Building ventilation thermal load regulation and control method and system based on thermal plume prediction

Abstract

The invention relates to the technical field of building thermal environment regulation and control, and discloses a building ventilation thermal load regulation and control method and system based on thermal plume prediction, and the method comprises the steps: constructing a curtain wall surface three-dimensional coordinate system and a thermal state temperature map, and obtaining a current thermal plume trajectory; similar trajectory matching is carried out in combination with a historical thermal plume trajectory database, and a thermal plume development path is predicted; based on a prediction result, establishing a compensation relationship between a floor load grading sequence and an adjacent floor feed-forward temperature, implementing time sequence dislocation dimming through spiral progressive control, and after time sequence dislocation dimming, performing thermal-optical coupling feedback correction; hot air can be effectively prevented from flowing backwards, the high-rise room temperature and the air conditioner load are reduced, indoor natural lighting is guaranteed, the building thermal environment is optimized, and building ventilation thermal load efficient regulation and control are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building thermal environment regulation, and more particularly, to a building ventilation thermal load regulation method and system based on thermal plume prediction. BACKGROUND

[0002] Glass curtain walls, with their transparency and light weight, have become the preferred solution for modern high-rise building envelope systems, and their facade form directly affects building ventilation paths and thermal environment balance. However, high-intensity solar radiation in summer directly acts on the outer glass surface of the curtain wall, inducing a transient temperature rise of over 60℃. Driven by buoyancy, these high-temperature areas form a "thermal plume" that rises rapidly along the curtain wall facade. This thermal plume is coupled with the thermal pressure channel formed by the open windows at the top of the building, changing the airflow direction of natural ventilation and creating a unique "curtain wall heat island microclimate" around the curtain wall. If the building ventilation thermal load regulation system still follows the traditional single-point light-temperature feedback approach, only the local temperature and light parameters are adjusted passively, and the thermal plume's rising path along the curtain wall and its temporal and spatial intensity cannot be predicted and intervened, leading to the continuous intrusion of high-temperature air carried by the thermal plume into the room, disrupting the thermal balance of the building ventilation system, and ultimately resulting in high air conditioning loads in high-rise buildings and difficulty in reducing the overall building ventilation thermal load energy consumption baseline.

[0003] Chinese patent application CN119041605A proposes an intelligent light-adjusting thermal insulation curtain wall and a light-adjusting method. The thermal insulation curtain wall is arranged with rotatable reflective prisms between the double-layer glass, which adjusts the visible light flux by controlling the angle through a program, and relies on the hollow layer to reduce the heat conduction between the outer and inner sheets. The control variable is only the preset illuminance-time curve, and real-time sensing and prediction of the external surface thermal plume trajectory are not involved. Chinese patent CN115478645B discloses a building curtain wall and a lighting adjustment method. The building curtain wall changes the light transmission area by rotating the entire separation layer to achieve uniform lighting, but still uses a static optical target as input, lacking an active inhibition mechanism for the thermal pressure transmission between floors induced by the thermal plume. Both of them stay at the "light-heat isolation" level, lacking a "trajectory prediction-load grading-neighbor layer feedforward" closed loop.

[0004] The existing blind area of the prior art is that when the surface of the outer glass is heated and forms an upward thermal plume due to solar radiation, if the upper window is in an open ventilation state, the thermal plume will directly "backflow" into the indoor space under the driving of the pressure difference, forming a "heat source-opening" transient coupling. At this time, even if the prism or the interlayer has reflected visible light to the comfort zone, due to the lack of real-time calculation of the thermal plume rising path and the thermal pressure gradient between the floors in the control variables, the system cannot reduce the light transmittance of the upper curtain wall in advance to weaken the intensity of the outer wall heat source, nor can it compensate for the adjacent layer feedforward for the high-temperature thermal plume that is about to rise in the lower layer, resulting in abnormal rise of the upper room temperature, long-time high-load operation of the air conditioner, and the energy consumption baseline is raised. Therefore, how to make the light adjustment ahead of the thermal plume and form a thermodynamic block between the floors has become a sub-allocation problem that needs to be broken through for the glass curtain wall heat island microclimate regulation. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, the present application provides a building ventilation heat load regulation method and system based on thermal plume prediction, which captures the current thermal plume trajectory by constructing a curtain wall surface three-dimensional coordinate system, predicts the thermal plume development path in combination with the historical thermal plume trajectory database, and establishes a floor load classification sequence and an adjacent layer feedforward temperature compensation relationship based on the path prediction result, which can effectively inhibit the backflow of hot air and realize the reduction of high-rise room temperature and building comprehensive energy consumption. Through the spiral progressive control of the time sequence staggered light adjustment scheme and the thermal-optical coupling feedback correction, the accuracy and dynamic adaptability of the glass curtain wall "heat island microclimate" regulation are improved, while ensuring indoor natural lighting, the building thermal environment regulation effect is further optimized.

[0006] To achieve the above object, the present application provides the following technical scheme:

[0007] The building ventilation heat load regulation method based on thermal plume prediction comprises:

[0008] A curtain wall surface three-dimensional coordinate system is constructed, a thermal state temperature map is constructed in the curtain wall surface three-dimensional coordinate system, and a current thermal plume trajectory line is constructed based on the thermal state temperature map; a historical thermal plume trajectory database is constructed, a similar historical trajectory of the current thermal plume trajectory line is extracted from the historical thermal plume trajectory database, and a development path prediction result of the current thermal plume is predicted based on the similar historical trajectory;

[0009] A floor load classification sequence is established by using the development path prediction result of the current thermal plume; an adjacent layer feedforward temperature compensation relationship is constructed based on the floor load classification sequence, and time sequence staggered light adjustment is implemented through spiral progressive control based on the adjacent layer feedforward temperature compensation relationship; after the time sequence staggered light adjustment is executed, thermal-optical coupling feedback correction is performed.

[0010] The method for constructing the current thermal plume trajectory line based on the thermal state temperature map comprises:

[0011] calculating a temperature gradient field based on the thermal temperature map, identifying and marking a thermal plume source point;

[0012] connecting the thermal plume source points according to the space-time dual criteria, constructing a current thermal plume trajectory line, and associating a current time label with the current thermal plume trajectory line.

[0013] The method for constructing the thermal temperature map comprises:

[0014] marking grid nodes on the curtain wall surface, collecting the temperature of each grid node at a preset sampling period under sunlight conditions, and generating a thermal temperature map at each collection time.

[0015] The identification of the thermal plume source point comprises:

[0016] For the thermal temperature map at each collection time, calculating a vertical temperature gradient;

[0017] When the vertical temperature gradient is greater than or equal to a temperature gradient threshold α, marking the corresponding grid node as a thermal plume source point.

[0018] The method for implementing time sequence misplacement dimming based on the adjacent layer feedforward temperature compensation relationship comprises:

[0019] generating a floor light transmittance control instruction sequence based on the adjacent layer feedforward temperature compensation relationship;

[0020] numbering the grid nodes on the curtain wall surface in a spiral path, distributing the grid nodes to different dimming groups according to the node number, and forming a spiral progressive dimming node sequence;

[0021] determining the time sequence misplacement execution time of each dimming group according to the floor light transmittance control instruction sequence, and executing time sequence misplacement dimming according to the spiral progressive dimming node sequence and the time sequence misplacement execution time.

[0022] The method for establishing a floor load classification sequence comprises:

[0023] calculating the thermal intrusion risk index of each floor based on the development path prediction result of the current thermal plume;

[0024] classifying the thermal load of each floor according to the thermal intrusion risk index, recording the thermal load classification result in order of floor height, and generating a floor load classification sequence.

[0025] The development path prediction result of the current thermal plume at least comprises a future thermal plume trajectory line;

[0026] The method for calculating the thermal intrusion risk index of each floor comprises:

[0027] Based on the prediction result of the current thermal plume development path, the probability P(z') that the future thermal plume trajectory line passes through each floor is calculated, and the temperature peak coefficient C and the window opening rate W(z') of each floor are calculated, wherein z' is the floor identifier. temp

[0028] The thermal intrusion risk index R(z') of each floor is equal to the product of P(z'), C temp and W(z').

[0029] The method for classifying the thermal load of each floor comprises:

[0030] When R(z') < β1, the floor z' is classified as a low-load floor.

[0031] When β1≤R(z')≤β2, the floor z' is classified as a medium-load floor.

[0032] When R(z') > β2, the floor z' is classified as a high-load floor; wherein β1 is a first threshold value, and β2 is a second threshold value.

[0033] The historical thermal plume trajectory database comprises historical thermal plume trajectory lines of the past n days.

[0034] The method for extracting similar historical trajectories of the current thermal plume trajectory line comprises:

[0035] Extracting the trajectory features of the current thermal plume trajectory line and the historical trajectory features of the historical thermal plume trajectory lines.

[0036] Performing a space-temperature-time triple similarity determination on the trajectory features of the current thermal plume trajectory line and the historical trajectory features of the historical thermal plume trajectory lines, and taking the historical thermal plume trajectory lines that satisfy the space-temperature-time triple similarity determination as similar historical trajectories to obtain k similar historical trajectories, and adjusting the related parameters of the space-temperature-time triple similarity determination until k is greater than or equal to the sample quantity threshold value if k is less than the sample quantity threshold value.

