CN107330636A - Engineering construction risk source based on 3DGIS+BIM technologies is monitored and artificial intelligence Forecasting Methodology in real time - Google Patents
Engineering construction risk source based on 3DGIS+BIM technologies is monitored and artificial intelligence Forecasting Methodology in real time Download PDFInfo
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Abstract
Monitored in real time and artificial intelligence Forecasting Methodology the invention discloses a kind of engineering construction risk source based on 3DGIS+BIM technologies, first using 3DGIS+BIM technique constructions three dimensional environmental model and construction management platform；Data are acquired secondly by automatic monitoring means, and upload to BIM construction management platforms；Then early warning value is set in BIM construction managements platform, when Monitoring Data exceedes early warning value, system will automatic bright aobvious alarm；It is finally based on adaptive grey Fourier Index Prediction Model all monitoring results are analyzed and handled, forecasting risk trend, the early warning before a certain value-at-risk is reached.The present invention effectively judges the safety and stability state of risk source based on data analysis, so as to take preventive measures in time, to reduce risk source danger and evade construction risk, effectively prevents accident, ensures construction safety.
Description
Technical field
The invention belongs to Construction of Civil Engineering field, more particularly to a kind of engineering construction wind based on 3DGIS+BIM technologies
Dangerous source is monitored and artificial intelligence Forecasting Methodology in real time.
Background technology
The risk source of Construction of Civil Engineering is primarily referred to as in process of construction, and engineering itself or neighboring area environment are produced
The factor of significant impact.Such as natural hybridized orbit (such as heavy rain, flood, mud-rock flow, hurricane, earthquake), geological conditions, peripheral ring
Border influence, construction technology and method etc..
Construction of Civil Engineering operation common peripheral environment positioned at Inner city is complicated, various buildings, buried pipe
Line is more and requires high to construction Deformation control.And the underground engineering in civil engineering is with more disguised big, ground physical mechanics
Parameter is inaccurate, construction technology is complicated, working space is limited, operating environment is severe, long construction period, unpredictable risk factors
It is many and to social environment influence it is big the features such as, further, since the understanding to underground engineering safety risk is not objective, risk management
In not science, the subjective reason such as the input of risk management is not in place, underground engineering construction, Frequent Accidents, situation is very severe.Such as
Subway work, which occurs, for Line of Nanjing Subway in 2007 causes Gas Pipe to be broken, and causes gas leakage to occur the thing that blast causes big fire
Part；Foundation pit collapse accident occurs for Hangzhou Subway within 2008, causes 21 people dead, and more than 10 people are injured.
With the fast development of Urbanization in China, empirical, type, the safety of men-to-men defense afterwards of Traditional project construction
Management mode can not be tackled, and it is inexorable trend to introduce efficient, intelligentized security risk prediction and management system.
The content of the invention
Goal of the invention：For problem above, the present invention proposes a kind of engineering construction risk source based on 3DGIS+BIM technologies
Monitor in real time and artificial intelligence Forecasting Methodology.
Technical scheme：To realize the purpose of the present invention, the technical scheme used is：One kind is based on 3DGIS+BIM technologies
Engineering construction risk source in real time monitoring and artificial intelligence Forecasting Methodology, specifically include following steps：
(1) three dimensional environmental model and BIM construction management platforms are set up using 3DGIS+BIM technologies；
(2) automatic data collection engineering construction risk source data, and upload to BIM construction management platforms；
(3) early warning value is set in BIM construction managements platform；
(4) adaptive grey Fourier Index Prediction Model is set up；
(5) real-time estimate of time series is carried out to engineering construction risk source monitoring data.
Step 1 is specifically included：
(1.1) terrain data drawing three-dimensional relief model is obtained；
(1.2) urban landscape data drawing three-dimensional city model is passed through；
(1.3) 3DGIS+BIM construction management platforms are set up.
Step 2 is specifically included：
(2.1) by automatic monitoring system, structure and the real time data of environment under external force are gathered；
(2.1) real time data passback 3DGIS+BIM construction management platforms.
Step 3 is specifically included：
(3.1) risk source data storehouse is set up, 3DGIS+BIM models is imported into, includes in system administration, risk source is carried out
Grade is assessed；
(3.2) early warning line is set on platform, and Monitoring Data is exceeded can automatic alarm.
