Microwave photon array system cross-scale simulation data compression method, device, medium and product

Abstract

The present application relates to the field of microwave photon, provide a kind of microwave photon array system cross-scale simulation data compression method, equipment, medium and product, the method comprises: preprocessing original microwave photon signal data, obtain denoising signal data and noise signal data;Based on DCT transformation and adaptive quantization, the denoising signal data is compressed;Noise signal data is extracted statistical characteristic parameter;According to the compression result of denoising signal data, reconstruct denoising signal data;According to the statistical characteristic parameter of noise signal data, reconstruct noise signal data;According to the denoising signal data and noise signal data of reconstruction, obtain reconstructed microwave photon signal data.The present application can effectively reduce the data scale of transmission and storage in the cross-scale simulation of microwave photon array system, help to improve system simulation evaluation efficiency.

Description

Technical Field

[0001] This invention relates to the field of microwave photonics, and more specifically, to a method, device, medium, and product for cross-scale simulation data compression of microwave photonic array systems. Background Technology

[0002] In the cross-scale simulation of microwave photonic array systems, due to the high operating frequency, wide dynamic range, and large scale span of the system, massive amounts of simulation data are generated. These microwave photonic signal data are frequently transmitted and stored between link models. Especially in distributed simulation scenarios, the transmission of massive amounts of simulation data between computing nodes will significantly increase the simulation time overhead of microwave photonic systems, thus severely restricting the cross-scale simulation efficiency of microwave photonic array systems. Therefore, there is an urgent need for data compression methods for microwave photonic signals.

[0003] Traditional data compression methods are mainly divided into two categories: lossless compression, such as Huffman coding, LZW coding (Lempel-Ziv-Welch coding), and arithmetic coding, which can completely recover the original data without information loss; and lossy compression, which allows for a certain degree of data loss in exchange for a higher compression ratio. This is achieved through techniques such as domain transformation (e.g., Discrete Cosine Transform, Wavelet Transform) and other methods to eliminate data redundancy, resulting in good performance in image, video, and file compression. However, traditional data compression algorithms are not specifically optimized for the characteristics of microwave photonic signal data, making it difficult to meet the data compression requirements of cross-scale simulations of microwave photonic array systems. Summary of the Invention

[0004] The present invention aims to provide a method, device, medium and product for cross-scale simulation data compression of microwave photonic array systems, in order to solve the problem that traditional data compression algorithms are not specifically optimized based on the characteristics of microwave photonic signal data, thus making it difficult to meet the data compression requirements of cross-scale simulation of microwave photonic array systems.

[0005] In a first aspect, the present invention provides a method for compressing cross-scale simulation data of a microwave photonic array system, comprising: The raw microwave photonic signal data is preprocessed to obtain denoised signal data and noise signal data; Compression of denoised signal data based on DCT transform and adaptive quantization; Extract statistical characteristic parameters from noise signal data; Denoising signal data reconstructed from the compression results of the denoised signal data; Noise signal data reconstructed based on the statistical characteristic parameters of the noise signal data; Based on the reconstructed denoised signal data and the noise signal data, the reconstructed microwave photonic signal data is obtained.

[0006] In a preferred embodiment, the preprocessing of the raw microwave photon signal data includes: The noise-containing signal data and noise signal data generated by each model in the microwave photonic link of the simulation system are stored independently. The noise data is subtracted from the noise signal data generated by each model in the microwave photonic link of the simulation system to obtain the separated denoised signal data and noise signal data.

[0007] In a preferred embodiment, the compression of the denoised signal data based on DCT transform and adaptive quantization includes: Perform DCT transform on the denoised signal data to obtain DCT coefficients; Obtain the value with the largest absolute value among the DCT coefficients; Set the desired compression signal-to-noise ratio; Initialize the quantization coefficients; Based on the largest absolute value and the quantization coefficient in the DCT coefficients, the DCT coefficients are quantized to obtain the quantized DCT coefficients. Perform inverse DCT transform on the quantized DCT coefficients to obtain the reconstructed signal data; The compressed signal-to-noise ratio is calculated based on the denoised signal data and the reconstructed signal data; Compare the calculated compressed signal-to-noise ratio with the expected compressed signal-to-noise ratio. If the calculated compressed signal-to-noise ratio reaches the expected compressed signal-to-noise ratio, the quantization ends; otherwise, the quantization coefficients are updated and the quantization process is repeated.

