CN113052503A - Forest fire risk assessment method for forest city cross-connection area based on PSR model - Google Patents
Forest fire risk assessment method for forest city cross-connection area based on PSR model Download PDFInfo
- Publication number
- CN113052503A CN113052503A CN202110476721.XA CN202110476721A CN113052503A CN 113052503 A CN113052503 A CN 113052503A CN 202110476721 A CN202110476721 A CN 202110476721A CN 113052503 A CN113052503 A CN 113052503A
- Authority
- CN
- China
- Prior art keywords
- forest fire
- data
- forest
- grid
- factor
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000012502 risk assessment Methods 0.000 title claims abstract description 34
- 238000000034 method Methods 0.000 title claims abstract description 22
- 230000004044 response Effects 0.000 claims abstract description 41
- 230000002265 prevention Effects 0.000 claims abstract description 12
- 238000004458 analytical method Methods 0.000 claims description 33
- 238000011156 evaluation Methods 0.000 claims description 17
- 238000006243 chemical reaction Methods 0.000 claims description 13
- 238000012545 processing Methods 0.000 claims description 7
- YBJHBAHKTGYVGT-ZKWXMUAHSA-N (+)-Biotin Chemical compound N1C(=O)N[C@@H]2[C@H](CCCCC(=O)O)SC[C@@H]21 YBJHBAHKTGYVGT-ZKWXMUAHSA-N 0.000 claims description 3
- FEPMHVLSLDOMQC-UHFFFAOYSA-N virginiamycin-S1 Natural products CC1OC(=O)C(C=2C=CC=CC=2)NC(=O)C2CC(=O)CCN2C(=O)C(CC=2C=CC=CC=2)N(C)C(=O)C2CCCN2C(=O)C(CC)NC(=O)C1NC(=O)C1=NC=CC=C1O FEPMHVLSLDOMQC-UHFFFAOYSA-N 0.000 claims description 3
- 238000011161 development Methods 0.000 abstract description 6
- 230000008447 perception Effects 0.000 abstract description 4
- 238000004364 calculation method Methods 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 6
- 241000282414 Homo sapiens Species 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 230000007613 environmental effect Effects 0.000 description 3
- 238000010606 normalization Methods 0.000 description 3
- 239000003086 colorant Substances 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 239000002360 explosive Substances 0.000 description 2
- 230000001939 inductive effect Effects 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 241000894007 species Species 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007123 defense Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 230000006698 induction Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 238000004451 qualitative analysis Methods 0.000 description 1
- 238000013441 quality evaluation Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000005728 strengthening Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001629 suppression Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
- G06Q50/265—Personal security, identity or safety
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
- Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture
- Y02A40/28—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture specially adapted for farming
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Health & Medical Sciences (AREA)
- Computer Security & Cryptography (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a forest fire risk assessment method for a forest city junction area based on a PSR model, which is characterized in that index data corresponding to the factors are extracted by methods such as keyword matching and the like according to special forest fire plan guidance of various regions and classified into P (pressure) indexes, S (state) indexes and R (response) indexes, a set of forest fire risk assessment calculation formula capable of effectively assessing forest fire in a forest city junction area is constructed, assessment results provide important technical support for advance prevention and perception, an emergency department can conveniently and timely master the development trend of potential masses and possible fires at the periphery, and reference guidance is provided for ordinary patrol; relevant departments and personnel can timely and effectively know the forest fire risk of the forest city cross-connection area, can timely and effectively prevent and treat the forest fire, and effectively reduces the occurrence of the fire.
Description
Technical Field
The invention relates to the field of urban public safety, in particular to a forest fire risk assessment method for a forest urban cross-connection area based on a PSR model.
Background
Forest fire refers to the action of forest fire which is not artificially controlled, freely spreads and expands in the range of forest lands and brings certain harm and loss to forests, forest ecosystems and human beings. Forest fires are natural disasters which are strong in burst, large in destructiveness and difficult to dispose and rescue. Forest fire prevention work is an important component of Chinese disaster prevention and reduction work, is important content of national public emergency system construction, is important work for accelerating forest development and strengthening the basis and premise of ecological construction, relates to forest resources and ecological safety, relates to life and property safety of people, and realizes early discovery, early movement and early suppression of forest fires, so that the forest fire is important work of forestry management departments in various regions.
At present, a set of effective risk assessment method is not provided for fire protection of the forest city cross-connection area, the risk condition of the forest city cross-connection area cannot be effectively mastered in time, and effective defense for the forest fire in the area cannot be achieved.
Disclosure of Invention
The invention aims to provide a forest fire risk assessment method for a forest city intersection region based on a PSR model, which can effectively assess the fire risk of the forest city intersection region, provide important technical support for advance prevention and perception, facilitate emergency departments to timely master the possible development trend of surrounding potential fires, and provide reference guidance for daily patrol.
