Wireless ultrasonic open channel flow prediction method based on artificial intelligence

Abstract

The invention discloses a wireless ultrasonic open channel flow prediction method based on artificial intelligence. The method comprises the steps of image acquisition, image preprocessing, establishment of an ultrasonic open channel flow prediction model and wireless ultrasonic open channel flow prediction. The invention belongs to the technical field of flow prediction, and particularly relates to a wireless ultrasonic open channel flow prediction method based on artificial intelligence. According to the scheme, a physical transmission matrix is constructed based on a physical transmission rule of water flow, and an adjacent matrix is constructed in combination with hydrological characteristics to fully excavate an interconnection relationship between nodes, so that when a large amount of hydrological data is processed, the flow prediction accuracy is improved; the flow prediction accuracy can be obviously improved; by updating and optimizing the state of each node, the method can better adapt to flow change and environmental disturbance, and the stability of flow prediction is improved; according to different space-time backgrounds, flow prediction is carried out adaptively; and the method can adapt to abnormal fluctuation conditions of long time span and different environmental conditions during open channel flow prediction, so that the professionality of open channel flow prediction is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of flow prediction, and in particular to a wireless ultrasonic open channel flow prediction method based on artificial intelligence. Background Art

[0002] Open channel flow prediction methods refer to a type of technical method used to predict the flow or flow rate of fluids passing through open water channels. The goal of this type of method is usually to establish a mathematical model to estimate or predict future flow based on flow-related data collected over a certain period of time. This type of prediction has important practical significance for water resource management, flood warning, environmental monitoring, agricultural irrigation and other fields. However, general open channel flow prediction methods ignore physical correlations and spatiotemporal dynamics, resulting in poor accuracy of flow prediction results; general open channel flow prediction methods are easily affected by environmental changes, lack real-time adaptability, and have poor ability to handle noisy flow data and incomplete flow data, which in turn leads to poor open channel flow prediction results. Summary of the invention

[0003] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a wireless ultrasonic open channel flow prediction method based on artificial intelligence. In view of the problem that the general open channel flow prediction method ignores physical correlation and spatiotemporal dynamics, resulting in poor accuracy of flow prediction results, this scheme constructs a physical transmission matrix based on the physical transmission law of water flow, calculates the weight of the adjacency matrix, and constructs the adjacency matrix in combination with the hydrological characteristics. The nodes can represent different types of hydrological characteristics at the same time, and fully explore the interconnection relationship between nodes, so that when processing a large amount of hydrological data, the flow prediction accuracy can be significantly improved; in view of the problem that the general open channel flow prediction method is easily affected by environmental changes, does not have real-time adaptability, and has poor ability to process noisy flow data and incomplete flow data, which leads to poor open channel flow prediction effect, this scheme updates and optimizes the state of each node to better adapt to flow changes and environmental disturbances, thereby improving the stability of flow prediction; introduces an adaptive feature selection layer and a self-attention mechanism to adaptively predict flow according to different spatiotemporal backgrounds; through the combination of graph structure and multi-layer perceptron, when predicting open channel flow, it can adapt to abnormal fluctuations of long spans and different environmental conditions, thereby improving the professionalism of open channel flow prediction.

[0004] The technical solution adopted by the present invention is as follows: The wireless ultrasonic open channel flow prediction method based on artificial intelligence provided by the present invention comprises the following steps:

[0005] Step S1: data collection;

[0006] Step S2: data preprocessing;

[0007] Step S3: Establishing an open channel flow prediction model;

[0008] Step S4: open channel flow prediction.

[0009] Furthermore, in step S1, the data collection is to collect historical wireless ultrasonic open channel flow data; the historical wireless ultrasonic open channel flow data includes meteorological data, hydrological data, soil data, sensor data, sampling time and open channel flow assessment level; the sensor data is collected by installing wireless ultrasonic sensors at different positions in the open channel; and the open channel flow assessment level is used as a data label.

