Field intensity prediction method of high-power microwave radiation field and related device

Abstract

The invention relates to the technical field of field intensity prediction, in particular to a field intensity prediction method of a high-power microwave radiation field and a related device. The method comprises the following steps: recording acquired data, determining field intensity data of microwave receiving antennas under each azimuth parameter according to each acquired sub-data, and constructing a sparse matrix field intensity data set according to the field intensity data of each microwave receiving antenna under each azimuth parameter; training a preset field intensity prediction model according to the sparse matrix field intensity data set to obtain a trained field intensity prediction model; and for each microwave receiving antenna, determining coordinates of a measurement point location, and inputting the coordinates into the field intensity prediction model to obtain a field intensity prediction value of the measurement point location output by the field intensity prediction model. According to the method, the complex nonlinear mapping relation, namely the mapping relation between the orientation parameters and the field intensity values, can be learned through the field intensity prediction model, deep features can be extracted from the collected data, high-precision field intensity prediction is achieved, and the prediction error is remarkably reduced.

Description

Technical Field

[0001] This application relates to the field of field strength prediction technology, and in particular to a method and related apparatus for predicting the field strength of a high-power microwave radiation field. Background Technology

[0002] In high-power microwave measurement systems, the radiation field situation diagram is a key parameter describing the performance of antenna radiation or received signals. Traditional microwave signal simulation methods typically employ deterministic or empirical models, but these methods suffer from the following technical problems: Insufficient accuracy: Empirical models have poor prediction accuracy in complex electromagnetic propagation environments and struggle to accurately reflect the actual field strength distribution; High computational complexity: Deterministic models require precise scene environment information, resulting in large computational loads and low efficiency; Weak generalization ability: Traditional models have poor adaptability to unknown scenes and struggle to handle complex situations such as nonlinear and non-uniform media; High measurement cost: High-power microwave measurement equipment requires high shielding effectiveness, leading to high equipment costs. Constructing a spatial radiation field situation requires simultaneous acquisition by numerous devices, further increasing measurement costs. A large number of test samples are also needed, making them inconvenient for application. Summary of the Invention

[0003] To address the aforementioned issues, this application provides a method and related apparatus for predicting the field strength of a high-power microwave radiation field. This method can learn complex nonlinear mapping relationships, i.e., the mapping relationship between azimuth parameters and field strength values, through a field strength prediction model. It can extract deep features from the collected data, achieve high-precision field strength prediction, and significantly reduce prediction errors.

[0004] According to one aspect of the embodiments of this application, a method for predicting the field strength of a high-power microwave radiation field is proposed, applied to a high-power microwave radiation system. The high-power microwave radiation system includes a radiation source located within the high-power microwave radiation field, multiple microwave receiving antennas, and a data acquisition device. Each microwave receiving antenna corresponds to a single measurement point. The radiation source is used to rotate around the high-power microwave radiation field formed by the microwave receiving antennas and the data acquisition device. The data acquisition device is used to collect data from the radiation source for each microwave receiving antenna during the rotation. The method includes: The data collected by the acquisition device during the rotation of the radiation source includes acquisition sub-data corresponding to each microwave receiving antenna. Each acquisition sub-data corresponds to multiple azimuth parameters formed by the radiation source during the rotation. Each azimuth parameter includes the azimuth angle, elevation angle and distance between the radiation source and the microwave receiving antenna. Based on the collected sub-data, the field strength data of each microwave receiving antenna under each azimuth parameter is determined, and the sparse matrix field strength dataset of the high-power microwave radiation field is constructed based on the field strength data of each microwave receiving antenna under each azimuth parameter. The preset field strength prediction model is trained based on the sparse matrix field strength dataset to obtain the trained field strength prediction model. For each microwave receiving antenna, the coordinates of the measurement point corresponding to the microwave receiving antenna are determined, and the coordinates are input into the field strength prediction model to obtain the field strength prediction value of the measurement point output by the field strength prediction model.

[0005] In the above scheme, determining the field strength data of each microwave receiving antenna under each azimuth parameter based on each of the acquired sub-data includes: For each microwave receiving antenna, the voltage amplitude of the microwave receiving antenna under multiple azimuth parameters is determined as the radiation source rotates around the high-power microwave radiation field, and each azimuth parameter corresponds to a single voltage amplitude. The field strength data corresponding to each of the azimuth parameters is determined based on the voltage amplitude of the microwave receiving antenna under each of the azimuth parameters.

