CN110598860B - Multi-station online wave cycle data prediction diagnosis method - Google Patents
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Abstract
The invention discloses a multi-station online wave cycle data prediction diagnosis method, which comprises a space-time sample test training stage and a space-time model dynamic calculation adjusting stage, wherein the space-time sample test training stage comprises two processes of time model test training and space model test training; the dynamic calculation and adjustment stage of the space-time model comprises four processes of mixed calculation diagnosis of a time model and a space model, weight adjustment of the space-time model, dynamic adjustment of the time model and dynamic adjustment of the space model; the time model adopts an RBF neural network model, the space model adopts a linear neural network model, and the method disclosed by the invention increases the space mutual analysis among a plurality of stations on the basis of the self-analysis of the single-station time sequence data, effectively combines the single-station time analysis and the multi-station space analysis, and improves the precision and the accuracy of the predictive diagnosis.
Description
Technical Field
The invention relates to the field of ocean wave monitoring, in particular to a multi-station online wave period data prediction and diagnosis method.
Background
At present, aiming at online wave cycle data monitored by a marine wave monitoring single station, only simple sending and receiving verification in a sea-land communication process can be carried out to judge whether a communication link has data errors. The existing online data receiving software can only simply judge the data range (such as more than or equal to 0 and less than 30 seconds), and cannot perform substantial diagnosis on specific data. The method aims at the defects of the prediction and diagnosis method of whether the data measured by the marine wave meter is abnormal or not, and the abnormal and vacant data of the marine wave meter cannot be timely and effectively found and supplemented, and the targeted judgment and identification are needed manually.
The existing data prediction diagnosis technology mainly comprises an autoregressive moving average model, a neural network model and the like, and the neural network model for time series prediction mainly comprises a linear neural network, a BP neural network, an RBF neural network and the like.
Most of the existing autoregressive moving average models and neural network models are used for processing existing data, and the models are often static and unchangeable or complex and time-consuming, so that the model preparation rate is low or the running speed is low, real-time updating of data in the online monitoring process is not considered, abnormal data need to be timely and effectively judged in the online ocean monitoring process, and the data in the absence of time need to be timely predicted and supplemented.
Meanwhile, marine monitoring data among a plurality of marine monitoring stations with similar space geographic distances have correlation, however, the correlation of the data among the plurality of stations is not considered in the prior art, so that the contact among the stations is not strong, and the data analysis is not complete and accurate enough.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method for predicting and diagnosing multi-station online wave cycle data, which aims to achieve the purposes of increasing the spatial mutual analysis among a plurality of stations, effectively combining the single-station time analysis and the multi-station spatial analysis and improving the precision and the accuracy of predictive diagnosis on the basis of the self-analysis of single-station time sequence data.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a multi-station online wave cycle data prediction diagnosis method comprises a space-time sample test training stage and a space-time model dynamic calculation adjusting stage, wherein the space-time sample test training stage comprises a time model test training stage and a space model test training stage; the dynamic calculation and adjustment stage of the space-time model comprises four processes of mixed calculation diagnosis of a time model and a space model, weight adjustment of the space-time model, dynamic adjustment of the time model and dynamic adjustment of the space model; the time model adopts an RBF neural network model, and the space model adopts a linear neural network model.
In the above scheme, the method for predicting diagnosis specifically includes the following steps:
(1) Training by using time samples to obtain an RBF neural network initial time model, and training by using space samples to obtain a linear neural network initial space model;
(2) Loading a trained RBF neural network initial time model and a trained linear neural network initial space model, performing actual prediction calculation one by one according to the latest time sequence input and space sequence input of real-time ocean waves obtained by a wave sensor to respectively obtain a time prediction result and a space prediction result, and obtaining a space-time comprehensive prediction result according to the time prediction result and the space prediction result by weighting calculation;
(3) Judging whether the data is abnormal or not by using the obtained space-time comprehensive prediction result and the actual measurement result at the next moment, and supplementing a predicted value as a current value if the data at the current moment is lack of measurement or the data is abnormal; and taking the measured value judged to be the effective value as the latest data to form a new time sample and a new space sample, and respectively using the new time sample and the new space sample for retraining and adjusting the RBF neural network dynamic time model and the retraining and adjusting the linear neural network dynamic space model, and performing dynamic prediction diagnosis on the data in the continuous training and adjusting process of the model.
