Dynamic simulation system of urban heat island effect based on multi-source data fusion

Abstract

The application discloses a city heat island effect dynamic simulation system based on multi-source data fusion and relates to the technical field of data simulation. The system calculates the artificial heat source intensity of a space unit by acquiring building function data, green land basic cooling capacity, water supply data and meteorological data, combining with the building function time period heat source coefficient. The actual cooling capacity of the green land is corrected based on the green land water state coefficient, and the cooling source supply is attenuated and corrected according to the distance between the surrounding green land and the space unit. Meanwhile, the weather amplification coefficient is obtained by weighting and coupling meteorological characteristics such as air temperature, humidity and wind speed. Finally, the heat source-cooling source imbalance index is calculated by coupling the artificial heat source intensity, the green land cooling source supply and the weather amplification coefficient, so as to realize the division of the city heat island risk level, the identification of the risk type and the spatial visualization output. The application can more truly reflect the formation reasons of the heat environment risk under different time and different space units.

Description

Technical Field

[0001] This invention relates to the field of data simulation technology, specifically to a dynamic simulation system for the urban heat island effect based on multi-source data fusion. Background Technology

[0002] The urban heat island effect refers to the phenomenon where the temperature in urban centers or highly developed areas is significantly higher than that in surrounding areas due to the combined effects of underlying surface structure, building density, heat emissions from human activities, and local meteorological conditions. With the continuous increase in urban construction intensity, the absorption and accumulation of solar radiation by building clusters, the release of artificial heat from traffic and commercial activities, the reduced evapotranspiration effect of hard paving, and changes in local wind speed, humidity, and radiation conditions result in significant spatial heterogeneity and temporal fluctuations in the urban thermal environment.

[0003] In existing technologies, the assessment of the urban heat island effect typically relies on remote sensing surface temperature retrieval, meteorological station monitoring, or microclimate models to simulate and analyze the temporal changes in the thermal environment. These methods can reflect the spatial distribution characteristics of the urban thermal environment to some extent, such as identifying high-temperature patches, analyzing temperature differences between densely built-up areas and green spaces, or creating heat island distribution maps based on historical temperature data. However, most techniques still primarily assess temperature field changes, essentially focusing on thermal environment characterization while neglecting the coupling effects between different types of heat source inputs and cold source regulation during heat island formation.

[0004] Specifically, while existing technologies can analyze the spatiotemporal distribution of heat island intensity through surface temperature inversion, changes in land cover type, or meteorological parameters, reflecting where it is hotter and how temperature changes, they struggle to further explain the underlying causes of thermal environment changes. In reality, the intensity of human activity and heat emission patterns corresponding to different building functions exhibit significant temporal differences. For example, the intensity of anthropogenic heat released by commercial, office, residential, and industrial functional areas differs during the day, night, or peak hours. If only temperature field changes are used as the primary assessment object, the heat island risk is easily simplified to a question of where the temperature is high, failing to effectively identify whether the heat island risk originates from increased anthropogenic heat emissions, insufficient cooling source supply, etc., thus limiting the targetedness and precision of heat island mitigation measures. Summary of the Invention

[0005] To address the problem that existing assessments of the urban heat island effect rely solely on temperature field changes and fail to provide a reliable basis for differentiated governance, this invention provides a dynamic simulation system for the urban heat island effect based on multi-source data fusion.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] In the first aspect, this application discloses a dynamic simulation system for urban heat island effect based on multi-source data fusion, including a data acquisition module, a heat source calculation module, a cold source calculation module, a cold source correction module, a weather amplification factor calculation module, and a result output module.

[0008] The data acquisition module is used to acquire building function data, green space basic cooling capacity, water supply data and meteorological data for each spatial unit;

[0009] The heat source calculation module is used to obtain various building areas from building function data and then calculate the proportion of various building functions. It also combines the preset building function time period heat source coefficients to obtain the standardized artificial heat source intensity of each space unit at different evaluation times.

[0010] The cold source calculation module is used to quantify the water supply data into the green space water state coefficient, and multiply it by the green space basic cooling capacity to calculate the actual green space cooling capacity.

[0011] The cold source correction module is used to correct the actual green space cooling capacity based on the distance between the surrounding green space and the current spatial assessment unit within the preset basic influence radius, thereby obtaining a standardized green space cold source supply.

[0012] The weather amplification factor calculation module is used to extract features from meteorological data and perform weighted coupling to obtain the weather amplification factor of spatial units at different evaluation times;

[0013] The results output module is used to couple the intensity of artificial heat sources, the supply of cold sources in green spaces, and the weather amplification factor to calculate the heat source-cold source imbalance index. Based on the heat source-cold source imbalance index, the heat island risk level is classified and the risk type is identified, thereby generating spatial visualization results.

[0014] Secondly, this application discloses a dynamic simulation method for the urban heat island effect based on multi-source data fusion, including the following steps:

[0015] Acquire building function data, green space basic cooling capacity, water supply data, and meteorological data for each spatial unit;

[0016] The area of ​​various buildings is obtained from the building function data, and the proportion of various building functions is calculated. The standardized artificial heat source intensity of each space unit at different evaluation times is obtained by summing the heat source coefficients of the preset building function time periods.

[0017] The water supply data is quantified into a green space water state coefficient, which is then multiplied by the green space basic cooling capacity to calculate the actual green space cooling capacity.

[0018] Based on the distance between the surrounding green space and the current spatial assessment unit within the preset basic influence radius, the actual green space cooling capacity is corrected to obtain a standardized green space cooling source supply.

[0019] Feature extraction and weighted coupling of meteorological data yield the weather amplification coefficient of spatial units at different assessment times;

[0020] The heat source-cold source imbalance index is calculated by coupling the intensity of artificial heat sources, the supply of cold sources in green spaces, and the weather amplification factor. Based on the heat source-cold source imbalance index, the heat island risk level is classified and the risk type is identified, thereby generating spatial visualization results.

[0021] Thirdly, this application discloses a computing device, including a memory and a processor.

[0022] The memory is used to store the computer program; the processor is used to execute the computer program to implement the steps of the aforementioned dynamic simulation method for urban heat island effect based on multi-source data fusion.

[0023] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0024] This application calculates the intensity of artificial heat sources at different assessment times using building function data, and constructs a heat source-cold source imbalance analysis model by combining the basic cooling capacity of green spaces, water supply status, and meteorological conditions. This enables the assessment of urban heat islands to no longer rely solely on temperature results, but to reflect the dynamic mechanisms in the formation process of the urban thermal environment. Compared with existing assessment methods that are mainly based on remote sensing temperature inversion or static green space indicators, this application can more realistically reflect the causes of thermal environment risks at different times and in different spatial units. Attached Figure Description

[0025] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts. Wherein: Figure 1 This is a block diagram of the urban heat island effect dynamic simulation system based on multi-source data fusion, as described in Embodiment 1 of the present invention. Figure 2 For based on Figure 1 A system block diagram with an extended cold source unit and a cold source distance adjustment unit; Figure 3 For based on Figure 2 A system block diagram with a hierarchical triggering module; Figure 4 For based on Figure 3 System block diagram of continuous recognition unit; Figure 5 A heat map showing the urban heat island risk level of a certain area at the first moment; Figure 6 A heat map showing the heat island risk level of a certain region at the second moment; Figure 7 A heat map showing the heat island risk level of a certain region at the third moment; Figure 8 A heat map showing the urban heat island risk level of a certain region at the fourth moment; Figure 9 A heat map showing the urban heat island risk level of a certain region at the fifth moment; Figure 10 A heat map showing the urban heat island risk level of a certain region at time 6. Figure 11 This is a flowchart of the dynamic simulation method for urban heat island effect based on multi-source data fusion, as described in Example 2. Detailed Implementation

[0026] It is readily understood that, based on the technical solution of this invention, those skilled in the art can propose various interchangeable structural methods and implementations without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention.

