A block thermal environment evaluation method, system, device and medium based on traffic heat dissipation characteristics

Abstract

The application relates to a kind of street block thermal environment evaluation method, system, equipment and medium based on traffic heat exhaust characteristics, which method includes: according to the static heat and humidity data of each fixed measuring point, the heat exhaust characteristics of each typical vehicle and fixed heat exhaust source of street block are quantified, and traffic heat exhaust source characteristic data are obtained;Based on the same time reference, the static heat and humidity data are spatiotemporally fused with the obtained mobile heat and humidity data, and the street block thermal environment distribution field is obtained;According to vehicle conversion coefficient, equivalent conversion processing is carried out on traffic flow, and traffic flow time series data are obtained;Traffic heat exhaust source characteristic data, street block thermal environment distribution field and traffic flow time series data are coupled and analyzed, a time series evolution model coupled with traffic flow and thermal environment is constructed according to the analysis result, and the thermal environment evaluation index of target street block in a preset time period is obtained by solving the model.The application improves the street block thermal environment evaluation precision under the background of modern urban three-dimensional traffic.

Description

Technical Field

[0001] This invention relates to the field of urban thermal environment assessment technology, and in particular to a method, system, equipment and medium for assessing the thermal environment of a neighborhood based on traffic heat exhaust characteristics. Background Technology

[0002] With the acceleration of global urbanization and the continuous increase in motor vehicle ownership, the urban heat island effect has become increasingly significant, seriously affecting the outdoor thermal comfort of urban residents, building energy consumption, and the ecological environment. Transportation system emissions account for a large proportion of the formation mechanism of the urban heat island. Furthermore, modern urban transportation has developed into a three-dimensional network including surface roads and underground rail transit and their ancillary facilities, resulting in highly dynamic and multi-dimensional heat emissions in both time and space. Therefore, systematically assessing the combined heat emissions generated by the coupling of surface and underground transportation systems on the microclimate at the street level has become a crucial issue that urgently needs to be addressed in the construction of green and low-carbon cities.

[0003] Currently, assessment techniques for urban thermal environment and the impact of traffic heat emissions mainly fall into the following two categories: One type of urban thermal environment observation method is based on fixed monitoring points. This method involves deploying a limited number of monitoring stations along roads or blocks and using temperature and humidity sensors to continuously observe near-surface air parameters over a long period to analyze the changing characteristics of the urban thermal environment. This technology is simple to operate, provides good data continuity, and can reflect the temporal evolution of the thermal environment. However, due to the limited number of monitoring points, it is difficult to reflect subtle spatial differences in the thermal environment within a block, especially to capture the instantaneous changes and diffusion characteristics of traffic heat emissions along roads. Furthermore, the monitoring results are usually superimposed with multiple background meteorological factors and heat source information, making it difficult to isolate the independent contribution of traffic heat emissions.

[0004] Another type of heat dissipation testing method focuses on a single vehicle or specific operating condition. This method typically measures parameters such as exhaust temperature and heat dissipation intensity of a single vehicle in a laboratory environment or within a limited road section, providing relatively accurate data on individual vehicle heat dissipation characteristics. However, the research object and test conditions are usually quite singular, making it difficult to represent the comprehensive heat dissipation under complex traffic flow conditions. More importantly, this method often fails to integrate with street-scale thermal environment observations, making it difficult to reveal the diffusion path of traffic heat dissipation in real urban spaces and its comprehensive impact on pedestrian activity spaces.

[0005] Therefore, those skilled in the art urgently need a comprehensive evaluation method that can combine multi-source traffic heat exhaust analysis with high-resolution thermal environment observation. Summary of the Invention

[0006] (a) Technical problems to be solved In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a method, system, equipment and medium for assessing the thermal environment of a street based on the heat exhaust characteristics of traffic, aiming to solve the technical problem that the existing technology is insufficient in terms of spatial coverage, heat source identification capability and multi-source coupling analysis, which makes it difficult to meet the technical requirements for accurate assessment of the thermal environment of a street in the context of three-dimensional traffic.

[0007] (II) Technical Solution To achieve the above objectives, the main technical solutions adopted by the present invention include: In a first aspect, embodiments of the present invention provide a method for assessing the thermal environment of a street block based on traffic heat exhaust characteristics, comprising: Based on the static damp heat data of each fixed measuring point in the target block, the heat exhaust characteristics of each typical vehicle and fixed heat exhaust source in the block are quantified to obtain traffic heat exhaust source characteristic data covering latent heat and sensible heat contributions. The system acquires mobile damp heat data of typical vehicles in the target block, and performs spatiotemporal fusion of static damp heat data and mobile damp heat data based on the same time reference to obtain the block thermal environment distribution field at the same spatiotemporal resolution. Based on the preset vehicle conversion factor, the traffic flow of the target block is equivalently converted to obtain the time series data of traffic flow for standard vehicle types; The characteristics of traffic heat sources, the distribution field of street thermal environment, and the time series data of traffic flow are coupled and analyzed. Based on the analysis results, a time series evolution model of traffic flow and thermal environment is constructed, and the thermal environment assessment index of the target street is obtained by solving the model within a preset time period.

[0008] Optionally, based on the static damp heat data obtained from each fixed measuring point in the target block, the heat exhaust characteristics of typical vehicles and fixed heat exhaust sources in the block are quantified to obtain traffic heat exhaust source characteristic data covering latent heat and sensible heat contributions, including: The static damp heat data, including air temperature, relative humidity and radiation temperature, collected at fixed measuring points are decomposed to extract the sensible heat flux and latent heat flux contributed by typical vehicles and fixed heat exhaust sources at each fixed measuring point. Based on the air temperature, the weights of sensible heat emissions from the powertrain and latent heat emissions from the air conditioning system of each typical vehicle are dynamically adjusted to obtain the sensible heat emission factor and latent heat emission factor of each typical vehicle in the current time period. The static heat emission intensity of a stationary heat source is obtained by analyzing the sensible heat flux and latent heat flux of the stationary heat source through convective sensible heat emission and evaporative latent heat emission. Based on sensible heat emission factors and latent heat emission factors, the sensible heat flux and latent heat flux of typical vehicles are corrected, and the obtained vehicle heat emission data and static heat emission intensity are assimilated and integrated to generate traffic heat emission source characteristic data that covers both latent heat and sensible heat contributions and distinguishes between mobile sources and stationary sources.