In order to achieve the purpose of the invention, the method for evaluating the forest fire risk of the forest city cross-connection area based on the PSR model comprises the following steps:
step S1: determining index ranges of various factors involved in risk assessment according to the forest fire plan;
step S2: determining factor attributes of each index in the index range determined in the step S1, determining specific indexes in the index range as pressure factors according to inducement factors of forest fires, determining specific indexes in the index range as state factors according to forest background conditions, and determining specific indexes in the index range as response determining factors according to forest fire observation patrol coverage conditions;
step S3: acquiring data of the indexes with determined attributes;
step S4: constructing a forest fire PSR evaluation framework of a forest city handover area, wherein P corresponds to a stress factor of a forest fire disaster, S corresponds to a state factor of the forest fire disaster, and R corresponds to forest fire prevention and rescue response;
step S5: establishing a standardized grid of a space target region, and setting the size of the standardized grid according to the area of a risk assessment region;
step S6: superposing the index data acquired in the step S3 to the grid established in the step S5, and respectively constructing a data set divided by the standard grid of P1 … … Pn, S1 & … … Sn and R1 … … Rn for each sub-index belonging to the pressure factor, the state factor and the response factor in each grid;
step S7: respectively calculating a total forest fire risk pressure factor value Prisk, a total forest fire risk state factor value Srisk and a total forest fire risk response factor value Rrisk in each grid according to a formula 1, a formula 2 and a formula 3 to form combined Prisk risk gridding data, Srisk risk gridding data and Rrisk risk gridding data;
Prisk＝wp1*P1+...+wpn*Pn (1)
Srisk＝ws1*S1+...+wsn*Sn (2)
Rrisk＝wr1*R1+...+wrn*Rn (3)
in the formula, wp1.. wpn, ws1.. wsn and wr1.. wrn are weight values of each index, the value range is 01, and Pn, Sn and Rn respectively belong to the number of indexes corresponding to pressure factors, state factors and response factors;
step S8: calculating to obtain forest fire risk assessment value PSRrisk gridding data of each grid according to one or more of formulas 410;
PSRrisk＝(P-R)/S (4)
PSRrisk＝(P-S)/R (5)
PSRrisk＝P-R-S (6)
PSRrisk＝P/R/S (7)
PSRrisk＝(P/S)-R (8)
PSRrisk＝(P/R)-S (9)
PSRrisk＝P/(S+R) (10)
in the formula, P, S, R is a forest fire risk pressure total factor Prisk, a forest fire risk state total factor Srisk and a forest fire risk response total factor Rrisk of each grid, which are obtained by formula (1) and formula (3);
step S9: and comparing the forest fire risk assessment value PSRrisk calculated in the step S8 with a preset high threshold and a preset low threshold, and judging the forest fire risk level of the area corresponding to the grid.
In some embodiments, after data of the index for which the attribute has been determined is acquired, each index data is subjected to normalization processing by equation (11),
in the formula, XjIs the j index, XmaxIs the maximum value of the j-th index, XminIs the minimum value of the j index, XijAnd normalizing the j index value.
In some embodiments, the weighting value of each index in step S7 is mainly determined from the weighting of the related expert experience.
In some embodiments, the evaluation method provided by the present application further includes a step of outputting a forest fire stress factor risk map, which specifically includes:
step SA 1: acquiring vector data determined as an index of the pressure factor P;
step SA 2: performing dot data vectorization and planar full-area data vector rasterization on the obtained vector data to respectively form dot data vectorization data and planar full-area data vector rasterization data;
step SA3, performing point density analysis, kernel density analysis and buffer area analysis on the dot-shaped data vectorized data, and outputting a grid map;
step SA4, performing raster point conversion processing on the raster image output in step SA3 and the planar full-area data vector rasterized data acquired in step SA2 to obtain raster point;
step SA5, overlapping the acquired grid turning points with the standardized grid space formed in the step S5, merging and summarizing overlapping results, and acquiring a total forest fire risk pressure factor Prisk of the overlapped grid according to the formula (1);
and step SA6, outputting a forest fire risk factor risk map according to the result of the total forest fire risk factor value Prisk obtained in the step SA 5.
In some embodiments, the evaluation method provided by the present invention further includes a step of outputting a forest fire state factor risk map, which specifically includes:
step SB 1: acquiring vector data determined as an index of a state factor S;
step SB 2: performing dot data vectorization and planar full-area data vector rasterization on the obtained vector data to respectively form dot data vectorization data and planar full-area data vector rasterization data;
step SB3: performing point density analysis, kernel density analysis and buffer area analysis on the dot data vectorization data, and outputting a grid map;
step SB4: performing raster point conversion processing on the raster image output in the step SB3 and the planar full-area data vector rasterized data acquired in the step SB2 to obtain raster point conversion;
step SB 5: the obtained grid turning points are overlapped with the standardized grid space formed in the step S5, the overlapping results are merged and gathered, and the forest fire risk state total factor value Srisk of the overlapped grid is obtained according to the formula (2);
and step SB6, outputting a forest fire state factor risk map according to the classification weighting result obtained in the step SB 5.
