A three-dimensional geographic target construction and analysis system based on neural network model

Abstract

The present invention discloses a three-dimensional geographic target construction and analysis system based on a neural network model, and relates to the technical field of three-dimensional geographic target construction. The present invention adopts a generative adversarial GANs network in deep learning to perform super-resolution reconstruction to improve the detail performance of the three-dimensional model; adopts an adversarial training method of a generator and a discriminator, the generator is responsible for generating a high-resolution three-dimensional model, and the discriminator is responsible for judging whether the generated model is true; combines a progressive generation strategy to gradually increase the resolution and complexity of the model; uses a stream processing framework Apache Kafka to perform real-time data processing and analysis, so that the model can reflect terrain changes in a timely manner; uses a stream processing pipeline to collect and process sensor data and satellite images in real time to detect and analyze terrain changes; and adopts an incremental update strategy to reduce data processing delays and consumption of computing resources.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional geographic target construction, and in particular to a three-dimensional geographic target construction and analysis system based on a neural network model. Background Art

[0002] In recent years, global climate change has led to frequent extreme weather events. The frequency and intensity of natural disasters such as geological disasters, floods, and heavy rainfall have increased significantly. These disasters have a significant impact on the terrain, often leading to topographic changes, including landslides, river diversions, and soil erosion. Three-dimensional models can provide a basis for emergency rescue and emergency response. If the three-dimensional geographic target construction and analysis system is not updated in a timely manner and cannot accurately reflect these changes, it will lead to multiple problems.

[0003] When natural disasters such as floods and landslides occur, emergency rescue and response rely on accurate terrain information. If the 3D model is not updated in a timely manner, rescue workers will rely on outdated information to make decisions, resulting in delays in rescue operations and waste of resources, and even greater losses and casualties. At the same time, disaster management and prevention require risk assessments based on accurate terrain and environmental data. For example, the delineation of flood risk areas needs to take into account the latest terrain changes and hydrological information. If the 3D model is not updated in a timely manner, it will lead to inaccurate risk assessments, which in turn affects the formulation and implementation of disaster prevention and mitigation measures.

[0004] Among existing three-dimensional geographic construction solutions, a common method is to use satellite remote sensing technology to obtain large-scale geographic information and use the acquired data to update the model. However, although satellite remote sensing has a wide coverage, its resolution and accuracy are limited, and there are delays in data acquisition and processing, which cannot meet the needs of real-time updates. Although drones and ground surveys can be combined, this is not effective for disaster areas. In addition, there are pre-deployed ground sensor networks to monitor terrain changes in real time and update the model through data transmission. However, ground sensors have limited coverage, high deployment and maintenance costs, and high complexity in data processing and integration. Therefore, a three-dimensional geographic target construction and analysis system based on a neural network model is urgently needed to solve such problems. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a three-dimensional geographic target construction and analysis system based on a neural network model, which solves the problems existing in the existing technology of using satellite remote sensing technology to obtain large-scale geographic information, with limited resolution and accuracy, and delays in data acquisition and processing, which cannot meet the needs of real-time updates; pre-deployed ground sensor networks are used to monitor terrain changes in real time and update models through data transmission, but the coverage of ground sensors is limited, the deployment and maintenance costs are high, and the data processing and integration are more complex.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0007] The present invention provides a three-dimensional geographic target construction and analysis system based on a neural network model, comprising:

[0008] The data acquisition module is responsible for acquiring geographic information from various data sources in real time, including high-frequency satellite images and meteorological data;

[0009] The data acquisition module includes:

[0010] Satellite image acquisition unit, used to periodically acquire satellite images, using high-resolution commercial satellites to obtain image data in real time; providing geographic information over a wide range; satellite images have a wide coverage area and can reflect terrain changes over a large area in real time;

[0011] Meteorological data acquisition unit, used to obtain real-time meteorological data, including rainfall, temperature, and humidity, through data interfaces between meteorological satellites and ground-based meteorological stations, to assist in detecting terrain changes. Meteorological data helps analyze and predict terrain changes, including soil erosion and landslides caused by rainfall.

[0012] Data transmission module, used to transmit collected data in real time;

[0013] The data transmission module includes:

[0014] Data transmission unit, which transmits the collected data to the data processing center in real time;

[0015] Apache Kafka cluster for real-time data transmission;

[0016] Deploy a Kafka cluster in the data processing center and configure brokers, zookeepers, and topics.

[0017] The data processing and fusion module uses the stream processing framework to process and analyze the collected data in real time;

[0018] The data processing and fusion modules include:

[0019] Apache Flink cluster for real-time data processing and analysis;

[0020] Deploy a Flink cluster in the data processing center and configure the jobmanager and taskmanager;

[0021] The stream processing pipeline unit processes and analyzes the transmitted data in real time. In the stream processing pipeline unit, stream processing programs are written using Flink's DataStream API to consume data from Kafka topics and perform data cleaning, format conversion, and feature extraction.

