Urban street environment optimization decision support method and system

Abstract

The invention relates to the technical field of public service management, in particular to an urban street environment optimization decision support method and system, and the method comprises the following steps: collecting behavior data, calculating a difference value to generate a thermodynamic gradient index, adjusting the weight to generate a pollution exposure correction coefficient if the difference value is out of limit, inputting terrain and building parameters into a ray tracing algorithm to construct a reflection path, screening a minimum sound energy return path to generate a geometric configuration parameter, calculating a difference value between a flow amplification and a shielding utilization rate to generate a greening regulation and control instruction, dividing areas according to a correction coefficient, and matching the instruction to generate an environment intervention deployment sequence. According to the method, a thermal gradient index correction pollution exposure risk evaluation system is constructed, human-centered intervention priorities are established, a ray tracing algorithm is utilized to analyze a geometrical relationship and optimize a component form, sound energy reflection interference is reduced, adaptive deformation of greening facilities is driven, and a physical space and dynamic behavior coupling mechanism is constructed. And the environment optimization precision is improved.

Description

Technical Field

[0001] This invention relates to the field of public service management technology, and in particular to a decision support method and system for optimizing urban street environment. Background Technology

[0002] The field of public service management technology mainly covers the use of information technology to coordinate and schedule the planning, execution, and management of administrative management, social public service resources, and urban infrastructure construction. It aims to assist relevant departments in completing planning, administrative tasks, and municipal management through data integration. One example is the traditional decision support method for optimizing urban street environments, which involves municipal planners carrying laser rangefinders, measuring tapes, and cameras to conduct on-site surveys of target streets. They manually measure physical parameters such as road width, building boundary spacing, and green belt area, recording the collected data by hand on paper survey forms. This data is then entered into spreadsheet software on a desktop computer for archiving. Computer-aided design software is used to create two-dimensional line drawings reflecting the current state of the street. Finally, designers manually annotate and modify the printed drawings to form a specific renovation and construction plan.

[0003] Current urban street environment optimization decisions rely on manual surveys and two-dimensional drawings, which only record static physical parameters while ignoring dynamic behavioral characteristics such as the duration of people's stay and activity fluctuations. Furthermore, two-dimensional drawings cannot simulate the complex physical reflection effects of the combination of street longitudinal sections and building facades on sound wave propagation. This results in a lack of quantitative analysis of pollution exposure risks and sound energy interference paths in the decision-making process, making it difficult to match the actual spatiotemporal distribution patterns of the population with the environmental governance measures. There is a mismatch between environmental governance measures and the actual exposure needs of the public, and it is impossible to effectively solve dynamic environmental problems such as noise superposition and heat island accumulation. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a decision support method and system for optimizing urban street environments.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A decision support method for optimizing urban street environment includes the following steps: S1: Collect the average dwell time, travel distance and fluctuation frequency of each person, perform normalized multiplication on the average dwell time, travel distance and fluctuation frequency to generate unit heat density, calculate the difference of unit heat density between adjacent time periods to generate heat gradient index, if the heat gradient index exceeds the preset threshold, increase the weight of the average concentration of pollutants to generate pollution exposure correction coefficient. S2: Input the street terrain elevation difference, ground slope parameters and building facade angle deviation into the ray tracing algorithm to construct a set of reflection paths, calculate the difference between the acoustic reflectivity and acoustic absorption coefficient of the building interface, and if the difference exceeds the interference benchmark value, then traverse the shield depth, window sill protrusion and component rotation angle to filter the minimum acoustic energy return path combination and generate geometric configuration parameters. S3: Collect the effective shading area, calculate the increase rate of pedestrian flow per unit heat density, calculate the difference between the increase rate of pedestrian flow and the utilization rate of the effective shading area, and if the utilization rate difference exceeds the set threshold, increase the extension length of green plants and the orientation angle of dense planting, and generate greening control instructions. S4: Divide the intervention demand areas according to the pollution exposure correction coefficient, match the intervention demand areas with the geometric configuration parameters and the greening control instructions, and generate an environmental intervention deployment sequence.

[0006] As a further aspect of the present invention, the pollution exposure correction coefficient includes a correction weighting factor and an exposure risk level determination value; The specific geometric configuration parameters are: shield depth setting value, windowsill protrusion setting value, and component rotation angle setting value; The greening control instructions include target values ​​for the projection extension length and target values ​​for the dense planting orientation angle; The environmental intervention deployment sequence specifically refers to the physical component adjustment actions, environmental early warning levels, and intervention execution sequence.

[0007] As a further aspect of the present invention, step S1 specifically comprises: S11: The average dwell time per person, the moving distance, and the fluctuation frequency within a preset time period are obtained through a distributed sensor network. The original data collected is processed to be dimensionless using the range transformation method to eliminate the influence of different dimensions on data fusion and generate standardized duration vector, distance vector, and frequency vector. S12: Perform a weighted multiplication operation on the duration vector, the distance vector, and the frequency vector to construct a composite index reflecting the intensity and dynamic change characteristics of crowd gathering in street space, and generate the unit thermal density; S13: Perform time-series slicing of the unit thermal density according to a preset time step, calculate the slope of thermal density change between the current time slice and the previous time slice, quantify the degree of abrupt change in the population aggregation state, and generate the thermal gradient index.

