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Article(id=1320165904948773219, tenantId=1146029695717560320, journalId=1306274063662022745, issueId=1320165840046117035, articleNumber=null, orderNo=null, doi=10.20168/j.1009-0029.2026.05.0107.09, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1738944000000, receivedDateStr=2025-02-08, revisedDate=1749139200000, revisedDateStr=2025-06-06, acceptedDate=null, acceptedDateStr=null, onlineDate=1792681679236, onlineDateStr=2026-10-22, pubDate=1778774400000, pubDateStr=2026-05-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792681679236, onlineIssueDateStr=2026-10-22, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792681679236, creator=13701087609, updateTime=1792681679236, updator=13701087609, issue=Issue{id=1320165840046117035, tenantId=1146029695717560320, journalId=1306274063662022745, year='2026', volume='45', issue='5', pageStart='1', pageEnd='148', issueExtLink='null', onlineDate='null', pubDate='1778774400000', pubDateStr='2026-05-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792681663762, creator='13701087609', updateTime=1792681663762, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=107, endPage=115, ext={EN=ArticleExt(id=1320165905426923876, articleId=1320165904948773219, tenantId=1146029695717560320, journalId=1306274063662022745, language=EN, title=Optimization study of county fire station layout based on accessibility, columnId=1307033132415349106, journalTitle=Fire Science and Technology, columnName=Emergency management study, runingTitle=null, highlight=null, articleAbstract=

Previous studies on the layout of fire stations predominantly focuses on urban areas, with limited attention given to counties primarily composed of rural regions. In response to the need for optimizing the layout of fire stations in counties, a comprehensive evaluation method for the accessibility of urban and rural fire rescue is proposed in this paper. Considering the differences in fire risks between urban and rural areas, different standards for accessibility classification are established. Fire rescue demand points are identified by integrating fire data and point of interest data, with urban fire stations and township full-time fire stations serving as supply points for fire rescue. The accessibility of fire rescue in the county is then weighted and comprehensively calculated based on the average ratio of fire direct property losses in urban and rural areas. Taking SD County as an example, 16 040 demand points were collected, and the existing 2 urban fire stations and 13 township full-time fire stations were used as supply points. The travel time for fire rescue was simulated based on real-time road conditions from online maps. A total of 29 evaluation scenarios were set within a full working day, each containing 16 040 samples, amounting to 465 160 samples in total. The results indicate that the cumulative proportion of demand points with accessibility levels Ⅰ and Ⅱ is 50.30%, with several areas experiencing excessively long response times. Analysis reveals that this is due to the lack of township full-time fire stations in these regions. Therefore, a proposal is made to construct 8 township full-time fire stations in these areas as an optimization plan. Upon re-evaluation, the cumulative proportion of demand points with accessibility levels Ⅰ and Ⅱ increased by 10.76% to 61.06% after the implementation of the optimization plan. The comprehensive evaluation method for urban and rural fire rescue accessibility proposed in this study can also be applied to the optimization of fire station layouts in other counties.

, authors=Wentao Zhao1, 2, Dingli Liu1, 2, Jing Yang1, 2, Diping Yuan2, authorsList=Wentao Zhao, Dingli Liu, Jing Yang, Diping Yuan, authorCompany=null, correspAuthors=Dingli Liu, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1320165910644638071, articleId=1320165904948773219, tenantId=1146029695717560320, journalId=1306274063662022745, language=CN, title=基于可达性的县域消防站布局优化研究, columnId=1307033132608287092, journalTitle=消防科学与技术, columnName=应急管理研究, runingTitle=null, highlight=null, articleAbstract=

现有的消防站布局方面的研究主要集中于城区，鲜有关注以乡村为主体的县域。本文针对县域消防站布局优化的需求，提出了城乡消防救援可达性综合评价方法。针对城乡火灾风险的差异，分别制定了城乡可达性等级划分标准。通过融合历史火灾数据和POI（兴趣点）数据确定消防救援需求点，以城市消防站和乡镇专职消防站为消防救援供应点。根据城乡平均火灾直接财产损失比值加权综合计算县域消防救援可达性。以SD县为例，收集了16 040个需求点，并以既有的2个城市消防站和13个乡镇专职消防站为供应点，基于在线地图实时路况仿真计算消防救援行驶时间。在一个完整的工作日内设置29个评估场景，每个场景包含16 040个样本，总计465 160个样本。结果表明，可达性等级为Ⅰ级和Ⅱ级的需求点数量累计占比为50.30%，且存在多个响应时间过长区域。分析发现，这是由于该区域缺少乡镇专职消防站。因此，提出在这些区域建设8个乡镇专职消防站为优化方案。经再次评价，优化方案实施后可达性等级为Ⅰ级和Ⅱ级的需求点数量累计占比提升了10.76%，达到61.06%。本文提出的城乡消防救援可达性综合评价方法还可应用于其他县域的消防站布局优化。

, authors=赵文涛1, 2, 刘顶立1, 2, 杨婧1, 2, 袁狄平2, authorsList=赵文涛, 刘顶立, 杨婧, 袁狄平, authorCompany=null, correspAuthors=刘顶立, authorNote=

赵文涛，长沙理工大学交通学院硕士研究生，主要从事消防救援资源配置及优化研究，湖南省长沙市天心区万家丽南路二段960号，410114。

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刘顶立，长沙理工大学交通学院副教授，。
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赵文涛，长沙理工大学交通学院硕士研究生，主要从事消防救援资源配置及优化研究，湖南省长沙市天心区万家丽南路二段960号，410114。

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赵文涛，长沙理工大学交通学院硕士研究生，主要从事消防救援资源配置及优化研究，湖南省长沙市天心区万家丽南路二段960号，410114。

