CN114528629A - Data baseline determination method and device and computer equipment - Google Patents
Data baseline determination method and device and computer equipment Download PDFInfo
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Abstract
The application relates to a method and a device for determining a data baseline and computer equipment. The method comprises the following steps: acquiring a bridge fluctuation data set in a target time period; the bridge fluctuation data set of the target time interval comprises bridge fluctuation data at each moment in the time sequence of the target time interval; dividing each bridge fluctuation data through a data self-adaptive segmented processing network to obtain each fluctuation data group; determining a bridge fluctuation baseline of each fluctuation data set according to each fluctuation data set and a two-dimensional probability density algorithm; and determining the bridge fluctuation baseline in the target time period according to the bridge fluctuation baseline of each fluctuation data set. By adopting the method, the accuracy of the acquired bridge fluctuation baseline can be improved.
Description
Technical Field
The application relates to the technical field of bridge measurement and calculation, in particular to a method and a device for determining a data baseline and computer equipment.
Background
Through research on data such as bridge health monitoring dynamic strain and deflection, the influence of multiple factors such as structural temperature (static state), vehicles (quasi-static state) and environmental noise on a bridge fluctuation baseline is found. In order to investigate the influence of different factors on the fluctuation condition of the bridge, the fluctuation data of the bridge is acquired in a target time period, and the fluctuation baseline of the bridge is determined by a bridge fluctuation baseline determination method according to the fluctuation data of the bridge.
At present, a method for determining a bridge fluctuation baseline commonly adopted in the industry obtains bridge fluctuation data in a bridge monitoring mode, and calculates all fluctuation data integrally to obtain the bridge fluctuation baseline. However, when the fluctuation range of the data is large, the baseline determination effect of the above algorithm is easily affected by the local peak, so that the overall bridge fluctuation baseline is shifted, and the accuracy of the acquired bridge fluctuation baseline is low.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a method, an apparatus and a computer device for determining a data baseline.
In a first aspect, the present application provides a method for determining a data baseline. The method comprises the following steps:
acquiring a bridge fluctuation data set in a target time period; the bridge fluctuation data set of the target time interval comprises bridge fluctuation data of each moment in the target time interval;
dividing each bridge fluctuation data through a data self-adaptive segmented processing network to obtain each fluctuation data group;
determining a bridge fluctuation baseline of each fluctuation data set according to each fluctuation data set and a two-dimensional probability density algorithm;
and determining the bridge fluctuation baseline in the target time period according to the bridge fluctuation baseline of each fluctuation data set.
Optionally, the data adaptive segmented processing network includes an adaptive segmented network and an evaluation network, and the dividing the bridge fluctuation data by the data adaptive segmented processing network to obtain each fluctuation data group includes:
determining each initial fluctuation data set and parameter values of the adaptive segmented network according to each bridge fluctuation data and the adaptive segmented network;
determining an evaluation value of each initial fluctuation data group according to each initial fluctuation data group and the evaluation network;
the parameter values of the adaptive segmented network corresponding to the initial fluctuation data set with the evaluation value smaller than the evaluation threshold value are listed in a tabu table of the adaptive segmented network, and the steps of determining each initial fluctuation data set and the parameter values of the adaptive segmented network according to each bridge fluctuation data and the adaptive segmented network are returned to be executed until all the initial fluctuation data sets with the evaluation values larger than the evaluation threshold value are determined;
and taking each initial fluctuation data set with the evaluation value larger than the evaluation threshold value as each fluctuation data set.
Optionally, the determining a bridge fluctuation baseline of each fluctuation data set according to each fluctuation data set and a two-dimensional probability density algorithm includes:
and aiming at each fluctuation data group, calculating by a two-dimensional probability density algorithm according to the fluctuation data group to obtain a bridge fluctuation baseline of the fluctuation data group.
Optionally, the determining a bridge fluctuation baseline in a target time period according to the bridge fluctuation baselines of each fluctuation data set includes:
selecting a bridge fluctuation baseline of the fluctuation data sets of the first time sequence according to the bridge fluctuation baseline of each fluctuation data set;
determining a new bridge fluctuation baseline according to the bridge fluctuation baseline of the fluctuation data set of the first time sequence and the bridge fluctuation baseline of the adjacent fluctuation data set; the new bridge fluctuation baseline represents the bridge fluctuation baseline of the fluctuation data group of the first time sequence and the adjacent fluctuation data group;
and taking the new bridge fluctuation baseline as the bridge fluctuation baseline of the fluctuation data set of the first time sequence, returning to execute the step of determining the new bridge fluctuation baseline according to the bridge fluctuation baseline of the fluctuation data set of the first time sequence and the bridge fluctuation baseline of the adjacent fluctuation data set until the new bridge fluctuation baseline represents the bridge fluctuation baseline of the bridge fluctuation data set of the target time period, and taking the new bridge fluctuation baseline as the bridge fluctuation baseline of the target time period.