[0022] The data fusion unit uses convolutional neural networks (CNNs) to perform feature extraction and multi-source data fusion, fusing data from different data sources to provide comprehensive geographic information;

[0023] Dynamic update module, which dynamically updates the 3D terrain model based on real-time processed data;

[0024] Dynamic update modules include:

[0025] Dynamic fusion unit, which uses Kalman filter algorithm to dynamically fuse data and update the model, combining real-time data and historical data to dynamically update the terrain model;

[0026] The model prediction unit uses the LSTM machine learning algorithm to predict terrain changes and adjust model parameters; it also predicts future terrain trends and makes model adjustments in advance;

[0027] Super-resolution reconstruction module, which generates high-precision models through super-resolution reconstruction technology to improve the details of 3D terrain models;

[0028] The super-resolution reconstruction module includes:

[0029] The GAN generation unit uses generative adversarial networks (GANs) to generate high-resolution models through adversarial training between the generator and the discriminator, thereby improving the model's detail performance.

[0030] The progressive generation unit adopts a progressive generation strategy to gradually increase the resolution and complexity of the model.

[0031] The present invention is further configured such that the specific steps of using Flink for real-time data processing and analysis include:

[0032] Set up the environment and deploy Apache Kafka and Apache Flink. Both Kafka and Flink are installed in the data processing center. Set up broker, zookeeper, and topics for Kafka, and job manager and task manager for Flink.

[0033] Obtain high-resolution satellite images and weather data, and send the data to Kafka topics through the Kafka producer API;

[0034] Use KafkaproducerAPI to write data sending code to send real-time data to Kafkatopics;

[0035] Write a stream processing program. The stream processing program is written as follows:

[0036] Use Flink's DataStream API to write stream processing programs and consume data from Kafka topics;

[0037] Filter out noise data and invalid data and convert the data into a unified format;

[0038] Extract useful feature information from the data, including terrain change characteristics;

[0039] Perform real-time analysis of extracted features to detect terrain changes;

[0040] The present invention is further configured to use a convolutional neural network (CNN) to fuse multi-source data in the following manner:

[0041] Normalize the satellite image data so that its pixel values ​​are between [0,1]. Where X is the original satellite image data matrix, X′ represents the normalized satellite image data matrix, max(X) and min(X) are the maximum and minimum values ​​of X respectively;

[0042] Normalize the meteorological data so that its value is between [0,1]. Where Y is the original meteorological data vector, Y' represents the normalized meteorological data vector; max(Y) and min(Y) are the maximum and minimum values ​​of Y respectively;

[0043] Build a convolutional neural network (CNN) model and define the CNN architecture:

[0044] Input layer: input high-resolution satellite images and meteorological data;

[0045] Satellite image input: shape is (H, W, C), where H is the height, W is the width, and C is the number of channels;

[0046] Meteorological data input: shape is (T, F), where T is the time step and F is the number of features;

[0047] Multiple convolutional layers are used to extract features of satellite images and meteorological data, Z = ReLU(Conv(X',W x )+b x ), X' is the normalized satellite image data matrix, W x is the convolution kernel weight matrix, b xis the bias vector, Conv represents the convolution operation, and ReLU is the activation function, which is defined as ReLU(z)=max(0,z);

[0048] Fuse the satellite image features and meteorological data features, F = Concat (Z, Y'), where Z represents the feature matrix extracted from the satellite image.

[0049] Y' is the normalized meteorological data vector, and Concat represents the feature concatenation operation;

[0050] Fully connected layer: Use the fully connected layer to process the fused features, O = ReLU (W f ·F+b f ), where W f represents the weight matrix of the fully connected layer, b f represents the bias vector, represents matrix multiplication;

[0051] Output layer: Generate fused geographic information, is the predicted fused geographic information vector, Softmax is the activation function, defined as For classification tasks, where i represents the i-th data point and j represents the category index;

[0052] The present invention is further configured such that the multi-source data fusion method using a convolutional neural network (CNN) also includes:

[0053] Perform model training: Use mean square error MSE as the loss function, Where N is the number of samples, is the predicted value of the i-th sample, Y i is the true value of the i-th sample;

[0054] Use the Adam optimization algorithm to update the model parameters and update the formula: η is the learning rate, θ is the model parameter vector, is the gradient of the loss function L with respect to the parameter θ;

[0055] Perform model evaluation and deployment, integrate the model into the Flink stream processing pipeline, and perform real-time data fusion and analysis;

[0056] The present invention is further configured to use the Kalman filter algorithm to dynamically fuse data and update the model in the following manner:

[0057] Define the system state space model, including the state transfer equation and observation equation,

[0058] State transfer equation: X k+1 =A k Xk +B k u k +w k , where X k+1 is the state vector at time k+1, representing the state of the terrain model, including terrain height and slope; A k is the state transfer matrix, that is, the state change of the system from time k to k+1; B k is the control input matrix, and the control input u k Convert to state change; u k That is, the control input vector at time k, which represents external influences, including rainfall, earthquake,

