Intelligent construction site environment quality real-time regulation and control method and system

Abstract

The invention discloses an intelligent construction site environment quality real-time regulation and control method and system, and relates to the technical field of intelligent construction site environment regulation and control. Continuous frame thermodynamic images of a construction area are collected, a heat source area H and a low-temperature area C are identified, an ultrasonic anemograph is combined, environment data of the construction area are obtained, a normalized data set S is constructed, and a normalized data set is obtained; constructing a thermal field stability index Wd according to the normalized data set S, further calculating a thermal field stability index difference value delta Wd, evaluating a thermal field stable state, constructing a thermal energy diffusion path and a path diffusion response factor Lj of the thermal energy diffusion path when the construction area is in an abnormal state, screening a risk path, and performing thermal energy conduction evaluation on the risk path to obtain a thermal energy conduction evaluation result. And the adaptation level of the heat energy diffusion path and the air supply equipment is judged, a heat energy conduction adjusting measure is generated, and the construction site environment is adjusted, so that the accuracy and intelligence of environment regulation and control and the system response efficiency are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent construction site environment regulation, in particular to an intelligent construction site environment quality real-time regulation method and system. BACKGROUND

[0002] In the field of modern construction engineering management, with the gradual popularization of intelligent construction site system and the in-depth application of digital construction means, the environmental perception and regulation capability of the construction site has gradually become an important part of intelligent construction, especially in high-latitude cold regions such as northeast forest regions, plateau and alpine regions, etc. The environmental quality regulation is no longer limited to traditional basic goals such as wet dust monitoring or noise identification, but gradually extends to more complex microclimate intervention categories such as local thermal environment control under low-temperature climate and work surface heat energy guidance. In this process, the heat source identification and heat flow guidance path regulation mechanism based on thermal image perception have become an important research direction to ensure the continuity, safety and energy efficiency of construction in cold scenes.

[0003] The existing construction site environment regulation technology mostly focuses on single-point ventilation or one-way heating, and only relies on passive monitoring by static sensors such as thermohygrographs and anemometers, lacking multi-source linkage control capability for dynamic heat flow evolution, and it is difficult to effectively identify the thermal source distribution heterogeneity, air stagnation zone formation characteristics and thermal diffusion path coupling relationship in the construction area. In particular, in winter basic operations such as bridge pile foundation excavation and concrete pouring, once the surrounding heat source is released and cannot form an effective diffusion channel to guide the work surface, it will lead to rapid heat loss at the work point, low heating efficiency, high manual intervention cost, poor ventilation efficiency, and serious constraints on construction progress and work quality.

[0004] The root cause of the above problems is that the existing system cannot realize the identification and judgment of the thermal field stability state of the construction area, lacks the quantitative modeling capability of the relationship between the thermal diffusion driving force and the path resistance between the local heat source and the cold source, and is difficult to automatically generate operable guidance measures in the case of wind flow deviation or thermal diffusion mismatch. Once the environmental regulation fails, it will cause serious consequences such as air heat stagnation area backflow, local cold end frosting, concrete thermal curing failure, etc. in the field, and even lead to intermittent work stoppage, equipment operation energy waste, early structural defects and a series of abnormal results, which significantly affect construction safety and cost control. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides an intelligent construction site environment quality real-time regulation method and system, which solves the problems mentioned in the background art.

[0006] To achieve the above purpose, the present application is realized by the following technical scheme: comprising the following steps: S1, using an infrared thermal imager and an ultrasonic anemometer to collect environmental data of the construction area in real time, and preprocessing to construct a normalized data set S; S2, according to the normalized data set S, analyzing the thermal field stability of the construction area, and when the thermal field stability is an abnormal state, performing thermal diffusion path analysis; S3, when the thermal field stability is an abnormal state, constructing a thermal energy diffusion path set, and analyzing the thermal energy diffusion of each thermal energy diffusion path in the thermal energy diffusion path set to mark the risk path, and collecting all risk paths to construct a thermal energy regulation candidate path set; S4, for the risk path in the thermal energy regulation candidate path set, performing path direction and air supply equipment wind direction adaptation degree analysis, evaluating the thermal energy conduction state of the thermal energy diffusion path, and generating second and third thermal energy conduction adjustment measures; S5, according to the second and third thermal energy conduction adjustment measures, real-time regulating the thermal energy conduction quality of the construction area.

[0007] Preferably, S1 includes the following specific steps: S11, deploying infrared thermal imagers and ultrasonic anemometers at the end of the tower crane arm, the top of the construction platform and the high position of the scaffold in the construction area, and connecting the infrared thermal imagers and ultrasonic anemometers to the edge server to collect thermal images and environmental data of the construction area in real time, and to perform thermal image preprocessing and environmental data preprocessing; The environmental data includes pixel temperature value T, heat source effective area Spath, heat source output power P and air supply equipment dominant wind direction angle θa; The thermal image preprocessing includes using median filtering to remove infrared flicker interference and using histogram equalization to enhance image contrast, and performing inter-frame temperature baseline calibration on each frame of thermal image to obtain standardized thermal image; The environmental data preprocessing includes using the maximum and minimum normalization method to normalize the environmental data to obtain the normalized data set S.

[0008] Preferably, S1 includes the following specific steps: S12, setting a data analysis time window, for each pixel in the standardized thermal image, performing first-order derivative calculation on the temperature value of the pixel within the data analysis time window to obtain the temperature change rate δT, evaluating the temperature change trend of each pixel, if δT≥1°C / min, it indicates that the pixel is a high temperature trend point, if δT<-0.5°C / min, it indicates that the pixel is a continuous cooling point, and all high temperature trend points and continuous cooling points are collected respectively to construct high temperature candidate point set Hcand and low temperature candidate point set Ccand; S13, according to the high-temperature candidate point set Hcand and the low-temperature candidate point set Ccand, using a density clustering algorithm to cluster the high-temperature trend points and the continuous cooling points into regional blocks respectively, and performing isolated pixel elimination and contour extraction on each regional block to identify the heat source region H and the low-temperature region C of the construction region.

[0009] Preferably, the specific steps of S2 include: S21, according to the normalized data set S, statistics the temperature mean and temperature standard deviation of each heat source region H and low-temperature region C, and coupling modeling the temperature mean and temperature standard deviation, constructing the temperature variation coefficient to measure the temperature uniformity of the construction region, and combining with the heat source output power data, coupling to construct the thermal field stability index Wd, wherein the calculation formula of the thermal field stability index Wd is: ; In the formula, σT represents the temperature standard deviation, Tavg represents the temperature mean, and σP represents the heat source output power standard deviation, and Pavg represents the heat source output power mean.