[0009] Optionally, mobile damp heat data of typical vehicles in the target block are acquired, and static damp heat data and mobile damp heat data are spatiotemporally fused based on the same time reference to obtain the block thermal environment distribution field at the same spatiotemporal resolution, including: Based on mobile monitoring units configured on multiple typical vehicles, the air temperature and relative humidity of the vehicles are collected in real time while they are in motion, and the collected data is filtered for thermal disturbance caused by the vehicles themselves to obtain mobile humid heat data. The static and mobile humidity and heat data were subjected to spatiotemporal standardization to ensure that the two sets of data have a uniform temporal sampling frequency and spatial grid resolution. Using static damp heat data from fixed measuring points as boundary constraints and mobile damp heat data as interpolation driving fields, the standardized static damp heat data and mobile damp heat data are fused to obtain the continuous thermal environment distribution field of the block at the target spatiotemporal resolution.

[0010] Optionally, based on a preset vehicle conversion factor, the traffic flow of the target block is equivalently converted to obtain time-series traffic flow data for standard vehicle types, including: Obtain raw traffic flow data for typical vehicle types in the target street within a preset time period; Based on the preset vehicle conversion factor table, the original traffic flow data of each typical vehicle type is multiplied by the corresponding conversion factor to convert it into an equivalent flow value in units of standard vehicle type; The equivalent flow values ​​of each vehicle type after conversion are summed to obtain the time series data of total traffic flow of mixed traffic under standard vehicle type. The total traffic flow time series data is discretized according to a preset time resolution to generate traffic flow time series data for standard vehicle models.

[0011] Optionally, traffic heat source characteristic data, street thermal environment distribution field, and traffic flow time series data are coupled and analyzed. Based on the analysis results, a time series evolution model of traffic flow and thermal environment is constructed, and the thermal environment assessment indicators of the target street within a preset time period are obtained by solving the model, including: Multi-dimensional correlation analysis was conducted on traffic heat source characteristic data, street thermal environment distribution field and traffic flow time series data to obtain the response characteristics of street thermal environment distribution under different traffic flow conditions. Based on traffic flow time series data, and combined with sensible heat emission factor and latent heat emission factor in traffic heat source characteristic data, a dynamic traffic heat input function is constructed. Using the street thermal environment distribution field as the initial state field, the dynamic traffic heat emission input function is coupled with the thermal parameters of the hard underlying surface of the street to construct a time-series evolution model that includes thermal diffusion equations and convective heat transfer equations. The temporal evolution model is numerically solved to simulate the spatiotemporal evolution of the thermal environment of the target block under dynamic changes in traffic flow, and the average heat island intensity and thermal comfort index within a preset time period are extracted as thermal environment assessment indicators.

[0012] Optionally, after coupling and analyzing traffic heat source characteristic data, street thermal environment distribution field, and traffic flow time series data, constructing a time series evolution model of traffic flow and thermal environment coupling based on the analysis results, and obtaining the thermal environment assessment index of the target street within a preset time period through model solving, it also includes... The thermal environment assessment indicators of each area of ​​the target block are compared with the preset thermal environment thresholds. Based on the comparison results, areas that exceed the thermal environment thresholds are marked as overheated street areas, and the degree of overheating in these areas is determined. Based on the contribution ratio of mobile heat sources and stationary heat sources in the traffic heat source characteristic data, an overheating source analysis was conducted on the overheated street area to obtain overheating source information including overheating type and heat emission intensity reduction target value for the overheated street area. Visualize and render the spatial distribution, overheating level, and overheating source information of overheated street areas to generate a digital thermal footprint map of the street with spatiotemporal dimensions. Based on the digital thermal footprint map of the street, combined with traffic flow time series data, overheating risk is predicted, and when the prediction results exceed the preset risk threshold, corresponding thermal risk warning information is generated.

[0013] Secondly, embodiments of the present invention provide a street thermal environment assessment system based on traffic heat exhaust characteristics, comprising: Humidity and heat monitoring unit; The data processing unit, connected to the heat and humidity monitoring unit, is used to execute the street thermal environment assessment method based on traffic heat exhaust characteristics described above.

[0014] Optionally, the data processing unit includes: The static heat exhaust characteristic quantification module is used to quantify the heat exhaust characteristics of typical vehicles and fixed heat exhaust sources in the target block based on the static damp heat data of each fixed measuring point, and obtain traffic heat exhaust source characteristic data covering the contributions of latent heat and sensible heat. The multi-source data spatiotemporal fusion module is used to acquire the mobile damp heat data of each typical vehicle in the target block, and to perform spatiotemporal fusion of static damp heat data and mobile damp heat data based on the same time base to obtain the block thermal environment distribution field under the same spatiotemporal resolution. The traffic flow equivalent conversion module is used to perform equivalent conversion processing on the traffic flow of the target block based on the preset vehicle conversion coefficient, and obtain the traffic flow time series data of standard vehicle types. The coupled evolution modeling and evaluation module is used to perform coupled analysis on traffic heat source characteristic data, street thermal environment distribution field and traffic flow time series data. Based on the analysis results, a time series evolution model of traffic flow and thermal environment coupling is constructed, and the thermal environment evaluation index of the target street is obtained by solving the model within a preset time period.

