CN106570650A - Fruit industry low temperature frost damage risk acquiring method - Google Patents
Fruit industry low temperature frost damage risk acquiring method Download PDFInfo
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- 235000013399 edible fruits Nutrition 0.000 title claims abstract description 70
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Abstract
The invention discloses a fruit industry low temperature frost damage risk acquiring method. The method mainly comprises the steps that a data collector acquires the temperature data and the geographic information data of a specific forest fruit planting area, and sends the data to the receiving platform of each place through a wireless data transmission module for storing; the receiving platform of each place sends the received data to the GIS server of a data application central apparatus room; a server GIS database carries out processing to acquire disaster-inducing factor risk index data, hazard-affected body vulnerability index data and disaster resistance and alleviation index data; according to the spatial distribution data of the disaster-inducing factor risk index data, the spatial distribution data of the hazard-affected body vulnerability index data and the spatial distribution data of the disaster resistance and alleviation ability index data, a low temperature frost damage comprehensive risk assessment model is established; and fruit industry low temperature frost damage risk division is carried out. The defects of unreasonable design, incomprehensive comprehensive consideration factor and inaccurate risk early warning of the fruit industry low temperature frost damage risk acquiring method in the prior art are overcome.
Description
Technical field
The present invention relates to industry of planting forest or fruit tress low temperature freezing-disaster Risk-recovery technical field, in particular it relates to a kind of forest and fruit low temperature
Freeze injury risk acquisition methods.
Background technology
Low temperature freezing-disaster natural phenomena, causes huge loss to industry of planting forest or fruit tress, the defence in prior art, to low temperature freezing-disaster
Aftersensation is known after often, prior art is not due to proposing practicable Risk-warning scheme, therefore takes prevention to arrange early
Apply, the economic interests for giving people the people cause greatly loss, while the method that prior art wind is nearly obtained, designs unreasonable, it is comprehensive
Close the factor for considering not comprehensive, cause Risk-warning inaccurate.
The content of the invention
It is an object of the present invention to be directed to the problems referred to above, a kind of forest and fruit low temperature freezing-disaster risk acquisition methods are proposed, with
The accuracy of low temperature freezing-disaster risk profile is realized, is formulated for government decision person and is reasonably prevented and reduced natural disasters policy and measure provides science
Foundation, the dynamic evaluation for low temperature freezing-disaster provide foundation and preparation, take measures early, reduce the loss that natural disaster is caused.
For achieving the above object, the technical solution used in the present invention is：A kind of forest and fruit low temperature freezing-disaster risk analyses side
Method, mainly includes：
Step 1：Data acquisition unit, the data acquisition unit integrated temperature sensor, amplification are placed in specific kind of plantation fruit area
Circuit, A/D modular converters, d GPS locating module and wireless data transfer module, the temperature sensor are passed using electric thermo-couple temperature
Sensor；
Step 2：Temperature simulation voltage signal is converted to digital signal by the A/D modular converters of data acquisition unit, is stored in deposit
In device, the positional information for being obtained with GPS location equipment immediately is sent to various places receiving platform simultaneously by wireless data transfer module
Storage, the various places receiving platform is again by the GIS server of the data is activation to market demand central machine room for receiving；
Step 3：The GIS database of server obtains the temperature observation data and geographic information data in specific kind of plantation fruit area,
And combine at remote sensing monitoring data, History Characteristic forest and fruit yield data and the socioeconomic data for obtaining storage in advance
Data after process are classified, are specifically divided into Flood inducing factors risk achievement data, hazard-affected body vulnerability inder data by reason
With combat a natural disaster mitigation achievement data；
Step 4：Flood inducing factors risk model is set up according to Flood inducing factors risk achievement data, according to hazard-affected body vulnerability inder
Data set up hazard-affected body Vulnerability Model, and combat a natural disaster mitigation capability model according to the foundation of mitigation achievement data is combated a natural disaster；
Step 5：Flood inducing factors risk each index space distribution, fragile according to hazard-affected body is obtained according to Flood inducing factors risk model
Property model obtains the spatial distribution of hazard-affected body vulnerability inder and obtains combating a natural disaster mitigation ability referring to according to mitigation capability model is combated a natural disaster
Target spatial distribution, with reference to the spatial distribution of each index in each model, sets up low temperature freezing-disaster integrated risk assessment models；
Step 6：By the size for comparing low temperature freezing-disaster integrated risk assessment models risk index, to industry of planting forest or fruit tress low temperature freezing-disaster wind
Danger just carries out regional classification；
Step 7：The regional classification result that client is obtained from server obtaining step 6, or the area that step 6 is obtained by server
Division result active push is checked to client by client.
Further, the Flood inducing factors risk achievement data include cold wave number of times, frost number of times, cold spell in later spring number of times,
Lowest temperature persistent period, extreme minimum temperature and cooling extent,
The hazard-affected body vulnerability inder data include population density size, cultivated area proportion, local special fruit tree area proportion and
Farm output level of development index,
It is described combat a natural disaster mitigation achievement data include GDP per capita numerical value, GDP numerical value, unit area labour force magnitude numerical value and
Mechanical total output numerical value.