[0037] The historical trajectory features of the historical thermal plume trajectory lines are associated with historical time labels.

[0038] The space-temperature-time triple similarity determination comprises a spatial position similarity determination, a temperature feature similarity determination, and a time similarity determination.

[0039] The condition for the time similarity determination is that the absolute value of the difference between the historical time label of the i'th historical thermal plume trajectory line and the current time label of the current thermal plume trajectory line is less than a time deviation threshold value.

[0040] ​The building ventilation thermal load regulation system based on thermal plume prediction is used for realizing the building ventilation thermal load regulation method based on thermal plume prediction, and comprises the following modules.

[0041] A thermal plume trajectory construction module is configured to construct a curtain wall surface three-dimensional coordinate system, construct a thermal state temperature atlas in the curtain wall surface three-dimensional coordinate system, and construct a current thermal plume trajectory line based on the thermal state temperature atlas.

[0042] A thermal plume trajectory prediction module is configured to construct a historical thermal plume trajectory database, extract a similar historical trajectory of the current thermal plume trajectory line from the historical thermal plume trajectory database, and predict a development path prediction result of the current thermal plume based on the similar historical trajectory.

[0043] A light regulation module is configured to establish a floor load hierarchical sequence by using the development path prediction result of the current thermal plume, construct a neighboring layer feedforward temperature compensation relationship based on the floor load hierarchical sequence, and implement time sequence staggered light regulation by using the spiral progressive control based on the neighboring layer feedforward temperature compensation relationship.

[0044] A feedback correction module is configured to perform thermal-light coupling feedback correction after the time sequence staggered light regulation is executed.

[0045] Compared with the prior art, the building ventilation thermal load regulation method based on thermal plume prediction has the following beneficial effects.

[0046] The closed-loop control of "trajectory prediction-hierarchical compensation-staggered execution-feedback correction" is adopted, so that the curtain wall light regulation is no longer only responsive to light, but the thermal plume rising channel is locked in advance, the heat is gradually reduced in stages before the heat invasion through the neighboring layer feedforward compensation, the optical and electrical impact caused by the synchronous action is eliminated by using the spiral progressive light regulation, and finally the high-rise room obtains lower surface temperature and less heat air backflow under the same sunlight condition, the air conditioning load is reduced, and the simultaneous optimization of light transmission, heat insulation and energy saving is realized. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0048] Figure 1 A principle flowchart of the building ventilation thermal load regulation method based on thermal plume prediction provided by the embodiments of the present application is shown in the following figure.

[0049] Figure 2 A method flowchart for establishing a floor load hierarchical sequence provided by the embodiments of the present application is shown in the following figure.

[0050] Figure 3 A method flowchart for generating a floor light transmittance control instruction sequence is provided for the embodiment of the present application.

[0051] Figure 4 A principle diagram of time sequence misplacement dimming is provided for the embodiment of the present application.

[0052] Figure 5 A method flowchart of thermal-optical coupling feedback correction is provided for the embodiment of the present application.

[0053] Figure 6 A functional module diagram of a building ventilation thermal load regulation system based on thermal plume prediction is provided for the embodiment of the present application. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0055] Embodiment 1:

[0056] Please refer to Figure 1 The embodiment provides a building ventilation thermal load regulation method based on thermal plume prediction, which comprises the following steps:

[0057] In step S10, a curtain wall surface three-dimensional coordinate system is constructed, and a current thermal plume trajectory line is constructed in the curtain wall surface three-dimensional coordinate system; a historical thermal plume trajectory database is constructed, a similar historical trajectory of the current thermal plume trajectory line is extracted from the historical thermal plume trajectory database, a common development law of the similar historical trajectory is analyzed, and a development path prediction result of the current thermal plume is predicted.

[0058] Further, step S10 comprises the following steps:

[0059] In step S11, a curtain wall surface three-dimensional coordinate system is constructed with the center of the bottom of the curtain wall as the origin, and a grid node is marked on the curtain wall surface.

[0060] The origin of the curtain wall surface three-dimensional coordinate system is set at the center of the curtain wall bottom. The position is selected according to the starting rule of heat transfer of the curtain wall. The curtain wall bottom is usually the initial area of heat accumulation, and serves as the geometric reference point of the building facade, which facilitates the coordination with the building structure coordinate system and reduces the error of space conversion. The axes of the curtain wall surface three-dimensional coordinate system are strictly defined to match the monitoring requirements of the thermal plume: the X-axis extends along the horizontal direction of the curtain wall, which is used to represent the temperature difference of different horizontal areas of the curtain wall. This is because the thermal plume will be horizontally offset by wind force, curtain wall material joint gaps, etc. during its upward movement, and the X-axis can accurately capture this offset feature. The Z-axis is vertically upward, directly corresponding to the core movement direction of the upward movement of the thermal plume. The starting, development and termination height of the thermal plume all need to be quantified by the Z-axis coordinate. The Y-axis is perpendicular to the curtain wall and points to the outside, providing a spatial reference for measuring the temperature difference between the curtain wall surface and the outdoor environment. The temperature difference is the core driving force for the formation of the thermal plume. The setting of the Y-axis makes the temperature difference measurement have a clear spatial reference, avoiding errors caused by chaotic collection positions of environmental temperature.

[0061] The grid nodes are marked at a standard width a of the curtain wall unit block. This setting is directly adapted to the characteristics of the curtain wall industrial construction. The curtain wall is usually composed of standardized unit blocks. Marking the nodes at a interval of a can make each grid node correspond to the physical position of the unit block one by one, which facilitates the integration of temperature sensors in the pre-set installation position of the unit block, without the need for additional modification of the curtain wall structure, reducing the construction difficulty and cost. At the same time, the uniform interval a forms a fixed spatial resolution, ensuring that the temperature data collected in different areas and at different times have consistent spatial scales, providing standardized input for subsequent temperature gradient calculation and trajectory reconstruction.

[0062] In the prior art, the traditional dimming system lacks a unified coordinate reference matching the space features of the curtain wall, and the temperature data is mostly scattered single-point measurement values, which cannot establish spatial correlation, resulting in that the position, range and movement direction of the thermal plume cannot be accurately positioned. Step S11 gives all temperature data a clear spatial coordinate attribute by constructing a curtain wall surface three-dimensional coordinate system, so that the discrete temperature values are converted into a data set with spatial structure; the marking of the grid nodes further divides the curtain wall surface into standardized monitoring units, realizing full coverage and ordering of temperature monitoring. The adaptability design of the grid nodes and unit blocks takes into account the monitoring accuracy and engineering feasibility, avoiding data loss or excessive engineering cost caused by unreasonable arrangement of monitoring points; the standardized spatial resolution ensures the comparability and consistency of the data, making it possible to compare the thermal plume characteristics of different floors and different time periods. The historical thermal plume trajectory coordinates stored in the historical thermal plume trajectory database in subsequent step S13 are based on the same coordinate system as the current thermal plume trajectory, so that the similarity matching of the current thermal plume trajectory and the historical thermal plume trajectory has a unified spatial scale, without the need for additional coordinate conversion, significantly improving the matching efficiency and accuracy. If this step is missing, the thermal state temperature map in subsequent step S12 will be a disordered set of temperature values without spatial coordinates, which cannot calculate the temperature gradient to identify the thermal plume source, resulting in a lack of accuracy basis for thermal plume monitoring and regulation.

[0063] Step S12, constructing a thermal state temperature map in the curtain wall surface three-dimensional coordinate system, and constructing a current thermal plume trajectory line based on the thermal state temperature map;

[0064] In the prior art, the traditional dimming system relies on static single-point temperature or light data, which cannot capture the dynamic evolution process of the thermal plume, and cannot quantitatively describe its trajectory, resulting in a lack of accurate basis for dimming control to address the movement of the thermal plume. Step S12 focuses on "dynamicizing static data, continuousizing discrete data, and characterizing complex motion", solving the core problem of real-time tracking and quantitative description of the thermal plume movement trajectory, and providing key dynamic data support for subsequent trajectory prediction and coordinated dimming.