[0027] In the current field of urban thermal environment management, especially in the direction of urban heat island risk assessment and dynamic simulation of thermal environment, the formation and evolution of thermal environment is considered to be a typical multi-factor coupled process. At the mechanism level, this thermal environment change is essentially a dynamic energy imbalance process formed by the combined effects of artificial heat source input, ecological cold source regulation and meteorological conditions. That is, urban building activities continuously release heat into the spatial environment, while ecological spaces such as green spaces and water bodies absorb and dissipate heat through evapotranspiration, shading and heat exchange mechanisms. At the same time, local temperature, humidity, wind speed and radiation conditions further affect the accumulation and diffusion of heat in space, thus jointly determining the thermal environment risk status of different spatial units at different times.

[0028] However, while existing technologies can dynamically assess urban thermal environments based on remote sensing temperature inversion, geographic information system analysis, time-series meteorological data, or microclimate simulation models, most technologies still primarily analyze temperature changes, leading to an inability to accurately identify the differences in the roles of various dominant factors in the formation of urban heat islands. Specifically, the lack of anthropogenic heat source coverage is evident in the fact that while existing technologies can statistically analyze building density or land use types, they cannot further differentiate the intensity of human activity and heat emissions at different times for different building functions such as commercial, office, residential, and industrial buildings. On the cold source side, while the system identifies green space coverage, it assumes that the cooling capacity of green spaces is constant, failing to consider the dynamic impact of soil moisture content, irrigation replenishment, and rainfall changes on evapotranspiration cooling capacity. Consequently, a unified dynamic correlation cannot be established between heat source enhancement, cold source attenuation, and weather amplification in the thermal environment formation process, resulting in ambiguity in the system's identification of the causes of urban heat island risk and consequently affecting the correspondence between risk assessment results and governance strategies.

[0029] For example, in urban core business districts during periods of sustained high temperatures in summer, existing thermal environment assessment systems can identify localized high-temperature areas through remote sensing images, but they cannot further determine whether the high-temperature risk stems from increased artificial heat dissipation due to peak commercial activity or from reduced cooling capacity of surrounding green spaces under prolonged water shortages. Without addressing these issues, existing thermal environment assessment systems will remain at the superficial stage of describing thermal environment results; the classification of heat island risk levels and the identification of risk types will lack a supporting mechanism, ultimately affecting the targetedness and precision of urban thermal environment governance measures.

[0030] After introducing the basic concept of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0031] Example 1

[0032] like Figure 1 The diagram shows a dynamic simulation system for the urban heat island effect based on multi-source data fusion, including a data acquisition module 101, a heat source calculation module 102, a cold source calculation module 103, a cold source correction module 104, a weather amplification factor calculation module 105, and a result output module 106.

[0033] It should be understood that the modules can be deployed on the same computing device or distributed across multiple computing devices to interact with each other via a network; the execution order of the modules can be flexibly adjusted according to specific application scenarios and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0034] The data acquisition module 101 is used to acquire building function data, green space basic cooling capacity, water supply data and meteorological data of each spatial unit.

[0035] Specifically, the spatial unit is the basic evaluation unit obtained by dividing the study area into grids. It can be a regular rectangular grid (e.g., 100m×100m, 200m×200m) or an irregular unit obtained by dividing according to administrative divisions, blocks or plot boundaries. The embodiments of the present invention are not limited to this.

[0036] Optionally, building function data and green space data can be derived from land use status maps, building survey data, POI (Point of Interest) data, or results based on high-resolution remote sensing image interpretation published by urban planning departments; rainfall data in water supply data can be derived from precipitation observation data or radar quantitative precipitation estimation products published by meteorological departments; irrigation data can be derived from irrigation scheduling records of municipal landscaping departments; meteorological data can be derived from meteorological station observations, reanalysis datasets, or numerical weather prediction model outputs. This embodiment of the invention is not limited to these sources.

[0037] Building function data includes at least residential, office, commercial, industrial, public service facilities, and transportation hubs. It should be understood that different building functions exhibit significantly different anthropogenic heat release rhythms throughout the day: for example, residential areas have concentrated energy consumption at night (lighting, air conditioning, cooking), office areas have concentrated energy consumption during the day (lighting, air conditioning, equipment), commercial areas have peak energy consumption from afternoon to night (air conditioning, cold chain, lighting), and industrial areas exhibit a nearly stable high heat source release throughout the day. This embodiment of the invention characterizes the temporal patterns of anthropogenic heat release by building function type, avoiding the high cost of directly collecting energy consumption data from each household.

[0038] The basic cooling capacity of green space is calculated by multiplying the green space ratio and the cooling coefficient. The green space ratio is obtained by the data acquisition module 101, and the cooling coefficient is obtained by matching the green space type obtained by the data acquisition module 101 with the preset vegetation type cooling coefficient.

[0039] Green space base cooling capacity The calculation formula is as follows:

[0040] ;

[0041] in, Representing spatial units The basic cooling capacity is a dimensionless quantity with a value ranging from 0 to 1. Representing spatial units The green space ratio is a dimensionless quantity, with a value ranging from 0 to 1. Representing spatial units The vegetation type cooling coefficient is a dimensionless quantity with a value ranging from 0 to 1.

[0042] Optionally, vegetation type cooling coefficient The shading and transpiration capacities of different vegetation types can be preset, as shown in the table below:

[0043]

[0044] The embodiments of the present invention are not limited thereto. For example, for a green space composed of a mixture of trees and lawns, the overall cooling coefficient of the unit can be obtained by area-weighted averaging.

[0045] To facilitate understanding of this application, a scenario is described below. A residential unit in a 200m x 200m complex has a total green area of ​​8000 m², accounting for 20% of the total area. Of this, 60% is forest and 40% is lawn. What is the green space ratio of this unit? =0.2, Cooling coefficient of comprehensive vegetation type =0.6×1.0+0.4×0.5=0.8, therefore the basic cooling capacity of this unit is... =0.2 × 0.8 = 0.16; In contrast, a suburban unit with the same green space ratio but whose green space consists entirely of sparse lawns, =0.3, The value of 0.06 reflects the difference in cooling capacity caused by differences in vegetation type under the same green space ratio.

[0046] The heat source calculation module 102 is used to obtain various building areas from building function data and then calculate the proportion of various building functions. It also combines the preset building function time period heat source coefficients to obtain the standardized artificial heat source intensity of each space unit at different evaluation times.

[0047] The calculation method for the intensity of the artificial heat source can be as follows: first, obtain the data for each spatial unit through the data acquisition module 101. The total building area of ​​all types of buildings is used to calculate the function of each type of building. In this unit Area proportion Then, based on the current assessment time... Query the corresponding heat source coefficient for the preset building function period. Finally, a weighted sum is performed on all building function types to obtain the standardized artificial heat source intensity of the unit at that moment. Standardized artificial heat source intensity The calculation formula is as follows:

[0048] ;

[0049] in, Representing spatial units At the assessment time The standardized artificial heat source intensity is a dimensionless quantity with a value range of 0 to 1; Indicates the spatial unit number; Indicates the evaluation time in hours, with a value range of 0 to 24; This indicates the building function type number, including types such as residential, office, commercial, industrial, public service facilities, and transportation hubs; Indicates the total number of building function types; Representing spatial units The Middle The area ratio of building functions is a dimensionless quantity with a value range of 0 to 1. Indicates the first Building-like functions at the time of evaluation The standardized heat source coefficient is a dimensionless quantity with a value ranging from 0 to 1.

[0050] The table below provides a reference for standardized heat source coefficients for different building functions:

[0051]

[0052] Optionally, the heat source coefficient of the building during different functional periods It can be based on the presets in the table above: for example, residential buildings. The value peaks between 8:00 PM and 11:00 PM (range 0.8-1.0), and decreases during daytime working hours (range 0.2-0.4); for office buildings... Peak values ​​are reached between 9:00 and 18:00 (range 0.8-1.0), with lower values ​​observed at night (range 0.1-0.2); for commercial buildings... Peak values ​​(0.7-1.0) are reached between 14:00 and 22:00; for industrial buildings... The values ​​are relatively stable at a high level throughout the day (ranging from 0.7 to 0.9); Transportation hubs... The heat source coefficient reaches its peak during the morning rush hour (7:00-9:00) and the evening rush hour (17:00-19:00) (with values ​​ranging from 0.7 to 0.9). The heat source coefficient curve for different time periods can be adjusted according to city type and seasonal differences, but this embodiment of the invention is not limited to this.