[0015] Thirdly, embodiments of the present invention provide an electronic device, comprising: At least one processor; and memory that is communicatively connected to at least one processor; The memory stores instructions that can be executed by at least one processor, which enables the at least one processor to perform the street thermal environment assessment method based on traffic heat exhaust characteristics described above.

[0016] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method for assessing the thermal environment of a street block based on traffic heat exhaust characteristics.

[0017] (III) Beneficial Effects The beneficial effects of the present invention are as follows: The street thermal environment assessment method based on traffic heat exhaust characteristics of the present invention, by adopting a data acquisition method that combines fixed measuring points and mobile monitoring, breaks through the limitations and judgment errors of traditional fixed monitoring points in terms of spatial coverage. It can simultaneously capture the static emission characteristics and dynamic diffusion process of traffic heat exhaust, obtain a street thermal environment distribution field with high spatiotemporal resolution, and thus improve the ability to characterize the spatial heterogeneity of the thermal environment inside the street.

[0018] Furthermore, this invention achieves a unified representation of multi-source heterogeneous data by spatiotemporally fusing static and mobile damp heat data under the same time reference and introducing a vehicle conversion factor to standardize traffic flow. Based on this, by constructing a temporal evolution model coupling traffic flow and the thermal environment, it can quantitatively analyze the dynamic contribution of traffic heat exhaust from different vehicle types and at different times to the street's thermal environment, thus meeting the needs of accurate assessment of the street's thermal environment in the context of modern urban three-dimensional transportation. Attached Figure Description

[0019] Figure 1 A flowchart illustrating a method for assessing the thermal environment of a street block based on traffic heat exhaust characteristics, provided in an embodiment of the present invention; Figure 2 A surface temperature distribution diagram of the motor vehicle lane area at 7:30 is provided as an embodiment of the present invention. Figure 3This is a surface temperature distribution diagram of the motor vehicle lane area at 10:30, provided as an embodiment of the present invention. Figure 4 This is a surface temperature distribution diagram of the motor vehicle lane area at 13:30 provided in an embodiment of the present invention. Figure 5 This is a surface temperature distribution diagram of the motor vehicle lane area at 16:30 provided in an embodiment of the present invention. Figure 6 This is a surface temperature distribution diagram of the motor vehicle lane area at 19:30 provided in an embodiment of the present invention. Figure 7 This is a schematic diagram of air temperature changes at measuring points in the atmosphere, ventilation shaft, and cooling tower, provided in an embodiment of the present invention. Figure 8 This is a schematic diagram of relative humidity changes at measuring points in the atmosphere, ventilation shaft, and cooling tower, provided in an embodiment of the present invention. Figure 9 This is a schematic diagram illustrating the temperature changes at the front and rear of a motor vehicle during driving, according to an embodiment of the present invention. Figure 10 This is a schematic diagram illustrating the changes in relative humidity at the front and rear of a motor vehicle during driving, as provided in an embodiment of the present invention. Detailed Implementation

[0020] To better explain and facilitate understanding of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] Prior to this, the definitions of the relevant technical terms used in this embodiment are as follows: The urban heat island effect refers to the phenomenon where urban areas are significantly warmer than surrounding rural or suburban areas due to human activities such as construction and transportation.

[0022] Traffic exhaust heat refers to the heat emitted into the urban environment by vehicles (such as fuel-powered vehicles and electric vehicles) and subway ancillary facilities (such as cooling towers and ventilation shafts), mainly including sensible heat and latent heat.

[0023] Heat dissipation characteristics: Heat dissipation characteristics refer to the characteristics of the heat released by a vehicle during operation.

[0024] Sensible heat: The heat absorbed or released when the temperature of an object changes, without being accompanied by a change in the state of matter.

[0025] Latent heat: The heat absorbed or released when a substance undergoes a change in state, while the temperature remains constant.

[0026] Street valley: A linear space enclosed by buildings on both sides and roads at the bottom, which is the core research area for the thermal environment of the neighborhood.

[0027] Traffic flow: Traffic flow refers to the number of vehicles passing through a specific road segment per unit of time.

[0028] Heat dissipation: Heat dissipation refers to the heat released into the environment by motor vehicles during operation.

[0029] Thermal comfort: A comprehensive evaluation index that reflects the degree of comfort of the human body in the surrounding thermal environment under specific environmental conditions.

[0030] refer to Figures 1 to 10 As shown in the embodiment of the present invention, a method for assessing the thermal environment of a street block based on traffic heat exhaust characteristics is proposed. The method includes: quantifying the heat exhaust characteristics of typical vehicles and fixed heat exhaust sources in the street block based on the static damp heat data of each fixed measuring point in the target street block, and obtaining traffic heat exhaust source characteristic data covering the contributions of latent heat and sensible heat; acquiring the mobile damp heat data of each typical vehicle in the target street block, and performing spatiotemporal fusion of the static damp heat data and the mobile damp heat data based on the same time reference to obtain the street block thermal environment distribution field at the same spatiotemporal resolution; performing equivalent conversion processing on the traffic flow of the target street block according to a preset vehicle conversion factor to obtain the traffic flow time series data of standard vehicle types; performing coupled analysis on the traffic heat exhaust source characteristic data, the street block thermal environment distribution field, and the traffic flow time series data, constructing a time series evolution model of traffic flow and thermal environment coupling based on the analysis results, and obtaining the thermal environment assessment index of the target street block within a preset time period through model solving.

[0031] This embodiment, by employing a data acquisition method combining fixed monitoring points and mobile monitoring, overcomes the limitations of traditional fixed monitoring points in spatial coverage and judgment errors. It can simultaneously capture the static emission characteristics and dynamic diffusion processes of traffic heat emissions, obtaining a high spatiotemporal resolution street thermal environment distribution field, thereby improving the ability to characterize the spatial heterogeneity of the street's internal thermal environment. Furthermore, this embodiment achieves a unified representation of multi-source heterogeneous data by spatiotemporally fusing static and mobile damp heat data under the same time reference and introducing a vehicle conversion factor to standardize traffic flow. Based on this, by constructing a temporal evolution model coupling traffic flow and the thermal environment, it can quantitatively analyze the dynamic contribution of traffic heat emissions from different vehicle types and at different times to the street's thermal environment, thus meeting the needs of accurate assessment of the street's thermal environment in the context of modern urban three-dimensional transportation.

