CN110222823B - Hydrological flow fluctuation situation identification method and system - Google Patents
Hydrological flow fluctuation situation identification method and system Download PDFInfo
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Abstract
The embodiment of the invention relates to the technical field of data processing, in particular to a hydrologic flow fluctuation situation identification method and a hydrologic flow fluctuation situation identification system. According to the method, each group of water ripple experimental data is used as a test set, the test set is identified by adopting a trained convolutional neural network to obtain a second identification accuracy, a first parameter of the trained convolutional neural network is adjusted to obtain a second parameter and is cached according to whether the second identification accuracy reaches a second set value or not, the cached parameter is averaged to obtain a third parameter, the constructed convolutional neural network is configured for the third time according to the third parameter, and the current water ripple data is input into the convolutional neural network completing the third configuration to realize the identification of the water ripple fluctuation situation, so that the convolutional neural network with strong generalization capability can be built based on the limited water ripple experimental data, and the accurate prediction and identification of the water ripple situation are realized based on the convolutional neural network.
Description
Technical Field
The embodiment of the invention relates to the technical field of data processing, in particular to a hydrologic flow fluctuation situation identification method and a hydrologic flow fluctuation situation identification system.
Background
The water ripple wave is a special wave, the water ripple wave does not belong to longitudinal waves or transverse waves, particle points in the water ripple wave move along an elliptical orbit, the projection of the movement in the vertical direction is like transverse waves, the projection in the horizontal direction is like longitudinal waves, and if two rows of waves are emitted from two wave sources at the same time, the energy emitted from the wave sources can not appear to accord with the two rules of cattle at the same time. Therefore, it becomes a difficult problem how to accurately predict and identify the fluctuation situation of the water ripple.
Disclosure of Invention
In view of this, the invention provides a method and a system for recognizing a fluctuation situation of hydrologic flow.
The embodiment of the invention provides a hydrologic flow fluctuation situation identification method, which comprises the following steps:
acquiring a plurality of groups of water ripple experimental data;
constructing a convolutional neural network;
aiming at each group of water ripple experimental data in the multiple groups of water ripple experimental data, inputting the water ripple experimental data as a test set into the convolutional neural network for training, so that the first identification accuracy of the trained convolutional neural network for identifying each group of water ripple experimental data in the other groups of water ripple experimental data reaches a first set value, and acquiring a first parameter of the trained convolutional neural network;
performing first configuration on the trained convolutional neural network based on the first parameter, and inputting the test set into the convolutional neural network after the first configuration is completed so as to obtain a second identification accuracy rate obtained by identifying the test set by the convolutional neural network after the first configuration is completed;
judging whether the second identification accuracy reaches a second set value or not; if so, caching the first parameter; if not, adjusting the first parameter to obtain a second parameter, performing second configuration on the convolutional neural network which completes the first configuration based on the second parameter so as to enable a second identification accuracy rate of the convolutional neural network which completes the second configuration for identifying the test set to reach a second set value, and caching the first parameter and the second parameter according to a set rule;
calculating the average value of the parameters obtained by caching as a third parameter, and performing third configuration on the constructed convolutional neural network according to the third parameter;
and acquiring current water ripple data, and inputting the current water ripple data into a convolution neural network which is configured for the third time so as to realize the identification of the water ripple fluctuation situation.