[0053] Suppose a 200m x 200m urban central business district unit has a building function composition of 70% office, 20% commercial, and 10% residential. At 14:00 (typical peak office hours), the heat source coefficients are taken as 0.95 for office buildings, 0.75 for commercial buildings, and 0.30 for residential buildings. Then, for this unit... =0.7×0.95+0.2×0.75+0.1×0.30=0.845; At 22:00 (a typical off-peak time for office work, extended hours for commercial businesses, and a peak time for residential businesses), the rates are 0.15 for office work, 0.85 for commercial businesses, and 0.90 for residential businesses. =0.7×0.15+0.2×0.85+0.1×0.90=0.365. It can be seen that the intensity of the man-made heat source varies significantly within the same spatial unit at different times, demonstrating the ability to dynamically characterize the function of the heat source over time.

[0054] The cold source calculation module 103 is used to quantify the water supply data into the green space water state coefficient, and multiply it by the green space basic cooling capacity to calculate the actual green space cooling capacity.

[0055] Actual green space cooling capacity The calculation formula is as follows:

[0056] ;

[0057] in, Representing spatial units At the assessment time The actual cooling capacity of green space is a dimensionless quantity, with a value range of 0 to 1. This is the aforementioned basic cooling capacity; Representing spatial units At the assessment time The green space moisture state coefficient is a dimensionless quantity with a value range of 0 to 1.

[0058] It should be understood that the cooling effect of green spaces mainly comes from the shading effect of the vegetation canopy and the latent heat loss caused by plant transpiration, with transpiration cooling contributing dominantly to the total cooling capacity of green spaces. When soil moisture is insufficient, plant stomata close, stomatal resistance increases, and the transpiration rate decreases significantly, leading to a reduction in latent heat flux. More energy is converted into sensible heat, and the cooling effect of green spaces diminishes accordingly. Therefore, green spaces should not be considered as static cold sources with constant cooling capacity, but rather as dynamic cold sources modulated in real time by moisture status. This invention introduces a moisture state coefficient... Basic cooling capacity Dynamic corrections were made to physically reconstruct the attenuation pattern of the cooling capacity of green spaces.

[0059] Specifically, based on the type of green space and meteorological data, different weights are assigned to the first-cycle precipitation data, the second-cycle precipitation data, and the irrigation data in the water replenishment data, and then a weighted calculation is performed. The weighted calculation result is then normalized by the ratio of the weighted calculation result to the reference evaporation to obtain the green space water state coefficient.

[0060] Among them, the weight of the first period precipitation data is greater than that of the second period precipitation data;

[0061] The first-cycle precipitation data, the second-cycle precipitation data, irrigation data, and reference evaporation are obtained from the water replenishment data in the data acquisition module 101.

[0062] Green space moisture state coefficient The calculation formula is as follows:

[0063] ;

[0064] in, Representing spatial units At the assessment time The green space water state coefficient is a dimensionless quantity with a value range of 0 to 1. Representing spatial units At the assessment time The cumulative rainfall within the first cycle is used as the first cycle precipitation data, and the unit is millimeters (mm). Representing spatial units At the assessment time The cumulative rainfall during the first two cycles is used as the precipitation data for the second cycle, in millimeters. Representing spatial units At the assessment time The cumulative amount of artificial irrigation in the previous 1-3 days, in millimeters; Representing spatial units At the assessment time Reference evaporation, in millimeters; , , These represent the contribution weights of the first-period precipitation data, the second-period precipitation data, and the irrigation data, respectively. They are dimensionless quantities, ranging from 0 to 1. The value ranges from 0.4 to 0.7. The value ranges from 0.2 to 0.4. Values ​​range from 0.3 to 0.6; This represents a smoothing amount to prevent the denominator from being too small, in millimeters, with a value range of 0.1 to 1; clip(·) represents the cutoff function, defined as: taking 0 when the independent variable is less than 0, taking the independent variable itself when the independent variable is between 0 and 1, and taking 1 when the independent variable is greater than 1.

[0065] Optionally, the first period can be the evaluation period. The three days prior to the assessment, the second cycle can be used as the assessment period. The present invention is not limited to the present invention as described in the last 7 days.

[0066] It should be understood that the first-cycle (short-cycle) precipitation has a more direct and significant impact on soil surface moisture content and plant transpiration, while the second-cycle (long-cycle) precipitation reflects the cumulative replenishment of deep soil moisture. Therefore, the weighting of the first-cycle precipitation data should be adjusted accordingly. Set the weight to be greater than the second period precipitation data ,For example Take 0.6, Taking 0.3 can more accurately reflect the actual transpiration water status of green space.

[0067] Optionally, weight , , Adjustments can also be made based on the type of green space: for example, for green spaces with deep root systems dominated by trees, the size can be appropriately increased. The weight (e.g., 0.35~0.4) is used to reflect its ability to utilize deep water; for shallow-rooted green spaces dominated by lawns, the weight can be appropriately increased. The weight of irrigation (e.g., 0.65~0.7) is used to reflect its dependence on surface moisture. Weight adjustments can also incorporate factors such as temperature and humidity from meteorological data; for example, the irrigation weight can be appropriately increased during periods of high temperature and dryness. (e.g., 0.5~0.6), but the embodiments of the present invention are not limited thereto.

[0068] Suppose a lawn / green space unit experiences continuous hot and sunny weather, and the cumulative rainfall over the previous 3 days is... The cumulative rainfall for the first 7 days was 0 mm. It is 2mm thick and requires no artificial irrigation. =0mm, reference evapotranspiration The daily weight is 7mm, and the weight is taken as follows: =0.65、 =0.25、 =0.5, smoothing amount =0.5mm, then the moisture state coefficient of this unit is... =clip((0.65×0+0.25×2+0.5×0) / (7+0.5),0,1)=clip(0.067,0,1)≈0.07, meaning the green space has almost lost its transpiration cooling capacity. Corresponding decay to Approximately 7%; conversely, if the unit undergoes an emergency irrigation of 15mm on top of the aforementioned, then =15mm, recalculate =clip((0+0.5+7.5) / 7.5,0,1)=clip(1.07,0,1)=1.0, the cooling capacity of the green space is fully restored. Back Original value. This example illustrates the high sensitivity of the water state coefficient to extreme drought and irrigation interventions.

[0069] It should be understood that in practical engineering applications, the process of irrigation or rainfall replenishing the soil, being absorbed by the roots, and then converted into transpiration cooling by the plant canopy typically involves a physiological time lag ranging from several hours to a day. To reflect this time lag process, embodiments of the present invention can... and The newly added supply amount on the same day (0-6 hours before the evaluation time t) is multiplied by a reduction factor (range 0.3-0.7, preferably 0.5), and the remaining historical supply amounts are included with their original weights; alternatively, exponential lag can be used to check the supply data, perform convolution smoothing, and then substitute it into the above formula. The embodiments of the present invention are not limited to this.

[0070] It should be understood that the purpose of using the above-mentioned normalized saturation function is: firstly, to introduce the clip truncation function to ensure... The value of is within the range of 0 to 1 to avoid negative values ​​or non-physical results exceeding 1; secondly, a smoothing factor is introduced. To avoid numerical overflow caused by the denominator approaching zero when the reference evapotranspiration is extremely small; thirdly, by using the ratio structure of water supply (precipitation + irrigation) to water demand (reference evapotranspiration), the concept of "water balance" of green space is physically restored, so that the water state coefficient can dynamically respond to changes in meteorological conditions.