[0032] To better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present invention can be understood more clearly and thoroughly, and that the scope of the present invention can be fully conveyed to those skilled in the art.

[0033] Specifically, refer to Figure 1 As shown in this embodiment, a method for assessing the thermal environment of a street block based on traffic heat exhaust characteristics is proposed. The method steps may include the following steps S100 to S400: S100. Based on the static damp heat data of each fixed measuring point in the target block, quantify the heat exhaust characteristics of each typical vehicle and fixed heat exhaust source in the block, and obtain traffic heat exhaust source characteristic data covering the contributions of latent heat and sensible heat.

[0034] In this embodiment, for typical vehicles of different types, such as fuel vehicles and electric vehicles, time-series measurements of temperature and humidity at the front and rear of the vehicle are conducted under typical operating conditions. Energy conversion methods are used to obtain vehicle heat emissions and their temporal distribution characteristics by vehicle type and operating condition. This overcomes the shortcomings of existing technologies that rely on single vehicle type or empirical parameters to estimate heat emissions, thereby improving the accuracy and applicability of vehicle heat emissions in urban energy budget analysis. Furthermore, this embodiment also performs refined separation of latent heat and sensible heat contributions. By performing flux decomposition on static damp heat data collected from fixed measuring points and combining it with dynamic sensible and latent heat emission factor corrections, accurate quantification of the heat emission characteristics of moving vehicles and fixed heat sources is achieved. Finally, traffic heat source characteristic data that comprehensively reflects the actual static thermal environment characteristics of the urban area is generated, providing accurate input parameters for subsequent thermal environment assessment. Specifically, step S100 may also include the following sub-steps S110 to S140: S110. Perform flux decomposition on the static damp heat data, including air temperature, relative humidity, and radiation temperature, collected at fixed measuring points, and extract the sensible heat flux and latent heat flux contributed by typical vehicles and fixed heat exhaust sources at each fixed measuring point.

[0035] For example, 10 thermal environment monitoring points were set up along the target street. Each point was equipped with an automatic temperature and humidity measuring instrument to continuously and periodically collect thermal environment parameters such as air temperature and relative humidity at the location of the point. The layout of the monitoring points followed the principle of combining road function type and spatial form, comprehensively considering factors such as road width, number of lanes, traffic flow density, street building layout, and street valley spatial structure. This ensured that the monitoring points covered different road cross-section types and typical street spatial characteristics, thereby guaranteeing the representativeness of the data at the spatial scale and the reliability of the statistical analysis process. At the same time, portable weather stations were set up at the monitoring points to collect environmental background parameters in real time, providing a unified environmental boundary condition for the subsequent coupling of vehicle heat exhaust and subway heat exhaust.

[0036] Furthermore, to obtain the surface thermal characteristic distribution of the motor vehicle lane and surrounding space, this embodiment measures the surface temperature of the motor vehicle lane area every three hours at a preset time interval using an infrared thermal imager. The measurement objects include the road paving surface, the facades of buildings along the street, and the outer surfaces of vehicles traveling within the road space. This obtains spatial distribution data of the surface temperature of the motor vehicle lane area at different time points. In one specific embodiment, the surface temperature distribution at the same location in the motor vehicle lane area at different times is as follows: Figures 2 to 6 As shown. This study focuses on the heat dissipation characteristics of stationary heat sources in the transportation sector, such as identifying outdoor units of the subway environmental control system within a block area, including cooling towers and ventilation shafts. Measurements were taken during peak subway operating hours, recording the temperature and relative humidity of the airflow. Specific data can be found in [reference needed]. Figure 7 , Figure 8 As shown.

[0037] S120. Based on the air temperature, dynamically adjust the weights of sensible heat emissions from the powertrain and latent heat emissions from the air conditioning system of each typical vehicle to obtain the sensible heat emission factor and latent heat emission factor of each typical vehicle in the current time period.

[0038] Furthermore, the ratio of sensible heat emissions from the vehicle's powertrain to latent heat emissions from the air conditioning system varies significantly under different temperature conditions. When the ambient temperature is low, the air conditioning cooling load is small, and the vehicle's heat dissipation is mainly due to sensible heat emissions from the powertrain. As the ambient temperature rises, the air conditioning system's operating load increases, and the proportion of latent heat emissions generated during its condensation process rises accordingly. Therefore, this embodiment introduces a weighting function with air temperature as the independent variable. By monitoring the air temperature at fixed measuring points in real time, the contribution weights of sensible and latent heat are dynamically adjusted to obtain sensible heat emission factors and latent heat emission factors that can truly reflect the thermal environment characteristics caused by the vehicle at the current time, avoiding the estimation deviation of heat dissipation caused by using a fixed ratio.

[0039] S130. Analyze the convective sensible heat emission and evaporative latent heat emission of the sensible heat flux and latent heat flux of the stationary heat source to obtain the static heat emission intensity of the stationary heat source.

[0040] For example, when analyzing stationary heat sources, it is necessary to differentiate them according to their heat dissipation methods: for outdoor units of subway environmental control systems, their cooling towers and ventilation shafts mainly dissipate heat through evaporation, releasing heat into the atmosphere as latent heat through the phase change of water, exhibiting obvious sensible heat emission characteristics; while for subway track heat dissipation, due to the effects of train braking, tunnel wall heat dissipation, and piston wind, its heat dissipation includes both convective sensible heat and a certain latent heat contribution. Therefore, this embodiment analyzes the proportion and intensity of convective sensible heat emission and evaporative latent heat emission for different types of stationary heat sources, thereby obtaining the static heat emission intensity that can accurately characterize the actual heat contribution of each stationary heat source.