In some embodiments, the evaluation method provided by the present invention further includes a step of outputting a forest fire response factor risk map, which specifically includes:
step SC 1: acquiring vector data determined as an index of the response factor R;
step SC 2: rasterizing the obtained vector number to obtain rasterized data;
step SC3, performing linear density analysis and buffer area analysis on the obtained rasterized data, and overlapping the linear density analysis result and the buffer area analysis result with the grid respectively;
step SC4, outputting the superimposed grid map, and performing grid point conversion;
step SC5, overlapping the acquired grid turning points with the standardized grid space formed in the step S5, merging and summarizing overlapping results, and acquiring a forest fire risk response total factor value Rrisk of the overlapped grid according to the formula (3);
and step SC6, outputting a forest fire state factor risk map according to the classification weighting result obtained in the step SC 5.
By adopting the technical scheme of the invention, at least the beneficial effects that can be achieved comprise:
1) according to the special plan guidance of forest fires in various places, index data corresponding to the factors are extracted by methods such as keyword matching and the like and classified into P (pressure) indexes, S (state) indexes and R (response) indexes, a set of forest fire risk assessment calculation formula capable of effectively assessing forest fires in forest city handover areas is constructed, assessment results provide important technical support for advance prevention and perception, emergency departments can conveniently and timely master the development trend of surrounding potential masses and possible fires, and reference guidance is provided for ordinary patrol; relevant departments and personnel can timely and effectively know the forest fire risk of the forest city cross-connection area, can timely and effectively prevent and treat the forest fire, and effectively reduces the occurrence of the fire. PSR (Pressure-State-Response), i.e., Pressure, status, Response. The evaluation framework is a commonly used evaluation framework in the sub-discipline of the ecosystem health evaluation in the environmental quality evaluation discipline, and is originally proposed by Canadian systemmercists David J.Rapport and Tony Friend (1979), and then developed by the economic cooperation and development Organization (OECD) and the environmental planning agency (UNEP) of the United nations in the eight and ninety years of the 20 th century together to research a framework system for environmental problems. The PSR model is widely applied to the fields of environmentality, ecology, city planning, treatment and the like, shows excellent logicality, integrity and feasibility, opens a new visual angle for preventing forest fires when being applied to the field of forest fires, has a better effect, and has the functions of bringing the pressure, the state and the response into a unified qualitative/quantitative analysis frame to obtain a comprehensive evaluation result.
2) And the data standardization processing is carried out on the index data, the dimension difference of each index is eliminated, and the problem that the evaluation result is influenced by the different dimension differences of each index is solved.
3) And the individual risk graph output is carried out on the pressure factors, the state factors and the response factors, so that the fire risk can be analyzed individually from the pressure, the state and the response three-latitude.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:
FIG. 1 is a flowchart describing a forest fire risk assessment method for a forest city intersection region based on a PSR model according to an embodiment of the present invention;
FIG. 2 is a flow chart of an output forest fire stress factor risk map according to an embodiment of the present invention;
FIG. 3 is a flow chart of outputting a forest fire status factor risk map according to an embodiment of the present invention;
FIG. 4 is a flow chart of outputting a forest fire response factor risk map according to an embodiment of the present invention;
FIG. 5 is a PSRrisk forest fire risk graph output by an embodiment of the present invention;
FIG. 6 is a schematic diagram illustrating the risk of a pressure factor output by an embodiment of the present invention;
FIG. 7 is a second schematic diagram illustrating the risk of pressure factors output by the embodiment of the present invention;
FIG. 8 is a schematic diagram illustrating the risk of a condition factor output by an embodiment of the present invention;
FIG. 9 is a second schematic diagram illustrating the risk of state factors output by the embodiment of the present invention;
FIG. 10 is a third schematic diagram of the risk of the condition factor output by the embodiment of the present invention;
FIG. 11 is a schematic diagram of response factor risk output by an embodiment of the present invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
Fig. 1 exemplarily shows a flow chart of the forest fire risk assessment method of the forest city intersection area based on the PSR model according to the embodiment. With reference to the drawings, the evaluation method in this embodiment includes the following steps:
step S1: determining index ranges of various factors involved in risk assessment according to the forest fire plan; for example, contents required for preventing and monitoring the forest fire in a certain area from the third chapter of prevention, monitoring and early warning are extracted from a forest fire emergency plan in the certain area, wherein the contents comprise weather forecast, holiday factors, deep-buried point distribution, major hazard sources, forest fire emergency equipment facilities, forest fire observation patrol coverage and the like, and the weather forecast, holiday factors, deep-buried point distribution, major hazard sources, forest fire emergency equipment facilities, forest fire observation patrol coverage and the like can be used as index ranges of various factors related to risk assessment;
