the heat flux trajectory, heat flux segments that conduct from outside to inside are screened according to the path direction. This aims to determine whether there is a tendency for heat to transfer from external components to the interior of the building through the heat-conducting structure during the operation of the photovoltaic modules. This identification process can be achieved by establishing a unified thermal resistance reference axis coordinate system, mapping the heat flux density per unit area to each node position according to the direction of the heat conduction path, and then combining the sign of the heat flow direction at each moment to determine the path segments with negative heat flux density or significantly less than the forward conduction benchmark, identifying them as reverse heat flux segments. Subsequently, within the identified reverse conduction segments, the maximum absolute value of the heat flux density in the time series is extracted, and the heat flux density value corresponding to this point is used as the reverse heat flux intensity parameter to quantify the maximum instantaneous heat input density during the thermal interference process from photovoltaics to the interior. This parameter reflects the peak energy input intensity in the thermal coupling path and helps identify time periods and path nodes with greater thermal disturbance risks. 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 map and the regulation of cold load sensitive areas, ensuring that the evaluation of thermal behavior has structural correlation and quantifiable interference intensity.

[0047] In the same heat flux section, the time interval in which the heat flux continuously exceeds the static reference value is determined, and the time span of the continuous heat transfer process is extracted as the heat flux continuous impact duration parameter; Determining the time interval during which the heat flux continuously exceeds the static baseline value within the same heat flux segment is intended to quantify the duration of the continuous impact of heat energy on the interior of the building through the reverse conduction path, thereby extracting the time characteristic parameter "heat flux continuous impact duration parameter" that reflects the persistence of thermal interference. The so-called "static baseline value" refers to the average steady-state heat flux density at each node of the heat conduction path when the building is in the absence of external thermal disturbances. It is usually obtained by statistically averaging heat flux data collected at night, during non-power generation periods, or when components are not operating. It reflects the background heat flux level of the structure under natural thermal equilibrium conditions. In practice, the reverse conduction segment in the heat flux trajectory can be first demarcated as a time window. The heat flux data for this segment is then compared hour by hour. The segments where the heat flux density exceeds the static baseline value at consecutive time points are selected, the start and end times are marked, and the span time is calculated as the duration of the reverse thermal disturbance. The duration parameter of the heat flux's continuous impact helps reveal that thermal interference is not an instantaneous pulse, but rather cumulative over time, posing a potential heat storage risk to the building's internal cooling load. Especially under conditions of high-heat-capacity envelope structures, its impact can continue beyond the system's steady-state regulation cycle. Therefore, this parameter is an indispensable dynamic thermal behavior indicator in constructing a reverse heat conduction interference map.

[0048] The reverse heat flux intensity parameter and the heat flux continuous impact duration parameter are bound to the original heat flux trajectory sequence to establish a thermal interference feature data pair with time tags and path position identifiers, which is used for subsequent association construction with building control parameters.

[0049] Binding the reverse heat flux intensity and duration parameters to the original heat flux trajectory sequence is intended to form an information structure with a complete temporal dimension and spatial path location. This allows for the construction of characteristic data pairs that reflect the thermal disturbance characteristics of specific building locations. This facilitates subsequent correlation with parameters such as temperature control targets and response strategies set in the building control system, supporting the assessment of thermal disturbance risk levels and regulatory decisions. The original heat flux trajectory sequence is a complete heat conduction path data set mapped and connected along the thermal resistance axis, following the path sequence from photovoltaic module to thermal conductive structure to indoor wall, based on three standardized physical quantities: heat flux density per unit area, absolute temperature difference, and power generation per unit area. This data typically includes the heat flux intensity and temperature rise level at each key node, as well as their temporal evolution trajectory. In practice, a structure record is constructed for each reverse thermal disturbance segment, containing fields such as start and end timestamps, path start and end coordinates, reverse heat flux intensity, duration, and the index position of the original heat flux trajectory within which the segment resides. This data is then used to generate a thermal disturbance characteristic data pair with temporal and spatial location properties. This binding process can be implemented using hash indexes or time-path double-key mapping, ensuring that each thermal interference event can be accurately mapped to the original thermal conduction behavior. This structured data combination not only facilitates the classification and quantitative mapping of thermal interference when constructing the subsequent thermal interference map, but also lays the data foundation for the formation of closed-loop control logic through dynamic feedback mechanisms with building control systems.