Optionally, the determining a bridge fluctuation baseline in a target time period according to the bridge fluctuation baselines of each fluctuation data set includes:
arranging the bridge fluctuation baselines of each fluctuation data set according to a time sequence;
and calculating by a smoothing algorithm according to the arranged bridge fluctuation baselines of each fluctuation data set to obtain the bridge fluctuation baselines in the target time period.
Optionally, the method further includes:
acquiring a bridge fluctuation data set in a sample time interval and each sample data group in the sample time interval;
and inputting the bridge fluctuation data set in the sample time period and each sample data group in the sample time period into the initial data adaptive segmented processing network, and training the initial data adaptive segmented processing network to obtain the data adaptive segmented processing network.
In a second aspect, the present application further provides a device for determining a data baseline. The device comprises:
the first acquisition module is used for acquiring a bridge fluctuation data set in a target time period; the bridge fluctuation data set of the target time interval comprises bridge fluctuation data of each moment in the target time interval;
the first determining module is used for dividing each bridge fluctuation data through a data self-adaptive segmented processing network to obtain each fluctuation data group;
the second determining module is used for determining the bridge fluctuation base line of each fluctuation data group according to each fluctuation data group and a two-dimensional probability density algorithm;
and the third determining module is used for determining the bridge fluctuation baseline in the target time period according to the bridge fluctuation baselines of all the fluctuation data sets.
Optionally, the data adaptive segmentation processing network includes an adaptive segmentation network and an evaluation network, and the first determining module is specifically configured to:
determining each initial fluctuation data set and parameter values of the adaptive segmented network according to each bridge fluctuation data and the adaptive segmented network;
determining an evaluation value of each initial fluctuation data group according to each initial fluctuation data group and the evaluation network;
the parameter values of the adaptive segmented network corresponding to the initial fluctuation data set with the evaluation value smaller than the evaluation threshold value are listed in a tabu table of the adaptive segmented network, and the steps of determining each initial fluctuation data set and the parameter values of the adaptive segmented network according to each bridge fluctuation data and the adaptive segmented network are returned to be executed until all the initial fluctuation data sets with the evaluation values larger than the evaluation threshold value are determined;
and taking each initial fluctuation data set with the evaluation value larger than the evaluation threshold value as each fluctuation data set.
Optionally, the second determining module is specifically configured to:
and aiming at each fluctuation data group, calculating by a two-dimensional probability density algorithm according to the fluctuation data group to obtain a bridge fluctuation baseline of the fluctuation data group.
Optionally, the third determining module is specifically configured to:
selecting a bridge fluctuation baseline of the fluctuation data sets of the first time sequence according to the bridge fluctuation baseline of each fluctuation data set;
determining a new bridge fluctuation baseline according to the bridge fluctuation baseline of the fluctuation data set of the first time sequence and the bridge fluctuation baseline of the adjacent fluctuation data set; the new bridge fluctuation baseline represents the bridge fluctuation baseline of the fluctuation data group of the first time sequence and the adjacent fluctuation data group;
and taking the new bridge fluctuation baseline as the bridge fluctuation baseline of the fluctuation data set of the first time sequence, returning to execute the step of determining the new bridge fluctuation baseline according to the bridge fluctuation baseline of the fluctuation data set of the first time sequence and the bridge fluctuation baseline of the adjacent fluctuation data set until the new bridge fluctuation baseline represents the bridge fluctuation baseline of the bridge fluctuation data set of the target time period, and taking the new bridge fluctuation baseline as the bridge fluctuation baseline of the target time period.
Optionally, the third determining module is specifically configured to:
arranging the bridge fluctuation baselines of each fluctuation data set according to a time sequence;
and calculating by a smoothing algorithm according to the arranged bridge fluctuation baselines of each fluctuation data set to obtain the bridge fluctuation baselines in the target time period.
Optionally, the apparatus further comprises:
the second acquisition module is used for acquiring the bridge fluctuation data set in the sample time interval and each sample data group in the sample time interval;
and the training module is used for inputting the bridge fluctuation data set in the sample period and each sample data group in the sample period into the initial data adaptive segmented processing network, and training the initial data adaptive segmented processing network to obtain the data adaptive segmented processing network.
In a third aspect, the present application provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of the method of any of the first aspects when the processor executes the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium. On which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any one of the first aspects.
In a fifth aspect, the present application provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, performs the steps of the method of any one of the first aspects.
According to the data baseline determination method, the data baseline determination device and the computer equipment, a bridge fluctuation data set in a target time period is obtained; the bridge fluctuation data set of the target time interval comprises bridge fluctuation data at each moment in the time sequence of the target time interval; dividing each bridge fluctuation data through a data self-adaptive segmented processing network to obtain each fluctuation data group; determining a bridge fluctuation baseline of each fluctuation data set according to each fluctuation data set and a two-dimensional probability density algorithm; and determining the bridge fluctuation baseline in the target time period according to the bridge fluctuation baseline of each fluctuation data set. The bridge fluctuation baselines of all the fluctuation data sets are obtained by segmenting the bridge fluctuation data set in the target time period and inputting the segmented fluctuation data sets into a probability density algorithm respectively, and the bridge fluctuation baselines of all the fluctuation data sets are combined to determine the bridge fluctuation baselines in the target time period, so that the accuracy of the obtained bridge fluctuation baselines is improved.
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FIG. 1 is a schematic flow chart diagram illustrating a method for determining a baseline for data in one embodiment;
FIG. 2 is a schematic diagram of a displacement-time coordinate system in one embodiment;
FIG. 3 is a flow chart illustrating the dividing step of the wave data group in one embodiment;
FIG. 4 is a schematic flow chart diagram illustrating the training steps of the data adaptive segmentation processing network in one embodiment;
FIG. 5 is a flow chart illustrating a method for determining a baseline of data in another embodiment;
FIG. 6 is a block diagram of an apparatus for determining a data baseline in one embodiment;
FIG. 7 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The method for determining the data baseline provided by the embodiment of the application can be applied to a terminal, a server, a system comprising the terminal and the server, and can be realized through interaction of the terminal and the server. The terminal may include, but is not limited to, various personal computers, notebook computers, tablet computers, and the like. The terminal is used for segmenting the bridge fluctuation data set in the target time period, inputting each segmented fluctuation data set into a probability density algorithm respectively to obtain a bridge fluctuation baseline of each fluctuation data set, combining the bridge fluctuation baselines of each fluctuation data set and determining the bridge fluctuation baseline in the target time period.
In one embodiment, as shown in fig. 1, a method for determining a data baseline is provided, which is described by taking the method as an example for a terminal, and includes the following steps:
and step S101, acquiring a bridge fluctuation data set in a target time interval.
The bridge fluctuation data set of the target time interval comprises bridge fluctuation data of each moment in the target time interval.
In this embodiment, the terminal acquires each bridge fluctuation data at the target time interval through the bridge monitoring sensor, and uses all the bridge fluctuation data at the target time interval as the bridge fluctuation data set at the target time interval. The target time interval may be, but is not limited to, acquired by the terminal in response to an operation of the user autonomously selecting the target time interval.
The bridge detection sensor can be any sensor capable of acquiring bridge fluctuation data. The bridge fluctuation data is two-dimensional displacement information of the bridge at a certain moment (the two-dimensional displacement information is represented as a straight line distance between the bridge vertical displacement information and the bridge horizontal displacement information and a coordinate origin in a two-dimensional coordinate system), and the two-dimensional displacement information of the bridge can be embodied in a displacement-time coordinate system, as shown in fig. 2, the abscissa axis of the displacement-time coordinate system is time, the ordinate axis is displacement distance, and any point in the coordinate system represents the two-dimensional displacement information of the bridge at a certain moment.
And S102, dividing the fluctuation data of each bridge through a data self-adaptive segmented processing network to obtain each fluctuation data group.
In this embodiment, the terminal divides the fluctuation data of each bridge into each fluctuation data group through a data adaptive segment processing network. The fluctuation data sets are all bridge fluctuation data contained in a certain interval time period, each bridge fluctuation data contained in the fluctuation data sets is continuous in a target time period, and the bridge fluctuation data contained in the two fluctuation data sets are different; the interval time periods corresponding to the fluctuation data groups can be different in length; all the bridge fluctuation data can be divided into at least 2 fluctuation data groups.