[0008] As a further aspect of the present invention, the process of generating the pollution exposure correction coefficient specifically includes: S14: Obtain the thermal gradient index and the preset gradient safety threshold, determine whether the dynamic changes of the crowd gathering are in the non-steady-state range, and if the thermal gradient index is greater than the gradient safety threshold, trigger the pollution weight adjustment mechanism to extract the basic pollution factor concentration values ​​in the current environment. S15: Based on the influence weight of the thermal gradient index on the concentration value of the basic pollutant, the influence of the thermal gradient index on the concentration value of the basic pollutant is nonlinearly amplified, and the comprehensive environmental pressure correction value is calculated using the exponential correction model to generate the pollution exposure correction coefficient. The formula for calculating the pollution exposure correction factor is as follows: ; in, This represents the pollution exposure correction factor. This represents the preset basic environmental weight constant. The thermal gradient exponent is represented by the calculated value. Represents the gradient safety threshold, This represents the gradient effect amplification factor. This represents the concentration value of the aforementioned basic pollutant. The growth coefficient of the concentration index.

[0009] As a further aspect of the present invention, the process of constructing the reflection path set and calculating the difference value is as follows: S21: Obtain the elevation difference of the street terrain to construct a three-dimensional landform model of the street canyon, combine the ground slope parameter to define the incident angle correction amount when the sound wave is reflected on the ground, and use the building facade angle deviation to establish an irregular scattering surface model of the building surface. S22: Input the sound source location and the receiver location into the ray tracing algorithm to simulate the multi-order reflection propagation process of sound waves between the three-dimensional terrain model and the irregular scattering surface model, trace the propagation trajectory of each effective sound ray, and generate the set of reflection paths; S23: For each path in the set of reflection paths, extract the material properties of the collision point, calculate the energy reflection ratio and energy absorption ratio of the material for a specific frequency of sound waves, subtract the two to obtain the energy residue characteristics of a single reflection, and generate the difference value between the sound wave reflectivity of the building interface and the sound wave absorption coefficient.

[0010] As a further aspect of the present invention, the process of filtering the geometric configuration parameters specifically includes: S24: Obtain the difference value and the interference reference value. If the difference value exceeds the allowable range, activate the parameter optimization iteration loop and set the initial search space and step size of the shield depth setting value, the windowsill protrusion setting value and the component rotation angle setting value. S25: In each iteration, combine the current parameters of the shield, windowsill and rotating component, update the geometry of the irregular scattering surface model, recalculate the cumulative acoustic energy of all sound rays reaching the receiving point, and select the parameter combination that can make the cumulative acoustic energy of the receiving point reach the minimum value. S26: Verify whether the building ventilation and lighting performance under the optimal parameter combination meets the constraints. If they do, lock the physical dimension values ​​corresponding to the combination and generate the geometric configuration parameters.

[0011] As a further aspect of the present invention, the calculation process of the utilization rate difference is specifically as follows: S31: Obtain the historical average value of the unit heat density as a baseline, calculate the percentage increase of the heat density in the current period relative to the historical average value, quantify the dynamic change of the street space bearing pressure, and generate the pedestrian flow increase rate; S32: Obtain the existing effective shielding area of ​​the street, calculate the amount of shielding space resources per person based on the current pedestrian traffic data, compare the amount of resources with the preset standard comfort shielding requirements, and calculate the actual usage saturation of the shielding facilities. S33: Map the increase rate of pedestrian traffic to the theoretically required growth rate of the shielding area, calculate the supply and demand gap between the theoretical growth rate and the current actual usage saturation, and generate the utilization rate difference.

[0012] As a further aspect of the present invention, the generation process of the greening control instruction specifically includes: S34: Obtain the utilization rate difference and the set threshold. When the utilization rate difference indicates insufficient shading supply, determine the urgency level of greening form adjustment based on the magnitude of the difference. S35: Calculate the additional projected area required for the plant canopy based on the urgency level, determine the required extension length of the plant branches through geometric inverse calculation, and generate the target value of the projected extension length; S36: Analyze the relationship between the solar incidence angle and the street orientation, calculate the leaf arrangement angle that maximizes the summer shading effect while minimizing the winter obstruction, determine the optimal orientation of the plant community, and generate the target value of the dense planting orientation angle. S37: Encapsulate the target value of the projection extension length and the target value of the dense planting orientation angle into an executable ecological regulation signal to generate the greening control instruction.

[0013] As a further aspect of the present invention, the specific steps of S4 are as follows: S41: Use the pollution exposure correction coefficient to conduct a gridded risk assessment of the street space, identify grid units with high pollution exposure risk and high population density, and mark them as the areas requiring intervention. S42: Extract the geometric configuration parameters and greening control instructions calculated for the intervention demand area, and establish a joint intervention scheme library for physical space transformation and ecological space optimization; S43: Based on the priority of mitigating environmental pressure, the operation items in the joint intervention scheme library are arranged in a time sequence to determine the execution time of the physical component adjustment action, the release conditions of the environmental early warning level, and the intervention execution sequence of each measure, thereby generating the environmental intervention deployment sequence.