Further, it is in the step 4, described to set up Flood inducing factors risk model specifically, according to computing formula：
Set up cause calamity because
Sub- risk model, H are Flood inducing factors risk sex index,For cold wave number of times weight, CW is cold wave number of times；For frost
Weight, FR are frost；For cold spell in later spring weight, CS is cold spell in later spring；For lowest temperature persistent period weight, TD is
The lowest temperature persistent period；For extreme minimum temperature weight, ET is extreme minimum temperature；Weigh for cooling extent
Weight, RC is cooling extent.
Further, in the step 2, hazard-affected body Vulnerability Model is set up, according to computing formula：, V is hazard-affected body vulnerability index,For population density
Weight, PD are population density；For cultivated area proportion weight, LA is cultivated area proportion；For local special fruit tree area
Proportion weight, FA are local special fruit tree area proportion；For farm output level of development weight, AP is farm output developing water
It is flat.
Further, in the step 4, mitigation capability model is combated a natural disaster in foundation, according to computing formula：, RE is to combat a natural disaster mitigation capacity levels;For GDP per capita weight, x1reFor GDP per capita;For the equal GDP weights in ground, x2reFor the equal GDP in ground；For unit face
Product rural laborer's weight, x3reFor unit area rural laborer；For mechanical total output weight, x4reAlways move for mechanical
Power.
Further, it is in step 5, described to set up low temperature freezing-disaster integrated risk assessment models, according to computing formula：, R is low temperature freezing-disaster integrated risk index；For the power of Flood inducing factors risk
Weight；H is Flood inducing factors risk；For the weight of supporting body vulnerability；V is supporting body vulnerability；To combat a natural disaster mitigation
Ability weight；RE is to combat a natural disaster mitigation ability, wherein, H, V, RE are the value obtained after normalization.
Further, in step 5, the spatial distribution of Flood inducing factors risk achievement data utilizes empty by GIS database
Interpolation method is by the Flood inducing factors wind that the lowest temperature and average temperature data are calculated day by day of the ground in certain period of meteorological site
Dangerous achievement data is interpolated to the spatial distribution data of this area, and specially GIS database turns to sky by IDW interpolation methods space
Between resolution be 1km2Raster map layer, in the case where GIS spatial analysis module is supported, to each single item evaluation index by distributing weight
Analysis is overlapped, Flood inducing factors risk spatial distribution is obtained；
Each area of index average planar value is converted by the spatial distribution of hazard-affected body vulnerability inder data by GIS database
For the point value at regional center (RC), its space is turned to 1km with IDW interpolation methods2It is for the raster data of unit, hazard-affected body is fragile
Property achievement data is interpolated to the spatial distribution data of this area, specially in GIS database turns to sky with IDW interpolation methods space
Between resolution be 1km2Raster map layer, in the case where GIS database spatial analysis module is supported, to each single item evaluation index by dividing
Analysis is overlapped with weight, supporting body vulnerability spatial distribution is obtained；
The spatial distribution for combating a natural disaster mitigation capacity index data is converted by the planar value that each is regional by index in GIS database
For the point value at regional center (RC), its space is turned to 1km with IDW interpolation methods2For the raster data of unit, specifically by
GIS database turns to spatial resolution as 1km with IDW interpolation methods space2Raster map layer, in ArcGIS spatial analysis module works
Under tool is supported, analysis is overlapped by weight is distributed to each single item evaluation index, ability spatial distribution of preventing and reducing natural disasters is obtained.
Further, the size of the weight carries out tax power to the weight of each factor by level method of acquiring and obtains.
A kind of forest and fruit low temperature freezing-disaster risk acquisition methods of various embodiments of the present invention, due to being mainly included in specific kind
Data acquisition unit, collecting temperature and geographical location information are placed in plantation fruit area, by temperature and geographical location information by wireless
Data transmission module sends to various places receiving platform and stores, and the various places receiving platform is again by the data is activation for receiving to data
Using the GIS server of central machine room；Monitoring location information of the GIS server according to acquisition, in the map of application system
Monitoring location scattergram is drawn, and in systems the position is bound with data acquisition unit, the temperature for collecting is included
In system, the GIS database of GIS server obtains the temperature observation data and geographic information data in specific kind of plantation fruit area,
And combine at remote sensing monitoring data, History Characteristic forest and fruit yield data and the socioeconomic data for obtaining storage in advance
Data after process are classified, are specifically divided into Flood inducing factors risk achievement data, hazard-affected body vulnerability inder data by reason
With combat a natural disaster mitigation achievement data；Flood inducing factors risk model is set up according to Flood inducing factors risk achievement data, according to hazard-affected body
Vulnerability inder data hazard-affected body frailty model, and mitigation capability model is combated a natural disaster according to the foundation of mitigation achievement data is combated a natural disaster；According to
The spatial distribution data of Flood inducing factors risk achievement data, the spatial distribution data of hazard-affected body vulnerability inder data and combat a natural disaster
The spatial distribution data of mitigation capacity index data, sets up low temperature freezing-disaster integrated risk assessment models；Low-temperature frozen to industry of planting forest or fruit tress
Evil fire risk district；Such that it is able to overcome the method that low temperature freezing-disaster risk is obtained in prior art, design unreasonable, consider
Factor is not comprehensive, causes the inaccurate defect of Risk-warning.
Other features and advantages of the present invention will be illustrated in the following description, also, partly be become from description
Obtain it is clear that or being understood by implementing the present invention.