[0071] The cold source correction module 104 is used to correct the actual green space cooling capacity based on the distance between the surrounding green space and the current spatial assessment unit within the preset basic influence radius to obtain a standardized green space cold source supply.

[0072] It should be understood that the cooling effect of green spaces is not limited to the spatial unit in which the green space is located. It can also affect spatial units within a certain range around the green space through processes such as cold air sinking, advection, and radiation exchange. However, this effect decreases with increasing distance. Therefore, the cooling contribution of surrounding green spaces needs to be added to the current assessment unit after being attenuated by distance in order to obtain the actual supply of green space cooling source received by that unit.

[0073] Specifically, standardized green space cooling supply The calculation process includes the following two steps:

[0074] The first step is to calculate the original cooling source supply for the green space:

[0075] ;

[0076] The second step is to perform saturation normalization on the original cold source supply:

[0077] ;

[0078] in, Representing spatial units At the assessment time The received raw green space cold source supply is a dimensionless quantity with a value greater than or equal to 0; Representing spatial units At the assessment time The standardized green space cooling source supply is a dimensionless quantity with a value range of 0 to 1; Indicates the number of the surrounding green space unit; This indicates the total number of surrounding green space units within the radius of influence of the foundation; Indicates surrounding green space units At the assessment time The actual cooling capacity of green spaces; Representing spatial units With surrounding green space units The distance between them, in meters (m); This represents the characteristic radius of cooling influence, also known as the basic influence radius, in meters, with a value range of 100 to 1000. This represents the cooling supply saturation coefficient, a dimensionless quantity, with a value ranging from 0.3 to 1.5, preferably from 0.5 to 1.0; It represents the base of the natural logarithm.

[0079] It should be understood that using an exponential decay function to characterize the cooling effect of green space with distance... The attenuation law physically corresponds to the energy dissipation process of cold air gradually decreasing due to turbulent mixing and heat exchange during horizontal transport in the near-surface layer. The greater the distance, the smaller the cooling contribution of the green space to the unit; when the distance exceeds the base's radius of influence... When the attenuation coefficient approaches 0, the corresponding cooling contribution can be ignored.

[0080] It should be understood that the physical significance of using a saturation normalization function is that when the number of surrounding green spaces is small, each additional green space can significantly improve the cooling supply of the unit; when the number of surrounding green spaces is large, due to the physical upper limit of the horizontal transport capacity of near-surface cold air, the marginal contribution of newly added green spaces to the cooling of the current unit tends to weaken and eventually saturate. This embodiment of the invention introduces a saturation coefficient. By controlling the saturation rate, the non-physical consequences caused by the linear and unbounded growth of the original cold source supply with the amount of green space are avoided.

[0081] like Figure 2 As shown, the cold source correction module 104 also includes an extended cold source unit 1041. The extended cold source unit 1041 is used to determine whether there is a water body in the current space unit that exceeds the preset area. If so, it obtains the water body area and water body distance in the current space unit and the preset influence range from the data acquisition module 101, and then calculates the water body cold source supply. Based on this, it corrects the standardized green space cold source supply to obtain the comprehensive cooling supply, which participates in the calculation of the heat source-cold source imbalance index.

[0082] It should be understood that urban lakes, rivers, and artificial water features, due to their high specific heat capacity and latent heat of vaporization, can absorb a large amount of heat during the day and release it through evaporation from the water surface, thus significantly reducing the near-surface air temperature of the water body itself and its surrounding area. This is especially true when the water body area is significantly larger (e.g., the area of ​​a water body unit exceeds a preset area threshold, ranging from 3000 to 10000 m²). 2 The preferred value is 5000m 2 In areas where only the contribution of green space as a cooling source is considered, the cooling source supply will be severely underestimated, leading to an overestimation of the heat island risk in that area. Therefore, embodiments of the present invention introduce an extended cooling source unit to incorporate the cooling effect of water bodies into the calculation of the overall cooling supply.

[0083] Specifically, the working process of the extended cold source unit 1041 includes: first, determining whether there is a water body exceeding a preset area threshold within the current space unit itself or its preset influence range; if so, obtaining the area of ​​the water body from the data acquisition module 101. and the distance between the water body and the current space unit. Then, the water body cooling source supply is calculated according to a distance attenuation form similar to that of the green space cooling source supply. .

[0084] Specifically, integrated cooling supply The calculation formula is as follows:

[0085] ;

[0086] in, Representing spatial units At the assessment time The overall cooling supply is a dimensionless quantity, with a value ranging from 0 to 1; That is, the aforementioned standardized green space cooling source supply ; Representing spatial units At the assessment time The water body cold source supply is a dimensionless quantity with a value range of 0 to 1; This represents the weighting coefficient of the water body cold source, which is a dimensionless quantity with a value range of 0.3 to 0.8, and preferably 0.5 to 0.7. Representing water body unit The area, in square meters; Representing spatial units With water body unit The distance between them, in meters.

[0087] Suppose a 200m x 200m waterfront commercial area unit has a green space ratio of only 5% (i.e. =0.05) is located in an area with scarce green space, but there is an urban lake of approximately 20,000 square meters located 300 meters east of the center of this unit. If only the cold source of green space is considered, If the value is approximately 0.05, the unit may be mistakenly identified as a high-heat island risk zone; after activating the extended cooling source unit, the value will be calculated based on the water area and distance. Approximately 0.7, take =0.6, then the overall cooling supply =clip(0.05+0.6×0.7,0,1)=clip(0.47,0,1)=0.47, the heat island risk assessment has been significantly revised to a reasonable level.

[0088] It should be understood that when there is no significant body of water in the study area, The term is set to 0, at which point the overall cooling supply... Degraded into a standardized green space cooling source supply This modular design does not affect the operation of other modules. It enhances the system's adaptability to different data bases.

[0089] The cold source correction module 104 also includes a cold source distance adjustment unit 1042, which is used to calculate the building enclosure degree based on the building height and density between the surrounding green space and the current spatial assessment unit within the basic influence radius, and dynamically adjust the basic influence radius accordingly. The actual green space cooling capacity is corrected by using the distance between the surrounding green space and the current spatial assessment unit within the dynamically adjusted basic influence radius.

[0090] Among them, the building enclosure degree is negatively correlated with the foundation influence radius; the foundation influence radius is obtained based on the green space ratio and green space type of the spatial unit;

[0091] The building height and density are obtained by the data acquisition module 101.

[0092] It should be understood that the enclosure effect of urban buildings significantly affects the horizontal transport of near-surface cold air: when surrounding buildings are tall and dense, the resulting "street valley" structure hinders the flow of cold air, compressing the cooling influence range of green spaces; conversely, when buildings are low and sparsely distributed, cold air can be transported over a wider area, and the cooling influence range of green spaces expands accordingly. Therefore, a fixed basic radius of influence cannot accurately reflect the differences in cold source propagation under different urban forms and needs to be dynamically adjusted according to the enclosure of surrounding buildings.

[0093] Specifically, the working process of the cold source distance adjustment unit 1042 includes:

[0094] First, calculate the spatial unit based on the building height and building density of all surrounding units within the basic influence radius. Corresponding building enclosure degree Building enclosure This can be represented by the weighted average of building height and the normalized product of building density, for example:

[0095] ;

[0096] in, Representing spatial units The building enclosure degree is a dimensionless quantity with a value range of 0 to 1; Representing spatial units The average building height within the radius of influence of the foundation, in meters; This represents the reference building height in meters. The value can be set as the 95th percentile of the building height in the study area. For example, it can be 60-100 m in a plain city and 150-200 m in a high-density urban core area. Representing spatial units The building density (the ratio of building footprint area to total unit area) within the radius of influence of the foundation is a dimensionless quantity, ranging from 0 to 1. When Greater than At that time, the height ratio is set to 1, meaning that for individual super high-rise buildings, their enclosure contribution will no longer be amplified to avoid... Exceeding 1 will cause subsequent Negative values ​​were found.