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journalId=1306274063662022745, articleId=1320165904948773219, language=EN, label=Fig. 2, caption=Distribution of various types of fire rescue demand points, figureFileSmall=7xKC/zkx/2y2j1FkC49lPg==, figureFileBig=k5toWROLLo2RUie6tn9OBw==, tableContent=null), ArticleFig(id=1320165917472965028, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=CN, label=图2, caption=各类型消防救援需求点分布, figureFileSmall=7xKC/zkx/2y2j1FkC49lPg==, figureFileBig=k5toWROLLo2RUie6tn9OBw==, tableContent=null), ArticleFig(id=1320165917569434021, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=EN, label=Fig. 3, caption=Percentage of the number of demand points with different accessibility levels of existed fire stations, figureFileSmall=CsnO4fwzZqbYQKIn9XjNIA==, figureFileBig=vSa6FekIPsbXNnPgq5tQBg==, tableContent=null), ArticleFig(id=1320165917649125798, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=CN, label=图3, caption=既有消防站不同可达性等级需求点数量占比, figureFileSmall=CsnO4fwzZqbYQKIn9XjNIA==, figureFileBig=vSa6FekIPsbXNnPgq5tQBg==, tableContent=null), ArticleFig(id=1320165917741400487, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=EN, label=Fig. 4, caption=Percentage of total number of demand points for different accessibility levels in urban and rural areas of existed fire stations, figureFileSmall=bctivtL7tHto0i5B6qfxPg==, figureFileBig=gcCTT5kscDrM1aXSImQWoA==, tableContent=null), ArticleFig(id=1320165917812703656, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=CN, label=图4, caption=既有消防站城乡不同可达性等级总需求点数量占比, figureFileSmall=bctivtL7tHto0i5B6qfxPg==, figureFileBig=gcCTT5kscDrM1aXSImQWoA==, tableContent=null), ArticleFig(id=1320165917892395433, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=EN, label=Fig. 5, caption=Distribution of demand points with different accessibility levels, figureFileSmall=hzadEm8O7PU0k64Xv5wJFg==, figureFileBig=DJElG3TF3Bl4wOwqQ5TYew==, tableContent=null), ArticleFig(id=1320165918001447338, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=CN, label=图5, caption=不同可达性等级需求点分布情况, figureFileSmall=hzadEm8O7PU0k64Xv5wJFg==, figureFileBig=DJElG3TF3Bl4wOwqQ5TYew==, tableContent=null), ArticleFig(id=1320165918072750507, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=EN, label=Fig. 6, caption=Location distribution of fire stations of SD County, figureFileSmall=3EoGkgOwsmZiE9VfhCCBEg==, figureFileBig=SY68lfxuyiYVBRJl4a0IrA==, tableContent=null), ArticleFig(id=1320165918198579628, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=CN, label=图6, caption=SD县消防站位置分布, figureFileSmall=3EoGkgOwsmZiE9VfhCCBEg==, figureFileBig=SY68lfxuyiYVBRJl4a0IrA==, tableContent=null), ArticleFig(id=1320165918278271405, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=EN, label=Fig. 7, caption=Percentage of total number of demand points with different accessibility levels for existed and proposing fire stations, figureFileSmall=dPLNluPJ7LXuFwaQwulGSA==, figureFileBig=9dsRGStsCia60kl5nXp9pw==, tableContent=null), ArticleFig(id=1320165918395711918, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=CN, label=图7, caption=既有+拟建消防站不同可达性等级总需求点数量占比, figureFileSmall=dPLNluPJ7LXuFwaQwulGSA==, figureFileBig=9dsRGStsCia60kl5nXp9pw==, tableContent=null), ArticleFig(id=1320165918538318255, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=EN, label=Fig. 8, caption=Distribution of demand points with different levels of accessibility, figureFileSmall=wm/s30gCeLcjEihWCsVCzQ==, figureFileBig=aRWhAwcg027IBdp/yFlDMw==, tableContent=null), ArticleFig(id=1320165918672535984, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=CN, label=图8, caption=不同可达性等级的需求点分布情况, figureFileSmall=wm/s30gCeLcjEihWCsVCzQ==, figureFileBig=aRWhAwcg027IBdp/yFlDMw==, tableContent=null), ArticleFig(id=1320165918764810673, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=EN, label=Table 1, caption=

Classification of fire rescue accessibility levels

, figureFileSmall=null, figureFileBig=null, tableContent=
等级响应时间
（城镇）/s		行驶时间（城镇）/s响应时间
（乡村）/s		行驶时间（乡村）/s描述
Ⅰ[60，300](0，240][60，480](0，420]有效覆
盖点
Ⅱ(300，480](240，420](480，660](420，600]可覆盖点
Ⅲ(480，660](420，600](660，840](600，780]不可覆
盖点
Ⅳ(660，+∞)(600，+∞)(840，+∞)(780，+∞)完全未
覆盖点
), ArticleFig(id=1320165918848696754, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=CN, label=表1, caption=

消防救援可达性等级划分

, figureFileSmall=null, figureFileBig=null, tableContent=
等级响应时间
（城镇）/s		行驶时间（城镇）/s响应时间
（乡村）/s		行驶时间（乡村）/s描述
Ⅰ[60，300](0，240][60，480](0，420]有效覆
盖点
Ⅱ(300，480](240，420](480，660](420，600]可覆盖点
Ⅲ(480，660](420，600](660，840](600，780]不可覆
盖点
Ⅳ(660，+∞)(600，+∞)(840，+∞)(780，+∞)完全未
覆盖点
), ArticleFig(id=1320165918919999923, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=EN, label=Table 2, caption=

Longitude and latitude of existed fire station sites

, figureFileSmall=null, figureFileBig=null, tableContent=
既有消防站经度纬度
ECFS-1111.741 861o27.261 860o
ECFS-2111.775 668o27.234 541o
ETFS-1111.782 385o27.329 682o
ETFS-2111.859 002o27.328 427o
ETFS-3111.879 441o27.290 109o
ETFS-4112.007 378o27.279 884o
ETFS-5111.908 758o27.180 826o
ETFS-6111.966 084o27.159 802o
ETFS-7112.043 124o27.145 432o
ETFS-8111.955 866o27.123 757o
ETFS-9111.906 854o27.080 405o
ETFS-10111.827 602o27.105 560o
ETFS-11111.718 423o27.170 525o
ETFS-12111.648 251o27.204 046o
ETFS-13111.680 624o27.276 166o
), ArticleFig(id=1320165919003886004, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=CN, label=表2, caption=