step S2: determining factor attributes of each index in the index range determined in step S1, and determining specific indexes in the index range as pressure factors according to weather, artificial induction factors and the like of forest fireBy PnDifferent specific indexes are shown, and n is a natural number; determining specific indexes in the index range as state factors according to forest background conditions such as forest flammability, major hazard source protection, emergency facilities and the like, and using SnDifferent specific indexes are shown, and n is a natural number; determining specific indexes in the index range as response determining factors according to forest fire observation patrol coverage conditions, and using RnDifferent specific indexes are shown, and n is a natural number;
step S3: data according to the determined attribute indexes, wherein the data is derived from data of related departments of weather and emergency; using a GIS tool and a keyword text tool to spatially draw data, and storing the data in a format of shp and coordinates in longitude and latitude coordinates;
step S4: constructing a forest fire PSR evaluation framework of a forest city handover area, wherein P corresponds to a stress factor of a forest fire disaster, S corresponds to a state factor of the forest fire disaster, and R corresponds to forest fire prevention and rescue response; expanding and replacing according to the geographical position, climate type and difference of human environment of each region, P type factors, S type factors and R type factors according to the business needs of emergency departments and the difficulty degree of data acquisition, but the judgment basis of the factors is still consistent;
step S5: establishing a standardized grid of a space target area by using GIS software, setting the size of the standardized grid according to the area of a risk assessment area, and taking 100 meters by 100 meters as a basic grid according to the current urban grid management traffic standard which generally operates;
step S6: performing grid division on the index data acquired in the step S3 in GIS software according to the standard grid generated in the step S5, namely overlaying the data acquired in the step S3 on the grid established in the step S5; after superposition, respectively constructing standard grid-divided data sets of P1 … … Pn, S1.… … Sn and R1 … … Rn for each sub-index belonging to the pressure factor, the state factor and the response factor;
step S7: respectively calculating a total forest fire risk pressure factor value Prisk, a total forest fire risk state factor value Srisk and a total forest fire risk response factor value Rrisk in each grid according to a formula 1, a formula 2 and a formula 3 to form combined Prisk risk gridding data, Srisk risk gridding data and Rrisk risk gridding data;
Prisk＝wp1*P1+...+wpn*Pn (1)
Srisk＝ws1*S1+...+wsn*Sn (2)
Rrisk＝wr1*R1+...+wrn*Rn (3)
in the formula, wp1 … wpn, ws1.. wsn and wr1.. wrn are weighted values of each index, and the value range is 01; pn, Sn and Rn respectively belong to the number of indexes corresponding to the pressure factor, the state factor and the response factor;
step S8: calculating to obtain forest fire risk assessment value PSRrisk gridding data of each grid according to one or more of formulas 410;
PSRrisk＝(P-R)/S (4)
PSRrisk＝(P-S)/R (5)
PSRrisk＝P-R-S (6)
PSRrisk＝P/R/S (7)
PSRrisk＝(P/S)-R (8)
PSRrisk ═ (P/R) one S (9)
PSRrisk＝P/(S+R) (10)
In the formula, P, S, R is obtained by the formula (1) to the formula (3) for each grid forest fire risk pressure total factor Prisk, forest fire risk state total factor Srisk and forest fire risk response total factor Rrisk respectively;
step S9: comparing the forest fire risk assessment value PSRrisk calculated in the step S8 with a preset high threshold and a preset low threshold, and judging the forest fire risk level of the area corresponding to the grid; when the grid forest fire risk assessment value PSRrisk is larger than a high threshold value, the area corresponding to the grid is a high-occurrence area with forest fire risk or an area needing key prevention and control; when the grid forest fire risk assessment value PSRiisk is smaller than a low threshold value, the area corresponding to the grid is a low-occurrence area of the forest fire risk; and when the grid forest fire risk assessment value PSRrisk is larger than the low threshold but smaller than the high threshold, the area corresponding to the grid is a middle risk area with forest fire risk.
In this embodiment, the specific index range of the forest fire PSR risk assessment factor is determined according to the forest fire plan of a specific area, as shown in table 1.
TABLE 1 forest fire PSR Risk assessment factor exemplary Table
| Data examples | Format | Source | |
| P pressure index | Temperature of | shp | Meteorological department |
| P pressure index | Humidity of air | shp | Meteorological department |
| P pressure index | Amount of rainfall | shp | Meteorological department |
| P pressure index | Number of deeply buried points | shp | Emergency department |
| P pressure index | Holiday | shp | Emergency department |
| P pressure index | Gas pipeline | shp | Emergency department |
| P pressure index | High-voltage wire | shp | Emergency department |
| P pressure index | Number of temples | shp | Emergency department |
| Index of S state | Forest seeds | shp | Department of planning and Natural resources |
| Index of S state | Number of voice sticks | shp | Emergency department |
| Index of S state | Number of propaganda cards | shp | Emergency department |
| Index of S state | Number of water storage barrels | shp | Emergency department |
| Index of S state | Number of reservoirs | shp | Emergency department |
| Index of S state | Number of watchtowers | shp | Emergency department |
| R response index | Number of patrols per day | shp | Emergency department |
| R response index | Number of rounds per day | shp | Emergency department |
| … | … | shp | … |
Note: the Shp format in the table is a general spatial data format and can be read by common GIS software, such as Arcgis, Qgis and the like. As those skilled in the art will appreciate, the above table 1 is merely an exemplary index, and does not mean that the index included in the P, S, R factor described in the present application can only be used as shown in table 1.
