CN111767649B - Soft foundation area transformer substation geological deformation safety assessment method - Google Patents
Soft foundation area transformer substation geological deformation safety assessment method Download PDFInfo
- Publication number
- CN111767649B CN111767649B CN202010585139.2A CN202010585139A CN111767649B CN 111767649 B CN111767649 B CN 111767649B CN 202010585139 A CN202010585139 A CN 202010585139A CN 111767649 B CN111767649 B CN 111767649B
- Authority
- CN
- China
- Prior art keywords
- model
- geological
- deformation
- transformer substation
- foundation
- 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
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B7/00—Measuring arrangements characterised by the use of electric or magnetic techniques
- G01B7/16—Measuring arrangements characterised by the use of electric or magnetic techniques for measuring the deformation in a solid, e.g. by resistance strain gauge
Abstract
The invention discloses a soft foundation area transformer substation geological deformation safety assessment method, which is based on a Beidou foundation enhancement network and utilizes a Beidou GNSS to automatically monitor the geological deformation of a soft foundation transformer substation; establishing an incidence relation (mathematical model) between monitoring point data and a geological structure of the transformer substation based on geological deformation monitoring data, and realizing real-time monitoring and early warning of geological deformation of the soft-foundation transformer substation; the invention can effectively improve the intelligent level of geological environment deformation monitoring and evaluation of the soft foundation transformer substation and ensure the safe and stable operation of the transformer substation.
Description
Technical Field
The invention relates to the field of geological environment deformation monitoring and protection of a power system transformer substation built in a soft foundation area.
Background
The transformer substations of the power grid are large in number and wide in distribution, the deformation degree of the transformer substation foundation directly influences the safe operation of the transformer substation grounding grid, and the operation safety of main equipment of the transformer substation is influenced when the transformer substation foundation is seriously deformed. Although the geological condition of the transformer substation is investigated during early-stage power construction, the geological hidden danger points cannot be completely avoided due to the limitation of construction land, and part of the transformer substation is built in a soft soil geological weak bearing area, namely a soft foundation area. The safety performance of primary and secondary electrical equipment in the transformer substation can be influenced by overlarge, too fast or relatively uneven geological deformation of a soft foundation of the transformer substation, and a plurality of key equipment face the danger of land loss, so that the safe and stable operation of the transformer substation is seriously threatened, and in recent years, accidents that a plurality of transformer substation grounding grids sink or deform occur.
Therefore, the soft soil foundation needs to be monitored in real time in the operation process of the transformer substation, and the deformation degree of the geological environment is evaluated according to the monitoring result, which is one of the preconditions for ensuring the safe operation of the transformer substation. The deformation monitoring of the soft foundation belongs to the field of engineering measurement, and currently, a traditional optical measurement method such as a level is generally adopted for settlement monitoring, a total station is adopted for settlement and horizontal displacement monitoring at the same time, and a static level can also be adopted for settlement monitoring. However, optical measurement generally requires manual operation, and since the line of sight is easily shielded, it is difficult to implement automatic monitoring in a substation, and an optical method cannot be implemented at night and under adverse weather conditions such as rainfall, heavy fog, strong wind, and the like; static levels, while convenient for automated monitoring, do not allow for displacement monitoring. Therefore, a technology for simultaneously carrying out settlement and horizontal displacement automatic monitoring on a soft foundation of a transformer substation is lacked at present, and meanwhile, how to scientifically determine a geological deformation control standard (threshold value) of the soft foundation transformer substation is also a problem to be solved urgently, and early warning is carried out after the deformation degree exceeds the control standard.
The geological deformation monitoring technology based on the Beidou foundation reinforcing net is gradually popularized and applied in China, is mainly applied to the fields of crustal movement, bridges, highways and the like, has the advantages of good real-time performance, high monitoring precision, safety, reliability and the like, and can be suitable for various meteorological conditions and at night; however, the method is still rarely reported to be used for monitoring the deformation of the geological environment of the soft soil foundation of the transformer substation.