[0014] A decision support system for optimizing urban street environment, the system being used to implement the aforementioned decision support method for optimizing urban street environment, the system comprising: The pollution exposure analysis module is used to collect average residence time, travel distance and fluctuation frequency, perform normalized product calculation to generate unit thermal density, and adjust the pollution factor weights based on the comparison results of thermal gradient index and preset threshold to generate pollution exposure correction coefficient. The geometric acoustic optimization module receives street terrain elevation differences, ground slope parameters, and building facade angle deviations. It uses a ray tracing algorithm to calculate the difference between the acoustic reflectivity and acoustic absorption coefficient of the building interface. It also filters the minimum sound energy return path by traversing the depth of the shield, the protrusion of the window sill, and the rotation angle of the components, and generates geometric configuration parameters. The greening ecological regulation module is used to collect the effective shaded area, calculate the increase rate of pedestrian flow per unit heat density and the difference in utilization rate, and adjust the extension length of green plants and the orientation angle of dense planting when the difference exceeds the limit, and generate greening regulation instructions. The intervention deployment decision module is used to divide the intervention demand area according to the pollution exposure correction coefficient, match the area with geometric configuration parameters and greening control instructions, and generate an environmental intervention deployment sequence that includes physical adjustment actions and execution timing.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, a thermal gradient index is constructed to transform dynamic crowd behavior into weights to correct the pollution exposure risk assessment system, establishing a human-centered intervention priority. A ray tracing algorithm is used to analyze the geometric relationship between street topography and building facades, and parametric traversal is used to optimize the shape of shielding and window sill components to reduce sound energy reflection interference from the physical path level. Based on the comparison of traffic increase and shielding utilization rate, green facilities are driven to adapt and deform. A decision-making mechanism coupling physical space and dynamic crowd behavior is constructed to ensure that the intervention strategy meets the actual use needs and improve the accuracy of street environment optimization. Attached Figure Description

[0016] Figure 1 This is the main flowchart of the urban street environment optimization decision support method of the present invention; Figure 2 This is a flowchart illustrating the generation process of the pollution exposure correction factor in this invention. Figure 3 This is a flowchart of the geometric configuration parameter filtering process of the present invention; Figure 4 This is a flowchart illustrating the generation process of greening control instructions in this invention. Figure 5 This is a flowchart for generating the environmental intervention deployment sequence of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the software-based technical solution is described in detail below with reference to system architecture diagrams and embodiments. It should be understood that the specific embodiments described herein are only for explaining the technical solutions of this invention and do not constitute a limitation on the scope of protection.

[0018] In the description of this invention, the system architecture relationships or data processing flows indicated by terms such as "layer," "module," "interface," "data flow," "client," and "server" are all defined based on the architecture diagram or flowchart corresponding to the embodiments. This way of describing is only used to clearly illustrate the logical relationships between the elements in the technical solution, and not to limit the physical deployment form. The term "multiple" includes two or more technical units, including but not limited to multiple data nodes, processing threads, service instances, or functional components and other scalable elements. The specific number is determined according to the actual business scenario and needs to be specifically specified.

[0019] Please see Figure 1 and Figure 2 This invention provides a technical solution: a decision support method for optimizing urban street environment, comprising the following steps: S1: Collect average dwell time, travel distance and fluctuation frequency, perform normalized multiplication on average dwell time, travel distance and fluctuation frequency to generate unit heat density, calculate the difference in unit heat density between adjacent time periods to generate heat gradient index, if heat gradient index exceeds preset threshold, increase the weight of average concentration of pollutants to generate pollution exposure correction coefficient. The specific steps of S1 are as follows: S11: The average dwell time, travel distance and fluctuation frequency per person within a preset time period are obtained through a distributed sensor network. The raw data is then processed to be dimensionless using the range transformation method to eliminate the influence of different dimensions on data fusion and generate standardized duration vector, distance vector and frequency vector. S12: Perform a weighted multiplication operation on the duration vector, distance vector, and frequency vector to construct a composite index reflecting the intensity and dynamic changes of crowd gathering in street space, and generate unit thermal density; S13: Perform time-series slicing of unit thermal density according to a preset time step, calculate the slope of thermal density change between the current time slice and the previous time slice, quantify the degree of abrupt change in the population aggregation state, and generate a thermal gradient index. The process of generating the pollution exposure correction factor specifically includes: S14: Obtain the thermal gradient index and the preset gradient safety threshold, determine whether the dynamic changes of the crowd gathering are in the non-steady state range, and if the thermal gradient index is greater than the gradient safety threshold, trigger the pollution weight adjustment mechanism to extract the basic pollution factor concentration values ​​in the current environment. S15: The influence weight of the thermal gradient index on the concentration values ​​of basic pollutants is nonlinearly amplified, and the comprehensive correction value of environmental pressure is calculated using the exponential correction model to generate the pollution exposure correction coefficient. The formula for calculating the pollution exposure correction factor is as follows: ; in, Represents the pollution exposure correction factor. This represents the preset basic environmental weight constant. This represents the calculated thermodynamic gradient exponent. Represents the gradient safety threshold. This represents the gradient effect amplification factor. Represents the concentration values ​​of basic pollutants. The growth coefficient of the concentration index; The pollution exposure correction factor includes the correction weighting factor and the exposure risk level determination value.