[0010] Further, in step S2, the data preprocessing is to perform data cleaning, data conversion, standardization processing and graph structure construction on the collected data; the data cleaning is to process missing values ​​and duplicate values ​​of the collected data; the data conversion is to convert the data into vector form; the standardization processing is based on the maximum and minimum normalization method to standardize the data; the graph structure construction is to construct a graph structure based on the historical wireless ultrasonic open channel flow data after standardization, each node represents the data collected at a specific location, and construct an adjacency matrix, which represents the connection relationship between nodes; the construction of the adjacency matrix is ​​specifically: based on the physical transmission law of water flow, a physical transmission matrix is ​​constructed, which is expressed as: ; Calculate the adjacency matrix weight, expressed as: ; ; Then complete the construction of the adjacency matrix, expressed as: ; Among them, K ij is the physical transmission matrix element between the i-th node and the j-th node, reflecting the physical characteristics between the nodes; α is the intensity coefficient; h ij , , and are the height difference of water level, sediment concentration difference, water temperature difference and precipitation difference between nodes; L ij is the horizontal distance of water levels between nodes; is the weight element of the adjacency matrix; and are the values ​​of the i-th node and the j-th node on the k-th feature respectively; N is the total number of features; and are the feature averages of the i-th node and the j-th node respectively; is the adjacency matrix element; and is the matrix weight; is the node data time difference; is the time adjustment factor; β is the adjustment parameter; is the characteristic factor; , , and is the adjustment factor; , , and They are the maximum water level height difference, sediment concentration difference, water temperature difference, and precipitation difference among all nodes; is the time decay factor.

[0011] Further, in step S3, the open channel flow prediction model is established by processing the graph structure of historical wireless ultrasonic open channel flow data, which specifically includes the following steps:

[0012] Step S31: Model design; the open channel flow prediction model uses a multi-layer perceptron to update the node information. The information of each layer of nodes is aggregated through the adjacency matrix of the graph structure, and then combined through MLP to update the node features, which is expressed as:

[0013] ;in, and are the feature vectors of nodes v at the kth layer and k-1th layer respectively; A vu represents the adjacency matrix element between node v and its neighbor node u; N(·) is the set of neighbor nodes; is a learnable parameter used to control the weight of self-connection; MLP k is the multilayer perceptron of the kth layer; n is the number of neighbor nodes;

[0014] Step S32: predicting the open channel flow assessment level; before the output layer, an adaptive feature selection layer is added, and the features of different layers are weightedly selected by introducing a self-attention mechanism, which is expressed as: ; Finally, the Softmax function is used to map the updated node features to the traffic evaluation level, which is expressed as: ;in, is the predicted open channel flow assessment level; Softmax(·) is the Softmax function; W out is the weight matrix of the output layer; is the node feature after adaptive feature selection; It is processed by the self-attention mechanism; is the learning parameter of the self-attention mechanism;

[0015] Step S33: model training; the historical wireless ultrasonic open channel flow data is pre-divided into a test set and a training set; during the training process, the training set is used for supervised learning, the loss function adopts the cross entropy loss, and a mixed regularization term is introduced to control the complexity, which is expressed as: ; Where n1 is the number of perceptron layers, is the weight matrix of the i1th layer multilayer perceptron; is the cross entropy loss; is the regularization coefficient; the model uses the gradient descent method to update the learning parameters; when the loss convergence or the maximum number of training times is reached, the training of the open channel flow prediction model is completed, and an accuracy threshold is set in advance. When the prediction accuracy of the trained open channel flow prediction model for the test set is higher than the accuracy threshold, the open channel flow prediction model is established; otherwise, the data set is re-divided to adjust the initial learning parameters and retrain the open channel flow prediction model.

[0016] Furthermore, in step S4, the open channel flow prediction is based on the established open channel flow prediction model, and meteorological data, hydrological data, soil data, sensor data and sampling time are collected in real time, and input into the open channel flow prediction model after preprocessing, and the open channel flow assessment level output by the model is used as the open channel flow prediction result.

[0017] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0018] (1) In view of the problem that general open channel flow prediction methods ignore physical correlation and spatiotemporal dynamics, resulting in poor accuracy of flow prediction results, this scheme constructs a physical transmission matrix based on the physical transmission law of water flow, calculates the weight of the adjacency matrix, and constructs an adjacency matrix based on hydrological characteristics. Nodes can represent different types of hydrological characteristics at the same time, and fully explore the interconnection relationship between nodes, so that when processing a large amount of hydrological data, the accuracy of flow prediction can be significantly improved.