[0050] In this embodiment, S033 is specifically: A two-dimensional coordinate domain is established with the reverse heat flux intensity parameter as the horizontal axis and the heat flux continuous impact duration parameter as the vertical axis, and the building temperature control target value offset range as the isovalue distribution line; 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 goal is to construct a mapping space that can visually express the degree of thermal interference, allowing different thermal conduction interference events to be clearly classified based on the two key dimensions of intensity and duration, thereby facilitating quantitative matching with the building's internal cooling load capacity. In specific implementation, the reverse heat flux intensity value extracted from each thermal interference event is first input as the horizontal coordinate. This value is usually expressed in W / m², reflecting the maximum heat density during the heat flow from outside to inside. The corresponding heat flux duration in seconds or minutes is then input as the vertical coordinate to reflect the duration of the heat flux interference. Subsequently, the building temperature control target offset range is superimposed on this two-dimensional coordinate system as contour lines. The difference between the upper and lower limits of the temperature control setpoint fluctuation allowed for each functional area or temperature-controlled space is defined as the control bandwidth (for example, if the target is 25°C and the allowable deviation is ±1°C, the offset range is 2°C). Based on this, the corresponding temperature control impacts of different thermal interference combinations (intensity + duration) are graded and labeled in the coordinate system, such as low-risk, medium-risk, and high-risk areas. These contour lines can be constructed through simulations to establish a heat flux impact curve on indoor temperature rise and infer the isothermal offset boundaries, or by fitting a function model to measured data, thereby forming a response threshold benchmark for the temperature control target on the two-dimensional map. This constructed two-dimensional coordinate domain not only makes the distribution characteristics of thermal interference impacts more intuitive but also provides a standardized underlying structure for the subsequent superposition of thermal conductive structural properties to form a multidimensional interference identification map.

[0051] According to the thermal resistance, thermal diffusivity and thickness of each structural layer in the thermal conductive structural 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; To further improve the accuracy of thermal disturbance assessment, it is necessary to incorporate the building's own thermal response parameters into the established two-dimensional coordinate domain, thereby constructing a structurally adaptable three-dimensional parameter set. This process uses the thermal resistance, thermal diffusivity, and structural thickness of each structural layer as the basic variables in the thermal conductivity structure properties to construct a set of building response correction factors corresponding to each thermal disturbance coordinate point (a combination of the reverse heat flux intensity parameter and the heat flux duration parameter). Specifically, the thermal resistance of each structural layer is first calculated using the thermal resistance formula R=L / λ, where L is the material thickness and λ is the material thermal conductivity. The thermal diffusivity α=λ / (ρ*c), where ρ is the density and c is the specific heat capacity, is then combined to estimate the heat transfer rate within the material. Subsequently, a response time correction factor and a buffer coefficient are established for each structural layer. These factors are combined to form a thermal response vector tied to the thermal disturbance parameter point, reflecting the building's actual response speed to thermal disturbances and its heat storage capacity. This vector is embedded at each point in the two-dimensional thermal disturbance coordinate domain, forming a three-dimensional parameter set with the reverse heat flux intensity, heat flux duration, and structural thermal response capacity as the coordinate axes. In this three-dimensional coordinate system, different thermal interference events are distinguished not only by intensity and duration but also by the thermal inertia of the building structure, enabling more precise zoning of heat conduction interference levels and the construction of a dynamic control model. Integrating material thermal properties into the graph analysis framework in this way helps improve the adaptability and generalization of thermal interference assessment across different building configurations.

[0052] The reverse heat flux intensity parameter, heat flux continuous impact duration parameter, building temperature control target value offset range, thermal resistance value included in the thermal conductive structural properties, thermal diffusivity and structure thickness are combined as a six-dimensional parameter set and mapped into the three-dimensional coordinate domain according to the coordinate axis embedding logic. The various thermal interference level areas are identified by superimposing the contour line density distribution and the thermal response weight, and a reverse heat conduction interference map is generated, which can be used to identify the heat conduction interference level of the cooling load inside the building.