[0020] This embodiment selects a commercial pedestrian street in a city as the monitoring object. The street is 500 meters long and 20 meters wide, and the monitoring period is set to the weekend peak hours from 14:00 to 14:15. First, a sensor network is constructed using 20 distributed sensor nodes (integrating Wi-Fi probes and UWB positioning modules) deployed on street lampposts to acquire raw data of pedestrians in the area at a preset time interval of 5 minutes. During the time interval of 14:00-14:05, the average dwell time per person within the coverage area of ​​a single monitoring point was collected as 12 minutes, the average movement distance per person was 150 meters, and the average position fluctuation frequency per person was 8 times / minute. The aforementioned distributed sensor network refers to a network system in which sensor nodes distributed in different locations in space self-organize through wireless communication technology to collaboratively sense, collect, and process information within the network coverage area. Subsequently, the above raw data is dimensionless processed using the range transformation method to eliminate dimensional differences. An empirical extreme value is set for this street scenario: the maximum dwell time. Minutes, minimum value Minutes; Maximum distance traveled meters, minimum value Meter; maximum fluctuation frequency times / minute, minimum value Times / minute. According to the range transformation formula... The standardized duration vector value is calculated. Distance vector value Frequency vector value .

[0021] Next, a weighted product operation is performed on the generated duration vector, distance vector, and frequency vector to generate the unit heat density. Based on the behavioral characteristics of people in commercial streets, weighting factors are set as follows: duration weight... Distance weight Frequency weight Calculate the unit thermal density. Record the unit heat density calculated in the previous time slice (13:55 to 14:00) as 0.255. Calculate the slope of the heat density change between the current time slice and the previous time slice, i.e., the heat gradient exponent, according to a preset 5-minute time step. Set a gradient safety threshold. This threshold is set based on the upper limit of fluctuations during historical periods of stability. (The calculation is based on...) 0.111 is greater than A value of 0.08 indicates that the population gathering is in a rapidly growing, non-steady-state phase, triggering the pollution weight adjustment mechanism. Simultaneously, the sensor network extracts the concentrations of basic pollutants in the environment, measuring the PM2.5 concentration. .

[0022] Finally, based on the above parameters, the pollution exposure correction coefficient is calculated using an exponential correction model. This step involves the following formulas: .in, Represents the pollution exposure correction factor; The preset basic environmental weight constant is 0.5. This value is determined by the environmental department based on the proportion of days with good or excellent air quality in the street in history, and is used to define the baseline risk level when there is no sudden change in population. The calculated thermal gradient exponent is 0.111. The gradient safety threshold is 0.08; The gradient effect amplification factor is set to 0.2. This parameter reflects the sensitivity of changes in population density to local airflow obstruction and is determined through wind tunnel experiments. The basic pollutant concentration value is 45; The concentration exponential growth coefficient, with a value of 0.01, is used to describe the nonlinear enhancing effect of pollutant concentration on health risk. The logic of multiplication and exponentiation in the formula is as follows: firstly, through... Calculate the excess proportion of the thermal gradient, using... The effect of ventilation obstruction caused by a sudden surge in population was quantified by linear weighting; subsequently, the effect was further quantified using... Calculate the background pressure multiplier of pollutants; finally, multiply both by the basic weights to achieve a comprehensive mapping from physical accumulation to environmental pressure. Substitute the values ​​for calculation: intermediate term 1, i.e., the population factor, is... ; The intermediate term 2, i.e., the pollution factor, is Final result The calculated result of 0.845 is the pollution exposure correction coefficient. This value is significantly higher than the basic weight of 0.5, indicating that under the current conditions of rapid population gathering and certain background pollution, the environmental exposure risk level has been raised from "low risk" to "medium-high risk", and subsequent spatial intervention needs to be initiated immediately.

[0023] Please see Figure 1 and Figure 3 S2: Input the street terrain elevation difference, ground slope parameters and building facade angle deviation into the ray tracing algorithm to construct a set of reflection paths, calculate the difference between the acoustic reflectivity and acoustic absorption coefficient of the building interface, and if the difference exceeds the interference benchmark value, then traverse the shield depth, window sill protrusion and component rotation angle to select the minimum acoustic energy return path combination and generate geometric configuration parameters. The specific process of constructing the reflection path set and calculating the difference value is as follows: S21: Obtain the elevation difference of the street terrain to construct a three-dimensional geomorphological model of the street canyon, combine the ground slope parameter to define the incident angle correction of the sound wave when it is reflected on the ground, and use the building facade angle deviation to establish an irregular scattering surface model of the building surface. S22: Input the location of the sound source and the location of the receiver into the ray tracing algorithm to simulate the multi-stage reflection and propagation process of sound waves between the three-dimensional terrain model and the irregular scattering surface model, track the propagation trajectory of each effective sound ray, and generate a set of reflection paths. S23: For each path in the set of reflection paths, extract the material properties of the collision point, calculate the energy reflection ratio and energy absorption ratio of the material for a specific frequency of sound waves, subtract the two to obtain the energy residue characteristics of a single reflection, and generate the difference value between the sound wave reflectivity and the sound wave absorption coefficient of the building interface. The process of selecting geometric configuration parameters specifically includes: S24: Obtain the difference value and the interference reference value. If the difference value exceeds the allowable range, activate the parameter optimization iteration loop and set the initial search space and step size for the shield depth setting value, the window sill protrusion setting value, and the component rotation angle setting value. S25: In each iteration, combine the current parameters of the shield, window sill and rotating component, update the geometry of the irregular scattering surface model, recalculate the cumulative acoustic energy of all sound rays reaching the receiving point, and select the parameter combination that can make the cumulative acoustic energy of the receiving point reach the minimum value. S26: Verify whether the building ventilation and lighting performance under the optimal parameter combination meets the constraints. If it does, lock the physical dimension values ​​corresponding to the combination and generate geometric configuration parameters. The specific geometric configuration parameters are the setting values ​​for the shield depth, the window sill protrusion, and the component rotation angle.

