High-resolution accumulated heat risk calculation method, device and system and storage medium

Abstract

The invention discloses a high-resolution cumulative thermal risk calculation method, device and system and a storage medium. The method comprises the steps of obtaining thermal environment data of villages and behavior data of villagers; according to the thermal environment data of the villages and the behavior data of the villagers, obtaining physiological equivalent temperature based on individual perception; a cumulative thermal risk indicator is calculated from the physiological equivalent temperature based on individual perception. By adopting the technical scheme of the invention, the thermal risk of rural community residents can be accurately evaluated and analyzed.

Description

Technical Field

[0001] The present invention belongs to the technical field of climate assessment, and particularly relates to a high-resolution cumulative heat risk calculation method, device, system, and storage medium. Background Art

[0002] Under the background of global warming and frequent extreme heatwaves, the heat risk has increased significantly, which will have an adverse impact on human health. Currently, for the research on heat risk at the community scale, urban form is often used as the research object, and the land surface temperature (LST) is combined with various variables, such as urban functional areas, urban density, urban form, population density, etc. to evaluate the spatial heat risk, and finally a risk map is drawn. The above research has explored the hazards of heat exposure and the impact of different environmental factors on heat risk, but there are also some deficiencies: 1. The existing heat risk assessment methods do not consider human behavior, resulting in inaccurate assessment results; for example, there is an unobstructed square in the core area of a community, and the ground of the square is paved with asphalt. Since the solar radiation intensity received by the square at noon is very high, the entire area is marked as a high heat risk area, but in fact, people do not appear in the square to move at noon, and the people living in this area do not have a high heat risk. 2. The simple meteorological indicators or thermal comfort indicators used in the current research do not consider the cumulative characteristics of the heat hazards suffered by the human body. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a high-resolution cumulative heat risk calculation method, device, system, and storage medium, which can accurately evaluate and analyze the heat risk of rural community residents.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] A high-resolution cumulative heat risk calculation method includes:

[0006] Step S1: Obtain the heat environment data of the village and the behavior data of the villagers;

[0007] Step S2: Obtain the physiologically equivalent temperature based on individual perception according to the heat environment data of the village and the behavior data of the villagers;

[0008] Step S3: Calculate the cumulative heat risk index according to the physiologically equivalent temperature based on individual perception.

[0009] Preferably, the heat environment data includes: air temperature, black globe temperature, relative humidity, and mean radiant temperature; the behavior data includes: the schedule and spatio-temporal trajectory of the residents; among them, the schedule is constructed based on the Markov model, and the spatio-temporal trajectory selects the path by the A* algorithm.

[0010] Preferably, the cumulative heat risk index The calculation formula is:

[0011] aPET i = PET(i, x, y)

[0012]

[0013] where aPET i is the PET value when the agent is at position (x, y) at time step i; W i is the heat stress weight according to time step i; N t is the total number of time steps in the cumulative calculation; is the cumulative heat index calculated based on the PET values at N t time steps.

[0014] The present invention provides a high-resolution cumulative heat risk calculation device, including:

[0015] An acquisition module for acquiring the heat environment data and the behavior data of villagers in a village;

[0016] A first calculation module for obtaining the physiologically equivalent temperature based on individual perception according to the heat environment data and the behavior data of villagers in a village;

[0017] A second calculation module for calculating the cumulative heat risk index according to the physiologically equivalent temperature based on individual perception.

[0018] Preferably, the heat environment data includes: air temperature, black globe temperature, relative humidity, and mean radiant temperature; the behavior data includes: the schedule and spatio-temporal trajectory of residents; wherein, the schedule is constructed based on the Markov model, and the spatio-temporal trajectory is selected for the path by the A* algorithm.

[0019] Preferably, the calculation formula of the cumulative heat risk index is:

[0020] aPET i = PET(i, x, y)

[0021]

[0022] where aPET i is the PET value when the agent is at position (x, y) at time step i; W i is the heat stress weight according to time step i; N t is the total number of time steps in the cumulative calculation; is the cumulative heat index calculated based on the PET values at N t time steps.

[0023] The present invention further provides a high-resolution cumulative heat risk calculation system, including: a memory and a processor, where a computer program run by the processor is stored on the memory, and the computer program executes a high-resolution cumulative heat risk calculation method when run by the processor.

[0024] The present invention further provides a storage medium, where a computer program is stored on the storage medium, and the computer program executes a high-resolution cumulative heat risk calculation method when running.

[0025] This method quantifies the cumulative heat risk of residents by combining the spatio-temporal behavior of residents with the thermal environment model of the village, and accurately evaluates the cumulative heat risk through the physiologically equivalent temperature (aPET) based on individual perception and the cumulative heat risk index (CHI). BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the provided drawings.

[0027] Figure 1 It is a flowchart of the high-resolution cumulative heat risk calculation method according to the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0029] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0030] Embodiment 1:

[0031] As Figure 1 shown, the embodiment of the present invention provides a high-resolution cumulative heat risk calculation method, including:

[0032] Step S1, obtaining the thermal environment data of the village and the behavior data of the villagers;

[0033] Step S2, obtaining the physiologically equivalent temperature based on individual perception (agent model) according to the thermal environment data of the village and the behavior data of the villagers;

[0034] Step S3: Calculate the cumulative heat risk index according to the physiologically equivalent temperature based on individual perception.

[0035] As an implementation manner of an embodiment of the present invention, the thermal environment data includes: air temperature, black globe temperature, relative humidity, and mean radiant temperature; the behavior data includes: the schedule and spatio-temporal trajectory of residents; wherein, the schedule is constructed based on the Markov model, and the spatio-temporal trajectory selects the path by the A* algorithm.

[0036] Furthermore, the calculation formula of the mean radiant temperature is as follows:

[0037]

[0038] wherein, T mrt is the mean radiant temperature; T g is the black globe temperature; T a is the air temperature; V a is the wind speed; D is the diameter of the black globe; ε is the emissivity of the black globe.

[0039] Furthermore, in step S2, the Agent-Based Model (ABM) is a computer simulation method used to study the behavior and interaction of complex systems composed of many individuals (called agents). ABM simulates the overall behavior of the system by modeling the characteristics, rules, and interactions of individual agents. In this study, constructing an agent-based rural resident behavior model includes the following three parts:

[0040] 1. Establishment of the agent time schedule: Determine the daily activity schedule of the agent by defining the key activity nodes on the time axis.

[0041] 2. Activities procedure setting: Set the execution rules and behavior patterns for each activity of the agent to reflect the activity preferences and behavior laws of residents during a specific period.

[0042] 3. Real-time location Variation: Dynamically update the position of the agent in the simulation space according to the activity arrangement and rules to form a complete spatial behavior trajectory.

[0043] These three parts together complete the setting of the behavior characteristics of the agent from time to space, and finally realize the trajectory simulation of all agents.

[0044] The agent's schedule is activity-driven. Specifically, the activities of rural residents are divided into two parts: fixed-time activities and random-time activities. The start time and duration of fixed-time activities are randomly assigned to all agents through a normal distribution, and the remaining parts in the schedule are filled with random-time activities. The distribution probability of random-time activities is calculated based on the Markov chain model, and finally, each agent forms a complete schedule. The start time and duration of each fixed-time activity, as well as the initial probability and duration of random-time activities, are obtained through questionnaire surveys.

[0045] However, in the actual survey, it was found that whether residents are engaged in farm work has a significant impact on their daily schedule. Residents engaged in farm work will go directly to the farm after getting up and go home for breakfast only after finishing the farm work; while residents not engaged in farm work will have breakfast directly after getting up early. This results in a large difference in the daily schedules of the two types of residents, especially in the schedules of fixed-time activities. Therefore, we divide the agents into two groups and set time characteristics according to whether they are engaged in farm work: one group is agents engaged in farm work, and the other group is agents not engaged in farm work (see Table 1).

[0046] Table 1

[0047]

[0048] In the embodiment of the present invention, to simulate the random distribution and conversion process of residents' random-time activities in time, a method based on the Markov chain is adopted to generate the transition probability matrix of random-time activities in each agent's schedule. The Markov chain is a memoryless random process, that is, the state of the system at the next time step is only related to its current state and is not directly affected by the states at earlier times.

[0049] The embodiment of the present invention uses a heuristic approach to iteratively adjust the transition probability matrix P based on the known steady-state distribution π. First, the target steady-state distribution π is estimated according to the survey data. Subsequently, a random initial transition probability matrix P is generated, and it is ensured that the sum of the elements in each row is 1. In the iterative process, the known steady-state distribution π is incorporated into the update step of P. Through a sufficient number of iterations, this heuristic method gradually modifies P so that the Markov chain can converge to the target steady-state distribution π.

[0050] The initial probability, activity duration, and time preference of random-time activities are shown in Table 2.

[0051] Table 2

[0052]

[0053] After determining the schedule of the agent, the setting of the time part of the resident behavior model is completed. Next, it is necessary to determine the trajectory of the agent, which involves the change of the agent's spatial position, that is, according to the activity time in the schedule, determine the specific position of the agent at each moment.

[0054] According to the different destinations, activities can be divided into fixed-destination activities, semi-fixed-destination activities, and random-destination activities.

[0055] 1. Fixed-destination activities

[0056] Include getting up, having breakfast, lunch, taking a nap, having dinner, and sleeping at home, as well as playing cards at the square pergola. The destinations of these activities are a fixed coordinate point. When the activity starts, the agent will go to the designated location and then start the activity after arriving.

[0057] 2. Semi-fixed-destination activities

[0058] Include farm work, exercise, taking the baby for a walk, square dancing, and passing-by activities. The destinations of these activities are a definite coordinate point randomly selected within a certain area. The agent first determines the target location within the area at the start of the activity and then goes to that location to carry out the activity.

[0059] 3. Random-destination activities

[0060] Include walking, standing and chatting, sitting alone, and chatting with multiple people activities. These activities determine the destination according to different behavior rules and sometimes re-determine a new destination during the activity. For example, for the sitting-alone and chatting-with-multiple-people activities, assume that agent i conducts the activity before lunch. First, it will go to the right chatting area. After arriving at the destination, if there is no other agent engaged in the same activity nearby, then agent i will conduct the sitting-alone activity at the destination; if there is an agent j engaged in the same activity nearby, then agent i will go to the location where agent j is, and at this time, the sitting-alone activity will turn into a chatting-with-multiple-people activity.

[0061] The embodiment of the present invention uses the A*(A-star) algorithm. A* is a heuristic search algorithm used to find the shortest path from the starting point to the target point in graph search problems

[45] , and is often used for path calculation of agents in the agent model. It combines the characteristics of minimum-cost first (Dijkstra algorithm) and heuristic search (such as greedy best-first search), and guides the search by estimating the total cost of the current path to ensure the optimality and efficiency of the path. The principle of the A* algorithm is to calculate an evaluation function f for each node n during the search process (n) , and its formula is as follows:

[0062] f (n) = g (n)+h (n)

[0063] Among them, g (n) is the actual distance from the starting node to node n; h (n) is the remaining estimated distance from node n to the target node; f (n) is the total cost of node n, representing the estimated minimum distance to reach the target node through node n.

[0064] The A* algorithm is used to simulate the path selection of village residents from one location to another. It considers the road network structure, walking cost, and environmental constraints to adapt to the actual environmental characteristics of the village, improving the efficiency and realism of path planning. By comprehensively evaluating the cost of the path (g (n) ) and the heuristic estimate of the target distance (h (n) ), the A algorithm generates the optimal path for each agent, effectively supporting the simulation of residents' behavior and path decision-making analysis in a dynamic environment.

[0065] Furthermore, based on the thermal environment data of the village and the behavior data of the villagers, through the LBT software, the physiologically equivalent temperature based on individual perception (agent model) is obtained.

[0066] As an implementation manner of the embodiment of the present invention, in step S3, based on the physiologically equivalent temperature (PET) value of the agent and combined with multiple time steps, the cumulative heat index of a single agent is quantified. To obtain the accurate PET value of the agent at each moment, the PET value (aPET i ) at each time step i is determined by the position (x, y) of the agent and the environmental conditions.

[0067] To consider the impact of heat stress over time, a weight is assigned to each time step This weight is calculated by the reciprocal cumulative sum of the step index:

[0068] This weighting scheme emphasizes the most recent time steps, making them have a greater influence compared to earlier time steps.

[0069] Within N t time steps, the cumulative heat index is calculated by the weighted average of the PET values of the agent. Specifically, The calculation formula of

[0070] aPET i = PET(i, x, y)

[0071]

[0072] Among them, aPETi PET value when the agent is at position (x, y) at time step i; W i is the heat stress weight according to time step i; N t is the total number of time steps in the cumulative calculation; is based on N t cumulative heat index calculated from the PET values at the time steps.

[0073] By taking the ratio of the sum of weighted PET values to the sum of weights, the time-varying characteristics of heat exposure can be captured, reflecting the overall heat stress of the agent during the analysis period; by combining spatial and temporal factors, it is used to evaluate the cumulative heat exposure risk.

[0074] Example 2:

[0075] The embodiment of the present invention also provides a high-resolution cumulative heat risk calculation device, including:

[0076] An acquisition module for acquiring the heat environment data of the village and the behavior data of the villagers;

[0077] A first calculation module for obtaining the physiologically equivalent temperature based on individual perception according to the heat environment data of the village and the behavior data of the villagers;

[0078] A second calculation module for calculating the cumulative heat risk index according to the physiologically equivalent temperature based on individual perception.

[0079] As an implementation manner of the embodiment of the present invention, the heat environment data includes: air temperature, black globe temperature, relative humidity, and mean radiant temperature; the behavior data includes: the schedule of residents and the spatio-temporal trajectory; among them, the schedule is constructed based on the Markov model, and the spatio-temporal trajectory selects the path by the A* algorithm.

[0080] As an implementation manner of the embodiment of the present invention, the cumulative heat risk index The calculation formula is:

[0081] aPET i = PET(i, x, y)

[0082]

[0083] where aPET i is the PET value when the agent is at position (x, y) at time step i; W i is the heat stress weight according to time step i; N t is the total number of time steps in the cumulative calculation; is based on N t cumulative heat index calculated from the PET values at the time steps.

[0084] Example 3:

[0085] An embodiment of the present invention further provides a high-resolution cumulative heat risk calculation system, including: a memory and a processor, where a computer program run by the processor is stored on the memory, and the computer program executes a high-resolution cumulative heat risk calculation method when run by the processor.

[0086] Example 4:

[0087] An embodiment of the present invention further provides a storage medium, where a computer program is stored on the storage medium, and the computer program executes a high-resolution cumulative heat risk calculation method when running.

[0088] The above-described embodiments are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A high-resolution cumulative thermal risk calculation method, characterized in that: include: Step S1, obtaining thermal environment data of the village and behavior data of villagers; Step S2: obtaining a physiological equivalent temperature based on individual perception according to the thermal environment data of the village and the behavior data of the villagers; Step S3: Calculate the cumulative heat risk index according to the physiological equivalent temperature based on individual perception.

2. The high-resolution cumulative thermal risk calculation method according to claim 1, characterized in that: Thermal environment data include: air temperature, black globe temperature, relative humidity, and mean radiation temperature; behavioral data include: residents' schedules and space-time trajectories; among them, the schedule is constructed based on the Markov model, and the space-time trajectory is selected by the A* algorithm.

3. The high-resolution cumulative thermal risk calculation method according to claim 2, characterized in that: Cumulative heat risk index The calculation formula is: aPET i =PET(i,x,y) Among them, aPET i is the PET value of the agent at position (x, y) at time step i; W i is the heat stress weight according to time step i; N t is the total number of time steps in the cumulative calculation; Based on N t The cumulative thermal index calculated from the PET value for the time step.

4. A high-resolution cumulative thermal risk calculation device, characterized in that: include: The acquisition module is used to obtain the thermal environment data of the village and the behavior data of the villagers; The first calculation module is used to obtain the physiological equivalent temperature based on individual perception according to the thermal environment data of the village and the behavior data of the villagers; The second calculation module is used to calculate the cumulative heat risk index according to the physiological equivalent temperature based on individual perception.

5. The high-resolution cumulative thermal risk calculation device according to claim 4, characterized in that: Thermal environment data include: air temperature, black globe temperature, relative humidity and mean radiation temperature; behavioral data include: residents' schedule and space-time trajectory; among them, the schedule is constructed based on the Markov model, and the space-time trajectory is selected by the A* algorithm.

6. The high-resolution cumulative thermal risk calculation device according to claim 5, characterized in that: Cumulative heat risk index The calculation formula is: aPET i =PET(i,x,y) Among them, aPET i is the PET value of the agent at position (x, y) at time step i; W i is the heat stress weight according to time step i; N t is the total number of time steps in the cumulative calculation; Based on N t The cumulative thermal index calculated from the PET values ​​for the time step.

7. A high-resolution cumulative thermal risk calculation system, characterized in that: include: A memory and a processor, wherein the memory stores a computer program executed by the processor, and when the computer program is executed by the processor, the high-resolution cumulative thermal risk calculation method according to any one of claims 1 to 3 is executed.

8. A storage medium, characterized in that: The storage medium stores a computer program, which executes the high-resolution cumulative thermal risk calculation method according to any one of claims 1 to 3 when running.

Images