CN105809914B - A kind of forest fires generation model early warning system based on fuzzy reasoning - Google Patents
A kind of forest fires generation model early warning system based on fuzzy reasoning Download PDFInfo
- Publication number
- CN105809914B CN105809914B CN201610180341.0A CN201610180341A CN105809914B CN 105809914 B CN105809914 B CN 105809914B CN 201610180341 A CN201610180341 A CN 201610180341A CN 105809914 B CN105809914 B CN 105809914B
- Authority
- CN
- China
- Prior art keywords
- fire
- forest
- values
- fuzzy number
- humidity
- 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.)
- Active
Links
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B31/00—Predictive alarm systems characterised by extrapolation or other computation using updated historic data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
- G06N5/048—Fuzzy inferencing
Abstract
The present invention relates to a kind of forest fires generation model early warning system and its concrete application based on fuzzy reasoning.The method that the model calculates fire risk is：(1) it is one group of ordered series of numbers from 0~1 to set fire section Q, Q；(2) Triangular Fuzzy Number A1, A2 ... the An under survey calculation special parameter 1, parameter 2 ... parameter n；(3) formula is brought into T values are calculated, W is positive fuzzy number；(4) formula is brought intoFire probability space CF values are calculated, the intermediate value of CF spatial values is fire probable value.
Description
Technical field
The invention belongs to application mathematical statistics field, occurs in particular to a kind of forest fires based on fuzzy reasoning
Model pre-warning system and its concrete application.
Background technology
Although always along with forest fire in being migrated in human history habitat, forest fire be not in general from
Right disaster, but the natural calamity of heavy damage ecological environment, threaten human life's property safety.Forest fire is not only broken up
The forest reserves, and heavy damage ecological environment.Forest fire is sudden by force, destructive power is big, it has also become 21 century Global Ecological
Safe biggest threat.The whole world is average annual at present occurs forest fire more than 20 ten thousand, burns 6,400,000 hectares of forest, it is total to account for Global Forests
The 1.8 ‰ of area.China is one of more serious country of forest fire, due to nature and history, forest fire frequency
Take place frequently life, and the destruction to China's Forest Resources and ecological environment is extremely serious, not only burns forest, reduces the density of crop, also broken
Bad forest structure, the value for reducing forest.In recent years, China's forest fire is further frequent, nineteen fifty to 2012 63
Nian Jian, add up that forest fire 79.8 ten thousand, 38,060,000 hectares of burned areas, equivalent to current average annual afforestation area occurs
More than 6 times, because of the people of calamity injures and deaths 33551.Wherein, occur on May 6th, 1987 to shock the world in the forest fire of Daxinganling forest area,
Areas of forests burned is 1,140,000 hm2, the cities and towns where two forestry bureaus, 9 forest farms have been burnt, 211 people die, 50,000 people's houselessness
It can return tired.Although 2012 are the fire damage less time, forest fire 3966 still occurs and rises, injured area nearly 1.4 ten thousand
Hectare, the people of injures and deaths 21.
The frequently-occurring and destructive importance for determining forest fires prevention and control of forest fires.And with advances in technology, utilization is advanced
Technological means and method realize that the efficient prevention and control of forest fires and automatic monitoring have been increasingly becoming possibility, the State Administration of Forestry also clearly carries
The objective of the struggle of " accelerating Forestry informationlization, drive forestry modernization " is gone out.Therefore, the efficiently woods based on information technology is studied
Fiery prevention and control system and key technology are extremely urgent.Therefore how it is scientific and effective to forest fires carry out early-warning and predicting, to greatest extent
The loss that occurs and brought by fire of reduction forest fires be always what Management offorestry department of China and scientific research department extremely paid close attention to
Problem, many achievements in research are generated for many years, but because China's fire research is started late, also have compared with developed countries
Larger gap.
In the research on forest fire prevention warning aspect, foreign countries are on the forefront always, wherein the most famous is
Canadian Forest fire indication system.The system is born in 1972.Its scientific research personnel with reference to the substantial amounts of country of Canada
Fire data and synoptic data, using Heat And Water Balance principle, Canadian Forest Service proposes to study in the form of model in nineteen sixty-eight
National forest-fire forecast system.Canadian Forest fire system (CFFDRS) is made up of four subsystems, weather conditions conducive to wildfires index
Subsystem (FOP) and fire load subsystem occur for system (WI), fire-resistance behavior subsystem (FBP), fire.Canadian fire day
Gas index system is built upon basic data (meteorological factor, Fuel loads calculating, the igniting examination of small-sized field of three aspects
Test) on the basis of, the experience fire danger prediction system developed with mathematical analysis with the method that field test is combined.System is in
Noon precipitation relative humidity, temperature, wind speed and preceding 24 hourly rainfall depth be 4 basic input variables, is reflected with 3 humidity codes
Fire complexity, rate of propagation and energyreleaserate.
Canadian Forest fire size class system is to develop one of most perfect, most widely used system on our times.It is gloomy
Forest fires danger climatic index system is its important component, and the index system is based on time lag-equilibrium moisture content theory, by gas
As condition and Fuel loads organically connect, the change for passing through weather condition calculates the change of Fuel loads.
Potential fire size class is determined further according to the Fuel loads of different size or position.Because the system contains fire and combustible
Water rate organically combines so that the system has obtained the generally approval of countries in the world forest fire protection circle.
After 1978, forest-fire forecast research is sent out from fire weather forecast to fire occurrence and forest-fire climate in China
Raw forecast development, and start to develop nationwide forest-fire forecast system.Heilongjiang Province in 1987 is gloomy to protect institute and Heilongjiang Province's science
Institute's Institute of Automation cooperation, develop " the gloomy anti-SF forests weather Automatic Telemetry System " in China.Joint Tsing-Hua University of Hong Kong University of Science and Thchnology
University etc. devises " green field thousand passes " wireless sense network system.In addition, Beijing Forestry University, Nanjing Forestry University, Northeast forestry
University, Hua Zhong Agriculture University, Nanjing Univ. of Posts and Telecommunications and Xian Electronics Science and Technology University etc. are passed in positive effort research based on wireless
Feel the key technology in the forest fire forecast system of net, obtain certain achievement.But up to the present, China is also national without establishing
Forest fires early-warning and predicting system.
Because the forest fire factor that makes a difference is numerous, and reason is complicated, it is difficult to accurate quantification is theoretically carried out, institute
With at present forest fire research in, mainly based on fire monitoring, when fire occurs the very first time grasp fire condition.This
Invention analyzes the latency for causing fire first, and the quantized data of various danger indexs is run and analysis develops China
Forest fire danger class system.
The content of the invention
The present invention predicted by Weighted Fuzzy forest fire according to specific weather conditions, can be to national most area
Risk of forest fire situation carry out early warning.Calculation procedure is as follows：
Step (1) weighted fuzzy reasoning method,
Step 1 proposes a kind of method of weighted fuzzy reasoning, and this is input value in the case of being adapted to, weighted value and can
The result leaned on all is fuzzy number.Propose weighted fuzzy reasoning method：
Case 1. assumes there is a fuzzy production rule R, and rule-based system is as follows：
R:if A1(w1)and A2(w2)and…An(wn) then (CF=w) (1)
Here A1,A2,…AnIt is proposition, their weight is w respectively1,w2,…wn, be definition span in [0,1]
Between fuzzy number, w be also the span that a fuzzy number defines represented between [0,1] rule certainty factor value
It is assumed that the fuzzy truth of proposition, so, the fuzzy truth of proposition can be assessed as follows:
Here,
W is positive fuzzy number
Step (2) Weighted Fuzzy forest fire prediction,
The generalized weighted of the parameter containing humiture obscures the pre- geodesic structure of forest fire and is defined as follows：
w1=extreme temperatures, here shown as Triangular Fuzzy Number 0.75,1,1
w2=humidity is extremely low, here shown as Triangular Fuzzy Number (0.75,1,1)
W represents that the ignition probability of forest fire is very high, and this is expressed as fuzzy number (0.75,1, a 1)
If it is assumed that w1=(0.75,1,1) and w2=(0.75,1,1), the probability that forest fire occurs is (0.75,1,1).Now, I
The weather data that obtains from the station of a sensor node, temperature and humidity is respectively D, S%.Now, to temperature, we are fixed
Adopted fire Pyatyi is as follows：(0,0.25,0.5,0.75,1),
Temperature Distribution be -40 degree to 60 degree, temperature value formula is：X=(T+40)/100,
If T=20 spends, it is 0.6 that the general formula X=(20+40)/100, which calculate X values,
Take X=0.6 two-stage up and down in fire Pyatyi, obtain Triangular Fuzzy Number corresponding to temperature be A1=(0.5,
0.6,0.75).
Similarly, humidity fire also divides five grades (0,0.25,0.5,0.75,1), and the bigger fire risk of humidity is smaller,
Humidity value formula is Y=(1-W%)
If it is 0.4 that W%=60% calculates Y value by formula Y=(1-W%),
Go Y=0.4 two-stage up and down in fire Pyatyi, obtain Triangular Fuzzy Number corresponding to humidity be A2=(0.25,
0.4,0.5).
A1=(0.5,0.6,0.75), A2=(0.25,0.3,0.5) (3)
To A1And A2Based on methodBlurring conclusion it is as follows：
Here it is 20 degree just to calculate temperature, the fuzzy truth of forest fire possibility when humidity is 60%.
(0.28,0.45,0.625) is exactly to consider possible fire probability space after temperature humidity in above formula (5), I
Choose intermediate value 0.45 and be used as fire probable value.
Specifically, the method for the present invention for being calculated by weighted fuzzy reasoning and predicting risk of forest fire probability,
When choosing two parameters of temperature and humidity, its calculation procedure is as follows：
(1) set fire Pyatyi section Q, Q is one group of ordered series of numbers from 0~1, it is preferred that Q be decile ordered series of numbers (0,0.25,
0.5,0.75,1)；
(2) the temperature D and humidity S% values of special time are measured, brings formula X=(D+40)/100, Y=1-S% into, respectively
Calculate X, Y value；
(3) take one group of numerical value closest to X, Y respectively in fire Pyatyi section, obtain,
Triangular Fuzzy Number A1=(X1, X, X2) corresponding to temperature, wherein, X1, X2 are two numbers in the Q of section closest to X,
Triangular Fuzzy Number A2=(Y1, Y, Y2) corresponding to humidity, wherein, Y1, Y2 are closest to Y two numbers in the Q of section；
(4) formula is brought intoT values are calculated, W is positive fuzzy number,
W1=extreme temperatures, Triangular Fuzzy Number (0.75,1,1) is expressed as,
W2=humidity is extremely low, is expressed as Triangular Fuzzy Number (0.75,1,1)；
(5) formula is brought intoFire probability space CF values are calculated, W values are (0.75,1,1), in CF spatial values
Value is the fire probable value under temperature D and humidity S%.
Brief description of the drawings
(equipment in left side is solar powered plate to Fig. 1 meteorological data collection nodes operative scenario in figure, to whole system
It is powered.Right-side device is wireless senser, and the data such as the temperature of collection, humidity, wind speed and precipitation are handled
After be sent to data center)
Fig. 2 Nanjing risk of forest fire early warning system radio node distributing position schematic diagram
Embodiment
The foundation and application of the Nanjing risk of forest fire early warning system of embodiment 1.
Forest fires generation model of the invention based on fuzzy reasoning develops the risk of forest fire that comparison is adapted to China's concrete condition
Early warning system, case study on implementation is by taking Nanjing as an example.
By the end of the year 2011,217.45 ten thousand hectares of Jiangsu Province's forest area coverage, 181.53 ten thousand hectares of forest land area, forest
Coverage rate 21.2%, live standing tree always accumulate 8700 ten thousand steres.Wherein, 45.93 ten thousand hectares of southern area of Jiangsu Province forest land area, forest is covered
Lid rate 23.3%, live standing tree always accumulate 1400 ten thousand steres；Regional 26.96 ten thousand hectares of forest land area, percentage of forest cover in Soviet Union
19.3%, live standing tree always accumulates 770 ten thousand steres；108.64 ten thousand hectares of North Jiangsu Area forest land area, percentage of forest cover 26.7%,
Live standing tree always accumulates 6530 ten thousand steres.The whole province of Jiangsu Province in 2011 occurs forest fire 51 and risen altogether, and the gross area 141.3 that overdoes is public
Hectare, 52.4 hectares of burned areas.Forest fire incidence is 5.1 times/100,000 hectares, aggrieved rate is 0.029 ‰.
To test the validity of present system, in Nanjing eastern suburb Purple Mountain southern foot and Nanjing Forestry University campus
Lay wireless sensing base station.The wireless sensor network can be with 4 parameters required for acquisition system, wireless sensor device part
Using 2 pieces of eZ430-RF2500T development boards and 1 solar energy rechargeable battery, the single-chip microcomputer including TIMSP430 on development board,
CC2500 wireless transceivers and antenna.CC2500 radio transceivers are operated in 2.4GHz frequency ranges, and message transmission rate control exists
250kbps.Rechargeable battery uses Cymbet companies EnerChip EP energy processors, is supplied using solar energy conversion equipment
Electricity, as shown in Figure 2.Wireless sensing node can be realized gathered a data every 1 minute, and by being wirelessly transferred, data are sent out
Deliver to remote terminal.
Table 1 is the average value of the Nanjing temperature of 12 months 2012, humidity, rainfall and wind speed, and data source is in
State's weather net.
1 Nanjing of table sunshine average time in 2012
Table 2 is to obtain Nanjing in 2012 based on the gloomy of fuzzy inference system using the method for the invention system-computed
Forest fire probability distribution of the forest fires calamity probability distribution based on fuzzy inference system
| Time | Temperature | Humidity | Probability |
| 2012-8-1 | 34.8 | 58 | 0.49 |
| 2012-8-2 | 34.3 | 63 | 0.4975 |
| 2012-8-3 | 31.1 | 79 | 0.5135 |
| 2012-8-4 | 34.3 | 64 | 0.4995 |
| 2012-8-5 | 33.8 | 65 | 0.499 |
| 2012-8-6 | 35.2 | 60 | 0.496 |
| 2012-8-7 | 34.4 | 62 | 0.496 |
| 2012-8-8 | 29.8 | 79 | 0.507 |
| 2012-8-9 | 28 | 84 | 0.508 |
| 2012-8-10 | 31.1 | 79 | 0.5135 |
| 2012-8-11 | 32 | 73 | 0.506 |
| 2012-8-12 | 33.2 | 70 | 0.506 |
| 2012-8-13 | 34.6 | 69 | 0.511 |
| 2012-8-14 | 34.7 | 73 | 0.5195 |
| 2012-8-15 | 33.2 | 72 | 0.51 |
| 2012-8-16 | 30.8 | 78 | 0.51 |
| 2012-8-17 | 32.6 | 75 | 0.513 |
| 2012-8-18 | 36.3 | 67 | 0.5155 |
| 2012-8-19 | 36 | 69 | 0.518 |
| 2012-8-20 | 33.5 | 72 | 0.5115 |
In practical application, the system can relatively accurately reflect Nanjing forest fire situation, wherein each group separate index number
Calculating is to choose the meteorological data surveyed every afternoon, although because weather is changeable, the data meeting that is calculated by the system
There is certain error, but use wireless sensor network technology, can be achieved on the uninterruptedly monitoring to meteorological factor in 24 hours,
The situation of each parameter index can be calculated at any time, can preferably realize prediction of forest fire disaster early warning, system proposed by the present invention
There is indicative significance for risk of forest fire early warning.
Need to illustrate and will be clear that above example is only used for explaining the present invention, be not intended as protecting the present invention
Protect the specific restriction of scope.
Claims (6)
1. a kind of method that risk of forest fire probability is calculated by weighted fuzzy reasoning model, methods described comprise the following steps：
(1) it is one group of ordered series of numbers from 0~1 to set fire section Q, Q；
(2) Triangular Fuzzy Number A1, A2 ... the An under survey calculation special parameter 1, parameter 2 ... parameter n；
(3) formula is brought intoT values are calculated, W is holotype
Paste triangular number；
(4) formula is brought intoFire probability space CF values are calculated, the intermediate value of CF spatial values is fire probable value；
Wherein, the special parameter described in step (2) is temperature D and humidity S% values, and corresponding Triangular Fuzzy Number is A1, A2；
Described A1, A2 computational methods are：
1) bring formula X=(D+40)/100, Y=1-S% into, calculate X, Y value respectively；
2) take one group of numerical value closest to X, Y respectively in the Q of section, obtain：
(a) Triangular Fuzzy Number A1=(X1, X, X2) corresponding to temperature, wherein, X1, X2 are two numbers in the Q of section closest to X,
(b) Triangular Fuzzy Number A2=(Y1, Y, Y2) corresponding to humidity, wherein, Y1, Y2 are closest to Y two numbers in the Q of section；
The positive fuzzy number values of W described in step (3) are,
W1=extreme temperatures, Triangular Fuzzy Number (0.75,1,1) is expressed as,
W2=humidity is extremely low, is expressed as Triangular Fuzzy Number (0.75,1,1).
2. according to the method for claim 1, it is characterised in that section Q described in step (1) for decile ordered series of numbers (0,0.25,
0.5,0.75,1)。
3. method according to claim 1 or 2, it is characterised in that the W values described in step (4) are (0.75,1,1).
4. application of any described methods of claim 1-3 in risk of forest fire is predicted.
5. application of any described methods of claim 1-3 in designing or making risk of forest fire source of early warning.
6. application of any described methods of claim 1-3 in risk of forest fire Early-warning Model is designed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610180341.0A CN105809914B (en) | 2016-03-25 | 2016-03-25 | A kind of forest fires generation model early warning system based on fuzzy reasoning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610180341.0A CN105809914B (en) | 2016-03-25 | 2016-03-25 | A kind of forest fires generation model early warning system based on fuzzy reasoning |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105809914A CN105809914A (en) | 2016-07-27 |
| CN105809914B true CN105809914B (en) | 2018-02-02 |
Family
ID=56454928
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610180341.0A Active CN105809914B (en) | 2016-03-25 | 2016-03-25 | A kind of forest fires generation model early warning system based on fuzzy reasoning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105809914B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115331116B (en) * | 2022-10-13 | 2023-04-18 | 潍坊绘圆地理信息有限公司 | Ground target accurate identification on-satellite fuzzy inference method based on multi-mode data |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101986358A (en) * | 2010-08-31 | 2011-03-16 | 彭浩明 | Neural network and fuzzy control fused electrical fire intelligent alarm method |
| CN102682560A (en) * | 2012-05-22 | 2012-09-19 | 哈尔滨工程大学 | Method and device for assessing level of fire interlock alarming in ship cabin |
| CN103325204A (en) * | 2013-06-07 | 2013-09-25 | 浙江工业大学 | Environment parameter perceiving fire hazard judgment method |
| CN103854413A (en) * | 2014-03-10 | 2014-06-11 | 南京林业大学 | FWI early warning system and application |
| CN103886385A (en) * | 2014-02-20 | 2014-06-25 | 中国林业科学研究院森林生态环境与保护研究所 | Method for predicting forest fire hazard day occurrence probability |
| CN105185022A (en) * | 2015-10-21 | 2015-12-23 | 国家电网公司 | Transformer substation fire detection system based on multi-sensor information combination and detection information combination method |
| CN105336081A (en) * | 2015-11-26 | 2016-02-17 | 电子科技大学中山学院 | Fuzzy neural network-based electrical fire monitoring terminal and processing steps thereof |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100592624B1 (en) * | 2003-09-09 | 2006-06-23 | 주식회사 진화이앤씨 | System for fire reasoning using many kind of fire sensor and method thereof |
-
2016
- 2016-03-25 CN CN201610180341.0A patent/CN105809914B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101986358A (en) * | 2010-08-31 | 2011-03-16 | 彭浩明 | Neural network and fuzzy control fused electrical fire intelligent alarm method |
| CN102682560A (en) * | 2012-05-22 | 2012-09-19 | 哈尔滨工程大学 | Method and device for assessing level of fire interlock alarming in ship cabin |
| CN103325204A (en) * | 2013-06-07 | 2013-09-25 | 浙江工业大学 | Environment parameter perceiving fire hazard judgment method |
| CN103886385A (en) * | 2014-02-20 | 2014-06-25 | 中国林业科学研究院森林生态环境与保护研究所 | Method for predicting forest fire hazard day occurrence probability |
| CN103854413A (en) * | 2014-03-10 | 2014-06-11 | 南京林业大学 | FWI early warning system and application |
| CN105185022A (en) * | 2015-10-21 | 2015-12-23 | 国家电网公司 | Transformer substation fire detection system based on multi-sensor information combination and detection information combination method |
| CN105336081A (en) * | 2015-11-26 | 2016-02-17 | 电子科技大学中山学院 | Fuzzy neural network-based electrical fire monitoring terminal and processing steps thereof |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105809914A (en) | 2016-07-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lin et al. | A fuzzy inference and big data analysis algorithm for the prediction of forest fire based on rechargeable wireless sensor networks | |
| Yeo et al. | A proposal for a site location planning model of environmentally friendly urban energy supply plants using an environment and energy geographical information system (E-GIS) database (DB) and an artificial neural network (ANN) | |
| CN102629294B (en) | Probability evaluation method of failure caused by typhoon to power transmission line | |
| Xue et al. | Impact of transmission tower-line interaction to the bulk power system during hurricane | |
| CN102590651B (en) | Measured lightning data-based transmission line failure probability evaluation method | |
| CN107292478B (en) | Method for acquiring influence situation of disaster on power distribution network | |
| CN103854413B (en) | A kind of forest fire weather index early warning system and application | |
| CN104794845A (en) | Forest fire alarming method based on fire danger rating forecast | |
| Wang et al. | Location optimization of wind plants using DEA and fuzzy multi-criteria decision making: A case study in Vietnam | |
| CN103646352A (en) | Electric-power emergency system evaluation model construction method based on frangibility | |
| CN104063750A (en) | Method for predicting influence of disasters to power system based on improved AHP-anti-entropy weight | |
| Lu et al. | Optimal allocation of fire extinguishing equipment for a power grid under widespread fire disasters | |
| CN112348276A (en) | Comprehensive energy system planning optimization method based on multiple elements and three levels | |
| Perera et al. | Optimal design of microgrids to improve wildfire resilience for vulnerable communities at the wildland-urban interface | |
| Yang et al. | Resilience assessment methodologies and enhancement strategies of multi‐energy cyber‐physical systems of the distribution network | |
| CN105809914B (en) | A kind of forest fires generation model early warning system based on fuzzy reasoning | |
| CN113610355B (en) | Urban energy system climate change fragile energy flow network evaluation method | |
| CN111815106A (en) | Power grid emergency method based on multi-source information | |
| CN116523381A (en) | Safety and environment-friendly comprehensive index evaluation method for power generation enterprises | |
| Wu et al. | Multiple criteria performance modelling and impact assessment of renewable energy systems—a literature review | |
| Sayarshad | Preignition risk mitigation model for analysis of wildfires caused by electrical power conductors | |
| Gang | Risk analysis and evaluation of wind electric farm construction | |
| CN105701731A (en) | Windproof safety assessment method for power grid | |
| CN112132397A (en) | Large-scale business district security risk fuzzy comprehensive evaluation method and device based on network analysis | |
| Liu et al. | Function design and realization of transmission line icing monitoring and early warning system combined with micro-meteorology and micro-topography in Guizhou |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20160727 Assignee: Jiangsu Yiwen Agroforestry Machinery Technology Co., Ltd. Assignor: Nanjing Forestry University Contract record no.: 2018990000301 Denomination of invention: Forest fire occurrence model early warning system based on fuzzy reasoning Granted publication date: 20180202 License type: Common License Record date: 20181115 |
|
| EE01 | Entry into force of recordation of patent licensing contract | ||
| EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20160727 Assignee: Nanjing Nokia Information Technology Co., Ltd. Assignor: Nanjing Forestry University Contract record no.: 2018990000318 Denomination of invention: Forest fire occurrence model early warning system based on fuzzy reasoning Granted publication date: 20180202 License type: Common License Record date: 20181128 |
|
| EE01 | Entry into force of recordation of patent licensing contract | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20191104 Address after: 601, room 01, building 108, East 210046, Gan Hua Street, Yao Road, Qixia District, Jiangsu, Nanjing Patentee after: Nanjing Nokia Information Technology Co., Ltd. Address before: Longpan road Xuanwu District of Nanjing city of Jiangsu Province, No. 159 210037 Patentee before: Nanjing Forestry University |
|
| TR01 | Transfer of patent right |