[0010] Preferably, the specific steps of S2 further include: S22, extracting the maximum value WdHmax of the thermal field stability index of the heat source region H and the maximum value WdCmax of the thermal field stability index of the low-temperature region C, and subtracting the maximum value WdCmax of the thermal field stability index of the low-temperature region C from the maximum value WdHmax of the thermal field stability index of the heat source region H to obtain the difference ΔWd of the thermal field stability index, and collecting the environmental data of the construction region under the historical normal state, calculating the difference ΔWd of the thermal field stability index of the construction region under the historical normal state, and performing statistical analysis, selecting the 90th percentile and the 95th percentile of the difference ΔWd of the thermal field stability as the first stability evaluation threshold Yz1 and the second stability evaluation threshold Yz2, and comparing the first stability evaluation threshold Yz1 and the second stability evaluation threshold Yz2 with the difference ΔWd of the thermal field stability index to evaluate the thermal field stability state of the construction region, and the specific evaluation content is as follows: If the difference ΔWd of the thermal field stability index is less than or equal to the first stability evaluation threshold Yz1, it indicates that the thermal field stability state of the construction region is in a stable state, and no processing is required at this time; If the first stability evaluation threshold Yz1 is less than the difference ΔWd of the thermal field stability index and the second stability evaluation threshold Yz2, it indicates that the thermal field stability state of the construction region is in a fluctuating state, at this time, the environmental data of the construction region is continuously monitored, and the data acquisition frequency is adjusted to 120% of the standard data acquisition frequency; If the difference ΔWd of the thermal field stability index is greater than or equal to the second stability evaluation threshold Yz2, it indicates that the thermal field stability state of the construction region is in an abnormal state, and the heat flow between the hot region and the cold region is unbalanced, at this time, a risk warning is issued, and a thermal energy diffusion path analysis is performed.

[0011] Preferably, S3 comprises the following steps: S31, when the thermal field at the construction area is in an abnormal state, according to the divided heat source area H and the low-temperature area C, a plurality of shortest heat energy diffusion paths are constructed using a graph search algorithm to obtain a heat energy diffusion path set, and for each heat energy diffusion path in the heat energy diffusion path set, a path diffusion response factor Lj is coupled and constructed according to the normalized data set S to quantify the heat energy diffusion capacity of the heat energy diffusion path, wherein the calculation formula of the path diffusion response factor Lj is: ; In the formula, Pavg represents the average heat source output power of the heat energy diffusion path, Hi represents the i-th heat source area, Ci represents the i-th low-temperature area, ΔT(Hi, Ci) represents the average temperature difference between the i-th heat source area and the i-th low-temperature area, d(Hi, Ci) represents the path length of the heat energy diffusion path from the i-th heat source area to the i-th low-temperature area, and Spath represents the cross-sectional area of the heat energy diffusion path.

[0012] S32, collect the effective heat energy diffusion path of the construction area under the historical normal state, construct a response factor sample set, and calculate the path diffusion response factor Lj according to the response factor sample set, and select the 85th percentile of the path diffusion response factor Lj as the path diffusion evaluation threshold Ljyz, and compare the path diffusion evaluation threshold Ljyz with the path diffusion response factor Lj to evaluate the feasibility of each heat energy diffusion path in the construction area. If the path diffusion response factor Lj is greater than or equal to the path diffusion evaluation threshold Ljyz, it is determined that the heat energy diffusion capacity of the heat energy diffusion path is qualified, and is marked as a feasible path. If the path diffusion response factor Lj is less than the path diffusion evaluation threshold Ljyz, it indicates that the heat energy diffusion capacity of the heat energy diffusion path is unqualified, and is marked as a risk path. All risk paths are collected to construct a heat energy regulation candidate path set.

[0013] Preferably, S4 comprises the following steps: S41, for each heat energy diffusion path in the heat energy regulation candidate path set, the heat energy diffusion path is output as a series of continuous path nodes P1, P2,..., Pm using a graph search algorithm, the heat energy diffusion path is divided into m-1 sub-paths, the coordinates of each path node are recorded, and the direction vector and the length L of each sub-path are calculated, and the main air supply device dominant wind direction angle θa is converted into a two-dimensional unit vector , wherein the two-dimensional unit vector , and the angle deviation amount Δθ of each sub-path is calculated, wherein the calculation formula of the angle deviation amount Δθ is: ; In the formula, arccos represents the inverse cosine function, , and respectively represent the direction vector and the length of the two-dimensional unit vector ; S42, multiply the angle deviation amount Δθ of each sub-path obtained by the length L of the sub-path, and sum the product of the angle deviation amount Δθ of each sub-path of the heat energy diffusion path and the length L of the sub-path, and divide by the total length of the heat energy diffusion path to obtain the path wind deviation index Fp.

[0014] Preferably, the S4 specific steps further comprise: S43, collect the environmental data of the construction area in the state of no regulation fault and effective heat conduction, calculate the path wind deviation index Fp for statistical analysis, and select the path wind deviation index Fp of the 85th percentile and the 95th percentile as the first wind deviation control threshold Ξ1 and the second wind deviation control threshold Ξ2, and compare the first wind deviation control threshold Ξ1 and the second wind deviation control threshold Ξ2 with the path wind deviation index Fp to evaluate the heat energy conduction state of the heat energy diffusion path. The specific evaluation content is as follows: If the path wind deviation index Fp is less than or equal to the first wind deviation control threshold Ξ1, it indicates that the diffusion loss of the heat energy diffusion path in the heat conduction process is within the standard range, and it is determined as the first adaptation level, which does not need to be processed at this time; If the first wind deviation control threshold Ξ1 is less than the path wind deviation index Fp and the second wind deviation control threshold Ξ2, it indicates that there is partial diffusion loss in the heat conduction process of the heat energy diffusion path, which is determined as the second adaptation level, and the second heat energy conduction adjustment measure is generated; If the path wind deviation index Fp is greater than or equal to the second wind deviation control threshold Ξ2, it indicates that there is serious diffusion loss in the heat conduction process of the heat energy diffusion path, which is determined as the third adaptation level, and the third heat energy conduction adjustment measure is generated.

[0015] Preferably, the S5 specific steps comprise: S51, according to the obtained second heat energy conduction adjustment measure and third heat energy conduction adjustment measure, real-time adjustment is carried out on each heat energy diffusion path of the construction scene; The second heat energy conduction adjustment measure includes increasing the wind speed level of the air supply equipment to the highest level, switching the rotation state of the air outlet guide assembly to form a fan-shaped flow guide area, and modifying the air supply rhythm to pulse type short intermittent air supply to improve the diffusion coverage; The third heat energy conduction adjustment measure includes adjusting the air supply cycle frequency to 150% of the standard air supply cycle frequency, and starting the auxiliary air supply equipment to change the wind direction angle, wherein the auxiliary air supply equipment includes air supply baffles and air guide plates, which are used to realize the directional turning of the wind direction.