A geological deformation monitoring method of the soft foundation transformer substation is provided by utilizing a transformer substation ground monitoring data resolving algorithm based on the Beidou foundation reinforcing network; establishing an incidence relation (mathematical model) between a geological settlement monitoring point and a geological structure of the transformer substation based on geological deformation monitoring data of the soft foundation transformer substation, and providing a transformer substation overall geological deformation safety assessment method based on geological settlement point monitoring information; and the real-time monitoring and early warning of the geological deformation of the soft foundation transformer substation are realized. The invention effectively improves the intelligent level of geological environment deformation monitoring and evaluation of the soft base station site transformer substation, and ensures the safe and stable operation of the transformer substation.
Disclosure of Invention
According to the invention, based on the Beidou foundation enhancement network, the Beidou GNSS is utilized to automatically monitor the geological deformation of the soft foundation transformer substation; establishing an incidence relation (mathematical model) between monitoring point data and a transformer substation geological structure based on geological deformation monitoring data, providing a transformer substation geological deformation safety comprehensive evaluation method based on monitoring information, and realizing real-time monitoring and early warning of the geological deformation of the soft-foundation transformer substation; the invention can effectively improve the intelligent level of geological environment deformation monitoring and evaluation of the soft foundation transformer substation and ensure the safe and stable operation of the transformer substation.
Based on the geological deformation monitoring data of the soft foundation transformer substation, searching an incidence relation between a geological settlement monitoring point and a geological structure of the transformer substation, and providing a transformer substation overall geological deformation degree evaluation method based on geological settlement point monitoring information;
firstly, establishing a geological generalized model and a numerical simulation model of a soft foundation transformer substation, obtaining a spatial-temporal evolution rule of a displacement field of the soft foundation under the action of a load by using a numerical analysis method, and determining a deformation control value of the soft foundation under a possible limit load condition; secondly, automatically monitoring the geological deformation of the soft foundation transformer substation by using a Beidou GNSS based on a Beidou foundation enhancement network; thirdly, based on the geological deformation monitoring data and the numerical simulation data of the first step, establishing an incidence relation model (artificial intelligence mathematical model) between the monitoring point data and the geological structure of the transformer substation, providing a transformer substation geological deformation safety assessment method based on monitoring information, and realizing real-time monitoring and early warning of the geological deformation of the soft foundation transformer substation, wherein the specific technical realization steps are as follows:
1. data collection and geological analysis
The method comprises the steps of carrying out site reconnaissance, collection and analysis on existing geological reconnaissance and basic design data of the transformer substation, finding out geological features of the transformer substation foundation, particularly soft soil distribution conditions and physical and mechanical property indexes of each soft soil layer, and finding out the influence of underground water on soft foundation deformation and related parameters;
2. establishing a geological generalization model and a numerical analysis model
Establishing a geological generalized model based on the step 1, wherein the geological generalized model comprises the thickness, distribution and physical and mechanical parameters of the foundation stratum of the transformer substation; simultaneously establishing a load model of the transformer substation, wherein the load model comprises load characteristics and distribution, variable load and accidental load acting on a foundation; then, establishing a numerical simulation model by using numerical simulation software FLAC3D on the basis of the geological generalized model and the load model;
the three-dimensional geological generalized model can be constructed based on CAD or 3DGIS, and the load model can adopt a nonlinear polynomial functionL(x,yZ) to describe the load characteristics and distribution;
3. determination of numerical simulation and monitoring control standard
On the basis of a creep constitutive model, performing long-term deformation evolution characteristic calculation on the soft foundation of the transformer substation under the load action by using the numerical simulation model established in the step 2, on one hand, obtaining a displacement field of the soft foundation of the transformer substation along with time change under the load action by using a numerical analysis method, determining a control value, namely a threshold value, of the deformation of the soft foundation under the possible ultimate load condition, and on the other hand, selecting 3-10 points with maximum deformation as key geological deformation monitoring control points;
calculating the long-term deformation evolution characteristics of the soft foundation under the load action by adopting a numerical simulation method based on common creep constitutive models (Merchant model, burgers model, power function model and Singh-Mitchell model), so as to obtain a long-term value under the condition of considering the ultimate load, and taking the long-term value as a deformation control value, namely a threshold value.