In a further technical scheme, in the step (1), training of the initial time model of the RBF neural network includes the following processes:
selecting wave period data of a single station as a processing training sample set, and setting training parameters of the RBF neural network time model, wherein the training parameters comprise the number of initial input layers, the number of hidden layer nodes and iteration precision; the testability training determines the number of input layers, the number of training samples and the number of hidden layer central nodes; and training to obtain an initial time model of the RBF neural network.
In a further technical solution, in the step (1), the training of the linear neural network initial space model includes the following processes:
1) Selecting N space monitoring stations, and normalizing wave period data of each station to obtain a space sample set;
2) Selecting 1 station P as an object for space model calculation, and using the rest N-1 station data as the input of a linear neural network;
3) Performing testability training by using different numbers of training samples, and determining a spatial model Ms which enables the training precision to be highest;
4) The number of samples corresponding to Ms is Np, and Np is the number of training samples during dynamic adjustment of the spatial model.
According to the technical scheme, the multi-station online wave periodic data prediction diagnosis method provided by the invention integrates the time and space characteristics of marine monitoring data, adopts an RBF neural network model for the time sequence data of a single station, adopts a linear neural network model for the space distribution data of a plurality of stations, and performs weighted calculation on the time calculation result and the space calculation result to form a final prediction value. And using the final prediction value for real-time diagnosis of the ocean online monitoring activity data. The invention has the following beneficial effects:
1. neural networks have advantages over traditional models
The neural network has strong nonlinear mapping capability. The method can complete highly complex input and output nonlinear mapping, and has good nonlinear fitting capability, which is incomparable with the traditional prediction method based on the least square method.
The neural network fitting result is more accurate than the mathematical model fitting result. The ARMA model requires that a time sequence is stable, so that logarithm is firstly taken and then difference is made on a data set during modeling, the establishment of a modeling neural network model is complex and troublesome, and a regular approximation real model can be found through training under the condition that the neural network does not know an internal mathematical model.
2. High operation speed, high adaptability and high accuracy
The RBF neural network time model has high operation speed and the dynamic learning and adjusting process of the model is high; the linear neural network space model has the advantages that the number and the scale of the spatial data point positions determine that the input of the space model is small, the calculation speed is high, the amount of dynamic training samples is small, and the high dynamic training speed of the linear neural network space model is guaranteed.
In the actual marine monitoring data diagnosis process, new data are continuously fed back to the learning and adjustment of the RBF neural network time model and the linear neural network space model along with the arrival of the new data, so that the whole calculation model has strong adaptability, and the effectiveness and the accuracy of data diagnosis in long-term marine online monitoring activities are ensured.
3. Temporal and spatial characteristics of ocean surveillance data are effectively combined
The space-time analysis is combined, the problem that the deviation of a simulation calculation result is large when the distance between a single station time sequence and a sample set and a central set is large is solved, and the problem that the error of a space analysis result is generally large due to the fact that the space factor is only emphasized in the single multi-station space analysis is also solved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.
Fig. 1 is a schematic diagram illustrating a principle of a method for predicting and diagnosing multi-station online wave cycle data according to an embodiment of the present invention;
FIG. 2 is a flowchart of a time model sample testing training process according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating a spatial model sample testing training process according to an embodiment of the present invention;
fig. 4 is a schematic flowchart (shown in two pages) illustrating a specific process of a multi-station online wave cycle data prediction diagnosis method according to an embodiment of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
The invention provides a method for predicting and diagnosing multi-station online wave cycle data, which comprises the following specific embodiments as shown in figure 1:
the method for predicting and diagnosing the multi-station online wave cycle data comprises a space-time sample test training stage and a space-time model dynamic calculation adjusting stage, wherein the space-time sample test training stage comprises a time model test training stage and a space model test training stage; the dynamic calculation and adjustment stage of the space-time model comprises four processes of mixed calculation diagnosis of the time model and the space model, weight adjustment of the space-time model, dynamic adjustment of the time model and dynamic adjustment of the space model; the time model adopts an RBF neural network model, and the space model adopts a linear neural network model.
1. Time-space sample test training phase
And training by using the time samples to obtain an RBF neural network initial time model, and training by using the space samples to obtain a linear neural network initial space model.
1. Time model test training, as shown in fig. 2:
1) And selecting representative sample data (30 days) with continuous single-station monitoring time, and dividing the sample data into a training sample and a testing sample, wherein the two types of samples are 15 days respectively.
