CN113987657A - Method for accurately and economically measuring street thermal environment in mobile mode - Google Patents
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Abstract
The invention discloses a method for accurately and economically measuring a street thermal environment in a mobile manner, wherein a representative space in a region to be measured is selected as a measuring point space, and an outdoor thermal environment is measured; calculating a general hot climate index of each measuring point space; classifying the measuring point spaces based on the sky view factor, and calculating the average value of the universal hot climate indexes of different types of measuring point spaces at different moments; determining the accuracy of the universal hot climate index of each measuring point space at each moment; establishing a platform about the accuracy of measuring point space, time and general hot climate indexes; calculating all continuous paths considering all moments in the platform; adopting a multi-objective optimization algorithm, and taking the accuracy and the measuring point space quantity as targets to screen out an optimized path; and carrying out moving observation on the urban thermal environment along the optimized path. The invention is used for solving the limitation of street thermal environment measurement in the prior art and achieving the purpose of improving the accuracy and the economical efficiency of outdoor thermal environment measurement.
Description
Technical Field
The invention relates to the field of urban thermal environment evaluation, in particular to an accurate and economic method for movably measuring a street thermal environment.
Background
High temperature caused by climate change can directly cause negative influence on ecological environment, social economy and human health, and the climate change becomes a hotspot problem in the global environment and health field. Especially in hot weather, the influence of urban heat islands may further impair outdoor thermal comfort, which prevents healthy outdoor activities and brings economic and social problems for sustainable urban development.
The street is a space where people mainly go out and do public activities in daily life, and the thermal environment level of the street can directly influence the life quality and physical and mental health of residents. The quality of outdoor thermal environments directly affects the types of activities, the activity time, and the user density of outdoor spaces of people outdoors. Therefore, the method creates a healthy and comfortable street thermal environment, reduces thermal influence and thermal hazard, is an important aspect for building green livable cities, and is beneficial to relieving urban heat island effect and achieving the aim of carbon neutralization. Quantitative evaluation of urban thermal environments is of great significance in revealing heat-related effects and performance of urban cooling measures, and is beneficial to quantifying the health and comfort of street thermal environments and dealing with urban thermal effects and thermal disasters.
In the prior art, according to different basic data sources, the quantitative evaluation method of the urban thermal environment can be divided into the following steps: remote sensing method, numerical simulation method and ground meteorological observation method. Among them, ground meteorological observation has been widely used to monitor and evaluate outdoor thermal environments of streets, blocks and prefecture streets, and the collected results thereof can be used to evaluate thermal comfort of the outdoor thermal environments. Ground meteorological observation, in turn, includes both fixed and mobile observations, capable of measuring air temperature and other parameters. The number of sensors in the measurement process is often limited due to the high price of the sensors in the ground meteorological observation. A small number of fixed sensor observation or moving observation modes are often adopted in research. However, in a street, the thermal environment of the measuring point is affected by the height of a building, the width of the street, vegetation, water and other cold and heat sources (such as an air conditioner outdoor unit and the like); for such a street with heterogeneity, it is difficult to characterize the thermal environment of the whole street using one measuring point in a fixed observation or several measuring points at random. Moreover, while the mobile observational approach can solve the problem of the number of sensors, moving from one point to another ignores the impact of temporal heterogeneity on the thermal environment; for example, when the sun suddenly shines on a measuring point, the radiation temperature may suddenly rise, and if the sudden change of the point is not considered in the moving measurement process, the high temperature is underestimated, the result of the evaluation analysis is directly influenced, and even the healthy trip of people is negatively influenced.
In conclusion, how to accurately and economically measure the outdoor thermal environment by adopting the ground meteorological observation method is a key problem to be solved urgently in the field.
Disclosure of Invention
The invention provides an accurate and economic method for mobile measurement of a street thermal environment, which aims to solve the limitation of the prior art on the street thermal environment measurement and achieve the purpose of improving the accuracy and the economy of outdoor thermal environment measurement.
