Thermal exposure risk time partition determination method and device, equipment and storage medium

Abstract

The invention provides a thermal exposure risk time partition determination method and device, equipment and a storage medium, and the method comprises the steps: obtaining a time sequence thermal exposure risk spatial distribution diagram layer of a target city under a preset time scale in a target period, carrying out the hierarchical clustering analysis based on the similarity of a thermal exposure risk spatial pattern, and obtaining a time sequence thermal exposure risk spatial distribution diagram layer; obtaining a time partition under a preset time scale; and carrying out space-time partition statistics on the thermal exposure risk based on the time partitions under the predetermined time scale to obtain an average urban thermal exposure risk space distribution diagram layer of the time partitions under the target time scale, and carrying out paired sample difference test so as to determine the reliability of the time partitions under the predetermined time scale according to a test result. According to the method, the spatial pattern of the thermal exposure risk is considered, so that quantitative division of the thermal exposure risk time partitions under different predetermined time scales is realized, the obtained time partitions are matched with the travel time of urban residents, and partition management and control of the urban thermal exposure risk can be effectively guided.

Description

Technical Field

[0001] The present invention relates to the field of heat exposure risk technology, and in particular to a method, device, equipment and storage medium for determining time zones for heat exposure risk. Background Art

[0002] Urban heat exposure risk represents the possibility of residents suffering health hazards from contact and exposure to high temperature conditions or adverse thermal environments. It is closely related to the urban living environment and residents' health, and profoundly affects the livability and sustainable management of the city.

[0003] Since both ambient temperature and urban population have high spatial heterogeneity and temporal variability, urban heat exposure risk also has high spatiotemporal variability; at the same time, limited by the availability of real resources, the targeted implementation of adaptation and mitigation measures for urban heat exposure risk needs to be carried out in different regions, which puts forward new requirements for the spatial and temporal zoning and verification methods of heat exposure risk.

[0004] Therefore, how to effectively and reliably determine the time zones of urban heat exposure risks in order to implement targeted zoning control of urban heat exposure risks is an urgent problem to be solved. Summary of the invention

[0005] The present invention provides a method, device, equipment and storage medium for determining time zones for heat exposure risks, which are used to solve the defects in the prior art that the time zones for urban heat exposure risks lack relevant scientific basis and the zoning control of risks cannot be effectively guided. In realizing the quantitative division of time zones for urban heat exposure risks, the spatial pattern of heat exposure risks is taken into account at the same time, thereby effectively guiding the zoning control of urban heat exposure risks.

[0006] The present invention provides a method for determining time zones of heat exposure risks, comprising: Obtain a time series heat exposure risk spatial distribution layer at a predetermined time scale for a target city within a target period; Based on the similarity of the spatial pattern of heat exposure risk, a hierarchical clustering analysis is performed on the time series heat exposure risk spatial distribution layer at the predetermined time scale to obtain the time partition at the predetermined time scale; Based on the time partitions at the predetermined time scale, performing spatiotemporal partition statistics on the heat exposure risk, and obtaining an average urban heat exposure risk spatial distribution layer of the time partitions at the target time scale; A paired sample difference test is performed on the average urban heat exposure risk spatial distribution layer of the time partition at the target time scale to determine the reliability of the time partition at the predetermined time scale based on the test results.

[0007] According to a method for determining time zones of heat exposure risk provided by the present invention, the step of obtaining a time series heat exposure risk spatial distribution layer at a predetermined time scale for a target city within a target period includes: For each predetermined time scale, determining a basic time unit of the predetermined time scale based on the predetermined time scale; Based on the basic time unit, dividing the target period into a plurality of time intervals of equal length; Determine a basic spatial unit, and divide the target city into a plurality of spatial regions based on the basic spatial unit; The spatial regions on the heat exposure risk layer in each time interval are sorted to obtain a time series heat exposure risk spatial distribution layer in the predetermined time scale.

[0008] According to a method for determining time zones of heat exposure risk provided by the present invention, the spatial regions on the heat exposure risk layer in each time interval are sorted to obtain a time series heat exposure risk spatial distribution layer in the predetermined time scale, including: According to the same spatial area order, the spatial areas on the heat exposure risk layer in each time interval are sorted to obtain the time series heat exposure risk spatial distribution layer in the predetermined time scale.

[0009] According to a method for determining time zones of heat exposure risk provided by the present invention, based on the similarity of the spatial pattern of heat exposure risk, a hierarchical clustering analysis is performed on the time series heat exposure risk spatial distribution layer at the predetermined time scale to obtain the time zones at the predetermined time scale, including: For the time series heat exposure risk spatial distribution layer at the predetermined time scale, hierarchical clustering analysis is performed based on the similarity of the heat exposure risk spatial pattern to obtain a specific number of multiple clustering results at different clustering levels; A target clustering level is determined, and according to the time attribute characteristics of the time interval contained in each clustering result under the target clustering level, the clustering result is defined to obtain the time partition under the predetermined time scale.

[0010] According to a method for determining time zones of heat exposure risk provided by the present invention, the time zones under the predetermined time scale are used to perform spatiotemporal statistics on heat exposure risk, and an average urban heat exposure risk spatial distribution layer of the time zones under the target time scale is obtained, including: Combining the time partitions at different predetermined time scales to obtain the time partitions at the target time scale; The average heat exposure risk value of each spatial area in the time partition at the target time scale is calculated to obtain the average urban heat exposure risk spatial distribution layer in the time partition at the target time scale.

[0011] According to a method for determining heat exposure risk time zones provided by the present invention, performing a paired sample difference test on the average urban heat exposure risk spatial distribution layer of the time zones under the target time scale comprises: The average urban heat exposure risk values ​​of different time zones in the same spatial area at the target time scale are taken as paired samples, and a paired sample difference test is performed on the spatial distribution layer of the average urban heat exposure risk of the time zones at the target time scale.