[0041] S140. Based on the sensible heat emission factor and the latent heat emission factor, the sensible heat flux and latent heat flux of typical vehicles are corrected, and the obtained vehicle heat emission data and static heat emission intensity are assimilated and integrated to generate traffic heat emission source characteristic data that covers the contributions of latent heat and sensible heat and distinguishes between mobile sources and stationary sources.

[0042] Furthermore, after correcting the vehicle thermal emission data, it is necessary to assimilate and integrate it with the static thermal emission intensity. Since vehicle heat emissions exhibit significant spatiotemporal dynamics, while the heat emissions from fixed heat sources (such as subway ventilation shafts, cooling towers, and commercial air conditioning outdoor units) are relatively stable, their heat release mechanisms and impact ranges differ. Therefore, this embodiment adopts a spatiotemporal matching principle, superimposing and fusing the corrected sensible and latent heat fluxes of vehicles within the same time period and the same street block with the corresponding static thermal emission intensity of the fixed heat source. During the fusion process, based on the spatial distribution of fixed measuring points, interpolation methods are used to expand discrete point source data into a continuous area source distribution, ensuring that the thermal contributions of mobile and fixed sources are coordinated and unified on a spatial scale.

[0043] S200: Acquire mobile damp heat data of typical vehicles in the target block, and perform spatiotemporal fusion of static damp heat data and mobile damp heat data based on the same time reference to obtain the block thermal environment distribution field under the same spatiotemporal resolution.

[0044] In this embodiment, to overcome the limitations of single fixed measuring points in spatial coverage and the shortcomings of mobile monitoring in temporal continuity, a deep fusion of static and mobile damp heat data is proposed. By using the same time reference as an anchor point, high temporal resolution fixed-point data is combined with high spatial coverage mobile trajectory data to generate a street thermal environment distribution field that has both temporal continuity and spatial detail. Specifically, step S200 includes the following steps S210 to S230: S210: Based on mobile monitoring units configured on multiple typical vehicles, the air temperature and relative humidity of the vehicles are collected in real time, and the collected data is filtered to remove the thermal disturbance of the vehicles themselves to obtain mobile damp heat data.

[0045] Furthermore, such as Figure 9 and Figure 10 As shown, since the areas of concentrated temperature and relative humidity changes during operation differ between gasoline-powered vehicles and electric vehicles, the monitoring units are positioned in different areas for different vehicle models. Specifically, the mobile monitoring unit is located at the rear of the gasoline-powered vehicle near the exhaust port, or at the front of the electric vehicle near the condenser exhaust port. Vehicle-specific thermal disturbances include: heat dissipation from the vehicle's powertrain, exhaust from the air conditioning system, and interference from the release of heat stored in the vehicle body on the surrounding airflow.

[0046] S220. Perform spatiotemporal standardization on static and mobile humidity data respectively to ensure that the two sets of data have a unified time sampling frequency and spatial grid resolution.

[0047] S230. Using static damp heat data from fixed measuring points as boundary constraints and moving damp heat data as interpolation driving fields, the standardized static damp heat data and moving damp heat data are fused to obtain the continuous thermal environment distribution field of the block at the target spatiotemporal resolution.

[0048] Furthermore, during the fusion process, static humidity and heat data from fixed measuring points are first used as boundary constraints for spatiotemporal interpolation to ensure that the fused thermal environment distribution field strictly matches the measured values ​​at the fixed points, thereby maintaining the accuracy of the time series. Subsequently, moving humidity and heat data are used as the interpolation driving field, utilizing their high spatial coverage characteristics to fill the spatial gaps between fixed measuring points. This embodiment spatially expands the discrete point data on the moving trajectory based on underlying surface features such as the layout of street buildings, road orientation, and vegetation distribution, so that the fusion result not only conforms to the continuity of the physical field but also reflects the heterogeneity of the local microclimate of the street.

[0049] S300: Based on the preset vehicle conversion factor, the traffic flow of the target block is equivalently converted to obtain the time series data of traffic flow for standard vehicle types.

[0050] In this embodiment, to standardize the quantitative scale of the impact of different vehicle types on the street's thermal environment, a vehicle conversion factor is introduced to convert complex mixed traffic flow into standard vehicle equivalents. Since different vehicle types differ significantly in physical size, power type, and heat dissipation intensity, directly using the original traffic flow cannot accurately reflect their comprehensive contribution to the thermal environment. Therefore, this embodiment formulates differentiated conversion factors based on vehicle heat dissipation characteristic data, uniformly converting the traffic flow of various vehicle types into standard passenger car equivalents, thereby providing a comparable and accumulative traffic flow benchmark for subsequent thermal environment contribution analysis. Specifically, step S300 may include the following sub-steps S310 to S340: S310: Obtain the original traffic flow data of each typical vehicle type in the target street within a preset time period.

[0051] S320. Based on the preset vehicle conversion factor table, the original traffic flow data of each typical vehicle type is multiplied by the corresponding conversion factor to convert it into an equivalent flow value in units of standard vehicle types.

[0052] Table 1. Vehicle Conversion Factor Table

[0053] S330. The equivalent flow values ​​of each vehicle type after conversion are summed to obtain the time series data of total traffic flow of mixed traffic under standard vehicle types.

[0054] S340. Discretize the total traffic flow time series data according to the preset time resolution to generate traffic flow time series data for standard vehicle models.

[0055] S400. The characteristic data of traffic heat source, the distribution field of street thermal environment and traffic flow time series data are coupled and analyzed. Based on the analysis results, a time series evolution model of traffic flow and thermal environment coupling is constructed, and the thermal environment assessment index of the target street in the preset time period is obtained by solving the model.