The invention is realized by the following technical scheme:
an accurate and economical method for mobile measurement of street thermal environment, comprising the steps of:
s1, selecting a representative space in the region to be measured as a measuring point space, and actually measuring or numerically simulating an outdoor thermal environment to obtain outdoor thermal environment data;
s2, calculating a general hot climate index of each measuring point space based on outdoor thermal environment data;
s3, classifying the measuring point spaces based on the sky view factor, and calculating the average value of the universal hot climate indexes of the measuring point spaces of different classes at different moments;
s4, determining the accuracy of the universal hot climate index of each measuring point space at each moment;
s5, establishing a platform about the accuracy and the economy of measuring point space, time and universal hot climate indexes;
s6, calculating all continuous paths considering all moments in the platform;
s7, screening out an optimized path by adopting a multi-objective optimization algorithm and taking the accuracy and the measuring point space quantity as targets;
and S8, carrying out moving observation of the urban thermal environment along the optimized path.
Aiming at the problems that a fixed observation method is difficult to represent the thermal environment of the whole street and a mobile observation method is easy to ignore time heterogeneity and the like due to the constraint of cost factors in the prior art, the invention provides an accurate and economic method for mobile measurement of the thermal environment of the street, which comprises the steps of firstly selecting a representative space in a region to be measured as a measuring point space and obtaining related outdoor thermal environment data in a mode of actual measurement or numerical simulation; if the experimental conditions/equipment are sufficient, the data can be obtained by adopting an actual measurement mode, and if the experimental conditions/equipment are relatively insufficient, the data can be obtained by adopting a numerical simulation mode. And calculating the universal hot climate index UTCI of each measuring point space based on the obtained outdoor thermal environment data, wherein each calculation result is related to the measuring time. And then classifying the measuring point spaces, wherein the classification standard is a sky View factor SVF (sky View factor), calculating the average value of the universal hot climate indexes of different types of measuring point spaces at different moments according to the calculated universal hot climate index of each measuring point space at each measuring moment, and calculating the accuracy of the universal hot climate index of each measuring point space at each moment based on the calculated average value. The sky view factor SVF is common knowledge in the art, and a person skilled in the art can determine an SVF value of each measurement point space according to the prior art, which is not described in detail in this application.
Based on the calculation and preparation work, the method continues to establish an optimization platform related to the space, time, accuracy and economy of the measuring points, calculates all paths considering all the time in the platform, and needs to keep the continuity of the paths in the calculation process for reasonable planning. Wherein the economy can be characterized by the number of measuring point spaces. And finally, optimizing paths friendly to both accuracy and measuring point space quantity by adopting a multi-objective optimization algorithm, and taking the paths as final optimized paths to carry out mobile observation of the urban thermal environment by using the optimized paths.
Compared with the prior art, the method comprises the following steps: (1) the method solves the problems of large input of manpower and material resources, small number of selected points or random point selection in the outdoor thermal environment meteorological observation process in the prior art, provides a high-efficiency, scientific, automatic, precise and intelligent measurement method, fills the blank of the prior art, and provides scientific and rigorous reference and support for thermal-related urban environment research and urban cooling measure research; (2) calculating a related parameter UTCI of outdoor thermal comfort evaluation through outdoor thermal environment data, combining the data with a parameterized platform to construct a path platform, and finally quickly obtaining an accurate and economic measurement path by adopting a multi-objective optimization algorithm, so that the number of sensors in a meteorological observation method is effectively reduced, the accuracy of outdoor thermal environment measurement is ensured, and the accuracy and the efficiency of outdoor thermal environment measurement are remarkably improved; (3) the method overcomes the defect that fixed observation or mobile observation only aims at fewer measuring points in the street in the prior art, realizes a measuring method which takes account of space-time heterogeneity, limit of the number of the measuring points and accuracy into account, and effectively avoids the condition that measurement is not accurate due to the influence of uneven thermal environment caused by individual measuring point space (such as cold and heat sources nearby) or special time (such as sudden exposure to the sun).
Further, in step S1, the outdoor thermal environment data includes: air temperature, relative humidity, black ball temperature, wind speed.
Further, in step S2, the method for calculating the universal hot climate index of each station space includes:
s201, calculating the height wind speed of 10m in each measuring point space according to actual measurement or numerical simulation height wind speed, and calculating the average radiation temperature of each measuring point space;
s202, calculating a general hot climate index of each measuring point space based on the height wind speed of 10m, the air temperature, the average radiation temperature and the relative humidity.