[0059] w k is the process noise, which is set to zero-mean normal distribution, and the covariance matrix is ​​expressed as Q k ;

[0060] Observation equation: z k =H k X k +v k , where z k is the observation vector at time k, representing the observation value extracted from satellite images and meteorological data; H k As the observation matrix, the state vector X k Mapped to observation space; v k is the observation noise, which is set to zero-mean normal distribution, and the covariance matrix is ​​R k ;

[0061] Set the initial conditions and initialize the state vector and covariance matrix at time k = 0;

[0062] Initial state vector: X0 = X init , X init That is, the initial state vector;

[0063] Initial covariance matrix: P0 = P init , P init That is, the initial covariance matrix;

[0064] The present invention is further configured such that the dynamic fusion of data and the model update method using the Kalman filter algorithm also include:

[0065] At each time step k, the state is predicted according to the state transition equation;

[0066] Status prediction: in is the prior state estimate at time k, is the posterior state estimate at time k-1;

[0067] Covariance prediction: Among them, P k|k-1 is the prior covariance matrix at time k, P k-1|k-1 is the posterior covariance matrix at time k-1;

[0068] The state is updated according to the observation equation,

[0069] Calculate the Kalman gain: Here K k is the Kalman gain at time k;

[0070] Status Update: Here is the posterior state estimate at time k; z k is the observation vector at time k;

[0071] Covariance update: P k|k =(IK k H k )P k|k-1 , where P k|k is the posterior covariance matrix at time k, and I is the identity matrix;

[0072] The present invention is further configured to use the LSTM machine learning algorithm to predict terrain changes as follows:

[0073] Collect historical topographic data, including terrain height and slope, as well as external data that influence topographic changes, including rainfall, temperature, and seismic activity;

[0074] Preprocess the data and divide the data into training set, validation set and test set according to time series;

[0075] Define the LSTM network architecture and perform model training;

[0076] Integrate the trained LSTM model into the geographic target construction and analysis system to predict terrain changes;

[0077] Acquire new terrain data and external impact data in real time from the data acquisition module, input the real-time data into the LSTM model, and generate terrain change prediction results;

[0078] The present invention is further configured to generate a high-resolution model through adversarial training of the generator and the discriminator in the following manner:

[0079] Input the acquired low-resolution terrain data, the shape is (H, W, C), where H is the height, W is the width, and C is the number of channels;

[0080] Perform multi-layer convolution and deconvolution operations, G(z)=ReLU(ConvTranspose(G conv (z),Wg )+b g ), where G(z) is the output of the generator, high-resolution terrain data, z is the input of low-resolution terrain data, G conv (z) is the output of the convolution layer in the generator, ConvTranspose represents the deconvolution operation, and W g is the weight matrix of the deconvolution layer, b g is the bias vector of the deconvolution layer;

[0081] Use the tanh activation function to generate high-resolution images. Represents the generated high-resolution terrain data;

[0082] The present invention is further configured such that the method of generating a high-resolution model through adversarial training of a generator and a discriminator further includes: defining a discriminator network:

[0083] Input layer: input real high-resolution terrain data or high-resolution data generated by the generator;

[0084] Extract features through multi-layer convolution operations and perform binary classification;

[0085] Output layer: Use sigmoid activation function for binary classification output;

[0086] Define the loss function and use the cross entropy loss function to maximize the probability that the discriminator believes that the generated data is real data;

[0087] Use the cross entropy loss function to maximize the probability that the discriminator correctly classifies real data and generated data;

[0088] Then initialize the network parameters of the generator and discriminator for training;

[0089] In each iteration, the generator and discriminator are trained alternately;

[0090] Training the discriminator:

[0091] Sampling a batch of data from real high-resolution terrain data as real samples;

[0092] Sample a batch of data from low-resolution terrain data and generate high-resolution data as generated samples through the generator;

[0093] Calculate the discriminator loss and update the discriminator parameters;

[0094] Training the generator:

[0095] Sampling a batch of data from low-resolution terrain data;

[0096] Generate high-resolution data through the generator;

[0097] Calculate the generator loss and update the generator parameters;

[0098] The present invention is further configured such that the workflow of the three-dimensional geographic target construction and analysis system is as follows:

[0099] Step 1. Data collection: Each data collection unit runs in parallel to obtain different types of geographic data in real time; the data is transmitted to the data processing center in real time through the data transmission unit;

[0100] Step 2: Data transmission and stream processing: Use the Kafka cluster for data transmission and the Flink stream processing pipeline to process and analyze the transmitted data in real time.

[0101] Step 3. Data fusion: In the data fusion unit, CNN is used to extract and fuse features from multi-source data to generate comprehensive geographic information.

[0102] Step 4. Dynamic update: The dynamic fusion unit combines real-time data and historical data and uses the Kalman filter algorithm to dynamically update the 3D terrain model. The model prediction unit uses the LSTM algorithm to predict terrain change trends and adjust model parameters in advance.