[0056] In this embodiment, to reveal the driving mechanism of traffic flow dynamics on the street's thermal environment, traffic heat emission source characteristic data, street thermal environment distribution field, and traffic flow time series data are deeply coupled and analyzed. By constructing a time series evolution model, not only can the thermal contribution of traffic heat emission at different time scales be quantified, but its diffusion and accumulation effects within the street space can also be simulated, ultimately outputting key indicators that can be used to assess the quality of the street's thermal environment. Specifically, step S400 may include the following sub-steps S410 to S440: S410. Conduct multi-dimensional correlation analysis on traffic heat source characteristic data, street thermal environment distribution field and traffic flow time series data to obtain the response characteristics of street thermal environment distribution under different traffic flow conditions.

[0057] Furthermore, a spatiotemporal correlation analysis method is employed to align traffic flow time-series data with the street thermal environment distribution field at the same spatiotemporal resolution, thereby identifying the time delay characteristics between traffic flow changes and thermal environment response. Simultaneously, based on the contribution ratio of sensible and latent heat in the traffic heat emission source characteristic data, cluster analysis is performed on the thermal environment response patterns under different vehicle type combinations to obtain the response characteristic maps of the street thermal environment to traffic heat emission during peak hours, off-peak hours, and nighttime hours. In addition, wavelet coherence analysis is introduced to reveal the co-evolutionary relationship between traffic flow fluctuations and thermal environment changes at different frequency scales, providing prior knowledge constraints for subsequent model construction.

[0058] S420. Based on traffic flow time series data, and combined with the sensible heat emission factor and latent heat emission factor in the traffic heat source characteristic data, a dynamic traffic heat input function is constructed.

[0059] Furthermore, based on the time-series traffic flow data of standard vehicle models, the sensible heat emission factors and latent heat emission factors of each vehicle model are weighted and fused according to the real-time traffic flow composition to obtain the comprehensive heat emission coefficient of the mixed traffic flow. Simultaneously, vehicle speed correction functions and ambient temperature correction functions are introduced to dynamically adjust the emission factors, reflecting the changes in vehicle heat emission characteristics under actual driving conditions. Then, combined with the spatial distribution characteristics of the street network, the traffic heat emission in the time series is mapped onto a two-dimensional spatial grid, forming a dynamic traffic heat emission input field with spatiotemporal distribution characteristics. The mathematical expression of the traffic heat emission input function is: (1) In equation (1), Q ( x,y,t )for t The total heat emitted by traffic at location (x,y) at any given time; n This represents the total number of typical vehicle models. N i ( t ) is the first i Type of vehicle t Traffic flow time-series data at any given moment; H i ( T ), L i ( T Let be the temperature of the i-th vehicle type in the current ambient temperature. T Sensible heat emission factor and latent heat emission factor; f H , f L These are the sensible heat emission correction factors and latent heat emission correction factors that vary with vehicle speed; these factors are proportional to vehicle speed. G ( x ,y) is a spatial distribution function based on the road network, used to distribute heat along traffic flow lines to the corresponding street grid cells.

[0060] S430. Using the street thermal environment distribution field as the initial state field, the dynamic traffic heat emission input function is coupled with the thermal parameters of the hard underlying surface of the street to construct a time-series evolution model that includes thermal diffusion equations and convective heat transfer equations.

[0061] Furthermore, the dynamic traffic heat emission input function is used as the external driving source term of the model and coupled with the heat storage and release characteristics parameters of the hard underlying surface of the block to establish the surface energy balance equation. Simultaneously, considering the heat transport process within the atmospheric boundary layer, turbulent diffusion coefficients and wind speed profile functions are introduced to construct convection-diffusion equations describing the horizontal and vertical heat transfer within the block. In addition, factors such as building shadows and street canyon geometry are embedded as boundary conditions into the model to improve the accuracy of the micro-thermal environment simulation of the block. The mathematical expression of the time-series evolution model is as follows: (2) In equation (2), T ( x,y,t This represents the distribution field of the street's thermal environment. t For time; α The thermal diffusivity; u ( x,y,t () represents the wind speed vector field; Q 1 represents the static heat emission intensity of a fixed heat source; Q 2 represents the dynamic heat storage and release flux of the underlying surface; ρ air density; C p This is the specific heat capacity of air.

[0062] S440. Numerical solution of the temporal evolution model is performed to simulate the spatiotemporal evolution of the thermal environment of the target block under dynamic changes in traffic flow, and the average heat island intensity and thermal comfort index within a preset time period are extracted as thermal environment assessment indicators.

[0063] In this embodiment, in order to apply the thermal environment assessment results to actual street thermal risk management and urban planning decisions, the following method steps S510 to S540 may be included after step S400: S510. Compare the thermal environment assessment indicators of each area of ​​the target block with the preset thermal environment threshold. Based on the comparison results, mark the areas that exceed the thermal environment threshold as overheated street areas and determine the degree of overheating of the area.

[0064] S520. Based on the contribution ratio of mobile heat sources and stationary heat sources in the traffic heat source characteristic data, conduct overheating source analysis on overheated street areas to obtain overheating source information of overheated street areas, including overheating type and heat emission intensity reduction target value.

[0065] S530: Visualize and render the spatial distribution, overheating level, and overheating source information of overheated street areas to generate a digital thermal footprint map of the street with spatiotemporal dimensions.

[0066] S540, based on the digital thermal footprint map of the street, combined with traffic flow time series data, performs overheating risk prediction, and generates corresponding thermal risk warning information when the prediction results exceed the preset risk threshold.

[0067] Furthermore, embodiments of the present invention also provide a street thermal environment assessment system based on traffic heat exhaust characteristics, comprising: The damp heat monitoring unit includes a fixed damp heat monitoring sensor and a mobile damp heat monitoring sensor. The fixed damp heat monitoring sensor is set at a fixed measuring point or the heat outlet of a fixed heat source to collect static damp heat data for each typical vehicle and the fixed heat source. The mobile damp heat monitoring sensor is set at the exhaust port or condenser heat outlet of a typical vehicle to collect mobile damp heat data for each typical vehicle.