[0006] In the above scheme, the step of constructing a sparse matrix field strength dataset of the high-power microwave radiation field based on the field strength data of each microwave receiving antenna under each of the azimuth parameters includes: Determine the location information of each microwave receiving antenna; For each microwave receiving antenna, the location information is associated and bound with the field strength data of the microwave receiving antenna under each of the azimuth parameters to construct a field strength subset of the microwave receiving antenna. The field strength subsets of each microwave receiving antenna are used as the sparse matrix field strength dataset.

[0007] In the above scheme, the step of training a preset field strength prediction model based on the sparse matrix field strength dataset to obtain the trained field strength prediction model includes: Determine a validation set corresponding to the sparse matrix field strength dataset, the validation set including the actual field strength data of each microwave receiving antenna under multiple azimuth parameters; The sparse matrix field strength dataset and the position information of the microwave receiving antenna are input into the preset field strength prediction model to obtain the field strength prediction value of the microwave receiving antenna under each of the azimuth parameters. The preset field strength prediction model is trained based on the error between the actual field strength data of the microwave receiving antenna under multiple azimuth parameters and the predicted field strength values ​​of the microwave receiving antenna under each of the azimuth parameters, to obtain the trained field strength prediction model.

[0008] In the above scheme, the method further includes: Determine the predicted field strength at each of the measurement points under each of the azimuth parameters; A microwave signal pattern for the high-power microwave radiation field is constructed based on the predicted field strength values ​​of each measurement point under each of the azimuth parameters.

[0009] In the above scheme, the azimuth angle is used to characterize the azimuth angle formed between the radiation source and the microwave receiving antenna, and the elevation angle is used to characterize the height distance between the radiation source and the microwave receiving antenna.

[0010] According to one aspect of the embodiments of this application, a field strength prediction device for a high-power microwave radiation field is proposed, applied to a high-power microwave radiation system. The high-power microwave radiation system includes a radiation source located in the high-power microwave radiation field, multiple microwave receiving antennas, and a data acquisition device. Each microwave receiving antenna corresponds to a single measurement point. The radiation source is used to rotate around the high-power microwave radiation field formed by the microwave receiving antennas and the data acquisition device. The data acquisition device is used to collect data collected by the radiation source for each microwave receiving antenna during the rotation. The device includes: The acquisition unit is used to record the acquisition data of the acquisition device during the rotation of the radiation source. The acquisition data includes acquisition sub-data corresponding to each microwave receiving antenna. Each acquisition sub-data corresponds to multiple azimuth parameters formed by the radiation source during the rotation. Each azimuth parameter includes the azimuth angle, elevation angle and distance between the radiation source and the microwave receiving antenna formed between them. The determining unit is used to determine the field strength data of each microwave receiving antenna under each azimuth parameter based on each of the collected sub-data, and to construct the sparse matrix field strength dataset of the high-power microwave radiation field based on the field strength data of each microwave receiving antenna under each azimuth parameter. The training unit is used to train the preset field strength prediction model based on the sparse matrix field strength dataset to obtain the trained field strength prediction model. The prediction unit is used to determine the coordinates of the measurement point corresponding to each microwave receiving antenna, input the coordinates into the field strength prediction model, and obtain the predicted field strength value of the measurement point output by the field strength prediction model.

[0011] According to one aspect of the embodiments of this application, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the field strength prediction method for high-power microwave radiation fields as described above.

[0012] According to one aspect of the embodiments of this application, a computer program product is provided, the computer program product including a computer program that is read and executed by a processor of an electronic device, causing the electronic device to perform the field strength prediction method for a high-power microwave radiation field as described above.

[0013] The beneficial effects of this application are as follows: This application first constructs a high-power microwave radiation system, which includes a radiation source located in a high-power microwave radiation field, multiple microwave receiving antennas, and acquisition equipment. The radiation source rotates around each microwave receiving antenna or the measured point within the high-power microwave radiation field. During this rotation, the acquisition equipment records the collected data under different azimuth parameters between the radiation source and the measured point, i.e., the acquired sub-data corresponding to each microwave receiving antenna. This data is used to construct training samples for a pre-defined field strength prediction model, i.e., a sparse matrix field strength dataset. Sparse matrix field strength datasets centrally represent the mapping relationship between azimuth parameters and field strength data. By training a pre-defined field strength prediction model using this dataset, the model learns this mapping relationship. Consequently, the trained model can accurately predict field strength values ​​for unknown azimuth parameters. Specifically, by inputting the coordinates of the measurement point corresponding to the microwave receiving antenna and the azimuth parameter formed by the antenna and the radiation source into the trained model, the predicted field strength value for that azimuth parameter can be obtained. Therefore, this application can learn complex nonlinear mapping relationships—that is, the mapping relationship between azimuth parameters and field strength values—through the field strength prediction model. This allows for the extraction of deep features from the collected data, achieving high-precision field strength prediction with a significant reduction in prediction error. Attached Figure Description