[0103] Step 5. Super-resolution reconstruction: The GAN generation unit generates a high-resolution 3D model through a generative adversarial network, and the progressive generation unit gradually improves the resolution and complexity of the model.

[0104] Compared with the prior art, the present invention has the following beneficial effects:

[0105] This paper uses the generative adversarial network (GANs) in deep learning to perform super-resolution reconstruction, improving the detail of the 3D model. It uses the adversarial training method of the generator and the discriminator, where the generator is responsible for generating high-resolution 3D models and the discriminator is responsible for determining whether the generated model is realistic. In combination with a progressive generation strategy, the resolution and complexity of the model are gradually increased.

[0106] This paper uses the stream processing framework Apache Kafka for real-time data processing and analysis, allowing the model to promptly reflect terrain changes. Through the stream processing pipeline, sensor data and satellite imagery are collected and processed in real time to detect and analyze terrain changes. Furthermore, an incremental update strategy is adopted to reduce data processing delays and computing resource consumption.

[0107] This invention introduces a dynamic update algorithm, adopts the Kalman filter algorithm, and dynamically integrates real-time data to update the model. By combining real-time data and historical data, the three-dimensional terrain model is updated in a timely manner. The dynamic update model based on the Kalman filter adjusts the terrain model parameters in real time. The machine learning algorithm LSTM is combined to predict terrain change trends and update the model in advance.

[0108] This solves the existing problems in the use of satellite remote sensing technology to obtain large-scale geographic information, which has limited resolution and accuracy, and there are delays in data acquisition and processing, which cannot meet the needs of real-time updates; pre-deployed ground sensor networks are used to monitor terrain changes in real time and update models through data transmission, but the coverage of ground sensors is limited, the deployment and maintenance costs are high, and the data processing and integration are complex. BRIEF DESCRIPTION OF THE DRAWINGS

[0109] Figure 1 A framework diagram of the three-dimensional geographic target analysis system based on the neural network model of the present invention. DETAILED DESCRIPTION

[0110] In order to enable those skilled in the art to better understand the solutions of the present invention, 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 embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0111] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0112] The present invention is described in further detail below with reference to the accompanying drawings:

[0113] Example 1

[0114] See also Figure 1 The present invention provides a three-dimensional geographic target construction and analysis system based on a neural network model, comprising:

[0115] The data acquisition module is responsible for acquiring geographic information from various data sources in real time, including high-frequency satellite images and meteorological data;

[0116] The data acquisition module includes:

[0117] Satellite image acquisition unit, used to periodically acquire satellite images, using high-resolution commercial satellites to obtain image data in real time; providing geographic information over a wide range; satellite images have a wide coverage area and can reflect terrain changes over a large area in real time;

[0118] Meteorological data acquisition unit, used to obtain real-time meteorological data, including rainfall, temperature, and humidity, through data interfaces between meteorological satellites and ground-based meteorological stations, to assist in detecting terrain changes. Meteorological data helps analyze and predict terrain changes, including soil erosion and landslides caused by rainfall.

[0119] Data transmission module, used to transmit collected data in real time;

[0120] The data transmission module includes:

[0121] Data transmission unit, which transmits the collected data to the data processing center in real time;

[0122] Apache Kafka cluster for real-time data transmission;

[0123] Deploy a Kafka cluster in the data processing center and configure brokers, zookeepers, and topics.

[0124] The data processing and fusion module uses the stream processing framework to process and analyze the collected data in real time;

[0125] The data processing and fusion modules include:

[0126] Apache Flink cluster for real-time data processing and analysis;

[0127] Deploy a Flink cluster in the data processing center and configure the jobmanager and taskmanager;

[0128] The stream processing pipeline unit processes and analyzes the transmitted data in real time. In the stream processing pipeline unit, stream processing programs are written using Flink's DataStream API to consume data from Kafka topics and perform data cleaning, format conversion, and feature extraction.

[0129] The data fusion unit uses convolutional neural networks (CNNs) to perform feature extraction and multi-source data fusion, fusing data from different data sources to provide comprehensive geographic information;

[0130] The specific steps for using Flink for real-time data processing and analysis include:

[0131] Set up the environment and deploy Apache Kafka and Apache Flink. Both Kafka and Flink are installed in the data processing center. Set up broker, zookeeper, and topics for Kafka, and job manager and task manager for Flink.