既有消防站点经纬度

, figureFileSmall=null, figureFileBig=null, tableContent=
既有消防站经度纬度
ECFS-1111.741 861o27.261 860o
ECFS-2111.775 668o27.234 541o
ETFS-1111.782 385o27.329 682o
ETFS-2111.859 002o27.328 427o
ETFS-3111.879 441o27.290 109o
ETFS-4112.007 378o27.279 884o
ETFS-5111.908 758o27.180 826o
ETFS-6111.966 084o27.159 802o
ETFS-7112.043 124o27.145 432o
ETFS-8111.955 866o27.123 757o
ETFS-9111.906 854o27.080 405o
ETFS-10111.827 602o27.105 560o
ETFS-11111.718 423o27.170 525o
ETFS-12111.648 251o27.204 046o
ETFS-13111.680 624o27.276 166o
), ArticleFig(id=1320165919083577781, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=EN, label=Table 3, caption=

Historical fire direct property losses, fire number and POI counts

, figureFileSmall=null, figureFileBig=null, tableContent=
符号描述数值
lu城镇火灾直接财产损失/万元1 146.19
lv乡村火灾直接财产损失/万元247.61
nu城镇火灾次数/次1 400
nv乡村火灾次数/次1 005
n3城镇POI数量/个3 496
n4乡村POI数量/个10 720
), ArticleFig(id=1320165919175852470, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=CN, label=表3, caption=

历史火灾直接财产损失、火灾次数和POI数量统计

, figureFileSmall=null, figureFileBig=null, tableContent=
符号描述数值
lu城镇火灾直接财产损失/万元1 146.19
lv乡村火灾直接财产损失/万元247.61
nu城镇火灾次数/次1 400
nv乡村火灾次数/次1 005
n3城镇POI数量/个3 496
n4乡村POI数量/个10 720
), ArticleFig(id=1320165919251349943, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=EN, label=Table 4, caption=

Weights of demand points

, figureFileSmall=null, figureFileBig=null, tableContent=
需求点类型权重权重值
城镇历史火灾点uf0.546 7
乡村历史火灾点vf0.209 3
城镇POIup0.223 3
乡村POIvp0.020 7
), ArticleFig(id=1320165919314264504, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=CN, label=表4, caption=

各需求点权重

, figureFileSmall=null, figureFileBig=null, tableContent=
需求点类型权重权重值
城镇历史火灾点uf0.546 7
乡村历史火灾点vf0.209 3
城镇POIup0.223 3
乡村POIvp0.020 7
), ArticleFig(id=1320165919389761977, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=EN, label=Table 5, caption=

D¯ T , t¯T, and v¯T of fire stations in SD Country

, figureFileSmall=null, figureFileBig=null, tableContent=
批次 D¯T/m t¯T/s v¯T/m/s
既有消防站布局3 448.98464.077.62
优化后消防站布局2 849.66404.267.25
变化幅度-599.32-59.81-0.37
), ArticleFig(id=1320165919482036666, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=CN, label=表5, caption=

SD县消防站点D¯ T、t¯T和v¯T

, figureFileSmall=null, figureFileBig=null, tableContent=
批次 D¯T/m t¯T/s v¯T/m/s
既有消防站布局3 448.98464.077.62
优化后消防站布局2 849.66404.267.25
变化幅度-599.32-59.81-0.37
), ArticleFig(id=1320165919561728443, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=EN, label=Table 6, caption=

Change in the percentage of the number of demand points for each accessibility class after the optimization scheme

, figureFileSmall=null, figureFileBig=null, tableContent=
场景Ⅰ级Ⅱ级Ⅲ级Ⅳ级
12:0019.32+5.8824.42+5.0920.87+1.7035.39-12.67
13:0020.94+6.1225.92+4.7221.03+1.8832.10-12.72
14:0019.96+5.6724.84+4.8820.26+2.0034.94-12.55
15:0019.46+6.0525.27+5.0918.91+2.2036.36-13.35
16:0019.22+5.8424.91+5.3519.21+1.9336.66-13.12
17:0018.07+6.0422.08+5.1119.98+2.2639.88-13.42
17:3018.40+5.9921.90+5.2619.79+1.9239.91-13.17
18:0018.42+5.7723.19+5.0921.20+2.3037.19-13.16
18:3019.93+5.8924.69+4.8021.49+2.3133.89-13.00
19:0021.92+6.0726.43+4.4120.14+2.0331.51-12.51
20:0022.24+6.0226.60+4.6619.05+1.3732.11-12.05
21:0024.51+6.4227.53+4.4519.26+1.4428.70-12.31
22:0026.91+6.7628.62+4.5418.65+0.6025.82-11.91
23:0028.40+6.8230.01+4.6218.13+0.0923.45-11.53
00:0030.70+7.1530.97+4.1016.80+0.0721.54-11.32
01:0029.09+6.8631.47+3.9816.32+0.3923.13-11.23
02:0031.06+6.9230.90+4.1815.32+0.4122.72-11.50
03:0031.91+6.9631.04+3.9514.77+0.3022.28-11.21
04:0030.44+6.5929.80+4.4115.21+0.5224.56-11.52
05:0028.60+6.3230.13+4.3115.22+1.0926.05-11.72
06:0025.59+5.9728.97+4.1716.84+1.4228.61-11.55
07:0021.33+5.7425.82+4.5320.77+1.7632.08-12.03
07:3019.88+5.3824.63+4.1920.92+2.1934.57-11.76
08:0020.05+5.6725.37+4.6220.99+1.9533.58-12.24
08:3020.03+5.6525.66+4.5920.31+1.8134.00-12.05
09:0019.06+5.5725.28+4.7820.01+2.0935.65-12.43
10:0017.43+5.3924.82+4.8420.02+2.3537.73-12.59
11:0016.85+5.6522.80+4.5920.57+1.8239.78-12.06
12:0019.08+5.9423.71+4.8619.59+2.5337.62-13.33
均值23.38+6.1726.92+4.5918.70+1.4331.00-12.20
), ArticleFig(id=1320165919645614524, tenantId=1146029695717560320, journalId=1306274063662022745, articleId=1320165904948773219, language=CN, label=表6, caption=