4. On-site automated monitoring
Based on a Beidou foundation enhancement network, installing a Beidou GNSS on the key monitoring control points selected in the step (3) to automatically monitor the geological deformation of the soft foundation transformer substation, and acquiring actual deformation monitoring data of the foundation based on a general GNSS coordinate resolving method;
5. mathematical modeling
Researching a data change space-time rule of key monitoring points based on the displacement field analysis result and engineering analogy and experience in the step (3), and establishing a space-time change rule mathematical model of the monitoring points by adopting an artificial intelligence method; the parameters of the mathematical model comprise the thickness of the soft foundation where the monitoring point is located, the compression modulus and the load; comparing the difference between the data obtained by the space-time change rule mathematical model and the actual monitoring data in the step (4), continuously feeding back the correction model, and finally determining a model suitable for specific projects, geology and load conditions through trial operation for a period of time;
a space-time change rule mathematical model of the monitoring point is established by adopting a Rough Set (RS) and a Grey wolf optimization algorithm (GHO) -Multivariate Adaptive Regression Spline (MARS) combined prediction model (GHO-MARS). The method comprises the following specific steps: firstly, screening input multi-factors based on an RS attribute reduction algorithm, eliminating redundant factors which have small influence on a prediction output result (soft basis deformation), and reserving main factors which have large influence on the prediction output result (soft basis deformation), thereby obtaining a simplest set of the input multi-factors; secondly, establishing a nonlinear mapping relation between the simplest set of influence factors obtained by the last screening and a prediction output result (soft foundation deformation) by adopting a MARS model, and optimizing two key parameters influencing the MARS modeling precision by adopting a GWOO algorithm: sample data minimum step size and end point. Finally, an artificial intelligence combined prediction model based on RS and GWO-MARS is obtained;
6. taking 100% of the control value obtained in the step (3) as a limit value, namely grade III; 85% of the control value is used as an alarm value, namely II level; 70% of the control value is used as an early warning value, namely I level, so that a three-level early warning system is established; and (5) judging which level of the early warning system the monitoring data obtained in the step (4) is located in, and carrying out classification and early warning on the safety level of the soft foundation geological deformation of the transformer substation.
The process completely describes the flow and the method of the transformer substation geological deformation safety evaluation based on the monitoring information, and can realize the real-time monitoring and early warning of the geological deformation of the soft foundation transformer substation; the method is different from the currently adopted method in that:
(1) The current method judges only based on the monitoring result, but the judging control standard is unclear, the invention definitely obtains the deformation control standard of the soft foundation under the conditions of specific projects, geology and loads through the numerical simulation of the step 3, and also provides scientific basis for determining the position of the key monitoring point;
(2) According to the invention, a mathematical model is established by monitoring data and stratum physical and mechanical characteristics of the soft foundation, such as the thickness, the compression modulus, the load and the like of the soft foundation, so that the geological characteristics and the load characteristics of the soft foundation of the transformer substation are fully reflected, and the method can adapt to possible variable load conditions or extreme climate conditions;
the analysis method used by the model in step 5 is an artificial neural network method, which is a known common artificial intelligence modeling method and is not described herein again.
Supplementary explanation, although the safety evaluation of the geological deformation of the transformer substation can be independently performed by directly utilizing the numerical simulation of the step 3, the safety evaluation needs to be performed by a professional engineer; the use method of the step 5 model obtained through the research process is very simple.
In the invention, in consideration of the dynamic situation that the geological condition of the soft foundation transformer substation is complex and the loading condition may change along with the service development, the detailed geological distribution condition and physical mechanical parameters of the foundation are determined through exploration, the spatial and temporal evolution rule of the soft foundation under the load action is obtained through numerical simulation, the geological deformation control threshold value is obtained, and the artificial intelligent mathematical model is established. And then substituting the monitoring data into the model to obtain theoretical control values of the monitoring data under different time scale space scales and loads, thereby providing scientific basis for determining the monitoring control values, ensuring the safe operation of the soft foundation transformer substation and being simple and convenient to use.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a transformer substation-soft foundation three-dimensional geological generalization model;
FIG. 3 is a load distribution characteristic curve (L(x, y, z) function);
FIG. 4 is a FLAC3D three-dimensional geology-load numerical analysis model;
FIG. 5 is a distribution rule of a soft foundation deformation space displacement field;
FIG. 6 is a soft basis characteristic point displacement-time evolution rule;
FIG. 7 is a mathematical model building implementation flow based on a rough set and GWOO-MARS;
fig. 8 shows the soft basis deformation prediction results.