[0132] Obtain high-resolution satellite images and weather data, and send the data to Kafka topics through the Kafka producer API;

[0133] Use KafkaproducerAPI to write data sending code to send real-time data to Kafkatopics;

[0134] Write a stream processing program. The stream processing program is written as follows:

[0135] Use Flink's DataStream API to write stream processing programs and consume data from Kafka topics;

[0136] Filter out noise data and invalid data and convert the data into a unified format;

[0137] Extract useful feature information from the data, including terrain change characteristics;

[0138] Perform real-time analysis of extracted features to detect terrain changes;

[0139] The method of multi-source data fusion using convolutional neural network CNN is as follows:

[0140] Normalize the satellite image data so that its pixel values ​​are between [0,1]. Where X is the original satellite image data matrix, X′ represents the normalized satellite image data matrix, max(X) and min(X) are the maximum and minimum values ​​of X respectively;

[0141] Normalize the meteorological data so that its value is between [0,1]. Where Y is the original meteorological data vector, Y' represents the normalized meteorological data vector; max(Y) and min(Y) are the maximum and minimum values ​​of Y respectively;

[0142] Build a convolutional neural network (CNN) model and define the CNN architecture:

[0143] Input layer: input high-resolution satellite images and meteorological data;

[0144] Satellite image input: shape is (H, W, C), where H is the height, W is the width, and C is the number of channels;

[0145] Meteorological data input: shape is (T, F), where T is the time step and F is the number of features;

[0146] Multiple convolutional layers are used to extract features of satellite images and meteorological data, Z = ReLU(Conv(X',W x )+b x ), X' is the normalized satellite image data matrix, W x is the convolution kernel weight matrix, b x is the bias vector, Conv represents the convolution operation, and ReLU is the activation function, which is defined as ReLU(z)=max(0,z);

[0147] Fuse the satellite image features and meteorological data features, F = Concat (Z, Y'), where Z represents the feature matrix extracted from the satellite image.

[0148] Y' is the normalized meteorological data vector, and Concat represents the feature concatenation operation;

[0149] Fully connected layer: Use the fully connected layer to process the fused features, O = ReLU (W f ·F+b f ), where W f represents the weight matrix of the fully connected layer, b f represents the bias vector, represents matrix multiplication;

[0150] Output layer: Generate fused geographic information, is the predicted fused geographic information vector, Softmax is the activation function, defined as For classification tasks, where i represents the i-th data point and j represents the category index;

[0151] The multi-source data fusion methods using convolutional neural networks (CNNs) also include:

[0152] Perform model training: Use mean square error MSE as the loss function, Where N is the number of samples, is the predicted value of the i-th sample, Y i is the true value of the i-th sample;

[0153] Use the Adam optimization algorithm to update the model parameters and update the formula: η is the learning rate, θ is the model parameter vector, is the gradient of the loss function L with respect to the parameter θ;

[0154] Perform model evaluation and deployment, integrate the model into the Flink stream processing pipeline, and perform real-time data fusion and analysis;

[0155] Dynamic update module, which dynamically updates the 3D terrain model based on real-time processed data;

[0156] Dynamic update modules include:

[0157] Dynamic fusion unit, which uses Kalman filter algorithm to dynamically fuse data and update the model, combining real-time data and historical data to dynamically update the terrain model;

[0158] The model prediction unit uses the LSTM machine learning algorithm to predict terrain changes and adjust model parameters; it also predicts future terrain trends and makes model adjustments in advance;

[0159] The dynamic fusion of data and model update method using Kalman filter algorithm is as follows:

[0160] Define the system state space model, including the state transfer equation and observation equation,

[0161] State transfer equation: X k+1 =A k X k +B k u k +w k , where X k+1 is the state vector at time k+1, representing the state of the terrain model, including terrain height and slope; A k is the state transfer matrix, that is, the state change of the system from time k to k+1; B k is the control input matrix, and the control input u k Convert to state change; u k That is, the control input vector at time k, which represents external influences, including rainfall, earthquake,

[0162] w k is the process noise, which is set to zero-mean normal distribution, and the covariance matrix is ​​expressed as Q k ;

[0163] Observation equation: z k =H k X k +v k , where z k is the observation vector at time k, representing the observation value extracted from satellite images and meteorological data; H k As the observation matrix, the state vector X kMapped to observation space; v k is the observation noise, which is set to zero-mean normal distribution, and the covariance matrix is ​​R k ;

[0164] Set the initial conditions and initialize the state vector and covariance matrix at time k = 0;

[0165] Initial state vector: X0 = X init , X init That is, the initial state vector;

[0166] Initial covariance matrix: P0 = P init , P init That is, the initial covariance matrix;

[0167] The dynamic fusion of data and model update methods using the Kalman filter algorithm also include:

[0168] At each time step k, the state is predicted according to the state transition equation;

[0169] Status prediction: in is the prior state estimate at time k, is the posterior state estimate at time k-1;

[0170] Covariance prediction: Among them, P k|k-1 is the prior covariance matrix at time k, P k-1|k-1 is the posterior covariance matrix at time k-1;

[0171] The state is updated according to the observation equation,

[0172] Calculate the Kalman gain: Here K k is the Kalman gain at time k;

[0173] Status Update: Here is the posterior state estimate at time k; z k is the observation vector at time k;

[0174] Covariance update: P k|k =(IK k H k )P k|k-1 , where P k|k is the posterior covariance matrix at time k, and I is the identity matrix;

[0175] The LSTM machine learning algorithm is used to predict terrain changes as follows:

[0176] Collect historical topographic data, including terrain height and slope, as well as external data that influence topographic changes, including rainfall, temperature, and seismic activity;

[0177] Preprocess the data and divide the data into training set, validation set and test set according to time series;

[0178] Define the LSTM network architecture and perform model training;

[0179] Integrate the trained LSTM model into the geographic target construction and analysis system to predict terrain changes;

[0180] Acquire new terrain data and external impact data in real time from the data acquisition module, input the real-time data into the LSTM model, and generate terrain change prediction results;

[0181] Super-resolution reconstruction module, which generates high-precision models through super-resolution reconstruction technology to improve the details of 3D terrain models;

[0182] The super-resolution reconstruction module includes:

[0183] The GAN generation unit uses generative adversarial networks (GANs) to generate high-resolution models through adversarial training between the generator and the discriminator, thereby improving the model's detail performance.