[0053] To accurately identify the level of thermal conduction interference caused by internal cooling loads in buildings, a multidimensional parameter fusion mechanism needs to be introduced into the thermal interference assessment model. By combining six core variables—the reverse heat flux intensity parameter, the heat flux duration parameter, the building temperature control target offset range, the thermal resistance value included in the thermal conductive structural properties, the thermal diffusivity, and the structural thickness—into a complete six-dimensional parameter set, the energy intensity, duration of action, temperature control deviation, and structural thermal response capability along the heat conduction path can be comprehensively covered. During the mapping process, a principal coordinate system is first constructed with the reverse heat flux intensity, the heat flux duration, and the temperature control target offset range as the three-dimensional spatial coordinate axes. The thermal resistance, thermal diffusivity, and structural thickness of each structural layer in the thermal conductive structural properties are then converted into correction coefficients reflecting the structural thermal inertia. These coefficients are then mapped to each coordinate point in the three-dimensional principal coordinate system as additional dimensions embedded in the logic, completing the fusion projection of the six-dimensional data in three-dimensional space. This mapping process converts structural parameters into weighted expressions through embedding rules, allowing the thermal interference parameters to obtain dynamic response characteristics in space, thereby constructing a set of visual reverse thermal interference maps to express the interference level distribution of heat conduction on the building cooling load under different locations and different thermal structure conditions.

[0054] In thermal disturbance level area identification, the "contour density distribution and thermal response weight overlay" is a regional delineation method based on spatial statistics and parameter weighting. Contour density distribution involves fitting the spatial range of thermal disturbance parameters in a three-dimensional coordinate domain as a continuous function to create boundaries with identical numerical distributions, thereby demarcating regions of varying thermal disturbance intensity. Thermal response weights, on the other hand, are correction factors reflecting the building's response speed and thermal storage capacity, constructed based on thermal resistance, thermal diffusivity, and thickness parameters extracted from the thermal conductivity structure. In practice, contour maps of thermal disturbance intensity and duration are first generated through data fitting and curve interpolation. Then, based on the structural correction weight corresponding to each coordinate point, the contour density spacing and coverage are adjusted to dynamically reflect the structural response capacity on the disturbance level boundaries. The resulting map not only clearly identifies high-intensity or long-duration heat-affected zones, but also assigns differentiated classification labels to different areas based on the building's ability to buffer thermal disturbances, providing a visual and quantitative basis for interference decision-making in building thermal management strategies.

[0055] S04. Match the reverse heat conduction interference map with a pre-established thermal risk boundary model to identify high-risk, controllable-risk, and low-risk thermal interference areas, and calculate a 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; In this embodiment, S04 specifically includes: The reverse heat flux intensity parameters, heat flux continuous impact duration parameters, building temperature control target value offset range, and thermal conductive structural attribute coordinates contained in the reverse heat conduction interference map are mapped into a unified parameter space. The parameters are then matched with the high-risk, controllable-risk, and low-risk intervals defined in the pre-established thermal risk boundary model. The corresponding thermal interference levels are then marked on the thermal interference points in the map based on the matching results. To map the reverse heat flux intensity parameters, heat flux duration parameters, building temperature control target offset range, and thermal conductivity structural properties contained in the reverse heat conduction interference map into a unified parameter space, these multidimensional physical parameters must first be normalized to maintain consistent dimensions and data scales for easy expression in a common coordinate system. This coordinate space can be constructed using a multidimensional numerical space or a tensor grid model, with each dimension corresponding to the aforementioned key thermal interference parameters. Subsequently, the parameter vector of each thermal interference point in the map is embedded into this unified parameter space to form a specific location. This is then mapped and matched against the risk level intervals in a pre-established thermal risk boundary model to identify whether it falls within the high-risk, controllable-risk, or low-risk boundary. This matching can be achieved using Euclidean distance classification, clustering algorithms, or support vector boundary models. After matching, the thermal interference points are labeled according to the thermal risk boundary label definition and their thermal interference level information is written back to the corresponding points in the map, forming a data structure with risk level classification, which provides a basic zoning basis for the subsequent calculation of cooling load correction factors. This process ensures the classification accuracy of different thermal interference points under multi-factor conditions and the physical rationality of the atlas partitioning.