[0016] The application discloses a real-time environment quality regulation system for a smart construction site, which comprises a data acquisition module, a thermal field stability analysis module, a risk path identification module, a thermal energy conduction evaluation module and an intelligent regulation module. The data acquisition module is used for acquiring environment data of the construction area in real time by using an infrared thermal imager and an ultrasonic anemometer, and performing preprocessing to construct a normalized data set S. The thermal field stability analysis module is used for analyzing the thermal field stability state of the construction area according to the normalized data set S, and performing thermal diffusion path analysis when the thermal field stability state is an abnormal state. The risk path identification module is used for constructing a thermal energy diffusion path set when the thermal field stability state is an abnormal state, analyzing the thermal energy diffusion of each thermal energy diffusion path in the thermal energy diffusion path set to mark a risk path, and collecting all the risk paths to construct a thermal energy regulation candidate path set. The thermal energy conduction evaluation module is used for performing path direction and air supply equipment wind direction adaptation degree analysis on the risk path in the thermal energy regulation candidate path set, evaluating the thermal energy conduction state of the thermal energy diffusion path, and generating second and third thermal energy conduction adjustment measures. The intelligent regulation module is used for regulating the thermal energy conduction quality of the construction area in real time according to the second and third thermal energy conduction adjustment measures.

[0017] The application provides a real-time environment quality regulation method and system for a smart construction site. (1) By arranging the infrared thermal imager and the ultrasonic anemometer in the construction area, a temperature change trend graph and a wind field flow direction graph are constructed, the spatial recognition and real-time dynamic tracking of the heat source area and the low-temperature area in the construction environment are realized, and the high-temperature trend points and the continuous cooling points in the construction area are quickly extracted and clustered based on the temperature change rate delta T and the density clustering algorithm, so that the defect that the traditional average temperature judgment method based on the whole area cannot reflect the heat flow directionality is avoided, compared with the existing scheme based on single-point temperature or static monitoring mechanism, the thermal energy diffusion direction accurate identification and response triggering in the dynamic abnormal state of the construction thermal field are realized, and the real-time performance and path controllability of the environment thermal regulation are greatly improved.

[0018] (2) By constructing a heat diffusion path evaluation model based on graph search and physical factor coupling, introducing path diffusion response factor Lj and path wind deviation index Fp as evaluation indexes, quantifying the "diffusion driving force" and "wind direction adaptation degree" of the path, evaluating the path conduction efficiency from two dimensions of heat diffusion capacity and wind power boost synergy, and setting the 85th percentile threshold as the evaluation line, the problem of no clear quantitative standard in the existing scheme is solved, and a quantifiable, evaluable and dynamically updated diffusion path intelligent screening mechanism is realized, providing a reliable foundation for the fine application of subsequent air supply regulation strategy.

[0019] (3) By constructing a multi-level adaptability judgment model based on heat energy conduction evaluation, according to the weighted evaluation results of the angle deviation amount Δθ and the path length of the heat energy diffusion path, three conduction adaptation levels are divided, and the second and third heat energy regulation strategies are generated, through the dynamic adjustable combination of air supply speed, wind direction angle, guiding mechanism and periodic rhythm, heat flow guidance, shunting and strengthening based on path state difference are realized, and the energy waste and local heat stagnation caused by the traditional "full-field integrated strong wind supply" mode are avoided, compared with the existing passive heat dissipation mechanism, this scheme has the ability of path level difference regulation and intelligent adaptive control, and significantly improves the heat energy conduction efficiency and regulation precision in the construction environment. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 A wisdom construction site environment quality real-time regulation method step schematic diagram is provided for the present application; Figure 2 A wisdom construction site environment quality real-time regulation system flow schematic diagram is provided for the present application; Figure 3 A heat source area H and low temperature area C identification step schematic diagram is provided for the present application; Figure 4 A construction area heat field stable state evaluation step schematic diagram is provided for the present application. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present application will be described clearly and completely 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, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0022] Embodiment 1 Please refer to Figure 1 The present application provides a wisdom construction site environment quality real-time regulation method, in order to achieve the above purpose, the present application realizes the following technical scheme: including the following steps: S1, real-time collection of environmental data of the construction area using an infrared thermal imager and an ultrasonic anemometer, and preprocessing to construct a normalized data set S; S2, analysis of the thermal field stability of the construction area according to the normalized data set S, and thermal diffusion path analysis when the thermal field stability is an abnormal state; S3, when the thermal field stability is an abnormal state, constructing a thermal energy diffusion path set and analyzing the thermal energy diffusion of each thermal energy diffusion path in the thermal energy diffusion path set to mark the risk path and collect all risk paths to construct a thermal energy regulation candidate path set; S4, for the risk path in the thermal energy regulation candidate path set, analyzing the degree of adaptation of the path direction to the air supply equipment wind direction, evaluating the thermal energy conduction state of the thermal energy diffusion path, and generating second and third thermal energy conduction adjustment measures; S5, real-time regulation of the thermal energy conduction quality of the construction area according to the second and third thermal energy conduction adjustment measures.

[0023] In this embodiment, by constructing a dynamic regulation mechanism integrating heat map recognition, data normalization analysis, thermal field stability evaluation and wind direction matching regulation, the problems of lag response to traditional construction site thermal field changes and lack of targeted regulation measures are solved. First, through the joint deployment of an infrared thermal imager and an ultrasonic anemometer, the construction area heat source and cold area can be accurately identified, and key environmental parameters can be obtained in real time, thereby avoiding the limitations of traditional manual inspection and improving the comprehensiveness of data collection and the real-time response. Secondly, by using the thermal field stability analysis and thermal energy diffusion path construction mechanism, the key path of thermal imbalance can be quickly identified when the thermal field has abnormal fluctuations, ensuring that the thermal energy regulation target is more targeted and spatially precise. In addition, the matching degree between the path direction and the air supply wind direction is evaluated, and hierarchical regulation measures are generated according to the evaluation results, which can realize intelligent adjustment of the air supply equipment wind direction, wind speed and rhythm, making the thermal energy diffusion more efficient and the flow more reasonable, thereby effectively reducing the influence of high temperature accumulation, cold and hot spot imbalance and other phenomena on construction safety and work comfort, dynamically adapting to the changes of the construction site environment, improving the precision, intelligence and system response efficiency of environmental regulation, and providing key technical support for smart construction.