[0068] The data processing unit, connected to the heat and humidity monitoring unit, is used to execute the street thermal environment assessment method based on traffic heat exhaust characteristics described above.

[0069] Furthermore, the data processing unit includes: The static heat exhaust characteristic quantification module is used to quantify the heat exhaust characteristics of typical vehicles and fixed heat exhaust sources in the target block based on the static damp heat data of each fixed measuring point, and obtain traffic heat exhaust source characteristic data covering the contributions of latent heat and sensible heat.

[0070] The multi-source data spatiotemporal fusion module is used to acquire the mobile damp heat data of each typical vehicle in the target block, and to perform spatiotemporal fusion of static damp heat data and mobile damp heat data based on the same time reference to obtain the block thermal environment distribution field under the same spatiotemporal resolution.

[0071] The traffic flow equivalent conversion module is used to perform equivalent conversion processing on the traffic flow of the target block based on the preset vehicle conversion coefficient, and obtain the time series data of traffic flow for standard vehicle types.

[0072] The coupled evolution modeling and evaluation module is used to perform coupled analysis on traffic heat source characteristic data, street thermal environment distribution field and traffic flow time series data. Based on the analysis results, a time series evolution model of traffic flow and thermal environment coupling is constructed, and the thermal environment evaluation index of the target street is obtained by solving the model within a preset time period.

[0073] Furthermore, embodiments of the present invention also provide an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described method for assessing the thermal environment of a street block based on traffic exhaust characteristics.

[0074] Finally, this embodiment of the invention also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method for assessing the thermal environment of a street block based on traffic heat exhaust characteristics.

[0075] In summary, this invention presents a method, system, equipment, and medium for assessing the thermal environment of urban blocks based on traffic heat emissions characteristics. First, by combining fixed monitoring points with mobile monitoring, the limitations of traditional fixed monitoring points in spatial coverage and judgment errors are overcome. This approach simultaneously captures the static emission characteristics and dynamic diffusion processes of traffic heat emissions, obtaining a high spatiotemporal resolution distribution field of the urban block's thermal environment, thereby improving the ability to characterize the spatial heterogeneity of the thermal environment within the block. Second, by spatiotemporally fusing static and mobile damp heat data under the same time reference and introducing vehicle conversion factors to standardize traffic flow, a unified representation of multi-source heterogeneous data is achieved. Finally, by constructing a temporal evolution model coupling traffic flow and the thermal environment, the dynamic contribution of traffic heat emissions from different vehicle types and at different times to the urban block's thermal environment can be quantitatively analyzed, meeting the needs for accurate assessment of the urban block's thermal environment in the context of modern urban three-dimensional transportation.

[0076] Since the systems / devices described in the above embodiments of the present invention are systems / devices used to implement the methods of the above embodiments of the present invention, those skilled in the art can understand the specific structure and modifications of the systems / devices based on the methods described in the above embodiments of the present invention, and therefore will not be repeated here. All systems / devices used in the methods of the above embodiments of the present invention fall within the scope of protection of the present invention.

[0077] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0078] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions.

[0079] It should be noted that in the description of this invention, the word "a" or "an" preceding a component does not exclude the existence of multiple such components. This invention can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. The use of terms such as first, second, third, etc., is merely for convenience and does not indicate any order. These terms can be understood as part of the component names.

[0080] Furthermore, it should be noted that in the description of this specification, the terms "one embodiment," "some embodiments," "embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0081] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning of the basic inventive concept, can make other changes and modifications to these embodiments.

[0082] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from the spirit and scope of the invention.

Claims

1. A method for assessing the thermal environment of a street block based on traffic heat exhaust characteristics, characterized in that, include: Based on the static damp heat data of each fixed measuring point in the target block, the heat exhaust characteristics of each typical vehicle and fixed heat exhaust source in the block are quantified to obtain traffic heat exhaust source characteristic data covering latent heat and sensible heat contributions. The system acquires mobile damp heat data of typical vehicles in the target block, and performs spatiotemporal fusion of static damp heat data and mobile damp heat data based on the same time reference to obtain the block thermal environment distribution field at the same spatiotemporal resolution. Based on the preset vehicle conversion factor, the traffic flow of the target block is equivalently converted to obtain the time series data of traffic flow for standard vehicle types; The characteristics of traffic heat sources, the distribution field of street thermal environment, and the time series data of traffic flow are coupled and analyzed. Based on the analysis results, a time series evolution model of traffic flow and thermal environment is constructed, and the thermal environment assessment index of the target street is obtained by solving the model within a preset time period.

2. The method as described in claim 1, characterized in that, Based on the static damp heat data obtained from various fixed measuring points in the target block, the heat emission characteristics of typical vehicles and fixed heat emission sources in the block are quantified, and traffic heat emission source characteristic data covering latent heat and sensible heat contributions are obtained, including: The static damp heat data, including air temperature, relative humidity and radiation temperature, collected at fixed measuring points are decomposed to extract the sensible heat flux and latent heat flux contributed by typical vehicles and fixed heat exhaust sources at each fixed measuring point. Based on the air temperature, the weights of sensible heat emissions from the powertrain and latent heat emissions from the air conditioning system of each typical vehicle are dynamically adjusted to obtain the sensible heat emission factor and latent heat emission factor of each typical vehicle in the current time period. The static heat emission intensity of a stationary heat source is obtained by analyzing the sensible heat flux and latent heat flux of the stationary heat source through convective sensible heat emission and evaporative latent heat emission. Based on sensible heat emission factors and latent heat emission factors, the sensible heat flux and latent heat flux of typical vehicles are corrected, and the obtained vehicle heat emission data and static heat emission intensity are assimilated and integrated to generate traffic heat emission source characteristic data that covers both latent heat and sensible heat contributions and distinguishes between mobile sources and stationary sources.