优化方案后各可达性等级需求点数量占比变化

, figureFileSmall=null, figureFileBig=null, tableContent=
场景Ⅰ级Ⅱ级Ⅲ级Ⅳ级
12:0019.32+5.8824.42+5.0920.87+1.7035.39-12.67
13:0020.94+6.1225.92+4.7221.03+1.8832.10-12.72
14:0019.96+5.6724.84+4.8820.26+2.0034.94-12.55
15:0019.46+6.0525.27+5.0918.91+2.2036.36-13.35
16:0019.22+5.8424.91+5.3519.21+1.9336.66-13.12
17:0018.07+6.0422.08+5.1119.98+2.2639.88-13.42
17:3018.40+5.9921.90+5.2619.79+1.9239.91-13.17
18:0018.42+5.7723.19+5.0921.20+2.3037.19-13.16
18:3019.93+5.8924.69+4.8021.49+2.3133.89-13.00
19:0021.92+6.0726.43+4.4120.14+2.0331.51-12.51
20:0022.24+6.0226.60+4.6619.05+1.3732.11-12.05
21:0024.51+6.4227.53+4.4519.26+1.4428.70-12.31
22:0026.91+6.7628.62+4.5418.65+0.6025.82-11.91
23:0028.40+6.8230.01+4.6218.13+0.0923.45-11.53
00:0030.70+7.1530.97+4.1016.80+0.0721.54-11.32
01:0029.09+6.8631.47+3.9816.32+0.3923.13-11.23
02:0031.06+6.9230.90+4.1815.32+0.4122.72-11.50
03:0031.91+6.9631.04+3.9514.77+0.3022.28-11.21
04:0030.44+6.5929.80+4.4115.21+0.5224.56-11.52
05:0028.60+6.3230.13+4.3115.22+1.0926.05-11.72
06:0025.59+5.9728.97+4.1716.84+1.4228.61-11.55
07:0021.33+5.7425.82+4.5320.77+1.7632.08-12.03
07:3019.88+5.3824.63+4.1920.92+2.1934.57-11.76
08:0020.05+5.6725.37+4.6220.99+1.9533.58-12.24
08:3020.03+5.6525.66+4.5920.31+1.8134.00-12.05
09:0019.06+5.5725.28+4.7820.01+2.0935.65-12.43
10:0017.43+5.3924.82+4.8420.02+2.3537.73-12.59
11:0016.85+5.6522.80+4.5920.57+1.8239.78-12.06
12:0019.08+5.9423.71+4.8619.59+2.5337.62-13.33
均值23.38+6.1726.92+4.5918.70+1.4331.00-12.20
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基于可达性的县域消防站布局优化研究
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赵文涛 1, 2 , 刘顶立 1, 2 , 杨婧 1, 2 , 袁狄平 2
消防科学与技术 | 应急管理研究 2026,45(5): 107-115
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消防科学与技术 |应急管理研究 2026 , 45 (5) : 107 -115
基于可达性的县域消防站布局优化研究
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赵文涛，长沙理工大学交通学院硕士研究生，主要从事消防救援资源配置及优化研究，湖南省长沙市天心区万家丽南路二段960号，410114。

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赵文涛，长沙理工大学交通学院硕士研究生，主要从事消防救援资源配置及优化研究，湖南省长沙市天心区万家丽南路二段960号，410114。

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赵文涛1, 2, 刘顶立1, 2 , 杨婧1, 2, 袁狄平2
作者信息
  • 1长沙理工大学 交通学院，湖南 长沙 410114
  • 2中国矿业大学深圳研究院，广东 深圳 518000
通讯作者:
刘顶立，长沙理工大学交通学院副教授，。
作者简介:

赵文涛，长沙理工大学交通学院硕士研究生，主要从事消防救援资源配置及优化研究，湖南省长沙市天心区万家丽南路二段960号，410114。

Optimization study of county fire station layout based on accessibility
Wentao Zhao1, 2, Dingli Liu1, 2 , Jing Yang1, 2, Diping Yuan2
Affiliations
  • 1School of Transportation, Changsha University of Science and Technology, Changsha Hunan 410114, China
  • 2Shenzhen Research Institute, China University of Mining and Technology, Shenzhen Guangdong 518000, China
出版时间: 2026-05-15 doi: 10.20168/j.1009-0029.2026.05.0107.09
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现有的消防站布局方面的研究主要集中于城区，鲜有关注以乡村为主体的县域。本文针对县域消防站布局优化的需求，提出了城乡消防救援可达性综合评价方法。针对城乡火灾风险的差异，分别制定了城乡可达性等级划分标准。通过融合历史火灾数据和POI（兴趣点）数据确定消防救援需求点，以城市消防站和乡镇专职消防站为消防救援供应点。根据城乡平均火灾直接财产损失比值加权综合计算县域消防救援可达性。以SD县为例，收集了16 040个需求点，并以既有的2个城市消防站和13个乡镇专职消防站为供应点，基于在线地图实时路况仿真计算消防救援行驶时间。在一个完整的工作日内设置29个评估场景，每个场景包含16 040个样本，总计465 160个样本。结果表明，可达性等级为Ⅰ级和Ⅱ级的需求点数量累计占比为50.30%，且存在多个响应时间过长区域。分析发现，这是由于该区域缺少乡镇专职消防站。因此，提出在这些区域建设8个乡镇专职消防站为优化方案。经再次评价，优化方案实施后可达性等级为Ⅰ级和Ⅱ级的需求点数量累计占比提升了10.76%，达到61.06%。本文提出的城乡消防救援可达性综合评价方法还可应用于其他县域的消防站布局优化。