[0019] (2) In view of the fact that general open channel flow prediction methods are easily affected by environmental changes, lack real-time adaptability, and have poor ability to process noisy flow data and incomplete flow data, which leads to poor open channel flow prediction results, this scheme updates and optimizes the state of each node to better adapt to flow changes and environmental disturbances and improve the stability of flow prediction; introduces an adaptive feature selection layer and self-attention mechanism to adaptively predict flow according to different spatiotemporal backgrounds; and through the combination of graph structure and multi-layer perceptron, it can adapt to abnormal fluctuations in long time spans and different environmental conditions when predicting open channel flow, thereby improving the professionalism of open channel flow prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic flow chart of the wireless ultrasonic open channel flow prediction method based on artificial intelligence provided by the present invention;

[0021] Figure 2 It is a schematic diagram of the process of step S3.

[0022] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0024] In the description of the present invention, it should be understood that terms such as “upper”, “lower”, “front”, “back”, “left”, “right”, “top”, “bottom”, “inside” and “outside” indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the present invention.

[0025] Example 1, see Figure 1 The present invention provides a wireless ultrasonic open channel flow prediction method based on artificial intelligence, which comprises the following steps:

[0026] Step S1: data collection, collecting historical wireless ultrasonic open channel flow data;

[0027] Step S2: Data preprocessing, data cleaning, data conversion, standardization and graph structure construction of the collected data;

[0028] Step S3: Establish an open channel flow prediction model, process the historical wireless ultrasonic open channel flow data through the graph structure, update the node characteristics, evaluate the flow level, and then complete the establishment of the open channel flow prediction model;

[0029] Step S4: open channel flow prediction: performing open channel flow prediction on the real-time collected data based on the established open channel flow prediction model.

[0030] Example 2, see Figure 1This embodiment is based on the above embodiment. In step S1, the historical wireless ultrasonic open channel flow data includes meteorological data, hydrological data, soil data, sensor data, sampling time and open channel flow assessment level; the sensor data is collected by installing wireless ultrasonic sensors at different positions of the open channel; the open channel flow assessment level is used as a data label; the meteorological data includes precipitation data, evaporation data, air temperature and humidity, and wind speed; the hydrological data includes flow velocity data, flow data, precipitation and water quality data; the soil data includes sediment concentration and groundwater level; the sensor data includes flow velocity and water temperature; the open channel flow assessment level includes low flow level, normal flow level, flood risk level and overload flow level.

[0031] Example 3, see Figure 1 This embodiment is based on the above embodiment. In step S2, data cleaning is to process missing values ​​and duplicate values ​​of the collected data; data conversion is to convert the data into vector form; standardization is to standardize the data based on the maximum and minimum normalization method; graph structure construction is to construct a graph structure based on the historical wireless ultrasonic open channel flow data after standardization, each node represents the data collected at a specific location, and construct an adjacency matrix, which represents the connection relationship between nodes; constructing the adjacency matrix is ​​specifically: constructing a physical transmission matrix based on the physical transmission law of water flow, expressed as: ; Calculate the adjacency matrix weight, expressed as: ; ; Then complete the construction of the adjacency matrix, expressed as: ; Among them, K ij is the physical transmission matrix element between the i-th node and the j-th node, reflecting the physical characteristics between the nodes; α is the intensity coefficient; h ij , , and are the height difference of water level, sediment concentration difference, water temperature difference and precipitation difference between nodes; L ij is the horizontal distance of water levels between nodes; is the weight element of the adjacency matrix; and are the values ​​of the i-th node and the j-th node on the k-th feature respectively; N is the total number of features; and are the feature averages of the i-th node and the j-th node respectively; is the adjacency matrix element; and is the matrix weight; is the node data time difference; is the time adjustment factor; β is the adjustment parameter; is the characteristic factor; , , and is the adjustment factor; , , and They are the maximum water level height difference, sediment concentration difference, water temperature difference, and precipitation difference among all nodes; is the time decay factor.

[0032] By performing the above operations, in view of the problem that general open channel flow prediction methods ignore physical correlation and spatiotemporal dynamics, resulting in poor accuracy of flow prediction results, this scheme constructs a physical transmission matrix based on the physical transmission laws of water flow, calculates the adjacency matrix weights, and constructs an adjacency matrix based on hydrological characteristics. Nodes can represent different types of hydrological characteristics at the same time, and fully explore the interconnection relationship between nodes, so that when processing large amounts of hydrological data, the accuracy of flow prediction can be significantly improved.

[0033] Example 4, see Figure 1 and Figure 2 This embodiment is based on the above embodiment. In step S3, the open channel flow prediction model is established by processing the graph structure of historical wireless ultrasonic open channel flow data, which specifically includes the following steps:

[0034] Step S31: Model design; the open channel flow prediction model uses a multi-layer perceptron to update the node information. The information of each layer of nodes is aggregated through the adjacency matrix of the graph structure, and then combined through MLP to update the node features, which is expressed as:

[0035] ;in, and are the feature vectors of nodes v at the kth layer and k-1th layer respectively; A vu represents the adjacency matrix element between node v and its neighbor node u; N(·) is the set of neighbor nodes; is a learnable parameter used to control the weight of self-connection; MLP k is the multilayer perceptron of the kth layer; n is the number of neighbor nodes;

[0036] Step S32: predicting the open channel flow assessment level; before the output layer, an adaptive feature selection layer is added, and the features of different layers are weightedly selected by introducing a self-attention mechanism, which is expressed as: ; Finally, the Softmax function is used to map the updated node features to the traffic evaluation level, which is expressed as: ;in, is the predicted open channel flow assessment level; Softmax(·) is the Softmax function; Wout is the weight matrix of the output layer; is the node feature after adaptive feature selection; It is processed by the self-attention mechanism; is the learning parameter of the self-attention mechanism;

[0037] Step S33: model training; the historical wireless ultrasonic open channel flow data is pre-divided into a test set and a training set; during the training process, the training set is used for supervised learning, the loss function adopts the cross entropy loss, and a mixed regularization term is introduced to control the complexity, which is expressed as: ; Where n1 is the number of perceptron layers, is the weight matrix of the i1th layer multilayer perceptron; is the cross entropy loss; is the regularization coefficient; the model uses the gradient descent method to update the learning parameters; when the loss convergence or the maximum number of training times is reached, the training of the open channel flow prediction model is completed, and an accuracy threshold is set in advance. When the prediction accuracy of the trained open channel flow prediction model for the test set is higher than the accuracy threshold, the open channel flow prediction model is established; otherwise, the data set is re-divided to adjust the initial learning parameters and retrain the open channel flow prediction model.

[0038] By performing the above operations, the general open channel flow prediction method is easily affected by environmental changes, lacks real-time adaptability, and has poor ability to process noisy flow data and incomplete flow data, which leads to poor open channel flow prediction results. This solution updates and optimizes the state of each node to better adapt to flow changes and environmental disturbances and improve the stability of flow prediction; introduces an adaptive feature selection layer and self-attention mechanism to adaptively predict flow according to different spatiotemporal backgrounds; through the combination of graph structure and multi-layer perceptron, it can adapt to abnormal fluctuations in long time spans and different environmental conditions when predicting open channel flow, thereby improving the professionalism of open channel flow prediction.

[0039] Example 5, see Figure 1 This embodiment is based on the above embodiment. In step S4, the open channel flow prediction is based on the established open channel flow prediction model. Meteorological data, hydrological data, soil data, sensor data and sampling time are collected in real time. After preprocessing, they are input into the open channel flow prediction model, and the open channel flow assessment level output by the model is used as the open channel flow prediction result.

[0040] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0041] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

[0042] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. A wireless ultrasonic open channel flow prediction method based on artificial intelligence, characterized by: The method comprises the following steps: Step S1: data collection, collecting historical wireless ultrasonic open channel flow data; Step S2: data preprocessing; Step S3: Establishing an open channel flow prediction model; Step S4: open channel flow prediction, performing open channel flow prediction on the real-time collected data based on the established open channel flow prediction model; Step S3 includes step S31: model design; the open channel flow prediction model updates the node information through a multi-layer perceptron, and the information of each layer of nodes is aggregated through the adjacency matrix of the graph structure, and then combined through MLP to update the node features, which is expressed as: ;in, and are the feature vectors of nodes v at the kth layer and k-1th layer respectively; A vu represents the adjacency matrix element between node v and its neighbor node u; N(·) is the set of neighbor nodes; is a learnable parameter used to control the weight of self-connection; MLP k is the multilayer perceptron of the kth layer; n is the number of neighbor nodes.

2. The method for predicting open channel flow rate using wireless ultrasonic wave based on artificial intelligence according to claim 1 is characterized in that: In step S3, the open channel flow prediction model is established by processing the graph structure of historical wireless ultrasonic open channel flow data, which specifically includes the following steps: Step S31: model design; Step S32: predicting the open channel flow assessment level; before the output layer, an adaptive feature selection layer is added, and the features of different layers are weightedly selected by introducing a self-attention mechanism, which is expressed as: ; Finally, the Softmax function is used to map the updated node features to the traffic evaluation level, which is expressed as: ;in, is the predicted open channel flow assessment level; Softmax(·) is the Softmax function; W out is the weight matrix of the output layer; is the node feature after adaptive feature selection; It is processed by the self-attention mechanism; is the learning parameter of the self-attention mechanism; Step S33: model training; the historical wireless ultrasonic open channel flow data is pre-divided into a test set and a training set; during the training process, the training set is used for supervised learning, the loss function adopts the cross entropy loss, and a mixed regularization term is introduced to control the complexity, which is expressed as: ; Among them, n1 is the number of perceptron layers, is the weight matrix of the i1th layer multilayer perceptron; is the cross entropy loss; is the regularization coefficient; the model uses the gradient descent method to update the learning parameters; when the loss convergence or the maximum number of training times is reached, the training of the open channel flow prediction model is completed, and an accuracy threshold is set in advance. When the prediction accuracy of the trained open channel flow prediction model for the test set is higher than the accuracy threshold, the open channel flow prediction model is established; otherwise, the data set is re-divided to adjust the initial learning parameters and retrain the open channel flow prediction model.

3. The method for predicting open channel flow rate using wireless ultrasonic wave based on artificial intelligence according to claim 2 is characterized by: In step S2, the data preprocessing is to perform data cleaning, data conversion, standardization and graph structure construction on the collected data; the data cleaning is to process missing values ​​and duplicate values ​​of the collected data; the data conversion is to convert the data into vector form; the standardization is to standardize the data based on the maximum and minimum normalization method.

4. The method for predicting open channel flow rate using wireless ultrasonic wave based on artificial intelligence according to claim 3 is characterized by: In step S2, the graph structure is constructed based on the standardized historical wireless ultrasonic open channel flow data, each node represents the data collected at a specific location, and an adjacency matrix is ​​constructed, and the adjacency matrix represents the connection relationship between the nodes; the construction of the adjacency matrix is ​​specifically: based on the physical transmission law of water flow, a physical transmission matrix is ​​constructed, which is expressed as: ; Calculate the adjacency matrix weights, expressed as: ; ; Then complete the construction of the adjacency matrix, expressed as: ; Among them, K ij is the physical transmission matrix element between the i-th node and the j-th node, reflecting the physical characteristics between the nodes; α is the intensity coefficient; h ij , , and are the height difference of water level, sediment concentration difference, water temperature difference and precipitation difference between nodes; L ij is the horizontal distance of water levels between nodes; is the weight element of the adjacency matrix; and are the values ​​of the i-th node and the j-th node on the k-th feature respectively; N is the total number of features; and are the feature averages of the i-th node and the j-th node respectively; is the adjacency matrix element; and is the matrix weight; is the node data time difference; is the time adjustment factor; β is the tuning parameter; is the characteristic factor; , , and is the adjustment factor; , , and They are the maximum water level height difference, sediment concentration difference, water temperature difference, and precipitation difference among all nodes; is the time decay factor.

5. The method for predicting open channel flow rate using wireless ultrasonic wave based on artificial intelligence according to claim 1 is characterized by: In step S1, the historical wireless ultrasonic open channel flow data includes meteorological data, hydrological data, soil data, sensor data, sampling time and open channel flow assessment level; the sensor data is collected by installing wireless ultrasonic sensors at different locations in the open channel; and the open channel flow assessment level is used as a data label.

6. The method for predicting open channel flow rate using wireless ultrasonic wave based on artificial intelligence according to claim 1 is characterized by: In step S4, the open channel flow prediction is based on the established open channel flow prediction model, and meteorological data, hydrological data, soil data, sensor data and sampling time are collected in real time. After preprocessing, they are input into the open channel flow prediction model, and the open channel flow assessment level output by the model is used as the open channel flow prediction result.

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