[0065] Further, step S12 includes:

[0066] Step S121, under the condition of sunlight, collecting the temperature of each grid node at a preset sampling period to generate a thermal state temperature map at each collection time;

[0067] Under the condition of sunlight, the temperature of the curtain wall surface grid node is collected by the distributed temperature sensor network at a preset sampling period. For example, the sampling period can be set to 2-10 seconds. The sensor network adopts a composite monitoring scheme combining an infrared thermal imager and a contact thermocouple: the infrared thermal imager covers the whole area of the curtain wall, providing high-resolution temperature distribution (spatial resolution up to 0.1a); the contact thermocouple is arranged at key nodes, such as plate joints, to compensate for the accuracy loss of infrared monitoring in complex reflection environment. This multi-modal data fusion method ensures the integrity and accuracy of the temperature field data. The thermal temperature map T(x, z, t) is stored in a three-dimensional matrix form, where x represents the X-axis coordinate of the grid node, corresponding to the horizontal position of the curtain wall; z represents the Z-axis coordinate, corresponding to the floor height; t represents the collection time, and the three together constitute a "space-time" three-dimensional data structure, which records the dynamic distribution of the curtain wall surface temperature completely. The high temperature of 60-70°C on the outer surface of the curtain wall and the formation of the rising hot air flow (thermal plume) in summer are caused by the direct action of solar radiation. Solar radiation is the core energy source for the temperature rise of the curtain wall surface and the formation of temperature difference with the outdoor environment. Without sunlight, such as at night or on rainy days, there is no significant heat accumulation on the curtain wall surface, the temperature difference with the environment is very small, and the thermal plume cannot be formed or its intensity can be ignored. Therefore, limiting the sunlight condition is to accurately focus on the nature of the problem, ensuring that the monitoring data is directly related to the core cause of "heat island microclimate".

[0068] Step S122, calculating the temperature gradient field based on the thermal temperature map, identifying and marking the thermal plume source point;

[0069] The calculation of the temperature gradient field needs to be calculated for each collection time of the thermal temperature map, and the vertical temperature gradient ATz(x, z, t) and the horizontal temperature gradient ATx(x, z, t) are calculated respectively. The calculation logic of ATz(x, z, t) is the difference between the temperature of the upper node and the current node at the same X-axis coordinate, that is, ATz(x, z, t) = T(x, z+1, t)-T(x, z, t); ATx(x, z, t) is the difference between the temperature of the right node and the current node at the same Z-axis coordinate, that is, ATx(x, z, t) = T(x+1, z, t)-T(x, z, t). The basis of this calculation logic is the basic law of heat transfer. The core movement of the thermal plume is vertical upward, and its formation is usually accompanied by a significant vertical temperature difference. ATz(x, z, t) directly reflects the strength of the rising hot air; ATx(x, z, t) reflects the lateral diffusion trend of the thermal plume, which can assist in judging the influence range of the thermal plume.

[0070] When the vertical temperature gradient ATz(x, z, t) is greater than or equal to the temperature gradient threshold a, the corresponding grid node is marked as a hot plume source point. The setting of the temperature gradient threshold a is based on the type of curtain wall material, and the setting method is: by comparing the thermal response characteristics of different material curtain walls under the same sunlight conditions, the minimum vertical temperature difference when the observable hot plume is formed is counted. Dark materials such as dark gray fluorocarbon paint aluminum plates have high solar absorptivity, and the surface temperature is easy to rise, so a smaller vertical temperature difference can form a hot plume, so a is set to a lower value; light-colored materials such as beige ceramic plates have low absorptivity, and a larger temperature difference is needed to form a hot plume, so a is set to a higher value; reflective materials such as mirror glass have low overall absorptivity, but local sunlight focusing is easy to occur, and the temperature of the focused area rises sharply, forming a local strong hot plume, so a lower a needs to be set to capture the local features. For example, when the curtain wall material is dark gray aluminum plate, a is set to 3°C; when it is beige ceramic plate, a is set to 5°C; when it is mirror glass, a is set to 2°C. When the ATz(x, z, t) of a certain grid node is greater than or equal to a, the grid node is marked as a hot plume source point, and its coordinates and corresponding temperature value are recorded.

[0071] The defect of the prior art is that only the high and low temperature of a single point is used to judge the heat source, and it is impossible to distinguish between "static high temperature points" and "dynamic hot plume source points". Some nodes may form static high temperature due to poor heat dissipation caused by material aging, but no rising hot air flow is generated. If a static high temperature point is mistakenly judged as a hot plume source point, it will lead to inaccurate control. Step S122 identifies the source point by temperature gradient instead of single-point temperature, accurately captures the core feature of "heat transfer strength", and avoids the interference of static high temperature. The temperature gradient field converts the static temperature distribution into a dynamic heat transfer trend, realizing the upgrade from "temperature value monitoring" to "heat flow direction identification"; based on the material characteristics, the temperature gradient threshold a is set, so that the hot plume source point identification is adapted to different curtain wall types, and the universality of the system is improved;

[0072] In step S123, the hot plume source points are connected according to the space-time double criteria, the current hot plume trajectory line is constructed, and the trajectory features of the current hot plume trajectory line are extracted. The current time label is associated with the current hot plume trajectory line.

[0073] The connection of the hot plume source points follows a "space-time" dual criterion: in space, the horizontal distance between the two hot plume source points is less than a preset horizontal connection threshold Dx, and the vertical distance is less than a preset vertical connection threshold Dz, the setting of Dx and Dz is based on the diffusion characteristics of the hot plume, generally Dx is 2 times the spatial resolution parameter, the spatial resolution is the standard width a of the curtain unit block, and Dz is 3 times the spatial resolution parameter, because the lateral diffusion range of the hot plume is limited during the rising process, and the continuity in the vertical direction is stronger; in time, the time interval between the two hot plume source points is less than or equal to a collection period, ensuring that the connected hot plume source points belong to the hot plume formed in the same period. The connection process adopts the "nearest neighbor principle", and preferentially connects the hot plume source points with the shortest spatial distance and the same time, to form a continuous hot plume trajectory line.

[0074] The trajectory features specifically include: a starting height h1, h1 is the Z-axis coordinate of the lowest point of the current hot plume trajectory line, reflecting the floor of origin of the hot plume; an ending height h2, h2 is the Z-axis coordinate of the highest point of the current hot plume trajectory line, reflecting the influence range of the hot plume; a trajectory offset angle θ, θ is the angle between the current hot plume trajectory line and the Z-axis, calculated by the X-axis coordinate difference Δx and the Z-axis coordinate difference Δz of adjacent nodes on the trajectory line, θ = arctan(Δx / Δz), reflecting the horizontal offset caused by wind and other factors; a temperature peak Tmax, Tmax is the highest temperature value of all nodes on the current hot plume trajectory line, reflecting the intensity of the hot plume. The current time label is associated with the current hot plume trajectory line. These trajectory feature parameters together constitute the identity label of the hot plume, and are stored in association with the coordinate data of the corresponding hot plume trajectory line, the collection time, and the environmental parameters.

[0075] The defect of the prior art is that even if the discrete hot plume source points are identified, they cannot be associated as continuous trajectories, resulting in the lack of key information such as the movement path and the influence height of the hot plume, and the inability to provide precise targeting basis for high-level dimming. Step S123 connects the source points by a space-time dual criterion, converts discrete points into continuous trajectories, and extracts feature parameters to quantify the movement law. The continuous trajectory line completely presents the spatial movement path of the hot plume, making the "start-development-termination" process of the hot plume visualized; the trajectory feature parameters simplify the complex hot plume movement into quantifiable indicators, providing structured input for the historical trajectory matching of subsequent step S13; the extraction of the trajectory offset angle θ can associate environmental factors such as wind speed, providing data support for analyzing the coupling relationship between the hot plume and the environment.

[0076] Step S12 solves the core problem of dynamic monitoring and quantification of thermal plume by "map construction-gradient calculation-trajectory extraction", realizes the "visualization, quantification and traceability" of thermal plume movement, and breaks the traditional system's "fuzzy perception" limitation of thermal plume; the time-sequenced trajectory data and characteristic parameters provide multi-dimensional input for subsequent prediction and control, so that active regulation has a data basis.

[0077] Step S13, constructing a historical thermal plume trajectory database, extracting similar historical trajectories of the current thermal plume trajectory line from the historical thermal plume trajectory database, analyzing the common development law of the similar historical trajectories, and predicting the development path prediction result of the current thermal plume.

[0078] Further, step S13 includes:

[0079] Step S131, collecting the historical temperature of each grid node in the past n days and the same sampling period and same collection time as the current monitoring, generating the historical thermal plume trajectory line of each collection time in the past n days, and constructing a historical thermal plume trajectory database;

[0080] The collection time length n of historical temperature needs to meet the statistical significance requirement, and the setting basis is the periodic law of thermal plume evolution. The thermal response of building curtain wall is affected by the sunshine period and weather changes. A sample size of more than 30 days can cover different typical environmental conditions such as sunshine intensity, outdoor temperature and wind speed, exclude the interference of short-term accidental factors on data law, and ensure that the database contains enough diverse thermal plume evolution modes, so n≥30. Data collection needs to strictly maintain consistency with the current monitoring, and the same sampling period and the same collection time are collected to ensure the uniformity of the time resolution of the data, so that the historical trajectory and the current trajectory have a direct comparison time reference. The generation of the historical thermal plume trajectory line completely reuses the method of step S12, that is, by collecting historical temperature to generate thermal state temperature map, calculating temperature gradient field to identify thermal plume source point, and then connecting the thermal plume source point according to the space-time double criterion to form the historical thermal plume trajectory line and extract the trajectory characteristics, and storing the trajectory characteristics of the historical thermal plume trajectory line into the historical thermal plume trajectory database.