[0184] Progressive generation unit, which adopts a progressive generation strategy to gradually increase the resolution and complexity of the model;

[0185] The way to generate a high-resolution model through adversarial training of the generator and the discriminator is:

[0186] Input the acquired low-resolution terrain data, the shape is (H, W, C), where H is the height, W is the width, and C is the number of channels;

[0187] Perform multi-layer convolution and deconvolution operations, G(z)=ReLU(ConvTranspose(G conv (z),W g )+b g ), where G(z) is the output of the generator, high-resolution terrain data, z is the input of low-resolution terrain data, G conv (z) is the output of the convolution layer in the generator, ConvTranspose represents the deconvolution operation, and W g is the weight matrix of the deconvolution layer, b g is the bias vector of the deconvolution layer;

[0188] Use the tanh activation function to generate high-resolution images. Represents the generated high-resolution terrain data;

[0189] Other methods for generating high-resolution models through adversarial training of generators and discriminators include:

[0190] Define the discriminator network:

[0191] Input layer: input real high-resolution terrain data or high-resolution data generated by the generator;

[0192] Extract features through multi-layer convolution operations and perform binary classification;

[0193] Output layer: Use sigmoid activation function for binary classification output;

[0194] Define the loss function and use the cross entropy loss function to maximize the probability that the discriminator believes that the generated data is real data;

[0195] Use the cross entropy loss function to maximize the probability that the discriminator correctly classifies real data and generated data;

[0196] Then initialize the network parameters of the generator and discriminator for training;

[0197] In each iteration, the generator and discriminator are trained alternately;

[0198] Training the discriminator:

[0199] Sampling a batch of data from real high-resolution terrain data as real samples;

[0200] Sample a batch of data from low-resolution terrain data and generate high-resolution data as generated samples through the generator;

[0201] Calculate the discriminator loss and update the discriminator parameters;

[0202] Training the generator:

[0203] Sampling a batch of data from low-resolution terrain data;

[0204] Generate high-resolution data through the generator;

[0205] Calculate the generator loss and update the generator parameters;

[0206] The system workflow is as follows: Step 1. Data collection: each data collection unit runs in parallel to obtain different types of geographic data in real time; the data is transmitted to the data processing center in real time through the data transmission unit;

[0207] Step 2: Data transmission and stream processing: Use the Kafka cluster for data transmission and the Flink stream processing pipeline to process and analyze the transmitted data in real time.

[0208] Step 3. Data fusion: In the data fusion unit, CNN is used to extract and fuse features from multi-source data to generate comprehensive geographic information.

[0209] Step 4. Dynamic update: The dynamic fusion unit combines real-time data and historical data and uses the Kalman filter algorithm to dynamically update the 3D terrain model. The model prediction unit uses the LSTM algorithm to predict terrain change trends and adjust model parameters in advance.

[0210] Step 5. Super-resolution reconstruction: The GAN generation unit generates a high-resolution 3D model through a generative adversarial network, and the progressive generation unit gradually improves the resolution and complexity of the model.

[0211] The present invention adopts the generative adversarial network (GANs) in deep learning for super-resolution reconstruction to improve the detail performance of the three-dimensional model; adopts the adversarial training method of the generator and the discriminator, the generator is responsible for generating a high-resolution three-dimensional model, and the discriminator is responsible for judging whether the generated model is realistic; combines the progressive generation strategy to gradually increase the resolution and complexity of the model; uses the stream processing framework Apache Kafka for real-time data processing and analysis, so that the model can reflect terrain changes in a timely manner; through the stream processing pipeline, sensor data and satellite images are collected and processed in real time to detect and analyze terrain changes; and adopts an incremental update strategy to reduce data processing delays and computing resource consumption; introduces a dynamic update algorithm, adopts the Kalman filter algorithm, and dynamically integrates real-time data to update the model; by combining real-time data and historical data, the three-dimensional terrain model is updated in a timely manner, and the terrain model parameters are adjusted in real time based on the dynamic update model of the Kalman filter; combines the machine learning algorithm LSTM to predict terrain change trends and update the model in advance.