Set up adaptive grey Fourier Index Prediction Model to be predicted material risk source time sequence, specifically include
Following steps：
(1) modeling is predicted with flow；
(2) background value improves；
(3) specific solution conversion；
(4) residual GM Technology Integration.
Beneficial effect：Compared with prior art, with following advantage：
(1) monitored in real time and forecasting system by engineering construction risk source, enhance intelligentized Risk-warning, realization pair
The precise positioning of risk source, the safety and stability state of risk source is effectively judged based on data analysis, so as to take prevention in time
Measure, to reduce risk source danger and evade construction risk, effectively prevents accident, ensures construction safety；
(2) real-time data transmission is realized by the overlap joint of automatic monitoring system and 3DGIS+BIM construction management platforms, is
Platform monitoring analysis provides accurately and effectively data source；By the analysis to data, dynamic monitoring management and risk are formed
Prediction；
(3) consider conventional model be difficult to because system is unstable and caused by irregular sequence perfect forecast, this
Invent the adaptation for optimizing Grey Prediction Model by methods such as background value improvement, specific solution conversion and residual GM Technology Integrations
Property, build adaptive grey Fourier Index Prediction Model, the overall precision of prediction of lifting.
Brief description of the drawings
Fig. 1 is railway traffic engineering construction material risk source monitoring and artificial intelligence Forecasting Methodology schematic diagram in real time；
Fig. 2 is railway traffic engineering construction material risk source method for real-time monitoring flow chart；
Fig. 3 is forecast model running schematic diagram.
Embodiment
Further is made to technical scheme below in conjunction with the accompanying drawings and by embodiment of railway traffic engineering construction
Explanation.
It is that track traffic material risk source monitors schematic diagram in real time as shown in Figure 1, is the great wind of track traffic as shown in Figure 2
Dangerous source method for real-time monitoring flow chart, track traffic material risk source method for real-time monitoring specifically includes following steps：
(1) three dimensional environmental model and BIM construction management platforms are set up using 3DGIS+BIM technologies；
Three dimensional environmental model is comprising in terrain data, urban landscape data, geological information, underground pipeline information and project
Portion's information.GIS-Geographic Information System (GIS) technology, BIM (BIM) technology.
(2) material risk source is monitored, and upload the data to 3DGIS+BIM construction management platforms；
Material risk source data is gathered in real time using automatic monitoring means.
(3) risk source is managed；
Early warning value is set to BIM construction managements platform, when Monitoring Data exceedes early warning value, the automatic bright aobvious alarm of system.
(4) adaptive grey Fourier Index Prediction Model is set up；
Based on gray system theory, improved by background value, particular value conversion and residual GM Technology Integration etc. three
Method, sets up prediction of the adaptive grey Fourier Index Prediction Model realization to material risk source time sequence.
Background value improves the application by integral term, eliminates the error that traditional background value formula is produced；Specific solution conversion is logical
Cross to set up and GMm (1,1) models of concept are minimized and based on reinforcing up-to-date information based on prediction curve and real data deviation
GMn (1,1) model of concept, is fitted prediction curve；Residual GM, which is integrated, passes through Fourier space and exponential smoothing amendment
The periodicity residual error of sequence and randomness residual error, to promote the reliability of forecast model.
(5) engineering construction material risk source time sequence is predicted.
All monitoring results are analyzed and handled based on adaptive grey Fourier Index Prediction Model, to engineering
Construction risk source carries out the real-time estimate of time series.
Step 1 specific implementation method is as follows：
(1.1) terrain data drawing three-dimensional relief model is obtained；
(1.2) urban landscape data drawing three-dimensional city model is passed through；
(1.3) 3DGIS+BIM construction management platforms are set up.
Step 2 specific implementation method is as follows：
(2.1) automatic monitoring data；
By automatic monitoring system, subway tunnel delta data under external force is gathered, data pass through optical fiber or net
The automatic real-time Transmission of network gives BIM construction management platforms.Required monitoring item, monitoring mode and measuring point are laid as shown in table 1.
Table 1
Automatic monitoring system based on platform has following advantage：
1. automate：, can after some Initialize installations and given monitoring plan is carried out (such as observation interval, issue)
Full-automatic observation is strictly performed according to plan, and records initial data automatically.