Based on the evaluation indexes in the PSR model, because the dimension of each index has difference, in order to unify the interaction among the factors and eliminate the influence of the dimension difference on the evaluation result, the data of each index is standardized by the formula (11),
in the formula, XjIs the j index, XmaxIs the maximum value of the j-th index, XminIs the minimum value of the j index, Xij' As the j index normalization value, normalization is performed by using a method.
When determining the weight of each index data related to the forest fire risk, the embodiment is mainly determined according to the weight of the experience of relevant experts, initially through the collection of relevant expert knowledge of an emergency department and the determination of the weight, and then can be adjusted according to business requirements.
When the index data and the grid are superimposed, any mode can be adopted, such as a manual mode, that is, an analyst superimposes the index data on the grid in response according to the target area and the target area condition; if a certain grid divided by the target area corresponds to a dense activity area of people, data corresponding to indexes of people belonging to the stress factors are superposed into the corresponding grid, and fixed factors existing in each area, such as weather factors, illumination factors, rainfall factors and the like, are superposed into each grid according to the belonging factors.
In order to analyze the forest fire risk from different factors more intuitively, the evaluation method also outputs a forest fire pressure factor risk map, a forest fire state factor risk map and a forest fire response factor risk map, and the forest fire risk is analyzed from different dimensions. Referring to fig. 2, the forest fire stress factor risk map in the present embodiment is output through the following steps:
step SA 1: acquiring vector data determined as an index of the pressure factor P;
step SA 2: performing dot data vectorization and planar full-area data vector rasterization on the obtained vector data to respectively form dot data vectorization data and planar full-area data vector rasterization data; wherein, the point data is a point factor inducing the forest fire, and the surface is a surface factor inducing the forest fire;
step SA3, performing point density analysis, kernel density analysis and buffer area analysis on the dot-shaped data vectorized data, and outputting a grid map;
step SA4, performing grid point conversion on the grid graph output in the step SA3 and the grid data of the planar full-area data vector acquired in the step SA2 by using an analysis tool in an Arctolbox to obtain grid point conversion;
step SA5, overlapping the acquired grid turning points with the standardized grid space formed in the step S5, merging and summarizing overlapping results, and acquiring a total forest fire risk pressure factor Prisk of the overlapped grid according to the formula (1); here, the superposition of points and a standard grid can be performed for the points using the spatial join tool in the arctolobox;
and step SA6, outputting a forest fire pressure factor risk map according to the classification weighting result obtained in the step SA 5.
The forest fire pressure factor risk graph output by the steps can be used for representing deep-buried point distribution, flammable and explosive point distribution or the like; as shown in fig. 6 and 7, the shp-format deep buried point distribution and the shp-format inflammable and explosive distribution are shown, respectively.
Referring to fig. 3, the risk map of forest fire state factors in the present embodiment is output through the following steps:
step SB 1: acquiring vector data determined as an index of a state factor S;
step SB 2: performing dot data vectorization and planar full-area data vector rasterization on the obtained vector data to respectively form dot data vectorization data and planar full-area data vector rasterization data;
step SB3, performing point density analysis, kernel density analysis and buffer area analysis on the dot-shaped data vectorized data, and outputting a grid map;
step SB4, carrying out raster point conversion processing on the raster image output in the step SB3 and the planar full-area data vector rasterized data acquired in the step SB2 by using an analysis tool in the Arctolbox to obtain raster point;
step SB 5: the obtained grid turning points are overlapped with the standardized grid space formed in the step S5, the overlapping results are merged and gathered, and the forest fire risk state total factor value Srisk of the overlapped grid is obtained according to the formula (2); here, the superposition of points and a standard grid can be performed for the points using the spatial join tool in the arctolobox;
and step SB6, outputting a forest fire state factor risk map according to the classification weighting result obtained in the step SB 5.
The forest fire state factor risk graph output by the steps can be used for representing forest species distribution, water storage pool distribution, billboard voice distribution or the like; as shown in fig. 8-10, a forest species distribution in shp format, a reservoir distribution in shp format, and an ignition point distribution in shp format are shown, respectively.
Referring to fig. 4, the forest fire response factor risk map in the present embodiment is output through the following steps:
step SC 1: acquiring vector data determined as an index of the response factor R;
step SC 2: rasterizing the obtained vector number to obtain rasterized data;
step SC3, performing linear density analysis and buffer area analysis on the obtained rasterized data, and overlapping the linear density analysis result and the buffer area analysis result with the grid respectively;
step SC4, outputting the superimposed grid map, and performing grid point conversion by using an analysis tool in an Arctolbox;
step SC5, overlapping the acquired grid turning points with the standardized grid space formed in the step S5, merging and summarizing overlapping results, and acquiring a forest fire risk response total factor value Rrisk of the overlapped grid according to the formula (3); here, the superposition of points and a standard grid can be performed for the points using the spatial join tool in the arctolobox;
and step SC6, outputting a forest fire state factor risk map according to the classification weighting result obtained in the step SC 5.