The universal hot climate index UTCI can be expressed as the following formula:
UTCI(Ta,MRT,v10,pa)＝Ta+Offset(Ta,MRT,v10,pa)；
wherein T isaAir temperature, MRT mean radiation temperature, v10Wind speed at height of 10m, paIs the relative humidity. The specific calculation process of the universal hot climate index UTCI can be realized by those skilled in the art, for example, by means of UTCI official website, and will not be described herein.
Further, the wind speed at the height of 10m is calculated by the following formula:
in the formula, v10Wind speed at height of 10m, vxTo actually measure the height of the wind speed, x is the height of the actually measured wind speed.
The wind speed of 10m height corresponding to the measuring position is calculated by the formula through the height of the measuring position and the actually measured wind speed.
Further, the average radiation temperature is calculated by the following formula:
wherein MRT is the mean radiant temperature, TgIs black sphere temperature, vxFor actually measuring the height of the wind speed, epsilon is the emissivity of the black sphere, D is the diameter of the black sphere, and TaIs the air temperature.
Further, in step S4, the accuracy of the universal hot climate index is calculated by the following formula:
in the formula, AI is the precision of the general hot climate index of a certain measuring point space at a certain moment, λ is the general hot climate index value of the measuring point space at the moment, and μ is the average value of the general hot climate indexes of the corresponding categories of the measuring point space at the moment.
Further, in step S6, the method of calculating all the continuous paths that take into account all the time points includes:
s601, dividing data into a plurality of time periods on a time vector;
s602, randomly distributing the time corresponding to each period of time;
s603, randomly selecting the corresponding measuring point space at each moment to establish a path, eliminating discontinuous paths and reserving continuous paths.
The scheme is combined with a space classification method, the influence of space-time heterogeneity on the outdoor thermal environment is comprehensively considered, the measurement accuracy can be accurately judged, then a moving path with extremely high accuracy is found out for a moving observation method of the urban thermal environment, and compared with the prior art, the interference of the time heterogeneity and the space heterogeneity is overcome.
Further, the multi-objective optimization algorithm in step S7 is a pareto frontier algorithm. The pareto front edge algorithm is an existing algorithm in multi-objective optimization, and is introduced into the field of street thermal environment measurement, so that an accurate and economic measurement path can be obtained quickly and efficiently, the number of sensors in a meteorological observation method is effectively reduced, the economy of the method is improved, and the accuracy of outdoor thermal environment measurement is guaranteed.
Further, in step S2, an Outdoor Comfort Calculator of the Ladybug software is used to calculate the universal hot climate index of each measurement point space.
The scheme is that a custom battery pack for calculating the UTCI is constructed by utilizing an outer Comfort battery of the Ladybug, and the required parameters are connected into the battery pack as variables, so that the UTCI value can be output, the calculation efficiency of the application is obviously improved, and the calculation result of the UTCI is favorably and quickly output.
Further, in step S5, the above platform is established using a wallaceti plug-in of the parameterization software Rhino-Grasshopper.
The method is characterized in that the Rhino-Grasshopper is the existing parameterized optimization platform software in the modeling field, and the Wallace plug-in is used in the scheme, so that the required platform can be simply and quickly established, and the subsequent path planning is fully prepared.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the invention provides an accurate and economic method for movably measuring the thermal environment of a street, solves the problems of large input of manpower and material resources, small point selection quantity or random point selection in the outdoor thermal environment meteorological observation process in the prior art, provides an efficient, scientific, automatic, accurate and intelligent measuring method, fills the blank of the prior art, and provides scientific and rigorous support and basis for thermal-related urban environment research and urban cooling measure research.
2. The invention relates to an accurate and economic method for mobile measurement of street thermal environment, which calculates the relevant parameter UTCI of outdoor thermal comfort evaluation through actually measured outdoor thermal environment data, and calculates the average value of UTCI of different spaces at different moments by combining a space classification method to obtain the accuracy of the UTCI, thereby comprehensively considering the influence of space-time heterogeneity on the outdoor thermal environment, accurately judging the measurement accuracy, finding out a mobile path with extremely high accuracy for a mobile observation method of urban thermal environment, and overcoming the interference of time and space heterogeneity compared with the prior art.
3. According to the method for accurately and economically and movably measuring the thermal environment of the street, relevant parameters UTCI of outdoor thermal comfort evaluation are calculated through actually measured outdoor thermal environment data, the actually measured data and a parameterized platform are combined to construct a path platform, and a multi-objective optimization algorithm is adopted to finally and quickly obtain an accurate and economical measurement path, so that the number of sensors in a meteorological observation method is effectively reduced, the accuracy of outdoor thermal environment measurement is ensured, and the accuracy and the efficiency of outdoor thermal environment measurement are remarkably improved.