For example, all the bridge fluctuation data are a1, a2, a3, a4, a5, a6, a7, a8, a9 and a10, and the terminal divides each bridge fluctuation data into three fluctuation data groups through a data adaptive segmentation processing network, wherein the first fluctuation data group is a1, a2 and a 3; the second fluctuation data group is a4, a 5; the third fluctuation data set is a6, a7, a8, a9, a 10. The specific division process will be described later in detail.
And S103, determining the bridge fluctuation base line of each fluctuation data set according to each fluctuation data set and a two-dimensional probability density algorithm.
In this embodiment, the terminal calculates each fluctuation data group by a two-dimensional probability density algorithm to obtain a bridge fluctuation baseline corresponding to each fluctuation data group. The bridge fluctuation baseline is a mimicry baseline of two-dimensional displacement information of the bridge in a certain period of time, the mimicry baseline can be expressed by a linear function, and the formula of the linear function is as follows:
y＝kx+c
in the above formula, x is a certain time of the bridge in a certain time period, y is two-dimensional displacement information of the bridge at the time, k is a slope of the mimicry datum line, and c is a parameter affecting the mimicry datum line. The specific calculation process will be described later in detail.
And step S104, determining the bridge fluctuation base line in the target time period according to the bridge fluctuation base lines of each fluctuation data set.
In the embodiment, the terminal arranges the bridge fluctuation baselines of each fluctuation data set in a displacement-time coordinate system, and then eliminates unsmooth connection points among the bridge fluctuation baselines of each fluctuation data set to obtain a smooth bridge fluctuation baseline; and the terminal takes the smooth bridge fluctuation baseline as a bridge fluctuation baseline in a target time period. The specific calculation process will be described later.
Based on the scheme, the bridge fluctuation data sets in the target time interval are segmented, the segmented fluctuation data sets are input into a probability density algorithm respectively to obtain the bridge fluctuation baselines of the fluctuation data sets, the bridge fluctuation baselines of the fluctuation data sets are combined to determine the bridge fluctuation baselines in the target time interval, and therefore the accuracy of the obtained bridge fluctuation baselines is improved.
Optionally, as shown in fig. 3, the data adaptive segmentation processing network includes an adaptive segmentation network and an evaluation network, and the data of each bridge fluctuation is divided by the data adaptive segmentation processing network to obtain each fluctuation data group, where the method includes:
step S301, determining each initial fluctuation data set and parameter values of the adaptive segmented network according to each bridge fluctuation data and the adaptive segmented network.
In this embodiment, the terminal divides each bridge fluctuation data into each initial fluctuation data group by the adaptive segment network according to each bridge fluctuation data, and obtains a parameter value of the adaptive segment network during the division operation. The working principle of the self-adaptive segmented network is that all bridge fluctuation data are divided into fluctuation data groups of different interval time intervals according to a time sequence. The adaptive segmented network can be any multi-data stream adaptive segmented algorithm which can realize the steps, and the parameter values of the adaptive segmented network are different during each iterative operation.
Step S302, determining the evaluation value of each initial fluctuation data group according to each initial fluctuation data group and the evaluation network.
In this embodiment, the terminal calculates, for each initial fluctuation data group, through the evaluation network according to each bridge fluctuation data in the initial fluctuation data group, to obtain an evaluation value of the fluctuation data group. The evaluation network may be, but is not limited to, a standard deviation algorithm, and the evaluation value of the wave data set is a standard deviation value of each bridge wave data included in the initial wave data set.
Step S303, the parameter values of the adaptive segmented network corresponding to the initial fluctuation data set with the evaluation value smaller than the evaluation threshold are listed in a taboo table of the adaptive segmented network, and the steps of determining each initial fluctuation data set and the parameter values of the adaptive segmented network according to each bridge fluctuation data and the adaptive segmented network are returned to be executed until all the initial fluctuation data sets with the evaluation values larger than the evaluation threshold are determined.
In the embodiment, the terminal stores the evaluation threshold value in advance, and selects the initial fluctuation data group with the evaluation value smaller than the evaluation threshold value from each fluctuation data group; the parameter values of the adaptive segmented network corresponding to the initial fluctuation data sets with evaluation values smaller than the evaluation threshold value are listed in a tabu table of the adaptive segmented network, and the step S301 is executed again; and when the terminal executes the operation, the adaptive segmented network is controlled to select parameter values except for the tabu table until the evaluation values of all the initial fluctuation data groups are greater than the evaluation threshold value, and the iterative operation is stopped.
In step S304, the initial fluctuation data group in which each evaluation value is larger than the evaluation threshold is set as each fluctuation data group.
In this embodiment, the terminal sets, as the respective fluctuation data groups, the initial fluctuation data groups each having an evaluation value larger than an evaluation threshold.
Based on the scheme, the terminal obtains each fluctuation data group through the data self-adaptive segmented processing network, so that the accuracy of calculating the fluctuation data of each bridge in different time periods is improved.
Optionally, determining a bridge fluctuation baseline of each fluctuation data set according to each fluctuation data set and a two-dimensional probability density algorithm, including: and aiming at each fluctuation data group, calculating by a two-dimensional probability density algorithm according to the fluctuation data group to obtain a bridge fluctuation baseline of the fluctuation data group.
In this embodiment, the terminal calculates, for each data group, each bridge fluctuation data of the fluctuation data group by using a two-dimensional probability density algorithm, to obtain a bridge fluctuation baseline of the fluctuation data group. The range of the bridge fluctuation baseline is consistent with the range of the interval time period corresponding to the fluctuation data set. The two-dimensional probability density algorithm may be, but is not limited to, a two-dimensional Basis Function (RBF). For example: the interval time period corresponding to a certain fluctuation data set is [ t, t +30], and then the calculation formula of the bridge fluctuation baseline corresponding to the fluctuation data set is as follows:
y＝kx+c，x∈(t，t+30)
in the above formula, x is a certain time of the bridge in the time period, y is two-dimensional displacement information of the bridge at the time, k is a slope of the fluctuation baseline of the bridge, and c is a parameter affecting the fluctuation baseline of the bridge.
Optionally, determining a bridge fluctuation baseline in a target time period according to the bridge fluctuation baselines of each fluctuation data set, including: selecting a bridge fluctuation baseline of the fluctuation data sets of the first time sequence according to the bridge fluctuation baseline of each fluctuation data set; determining a new bridge fluctuation baseline according to the bridge fluctuation baseline of the fluctuation data set of the first time sequence and the bridge fluctuation baseline of the adjacent fluctuation data set; the new bridge fluctuation baseline represents the bridge fluctuation baseline of the fluctuation data group of the first time sequence and the adjacent fluctuation data group; and taking the new bridge fluctuation baseline as the bridge fluctuation baseline of the fluctuation data set of the first time sequence, returning and executing the bridge fluctuation baseline of the fluctuation data set according to the first time sequence and the bridge fluctuation baseline of the adjacent fluctuation data set, determining the new bridge fluctuation baseline until the new bridge fluctuation baseline represents the bridge fluctuation baseline of the bridge fluctuation data set of the target time period, and taking the new bridge fluctuation baseline as the bridge fluctuation baseline of the target time period.
In this embodiment, the terminal sorts each fluctuation data group according to a time sequence, and selects a fluctuation data group arranged at the first position in the time sequence; and obtaining a new bridge fluctuation baseline through a smoothing processing algorithm according to the bridge fluctuation baseline corresponding to the fluctuation data set and the bridge fluctuation baseline adjacent to the fluctuation data set (namely the second-position fluctuation data set arranged in the time sequence), wherein the new bridge fluctuation baseline can represent the first-position fluctuation data set and each bridge fluctuation data of the fluctuation data sets adjacent to the fluctuation data set.
And the terminal combines the first fluctuation data group with the second fluctuation data group to obtain a new fluctuation data group, uses the new fluctuation data group as the first fluctuation data group (the bridge fluctuation baseline corresponding to the first fluctuation data group is the new bridge fluctuation baseline), and returns to execute the steps until the obtained new bridge fluctuation baseline can represent all fluctuation data groups in the target time period. And the terminal takes the bridge fluctuation base line of all the fluctuation data sets which can represent the target time interval as the bridge fluctuation base line of the target time interval.
The smoothing algorithm may be, but is not limited to, an averaging algorithm, and the specific calculation process is to average slopes corresponding to two bridge fluctuation baselines, and also average parameters affecting the bridge fluctuation baselines, so as to obtain a linear function relation of the new bridge fluctuation baselines. For example: the bridge fluctuation baseline of the A fluctuation data set is as follows: k1x + c 1; the bridge fluctuation baseline of the B fluctuation data set is as follows: and y is k2x + c2, the fluctuation baselines of the new bridge corresponding to the two fluctuation data sets are:
y＝k3x+c3
k3＝(k1+k2)/2
c3＝(c1+c2)/2
in the above formula, k1, k2 and k3 are the slopes of the fluctuation baselines of the bridges, and c1, c2 and c3 are the parameters of the fluctuation baselines of the bridges.
Based on the scheme, the bridge fluctuation base line in the target time period is determined by the bridge fluctuation base line of each fluctuation data set, so that the accuracy of the obtained bridge fluctuation base line is higher.
Optionally, determining a bridge fluctuation baseline in a target time period according to the bridge fluctuation baselines of each fluctuation data set, including: arranging the bridge fluctuation baselines of each fluctuation data set according to a time sequence; and calculating by a smoothing algorithm according to the arranged bridge fluctuation baselines of each fluctuation data set to obtain the bridge fluctuation baselines in the target time period.
In this embodiment, the terminal may arrange each fluctuation data set in a displacement-time coordinate system according to a time sequence, and obtain a bridge fluctuation baseline of each unsmooth fluctuation data set in the coordinate system. And the terminal carries out smoothing treatment on the bridge fluctuation base line of each fluctuation data set through a smoothing treatment algorithm, and integrates and treats each bridge fluctuation base line into a smooth bridge fluctuation base line. And the terminal takes the combined bridge fluctuation baseline as a bridge fluctuation baseline in a target time period.
Based on the scheme, the bridge fluctuation base line in the target time period is determined by the bridge fluctuation base line of each fluctuation data set, so that the accuracy of the obtained bridge fluctuation base line is higher.
Optionally, as shown in fig. 4, the method further includes:
step S401, a bridge fluctuation data set of a sample time interval and each sample data group of the sample time interval are obtained.
In this embodiment, the terminal obtains the bridge fluctuation data set at the sample time interval and each sample data at the sample time interval. The specific processing procedure of this step can refer to the related explanation of step S101, which is not described herein again.
Step S402, inputting the bridge fluctuation data set in the sample time interval and each sample data group in the sample time interval into an initial data adaptive segmented processing network, and training the initial data adaptive segmented processing network to obtain the data adaptive segmented processing network.
In this embodiment, the terminal inputs the bridge fluctuation data set at the sample time interval and each sample data group at the sample time interval into the initial data adaptive segmented processing network, and trains the initial data adaptive segmented processing network to obtain the data adaptive segmented processing network.
Based on the scheme, the initial data adaptive segmented processing network is trained through the acquired bridge fluctuation data set in the sample time period and each sample data group in the sample time period to obtain the data adaptive segmented processing network, and a basis is provided for subsequently operating the bridge fluctuation data set in the target time period.
The present application further provides an example of determining a data baseline, as shown in fig. 5, a specific processing procedure includes the following steps:
step S501, a bridge fluctuation data set in a target time interval is obtained.
The bridge fluctuation data set of the target time interval comprises bridge fluctuation data of each moment in the time sequence of the target time interval.
Step S502, determining each initial fluctuation data group and parameter values of the adaptive segmented network according to each bridge fluctuation data and the adaptive segmented network.
Step S503, determining the evaluation value of each initial fluctuation data group according to each initial fluctuation data group and the evaluation network.
In step S504, it is determined whether or not there is an evaluation value of the initial fluctuation data group smaller than the evaluation threshold.
If yes, the parameter value of the adaptive segmented network corresponding to the initial fluctuation data set smaller than the evaluation threshold value is listed in a taboo table of the adaptive segmented network, and the step S502 is returned to be executed; if not, step S505 is performed.
In step S505, the initial fluctuation data group in which each evaluation value is larger than the evaluation threshold is set as each fluctuation data group.
And S506, calculating by a two-dimensional probability density algorithm according to the fluctuation data groups aiming at each fluctuation data group to obtain a bridge fluctuation baseline of the fluctuation data group.
And step S507, selecting the bridge fluctuation baseline of the fluctuation data sets of the first time sequence according to the bridge fluctuation baseline of each fluctuation data set.
And step S508, determining a new bridge fluctuation baseline according to the bridge fluctuation baseline of the fluctuation data set of the first time sequence and the bridge fluctuation baseline of the adjacent fluctuation data set.
Wherein the new bridge fluctuation baseline represents the bridge fluctuation baseline of the fluctuation data set of the first time series and the adjacent fluctuation data set.
Step S509, determine whether the new bridge fluctuation baseline represents a bridge fluctuation baseline of the bridge fluctuation data set in the target time period.
If yes, go to step S510; if not, taking the new bridge fluctuation baseline as the bridge fluctuation baseline of the fluctuation data set of the first time sequence, and returning to execute the step S508.
And step S510, taking the new bridge fluctuation baseline as a bridge fluctuation baseline in a target time period.
It should be understood that, although the steps in the flowcharts related to the embodiments as described above are sequentially displayed as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.
Based on the same inventive concept, the embodiment of the present application further provides a data baseline determination device for implementing the above-mentioned data baseline determination method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so specific limitations in one or more embodiments of the device for determining a data baseline provided below can be referred to the limitations of the method for determining a data baseline in the foregoing description, and are not described herein again.
In one embodiment, as shown in fig. 6, there is provided a data baseline determination apparatus, including: a first obtaining module 610, a first determining module 620, a second determining module 630, and a third determining module 640, wherein:
a first obtaining module 610, configured to obtain a bridge fluctuation data set in a target time period; the bridge fluctuation data set of the target time interval comprises bridge fluctuation data at each moment in the time sequence of the target time interval;
the first determining module 620 is configured to divide each of the bridge fluctuation data by a data adaptive segmentation processing network to obtain each fluctuation data group;
a second determining module 630, configured to determine a bridge fluctuation baseline of each fluctuation data set according to each fluctuation data set and a two-dimensional probability density algorithm;
and a third determining module 640, configured to determine a bridge fluctuation baseline in a target time period according to the bridge fluctuation baseline of each fluctuation data set.
Optionally, the data adaptive segmentation processing network includes an adaptive segmentation network and an evaluation network, and the first determining module 620 is specifically configured to:
determining each initial fluctuation data set and parameter values of the adaptive segmented network according to each bridge fluctuation data and the adaptive segmented network;
determining the evaluation value of each initial fluctuation data group according to each initial fluctuation data group and the evaluation network;
the parameter values of the self-adaptive segmented network corresponding to the initial fluctuation data set with the evaluation value smaller than the evaluation threshold value are listed in a taboo table of the self-adaptive segmented network, and the steps of determining each initial fluctuation data set and the parameter values of the self-adaptive segmented network according to each bridge fluctuation data and the self-adaptive segmented network are returned to be executed until all the initial fluctuation data sets with the evaluation values larger than the evaluation threshold value are determined;
and taking the initial fluctuation data group with each evaluation value larger than the evaluation threshold value as each fluctuation data group.
Optionally, the second determining module 630 is specifically configured to:
and aiming at each fluctuation data group, calculating by a two-dimensional probability density algorithm according to the fluctuation data group to obtain a bridge fluctuation baseline of the fluctuation data group.
Optionally, the third determining module 640 is specifically configured to:
selecting a bridge fluctuation baseline of the fluctuation data sets of the first time sequence according to the bridge fluctuation baseline of each fluctuation data set;
determining a new bridge fluctuation baseline according to the bridge fluctuation baseline of the fluctuation data set of the first time sequence and the bridge fluctuation baseline of the adjacent fluctuation data set; the new bridge fluctuation baseline represents the bridge fluctuation baseline of the fluctuation data group of the first time sequence and the adjacent fluctuation data group;
and taking the new bridge fluctuation baseline as the bridge fluctuation baseline of the fluctuation data set of the first time sequence, returning and executing the bridge fluctuation baseline of the fluctuation data set according to the first time sequence and the bridge fluctuation baseline of the adjacent fluctuation data set, determining the new bridge fluctuation baseline until the new bridge fluctuation baseline represents the bridge fluctuation baseline of the bridge fluctuation data set of the target time period, and taking the new bridge fluctuation baseline as the bridge fluctuation baseline of the target time period.
Optionally, the third determining module 640 is specifically configured to:
arranging the bridge fluctuation baselines of each fluctuation data set according to a time sequence;
and calculating by a smoothing algorithm according to the arranged bridge fluctuation baselines of each fluctuation data set to obtain the bridge fluctuation baselines in the target time period.
Optionally, the apparatus further comprises:
the second acquisition module is used for acquiring the bridge fluctuation data set in the sample time interval and each sample data group in the sample time interval;
and the training module is used for inputting the bridge fluctuation data set in the sample period and each sample data group in the sample period into the initial data adaptive segmented processing network, and training the initial data adaptive segmented processing network to obtain the data adaptive segmented processing network.
The various modules in the data baseline determination apparatus described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 7. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a method of determining a data baseline. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on a shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 7 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the above method embodiments when executing the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
In an embodiment, a computer program product is provided, comprising a computer program which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
It should be noted that, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), Magnetic Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the various embodiments provided herein may be, without limitation, general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, or the like.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above examples only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.
Claims (10)
1. A method for determining a data baseline, the method comprising:
acquiring a bridge fluctuation data set in a target time period; the bridge fluctuation data set of the target time interval comprises bridge fluctuation data of each moment in the target time interval;
dividing each bridge fluctuation data through a data self-adaptive segmented processing network to obtain each fluctuation data group;
determining a bridge fluctuation baseline of each fluctuation data set according to each fluctuation data set and a two-dimensional probability density algorithm;
and determining the bridge fluctuation baseline in the target time period according to the bridge fluctuation baseline of each fluctuation data set.
2. The method of claim 1, wherein the data adaptive segmentation processing network comprises an adaptive segmentation network and an evaluation network, and the dividing each bridge wave data into wave data groups by the data adaptive segmentation processing network comprises:
determining each initial fluctuation data set and parameter values of the adaptive segmented network according to each bridge fluctuation data and the adaptive segmented network;
determining an evaluation value of each initial fluctuation data group according to each initial fluctuation data group and the evaluation network;
the parameter values of the adaptive segmented network corresponding to the initial fluctuation data set with the evaluation value smaller than the evaluation threshold value are listed in a tabu table of the adaptive segmented network, and the steps of determining each initial fluctuation data set and the parameter values of the adaptive segmented network according to each bridge fluctuation data and the adaptive segmented network are returned to be executed until all the initial fluctuation data sets with the evaluation values larger than the evaluation threshold value are determined;
and taking each initial fluctuation data set with the evaluation value larger than the evaluation threshold value as each fluctuation data set.
3. The method of claim 1, wherein determining a bridge fluctuation baseline for each of the fluctuation data sets based on each of the fluctuation data sets and a two-dimensional probability density algorithm comprises:
and aiming at each fluctuation data group, calculating by a two-dimensional probability density algorithm according to the fluctuation data group to obtain a bridge fluctuation baseline of the fluctuation data group.
4. The method of claim 1, wherein determining the bridge fluctuation baseline for the target time period from the bridge fluctuation baselines of each of the fluctuation data sets comprises:
selecting a bridge fluctuation baseline of the fluctuation data sets of the first time sequence according to the bridge fluctuation baseline of each fluctuation data set;
determining a new bridge fluctuation baseline according to the bridge fluctuation baseline of the fluctuation data set of the first time sequence and the bridge fluctuation baseline of the adjacent fluctuation data set; the new bridge fluctuation baseline represents the bridge fluctuation baseline of the fluctuation data group of the first time sequence and the adjacent fluctuation data group;
and taking the new bridge fluctuation baseline as the bridge fluctuation baseline of the fluctuation data set of the first time sequence, returning to execute the step of determining the new bridge fluctuation baseline according to the bridge fluctuation baseline of the fluctuation data set of the first time sequence and the bridge fluctuation baseline of the adjacent fluctuation data set until the new bridge fluctuation baseline represents the bridge fluctuation baseline of the bridge fluctuation data set of the target time period, and taking the new bridge fluctuation baseline as the bridge fluctuation baseline of the target time period.
5. The method of claim 1, wherein determining the bridge fluctuation baseline for the target time period from the bridge fluctuation baselines of each of the fluctuation data sets comprises:
arranging the bridge fluctuation baselines of each fluctuation data set according to a time sequence;
and calculating by a smoothing algorithm according to the arranged bridge fluctuation baselines of each fluctuation data set to obtain the bridge fluctuation baselines in the target time period.
6. The method of claim 2, further comprising:
acquiring a bridge fluctuation data set in a sample time interval and each sample data group in the sample time interval;
and inputting the bridge fluctuation data set in the sample time period and each sample data group in the sample time period into the initial data adaptive segmented processing network, and training the initial data adaptive segmented processing network to obtain the data adaptive segmented processing network.
7. An apparatus for determining a data baseline, the apparatus comprising:
the first acquisition module is used for acquiring a bridge fluctuation data set in a target time period; the bridge fluctuation data set of the target time interval comprises bridge fluctuation data of each moment in the target time interval;
the first determining module is used for dividing each bridge fluctuation data through a data self-adaptive segmented processing network to obtain each fluctuation data group;
the second determining module is used for determining the bridge fluctuation base line of each fluctuation data group according to each fluctuation data group and a two-dimensional probability density algorithm;
and the third determining module is used for determining the bridge fluctuation baseline in the target time period according to the bridge fluctuation baselines of all the fluctuation data sets.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 6.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 6 when executed by a processor.
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