[0212] The above content is only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A three-dimensional geographic target construction and analysis system based on a neural network model, characterized in that: include: Data acquisition module, including: A satellite image acquisition unit, used for periodically acquiring satellite images; Meteorological data acquisition unit, which acquires meteorological data in real time, including rainfall, temperature, and humidity, and assists in terrain change detection; Data transmission module, including: Data transmission unit, which transmits the collected data to the data processing center in real time; Apache Kafka cluster for real-time data transmission; Data processing and fusion module, including: Apache Flink cluster for real-time data processing and analysis; Stream processing pipeline unit, which processes and analyzes transmitted data in real time; The data fusion unit uses convolutional neural network (CNN) to perform feature extraction and multi-source data fusion, fusing data from different data sources to provide comprehensive geographic information; Dynamic update modules, including: Dynamic fusion unit, which uses Kalman filter algorithm to dynamically fuse data and update models, combines real-time data and historical data, and dynamically updates terrain models; Model prediction unit, which uses LSTM machine learning algorithm to predict terrain changes and adjust model parameters; Super-resolution reconstruction module, including: The GAN generation unit uses the generative adversarial network GANs to generate a high-resolution model through adversarial training of the generator and the discriminator; The progressive generation unit adopts a progressive generation strategy to gradually increase the resolution and complexity of the model.

2. A three-dimensional geographic target construction and analysis system based on a neural network model according to claim 1, characterized in that: The specific steps for using Flink for real-time data processing and analysis include: Set up the environment and deploy Apache Kafka and Apache Flink. Both Kafka and Flink are installed in the data processing center. Set up broker, zookeeper, and topics for Kafka, and set up job manager and task manager for Flink. Obtain high-resolution satellite images and weather data, and send the data to Kafkatopics through the KafkaproducerAPI; Use KafkaproducerAPI to write data sending code to send real-time data to Kafkatopics; Write a stream processing program. The stream processing program is written as follows: Use Flink's DataStream API to write stream processing programs to consume data from Kafka topics; Filter out noise data and invalid data, and convert the data into a unified format; Extract useful feature information from the data, including terrain change characteristics; The extracted features are analyzed in real time to detect terrain changes.

3. A three-dimensional geographic target construction and analysis system based on a neural network model according to claim 2, characterized in that: The method of using convolutional neural network CNN for multi-source data fusion is as follows: Normalize the satellite image data so that its pixel values ​​are between [0,1]. Where X is the original satellite image data matrix, X′ represents the normalized satellite image data matrix, max(X) and min(X) are the maximum and minimum values ​​of X respectively; Normalize the meteorological data so that its value is between [0,1]. Where Y is the original meteorological data vector, Y' represents the normalized meteorological data vector; max(Y) and min(Y) are the maximum and minimum values ​​of Y respectively; Build a convolutional neural network CNN model and define the CNN architecture: Input layer: input high-resolution satellite images and meteorological data; Satellite image input: shape is (H, W, C), where H is the height, W is the width, and C is the number of channels; Meteorological data input: shape is (T, F), where T is the time step and F is the number of features; Multiple convolutional layers are used to extract features of satellite images and meteorological data, Z = ReLU (Conv (X ′, W x )+b x ), X' is the normalized satellite image data matrix, W x is the convolution kernel weight matrix, b x is the bias vector, Conv represents the convolution operation, and ReLU is the activation function, which is defined as ReLU(z)=max(0,z); The satellite image features and meteorological data features are fused, F = Concat (Z, Y′), where Z represents the feature matrix extracted from the satellite image. Y′ is the normalized meteorological data vector, and Concat represents the concatenation operation of features; Fully connected layer: Use the fully connected layer to process the fused features, O = ReLU (W f ·F+b f ), where W f represents the weight matrix of the fully connected layer, b f represents the bias vector, represents the matrix multiplication; Output layer: Generate fused geographic information. is the predicted fused geographic information vector, and Soft max is the activation function, defined as For classification tasks, where i represents the i-th data point and j represents the category index.

4. A three-dimensional geographic target construction and analysis system based on a neural network model according to claim 3, characterized in that: The multi-source data fusion method using convolutional neural network CNN also includes: Model training: Use mean square error MSE as the loss function. Where N is the number of samples, is the predicted value of the i-th sample, Y i is the true value of the i-th sample; Use the Adam optimization algorithm to update the model parameters and update the formula: η is the learning rate, θ is the model parameter vector, is the gradient of the loss function L with respect to the parameter θ; Evaluate and deploy models, integrate models into Flink stream processing pipelines, and perform real-time data fusion and analysis.