[0024] Embodiment 2 Please refer to Figure 1 and Figure 3 , specifically: the specific steps of S1 include: S11, deploy infrared thermal imagers and ultrasonic anemometers at the end of the tower crane arm, the top of the construction platform and the high position of the scaffold in the construction area, and connect the infrared thermal imagers and ultrasonic anemometers to the edge server to collect thermal images and environmental data of the construction area in real time, and perform thermal image preprocessing and environmental data preprocessing; The environmental data includes pixel temperature value T, heat source effective area Spath, heat source output power P, and air supply equipment dominant wind direction angle θa. The pixel temperature value T represents the temperature of each pixel in the thermal image of the construction area, which is obtained by the infrared thermal imager; The heat source effective area Spath represents the heat radiation area of the heat source region, which is used to calculate the heat radiation capacity of the heat source region, and is obtained by contour extraction on the heat source region; The heat source output power P represents the heat energy output power of the heat source equipment, which is obtained by reading the electric power of the heat source equipment; The air supply equipment dominant wind direction angle θa represents the wind direction angle of the air supply equipment guiding the diffusion of hot air in the construction area, which is obtained by the air supply equipment work log; The thermal image preprocessing includes using median filtering to remove infrared flicker interference and using histogram equalization to enhance image contrast, and performing inter-frame temperature baseline calibration on each frame of thermal image to obtain standardized thermal image, wherein the inter-frame temperature baseline calibration can ensure the consistency and comparability of temperature data of each frame of image on the time axis, and eliminate the temperature baseline deviation caused by equipment fluctuation, environmental mutation or short-term drift of infrared detector; The environmental data preprocessing includes using maximum-minimization normalization method to normalize the environmental data to obtain a normalized data set S.

[0025] The specific steps of S1 further include: S12, set a data analysis time window, for each pixel in the standardized thermal image, calculate the first derivative of the temperature value of the pixel within the data analysis time window to obtain the temperature change rate δT, evaluate the temperature change trend of each pixel, if δT≥1°C / min, it represents that the pixel is a high temperature trend point, if δT<-0.5°C / min, it represents that the pixel is a continuous cooling point, and all high temperature trend points and continuous cooling points are collected respectively to construct high temperature candidate point set Hcand and low temperature candidate point set Ccand; S13, according to the high temperature candidate point set Hcand and the low temperature candidate point set Ccand, use the density clustering algorithm to cluster the high temperature trend points and the continuous cooling points into region blocks respectively, and perform isolated pixel elimination and contour extraction on each region block to identify the heat source region H and the low temperature region C of the construction area.

[0026] Density clustering algorithm is a clustering algorithm based on point density distribution, such as DBSCAN algorithm, which is used in the scheme to accurately extract the spatial hot spot area from the local thermal field fluctuation, and obtain the heat source area H and the low temperature area C.

[0027] In this embodiment, by arranging the infrared thermal imager and the ultrasonic anemometer at multiple points at the end of the tower crane arm, the top of the construction platform and the high position of the scaffold, the data collection of the thermal environment of the construction area is realized, the coverage and spatial perception accuracy of the thermal image are improved, and the image preprocessing methods such as median filtering and histogram equalization are combined to effectively eliminate the flicker interference and insufficient contrast of the infrared image, providing a clear and stable image basis for subsequent heat source identification. Through the calculation of the temperature change rate, the system can not only identify the current thermal distribution state, but also has the dynamic identification ability of the thermal energy change trend, so that the early perception is realized before the heat source spreads, the forward-looking of the regulation and control is enhanced, at the same time, the density clustering and isolated pixel elimination technology are used to aggregate and extract the contour of the high temperature and cooling pixels, avoiding the misidentification problem under the traditional single-point threshold judgment mode, and accurately constructing the spatial boundary of the heat source area H and the low temperature area C, improving the robustness of the thermal anomaly identification. On this basis, by deploying the ultrasonic anemometer and calling the working log of the air supply equipment, the environmental parameters such as heat source radiation area, output power, pixel temperature and dominant wind direction are comprehensively collected and normalized to construct a unified standard data set, providing high-quality input support for subsequent heat diffusion path judgment and regulation strategy generation, and significantly improving the intelligent decision-making ability and real-time response level of the system.

[0028] Embodiment 3 For reference Figure 1 and Figure 4 , in particular: the specific steps of S2 include: S21, according to the normalized data set S, the temperature mean and temperature standard deviation of each heat source area H and low temperature area C are calculated, and the temperature mean and temperature standard deviation are coupled to build a temperature variation coefficient to measure the temperature uniformity of the construction area, and the heat source output power data is coupled to build a thermal field stability index Wd, wherein the calculation formula of the thermal field stability index Wd is: ; In the formula, σT represents the temperature standard deviation, Tavg represents the temperature mean, σP represents the heat source output power standard deviation, and Pavg represents the heat source output power mean.

[0029] Formula derivation process and physical meaning: the formula carries out data analysis on the normalized data set S after dimensionless processing, considers the spatial temperature distribution uniformity of the construction area and the consistency of the heat source energy output, and couples to build a comprehensive dimensionless feature index, which can not only reflect the consistency of the temperature distribution, but also reflect the reliability of the heat source energy output. First, the formula σT / Tavg represents the temperature distribution uniformity of the construction area, and the formula σP / Pavg represents the consistency of the heat source energy output. The formula σT / Tavg*σP / Pavg represents the coupling of the two, and the formula Wd represents the thermal field stability index. represents the temperature variation coefficient, which is a spatial stability index of the heat field temperature distribution stability formula, and then, represents the power variation coefficient, which reflects the consistency of the heat source equipment heating, and finally couples to form the product structure of the temperature variation coefficient x (1 + power variation coefficient), which together determines whether the region is at risk of instability.

[0030] The specific example is as follows: Assuming that a construction site is a concrete curing construction site, the collected environmental data includes a temperature standard deviation σT of 2.5°C, a temperature average Tavg of 36.2°C, a heat source output power standard deviation of 42.8w, and a heat source output power average of 120w. At this time, the heat field stability index Wd is calculated, and the specific value of the heat field stability index Wd is 0.093; The specific steps of S2 further include: S22, extract the maximum value WdHmax of the heat field stability index of the heat source region H and the maximum value WdCmax of the heat field stability index of the low-temperature region C, and subtract the maximum value WdCmax of the heat field stability index of the low-temperature region C from the maximum value WdHmax of the heat field stability index of the heat source region H to obtain the difference ΔWd of the heat field stability index, collect the environmental data of the construction region under the historical normal state, calculate the difference ΔWd of the heat field stability index of the construction region under the historical normal state, and perform statistical analysis, select the 90th percentile and the 95th percentile of the heat field stability difference ΔWd as the first stability evaluation threshold Yz1 and the second stability evaluation threshold Yz2, and compare the first stability evaluation threshold Yz1 and the second stability evaluation threshold Yz2 with the heat field stability difference ΔWd to evaluate the heat field stability state of the construction region, and the specific evaluation content is as follows: If the heat field stability difference ΔWd is less than or equal to the first stability evaluation threshold Yz1, it indicates that the heat field stability state of the construction region is in a stable state, and no treatment is required at this time; If the first stability evaluation threshold Yz1 is less than the heat field stability difference ΔWd and the heat field stability difference ΔWd is less than the second stability evaluation threshold Yz2, it indicates that the heat field stability state of the construction region is in a fluctuating state, at which time the environmental data of the construction region is continuously monitored, and the data collection frequency is adjusted to 120% of the standard data collection frequency; If the heat field stability difference ΔWd is greater than or equal to the second stability evaluation threshold Yz2, it indicates that the heat field stability state of the construction region is in an abnormal state, and the heat flow between the hot zone and the cold zone is unbalanced, at which time a risk warning is issued, and a heat energy diffusion path analysis is performed.