The forest fire pressure factor risk graph output by the steps can be used for representing fire fighting channel distribution or the like; as shown in fig. 11, the map of the fire passage in shp format is shown.
On the basis of the output forest fire pressure factor risk map, the output forest fire state factor risk map and the output forest fire response factor risk map, a forest fire risk map can be generated by using software such as GIS software. The generated forest fire risk map may be as shown in fig. 5. In the embodiment, different colors are adopted in a forest fire risk map generated by GIS software to represent risk areas of different levels, and since a color drawing is not allowed to be used in the application document, gray with different depths is used to represent risk areas of different levels, as shown in fig. 5, a dark gray grid represents a forest fire high risk area, a medium gray grid represents a forest fire high risk area, a white grid represents a forest fire medium risk area, a gray grid (with the largest area) represents a forest fire low risk area, and the dark gray, the medium gray and the white are all areas with dense human activities near the forest and need to be protected more. Certainly, in practical application, red, orange, yellow, gray, blue or other color grids can be used to represent different forest fire area levels, for example, a red grid represents a forest fire high risk area, an orange grid represents a forest fire high risk area, a yellow grid represents a forest fire medium high risk area, a gray grid represents a forest fire low risk area, and red grids, orange grids and yellow grids are all areas with dense human activities near the forest, and additional prevention and control are needed.
And a graphic result display form is adopted, so that the evaluation result is more visual, different colors displayed by the target grid risk level are set, and related personnel can directly judge the fire risk level of the corresponding region of the target region through the grid color.
The evaluation method provided by the invention determines a target area, determines an index range according to the requirement of the target area, and then determines the attribution of indexes in the index range; after the target area is determined, a spatial target area grid is established, data corresponding to indexes are obtained from relevant departments, the index data are superposed to the established target area grid, grid data of each grid are calculated by using a PSR model, and fire risk grade judgment of the target area is realized according to the grid data.
According to the special forest fire plan guidance of various regions, index data corresponding to the factors are extracted by methods such as keyword matching and the like to be classified into P (pressure) indexes, S (state) indexes and R (response) indexes, a set of calculation formula capable of evaluating forest fire risks in a forest city handover region is constructed, important technical support is provided for advance prevention and perception, an emergency department can conveniently and timely master the possible development trend of surrounding potential masses and fires, and reference guidance is provided for ordinary inspection.
The above embodiments are only for illustrating the patented technical solutions of the present invention and are not limiting, and modifications or equivalent substitutions made by those skilled in the art to the patented technical solutions of the present invention are included in the claims of the present invention as long as they do not depart from the spirit and scope of the patented technical solutions of the present invention.
Claims (6)
1. A forest fire risk assessment method for a forest city cross-connection area based on a PSR model is characterized by comprising the following steps:
step S1: determining index ranges of various factors involved in risk assessment according to the forest fire plan;
step S2: determining factor attributes of each index in the index range determined in the step S1, determining specific indexes in the index range as pressure factors according to inducement factors of forest fires, determining specific indexes in the index range as state factors according to forest background conditions, and determining specific indexes in the index range as response determining factors according to forest fire observation patrol coverage conditions;
step S3: acquiring data of the indexes with determined attributes;
step S4: constructing a forest fire PSR evaluation framework of a forest city handover area, wherein P corresponds to a stress factor of a forest fire disaster, S corresponds to a state factor of the forest fire disaster, and R corresponds to forest fire prevention and rescue response;
step S5: establishing a standardized grid of a space target region, and setting the size of the standardized grid according to the area of a risk assessment region;
step S6: superposing the index data acquired in the step S3 to the grid established in the step S5, and respectively constructing a data set divided by the standard grid of P1 … … Pn, S1 & … … Sn and R1 … … Rn for each sub-index belonging to the pressure factor, the state factor and the response factor in each grid;
step S7: respectively calculating a total forest fire risk pressure factor value Prisk, a total forest fire risk state factor value Srisk and a total forest fire risk response factor value Rrisk in each grid according to a formula 1, a formula 2 and a formula 3 to form combined Prisk risk gridding data, Srisk risk gridding data and Rrisk risk gridding data;
Prisk＝wp1*P1+...+wpn*Pn (1)
Srisk＝ws1*S1+...+wsn*Sn (2)
Rrisk＝wr1*R1+...+wrn*Rn (3)
in the formula, wp1.. wpn, ws1.. wsn and wr1.. wrn are weight values of each index, the value range is 0-1, and Pn, Sn and Rn respectively belong to the number of indexes corresponding to pressure factors, state factors and response factors;
step S8: calculating to obtain forest fire risk assessment value PSRrisk gridding data of each grid according to one or more of formulas 4-10;
PSRrisk＝(P-R)/S (4)
PSRrisk＝(P-S)/R (5)
PSRrisk＝P-R-S (6)
PSRrisk＝P/R/S (7)
PSRrisk＝(P/S)-R (8)
PSRrisk＝(P/R)-S (9)
PSRrisk＝P/(S+R) (10)
in the formula, P, S, R is a forest fire risk pressure total factor Prisk, a forest fire risk state total factor Srisk and a forest fire risk response total factor Rrisk of each grid, which are obtained by formula (1) and formula (3);
step S9: and comparing the forest fire risk assessment value PSRrisk calculated in the step S8 with a preset high threshold and a preset low threshold, and judging the forest fire risk level of the area corresponding to the grid.