2. it is intelligent：Realize unattended deformation monitoring, and the energy encountered problems with certain adaptation environment and processing
Power.At a time when some monitoring point is kept off during such as the observation of certain phase, instrument is unable to reading, and software meeting automatic control survey robot is tasted
Examination measurement several times, if still not all right, is voluntarily retried every a period of time again, until observation is normal.
3. ripe data processing method：Locate after being carried out to the initial data of collection according to the data processing model of setting
Reason, removes the various errors introduced in measurement process, and finally fitting reflects the data for best suiting practical distortion.
4. data storage and management：A large amount of original observed datas of collection are carried out using SQL Server database technologys
Storage and management, and data are inquired about and analyzed.
5. various result output：Platform can carry out form to the data after initial data and analyzing and processing, draw deformation
Conditional curve, and the curvilinear figure of drafting can be printed out.
6. automatic real-time early warning, alarm：According to setting limit difference to more than allow limit difference deformation point carry out real-time early warning,
Alarm, and show the value that deformation is transfinited.
Step 3 specific implementation method is as follows：
(3.1) risk source is managed；
The monitoring measurement Con trolling index proposed according to designing unit, by the alert status of monitoring point in work progress by serious
Degree is ascending to be divided into three-level：Yellow monitoring and warning, orange monitoring and warning and red monitoring and warning.
1. yellow monitoring and warning：" dual control " index (variable quantity, rate of change) exceedes the 70% of monitoring measurement controlling value
When, or one of dual control index is when exceeding the 85% of monitoring measurement controlling value；
2. orange monitoring and warning：One of when " dual control " index exceedes the 85% of monitoring measurement controlling value, or dual control index
During more than monitoring measurement controlling value；
3. red monitoring and warning：" dual control " index exceedes monitoring measurement controlling value, or actual measurement rate of change occurs drastically
During growth.
The positional information of monitoring point is added in the 3DGIS+BIM models of structure, can be quick to monitoring point by model
Positioning, when being broken down so as to monitoring device or no longer meeting monitoring accuracy requirement, equipment replacement can be completed in time, it is ensured that monitoring
The authenticity of data.
(3.2) Risk-warning；
By setting early warning line on platform, once Monitoring Data is exceeded, platform can propose alarm automatically.
Warning information list display the monitoring point quantity of each project, red early warning number, orange warning number, yellow early warning
Keep count of, the detailed report of each early warning can be checked.
The specific implementation of step 4 and step 5 is as follows：
By setting up adaptive grey Fourier Index Prediction Model, the reality of time series is carried out to engineering construction risk source
When predict.Model is specifically described below to set up and analysis calculating process.
(1) GM (1,1) modeling and pre- flow gauge
Assuming that there is a time series to be X(0)=(x(0)(1), x(0)(2) ..., x(0)(n)), its be positive number, it is equidistant n when
Between put upper data and constituted.The present invention illustrates GM (1,1) modeling and pre- flow gauge based on this time sequence.
Step 1：To X(0)Generated
Original series X(0)Kenel be probably in disorder, it is difficult to find out the data of its rule of development.In order to which its is implicit
Rule excavates out, can pass through many generation techniques and original in disorder sequence is carried out into one or many generations, to reduce data
Randomness, and it is regular to lift its.Wherein, one-accumulate generation is the most frequently used a kind of technology in GM (1,1) model, and it is transported
It is
Wherein, X(1)=(x(1)(1), x(1)(2) ..., x(1)(n) sequence after once generating) is represented, is represented herein once
Sequence after Accumulating generation.
Step 2：Carry out smooth than examining
To ensure GM (1,1) model modelings and the precision and reliability of prediction, modeling sequence must assure that certain light
Slide than ρ (k), its formula is：
The result of smooth ratio, which need to fall within feasible interval, can just ensure the precision and reliability of modeling and prediction.
Step 3：Level is carried out than examining
Level is than the operational formula of inspection：
Likewise, level also needs to fall within feasible interval than the result of inspection can just ensure the acceptable degree of forecast model.
Step 4：To X(1)Make close to generation
In grey differential equation formula, x(1)(t=k) value will be seated x(1)And x (k-1)(1)(k) (because x between(1)
(t) it is monotonically increasing function), therefore can x(1)(t=k) it will be indicated as：
z(1)(k)=α x(1)(k)+(1-α)x(1)(k-1)
Wherein, α ∈ [0,1].In general, if without particular requirement, the mode close to average generation, i.e. α=0.5 can be adopted.
