A Smart Travel Decision-making Platform and Method Based on Thermal Early Warning and Thermal Adaptation

Abstract

The present invention discloses an intelligent travel decision-making platform and method based on heat warning and heat adaptation. The platform includes a data collection module, a microclimate prediction module, a data post-processing module, a storage module, a retrieval module, a decision-making module, and an interaction module. The present invention is used to solve the problem that there is no decision-making system and method for urban heat research that is universal at all levels in the prior art, and to construct an urban heat warning and heat adaptation decision-making platform that is universal at all levels, so as to provide guiding opinions for people to travel healthily and safely in a heat-affected environment, and at the same time provide systematic and scientific support for accurately evaluating the urban heat environment and for the community to sustainably respond to high temperatures.

Description

Technical Field

[0001] The present invention relates to the field of urban high - temperature research, and particularly to an intelligent travel decision - making platform and method based on heat warning and heat adaptation. Background Art

[0002] Currently, affected by heatwaves and the urban heat island effect, many cities are severely threatened by high temperatures. With the rapid development of global warming and urbanization, the urban heat island effect will further deteriorate, and extreme high temperatures will become more common. High temperature is one of the key driving factors for various diseases related to the respiratory system, digestive system, skin heat damage, cardiovascular system, eyes, and metabolism and urinary system. It not only reduces human thermal comfort but also leads to an increase in morbidity and mortality.

[0003] Extreme high temperatures also hinder urban sustainable development in many other aspects. To alleviate heat discomfort and heat stress caused by high temperatures, people's heavy reliance on air - conditioning facilities has led to a significant increase in electricity consumption. In addition, extreme high temperatures lead to social inequality. For example, the availability and operability of air - conditioning and refrigeration facilities contribute to economic - related inequalities; heat - induced mortality and morbidity are more prominent in vulnerable groups such as children, the elderly, pregnant women, and patients. High temperatures will further cause economic losses due to decreased labor productivity and negative impacts on agricultural output, health, transportation, energy, and assets. Implementing heat mitigation and heat adaptation strategies is crucial for ensuring society's protection from urban high - temperature threats.

[0004] Addressing the high - temperature challenge requires not only reducing greenhouse gases but also a series of emergency strategies to mitigate heat - induced impacts by building high - temperature - resilient cities and communities. In particular, the construction of high - temperature - resilient cities and communities should predict, prepare for, and adapt to extreme high temperatures through means such as planning, design, construction, and operation to ensure that society can withstand, respond to, and quickly recover from high - temperature disruptions. Urban heat mitigation and heat adaptation are the keys to building cool cities and communities. The implementation of mitigation strategies can reduce urban temperature, improve thermal comfort, and alleviate other negative impacts.

[0005] However, simply relying on heat mitigation strategies by urban planners and designers cannot comprehensively solve the urban high - temperature problem. There is a lack of a decision - making platform for urban heat warning and heat adaptation in the existing technology, and conventional decision - making methods cannot adapt to the high heterogeneity of people's activities. Therefore, there is an urgent need for a real - time, intelligent, and comprehensive warning and decision - making platform to accurately inform people of heat impacts and adaptation strategies, provide management basis for urban managers, service guidance for the community level, and guidance for individuals to travel healthily and safely in a heat - affected environment. Summary of the Invention

[0006] The present invention provides an intelligent travel decision-making platform and method based on heat warning and heat adaptation, so as to solve the problem in the prior art that there is no decision-making system for urban heat research that is universal at all levels, and to construct an urban heat warning and heat adaptation decision-making platform that can be used at all levels, provide guidance for people to travel healthily and safely in a heat-affected environment, and at the same time provide systematic scientific support for accurately evaluating the urban heat environment and for the community to sustainably respond to high temperatures.

