CN114970982B - Prediction method and system for inhaul cable temperature extreme value - Google Patents
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Abstract
The invention discloses a prediction method and a prediction system for a guy cable temperature extremum, which are characterized in that a training set is constructed by collecting a plurality of temperature data and corresponding meteorological data of the guy cable under a plurality of different meteorological conditions and using the plurality of temperature data and the corresponding meteorological data; constructing a prediction model which takes a plurality of meteorological data as input quantity and temperature data of a inhaul cable as output quantity, and training the prediction model by using training data in a training set; acquiring a weather time sequence of a target inhaul cable in a target period, and sequentially inputting weather data in the weather time sequence into the prediction model to obtain a temperature time sequence corresponding to the target inhaul cable in the target period; and performing extremum fitting on the temperature data of the temperature time sequence to obtain a temperature extremum of the target inhaul cable in a target period. The invention can accurately measure the temperature extremum of the inhaul cable, thereby providing a foundation for the safety monitoring of the cable-stayed bridge.
Description
Technical Field
The invention relates to the field of bridge engineering, in particular to a prediction method and a prediction system for a temperature extremum of a inhaul cable.
Background
The stay cable is a main stress component of the cable-stayed bridge, and the main girder deformation caused by the temperature change of the stay cable has an important influence on the safe operation of the cable-stayed bridge. The temperature extreme value of the inhaul cable is accurately predicted, the design work of the cable-stayed bridge is facilitated, and the safe operation of the cable-stayed bridge is ensured.
In the current design specification, the regulation about the temperature extremum of the cable-stayed bridge cable is not clear. In the prior art, few studies are performed on the temperature extreme value of the inhaul cable. In general, a long-term and large amount of observation data is required to determine the extreme value of structural load, and a method for predicting and calculating the temperature extreme value of the inhaul cable by using the observation data of a short time is also lacking in the prior art. The technology combines long-term meteorological data by means of a machine learning method, expands and obtains a multi-year inhaul cable temperature data set on the basis of actually measured inhaul cable temperature data, and has good innovation application value.
Disclosure of Invention
The invention provides a prediction method and a prediction system for a temperature extremum of a inhaul cable, which are used for solving the technical problem that the temperature extremum of the inhaul cable cannot be accurately measured in the prior art.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
a prediction method of a guy cable temperature extremum comprises the following steps:
acquiring a plurality of temperature data and corresponding meteorological data of a guy cable under a plurality of different meteorological conditions, and constructing a training set by using the plurality of temperature data and the corresponding meteorological data;
constructing a prediction model which takes a plurality of meteorological data as input quantity and temperature data of a inhaul cable as output quantity, and training the prediction model by using training data in a training set;
Acquiring a weather time sequence of a target inhaul cable in a target period, and sequentially inputting weather data in the weather time sequence into the prediction model to obtain a temperature time sequence corresponding to the target inhaul cable in the target period;
And performing extremum fitting on the temperature data of the temperature time sequence to obtain a temperature extremum of the target inhaul cable in a target period.
Preferably, the weather data includes: atmospheric temperature, wind speed, wind direction, solar radiation; the temperature data comprise average values of temperatures at a plurality of different positions on the same axial section of the inhaul cable.
Preferably, a prediction model with a plurality of meteorological data as input quantity and stay cable temperature data as output quantity is constructed, and the prediction model is realized through GBDT (Gradient Boosting Decision Tree, gradient lifting tree) algorithm, the GBDT algorithm adopts a gradient descent method to train parameters, and the objective function of gradient descent is as follows:
Wherein f m (x) represents a learner obtained by the mth round of iteration, x i represents the ith training data, n represents the total number of training data, and f (x i) represents a predicted value of the learner on the ith training data; representing learning objectives of the ith training data.
Preferably, the temperature data of the temperature time sequence is subjected to extremum fitting by adopting a maximum entropy method, and a constraint condition formula adopted by the maximum entropy method is as follows:
Wherein: d is an integration interval; x is a random variable, and f (x) is a random variable probability density function; g i (x) is a function of x; m is the order of the moment used; m i is the i-th order random variable origin moment.