Optionally, each set of ripple experimental data in the multiple sets of ripple experimental data includes n consecutive three-dimensional coordinates of at least one first pixel in a preset time period;
the first recognition accuracy is calculated by the following method:
wherein the content of the first and second substances,
pre 1 is the first recognition accuracy;
n 1 predicting the three-dimensional coordinates of at least one first pixel in each group of water ripple experimental data at the next moment of each three-dimensional coordinate in a preset time period by adopting a trained convolutional neural network and predicting the correct time sum;
the second recognition accuracy is calculated by the following method:
pre 2 a second recognition accuracy;
n 2 predicting three-dimensional coordinates of at least one first pixel in the water wave experimental data corresponding to the test set at the next moment of each three-dimensional coordinate in a preset time period by adopting a convolution neural network which is configured for the first time, and predicting the correct time sum;
optionally, the caching the first parameter and the second parameter according to a set rule includes:
carrying out weighted summation on the first parameter and the second parameter according to a set formula, and caching a weighted summation value; the set formula is as follows:
wherein the content of the first and second substances,
sum i taking the ith group of water ripple experimental data as a weighted summation value when the test set is formed, i belongs to [1,m ∈ ]]∩i∈Z；
para 1-i Taking the ith group of water ripple experimental data as a first parameter of the convolutional neural network which is correspondingly trained when the test set is used;
para 2-i when the ith group of water wave experimental data is used as a test set and the corresponding convolutional neural network which finishes training identifies the ith group of water wave experimental data to obtain a second identification accuracy rate which does not reach a second set value, the first parameter para is subjected to the first parameter para 1-i And adjusting the obtained second parameter.
Optionally, the step of inputting the current water ripple data into a convolution neural network configured for the third time to realize identification of the water ripple fluctuation situation includes:
inputting the current-time three-dimensional coordinate into the convolution neural network which completes the third configuration so that the convolution neural network which completes the third configuration identifies the next-time three-dimensional coordinate of the at least one second pixel.
The embodiment of the invention also provides a hydrologic flow fluctuation situation recognition system, which comprises:
the experimental data acquisition module is used for acquiring a plurality of groups of water wave experimental data;
the convolutional neural network construction module is used for constructing a convolutional neural network;
a first parameter obtaining module, configured to, for each group of water ripple experimental data in the multiple groups of water ripple experimental data, use the water ripple experimental data as a test set, input other groups of water ripple experimental data in the multiple groups of water ripple experimental data except the test set into the convolutional neural network for training, so that a first recognition accuracy of the trained convolutional neural network when recognizing each group of water ripple experimental data in the other groups of water ripple experimental data reaches a first set value, and obtain a first parameter of the trained convolutional neural network;
the identification accuracy rate calculation module is used for carrying out first configuration on the trained convolutional neural network based on the first parameter, and inputting the test set into the convolutional neural network which completes the first configuration to obtain second identification accuracy rate obtained by identifying the test set by the convolutional neural network which completes the first configuration;
the parameter caching module is used for judging whether the second identification accuracy reaches a second set value or not; if so, caching the first parameter; if not, adjusting the first parameter to obtain a second parameter, performing second configuration on the convolutional neural network which completes the first configuration based on the second parameter so that a second identification accuracy rate of the convolutional neural network which completes the second configuration for identifying the test set reaches a second set value, and caching the first parameter and the second parameter according to a set rule;
the convolutional neural network configuration module is used for calculating the average value of the parameters obtained by caching as a third parameter and carrying out third configuration on the constructed convolutional neural network according to the third parameter;
and the identification module is used for acquiring current water ripple data and inputting the current water ripple data into the convolution neural network which completes the third configuration so as to realize the identification of the water ripple fluctuation situation.
Optionally, each set of ripple experimental data in the multiple sets of ripple experimental data includes n consecutive three-dimensional coordinates of at least one first pixel within a preset time period;
the first recognition accuracy is calculated by the following method:
wherein the content of the first and second substances,
pre 1 is the first recognition accuracy;
n 1 predicting the three-dimensional coordinates of at least one first pixel in each group of water ripple experimental data at the next moment of each three-dimensional coordinate in a preset time period by adopting a trained convolutional neural network and predicting the correct time sum;
the second recognition accuracy is calculated by the following method:
pre 2 a second recognition accuracy;
n 2 predicting three-dimensional coordinates of at least one first pixel in the water wave experimental data corresponding to the test set at the next moment of each three-dimensional coordinate in a preset time period by adopting a convolution neural network which is configured for the first time, and predicting the correct time sum;
optionally, the multiple groups of water ripple experimental data are m groups, and the parameter caching module caches the first parameter and the second parameter according to a set rule in the following manner:
carrying out weighted summation on the first parameter and the second parameter according to a set formula, and caching a weighted summation value; the set formula is as follows:
wherein the content of the first and second substances,
sum i taking the ith group of water ripple experimental data as a weighted summation value when the test set is formed, i belongs to [1,m ∈ ]]∩i∈Z；
para 1-i The convolution neural net which is used for completing training and corresponds to the ith group of water wave experimental data when being used as a test setA first parameter of the complex;
para 2-i when the ith group of water wave experimental data is used as a test set and the corresponding convolutional neural network which finishes training identifies the ith group of water wave experimental data to obtain a second identification accuracy rate which does not reach a second set value, the first parameter para is subjected to the first parameter para 1-i And adjusting the obtained second parameter.