[0056] Based on the spatial distribution of the marked high-risk, controllable-risk, and low-risk thermal interference points, thermal interference areas with continuous boundaries are aggregated. The spatial distribution characteristics of each type of thermal interference area are statistically analyzed, including occupied area, distribution density, boundary direction, and relative position relationship with the cooling load target area. When constructing thermal interference zones based on the spatial distribution of high-risk, controllable-risk, and low-risk thermal interference points, the classified thermal interference points must first be spatially located in geographic coordinates or building structural coordinate systems. Density-based clustering algorithms (such as DBSCAN) or methods based on spatial connectivity analysis can be used to cluster adjacent thermal interference points of the same category to form thermal interference zones with continuous boundaries and consistent categories. After clustering, boundary tracing algorithms (such as the alpha shape method or boundary scanning) are used to identify the geometric outline of each thermal interference zone and calculate its occupied area. 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 enclosing rectangle or principal direction analysis) are used to determine the primary orientation of the zone boundaries. Furthermore, spatial analysis techniques (such as nearest neighbor calculation or overlap metric) are used in conjunction with building design drawings or target cooling load zone coordinate data to clarify the relative position of the thermal interference zone and the target cooling load zone, providing precise 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 delineation is highly consistent with the physical space.

[0057] Based on the spatial distribution of each type of thermal interference area and the heat transfer characteristics of each structural layer in the thermal conductive structure, including thermal resistance, thermal diffusivity, and structural thickness, a quantitative model reflecting the influence of the heat conduction path was constructed to extract the energy offset of the area affecting the building cooling load. To construct a quantitative model of the impact of heat conduction paths based on the spatial distribution of each type of thermal interference region and the heat transfer characteristics of each structural layer within the thermal conductivity structure, the spatial location of the thermal interference region must first be mapped to the building envelope it covers. Key thermal parameters such as thermal resistance, thermal diffusivity, and structural thickness of structural units within the region, such as walls, roofs, and glass curtain walls, can be extracted. 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 resistances of each structural layer to reflect the overall heat transfer capacity. Combined with thermal diffusivity to model transient thermal response, the one-dimensional unsteady-state heat conduction equation is used to solve 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 region, the intensity of the reverse heat input from the region 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 cooling load temperature zone to extract the energy offset caused by the thermal interference region on the cooling load, which serves as the core reference value for subsequent energy consumption correction. This modeling process not only takes into account the thermal properties of the material, but also integrates the regional spatial morphology to achieve a quantitative expression of the heat conduction interference capability.

[0058] The energy offset corresponding to each type of thermal interference area is converted into a cooling load impact value per unit area. Combined with the regional coverage and interference duration weight, the 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 distribution and energy efficiency management.

[0059] To convert the energy offset corresponding to each type of thermal interference zone into a cooling load impact per unit area, the energy offset (in joules or kilowatt-hours) extracted in the previous stage is first combined with the actual projected area of ​​the zone to perform an energy density conversion, resulting in a cooling load impact per unit area (e.g., W / m²). Next, weighting factors are assigned based on the zone's coverage within the overall building space (i.e., the ratio of the zone's area to the total controlled area) and the duration of the thermal interference. 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 added based on the number of hours exceeding a set threshold. Finally, the cooling load impact per unit area and the weighting factors are combined using a weighted product model to calculate a cooling load correction factor (e.g., +15 W / m²) for each type of thermal interference zone. The cooling load correction factors for each zone are then mapped to each functional area of ​​the building according to their spatial coordinates, forming a cooling load correction factor cluster covering the building space. For example, if a west-facing office area experiences a high heat disturbance for three consecutive hours during a period of intense sunlight, with an energy offset of 900 Wh and an area of ​​30 m², the cooling load impact per unit area is 30 Wh / m². Combining a coverage weight of 0.8 and a disturbance duration weight of 1.2, the cooling load correction factor is: 30 × 0.8 × 1.2 = 28.8 Wh / m². This result is incorporated into the overall building cooling load correction map, providing quantitative input for localized load increase scheduling and energy efficiency optimization of the air conditioning system.