[0097] Second, based on the building enclosure degree Radius of influence on the foundation Dynamic adjustments are made to obtain the adjusted radius of influence. :

[0098] ;

[0099] in, Representing spatial units The radius of influence after dynamic adjustment, in meters; Indicates the aforementioned basic radius of influence; It represents the attenuation intensity coefficient of the enclosure degree on the radius of influence. It is a dimensionless quantity with a value range of 0.3 to 0.8, preferably 0.5 to 0.7.

[0100] It should be understood that, as can be seen from the above formula, when the building enclosure degree When it approaches 0 (i.e., open areas with sparse and low-rise buildings). Approximately equal to The impact range of green space cooling remains at the base value; when the building enclosure degree When it approaches 1 (i.e., the densely built, towering "street valley" area). Reduce to With (1- The product of ) significantly compresses the range of influence of green space cooling, thus truly reflecting the physical fact that the propagation of cold source is hindered in a high-density built environment.

[0101] Optionally, the radius of influence of the base Green space ratio can be based on spatial unit and vegetation type cooling coefficient Comprehensive settings: For example, when When the value is greater than 0.3 and the forest is mainly composed of arbor forests (strong transpiration and cooling, significant cold air sinking), a larger value can be set. (Values ​​range from 600 to 1000 m); when When the concentration is between 0.1 and 0.3 and the mixture consists of trees, shrubs, and grasses, a medium concentration can be set. (Values ​​range from 300 to 600 m); when When the value is less than 0.1 or when the lawn is sparse (with a weaker cooling effect), a smaller value can be set. (Values ​​range from 100 to 300 m), but the embodiments of the present invention are not limited thereto.

[0102] The actual cooling capacity of green spaces is corrected by using the distance between the surrounding green spaces within the dynamically adjusted basic influence radius and the current spatial assessment unit. That is, the basic influence radius is incorporated into the aforementioned standardized formula for calculating green space cooling source supply. Replace with dynamically adjusted This allows for a modified cold source supply that takes into account differences in building enclosure.

[0103] Assuming the radius of influence of the base =500m, attenuation intensity coefficient =0.6. For a certain high-rise central business district unit, the average building height of its surrounding buildings is... =150m (take) =200m), building density If the value is 0.5, then the building enclosure degree of this unit is... =(150 / 200)×0.5=0.375, the radius of influence after dynamic adjustment =500×(1-0.6×0.375)=500×0.775=387.5m, meaning the cold source propagation range is compressed by approximately 23%; while for a certain suburban low-density residential unit, =20m =0.15, =(20 / 200)×0.15=0.015, =500×(1-0.6×0.015)=495.5m, with almost no attenuation. This example visually illustrates the difference in the cold source propagation range of the same green space under different urban forms.

[0104] The weather amplification factor calculation module 105 is used to extract features from meteorological data and perform weighted coupling to obtain the weather amplification factor of spatial units at different evaluation times; specifically, it includes:

[0105] The data acquisition unit 1051 is used to acquire the temperature, relative humidity and wind speed at the corresponding assessment time from meteorological data and perform standardization processing.

[0106] The data calculation unit 1052 is used to input the standardized temperature, relative humidity and wind speed into the weather amplification model to obtain the weather amplification factor;

[0107] The weather amplification model is constructed based on the positive correlation between air temperature and relative humidity and the weather amplification effect, and the negative correlation between wind speed and the weather amplification effect.

[0108] Specifically, the standardization process involves: converting the original temperature... relative humidity and wind speed Standardized temperatures are obtained by mapping the normalization function to the 0-1 interval. Standardized relative humidity and standardized wind speed The normalized reference value range for air temperature can be: initial reference temperature 25~30℃ (preferably 28℃), high heat reference temperature 35~40℃ (preferably 38℃); the normalized reference value range for relative humidity can be: initial reference 60%~70% (preferably 65%), high humidity reference 85%~95% (preferably 90%); the normalized reference wind speed range can be 2~4m / s (preferably 3 m / s).

[0109] Weather magnification factor The calculation formula (weather amplification model) is as follows:

[0110] ;

[0111] in, Representing spatial units At the assessment time The weather amplification factor is a dimensionless quantity, ranging from 1 to (1+). + + ); , , Representing spatial units At the assessment time The measured air temperature (in degrees Celsius), relative humidity (in percentage), and wind speed (in meters per second). , , These represent the standardized air temperature, relative humidity, and wind speed, respectively. They are all dimensionless quantities with values ​​ranging from 0 to 1. , , These represent the weighting coefficients for air temperature, relative humidity, and wind speed, respectively. They are dimensionless quantities, ranging from 0 to 1. The preferred value is 0.4~0.6 (temperature is the dominant factor). The preferred value is 0.1 to 0.3. The preferred values ​​are 0.2 to 0.4, and the sum of the three values ​​should preferably not exceed 1.5.

[0112] It should be understood that the higher the temperature, the more likely an anthropogenic heat source of the same intensity will create a thermal risk in that unit. It is positively correlated with the weather amplification effect; the higher the relative humidity, the weaker the body's ability to dissipate heat through sweat evaporation, the stronger the perceived heat sensation, and the greater the air heat capacity in a high-humidity environment, making it easier for heat to accumulate. Positively correlated with the weather amplification effect; the higher the wind speed, the stronger the air exchange near the ground, the easier it is for sensible heat to be carried away and diffused, and the lower the risk of a heat island effect. Through (1- The form of ) is negatively correlated with the weather amplification effect. This embodiment of the invention unifies the physical influence of the three meteorological elements into a dimensionless amplification coefficient through the above-described weighted coupling method, and uses a constant 1 as a baseline term to ensure that when all meteorological elements are in a "non-amplified" state, The value remains 1, and there will be no non-physical reduction of the artificial heat source.

[0113] For example, during a typical hot summer afternoon, the temperature =36℃, relative humidity =80%, wind speed =1.0 m / s. Taking a reference temperature of 28℃, a high-heat reference of 38℃, a reference humidity of 65%, a high-humidity reference of 90%, and a reference wind speed of 3 m / s, then... =(36-28) / (38-28)=0.8、 =(80-65) / (90-65)=0.6、 =1 / 3=0.33. Take... =0.5、 =0.2、 =0.3, then =1+0.5×0.8+0.2×0.6+0.3×(1-0.33)=1+0.4+0.12+0.20=1.72, meaning that under typical hot, humid, and low-wind conditions, the weather pattern is approximately 72% larger; conversely, under cool and windy conditions... =27℃ =55%, =4 m / s, =0、 =0、 =1, =1+0+0+0=1, meaning no amplification.

[0114] By cooperating with the aforementioned heat source calculation module 102, cold source calculation module 103, cold source correction module 104 (including extended cold source unit and cold source distance adjustment unit), and weather amplification coefficient calculation module 105, the embodiments of the present invention can produce the following technical effects:

[0115] First, the synergy between the time-based functionalization of heat sources and the moisture state-based functionalization of cold sources: By dividing the "heat source" into time-based parameters according to building functions, we can obtain... Simultaneously, the "cold source" is dynamically unfolded according to its moisture state, resulting in... Both fall within the 0-1 range and have completely consistent time resolution, allowing the crucial question of "when and where the heat source-cold source mismatch exists" to be accurately captured on a unified dimension and time axis. For example, if peak nighttime energy consumption in residential areas coincides with the cooling failure of drought-stricken green spaces, the two will work together to create a significant nighttime imbalance, a phenomenon that is difficult to identify in traditional static assessments.

[0116] Second, the synergy of distance attenuation, saturation constraints, and dynamic adjustment of building enclosure: In the calculation of standardized cold source supply, exponential distance attenuation is used to characterize the spatial limitations of cold source propagation; a saturation normalization function is used to avoid the linear accumulation of cold source capacity with the amount of green space; and then, building enclosure is used... By dynamically compressing the basic radius of influence, the three factors work together to ensure that the spatial distribution of cold source supply conforms to physical facts (obstructed by street valleys) and avoids numerical divergence (constrained by saturation), thereby generating a spatially continuous and smooth cold source field with a reasonable and controllable magnitude, providing high-quality input for subsequent indexed comparisons.