Preferably, the temperature data of the temperature time sequence is subjected to extremum fitting to obtain a temperature extremum of the target inhaul cable in a target period, and the method specifically comprises the following steps:
inputting the meteorological data in the meteorological time sequence into the prediction model for multiple times to obtain a temperature time sequence predicted for multiple times;
performing extremum fitting on each predicted temperature time sequence to obtain each predicted temperature extremum;
and obtaining an average value of the temperature extreme values predicted for a plurality of times as a final temperature extreme value.
A computer system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method described above when the computer program is executed.
The invention has the following beneficial effects:
1. According to the prediction method and the prediction system for the temperature extremum of the inhaul cable, a training set is constructed by collecting a plurality of temperature data and corresponding meteorological data of the inhaul cable under a plurality of different meteorological conditions and using the plurality of temperature data and the corresponding meteorological data; constructing a prediction model which takes a plurality of meteorological data as input quantity and temperature data of a inhaul cable as output quantity, and training the prediction model by using training data in a training set; acquiring a weather time sequence of a target inhaul cable in a target period, and sequentially inputting weather data in the weather time sequence into the prediction model to obtain a temperature time sequence corresponding to the target inhaul cable in the target period; and performing extremum fitting on the temperature data of the temperature time sequence to obtain a temperature extremum of the target inhaul cable in a target period. The invention can accurately measure the temperature extremum of the inhaul cable, thereby providing a foundation for the safety monitoring of the cable-stayed bridge.
2. In the preferred scheme, the invention trains GBDT models by utilizing the cable temperature actually measured data in a short period of time, obtains cable temperature data for many years by combining long-term historical meteorological data, and then fits cable temperature extremum distribution by a maximum entropy method, thereby further improving the accuracy of cable temperature extremum measurement.
In addition to the objects, features and advantages described above, the present invention has other objects, features and advantages. The invention will be described in further detail with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
FIG. 1 is a flow chart of a method for predicting a cable temperature extremum in accordance with a preferred embodiment of the present invention.
Detailed Description
Embodiments of the invention are described in detail below with reference to the attached drawings, but the invention can be implemented in a number of different ways, which are defined and covered by the claims.
Embodiment one:
The embodiment discloses a prediction method of a inhaul cable temperature extremum, which comprises the following steps:
acquiring a plurality of temperature data and corresponding meteorological data of a guy cable under a plurality of different meteorological conditions, and constructing a training set by using the plurality of temperature data and the corresponding meteorological data;
constructing a prediction model which takes a plurality of meteorological data as input quantity and temperature data of a inhaul cable as output quantity, and training the prediction model by using training data in a training set;
Acquiring a weather time sequence of a target inhaul cable in a target period, and sequentially inputting weather data in the weather time sequence into the prediction model to obtain a temperature time sequence corresponding to the target inhaul cable in the target period;
And performing extremum fitting on the temperature data of the temperature time sequence to obtain a temperature extremum of the target inhaul cable in a target period.
In addition, in the present embodiment, a computer system is also disclosed, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the above method when executing the computer program.
According to the prediction method and the prediction system for the temperature extremum of the inhaul cable, a training set is constructed by collecting a plurality of temperature data and corresponding meteorological data of the inhaul cable under a plurality of different meteorological conditions and using the plurality of temperature data and the corresponding meteorological data; constructing a prediction model which takes a plurality of meteorological data as input quantity and temperature data of a inhaul cable as output quantity, and training the prediction model by using training data in a training set; acquiring a weather time sequence of a target inhaul cable in a target period, and sequentially inputting weather data in the weather time sequence into the prediction model to obtain a temperature time sequence corresponding to the target inhaul cable in the target period; and performing extremum fitting on the temperature data of the temperature time sequence to obtain a temperature extremum of the target inhaul cable in a target period. The invention can accurately measure the temperature extremum of the inhaul cable, thereby providing a foundation for the safety monitoring of the cable-stayed bridge.