[0060] S05. Combine the thermal interference classification results with the cooling load correction factor to establish a dynamic feedback control process, continuously analyze the heat flux change trend, and when the thermal interference area continues to meet high-risk conditions, perform thermal conductivity structure insulation adjustment operations or building cooling source allocation operations to achieve synchronous coordination of cooling load prediction and thermal conductivity impact.

[0061] In this embodiment, S05 specifically includes: Based on the thermal interference classification results and cooling load correction factors, a dynamic identification model for thermal risk status was constructed. The heat flux intensity variation curve of each area and the corresponding cooling load correction value were input into the model to form a thermal interference monitoring dataset with time dimension and risk level labels. This process forms the foundation for building a dynamic control mechanism. Its core lies in leveraging the correlation between thermal disturbance classification results and cooling load correction factors to establish a dynamic thermal risk status identification model. This model continuously monitors the heat flux intensity variation curves of each thermal disturbance zone within the building, uses these as input variables, and associates them with the cooling load correction values ​​obtained from previous assessments to generate time-series data pairs. Specifically, a multidimensional time series analysis method is employed to extract the instantaneous gradient, fluctuation amplitude, and trend slope of the heat flux variation in each zone. These data are then labeled and graded based on the respective cooling load correction factor weights. By establishing dynamic risk identification criteria within this model, each monitored data point is assigned a "high risk," "controllable risk," or "low risk" classification based on its heat flux variation characteristics and energy consumption sensitivity. This ultimately creates a thermal disturbance monitoring dataset with time tags, spatial locations, and risk levels, providing a precise foundation for subsequent control strategies.

[0062] Continuously obtain the heat flux intensity change value of each monitoring point in the heat flux trajectory, and calculate the thermal interference intensity change rate by combining the time series analysis method. By comparing it with the high-risk judgment threshold set in the risk identification model, the thermal interference area that meets the high-risk status is identified; This process aims to identify areas of a building with thermal risk in real time. Its core approach is to use time series analysis to extract heat flux trends and determine whether a high-risk thermal disturbance has occurred. Specifically, the system continuously collects heat flux intensity changes at each monitoring point in the heat flux trajectory, constructing a continuous series of heat flux changes over time. By applying time series analysis methods such as moving average analysis, differencing, sliding window slope calculation, or exponentially 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 be used to calculate the heat flux trend value for each monitoring point over the past 10 minutes and compare it with a preset high-risk threshold (e.g., a rate of change exceeding 5 W / m²·min) in the thermal risk identification model. If the threshold is exceeded for several consecutive time periods, the system identifies the area as a high-risk thermal disturbance zone. This method not only reflects the heat flux intensity itself but also reveals its dynamic characteristics, helping to predict areas with sudden increases in cooling load pressure and providing early warning signals for subsequent control measures.

[0063] For the identified high-risk thermal interference areas, the thermal conductivity structural properties, including thermal resistance, thermal diffusivity, and structure thickness, are combined to calculate the locations in the thermal conductivity path where thermal insulation adjustment can be implemented and their upper limit, thereby determining the thermal insulation adjustment operation strategy for the thermal conductivity structure. To develop effective intervention measures for identified high-risk thermal interference areas, the system assesses their thermal conductivity and potential for regulation by combining the corresponding thermally conductive structural properties, including thermal resistance, thermal diffusivity, and structural thickness. Specifically, the system first quantifies the thermophysical properties of each structural layer in the thermal interference path. The total thermal resistance of each layer is calculated layer by layer based on the one-dimensional steady-state heat transfer equation R=L / λ (where R is thermal resistance, L is layer thickness, and λ is thermal conductivity). Subsequently, the system analyzes the building material property database to determine whether materials with variable thermal conductivity (such as phase-change insulation coatings or adjustable density infill layers) are included, identifying specific areas where insulation adjustments can be made. For example, if a thermal path includes 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 a thermal insulation coating with a thermal conductivity of 0.03W / m·K) can raise its thermal resistance to the target value. Thermal diffusivity and thermal inertia analysis are combined to set an upper limit for the response of these adjustments to avoid disrupting the overall thermal balance. The final output of the adiabatic adjustment operation strategy clearly includes the adjustment location, the amount of change in material parameters, and the corresponding thermal resistance target value, providing executable guidance for physical intervention.

[0064] At the same time, based on the change in the cooling load correction factor of the identified high-risk thermal interference area and the current cooling source supply capacity, it is determined whether there is cooling source allocation margin. If the judgment result is that there is cooling source allocation margin, a cooling source allocation operation strategy is generated, which includes the target area location, load adjustment coefficient and cooling path; After identifying a high-risk thermal interference area, it is necessary to assess whether the area can achieve effective cooling control by allocating cooling sources. Specifically, the system first reads the temporal variation of the cooling load correction factor for the thermal interference area and calculates its cooling capacity gap 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 margin, and water system return water temperature and flow margin, to construct a model of the current cooling source supply capacity. The peak rate of change of the cooling load correction factor is then compared with the dispatchable cooling capacity in the cooling source supply capacity. If the condition of "dispatched cooling capacity ≥ cooling capacity gap in the thermal interference area" is met, it is determined that there is margin for cooling source allocation. If this condition is met, the cooling source allocation path is further determined, such as providing cooling through the nearest cooling source branch or adjusting the intelligent air supply system zoning control. For example, if the cooling load correction factor corresponding to the thermal disturbance in an office building increases to +35W / m², and the central air conditioning system's branch fans in adjacent areas still have 50W / m² of available cooling capacity, a cooling resource allocation operation strategy can be generated. This strategy includes control parameters such as the spatial location of the target area, a set load adjustment factor (such as adjusting the target temperature by 2°C), and a cooling capacity allocation path (such as activating the north branch fan and increasing the chilled water flow rate) to guide the system in implementing energy reconstruction and coordinated cooling load distribution.

[0065] The thermal insulation adjustment operation strategy of the heat-conducting structure and the cold source allocation operation strategy are executed simultaneously to complete the joint intervention of the thermal interference effect and the cooling load response, and realize the coordinated regulation of the cooling load prediction and thermal conductivity effect in the building thermal management system.

[0066] After completing the construction of the thermal conductivity structure adiabatic adjustment operation strategy and the cold source allocation operation strategy, the two are executed simultaneously to achieve two-way control of the heat conduction source and the indoor temperature control terminal when thermal interference continues to increase, thereby improving the response speed and control accuracy of the building thermal management system. In specific implementation, the system first executes operations that can implement adiabatic adjustment in the heat conduction path based on the spatial location of the high-risk thermal interference area, such as 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 backplane to suppress heat input; at the same time, the intelligent air conditioning system is dispatched to inject additional cold sources into the area, adjusting the fan air supply rate and cold water temperature to compensate for the rapid accumulation of indoor heat. The entire process maintains the synchronization of control logic through the linkage mechanism of the thermal interference level label and the cooling load correction factor. For example, if a sudden increase in reverse heat flux intensity occurs in a conference room and the duration of the heat flux exceeds a specified limit, the system simultaneously triggers the adiabatic state switch of the exterior metal thermal conductive panels (reducing thermal conductivity from 15W / m·K to 3W / m·K) and activates the southbound cooling branch to inject an additional 10kWh of cooling capacity per hour, ensuring that the thermal interference level in the area is reduced from high risk to controllable within ten minutes. Ultimately, this achieves a coordinated and stable response to thermal behavior suppression and indoor cooling load response. This prevents delayed or ineffective responses from any single measure and ensures a dynamic balance between energy efficiency optimization and indoor comfort.

[0067] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are fully or partially 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 accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more 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.

[0068] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean 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 the present application.

[0069] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0070] 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 example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0071] Units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0072] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0073] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. The integrated energy-saving design and analysis method for new energy buildings is characterized by: The specific steps include: 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 synchronously collected through optical signal transmission, forming a data channel covering the photovoltaic heating path and 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 conductivity 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 continuous impact duration parameter. Combine these parameters with the temperature control target and thermal conductivity structural properties set in the building to construct a reverse heat conduction interference map to determine the degree of heat conduction interference of the building's internal cooling load; S04. Match the reverse heat conduction interference map with the pre-established thermal risk boundary model to identify high-risk, controllable-risk, and low-risk heat 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 heat interference area; S05. Combine the heat interference classification results with the cooling load correction factor to establish a dynamic feedback control process, continuously analyze the heat flux change trend, and when the heat interference area continues to meet the high-risk conditions, perform the heat conduction structure insulation adjustment operation or the building cold source allocation operation to achieve synchronous coordination of cooling load prediction and heat conduction impact.