[0117] Third, the complementarity of green space cooling sources and water body cooling sources: by expanding the introduction of cooling source units, the water body cooling source... With green space cold source The weighted average is used to calculate the overall cooling supply. This ensures that waterfront areas with low green space ratios (such as riverside / lakeside commercial areas) are not misclassified as high-risk areas; when data conditions are limited, the water body item is automatically reset to zero, and the green space cold source is used for calculations independently, thus achieving a highly robust modular design that "activates when data is available and degrades when data is missing".

[0118] Fourth, the coupling of meteorological amplification and heat source-cold source: weather amplification coefficient. The effects of three meteorological elements—temperature, humidity, and wind speed—are integrated and acted upon in a product form. In this way, even if the intensity of anthropogenic heat sources and the cold sources of green spaces are the same, the risk level of the urban heat island will be naturally differentiated under different meteorological conditions (such as the hot and dry summer season versus the cool and windy post-transit period), avoiding the identification error of "same source, same cooling but different risks" caused by simply treating meteorological conditions as exogenous variables.

[0119] The output module 106 is used to couple the intensity of artificial heat sources, the supply of cold sources in green spaces, and the weather amplification factor to calculate the heat source-cold source imbalance index, and to classify the heat island risk level and identify the risk type based on the heat source-cold source imbalance index, thereby generating spatial visualization results.

[0120] The formula for calculating the heat source-cold source imbalance index is as follows:

[0121] ;

[0122] in, Representing spatial units At the assessment time The heat source-cold source imbalance index is a dimensionless quantity with a value greater than or equal to 0. This indicates the intensity of the aforementioned standardized artificial heat source; This represents the aforementioned weather amplification factor; This indicates that the aforementioned standardized green space cooling source supply (which can be replaced by a comprehensive cooling supply when the extended cooling source unit is activated) is being used. ); This represents the smoothing constant, which is a dimensionless quantity with a value range of 0.05 to 0.2, preferably 0.05 to 0.1.

[0123] It should be understood that the smoothing constant Its purpose is to avoid the supply of cold source. The numerical overflow caused by the denominator being zero when approaching 0, and its physical meaning corresponds to the background heat dissipation capacity of the city other than green spaces and water bodies (such as free air convection, building material albedo, shading, etc.). A smaller value (e.g., 0.05) indicates weaker background heat dissipation, and the heat source-cold source imbalance index is more sensitive to the lack of a cold source; when... When a larger value is taken (such as 0.2), the sensitivity to the lack of cold source is reduced accordingly.

[0124] It should be understood that the heat source-cold source imbalance index It is a dimensionless relative risk index and does not represent the actual temperature rise (Celsius). Its purpose is to sort, classify, warn, and map the heat island risk of different spatial units and at different assessment times, thereby supporting the dynamic assessment and governance decision-making of urban heat islands.

[0125] Specifically, the classification of heat island risk levels can be based on The quantile threshold is used for example: Units located above the 95th percentile of the study area are classified as high-risk, units located between the 80th and 95th percentiles are classified as medium-high-risk, units located between the 50th and 80th percentiles are classified as medium-risk, and units located below the 50th percentile are classified as low-risk. However, this embodiment of the invention is not limited to these categories.

[0126] Specifically, risk type identification can be based on The time period exceeding the threshold and its corresponding dominant building functions are considered. preset threshold The range of values ​​is the same time period of the same day in the study area. 80th to 95th quantiles:

[0127] (1) Residential nighttime imbalance type: when the spatial unit Residential building function ratio Exceeding the preset threshold (Value range 0.5~0.7, preferably 0.6), and its value during nighttime hours (e.g., 19:00~24:00). Exceeding the preset threshold If so, it is identified as a residential nighttime imbalance type, corresponding to the risk of a nighttime community heat island;

[0128] (2) Daytime imbalance in office space: when the space unit Office building function ratio Exceeding the preset threshold (Value range 0.5~0.7, preferably 0.6), and its value during daytime hours (e.g., 9:00~18:00). Exceeding the preset threshold If so, it is identified as an imbalanced daytime work schedule;

[0129] (3) Commercial delay imbalance type: when spatial units Commercial building function ratio Exceeding the preset threshold (Value range 0.4~0.6, preferably 0.5), and its value during the afternoon to nighttime period (e.g., 14:00~22:00). Exceeding the preset threshold If so, it is identified as a business delay imbalance type;

[0130] (4) Industrial imbalance type: when spatial units Industrial building function ratio Exceeding the preset threshold (Value range 0.4~0.6, preferably 0.5), and its value across multiple time periods throughout the day (more than 4 consecutive evaluation times). Continuously exceeding the preset threshold If so, it is identified as an industrial persistent imbalance type;

[0131] (5) Green space moisture attenuation type: when spatial unit green space ratio Higher than the preset threshold (Value range 0.2~0.4, preferably 0.3) but the moisture state coefficient Below the preset threshold (Value range 0.2~0.4, preferably 0.3), and Exceeding the preset threshold If the risk is identified as "green space moisture depletion type", corresponding to "green space failure type" risk, it indicates that irrigation scheduling needs to be strengthened.

[0132] (6) Mixed-function imbalance: When the proportion of any building function in spatial unit i does not exceed the corresponding threshold mentioned above, but its Still exceeds the preset threshold If the imbalance is identified as a mixed-function type, it corresponds to the thermal risk caused by the combined effects of multiple building functions. When addressing this issue, a comprehensive diagnosis should be conducted based on the specific building function and structure of the unit.

[0133] For example, in a city, the assessment period is at 2:00 PM, within the study area. The 80th percentile is 3.5 and the 95th percentile is 5.2. (This refers to a unit in an old residential community.) =0.75 (exceeding) =0.6), at 22:00 the night this unit =4.8 (exceeding) A value of 4.0 indicates a residential nighttime imbalance type; a unit in an office park in a high-tech zone. =0.68 (exceeding) =0.6), at 15:00 during the day =5.5 (exceeding) (Value 5.0), identified as daytime imbalance type in office work; a large suburban park unit =0.45 (exceeding) =0.3), but due to long-term lack of irrigation, =0.15 (lower than) =0.3), =4.3 (exceeding) (Value 4.0) is identified as a green space moisture depletion type, and the system gives a treatment prompt of "It is recommended to start emergency irrigation".

[0134] It should be understood that differentiated mitigation measures can be taken for the different types of heat island risks mentioned above: for example, the nighttime imbalance in residential areas can be mitigated by promoting cool roofs and nighttime ventilation designs; the daytime imbalance in offices can be mitigated by facade shading and staggered energy use; and the water depletion in green spaces can be mitigated by strengthening summer irrigation scheduling. The embodiments of this invention are not limited to these.

[0135] Specifically, spatial visualization results can be presented in the form of GIS maps, heat maps, and time-series animations. For example, using grid cells as the base map, each cell can be gradient-colored according to its heat island risk level, and risk type labels can be overlaid. The hourly variation curve provides city managers with intuitive and interactive decision support. The specific implementation method of visualization is a conventional technology in this field and will not be elaborated here.

[0136] like Figure 5 The image shown is a heat map of the urban heat island risk level in a certain area at the first moment. Figure 5 Characterized by mild meteorological conditions, high green coverage, and low artificial heat source coefficient. For example... Figure 6 The image shown is a heat map of the urban heat island risk level in a certain region at the second time point. Figure 6 The eastern spatial unit exhibits a high intensity of artificial heat sources (possibly due to the large proportion of commercial and transportation hub buildings). For example... Figure 7 The image shown is a heat map of the urban heat island risk level in a certain region at the third time point. Figure 7 This indicates a general warming trend, with high-risk clusters on the eastern side. For example... Figure 8 The image shown is a heat map of the urban heat island risk level in a certain region at the fourth time point. Figure 8 This indicates that the western side benefits from the cold source term. For example... Figure 9 The image shown is a heat map of the urban heat island risk level in a certain region at the fifth moment. Figure 9 This indicates a persistent high risk on the eastern side, leading to a decline in the effectiveness of the cold source. For example... Figure 10 The image shown is a heat map of the urban heat island risk level in a certain region at time 6. Figure 10 This indicates an increased risk on the east side, while the formation of a connected cold source in the middle provides benefits.