[0024] Following the high-risk status identified in S1, the system retrieves street geographic information data, obtaining a street elevation difference of 2.5 meters, a ground slope parameter of 3°, and a building facade angle deviation of 5° (inclination relative to the vertical plane). These parameters are then input into a ray tracing algorithm to construct a 3D terrain model and an irregular scattering surface model. The ray tracing algorithm is a calculation method that simulates the propagation path of light or sound waves in three-dimensional space to calculate the distribution of physical fields. In this embodiment, it is specifically used to trace the reflection and scattering trajectories of sound waves between building surfaces and the ground. The sound source point is defined as 0.5 meters above the ground at the street centerline (simulating vehicle tire noise), and the receiving point is 1.5 meters above the ground at ear height. In the simulation, the propagation trajectories of 1000 effective sound rays are traced, generating a set of reflection paths. For a path in the set reflected from a building's glass curtain wall to the receiving point, the system extracts the material properties of that collision point. As shown in Table 1, the system calls the material acoustics database.

[0025] Table 1. Acoustic property parameters of building interface materials: ;

[0026] Referring to Table 1, for the glass curtain wall path, extract the data in the table to calculate the difference value: The interference baseline value was set at 0.60, which was established based on the noise limit in commercial areas in the "Environmental Noise Quality Standard" and represents the minimum requirement for sound energy attenuation. Since the calculated difference value of 0.87 is greater than the interference baseline value of 0.60, the current building interface sound focusing effect is considered significant, necessitating the activation of the parameter optimization iterative loop. The geometric configuration parameter selection process was initiated, setting the shield depth search space to 0.2m to 0.8m with a step size of 0.1m; the window sill protrusion search space to 0.1m to 0.5m with a step size of 0.05m; and the component rotation angle search space to 0° to 45° with a step size of 5°. In the first iteration, a combination of 0.2m shield depth, 0.1m window sill protrusion, and 0° rotation angle was selected. After updating the scattering surface model, ray tracing calculations were performed again, and the cumulative sound energy level at the receiving point was measured to be 75dB. After multiple iterations, when the selected values ​​were a shield depth of 0.6m, a windowsill protrusion of 0.3m, and a component rotation angle of 30°, the perforated aluminum plate material characteristics intervened in the reflection path, causing some strong reflection paths to be scattered or absorbed. The recalculated cumulative sound energy level dropped to 62dB, reaching the minimum value within the search space. Subsequently, the building ventilation performance under this combination was verified. The calculated increase in drag coefficient was 12% (less than the 20% constraint limit), and the decrease in daylight factor was 8% (less than the 15% constraint limit), meeting the physical performance constraints. Therefore, this set of physical dimension values ​​was locked, and the geometric configuration parameters were generated: shield depth 0.6m, windowsill protrusion 0.3m, and component rotation angle 30°.

[0027] Please see Figure 1 and Figure 4 S3: Collect the effective shaded area, calculate the increase rate of pedestrian flow per unit heat density, calculate the difference between the increase rate of pedestrian flow and the utilization rate of the effective shaded area. If the utilization rate difference exceeds the set threshold, increase the extension length of green plants and the orientation angle of dense planting, and generate greening control instructions. The calculation process for the utilization rate difference is as follows: S31: Obtain the historical average value of unit heat density as a baseline, calculate the percentage increase of heat density in the current period relative to the historical average value, quantify the dynamic changes in the pressure on street space, and generate the increase rate of pedestrian flow. S32: Obtain the existing effective shelter area of ​​the street, calculate the per capita shelter space resource based on the current pedestrian flow data, compare the resource amount with the preset standard comfort shelter requirements, and calculate the actual usage saturation of the shelter facilities. S33: Map the growth rate of pedestrian traffic to the theoretically required growth rate of the shielding area, calculate the supply and demand gap between the theoretical growth rate and the current actual saturation level, and generate the utilization rate difference. The process of generating greening control instructions specifically includes: S34: Obtain the utilization rate difference and set a threshold. When the utilization rate difference indicates insufficient shading supply, determine the urgency level of greening form adjustment based on the magnitude of the difference. S35: Calculate the additional projected area required for the plant canopy based on the urgency level, determine the required extension length of the plant branches through geometric inverse calculation, and generate the target value of the projected extension length. S36: Analyze the relationship between the solar incidence angle and the street orientation, calculate the leaf arrangement angle that maximizes the summer shading effect while minimizing the winter obstruction, determine the optimal orientation of the plant community, and generate the target value of the dense planting orientation angle. S37: Encapsulate the target value of the projected extension length and the target value of the dense planting orientation angle into an executable ecological regulation signal to generate greening control instructions; The greening control instructions include target values ​​for the projected extension length and target values ​​for the orientation angle of dense planting.