[0031] In this embodiment, by constructing a thermal field stability index Wd and introducing its difference ΔWd as a core evaluation indicator, high-precision dynamic monitoring of the thermal field stability state in the construction area is achieved. This effectively compensates for the shortcomings of existing construction site environmental monitoring in identifying coupled fluctuations in the thermal field. A temperature variation coefficient is constructed using the temperature mean and standard deviation to intuitively reflect the uniformity of temperature distribution in the construction area. Simultaneously, a power fluctuation factor is introduced to measure the consistency of energy output from heat source equipment. Coupled analysis with temperature indicators not only improves the sensitivity to changes in the thermal field but also enhances the ability to identify instability in heat supply. Furthermore, based on historical stable state sample datasets, percentile analysis is employed. Adaptive setting of dual assessment thresholds avoids the traditional threshold setting mode based on human experience, improving the scientific nature and universality of the assessment mechanism. During the assessment process, based on different ranges of the thermal field stability index difference ΔWd, it corresponds to three states: "stable," "fluctuating," and "abnormal," and automatically links the sampling frequency adjustment and risk warning mechanism. This enables the control system to have on-demand response and hierarchical scheduling capabilities, effectively reducing the potential impact of thermal field fluctuations on the health of construction personnel and equipment operation. Overall, through stability difference assessment and dynamic adjustment strategies, thermal energy status identification and risk response are achieved, significantly improving the real-time performance, accuracy, and intelligence level of smart construction site environmental quality management. Example 4

[0032] Please refer to Figure 1 Specifically: The specific steps of S3 include: S31. When the thermal field in the construction area is in an abnormal state, based on the divided heat source region H and low temperature region C, a graph search algorithm is used to construct multiple shortest thermal energy diffusion paths to obtain a set of thermal energy diffusion paths. For each thermal energy diffusion path in the set, a path diffusion response factor Lj is coupled and constructed based on the normalized data set S to quantify the thermal energy diffusion capacity of the path. The formula for calculating the path diffusion response factor Lj is as follows: ; In the formula, Pavg represents the average output power of the heat source along the heat diffusion path, Hi represents the i-th heat source region, Ci represents the i-th low temperature region, ΔT(Hi, Ci) represents the average temperature difference between the i-th heat source region and the i-th low temperature region, d(Hi, Ci) represents the path length of the heat diffusion path from the i-th heat source region to the i-th low temperature region, and Spath represents the cross-sectional area of ​​the heat diffusion path. Graph search algorithm is an algorithmic tool used to search for the optimal path or all feasible paths in a graph structure. It is used to search for all feasible paths from the heat source region H to the low temperature region C in a graph structure, and to construct multiple candidate paths according to a set objective, such as the shortest path. The path length is obtained by calculating the spatial Euclidean distance between the endpoints of the heat diffusion path by calling up the thermal image of the construction area. The cross-sectional area of ​​the heat diffusion path represents the effective conduction cross-sectional area of ​​the heat diffusion path in space, which is obtained by identifying the BIM structural diagram of the construction area; Formula derivation and physical meaning: The formula is used to evaluate the feasibility of heat diffusion from the heat source area to the low temperature area. Its essence is the driving intensity of heat diffusion that can be carried by "unit path length × unit path cross section". The numerator is the measure factor of heat energy diffusion driving force, which consists of two physical quantities: the average power of the heat source and the average temperature difference. The product of the two represents the "heat flux potential energy" of diffusion, that is, the diffusion energy intensity corresponding to the unit temperature difference per unit time. The larger the temperature difference, the stronger the heating power and the greater the heat diffusion intensity. The denominator represents the path resistance factor, which represents the path load that needs to be overcome in the process of heat diffusion from the heat source area to the low temperature area. The longer the heat energy diffusion path, the greater the heat attenuation. The smaller the cross-sectional area of ​​the heat energy diffusion path, the higher the heat energy density, and the easier it is to cause heat flow obstruction. Finally, the measure factor of heat energy diffusion driving force and the path resistance factor are coupled and modeled to construct the path diffusion response factor Lj, which truly reflects the feasibility and efficiency of heat energy cross-space migration in the construction area and provides a reliable criterion basis for the selection of subsequent control paths. Specific examples are as follows: Suppose that in a construction area of ​​a high-altitude, cold-weather worksite, there is a heat diffusion path from a heat source region to a low-temperature region. The collected data includes an average heat source output power (Pavg) of 1800 W, an average temperature difference of 16.2°C between the heat source region H and the low-temperature region C, a path length d(Hi, Ci) of 3.2 m, and a cross-sectional area Spath of 0.12 m². 2 At this point, the path diffusion response factor Lj for calculating the heat energy diffusion path is 75.9 kW / m. 3 This indicates that the heat energy transferred per unit volume of heat conduction path is 75.9 kW when the average temperature difference is 16.2°C.

[0033] S32. Collect effective heat energy diffusion paths in the construction area under historical normal conditions, construct a response factor sample set, calculate the path diffusion response factor Lj based on the response factor sample set, and select the 85th percentile path diffusion response factor Lj as the path diffusion assessment threshold Ljyz. Compare and analyze the path diffusion assessment threshold Ljyz with the path diffusion response factor Lj to evaluate the feasibility of each heat energy diffusion path in the construction area. If the path diffusion response factor Lj ≥ the path diffusion assessment threshold Ljyz, the heat energy diffusion capacity of the heat energy diffusion path is deemed qualified and marked as a scalable path. If the path diffusion response factor Lj < the path diffusion assessment threshold Ljyz, the heat energy diffusion capacity of the heat energy diffusion path is deemed unqualified and marked as a risk path. Collect all risk paths to construct a set of candidate paths for heat energy regulation.

[0034] In this embodiment, by constructing a path diffusion response factor Lj and combining it with percentile threshold analysis, a high-precision quantitative assessment of the feasibility and conduction efficiency of heat energy diffusion paths within the construction area is achieved. This overcomes the technical bottleneck of traditional environmental thermal control systems that rely solely on fuzzy judgments based on single-point temperature or wind speed data. Based on actual parameters such as heat source output power, temperature difference, path length, and conduction cross-sectional area, this method establishes a physical model coupling heat diffusion driving force and transmission resistance. From the dual perspectives of heat flux potential energy and path load, it comprehensively reflects the heat conduction capacity in complex construction spaces. Simultaneously, by introducing a graph search algorithm to automatically construct multiple heat energy diffusion paths, the system is able to actively identify heat energy in the path network. The ability to identify "short channels" and "bottleneck areas" provides a structured and hierarchical path foundation for subsequent regulation. Based on this, a response factor distribution model is constructed using sample data under normal construction conditions, and the 85th percentile is set as a reasonable feasibility assessment threshold to ensure that the marked risk paths have statistical significance and practical reference value. This method not only improves the scientificity and accuracy of heat diffusion path selection, but also provides physical quantity support and evaluation basis for optimizing thermal field regulation strategies. It significantly enhances the timeliness of response, accuracy of regulation, and system adaptability of smart construction sites when facing thermal field anomalies, and provides key support for building a highly reliable and low-energy dynamic thermal environment management and control mechanism. Example 5