Step 5：Estimate the parameter of GM (1,1) basic model
The basic conception of GM (1,1) model, is exactly that the sequence X after generation is fitted using grey differential equation(1), with progress after
Continuous modeling and prediction.Grey differential equation is：
Wherein, α is referred to as development coefficient, is referred to as grey input quantity.However, grey differential equation system is continuous function, it is necessary to pass through
By means of the mode in generation, using the shadow equation of grey difference equation, that is, GM (1,1) model, the parameter of grey differential equation is estimated, then
In generation, returns grey differential equation modeling.The grey difference equation of GM (1,1) model is：
x(0)(k)+az(1)(k)=b
In GM (1,1) model, original series and formation sequence are discrete series, it is therefore necessary to by the side by means of generation
Formula, is borrowed for the differential equation with difference equation, and it is borrowed is for mode：
x(1)(t)=z(1)(k)
Then, grey difference equation and least squares method are utilized, you can obtain the estimate of basic model.Order：
It can obtain：
Step 6：Determine grey differential equation and time response formula
After a and the decision of b values, grey differential equation formula is taken back, and ask the general solution of its single order linear differential equation to be：
The primary condition of GM (1,1) model is set in (1, x(1)(1)), can obtain specific solution is：
Also known as time response formula.As 1≤t≤n, the result of gained is referred to as the analogue value；Work as t>During n, its result is referred to as pre-
Measured value.Therefore, GM (1,1) p step predicted values are：
Step 7：Try to achieve X(1)Simulation and forecasting sequence
By X(1)Simulation and forecasting sequence arrange and be：
ArriveFor the simulation assessed value of GM (1,1) model；ArriveFor GM (1,1) mould
The forecast assessment value of type.
Step 8：It is rightCarry out inverse generation in the hope of
, will using the mode of inverse generationIt is reduced toSimulation and forecasting sequence for original series.Reduction
Mode be generating function inverse function.So that one-accumulate is generated as an example, its inverse generation is the method that inverse one-accumulate is generated,
I.e.：
Step 9：Examine error
The error of original series kth point is defined as：
ε=(ε (2), ε (3) ..., ε (n))
For error sequence, error-checking criterion includes：
1. error sum of squares：
S=εTε
2. relative error：
3. average relative error：
(2) background value improves
Background value is the important ring of influence precision of prediction in GM (1,1) arithmetic logic, and the adjustment of wherein α values is background value
Key parameter.It is below the computing flow of improvement GM (1,1) Model Background value：
Because grey differential equation is a kind of exponential function, therefore the present embodiment makes x(1)(t) it is：
x(1)(t)=AeBt+C
Then, formula is substituted into
It can obtain：
T is equal to k, k-1 and k-2 Fen Do and substitutes into formula, can be obtained：
x(1)(k)=AeBk+C
x(1)(k-1)=AeB(k-1)+C
x(1)(k-2)=AeB(k-2)+C
It can be calculated：
B=lnx(0)(k)-lnx(0)(k-1)
B values generation time formula , The can be obtained into A is
A values and B values are substituted into formula, and calculate C values and is：
Finally, B values and C values are substituted into formula, the background value after must can improving is：
In the arithmetic logic that new background value is substituted into GM (1,1) model, you can GM (1,1) model after being improved.
To avoid confusion, alleged GM (1,1) is GM (1,1) model after improving below.
(3) specific solution conversion
It is the running concept for showing GM (1,1) model as shown in Figure 3, wherein, x(1)(1) x is arrived(1)(n) it is original series
One-accumulate formation sequence, is the modeling foundation of GM (1,1) model mainly.It can be seen that in figure, influence the key of precision of prediction
Point is whether forecasting sequence can fall in Probability Area.However, due to the unstability of System Development, GM (1,1) model differs
It is fixed to fall in Probability Area every time.
(4) residual GM Technology Integration
Effectively to grasp the pulsation of System Development, the present embodiment also integrates residual error in addition to improving background value, changing specific solution
Correction technique, including the periodic error of Fourier space (Fourier series) modified grey model is included, and use
Exponential smoothing (exponential smoothing technique) corrects remaining random error, and is named as EFGMm
(1,1) model and EFGMn (1,1) model, to lift the self adaptive of grey prediction.