2) The sample data is processed into data sequences with the length of 1 hour and equal time intervals, namely 360 groups of training samples and 360 groups of test samples, and normalization processing is carried out (the numerical range is processed to be between-1, 1).
3) The number of training samples is set to be 200, the iteration precision is set to be 0.005, the maximum iteration number is 500, the iteration step length is set to be 0.01, and the clustering segmentation factor is set to be 0.85 when the RBF neural network time model is trained.
4) And respectively training and testing the number of input layers of the RBF neural network time model by taking 24, 36, 48 and 60 as the number of input layers of the RBF neural network time model, obtaining the number of the input layers corresponding to the model with the best test accuracy, recording the number as Ni, and taking the number of the input layers of the model as Ni.
5) And (5) training and testing by using different training sample numbers with 10 as step length between [200 and 360], recording the training sample number corresponding to the model with the best test accuracy as Ns, and taking the training sample number as Ns.
6) And (3) training and testing by using different segmentation factors with 0.01 as a step length between [0.5 and 2], obtaining the segmentation factor corresponding to the model with the best test accuracy and marking as pp, wherein the number of the corresponding hidden layer central nodes is Nt when the value of the clustering segmentation factor is taken as pp.
7) And training to obtain an initial time model Mt of the RBF neural network by using the number of input layers as Ni, the number of samples as Ns, the iteration precision as 0.005, the maximum iteration number as 500, the iteration step length as 0.01 and the number of nodes of the hidden layer as Nt (a clustering partition factor is pp).
2. The training phase of the spatial model test is shown in fig. 3:
1) N (N > 3) ocean monitoring stations with similar spatial positions are selected, and representative sample data (3 days) with continuous monitoring time is selected for each station.
2) The sample data is processed into data sequences with the length of 1 hour and equal time intervals, namely 120 groups, and normalization processing is carried out (the numerical range is processed to be between-1, 1).
3) Selecting 1 station P from N ocean monitoring stations as an object of linear neural network space model calculation of on-line wave period data, and taking wave period data of each moment of the rest N-1 stations as N-1 inputs of a linear neural network.
4) And (3) training and testing by using different training sample numbers with 1 as a step length between [2 and 72], so as to obtain a model (containing various parameters) Ms with the best test accuracy, wherein the training sample number corresponding to the Ms is recorded as Np. In the dynamic adjustment calculation stage of the spatial model, the number of dynamic training samples is Np.
2. The spatio-temporal model dynamically calculates the adjustment phase (taking P monitoring station as an example), as shown in fig. 4:
in the dynamic calculation and adjustment stage of the space-time model, the pre-trained time model, space model and space-time model weight coefficient of the P station are used as actual calculation diagnosis. In the actual monitoring activity of the ocean waves of the P station, new wave period data are continuously obtained, time model calculation and space model calculation are continuously carried out, a prediction result is calculated according to a space-time weight coefficient, an actual measurement value and a predicted value are compared, whether the actual measurement value is an effective value or not is judged, if the actual measurement value is not measured or is judged to be an invalid value, the predicted value is used as a current effective value, and if the actual measurement value is judged to be an effective value, the actual measurement value is used for dynamic feedback adjustment of the space-time weight, the time model and the space model, so that the latest data characteristics and the change rule are continuously adapted. And during the P-station ocean wave monitoring activity, the space-time model calculation and adjustment are carried out at each monitoring moment until the P-station ocean wave monitoring activity is finished.
The whole process comprises the following steps:
1) And starting to perform space-time model dynamic calculation and adjustment when the P station has continuous Ni effective monitoring values. And loading an RBF neural network initial time model Mt and a linear neural network initial space model Ms, recording the initial time sample set of Mt as S, and then the sample number of S is the optimal time sample number Ns obtained in the training stage of the time model.
2) The number of the new samples of the time model is set to 0, namely Nc =0.
3) And finding the latest Np monitoring moments when all the N station bit data are complete and effective according to the current moment. Np measured values Ca for P-site; respectively calculating Np time predicted values Ct of the P station positions by using Mt, wherein the time predicted value at each moment t is obtained by inputting Ns continuous effective values before t (not containing t) into the Mt for calculation; respectively calculating Np space predicted values Cs of the P stations by using Ms, wherein the space predicted value of each time t is obtained by inputting N-1 effective values of N-1 stations (not containing P stations) at the time t into the Ms.