Detailed Description
The process of the present invention is further illustrated in detail by the following examples, but the scope of the process of the present invention is not limited thereto.
Example 1: as shown in fig. 1, the soft foundation area transformer substation geological deformation safety assessment method is as follows:
1. collecting data and analyzing geology
Performing site reconnaissance, collecting and analyzing existing geological reconnaissance and basic design data of the transformer substation, finding out geological characteristics of the transformer substation foundation, particularly soft soil distribution conditions and physical and mechanical property indexes of each soft soil layer, and finding out the influence and related parameters of underground water on the deformation of the soft foundation;
2. establishing a geological generalization model and a numerical analysis model
Establishing a three-dimensional geological generalized model by adopting CAD or 3DGIS based on the step 1, wherein the three-dimensional geological generalized model comprises the thickness, distribution and physical and mechanical parameters of the foundation stratum of the transformer substation, and the established soft foundation three-dimensional geological generalized model of the transformer substation is shown in a figure 2; based on the aforesaid non-linear polynomial function at the same timeL(x,y,z) Constructing a load model of the substation, including the load characteristics and distribution acting on the foundation, variable load and accidental load, and using a non-linear polynomial function as shown in FIG. 3L(x,y,z) The described load distribution characteristic surface; then utilizing commercial numerical simulation software FLAC on the basis of construction of a geological generalized model and a load model 3D Establishing a three-dimensional numerical analysis model, and establishing a transformer substation soft foundation-load three-dimensional geological probability model based on a FLAC3D finite difference method in FIG. 4;
3. determination of numerical simulation and monitoring control standard
Performing long-term deformation evolution characteristic calculation on the soft foundation of the transformer substation under the load action by using the numerical analysis model established in the step 2, on one hand, obtaining a displacement field (see figure 5) of the soft foundation of the transformer substation along with time change under the load action by using a numerical analysis method based on a common creep constitutive model (Merchant model, burgers model, power function model and Singh-Mitchell model), and on the other hand, obtaining a long-term value under the condition of considering the ultimate load, and accordingly, determining a control value (see a control value shown in figure 6) of deformation of the soft foundation under the possible ultimate load condition, namely a threshold value, and on the other hand, selecting 3-10 points with the maximum deformation as key geological deformation monitoring control points;
4. on-site automated monitoring
Based on a Beidou foundation enhancement network, a Beidou GNSS is installed on the key monitoring control points selected in the step (3) to automatically monitor the geological deformation of the soft foundation transformer substation, and actual deformation monitoring data of the foundation are obtained based on a general GNSS coordinate resolving method;
5. mathematical modeling
Researching the data change space-time law of the key monitoring points based on the displacement field analysis result, engineering analogy and experience in the step 3, and establishing a space-time change law mathematical model of the monitoring points by adopting the artificial intelligence combined prediction model based on RS and GWO-MARS, wherein the specific implementation flow is shown in FIG. 7; the mathematical model comprises parameters including the thickness of the soft foundation where the monitoring point is locatedhCompression modulusE s Load, and the likeL(ii) a Mathematical model for comparing temporal-spatial change rulesf（h,E s ,L) The obtained data and the actual monitoring data in the step 4xContinuously feeding back the correction model, and finally determining a model suitable for specific projects, geology and load conditions after a period of trial operation, wherein a soft foundation deformation prediction result based on the established artificial intelligence mathematical model is shown in FIG. 8;
6. taking 100% of the control value obtained in the step 3 as a limit value, namely grade III; 85% of the control value is used as an alarm value, namely II level; 70% of the control value is used as an early warning value, namely I level, so that a three-level early warning system is established; and (5) judging which level of the early warning system the monitoring data obtained in the step (4) are located in, and thus carrying out transformer substation soft foundation geological deformation safety level division. In addition, the artificial intelligence mathematical model established based on the step 5 can predict the soft foundation deformation values of the next days or even months, so as to achieve the purpose of risk early warning.
Claims (4)
1. A soft foundation area transformer substation geological deformation safety assessment method is characterized by comprising the following steps:
(1) Performing site reconnaissance, collecting and analyzing existing geological reconnaissance and basic design data of the transformer substation, finding out geological characteristics of the transformer substation foundation, particularly soft soil distribution conditions and physical and mechanical property indexes of each soft soil layer, and finding out the influence and related parameters of underground water on the deformation of the soft foundation;
(2) Establishing a geological generalization model and a numerical analysis model
Establishing a geological generalized model based on the step (1), wherein the geological generalized model comprises the thickness, distribution and physical and mechanical parameters of the foundation stratum of the transformer substation; simultaneously establishing a load model of the transformer substation, wherein the load model comprises load characteristics and distribution, variable load and accidental load acting on a foundation; then numerical simulation software FLAC is utilized on the basis of the geological generalization model and the load model 3D Establishing a numerical simulation model;
(3) Determination of numerical simulation and monitoring control standard
On the basis of a creep constitutive model, performing long-term deformation evolution characteristic calculation on the soft foundation of the transformer substation under the load action by using the numerical simulation model established in the step (2), on one hand, obtaining a displacement field of the soft foundation of the transformer substation along with time change under the load action by using a numerical analysis method, determining a control value, namely a threshold value, of the deformation of the soft foundation under a possible limit load condition, and on the other hand, selecting 3-10 points with maximum deformation as key geological deformation monitoring control points;
(4) On-site automated monitoring
Based on a Beidou foundation enhancement network, installing a Beidou GNSS on the key monitoring control points selected in the step (3) to automatically monitor the geological deformation of the soft foundation transformer substation, and acquiring actual deformation monitoring data of the foundation based on a general GNSS coordinate resolving method;
(5) Mathematical modeling
Researching a data change space-time rule of key monitoring points based on the displacement field analysis result and engineering analogy and experience in the step (3), and establishing a space-time change rule mathematical model of the monitoring points by adopting an artificial intelligence method; the parameters of the mathematical model comprise the thickness of the soft foundation where the monitoring point is located, the compression modulus and the load; comparing the difference between the data obtained by the space-time change rule mathematical model and the actual monitoring data in the step (4), continuously feeding back the correction model, and finally determining a model suitable for specific projects, geology and load conditions through trial operation for a period of time;
(6) Taking 100% of the control value obtained in the step (3) as a limit value, namely grade III; 85% of the control value is used as an alarm value, namely II level; 70% of the control value is used as an early warning value, namely I level, so that a three-level early warning system is established; and (5) judging which level of the early warning system the monitoring data obtained in the step (4) is located in, and carrying out classification and early warning on the safety level of the soft foundation geological deformation of the transformer substation.
2. The soft foundation area substation geological deformation safety evaluation method according to claim 1, characterized in that: the geologic generalized model is built by CAD or 3DGIS, and nonlinear polynomial function is adoptedL(x,yZ) to construct a loading model.
3. The soft foundation area substation geological deformation safety evaluation method according to claim 1, characterized in that: the creep constitutive model is a Merchant model, a Burgers model, a power function model or a Singh-Mitchell model, the long-term deformation evolution characteristics of the soft foundation under the load action are calculated by adopting a numerical simulation method, so that a long-term value under the condition of considering the ultimate load is obtained and is used as a deformation control value, namely a threshold value.
4. The soft foundation area substation geological deformation safety assessment method according to claim 1, characterized in that the building step of the time-space change rule mathematical model of the monitoring points is as follows: firstly, screening input multi-factors based on an RS attribute reduction algorithm, eliminating redundant factors which have small influence on a prediction output result, namely soft basis deformation, and reserving main factors which have large influence on the prediction output result, namely the soft basis deformation, so as to obtain a simplest set of the input multi-factors; secondly, establishing a nonlinear mapping relation between the simplest set of influence factors obtained by the last step of screening and a prediction output result by adopting a multivariate self-adaptive regression spline MARS model, and optimizing two key parameters influencing MARS modeling precision by adopting a wolf optimization algorithm GWOO: sample data minimum step size and end point; and finally obtaining an artificial intelligence combined prediction model based on the rough set and the GPO-MARS.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010585139.2A CN111767649B (en) | 2020-06-23 | 2020-06-23 | Soft foundation area transformer substation geological deformation safety assessment method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010585139.2A CN111767649B (en) | 2020-06-23 | 2020-06-23 | Soft foundation area transformer substation geological deformation safety assessment method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111767649A CN111767649A (en) | 2020-10-13 |
| CN111767649B true CN111767649B (en) | 2023-01-31 |
Family
ID=72722218
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010585139.2A Active CN111767649B (en) | 2020-06-23 | 2020-06-23 | Soft foundation area transformer substation geological deformation safety assessment method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111767649B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113987949B (en) * | 2021-11-03 | 2024-05-07 | 燕山大学 | Data-driven-based plate strip steel deformation resistance prediction method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106679625A (en) * | 2016-12-05 | 2017-05-17 | 安徽继远软件有限公司 | High-precision deformation monitoring method of wide-area electric iron tower based on Beidou system |
| CN106884442A (en) * | 2017-04-25 | 2017-06-23 | 西安理工大学 | A kind of implementation of cheuch shape high fill foundation multi- scenarios method monitoring system |
| CN107764231A (en) * | 2017-10-13 | 2018-03-06 | 天津市勘察院 | A kind of building deformation monitoring system and method based on the enhancing of Big Dipper ground |
-
2020
- 2020-06-23 CN CN202010585139.2A patent/CN111767649B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106679625A (en) * | 2016-12-05 | 2017-05-17 | 安徽继远软件有限公司 | High-precision deformation monitoring method of wide-area electric iron tower based on Beidou system |
| CN106884442A (en) * | 2017-04-25 | 2017-06-23 | 西安理工大学 | A kind of implementation of cheuch shape high fill foundation multi- scenarios method monitoring system |
| CN107764231A (en) * | 2017-10-13 | 2018-03-06 | 天津市勘察院 | A kind of building deformation monitoring system and method based on the enhancing of Big Dipper ground |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111767649A (en) | 2020-10-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Liu et al. | Algorithms for intelligent prediction of landslide displacements | |
| CN113779835A (en) | AI and intelligent monitoring system based deep and large foundation pit safety early warning method | |
| CN111260187A (en) | Intelligent mine geological environment information evaluation system and evaluation method | |
| KR101824662B1 (en) | Method for Management of River Facility through River Diagnosis | |
| US20210404139A1 (en) | System for bridge scour multi-source monitoring, monitoring method thereof, and scour depth evaluating method thereof | |
| CN102867110A (en) | Rainstorm disaster risk evaluation method for foundation slope of transmission line tower | |
| CN113283802A (en) | Landslide risk assessment method for complex and difficult mountain area | |
| CN109886456B (en) | Debris flow prediction method based on PCA and mixed kernel function LSSVR | |
| CN113379105A (en) | Site earthquake liquefaction disaster rapid estimation method based on artificial intelligence | |
| CN113554849A (en) | Air-ground monitoring slope system and instability risk assessment method thereof | |
| CN112966722A (en) | Regional landslide susceptibility prediction method based on semi-supervised random forest model | |
| CN113610301B (en) | Prediction algorithm for representing ground settlement time sequence evolution | |
| CN115359629B (en) | Regional landslide hazard early warning method based on sequential evaluation method | |
| CN114357691A (en) | Power facility geological foundation deformation safety assessment method | |
| CN115017591A (en) | Building structure group earthquake-resistant performance evaluation method | |
| CN109299208B (en) | Intelligent visual risk assessment method for transmission tower in typhoon disaster | |
| CN115730684A (en) | Air quality detection system based on LSTM-CNN model | |
| CN111915158A (en) | Rainstorm disaster weather risk assessment method, device and equipment based on Flood Area model | |
| CN112330184A (en) | Construction risk prediction method, device, equipment and medium | |
| CN111767649B (en) | Soft foundation area transformer substation geological deformation safety assessment method | |
| CN112967475A (en) | Intelligent regional landslide monitoring and management method and device | |
| CN117217058A (en) | Method, system, equipment and medium for monitoring hydraulic structure safety of pumping and storing power station | |
| CN115270527A (en) | Real-time assessment method, equipment and storage medium for road collapse risk | |
| CN113657610A (en) | Hail climate characteristic prediction method based on random forest | |
| CN116777079A (en) | Desertification intrusion disaster prediction method based on Bayesian interlayer structure model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |