CN116029151B - Water flow resistance prediction method, training method, flow prediction method and device - Google Patents
Water flow resistance prediction method, training method, flow prediction method and device Download PDFInfo
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Abstract
The disclosure provides a water flow resistance prediction method, a training method, a flow prediction method and a flow prediction device, which can be applied to the field of hydraulic engineering. The method comprises the following steps: processing the first digital elevation model data based on a preset optimization algorithm to obtain second digital elevation model data, wherein the first digital elevation model data is generated after elevation data acquisition of a riverbed of a river to be detected; determining target elevation data according to the first digital elevation model data and the second digital elevation model data; and inputting the target elevation data and river depth data of the river to be detected into a target water flow resistance prediction model, and outputting the target water flow resistance.
Description
Technical Field
The present disclosure relates to the field of hydraulic engineering, and in particular, to a water flow resistance prediction method, a training method, a flow prediction method, a device, a medium, and a program product.
Background
The flood flow prediction has very important significance for the stable operation of hydraulic engineering facilities and the protection of natural disasters. The related art method for predicting the flow rate of the flood generally calculates the flow rate and the flow rate of the river using a resistance formula. However, the calculation accuracy of the water flow resistance in the related technology is low, and the accuracy requirement of the flow prediction in the related application scene is difficult to meet.
Disclosure of Invention
In view of the foregoing, the present disclosure provides a water flow resistance prediction method, a training method, a flow prediction method, an apparatus, a medium, and a program product.
According to a first aspect of the present disclosure, there is provided a water flow resistance prediction method including:
Processing the first digital elevation model data based on a preset optimization algorithm to obtain second digital elevation model data, wherein the first digital elevation model data is generated after elevation data acquisition of a riverbed of a river to be detected;
Determining target elevation data according to the first digital elevation model data and the second digital elevation model data; and
And inputting the target elevation data and river depth data of the river to be detected into a target water flow resistance prediction model, and outputting the target water flow resistance.
Another aspect of the present disclosure provides a training method of a water flow resistance prediction model, including:
The method comprises the steps of obtaining a training sample, wherein the training sample comprises sample first elevation model data, sample river depth data and sample target water flow resistance, and the sample first elevation model data is generated by acquiring elevation data of a river bed of a river to be detected;
Processing the first digital elevation model data of the sample based on a preset optimization algorithm to obtain the second digital elevation model data of the sample;
Determining sample target elevation data according to the sample first digital elevation model data and the sample second digital elevation model data;
inputting the sample target elevation data and the sample river depth data into an initial water flow resistance prediction model, and outputting predicted target water flow resistance; and
Training an initial water flow resistance prediction model by using the predicted target water flow resistance and the sample target water flow resistance to obtain a trained target water flow resistance prediction model;
the target water flow resistance prediction model is used for the water flow resistance prediction method.
Another aspect of the present disclosure provides a traffic prediction method, including:
According to the water flow resistance prediction method, determining the target water flow resistance of the river to be detected; and
And determining the target flow of the river to be detected according to the target water flow resistance.
Another aspect of the present disclosure provides a water flow resistance prediction apparatus, including:
The first optimization processing module is used for processing the first digital elevation model data based on a preset optimization algorithm to obtain second digital elevation model data, wherein the first digital elevation model data is generated after elevation data acquisition of a river bed of a river to be detected;
The target elevation data determining module is used for determining target elevation data according to the first digital elevation model data and the second digital elevation model data; and
The target water flow resistance determining module is used for inputting the target elevation data and river depth data of the river to be detected into the target water flow resistance prediction model and outputting the target water flow resistance.
Another aspect of the present disclosure provides a flow prediction apparatus, comprising:
The target water flow resistance prediction module is used for determining the target water flow resistance of the river to be detected according to the water flow resistance prediction method; and
And the target flow determining module is used for determining the target flow of the river to be detected according to the target water flow resistance.
Another aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method described above.
Another aspect of the present disclosure also provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the above-described method.
Another aspect of the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the above method.
According to the water flow resistance prediction method, the training method, the flow prediction method, the device, the equipment, the medium and the program product, the first digital elevation model data acquired from the river to be detected is processed by utilizing the preset optimization algorithm, so that the characteristics of the river bed landform of the river to be detected are more accurately represented, further, the technical problem of lower simulation accuracy caused by simulating the river bed roughness through the river bed centerline elevation data in the related art can be avoided by determining the target elevation data according to the processed second digital elevation model data and the first digital elevation model data, and then the target water flow resistance is obtained according to the target elevation data and the river depth data, so that the technical effect of the water flow resistance prediction accuracy can be improved.
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The foregoing and other objects, features and advantages of the disclosure will be more apparent from the following description of embodiments of the disclosure with reference to the accompanying drawings, in which:
FIG. 1 schematically illustrates an application scenario diagram of a water flow resistance prediction method, apparatus according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow chart of a water flow resistance prediction method according to an embodiment of the present disclosure;
FIG. 3A schematically illustrates a schematic diagram of a detection device according to an embodiment of the present disclosure;
FIG. 3B schematically illustrates a schematic diagram of a river to be detected in accordance with an embodiment of the present disclosure;
FIG. 4 schematically illustrates a flow chart of a water flow resistance prediction method according to an embodiment of the present disclosure;
FIG. 5 schematically illustrates a flow chart of a flow prediction method according to an embodiment of the present disclosure;
FIG. 6 schematically shows a block diagram of a water flow resistance prediction device according to an embodiment of the present disclosure;
FIG. 7 schematically illustrates a block diagram of a flow prediction device according to an embodiment of the present disclosure;
FIG. 8 schematically illustrates a block diagram of a training device of a water flow resistance prediction model according to an embodiment of the present disclosure; and
Fig. 9 schematically illustrates a block diagram of an electronic device adapted to implement a water flow resistance prediction method, a training method of a water flow resistance prediction model, a flow prediction method, according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a convention should be interpreted in accordance with the meaning of one of skill in the art having generally understood the convention (e.g., "a system having at least one of A, B and C" would include, but not be limited to, systems having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
In the technical scheme of the disclosure, the related data (such as including but not limited to personal information of a user) are collected, stored, used, processed, transmitted, provided, disclosed, applied and the like, all conform to the regulations of related laws and regulations, necessary security measures are adopted, and the public welcome is not violated.
Embodiments of the present disclosure provide a water flow resistance prediction method, training method, flow prediction method, apparatus, medium, and program product. The water flow resistance prediction method comprises the following steps: processing the first digital elevation model data based on a preset optimization algorithm to obtain second digital elevation model data, wherein the first digital elevation model data is generated after elevation data acquisition of a riverbed of a river to be detected; determining target elevation data according to the first digital elevation model data and the second digital elevation model data; and inputting the target elevation data and river depth data of the river to be detected into a target water flow resistance prediction model, and outputting the target water flow resistance.
According to the embodiment of the disclosure, the first digital elevation model data acquired from the river to be detected is processed by using the preset optimization algorithm, so that the river bed landform characteristics of the river to be detected are more accurately represented, the target elevation data are determined according to the second digital elevation model data and the first digital elevation model data which are obtained through processing, the technical problem of low simulation accuracy caused by simulating the river bed roughness through the river bed centerline elevation data in the related art can be avoided, and then the target water flow resistance is obtained according to the target elevation data and the river depth data, so that the technical effect of the prediction accuracy of the water flow resistance can be improved.
Fig. 1 schematically illustrates an application scenario diagram of a water flow resistance prediction method and apparatus according to an embodiment of the present disclosure.
As shown in fig. 1, an application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is a medium used to provide a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 through the network 104 using at least one of the first terminal device 101, the second terminal device 102, the third terminal device 103, to receive or send messages, etc. Various communication client applications, such as a shopping class application, a web browser application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc. (by way of example only) may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103.
The first terminal device 101, the second terminal device 102, the third terminal device 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server (by way of example only) providing support for websites browsed by the user using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as the user request, and feed back the processing result (e.g., the web page, information, or data obtained or generated according to the user request) to the terminal device.
It should be noted that the water flow resistance prediction method provided by the embodiments of the present disclosure may be generally performed by the server 105. Accordingly, the water flow resistance prediction apparatus provided by the embodiments of the present disclosure may be generally provided in the server 105. The water flow resistance prediction method provided by the embodiments of the present disclosure may also be performed by a server or a server cluster that is different from the server 105 and is capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and/or the server 105. Accordingly, the water flow resistance prediction apparatus provided by the embodiments of the present disclosure may also be provided in a server or a server cluster that is different from the server 105 and is capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and/or the server 105.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
The water flow resistance prediction method of the disclosed embodiment will be described in detail with reference to fig. 2 to 3 based on the scenario described in fig. 1.
Fig. 2 schematically shows a flow chart of a water flow resistance prediction method according to an embodiment of the present disclosure.
As shown in fig. 2, the water flow resistance prediction method of this embodiment includes operations S210 to S230.
In operation S210, the first digital elevation model data is processed based on a preset optimization algorithm to obtain second digital elevation model data, where the first digital elevation model data is generated after elevation data acquisition of a river bed of a river to be detected.
In operation S220, target elevation data is determined from the first digital elevation model data and the second digital elevation model data.
In operation S230, the target elevation data and the river depth data of the river to be detected are input to the target water flow resistance prediction model, and the target water flow resistance is output.
According to the embodiment of the disclosure, the first Digital elevation model data may be Digital elevation model data (Digital ElevationModel, DEM) obtained after the to-be-detected river performs the geomorphic feature detection, and the geomorphic feature of the to-be-detected river may be more accurately represented by the first Digital elevation model data, so that the loss of the geomorphic feature information is avoided.
The first digital elevation model data may be detected by any detection method or detection technique, for example, the first digital elevation data may be detected by a sonar technique. However, the method is not limited thereto, and the digital elevation data may be obtained by detecting other detection methods such as total station detection, and the specific method for obtaining the first digital elevation data by detecting is not limited in the embodiments of the present disclosure.
According to the embodiment of the disclosure, the preset optimization calculation method can comprise a regression algorithm in the related technology, so that trending processing of the first digital elevation data is realized, and the characterization accuracy of the geomorphic characteristics of the river bed is improved. The embodiment of the present disclosure does not limit the specific type of the preset optimization algorithm, and a person skilled in the art may design the preset optimization algorithm according to actual requirements.
According to the embodiment of the disclosure, the target water flow resistance prediction model may be constructed based on a deep learning algorithm, for example, the target water flow resistance prediction model is constructed based on a deep learning algorithm such as a cyclic neural network algorithm, a long-short-term memory network algorithm, a convolutional neural network algorithm, and the like. But not limited thereto, the target water flow resistance prediction model may be constructed based on a water flow resistance prediction equation in the related art.
According to the embodiment of the disclosure, the first digital elevation model data acquired from the river to be detected is processed by using the preset optimization algorithm, so that the river bed landform characteristics of the river to be detected are more accurately represented, the target elevation data are determined according to the second digital elevation model data and the first digital elevation model data which are obtained through processing, the technical problem of low simulation accuracy caused by simulating the river bed roughness through the river bed centerline elevation data in the related art can be avoided, and then the target water flow resistance is obtained according to the target elevation data and the river depth data, so that the technical effect of the prediction accuracy of the water flow resistance can be improved.
According to the embodiment of the disclosure, the river bed of the river to be detected is paved with the pedal stones, and the pedal stones are arranged along the width direction of the river to be detected.
According to the embodiment of the disclosure, the direction in which the pedal stones are paved on the river bed can form any angle with the central line of the river bed, for example, can be perpendicular to the central line of the river bed, or can also form an included angle of any angle of 30 degrees, 45 degrees and the like with the central line of the river bed.
According to an embodiment of the present disclosure, the water flow resistance prediction method may further include the operations of:
And receiving the first digital elevation model data sent by the sampling system, wherein the sampling system acquires data of a to-be-detected area of the river to be detected according to the detection devices arranged in an array form and then generates the first digital elevation model data.
Fig. 3A schematically illustrates a schematic diagram of a detection device according to an embodiment of the present disclosure.
Fig. 3B schematically shows a schematic diagram of a river to be detected according to an embodiment of the present disclosure.
As shown in connection with fig. 3A and 3B, the river to be inspected may flow in the water flow direction. The pedal stone 311 can be laid along the width direction on the river bed of the river to be detected. In the manner shown in fig. 3A, in the river to be detected area 310, a foot stone 311 may be laid on the river bed along the width direction. The detection devices 321 may be arranged in an array form in the to-be-detected area 310, so as to facilitate data acquisition of the river to be detected and generate the first digital elevation model data.
For example, the detection devices 321 may be arranged along the 1/6 river width position to the 5/6 river width position of the width direction of the area 310 to be detected, with 1/6 river width (river width) of the river to be detected as the interval width, to form sampling points distributed in an array in the area 310 to be detected.
According to an embodiment of the present disclosure, the preset optimization algorithm comprises at least one of:
least square method, maximum likelihood estimation algorithm, linear regression method, newton iteration method.
According to the embodiment of the disclosure, the first digital elevation model data can be processed based on the least square method to realize trending of the first digital elevation model data, so that the obtained second digital elevation data can be more accurately fitted with the geomorphic characteristics of the river bed of the river to be detected.
According to an embodiment of the present disclosure, determining the target elevation data from the first digital elevation model data and the second digital elevation model data may include the operations of:
determining third digital elevation model data based on a difference between the first digital elevation model data and the second digital elevation model data; and calculating the statistical second moment of the third digital elevation model data to obtain the target elevation data.
In one embodiment of the present disclosure, the target elevation data may be input to a target water flow resistance prediction model constructed based on a deep learning algorithm,
According to the embodiment of the disclosure, the target water flow resistance prediction model is constructed based on a water flow resistance equation.
In operation S230, inputting the target elevation data and the river depth data of the river to be detected into the target water flow resistance prediction model may include the following operations.
And inputting the quotient of the river depth data and the target elevation data as target inundation data into a target water flow resistance prediction model.
In one comparative example of the present disclosure, the resistance due to water flow can be expressed by a function including the depth d of water of a river and the roughness k of a river bed. Therefore, the sediment particle diameter parameter D i of the river channel can be based; or the elevation parameter sigma of the river bed section of the river channel is quantified, so that the river resistance can be quantitatively calculated.
For example, the water flow resistance may be calculated by the following formula (1), i.e., the fraglen resistance formula.
In the formula (1), D represents the average water depth (river depth data) of a river segment in a river, D 84 represents the particle size of sediment corresponding to the 84 th percentile in the sediment in the river,Indicating the resistance to water flow. D/D 84 represents the flooding degree of the river.
According to the embodiments of the present disclosure, the inventors found that the method for calculating the water flow resistance in the related art has low calculation accuracy, and it is difficult to accurately detect the water flow resistance in the river. Therefore, the target elevation data can be obtained through the water flow resistance prediction method in the embodiment, so that the target elevation data can be used for accurately quantifying the river bed roughness, and the quotient of the river depth data and the target elevation data is used as the target inundation degree to be input into the target water flow resistance prediction model constructed based on the water flow resistance equation, so that the output target water flow resistance can be more accurate.
For example, a target water flow resistance model may be constructed based on equation (2), resulting in a target water flow resistance.
In the formula (2), d represents river depth data of a river to be detected, σ z,3d represents target elevation data,Indicating a target water flow resistance.
It should be noted that, corresponding parameters (e.g., parameter 5.8, parameter 1.2) in the formula (2) may be used as parameters of the target water flow resistance prediction model. The parameters may be determined by a training method in the related art, such as a gradient descent algorithm, or based on a parameter calibration method, and the specific method for determining the parameters of the target water flow resistance prediction model is not limited in the embodiments of the present disclosure, and may be selected by those skilled in the art according to actual needs.
Fig. 4 schematically shows a flowchart of a water flow resistance prediction method according to an embodiment of the present disclosure.
As shown in fig. 4, the training method of the water flow resistance prediction model of this embodiment may include operations S410 to S450.
In operation S410, a training sample is obtained, where the training sample includes sample first elevation model data, sample river depth data, and sample target water flow resistance, and the sample first elevation model data is generated after elevation data acquisition of a river bed of a river to be detected by the sample.
In operation S420, the sample first digital elevation model data is processed based on a preset optimization algorithm to obtain sample second digital elevation model data.
In operation S430, sample target elevation data is determined from the sample first digital elevation model data and the sample second digital elevation model data.
In operation S440, the sample target elevation data and the sample river depth data are input to the initial water flow resistance prediction model, and the predicted target water flow resistance is output.
In operation S450, an initial water flow resistance prediction model is trained using the predicted target water flow resistance and the sample target water flow resistance, resulting in a trained target water flow resistance prediction model.
The target water flow resistance prediction model is used for the water flow resistance prediction method.
According to the embodiment of the present disclosure, the initial water flow resistance prediction model may be constructed based on a deep learning algorithm, or may also be constructed based on a water flow resistance equation, and the specific algorithm type of the initial water flow resistance prediction model is not limited in the embodiment of the present disclosure.
According to an embodiment of the present disclosure, an initial target water flow resistance prediction model is constructed based on a water flow resistance equation.
In operation S440, inputting the sample target elevation data and the sample river depth data into the initial water flow resistance prediction model may include the operations of:
and (3) inputting the quotient of the sample river depth data and the sample target elevation data as sample target inundation data into an initial target water flow resistance prediction model.
According to an embodiment of the present disclosure, an initial target water flow resistance prediction model may be constructed based on the following equation (3), for example.
In the formula (3), d represents sample river depth data of a river to be detected by a sample, σ z,3d represents sample target elevation data,Representing the predicted target water flow resistance, and a and b represent the first and second parameters of the initial target water flow resistance prediction model, respectively.
Sample target elevation data obtained through the training method provided by the embodiment of the present disclosure, or sample target elevation data based on the water flow resistance prediction method provided by the foregoing embodiment may also be obtained. And inputting the predicted target water flow resistance and the sample target water flow resistance output by the initial target water flow resistance prediction model into a loss function to obtain a loss value, and adjusting a first parameter a and a second parameter b in a formula (3) by using the loss value until the first parameter a and the second parameter b obtained when the loss function converges can be used as parameters of the trained target water flow resistance prediction model, so that the trained target water flow resistance prediction model is obtained.
It should be noted that, technical terms in the training method provided in the embodiments of the present disclosure have the same or corresponding technical attributes as those in the water flow resistance prediction method, and embodiments of the present disclosure are not described herein again.
Fig. 5 schematically illustrates a flow chart of a flow prediction method according to an embodiment of the present disclosure.
As shown in fig. 5, the flow prediction method may include operations S510 to S520.
In operation S510, a target water flow resistance of the river to be detected is determined according to the water flow resistance prediction method described above.
In operation S520, a target flow rate of the river to be detected is determined according to the target water flow resistance.
According to an embodiment of the present disclosure, the predicted flow rate of the river channel may be calculated based on the following formulas (4) and (5).
In the formulas (4) and (5), Q is the target flow of the river to be detected, and g is the gravitational acceleration. d is the average water depth of the river to be detected (river depth data of the river to be detected), w is the average river width of the river to be detected, S is the river bed gradient,The daily water flow resistance can be expressed.
According to the embodiments of the present disclosure, a high-accuracy target water flow resistance can be detected due to and in addition to the water flow resistance prediction method provided by the above embodiments. Therefore, the target flow rate of the river to be detected is determined based on the target water flow resistance, the detection accuracy of the flow rate of the river to be detected can be further improved, the flow rate condition of the river to be detected can be accurately predicted, and the technical effect of accurately predicting the river torrential flood outbreak risk is achieved.
Fig. 6 schematically shows a block diagram of a water flow resistance prediction apparatus according to an embodiment of the present disclosure.
As shown in fig. 6, the water flow resistance prediction apparatus 600 of this embodiment includes a first optimization processing module 610, a target elevation data determination module 620, and a target water flow resistance determination module.
The first optimization processing module 610 is configured to process the first digital elevation model data based on a preset optimization algorithm to obtain second digital elevation model data, where the first digital elevation model data is generated after elevation data acquisition of a river bed of a river to be detected.
The target elevation data determining module 620 is configured to determine target elevation data according to the first digital elevation model data and the second digital elevation model data.
The target water flow resistance determining module 630 is configured to input the target elevation data and the river depth data of the river to be detected into the target water flow resistance prediction model, and output the target water flow resistance.
According to an embodiment of the present disclosure, the target elevation data determining module includes: the third digital elevation model data determining sub-module and the target elevation data determining sub-module.
And a third digital elevation model data determining sub-module for determining third digital elevation model data based on a difference between the first digital elevation model data and the second digital elevation model data.
And the target elevation data determining sub-module is used for calculating the statistical second moment of the third digital elevation model data to obtain target elevation data.
According to the embodiment of the disclosure, the target water flow resistance prediction model is constructed based on a water flow resistance equation.
The target water flow resistance determination module includes a target water flow resistance determination sub-module.
The target water flow resistance determination submodule is used for inputting the quotient of the river depth data and the target elevation data as target inundation data into the target water flow resistance prediction model.
According to the embodiment of the disclosure, the river bed of the river to be detected is paved with the pedal stones, and the pedal stones are arranged along the width direction of the river to be detected.
According to an embodiment of the present disclosure, the water flow resistance prediction apparatus further includes: and a data receiving module.
The data receiving module is used for receiving the first digital elevation model data sent by the sampling system, wherein the sampling system generates the first digital elevation model data after data acquisition is carried out on the to-be-detected area of the river to be detected according to the detection devices arranged in an array mode.
According to an embodiment of the present disclosure, the preset optimization algorithm comprises at least one of:
least square method, maximum likelihood estimation algorithm, linear regression method, newton iteration method.
Fig. 7 schematically shows a block diagram of a flow rate prediction device according to an embodiment of the present disclosure.
As shown in fig. 7, the flow rate prediction apparatus 700 of this embodiment includes a target water flow resistance prediction module 710 and a target flow rate determination module 720.
The target water flow resistance prediction module 710 is configured to determine a target water flow resistance of the river to be detected according to the water flow resistance prediction method described above.
And the target flow determining module 720 is configured to determine the target flow of the river to be detected according to the target water flow resistance.
Fig. 8 schematically shows a block diagram of a training device of a water flow resistance prediction model according to an embodiment of the present disclosure.
As shown in fig. 8, the training apparatus 800 of the water flow resistance prediction model of this embodiment includes a sample acquisition module 810, a second optimization processing module 820, a sample target elevation data determination module 830, a predicted target water flow resistance determination module 840, and a training module 850.
The sample obtaining module 810 is configured to obtain a training sample, where the training sample includes sample first elevation model data, sample river depth data, and sample target water flow resistance, and the sample first elevation model data is generated after elevation data collection of a river bed of a river to be detected by the sample.
The second optimization processing module 820 is configured to process the sample first digital elevation model data based on a preset optimization algorithm to obtain sample second digital elevation model data.
The sample target elevation data determining module 830 is configured to determine sample target elevation data according to the sample first digital elevation model data and the sample second digital elevation model data.
The predicted target water flow resistance determination module 840 is configured to input the sample target elevation data and the sample river depth data to the initial water flow resistance prediction model, and output a predicted target water flow resistance.
The training module 850 is configured to train the initial water flow resistance prediction model using the predicted target water flow resistance and the sample target water flow resistance to obtain a trained target water flow resistance prediction model.
Wherein the target water flow resistance prediction model is used for the water flow resistance prediction method as described above.
According to an embodiment of the present disclosure, an initial target water flow resistance prediction model is constructed based on a water flow resistance equation.
Wherein the predicted target water flow resistance determination module 840 includes a predicted target water flow resistance determination sub-module.
The predicted target water flow resistance determination submodule is used for inputting the quotient of the sample river depth data and the sample target elevation data as sample target inundation data into the initial target water flow resistance prediction model.
According to embodiments of the present disclosure, any of the first optimization processing module 610, the target elevation data determination module 620, and the target water flow resistance determination module, or the target water flow resistance prediction module 710 and the target flow determination module 720, or the sample acquisition module 810, the second optimization processing module 820, the sample target elevation data determination module 830, the predicted target water flow resistance determination module 840, and the training module 850 may be combined in one module to be implemented, or any of the modules may be split into a plurality of modules. Or at least some of the functionality of one or more of the modules may be combined with, and implemented in, at least some of the functionality of other modules. According to embodiments of the present disclosure, at least one of the first optimization processing module 610, the target elevation data determination module 620, and the target water flow resistance determination module, or the target water flow resistance prediction module 710 and the target flow determination module 720, or the sample acquisition module 810, the second optimization processing module 820, the sample target elevation data determination module 830, the predicted target water flow resistance determination module 840, and the training module 850 may be implemented at least in part as hardware circuitry, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable manner of integrating or packaging the circuitry, or in any one of or a suitable combination of three of software, hardware, and firmware. Either the first optimization processing module 610, the target elevation data determination module 620, and the target water flow resistance determination module, or the target water flow resistance prediction module 710 and the target flow determination module 720, or at least one of the sample acquisition module 810, the second optimization processing module 820, the sample target elevation data determination module 830, the predicted target water flow resistance determination module 840, and the training module 850 may be at least partially implemented as a computer program module that, when executed, performs the corresponding functions.
Fig. 9 schematically illustrates a block diagram of an electronic device adapted to implement a water flow resistance prediction method, a training method of a water flow resistance prediction model, a flow prediction method, according to an embodiment of the present disclosure.
As shown in fig. 9, an electronic device 900 according to an embodiment of the present disclosure includes a processor 901 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 902 or a program loaded from a storage portion 908 into a Random Access Memory (RAM) 903. The processor 901 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. Processor 901 may also include on-board memory for caching purposes. Processor 901 may include a single processing unit or multiple processing units for performing the different actions of the method flows according to embodiments of the present disclosure.
In the RAM 903, various programs and data necessary for the operation of the electronic device 900 are stored. The processor 901, the ROM 902, and the RAM 903 are connected to each other by a bus 904. The processor 901 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 902 and/or the RAM 903. Note that the program may be stored in one or more memories other than the ROM 902 and the RAM 903. The processor 901 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in the one or more memories.
According to an embodiment of the disclosure, the electronic device 900 may also include an input/output (I/O) interface 905, the input/output (I/O) interface 905 also being connected to the bus 904. The electronic device 900 may also include one or more of the following components connected to the I/O interface 905: an input section 906 including a keyboard, a mouse, and the like; an output portion 907 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage portion 908 including a hard disk or the like; and a communication section 909 including a network interface card such as a LAN card, a modem, or the like. The communication section 909 performs communication processing via a network such as the internet. The drive 910 is also connected to the I/O interface 905 as needed. A removable medium 911 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on the drive 910 so that a computer program read out therefrom is installed into the storage section 908 as needed.
The present disclosure also provides a computer-readable storage medium that may be embodied in the apparatus/device/system described in the above embodiments; or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, the computer-readable storage medium may include ROM 902 and/or RAM 903 and/or one or more memories other than ROM 902 and RAM 903 described above.
Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowcharts. The program code, when executed in a computer system, causes the computer system to perform the methods provided by embodiments of the present disclosure.
The above-described functions defined in the system/apparatus of the embodiments of the present disclosure are performed when the computer program is executed by the processor 901. The systems, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed, and downloaded and installed in the form of a signal on a network medium, via communication portion 909, and/or installed from removable medium 911. The computer program may include program code that may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
In such an embodiment, the computer program may be downloaded and installed from the network via the communication portion 909 and/or installed from the removable medium 911. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 901. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
According to embodiments of the present disclosure, program code for performing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, such computer programs may be implemented in high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. Programming languages include, but are not limited to, such as Java, c++, python, "C" or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. 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 or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure and/or in the claims may be provided in a variety of combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.
The embodiments of the present disclosure are described above. These examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the embodiments cannot be used advantageously in combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the disclosure, and such alternatives and modifications are intended to fall within the scope of the disclosure.
Claims (8)
1. A water flow resistance prediction method, comprising:
Processing first digital elevation model data based on a regression algorithm to obtain second digital elevation model data, wherein the first digital elevation model data is generated after elevation data acquisition of a riverbed of a river to be detected;
Determining target elevation data according to the first digital elevation model data and the second digital elevation model data; and
Inputting the quotient of the river depth data and the target elevation data as target inundation data into a target water flow resistance prediction model to output target water flow resistance, wherein the target water flow resistance prediction model is constructed based on a water flow resistance equation, and the target water flow resistance prediction model is determined based on the following modes:
Obtaining a training sample, wherein the training sample comprises sample first elevation model data, sample river depth data and sample target water flow resistance, and the sample first elevation model data is generated after elevation data acquisition of a river bed of a river to be detected by a sample;
Processing the first digital elevation model data of the sample based on a regression algorithm to obtain second digital elevation model data of the sample;
determining sample target elevation data according to the sample first digital elevation model data and the sample second digital elevation model data;
inputting the quotient of the sample river depth data and the sample target elevation data as sample target inundation data into an initial water flow resistance prediction model, and outputting predicted target water flow resistance; and
Training the initial water flow resistance prediction model by utilizing the predicted target water flow resistance and the sample target water flow resistance to obtain a trained target water flow resistance prediction model;
The initial water flow resistance prediction model is constructed based on a water flow resistance equation.
2. The method of claim 1, wherein determining target elevation data from the first digital elevation model data and the second digital elevation model data comprises:
determining third digital elevation model data based on a difference between the first digital elevation model data and the second digital elevation model data; and
And calculating the statistical second moment of the third digital elevation model data to obtain the target elevation data.
3. The method of claim 1, wherein a footboard stone is laid on a river bed of the river to be detected, the footboard stone being arranged along a width direction of the river to be detected.
4. The method of claim 1, further comprising:
And receiving the first digital elevation model data sent by a sampling system, wherein the sampling system acquires data of a to-be-detected area of the river to be detected according to detection devices arranged in an array form and then generates the first digital elevation model data.
5. The method of claim 1, wherein the regression algorithm comprises at least one of:
least square method, maximum likelihood estimation algorithm, linear regression method, newton iteration method.
6. A traffic prediction method, comprising:
the water flow resistance prediction method according to any one of claims 1 to 5, determining a target water flow resistance of a river to be detected; and
And determining the target flow of the river to be detected according to the target water flow resistance.
7. A water flow resistance prediction device comprising:
the first optimization processing module is used for processing the first digital elevation model data based on a regression algorithm to obtain second digital elevation model data, wherein the first digital elevation model data is generated after elevation data acquisition of a river bed of a river to be detected;
The target elevation data determining module is used for determining target elevation data according to the first digital elevation model data and the second digital elevation model data; and
The target water flow resistance determining module is used for inputting the quotient of the river depth data and the target elevation data as target flooding data to the target water flow resistance prediction model and outputting target water flow resistance;
the target water flow resistance prediction model is constructed based on a water flow resistance equation, and is determined based on the following modes:
Obtaining a training sample, wherein the training sample comprises sample first elevation model data, sample river depth data and sample target water flow resistance, and the sample first elevation model data is generated after elevation data acquisition of a river bed of a river to be detected by a sample;
Processing the first digital elevation model data of the sample based on a regression algorithm to obtain second digital elevation model data of the sample;
determining sample target elevation data according to the sample first digital elevation model data and the sample second digital elevation model data;
inputting the quotient of the sample river depth data and the sample target elevation data as sample target inundation data into an initial water flow resistance prediction model, and outputting predicted target water flow resistance; and
Training the initial water flow resistance prediction model by utilizing the predicted target water flow resistance and the sample target water flow resistance to obtain a trained target water flow resistance prediction model;
The initial water flow resistance prediction model is constructed based on a water flow resistance equation.
8. A flow prediction device, comprising:
A target water flow resistance prediction module for determining a target water flow resistance of a river to be detected according to the water flow resistance prediction method of any one of claims 1 to 5; and
And the target flow determining module is used for determining the target flow of the river to be detected according to the target water flow resistance.
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