[0081] The multiplexing ensures that the feature dimensions and generation criteria of the historical thermal plume trajectory line are completely consistent with those of the current thermal plume trajectory line, avoiding the problem of incomparable features caused by differences in technical logic. The storage structure of the historical thermal plume trajectory database needs to realize the three-dimensional association of "trajectory-feature-environment". Each historical thermal plume trajectory line is associated with its complete coordinate data, extracted historical starting height, historical ending height, historical trajectory offset angle, historical temperature peak, and corresponding historical environmental parameters (solar intensity, outdoor temperature, wind speed) and historical time labels such as date and collection time. When storing, double indexing is performed according to "time dimension + feature dimension". The time index facilitates quick positioning of historical data at the same time, and the feature index provides an efficient search path for subsequent similarity matching. The unified trajectory generation logic ensures the homogeneity and comparability of historical data and current data, providing a prerequisite for the accuracy of similarity matching; the three-dimensional association of the storage structure realizes the deep binding of trajectory features and environmental conditions, enabling the subsequent prediction to consider the coupling effects of the thermal plume itself and the external environment; the double indexing design improves the data retrieval efficiency, meeting the real-time requirements of dimming control.

[0082] Step S132, similarity matching of the current thermal plume trajectory line with the historical thermal plume trajectory database is performed to extract similar historical trajectories;

[0083] The trajectory features of the current thermal plume trajectory line and the historical trajectory features of the historical thermal plume trajectory lines in the historical thermal plume trajectory database are subjected to spatial-temperature-time triple similarity determination. The historical thermal plume trajectory lines that satisfy the spatial-temperature-time triple similarity determination are regarded as similar historical trajectories, and a total of k similar historical trajectories are obtained. If k is less than the sample quantity threshold, the related parameters of the spatial-temperature-time triple similarity determination are adjusted until k is greater than or equal to the sample quantity threshold.

[0084] The space-temperature-time triple similarity determination includes a space position similarity determination, a temperature characteristic similarity determination, and a time similarity determination. A condition for the space position similarity determination is that an absolute value of a difference between a historical starting height of an i'th historical thermal plume trajectory and a starting height of a current thermal plume trajectory is less than a space distance threshold value, and an absolute value of a difference between a historical trajectory deflection angle of the i'th historical thermal plume trajectory and a trajectory deflection angle of the current thermal plume trajectory is less than an angle deviation threshold value. The space distance threshold value is set as an integer multiple of a, and the basis is that a minimum change unit of the starting height corresponds to a vertical spacing of a grid node, that is, the integer multiple of a can ensure that the difference judgment and the spatial accuracy of monitoring match. For example, it can be set as 2a. The angle deviation threshold value is set according to the influence of wind on thermal plume deflection. By statistically analyzing the fluctuation range of the thermal plume deflection angle under different wind speeds, the minimum angle difference that can distinguish the influence of different wind is determined. When the wind speed is small, the deflection angle fluctuation is small, and the angle deviation threshold value can be set to a small value. When the wind speed fluctuates greatly, the angle deviation threshold value is appropriately relaxed to ensure that the trajectories under the influence of the same wind are captured. For example, the angle deviation threshold value is set to 10 degrees. i' is an index of the historical thermal plume trajectory line in the historical thermal plume trajectory database.

[0085] A condition for the temperature characteristic similarity determination is that an absolute value of a difference between a historical temperature peak value of the i'th historical thermal plume trajectory line and a temperature peak value of the current thermal plume trajectory line is less than a temperature deviation threshold value. The temperature deviation threshold value is based on a distinguishable range of thermal plume intensity. By analyzing the temperature peak value fluctuation of the same thermal plume trajectory line (i.e., the same starting height and trajectory deflection angle) in the historical thermal plume trajectory database, the maximum temperature deviation that does not cause significant differences in evolution patterns is determined. The temperature fluctuation tolerance of a high-intensity thermal plume is slightly larger, and the temperature fluctuation tolerance of a low-intensity thermal plume needs to be strictly controlled. For example, the temperature deviation threshold value is set to 5°C. If the temperature peak value of the current thermal plume trajectory line is 65°C, the historical temperature peak value of the historical thermal plume trajectory line is within the range of 60-70°C, which satisfies the temperature characteristic similarity.

[0086] A condition for the time similarity determination is that an absolute value of a difference between a historical time label of the i'th historical thermal plume trajectory line and a current time label of the current thermal plume trajectory line is less than a time deviation threshold value. The time deviation threshold value is based on the rate of change of the sun angle. The sun angle changes slowly in a short time, the differences in sunlight conditions, environmental temperature, etc. at similar times are small, and the similarity of thermal plume evolution patterns is high. By statistically analyzing the influence of the change of the sun angle on the characteristics of the thermal plume, the time deviation threshold value is determined to ensure the consistency of the environment at similar times. For example, the time deviation threshold value can be set to 30 minutes. If the current time label of the current thermal plume trajectory line is 14:00, the historical time label of the historical thermal plume trajectory line is within the range of 13:30-14:30, which satisfies the time similarity.

[0087] The setting of the sample quantity threshold is based on ensuring the accuracy of the hot plume development path prediction, and needs to meet the similar sample quantity sufficient to support rule analysis. For example, the sample quantity threshold is set to 3. When k < 3, the related parameters of the space-temperature-time triple similarity judgment are adjusted. The related parameters refer to the core threshold parameters in the triple similarity judgment dimension, including the spatial distance threshold and the angle deviation threshold of the spatial position similarity judgment, the temperature deviation threshold of the temperature characteristic similarity judgment, and the time deviation threshold of the time similarity judgment. The related parameters of the space-temperature-time triple similarity judgment are adjusted, specifically, the core threshold parameters are relaxed in the order of "space→angle→time", and the dimensions with less impact on prediction accuracy are preferentially relaxed. For example, the spatial distance threshold is relaxed from 2a to 3a, and the angle deviation threshold is relaxed from 10 degrees to 15 degrees, balancing the sample quantity and accuracy. The reason for setting the sample quantity threshold is that the formation and development of the hot plume are affected by multiple factors such as solar intensity, material properties, and wind speed. A single or small number of historical trajectories are easily disturbed by accidental factors (such as instantaneous gusts and local material heat dissipation anomalies), and cannot reflect the "typical evolution pattern under similar initial conditions". Only when the sample quantity reaches a certain size, the common characteristics of multiple trajectories can offset accidental fluctuations and present stable evolution rules. For example, hot plumes with the same initial height, trajectory offset angle, and temperature peak value may have a lower termination height due to instantaneous cloud cover, but the common termination height of 3 or more similar trajectories can filter out such abnormalities and form a stable correlation logic of "initial-development-termination", providing a reliable basis for prediction. The core reason for relaxing the threshold in the order of "space→angle→time" is that each dimension has different impacts on prediction accuracy, and the dimensions with less impact are preferentially relaxed to supplement the sample. Among them, the spatial dimension has the least impact: the core of the hot plume rises vertically, and a deviation of 1-2 grid spacings in the initial height still converges in the upper layer, so the impact of relaxation is small; the angle dimension has a moderate impact: the offset angle is related to horizontal diffusion, but the core prediction of arrival time, temperature decay, etc. is stable, so relaxation can include more samples; the time dimension has the greatest impact: it is directly related to the core driving factors such as sunlight, so early relaxation may lead to prediction failure due to environmental differences, so it is the last to relax.

[0088] The existing technology cannot achieve accurate similarity matching of hot plume trajectories, and often leads to prediction deviation due to single matching dimension or insufficient sample quantity. Step S132 solves the problem of insufficient matching accuracy by considering the influence of spatial position, temperature intensity, and time environment through triple-dimension similarity judgment. The sample quantity dynamic adjustment mechanism balances the representativeness and accuracy of the sample. If this step is missing, it will not be able to select effective reference trajectories from historical data, and subsequent prediction can only rely on real-time data, which cannot reflect the guiding value of historical experience.

[0089] Step S133, analyze the common development rules of similar historical trajectories, and predict the development path prediction result of the current hot plume.

[0090] Based on the k similar historical trajectories matched in step S132, the common features of the subsequent development path are extracted, and the evolution trajectory of the current hot plume in the next m minutes is determined, including the predicted termination height h2 pre , the maximum diffusion width w max , the time t arrival (z') to reach each floor, the predicted trajectory offset angle θ pre , and the future hot plume trajectory line, where z' is the floor identifier, and these prediction contents provide core parameters for risk assessment in step S21.h2 pre is the arithmetic mean of the historical termination heights of the k similar historical trajectories; the maximum diffusion width of the k similar historical trajectories is extracted, and the median is taken as w max ; θ pre is the arithmetic mean of the historical trajectory offset angles of the k similar historical trajectories; the average time for the hot plume in the similar historical trajectories to reach each floor from the starting height is calculated as t arrival (z').

[0091] The generation of the future hot plume trajectory line needs to be based on real-time trajectory, refer to historical commonness, and be constrained by quantitative parameters, and is constructed through multi-dimensional coordination to ensure consistency with the actual evolution law of the hot plume. First, the endpoint of the current hot plume trajectory line is strictly anchored as the starting point, which is the actual spatial position of the hot plume, so as to avoid "starting point misalignment" with the historical trajectory, ensure the spatial continuity of the future trajectory with the currently monitored trajectory, and avoid the problem of breaking the real-time motion trend. Second, the common features of the subsequent development of the k similar historical trajectories are extracted as the path reference: including the overall path trend extending from the "equivalent position of the current trajectory endpoint" to the termination height, such as vertical upward movement as the main trend, uniform trend with small horizontal offset, distribution range of historical trajectory offset angle, diffusion rhythm with the increase of floor height, the distribution range of historical trajectory offset angle provides a stability reference for the horizontal offset of the future trajectory, such as similar trajectories with 5°-8° offset, the future trajectory follows this interval, and the diffusion rhythm with the increase of floor height may be, for example, the common law of slow horizontal diffusion at low floors and accelerated diffusion at medium and high floors. These features ensure that the motion mode of the future trajectory conforms to the historical evolution logic, rather than isolated coordinate splicing. Finally, the predicted parameters are used as hard constraints: the vertical endpoint of the trajectory line must be accurately aligned with h2 pre , the horizontal maximum extension range must be bounded by w max , and the overall horizontal offset direction must be consistent with θ pre . Through the triple coordination of "starting point + common reference + parameter constraint", a continuous trajectory extending from the endpoint of the current hot plume trajectory line to h2 pre is finally constructed, forming a complete future hot plume trajectory line.

[0092] The evolution of the thermal plume has regularity. Similar initial conditions such as starting height, trajectory deviation angle, temperature peak, and time environment often lead to similar development paths. The subsequent evolution process of the historical trajectory provides a reliable reference for the development of the current thermal plume. The existing technology can only monitor the thermal plume in real time and cannot predict its future trend and influence range, resulting in the light control always being in a passive response state. Step S133 realizes the prediction function through historical trajectory regularity analysis, changes passive response to active prevention. If this step is missing, the adjacent layer coordinated dimming in step S20 will not be able to block the thermal plume transmission in time due to the lack of advance, and the regulation effect will be greatly reduced. The prediction result of step S13 provides accurate basis in time and space dimensions for the regulation strategy of step S20, so that the floor load grading and adjacent layer compensation operations can be carried out in advance according to the expected path of the thermal plume, and the "monitoring-prediction-regulation" closed loop is realized. Compared with single monitoring or single regulation, the response efficiency and control accuracy of the system are greatly improved.

[0093] Step S13 mainly solves the technical problem of lack of prediction ability in the development of the thermal plume, which specifically includes two core problems: first, discrete heat source points cannot reflect the continuous rising path of the thermal plume. Even if the existing technology identifies discrete thermal plume source points, it cannot associate them into a continuous trajectory, resulting in the lack of key information such as the movement path and influence height of the thermal plume. Second, the real-time detected thermal plume lacks historical reference and cannot predict its future development trend. The existing technology only relies on real-time data for passive response and cannot use historical evolution rules to guide regulation. Through the construction of a historical thermal plume trajectory database, three-dimensional similarity matching, and regularity analysis, step S13 accurately solves the above problems and realizes intelligent prediction of the development path of the thermal plume. Three-dimensional similarity determination is constrained by space, temperature, and time, ensuring that the similar historical trajectories selected have high reference value. Based on the common rules of similar historical trajectories, the future development path of the current thermal plume such as the termination height, diffusion width, and arrival time can be accurately predicted, breaking the limitation of traditional technology that "can only monitor the present and cannot predict the future".

[0094] Step S20, using the development path prediction result of the current thermal plume, establishes a floor load grading sequence; based on the floor load grading sequence, a neighbor layer feedforward temperature compensation relationship is constructed to generate a floor light transmittance control instruction sequence; the floor light transmittance control instruction sequence is converted into a time sequence staggered dimming scheme, and the time sequence staggered dimming is implemented through spiral progressive control; after the time sequence staggered dimming is executed, a thermal-optical coupling feedback correction is performed.

[0095] Further, step S20 includes:

[0096] Step S21, using the current thermal plume development path prediction result, calculate the thermal intrusion risk index of each floor, establish the floor load classification sequence;

[0097] Please refer to Figure 2 As shown, further, step S21 includes:

[0098] Step S211, based on the current thermal plume development path prediction result, calculate the thermal intrusion risk index of each floor;

[0099] The thermal intrusion risk index is a quantitative indicator that comprehensively reflects the degree of influence of the floor by the thermal plume, and its core role is to convert the dynamic characteristics of the thermal plume into a hierarchical load basis that can be used for dimming control. The thermal plume development path prediction result includes parameters such as termination height, maximum diffusion width, and time of arrival at each floor, which provide spatial and temporal dimension basis input for index calculation.

[0100] Step S211 calculates the thermal intrusion risk index of each floor based on the current thermal plume development path prediction result. First, determine the probability P(z') of the future thermal plume trajectory line passing through each floor, the probability P(z') is obtained by relying on the motion data of the future thermal plume trajectory line in the future prediction time window in the prediction result, and the duration of the future thermal plume trajectory line passing through the height range corresponding to the floor z' is calculated. The ratio of this duration to the prediction time window is determined as the probability P(z'). P(z') directly reflects the possibility of the floor being covered by the thermal plume, and the longer the coverage time, the higher the affected probability. Then calculate the temperature peak coefficient C temp , the calculation of the temperature peak coefficient C temp needs to obtain the predicted maximum temperature of the thermal plume passing near the floor z' in the prediction result, and then obtain the outdoor environment temperature at the same time, the ratio of the two is the temperature peak coefficient C temp . The environment temperature is collected by a distributed temperature and humidity sensor preset on the building outer wall, the collection position avoids direct sunlight and airflow dead angle to ensure data representativeness; the predicted maximum temperature of the thermal plume is extracted from the temperature parameter corresponding to the floor height in the development path prediction result. C temp quantifies the heat intensity of the thermal plume itself, the larger the ratio, the stronger the potential for indoor temperature rise after thermal intrusion. Finally, obtain the window opening rate W(z') of each floor, the window opening rate W(z') needs to be obtained by monitoring the window opening state in real time through the position sensor installed on each floor window, and then calculating the total area of all open windows in the floor, and then comparing it with the total design area of the curtain wall window in the floor. The opening of the window is a direct channel for hot air to enter the indoor, the higher the window opening rate, the more smooth the actual path of thermal intrusion, and the higher the risk transmission efficiency. The calculation formula of the thermal intrusion risk index R(z') is R(z')=P(z')×C tempXW(z'), the design logic of the formula is based on the three elements of thermal intrusion: contact possibility, energy intensity and transmission channel, the product of the three makes the index fully and accurately map the actual thermal load state, and any change in any element will directly affect the index value, avoiding the deviation caused by single factor evaluation.

[0101] Step S212, according to the thermal intrusion risk index, the thermal load of each floor is graded; the thermal load grading results are recorded in order of floor height to generate a floor load grading sequence.

[0102] According to the size of R(z'), the floor is dynamically divided into three thermal load levels: first, set the first threshold β1 and the second threshold β2, the setting of β1 and β2 needs to combine the use characteristics and thermal demand differences of building types: the personnel density of office buildings is stable and long-term stay, the sensitivity to temperature fluctuation is medium, the threshold setting needs to balance energy saving and office comfort; the personnel activity time of residential buildings is flexible, the temperature stability requirement is high during night rest, the threshold setting needs to be more biased towards low load control; the personnel flow of commercial buildings is large, the space openness is high, the heat exchange is frequent, the threshold setting needs to adapt to high load tolerance. The setting method is to monitor the thermal load of the same type of building for not less than 30 days, record the indoor temperature change and personnel comfort feedback under different R(z'), determine the maximum R(z') that does not affect the comfort as β2, and determine the minimum R(z') that can maintain comfort without additional dimming as β1. Based on this, when R(z') < β1, the floor z' is classified as low load layer; when β1≤R(z')≤β2, the floor z' is classified as medium load layer; when R(z')>β2, the floor z' is classified as high load layer. After classification, the classification results are arranged in order from low to high or from high to low, forming a floor load grading sequence, and each element in the sequence corresponds to a floor and its thermal load level.

[0103] Step S21 solves the problem that the traditional static floor division cannot quantify the dynamic thermal load difference. The existing technology divides the load area mainly by fixed floor height or orientation, ignoring the movement trajectory and intensity fluctuation of thermal plume with time, resulting in mismatch between dimming control and actual thermal load. Through the calculation of the three elements of thermal intrusion risk index, the transformation from "static area division" to "dynamic risk quantification" is realized, the probability P(z') captures the influence of time dimension, the temperature peak coefficient C tempThe influence strength of the energy capture dimension is captured by the windowing rate W(z'), and the channel state of the transmission dimension is captured by the windowing rate W(z'), which makes the thermal load evaluation of each floor accurate and real-time. Step S21 provides the core basis for the subsequent step S22 of adjacent layer cooperative control. The floor load classification sequence directly determines the basic dimming strategy of each floor and the compensation strength of adjacent layers. If this step is missing, step S22 will lose the quantitative standard of hierarchical control and can only use a unified dimming mode, which cannot implement precise control for high-load floors. Step S21 realizes the differentiated identification of floor thermal load, enabling the dimming control to shift from "average allocation" to "on-demand allocation", and providing a hierarchical effect evaluation benchmark for subsequent feedback correction. By comparing the actual cooling amplitudes of floors with different load levels, the weak links of the control strategy can be quickly located. In addition, the thermal intrusion risk index calculation incorporates the human factor of window opening behavior into the thermal load evaluation, solving the problem of uncontrolled thermal intrusion caused by human window opening in traditional systems, and enabling the dimming control to dynamically adapt to the load changes caused by personnel usage habits.