[0008] In a preferred embodiment, the compression result of the denoised signal data includes the values ​​and positions of the non-zero terms in the quantized DCT coefficients.

[0009] In a preferred embodiment, the denoised signal data reconstructed based on the compression result of the denoised signal data includes: Based on the compression result of the denoised signal data, that is, the value and position of the non-zero terms in the quantized DCT coefficients, the complete DCT coefficients are recovered by padding with zeros at other positions; Perform an inverse DCT transform on the complete DCT coefficients to obtain the reconstructed denoised signal data.

[0010] In a preferred embodiment, based on the Gaussian distribution assumption of noise, the statistical characteristic parameters of the noise signal data include the mean and standard deviation of the noise signal data.

[0011] In a preferred embodiment, the noise signal data reconstructed based on the statistical characteristic parameters of the noise signal data includes: Based on the assumption of Gaussian distribution of noise, a random number generator is used to generate a Gaussian noise sequence with a mean of 0 and a standard deviation of 1. Based on the statistical characteristic parameters of the noise signal data, namely the mean and standard deviation of the noise signal data, the reconstructed noise signal data is obtained by combining the Gaussian noise sequence.

[0012] In a second aspect, the present invention provides an electronic device, comprising: At least one processor; and a memory communicatively connected to said at least one processor; The memory stores instructions that can be executed by the at least one processor, and the at least one processor executes the instructions stored in the memory to perform the above-described method.

[0013] Thirdly, the present invention provides a computer-readable storage medium for storing instructions that, when executed, enable the above-described method to be implemented.

[0014] Fourthly, the present invention provides a computer program product that, when invoked by a computer, causes the computer to execute the above-described method.

[0015] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: This invention designs different compression algorithms for signal data and noise data generated from microwave photonic system simulations to improve the compression ratio of microwave photonic signal data. For the denoised signal data of microwave photonic systems, this invention employs a compression algorithm based on DCT transform and adaptive quantization to compress the signal data while ensuring signal reconstruction accuracy. For the noise signal data of microwave photonic systems, this invention employs a compression algorithm based on statistical characteristic parameter extraction and noise signal reconstruction to compress the noise data. Therefore, this invention can effectively reduce the data scale transmitted and stored in cross-scale simulations of microwave photonic array systems, contributing to improved system simulation evaluation efficiency. Attached Figure Description

[0016] Figure 1 A flowchart of a cross-scale simulation data compression method for a microwave photonic array system provided in an embodiment of the present invention.

[0017] Figure 2a This is a waveform diagram of the original denoised signal data in an application example of the present invention.

[0018] Figure 2b This is a waveform diagram of the reconstructed denoised signal data in an application example of the present invention.

[0019] Figure 3aThis is a waveform diagram of the original noise signal data in an application example of the present invention.

[0020] Figure 3b This is a waveform diagram of the reconstructed noise signal data in an application example of the present invention.

[0021] Figure 4a This is a waveform diagram of the original microwave photonic signal data in an application example of the present invention.

[0022] Figure 4b This is a waveform diagram of the reconstructed microwave photonic signal data in an application example of the present invention.

[0023] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0025] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0026] like Figure 1 As shown in the figure, this invention provides a method for compressing cross-scale simulation data of a microwave photonic array system, comprising two parts: a compression process of the original microwave photonic signal data and a decompression process of the compressed microwave photonic signal data, as described below: Part 1: The compression process of the raw microwave photonic signal data, including the following steps: Step 1: Preprocess the raw microwave photon signal data to obtain denoised signal data and noise signal data.

[0027] This invention utilizes the difference in energy distribution between signal data and noise data in microwave photonic signals, employing different compression methods for each. Signal data, after DCT transformation, concentrates its energy on a few DCT coefficients, allowing for compression by leveraging the energy concentration characteristic of DCT. Noise data, however, exhibits a uniform energy distribution across all frequencies in the frequency domain, lacking energy concentration; therefore, DCT-based compression methods are ineffective for random and irregular noise data. Consequently, this invention first preprocesses the microwave photonic signal data, separating the signal and noise data to obtain denoised signal data and noise signal data.

[0028] It is worth noting that the noisy signal data and noise signal data generated by the various models in the microwave photonic link of the simulation system involved in this invention are stored independently. This provides technical convenience for the present invention to perform different compression processing on the signal data and noise signal data. In this invention, the method for preprocessing the original microwave photonic signal data is to subtract the noise signal data from the noisy signal data generated by each model in the microwave photonic link of the simulation system, thereby obtaining the separated denoised signal data and noise signal data. For ease of description, the various types of data are represented as follows: Raw microwave photon signal data ; Denoising signal data ; Noise signal data ; in, .