[0007] The present invention is realized through the following technical solutions:

[0008] An intelligent travel decision-making platform based on heat warning and heat adaptation, comprising:

[0009] A data collection module, configured to collect urban morphological data, dynamic meteorological data, social and economic data, and heat adaptation strategy data within a specified area;

[0010] A microclimate prediction module, configured to extract urban morphological data and dynamic meteorological data from the data collection module and simulate the dynamic microclimate of a specified area;

[0011] A data post-processing module, configured to extract microclimate parameters from the microclimate prediction module and calculate heat health risk meteorological indicators based on the microclimate parameters;

[0012] A storage module, configured to store the social and economic data, heat adaptation strategy data, and heat health risk meteorological indicators;

[0013] A retrieval module, configured to retrieve heat adaptation strategies suitable for the current user category and heat adaptation needs in the storage module according to the user category and heat adaptation needs, and send them to the decision-making module;

[0014] A decision-making module, configured to make heat warning decisions and / or heat adaptation decisions according to the heat adaptation strategies and in combination with a preset heat health risk meteorological indicator threshold;

[0015] An interaction module, configured to obtain the user category and the user's heat adaptation needs, and output the heat warning decisions and / or heat adaptation decisions to the user.

[0016] Aiming at the problem in the prior art that there is no decision-making system for urban heat research that is universal at all levels, the present invention first proposes an intelligent travel decision-making platform based on heat warning and heat adaptation, and this platform at least includes the following modules:

[0017] A data collection module, and the types of data collected include urban morphological data, dynamic meteorological data, social and economic data, and heat adaptation strategy data within a specified area, providing basic data support for the operation of the entire platform.

[0018] A microclimate prediction module for predicting the dynamic microclimate of a specified area through numerical simulation. The prediction of the dynamic microclimate is based on urban morphology data and dynamic meteorological data. The use of dynamic meteorological data enables the microclimate predicted by the decision-making platform of the present application to be updated in real time according to changes in meteorological data, so as to ensure the timeliness and accuracy of decision-making.

[0019] A data post-processing module for post-processing based on the predicted dynamic microclimate and calculating the required meteorological indicators of heat health risk; among them, the meteorological indicators of heat health risk can be any indicators in the prior art that are closely related to thermal comfort and / or heat health. The specific indicator parameters are not limited herein, and those skilled in the art can make adaptive settings according to the specific application environment; of course, the microclimate parameters required for calculating different meteorological indicators of heat health risk are also different, and can be extracted from the predicted dynamic microclimate according to actual needs.

[0020] A storage module for jointly storing the above-mentioned collected socioeconomic data, heat adaptation strategy data, and calculated meteorological indicators of heat health risk, providing support for later information retrieval and decision-making, and realizing integrated intelligent prediction and decision-making.

[0021] A retrieval module for retrieving heat adaptation strategies suitable for the current user category and heat adaptation needs in the storage module according to the user category and heat adaptation needs, and sending them to the decision-making module. The retrieval module can retrieve data from the storage module to obtain possible adaptation strategies; among them, the heat adaptation strategies required by users of different types or social information are different. For example, when the user is a government department, an enterprise, a community, an individual, etc., the purpose of using this platform is different. Therefore, this solution needs to retrieve appropriate heat adaptation strategies according to the user category; at the same time, even for users of the same type, due to individual characteristic differences and high heterogeneity of activities, the matching heat adaptation strategies may also be different. Therefore, this solution also needs to retrieve appropriate heat adaptation strategies according to specific heat adaptation needs. It should be noted that the heat adaptation needs can be directly or indirectly obtained from the user's input information.

[0022] A decision-making module that receives the heat adaptation strategies sent by the retrieval module and makes heat warning decisions and / or heat adaptation decisions in combination with the preset thresholds of meteorological indicators of heat health risk. Through the decision-making module of the present application, different heat comfort index warnings can be provided for different social characteristic groups. When the corresponding index exceeds the set threshold, it will automatically alarm or provide users with detailed suggestions for adapting to high temperatures to meet the conditions for healthy travel, so as to make heat warning decisions that meet the current user needs. In addition, through the decision-making module, different heat adaptation decisions can be provided for the heat adaptation needs of different social characteristic groups, providing service guidance at the community level and guiding opinions for individuals to travel healthily and safely in a heat-affected environment.

[0023] This application realizes the human-computer interaction between users and the platform through an interaction module.

[0024] It can be seen that this application: (1) combines dual means of thermal evaluation warning and thermal adaptation strategy selection to construct a smart city travel decision-making system under high-temperature hazards, which can be specific to the climate of local urban areas such as communities, fully considering the high heterogeneity of people's activities. A complete smart city thermal warning and thermal adaptation decision-making platform is constructed from aspects such as thermal-related data collection, microclimate prediction and visualization, and adaptation strategy selection, providing scientific and reasonable guidance for people to travel healthily and safely in a heat-affected environment. At the same time, it provides support for government departments, social organizations, etc. to accurately evaluate the urban heat in a designated area, and provides systematic scientific support for regional sustainable and resilient responses to high temperatures with communities as units; (2) through in-depth analysis of urban form, meteorology, social economy, and thermal adaptation strategy data, after predicting, post-processing, storing, retrieving, and analyzing the data, a thermal warning and thermal adaptation decision-making platform is constructed, providing a reliable and accurate scientific basis for user management decision-making, and being able to improve the management level of thermal health and thermal safety during travel, making the response strategy to the urban thermal environment no longer stay in the theoretical stage, but having a practical and feasible comprehensive execution platform, filling the gap in the existing technology; (3) enables different types of stakeholders to transform high-temperature adaptation plans / guidelines / technologies into the operations of communities, industries, and individual behaviors, with good commercial value; for example, through the decision-making platform of this application, individuals with different demographic characteristics (such as age, health, gender, economic status) can obtain accurate information related to the type, location, and path of outdoor activities, time and duration, and the availability of basic cooling facilities and cooling centers; (4) the decision-making platform of this application can subjectively reduce the impact of high temperatures and reduce social injustice caused by high temperatures. For example, it is beneficial to assist enterprise managers in improving the heat adaptation ability of workers, thereby improving work efficiency and economic benefits in hot weather.

[0025] Furthermore, the urban form data includes one or more of the land utilization rate, building coverage area, building height, building shape, urban street information, permeable surface, impermeable surface, surface material, vegetation coverage area, vegetation type, and vegetation shape in a designated area;

[0026] The dynamic meteorological data includes one or more of the underlying surface temperature, air temperature, relative humidity, wind speed, solar radiation, and heat-related pollutant data in a designated area;

[0027] The social economy data includes the demographic characteristics in a designated area;

[0028] The heat adaptation strategy data includes heat adaptation strategies for different characteristic populations within a specified area.

[0029] This solution defines the specific collection content of the data collection module. Among them:

[0030] Urban form data is mainly used to reflect the factors that will affect the outdoor thermal environment in urban construction. Its specific data can be obtained from government planning departments or collected through remote sensing technologies (such as MODIS, Landsat, Lida).

[0031] Dynamic meteorological data mainly includes factors that can characterize the urban thermal environment, so as to make full preparations for calculating the meteorological indicators of heat health risks in the later stage. It can be obtained through remote sensing technologies, collected by meteorological stations at all levels, or obtained by means of numerical simulation.

[0032] Socio-economic data mainly includes population characteristics. The population characteristics in this application include, but are not limited to, factors related to individuals and related to heat adaptation ability, such as education, age, gender, income, work type, health, housing, etc., which can be obtained through census data, questionnaires, interviews, etc.

[0033] Heat adaptation strategy data refers to the habitual behaviors within the region to reduce heat hazards by changing policies or actions. For example: drinking ice water, turning on the air conditioner, reducing clothing, taking shelter in the shade, etc. during high temperatures. It can be obtained through literature collection, questionnaires, on-site visits, etc.

[0034] Furthermore, the microclimate prediction module simulates the microclimate in a specified area at a specified time based on the dynamic meteorological data and makes local corrections based on the urban form data.

[0035] The simulation and prediction of the microclimate can be obtained by using software such as GIS, WRF, ENVI-met, Rhino, etc. Among them, the WRF model has the advantage of estimating large-scale weather with low resolution, while ENVI-met has the advantage of predicting the climate at the scale of buildings, streets, blocks and areas with high resolution. This solution also makes local corrections to the simulated and predicted microclimate in combination with the urban form data to overcome the influence of urban buildings / facilities, etc. on the local climate and obtain more accurate local climate data such as wind speed.

[0036] Furthermore, the meteorological indicators of heat health risk include one or more of wet bulb globe temperature, mean radiant temperature, physiological equivalent temperature, and universal thermal climate index. In this solution, preferably, one or more of wet bulb globe temperature (WBGT), mean radiant temperature (Tmrt), physiological equivalent temperature (PET), and universal thermal climate index (UTCI) are used as meteorological indicators of heat health risk. The common feature of these indicators is that they can reasonably characterize thermal comfort and heat health, and can provide an accurate basis for the subsequent decision-making process in this application.