Optionally, the current ripple data includes a current-time three-dimensional coordinate of at least one second pixel, and the identification module inputs the current ripple data into a convolutional neural network configured for the third time to realize identification of a ripple fluctuation situation of the water by:
inputting the current-time three-dimensional coordinate into the convolution neural network which completes the third configuration so that the convolution neural network which completes the third configuration identifies the next-time three-dimensional coordinate of the at least one second pixel.
The embodiment of the invention also provides electronic equipment which comprises a memory, a processor and a computer program which is stored on the memory and can be run on the processor, wherein the processor realizes the hydrologic flow fluctuation situation recognition method when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, which comprises a computer program, and the computer program controls the electronic equipment where the readable storage medium is located to execute the hydrologic flow fluctuation situation recognition method when running
According to the hydrological flow fluctuation situation identification method and the hydrological flow fluctuation situation identification system, each group of water ripple experimental data in multiple groups of water ripple experimental data is used as a test set, the test set is identified by adopting a trained convolutional neural network to obtain a second identification accuracy, a first parameter of the trained convolutional neural network is adjusted according to whether the second identification accuracy reaches a second set value to obtain a second parameter, the first parameter and the second parameter are cached, the cached parameter is averaged to obtain a third parameter, the constructed convolutional neural network is configured for the third time according to the third parameter, and the obtained current water ripple data is input into the convolutional neural network which completes the third configuration to realize the identification of the water ripple situation.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a block diagram of an electronic device according to an embodiment of the present invention.
Fig. 2 is a flowchart of a method for recognizing a fluctuation situation of a hydrological flow according to an embodiment of the present invention.
Fig. 3 is a block diagram of a hydrologic flow fluctuation situation recognition system according to an embodiment of the present invention.
An icon:
10-an electronic device; 11-a memory; 12-a processor; 13-a network module;
20-a hydrological flow fluctuation situation recognition system; 21-an experimental data acquisition module; 22-a convolutional neural network construction module; 23-a first parameter acquisition module; 24-an identification accuracy calculation module; 25-parameter cache module; 26-a convolutional neural network configuration module; 27-identification module.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
The inventor finds that the prior art is difficult to accurately predict and identify the fluctuation situation of the water ripple. On one hand, the accuracy of the common prediction and identification method is low; on the other hand, a large amount of experimental data is needed for simulation in the early stage of some methods with high prediction and identification accuracy, the labor cost and the research and development cost are high, and in addition, the generalization capability of the prediction and identification methods with high accuracy is low.
The above prior art solutions have all the defects which are the results of the inventor after practice and careful study, so that the discovery process of the above problems and the solutions proposed by the following embodiments of the present invention to the above problems should be the contribution of the inventor to the present invention in the process of the present invention.
Based on the research, the embodiment of the invention provides a method and a system for recognizing the fluctuation situation of the hydrological flow, which can build a convolutional neural network with strong generalization capability based on limited experimental data of the water ripple and realize accurate prediction and recognition of the fluctuation situation of the water ripple based on the convolutional neural network.