[0029] For other similar simulation systems, if the model does not calculate and store noise data separately, filtering, coherent accumulation, statistical detection and other methods can be used to separate the signal and noise during data preprocessing.

[0030] Step 2: Compress the denoised signal data based on DCT transform and adaptive quantization.

[0031] For the denoised signal data obtained after preprocessing the original microwave photonic signal data, after DCT transformation, the energy will be concentrated on a few DCT coefficients. This characteristic means that discarding some low-energy DCT coefficients will not have a significant impact on the reconstruction accuracy of the signal data. This invention utilizes this characteristic to achieve compression of microwave photonic signal data.

[0032] Microwave photonic signal data is a one-dimensional discrete-time domain signal sequence generated at a certain sampling rate. Equation (1) is used to perform DCT transformation on the denoised signal data: , (1) in, As shown in equation (2): (2) DCT coefficients obtained after DCT transformation Length and denoised signal data The lengths are consistent, but the energy is concentrated on a few coefficients. Utilizing the characteristics of DCT transform, setting some low-energy DCT coefficients to zero will not cause a significant decrease in signal reconstruction accuracy. Therefore, under the premise of permissible accuracy loss, it is only necessary to transmit and store the non-zero DCT coefficients and their positions, thereby achieving the purpose of data compression.

[0033] To balance reconstruction accuracy and data compression ratio, this invention performs adaptive quantization on microwave photonic signal data under different modulation types, pulse parameters, carrier frequency / power, and other parameters, ensuring that the compression ratio is maximized while meeting reconstruction accuracy requirements. Therefore, this invention employs a compression signal-to-noise ratio... To measure the accuracy of data reconstruction, it can be calculated based on equation (3): (3) in, Represents the denoised signal data. This represents the reconstructed signal data obtained based on the adaptively quantized DCT coefficients. A larger value indicates higher reconstruction accuracy and higher quality of reconstructed signal data. Compared with the original denoised signal data The smaller the error between them.

[0034] DCT coefficients The process of adaptive quantization is as follows: 1) Preprocess the raw microwave photonic signal data to obtain denoised signal data under the current modulation type, pulse parameters, carrier frequency / power and other parameters; 2) Perform DCT transformation on the denoised signal data according to equation (1) to obtain the unquantized DCT coefficients; 3) Obtain the value with the largest absolute value among the DCT coefficients, denoted as ; 4) Set the expected compression signal-to-noise ratio ; 5) Initialize quantization coefficients ,in ; 6) Based on the largest absolute value and the quantization coefficient in the DCT coefficients, the DCT coefficients are sequentially processed according to equation (4). The sequence is quantized to obtain the quantized DCT coefficients. ; , (4) 7) Quantized DCT coefficients Perform inverse DCT to obtain reconstructed signal data. ; 8) Based on the denoised signal data and the reconstructed signal data, calculate the compressed signal-to-noise ratio according to equation (3). ; 9) Compare the calculated compression signal-to-noise ratios. and expected compressed signal-to-noise ratio : like If the conditions are met, the quantification process ends. like If the condition is not met, update the quantization coefficient. And repeat steps 5) to 9).

[0035] In step 9), the quantization coefficients can be updated using iterative methods, optimization algorithms, adaptive slopes, and other algorithms. By quantization coefficients Adjust to a suitable value so that The conditions are established.

[0036] DCT coefficients after adaptive quantization There will be a large number of terms with a value of zero, which will be transmitted and stored as quantized DCT coefficients. The values ​​and positions of non-zero items are determined to reduce the data size.

[0037] Step 3: Extract statistical characteristic parameters from the noise signal data.

[0038] For the noise signal data obtained by preprocessing the original microwave photon signal data, its energy is uniformly distributed across all frequencies in the frequency domain and there is no energy concentration characteristic. Therefore, the coefficients after DCT transformation do not have sparsity, making it difficult to reduce the data size using the above quantization methods.

[0039] For cross-scale simulations of microwave photonic array systems, noise signal data is characterized by randomness and irregularity. Therefore, the specific numerical value of the noise signal at each moment is usually not of concern; rather, the statistical characteristics of its intensity and energy distribution are more important. These statistical parameters can be used to reconstruct the noise signal data. Therefore, this invention achieves noise signal data compression by extracting the statistical parameters of the noise signal data and transmitting and storing only these parameters during cross-scale simulations of the microwave photonic array system.