Assuming that the time series for having a pen data is x(0)=(x(0)(1), x(0)(2) ..., x(0)(n)), the present embodiment is with this
The arithmetic logic of EFGMm (1,1) models and EFGMn (1,1) model is described as follows for target.Due to EFGMm (1,1) models with
Only in the kenel of residual sequence, different (EFGMm (1,1) residual sequence has n to the difference of EFGMn (1,1) model calculation logic
Data, because its not using any specified point as specific solution where；EFGMn (1,1) is then to have n-1, because it using nth point is used as spy
Fixed solution), but the correctness of calculation is had no effect on, elastic it can adjust.For purposes of illustration only, it is following with EFGMm (1,1) model exemplified by.
Step 1：System Development trend is grasped using GMm (1,1), and calculates its residual error；
X is calculated using GMm (1,1) model(0)One-step prediction valueAnd a residual sequence E '(0)For：
E′(0)=(ε '(0)(1),ε′(0)(2),...,ε′(0)(n))
Step 2：Utilize residual sequence of Fourier space amendment；
Now, is extracted using Fourier space and lies in a residual sequence E '(0)In periodicity speciality, that is, pass through
Following equation is fitted a residual sequence E '(0)。
Wherein, T is a residual sequence E '(0)Length, be n-1.Least squares method deploys result：
C=(PTP)-1PTE′(0)
Wherein：
A residual error simulated series, which can be obtained, is：
The concept of Fourier space is a residual sequence ε '(0)Frequency spectrum (frequency spectra) is converted into,
And extract its low-frequency part.In addition, Fourier space can sift out high-frequency part (being the part of noise), for one
The simulation of secondary residual sequence has good effect, and can obtain quadratic residue sequence E "(0)For：
E″(0)=(ε "(0)(2),ε″(0)(3),...,ε″(0)(n))
Step 3：Utilization index exponential smoothing amendment quadratic residue sequence；
Now, utilization index exponential smoothing, which is extracted, lies in quadratic residue sequence E "(0)In randomness speciality, that is, pass through
Following equation fitting quadratic residue sequence E "(0)
Wherein, φ is smoothing factor (smoothing coefficient), and 0<φ<1.
The present embodiment tries to achieve the smoothing factor value minimum with quadratic residue sequence error by optimized technology herein, i.e.,
Solve the minimum value of following formula.
It can be further broken into：
Therefore, function can will be optimized to be write as：
After obtaining optimum value, you can try to achieve the one-step prediction value of quadratic residue sequence, be：
Finally combine above correction value, you can the one-step prediction value for trying to achieve original series is：
Above-described algorithm is a circulation, through the technology of rolling, can constantly be predicted and update the system
The pulsation of development.
The present embodiment sets up two new-type Grey Prediction Model , Fen Do for GMm (1,1) models and GMn (1,1) model.
(1) GMm (1,1) model
GMm (1,1) model is to be solved using least squares method based on simulated series and model the pre- of sequence deviation minimum
Model is surveyed, this mode is not with X(1)On any component as primary condition, but entered with the optimized angle of series model precision
Row prediction, has suitable benefit to the lifting of precision of prediction.
The calculation logic of GMm (1,1) model is as follows, coefficient value to be ordered is set into unknown herein, and set up following calculating formula：
Represented in the way of matrix, be：
Order：
Therefore, the estimate that can calculate c using least squares method is：
WillGeneration, which returns formula and can obtain the time response formula of GMm (1,1) model, is：
Therefore, the one-step prediction value of original series is：
Through the running of above-mentioned formula, you can obtain the grey forecasting model minimized based on modeling data deviation.
(2) GMn (1,1) model
Traditional GM (1,1) model is with sequence X(1)One-component as gray model primary condition, it is but this
Mode is to the utilization of fresh information and insufficient.If can be with X(1)N-th of component as primary condition, then the utilization of fresh information is just
Can be more abundant, the trend of sequence is grasped can be more accurate, and prediction curve can be also substantially improved and falls probability in Probability Area.
In consideration of it, GMn (1,1) model is then that specific solution is set in into (n, x(1)(n) on), that is, one newest in simulated series
Numerical value.
The calculation logic of GMn (1,1) model is as follows.By primary condition (n, x(1)(n) the general of grey differential equation formula) is substituted into
In solution, you can the time response formula for obtaining GMn (1,1) model is：
Therefore, the 1 step predicted value that also can obtain GMn (1,1) model is：
Therefore, X can further be derived(0)1 step predicted value be：
According to the running of above-mentioned formula, you can the gray model in update based on primary condition is obtained, to lift ash
Grasp ability of the color model to future trend.