[0014] Figure 1 This is a system architecture diagram of the field strength prediction method for high-power microwave radiation fields provided in the embodiments of this application. Figure 2 A flowchart illustrating the field strength prediction method for high-power microwave radiation fields provided in this application embodiment; Figure 3 A schematic diagram of a high-power microwave radiation field provided for an embodiment of this application; Figure 4 A schematic diagram for spatial modeling of the radiation field; Figure 5This is an interpolated cross-section view with a height of 1 meter; Figure 6 This is an interpolated cross-section view at a height of 2 meters; Figure 7 This is an interpolated cross-section view at a height of 3 meters; Figure 8 This is an interpolated cross-section view at a height of 5 meters; Figure 9 This is a cross-sectional view of the interpolated field strength in the angular direction. Figure 10 This is a cross-sectional view of the field strength interpolation in the height direction; Figure 11 This is a cross-sectional view of the field strength interpolation in the range direction; Figure 12 A block diagram of a field strength prediction device for a high-power microwave radiation field provided in an embodiment of this application; Figure 13 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application; Figure 14 This is a schematic diagram of the structure of a server provided in an embodiment of this application. Detailed Implementation

[0015] To enable those skilled in the art to better understand the solutions of this application, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0016] It should be noted that while some processes described in the specification, claims, and accompanying drawings include multiple steps appearing in a specific order, it should be clearly understood that these steps may not be performed in the order they appear herein, or may be performed in parallel. The step numbers are merely used to distinguish different steps and do not themselves represent any execution order. Furthermore, descriptions such as "first," "second," or "objective" in this document are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. "Multiple" in this document refers to at least two.

[0017] It is worth noting that in the specific embodiments of this application, data such as collected data and field strength data are involved. When the above embodiments of this application are applied to specific products or technologies, permission or consent from the target object is required, and the collection, use, and processing of related data must comply with relevant laws, regulations, and standards. For example, when an embodiment of this application needs to obtain data such as collected data and field strength data, separate permission or consent from the target object can be obtained through pop-up windows or redirection to a confirmation page. After obtaining separate permission or consent from the target object, the collected data, field strength data, and other related data used to enable the embodiment of this application to operate normally can then be obtained.

[0018] Please see Figure 1 , Figure 1 This is a system architecture diagram of the field strength prediction method for high-power microwave radiation fields provided in this application embodiment. It includes a terminal 140, an Internet connection 130, a gateway 120, a server 110, etc.

[0019] Terminal 140 can take various forms, including desktop computers, laptops, PDAs (personal digital assistants), mobile phones, vehicle terminals, and dedicated terminals. Furthermore, it can be a single device or a collection of multiple devices. For example, multiple desktop computers can be interconnected via a local area network, sharing a single monitor to work collaboratively, forming a single terminal 140. Terminal 140 can communicate with the Internet 130 via wired or wireless means to exchange data.

[0020] Server 110 refers to a computer system capable of providing certain services to terminal 140. Compared to ordinary terminal 140, server 110 has higher requirements in terms of stability, security, and performance. Server 110 can be a single high-performance computer in a network platform, a cluster of multiple high-performance computers, a portion of a single high-performance computer (e.g., a virtual machine), or a combination of portions of multiple high-performance computers (e.g., virtual machines). Server 110 can also communicate with the Internet 130 via wired or wireless means to exchange data.

[0021] Gateway 120, also known as an internetwork connector or protocol converter, is a computer system or device that acts as a translator, enabling network interconnection at the transport layer. It bridges the gap between two systems using different communication protocols, data formats, languages, or even completely different architectures. Gateways can also provide filtering and security functions. Messages sent from terminal 140 to server 110 are forwarded to the corresponding server 110 via gateway 120. Messages sent from server 110 to terminal 140 are also forwarded to the corresponding terminal 140 via gateway 120.

[0022] The following provides a detailed description of the specific implementation methods of the embodiments of this application: Please see Figure 2 , Figure 2 This is a flowchart illustrating the field strength prediction method for a high-power microwave radiation field provided in this application embodiment. The field strength prediction method for a high-power microwave radiation field can be implemented by server 110 and / or terminal 140. Figure 2 The field strength prediction methods for high-power microwave radiation fields shown include: Step 210: Record the data collected by the acquisition device during the rotation of the radiation source. The data collected includes acquisition sub-data corresponding to each microwave receiving antenna. Each acquisition sub-data corresponds to multiple azimuth parameters formed by the radiation source during the rotation. Each azimuth parameter includes the azimuth angle, elevation angle and distance between the radiation source and the microwave receiving antenna. Step 220: Determine the field strength data of each microwave receiving antenna under each azimuth parameter based on each of the collected sub-data, and construct the sparse matrix field strength dataset of the high-power microwave radiation field based on the field strength data of each microwave receiving antenna under each azimuth parameter. Step 230: Train the preset field strength prediction model based on the sparse matrix field strength dataset to obtain the trained field strength prediction model. Step 240: For each microwave receiving antenna, determine the coordinates of the measurement point corresponding to the microwave receiving antenna, input the coordinates into the field strength prediction model, and obtain the field strength prediction value of the measurement point output by the field strength prediction model.

[0023] The complete embodiments of this application are explained in detail below: First, the overall approach of this application can be summarized into the following five parts: Step 1: High-power microwave field strength data acquisition and preprocessing. Microwave signal field strength data are obtained through measurement or simulation, and a sparse matrix field strength dataset is constructed. The collected field strength data is normalized to eliminate the influence of dimensions; The K-means clustering method is used to spatially decompose the sample points and establish a training set for the sub-neural network modules.

[0024] Step 2: Model building. Construct a field strength prediction model based on neural networks (i.e., a pre-defined field strength prediction model). The model consists of an input layer, a hidden layer, and an output layer, where the hidden layer uses Gaussian radial basis functions as activation functions; The Levenberg-Marquardt algorithm was used to train the neural network model and optimize the network weights and bias parameters.

[0025] Step 3: Model training and optimization. The preprocessed sparse matrix field strength data is used as input, and the corresponding microwave signal pattern (that is, the field strength values ​​of each microwave receiving antenna under each azimuth parameter are used to construct a whole microwave signal pattern) is used as output. The prediction error is minimized by updating the neural network weights using gradient descent. Cross-validation is used to evaluate model performance and prevent overfitting. The generalization ability of the model can be optimized by adjusting hyperparameters such as the number of neurons in the hidden layer and the learning rate.

[0026] Step 4: Microwave signal radiation field interpolation. For the location to be interpolated (i.e., the location of the radiation source as a variable), its spatial coordinate parameters are input into the trained neural network model. Since the coordinates of the microwave receiving antenna are known, an azimuth parameter can be obtained by inputting the location to be interpolated (i.e., the location of the radiation source). Since the field strength prediction model learns the mapping relationship between the azimuth parameter and the field strength value, the predicted field strength value of the location to be interpolated can be obtained by inputting the azimuth parameter.

[0027] The model outputs the predicted field strength at that location; The prediction results are smoothed using bilinear interpolation or cubic spline interpolation methods. Generate a complete three-dimensional microwave signal pattern to achieve situational visualization.

[0028] Step 5: Performance evaluation and verification. Interpolation accuracy is evaluated using metrics such as root mean square error (RMSE) and mean absolute error (MAE). The effectiveness of the field strength prediction model was verified by comparing the measured data with the predicted data.

[0029] Specifically, the following is a detailed explanation of the four parts mentioned above: Modeling of high-power radiation field measurements; Measurement of high-power microwave radiation sources, such as Figure 3 As shown, the data acquisition equipment is arranged radially around the radiation source on a concentric ring centered on the radiation source. Each test point (i.e., the point being measured, or the microwave receiving antenna) can be deployed. The microwave receiving antenna can be, for example, an antenna. Due to the influence of antenna height, the equipment is generally deployed at heights such as 1 meter, 2 meters, 3 meters, and 4 meters. It can collect field strength values ​​at different distances, angles (i.e., azimuth angles), and heights (i.e., elevation angles). The interpolation graph of the angular direction is shown below. Figure 9As shown, the height direction interpolation section is as follows: Figure 10 As shown, the distance direction interpolation tangent is as follows: Figure 11 As shown.

[0030] Spatial modeling of radiation fields; The field strength of the high-power microwave radiation field is modeled in space using 3D spherical data. This is based on the deployment of the acquisition equipment, such as... Figure 4 As shown, H1, H2, H3, and H4 are measurement points for spatial height changes, and measurement points 1, 5, 9, 13, 17, and 21 are measurement points for distance changes. The test data is presented in a spherical matrix formed by using the azimuth and elevation angles of the measurement points as input vectors and the measured field strength as the output value.

[0031] Sparse matrix formula; For the model, the numerical field strength mapping from the input layer to the output layer can be represented as: y = W3·σ(W2·σ(W1·x + b1) + b2) + b3 in: x = [θ, φ, a] The input vector is (azimuth θ, elevation φ, distance a). y represents the output field strength value; W1 is the weight matrix from the input layer to hidden layer 1 (2×10 dimensions). W2 is the weight matrix (10×10 dimensions) from hidden layer 1 to hidden layer 2. W3 is the weight matrix from hidden layer 2 to the output layer (10×1 dimension). b1, b2, b3 are the bias vectors for each layer; σ(·) is the Sigmoid activation function: σ(z) = 1 / (1 + e^(-z / z)) -z ).

[0032] Matrix expansion form; Expanding the above formula into matrix operations:

[0033] This represents the weight from the i-th input to the j-th hidden neuron; This represents the weights from the j-th hidden neuron to the k-th hidden neuron; This represents the weight from the k-th hidden neuron to the output neuron.

[0034] Field strength data preprocessing and format conversion, input data standardization; The FANN_FLO model requires input data as floating-point arrays and necessitates normalization to improve training stability. According to the training data format requirements of the FANN library, the field strength data needs to be converted to the following structure: File format: Each line contains input (angle coordinates) and output (field strength value), in the following format: num_train_datanum_inputnum_output, where each subsequent line contains the input vector and the output vector, respectively.

[0035] Normalization method: Min-max scaling is used to compress the field strength data to the range [0, 1]. The formula is as follows:

[0036] Coordinate System 1: Microwave radiation patterns typically use azimuth and elevation as variables, requiring the polar coordinates (θ, φ) of the measured data to be converted to Cartesian coordinates (x, y, z) or directly used as angle input. For example, in 3D radiation pattern interpolation, the input layer can contain two neurons (azimuth and elevation), and the output layer has one neuron (field strength value).

[0037] 2. FANN_FLO network structure configuration: Model topology design; According to the search results, the typical structure of FANN_FLO is a feedforward neural network, which includes: Input layer: The number of neurons is equal to the dimension of the input parameters (e.g., azimuth angle + elevation angle = 2 neurons).

[0038] Hidden layers: 1-2 layers are recommended, with each layer containing 2-5 times the number of neurons as the input layer. For example, a hidden layer with 10 neurons can effectively capture the non-linear characteristics of the pattern.

[0039] Output layer: 1 neuron, corresponding to the interpolated field strength value.

[0040] Activation function and training parameters; Hidden layer: The Sigmoid function (FANN_SIGMOID) is used because of its adaptability to non-linear mapping.

[0041] Learning rate: The initial value is set to 0.1, and it is dynamically adjusted based on the validation set error.

[0042] Training algorithm: RPROP (Resilient Backpropagation) is used, which is suitable for continuous value prediction.

[0043] 3. Training Sample Generation and Model Training: Data augmentation strategies; To improve interpolation accuracy, the training set can be expanded by combining it with traditional interpolation methods: Hybrid interpolation method: The measured principal plane (azimuth and elevation) data are combined with the model parameter estimation through weighted summation to generate pseudo-3D data.

[0044] Random sampling: Randomly offset the measurement data by an angle (±1°) to simulate noise interference and enhance the robustness of the model.

[0045] Model training process; Data loading: Use the fann_read_train_from_file function to read the preprocessed training data.

[0046] Network initialization: The network is built using the `fann_create` function. Example code snippet: Create a model: `struct fann *ann = fann_create_standard(3, 2, 10, 1)`, with 2 inputs, 10 hidden neurons, and 1 output. Set the activation function for the hidden function: `fann_set_activation_function_hidden(ann, FANN_SIGMOID);` Set the activation function output: `fann_set_activation_function_output(ann, FANN_LINEAR);` Training execution: Call the fann_train_on_data function to set the maximum number of iterations (e.g., 10,000 times) and the target mean squared error (MSE < 1e-5). Model saving: Save the network parameters using fann_save after training is complete.

[0047] 4. Interpolation simulation and accuracy evaluation: directional pattern interpolation prediction; For unmeasured angles (θ, φ), the coordinates are input through the fann_run function, and the model outputs the interpolated field strength value to reconstruct the full-space radiation pattern. For example, grid points at 360° azimuth and 180° elevation angles are predicted to generate a complete 3D radiation pattern.

[0048] Error assessment metrics; The following metrics were used to verify the interpolation effect: Root Mean Square Error (RMSE): Measures the overall deviation between predicted and actual values. The formula is:

[0049] Equivalent Spurious Signal (ESS): Used to evaluate the consistency between the main lobe and side lobes of the radiation pattern, defined as the difference in power spectrum between the interpolated radiation pattern and the measured value.

[0050] Angular correlation coefficient (k): quantifies the similarity of the shape of the direction pattern, with a value range of [-1, 1]. The closer to 1, the better the consistency.

[0051] In the reconstruction of the radiation field situation map of a 3D antenna radiation source, a machine learning model is used to interpolate the field strength from 2D principal plane measurement data to the full space field strength: Input: azimuth (θ), elevation (φ), and distance (a); Output: Field strength (dBm); Accuracy: After training with 1000 sets of measurement data, RMSE can be controlled within 0.5 dB and ESS is below -30 dB.

[0052] In summary, this application first constructs a high-power microwave radiation system, which includes a radiation source located in a high-power microwave radiation field, multiple microwave receiving antennas, and acquisition equipment. The radiation source rotates around each microwave receiving antenna or measurement point within the high-power microwave radiation field. During this rotation, the acquisition equipment records data collected under different azimuth parameters between the radiation source and the measurement point; this data corresponds to the acquisition sub-data for each microwave receiving antenna. This data is used to construct training samples for a pre-defined field strength prediction model, i.e., a sparse matrix field strength dataset. The azimuth parameters include the azimuth angle and elevation angle formed between the radiation source and the microwave receiving antennas, and the distance between them. The azimuth angle and elevation angle characterize the height and angle between the radiation source and the microwave receiving antennas. Combined with the distance between them, and using a small amount of test point information (the sparse matrix field strength dataset), the corresponding field strength data under other azimuth parameters of the radiation source are retrieved.

[0053] Sparse matrix field strength datasets centrally represent the mapping relationship between azimuth parameters and field strength data. By training a pre-defined field strength prediction model using this dataset, the model learns this mapping relationship. Consequently, the trained model can accurately predict field strength values ​​for unknown azimuth parameters. Specifically, by inputting the coordinates of the measurement point corresponding to the microwave receiving antenna and the azimuth parameter formed by the antenna and the radiation source into the trained model, the predicted field strength value for that azimuth parameter can be obtained. Therefore, this application can learn complex nonlinear mapping relationships—that is, the mapping relationship between azimuth parameters and field strength values—through the field strength prediction model. This allows for the extraction of deep features from the collected data, achieving high-precision field strength prediction with a significant reduction in prediction error.

[0054] Please see Figure 12 , Figure 12 This is a schematic diagram of the structure of a field strength prediction device for a high-power microwave radiation field provided in an embodiment of this application. The field strength prediction device for a high-power microwave radiation field is applied to computer equipment, and may include: The acquisition unit 401 is used to record the acquisition data of the acquisition device during the rotation of the radiation source. The acquisition data includes acquisition sub-data corresponding to each microwave receiving antenna. Each acquisition sub-data corresponds to multiple azimuth parameters formed by the radiation source during the rotation. Each azimuth parameter includes the azimuth angle, elevation angle and distance between the radiation source and the microwave receiving antenna formed between the radiation source and the microwave receiving antenna. The determining unit 402 is used to determine the field strength data of each microwave receiving antenna under each azimuth parameter based on each of the collected sub-data, and to construct a sparse matrix field strength dataset of the high-power microwave radiation field based on the field strength data of each microwave receiving antenna under each azimuth parameter. Training unit 403 is used to train a preset field strength prediction model based on the sparse matrix field strength dataset to obtain a trained field strength prediction model. The prediction unit 404 is used to determine the coordinates of the measurement point corresponding to each microwave receiving antenna, input the coordinates into the field strength prediction model, and obtain the field strength prediction value of the measurement point output by the field strength prediction model.

[0055] Reference Figure 13 , Figure 13 To implement the structural block diagram of a portion of the terminal 140 in this application embodiment, the terminal 140 includes: a radio frequency (RF) circuit 710, a memory 715, an input unit 730, a display unit 740, a sensor 750, an audio circuit 760, a wireless fidelity (WiFi) module 770, a processor 780, and a power supply 790, among other components. Those skilled in the art will understand that... Figure 13 The terminal 140 structure shown does not constitute a limitation on a mobile phone or computer, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0056] The RF circuit 710 can be used to receive and transmit signals during information transmission or calls. In particular, it receives downlink information from the base station and processes it with the processor 780; in addition, it transmits uplink data to the base station.

[0057] The memory 715 can be used to store software programs and modules. The processor 780 executes various terminal functions and high-power microwave radiation field intensity prediction processing by running the software programs and modules stored in the memory 715.

[0058] The input unit 730 can be used to receive input numeric or character information, and to generate key signal inputs related to the terminal's settings and function control. Specifically, the input unit 730 may include a touch panel 731 and other input devices 732.

[0059] The display unit 740 can be used to display input or provided information, as well as various menus of the terminal. The display unit 740 may include a display panel 741.

[0060] Audio circuitry 760, speaker 761, and microphone 762 provide an audio interface.

[0061] In this embodiment, the processor 780 included in the terminal 140 can execute the field strength prediction method for the high-power microwave radiation field of the previous embodiment.

[0062] The terminal 140 in this application embodiment includes, but is not limited to, mobile phones, computers, intelligent voice interaction devices, smart home appliances, vehicle terminals, and aircraft. This application embodiment can be applied to various scenarios, including but not limited to cloud technology, artificial intelligence, smart transportation, and assisted driving.

[0063] Figure 14 This is a partial structural block diagram of a server 110 implementing an embodiment of this application. The server 110 can vary significantly due to different configurations or performance characteristics, and may include one or more central processing units (CPUs) 822 (e.g., one or more processors) and memory 832, and one or more storage media 830 (e.g., one or more mass storage devices) for storing application programs 842 or data 844. The memory 832 and storage media 830 can be temporary or persistent storage. The program stored in the storage media 830 may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the server 110. Furthermore, the CPU 822 may be configured to communicate with the storage media 830 and execute the series of instruction operations in the storage media 830 on the server 110.

[0064] Server 110 may also include one or more power supplies 826, one or more wired or wireless network interfaces 850, one or more input / output interfaces 858, and / or one or more operating systems 841, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0065] The central processing unit 822 in server 110 can be used to execute the field strength prediction method for high-power microwave radiation fields according to embodiments of this application.

[0066] This application also provides a computer-readable storage medium for storing program code for executing the field strength prediction method for high-power microwave radiation fields in the foregoing embodiments.

[0067] This application also provides a computer program product, which includes a computer program. A processor of a computer device reads and executes the computer program, causing the computer device to perform the above-described method for predicting the field strength of a high-power microwave radiation field.

[0068] Furthermore, the terms “comprising” and “including”, and any variations thereof, are intended to cover non-exclusive inclusion, such that a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or apparatus.

[0069] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0070] It should be understood that in the description of the embodiments of this application, "multiple" means two or more, "greater than", "less than", "exceeding" etc. are understood to exclude the number itself, and "above", "below", "within" etc. are understood to include the number itself.

[0071] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0072] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0073] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0074] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, rotating hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0075] It should also be understood that the various implementation methods provided in this application can be combined arbitrarily to achieve different technical effects.

[0076] In the embodiments of this application, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0077] The above is a detailed description of the embodiments of this application. However, this application is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A method for predicting the field strength of a high-power microwave radiation field, characterized in that, An application is made in a high-power microwave radiation system, the high-power microwave radiation system comprising a radiation source located in a high-power microwave radiation field, multiple microwave receiving antennas, and a data acquisition device, each of the microwave receiving antennas corresponding to a single measurement point, the radiation source being used to rotate around the high-power microwave radiation field formed by the microwave receiving antennas and the data acquisition device, the data acquisition device being used to acquire data from the radiation source for each of the microwave receiving antennas during the rotation, the method comprising: The data collected by the acquisition device during the rotation of the radiation source includes acquisition sub-data corresponding to each microwave receiving antenna. Each acquisition sub-data corresponds to multiple azimuth parameters formed by the radiation source during the rotation. Each azimuth parameter includes the azimuth angle, elevation angle and distance between the radiation source and the microwave receiving antenna. Based on the collected sub-data, the field strength data of each microwave receiving antenna under each azimuth parameter is determined, and the sparse matrix field strength dataset of the high-power microwave radiation field is constructed based on the field strength data of each microwave receiving antenna under each azimuth parameter. The preset field strength prediction model is trained based on the sparse matrix field strength dataset to obtain the trained field strength prediction model. For each microwave receiving antenna, the coordinates of the measurement point corresponding to the microwave receiving antenna are determined, and the coordinates are input into the field strength prediction model to obtain the field strength prediction value of the measurement point output by the field strength prediction model.

2. The method for predicting the field strength of a high-power microwave radiation field according to claim 1, characterized in that, The step of determining the field strength data of each microwave receiving antenna under each azimuth parameter based on each of the acquired sub-data includes: For each microwave receiving antenna, the voltage amplitude of the microwave receiving antenna under multiple azimuth parameters is determined as the radiation source rotates around the high-power microwave radiation field, and each azimuth parameter corresponds to a single voltage amplitude. The field strength data corresponding to each of the azimuth parameters is determined based on the voltage amplitude of the microwave receiving antenna under each of the azimuth parameters.

3. The method for predicting the field strength of a high-power microwave radiation field according to claim 1, characterized in that, The step of constructing a sparse matrix field strength dataset of the high-power microwave radiation field based on the field strength data of each microwave receiving antenna under each of the azimuth parameters includes: Determine the location information of each microwave receiving antenna; For each microwave receiving antenna, the location information is associated and bound with the field strength data of the microwave receiving antenna under each of the azimuth parameters to construct a field strength subset of the microwave receiving antenna. The field strength subsets of each microwave receiving antenna are used as the sparse matrix field strength dataset.

4. The method for predicting the field strength of a high-power microwave radiation field according to claim 3, characterized in that, The step of training a preset field strength prediction model based on the sparse matrix field strength dataset to obtain a trained field strength prediction model includes: Determine a validation set corresponding to the sparse matrix field strength dataset, the validation set including the actual field strength data of each microwave receiving antenna under multiple azimuth parameters; The sparse matrix field strength dataset and the position information of the microwave receiving antenna are input into the preset field strength prediction model to obtain the field strength prediction value of the microwave receiving antenna under each of the azimuth parameters. The preset field strength prediction model is trained based on the error between the actual field strength data of the microwave receiving antenna under multiple azimuth parameters and the predicted field strength values ​​of the microwave receiving antenna under each of the azimuth parameters, to obtain the trained field strength prediction model.

5. The method for predicting the field strength of a high-power microwave radiation field according to claim 1, characterized in that, The method further includes: Determine the predicted field strength at each of the measurement points under each of the azimuth parameters; A microwave signal pattern for the high-power microwave radiation field is constructed based on the predicted field strength values ​​of each measurement point under each of the azimuth parameters.

6. The method for predicting the field strength of a high-power microwave radiation field according to claim 1, characterized in that, The azimuth angle is used to characterize the azimuth angle formed between the radiation source and the microwave receiving antenna, and the elevation angle is used to characterize the height distance between the radiation source and the microwave receiving antenna.

7. A field strength prediction device for a high-power microwave radiation field, characterized in that, An apparatus for use in high-power microwave radiation systems includes a radiation source located in a high-power microwave radiation field, multiple microwave receiving antennas, and a data acquisition device. Each microwave receiving antenna corresponds to a single measurement point. The radiation source rotates around the high-power microwave radiation field formed by the microwave receiving antennas and the data acquisition device. The data acquisition device collects data from the radiation source for each microwave receiving antenna during the rotation. The apparatus includes: The acquisition unit is used to record the acquisition data of the acquisition device during the rotation of the radiation source. The acquisition data includes acquisition sub-data corresponding to each microwave receiving antenna. Each acquisition sub-data corresponds to multiple azimuth parameters formed by the radiation source during the rotation. Each azimuth parameter includes the azimuth angle, elevation angle and distance between the radiation source and the microwave receiving antenna formed between them. The determining unit is used to determine the field strength data of each microwave receiving antenna under each azimuth parameter based on each of the collected sub-data, and to construct the sparse matrix field strength dataset of the high-power microwave radiation field based on the field strength data of each microwave receiving antenna under each azimuth parameter. The training unit is used to train the preset field strength prediction model based on the sparse matrix field strength dataset to obtain the trained field strength prediction model. The prediction unit is used to determine the coordinates of the measurement point corresponding to each microwave receiving antenna, input the coordinates into the field strength prediction model, and obtain the predicted field strength value of the measurement point output by the field strength prediction model.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the field strength prediction method for high-power microwave radiation fields as described in any one of claims 1 to 6.

9. A computer program product, the computer program product comprising a computer program, characterized in that, The computer program is read and executed by the processor of the electronic device, causing the electronic device to perform the field strength prediction method for the high-power microwave radiation field as described in any one of claims 1 to 6.

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