Below by drawings and Examples, technical scheme is described in further detail.
Description of the drawings
Accompanying drawing is used for providing a further understanding of the present invention, and constitutes a part for description, the reality with the present invention
Applying example is used for explaining the present invention together, is not construed as limiting the invention.In the accompanying drawings：
Fig. 1 is a kind of forest and fruit low temperature freezing-disaster risk acquisition methods schematic diagram described in the embodiment of the present invention.
Specific embodiment
The preferred embodiments of the present invention are illustrated below in conjunction with accompanying drawing, it will be appreciated that preferred reality described herein
Apply example and be merely to illustrate and explain the present invention, be not intended to limit the present invention.
Specifically, a kind of forest and fruit low temperature freezing-disaster risk acquisition methods, explain the present invention, low temperature by taking Xinjiang region as an example
Freeze injury is to endanger Xinjiang characteristic industry of planting forest or fruit tress to continue one of three big disasters of efficient and healthful development, by period freezing injury of fruit trees of surviving the winter
The collection of history the condition of a disaster data and the foundation of meteorogical phenomena database, study time of origin, critical temperature and every meteorology of freeze injury
Factor Diurnal Variation for many years, illustrates the relation of wintering of fruit tree freeze injury and meteorological factor.According to 67, November-May next year Xinjiang
1951-2013 years days of meteorological site cold wave number of times, frost, cold spell in later spring, the lowest temperature persistent period, extreme minimum temperature, cooling
Amplitude determines that Xinjiang wintering of fruit tree phase and cold spell in later spring occur the meteorological index of freeze injury.Integrated economics index and local special fruit tree area ratio
Index Establishment index system being waited again, overall merit being carried out to Xinjiang characteristic industry of planting forest or fruit tress low temperature freezing-disaster risk assessment, achievement in research is
The determination in emphasis prevention and control area provides decision support, formulates for government decision person and reasonably prevents and reduces natural disasters policy and measure provides science
Foundation, the dynamic evaluation for low temperature freezing-disaster provide foundation and preparation.
Data acquisition unit, data acquisition unit integrated temperature sensor, amplifying circuit, A/ are placed in specific kind of plantation fruit area
The functional modules such as D modular converters, GPS location equipment, wireless data transmission.Precision, sensitivity of this application to temperature sensor
It is not high with linear requirements, but as outdoor weather is complicated and changeable, and need long-time stable to work, it requires equipment performance
It is reliable, long service life, measurement range width（-50℃~100℃）, various weather etc. are adapted to, therefore electric thermo-couple temperature can be adopted
Sensor.The principle of thermocouple temperature sensor is by the conductor of two kinds of different components（Referred to as thermocouple silk material or thermode）Two
Termination synthesis loop, when the temperature at two abutments is different, will produce electromotive force in the loop, according to thermo-electromotive force and temperature
The functional relationship of degree, obtains temperature value.
According to the substance and research purpose of low temperature freezing-disaster fire risk district, the basic data that this research needs from
《Xinjiang Uygur Autonomous Regions's statistical yearbook in 2012》With《China Statistical Yearbook》, the population density, arable land face including 2012
Product proportion, local special fruit tree area proportion, farm output level of development, GDP per capita, GDP, the total power of farm machinery and unit
The data such as area rural laborer.The meteorological data that research needs is from China Meteorological Sharing Services for Scientific Data net including new
Autonomous region of boundary Uygur 67 in November, -2013 meteorological site nineteen fifty-one, December, January, 2 months, March, April, the China ground in May
Face climatological data earning in a day data set（Day by day the lowest temperature, mean temperature）Etc. data.Geographic information data source of data is in country
Basic Geographic Information System.
Flood inducing factors risk it refer to meteorological disaster intensity of anomaly, it is mainly (strong by meteorological Flood inducing factors activity scale
Degree) and the activity frequency (probability) determine.General Flood inducing factors intensity is bigger, and the frequency is higher, the destruction caused by meteorological disaster
Loss is more serious, and the risk of meteorological disaster is also bigger.
Flood inducing factors risk index：
(1) cold wave number of times（CW）
After cold wave refers to that a certain regional cold air passes by, temperature declined in 24 hours>=8 DEG C, and the lowest temperature drops to<=4℃；Or
Mercury dropped in 48 hours>=10 DEG C, and the lowest temperature drops to<=4℃；Or temperature continuously declines in 72 hours>=12 DEG C, and
And the lowest temperature exists<=4 DEG C of weather.
(2) frost（FR）
Crop growth season (after the March of North SinKiang, South Sinkiang is after 2 months) interior cold air invasion, temperature decrease to less than 0 DEG C）
(3) cold spell in later spring (CS)
Cold spell in later spring " refers to that the early spring (after the March of North SinKiang, South Sinkiang is after 2 months) temperature gos up very fast, and in later stage in spring (April or 5
Month) the temperature weather phenomenon low compared with normal year.
(4) lowest temperature persistent period (TD)
When temperature drops to the critical temperature branch of fruit tree（- 25～-30 DEG C）When low temperature continuous time longer freeze injury it is more serious,
Bud（- 18～-22 DEG C）When low temperature continuous time longer freeze injury it is more serious.
(5) extreme minimum temperature (ET) such as table 1：
Table 1
(6) cooling extent (RC)
Strong cooling refers to that 24 h of daily mean temperature declines 8-10 DEG C or 48 h decline>=10.0 DEG C, and the lowest temperature<=5.0 DEG C
Cooling weather process.
Flood inducing factors risk model (H)：
According to Flood inducing factors risk index system, low temperature freezing-disaster Flood inducing factors risk model is built, computing formula is as follows：
In formula, H is cause
Calamity factor risk sex index,Cold wave number of times weight, CW are cold wave number of times；For frost weight, FR is frost；For cold spell in later spring weight, CS is cold spell in later spring；For lowest temperature persistent period weight, when TD is that the lowest temperature continues
Between；For extreme minimum temperature weight, ET is extreme minimum temperature；For cooling extent weight, RC is cooling extent.
Supporting body vulnerability inder：
Supporting body vulnerability it refer to may be subject to meteorological disaster threaten all personnel and property injury or the extent of damage, such as
Personnel, domestic animal, house, industry of planting forest or fruit tress, life line etc., a regional population and property are more concentrated, and vulnerability is higher, can suffer from diving
Bigger losing, meteorological disaster risk is bigger.
（1）Population density (PD)
The big I of population density embodies the degree of a regional densely populated situation, and population density is bigger, and unit area meets with
Also many by the number of disaster, supporting body vulnerability is bigger.
（2）Cultivated area proportion (LA)
Cultivated area ratio reflects the exposure level of local special fruit tree and low temperature freezing-disaster factor, and the desired value of proportion is bigger, area
Vulnerability it is bigger.
（3）Local special fruit tree area proportion (FA)
When local special fruit tree is subject to Freezing stress, local special fruit tree cultivated area is bigger, and disaster area is heavier.Supporting body vulnerability is just
Bigger, loss is also corresponding more serious.Therefore, local special fruit tree area proportion is for supporting body low temperature freezing-disaster calamity source
Individual important indicator.
（4）Farm output level of development (AP)
Consider that the Per Unit Area Grain Yield in a region can reflect the level of production of this area's planting industry, so as to weigh from agriculture angle
Amount agricultural development level.The high area of agricultural development level, when natural disaster is coerced, the extent of injury being subjected to is also more
It is high.Conversely, the low area of farm output level of development, the corresponding extent of damage can also be reduced.
Supporting body Vulnerability Model（V）：
Supporting body vulnerability level is to affect one of Fundamentals of calamity source size, considers population density (PD), ploughs
Ground area proportion (LA), local special fruit tree area proportion (FA) and farm output level of development (AP), set up supporting body vulnerability wind
Dangerous evaluation model, i.e.,:
In formula, V is supporting body vulnerability index,For population density weight weight, PD is population density；For arable land
Area proportion weight, LA are cultivated area proportion；For local special fruit tree area proportion weight, FA is local special fruit tree area ratio
Weight；For farm output level of development weight, AP is farm output level of development；
Ability of preventing and reducing natural disasters it refer to and meteorological disaster resisted and recovery extent by disaster area, including emergency managerial ability, mitigation
Input resource preparation etc., ability of preventing and reducing natural disasters is higher, and the potential loss that can suffer from is less, and meteorological disaster risk is less.
Prevent and reduce natural disasters capacity index:
（1）GDP per capita (x1re)
GDP per capita is to weigh the important symbol of a regional economy situation, and its value is higher, and economic level is higher, and Disaster Defense Capability is got over
By force.Circumferential edge obtains each district city GDP per capita value in Xinjiang by statistical yearbook data.
（2）Ground GDP (x2re)
Ground GDP is the important symbol for weighing a regional economy situation, and its value is higher, and ability of preventing and reducing natural disasters is stronger.Count herein
The each district city ground in Xinjiang GDP values are obtained according to by statistical yearbook data.
（3）Unit area rural laborer (x3re)
The power that the anthropic factor of unit area rural laborer's reflecting regional affects, unit area rural laborer are more, then resist
Calamity ability is stronger.
（4）Mechanical total output (x4re)
Mechanical total output reflects this area's opposing disaster at utmost, is the direct embodiment of anti-disaster ability.
Prevent and reduce natural disasters capability model（RE）:
Mitigation ability is combated a natural disaster, refers to that people reduce the energy of low temperature freezing-disaster casualty loss by low-temperature resistance freeze injury action
Power, it is generally recognized that subjectivity key element, by GDP per capita (x1re), GDP (x2re), unit area rural area
Labour force (x3re), mechanical total output (x4re) build and combat a natural disaster mitigation capability model.In formula, RE is to combat a natural disaster mitigation energy range
Degree;For GDP per capita weight, x1reFor GDP per capita;For the equal GDP weights in ground, x2reFor the equal GDP in ground；For list
Plane accumulates rural laborer's weight, x3reFor unit area rural laborer；For mechanical total output weight, x4reIt is total for machinery
Power.
Having an effect and process from low temperature freezing-disaster risk, regional characteristics industry of planting forest or fruit tress low temperature freezing-disaster integrated risk are commented
Estimate the Flood inducing factors risk (H) for generally comprising two objectivity key elements and a subjectivity key element, i.e. low temperature freezing-disaster, supporting body
Vulnerability (V) and combat a natural disaster mitigation ability (RE).By the risk elements of comprehensive three individual event disasters, Xinjiang characteristic woods is built
Fruit industry low temperature freezing-disaster integrated risk index R, is defined as follows:In formula, R is low temperature
Freeze injury integrated risk index;For the weight of Flood inducing factors risk;H is Flood inducing factors risk;It is crisp for supporting body
The weight of weak property;V is supporting body vulnerability;To combat a natural disaster mitigation ability weight;RE is to combat a natural disaster mitigation ability.Wherein, H, V,
RE is the value obtained after normalization.By the size for comparing risk indicator R, by means of ArcGIS functions, to each department
Low temperature freezing-disaster risk height evaluated.
The present invention, according to the principle of analytic hierarchy process (AHP), is different compositing factors first by PROBLEM DECOMPOSITION, according to factor it
Between influence each other and its hierarchical cluster combined with membership, form an orderly hierarchy Model, i.e. one-level evaluation
Index and two-level appraisement index.Low temperature freezing-disaster one-level evaluation index（Two-level appraisement index）Respectively:Flood inducing factors risk mould
Type (cold wave number of times, frost, cold spell in later spring, lowest temperature persistent period, extreme minimum temperature, cooling extent);Supporting body vulnerability
Model (population density, cultivated area proportion, local special fruit tree area proportion, farm output level of development) and combat a natural disaster mitigation ability mould
Type (GDP per capita, the equal GDP in ground, the total power of farm machinery, unit area rural laborer).According to the correlation step of analytic hierarchy process (AHP)
Suddenly tax power is carried out to each factor, each monomial factor weight and comprehensive weight are shown in Table 2- tables 5.
2 Xinjiang characteristic industry of planting forest or fruit tress low temperature freezing-disaster Flood inducing factors risk of table（H）Subitem weight index
Note;Judgment matrix approach ratio:0.0583;Weight 0.4652 to general objective.
3 Xinjiang characteristic industry of planting forest or fruit tress low temperature freezing-disaster supporting body vulnerability of table（V）Subitem weight index
Note;Judgment matrix approach ratio:0.0580;Weight 0.3438 to general objective.
4 Xinjiang characteristic industry of planting forest or fruit tress low temperature freezing-disaster of table is prevented and reduced natural disasters ability（RE）Subitem weight index
Note;Judgment matrix approach ratio:0.0039;Weight 0. 0.1910 to general objective.
5 Xinjiang characteristic industry of planting forest or fruit tress low temperature freezing-disaster integrated risk of table assessment (R) subitem weight index
Flood inducing factors risk assesses each index space distribution：
In ArcGIS utilization space interpolation method by 67 in November, -2013 meteorological site nineteen fifty-one, December, January, 2 months, March,
April, the Chinese terrestrial climate data earning in a day data set in May（Day by day the lowest temperature, mean temperature）Etc. the cold wave that data are calculated
Number, frost, cold spell in later spring, lowest temperature persistent period, extreme minimum temperature, cooling extent are interpolated to the space point for entirely studying area
Cloth.After practice, drawn by interpolation result：
From the point of view of the distribution of cold wave number of times, In The North of Xinjiang generation cold wave number of times is more, nearly all more than 4500 times；Southern Xinjiang is sent out
Raw cold wave number of times is less, and, below 4500 times, only the indivedual counties and cities in Kaxgar Prefecture are higher for great majority.
From the point of view of the distribution of frost number of times, In Altay, xinjiang, Ili Prefecture, Hotan Prefecture's generation frost number of times are more,
Nearly all more than 180 times；Aksu, Xinjiang, Bayangolmongol Autonomous Prefecture generation frost number of times are less, and great majority exist
Less than 180 times.
From the point of view of the distribution of cold spell in later spring number of times, In The North of Xinjiang generation cold spell in later spring number of times is more, nearly all more than 180 times；Newly
Generation cold spell in later spring number of times in boundary south is less, and, below 180 times, only the indivedual counties and cities in Kaxgar Prefecture are higher for great majority.
From the point of view of bud lowest temperature continuous time and its distribution, the bud lowest temperature persistent period, longer freeze injury was more serious, newly
The Kaxgar Prefecture in the boundary west and south, Hotan Prefecture are the most serious, are secondly Aksu Prefecture and Ili Prefecture subregion, In The East Xinjiang
The northern bud lowest temperature persistent period is shorter, and the probability that freeze injury occurs is relatively low.
From the point of view of branch lowest temperature continuous time and its distribution, the branch lowest temperature persistent period, longer freeze injury was more serious, newly
The Kaxgar Prefecture in the boundary west and south, Hotan Prefecture are the most serious, are secondly Aksu Prefecture and Ili Prefecture subregion, In The East Xinjiang
The northern branch lowest temperature persistent period is shorter, and the probability that freeze injury occurs is relatively low.
From the point of view of extreme minimum temperature distribution, the degree that freeze injury occurs is in step wise reduction trend from north orientation south.
From the point of view of cooling extent distribution, In The North of Xinjiang generation cold wave number of times is more, nearly all more than 3900 times；Xin Jiangnan
Portion's generation cold wave number of times is less, and, below 3900 times, only the indivedual counties and cities in Kaxgar Prefecture are higher for great majority.
Flood inducing factors risk spatial distribution
In the assessment of Flood inducing factors risk, each index is using limited meteorological site in meteorological data, with IDW in ArcGIS
Interpolation method space turns to spatial resolution for 1km2Raster map layer, ArcGIS spatial analysis module tool support under, to each
Single item evaluation index is overlapped analysis by weight is distributed, obtains Flood inducing factors risk spatial distribution map.By stack result
Understand：There is low temperature freezing-disaster in In Altay, xinjiang, Mongolian Autonomous Prefecture of Bortala, Ili Prefecture, Kaxgar Prefecture, Hotan Prefecture
Risk it is higher, Aksu Prefecture, Bayangolmongol Autonomous Prefecture occur low temperature freezing-disaster risk it is relatively low.
（1）The each index space distribution of supporting body vulnerability
Supporting body vulnerability data is obtained in units of administrative areas at the county level, and each evaluation index carries out surface layer data to raster data
Conversion.Each county of index (city) level average planar value is converted into into the point at county (city) geographic center under ArcGIS platforms
Value, is turned to its space with 1km with IDW interpolation methods2For the raster data of unit.By population density, cultivated area proportion, characteristic
Woods fruit area proportion, farm output level of development are interpolated to the spatial distribution for entirely studying area.After invention is put into practice, by interpolation
As a result understand：
From the point of view of population density distribution：Kaxgar Prefecture, Hetian City, one-level Urumqi City of Ili Prefecture part counties and cities and its periphery
Concentration is compared in counties and cities' population distribution, and other areas more disperse.
From the point of view of cultivated area is than redistribution：Western Xinjiang especially Emin County, Huocheng County, Ili Prefecture, Kaxgar Prefecture
Other counties and cities' cultivated area proportion is larger, and other areas are relatively small.
From the point of view of local special fruit tree area is than redistribution：Kaxgar Prefecture, Turfan Prefecture, Bayangolmongol Autonomous Prefecture and
The area such as Shihezi local special fruit tree area is more concentrated than redistribution, and other areas are less.
From the point of view of the distribution of farm output level of development：Kaxgar Prefecture, Bayangolmongol Autonomous Prefecture, Ili Prefecture agricultural are produced
Amount level of development is higher, and other areas are relatively low.
In supporting body vulnerability assessment, each index uses the statistical data of statistical yearbook Zhong Ge counties and cities in 2012,
Spatial resolution is turned to as 1km with IDW interpolation methods space in ArcGIS2Raster map layer, in ArcGIS spatial analysis module works
Under tool is supported, analysis is overlapped by weight is distributed to each single item evaluation index, supporting body vulnerability spatial distribution map is obtained.
From stack result：Turfan Prefecture, Kaxgar Prefecture, Ili Prefecture vulnerability are higher, and it is bigger to can suffer from potential loss,
Meteorological disaster risk is bigger.
（1）The each index space distribution of ability of preventing and reducing natural disasters
Capacity data of preventing and reducing natural disasters is obtained in units of administrative areas at the county level, and each evaluation index carries out surface layer data to raster data
Conversion.Each county of index (city) level average planar value is converted into into the point at county (city) geographic center under ArcGIS platforms
Value, is turned to its space with 1km with IDW interpolation methods2For the raster data of unit.By GDP per capita, the equal GDP in ground, agricultural machinery are total
Power, unit area rural laborer are interpolated to the spatial distribution for entirely studying area.From interpolation result：
From the point of view of GDP per capita distribution：Turfan Prefecture, Shihezi, Bayangolmongol Autonomous Prefecture, Karamay City are per capita
GDP is higher, and other areas are relatively low.
From the point of view of the equal GDP distributions in ground：Kaxgar Prefecture, Ili Prefecture, Shihezi, Bayangolmongol Autonomous Prefecture ground are
GDP is higher, and other areas are relatively low.
From the point of view of unit area rural laborer distribution：It is Kaxgar Prefecture, Ili Prefecture, Bayangolmongol Autonomous Prefecture, prosperous
Lucky autonomous prefecture of the Hui ethnic group, the product rural laborer's distribution of Hotan Prefecture's plane are relatively concentrated, and other areas are relatively relatively decentralized.
From the point of view of total power of farm machinery distribution：Aksu Prefecture, Bayangolmongol Autonomous Prefecture, Turfan Prefecture agricultural
Mechanical total output is higher, and other areas are relatively low.
（2）Ability of preventing and reducing natural disasters spatial distribution
Each index in capability evaluation of preventing and reducing natural disasters uses the statistical data of statistical yearbook Zhong Ge counties and cities in 2012, in ArcGIS
Spatial resolution is turned to as 1km with IDW interpolation methods space2Raster map layer, ArcGIS spatial analysis module tool support under,
Analysis is overlapped by weight is distributed to each single item evaluation index, ability spatial distribution map of preventing and reducing natural disasters is obtained.By superposition knot
Fruit understands：Qarkilik County, Hami City, Shanshan County, Karamay City, Yiwu County, Kuytun City, Shihezi, Shawan County, Korla City
Ability of preventing and reducing natural disasters is stronger, and other counties and cities are relatively weak.
Due to the limited meteorological site in meteorological data Source Study area used in the assessment of Flood inducing factors risk, in ArcGIS
In spatial resolution is turned to as 1km with IDW interpolation methods space2Raster map layer, and supporting body vulnerability and ability of preventing and reducing natural disasters
Each achievement data in assessment is obtained in units of administrative areas at the county level, and data county actually (city) level is average, spatial distribution expression
For point county's VectorLayer.There is certain spatial diversity in both.To eliminate both differences, need to refer to supporting body vulnerability assessment
Mark and each evaluation index of ability of preventing and reducing natural disasters carry out surface layer data to the conversion of raster data.It is index is every under ArcGIS platforms
The average planar value of individual county (city) level is converted into the point value at county (city) geographic center, equally with IDW interpolation methods by its spatialization
It is with 1km2For the raster data of unit.In the case where ArcGIS spatial analysis module tool is supported, to each single item evaluation index by
Distribution weight is overlapped analysis, and (Flood inducing factors risk, supporting body are fragile to obtain 3 low temperature freezing-disaster risk assessment Asia indexs
Property and ability of preventing and reducing natural disasters) spatial distribution map, finally three is superimposed, obtain Xinjiang characteristic industry of planting forest or fruit tress low temperature freezing-disaster comprehensive assessment
Index space scattergram.Prevention and control region is divided according to risk height result, decision data is provided for government department.
Following beneficial effect can at least be reached：The accuracy of low temperature freezing-disaster risk profile is realized, is government decision person's system
Fixed reasonably prevent and reduce natural disasters policy and measure offer scientific basis, the dynamic evaluation for low temperature freezing-disaster provide foundation and preparation, and
Early take measures, reduce the loss that natural disaster is caused.
Finally it should be noted that：The preferred embodiments of the present invention are the foregoing is only, the present invention is not limited to,
Although being described in detail to the present invention with reference to the foregoing embodiments, for a person skilled in the art, which still may be used
To modify to the technical scheme described in foregoing embodiments, or equivalent is carried out to which part technical characteristic.
All any modification, equivalent substitution and improvements within the spirit and principles in the present invention, made etc., should be included in the present invention's
Within protection domain.
Claims (8)
1. a kind of forest and fruit low temperature freezing-disaster risk acquisition methods, it is characterised in that include：
Step 1：Data acquisition unit, the data acquisition unit integrated temperature sensor, amplification are placed in specific kind of plantation fruit area
Circuit, A/D modular converters, d GPS locating module and wireless data transfer module, the temperature sensor are passed using electric thermo-couple temperature
Sensor；
Step 2：Temperature simulation voltage signal is converted to digital signal by the A/D modular converters of data acquisition unit, is stored in deposit
In device, the positional information for being obtained with GPS location equipment immediately is sent to various places receiving platform simultaneously by wireless data transfer module
Storage, the various places receiving platform is again by the GIS server of the data is activation to market demand central machine room for receiving；
Step 3：GIS server draws monitoring location according to the monitoring location information for obtaining in the map of application system
Scattergram, and in systems the position is bound with data acquisition unit, the temperature for collecting is shown in systems, GIS service
The GIS database of device obtains the temperature observation data and geographic information data in specific kind of plantation fruit area, and combines acquisition in advance
The remote sensing monitoring data of storage, History Characteristic forest and fruit yield data and socioeconomic data are processed, after process
Data are classified, and are specifically divided into Flood inducing factors risk achievement data, hazard-affected body vulnerability inder data and combat a natural disaster mitigation and refer to
Mark data；
Step 4：Flood inducing factors risk model is set up according to Flood inducing factors risk achievement data, according to hazard-affected body vulnerability inder
Data set up hazard-affected body Vulnerability Model, and combat a natural disaster mitigation capability model according to the foundation of mitigation achievement data is combated a natural disaster；
Step 5：Flood inducing factors risk each index space distribution, fragile according to hazard-affected body is obtained according to Flood inducing factors risk model
Property model obtains the spatial distribution of hazard-affected body vulnerability inder and obtains combating a natural disaster mitigation ability referring to according to mitigation capability model is combated a natural disaster
Target spatial distribution, with reference to the weight size and spatial distribution of each index in each model, sets up the assessment of low temperature freezing-disaster integrated risk
Model；
Step 6：By the size for comparing low temperature freezing-disaster integrated risk assessment models risk index, to industry of planting forest or fruit tress low temperature freezing-disaster wind
Danger just carries out regional classification；
Step 7：The regional classification result that client is obtained from server obtaining step 6, or the area that step 6 is obtained by server
Division result active push is checked to client by client.
2. forest and fruit low temperature freezing-disaster risk analysis method according to claim 1, it is characterised in that in step 2, described
Flood inducing factors risk achievement data includes cold wave number of times, frost number of times, cold spell in later spring number of times, the lowest temperature persistent period, extreme
Minimum temperature and cooling extent,
The hazard-affected body vulnerability inder data include population density size, cultivated area proportion, local special fruit tree area proportion and
Farm output level of development index,
It is described combat a natural disaster mitigation achievement data include GDP per capita numerical value, GDP numerical value, unit area labour force magnitude numerical value and
Mechanical total output numerical value.
3. forest and fruit low temperature freezing-disaster risk acquisition methods according to claim 2, it is characterised in that institute
State in step 4, it is described to set up Flood inducing factors risk model specifically, according to computing formula：Set up Flood inducing factors risk mould
Type, H are Flood inducing factors risk sex index,For cold wave number of times weight, CW is cold wave number of times；For frost weight, FR
For frost；For cold spell in later spring weight, CS is cold spell in later spring；For lowest temperature persistent period weight, TD is the lowest temperature
Persistent period；For extreme minimum temperature weight, ET is extreme minimum temperature；For cooling extent weight, RC is drop
Warm amplitude.