Embodiment two:
The second embodiment is a preferred embodiment of the first embodiment, which is different from the first embodiment in that the specific steps of the prediction method for the temperature extremum of the inhaul cable are introduced, and specifically includes the following steps:
The cable-stayed bridge mainly comprises a stressed member, wherein the temperature change of a stay cable can lead the stay cable to be stretched or shortened, so that the girder is deformed, and the safe operation of a high-speed train is threatened. The reasonable determination of the temperature extreme value is beneficial to the development of cable-stayed bridge design work, and the reasonable and safe operation of the cable-stayed bridge of the high-speed railway can be ensured.
As shown in FIG. 1, the cable temperature extremum prediction method of the invention comprises the following steps:
A1. Acquiring inhaul cable temperature data in a period of time;
in the step A1, a cable segment test is carried out somewhere, five measuring points are arranged on the section of a cable, and the average value of the measuring points is taken as the temperature of the cable. The cable temperature is collected on line in real time, and the cable temperature is uploaded to the cloud platform once every half an hour.
A2. Collecting meteorological data and temperature data in a corresponding time period to form a sample set, and constructing a characteristic project;
in step A2, the meteorological data includes atmospheric temperature, wind speed, wind direction, solar radiation. The sample set is composed of measured cable temperature and collected weather data over a corresponding period of time. The sample set also requires a preprocessing process to exclude samples with missing information in the sample items. The sample set is divided into a training set and a test set, and the training set and the test set are divided according to the proportion of 80% and 20%. Construction of a feature engineering is an important step in building a machine learning model, the feature engineering of which includes the atmospheric temperature over the past 48 hours, the wind speed over the past 48 hours, the solar radiation over the past 48 hours, and time-dependent features. The time-related features include two parts, namely, the number of days (the day of the year) and the number of time sequences (the time of day)
The dataset characterization engineering is as follows:
A3. Training a model by adopting GBDT algorithm, and putting the collected long-term historical meteorological data into the model for training to obtain stay cable long-term temperature data;
in the step A3, a GBDT algorithm is adopted to train a model, and the collected long-term historical meteorological data are put into the model for training to obtain the stay cable long-term temperature data. The GBDT algorithm, also known as the integrated decision tree algorithm, is an algorithm that fits multiple decision trees in series. The decision process of the decision tree algorithm is as follows:
The GBDT algorithm fits the decision tree with a gradient descent algorithm, i.e., the learner that each iteration generates learns a negative gradient with a target equal to the loss function L (F m (x), y).
Wherein: -representing a learning objective of the mth round of iterative learner corresponding to the i-th input;
F m-1(xi), which is the prediction output of the m-1 th round of iterative learner to x i;
L (F m-1(xi),yi), which represents a loss function used for measuring the error between the model output and the real result, common loss functions include a mean square loss function, an exponential loss function and the like.
Fitting by the mth learnerAn objective function consisting of learning the objective function in a manner that minimizes the mean square error:
Wherein: f j (x) the learner obtained by the j-th round of stacking;
Representing the learning objective.
The optimal parameters of GBDT model are obtained by using gridding searching method. After the model is trained, years of historical meteorological data are put into the model, and years of inhaul cable temperature data are obtained.
A4. Performing extremum fitting on the temperature data of the inhaul cable for many years by adopting maximum entropy release;
A5. And (3) repeating the steps A3 and A4 to obtain the average value of the fitting parameters of the extremum distribution, and obtaining the temperature extremum with probability guarantee meaning of the inhaul cable.
In the step A4, the maximum entropy method is adopted to carry out extremum fitting on the temperature data of the multi-year inhaul cable. The principle of the maximum entropy method is as follows: when the unknown probability distribution is inferred after partial information is grasped, a proper constraint condition is selected, and the probability distribution with the maximum entropy is selected, namely:
The constraint condition formula adopted is as follows:
Wherein: d is an integration interval;
gi (x) is a function of x;
m is the order of the moment used;
mi is the moment of origin of the ith order random variable.