5. A three-dimensional geographic target construction and analysis system based on a neural network model according to claim 4, characterized in that: The dynamic fusion and model update method of data using Kalman filter algorithm is as follows: Define the system state space model, including state transfer equations and observation equations, State transfer equation: X k+1 =A k X k +B k u k +w k , where X k+1 is the state vector at time k+1, indicating the state of the terrain model, including terrain height and slope; A k is the state transfer matrix, that is, the state change of the system from time k to k+1; B k is the control input matrix, and the control input u k Transformed to state change; u k That is, the control input vector at time k represents external influences, including rainfall, earthquake, w k is the process noise, set to zero-mean normal distribution, and the covariance matrix is ​​expressed as Q k ; Observation equation: z k =H k X k +v k , where z k is the observation vector at time k, representing the observation value extracted from satellite images and meteorological data; H k As the observation matrix, the state vector X k Mapped to observation space; v k is the observation noise, set to zero-mean normal distribution, and the covariance matrix is ​​R k ; Set the initial conditions, and initialize the state vector and covariance matrix at time k = 0; Initial state vector: X0 = X init , X init That is, the initial state vector; Initial covariance matrix: P0 = P init , P init That is, the initial covariance matrix.

6. A three-dimensional geographic target construction and analysis system based on a neural network model according to claim 5, characterized in that: The dynamic fusion and model update methods of using Kalman filter algorithm for data also include: At each time step k, the state prediction is performed according to the state transfer equation; Status prediction: in is the prior state estimate at time k, is the posterior state estimate at time k-1; Covariance prediction: Where P k|k-1 is the prior covariance matrix at time k, P k-1|k-1 is the posterior covariance matrix at time k-1; The state is updated according to the observation equation, Calculate the Kalman gain: Here K k is the Kalman gain at time k; Status Update: Here is the posterior state estimate at time k; z k is the observation vector at time k; Covariance update: P k|k =(IK k H k ) k|k-1 , where P k|k is the posterior covariance matrix at time k, and I is the identity matrix.

7. A three-dimensional geographic target construction and analysis system based on a neural network model according to claim 6, characterized in that: The LSTM machine learning algorithm is used to predict terrain changes as follows: Collect historical topographic data, including topographic height and slope, and external data that influence topographic changes, including rainfall, temperature, and seismic activity; Preprocess the data and divide the data into training set, validation set and test set according to time series; Define the LSTM network architecture and perform model training; Integrate the trained LSTM model into the geographic target construction and analysis system to predict terrain changes; New terrain data and external impact data are obtained from the data acquisition module in real time, and the real-time data is input into the LSTM model to generate terrain change prediction results.

8. A three-dimensional geographic target construction and analysis system based on a neural network model according to claim 7, characterized in that: The way to generate a high-resolution model through adversarial training of the generator and the discriminator is: Input the acquired low-resolution terrain data, the shape is (H, W, C), where H is the height, W is the width, and C is the number of channels; Perform multi-layer convolution and deconvolution operations, G(z) = ReLU(ConvTranspose(G conv (z),W g )+b g ), where G(z) is the output of the generator, high-resolution terrain data, z is the input of low-resolution terrain data, G conv (z) is the output of the convolution layer in the generator, ConvTranspose represents the deconvolution operation, and W g is the weight matrix of the deconvolution layer, b g is the bias vector of the deconvolution layer; Use the tanh activation function to generate high-resolution images. Represents the generated high-resolution terrain data.

9. A three-dimensional geographic target construction and analysis system based on a neural network model according to claim 8, characterized in that: The method of generating a high-resolution model through adversarial training of the generator and the discriminator also includes: defining the discriminator network: Input layer: input real high-resolution terrain data or high-resolution data generated by the generator; Features are extracted through multi-layer convolution operations for binary classification; Output layer: Use sigmoid activation function for binary classification output; Define the loss function and use the cross entropy loss function to maximize the probability that the discriminator considers the generated data to be real data; Use the cross entropy loss function to maximize the probability that the discriminator correctly classifies real data and generated data; Then initialize the network parameters of the generator and discriminator for training; In each iteration, the generator and discriminator are trained alternately; Training the discriminator: Sampling a batch of data from real high-resolution terrain data as real samples; Sample a batch of data from low-resolution terrain data and generate high-resolution data as generated samples through the generator; Calculate the discriminator loss and update the discriminator parameters; Training the generator: Sample a batch of data from low-resolution terrain data; Generate high-resolution data through the generator; Calculate the generator loss and update the generator parameters.

10. A three-dimensional geographic target construction and analysis system based on a neural network model according to claim 9, characterized in that: The workflow of the 3D geographic target construction and analysis system is as follows: Step 1. Data collection: each data collection unit runs in parallel to obtain different types of geographic data in real time; the data is transmitted to the data processing center in real time through the data transmission unit; Step 2: Data transmission and stream processing: Use the Kafka cluster for data transmission and the Flink stream processing pipeline to process and analyze the transmitted data in real time. Step 3. Data fusion: In the data fusion unit, CNN is used to extract and fuse the features of multi-source data to generate comprehensive geographic information. Step 4. Dynamic update: The dynamic fusion unit combines real-time data and historical data and uses the Kalman filter algorithm to dynamically update the three-dimensional terrain model; the model prediction unit uses the LSTM algorithm to predict the terrain change trend and adjust the model parameters in advance; Step 5. Super-resolution reconstruction: The GAN generation unit generates a high-resolution three-dimensional model through a generative adversarial network, and the progressive generation unit gradually improves the resolution and complexity of the model.

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