2. The integrated energy-saving design and analysis method for new energy buildings according to claim 1 is 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 points of the metal heat-conducting structure, and the corresponding wall surface inside the building in a unified manner according to the time sequence of optical signal collection, and use equal-interval interpolation and reconstruction to achieve cross-channel time synchronization; S022. Convert the aligned heat flow 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, thereby uniformly constructing a standardized physical quantity sequence; S023. Extract the thermal resistance parameters of the conduction path based on the thermal conductivity and structural dimensions of the heat-conducting structure. Establish the heat flux intensity distribution by combining the heat flux density per unit area and the absolute temperature difference. Simultaneously, 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 the uniformly constructed thermal resistance reference axis coordinate system, the heat flux density per unit area, the absolute temperature difference, and the power generation value per unit area are mapped and embedded in the order of the paths, and combined to form the heat flux trajectory and thermal response profile, which are used to express the complete heat conduction path characteristics from photovoltaic to indoor.

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

4. The integrated energy-saving design and analysis method for new energy buildings according to claim 3 is characterized in that: S023 specifically: According to the material type corresponding to the heat-conducting structure, the thermal conductivity value is obtained. Combined with the thickness and cross-sectional area of ​​the structure, the thermal resistance value is calculated based on 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 combined with the corresponding thermal resistance value to construct a heat flux intensity distribution sequence facing the heat conduction path, reflecting the degree of heat input concentration at different locations; The heat capacity and thermal diffusivity parameters of the filling layer and surface materials in the building envelope structure are extracted to form a thermal inertia parameter group associated with the structural layout, which is used to support the modeling of heat storage characteristics in the subsequent dynamic thermal response process.

5. The new energy building integrated energy-saving design and analysis method according to claim 4 is characterized in that: S024 is specifically: A thermal resistance reference axis coordinate system was established, and the heat conduction path was sequentially expanded according to the three key thermal locations: photovoltaic modules, heat conduction structures, and indoor walls. The thermal resistance values ​​were cumulatively arranged along the axis to form a two-dimensional mapping domain with continuous thermal resistance coordinates. The unit area heat flux density data sequence is mapped to the corresponding photovoltaic module position along the thermal resistance coordinate axis, the absolute temperature difference data sequence is mapped to the heat conduction structure node, and the unit area power generation value sequence is synchronously mapped to the initial point of the photovoltaic module. The three types of data form a heat input-conduction-response path relationship; According to the relative distance of each point on the thermal resistance axis and the physical sequence order, the three types of mapped standardized data are connected in trajectory to construct a heat flux trajectory and thermal response profile that reflects the heat flow change trend and temperature rise response law along the thermal resistance change path.

6. The integrated energy-saving design and analysis method for new energy buildings according to claim 1 is characterized in that: S03 specifically includes the following steps: S031. Extract the heat flux direction change data on the path from the photovoltaic module to the interior of the building from the heat flux trajectory and thermal response profile, and extract the reverse heat flux intensity parameter and the heat flux continuous impact duration parameter based on the section where the heat flow direction changes from outside to inside; S032. Match the extracted reverse heat flux intensity parameter and heat flux continuous impact duration parameter with the temperature control targets corresponding to the areas within the building, and combine the thermal resistance, structural thickness, and thermal diffusivity of each material in the thermal conductive structure to form a set of building thermal conductive structure attributes; S033. Construct a coordinate mapping domain in three-dimensional space based on the reverse heat flux intensity parameter, the heat flux continuous impact duration parameter, the temperature control target, and the heat conduction structural properties. Map different thermal interference characteristics to the coordinate domain to form a partition expression, and generate a reverse heat conduction interference map for determining the degree of heat conduction interference of the cooling load inside the building.