2. The PSR model-based forest fire risk assessment method for forest urban intersection areas, according to claim 1, is characterized in that: after data of the index for which the attribute has been determined is acquired, each index data is normalized by equation (11),
in the formula, XjIs the j index, XmaxIs the maximum value of the j-th index, XminIs the minimum value of the j index, Xij' is the j index normalized value.
3. The PSR model-based forest fire risk assessment method for forest urban intersection areas, according to claim 1, is characterized in that: the weighting values of the indexes in step S7 are mainly determined from the weights of the related expert experiences.
4. The PSR model-based forest fire risk assessment method for forest urban intersection areas, according to claim 1, is characterized in that: the method also comprises a step of outputting a forest fire pressure factor risk map, and the step specifically comprises the following steps:
step SA 1: acquiring vector data determined as an index of the pressure factor P;
step SA 2: performing dot data vectorization and planar full-area data vector rasterization on the obtained vector data to respectively form dot data vectorization data and planar full-area data vector rasterization data;
step SA3: performing point density analysis, kernel density analysis and buffer area analysis on the dot data vectorization data, and outputting a grid map;
step SA4: performing raster point conversion processing on the raster image output by the step SA3 and the planar full-area data vector rasterized data acquired by the step SA2 to obtain raster point conversion;
step SA5: the obtained grid turning points are overlapped with the standardized grid space formed in the step S5, the overlapping results are combined and gathered, and the total forest fire risk pressure factor value Prisk of the overlapped grid is obtained according to the formula (1);
step SA6: and (4) outputting a forest fire risk factor risk map according to the result of the total forest fire risk factor value Prisk obtained in the step SA 5.
5. The PSR model-based forest fire risk assessment method for forest urban intersection areas, according to claim 1, is characterized in that: the method also comprises a step of outputting a forest fire state factor risk map, and the step specifically comprises the following steps:
step SB 1: acquiring vector data determined as an index of a state factor S;
step SB 2: performing dot data vectorization and planar full-area data vector rasterization on the obtained vector data to respectively form dot data vectorization data and planar full-area data vector rasterization data;
step SB3: performing point density analysis, kernel density analysis and buffer area analysis on the dot data vectorization data, and outputting a grid map;
step SB4: performing raster point conversion processing on the raster image output in the step SB3 and the planar full-area data vector rasterized data acquired in the step SB2 to obtain raster point conversion;
step SB 5: the obtained grid turning points are overlapped with the standardized grid space formed in the step S5, the overlapping results are merged and gathered, and the forest fire risk state total factor value Srisk of the overlapped grid is obtained according to the formula (2);
step SB6: and outputting a forest fire state factor risk map according to the classification weighting result obtained in the step SB 5.