4. The method for accurately and economically measuring the thermal environment of the street in a mobile manner overcomes the defect that the prior art only aims at fixed observation or mobile observation of few measuring points of the street, realizes the measuring method which gives consideration to space-time heterogeneity, limit of the number of the measuring points and accuracy, and effectively avoids the situation that the measurement is not accurate due to the uneven thermal environment caused by the space of individual measuring points (such as the nearby cold and heat sources) or special moments (such as sudden exposure to the sun).
5. The invention discloses an accurate and economic method for mobile measurement of a street thermal environment, which introduces a parameterized platform into the field of street thermal environment measurement, so that the finally obtained path of a meteorological mobile observation method is visualized, and the display effect is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
FIG. 1 is a schematic step diagram of an embodiment of the present invention;
FIG. 2 is a schematic flow chart of an embodiment of the present invention;
FIG. 3 is a schematic view of the arrangement of the measurement points of the SC roadway in the embodiment of the present invention;
FIG. 4 is a schematic view of measuring point arrangement of a JX slope in the embodiment of the present invention;
FIG. 5 is a diagram illustrating an optimization result according to an embodiment of the present invention;
fig. 6 is a schematic diagram of an optimized path according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention. In the description of the present application, it is to be understood that the terms "front", "back", "left", "right", "upper", "lower", "vertical", "horizontal", "high", "low", "inner", "outer", etc. indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience in describing the present invention and simplifying the description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus should not be construed as limiting the scope of the present application.
Example 1:
an accurate and economical method for mobile measurement of street thermal environment as shown in fig. 1, comprising:
s1, selecting a representative space in the area to be measured as a measuring point space, and acquiring outdoor thermal environment data of each measuring point space at different moments; wherein, the representative space needs to consider both the spatial position with universality/generality and the spatial position with particularity/abnormality in the region to be measured. Wherein, the outdoor thermal environment data can be obtained by means of actual measurement and/or numerical simulation.
S2, calculating the general hot climate index of each measuring point space based on the outdoor thermal environment data:
calculating the height wind speed of 10m in each measuring point space:
in the formula, v10Wind speed at height of 10m, vxTo actually measure the height of the wind speed, x is the height of the actually measured wind speed.
Calculating the average radiation temperature of each measuring point space:
wherein MRT is the mean radiant temperature, TgIs black sphere temperature, vxFor actually measuring the height of the wind speed, epsilon is the emissivity of the black sphere, D is the diameter of the black sphere, and TaIs the air temperature.
And calculating the universal hot climate index UTCI of the space of each measuring point based on the height wind speed of 10m, the air temperature, the average radiation temperature and the relative humidity.
S3, classifying the measuring point spaces based on the sky view factor, and calculating the average value of the universal hot climate indexes of the measuring point spaces of different classes at different moments;
s4, determining the precision AI of the universal hot climate index of each measuring point space at each moment:
in the formula, λ is a general hot climate index value of the measurement point space at the time, and μ is an average value of the general hot climate indexes of the corresponding categories of the measurement point space at the time.
S5, establishing a platform about the accuracy and the economy of measuring point space, time and universal hot climate indexes;
s6, calculating all continuous paths considering all time points in the platform:
dividing data into a plurality of time periods on a time vector; randomly distributing the time corresponding to each period of time; and randomly selecting the corresponding measuring point space at each moment to establish a path, eliminating discontinuous paths and reserving continuous paths.
S7, screening out an optimized path by adopting a multi-objective optimization algorithm and taking the accuracy and the measuring point space quantity as targets;
and S8, carrying out moving observation of the urban thermal environment along the optimized path.
Example 2:
an accurate and economical method for mobile measurement of street thermal environment is shown in fig. 2, and this embodiment takes street thermal environment measurement of SC lane and JX slope in Chongqing city as an example to explain the method.
The method comprises the following steps: data acquisition
And selecting a representative space in the field as a measuring point to measure the outdoor thermal environment, wherein the representative space comprises air temperature (Ta), relative humidity (Rh), black ball temperature (Tg), wind speed (v), time interval of data acquisition and the like (determined according to point changing time).