3. The method as described in claim 1, characterized in that, The system acquires mobile damp heat data for typical vehicles in the target street area, and performs spatiotemporal fusion of static and mobile damp heat data based on the same time reference to obtain the street thermal environment distribution field at the same spatiotemporal resolution, including: Based on mobile monitoring units configured on multiple typical vehicles, the air temperature and relative humidity of the vehicles are collected in real time while they are in motion, and the collected data is filtered for thermal disturbance caused by the vehicles themselves to obtain mobile humid heat data. The static and mobile humidity and heat data were subjected to spatiotemporal standardization to ensure that the two sets of data have a uniform temporal sampling frequency and spatial grid resolution. Using static damp heat data from fixed measuring points as boundary constraints and mobile damp heat data as interpolation driving fields, the standardized static damp heat data and mobile damp heat data are fused to obtain the continuous thermal environment distribution field of the block at the target spatiotemporal resolution.

4. The method as described in claim 1, characterized in that, Based on the preset vehicle conversion factor, the traffic flow of the target block is equivalently converted to obtain the time-series traffic flow data for standard vehicle types, including: Obtain raw traffic flow data for typical vehicle types in the target street within a preset time period; Based on the preset vehicle conversion factor table, the original traffic flow data of each typical vehicle type is multiplied by the corresponding conversion factor to convert it into an equivalent flow value in units of standard vehicle type; The equivalent flow values ​​of each vehicle type after conversion are summed to obtain the time series data of total traffic flow of mixed traffic under standard vehicle type. The total traffic flow time series data is discretized according to a preset time resolution to generate traffic flow time series data for standard vehicle models.

5. The method as described in claim 1, characterized in that, The characteristics of traffic heat sources, the distribution field of street thermal environment, and the time series data of traffic flow are coupled and analyzed. Based on the analysis results, a time series evolution model of traffic flow and thermal environment is constructed. The thermal environment assessment indicators of the target street within a preset time period are obtained by solving the model, including: Multi-dimensional correlation analysis was conducted on traffic heat source characteristic data, street thermal environment distribution field and traffic flow time series data to obtain the response characteristics of street thermal environment distribution under different traffic flow conditions. Based on traffic flow time series data, and combined with sensible heat emission factor and latent heat emission factor in traffic heat source characteristic data, a dynamic traffic heat input function is constructed. Using the street thermal environment distribution field as the initial state field, the dynamic traffic heat emission input function is coupled with the thermal parameters of the hard underlying surface of the street to construct a time-series evolution model that includes thermal diffusion equations and convective heat transfer equations. The temporal evolution model is numerically solved to simulate the spatiotemporal evolution of the thermal environment of the target block under dynamic changes in traffic flow, and the average heat island intensity and thermal comfort index within a preset time period are extracted as thermal environment assessment indicators.

6. The method as described in claim 1, characterized in that, After coupling and analyzing traffic heat source characteristic data, street thermal environment distribution field, and traffic flow time series data, and constructing a time series evolution model of traffic flow and thermal environment coupling based on the analysis results, and obtaining the thermal environment assessment index of the target street within a preset time period through model solving, it also includes... The thermal environment assessment indicators of each area of ​​the target block are compared with the preset thermal environment thresholds. Based on the comparison results, areas that exceed the thermal environment thresholds are marked as overheated street areas, and the degree of overheating in these areas is determined. Based on the contribution ratio of mobile heat sources and stationary heat sources in the traffic heat source characteristic data, an overheating source analysis was conducted on the overheated street area to obtain overheating source information including overheating type and heat emission intensity reduction target value for the overheated street area. Visualize and render the spatial distribution, overheating level, and overheating source information of overheated street areas to generate a digital thermal footprint map of the street with spatiotemporal dimensions. Based on the digital thermal footprint map of the street, combined with traffic flow time series data, overheating risk is predicted, and when the prediction results exceed the preset risk threshold, corresponding thermal risk warning information is generated.

7. A street thermal environment assessment system based on traffic heat exhaust characteristics, characterized in that, include: Humidity and heat monitoring unit; The data processing unit, connected to the heat and humidity monitoring unit, is used to execute a street thermal environment assessment method based on traffic heat exhaust characteristics as described in any one of claims 1-6.

8. The system as described in claim 7, characterized in that, The data processing unit includes: The static heat exhaust characteristic quantification module is used to quantify the heat exhaust characteristics of typical vehicles and fixed heat exhaust sources in the target block based on the static damp heat data of each fixed measuring point, and obtain traffic heat exhaust source characteristic data covering the contributions of latent heat and sensible heat. The multi-source data spatiotemporal fusion module is used to acquire the mobile damp heat data of each typical vehicle in the target block, and to perform spatiotemporal fusion of static damp heat data and mobile damp heat data based on the same time base to obtain the block thermal environment distribution field under the same spatiotemporal resolution. The traffic flow equivalent conversion module is used to perform equivalent conversion processing on the traffic flow of the target block based on the preset vehicle conversion coefficient, and obtain the traffic flow time series data of standard vehicle types. The coupled evolution modeling and evaluation module is used to perform coupled analysis on traffic heat source characteristic data, street thermal environment distribution field and traffic flow time series data. Based on the analysis results, a time series evolution model of traffic flow and thermal environment coupling is constructed, and the thermal environment evaluation index of the target street is obtained by solving the model within a preset time period.

9. An electronic device, characterized in that, include: At least one processor; and memory that is communicatively connected to at least one processor; The memory stores instructions that can be executed by at least one processor, which enables the at least one processor to perform a street thermal environment assessment method based on traffic heat exhaust characteristics as described in any one of claims 1-6.

10. A computer-readable storage medium storing computer-executable instructions thereon, characterized in that, When executed by a processor, the computer-executable instructions implement a method for assessing the thermal environment of a neighborhood based on traffic heat exhaust characteristics as described in any one of claims 1-6.

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