[0104] Step S22, constructing an adjacent layer feedforward temperature compensation relationship based on the floor load classification sequence, and generating a floor transmittance control instruction sequence based on the adjacent layer feedforward temperature compensation relationship;

[0105] Referring to Figure 3 Further, step S22 includes:

[0106] Step S221, constructing an adjacent layer feedforward temperature compensation relationship based on the floor load classification sequence;

[0107] Step S222, calculating the transmittance adjustment amount of each floor according to the floor's own thermal load level and the adjacent layer feedforward temperature compensation relationship, and forming a floor transmittance control instruction sequence.

[0108] The adjacent-floor feedforward temperature compensation relationship is the core mechanism for establishing the correlation between dimming strategies on different floors. Its design logic stems from the vertical transfer characteristics of heat plumes. Heat plumes rise from their source point on lower floors and continuously conduct heat to the upper floors they pass through; dimming on a single floor cannot break this transmission chain. The floor load classification sequence provides load level input for this relationship, with different levels corresponding to different basic control requirements and compensation priorities. Based on the floor load classification sequence, an adjacent-floor temperature compensation relationship is established for each floor. The compensation rules for the adjacent floor temperature compensation relationship are set based on the physical laws of heat plume transfer: When the lower floor z'-1 is a high-load floor, it indicates that a large amount of high-temperature hot air is moving upward. As the next station in the transfer path, floor z' needs to reduce its light transmittance in advance to reduce its own heat absorption and avoid superposition effect with the rising hot air. Therefore, the adjacent floor compensation amount δ1 is set. When the upper floor z'+1 is a high-load floor, it indicates that the hot plume has reached this height and may form a local high-pressure zone. The air on floor z' is easily drawn upward to form a "chimney effect" and accelerate heat transfer. Therefore, the adjacent floor compensation amount δ2 is set to reduce its own light transmittance and slow down the upward speed of the airflow. The settings of δ1 and δ2 need to be determined through heat plume transfer experiments. In these experiments, the transfer rate of the heat plume between floors is monitored under different load levels. The minimum transmittance adjustment range that reduces the transfer rate is statistically analyzed and used as the base compensation amount. This is then adjusted based on the building's floor height. The greater the floor height, the longer the heat plume diffuses between floors, and the compensation amount can be appropriately reduced; conversely, the smaller the floor height, the faster the transfer, and the compensation amount needs to be appropriately increased. When floor z' faces high loads from adjacent floors above and below, δ1 and δ2 are superimposed to form a dual compensation to cope with the bidirectional heat transfer pressure.

[0109] For floors with different load levels, different basic light transmittance adjustment values ​​δ are set. base The low-load layer is minimally affected by the thermal plume and requires no additional adjustment to its transmittance; therefore, δ base Set to 0 to maintain the standard light transmittance required for indoor lighting; the medium-load layer is moderately affected and needs to appropriately reduce light transmittance to suppress heat absorption, δ base The settings are based on the average temperature rise under medium load, ensuring that adjustments can offset most of the temperature increase; the high-load layer is most significantly affected, requiring a substantial reduction in transmittance, δ base The setting aims to block the main heat from entering. The δ values ​​corresponding to each load level... base Long-term field testing and calibration are required to record different δ values. base For each floor with different load levels, the minimum adjustment range that maintains the temperature within the comfortable range is selected as the final value. Based on the basic adjustment, compensation amounts δ1 or δ2 from adjacent floors are added to obtain the light transmittance adjustment. The calculation of the light transmittance adjustment must follow the principle of "basic adjustment first, compensation added later," first determining the δ corresponding to its own load. baseAccording to the adjacent floor state, whether to add δ1, δ2 or the sum of the two is determined. The floor light transmittance control instruction sequence is composed of the light transmittance adjustment amount of each floor, floor identification, execution priority and other information, wherein the execution priority is set according to the load level, and the instruction priority of the high-load floor is higher than that of the medium and low-load floor, so as to ensure that the key area is regulated first.

[0110] Step S22 solves the problem that single-floor dimming cannot block the heat plume transmission chain. In the prior art, each floor dimming system operates independently and adjusts only according to its own temperature or light data, resulting in continuous upward transmission of the heat plume generated by the lower layer, and the upper layer becomes a “heat collection area”. Even if the upper layer is dimmed, it cannot offset the heat input of the lower layer. Through the adjacent floor feedforward temperature compensation relationship, the dispersed floor dimming is converted into a coordinated system, the upper layer intervenes in advance when the lower layer is in high load, the middle layer actively buffers when the upper layer is in high load, and a vertical heat transmission blocking belt is formed. The floor load grading sequence provides a clear trigger condition for the adjacent floor feedforward temperature compensation relationship. Only when the adjacent layer is in high load level, the compensation is started, avoiding the problem of insufficient light caused by indiscriminate compensation. The adjacent floor feedforward temperature compensation relationship converts the grading result into specific regulation actions, so that the value of grading falls to the dimming effect. If step S22 is missing, the grading result of step S21 can only be used for single-floor dimming, and cannot take advantage of layered coordination. The vertical transmission problem of the heat plume is still not solved, and the phenomenon of abnormal temperature rise in the high-rise room continues to exist. Step S22 realizes “joint defense and control” between floors, breaks the limitations of traditional system regulation, and makes the regulation action earlier than the arrival time of the heat plume through feedforward compensation, from passive response to active prevention. This compensation mechanism can indirectly suppress the strengthening of the “chimney effect”. When the middle floor reduces the light transmittance, the temperature gradient on the surface of the curtain wall tends to be flat, and the air flow upward power is weakened, not only reducing the heat invasion of the upper layer, but also reducing the overall heat exchange strength of the building, further optimizing the energy consumption.

[0111] Step S23 converts the floor light transmittance control instruction sequence into a time sequence staggered dimming scheme, and implements time sequence staggered dimming through spiral progressive control.

[0112] The core of step S23 is to solve the dual problems of optical impact and electrical power peak value caused by large-area synchronous dimming. The optical impact is the stimulation of instantaneous light and dark mutation to indoor and outdoor personnel vision, and the power peak value is caused by the sudden increase of circuit load due to the simultaneous start and stop of a large number of dimming units. Both of them will reduce the practicability and safety of the system. The combination of spiral progressive control and time sequence staggered strategy realizes the smoothness of the dimming process and the balance of electrical load.

[0113] Further, step S23 includes:

[0114] Step S231, the grid nodes on the curtain wall surface are numbered in a spiral path, and the grid nodes are distributed to different dimming groups according to the node number, forming a spiral progressive dimming node sequence;

[0115] The implementation of numbering in a spiral path needs to take the three-dimensional coordinate system of the curtain wall surface constructed in step S11 as the reference, and determine that the numbering starting point is the center of the bottom of the curtain wall corresponding to the origin of the coordinate system. The starting point is selected according to the visual perception law, and the brightness change in the central area has a relatively gentle effect on the human eye. Expanding from the center outward can reduce the perception intensity of visual mutation. The numbering direction is clockwise, and the basis is that most human eyes are more adaptable to dynamic changes in the clockwise direction, and it matches the common installation sequence of the curtain wall unit blocks, reducing the spatial dislocation of numbering and physical structure. The numbering process starts from the center point, extends outward in a clockwise direction, covers adjacent grid nodes in each circle, ensures that the numbering sequence and the spatial position form a continuous spiral trajectory, and each grid node corresponds to a unique numbering value. The grouping rule is based on the modulo operation of the numbering. Set the number of groups n1, divide all nodes by the remainder of the number of groups n1, and divide them into n1 dimming groups. The setting of the number of groups n1 needs to balance the dimming smoothness and execution efficiency: if n1 is too small, adjacent nodes are easily grouped into the same group, and the optical impact cannot be eliminated; if n1 is too large, the total dimming time is too long, affecting the real-time performance of thermal regulation. The setting method is to record the subjective evaluation of the dimming process by personnel when n1 is different through visual comfort test, and determine the minimum n1 that can eliminate visual impact and meet real-time performance requirements by combining the total dimming time requirement. Usually, n1 is valued at 4. For example, n1=4, the nodes with a remainder of 0 when divided by 4 are grouped into the first group, the nodes with a remainder of 1 are grouped into the second group, the nodes with a remainder of 2 are grouped into the third group, and the nodes with a remainder of 3 are grouped into the fourth group, forming a spiral progressive dimming node sequence composed of four dimming groups.

[0116] Step S232, according to the floor light transmittance control instruction sequence, determine the timing execution time of each dimming group; according to the spiral progressive dimming node sequence and the timing execution time, execute the timing staggered dimming.