7. The integrated energy-saving design and analysis method for new energy buildings according to claim 6 is characterized in that: S031 specifically: In the heat flux trajectory, the heat flux segment that conducts from outside to inside is 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 interference energy density in the heat conduction path; In the same heat flux section, the time interval in which the heat flux continuously exceeds the static reference value is determined, and the time span of the continuous heat transfer process is extracted as the heat flux continuous impact duration parameter; The reverse heat flux intensity parameter and the heat flux continuous impact duration parameter are bound to the original heat flux trajectory sequence to establish a thermal interference feature data pair with time tags and path position identifiers, which is used for subsequent association construction with building control parameters.

8. The new energy building integrated energy-saving design and analysis method according to claim 7 is characterized in that: S033 specifically: A two-dimensional coordinate domain is established with the reverse heat flux intensity parameter as the horizontal axis and the heat flux continuous impact duration parameter as the vertical axis, and the building temperature control target value offset range as the isovalue distribution line; According to the thermal resistance, thermal diffusivity and thickness of each structural layer in the thermal conductive structural 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 continuous impact duration parameter, building temperature control target value offset range, thermal resistance value included in the thermal conductive structural properties, thermal diffusivity and structure thickness are combined as a six-dimensional parameter set and mapped into the three-dimensional coordinate domain according to the coordinate axis embedding logic. The various thermal interference level areas are identified by superimposing the contour line density distribution and the thermal response weight, and a reverse heat conduction interference map is generated, which can be used to identify the heat conduction interference level of the cooling load inside the building.

9. The new energy building integrated energy-saving design and analysis method according to claim 1 is characterized in that: S04 specifically includes: The reverse heat flux intensity parameters, heat flux continuous impact duration parameters, building temperature control target value offset range, and thermal conductive structural attribute coordinates contained in the reverse heat conduction interference map are mapped into a unified parameter space. The parameters are then matched with the high-risk, controllable-risk, and low-risk intervals defined in the pre-established thermal risk boundary model. The corresponding thermal interference levels are then marked on the thermal interference points in the map based on the matching results. Based on the spatial distribution of the marked high-risk, controllable-risk, and low-risk thermal interference points, thermal interference areas with continuous boundaries are aggregated. The spatial distribution characteristics of each type of thermal interference area are statistically analyzed, including occupied area, distribution density, boundary direction, and relative position relationship with the cooling load target 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 conductive structure, including thermal resistance, thermal diffusivity, and structural thickness, a quantitative model reflecting the influence of the heat conduction path was constructed to extract the energy offset of the area affecting the building cooling load. The energy offset corresponding to each type of thermal interference area is converted into a cooling load impact value per unit area. Combined with the regional coverage and interference duration weight, the 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 distribution and energy efficiency management.

10. The new energy building integrated energy-saving design and analysis method according to claim 1 is characterized in that: S05 specifically includes: Based on the thermal interference classification results and cooling load correction factors, a dynamic identification model for thermal risk status was constructed. The heat flux intensity variation curve of each area and the corresponding cooling load correction value were input into the model to form a thermal interference monitoring dataset with time dimension and risk level labels. Continuously obtain the heat flux intensity change value of each monitoring point in the heat flux trajectory, and calculate the thermal interference intensity change rate by combining the time series analysis method. By comparing it with the high-risk judgment threshold set in the risk identification model, the thermal interference area that meets the high-risk status is identified; For the identified high-risk thermal interference areas, the thermal conductivity structural properties, including thermal resistance, thermal diffusivity, and structure thickness, are combined to calculate the locations in the thermal conductivity path where thermal insulation adjustment can be implemented and their upper limit, thereby determining the thermal insulation adjustment operation strategy for the thermal conductivity structure. At the same time, based on the change in the cooling load correction factor of the identified high-risk thermal interference area and the current cooling source supply capacity, it is determined whether there is cooling source allocation margin. If the judgment result is that there is cooling source allocation margin, a cooling source allocation operation strategy is generated, which includes the target area location, load adjustment coefficient and cooling path; The thermal insulation adjustment operation strategy of the heat-conducting structure and the cold source allocation operation strategy are executed simultaneously to complete the joint intervention of the thermal interference effect and the cooling load response, and realize the coordinated regulation of the cooling load prediction and thermal conductivity effect in the building thermal management system.

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