县域  /  消防站  /  可达性  /  消防救援  /  火灾数据统计

Previous studies on the layout of fire stations predominantly focuses on urban areas, with limited attention given to counties primarily composed of rural regions. In response to the need for optimizing the layout of fire stations in counties, a comprehensive evaluation method for the accessibility of urban and rural fire rescue is proposed in this paper. Considering the differences in fire risks between urban and rural areas, different standards for accessibility classification are established. Fire rescue demand points are identified by integrating fire data and point of interest data, with urban fire stations and township full-time fire stations serving as supply points for fire rescue. The accessibility of fire rescue in the county is then weighted and comprehensively calculated based on the average ratio of fire direct property losses in urban and rural areas. Taking SD County as an example, 16 040 demand points were collected, and the existing 2 urban fire stations and 13 township full-time fire stations were used as supply points. The travel time for fire rescue was simulated based on real-time road conditions from online maps. A total of 29 evaluation scenarios were set within a full working day, each containing 16 040 samples, amounting to 465 160 samples in total. The results indicate that the cumulative proportion of demand points with accessibility levels Ⅰ and Ⅱ is 50.30%, with several areas experiencing excessively long response times. Analysis reveals that this is due to the lack of township full-time fire stations in these regions. Therefore, a proposal is made to construct 8 township full-time fire stations in these areas as an optimization plan. Upon re-evaluation, the cumulative proportion of demand points with accessibility levels Ⅰ and Ⅱ increased by 10.76% to 61.06% after the implementation of the optimization plan. The comprehensive evaluation method for urban and rural fire rescue accessibility proposed in this study can also be applied to the optimization of fire station layouts in other counties.

county  /  fire station  /  accessibility  /  fire rescue  /  fire data statistics
赵文涛, 刘顶立, 杨婧, 袁狄平. 基于可达性的县域消防站布局优化研究. 消防科学与技术, 2026 , 45 (5) : 107 -115 . DOI: 10.20168/j.1009-0029.2026.05.0107.09
Wentao Zhao, Dingli Liu, Jing Yang, Diping Yuan. Optimization study of county fire station layout based on accessibility[J]. Fire Science and Technology, 2026 , 45 (5) : 107 -115 . DOI: 10.20168/j.1009-0029.2026.05.0107.09
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截至2024年底，我国共有2 846个县级行政区，常住人口城镇化率为67.00%［1］。县域作为国家治理的基本单元，涵盖了城镇和乡村两大类型的区域。城镇地区消防基础设施相对完善，消防救援响应时间较短，但人口密集，经济活动频繁，火灾风险相对集中，一旦发生火灾，危险程度较大。然而，乡村地区作为县域地理空间的主体部分，其消防安全状况也直接影响县域整体的消防安全水平。随着乡村经济的发展，乡村地区的消防安全问题日益突出［2-3］。据统计，2024年前8个月全国共接到火灾报警66万起，其中乡村火灾39.1万起，占总数的59.1%［4］。乡村地区房屋多为木质或砖木结构，耐火等级低，道路狭窄，消防基础设施薄弱，部分村庄甚至没有专门的消防设施。此外，乡镇专职消防站的消防车辆数量不足，难以充分满足扑救火灾与应急救援的实际需要［5］。乡村地区通常距离消防站较远，可达性普遍低于城镇，且由于地理环境复杂、交通条件有限等因素，其可达性评估难度较大。随着乡村地区火灾的频发，研究县域消防救援可达性，对优化消防救援力量布局有重要意义。
在消防救援可达性方面，国内外学者已经进行了很多研究。可达性是从一点到达另一点的难易程度［6］，是评估城市服务设施布局是否合理的一个关键指标。经典的可达性模型有最近距离法、基于GIS的空间分析法、累计机会法、时空可达性法、引力模型法等。最近距离法遵循最小距离原则，适合数据规模较小的场景，但对初始样本顺序较为敏感［7］；基于GIS的空间分析法融合多源数据，适用于地理空间数据丰富且具有空间关联性的情况，但对数据精度与质量要求高［8］；累计机会法的核心是累计一定范围内的机会数量，适用于公共设施的可达性评价，但难以反映空间竞争与选择行为［9-10］；时空可达性法具备综合性、个体性和动态性，适用于城市交通的规划和评估，但对数据和模型要求高［11-12］；引力模型法基于吸引力与距离衰减原理，适用于相对稳定的时空尺度场景，对数据质量依赖度高［13］。可见，模型各有优劣，适用于不同场景。
在消防救援可达性评估方面，TOMASIELLO D B等［10］引入了时间间隔累积可达性指标，减轻任意选择的出行时间对累积可达性分析的影响。干靓等［14］利用高斯两步移动搜寻法，从供需协调的视角对城市应急救援可达性进行评估。随着地理信息系统（GIS）的日益成熟，学者开始利用其强大的空间可视化能力深入分析消防救援的可达性。周添等［15］建立了基于区域视角的特长公路隧道消防救援可达性评价模型。刘伟军等［16］构建了一个基于时间加权的消防救援评估模型，计算区域总体有效覆盖率。此外，在消防救援力量布局方面，王宁等［17］构建了多目标选址覆盖模型为城市消防站选址。贺俊杰等［18］从火灾风险的角度出发，构建了一套区域火灾风险评估体系。WANG K等［19］基于历史火灾事故数据和专家判断，确定了各类别兴趣点（POI）的火灾风险权重，提出一种新的城市火灾风险评估方法。曹跃等［20］基于拉格朗日松弛框架，构建消防站固定费用选址的整数规划模型，目标是最小限度地降低救援等待损失和消防站建设费用。顾彧等［21］则以超立方体排队均衡为基础，引入排队理论和多目标优化算法，构建消防站布局和调度优化模型。
可以看出，前人的研究主要关注城市消防救援力量。对县域内乡村的消防救援关注较少。传统研究多以城市整体划分可达性等级，但现在乡村火灾比例越来越高，县域消防救援力量的建设与布局问题日益受到关注。越来越多的乡镇专职消防站被建立，但如何科学布局这些力量，使其发挥最大效能仍然是一个难点。
因此，本文提出一种城乡消防救援可达性综合评价方法。以SD县为例进行验证。针对城乡火灾风险的差异，分别制定了城乡可达性等级划分标准。通过融合POI和历史火灾数据的16 040个点作为消防救援需求点，以2个既有城市消防站和13个乡镇专职消防站作为消防救援供应点。根据城乡平均火灾直接财产损失比值加权综合计算县域消防救援可达性。对既有消防站的布局进行分析，并提出优化方案，最后再次用城乡消防救援可达性综合评价方法验证优化方案的可行性。
1　城乡消防救援可达性综合评价方法
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1.1　可达性时间指标
各国在城市消防救援的响应时间方面存在差异。美国消防标准规定，对城市消防救援部门，在90%的情况下，警报响应后第一个消防单位必须在240 s的行程时间内到达火灾现场［22］。日本消防救护车到达现场的平均时间是564 s［23］。在我国，900 s消防时间分配为：发现起火240 s、报警和指挥中心处警150 s、接到指令出动60 s、行车到场240 s、开始出水扑救210 s［24］。240 s的行驶时间和60 s的行动准备时间即300 s为可达性等级Ⅰ的目标响应时间。出水扑救中的210 s以30 s为灭火救援准备时间，180 s响应救援，即480 s为等级Ⅱ的目标响应时间。设定660 s为等级Ⅲ的目标响应时间，若超过660 s则归为等级Ⅳ，据此划分城镇消防救援可达性等级。我国并没有明确规定乡村消防救援响应时间。乡村地区的火灾风险比较分散，对单体建筑造成损失的容忍度更高，消防救援响应时间目标值可长于城镇地区。在城镇消防救援可达性等级划分的基础上，相应地将各等级增加180 s，以此来划分乡村地区的可达性等级。本文消防救援可达性等级划分如表1所示。
1.2　可达性综合评价模型
为了全面评估消防救援需求点的火灾风险，本文将根据消防救援需求点的来源和地理位置，将其划分为4种类型：城镇POI、乡村POI、城镇历史火灾点和乡村历史火灾点。大部分的历史火灾发生在建筑内，故部分POI和历史火灾点的经纬度是相近的，甚至接近于重叠。这种情形下，可认为该位置的火灾风险更高、对消防救援可达性综合评价结果影响更大。各类型将被赋予不同的权重值，以反映其潜在的火灾风险和对救援资源的需求。各类型权重总和如式（1）所示。
uf+vf+up+vp=1
式中：uf为城镇火灾权重；up为城镇POI权重；vf为乡村火灾权重；vp为乡村POI权重。
城镇发生火灾可能导致的损失通常大于乡村，城镇类型的权重应高于乡村类别。本文采用火灾直接财产损失计算城镇与乡村POI和历史火灾点的风险权重，对县域内城乡地区的历史火灾直接财产损失进行了统计分析。这些数据被用于计算城镇和乡村需求点的风险权重比值，进而为城镇和乡村需求点分配不同的权重。选取历史时间段内的火灾数据，城镇和乡村的总权重比值由式（2）得到。
(uf+up):(vf+vp)=lunu:lvnv
式中：lu为城镇直接财产损失；lv为乡村直接财产损失；nu为城镇火灾次数；nv为乡村火灾次数。
同时，考虑到历史火灾点不仅代表了过去的火灾事件，也预示着潜在的火灾风险，因此其权重应高于POI。本文中城镇和乡村的历史火灾与POI的权重比值分别通过式（3）式（4）计算设定。
uf:up=n3:nu
vf:vp=n4:nv
式中：n3为城镇POI数量；n4为乡村POI数量。
假设在一个地区有n个消防救援需求点、m个消防救援供应点，选取某时间段内连续时刻的k个评估场景，对这一地区的消防救援需求点数量占比进行评估。在第j个评估场景对应的时刻tj，第i个消防救援需求点与行程时间最短的消防救援供应点之间的行驶时间和行驶距离分别表示为ti，tj和Di，tj。则某一评估时刻tj所有消防救援需求点的平均距离D¯tj、平均时间t¯tj以及平均速度v¯tj可以分别通过式（5）～式（7）计算得到。
D¯tj=ufn1∑i=1n1Di,tj+vfn2∑i=1n2Di,tj+upn3∑i=1n3Di,tj+vpn4∑i=1n4Di,tj
t¯tj=ufn1∑i=1n1ti,tj+vfn2∑i=1n2ti,tj+upn3∑i=1n3ti,tj+vpn4∑i=1n4ti,tj
v¯tj=D¯tjt¯tj
式中：n1为城镇历史火灾点数量；n2为乡村历史火灾点数量。
所有评估场景的总平均距离D¯T、总平均时间t¯T以及总平均速度v¯T计算方程式如式（8）～式（10）所示。对不同时间段的平均值进行加权求和，从而更加准确地反映了整个评估期间的总平均情况。
D¯T=12⋅1tk-t1∑j=1k-1(tj+1-tj)(D¯tj+D¯tj+1)
t¯T=12⋅1tk-t1∑j=1k-1(tj+1-tj)(t¯tj+t¯tj+1)
v¯T=12⋅1tk-t1∑j=1k-1(tj+1-tj)(v¯tj+v¯tj+1)
每一个计算场景tj，可以计算出不同可达性等级需求点数量的比值Rtj,q，计算方程如式（11）所示。
Rtj,q=Ctj,qCtj,I+Ctj,II+Ctj,III+Ctj,IV,q=I,II,III,IV 
式中：Ctj,q为tj时刻q等级所包含的需求点数量。
为了全面评估该区域消防救援的可达性，可以通过时间加权方法计算一个估计周期（如1天、1周或1个月）内不同可达性等级的总需求点数量占比Rq，T，可以根据式（12）计算。
          Rq,T=1tk-t1∑j=1k-1(tj+1-tj)Rtj,q+Rtj+1,q2,q=I,II,III,IV
1.3　仿真计算及验证方法
消防救援可达性仿真计算技术路线如图1所示。
通过仿真计算，对城乡消防救援可达性进行综合评价。首先对消防站与消防救援需求点的结构化地址进行预处理，解析出对应的经度与纬度。然后以消防站为起点、消防救援需求点为终点，依据解析后的经纬度构建批量算路参数，并通过调用在线地图API，获取消防车行驶的时间与距离［15，25］。最后按可达性综合评价模型进行计算、评估并可视化。文中，通过调用百度地图批量算路API，根据实时路况仿真计算消防车行驶时间与距离。
2　研究区域
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2.1　研究区域概况
SD县位于湖南省中部，地处湘中丘陵地带，地势南北高、中部低，形成阶梯式倾斜的地貌。该县南北长约59 km，东西宽约56.7 km，总面积为1 778 km2。县辖3个街道、18个镇和4个乡。从地形看，SD县以丘岗地为主，占比达61.18%；山地次之，占比21.69%；而平原多是溪谷平原，占比仅10.85%。综合看，SD县的地形较为复杂，主要以丘陵和山地为主，平原面积相对较少。
2.2　消防救援供应点
以SD县既有2个城市消防站和13个乡镇专职消防站为消防救援供应点。根据消防救援部门提供的数据，可获得SD县的城市消防站、专职消防队信息。SD县现有1个一级城市消防站和1个二级城市消防站及13个乡镇专职消防站。其中，一级城市消防站中配有消防员19人、专职消防员12人，二级城市消防站中配有专职消防员21人。通过消防救援部门收集、实地调研及网络地图检索，可获取到城市消防站（Existed City Fire Station，ECFS）及各乡镇专职消防站（Existed Township Fire Station，ETFS）的精确位置，并记录其结构化地址。再调用百度地图的地理编码Web服务API，将结构化地址解析为经度和纬度，见表2。
2.3　消防救援需求点
消防救援需求点的来源主要有两个方面。
1）根据消防救援部门提供的2021年初至2024年6月底的历史火灾数据，对频繁发生火灾地点进行细致筛选，在农村地区以村、组、冲为单位确定需求点地址，城区以街道地址确认需求点地址。共得到乡村历史火灾点580个、城镇历史火灾点1 244个。对上述筛选后的历史火灾地址信息进行了结构化处理，并转化为经纬度坐标，以此作为消防救援需求点。
2）借助在线地图获取了POI数据，按照中餐厅、星级酒店、购物中心、高等院校、综合医院、乡镇、村庄、组、冲等关键词进行筛选，覆盖了美食、酒店、购物、生活服务、行政地标等多个领域的建筑类型。共得到城镇POI数据3 496个和乡村POI数据10 720个。本文将POI作为潜在的消防救援需求点。
在一定程度上，消防救援需求点可以表征区域火灾风险［19］，需求点分布越密集，意味着该区域的火灾风险越高。为了使各类型的消防救援需求点分布情况更为直观，采用核密度聚类技术进行可视化处理［26］，见图2。不同颜色用来区分火灾风险密度：红色、绿色、蓝色分别对应火灾风险密度高、中、低，而蓝色则表示火灾风险密度较低的区域。由图2可知，SD县乡村地区火灾风险密度显著低于城镇区域。
根据SD县消防救援部门提供的历史火灾数据，经过统计分析得到该县城镇和乡村在一定时期的总直接财产损失、火灾次数和POI数量，如表3所示。
将数据处理结果代入式（2）～式（4），得出4种需求点类型的权重。具体权重分配如下：城镇历史火灾点的权重最高，其次是城镇POI，然后是乡村历史火灾点，最后是乡村POI。各需求点类型权重划分见表4。
2.4　评估时刻
为了准确评估SD县的消防救援总需求点数量占比，选取一个完整的工作日（从当天12：00至次日12：00）作为评估周期，每隔1 h设置连续的评估场景，涵盖不同交通特征（如拥堵、平稳、畅通）。在出行高峰期，进行局部加密场景，即在7：30和8：30以及17：30和18：30增设4个评估场景。共设置既有消防站和拟建优化2个批次、29个评估场景。调用百度地图API，实现了对实时行驶时间和距离的计算。
3　既有消防站效果评价
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3.1　既有消防站可达性分析
对既有消防站批次的29个评估场景进行数据挖掘和分析，得到了465 160个有效样本。结果显示，可达性等级为Ⅰ、Ⅱ、Ⅲ的需求点数量占比在白天明显小于晚上，而Ⅳ等级则正好相反。图3中所呈现的变化趋势显示，SD县各可达性等级的需求点数量占比在一天内不断波动。等级为Ⅰ的需求点数量占比在早晚交通高峰时段，即7：30及17：30附近较低，而在凌晨0：00-3：00期间，需求点数量占比则处于最高阶段。这可能与交通流量有关，在白天，尤其是早晚高峰阶段，车流量较多，导致消防救援响应缓慢，进而影响消防救援可达性；而在晚上，车流量相对减少，道路更通畅，从而使得消防救援服务快速响应，提高消防救援可达性。此外可以看出，在既有消防站的救援服务下，RⅠ，T为23.38%，RⅡ，T为26.92%，RⅢ，T为18.70%，RⅣ，T为31.00%。而RⅠ，T和RⅡ，T的累计占比甚至不足60%，这表明县域内的消防救援服务仍有待提高。这种情况可能与SD县的地形特点有关，该县82.87%的地形为丘陵和山地，平原面积相对较少。此外，该县有2个城市消防站和13个乡镇专职消防站，但是要负责1 778 km2的区域，这无疑增加了及时响应救援的难度。
在既有消防站点的消防救援服务下，SD县域内的城镇和乡村的不同可达性等级需求点数量占比如图4所示。总体看，城镇在可达性等级为Ⅰ、Ⅱ、Ⅲ的需求点数量占比均高于乡村，而乡村在可达性等级为Ⅳ的需求点数量占比显著高于城镇。这表明城乡之间在消防救援资源的分配和利用上存在不均衡性。这主要是因为城市消防站和乡镇专职消防站，一般设在辖区内的适中位置，且靠近便于车辆迅速出动的临街地段［5，27］，能对城区和乡镇提供有效的消防救援覆盖；相比之下，乡村地区大多距离消防站较远，消防救援服务的响应时间更长，从而大大降低了消防救援的可达性。此外，部分乡镇缺乏专门的消防站，这一定程度上加剧了乡村地区消防救援覆盖不足的现象。因此，亟须对既有的消防救援力量进行优化调整。
3.2　结果可视化分析
以当天12：00的计算结果为例进行可视化，见图5。根据消防救援供应点到消防救援需求点的行驶时间，将各可达性等级所覆盖的需求点划分为Ⅰ、Ⅱ、Ⅲ、Ⅳ 4个等级，并分别用绿、黄、橙、红表示。可以看出，在4个区域内有大量红色需求点。这表明这些区域缺乏相应的消防站点，主要原因为该区域缺少乡镇专职消防站，导致了消防救援响应时间的延长。
4　优化方案效果评价
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4.1　优化方案设计
根据既有消防站批次的可达性和可视化分析结果，发现部分地区缺少乡镇专职消防站，导致该乡村地区无法得到及时的消防救援服务。消防站的建设需要大量的资金投入，因此必须在有限的资金投入下，实现消防站建设的高效性和经济性，同时兼顾最大限度地均衡覆盖辖区，既避免消防救援力量过剩，也防止出现消防救援力量严重不足的情况。该县的一个镇（乡）与县城相毗邻，距离城市消防站很近，主要依托于县城的消防救援。故暂不建设乡镇专职消防站，随着城市化进程的推进，未来可在该镇（乡）建设城市消防站。基于以上要求，为优化该县域的消防救援力量布局，在图5中4个区域所对应的8个乡镇地区建设乡镇专职消防站，如图6所示。图6中，黄星代表既有的城市消防站和既有的乡镇专职消防站，红星代表着拟建的乡镇专职消防站。计划在原有15个消防救援站点的基础上，建设8个乡镇专职消防站。
4.2　优化方案评价
对拟建优化批次的29个评估场景进行数据挖掘和分析，得到了465 160个有效样本。SD县既有消防站和优化实施后的消防站点消防出警的总平均距离D¯T、总平均时间t¯T、总平均速度v¯T的计算结果如表5所示。在消防站布局优化后，消防车辆D¯T减少了599.32 m，t¯T减少了59.81 s，v¯T提升了0.37 m/s。可以看出，虽然D¯T和tT都有所减少，但优化后的消防救援行驶时间仍超过4 min。SD县多丘陵、山地，复杂的地理环境使得在8个乡镇专职消防站投入使用后，t¯T的提升幅度仍有限。
如图7所示，消防站布局优化后，可达性等级为Ⅰ的总需求点数量占比RⅠ，T提升6.17%，达到29.55%；等级为Ⅱ的总需求点数量占比提升4.59%，达到31.51%；等级为Ⅳ的总需求点数量占比降低12.20%，达到18.80%。RⅠ，T和RⅡ，T的累计占比较之前提升10.76%，但不超过65%。这可能是由于部分偏远地区，消防站点覆盖不足，导致行驶距离和时间仍然较长，进而降低消防救援的可达性。因此，还需进一步完善乡镇专职消防站的建设，并建立志愿消防队，优化消防救援力量布局。
县域内各可达性等级需求点数量占比变化如表6所示。说明：在每个评估场景中，第一组数据的第一个数值（如19.32）代表既有消防站点的需求点数量占比（19.32%），而随后的数值（如5.88）则反映了优化方案使用后需求点数量占比的变化幅度（5.88%）。可以看出，等级为Ⅰ、Ⅱ、Ⅲ的需求点数量占比几乎都呈现增长趋势，其中等级Ⅰ的增长幅度最大，这可能是由于建设乡镇专职消防站后，增强对乡镇周边的乡村区域覆盖能力，从而提高了消防救援可达性，使得等级为Ⅰ的需求点比例更多，但该县的消防救援服务总体水平仍有待提升。
4.3　结果可视化
以当天12：00的计算结果为例进行可视化。如图8所示。可以看出，在优化布局后，区域1所代表的城区周围需求点比例变化不大，而在区域2～4，大部分需求点从Ⅳ提升至Ⅱ和Ⅲ等级，表明这些区域的消防救援能力得到显著改善。但整体来看，Ⅰ等级的需求点大多集中在城区和镇区中心附近，其他大部分需求点，尤其是位于乡村和一些偏远地区的，仍然处于Ⅳ等级，说明这些地区的消防救援覆盖能力仍然不足。优化布局后，尽管消防救援力量有所提升，但该县的消防救援力量仍有待进一步加强。
在乡村地区，由于地域辽阔、交通不便以及人口密度相对较低，部分地区难以及时有效地应对突发火灾事件。因此，有必要建立乡村志愿消防队，与城市消防站和乡镇专职消防站形成互补，增强乡村消防工作的覆盖面，缩短灭火救援作战时间和作战半径。
5　结 论
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通过消防救援部门收集、实地调研及网络地图检索获取消防救援供应点和需求点信息，并基于在线地图实时路况仿真计算消防救援行驶时间，使用城乡消防救援可达性综合评价方法优化了县域消防站布局。以SD县为例，设置既有消防站和拟建优化2个批次、29个评估场景，每个场景包含16 040个样本，总计930 320个样本。基于这些数据计算得出以下结论。
1）对既有消防站布局评价，RⅠ，T为23.38%，RⅡ，T为26.92%，RⅢ，T为18.70%，RⅣ，T为31.00%。而RⅠ，T和RⅡ，T的累计占比甚至不足60%，且可视化结果存在多个Ⅳ等级区域，这表明县域内的消防救援服务仍有待提高。
2）方案优化后，消防车辆D¯T减少了599.32 m，达到2 849.66 m；t¯T减少了59.81 s，达到404.26 s。复杂的地理环境使得在8个乡镇专职消防站投入使用后，t¯T的提升幅度仍有限。
3）方案优化后，可达性等级Ⅰ的总需求点数量占比RⅠ，T将提升6.17%，达到29.55%，可达性等级Ⅱ的总需求点数量占比RⅡ，T提升4.59%，达到31.51%，但仍存在部分区域未得到充分覆盖。
SD县还需加强消防站建设，在拟建乡镇专职消防站投入使用后，应进一步完善乡镇专职消防站的建设，并着手建立村组“乡村志愿消防队”。随着车路协同技术发展，通行效率将提升，未来消防救援可达性有望得到提升。此外，本文所提出的城乡消防救援可达性综合评价方法还适用于其他县域的计算。未来研究中，将建立基于城乡不同类型需求点的火灾风险加权的可达性评价模型。

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doi: 10.20168/j.1009-0029.2026.05.0107.09
  • 接收时间：2025-02-08
  • 首发时间：2026-10-22
  • 出版时间：2026-05-15
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  • 收稿日期：2025-02-08
  • 修回日期：2025-06-06
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    1长沙理工大学 交通学院，湖南 长沙 410114
    2中国矿业大学深圳研究院，广东 深圳 518000

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刘顶立，长沙理工大学交通学院副教授，。
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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