6. The PSR model-based forest fire risk assessment method for forest urban intersection areas, according to claim 1, is characterized in that: the method also comprises a step of outputting a forest fire response factor risk map, and the step specifically comprises the following steps:
step SC 1: acquiring vector data determined as an index of the response factor R;
step SC 2: rasterizing the obtained vector number to obtain rasterized data;
step SC3: performing linear density analysis and buffer area analysis on the obtained rasterized data, and respectively overlapping a linear density analysis result and a buffer area analysis result with the grid;
step SC4: outputting the superposed grid graph, and performing grid point conversion;
step SC5: the obtained grid turning points are overlapped with the standardized grid space formed in the step S5, the overlapping results are merged and gathered, and the forest fire risk response total factor value Rrisk of the overlapped grid is obtained according to the formula (3);
step SC6: and outputting a forest fire state factor risk map according to the classification weighting result obtained in the step SC 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110476721.XA CN113052503B (en) | 2021-04-29 | 2021-04-29 | Forest fire risk assessment method for forest urban junction area based on PSR model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110476721.XA CN113052503B (en) | 2021-04-29 | 2021-04-29 | Forest fire risk assessment method for forest urban junction area based on PSR model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113052503A true CN113052503A (en) | 2021-06-29 |
| CN113052503B CN113052503B (en) | 2023-10-24 |
Family
ID=76517818
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110476721.XA Active CN113052503B (en) | 2021-04-29 | 2021-04-29 | Forest fire risk assessment method for forest urban junction area based on PSR model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113052503B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104537206A (en) * | 2014-11-28 | 2015-04-22 | 国网上海市电力公司 | PSR model based grid infrastructure vulnerability evaluation method |
| CN104732447A (en) * | 2014-04-23 | 2015-06-24 | 国家电网公司 | Method for establishing important power grid infrastructure vulnerability index system |
| US20160061476A1 (en) * | 2014-09-03 | 2016-03-03 | Oberon, Inc. | Environmental Sensor Device |
| CN107909283A (en) * | 2017-11-17 | 2018-04-13 | 武汉科技大学 | A kind of Urban Fire Risk appraisal procedure based on a reference value |
| CN112712275A (en) * | 2021-01-07 | 2021-04-27 | 南京大学 | Forest fire risk assessment method based on Maxent and GIS |
-
2021
- 2021-04-29 CN CN202110476721.XA patent/CN113052503B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104732447A (en) * | 2014-04-23 | 2015-06-24 | 国家电网公司 | Method for establishing important power grid infrastructure vulnerability index system |
| US20160061476A1 (en) * | 2014-09-03 | 2016-03-03 | Oberon, Inc. | Environmental Sensor Device |
| CN104537206A (en) * | 2014-11-28 | 2015-04-22 | 国网上海市电力公司 | PSR model based grid infrastructure vulnerability evaluation method |
| CN107909283A (en) * | 2017-11-17 | 2018-04-13 | 武汉科技大学 | A kind of Urban Fire Risk appraisal procedure based on a reference value |
| CN112712275A (en) * | 2021-01-07 | 2021-04-27 | 南京大学 | Forest fire risk assessment method based on Maxent and GIS |
Non-Patent Citations (2)
| Title |
|---|
| 于海龙;邬伦: "森林火灾现场视频图像传输方案研究", 地理信息世界, no. 004, pages 40 - 44 * |
| 王国萍;闵庆文;丁陆彬;何思源;李禾尧: "基于PSR模型的国家公园综合灾害风险评估指标体系构建", 生态学报, no. 22, pages 36 - 48 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113052503B (en) | 2023-10-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113487181B (en) | Urban area ecological security pattern evaluation method | |
| Vogiatzis | Airport environmental noise mapping and land use management as an environmental protection action policy tool. The case of the Larnaka International Airport (Cyprus) | |
| de Almeida et al. | Evaluating ten years of management effectiveness in a mangrove protected area | |
| CN112686581B (en) | Natural disaster secondary dangerous chemical accident risk assessment method and assessment system | |
| CN105894115A (en) | Regional port major hazard source quantitative risk assessment method | |
| KR102427026B1 (en) | Method and server for visualization of disaster response information | |
| Guan et al. | Risk assessment method for industrial accident consequences and human vulnerability in urban areas | |
| Zhang et al. | Multi-scale accessibility performance of shelters types with diversity layout in coastal port cities: A case study in Nagoya City, Japan | |
| CN113052503A (en) | Forest fire risk assessment method for forest city cross-connection area based on PSR model | |
| Nefros et al. | Geographical Information Systems and Remote Sensing Techniques to Reduce the Impact of Natural Disasters in Smart Cities | |
| Marinova | New map symbol system for disaster management | |
| Cocco et al. | Applying Geodesign in Urban Planning Case Study of Pampulha, Belo Horizonte. Brazilian | |
| Elvas et al. | Data fusion and visualization towards city disaster management: Lisbon case study | |
| Martyn et al. | Informational graphic technologies for fire safety level determination in special purpose buildings | |
| Pesaresia et al. | GIS4RISKS project. Synergic use of GIS applications for analysing volcanic and seismic risks in the pre and post event | |
| Cillis et al. | Fire planning of urban-rural interface in open source gis environment: Case study of the apulia region (southern italy) | |
| Martines et al. | Detecting stepping-stones for connectivity planning in local-regional scale | |
| Platt | A model of exurban land-use change and wildfire mitigation | |
| Schaller et al. | ArcGIS modelBuilder applications for landscape development planning in the region of Munich, Bavaria | |
| Ishiwatari | Regional Policies and Initiatives on Climate Change and Disaster Risks: How Can Peacebuilding Assistance and Climate Change Adaptation Be Integrated? | |
| Nadaline et al. | Area of occupancy of Brachycephalus coloratus Ribeiro, Blackburn, Stanley, Pie & Bornschein, 2017 (Anura, Brachycephalidae), endemic to the Serra da Baitaca, Brazil, and its implications for the conservation and Green Status of the species | |
| Montoya et al. | Differences in the risk profiles and risk perception of flammable liquid hazards in San Luis Potosi, Mexico | |
| Brumarova et al. | Creating of risk maps | |
| Ohlson et al. | A wildfire risk management system—an evolution of the wildfire threat rating system | |
| CN118278594B (en) | Mobile source route selection method, equipment and medium based on numerical simulation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |