CN104462774A - Urban road and low-lying area water accumulation forecasting method based on water tank model - Google Patents
Urban road and low-lying area water accumulation forecasting method based on water tank model Download PDFInfo
- Publication number
- CN104462774A CN104462774A CN201410631659.7A CN201410631659A CN104462774A CN 104462774 A CN104462774 A CN 104462774A CN 201410631659 A CN201410631659 A CN 201410631659A CN 104462774 A CN104462774 A CN 104462774A
- Authority
- CN
- China
- Prior art keywords
- area
- forecast
- forecasting
- water
- period
- 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.)
- Pending
Links
Landscapes
- Sewage (AREA)
Abstract
The invention provides an urban road and low-lying area water accumulation forecasting method based on a water tank model. The method comprises the following steps that (1) an urban hydrological forecasting boundary is determined; (2) the impervious area percentage of an underlying surface in a forecasting region is calculated; (3) rainwater is collected; (4) forecasting time periods are determined; (5) water accumulation forecasting is conduced through the water tank model. According to the method, rainfall is generalized into inflow water of a water tank through the water tank model, drainage is generalized into outflow water of the water tank, and the water accumulation volume of the water tank in each time period is calculated and converted into a water accumulation depth, so that accurate and effective urban road and low-lying area water accumulation forecasting is achieved, and a thought is provided for an urban area rainfall flood simulation study; the application range is wide, operation is simple, the forecasting speed is high, the accuracy is high, and the method has high promotional value.
Description
Technical field
The present invention relates to Urban Hydrologic forecasting technique field, especially downtown roads and low laying areas depth of accumulated water forecasting model foundation and realize automatic forecasting early warning.
Background technology
The development of urbanization, changes topography and geomorphology and the free drainage system of original region directly or indirectly.The underground pipe network that sewerage system has been thickened by original earth's surface irrigation canals and ditches, rivers and lakes.Drainage pipeline networks scarce capacity will cause road surface and lowered zones ponding, very large on the impact such as traffic, production, life, and it is densely populated to add Inner city, and building is concentrated, industry and commerce and traffic flourishing, the loss meeting with heavy rain is quite serious.
By analyzing the historical summaries such as city rainfall over the years, road water level monitoring data, Water Year Book, we find that city rainfall and road ponding have cause-effect relationship closely, and have followed the rule rising and disappear.After rainfall, the speed of road ponding is quickly, and ponding process and rainfall intensity, distribution of rainfall, the artificial comparatively Important Relations that drains flooded fields.
The process that conventional forecasting model has runoff yield and confluxes, these produce Confluence Model and are generally applicable to basin type forecast.Be not suitable for the downtown roads that almost can ignore Process of Confluence and low laying areas ponding forecasts.As the heavy rain hydrologic forecast model SWMM of more famous forecast urban waterlogging, but still lack very much for the utility model of region and city reality.
Summary of the invention
The invention provides a kind of urban road based on tank model and low laying areas ponding forecasting procedure.
For solving the problems of the technologies described above, the present invention adopts following technical scheme:
Based on urban road and the low laying areas ponding forecasting procedure of tank model, comprise the steps:
1) Urban Hydrologic forecast border is determined: forecast boundary line divides according to the layout of city river and underdrainage pipe network to determine forecast area, and measure the total area S of forecast area
0;
2) underlying surface Permeable stratum area number percent η in CALCULATING PREDICTION region
0: according to incity, city actual measurement current relief map or satellite remote sensing figure and urban land use functional planning figure, be multiple plot by underlying surface according to Type division, and measure its area S respectively
1, S
2s
n, the Permeable stratum area number percent η in heterogeneity plot is obtained by tabling look-up
1, η
2η
n, underlying surface Permeable stratum area number percent η in forecast area
0computing formula as follows,
Underlying surface Permeable stratum area number percent η in described forecast area
0need according to remote sensing figure, planning plot ratio and Green Space Index, and carry out adjustment correction in conjunction with soil moisture content, bury of groundwater and raininess.Gather the water level at road section hydrometric station in forecast area, draw the stage hydrograph in the period, the fluctuation trend in conjunction with this stage hydrograph is revised forecast result.Described plot comprises building district, high school district, factory district, cottage area, warehouse area, river course, lake and greenery patches district.
3) rain collection: use remote gauged rainfall data, after single station quantity of precipitation gathers, asks forecast area rainfall by arithmetic mean method or Thiessen polygon method;
4) forecasting period is determined: according to size, forecast area is at least marked off three forecasting periods.Described forecasting period choose with reference to following standard: forecast area area≤10km
2, forecasting period is long gets 10 minutes; 10km
2< forecast area area≤20km
2, forecasting period is long gets 20 minutes; 20km
2< forecast area area≤80km
2, forecasting period is long gets 30 minutes; 80km
2< forecast area area≤150km
2, forecasting period is long gets 60 minutes.
5) utilize tank model to carry out ponding forecast, specifically comprise the steps:
51) determine to forecast website;
52) adopt tank model that rainfall is generalized as water tank water intake amount, water discharge is generalized as water tank aquifer yield;
53) forecasting period is chosen, by the rainfall data period;
54) calculate each forecasting period rainfall deduct water discharge after effective precipitation, and calculate period accumulation effective precipitation.
Described period accumulation effective precipitation P
long-pending(n+1) computing formula is as follows:
P
long-pending(n+1)=P
long-pending(n)+P
onlyn (), if P
long-pending(n+1) <0, then P
long-pending(n+1)=0;
Wherein P
long-pendingn () is upper period accumulation effective precipitation, P
onlyn () is this period effective precipitation.
55) according to period accumulation effective precipitation and depth of accumulated water relation table, check in each period accumulation depth of accumulated water, and draw period accumulation depth of accumulated water sequence;
56) according to period accumulation depth of accumulated water sequential value, export forecast result eigenwert, realize ponding forecast.
Described forecast result eigenwert comprise depth of water peak value, peak current between and estimate regression time, wherein depth of water peak value is the maximum water depth value of period accumulation depth of accumulated water sequential value, be the time that this depth of water peak value is corresponding between peak is current, expectation regression time is the last depth of water of period accumulation depth of accumulated water sequential value is the time of zero.
From above technical scheme, the present invention adopts tank model that rainfall is generalized as water tank water intake, draining is generalized as water tank water outlet, by calculating each period water tank water accumulating volume and being converted into ponding deeply, realize accurate and effective urban road and the forecast of low laying areas ponding, for the modeling effort of city rain flood provides thinking, applied widely, simple to operate, forecast speed is fast, precision is high, has good promotional value.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of urban road of the present invention and low laying areas ponding forecasting procedure;
Fig. 2 is the process flow diagram that tank model of the present invention carries out ponding forecast;
Fig. 3 is accumulation effective precipitation and depth of accumulated water relation coordinate diagram in the present invention.
Embodiment
Below in conjunction with accompanying drawing, a kind of preferred implementation of the present invention is described in detail.
By the analysis to historical summary, after finding rainfall, produce the speed of road ponding quickly, and the similar water tank retaining of ponding process, tank model is generalized as water tank water intake rainfall, and draining is generalized as water tank water outlet.Propose a kind of urban road based on tank model and low laying areas ponding according to the present invention of this principle to forecast and invent.
The present invention, by calculating each period water tank water accumulating volume and being converted into depth of accumulated water, realizes ponding forecast.Therefore, this model needs preparation two class parameter before forecast.One class is drainability, the depth of runoff that namely drains flooded fields (unit is mm); One class is accumulation effective precipitation (unit is mm) and (unit is corresponding relation coordinate diagram (shown in accompanying drawing 3) m) to depth of accumulated water, the ponding in wherein accumulation effective precipitation and water tank.Collect this two classes parameter, need arrange historical summary and analyze, and according to real-time prediction process, with practical experience correction.
As shown in Figure 1, the present invention includes following steps:
100, Urban Hydrologic forecast border is determined: forecast boundary line divides according to the layout of city river and underdrainage pipe network to determine forecast area, and measure the total area S of forecast area
0.
The boundary line in nature basin divides according to landform, landforms and level line on topomap, and in urban area, forecast boundary line divides according to the situation of landform and urban drainage pipe network.Sewerage system is made up of pipe network and the draining network of waterways, and the storm flood that urban size produces first enters pipe network usually, then enters the network of waterways, and the layout with reference to city river and underdrainage pipe network divides forecast area.
Will confirm the underlying surface situation in this forecast area after dividing forecast area, the particularly importantly determination of Permeable stratum area, it is very large to rain flood process influence.
200, underlying surface Permeable stratum area number percent η in CALCULATING PREDICTION region
0: according to incity, city actual measurement current relief map or satellite remote sensing figure and urban land use functional planning figure, be multiple plot by underlying surface according to Type division, and measure its area S respectively
1, S
2s
n, the Permeable stratum area number percent η in heterogeneity plot is obtained by tabling look-up
1, η
2η
n, underlying surface Permeable stratum area number percent η in forecast area
0computing formula as follows:
Described plot comprises building district, high school district, factory district, cottage area, warehouse area, river course, lake and greenery patches district, the Permeable stratum area number percent η in dissimilar plot
nas shown in table 1.
Table 1 urban area Permeable stratum area number percent table
The Permeable stratum area number percent in above typical plot, the data obtained by factual survey, when the concrete hydrology calculates, also according to actual conditions and remote sensing figure, suitably adjustment such as planning plot ratio, Green Space Index etc., specifically adjustment correction can be carried out in conjunction with soil moisture content, bury of groundwater and raininess.
300, rain collection:
Due to flood peak, city current between short, rain flood process completes very soon, and therefore need to obtain rainfall data as early as possible, rainfall data uses remote gauged rainfall data, after single station quantity of precipitation gathers, asks forecast area rainfall by arithmetic mean method or Thiessen polygon method.
Water level, data on flows can also be used for local inflow, forecast reference and correction, gather the water level at road section hydrometric station in forecast area, draw the stage hydrograph in the period, the fluctuation trend in conjunction with this stage hydrograph is revised forecast result.
400, forecasting period is determined: according to size, forecast area is at least marked off three forecasting periods.Described forecasting period choose with reference to following standard: forecast area area≤10km
2, forecasting period is long gets 10 minutes; 10km
2< forecast area area≤20km
2, forecasting period is long gets 20 minutes; 20km
2< forecast area area≤80km
2, forecasting period is long gets 30 minutes; 80km
2< forecast area area≤150km
2, forecasting period is long gets 60 minutes.
500, tank model is utilized to carry out ponding forecast, by reference to the accompanying drawings 2, specifically comprise the steps:
501: determine to forecast website;
502: adopt tank model that rainfall is generalized as water tank water intake amount, water discharge is generalized as water tank aquifer yield, as 1 hour rainfall 30mm, 2 hours rainfall 50mm etc.;
503: choose forecasting period, computer disposal makes the rainfall data period;
504: calculate each forecasting period rainfall deduct water discharge after effective precipitation, can be negative value, and calculate period accumulation effective precipitation.
Described period accumulation effective precipitation P
long-pending(n+1) computing formula is as follows:
P
long-pending(n+1)=P
long-pending(n)+P
onlyn (), if P
long-pending(n+1) <0, then P
long-pending(n+1)=0 (when result is less than 0, counting 0, because excess surface water can not be negative);
Wherein P
long-pendingn () is upper period accumulation effective precipitation, P
onlyn () is this period effective precipitation.
505: according to period accumulation effective precipitation and depth of accumulated water relation coordinate diagram (shown in accompanying drawing 3), check in each period accumulation depth of accumulated water, and draw period accumulation depth of accumulated water sequence;
506: according to period accumulation depth of accumulated water sequential value, export forecast result eigenwert, realize ponding forecast.
Described forecast result eigenwert comprise depth of water peak value, peak current between and estimate regression time, wherein depth of water peak value is the maximum water depth value of period accumulation depth of accumulated water sequential value, be the time that this depth of water peak value is corresponding between peak is current, expectation regression time is the last depth of water of period accumulation depth of accumulated water sequential value is the time of zero.
The above embodiment is only be described the preferred embodiment of the present invention; not scope of the present invention is limited; under not departing from the present invention and designing the prerequisite of spirit; the various distortion that those of ordinary skill in the art make technical scheme of the present invention and improvement, all should fall in protection domain that claims of the present invention determine.
Claims (7)
1., based on urban road and the low laying areas ponding forecasting procedure of tank model, it is characterized in that, comprise the steps:
1) Urban Hydrologic forecast border is determined: forecast boundary line divides according to the layout of city river and underdrainage pipe network to determine forecast area, and measure the total area S of forecast area
0;
2) underlying surface Permeable stratum area number percent η in CALCULATING PREDICTION region
0: according to incity, city actual measurement current relief map or satellite remote sensing figure and urban land use functional planning figure, be multiple plot by underlying surface according to Type division, and measure its area S respectively
1, S
2s
n, the Permeable stratum area number percent η in heterogeneity plot is obtained by tabling look-up
1, η
2η
n, underlying surface Permeable stratum area number percent η in forecast area
0computing formula as follows,
3) rain collection: use remote gauged rainfall data, after single station quantity of precipitation gathers, asks forecast area rainfall by arithmetic mean method or Thiessen polygon method;
4) forecasting period is determined: according to size, forecast area is at least marked off three forecasting periods;
5) utilize tank model to carry out ponding forecast, specifically comprise the steps:
51) determine to forecast website;
52) adopt tank model that rainfall is generalized as water tank water intake amount, water discharge is generalized as water tank aquifer yield;
53) forecasting period is chosen, by the rainfall data period;
54) calculate each forecasting period rainfall deduct water discharge after effective precipitation, and calculate period accumulation effective precipitation;
55) according to period accumulation effective precipitation and depth of accumulated water relation table, check in each period accumulation depth of accumulated water, and draw period accumulation depth of accumulated water sequence;
56) according to period accumulation depth of accumulated water sequential value, export forecast result eigenwert, realize ponding forecast.
2. ponding forecasting procedure according to claim 1, is characterized in that, step 2) in, underlying surface Permeable stratum area number percent η in described forecast area
0need according to remote sensing figure, planning plot ratio and Green Space Index, and carry out adjustment correction in conjunction with soil moisture content, bury of groundwater and raininess.
3. ponding forecasting procedure according to claim 1, is characterized in that, step 2) in, described plot comprises building district, high school district, factory district, cottage area, warehouse area, river course, lake and greenery patches district.
4. ponding forecasting procedure according to claim 1, is characterized in that, gathers the water level at road section hydrometric station in forecast area, and draw the stage hydrograph in the period, the fluctuation trend in conjunction with this stage hydrograph is revised forecast result.
5. ponding forecasting procedure according to claim 1, is characterized in that, step 4) in, described forecasting period choose with reference to following standard: forecast area area≤10km
2, forecasting period is long gets 10 minutes; 10km
2< forecast area area≤20km
2, forecasting period is long gets 20 minutes; 20km
2< forecast area area≤80km
2, forecasting period is long gets 30 minutes; 80km
2< forecast area area≤150km
2, forecasting period is long gets 60 minutes.
6. ponding forecasting procedure according to claim 1, is characterized in that, step 54) in, described period accumulation effective precipitation P
long-pending(n+1) computing formula is as follows,
P
long-pending(n+1)=P
long-pending(n)+P
onlyn (), if P
long-pending(n+1) <0, then P
long-pending(n+1)=0;
Wherein P
long-pendingn () is upper period accumulation effective precipitation, P
onlyn () is this period effective precipitation.
7. ponding forecasting procedure according to claim 1, it is characterized in that, step 56) in, described forecast result eigenwert comprise depth of water peak value, peak current between and estimate regression time, wherein depth of water peak value is the maximum water depth value of period accumulation depth of accumulated water sequential value, be the time that this depth of water peak value is corresponding between peak is current, expectation regression time is the last depth of water of period accumulation depth of accumulated water sequential value is the time of zero.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410631659.7A CN104462774A (en) | 2014-11-11 | 2014-11-11 | Urban road and low-lying area water accumulation forecasting method based on water tank model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410631659.7A CN104462774A (en) | 2014-11-11 | 2014-11-11 | Urban road and low-lying area water accumulation forecasting method based on water tank model |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN104462774A true CN104462774A (en) | 2015-03-25 |
Family
ID=52908803
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410631659.7A Pending CN104462774A (en) | 2014-11-11 | 2014-11-11 | Urban road and low-lying area water accumulation forecasting method based on water tank model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104462774A (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105260607A (en) * | 2015-10-20 | 2016-01-20 | 华中科技大学 | Serial connection and parallel connection coupling multi-model hydrological forecasting method |
| CN105550803A (en) * | 2015-12-08 | 2016-05-04 | 哈尔滨航天恒星数据系统科技有限公司 | Urban water-logging analysis method and urban water-logging analysis system |
| CN106709608A (en) * | 2017-01-09 | 2017-05-24 | 泰华智慧产业集团股份有限公司 | Method and system for forecasting degree of influence of ponding on areas in city |
| CN107220496A (en) * | 2017-05-26 | 2017-09-29 | 上海市气象灾害防御技术中心 | A kind of urban rainstorm waterlogging assesses modeling method |
| CN108182543A (en) * | 2018-01-17 | 2018-06-19 | 福建四创软件有限公司 | One kind becomes more meticulous grid waterlogging water logging forecasting procedure |
| CN108491653A (en) * | 2018-03-29 | 2018-09-04 | 中国科学院地球化学研究所 | A kind of karst rainfall erosivity computational methods |
| CN110160550A (en) * | 2019-04-29 | 2019-08-23 | 东南大学 | A kind of city route bootstrap technique based on the prediction of road ponding |
| CN113781813A (en) * | 2021-10-22 | 2021-12-10 | 北京声智科技有限公司 | Early warning method, system, device and electronic equipment |
| CN114808823A (en) * | 2022-04-28 | 2022-07-29 | 南通银烛节能技术服务有限公司 | Intelligent control method and system for quickly cleaning accumulated liquid on road surface of sweeper |
| CN114814995A (en) * | 2022-03-31 | 2022-07-29 | 武汉达梦数据技术有限公司 | Early warning method and device for urban rainfall abnormality |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202757662U (en) * | 2012-08-31 | 2013-02-27 | 唐山现代工控技术有限公司 | Urban road water accumulation monitoring device |
-
2014
- 2014-11-11 CN CN201410631659.7A patent/CN104462774A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202757662U (en) * | 2012-08-31 | 2013-02-27 | 唐山现代工控技术有限公司 | Urban road water accumulation monitoring device |
Non-Patent Citations (3)
| Title |
|---|
| 王清川等: "河北省廊坊市城市积涝动态预报预警系统研制", 《干旱气象》 * |
| 薛燕: "城市洪水预报特点与方法解析", 《中国水利学会2010学术年会论文集（下册）》 * |
| 顾升波等: "城市内涝低洼风险区损失评估及预防对策", 《中固市政工程》 * |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105260607A (en) * | 2015-10-20 | 2016-01-20 | 华中科技大学 | Serial connection and parallel connection coupling multi-model hydrological forecasting method |
| CN105550803A (en) * | 2015-12-08 | 2016-05-04 | 哈尔滨航天恒星数据系统科技有限公司 | Urban water-logging analysis method and urban water-logging analysis system |
| CN106709608A (en) * | 2017-01-09 | 2017-05-24 | 泰华智慧产业集团股份有限公司 | Method and system for forecasting degree of influence of ponding on areas in city |
| CN107220496A (en) * | 2017-05-26 | 2017-09-29 | 上海市气象灾害防御技术中心 | A kind of urban rainstorm waterlogging assesses modeling method |
| CN107220496B (en) * | 2017-05-26 | 2020-06-12 | 上海市气象灾害防御技术中心 | Urban rainstorm waterlogging assessment modeling method |
| CN108182543A (en) * | 2018-01-17 | 2018-06-19 | 福建四创软件有限公司 | One kind becomes more meticulous grid waterlogging water logging forecasting procedure |
| CN108491653B (en) * | 2018-03-29 | 2020-07-07 | 中国科学院地球化学研究所 | Karst region rainfall erosion force calculation method |
| CN108491653A (en) * | 2018-03-29 | 2018-09-04 | 中国科学院地球化学研究所 | A kind of karst rainfall erosivity computational methods |
| CN110160550A (en) * | 2019-04-29 | 2019-08-23 | 东南大学 | A kind of city route bootstrap technique based on the prediction of road ponding |
| CN110160550B (en) * | 2019-04-29 | 2022-07-08 | 东南大学 | Urban route guiding method based on road ponding prediction |
| CN113781813A (en) * | 2021-10-22 | 2021-12-10 | 北京声智科技有限公司 | Early warning method, system, device and electronic equipment |
| CN114814995A (en) * | 2022-03-31 | 2022-07-29 | 武汉达梦数据技术有限公司 | Early warning method and device for urban rainfall abnormality |
| CN114814995B (en) * | 2022-03-31 | 2022-11-22 | 武汉达梦数据技术有限公司 | Urban waterlogging early warning method and device |
| CN114808823A (en) * | 2022-04-28 | 2022-07-29 | 南通银烛节能技术服务有限公司 | Intelligent control method and system for quickly cleaning accumulated liquid on road surface of sweeper |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104462774A (en) | Urban road and low-lying area water accumulation forecasting method based on water tank model | |
| CN106884405B (en) | Inrush type mountain flood assay method for a kind of Cross Some Region Without Data | |
| CN111062125B (en) | Hydrological effect evaluation method for sponge type comprehensive pipe gallery | |
| CN102201034A (en) | Method for estimating nonpoint source pollution load of village adjacent to river | |
| CN106372328A (en) | Drainage pipeline repair method and system based on drainage ability evaluation | |
| Jeppesen et al. | Modelling the historical water cycle of the Copenhagen area 1850–2003 | |
| CN105651484A (en) | Adaptive rainwater pipe network monitoring method and adaptive rainwater pipe network monitoring system | |
| CN110838079A (en) | Intelligent city flood control and drainage remote monitoring system and method | |
| CN110222427A (en) | A kind of analysis method based on mathematical model urban waterlogging | |
| CN103886151A (en) | Method for establishing simplified hydraulic model of urban downward-concave overpass area under different rainstorm situations | |
| Ben-Daoud et al. | The use of low impact development technologies in the attenuation of flood flows in an urban area: Settat city (Morocco) as a case | |
| Ahamed et al. | Urban flood modeling and management using SWMM for new RR Pet region, Vijayawada, India | |
| Liu et al. | Review of the status of urban flood monitoring and forecasting in TC region | |
| CN117033856A (en) | Method for determining urban inundation area of short-duration heavy rainfall | |
| CN115563740B (en) | Urban surface mixed runoff yield calculation method based on drainage pipe network distribution | |
| CN109978273B (en) | Mine water inflow prediction method based on landform water control | |
| Méndez | Hydraulic analysis of urban drainage systems with conventional solutions and sustainable technologies: Case study in Quito, Ecuador | |
| CN110889185A (en) | Peak flow analysis method for small watershed and application thereof | |
| Thein | Modelling of short duration rainfall IDF equation for Sagaing region, Myanmar | |
| Douglas et al. | Characterisation of urban streams and urban flooding | |
| Zhao et al. | The study of urban rainstorm waterlogging scenario simulation based on GIS and SWMM model—Take the example of Kunming Dongfeng East Road catchment area | |
| Awang Ali et al. | Model reliability assessment: A hydrodynamic modeling approach for flood simulation in Damansara catchment using InfoWorks RS | |
| Deka et al. | Simulation of drainage system using storm water management model (SWMM 5.1) | |
| Alawneh et al. | Modeling of groundwater recharge by rainwater harvesting-Wadi Bayer (Case Study) | |
| Doan et al. | Evaluating the impacts of an improved sewer system on city flood inundations using MIKE Urban Model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| CB02 | Change of applicant information |
Address after: Daoxiang road Hefei City, Anhui province 230031 Shushan New Industrial Park No. 1 Applicant after: In the three data technology Limited by Share Ltd Address before: Daoxiang road Hefei City, Anhui province 230031 Shushan New Industrial Park No. 1 Applicant before: Hefei Sanli Automation Engineering Co., Ltd. |
|
| COR | Change of bibliographic data | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20150325 |
|
| RJ01 | Rejection of invention patent application after publication |