[0040] The noise signal data is a discrete-time sequence with the same length as the denoised signal data. The microwave photonic array system simulation involved in this invention is based on the Gaussian distribution assumption of the noise, and the mean of its noise signal data is... The standard deviation can be calculated using equation (5). It can be calculated using equation (6): (5) (6) In cross-scale simulation of microwave photonic array systems, only the mean of transmitted noise signal data is considered. and standard deviation Instead of transmitting the original noise signal data, it compresses the noise signal data.

[0041] It is worth noting that for other types of noise signal data that are not Gaussian distributed, similar methods can be used to extract statistical characteristic parameters and reconstruct the noise signal data by combining their statistical distribution characteristics.

[0042] In the compression process of the original signal in the first part, after steps one to three above, the resulting compressed wave photon signal data consists of two parts: The first is the compression result of the denoised signal data, that is, the quantization DCT coefficients of the denoised signal data. The values ​​and positions of non-zero items in the sequence; Second, the statistical characteristic parameters of the noise signal data, namely the mean of the noise signal data. and standard deviation Statistical characteristic parameters.

[0043] Part Two: The decompression process of compressed microwave photonic signal data, including the following steps: Step 1: Reconstruct the denoised signal data based on the compression result of the denoised signal data.

[0044] First, based on the compression results of the denoised signal data obtained in the first part, i.e., the values ​​and positions of the non-zero terms in the quantized DCT coefficients, the complete DCT coefficients are recovered by padding with zeros at other positions. .

[0045] Then, for the complete DCT coefficients The inverse DCT transformation is performed using equation (7) to obtain the reconstructed denoised signal data. .

[0046] , (7) Step 2: Reconstruct the noise signal data based on the statistical characteristic parameters of the noise signal data.

[0047] For the Gaussian distributed noise signal in the simulation of the microwave photonic array system involved in this invention, the mean value obtained from its first part is used. and standard deviation By using statistical parameters, we can obtain reconstructed noise signal data with identical distribution and statistical characteristic parameters.

[0048] First, a Gaussian noise sequence with a mean of 0 and a standard deviation of 1 is generated using a random number generator. The sequence's length is the same as the length of the noise signal data, and is denoted as [the sequence is missing here]. , .

[0049] Then, based on the statistical characteristic parameters of the noise signal data, namely the mean and standard deviation of the noise signal data, the reconstructed noise signal data is obtained by combining the Gaussian noise sequence according to equation (8).

[0050] , (8) Step 3: Based on the reconstructed denoised signal data and noise signal data, obtain the reconstructed microwave photonic signal data.

[0051] After steps one and two, the reconstructed denoised signal data are obtained respectively. and reconstructed noise signal data The reconstructed microwave photon signal data is obtained by calculating according to equation (9). .

[0052] , (9) After the decompression process of the compressed signal data in the second part, the reconstructed microwave photonic signal data can be obtained. The following two metrics are used to evaluate the reconstruction accuracy of microwave photonic signal data: 1) Calculate the compression signal-to-noise ratio according to equation (3). To evaluate the reconstruction accuracy of adaptive quantization compression for denoised signal data; 2) Calculate the original microwave photon signal data respectively. signal-to-noise ratio and reconstructed microwave photonic signal data signal-to-noise ratio Then, the deviation between the two is calculated using equation (10). This is used to evaluate the impact of noise data compression on reconstruction accuracy.

[0053] (10) An application example: This invention uses a broadband microwave photonic array system link simulation as an example for illustration. The system has a sampling rate of 100 GHz, a simulation step time window length of 100 ns, and pulse parameters of the signal source model as follows: pulse width 10 ns, repetition frequency delay 30 ns, carrier frequency 3 GHz, and signal power 20 dBm.

[0054] The process of compressing the raw microwave photonic signal data of the broadband microwave photonic array system in this application example is as follows: Step 1: Preprocess the raw microwave photon signal data to obtain denoised signal data and noise signal data.

[0055] Based on the parameter settings of the signal source model in the application example, with 10,000 data points included in one simulation step, the original microwave photon signal data can be obtained. and noise signal data , .

[0056] Original microwave photon signal data Subtract noise signal data Thus, the denoised signal data is obtained. This completes the preprocessing of microwave photonic signal data.