[0012] According to a method for determining time zones of heat exposure risk provided by the present invention, the reliability of the time zones under the predetermined time scale is determined according to the test results, including: If the significance level test result parameter value is less than the preset significance level test threshold, then determining that the time partition under the predetermined time scale is a reliable result under the significance level; If the significance level test result parameter value is not less than a preset significance level test threshold, it is determined that the time partition under the predetermined time scale is an unreliable result under the significance level.

[0013] The present invention also provides a device for determining time zones of heat exposure risks, comprising: An acquisition module is used to obtain a time series heat exposure risk spatial distribution layer at a predetermined time scale for a target city within a target period; A clustering module, for performing a hierarchical clustering analysis on the time series heat exposure risk spatial distribution layer at the predetermined time scale based on the similarity of the heat exposure risk spatial pattern, to obtain the time partition at the predetermined time scale; A statistical module, for performing spatiotemporal statistics on heat exposure risk based on the time partitions at the predetermined time scale, and obtaining an average urban heat exposure risk spatial distribution layer for the time partitions at the target time scale; The test module is used to perform a paired sample difference test on the average urban heat exposure risk spatial distribution layer of the time partition at the target time scale, so as to determine the reliability of the time partition at the predetermined time scale according to the test results.

[0014] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for determining the time partition of heat exposure risk as described in any one of the above is implemented.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for determining the time partition of heat exposure risk as described in any one of the above is implemented.

[0016] The method, device, equipment and storage medium for determining the time zones of heat exposure risks provided by the present invention perform hierarchical clustering analysis on the time series heat exposure risk spatial distribution layer at a predetermined time scale of the target city within the target period, taking into account the spatial pattern of heat exposure risks, so that the time zones obtained by the clustering analysis can match the travel time of urban residents, thereby realizing the quantitative division of time zones at different predetermined time scales, and effectively guiding the zoning control of urban heat exposure risks; by performing spatiotemporal zoning statistics on heat exposure risks based on time zones at a predetermined time scale, an average urban heat exposure risk spatial distribution layer of the time zones at the target time scale is obtained, and then a paired sample difference test is performed on the average urban heat exposure risk spatial distribution layer of the time zones at the target time scale, so that the reliability of the time zones at the predetermined time scale obtained by the clustering analysis can be further effectively verified through the test results, which is conducive to the final output of reliable and effective time zoning results. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flow chart of a method for determining time zones for heat exposure risks provided in an embodiment of the present invention.

[0019] Figure 2 It is a schematic diagram of the spatial distribution of the average heat exposure risk of a target city within 24 hours provided by an embodiment of the present invention.

[0020] Figure 3 It is a schematic diagram of the spatial distribution of the average heat exposure risk of a target city over 31 days provided by an embodiment of the present invention.

[0021] Figure 4 It is a schematic diagram of the hierarchical clustering pattern of urban heat exposure risk time series at different predetermined time scales in a target city provided by an embodiment of the present invention.

[0022] Figure 5 It is a schematic diagram of the spatial distribution of average urban heat exposure risks in different time zones of a target city provided by an embodiment of the present invention.

[0023] Figure 6 It is a structural schematic diagram of a device for determining time zones for heat exposure risks provided in an embodiment of the present invention.

[0024] Figure 7 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] At present, there is no clear and definite method for quantitative determination of time zones of heat exposure risk. On an hourly scale, the intraday zones of heat exposure risk are often simply and qualitatively divided into natural daytime and natural nighttime according to sunrise and sunset times. However, on the one hand, this time zone does not match the travel time of urban residents to a certain extent, and lacks the reference value and significance of applicability on a daily scale; on the other hand, it does not take into account the spatial heterogeneity of heat exposure risk, an important risk characteristic. Therefore, there is currently a lack of reliable methods for the time zone division of urban heat exposure risk, and the zoning control of urban heat exposure risk cannot be effectively guided.

[0027] In response to the above problems, the present invention takes into account the spatial pattern of heat exposure risk, thereby realizing the quantitative division of time zones of heat exposure risk under different predetermined time scales. The obtained time zones match the travel time of urban residents, which can effectively guide the zoning control of urban heat exposure risk.

[0028] Figure 1 is a flow chart of a method for determining time zones for heat exposure risk provided by an embodiment of the present invention. Figure 1 The embodiment of the present invention provides a method for determining time zones of heat exposure risk, and the method may specifically include the following steps: Step 110, obtaining a time series heat exposure risk spatial distribution layer at a predetermined time scale for a target city within a target period.

[0029] It should be noted that the execution subject of the heat exposure risk time partition determination method provided in the embodiment of the present invention may be an electronic device, a component in an electronic device, an integrated circuit or a chip. The electronic device may be a mobile electronic device or a non-mobile electronic device. Exemplarily, the mobile electronic device may be a mobile phone, a tablet computer, a laptop computer, a PDA, a wearable device, an ultra-mobile personal computer (UMPC), a netbook or a personal digital assistant (PDA), etc., and the non-mobile electronic device may be a server, a network attached storage (NAS), a personal computer (PC), a television (TV), a teller machine or a self-service machine, etc., and the embodiment of the present invention does not specifically limit this.

[0030] Specifically, the target period may refer to a heat exposure risk research period determined according to actual needs. For example, the target period may be a month of a year or a day of a month.

[0031] Specifically, the target city may refer to a heat exposure risk study city determined according to actual needs. It should be noted that the heat exposure risk time zone determination method provided in the embodiment of the present invention can be applied not only to heat exposure risk study scenarios in a specific city, but also to heat exposure risk study scenarios in other regions, for example, it can be applied to heat exposure risk study scenarios in provinces, heat exposure risk study scenarios in counties, etc., and the present invention does not make specific limitations here.