4) When Cs is identical to Ct for all values, take W =0.5; when Cs and Ct are not identical, calculating to obtain optimal W within the range of [0,1], and enabling the distance between W and Cs plus (1-W) Ct and Ca to be minimum, wherein Cs, ct and Ca all contain Np values, and the monitoring time of corresponding values among Cs, ct and Ca is identical.
5) tn records the current latest data time. Inputting effective values of Ni latest moments before tn (without tn) of the P station position into the Mt to calculate a time prediction result Rt, and finally predicting a value Rp = Rt. And judging whether other N-1 stations except P have data missing at the tn moment, and turning to 7) if the data missing exists.
6) And inputting the N-1 monitoring values of the rest N-1 stations (without P stations) at the time tn into Ms to calculate a spatial prediction value Rs. And calculating a space-time predicted value Rp = W Rs + (1-W) Rt.
7) Checking and judging whether the P station position has wave period actual measurement data at the new time tn, if the wave period actual measurement data is lacked, using Rp as a P station position tn time effective value, and turning to 21); if there is measured data, it is recorded as Ra, if abs (Rp-Ra) >0.25, the measured value is not valid, and Rp is used as the effective value of P station tn, and the process goes to 21).
8) And judging whether other N-1 stations except P have data missing at the tn moment, and turning to 13) if the data missing exists. Ra replaced the oldest data in Ca; rt replaces the longest time data in the Ct; rs replaced the oldest time data in Cs.
9) When Cs is identical to Ct for all values, take W =0.5; when Cs and Ct are not identical, recalculating the optimal space-time weight Wn in the range of [0,1] to minimize the distance between Wn Cs + (1-Wn) Ct and Ca, wherein Cs, ct and Ca all contain Np values, and the monitoring time of corresponding values among Cs, ct and Ca is identical. Adjust W to W = W0.5 + wn 0.5.
10 N-1 data of N-1 stations except P at the time tn are taken as space model sample input, and Ra is taken as space model sample output to form the latest space sample B.
11 All N stations take the historical values of Np-1 latest time, except at time tn. The missing number of time instants is checked and removed from the Np-1 time instant data sets, plus B, to form Nps spatial sample sets (Nps > = 1).
12 Nps spatial samples are used to train the linear neural network spatial model, obtain a new spatial model and update the replacement Ms.
13 Ni latest time data except tn time of P station are used as time model sample input, ra is used as time model sample output, and the latest time sample a is formed.
14 Calculate the shortest distance DisS between the sample input of A and each of the sample inputs of S. Go to 21 if dis < Ni 0.005).
15 Add a to the time sample set S, let Nc = Nc +1; if Nc > = Ni 0.3, go to 18).
16 Compute the minimum distance DisT between the sample input of a and each central node in T, and compute the minimum distance DisM between each central node in T. When DisT > DisM 0.7 and DisS > DisM 0.5, the sample input of A is used as a new center to add T, the oldest sample Sh is found from the time sample set, the center with the minimum distance to the sample input of Sh is found from T and deleted, and the number of the centers of T is kept unchanged.
17 Sh is removed from the time sample set S and a transition is made to 20).
18 Find the oldest sample Sh from S, remove Sh.
19 Find the oldest sample S from the current S 0 All samples in S are taken as distance S 0 The distances of (D) are sorted from small to large (S) 0 First sample) and use L 0 ,L 1 ,...,L Ns-1 Represents the sorted samples and S 0 Calculating a distance difference D 1 ＝L 1 -L 0 ，D 2 ＝L 2 -L 1 ,…,D Ns-1 ＝L Ns-1 -L Ns-2 . To D 1 ,D 2 ,...,D Ns-1 Taking Nt maximum values, and comparing with the Nt valuesThe set of the corresponding Nt sample inputs serves as a new set of central nodes T.
20 According to the latest time sample set and the hidden layer center set T, recalculating the time model output weight Wd, and updating and adjusting the time model Mt.
21 Go to 22) if the ocean wave monitoring activity is over); otherwise, waiting for the next time to arrive, and going to 5).
22 The P-station spatio-temporal model dynamic computational diagnostic process is ended.
In the above process steps, the hybrid computation diagnosis process of the temporal model and the spatial model includes steps 3) -7); the dynamic space-time weight adjusting process comprises steps 8) and 9); the multi-station space model training and adjusting process comprises steps 10) -12); the single-site time model dynamic adjustment process comprises steps 13) -20).
Wherein, in steps 4) and 9), when Cs and Ct are not identical, calculating the optimal W within the range of [0,1] to minimize the distance between W and Cs plus (1-W) Ct and Ca, and the method comprises the following steps:
(1) recording Np numerical values of n = Np and Cs as x1, x2, \ 8230, xn according to the monitoring time sequence; np numerical values of Ct are y1, y2, \8230;, yn according to the sequence of monitoring time; np values of Ca are z1, z2, \8230, zn according to the monitoring time sequence.
(2) Solving for W that minimizes the distance between W and Cs + (1-W) Ct, i.e. solving for
The smallest W.
③
④
There is a minimum value for the quadratic polynomial.
(5) Get
Judging whether the W value is [0,1]]In the range, when W is less than 0, takeW =0; when W > 1, take W =1.
On-line ocean wave cycle data is continuously obtained at fixed time intervals of 10 minutes, 30 minutes, 60 minutes and the like, and 100% transmission efficiency is difficult to guarantee when the data is transmitted to a software processing system on land from the sea by processes of wave sensors, data packaging, data wireless transmission, data unpacking and the like.
When a multi-station space model is calculated, the model input of a certain station is the monitoring value of other stations at the same time, and when any station data is missing at the present time, the calculation of the space model can not be carried out. And establishing an RBF neural network time model for each station, diagnosing and filling abnormal and missing data, and using complete and continuous data of each station for calculation of a linear neural network space model so as to further perform mixed calculation of the time model and the space model.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (2)
1. A multi-station online wave cycle data prediction diagnosis method is characterized by comprising a space-time sample test training stage and a space-time model dynamic calculation adjusting stage, wherein the space-time sample test training stage comprises a time model test training stage and a space model test training stage; the dynamic calculation and adjustment stage of the space-time model comprises four processes of mixed calculation diagnosis of a time model and a space model, weight adjustment of the space-time model, dynamic adjustment of the time model and dynamic adjustment of the space model; the time model adopts an RBF neural network model, and the space model adopts a linear neural network model;
the method for predictively diagnosing specifically comprises the following processes:
(1) Training by using time samples to obtain an RBF neural network initial time model, and training by using space samples to obtain a linear neural network initial space model;
(2) Loading a trained RBF neural network initial time model and a trained linear neural network initial space model, performing actual prediction calculation one by one according to the latest time sequence input and space sequence input of real-time ocean waves obtained by a multi-station wave sensor to respectively obtain a time prediction result and a space prediction result, and obtaining a space-time comprehensive prediction result according to the time prediction result and the space prediction result by weighting calculation;
(3) Judging whether the data is abnormal or not by using the obtained space-time comprehensive prediction result and the actual measurement result at the next moment, and supplementing a predicted value as a current value if the data at the current moment is lack of measurement or the data is abnormal; the measured value which is judged to be the effective value is used as the latest data to form a new time sample and a new space sample which are respectively used for retraining and adjusting the RBF neural network dynamic time model and retraining and adjusting the linear neural network dynamic space model, and dynamic predictive diagnosis of the data is carried out in the continuous training and adjusting of the model;
in the step (1), the training of the linear neural network initial space model comprises the following processes:
1) Selecting N space monitoring stations, and normalizing wave period data of each station to obtain a space sample set;
2) Selecting 1 station P as an object of space model calculation, and using the rest N-1 station data as the input of a linear neural network;
3) Performing testability training by using different numbers of training samples, and determining a spatial model Ms which enables the training precision to be highest;
4) The number of samples corresponding to Ms is Np, and Np is the number of training samples during dynamic adjustment of the spatial model.
2. The method for predictively diagnosing multi-site online wave cycle data as set forth in claim 1, wherein the training of the initial time model of the RBF neural network in step (1) comprises the following steps:
selecting wave period data of a single station as a processing training sample set, and setting training parameters of an RBF neural network time model, wherein the training parameters comprise the number of initial input layers, the number of nodes of a hidden layer and iteration precision; the testability training determines the number of input layers, the number of training samples and the number of hidden layer central nodes; and training to obtain an initial time model of the RBF neural network.
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