4. forest and fruit low temperature freezing-disaster risk acquisition methods according to claim 3, it is characterised in that in the step 4,
Hazard-affected body Vulnerability Model is set up, according to computing formula：, V is
Hazard-affected body vulnerability index,For population density weight weight, PD is population density；For cultivated area proportion weight, LA
For cultivated area proportion；For local special fruit tree area proportion weight, FA is local special fruit tree area proportion；For agricultural product
Amount level of development weight, AP are farm output level of development.
5. forest and fruit low temperature freezing-disaster risk acquisition methods according to claim 4, its feature exist
In, in the step 4, mitigation capability model is combated a natural disaster in foundation, according to computing formula：, RE is to combat a natural disaster mitigation energy range
Degree；For GDP per capita weight, x1reFor GDP per capita；For the equal GDP weights in ground, x2reFor the equal GDP in ground；For unit
Area rural laborer's weight, x3reFor unit area rural laborer；For mechanical total output weight, x4reAlways move for mechanical
Power.
6. forest and fruit low temperature freezing-disaster risk acquisition methods according to claim 5, it is characterised in that in step 5, described
Low temperature freezing-disaster integrated risk assessment models are set up, according to computing formula：
, R is low temperature freezing-disaster integrated risk index；For Flood inducing factors risk
Weight；H is Flood inducing factors risk；For the weight of supporting body vulnerability；V is supporting body vulnerability；To combat a natural disaster
Mitigation ability weight；RE is to combat a natural disaster mitigation ability, wherein, H, V, RE are the value obtained after normalization.
7. forest and fruit low temperature freezing-disaster risk acquisition methods according to claim 1, it is characterised in that in step 5, cause calamity
The spatial distribution of factor risk achievement data is by GIS database utilization space interpolation method by certain period of meteorological site
The ground Flood inducing factors risk achievement data that the lowest temperature and average temperature data are calculated day by day is interpolated to the space of this area
Distributed data, specially GIS database turn to spatial resolution for 1km by IDW interpolation methods space2Raster map layer, in GIS
Under spatial analysis module is supported, analysis is overlapped by weight is distributed to each single item evaluation index, Flood inducing factors risk is obtained
Property spatial distribution；
Each area of index average planar value is turned by the spatial distribution of hazard-affected body vulnerability inder data by GIS database
The point value at regional center (RC) is turned to, its space is turned to 1km with IDW interpolation methods2It is for the raster data of unit, hazard-affected body is crisp
Weak property achievement data is interpolated to the spatial distribution data of this area, is turned to IDW interpolation methods space specially in GIS database
Spatial resolution is 1km2Raster map layer, in the case where GIS database spatial analysis module is supported, to each single item evaluation index by
Distribution weight is overlapped analysis, obtains supporting body vulnerability spatial distribution；
The spatial distribution for combating a natural disaster mitigation capacity index data is converted by the planar value that each is regional by index in GIS database
For the point value at regional center (RC), its space is turned to 1km with IDW interpolation methods2For the raster data of unit, specifically by
GIS database turns to spatial resolution as 1km with IDW interpolation methods space2Raster map layer, in ArcGIS spatial analysis module works
Under tool is supported, analysis is overlapped by weight is distributed to each single item evaluation index, ability spatial distribution of preventing and reducing natural disasters is obtained.
8. forest and fruit low temperature freezing-disaster risk acquisition methods according to any one of claim 3-6, it is characterised in that described
The size of weight carries out tax power to the weight of each factor by level method of acquiring and obtains.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102646219A (en) * | 2012-02-27 | 2012-08-22 | 兰州大学 | Method for pre-warning snow disaster in pasturing area |
| CN204101076U (en) * | 2014-07-09 | 2015-01-14 | 中国农业科学院农业环境与可持续发展研究所 | One planting fruit-trees frost calamity monitoring system |
-
2016
- 2016-11-09 CN CN201610985403.5A patent/CN106570650A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102646219A (en) * | 2012-02-27 | 2012-08-22 | 兰州大学 | Method for pre-warning snow disaster in pasturing area |
| CN204101076U (en) * | 2014-07-09 | 2015-01-14 | 中国农业科学院农业环境与可持续发展研究所 | One planting fruit-trees frost calamity monitoring system |
Non-Patent Citations (1)
| Title |
|---|
| 李红英等: "基于GIS 的宁夏晚霜冻害风险评估与区划", 《自然灾害学报》 * |
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Inventor after: Guo Jing Inventor after: Chen Meng Inventor after: Lu Tianping Inventor after: Zhang Dongya Inventor after: Wang Ranghui Inventor after: Liu Yongping Inventor after: Zhang Renping Inventor after: Guo Xing Inventor after: Chen Xin Inventor before: Guo Jing Inventor before: An Lu Inventor before: Wang Lei Inventor before: Lu Mingyan Inventor before: Qi Cheng Inventor before: Chen Meng Inventor before: Lu Tianping Inventor before: Zhang Dongya Inventor before: Wang Ranghui Inventor before: Liu Yongping Inventor before: Zhang Renping Inventor before: Guo Xing Inventor before: Chen Xin |
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Application publication date: 20170419 |