FIG. 3 is a schematic view of the station arrangement of the SC roadway, in which A is provided1、B1、C1、D1、E1Five measuring points;
FIG. 4 is a schematic view of the measurement point arrangement of JX slope, wherein B is provided2、C2、D2、E2、F2、G2Six stations, each arrow in fig. 4 indicating a downhill direction.
The following table shows the G2 points at half hour intervals from 8:00 to 19: 30 collected partial data.
Table 1 partial measurement data of point G2
Step two: analytical calculations
(1) The battery pack of "calculate wind speed at 10m height" was constructed by the Rhino & Grasshopper platform, and the calculation formula is as follows:
wherein v isxThe wind speed at 1.1m height was taken and x was 1.1 m.
(2) The battery pack of "calculating the mean radiant temperature MRT" was constructed by continuing with the Rhino & Grasshopper platform, and the calculation formula is as follows:
in the formula, TgIs black sphere temperature, vxFor actual measurement of wind speed, ε is the emissivity of the black sphere, D is the diameter of the black sphere, TaIs the air temperature.
(3) And (3) constructing a 'calculation UTCI' battery pack by using an outer Comfort Calculator of Ladybug, connecting required parameters serving as variables into the battery pack, and outputting a UTCI value.
The battery may be expressed as a function of the form:
UTCI(Ta,MRT,v10,pa)＝Ta+Offset(Ta,MRT,v10,pa)。
again taking the example at point G2, the following table shows the fractional values calculated at point G2:
table 2 average partial radiation temperature at measurement point G2 and UTCI values
(4) The measuring points of the two fields are classified according to SVF, and are divided into two categories of SVF more than 0.1 and SVF less than or equal to 0.1;
calculating the average value of UTCI of different types of measuring points at different moments, wherein partial results are shown in the following table:
TABLE 3 partial mean values of UTCI at different times for different classes of measuring points
Step three: determining a value
Calculating the UTCI precision (AI) of different measuring points at each moment:
in the formula, λ is a general hot climate index value of the measurement point space at the time, and μ is an average value of the general hot climate indexes of the corresponding categories of the measurement point space at the time.
The calculation results of this example are as follows:
TABLE 4 precision AI at different times for each measurement point
| 8: 00 | 8: 30 | 9: 00 | 9: 30 | 10: 00 | 10: 30 | 11: 00 | 11: 30 | 12: 00 | 12: 30 | 13: 00 | 13: 30 | 14: 00 | 14: 30 | 15: 00 | 15: 30 | 16: 00 | 16: 30 | 17: 00 | 17: 30 | 18: 00 | 18: 30 | 19: 00 | 19: 30 | |
| A1 | 0.9 9 | 0. 99 | 1.0 0 | 1.0 0 | 0.9 4 | 0.9 2 | 0.9 5 | 0.9 7 | 0.9 7 | 0.9 8 | 0.9 8 | 0.9 9 | 0.9 4 | 0.8 9 | 0.8 5 | 0.8 8 | 0.8 4 | 0.8 4 | 0.8 6 | 0.8 7 | 0.8 8 | 0.9 2 | 0.9 6 | 0.9 8 |
| B1 | 0.9 8 | 0. 98 | 0.9 7 | 0.9 3 | 0.9 1 | 0.9 0 | 0.9 0 | 0.9 5 | 0.9 8 | 0.9 7 | 0.9 7 | 0.9 7 | 0.9 8 | 0.9 8 | 0.9 9 | 0.9 8 | 0.9 9 | 1.0 0 | 1.0 0 | 0.9 9 | 1.0 0 | 0.9 8 | 0.9 8 | 0.9 9 |
| C1 | 0.9 8 | 0. 98 | 0.9 7 | 0.9 3 | 0.9 1 | 0.9 0 | 0.9 0 | 0.9 5 | 0.9 8 | 0.9 7 | 0.9 7 | 0.9 7 | 0.9 8 | 0.9 8 | 0.9 9 | 0.9 8 | 0.9 9 | 1.0 0 | 1.0 0 | 0.9 9 | 1.0 0 | 0.9 8 | 0.9 8 | 0.9 9 |
| D1 | 1.0 0 | 0. 99 | 0.9 8 | 0.9 8 | 0.9 9 | 0.9 9 | 0.9 5 | 0.9 1 | 0.9 2 | 0.9 1 | 0.9 1 | 0.9 1 | 0.9 0 | 0.8 9 | 0.9 1 | 0.9 4 | 0.9 4 | 0.9 3 | 0.9 3 | 0.9 5 | 0.9 7 | 0.9 9 | 0.9 8 | 0.9 9 |
| E1 | 1.0 0 | 1. 00 | 0.9 9 | 0.9 8 | 0.9 5 | 0.9 3 | 0.9 0 | 0.8 8 | 0.8 9 | 0.8 9 | 0.8 9 | 0.9 0 | 0.9 5 | 1.0 0 | 0.9 4 | 0.9 4 | 0.9 0 | 0.9 1 | 0.9 3 | 0.9 2 | 0.9 1 | 0.9 3 | 0.9 7 | 0.9 9 |
| B2 | 0.9 9 | 1. 00 | 1.0 0 | 0.9 9 | 0.9 8 | 0.9 6 | 0.9 7 | 0.9 7 | 0.9 6 | 0.9 6 | 0.9 7 | 1.0 0 | 0.9 7 | 0.9 6 | 0.9 7 | 0.9 6 | 0.9 5 | 0.9 5 | 0.9 5 | 0.9 5 | 0.9 4 | 0.9 5 | 0.9 5 | 0.9 6 |
| C2 | 0.9 9 | 0. 99 | 0.9 8 | 0.9 6 | 0.9 1 | 0.8 6 | 0.8 5 | 0.8 3 | 0.8 3 | 0.9 0 | 0.9 7 | 0.9 8 | 0.9 6 | 0.9 5 | 0.8 6 | 0.8 5 | 0.8 9 | 0.8 9 | 0.9 1 | 0.9 4 | 0.9 4 | 0.9 5 | 0.9 5 | 0.9 6 |
| D2 | 1.0 0 | 0. 98 | 0.9 7 | 0.9 7 | 0.9 8 | 0.9 9 | 0.9 8 | 0.9 8 | 0.9 7 | 0.9 7 | 0.9 9 | 1.0 0 | 0.9 9 | 0.9 8 | 0.9 9 | 0.9 9 | 0.9 9 | 0.9 8 | 0.9 8 | 0.9 8 | 0.9 7 | 0.9 7 | 0.9 7 | 0.9 7 |
| E2 | 0.9 9 | 0. 99 | 0.9 8 | 0.9 9 | 0.9 8 | 0.8 9 | 0.9 0 | 0.8 9 | 0.9 1 | 0.8 9 | 0.9 6 | 0.9 6 | 0.9 3 | 0.9 7 | 0.9 4 | 0.9 3 | 1.0 0 | 0.9 9 | 0.9 9 | 0.9 8 | 0.9 8 | 0.9 7 | 0.9 7 | 0.9 8 |
| F2 | 0.9 9 | 0. 98 | 0.9 7 | 0.9 6 | 0.9 6 | 0.9 5 | 0.9 9 | 0.9 9 | 0.9 9 | 0.9 9 | 0.9 8 | 1.0 0 | 0.9 8 | 0.9 7 | 0.9 8 | 0.9 7 | 0.9 7 | 0.9 7 | 0.9 7 | 0.9 7 | 0.9 7 | 0.9 8 | 0.9 8 | 0.9 9 |
| G2 | 0.9 8 | 0. 99 | 1.0 0 | 0.9 7 | 0.9 3 | 0.9 7 | 0.9 5 | 0.9 4 | 0.9 2 | 0.9 9 | 0.9 8 | 0.9 8 | 0.9 7 | 0.9 8 | 0.9 2 | 0.9 2 | 0.8 9 | 0.8 7 | 0.9 0 | 0.9 6 | 0.9 6 | 0.9 7 | 0.9 7 | 0.9 8 |
Step four: construction platform
And establishing measuring points, time and corresponding UTCI precision in an optimization platform (Rhino & Grasshopper platform Wallace), and inputting the calculated AI value into the platform.
Step five: determining a continuous path
And (4) establishing a battery pack which automatically screens all paths considered at all times on the platform, and obtaining a result. The method comprises the following specific steps:
(1) a Random Reduce battery is used in an optimization platform, and data on a time vector is divided into m sections;
(2) using a Random Reduce battery to randomly distribute the corresponding time of each period of time;
(3) and (3) using a Random battery and a Random Reduce battery to randomly select a corresponding measuring point at the moment so as to ensure the continuity of the path.
Step six: multi-objective optimization
And determining an accurate optimization result with a proper number of measuring points by adopting pareto multi-objective optimization, and outputting an optimization path in a platform in a visual mode. The optimization result of this embodiment is shown in fig. 5, where the points in the box are the optimal solution, that is, the optimal path is finally obtained, the solution has a small number of measured points (high measurement efficiency) and high accuracy (accurate measurement result), and is most friendly to economy and accuracy after comprehensive evaluation.
After the optimal scheme is unfolded and beautified, the path schematic diagrams of the two sites as shown in fig. 6 can be obtained, and the path is taken as the basis to carry out the thermal environment movement observation of the two sites.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Claims (10)
1. An accurate and economical method for mobile measurement of the thermal environment of a street, comprising the steps of:
s1, selecting a representative space in the region to be measured as a measuring point space, and actually measuring or numerically simulating an outdoor thermal environment to obtain outdoor thermal environment data;
s2, calculating a general hot climate index of each measuring point space based on outdoor thermal environment data;
s3, classifying the measuring point spaces based on the sky view factor, and calculating the average value of the universal hot climate indexes of the measuring point spaces of different classes at different moments;
s4, determining the accuracy of the universal hot climate index of each measuring point space at each moment;
s5, establishing a platform about the accuracy and the economy of measuring point space, time and universal hot climate indexes;
s6, calculating all continuous paths considering all moments in the platform;
s7, screening out an optimized path by adopting a multi-objective optimization algorithm and taking the accuracy and the measuring point space quantity as targets;
and S8, carrying out moving observation of the urban thermal environment along the optimized path.
2. The method for accurately and economically mobile measuring street thermal environment according to claim 1, wherein in step S1, said outdoor thermal environment data comprises: air temperature, relative humidity, black ball temperature, wind speed.
3. The method for accurately and economically mobile measuring the thermal environment of the street as claimed in claim 1, wherein the step S2 is performed by calculating the universal hot climate index of each station space, comprising:
s201, calculating the height wind speed of 10m in each measuring point space according to actual measurement or numerical simulation height wind speed, and calculating the average radiation temperature of each measuring point space;
s202, calculating a general hot climate index of each measuring point space based on the height wind speed of 10m, the air temperature, the average radiation temperature and the relative humidity.
4. An accurate and economical method for mobile measurement of street thermal environment according to claim 3, characterized in that the wind speed at 10m altitude is calculated by the following formula:
in the formula, v10Wind speed at height of 10m, vxTo actually measure the height of the wind speed, x is the height of the actually measured wind speed.
5. An accurate and economical method for mobile measurement of the thermal environment of streets according to claim 3, characterized in that the average radiation temperature is calculated by the following formula:
wherein MRT is the mean radiant temperature, TgIs black sphere temperature, vxFor actually measuring the height of the wind speed, epsilon is the emissivity of the black sphere, D is the diameter of the black sphere, and TaIs the air temperature.
6. An accurate and economical method for mobile measurement of street thermal environment according to claim 1, wherein in step S4, the accuracy of the universal hot climate index is calculated by the following formula:
in the formula, AI is the precision of the general hot climate index of a certain measuring point space at a certain moment, λ is the general hot climate index value of the measuring point space at the moment, and μ is the average value of the general hot climate indexes of the corresponding categories of the measuring point space at the moment.
7. An accurate and economical method for mobile measurement of street thermal environment according to claim 1, wherein in step S6, the method for calculating all continuous paths considering all time includes:
s601, dividing data into a plurality of time periods on a time vector;
s602, randomly distributing the time corresponding to each period of time;
s603, randomly selecting the corresponding measuring point space at each moment to establish a path, eliminating discontinuous paths and reserving continuous paths.
8. An accurate and economical method for mobile measurement of street thermal environment as claimed in claim 1, wherein the multi-objective optimization algorithm in step S7 is pareto frontier algorithm.
9. The method of claim 1, wherein in step S2, the universal thermal climate index of each station space is calculated by using the Outdoor Comfort Calculator of the Ladybug software.
10. An accurate and economical method for mobile measurement of street thermal environment according to claim 1, characterized in that in step S5, the platform is built using the parameter software Rhino-Grasshopper wallacet plug-in.
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