[0057] Step 2: Compress the denoised signal data based on DCT transform and adaptive quantization.

[0058] For denoised signal data The DCT transformation of the denoised signal data can be completed using equation (1), and the DCT coefficients can be obtained. , At this point, the energy will be concentrated on a few DCT coefficients.

[0059] The DCT coefficients are processed according to the following procedure. Perform adaptive quantization: 1) Obtain the denoised signal data through preprocessing in step one. ; 2) Apply equation (1) to the denoised signal data Perform DCT transform to obtain unquantized DCT coefficients. ; 3) Obtain the value with the largest absolute value among the DCT coefficients, denoted as ; 4) Set the expected compression signal-to-noise ratio ; 5) Initialize quantization coefficients ; 6) Based on the largest absolute value and the quantization coefficient in the DCT coefficients, the DCT coefficients are sequentially processed according to equation (4). Quantization is performed to obtain the quantized DCT coefficients. ; 7) Quantized DCT coefficients Perform inverse DCT to obtain reconstructed signal data. ; 8) Based on the denoised signal data and the reconstructed signal data, calculate the compressed signal-to-noise ratio according to equation (3). ; 9) Compare the calculated compression signal-to-noise ratios. and expected compressed signal-to-noise ratio : When quantization coefficient hour, If the condition is not met, the method for updating the quantization coefficient in this application example is... Update quantization coefficients Then, repeat steps 5) to 9). hour ,at this time Once the conditions are met, the adaptive quantization process ends.

[0060] At this point, the quantized DCT coefficients The number of zero terms in the non-zero data is 664, achieving a compression ratio of 15.06 times compared to the original signal data of length 10,000. This is achieved through the transmission and storage of DCT coefficients. The values ​​and positions of non-zero items in the data can significantly reduce the data size.

[0061] Step 3: Extract statistical characteristic parameters from the noise signal data.

[0062] Noise signal data obtained from preprocessing raw microwave photonic signal data Statistical characteristic parameters are extracted, and the mean of the noise signal data is calculated using equation (5). The standard deviation of the noise signal data is calculated using equation (6). Noise signal data compression is achieved by transmitting and storing statistical characteristic parameters of the noise signal data.

[0063] In the process of compressing microwave photonic signal data, after steps one through three described above, the resulting compressed signal data consists of two parts: one is the compressed result of the denoised signal data, i.e., the quantized DCT coefficients. The values ​​and positions of non-zero terms, and secondly, the statistical characteristic parameters of the noise signal data, namely the mean of the noise signal data. and standard deviation Statistical characteristic parameters.

[0064] The process of decompressing the compressed microwave photonic signal data of the broadband microwave photonic array system in this application example is as follows: Step 1: Reconstruct the denoised signal data based on the compression result of the denoised signal data.

[0065] First, based on the compression result of the denoised signal data obtained during the compression process, namely the values ​​and positions of the 664 non-zero terms in the quantized DCT coefficient sequence, the complete DCT coefficients are recovered by padding with zeros at other positions. .

[0066] Then, for the complete DCT coefficients The inverse DCT transformation is performed using equation (7) to obtain the reconstructed denoised signal data. .

[0067] Raw denoised signal data of microwave photons The waveform is as follows Figure 2a As shown, the reconstructed denoised signal data The waveform is as follows Figure 2b As shown, comparison Figure 2a and Figure 2b As can be seen from the waveform, the waveform of the reconstructed denoised signal data has a high degree of consistency with the waveform of the original denoised signal data.

[0068] Step 2: Reconstruct the noise signal data based on the statistical characteristic parameters of the noise signal data.

[0069] Mean of noise signal data obtained from the compression process and standard deviation By using statistical characteristic parameters, we can obtain reconstructed noise signal data with the same distribution and statistical characteristic parameters.

[0070] First, a Gaussian noise sequence with a mean of 0 and a standard deviation of 1 is generated using a random number generator. , .

[0071] Then, based on the statistical characteristic parameters of the noise signal data, namely the mean and standard deviation of the noise signal data, the reconstructed noise signal data is calculated using equation (8). .

[0072] Raw noise signal data of microwave photons The waveform is as follows Figure 3a As shown, the reconstructed noise signal data The waveform is as follows Figure 3b As shown.

[0073] Step 3: Based on the reconstructed denoised signal data and noise signal data, obtain the reconstructed microwave photonic signal data.

[0074] After steps one and two, the reconstructed denoised signal data are obtained respectively. and reconstructed noise signal data The reconstructed microwave photon signal data is obtained by calculating according to equation (9). .

[0075] Raw microwave photon signal data The waveform is as follows Figure 4a As shown, the reconstructed microwave photonic signal data The waveform is as follows Figure 4b As shown, comparison Figure 4a and Figure 4b It can be seen that the waveform of the reconstructed microwave photonic signal data has a high degree of consistency with the waveform of the original microwave photonic signal data.

[0076] The two metrics used to evaluate the microwave photonic signal reconstruction accuracy in this invention are as follows: 1) The compression signal-to-noise ratio is calculated according to equation (3) as follows: This indicates that the adaptive quantization method described in this invention can achieve a good balance between reconstruction accuracy and compression ratio; 2) Calculate the raw microwave photon signal data The signal-to-noise ratio is and reconstructing microwave photonic signal data The signal-to-noise ratio is Then, the deviation between the two is calculated using equation (10). This indicates that the method described in this invention can reconstruct the original microwave photonic signal data with high accuracy.

[0077] Furthermore, regarding data compression ratio, the method described in this invention achieves a compression ratio of 15.06 times in the application examples; while directly compressing the original microwave photonic signal data... Performing DCT transformation and adaptive quantization, however, results in challenges in achieving the same compression signal-to-noise ratio due to the lack of energy concentration in the noise signal. If so, 4268 non-zero DCT coefficients need to be retained, and the compression ratio will be only 2.34.

[0078] It can be seen that by utilizing the differential characteristics of signal data and noise data in microwave photonic array systems, this invention designs corresponding data compression methods, which can significantly improve the compression ratio of microwave photonic signal data while ensuring signal reconstruction accuracy.

[0079] Based on the same technical concept, embodiments of the present invention also provide an electronic device that can implement the embedded software code generation method flow provided in the above embodiments of the present invention. In one embodiment, the electronic device may be a server, a terminal device, or other electronic devices. Figure 5 As shown, the electronic device may include: At least one processor and a memory connected to the at least one processor. In this embodiment of the invention, the specific connection medium between the processor and the memory is not limited. Figure 5The example used is the connection between the processor and memory via a bus. The bus... Figure 5 The connections between other components are indicated by thick lines and are for illustrative purposes only, not as limiting information. Buses can be divided into address buses, data buses, control buses, etc., but for ease of representation, [the specific bus type is not shown here]. Figure 5 The processor is represented by a single thick line, but this does not imply that there is only one bus or one type of bus. Alternatively, a processor can also be called a controller; there are no restrictions on the name.

[0080] In this embodiment of the invention, the memory stores instructions that can be executed by at least one processor. By executing the instructions stored in the memory, at least one processor can execute an embedded software code generation method as described above.

[0081] The processor is the control center of the device. It can connect to various parts of the control device through various interfaces and lines. By running or executing instructions stored in memory and calling data stored in memory, it can monitor the device's various functions and process data, thereby enabling overall monitoring of the device.

[0082] In an alternative design, the processor may include one or more processing units. The processor may integrate an application processor and a modem processor, wherein the application processor primarily handles the operating system, user interface, and applications, while the modem processor primarily handles wireless communication. It is understood that the modem processor may also not be integrated into the processor. In some embodiments, the processor and memory may be implemented on the same chip; in some embodiments, they may also be implemented separately on separate chips.

[0083] The processor can be a general-purpose processor, such as a CPU, digital signal processor, application-specific integrated circuit, field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the embedded software code generation method disclosed in the embodiments of this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0084] Memory, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory can include at least one type of storage medium, such as flash memory, hard disk, multimedia cards, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), and electrically erasable programmable read-only memory (EPROM). Only memory (EEPROM), magnetic storage, magnetic disks, optical disks, etc. A memory is any other medium capable of carrying or storing desired program code in the form of instructions or data structures, and accessible by a computer, but is not limited thereto. The memory in embodiments of this invention can also be a circuit or any other device capable of performing storage functions for storing program instructions and / or data.

[0085] By designing and programming the processor, the code corresponding to the embedded software code generation method described in the foregoing embodiments can be embedded into the chip, enabling the chip to execute the steps of the method described in the foregoing embodiments during runtime. How to design and program the processor is a technique well-known to those skilled in the art and will not be elaborated upon here.

[0086] Based on the same inventive concept, embodiments of the present invention also provide a storage medium storing computer instructions that, when executed on a computer, cause the computer to execute an embedded software code generation method described above.

[0087] In some alternative embodiments, the present invention also provides that various aspects of the embedded software code generation method can also be implemented in the form of a program product comprising program code, which, when the program product is run on a device, causes the control device to perform the steps in the embedded software code generation method according to various exemplary embodiments of the present invention described above.

[0088] It should be noted that although several units or sub-units of the apparatus have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the invention, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units. Furthermore, although the operation of the method of the invention is described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0089] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can be implemented in one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs) containing computer-usable program code. The form of a computer program product implemented on ROM, optical memory, etc.

[0090] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a server, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0091] Program code for performing the operations of this invention can be written using any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0092] In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0093] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0094] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0095] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for compressing cross-scale simulation data of a microwave photonic array system, characterized in that, include: The raw microwave photonic signal data is preprocessed to obtain denoised signal data and noise signal data; Compression of denoised signal data based on DCT transform and adaptive quantization; Extract statistical characteristic parameters from noise signal data; Denoising signal data reconstructed from the compression results of the denoised signal data; Noise signal data reconstructed based on the statistical characteristic parameters of the noise signal data; Based on the reconstructed denoised signal data and the noise signal data, the reconstructed microwave photonic signal data is obtained.

2. The method for compressing cross-scale simulation data of a microwave photonic array system according to claim 1, characterized in that, The preprocessed raw microwave photon signal data includes: The noise-containing signal data and noise signal data generated by each model in the microwave photonic link of the simulation system are stored independently. The noise data is subtracted from the noise signal data generated by each model in the microwave photonic link of the simulation system to obtain the separated denoised signal data and noise signal data.

3. The method for compressing cross-scale simulation data of a microwave photonic array system according to claim 1, characterized in that, The compression of denoised signal data based on DCT transform and adaptive quantization includes: Perform DCT transform on the denoised signal data to obtain DCT coefficients; Obtain the value with the largest absolute value among the DCT coefficients; Set the desired compression signal-to-noise ratio; Initialize the quantization coefficients; Based on the largest absolute value and the quantization coefficient in the DCT coefficients, the DCT coefficients are quantized to obtain the quantized DCT coefficients. Perform inverse DCT transform on the quantized DCT coefficients to obtain the reconstructed signal data; The compressed signal-to-noise ratio is calculated based on the denoised signal data and the reconstructed signal data; Compare the calculated compressed signal-to-noise ratio with the expected compressed signal-to-noise ratio. If the calculated compressed signal-to-noise ratio reaches the expected compressed signal-to-noise ratio, the quantization ends; otherwise, the quantization coefficients are updated and the quantization process is repeated.

4. The method for compressing cross-scale simulation data of a microwave photonic array system according to claim 3, characterized in that, The compression result of the denoised signal data includes the value and position of the non-zero terms in the quantized DCT coefficients.

5. The method for compressing cross-scale simulation data of a microwave photonic array system according to claim 1, characterized in that, Based on the Gaussian distribution assumption of noise, the statistical characteristic parameters of the noise signal data include the mean and standard deviation of the noise signal data.

6. The method for compressing cross-scale simulation data of a microwave photonic array system according to claim 4, characterized in that, The denoised signal data reconstructed based on the compression result of the denoised signal data includes: Based on the compression result of the denoised signal data, that is, the value and position of the non-zero terms in the quantized DCT coefficients, the complete DCT coefficients are recovered by padding with zeros at other positions; Perform an inverse DCT transform on the complete DCT coefficients to obtain the reconstructed denoised signal data.

7. The method for compressing cross-scale simulation data of a microwave photonic array system according to claim 5, characterized in that, The noise signal data reconstructed based on the statistical characteristic parameters of the noise signal data includes: Based on the assumption of Gaussian distribution of noise, a random number generator is used to generate a Gaussian noise sequence with a mean of 0 and a standard deviation of 1. Based on the statistical characteristic parameters of the noise signal data, namely the mean and standard deviation of the noise signal data, the reconstructed noise signal data is obtained by combining the Gaussian noise sequence.

8. An electronic device, characterized in that, include: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, which executes the instructions stored in the memory to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store instructions that, when executed, cause the method as described in any one of claims 1-7 to be implemented.

10. A computer program product, characterized in that, When the computer program product is invoked by a computer, it causes the computer to perform the method as described in any one of claims 1-7.

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