[0032] Specifically, the predetermined time scale may refer to a single-dimensional time scale determined according to actual needs, such as a monthly scale, a daily scale, an hourly scale, etc. There may be multiple predetermined time scales, and each predetermined time scale may have a corresponding time series heat exposure risk spatial distribution layer, for example, a monthly series heat exposure risk spatial distribution layer at a monthly scale, a date series heat exposure risk spatial distribution layer at a daily scale, and a time period series heat exposure risk spatial distribution layer at an hourly scale.

[0033] It should be noted that the predetermined time scale can be determined according to the target period and cannot exceed the time range of the target period. For example, if the target period is a certain month, the predetermined time scale can be determined as a day scale and an hour scale, and cannot include a month scale; if the target period is a certain year, the predetermined time scale can be determined as two time scales of a month scale and a day scale, or can be determined as three time scales of a month scale, a day scale, and an hour scale.

[0034] In an embodiment of the present invention, time series heat exposure risk spatial distribution layers at multiple predetermined time scales of a target city within a target period may be obtained, so as to perform hierarchical clustering analysis respectively to obtain corresponding time partitions.

[0035] The embodiment of the present invention realizes the division of time zones by utilizing the heat exposure risk spatial distribution layer, taking into account the important risk characteristic property of spatial heterogeneity of heat exposure risk, which is conducive to more effectively guiding the zoning control of urban heat exposure risk.

[0036] Step 120, based on the similarity of the spatial pattern of heat exposure risk, a hierarchical clustering analysis is performed on the time series heat exposure risk spatial distribution layer at the predetermined time scale to obtain the time partition at the predetermined time scale.

[0037] Specifically, the time series heat exposure risk spatial distribution layer at each predetermined time scale can be respectively subjected to hierarchical clustering analysis based on the similarity of the heat exposure risk spatial pattern to obtain time partitions at each predetermined time scale. There can be at least two time partitions at each predetermined time scale.

[0038] In some embodiments, a hierarchical clustering analysis can be performed on the date series heat exposure risk spatial distribution layer at the daily scale to obtain time partitions at the daily scale; a hierarchical clustering analysis can be performed on the time period series heat exposure risk spatial distribution layer at the hourly scale to obtain time partitions at the hourly scale; and a hierarchical clustering analysis can also be performed on the month series heat exposure risk spatial distribution layer at the monthly scale to obtain time partitions at the monthly scale.

[0039] In some embodiments, the time partitions at the day scale may include two time partitions of "rest days" and "working days", the time partitions at the hour scale may include two time partitions of "daytime" and "night", and the time partitions at the month scale may include two time partitions of "cold season" and "hot season".

[0040] Step 130, based on the time partitions at the predetermined time scale, perform spatiotemporal partition statistics on the heat exposure risk to obtain an average urban heat exposure risk spatial distribution layer of the time partitions at the target time scale.

[0041] Specifically, the target time scale may be a multidimensional time scale composed of a plurality of different predetermined time scales, and the time partition under the target time scale may be obtained by combining the time partitions under different predetermined time scales. For example, when the predetermined time scale is a day scale or an hour scale, the target time scale is a two-dimensional time scale of a day scale and an hour scale.

[0042] In some embodiments, when the predetermined time scale is a day scale and an hour scale, the time partitions under the target time scale may include “daytime on weekends”, “nighttime on weekends”, “daytime on weekdays” and “nighttime on weekdays”.

[0043] In some embodiments, when the predetermined time scale is a monthly scale, a daily scale, and an hourly scale, the time partitions under the target time scale may include "daytime on holidays in cold season", "night on holidays in cold season", "daytime on weekdays in cold season", "night on weekdays in cold season", "daytime on holidays in hot season", "night on holidays in hot season", "daytime on weekdays in hot season", and "night on weekdays in hot season".

[0044] In an embodiment of the present invention, based on the preliminary division of each time zone at a predetermined time scale, the heat exposure risk can be statistically analyzed by spatiotemporal zoning with the basic spatial unit as the object, so as to obtain the average urban heat exposure risk spatial distribution layer of each time zone at the target time scale.

[0045] Step 140, performing a paired sample difference test on the average urban heat exposure risk spatial distribution layer of the time partition at the target time scale, so as to determine the reliability of the time partition at the predetermined time scale according to the test result.

[0046] Specifically, a paired sample difference test can be performed on the average urban heat exposure risk spatial distribution layer of any two time zones under the target time scale. The difference test can be used to compare whether the differences between the samples of the average urban heat exposure risk spatial distribution layer of the two time zones are significant, thereby determining the reliability of each time zone under the predetermined time scale that has been preliminarily divided based on the significance level test results.

[0047] The embodiment of the present invention performs a hierarchical clustering analysis on the time series heat exposure risk spatial distribution layer of the target city at a predetermined time scale within the target period, taking into account the spatial pattern of heat exposure risk, so that the time partitions obtained by the cluster analysis can match the travel time of urban residents, thereby realizing the quantitative division of time partitions at different predetermined time scales, which can effectively guide the zoning control of urban heat exposure risks; by performing spatiotemporal zoning statistics on the heat exposure risk based on the time partitions at the predetermined time scale, the average urban heat exposure risk spatial distribution layer of the time partitions at the target time scale is obtained, and then the paired sample difference test is performed on the average urban heat exposure risk spatial distribution layer of the time partitions at the target time scale, so that the reliability of the time partitions at the predetermined time scale obtained by the cluster analysis can be further effectively verified through the test results, which is conducive to the final output of reliable and effective time partition results.

[0048] In some optional embodiments, obtaining a time series heat exposure risk spatial distribution layer at a predetermined time scale for a target city within a target period may specifically include: Step 111: for each predetermined time scale, determine a basic time unit of the predetermined time scale based on the predetermined time scale.

[0049] Specifically, for each predetermined time scale, a basic time unit on the time series at each predetermined time scale may be determined respectively.

[0050] In some embodiments, for a date sequence on a day scale, the day may be used as a basic time unit; for a time period sequence on an hour scale, the hour may be used as a basic time unit.

[0051] Step 112: Based on the basic time unit, divide the target period into a plurality of time intervals of equal length.

[0052] Specifically, the target period may be divided into a plurality of time intervals of the same length based on the basic time units corresponding to the respective predetermined time scales.

[0053] In some embodiments, assuming that the target period is October 2020, October can be divided into 31 dates based on the basic time unit of the day scale, and October can be divided into 24 hours based on the time period sequence of the hour scale; assuming that the target period is September 2020, September can be divided into 30 dates based on the basic time unit of the day scale, and September can be divided into 24 hours based on the time period sequence of the hour scale.

[0054] Step 113, determining a basic spatial unit, and dividing the target city into a plurality of spatial regions based on the basic spatial unit.

[0055] Specifically, the basic space unit may refer to a regular grid set according to actual needs. The regular grid may be a grid with the same length and width, or a grid with different lengths and widths.

[0056] Specifically, the target city can be divided into n spatial regions of the same size (i.e., the same area) based on the basic spatial unit, and the average heat exposure risk value HER of each spatial region in each time interval can be calculated. n , and obtain the heat exposure risk layer of each spatial area in each time interval.

[0057] In some embodiments, the basic spatial unit may be determined as a regular grid of 250 meters by 250 meters, thereby dividing the target city into a plurality of spatial regions of the same area of ​​250 meters by 250 meters.

[0058] Step 114, sorting the spatial regions on the heat exposure risk layer in each time interval to obtain a time series heat exposure risk spatial distribution layer in the predetermined time scale.

[0059] In some embodiments, the spatial areas on each day's heat exposure risk layer can be sorted with the spatial area as the horizontal coordinate and the date sequence on the daily scale as the vertical coordinate to obtain a date sequence heat exposure risk spatial distribution layer on the daily scale; the spatial areas on each hour's heat exposure risk layer can be sorted with the spatial area as the horizontal coordinate and the time period sequence on the hourly scale as the vertical coordinate to obtain a time period sequence heat exposure risk spatial distribution layer on the hourly scale.

[0060] Figure 2 24-hour average heat exposure risk spatial distribution diagram of the target city provided by the embodiment of the present invention. Figure 2 , the higher the brightness of the space area, the higher the average heat exposure risk value; the lower the brightness of the space area, the lower the average heat exposure risk value. It can be seen that the brightness from period 8 to period 19 is high, and the average heat exposure risk value is high; the brightness from period 19 to period 8 is low, and the average heat exposure risk value is low.

[0061] Figure 3 31 days average heat exposure risk spatial distribution diagram of the target city provided by the embodiment of the present invention. Figure 3 , the higher the brightness of the space area, the higher the average heat exposure risk value; the lower the brightness of the space area, the lower the average heat exposure risk value. It can be seen that the brightness on the 1st to 8th, 11th, 24th to 25th, 17th to 18th and 31st are all high, and the average heat exposure risk value is high; the brightness on the 9th to 10th, 12th to 16th, 19th to 23rd and 26th to 30th are all low, and the average heat exposure risk value is low.

[0062] The embodiment of the present invention divides the time intervals and spatial areas, and sorts the spatial areas on the heat exposure risk layer in each time interval, which is conducive to ensuring the consistency of the spatial pattern of heat exposure risk layers in different time series, taking into account the spatial pattern of heat exposure risks, thereby effectively guiding the zoning control of urban heat exposure risks.

[0063] In some optional embodiments, the sorting of the spatial regions on the heat exposure risk layer in each time interval to obtain the time series heat exposure risk spatial distribution layer in the predetermined time scale may specifically include: sorting the spatial regions on the heat exposure risk layer in each time interval in the same spatial region order to obtain the time series heat exposure risk spatial distribution layer in the predetermined time scale.

[0064] Specifically, after the target city is divided into multiple spatial regions of the same size, each spatial region can be uniquely numbered, and the spatial region sorting order can be determined according to the numbers corresponding to each spatial region. For example, the spatial region order can be: spatial region 1, spatial region 2, spatial region 3, ..., spatial region n.

[0065] Specifically, for each predetermined time scale, the spatial regions on the heat exposure risk layer in each time interval can be sorted according to the same spatial region sorting order to obtain the time series heat exposure risk layer in the predetermined time scale. Among them, the relative spatial position of the same spatial region in the time series is the same, which is conducive to ensuring the consistency of the spatial pattern of heat exposure risk layers in different time series.

[0066] The embodiment of the present invention sorts the spatial areas on the heat exposure risk layer in each time interval according to the same spatial area order, so that the relative spatial position of the same spatial area in the time series is the same, which is conducive to ensuring the consistency of the spatial pattern of heat exposure risk layers in different time series, thereby effectively guiding the zoning control of urban heat exposure risks.

[0067] In some optional embodiments, based on the similarity of the spatial pattern of heat exposure risk, a hierarchical clustering analysis is performed on the time series heat exposure risk spatial distribution layer at the predetermined time scale to obtain the time partition at the predetermined time scale, which may specifically include: Step 121, performing a hierarchical clustering analysis on the time series heat exposure risk spatial distribution layer at the predetermined time scale based on the similarity of the heat exposure risk spatial pattern, to obtain a specific number of multiple clustering results at different clustering levels; Step 122, determining a target clustering level, defining the clustering results according to the time attribute characteristics of the time interval contained in each clustering result under the target clustering level, and obtaining time partitions under a predetermined time scale.

[0068] Specifically, the target clustering level may refer to a clustering level determined according to the number requirement of clustering results.

[0069] Specifically, after performing hierarchical clustering analysis, multiple clustering levels can be obtained, and each clustering level can include a specific number of clustering results. According to the number of clustering results actually required, a clustering level that meets the number of clustering results can be selected as the target clustering level.