[0137] like Figure 3 As shown, the system also includes a graded triggering module 107, which is used to obtain the heat source-cold source imbalance index and the corresponding heat island risk level of each space unit within the sliding historical window. If the heat island risk level is lower than the preset safety level and the change of the heat source-cold source imbalance index at the same historical assessment time does not exceed the threshold, the update frequency is reduced.

[0138] It should be understood that urban heat island dynamic simulation systems need to balance timeliness and computational cost in practical engineering applications: for spatial units with low heat island risk and relatively stable heat source-cold source relationships, frequent high-resolution calculations are neither practically meaningful nor do they consume a lot of computing power; while for spatial units with high heat island risk or rapidly changing conditions, it is necessary to increase the update frequency and provide timely warnings. Therefore, this embodiment of the invention introduces a hierarchical triggering module to adaptively schedule the calculation update frequency for different spatial units and different time periods.

[0139] Specifically, the working process of the hierarchical triggering module 107 includes:

[0140] First, for each spatial unit The result output module 106 retrieves the result within the sliding history window. The sequence and corresponding heat island risk level. The sliding historical window is defined by the window length D and the number of daily assessment times M: the window length D represents the number of days to backtrack, ranging from 3 to 14 days, preferably 7 days; the number of daily assessment times M represents the number of times the unit is sampled each day, ranging from 1 to 24, preferably 24 (i.e., once per hour). When the window length D=7 and M=24, a total of 168 data points are obtained for this unit within the sliding historical window. Sampled values. Subsequent comparisons of "historical same assessment time" refer to the D sampled values ​​in the past D days that correspond to the hour position of the current assessment time t;

[0141] Second, determine whether the current heat island risk level of the unit is lower than the preset safety level (the value range is "low risk" to "medium risk", with "medium risk" preferred), and at the same historical assessment time, calculate its risk level. The magnitude of the change (e.g., standard deviation or the difference between the maximum and minimum values), and compared with a preset change threshold (the value range is the historical value of this unit). Compare with the average of 15%~30%, preferably 20%;

[0142] Third, if the heat island risk level is lower than the preset safety level and the historical change range does not exceed the threshold, it indicates that the unit is in a stable low-risk state, so its update frequency is reduced (for example, from a 1-hour step to a 3-6 hour step, or the previous calculation result is directly used when the data has not changed).

[0143] For example, in a low-density residential area in the suburbs, the same time period over the past 7 days... The sequence is {1.2,1.3,1.1,1.2,1.3,1.2,1.2}, with a mean of 1.21. The difference between the maximum and minimum values ​​is only 0.2, which is far below the 20% threshold of the mean (0.24). Furthermore, the current level is "low risk". The update frequency of this unit has been automatically extended from 1 hour to 3 hours.

[0144] like Figure 4 As shown, the result output module 106 also includes a continuous identification unit 1061, which is used to obtain the heat source-cold source imbalance index at continuous evaluation time, and calculate the growth rate and growth acceleration of the heat source-cold source imbalance index at adjacent evaluation time. If the growth rate and growth acceleration both exceed the threshold, an early warning measure is triggered.

[0145] It should be understood that the time of a single evaluation It can only reflect the current instantaneous level of heat island risk and cannot capture the rapid accumulation trend of risk; while for some extreme high-temperature events or sudden heat island events, it is often accompanied by The growth is rapid and accelerating. Therefore, this embodiment of the invention introduces a continuous recognition unit 1061 to identify the growth rate from two dimensions: first-order change (growth rate) and second-order change (growth acceleration). We will conduct joint monitoring of dynamic trends to enhance early warning capabilities.

[0146] Specifically, the operation of the continuous identification unit 1061 includes:

[0147] First, obtain spatial units from the result output module 106. During continuous evaluation time , , The heat source-cold source imbalance index;

[0148] Second, calculate the growth rate of adjacent evaluation times. ;

[0149] Third, calculate the growth acceleration between adjacent evaluation times. ;

[0150] Fourth, With the preset speed threshold Compare( The value range is the history of this unit. The 90th quantile (preferably 0.5~1.0) will be... Compared with the preset acceleration threshold Compare( The value range is the history of this unit. The 90th quantile, preferably 0.2~0.5); if Greater than and Greater than If both conditions are met, the unit is determined to be in a phase of rapid accumulation of heat island risk, triggering early warning measures. These measures may include, but are not limited to: pushing early warning information to the city management platform, issuing high-temperature warnings to the public, automatically initiating emergency irrigation, and temporarily adjusting energy consumption scheduling. This embodiment of the invention is not limited to these measures.

[0151] The system periodically calibrates model parameters using locally measured data. Data available for calibration includes meteorological data, manual inspection results, resident heat complaint records, garden irrigation records, and high-temperature health event data, among others. Calibration content includes the heat source coefficient of building functions, the cooling coefficient of vegetation types, the moisture state coefficient of green spaces, the radius of influence of the foundation, the weather amplification factor, and the threshold for the urban heat island risk level. Through continuous calibration, the system can adapt to different cities, seasons, and management scenarios.

[0152] For slow variables such as building function data and green space data (with a change cycle typically lasting months or years), the data acquisition module 101 monitors their hash values ​​or version numbers. When the data source is not updated, the heat source calculation module 102 and the cold source calculation module 103 (the basic part) directly reuse the previous calculation results, only updating fast variables (with a change cycle on the hourly level) that are affected by meteorological conditions, such as the moisture state coefficient and the weather amplification coefficient. When the data source is updated (e.g., urban planning adjustments, new green spaces), the corresponding slow variables are recalculated. By combining the pre-calculation of slow variables with the real-time calculation of fast variables, the overall computing power consumption of the system can be significantly reduced while ensuring the timeliness of the assessment.

[0153] By combining the hierarchical triggering module and the continuous identification unit, the embodiments of this application can produce the following technical effects:

[0154] The tiered triggering module redistributes computing resources in the "spatial" dimension: releasing computing power from stable, low-risk areas and concentrating it on high-risk or rapidly changing areas; the continuous identification unit jointly monitors dynamic trends in the "temporal" dimension—through... and The dual threshold discrimination can be used to... Before the preset security level of the tiered trigger module is reached, potential risks that are rapidly accumulating are identified in advance. The two work together to form a two-layer risk monitoring mechanism of "spatial dynamic scheduling + time trend early warning", which enables the present invention to have both low computing power consumption and high early warning timeliness.

[0155] In summary, this application has the following technical effects:

[0156] First, without relying on satellite surface temperature data, the assessment of urban heat islands is transformed from "temperature result observation" to "dynamic imbalance simulation of heat source-cold source", which realizes the physical reconstruction of the heat island formation mechanism and makes up for the shortcomings of existing technologies that post-event observations cannot support forward-looking interventions.

[0157] Second, by combining methods such as time-based building functions, state-based green space cold source moisture, cold source space attenuation and saturation constraints, dynamic correction of building enclosure radius, and comprehensive cold source expansion (green space + water body), a dynamic risk index for urban heat island with clear mechanism, calculable, verifiable and implementable is constructed.

[0158] Third, through the hierarchical triggering module and continuous identification unit, adaptive monitoring, scheduling and early warning of spatial units with different risk levels and different trends are realized, which significantly reduces computing power consumption while improving the timeliness of early warning.

[0159] Fourth, the system adopts a modular design, and each module can be deployed and upgraded independently. When some data (such as water bodies, irrigation, building height, etc.) are temporarily unavailable, only the green space basic cold source module can be used as a simplified version. After the data is complete, advanced modules such as extended cold source and enclosure adjustment can be used, which has good engineering adaptability.