[0037] Furthermore, the interaction module includes:

[0038] A feature collection unit, which is used to collect the demographic characteristics and heat adaptation needs of the current user, and provide a basis for the work of the retrieval module and the decision-making module, so as to ensure that the retrieval and decision-making results can meet the actual needs of the current user;

[0039] An output unit, which is used to output the final decision to the user.

[0040] Furthermore, the retrieval module includes:

[0041] A user identification unit, which is used to identify whether the current user category belongs to a government organization, a social organization, a community institution or an individual;

[0042] A classification unit, which is used to divide the current user's category according to the demographic characteristics when the current user belongs to an individual;

[0043] A first retrieval unit, which is used to retrieve heat adaptation strategies suitable for the current user category in the storage module;

[0044] A second retrieval unit, which is used to retrieve heat adaptation strategies suitable for the current heat adaptation needs in the storage module.

[0045] In order to more fully meet the different needs of different users, the retrieval module of this solution also includes a user identification unit, which is used to classify and identify the current user. It can ask the current user which one of a government organization, a social organization, a community institution or an individual belongs to through a man-machine interaction method, so as to provide a sufficient basis for subsequent retrieval and decision-making, and ensure that this application can provide decision-making information related to heat warning and heat adaptation for users at all levels at the same time.

[0046] In addition, if the current user belongs to an individual, the classification unit is also started. Through a man-machine interaction method, the demographic characteristics of the current user are asked, and classification is carried out according to the demographic characteristics input by the user, so as to overcome the decision-making differences brought about by the high heterogeneity of the physical fitness and travel activities of different groups. Of course, the specific classification rules of the classification unit based on demographic characteristics are preset by those skilled in the art according to the actual situation, and will not be limited here.

[0047] The first retrieval unit and the second retrieval unit respectively retrieve the heat adaptation strategy in the storage module according to the classification and heat adaptation needs of the current user, using the aforementioned heat adaptation strategy data as a database. Finally, the decision module makes a heat warning decision based on the retrieval results and the threshold of the thermal health risk meteorological indicator.

[0048] It should be noted that the specific decision-making rules and the thresholds corresponding to each thermal health risk meteorological indicator are preset in advance by technical personnel in this field according to actual conditions and can be flexibly adjusted according to climate change, and this application does not limit this. This application seeks protection for the technical concept of implementing decisions related to heat warning and heat adaptation through this new decision-making platform, rather than specific decision-making rules. In addition, the specific decision results depend on the thermal adaptation needs of different users. For example, when the user is a government management department, the decision result may be a warning for areas with severe local thermal environment; when the user is at the community or social group level, the decision result may be a visual guide for residents to cool off or the distribution of cooling facilities; when the user is an individual, the decision result may be suggestions on travel routes, time, etc., or information guidance on surrounding thermal comfort areas.

[0049] A smart travel decision-making method based on heat warning and heat adaptation includes the following steps:

[0050] S1. Collect urban morphology data, dynamic meteorological data, socioeconomic data, and heat adaptation strategy data within the designated area;

[0051] S2, extracting urban morphological data and dynamic meteorological data from the data collection module to simulate the dynamic microclimate of a designated area;

[0052] S3, extracting microclimate parameters, and calculating thermal health risk meteorological indicators based on the microclimate parameters;

[0053] S4, storing the socioeconomic data, heat adaptation strategy data, and heat health risk meteorological indicators as standby data;

[0054] S5, inputting a user category and a thermal adaptation requirement, and retrieving a thermal adaptation strategy that is adapted to the current user category and the thermal adaptation requirement from the standby data;

[0055] S6. Making a heat warning decision and / or heat adaptation decision according to the heat adaptation strategy and in combination with a preset heat health risk meteorological indicator threshold;

[0056] S7. Output the heat warning decision and / or heat adaptation decision to the user.

[0057] Furthermore, the urban morphology data is obtained from government departments or collected through remote sensing technology;

[0058] The dynamic meteorological data is obtained through meteorological departments, collected through remote sensing technology, or obtained through numerical simulation;

[0059] The socioeconomic data is obtained through census data and / or questionnaire surveys;

[0060] The heat adaptation strategy data is obtained through on-site visits and / or questionnaire surveys.

[0061] Furthermore, the method for simulating the dynamic microclimate of a specified area includes: simulating the microclimate of the specified area within a specified time according to the dynamic meteorological data, and making local corrections based on urban morphology data; the heat health risk meteorological indicators include one or more of wet bulb globe temperature, mean radiant temperature, physiological equivalent temperature, and universal thermal climate index.

[0062] Furthermore, in step S5, first judge the primary category of the user according to the input user category, and the primary category includes government organizations, social groups, community institutions, and individuals;

[0063] If the primary category of the user belongs to an individual, then collect the demographic characteristics of the current user, and divide the secondary category of the current user according to the demographic characteristics.

[0064] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0065] 1. The intelligent travel decision-making platform and method based on heat warning and heat adaptation of the present invention combines the dual means of heat assessment and warning and heat adaptation strategy selection to construct an intelligent city travel decision-making system under high-temperature hazards, which can be specific to the climate of local urban areas such as communities, fully considering the high heterogeneity of people's activities. A complete intelligent city heat warning and heat adaptation decision-making platform is constructed from aspects such as heat-related data collection, microclimate prediction and visualization, and adaptation strategy selection, providing scientific and reasonable guidance for people to travel healthily and safely in a heat-affected environment. At the same time, it provides support for government departments, social groups, etc. to accurately evaluate the urban heat in a specified area, and provides systematic and scientific support for regional sustainable and resilient responses to high temperatures with communities as units.

[0066] 2. The intelligent travel decision-making platform and method based on heat warning and heat adaptation of the present invention, through in-depth analysis of urban morphology, meteorology, socioeconomic, and heat adaptation strategy data, constructs a heat warning and heat adaptation decision-making platform after predicting, post-processing, storing, retrieving, and analyzing the data, providing a reliable and accurate scientific basis for user management decision-making, and being able to improve the heat health and heat safety management level of travel, making the coping strategies for the urban heat environment no longer stay in the theoretical stage, but having a practical and feasible comprehensive execution platform, filling the gap in the prior art.

[0067] 3. The intelligent travel decision-making platform and method based on heat warning and heat adaptation of the present invention enable different types of stakeholders to transform heat adaptation plans / guidelines / technologies into the operations of communities and industries and individual behaviors, and have good commercial value.

[0068] 4. The intelligent travel decision-making platform and method based on heat warning and heat adaptation of the present invention can subjectively reduce the impact of high temperature on residents' lives and work, improve the heat tolerance of vulnerable groups, reduce the heat vulnerability of residents and the environment, and thus reduce social injustice caused by high temperature. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of this application, and do not limit the embodiments of the present invention. In the drawings:

[0070] Figure 1 It is a schematic diagram of the decision-making platform in the specific embodiment of the present invention;

[0071] Figure 2 It is a schematic flow diagram of the decision-making method in the specific embodiment of the present invention;

[0072] Figure 3 It is a decision-making schematic diagram in a specific embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0073] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and the drawings. The illustrative embodiments and descriptions thereof of the present invention are only used to explain the present invention and do not limit the present invention. In the description of this application, it should be understood that the orientation or positional relationship indicated by the terms "front", "rear", "left", "right", "up", "down", "vertical", "horizontal", "high", "low", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the protection scope of this application.

[0074] Embodiment 1:

[0075] As Figure 1 shown, an intelligent travel decision-making platform based on heat warning and heat adaptation includes:

[0076] A data collection module for collecting urban form data, dynamic meteorological data, social economic data, and heat adaptation strategy data within a specified area;

[0077] A microclimate prediction module, configured to extract urban morphological data and dynamic meteorological data from the data collection module, and simulate the dynamic microclimate of a specified area;

[0078] A data post - processing module, configured to extract microclimate parameters from the microclimate prediction module, and calculate heat - health risk meteorological indices based on the microclimate parameters;

[0079] A storage module, configured to store the socioeconomic data, heat adaptation strategy data, and heat - health risk meteorological indices;

[0080] A retrieval module, configured to retrieve heat adaptation strategies suitable for the current user category and heat adaptation needs from the storage module according to the user category and heat adaptation needs, and send them to the decision - making module;

[0081] A decision - making module, configured to make heat warning decisions and / or heat adaptation decisions according to the heat adaptation strategies and in combination with a preset threshold of heat - health risk meteorological indices;

[0082] An interaction module, configured to obtain the user category and the user's heat adaptation needs, and output the heat warning decisions and / or heat adaptation decisions to the user.

[0083] Wherein, the urban morphological data includes one or more of land utilization rate, building coverage area, building height, building shape, urban street information, pervious surface, impervious surface, surface material, vegetation coverage area, vegetation species, and vegetation shape in a specified area;

[0084] The dynamic meteorological data includes one or more of underlying surface temperature, air temperature, relative humidity, wind speed, solar radiation, and heat - related pollutant data in a specified area;

[0085] The socioeconomic data includes the population characteristics in a specified area;

[0086] The heat adaptation strategy data includes heat adaptation strategies for different - characteristic populations in a specified area.

[0087] In this embodiment, the microclimate prediction module simulates the microclimate of a specified area within a specified time according to the dynamic meteorological data, and makes local corrections based on the urban morphological data.

[0088] In this embodiment, the heat - health risk meteorological indices include wet - bulb globe temperature, mean radiant temperature, physiological equivalent temperature, and universal thermal climate index.

[0089] In a more preferred embodiment:

[0090] The interaction module includes:

[0091] A feature collection unit, configured to collect the population characteristics and heat adaptation needs of the current user;

[0092] An output unit for outputting a final decision to the user.

[0093] The retrieval module includes:

[0094] A user identification unit for identifying whether the current user category belongs to a government organization, a social organization, a community institution or an individual;

[0095] A classification unit for classifying the current user's category according to the population characteristics when the current user belongs to an individual;

[0096] A first retrieval unit for retrieving a heat adaptation strategy suitable for the current user category in the storage module;

[0097] A second retrieval unit for retrieving a heat adaptation strategy suitable for the current heat adaptation requirement in the storage module.

[0098] In a more preferred embodiment, the method for simulating the dynamic microclimate of a specified area includes: simulating the microclimate of the specified area within a specified time according to the dynamic meteorological data, and making local corrections based on the urban morphology data;

[0099] In a more preferred embodiment, when obtaining the user category, first determine the first-level category of the user according to the input user category, and the first-level category includes government organizations, social organizations, community institutions, and individuals;

[0100] If the first-level category of the user belongs to an individual, then collect the population characteristics of the current user, and classify the second-level category of the current user according to the population characteristics.

[0101] The decision-making platform built in this embodiment provides a comprehensive heat environment response platform that can serve government organizations, social organizations, community institutions and individuals at the same time, and can contribute to the city's response to heat hazards at all levels, such as:

[0102] When the user is a government organization, through this embodiment, the microclimate environment of the area can be predicted in a timely manner, and early warnings can be issued for high-temperature areas in a timely manner to avoid staying or working in high-temperature areas for a long time; warning outdoor workers of heat safety and adopting heat adaptation strategies at the municipal construction level to reduce the harm caused by high temperature, and arranging relevant departments to take effective cooling measures.

[0103] When the user is a social organization or a community institution, through this embodiment, accurate and visual cooling points or the distribution of cooling facilities can be provided for residents to guide residents to travel or carry out outdoor activities healthily and safely, such as when it is thermally comfortable and safe for residents to travel; if necessary to travel, where there are cooling places for residents to cool down, etc.

[0104] When the user is an individual, through this embodiment, retrieval can be performed according to different personal demographic characteristics (age, health, gender, economic status, etc.) and the types of outdoor activities required, to obtain the thermal comfort index for travel, and get suggestions for reaching the destination (including routes, time, etc.) that are more comfortable, healthy, and safe as shown in Figure 3 , and at the same time, information such as cooling points, cooling facilities, and public air-conditioned places around or along the way can be prompted / queried.

[0105] Embodiment 2:

[0106] A smart travel decision-making method based on heat warning and heat adaptation, as shown in Figure 2 , includes the following steps:

[0107] Step 1: Data collection

[0108] Data collection includes 4 categories: urban form data, meteorological data, socioeconomic data, and heat adaptation strategy data.

[0109] Urban form data includes land use rate, building coverage area, building height, building shape, urban street information, permeable surface, impermeable surface, surface material, vegetation coverage area, vegetation species, and vegetation shape. The data can be obtained from the planning department, or collected through remote sensing products (such as MODIS, Landsat, Lidar), relevant companies, and individuals.

[0110] Meteorological data includes underlying surface temperature, air temperature, relative humidity, wind speed, solar radiation, and heat-related pollutants (such as NO x , O3). Meteorological data is dynamically changing and can be collected through remote sensing, national meteorological stations, urban meteorological stations, mobile climate stations, numerical simulations, etc.

[0111] Socioeconomic data reflects demographic characteristics such as education, age, gender, income, work type, health, and housing, and can be obtained through census data, questionnaires, interviews, etc.

[0112] Heat adaptation strategy data refers to behaviors that reduce heat-related hazards by changing policies or actions, such as drinking ice water, turning on the air conditioner, reducing clothing, taking shelter in the shade, etc. at high temperatures. These data need to be obtained through literature collection and questionnaires.

[0113] Step 2: Microclimate prediction

[0114] Microclimate prediction can be obtained by using software such as GIS, WRF, ENVI-met, Rhino, etc. based on the urban morphology data and meteorological data in Step 1. The WRF model can be used to estimate large-scale weather at a low resolution, while ENVI-met can predict the climate at the scales of buildings, streets, blocks, and areas at a high resolution.

[0115] In this embodiment, taking Rhino software as an example, first, the temperature and humidity of the local microclimate are simulated using the Dragonfly plug-in in Rhino through the collected meteorological data and urban morphology data; then, the accuracy of the simulation is verified by combining the on-site measured data. If the results are similar, the model is considered valid. If the results have a large gap, the parameters need to be adjusted again until the results are similar before proceeding to the next simulation; then, using the accurate temperature and humidity data and combining with the urban morphology data, the local wind environment is simulated using the OpenFOAM or butterfly plug-in to obtain the predicted wind speed.

[0116] Step 3: Data post-processing

[0117] According to the parameters of the predicted microclimate obtained in Step 2, including air temperature, humidity, wind speed, etc., these parameters are post-processed as indicators closely related to thermal comfort and thermal health (such as WBGT, Tmrt, PET, UTCI).

[0118] Step 4: Data storage

[0119] First, the socioeconomic data and heat adaptation strategy data in Step 1 are pre-stored in the platform; then, the thermal comfort and thermal health indicators obtained from the post-processing in Step 3 are stored in the platform to provide support for later information retrieval and decision-making, realizing integrated intelligent prediction and decision-making.

[0120] Step 5: Information retrieval

[0121] Retrieve data for possible adaptation strategies. Users can input various demographic characteristics (such as age, health status) and social information on workload and intensity. Users can be individuals, service providers, community healthcare, community centers, government management departments, and other social organizations, etc.

[0122] Step 6: Heat warning and heat adaptation decision-making

[0123] The platform provides different heat comfort index warnings for different social characteristic groups. When the index exceeds the threshold, it will automatically alarm or provide users with detailed suggestions for adapting to high temperatures to meet the conditions for healthy travel.

[0124] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0125] It should be noted that in this text, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article or device. In addition, the term "connected" used in this text, without special explanation, can be directly connected or indirectly connected through other components.

Claims

1. A smart travel decision-making platform based on heat warning and heat adaptation, characterized in that, include: A data collection module, which is used to collect urban morphology data, dynamic meteorological data, socio-economic data, and heat adaptation strategy data within a specified area; The urban morphology data include land utilization rate, building coverage area, building height, building size, urban street information, permeable surface, impermeable surface, surface material, vegetation coverage area, vegetation type and vegetation size in the specified area; the dynamic meteorological data include underlying surface temperature, air temperature, relative humidity, wind speed, solar radiation and heat-related pollutant data in the specified area; the socio-economic data include population characteristics in the specified area; the heat adaptation strategy data include heat adaptation strategies of different population characteristics in the specified area; A microclimate prediction module, used to extract urban morphology data and dynamic meteorological data from the data collection module and simulate the dynamic microclimate of a designated area; A data post-processing module, used for extracting microclimate parameters from the microclimate prediction module and calculating thermal health risk meteorological indicators based on the microclimate parameters; A storage module, used to store the socio-economic data, heat adaptation strategy data, and heat health risk meteorological indicators; The thermal health risk meteorological index includes one or more of wet globe temperature, mean radiation temperature, physiological equivalent temperature, and general thermal climate index; A retrieval module is used to retrieve a thermal adaptation strategy that is suitable for the current user category and thermal adaptation requirement in the storage module according to the user category and the thermal adaptation requirement, and send it to the decision module; A decision-making module, used to make a heat warning decision and / or a heat adaptation decision according to the heat adaptation strategy and in combination with a preset heat health risk meteorological indicator threshold; An interaction module, used to obtain a user category and a user's heat adaptation requirement, and output the heat warning decision and / or heat adaptation decision to the user; The smart travel decision-making method based on the smart travel decision-making platform includes the following steps: S1. Collect urban morphology data, dynamic meteorological data, socioeconomic data, and heat adaptation strategy data within the designated area; S2, extracting urban morphological data and dynamic meteorological data from the data collection module to simulate the dynamic microclimate of a designated area; S3, extracting microclimate parameters, and calculating thermal health risk meteorological indicators based on the microclimate parameters; S4, storing the socioeconomic data, heat adaptation strategy data, and heat health risk meteorological indicators as standby data; S5. Input user category and thermal adaptation requirements; Determine the primary category of the user based on the input user category, where the primary category includes government organizations, social groups, community organizations, and individuals; if the primary category of the user is an individual, collect the demographic characteristics of the current user, and divide the secondary category of the current user based on the demographic characteristics; Retrieving a thermal adaptation strategy adapted to the current user category and thermal adaptation requirements from the standby data; S6. Making a heat warning decision and / or heat adaptation decision according to the heat adaptation strategy and in combination with a preset heat health risk meteorological indicator threshold; S7. Outputting the heat warning decision and / or heat adaptation decision to the user; When the user is a government organization, it can predict the regional microclimate environment and issue early warnings for high temperature areas in a timely manner; When the user is a social organization or a community institution, provide accurate and visual distribution of cooling points or cooling facilities for residents, and guide residents to travel or engage in outdoor activities healthily and safely; When the user is an individual, retrieve according to the different demographic characteristics of the individual and the types of outdoor activities required, obtain the thermal comfort index for travel, get suggestions for reaching the destination including the route and time, and at the same time prompt / query information about cooling points, cooling facilities or public air-conditioned places around or along the way.

2. The intelligent travel decision-making platform based on thermal early warning and thermal adaptation according to claim 1, characterized in that The microclimate prediction module simulates the microclimate in a specified area at a specified time based on dynamic meteorological data and makes local corrections based on urban morphology data.

3. The intelligent travel decision-making platform based on thermal early warning and thermal adaptation according to claim 1, wherein The interaction module includes: A feature collection unit for collecting the demographic characteristics and heat adaptation needs of the current user; An output unit for outputting the final decision to the user.

4. The intelligent travel decision-making platform based on thermal early warning and thermal adaptation according to claim 3, characterized in that, The retrieval module includes: A user identification unit for identifying whether the current user category belongs to a government organization, a social organization, a community institution or an individual; A classification unit for classifying the current user category according to the demographic characteristics when the current user is an individual; A first retrieval unit for retrieving heat adaptation strategies suitable for the current user category in the storage module; A second retrieval unit for retrieving heat adaptation strategies suitable for the current heat adaptation needs in the storage module.

5. The intelligent travel decision-making platform based on heat warning and heat adaptation according to claim 1, characterized in that The urban morphology data is obtained from government departments or collected through remote sensing technology; The dynamic meteorological data is obtained through meteorological departments, or collected through remote sensing technology, or obtained through numerical simulation; The social and economic data is obtained through census data and / or questionnaires; The heat adaptation strategy data is obtained through on-site visits and / or questionnaires.

6. The intelligent travel decision-making platform based on heat warning and heat adaptation according to claim 1, characterized in that The method for simulating the dynamic microclimate of a specified area includes: simulating the microclimate in a specified area at a specified time based on dynamic meteorological data and making local corrections based on urban morphology data; The heat health risk meteorological indicators include one or more of wet bulb globe temperature, mean radiant temperature, physiological equivalent temperature, and universal thermal climate index.

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