[0117] The determination of the execution time of the timing misalignment takes the reference time t1 as the starting point, and the reference time t1 is the starting point of the first control cycle after the generation of the floor light transmittance control instruction sequence, ensuring that the time delay of instruction generation and execution is minimized. The time interval Δt of each dimming group needs to be associated with the light transmittance adjustment amount. The larger the adjustment amount, the more significant the brightness change of the dimming unit, and the higher the sensitivity of the human eye to the change. A longer interval time is needed to adapt; the smaller the adjustment amount, the more gradual the change, and the interval can be shortened to improve efficiency. The setting method of Δt is to measure the visual adaptation time under different light transmittance adjustment amounts, establish the corresponding relationship between the light transmittance adjustment amount and the adaptation time, and take 70% of the adaptation time as Δt to ensure comfort and avoid low efficiency. The execution time of the n1 dimming groups is as follows: the first group is executed at time t1, and the n1th group is executed at time t1+(n1-1)Δt. As shown in Figure 4 when n1=4, the four groups execute dimming in sequence according to the time axis: the first group is executed at time t1, the second group is executed at time t1+Δt, the third group is executed at time t1+2Δt, and the fourth group is executed at time t1+3Δt. During the execution process, the grid nodes in each dimming group synchronously receive the light transmittance adjustment instruction and complete the brightness change at the preset adjustment rate. The adjustment rate is matched with Δt to ensure that the nodes in the group complete dimming before the adjacent group starts, avoiding cross-group timing overlap.

[0118] Step S23 solves the problem of optical impact and power peak of traditional synchronous dimming. In the prior art, all dimming units act simultaneously, the brightness of the curtain wall surface changes instantaneously, forming a strong contrast between light and dark, and a large number of units consume power at the same time, causing the circuit current to rise suddenly, which may trigger overload protection. By numbering the nodes in a spiral path, adjacent grid nodes are assigned to different dimming groups, avoiding synchronous changes in the same area, presenting a gradual transition effect that spreads from the center outward, reducing the perception of light and dark changes; time sequence offset execution disperses electrical load to multiple time segments, only 1 / n1 dimming units operate at each time, and the circuit load is controlled within a safe range. Step S23 converts the abstract instruction sequence generated by step S22 into executable physical actions. If this step is missing, the coordinated dimming strategy of step S22 will fail due to improper execution, and the expected heat prevention effect cannot be achieved, and even system failure may occur. Step S23 realizes the dual improvement of visual comfort and electrical safety, and the spatial distribution of the spiral path and the vertical upward trajectory of the thermal plume form a complement, and the central area dimming can preferentially suppress the heat absorption of the core thermal plume, further strengthening the heat regulation effect. Time sequence offset execution enhances the adaptability of the system to voltage fluctuations in the power grid, and dispersed load changes are less likely to cause voltage drops, improving the reliability of the system in unstable power supply environments. Node numbering is based on standardized grid nodes, ensuring that the numbering rules are consistent with the spatial resolution of the monitoring unit, so that the division of dimming groups can accurately correspond to the thermal plume affected area, avoiding the misalignment of dimming and thermal load areas.

[0119] Step S24, after time sequence offset dimming execution, collect the dimming temperature map, compare the dimming temperature map with the thermal temperature map before dimming, and perform thermal-optical coupling feedback correction.

[0120] Thermal-optical coupling feedback correction refers to correcting the key parameters affecting thermal plume monitoring and dimming control by monitoring the actual thermal effect after dimming, so that the system can adapt to the performance deviation caused by environmental changes and aging. The core is to establish a closed-loop logic of "control-monitoring-correction".

[0121] Please refer to Figure 5 As shown, further, step S24 comprises:

[0122] Step S241, after time sequence offset dimming execution, collect the dimming temperature map, compare the dimming temperature map with the thermal temperature map before dimming, and calculate the actual cooling amplitude of each grid node;

[0123] The collection of the temperature map after dimming needs to reuse the monitoring system of S121, and is started in the first sampling period after the completion of dimming execution, so as to ensure that the time interval between the collection time and the completion time of dimming is the shortest, and the environmental factor interference is reduced. The collection method is consistent with S121, and the temperature of each grid node is obtained through the composite monitoring scheme of the infrared thermal imager and the contact thermocouple, and the temperature map after dimming is generated. The calculation method of the actual cooling amplitude of each grid node is that the temperature value of each grid node in the thermal state temperature map before dimming is subtracted from the temperature value of the corresponding grid node in the temperature map after dimming. The actual cooling amplitude is positive, which indicates that the dimming produces a cooling effect, and is negative, which indicates that an abnormal temperature rise occurs.

[0124] In step S242, the expected cooling amplitude of each grid node is calculated, the actual cooling amplitude of each grid node is compared with the expected cooling amplitude to calculate the effect deviation, and an effect deviation record table is established.

[0125] The calculation of the expected cooling amplitude needs to be based on the thermal plume development path prediction result of step S133 and the light transmittance adjustment amount of step S222. The core logic is that there is a correlation between the light transmittance adjustment amount and the cooling effect, and the cooling effect produced by the same light transmittance adjustment amount is different when the thermal plume intensity is different. The specific calculation process needs to extract similar historical trajectories that meet the spatial-temperature-time triple similarity judgment from the historical thermal plume trajectory database, obtain the historical cooling amplitude of these similar historical trajectories under the same light transmittance adjustment amount, calculate the statistical mean of the historical cooling amplitude as the basic expected value, and then correct it in combination with the temperature peak Tmax of the current thermal plume and the average temperature peak Tavg of the similar historical trajectories. The correction coefficient Kcorr=Tmax / Tavg, and the final expected cooling amplitude=the basic expected value×Kcorr. The basis of this calculation method is that the thermal plume intensity directly affects the cooling demand and effect. The higher the temperature peak, the greater the theoretical cooling potential that can be achieved under the same light transmittance adjustment amount. Through the correction coefficient, the expected value can be more consistent with the actual characteristics of the current thermal plume. The calculation method of the effect deviation is: the expected cooling amplitude minus the actual cooling amplitude. The positive effect deviation indicates that the actual cooling does not meet the expectation, and the thermal regulation is insufficient; the negative effect deviation indicates that the cooling exceeds the expectation, which may lead to insufficient illumination. The establishment of the effect deviation record table needs to include the deviation occurrence time, the deviation position, the effect deviation value, and the environmental parameters (outdoor temperature, wind speed, and solar intensity) at that time. These information provides multi-dimensional input for subsequent deviation trend analysis, and the environmental parameters can help to distinguish whether the deviation is caused by system parameter problems or external environmental mutations.

[0126] In step S243, the deviation trend in the effect deviation record table is analyzed, and adaptive adjustment is performed on the key parameters.

[0127] The key parameters include temperature gradient threshold a, adjacent layer compensation amount d1 and d2, and prediction time window m, which respectively affect the sensitivity of hot plume source point identification, the regulation strength of adjacent layer cooperation, and the time advance of trajectory prediction, and are the core factors determining the control accuracy of the system. The deviation trend analysis needs to set statistical standards: k' times of continuous dimming is an analysis period, the setting of k' is based on the control frequency, and k' is usually 3 to ensure that enough deviation samples are included in the period to exclude accidental factors; 60% of the grid nodes are set as the deviation coverage threshold to ensure that the deviation is regional universality rather than local abnormality. When more than 60% of the grid nodes in k' times of continuous dimming appear positive deviation, it indicates that the system response to the hot plume is lagging or the regulation strength is insufficient, and the sensitivity and regulation strength need to be improved: reducing a can make the system identify the hot plume source point earlier and start the regulation in advance; increasing d1 and d2 can strengthen the blocking effect of adjacent layer cooperation; and shortening m can make the prediction result closer to the real-time change and reduce the time lag. When more than 60% of the area appears negative deviation, it indicates that the system response is too aggressive, and the sensitivity and regulation strength need to be reduced: increasing a can avoid misjudging static high temperature as a hot plume source point; reducing d1 and d2 can prevent insufficient illumination caused by excessive dimming; and extending m can make the prediction more stable and avoid frequent adjustments. The adjustment range of the parameters needs to be determined based on the size of the deviation, and the larger the absolute value of the deviation, the larger the adjustment range. The setting method is to establish a linear relationship between the average value of the effect deviation and the adjustment range to ensure that the adjustment amount of the key parameters matches the deviation degree. The adjusted key parameters need to be updated to the system configuration, and the adjustment event and adjustment range are marked in the effect deviation record table to provide a reference for the deviation analysis of the subsequent period.

[0128] Step S24 solves the problem that fixed parameters cannot adapt to dynamic environment. In the prior art, system parameters are mostly fixed values preset at the factory, and as the seasons change, the curtain wall materials age, and the environmental conditions change, the deviation of the fixed parameters from the actual demand gradually increases, leading to the attenuation of the control effect. Through the heat-light coupling feedback correction, the dynamic self-adaptation of the parameters is realized, so that the system can continuously match the hot plume evolution law and the dimming demand. Step S24 provides the system with self-learning ability, which is a key link connecting the "monitoring-prediction-regulation" closed loop. If step S24 is missing, the control accuracy of the system will continuously decrease over time, and the system cannot maintain the effect of reducing the temperature on the high floor and optimizing energy consumption for a long time. The adaptive adjustment can improve the long-term stability of the system performance, and the parameter adjustment can offset the influence of long-term factors such as material aging and seasonal change. At the same time, the environmental parameter correlation of the deviation record table can help identify the influence of special weather (such as sudden gusts and cloudy weather) on the regulation effect, and provide data support for subsequent optimization.

[0129] Embodiment 2

[0130] This embodiment is based on embodiment 1 and provides a building ventilation heat load regulation system based on hot plume prediction, such asFigure 6 As shown, comprising:

[0131] Hot plume trajectory construction module: for constructing a curtain wall surface three-dimensional coordinate system, and constructing a current hot plume trajectory line in the curtain wall surface three-dimensional coordinate system;

[0132] Hot plume trajectory prediction module: for constructing a historical hot plume trajectory database, extracting a similar historical trajectory of the current hot plume trajectory line from the historical hot plume trajectory database, analyzing the common development law of the similar historical trajectory, and predicting a development path prediction result of the current hot plume;

[0133] Light adjusting module: using the development path prediction result of the current hot plume to establish a floor load grading sequence; constructing a neighbor layer feedforward temperature compensation relationship based on the floor load grading sequence, generating a floor light transmittance control instruction sequence; converting the floor light transmittance control instruction sequence into a time sequence staggered light adjusting scheme, and implementing time sequence staggered light adjusting through spiral progressive control;

[0134] Feedback correction module: for performing hot-light coupling feedback correction after the time sequence staggered light adjusting is executed.

[0135] Further, in the light adjusting module, the method for establishing the floor load grading sequence comprises:

[0136] Step S211: calculating a heat intrusion risk index of each floor based on the development path prediction result of the current hot plume;

[0137] Step S212: performing heat load grading on each floor according to the heat intrusion risk index; recording the heat load grading result in the order of floor height to generate a floor load grading sequence.

[0138] Further, in the light adjusting module, the method for implementing time sequence staggered light adjusting through spiral progressive control comprises:

[0139] Step S231: numbering nodes of the grid nodes on the curtain wall surface in a spiral path, and distributing the grid nodes to different light adjusting groups according to the node number to form a spiral progressive light adjusting node sequence;

[0140] Step S232: determining a time sequence staggered execution time of each light adjusting group according to the floor light transmittance control instruction sequence; and executing time sequence staggered light adjusting according to the spiral progressive light adjusting node sequence and the time sequence staggered execution time.

[0141] The method and system of the present application can be implemented in many ways. For example, the method and system of the present application can be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the method is only for illustration, and the steps of the method of the present application are not limited to the above specifically described order, unless otherwise specifically stated.

[0142] In addition, parts of the above technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of corresponding technical solutions in the prior art are not described in detail to avoid excessive repetition.

[0143] The specific embodiments described above are further explained in further detail by the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for regulating the thermal load of building ventilation based on thermal plume prediction, characterized in that, The method comprises: constructing a curtain wall surface three-dimensional coordinate system, constructing a thermal state temperature map in the curtain wall surface three-dimensional coordinate system, constructing a current thermal plume trajectory line based on the thermal state temperature map, constructing a historical thermal plume trajectory database, extracting a similar historical trajectory of the current thermal plume trajectory line from the historical thermal plume trajectory database, and predicting a development path prediction result of the current thermal plume based on the similar historical trajectory; establishing a floor load classification sequence using the development path prediction result of the current thermal plume, constructing a neighboring layer feedforward temperature compensation relationship based on the floor load classification sequence, and implementing time sequence staggered dimming through spiral progressive control based on the neighboring layer feedforward temperature compensation relationship; and performing thermal-optical coupling feedback correction after the time sequence staggered dimming is executed.

2. The building ventilation thermal load regulation method based on thermal plume prediction of claim 1, wherein, The method for constructing the current thermal plume trajectory line based on the thermal state temperature map comprises: calculating a temperature gradient field based on the thermal state temperature map, identifying and marking a thermal plume source point; connecting the thermal plume source point according to a space-time dual criterion, constructing a current thermal plume trajectory line, and associating a current time label with the current thermal plume trajectory line.

3. The building ventilation thermal load regulation method based on thermal plume prediction of claim 2, wherein, The method for constructing the thermal state temperature map comprises: marking grid nodes on a curtain wall surface, collecting temperatures of each grid node at a preset sampling period under sunlight conditions, and generating a thermal state temperature map at each collection time.

4. The building ventilation thermal load regulation method based on thermal plume prediction of claim 3, wherein, The identification of the thermal plume source point comprises: calculating a vertical temperature gradient for the thermal state temperature map at each collection time; when the vertical temperature gradient is greater than or equal to a temperature gradient threshold value α, marking the corresponding grid node as a thermal plume source point.

5. The method of claim 4, wherein, The method for implementing time sequence staggered dimming through spiral progressive control based on the neighboring layer feedforward temperature compensation relationship comprises: generating a floor light transmittance control instruction sequence based on the neighboring layer feedforward temperature compensation relationship; numbering the grid nodes on the curtain wall surface according to a spiral path, distributing the grid nodes to different dimming groups according to the node numbering, and forming a spiral progressive dimming node sequence; determining time sequence staggered execution time of each dimming group according to the floor light transmittance control instruction sequence, and executing time sequence staggered dimming according to the spiral progressive dimming node sequence and the time sequence staggered execution time.

6. The method of claim 5, wherein, The method for establishing the floor load classification sequence comprises: calculating a thermal intrusion risk index of each floor based on the development path prediction result of the current thermal plume; classifying the thermal load of each floor according to the thermal intrusion risk index, recording the thermal load classification result in order of floor height, and generating a floor load classification sequence.

7. The method of claim 6, wherein, The development path prediction result of the current thermal plume at least comprises a future thermal plume trajectory line; The method for calculating the thermal intrusion risk index of each floor comprises: Based on the prediction result of the current hot plume development path, the probability P(z') of the future hot plume trajectory line passing through each floor is calculated, and the temperature peak coefficient C and the window opening rate W(z') of each floor are calculated, where z' is the floor identifier. temp and the window opening rate W(z') of each floor, where z' is the floor identifier. The thermal intrusion risk index R(z') of each floor is equal to the product of P(z'), C temp and W(z').

8. The method of claim 7, wherein, The method for classifying the thermal load of each floor comprises: when R(z') < β1, classifying the floor z' as a low load layer; when β1 ≤ R(z') ≤ β2, classifying the floor z' as a medium load layer; when R(z') > β2, classifying the floor z' as a high load layer; wherein β1 is a first threshold value, and β2 is a second threshold value.

9. The method of claim 8, wherein, The historical thermal plume trajectory database comprises historical thermal plume trajectory lines of the past n days; The method for extracting the similar historical trajectory of the current thermal plume trajectory line comprises: extracting a trajectory feature of the current thermal plume trajectory line and a historical trajectory feature of the historical thermal plume trajectory line; performing a space-temperature-time triple similarity determination on the trajectory feature of the current thermal plume trajectory line and the historical trajectory feature of the historical thermal plume trajectory line, regarding the historical thermal plume trajectory line satisfying the space-temperature-time triple similarity determination as a similar historical trajectory, obtaining k similar historical trajectories, and adjusting related parameters of the space-temperature-time triple similarity determination until k is greater than or equal to a sample quantity threshold if k is less than the sample quantity threshold.

10. The method of claim 9, wherein, the historical trajectory feature of the historical thermal plume trajectory line is associated with a historical time label; the space-temperature-time triple similarity determination includes a space position similarity determination, a temperature feature similarity determination, and a time similarity determination; a condition of the time similarity determination is that an absolute value of a difference between the historical time label of the i'th historical thermal plume trajectory line and a current time label of the current thermal plume trajectory line is less than a time deviation threshold.

11. A building ventilation thermal load regulation system based on thermal plume prediction for implementing the building ventilation thermal load regulation method based on thermal plume prediction according to any one of claims 1-10, characterized in that, the system comprises: a thermal plume trajectory construction module configured to construct a curtain surface three-dimensional coordinate system, construct a thermal state temperature map in the curtain surface three-dimensional coordinate system, and construct a current thermal plume trajectory line based on the thermal state temperature map; a thermal plume trajectory prediction module configured to construct a historical thermal plume trajectory database, extract similar historical trajectories of the current thermal plume trajectory line from the historical thermal plume trajectory database, and predict a development path prediction result of the current thermal plume based on the similar historical trajectories; a dimming module configured to establish a floor load classification sequence using the development path prediction result of the current thermal plume, construct a neighboring layer feedforward temperature compensation relationship based on the floor load classification sequence, and implement time sequence staggered dimming through spiral progressive control based on the neighboring layer feedforward temperature compensation relationship; a feedback correction module configured to perform thermal-optical coupling feedback correction after the time sequence staggered dimming is executed.

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