[0035] Please refer to Figure 1 Specifically: The specific steps of S4 include: S41. For each heat diffusion path in the set of candidate heat regulation paths, use a graph search algorithm to output the heat diffusion path as a series of continuous path nodes P1, P2, ..., Pm, divide the heat diffusion path into m-1 sub-paths, record the coordinates of each path node, and calculate the direction vector of each sub-path. And the length L, and convert the prevailing wind direction angle θa of the air supply equipment into a two-dimensional unit vector. , where the two-dimensional unit vector And calculate the included angle deviation Δθ for each sub-path segment, where the formula for calculating the included angle deviation Δθ is: ; In the formula, arccos represents the arccosine function. and They represent direction vectors respectively. and two-dimensional unit vector The modulus length; Formula Derivation and Physical Meaning: The formula originates from the two-dimensional vector angle formula and is used to calculate the angle between the direction of each sub-path in the heat diffusion path and the air supply direction of the air supply equipment. In other words, it determines whether the airflow is aligned with the path, thus assessing whether the path requires heat guidance. The smaller the angle deviation Δθ, the more consistent the airflow direction is with the path, which is beneficial for heat diffusion. Here, the numerator is a two-dimensional unit vector. and the direction vector of the sub-path The dot product represents the degree of overlap between the path direction and the wind direction. The larger the dot product, the more consistent the directions. The denominator represents the product of the length of the path segment and the length of the wind direction vector. Finally, the numerator is divided by the denominator to obtain the cosine value of the angle. Then, the cosine value of the angle is restored to the angle by the arccosine function. Specific examples are as follows: Suppose there exists a sub-path of a heat diffusion path, with the starting and ending points of the sub-path being (10, 8) and (22, 16) respectively. The direction vector is... Given (12, 8), the prevailing wind direction angle θa of the air supply equipment is 30°, which is converted into a unit two-dimensional direction vector. =(0.866, 0.5), at this time the angle deviation Δθ of the sub-path is calculated to be 3.5°.

[0036] S42. Multiply the angle deviation Δθ of each sub-path segment by the length L of the sub-path, sum the product of the angle deviation Δθ of each sub-path segment and the length L of the sub-path, and divide by the total length of the heat diffusion path to obtain the path wind deviation index Fp.

[0037] The specific steps in S4 also include: S43. Collect environmental data of the construction area under conditions of no control failures and effective heat conduction, calculate the path wind deviation index Fp, perform statistical analysis, and select the 85th and 95th percentile path wind deviation indices Fp as the first wind deviation control threshold Ξ1 and the second wind deviation control threshold Ξ2. Compare and analyze the first wind deviation control threshold Ξ1 and the second wind deviation control threshold Ξ2 with the path wind deviation index Fp to evaluate the heat conduction status of the heat diffusion path. The specific evaluation content is as follows: If the path wind deviation index Fp ≤ the first wind deviation control threshold Ξ1, it means that the diffusion loss of the heat energy diffusion path in the heat conduction process is within the standard range, and it is judged as the first adaptation level. At this time, no processing is required. If the first wind deflection control threshold Ξ1 < the path wind deviation index Fp < the second wind deflection control threshold Ξ2, it indicates that there is some diffusion loss in the heat energy diffusion path during the heat conduction process, and it is judged as the second adaptation level, and the second heat energy conduction regulation measures are generated. If the path wind deviation index Fp is greater than or equal to the second wind deviation control threshold Ξ2, it indicates that there is a serious diffusion loss in the heat energy diffusion path during the heat conduction process, and it is judged as the third adaptation level, and the third heat energy conduction regulation measure is generated.

[0038] In this embodiment, a path wind deviation index Fp is constructed to quantitatively assess the fit between the heat diffusion path and the air supply direction. This compensates for the shortcomings of existing smart construction site environmental control methods in terms of heat guidance accuracy and path selection rationality. Based on a graph search algorithm, each heat diffusion path is decomposed into multiple sub-paths, and combined with the dominant wind direction of the air supply equipment, it is converted into a two-dimensional vector. By calculating the angle deviation between the wind direction and the path direction segment by segment, the consistency between the airflow and the heat diffusion direction is accurately characterized, effectively reflecting the boosting or hindering effect of wind energy on heat diffusion in the spatial path. This is especially suitable for scenarios with complex construction spaces and frequent wind field disturbances. At the same time, the deviation angle of each segment is multiplied by the sub-path length. By constructing a deviation integral index that considers spatial span and directional error, the evaluation results are made more physically realistic and have engineering reference value. Furthermore, by combining normal conduction sample data to construct a percentile threshold model, a hierarchical classification of the path heat conduction state is achieved, providing a basis for the formulation of subsequent differentiated adjustment measures. Compared with the traditional control mode based on static temperature distribution, this method realizes a three-in-one control logic of "wind direction-path coupling-adaptation", which improves the efficiency of heat conduction path adaptability identification and the accuracy of thermal field intervention. It also enhances the robustness and responsiveness of the thermal environment control system in the construction area under dynamic changing conditions, and further promotes the development of smart construction site thermal control systems towards refinement and intelligence. Example 6

[0039] Please refer to Figure 1 Specifically: The S5 steps include: S51. Based on the obtained second and third heat conduction regulation measures, adjust each heat diffusion path in the construction scenario in real time. The second heat conduction regulation measures include increasing the wind speed level of the air supply equipment to the highest level, switching the rotation state of the air outlet guide component to form a fan-shaped guide area, and modifying the air supply rhythm to pulsed short-interval air supply to improve the diffusion coverage. The third heat transfer regulation measure includes adjusting the air supply cycle frequency to 150% of the standard air supply cycle frequency and activating auxiliary air supply equipment to change the wind direction angle. The auxiliary air supply equipment includes air supply baffles and air guides to achieve directional deflection of the wind direction.

[0040] In this embodiment, a hierarchical control strategy is used to achieve real-time optimization of the heat conduction path in the construction area, effectively improving the intervention capability and adjustment accuracy of the smart construction site thermal environment management system under complex dynamic conditions. Based on the evaluation results, different levels of heat conduction adjustment measures are intelligently triggered, deeply coupling the control actions with the path adaptation state. For the heat diffusion path of the second adaptation level, the system strengthens the power output and wind field guidance capability of heat at the diffusion path end by increasing the wind speed of the air supply equipment to the highest level, switching the rotation state of the air outlet guide component to form a fan-shaped guide zone, and introducing a pulsed short-interval air supply strategy, thereby improving the uniformity of heat flow coverage and the stability of conduction. Qualitatively, for the heat diffusion path of the third adaptation level, the system further superimposes auxiliary air supply equipment, including air supply baffles and air guides, to achieve precise deflection of air direction and local enhanced guidance. It is particularly suitable for construction structural environments with many spatial obstructions and multiple path deflections. In addition, by increasing the frequency of air supply cycles, the continuity and penetration of airflow are enhanced, effectively alleviating the problem of local heat accumulation caused by path obstruction or excessive air deviation. This improves the system's adjustment flexibility and adaptability, reduces system energy waste and ineffective operation, and enhances the thermal field uniformity and working environment comfort of the construction area. It provides more reliable dynamic support for ensuring the health of construction personnel and the safe operation of equipment. Example 7

[0041] Please refer to Figure 2 A smart construction site environmental quality real-time control system includes a data acquisition module, a thermal field stability analysis module, a risk path identification module, a heat energy conduction assessment module, and an intelligent control module. The data acquisition module is used to collect environmental data of the construction area in real time using an infrared thermal imager and an ultrasonic anemometer, and to preprocess the data to construct a normalized dataset S. The thermal stability analysis module is used to analyze the thermal stability state of the construction area based on the normalized data set S, and to perform thermal diffusion path analysis when the thermal stability state is abnormal. The risk path identification module is used to construct a set of thermal energy diffusion paths when the thermal field is in an abnormal stable state, and to analyze the thermal energy diffusion of each thermal energy diffusion path in the set of thermal energy diffusion paths in order to mark risk paths, and to collect all risk paths to construct a set of candidate thermal energy regulation paths. The heat conduction assessment module is used to analyze the compatibility between the path direction and the air supply equipment direction of the risk path in the set of heat regulation candidate paths, evaluate the heat conduction status of the heat diffusion path, and generate the second and third heat conduction regulation measures. The intelligent control module is used to control the heat conduction quality of the construction area in real time based on the second and third heat conduction control measures.

[0042] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A method for real-time regulation of environment quality in a smart construction site, characterized in that: The method comprises the following steps: S1, real-time collection of environmental data of the construction area using an infrared thermal imager and an ultrasonic anemometer, and preprocessing, to construct a normalized data set S; S2, analysis of the thermal field stability of the construction area according to the normalized data set S, and when the thermal field stability is an abnormal state, thermal diffusion path analysis is performed; S3, when the thermal field stability is an abnormal state, a thermal energy diffusion path set is constructed, and the thermal energy diffusion of each thermal energy diffusion path in the thermal energy diffusion path set is analyzed to mark a risk path, and all risk paths are collected to construct a thermal energy regulation candidate path set; S4, for the risk path in the thermal energy regulation candidate path set, the degree of adaptation of the path direction to the air direction of the air supply equipment is analyzed, the thermal energy conduction state of the thermal energy diffusion path is evaluated, and second and third thermal energy conduction regulation measures are generated; S5, real-time regulation of the thermal energy conduction quality of the construction area according to the second and third thermal energy conduction regulation measures. 2.The real-time environment quality regulation method of a smart construction site according to claim 1, characterized in that: The specific steps of S1 include: S11, deploying infrared thermal imagers and ultrasonic anemometers at the end of the tower crane arm, the top of the construction platform, and the high position of the scaffold in the construction area, and connecting the infrared thermal imagers and ultrasonic anemometers to the edge server to real-time collection of thermal images and environmental data of the construction area, and thermal image preprocessing and environmental data preprocessing; The environmental data includes pixel temperature value T, heat source effective area Spath, heat source output power P, and air supply equipment dominant wind direction angle θa; The thermal image preprocessing includes using median filtering to remove infrared flicker interference and using histogram equalization to enhance image contrast, and performing inter-frame temperature baseline calibration on each thermal image to obtain a standardized thermal image; The environmental data preprocessing includes using the maximum-minimum normalization method to normalize the environmental data to obtain the normalized data set S. 3.The real-time environment quality regulation method of a smart construction site according to claim 2, characterized in that: The specific steps of S1 further include: S12, setting a data analysis time window, for each pixel in the standardized thermal image, performing first-order derivative calculation on the temperature value of the pixel within the data analysis time window to obtain the temperature change rate δT, evaluating the temperature change trend of each pixel, if δT≥1°C / min, it indicates that the pixel is a high temperature trend point, if δT<-0.5°C / min, it indicates that the pixel is a continuous cooling point, and all high temperature trend points and continuous cooling points are collected respectively to construct high temperature candidate point set Hcand and low temperature candidate point set Ccand; S13, according to the high temperature candidate point set Hcand and the low temperature candidate point set Ccand, using a density clustering algorithm to cluster the high temperature trend points and the continuous cooling points into region blocks respectively, and performing isolated pixel removal and contour extraction on each region block to identify the heat source region H and the low temperature region C of the construction area.

4. The real-time environment quality regulation method of a smart construction site according to claim 3, characterized in that: The specific steps of S2 include: S21, according to the normalized data set S, the temperature mean and temperature standard deviation of each heat source area H and low temperature area C are counted, and the temperature mean and temperature standard deviation are coupled to build a temperature variation coefficient to measure the temperature uniformity of the construction area, and the heat field stability index Wd is coupled and built by combining the heat source output power data, wherein the heat field stability index Wd calculation formula is: ; In the formula, σT represents the temperature standard deviation, Tavg represents the temperature mean value, σP represents the heat source output power standard deviation, and Pavg represents the heat source output power mean value.

5. The real-time environment quality regulation method of a smart construction site according to claim 4, characterized in that: The specific steps of S2 further include: S22, extract the heat field stability index maximum value WdHmax of the heat source area H and the heat field stability index maximum value WdCmax of the low-temperature area C, subtract the heat field stability index maximum value WdCmax of the low-temperature area C from the heat field stability index maximum value WdHmax of the heat source area H to obtain the heat field stability index difference value AWd, collect the environmental data of the construction area under the historical normal state, calculate the heat field stability index difference value AWd of the construction area under the historical normal state, and perform statistical analysis, select the 90th percentile and the 95th percentile of the heat field stability difference AWd as the first stability evaluation threshold Yz1 and the second stability evaluation threshold Yz2, and compare the first stability evaluation threshold Yz1 and the second stability evaluation threshold Yz2 with the heat field stability difference AWd to evaluate the heat field stability state of the construction area, and the specific evaluation content is as follows: If the heat field stability index difference AWd is less than or equal to the first stability evaluation threshold Yz1, it indicates that the heat field stability state of the construction area is in a stable state, and no treatment is required at this time; If the first stability evaluation threshold Yz1 is less than the heat field stability index difference AWd and the heat field stability index difference AWd is less than the second stability evaluation threshold Yz2, it indicates that the heat field stability state of the construction area is in a fluctuating state, at this time, the environmental data of the construction area is continuously monitored, and the data collection frequency is adjusted to 120% of the standard data collection frequency; If the heat field stability index difference AWd is greater than or equal to the second stability evaluation threshold Yz2, it indicates that the heat field stability state of the construction area is in an abnormal state, and the heat flow between the hot area and the cold area is unbalanced, at this time, a risk warning is issued, and the heat energy diffusion path analysis is performed. 6.The real-time environment quality regulation method of a smart construction site according to claim 5, wherein: S3 includes the following specific steps: S31, when the thermal field at the construction area is in an abnormal state, according to the divided heat source area H and the low temperature area C, a plurality of shortest heat energy diffusion paths are constructed using a graph search algorithm to obtain a heat energy diffusion path set, and for each heat energy diffusion path in the heat energy diffusion path set, according to the normalized data set S, a path diffusion response factor Lj is coupled to be constructed to quantify the heat energy diffusion capacity of the heat energy diffusion path, wherein the path diffusion response factor Lj calculation formula is: ; In the formula, Pavg represents the average heat source output power of the heat energy diffusion path, Hi represents the i-th heat source area, Ci represents the i-th low-temperature area, AT(Hi, Ci) represents the average temperature difference between the i-th heat source area and the i-th low-temperature area, d(Hi, Ci) represents the path length of the heat energy diffusion path from the i-th heat source area to the i-th low-temperature area, and Spath represents the cross-sectional area of the heat energy diffusion path; S32, collect the effective heat energy diffusion path of the construction area under the historical normal state, construct a response factor sample set, calculate the path diffusion response factor Lj according to the response factor sample set, select the 85th percentile of the path diffusion response factor Lj as the path diffusion evaluation threshold Ljyz, and compare the path diffusion evaluation threshold Ljyz with the path diffusion response factor Lj to evaluate the feasibility of each heat energy diffusion path in the construction area. If the path diffusion response factor Lj is greater than or equal to the path diffusion evaluation threshold Ljyz, it is determined that the heat energy diffusion capacity of the heat energy diffusion path is qualified, and is marked as a feasible path. If the path diffusion response factor Lj is less than the path diffusion evaluation threshold Ljyz, it indicates that the heat energy diffusion capacity of the heat energy diffusion path is unqualified, and is marked as a risk path. All risk paths are collected to construct a heat energy regulation candidate path set.

7. The real-time environment quality regulation method of a smart construction site according to claim 6, characterized in that: S4 includes the following specific steps: S41, for each thermal energy diffusion path in the set of thermal energy regulation candidate paths, using a graph search algorithm to output the thermal energy diffusion path as a series of consecutive path nodes P1, P2,..., Pm, divide the thermal energy diffusion path into m-1 sub-paths, record the coordinates of each path node, and calculate the direction vector of each sub-path and length L, and convert the main wind direction angle θa of the air supply device into a two-dimensional unit vector where the two-dimensional unit vector = (cos θa, sin θa), and calculate the angular deviation amount Δθ of each sub-path, where the angular deviation amount Δθ calculation formula is: ; where arccos denotes the inverse cosine function, and denote the magnitude of the directional vector and the two-dimensional unit vector respectively. S42, multiply the included angle deviation amount Δθ of each sub-path obtained by the length L of the sub-path, and sum the product of the included angle deviation amount Δθ of each sub-path of the heat energy diffusion path and the length L of the sub-path, and divide by the total length of the heat energy diffusion path to obtain the path wind deviation index Fp. 8.The real-time environment quality regulation method of a smart construction site according to claim 7, wherein: S4, the specific steps further comprise: S43, collect the environmental data of the construction area in the state of no regulation failure and effective heat conduction, calculate the path wind deviation index Fp for statistical analysis, and select the path wind deviation index Fp of the 85th percentile and the 95th percentile as the first wind deviation control threshold Ξ1 and the second wind deviation control threshold Ξ2, and compare the first wind deviation control threshold Ξ1 and the second wind deviation control threshold Ξ2 with the path wind deviation index Fp for comparative analysis to evaluate the heat energy conduction state of the heat energy diffusion path, and the specific evaluation contents are as follows: If the path wind deviation index Fp is less than or equal to the first wind deviation control threshold Ξ1, it indicates that the diffusion loss of the heat energy diffusion path in the heat conduction process is within the standard range, and it is determined as the first adaptive level, and no processing is required at this time; If the first wind deviation control threshold Ξ1 is less than the path wind deviation index Fp and the second wind deviation control threshold Ξ2, it indicates that there is partial diffusion loss in the heat conduction process of the heat energy diffusion path, and it is determined as the second adaptive level, and the second heat energy conduction adjustment measure is generated; If the path wind deviation index Fp is greater than or equal to the second wind deviation control threshold Ξ2, it indicates that there is serious diffusion loss in the heat conduction process of the heat energy diffusion path, and it is determined as the third adaptive level, and the third heat energy conduction adjustment measure is generated. 9.The real-time environment quality regulation method of a smart construction site according to claim 8, wherein: S5, the specific steps comprise: S51, according to the obtained second heat energy conduction adjustment measure and third heat energy conduction adjustment measure, real-time adjustment is made to each heat energy diffusion path of the construction scene; The second heat energy conduction adjustment measure includes increasing the wind speed level of the air supply equipment to the highest level, switching the rotation state of the air outlet guide assembly to form a fan-shaped flow guide area, and modifying the air supply rhythm to pulse type short intermittent air supply to improve the diffusion coverage; The third heat energy conduction adjustment measure includes adjusting the air supply cycle frequency to 150% of the standard air supply cycle frequency, and starting the auxiliary air supply equipment to change the wind direction angle, wherein the auxiliary air supply equipment includes air supply baffles and air guide plates for realizing directional turning of the wind direction. 10.A real-time regulation system for environment quality of a smart construction site, applied to the real-time regulation method for environment quality of a smart construction site according to any one of claims 1-9, characterized in that: It comprises a data acquisition module, a thermal field stability analysis module, a risk path identification module, a heat energy conduction evaluation module and an intelligent control module; The data acquisition module is used to collect the environmental data of the construction area in real time using an infrared thermal imager and an ultrasonic anemometer, and to preprocess the data to construct a normalized data set S; The thermal field stability analysis module is used to analyze the thermal field stability state of the construction area according to the normalized data set S, and to analyze the heat diffusion path when the thermal field stability state is abnormal; The risk path identification module is used to construct a heat energy diffusion path set when the thermal field stability state is abnormal, and to analyze the heat energy diffusion of each heat energy diffusion path in the heat energy diffusion path set to mark the risk path, and to collect all the risk paths to construct a heat energy regulation candidate path set; The thermal energy conduction evaluation module is configured to analyze the degree of adaptation of the path direction and the air direction of the air supply device for the risk path in the thermal energy regulation candidate path set, evaluate the thermal energy conduction state of the thermal energy diffusion path, and generate a second thermal energy conduction regulation measure and a third thermal energy conduction regulation measure; The intelligent regulation module is configured to regulate the thermal energy conduction quality of the construction area in real time according to the second thermal energy conduction regulation measure and the third thermal energy conduction regulation measure.

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