[0028] After determining the parameters of the hard physical components, the system further implements soft ecological regulation. First, effective shading area data is collected; the total effective shading area provided by the existing tree canopies and shading facilities along the street is... Square meters. Retrieve the unit heat density of 0.366 calculated in step S1, and obtain the historical average heat density of 0.280 for this period as a baseline. Calculate the rate of increase in pedestrian traffic per unit heat density. Next, based on the current real-time monitored pedestrian flow of 1200 people, the amount of sheltered space resources available per person is calculated as follows: Square meters per person. The standard comfort shading requirement is set at 0.5 square meters per person (based on the Summer Human Thermal Comfort Model (PMV)). The aforementioned Summer Human Thermal Comfort Model (PMV) refers to the Predicted Average Voting Model, used to comprehensively evaluate the subjective feeling of the human body towards the thermal environment. In this embodiment, this model is used to inversely deduce the minimum per capita shading area required to maintain a thermally neutral state. The actual utilization saturation of the shading facilities is calculated. This means demand is 1.50 times supply. Mapping the 30.7% increase in pedestrian traffic to the theoretically required growth rate of shaded area, with a mapping coefficient of 1.0, the theoretical growth rate is also 30.7%. Calculate the theoretical required area. Square meters. Calculate the utilization rate difference, i.e., the supply-demand gap ratio. The utilization rate difference threshold was set at 15%. Since 30.7% was greater than 15%, it was determined that the shading supply was severely insufficient, and the green plant parameters were increased.

[0029] Based on the utilization rate difference of 30.7%, the urgency level was determined to be "Level 1 Emergency". Based on this level, the system calculated the additional projected area required for the plant canopy. Square meters. Assuming the number of variable green plant units along the street to be adjusted is 50, then each plant needs to increase its projected area by 2.456 square meters. Through geometric inverse calculation, assuming the tree canopy has a circular projection, according to the formula... Since the basic crown radius already exists, the branch extension length needs to be calculated. If the initial radius is 2 meters, its projected area is 12.56 square meters, then the target area is 15.016 square meters. The calculated target radius needs to reach 2.18 meters, which is the target value for the projected extension length. Meters. Simultaneously, the solar incidence angle was analyzed. The monitoring day was set as the summer solstice, with a local solar altitude angle of 60° and an azimuth angle of 45° west of south at 14:00. The street direction was east-west. To maximize shading, the leaf arrangement angle was calculated. The system simulated the ground shading rate under different leaf angles. The results showed that shading was maximized when the leaf surface normal was parallel to the solar incidence, but winter lighting needed to be considered. After comprehensive calculation, the optimal leaf tilt angle that maximizes summer shading while minimizing winter obstruction was determined to be 45° relative to the horizontal plane, facing southwest. Therefore, a target value for the dense planting orientation angle was generated. That is, the southwest direction. Finally, the target value of the projected extension length of 0.18 meters and the target value of the dense planting orientation angle of 225° are packaged together to generate greening control instructions.

[0030] Please see Figure 1 and Figure 5 S4: Based on the pollution exposure correction coefficient, divide the intervention demand areas, match the intervention demand areas with geometric configuration parameters and greening control instructions, and generate an environmental intervention deployment sequence; The specific steps of S4 are as follows: S41: Use pollution exposure correction coefficients to conduct gridded risk assessments of street spaces, identify grid units with high pollution exposure risk and high population density, and mark them as areas requiring intervention. S42: Extract the geometric configuration parameters and greening control instructions generated for the areas requiring intervention, and establish a joint intervention scheme library for physical space transformation and ecological space optimization; S43: Based on the priority of mitigating environmental pressure, the operational items in the joint intervention program library are arranged in sequence to determine the execution time of physical component adjustment actions, the release conditions of environmental early warning levels, and the intervention execution sequence of various measures, thereby generating an environmental intervention deployment sequence; The environmental intervention deployment sequence specifically refers to the physical component adjustment actions, environmental early warning levels, and the timing of intervention execution.

[0031] This step performs the final deployment based on the calculation results of S1 to S3. First, the pollution exposure correction factor calculated in S1 is used. A grid-based risk assessment of the street space was conducted. The street layout was divided into... The grid consists of 50 cells. Grids with a value greater than 0.80 were defined as "high-risk areas." Five consecutive grid units (numbered G20-G24) located in the middle section of the street, a densely populated area of ​​restaurants, were identified as areas requiring intervention. These areas not only have high population density (i.e., high thermal density) but also significant pollution exposure risks. Parameters calculated for these five grid units were extracted: geometric configuration parameters from S2 (shield depth 0.6m, window sill protrusion 0.3m, component rotation angle 30°) and greening control instructions from S3 (projection extension length 0.18m, dense planting orientation angle 225°). A joint intervention scheme library was established.

[0032] Based on the priority of mitigating environmental pressure, physical component adjustments are set as the highest priority due to their fast response speed and direct aerodynamic effects; greening adjustments are secondary because their effective period is longer. The operation sequence is as follows: 1. T+0s (Immediate Execution): Issue an environmental warning level of "Orange Warning" to the management terminal, prompting pedestrian flow guidance; 2. T+30s (Physical Response): Issue a physical component adjustment command, driving the intelligent building facade components in areas G20-G24 to rotate and extend, adjusting to a shielding depth of 0.6m and an angle of 30° to disperse sound focusing and improve local ventilation; 3. T+60s (Ecological Response): Issue a greening control command, controlling the intelligent planting containers in this area to adjust their orientation to 225° and extending the retractable branch support structure by 0.18m to supplement shading and absorb particulate matter. The final environmental intervention deployment sequence is generated as follows: {Action 1: Orange alert issued; Action 2: Facade components (0.6m, 0.3m, 30°), delayed 30s; Action 3: Greening unit (0.18m, 225°), delayed 60s}. The orderly execution of this sequence ensures rapid adaptive optimization of the spatial environment during periods of high pollution exposure risk.

[0033] A decision support system for optimizing urban street environment, the system being used to execute the aforementioned decision support method for optimizing urban street environment, the system comprising: The pollution exposure analysis module is used to collect average residence time, travel distance and fluctuation frequency, perform normalized product calculation to generate unit thermal density, and adjust the pollution factor weights based on the comparison results of thermal gradient index and preset threshold to generate pollution exposure correction coefficient. The geometric acoustic optimization module receives street terrain elevation differences, ground slope parameters, and building facade angle deviations. It uses a ray tracing algorithm to calculate the difference between the acoustic reflectivity and acoustic absorption coefficient of the building interface. It also filters the minimum sound energy return path by traversing the depth of the shield, the protrusion of the window sill, and the rotation angle of the components, and generates geometric configuration parameters. The greening ecological regulation module is used to collect the effective shaded area, calculate the increase rate of pedestrian flow per unit heat density and the difference in utilization rate, and adjust the extension length of green plants and the orientation angle of dense planting when the difference exceeds the limit, and generate greening regulation instructions. The intervention deployment decision module is used to divide the intervention demand area according to the pollution exposure correction coefficient, match the area with geometric configuration parameters and greening control instructions, and generate an environmental intervention deployment sequence that includes physical adjustment actions and execution timing.

[0034] The above embodiments illustrate preferred embodiments of the present invention. Any equivalent adjustments to the technical solution based on software engineering methods are within the scope of protection, including but not limited to: implementing algorithm logic using different programming languages, refactoring functional modules into services, adjusting data interaction protocols, and optimizing resource scheduling strategies. Any implementation scheme derived from reasonable modifications to the data processing flow, service call chain, or system architecture layer without departing from the core technology of the present invention should be considered within the scope of protection defined by the claims of the present invention.

Claims

1. A decision support method for optimizing urban street environment, characterized in that, Includes the following steps: S1: Collect the average dwell time, travel distance and fluctuation frequency of each person, perform normalized multiplication on the average dwell time, travel distance and fluctuation frequency to generate unit heat density, calculate the difference of unit heat density between adjacent time periods to generate heat gradient index, if the heat gradient index exceeds the preset threshold, increase the weight of the average concentration of pollutants to generate pollution exposure correction coefficient. S2: Input the street terrain elevation difference, ground slope parameters and building facade angle deviation into the ray tracing algorithm to construct a set of reflection paths, calculate the difference between the acoustic reflectivity and acoustic absorption coefficient of the building interface, and if the difference exceeds the interference benchmark value, then traverse the shield depth, window sill protrusion and component rotation angle to filter the minimum acoustic energy return path combination and generate geometric configuration parameters. S3: Collect the effective shading area, calculate the increase rate of pedestrian flow per unit heat density, calculate the difference between the increase rate of pedestrian flow and the utilization rate of the effective shading area, and if the utilization rate difference exceeds the set threshold, increase the extension length of green plants and the orientation angle of dense planting, and generate greening control instructions. S4: Divide the intervention demand areas according to the pollution exposure correction coefficient, match the intervention demand areas with the geometric configuration parameters and the greening control instructions, and generate an environmental intervention deployment sequence.

2. The urban street environment optimization decision support method according to claim 1, characterized in that, The pollution exposure correction factor includes a correction weighting factor and an exposure risk level determination value; The specific geometric configuration parameters are: shield depth setting value, windowsill protrusion setting value, and component rotation angle setting value; The greening control instructions include target values ​​for the projection extension length and target values ​​for the dense planting orientation angle; The environmental intervention deployment sequence specifically refers to the physical component adjustment actions, environmental early warning levels, and intervention execution sequence.

3. The urban street environment optimization decision support method according to claim 2, characterized in that, The specific steps of S1 are as follows: S11: The average dwell time per person, the moving distance, and the fluctuation frequency within a preset time period are obtained through a distributed sensor network. The original data collected is processed to be dimensionless using the range transformation method to eliminate the influence of different dimensions on data fusion and generate standardized duration vector, distance vector, and frequency vector. S12: Perform a weighted multiplication operation on the duration vector, the distance vector, and the frequency vector to construct a composite index reflecting the intensity and dynamic change characteristics of crowd gathering in street space, and generate the unit thermal density; S13: Perform time-series slicing of the unit thermal density according to a preset time step, calculate the slope of thermal density change between the current time slice and the previous time slice, quantify the degree of abrupt change in the population aggregation state, and generate the thermal gradient index.

4. The urban street environment optimization decision support method according to claim 3, characterized in that, The process of generating the pollution exposure correction factor specifically includes: S14: Obtain the thermal gradient index and the preset gradient safety threshold, determine whether the dynamic changes of the crowd gathering are in the non-steady-state range, and if the thermal gradient index is greater than the gradient safety threshold, trigger the pollution weight adjustment mechanism to extract the basic pollution factor concentration values ​​in the current environment. S15: Based on the influence weight of the thermal gradient index on the concentration value of the basic pollutant, the influence of the thermal gradient index on the concentration value of the basic pollutant is nonlinearly amplified, and the comprehensive environmental pressure correction value is calculated using the exponential correction model to generate the pollution exposure correction coefficient. The formula for calculating the pollution exposure correction factor is as follows: in, This represents the pollution exposure correction factor. This represents the preset basic environmental weight constant. The thermal gradient exponent is represented by the calculated value. Represents the gradient safety threshold, This represents the gradient effect amplification factor. This represents the concentration value of the aforementioned basic pollutant. The growth coefficient of the concentration index.

5. The urban street environment optimization decision support method according to claim 2, characterized in that, The specific process of constructing the reflection path set and calculating the difference value is as follows: S21: Obtain the elevation difference of the street terrain to construct a three-dimensional landform model of the street canyon, combine the ground slope parameter to define the incident angle correction amount when the sound wave is reflected on the ground, and use the building facade angle deviation to establish an irregular scattering surface model of the building surface. S22: Input the sound source location and the receiver location into the ray tracing algorithm to simulate the multi-order reflection propagation process of sound waves between the three-dimensional terrain model and the irregular scattering surface model, trace the propagation trajectory of each effective sound ray, and generate the set of reflection paths; S23: For each path in the set of reflection paths, extract the material properties of the collision point, calculate the energy reflection ratio and energy absorption ratio of the material for a specific frequency of sound waves, subtract the two to obtain the energy residue characteristics of a single reflection, and generate the difference value between the sound wave reflectivity of the building interface and the sound wave absorption coefficient.

6. The urban street environment optimization decision support method according to claim 5, characterized in that, The process of filtering the geometric configuration parameters specifically includes: S24: Obtain the difference value and the interference reference value. If the difference value exceeds the allowable range, activate the parameter optimization iteration loop and set the initial search space and step size of the shield depth setting value, the windowsill protrusion setting value and the component rotation angle setting value. S25: In each iteration, combine the current parameters of the shield, windowsill and rotating component, update the geometry of the irregular scattering surface model, recalculate the cumulative acoustic energy of all sound rays reaching the receiving point, and select the parameter combination that can make the cumulative acoustic energy of the receiving point reach the minimum value. S26: Verify whether the building ventilation and lighting performance under the optimal parameter combination meets the constraints. If they do, lock the physical dimension values ​​corresponding to the combination and generate the geometric configuration parameters.

7. The urban street environment optimization decision support method according to claim 2, characterized in that, The calculation process for the utilization rate difference is as follows: S31: Obtain the historical average value of the unit heat density as a baseline, calculate the percentage increase of the heat density in the current period relative to the historical average value, quantify the dynamic change of the street space bearing pressure, and generate the pedestrian flow increase rate; S32: Obtain the existing effective shielding area of ​​the street, calculate the amount of shielding space resources per person based on the current pedestrian traffic data, compare the amount of resources with the preset standard comfort shielding requirements, and calculate the actual usage saturation of the shielding facilities. S33: Map the increase rate of pedestrian traffic to the theoretically required growth rate of the shielding area, calculate the supply and demand gap between the theoretical growth rate and the current actual usage saturation, and generate the utilization rate difference.

8. The urban street environment optimization decision support method according to claim 7, characterized in that, The generation process of the greening control instructions specifically includes: S34: Obtain the utilization rate difference and the set threshold. When the utilization rate difference indicates insufficient shading supply, determine the urgency level of greening form adjustment based on the magnitude of the difference. S35: Calculate the additional projected area required for the plant canopy based on the urgency level, determine the required extension length of the plant branches through geometric inverse calculation, and generate the target value of the projected extension length; S36: Analyze the relationship between the solar incidence angle and the street orientation, calculate the leaf arrangement angle that maximizes the summer shading effect while minimizing the winter obstruction, determine the optimal orientation of the plant community, and generate the target value of the dense planting orientation angle. S37: Encapsulate the target value of the projection extension length and the target value of the dense planting orientation angle into an executable ecological regulation signal to generate the greening control instruction.

9. The urban street environment optimization decision support method according to claim 8, characterized in that, The specific steps of S4 are as follows: S41: Use the pollution exposure correction coefficient to conduct a gridded risk assessment of the street space, identify grid units with high pollution exposure risk and high population density, and mark them as the areas requiring intervention. S42: Extract the geometric configuration parameters and greening control instructions calculated for the intervention demand area, and establish a joint intervention scheme library for physical space transformation and ecological space optimization; S43: Based on the priority of mitigating environmental pressure, the operation items in the joint intervention scheme library are arranged in a time sequence to determine the execution time of the physical component adjustment action, the release conditions of the environmental early warning level, and the intervention execution sequence of each measure, thereby generating the environmental intervention deployment sequence.

10. A decision support system for optimizing urban street environment, characterized in that, The system is used to implement the urban street environment optimization decision support method according to any one of claims 1-9, and the system includes: The pollution exposure analysis module is used to collect average residence time, travel distance and fluctuation frequency, perform normalized product calculation to generate unit thermal density, and adjust the pollution factor weights based on the comparison results of thermal gradient index and preset threshold to generate pollution exposure correction coefficient. The geometric acoustic optimization module receives street terrain elevation differences, ground slope parameters, and building facade angle deviations. It uses a ray tracing algorithm to calculate the difference between the acoustic reflectivity and acoustic absorption coefficient of the building interface. It also filters the minimum sound energy return path by traversing the depth of the shield, the protrusion of the window sill, and the rotation angle of the components, and generates geometric configuration parameters. The greening ecological regulation module is used to collect the effective shaded area, calculate the increase rate of pedestrian flow per unit heat density and the difference in utilization rate, and adjust the extension length of green plants and the orientation angle of dense planting when the difference exceeds the limit, and generate greening regulation instructions. The intervention deployment decision module is used to divide the intervention demand area according to the pollution exposure correction coefficient, match the area with geometric configuration parameters and greening control instructions, and generate an environmental intervention deployment sequence that includes physical adjustment actions and execution timing.

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