And (3) introducing a Lagrangian multiplier to solve the extremum under the constraint condition, and enabling the Lagrangian multiplier to:
let L to f (x) be offset equal to zero, and the arrangement can be obtained:
If g i(x)＝xi is taken, namely moment constraint is taken, let:
The maximum entropy probability density function is thus obtained as:
the distribution function of the maximum entropy model obtainable according to equation (7) is:
Solving the undetermined lagrangian multiplier λi (i=1, 2, …, m) results in the probability density function being obtained. The lagrangian multiplier can be solved by adopting a Newton iteration method, and the solving equation is as follows:
λ(k)＝λ(k-1)-(J(k))-1j(k)
In A5, repeating A3 and A4 to obtain the average value of the extremum distribution fitting parameters, and obtaining the temperature extremum with probability guarantee meaning of the inhaul cable. In the GBDT model, there is a parameter of sub-sampling rate, i.e. part of the training set data is selected for training, which makes the model have a certain randomness. In order to ensure the stability of a calculation result, the historical meteorological data of years are put into a model for training for many times, extremum distribution fitting is carried out on the cable temperature of years obtained each time, the average of fitting distribution parameters is taken as a final value, and finally the extremum with probability guarantee significance on the cable temperature is obtained.
In summary, the cable temperature extremum can be accurately measured by training GBDT models by utilizing cable temperature actually measured data in a short period of time, obtaining cable temperature data of many years by combining long-term historical meteorological data, and fitting cable temperature extremum distribution by a maximum entropy method, so that a foundation is provided for safety monitoring of a cable-stayed bridge.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (3)
1. The prediction method of the inhaul cable temperature extreme value is characterized by comprising the following steps of:
acquiring temperature data of a inhaul cable, collecting meteorological data corresponding to the temperature data of the inhaul cable in time, and constructing a training set by using the temperature data and the corresponding meteorological data;
constructing a prediction model which takes a plurality of meteorological data as input quantity and temperature data of a inhaul cable as output quantity, and training the prediction model by using training data in a training set;
Acquiring a weather time sequence of a target inhaul cable in a target period, and sequentially inputting weather data in the weather time sequence into the prediction model to obtain a temperature time sequence corresponding to the target inhaul cable in the target period;
Performing extremum fitting on the temperature data of the temperature time sequence to obtain a temperature extremum of the target inhaul cable in a target period;
the meteorological data includes: atmospheric temperature, wind speed, wind direction, solar radiation; the temperature data comprise average values of temperatures at a plurality of different positions on the same axial section of the inhaul cable;
The method comprises the steps of constructing a prediction model which takes a plurality of meteorological data as input quantity and stay cable temperature data as output quantity, and realizing the prediction model through GBDT algorithm, wherein the GBDT algorithm adopts a gradient descent method to train parameters, and an objective function of gradient descent is as follows:
Wherein f m (x) represents a learner obtained by the mth round of iteration, x i represents the ith training data, n represents the total number of training data, and f (x i) represents a predicted value of the learner on the ith training data; a learning object representing the ith training data;
Performing extremum fitting on the temperature data of the temperature time sequence to obtain a temperature extremum of the target inhaul cable in a target period, wherein the method specifically comprises the following steps of:
inputting the meteorological data in the meteorological time sequence into the prediction model for multiple times to obtain a temperature time sequence predicted for multiple times;
performing extremum fitting on each predicted temperature time sequence to obtain each predicted temperature extremum;
and obtaining an average value of the temperature extreme values predicted for a plurality of times as a final temperature extreme value.
2. The method for predicting the temperature extremum of the inhaul cable according to claim 1, wherein the extremum fitting is performed on the temperature data of the temperature time sequence by adopting a maximum entropy method, and a constraint condition formula adopted by the maximum entropy method is as follows:
Wherein D is an integration interval; x is a random variable, and f (x) is a random variable probability density function; g i (x) is a function of x; m is the order of the moment used; m i is the i-th order random variable origin moment.
3. A computer system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any of the preceding claims 1 to 2 when the computer program is executed.
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