[0160] Example 2

[0161] like Figure 11 As shown in the figure, this embodiment introduces a dynamic simulation method for the urban heat island effect based on multi-source data fusion, including the following steps:

[0162] S201. Obtain building function data, green space basic cooling capacity, water supply data and meteorological data for each spatial unit;

[0163] S202. Obtain the area of ​​various types of buildings from the building function data, calculate the proportion of various building functions, and sum the heat source coefficients of the preset building function time periods to obtain the standardized artificial heat source intensity of each space unit at different evaluation times.

[0164] S203. Quantify the water supply data into a green space water state coefficient, and multiply it by the green space basic cooling capacity to calculate the actual green space cooling capacity;

[0165] S204. Based on the distance between the surrounding green space and the current spatial assessment unit within the preset basic influence radius, the actual green space cooling capacity is corrected to obtain a standardized green space cooling source supply.

[0166] S205. Feature extraction and weighted coupling of meteorological data are performed to obtain the weather amplification coefficient of spatial units at different assessment times;

[0167] S206. The heat source-cold source imbalance index is calculated by coupling the intensity of artificial heat sources, the supply of cold sources in green spaces, and the weather amplification factor. Based on the heat source-cold source imbalance index, the heat island risk level is classified and the risk type is identified, and spatial visualization results are generated accordingly.

[0168] This embodiment has the same beneficial effects as Embodiment 1.

[0169] Example 3

[0170] This embodiment describes a computing device, including a memory 301 and a processor 302.

[0171] The memory is used to store the computer program; the processor is used to execute the computer program to implement the steps of the aforementioned dynamic simulation method for the urban heat island effect based on multi-source data fusion.

[0172] This embodiment has the same beneficial effects as Embodiment 1.

[0173] The technical scope of this invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of this invention, and all such modifications and variations should fall within the protection scope of this invention.

Claims

1. A dynamic simulation system for urban heat island effect based on multi-source data fusion, characterized in that, include: The data acquisition module is used to acquire building function data, green space basic cooling capacity, water supply data and meteorological data for each spatial unit; The heat source calculation module is used to obtain various building areas from building function data and then calculate the proportion of various building functions. It also combines the preset building function time period heat source coefficients to obtain the standardized artificial heat source intensity of each space unit at different evaluation times. The cold source calculation module is used to quantify the moisture replenishment data into a green space moisture state coefficient, and multiply it by the basic cooling capacity of the green space to calculate the actual cooling capacity of the green space. Specifically, quantifying the moisture replenishment data into the green space moisture state coefficient includes the following steps: Based on green space type and meteorological data, different weights are assigned to the first-cycle precipitation data, second-cycle precipitation data, and irrigation data in the water replenishment data, and then a weighted calculation is performed. The weighted calculation result is then normalized by the ratio of the weighted calculation result to the reference evaporation to obtain the green space water state coefficient. Among them, the weight of the first-cycle precipitation data is greater than that of the second-cycle precipitation data. The first-cycle precipitation data, second-cycle precipitation data, irrigation data, and reference evaporation are obtained from the water replenishment data in the data acquisition module. The first cycle is a short cycle, and the second cycle is a long cycle. The cold source correction module is used to correct the actual green space cooling capacity based on the distance between the surrounding green space and the current spatial assessment unit within the preset basic influence radius, so as to obtain a standardized green space cold source supply. The weather amplification factor calculation module is used to extract features from meteorological data and weighted couple them to obtain the weather amplification factor of spatial units at different evaluation times; The results output module is used to couple the intensity of artificial heat sources, the supply of cold sources in green spaces, and the weather amplification factor to calculate the heat source-cold source imbalance index, and to classify the heat island risk level and identify the risk type based on the heat source-cold source imbalance index, thereby generating spatial visualization results. The formula for calculating the heat source-cold source imbalance index is as follows: ； in, This indicates the heat source-cold source imbalance index; Indicates the standardized intensity of an artificial heat source; Indicates the weather amplification factor; This indicates a standardized supply of cooling sources for green spaces; This represents the smoothing constant.

2. The urban heat island effect dynamic simulation system based on multi-source data fusion according to claim 1, characterized in that, In the data acquisition module, building function data includes at least residential, office, commercial, industrial, public service facilities, and transportation hubs; The basic cooling capacity of green space is calculated by multiplying the green space ratio and the cooling coefficient. The green space ratio is obtained by the data acquisition module, and the cooling coefficient is obtained by matching the green space type obtained by the data acquisition module with the preset vegetation type cooling coefficient.

3. The urban heat island effect dynamic simulation system based on multi-source data fusion according to claim 1, characterized in that, The cold source correction module also includes an extended cold source unit. The extended cold source unit is used to determine whether there is a water body in the current space unit that exceeds the preset area. If so, it obtains the water body area and water body distance in the current space unit and the preset influence range from the data acquisition module, and then calculates the water body cold source supply. Based on this, the standardized green space cold source supply is corrected to obtain the comprehensive cooling supply, which participates in the calculation of the heat source-cold source imbalance index.

4. The urban heat island effect dynamic simulation system based on multi-source data fusion according to claim 1, characterized in that, The weather amplification factor calculation module specifically includes: The data acquisition unit is used to obtain the temperature, relative humidity, and wind speed at the corresponding assessment time from meteorological data and to standardize them. The data calculation unit is used to input standardized temperature, relative humidity and wind speed into the weather amplification model to obtain the weather amplification factor; The weather amplification model is constructed based on the positive correlation between air temperature and relative humidity and the weather amplification effect, and the negative correlation between wind speed and the weather amplification effect.

5. The urban heat island effect dynamic simulation system based on multi-source data fusion according to claim 1, characterized in that, The cold source correction module also includes a cold source distance adjustment unit, which is used to calculate the building enclosure degree based on the building height and density between the surrounding green space and the current spatial assessment unit within the basic influence radius, and dynamically adjust the basic influence radius accordingly. The actual green space cooling capacity is corrected by using the distance between the surrounding green space and the current spatial assessment unit within the dynamically adjusted basic influence radius. Among them, the building enclosure degree is negatively correlated with the foundation influence radius; the foundation influence radius is obtained based on the green space ratio and green space type of the spatial unit; The building height and density are obtained by the data acquisition module.

6. The urban heat island effect dynamic simulation system based on multi-source data fusion according to claim 1, characterized in that, The system also includes a graded triggering module, which is used to obtain the heat source-cold source imbalance index and the corresponding heat island risk level of each spatial unit within the sliding historical window. If the heat island risk level is lower than the preset safety level and the change of the heat source-cold source imbalance index at the same historical assessment time does not exceed the threshold, the update frequency is reduced.

7. A dynamic simulation method for urban heat island effect based on multi-source data fusion, applied to the dynamic simulation system for urban heat island effect based on multi-source data fusion as described in any one of claims 1-6, characterized in that, The method includes the following steps: Acquire building function data, green space basic cooling capacity, water supply data, and meteorological data for each spatial unit; The area of ​​various buildings is obtained from the building function data, and the proportion of various building functions is calculated. The standardized artificial heat source intensity of each space unit at different evaluation times is obtained by summing the heat source coefficients of the preset building function time periods. The water supply data is quantified into a green space water state coefficient, which is then multiplied by the green space basic cooling capacity to calculate the actual green space cooling capacity. Based on the distance between the surrounding green space and the current spatial assessment unit within the preset basic influence radius, the actual green space cooling capacity is corrected to obtain a standardized green space cooling source supply. Feature extraction and weighted coupling of meteorological data yield the weather amplification coefficient of spatial units at different assessment times; The heat source-cold source imbalance index is calculated by coupling the intensity of artificial heat sources, the supply of cold sources in green spaces, and the weather amplification factor. Based on the heat source-cold source imbalance index, the heat island risk level is classified and the risk type is identified, thereby generating spatial visualization results.

8. A computing device, characterized in that, include: Memory, used to store computer programs; A processor is configured to execute the computer program to implement the steps of the dynamic simulation method for urban heat island effect based on multi-source data fusion as described in claim 7.

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