[0070] In some embodiments, after performing hierarchical clustering analysis, one clustering result at the first clustering level (i.e., the time series heat exposure risk spatial distribution layer belongs to a large cluster), two clustering results of cluster 1 and cluster 2 at the second clustering level, and four clustering results of cluster 11, cluster 12, cluster 21, and cluster 22 at the third clustering level can be obtained. Among them, cluster 1 includes cluster 11 and cluster 12, and cluster 2 includes cluster 21 and cluster 22. When the number of required clustering results is 2, the second clustering level can be selected as the target clustering level; when the number of required clustering results is 4, the third clustering level can be selected as the target clustering level.

[0071] Specifically, the time attribute characteristics of the time intervals included in different clustering results are also different.

[0072] Specifically, each clustering result under the target clustering level may include the spatial distribution of heat exposure risk under multiple time intervals. The clustering results may be defined according to the time attribute characteristics of the multiple time intervals contained in each clustering result to obtain the time partition under a predetermined time scale.

[0073] In some embodiments, when the target clustering level is the second clustering level, cluster 1 can be defined according to the time attribute characteristics of multiple time intervals included in cluster 1, and cluster 2 can be defined according to the time attribute characteristics of multiple time intervals included in cluster 2.

[0074] Figure 4 It is a schematic diagram of the hierarchical clustering pattern of urban heat exposure risk time series at different predetermined time scales in a target city provided by an embodiment of the present invention.

[0075] Reference Figure 4 In some embodiments, a hierarchical clustering analysis is performed on the date series heat exposure risk spatial distribution layer at the day scale. When the target clustering level is determined to be the second clustering level, two clustering results, cluster 1 (1-8 days, 11 days, 24-25 days, 17-18 days, and 31 days) and cluster 2 (9-10 days, 12-16 days, 19-23 days, and 26-30 days), can be obtained. Cluster 1 and cluster 2 can be defined as "rest day" and "working day" respectively according to the time attribute characteristics of the specific dates contained in the two clustering results.

[0076] In some embodiments, hierarchical clustering analysis is performed on the spatial distribution layer of heat exposure risk of time series at the hourly scale, and two clustering results, cluster 3 (time periods 9-19) and cluster 4 (time periods 1-8 and time periods 20-24), can be obtained. Cluster 3 and cluster 4 can be defined as "daytime" and "nighttime" respectively according to the time attribute characteristics of the specific dates contained in the two clustering results.

[0077] The embodiment of the present invention performs a hierarchical clustering analysis on the spatial distribution layer of the time series heat exposure risk at a predetermined time scale, and defines the clustering results according to the time attribute characteristics of the time series contained in each clustering result, which is conducive to the quantitative division of time zones at different predetermined time scales, thereby effectively guiding the zoning control of urban heat exposure risks.

[0078] In some optional embodiments, the time partitioning based on the predetermined time scale is used to perform spatiotemporal statistics on the heat exposure risk to obtain an average urban heat exposure risk spatial distribution layer of the time partitions at the target time scale, which may specifically include: Step 131, combining time partitions at different predetermined time scales to obtain time partitions at a target time scale; Step 132, calculate the average heat exposure risk value of each spatial area in the time partition at the target time scale, and obtain the average urban heat exposure risk spatial distribution layer in the time partition at the target time scale.

[0079] Specifically, there may be multiple predetermined time scales, and one predetermined time scale may have multiple time partitions. The time partitions at different predetermined time scales may be combined to obtain multiple time partitions at the target time scale, thereby calculating the average heat exposure risk value HER of each spatial area in each time partition at the target time scale. ` n , and obtain the average urban heat exposure risk spatial distribution layer in each time zone at the target time scale.

[0080] In some embodiments, the time partitions "rest day" and "weekday" on a day scale and the time partitions "day" and "night" on an hour scale can be combined to obtain the time partitions "rest day", "rest night", "weekday day" and "weekday night" on a target time scale.

[0081] Figure 5 It is a schematic diagram of the spatial distribution of average urban heat exposure risks in different time zones of a target city provided by an embodiment of the present invention.

[0082] Reference Figure 5 In some embodiments, the heat exposure risk of the target city can be statistically analyzed based on the time partitions at the hourly scale and the time partitions at the daily scale, and the average urban heat exposure risk spatial distribution of weekday daytime, weekday night, weekend daytime, and weekend night can be obtained. It can be seen that the brightness of weekday daytime and weekend daytime is high, and the average heat exposure risk value is high; the brightness of weekday night and weekend night is low, and the average heat exposure risk value is low.

[0083] The embodiment of the present invention calculates the average heat exposure risk value of each spatial area in the time partition at the target time scale, obtains the average urban heat exposure risk spatial distribution layer in the time partition at the target time scale, and then can perform a paired sample difference test. Through the test results, the reliability of the time partition at the predetermined time scale obtained by cluster analysis can be further effectively verified, which is conducive to the final output of reliable and effective time partition results.

[0084] In some optional embodiments, the paired sample difference test is performed on the spatial distribution layer of average urban heat exposure risk in time partitions at the target time scale, which can specifically include: taking the average urban heat exposure risk values ​​of different time partitions in the same spatial area at the target time scale as paired samples, and performing a paired sample difference test on the spatial distribution layer of average urban heat exposure risk in time partitions at the target time scale.

[0085] Specifically, any predetermined time scale can be selected as the partition difference test scale, and other time scales outside the partition difference test scale can be used as control variable scales to perform paired sample difference test.

[0086] Specifically, the test results may include significance level test results and differential effect results. The significance level test results may be used to determine whether the time partitions at a predetermined scale obtained by cluster analysis are reliable results, and the differential effect results may be used to determine the difference levels between the time partitions at a predetermined scale obtained by cluster analysis.

[0087] Table 1 is the significance test result of the time-zone difference of heat exposure risk in the target city provided by the embodiment of the present invention.

[0088]

[0089] Referring to Table 1, in some embodiments, when the hourly scale is selected as the zoning difference test scale and the daily scale is selected as the control variable scale, the average urban heat exposure risk values ​​of the same spatial area during weekday daytime and weekday night can be used as paired samples for difference test, and the significance level is 0.001, and the difference effect is 0.2968; the average urban heat exposure risk values ​​of the same spatial area during weekend daytime and weekend night can be used as paired samples for difference test, and the significance level is 0.001, and the difference effect is 0.3079.

[0090] When the day scale is selected as the zoning difference test scale and the hour scale is selected as the control variable scale, the average urban heat exposure risk values ​​in the same spatial area during the daytime on weekdays and the daytime on weekends can be used as paired samples for difference tests, and the significance level is 0.001, and the difference effect is 0.2071; the average urban heat exposure risk values ​​in the same spatial area during the night on weekdays and the night on weekends can be used as paired samples for difference tests, and the significance level is 0.001, and the difference effect is 0.2568.

[0091] The embodiment of the present invention uses the average urban heat exposure risk values ​​of different time partitions in the same spatial area at the target time scale as paired samples to perform a difference test. The test results can further effectively verify the reliability of the time partition at the predetermined time scale obtained by cluster analysis, which is conducive to the final output of reliable and effective time partition results.

[0092] In some optional embodiments, determining the reliability of the time partition under the predetermined time scale according to the test result may specifically include: Step 141, if the significance level test result parameter value is less than a preset significance level threshold, then determining that the time partition under the predetermined time scale is a reliable result under the significance level; Step 142: If the significance level test result parameter value is not less than the preset significance level test threshold, it is determined that the time partition under the predetermined time scale is an unreliable result under the significance level.

[0093] Specifically, the smaller the parameter value (p-value) of the significance level test result is, the more reliable the time partition division result at the predetermined time scale obtained by cluster analysis is.

[0094] In some embodiments, the preset significance level threshold can be set to 0.001. If the parameter value of the significance level test result p<0.001, it can be determined that the time partition at the predetermined time scale obtained by the cluster analysis is a reliable result at the significance level of 0.001; if the parameter value of the significance level test result p≥0.001, it can be determined that the time partition at the predetermined time scale obtained by the cluster analysis is an unreliable result at the significance level of 0.001. It should be noted that the preset significance level threshold is usually set to 0.1, 0.05 and 0.01. The embodiment of the present invention sets a more stringent preset significance level threshold (0.001) for reliability verification, which can more effectively illustrate the reliability of the time partition at the predetermined time scale of the final output, thereby more effectively guiding the zoning control of urban heat exposure risks.

[0095] Referring to Table 1, it can be seen that there are significant differences in urban heat exposure risks between time partition groups at the hourly scale and the daily scale (p<0.001), which shows that the time partition results obtained by clustering at the predetermined time scale are reliable.

[0096] Specifically, the larger the difference effect parameter value (es value), the greater the degree of partition difference. In some embodiments, if the difference effect result is 0<es<0.3, it can be said that the degree of difference is small; if the difference effect result is 0.3≤es<0.7, it can be said that the degree of difference is medium; if the difference effect result is 0.7≤es<1, it can be said that the degree of difference is large.

[0097] Referring to Table 1, it can be seen that the difference in the spatial distribution of average urban heat exposure risk between weekend daytime and weekend nighttime is moderate, while the difference in the spatial distribution of average urban heat exposure risk between weekday daytime and weekday nighttime, between weekday daytime and weekend daytime, and between weekday night and weekend nighttime is small.

[0098] The embodiment of the present invention can effectively verify the reliability of the time partition at a predetermined time scale obtained by cluster analysis through the significance level test result, which is conducive to the final output of reliable and effective time partition results.

[0099] In order to enable those skilled in the art to better understand the embodiment of the present invention, the embodiment of the present invention is described below by using a specific example.

[0100] (1) For the hourly time period series and the daily date series, the target period (October 2020) is divided into 24 time periods and 31 dates, taking hours and days as the basic time units respectively.

[0101] (2) The basic spatial unit is determined as a regular grid of 250 m × 250 m. The target city is divided into 25,035 spatial regions of equal area, and the average heat exposure risk value of each spatial region is calculated.

[0102] (3) Divide the time series at the two time scales into two time partitions respectively.

[0103] According to the clustering results, the date series on a daily scale can be divided into cluster 1 (1st to 8th, 11th, 24th to 25th, 17th to 18th and 31st) and cluster 2 (9th to 10th, 12th to 16th, 19th to 23rd and 26th to 30th). According to the attribute characteristics of the specific dates contained in the two clusters, cluster 1 and cluster 2 can be defined as "rest days" and "working days" respectively. Among them, "rest days" include holidays (National Day and Mid-Autumn Festival holidays 1st to 8th) and normal weekends (11th, 24th to 25th, 17th to 18th, 31st). For urban risk management, the same risk management measures and strategies can be applied to the two rest days, specific holidays and regular weekends, to a certain extent.

[0104] The hourly time series is divided into cluster 1 (time periods 9-19) and cluster 2 (time periods 1-8 and time periods 20-24). According to the attribute characteristics of the specific time periods contained in the two clusters, cluster 1 and cluster 2 can be defined as "cluster daytime" (08:00-19:00) and "cluster nighttime" (19:00-08:00), respectively. The time partitions "cluster daytime" and "cluster nighttime" obtained by clustering algorithm are more consistent with the daily travel time of urban residents than the "natural daytime" (06:00-18:00) and "natural nighttime" (18:00-06:00) directly divided according to the sunrise time (around 06:00) and sunset time (around 17:30-18:00) in October.

[0105] Combining the time zoning results on the two time scales, it can be seen that the quantitative division of time zones through clustering algorithms has a more effective guiding significance for the zoning management of urban heat exposure risks.

[0106] (4) Based on the preliminary hourly and daily scale divisions, the heat exposure risk of the target city can be statistically analyzed in time and space, and the average spatial distribution of urban heat exposure risk during weekday daytime, weekday night, weekend daytime, and weekend nighttime can be obtained.

[0107] (5) A paired sample difference test was performed on the average heat exposure risk layer of the time-space partitions. The test results showed that there were significant differences in urban heat exposure risks between the time partition groups at the hourly scale and the daily scale (p < 0.001), indicating that the preliminary division results of the time partitions obtained in step (3) are reliable and the proposed method for quantitatively determining the time partitions of urban heat exposure risks is effective.

[0108] The heat exposure risk time zone determination device provided by the present invention is described below. The heat exposure risk time zone determination device described below and the heat exposure risk time zone determination method described above can refer to each other.

[0109] Figure 6 Schematic diagram of the structure of the device for determining the time zones of heat exposure risk provided by an embodiment of the present invention. Figure 6 The embodiment of the present invention provides a device for determining time zones of heat exposure risks, and the device may specifically include the following modules: An acquisition module 610 is used to acquire a time series heat exposure risk spatial distribution layer at a predetermined time scale of a target city within a target period; A clustering module 620 is used to perform a hierarchical clustering analysis on the time series heat exposure risk spatial distribution layer at the predetermined time scale based on the similarity of the heat exposure risk spatial pattern, so as to obtain a time partition at the predetermined time scale; A statistical module 630 is used to perform spatiotemporal statistics on heat exposure risks based on the time partitions at the predetermined time scale, and obtain an average urban heat exposure risk spatial distribution layer of the time partitions at the target time scale; The test module 640 is used to perform a paired sample difference test on the average urban heat exposure risk spatial distribution layer of the time partition at the target time scale, so as to determine the reliability of the time partition at the predetermined time scale according to the test result.

[0110] In some optional embodiments, the acquisition module 610 is specifically used to: For each predetermined time scale, determining a basic time unit of the predetermined time scale based on the predetermined time scale; Based on the basic time unit, dividing the target period into a plurality of time intervals of equal length; Determine a basic spatial unit, and divide the target city into a plurality of spatial regions based on the basic spatial unit; The spatial regions on the heat exposure risk layer in each time interval are sorted to obtain a time series heat exposure risk spatial distribution layer in the predetermined time scale.

[0111] In some optional embodiments, the acquisition module 610 is specifically used to: According to the same spatial area order, the spatial areas on the heat exposure risk layer in each time interval are sorted to obtain the time series heat exposure risk spatial distribution layer in the predetermined time scale.

[0112] In some optional embodiments, the clustering module 620 is used to: For the time series heat exposure risk spatial distribution layer at the predetermined time scale, hierarchical clustering analysis is performed based on the similarity of the heat exposure risk spatial pattern to obtain clustering results at different clustering levels; A target clustering level is determined, and according to the time attribute characteristics of the time series contained in each clustering result under the target clustering level, the clustering result is defined to obtain a time partition under a predetermined time scale.

[0113] In some optional embodiments, the statistics module 630 is specifically used to: Combining the time partitions at different predetermined time scales to obtain the time partitions at the target time scale; The average heat exposure risk value of each spatial area in the time partition at the target time scale is calculated to obtain the average urban heat exposure risk spatial distribution layer in the time partition at the target time scale.

[0114] In some optional embodiments, the inspection module 640 is used to: The average urban heat exposure risk values ​​of different time zones in the same spatial area at the target time scale are taken as paired samples, and a paired sample difference test is performed on the spatial distribution layer of the average urban heat exposure risk of the time zones at the target time scale.

[0115] In some optional embodiments, the inspection module 640 is used to: If the significance level test result parameter value is less than the preset significance level test threshold, then determining that the time partition under the predetermined time scale is a reliable result under the significance level; If the significance level test result parameter value is not less than a preset significance level test threshold, it is determined that the time partition under the predetermined time scale is an unreliable result under the significance level.

[0116] The embodiment of the present invention performs a hierarchical clustering analysis on the time series heat exposure risk spatial distribution layer of the target city at a predetermined time scale within the target period, taking into account the spatial pattern of heat exposure risk, so that the time partitions obtained by the cluster analysis can match the travel time of urban residents, thereby realizing the quantitative division of time partitions at different predetermined time scales, which can effectively guide the zoning control of urban heat exposure risks; by performing spatiotemporal zoning statistics on the heat exposure risk based on the time partitions at the predetermined time scale, the average urban heat exposure risk spatial distribution layer of the time partitions at the target time scale is obtained, and then the paired sample difference test is performed on the average urban heat exposure risk spatial distribution layer of the time partitions at the target time scale, so that the reliability of the time partitions at the predetermined time scale obtained by the cluster analysis can be further effectively verified through the test results, which is conducive to the final output of reliable and effective time partition results.

[0117] Figure 7 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 7As shown, the electronic device may include: a processor 710, a communication interface 720, a memory 730 and a communication bus 740, wherein the processor 710, the communication interface 720 and the memory 730 communicate with each other through the communication bus 740. The processor 710 may call the logic instructions in the memory 730 to execute the method for determining the time partition of heat exposure risk, which includes: obtaining a time series heat exposure risk spatial distribution layer under a predetermined time scale of a target city in a target period; performing hierarchical clustering analysis on the time series heat exposure risk spatial distribution layer under the predetermined time scale based on the similarity of the heat exposure risk spatial pattern to obtain the time partition under the predetermined time scale; performing spatiotemporal partition statistics on the heat exposure risk based on the time partition under the predetermined time scale to obtain the average city heat exposure risk spatial distribution layer of the time partition under the target time scale; performing a paired sample difference test on the average city heat exposure risk spatial distribution layer of the time partition under the target time scale to determine the reliability of the time partition under the predetermined time scale according to the test result.

[0118] In addition, the logic instructions in the above-mentioned memory 730 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0119] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the method for determining the time partition of heat exposure risk provided by the above-mentioned methods, the method comprising: obtaining a time series heat exposure risk spatial distribution layer at a predetermined time scale of a target city within a target period; based on the similarity of the spatial pattern of heat exposure risk, performing a hierarchical clustering analysis on the time series heat exposure risk spatial distribution layer at the predetermined time scale to obtain the time partition at the predetermined time scale; based on the time partition at the predetermined time scale, performing spatiotemporal partition statistics on the heat exposure risk to obtain an average urban heat exposure risk spatial distribution layer of the time partition at the target time scale; performing a paired sample difference test on the average urban heat exposure risk spatial distribution layer of the time partition at the target time scale to determine the reliability of the time partition at the predetermined time scale according to the test results.

[0120] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0121] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0122] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining time zones of heat exposure risk, characterized in that: include: Obtain a time series heat exposure risk spatial distribution layer at a predetermined time scale for a target city within a target period; Based on the similarity of the spatial pattern of heat exposure risk, a hierarchical clustering analysis is performed on the time series heat exposure risk spatial distribution layer at the predetermined time scale to obtain the time partition at the predetermined time scale; Based on the time partitions at the predetermined time scale, performing spatiotemporal partition statistics on the heat exposure risk, and obtaining an average urban heat exposure risk spatial distribution layer of the time partitions at the target time scale; A paired sample difference test is performed on the average urban heat exposure risk spatial distribution layer of the time partition at the target time scale to determine the reliability of the time partition at the predetermined time scale based on the test results.

2. The method for determining heat exposure risk time zones according to claim 1, characterized in that: The step of obtaining a time series heat exposure risk spatial distribution layer at a predetermined time scale for a target city within a target period includes: For each predetermined time scale, determining a basic time unit of the predetermined time scale based on the predetermined time scale; Based on the basic time unit, dividing the target period into a plurality of time intervals of equal length; Determine a basic spatial unit, and divide the target city into a plurality of spatial regions based on the basic spatial unit; The spatial regions on the heat exposure risk layer in each time interval are sorted to obtain a time series heat exposure risk spatial distribution layer in the predetermined time scale.

3. The method for determining heat exposure risk time zones according to claim 2, characterized in that: The spatial regions on the heat exposure risk layer in each time interval are sorted to obtain the time series heat exposure risk spatial distribution layer in the predetermined time scale, including: According to the same spatial area order, the spatial areas on the heat exposure risk layer in each time interval are sorted to obtain the time series heat exposure risk spatial distribution layer in the predetermined time scale.

4. The method for determining heat exposure risk time zones according to claim 1, characterized in that: Based on the similarity of the spatial pattern of heat exposure risk, a hierarchical clustering analysis is performed on the time series heat exposure risk spatial distribution layer at the predetermined time scale to obtain the time partition at the predetermined time scale, including: For the time series heat exposure risk spatial distribution layer at the predetermined time scale, hierarchical clustering analysis is performed based on the similarity of the heat exposure risk spatial pattern to obtain a specific number of multiple clustering results at different clustering levels; A target clustering level is determined, and according to the time attribute characteristics of the time interval contained in each clustering result under the target clustering level, the clustering result is defined to obtain the time partition under the predetermined time scale.

5. The method for determining heat exposure risk time zones according to claim 1, characterized in that: The time partitioning based on the predetermined time scale is used to perform spatiotemporal statistics on the heat exposure risk, and obtain an average urban heat exposure risk spatial distribution layer of the time partitions under the target time scale, including: Combining the time partitions at different predetermined time scales to obtain the time partitions at the target time scale; The average heat exposure risk value of each spatial area in the time partition at the target time scale is calculated to obtain the average urban heat exposure risk spatial distribution layer in the time partition at the target time scale.

6. The method for determining heat exposure risk time zones according to claim 1, characterized in that: The paired sample difference test on the average urban heat exposure risk spatial distribution layer of the time partition under the target time scale includes: The average urban heat exposure risk values ​​of different time zones in the same spatial area at the target time scale are taken as paired samples, and a paired sample difference test is performed on the spatial distribution layer of the average urban heat exposure risk of the time zones at the target time scale.

7. The method for determining heat exposure risk time zones according to claim 1, characterized in that: Determining the reliability of the time partition under the predetermined time scale according to the test result includes: If the significance level test result parameter value is less than the preset significance level test threshold, then determining that the time partition under the predetermined time scale is a reliable result under the significance level; If the significance level test result parameter value is not less than a preset significance level test threshold, it is determined that the time partition under the predetermined time scale is an unreliable result under the significance level.

8. A device for determining time zones for heat exposure risk, characterized in that: include: An acquisition module is used to obtain a time series heat exposure risk spatial distribution layer at a predetermined time scale for a target city within a target period; A clustering module, for performing a hierarchical clustering analysis on the time series heat exposure risk spatial distribution layer at the predetermined time scale based on the similarity of the heat exposure risk spatial pattern, to obtain the time partition at the predetermined time scale; A statistical module, for performing spatiotemporal statistics on heat exposure risk based on the time partitions at the predetermined time scale, and obtaining an average urban heat exposure risk spatial distribution layer for the time partitions at the target time scale; The test module is used to perform a paired sample difference test on the average urban heat exposure risk spatial distribution layer of the time partition at the target time scale, so as to determine the reliability of the time partition at the predetermined time scale according to the test results.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for determining the time zones of heat exposure risks according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for determining the time zones of heat exposure risks according to any one of claims 1 to 7 is implemented.

Images