GM (1,1) models have been formally the forecast model with reliability and practicality on numerous documents,.However,
It should also be appreciated that GM (1,1) models are unsatisfactory for the prediction effect of non-stationary system developmental sequence from document.By to passing
The improvement of GM (1,1) Model Background value calculation of uniting, and the specific solution of conversion fall within the probability of Probability Area to increase, after improvement
GMm (1,1) models or GMn (1,1) models grasp ability for the trend of turning point and also increase therewith, then be aided with residual GM
Etc. technology, then the error of turning point can be effectively corrected, to promote the precision of prediction of material risk source Monitoring Data.
(5) engineering construction material risk source time sequence is predicted.
All monitoring results are analyzed and handled based on adaptive grey Fourier Index Prediction Model, to engineering
Construction risk source carries out the real-time estimate of time series.When predicted value exceedes early warning value, the automatic bright aobvious alarm of system.
Claims (7)
1. a kind of engineering construction risk source based on 3DGIS+BIM technologies is monitored and artificial intelligence Forecasting Methodology in real time, its feature
It is：Specifically include following steps：
(1) three dimensional environmental model and BIM construction management platforms are set up using 3DGIS+BIM technologies；
(2) automatic data collection engineering construction risk source data, and upload to BIM construction management platforms；
(3) early warning value is set in BIM construction managements platform；
(4) adaptive grey Fourier Index Prediction Model is set up；
(5) real-time estimate of time series is carried out to engineering construction risk source monitoring data.
2. the engineering construction risk source according to claim 1 based on 3DGIS+BIM technologies is monitored and artificial intelligence in real time
Forecasting Methodology, it is characterised in that：The step 1 is specifically included：
(1.1) terrain data drawing three-dimensional relief model is obtained；
(1.2) urban landscape data drawing three-dimensional city model is passed through；
(1.3) 3DGIS+BIM construction management platforms are set up.
3. the engineering construction risk source according to claim 1 based on 3DGIS+BIM technologies is monitored and artificial intelligence in real time
Forecasting Methodology, it is characterised in that：The step 2 is specifically included：
(2.1) by automatic monitoring system, structure and the real time data of environment under external force are gathered, 3DGIS+ is returned
BIM construction management platforms.
4. the engineering construction risk source according to claim 1 based on 3DGIS+BIM technologies is monitored and artificial intelligence in real time
Forecasting Methodology, it is characterised in that：The step 3 is specifically included：
(3.1) risk source data storehouse is set up, 3DGIS+BIM models is imported into, includes in system administration, grade is carried out to risk source
Assess；
(3.2) early warning line is set on platform, and Monitoring Data is exceeded can automatic alarm.
5. the engineering construction risk source according to claim 1 based on 3DGIS+BIM technologies is monitored and artificial intelligence in real time
Forecasting Methodology, it is characterised in that：Set up adaptive grey Fourier Index Prediction Model and the time is carried out to engineering construction risk source
The real-time estimate of sequence, specifically includes following steps：
(1) modeling and pre- flow gauge；
(2) background value improves；
(3) specific solution conversion；
(4) residual GM Technology Integration.
(5) the automatic bright aobvious alarm in Risk-warning corresponding position in 3DGIS+BIM models.
6. the engineering construction risk source according to claim 5 based on 3DGIS+BIM technologies is monitored and artificial intelligence in real time
Forecasting Methodology, it is characterised in that：The forecast model using least squares method solve based on simulated series with modeling sequence from
Difference is minimized, and is not used as primary condition using any component of sequence.
7. the engineering construction risk source according to claim 5 based on 3DGIS+BIM technologies is monitored and artificial intelligence in real time
Forecasting Methodology, it is characterised in that：The forecast model is used as primary condition using n-th of component of sequence.
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| CN115167212B (en) * | 2022-07-13 | 2023-09-26 | 中交第三航务工程局有限公司 | Dynamic construction control system and method for foundation pit based on monitoring platform |
| CN116363600A (en) * | 2023-06-01 | 2023-06-30 | 深圳恒邦新创科技有限公司 | Method and system for predicting maintenance operation risk of motor train unit |
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