Fig. 1 shows a block diagram of an electronic device 10 according to an embodiment of the present invention. The electronic device 10 in the embodiment of the present invention has functions of data storage, transmission, and processing, and as shown in fig. 1, the electronic device 10 includes: the device comprises a memory 11, a processor 12, a network module 13 and a hydrologic flow fluctuation situation recognition system 20.
The memory 11, the processor 12 and the network module 13 are electrically connected directly or indirectly to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 11 stores a hydrological flow fluctuation situation recognition system 20, the hydrological flow fluctuation situation recognition system 20 includes at least one software function module which can be stored in the memory 11 in a form of software or firmware (firmware), and the processor 12 executes various function applications and data processing by running a software program and a module stored in the memory 11, such as the hydrological flow fluctuation situation recognition system 20 in the embodiment of the present invention, so as to implement the hydrological flow fluctuation situation recognition method in the embodiment of the present invention.
The memory 11 is used for storing a program, and the processor 12 executes the program after receiving the execution instruction. The processor 12 may be an integrated circuit chip having data processing capabilities. The Processor 12 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like. The various methods, steps and logic blocks disclosed in embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The network module 13 is used for establishing communication connection between the electronic device 10 and other communication terminal devices through a network, and implementing transceiving operation of network signals and data. The network signal may include a wireless signal or a wired signal.
The embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a computer program, and the computer program controls, when running, the electronic device 10 where the computer-readable storage medium is located to execute the following method for recognizing the fluctuation situation of the hydrologic flow rate.
Fig. 2 shows a flowchart of a method for recognizing a fluctuation situation of a hydrological flow according to an embodiment of the present invention. The method steps defined by the method-related flow, as applied to the electronic device 10, may be implemented by the processor 12. The specific process shown in FIG. 2 will be described in detail below:
and S21, acquiring multiple groups of water ripple test data.
In this example, the water wave test data are m groups.
And S22, constructing a convolutional neural network.
And constructing the obtained convolutional neural network for subsequent training and recognition.
And S23, training the convolutional neural network, and acquiring a first parameter of the trained convolutional neural network.
In this embodiment, each of the m groups of water wave test data is used as a test set, and the m-1 groups of water wave test data are used as a training set to train the convolutional neural network constructed in step S22.
In the embodiment, each set of water ripple test data includes n consecutive three-dimensional coordinates of at least one first pixel within a preset period.
Wherein, the training process is as follows:
taking the ith group of water wave experimental data as an example of a test set, predicting the three-dimensional coordinate of at least one first pixel in each group of water wave experimental data in the m-1 groups of water wave experimental data at the next moment corresponding to each three-dimensional coordinate in n continuous three-dimensional coordinates in a preset time period, and further obtaining a first identification accuracy pre 1 . As can be appreciated, the first recognition accuracy pre is obtained 1 A total of m-1, if m-1, first recognition accuracy rates pre 1 All reach the first set value (in this embodiment, the first set value may be set to 80%), and the first parameter para of the convolutional neural network which is trained at this time is obtained 1-i It is understood that para 1-i And taking the ith group of water ripple experimental data as a first parameter of the convolutional neural network which is correspondingly trained when the test set is finished.
In this embodiment, the first recognition accuracy is calculated by:
wherein the content of the first and second substances,
pre 1 is the first recognition accuracy;
n 1 predicting the three-dimensional coordinates of at least one first pixel in each group of water ripple experimental data at the next moment of each three-dimensional coordinate in a preset time period by adopting a trained convolutional neural network and predicting the correct time sum;
for another example, taking m =5, taking the 1 st group of water wave experimental data as the test set as an example, the 2 nd to 5 th groups of water wave experimental data are input into the convolutional neural network constructed in step S22, and the parameters of the convolutional neural network are adjusted so that the convolutional neural network recognizes the 2 nd to 5 th groups of water wave experimental data to obtain the first recognition accuracy pre 1 Reaches 80%, the first parameter para at this time is obtained 1-1 。
And S24, performing first configuration on the trained convolutional neural network based on the first parameter, and inputting the test set into the convolutional neural network after the first configuration so as to obtain a second identification accuracy rate obtained by identifying the test set by the convolutional neural network after the first configuration.
Take m =5 as an example, based on the first parameter para 1-1 Performing first configuration on the trained convolutional neural network, inputting the 1 st group of water ripple experiment data into the convolutional neural network which completes the first configuration to obtain a second identification accuracy pre obtained by identifying the test set by the convolutional neural network which completes the first configuration 2 Wherein:
pre 2 a second recognition accuracy;
n 2 predicting three-dimensional coordinates of at least one first pixel in the water wave experimental data corresponding to the test set at the next moment of each three-dimensional coordinate in a preset time period by adopting a convolution neural network which is configured for the first time, and predicting the correct time sum;
it can be understood that the second recognition accuracy rate obtained by recognizing the 1 st group of water wave experimental data by the convolutional neural network trained by taking the 2 nd to 5 th groups of water wave experimental data as the training set is pre 2 。
And S25, judging whether the second identification accuracy reaches a second set value or not, and executing corresponding operation according to the judgment result.
In the embodiment, the second set value is higher than the first set value, for example, the second set value may be 90%.
If pre 2 Up to 90% of para 1-1 Buffer memory if pre 2 Not up to 90% for para 1-1 Adjusting to obtain the second parameter para using the 1 st group of water wave experimental data as the test set 2-1 . Wherein the second parameter para is based on 2-1 The second configuration of the convolutional neural network which completes the first configuration can enable the convolutional neural network which completes the second configuration to identify the second identification accuracy pre of the test set 2 Up to 90%.
Further, for para 1-1 And para 2-1 And carrying out weighted summation according to a set formula, and caching a weighted summation value.
In this embodiment, the formula is set as:
wherein the content of the first and second substances,
sum i weighted sum of the ith set of water ripple experimental data as test set，i∈[1,m]∩i∈Z；
para 1-i When the ith group of water wave experimental data is used as a test set, corresponding first parameters of the convolutional neural network completing training;
para 2-i when the ith group of water wave experimental data is used as a test set and the corresponding convolutional neural network which finishes training identifies the ith group of water wave experimental data to obtain a second identification accuracy rate which does not reach a second set value, the first parameter para is subjected to the first parameter para 1-i And adjusting the obtained second parameter.
For example, for para 1-1 And para 2-1 Carrying out weighted summation according to a set formula to obtain:
will sum 1 And carrying out caching.
It is understood that the above steps are iterated 5 times, in other words, the total number of buffered parameters is 5, the buffered parameters include at least one of the first parameter and the weighted sum value, and for another example, the buffered parameters are: sum 1 、para 1-2 、para 1-3 、sum 4 And para 1-5 。
And S26, calculating the average value of the parameters obtained by caching as a third parameter, and performing third configuration on the constructed convolutional neural network according to the third parameter.
For example, to sum 1 、para 1-2 、para 1-3 、sum 4 And para 1-5 And averaging to obtain a third parameter, and performing third configuration on the constructed convolutional neural network according to the third parameter.
And S27, acquiring current water ripple data, and inputting the current water ripple data into a convolution neural network which is configured for the third time so as to realize the identification of the water ripple fluctuation situation.
In this embodiment, the current water wave data includes a current time three-dimensional coordinate of at least one second pixel.
The method for identifying and predicting the current water wave data comprises the following steps: and inputting the current-time three-dimensional coordinate of the at least one second pixel into the convolution neural network which completes the third configuration so that the convolution neural network which completes the third configuration can identify the next-time three-dimensional coordinate of the at least one second pixel, thereby realizing accurate prediction and identification of the water ripple fluctuation situation.
In this embodiment, the convolutional neural network is trained by using each group of water ripple test data in m groups of water ripple test data as a test set and using m-1 groups of water ripple test data as a training set, so that the generalization capability of the convolutional neural network can be increased on the basis of a limited group of water ripple test data.
In addition, the first parameter is adjusted to obtain the second parameter on the premise that the second identification accuracy rate obtained by identifying the test set by adopting the convolutional neural network which is configured for the first time does not reach the second set value, and the weighted summation is carried out on the basis of the first parameter and the second parameter, so that the weights of the training set and the test set can be taken into consideration, and the accuracy of the water wave fluctuation situation identification is further improved.
Furthermore, the convolutional neural network is configured by adopting the third parameter obtained by calculation, so that the smooth processing of the convolutional neural network can be realized, the volatility of the recognition result is reduced, and the accuracy of the water wave fluctuation situation recognition is further improved.
On the basis, as shown in fig. 3, an embodiment of the present invention provides a hydrologic flow fluctuation situation recognition system 20, where the hydrologic flow fluctuation situation recognition system 20 includes: the device comprises an experimental data acquisition module 21, a convolutional neural network construction module 22, a first parameter acquisition module 23, an identification accuracy calculation module 24, a parameter cache module 25, a convolutional neural network configuration module 26 and an identification module 27.
And the experimental data acquisition module 21 is configured to acquire multiple groups of water ripple experimental data.
Since the experimental data acquisition module 21 is similar to the implementation principle of step S21 in fig. 2, it will not be further described here.
And the convolutional neural network construction module 22 is used for constructing a convolutional neural network.
Since the convolutional neural network building block 22 is similar to the implementation principle of step S22 in fig. 2, it will not be further described here.
A first parameter obtaining module 23, configured to, for each group of water ripple experimental data in the multiple groups of water ripple experimental data, use the water ripple experimental data as a test set, input other groups of water ripple experimental data in the multiple groups of water ripple experimental data except the test set into the convolutional neural network for training, so that a first recognition accuracy of the trained convolutional neural network when recognizing each group of water ripple experimental data in the other groups of water ripple experimental data reaches a first set value, and obtain a first parameter of the trained convolutional neural network; .
Since the first parameter obtaining module 23 is similar to the implementation principle of step S23 in fig. 2, it will not be further described here.
And the identification accuracy calculation module 24 is configured to perform first configuration on the trained convolutional neural network based on the first parameter, and input the test set into the first configured convolutional neural network to obtain a second identification accuracy obtained by identifying the test set by the first configured convolutional neural network.
Since the recognition accuracy calculation module 24 is similar to the implementation principle of step S24 in fig. 2, it will not be further described here.
The parameter caching module 25 is configured to determine whether the second recognition accuracy reaches a second set value; if so, caching the first parameter; if not, adjusting the first parameter to obtain a second parameter, performing second configuration on the convolutional neural network which completes the first configuration based on the second parameter, so that a second identification accuracy rate of the convolutional neural network which completes the second configuration for identifying the test set reaches a second set value, and caching the first parameter and the second parameter according to a set rule.
Since the implementation principle of the parameter caching module 25 is similar to that of step S25 in fig. 2, no further description is provided here.
And the convolutional neural network configuration module 26 is configured to calculate an average value of the parameters obtained by caching as a third parameter, and perform third configuration on the constructed convolutional neural network according to the third parameter.
Since the convolutional neural network configuration module 26 is similar to the implementation principle of step S26 in fig. 2, it will not be further described here.
And the identification module 27 is configured to acquire current water ripple data, and input the current water ripple data into the convolution neural network configured for the third time to identify the water ripple fluctuation situation.
Since the identification module 27 is similar to the implementation principle of step S27 in fig. 2, it will not be further described here.
In summary, the method and the system for recognizing the fluctuation situation of the hydrological flow provided by the embodiment of the invention can build a convolutional neural network with strong generalization capability based on limited experimental data of the water ripple and realize accurate prediction and recognition of the fluctuation situation of the water ripple based on the convolutional neural network.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus and method embodiments described above are merely illustrative and, for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist alone, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A hydrologic flow fluctuation situation recognition method is characterized by comprising the following steps:
acquiring a plurality of groups of water ripple experimental data;
constructing a convolutional neural network;
aiming at each group of water ripple experimental data in the multiple groups of water ripple experimental data, inputting the water ripple experimental data as a test set into the convolutional neural network for training, so that the first identification accuracy of the trained convolutional neural network for identifying each group of water ripple experimental data in the other groups of water ripple experimental data reaches a first set value, and acquiring a first parameter of the trained convolutional neural network;
performing first configuration on the trained convolutional neural network based on the first parameter, and inputting the test set into the convolutional neural network after the first configuration is completed so as to obtain a second identification accuracy rate obtained by identifying the test set by the convolutional neural network after the first configuration is completed;
judging whether the second identification accuracy reaches a second set value or not; if so, caching the first parameter; if not, adjusting the first parameter to obtain a second parameter, performing second configuration on the convolutional neural network which completes the first configuration based on the second parameter so that a second identification accuracy rate of the convolutional neural network which completes the second configuration for identifying the test set reaches a second set value, and caching the first parameter and the second parameter according to a set rule;
calculating the average value of the parameters obtained by caching as a third parameter, and carrying out third configuration on the constructed convolutional neural network according to the third parameter;
acquiring current water ripple data, and inputting the current water ripple data into a convolution neural network which is configured for the third time so as to realize the identification of the water ripple fluctuation situation;
each group of water ripple experimental data in the multiple groups of water ripple experimental data comprises n continuous three-dimensional coordinates of at least one first pixel in a preset time period;
the first recognition accuracy is calculated by the following method:
wherein, the first and the second end of the pipe are connected with each other,
pre 1 is the first recognition accuracy;
n 1 predicting the three-dimensional coordinates of at least one first pixel in each group of water ripple experimental data at the next moment of each three-dimensional coordinate in a preset time period by adopting a trained convolutional neural network and predicting the correct time sum;
the second recognition accuracy is calculated by the following method:
pre 2 a second recognition accuracy;
n 2 for testing the paired test sets by using the convolution neural network which completes the first configurationPredicting three-dimensional coordinates of at least one first pixel in the corresponding water wave experimental data at the next moment of each three-dimensional coordinate in a preset time period and predicting the correct time sum;
the method comprises the following steps of performing caching on a first parameter and a second parameter according to a set rule, wherein the multiple groups of water ripple experimental data are m groups, and the caching step comprises the following steps:
carrying out weighted summation on the first parameter and the second parameter according to a set formula, and caching a weighted summation value; the set formula is as follows:
wherein the content of the first and second substances,
sum i taking the ith group of water ripple experimental data as a weighted summation value when the test set is formed, i belongs to [1,m ∈ ]]∩i∈Z；
para 1-i Taking the ith group of water ripple experimental data as a first parameter of the convolutional neural network which is correspondingly trained when the test set is used;
para 2-i when the ith group of water wave experimental data is used as a test set and the corresponding convolutional neural network which finishes training identifies the ith group of water wave experimental data to obtain a second identification accuracy rate which does not reach a second set value, the first parameter para is subjected to the first parameter para 1-i And adjusting the obtained second parameter.
2. The method for recognizing the fluctuation situation of the hydrological flow according to claim 1, wherein the current water ripple data includes a current-time three-dimensional coordinate of at least one second pixel, and the step of inputting the current water ripple data into a convolutional neural network which is configured for the third time to realize recognition of the fluctuation situation of the water ripple comprises:
inputting the current-time three-dimensional coordinate into the convolution neural network which completes the third configuration so that the convolution neural network which completes the third configuration identifies the next-time three-dimensional coordinate of the at least one second pixel.
3. A hydrological flow fluctuation situation recognition system is characterized by comprising:
the experimental data acquisition module is used for acquiring a plurality of groups of water wave experimental data;
the convolutional neural network construction module is used for constructing a convolutional neural network;
a first parameter obtaining module, configured to, for each group of water ripple experimental data in the multiple groups of water ripple experimental data, use the water ripple experimental data as a test set, input other groups of water ripple experimental data in the multiple groups of water ripple experimental data except the test set into the convolutional neural network for training, so that a first recognition accuracy of the trained convolutional neural network when recognizing each group of water ripple experimental data in the other groups of water ripple experimental data reaches a first set value, and obtain a first parameter of the trained convolutional neural network;
the identification accuracy rate calculation module is used for carrying out first configuration on the trained convolutional neural network based on the first parameter, and inputting the test set into the convolutional neural network which completes the first configuration to obtain second identification accuracy rate obtained by identifying the test set by the convolutional neural network which completes the first configuration;
the parameter caching module is used for judging whether the second identification accuracy reaches a second set value or not; if so, caching the first parameter; if not, adjusting the first parameter to obtain a second parameter, performing second configuration on the convolutional neural network which completes the first configuration based on the second parameter so that a second identification accuracy rate of the convolutional neural network which completes the second configuration for identifying the test set reaches a second set value, and caching the first parameter and the second parameter according to a set rule;
the convolutional neural network configuration module is used for calculating the average value of the parameters obtained by caching as a third parameter and carrying out third configuration on the constructed convolutional neural network according to the third parameter;
the identification module is used for acquiring current water ripple data and inputting the current water ripple data into a convolution neural network which completes the third configuration so as to realize the identification of the water ripple fluctuation situation;
each group of water ripple experimental data in the multiple groups of water ripple experimental data comprises n continuous three-dimensional coordinates of at least one first pixel in a preset time period;
the first recognition accuracy is calculated by the following method:
wherein, the first and the second end of the pipe are connected with each other,
pre 1 is the first recognition accuracy;
n 1 predicting the three-dimensional coordinates of at least one first pixel in each group of water ripple experimental data at the next moment of each three-dimensional coordinate in a preset time period by adopting a trained convolutional neural network and predicting the correct time sum;
the second recognition accuracy is calculated by the following method:
pre 2 a second recognition accuracy;
n 2 predicting three-dimensional coordinates of at least one first pixel in the water wave experimental data corresponding to the test set at the next moment of each three-dimensional coordinate in a preset time period by adopting a convolution neural network which completes the first configuration, and predicting the correct time sum;
the multiple groups of water ripple experimental data are m groups, and the parameter caching module caches the first parameter and the second parameter according to a set rule in the following mode:
carrying out weighted summation on the first parameter and the second parameter according to a set formula, and caching a weighted summation value; the set formula is as follows:
wherein the content of the first and second substances,
sum i the weighted summation value of the ith group of water wave experimental data as the test set,i∈[1,m]∩i∈Z；
para 1-i taking the ith group of water ripple experimental data as a first parameter of the convolutional neural network which is correspondingly trained when the test set is used;
para 2-i when the ith group of water wave experimental data is used as a test set and the corresponding convolutional neural network completing training identifies the ith group of water wave experimental data to obtain a second identification accuracy rate which does not reach a second set value, the first parameter para is subjected to the first parameter para 1-i And adjusting the obtained second parameter.
4. The hydrologic flow fluctuation situation recognition system of claim 3, wherein the current water ripple data comprises a current-time three-dimensional coordinate of at least one second pixel, and the recognition module inputs the current water ripple data into a convolution neural network configured for the third time to realize recognition of the water ripple fluctuation situation by:
inputting the current-time three-dimensional coordinate into the convolution neural network which completes the third configuration so that the convolution neural network which completes the third configuration identifies the next-time three-dimensional coordinate of the at least one second pixel.
5. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the hydrological flow fluctuation situation recognition method of any one of claims 1 to 2 when executing the computer program.
6. A computer-readable storage medium, wherein the computer-readable storage medium comprises a computer program, and the computer program controls an electronic device where the computer-readable storage medium is located to execute the hydrologic flow fluctuation situation recognition method according to any one of claims 1-2.
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