CN114782559A - Frequency spectrum data compression method and device based on image DCT (discrete cosine transform) - Google Patents

Abstract

The embodiment of the application provides a frequency spectrum data compression method and device based on image DCT (discrete cosine transformation), wherein the method comprises the following steps: equally dividing the received frequency spectrum data at the same time into three groups of frequency parts, respectively mapping to R, G, B three channels according to the signal intensity and splicing into an RGB picture; performing discrete cosine transformation on the RGB picture, and predicting errors under different compression ratios by the feature matrix subjected to the discrete cosine transformation according to a neural network until the errors are within a set acceptable range; the method and the device can effectively improve the compression storage capacity of the monitoring spectrum data.

Description

Spectral data compression method and device based on image DCT transform

technical field

The present application relates to the field of radio monitoring, and in particular, to a method and device for compressing spectral data based on image DCT transform.

Background technique

In recent years, with the rapid development of the construction of radio spectrum monitoring equipment and facilities, the scale of the radio spectrum monitoring network has become larger and larger, and the number of fixed stations, mobile stations, transportable stations and other equipment has increased rapidly. The increase in the number of station equipment has made the development of radio spectrum monitoring scanning, measurement and positioning more convenient and efficient. In the process of using wireless signal monitoring equipment to carry out work, various types of massive radio spectrum monitoring data are generated. How to quickly and effectively It is important to store this data for later analysis.

The measured data shows that at a real-time bandwidth of 40MHz, the amount of spectrum data collected is about 3GB/hour, and a long-term, multi-device collection will inevitably generate a large amount of spectrum data. However, the storage capacity of monitoring equipment has an upper limit, and long-term spectrum monitoring and collection will bring great challenges to its storage capacity. Therefore, how to compress the spectrum data and improve the data storage capacity of the monitoring system is an urgent problem to be solved.

Data compression refers to reducing the amount of data to reduce storage space, improving transmission, storage and processing efficiency, or reorganizing data according to certain algorithms to reduce data redundancy and storage space without losing useful information. a technology.

The main function of data compression is to reduce the amount of data in the process of data transmission or transfer. There is redundancy in the data to be recorded and transmitted, that is, some data appear in predictable locations, and this part of redundant data can be processed through data compression. At the same time, the correlation between adjacent data and the spectral data have certain regularity and periodicity, so some transformations can be used to remove the correlation redundancy as much as possible.

At the level of compressed data integrity, compression techniques can be divided into two categories: lossy compression and lossless compression. As the name implies, lossy compression means that the compressed data is different from the original data after decompression, and the specific differences vary depending on the algorithm, while lossless compression means that the decompressed data is 100% the same as the original data. In addition, lossy compression has a lower compression rate than lossless compression because it discards some unimportant content, and because both have their own advantages, the application scenarios are also different.

SUMMARY OF THE INVENTION

In view of the problems in the prior art, the present application provides a spectral data compression method and device based on image DCT transformation, which can effectively improve the compression and storage capability of monitoring spectral data.

In order to solve at least one of the above problems, the present application provides the following technical solutions:

In a first aspect, the present application provides a spectral data compression method based on image DCT transformation, including:

Divide the spectrum data received at the same time into three groups of frequency parts, and map them to the R, G, and B channels according to the signal strength, and form an RGB picture;

The discrete cosine transform is performed on the RGB picture, and the feature matrix after the discrete cosine transform predicts the error under different compression rates according to the neural network, until the error is within the set acceptable range.

Further, dividing the received spectrum data at the same time into three groups of frequency parts, including:

The received spectrum data is represented as a two-dimensional array, and the data is normalized to the range of 0-255 and rounded up. The horizontal axis is frequency, the vertical axis is time, and the number of rows is 1/3 of the number of columns. Rounded up;

If the number of columns is an integer multiple of 3, it is directly divided into three equal parts. If the number of columns is not divisible by three, zero columns are filled on the right side until the number of columns is an integer multiple of 3.

Further, the spectrum data received at the same time is divided into three groups of frequency parts, and according to the signal strength, they are respectively mapped to the R, G, and B channels and assembled into an RGB picture, including:

Divide the frequency parts into three groups equally and add a marked row in the first row, the original data column is 255, and the added all zero column is 0;

Divide the expanded array into three equal columns, map the three matrixes after three equal divisions to the magnitudes of R, G, and B according to their numerical values, and form an RGB picture, in which the left matrix corresponds to the first matrix. The one-dimensional R channel, the left second matrix corresponds to the second-dimensional G channel, and the rightmost matrix corresponds to the third-dimensional B channel.

Further, the feature matrix after the discrete cosine transform predicts the error under different compression rates according to the neural network, until the error is within the set acceptable range, including:

Select a specific threshold to filter feature values through a pre-trained machine learning model;

Traverse all thresholds in the range of [0.01, 19.51] according to the length of 0.5 steps, input the length, width and threshold of the image to be compressed into the trained machine learning model, output the image quality prediction value and dynamically select the compression rate according to the demand , until the error is within the set acceptable range.

In a second aspect, the present application provides a spectral data compression device based on image DCT transformation, comprising:

The RGB mapping module is used to divide the spectrum data received at the same time into three groups of frequency parts, and map them to the R, G, and B channels according to the signal strength, and form an RGB picture;

The data compression module is used to perform discrete cosine transform on the RGB picture, and the feature matrix after the discrete cosine transform predicts the error under different compression rates according to the neural network, until the error is within the set acceptable range.

In a third aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, the processor implements the image-based DCT transformation when the processor executes the program The steps of the spectral data compression method.

In a fourth aspect, the present application provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, implements the steps of the method for compressing spectral data based on image DCT transform.

In a fifth aspect, the present application provides a computer program product, comprising a computer program/instruction, when the computer program/instruction is executed by a processor, the steps of the method for compressing spectral data based on image DCT transform are implemented.

It can be seen from the above technical solutions that the present application provides a method and device for compressing spectral data based on image DCT transformation. By adopting the RGB three-channel aliasing method, the compression rate is improved without causing excessive data recovery error loss. This can effectively improve the compression storage capability of the monitoring spectrum data.

Description of drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are For some embodiments of the present application, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without any creative effort.

FIG. 1 is one of the schematic flowcharts of the spectral data compression method based on image DCT transform in an embodiment of the application;

FIG. 2 is the second schematic flowchart of the spectral data compression method based on image DCT transform in the embodiment of the application;

3 is a third schematic flowchart of a method for compressing spectral data based on image DCT transform in an embodiment of the application;

4 is a fourth schematic flowchart of a method for compressing spectral data based on image DCT transform in an embodiment of the present application;

5 is a structural diagram of an apparatus for compressing spectral data based on image DCT transform in an embodiment of the present application;

6 is a schematic diagram of normalized spectral data displayed by a grayscale image in a specific embodiment of the application;

FIG. 7 is a schematic diagram of mapping the frequency of a grayscale image of spectral data into three channels after three equal divisions into three channels of R, G, and B in a specific embodiment of the application;

8 is a schematic diagram of splicing three channels into an RGB picture in a specific embodiment of the application;

FIG. 9 is a schematic structural diagram of an electronic device in an embodiment of the present application.

Detailed ways

In order to make the purposes, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments It is a part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

The acquisition, storage, use, and processing of data in the technical solution of this application are in compliance with the relevant provisions of national laws and regulations.

Considering the problems existing in the prior art, the present application provides a spectral data compression method and device based on image DCT transformation. By adopting the RGB three-channel aliasing method, the compression rate is improved without causing too much extra data. The error loss is recovered, thereby effectively improving the compression and storage capacity of the monitoring spectrum data.

In order to effectively improve the ability to compress and store monitoring spectral data, the present application provides an embodiment of a spectral data compression method based on image DCT transformation. Referring to FIG. 1 , the spectral data compression method based on image DCT transformation specifically includes the following: content:

Step S101: Divide the spectrum data received at the same time into three groups of frequency parts equally, and map them to the R, G, and B channels respectively according to the signal strength and form an RGB picture;

Specific steps are as follows:

1. Represent the received spectrum data as a two-dimensional array, the horizontal axis is frequency, the vertical axis is time, and the data is normalized to the range of 0-255, and rounded up; the number of rows is 1 of the number of columns /3 is rounded up.

2. If the number of columns is an integer multiple of 3, it can be directly divided into three equal parts; if the number of columns is not divisible by three, zero columns are filled on the right side until the number of columns is an integer multiple of 3.

3. Add a marked row in the first row, the original data column is 255, and the added all zero column is 0.

4. Divide the expanded array into thirds by column:

5. Map the three matrices to the magnitudes of R, G, and B according to their numerical values:

此时数据为0-255之间的数目，可直接取整并对应到RGB通道的色彩强度。The left matrix corresponds to the first-dimensional R channel, the left second matrix corresponds to the second-dimensional G channel, and the rightmost matrix corresponds to the third-dimensional B channel: calculated by data normalization

At this time, the data is a number between 0-255, which can be directly rounded and corresponding to the color intensity of the RGB channel.

Step S102 : Perform discrete cosine transform on the RGB picture, and predict the error under different compression rates on the feature matrix after the discrete cosine transform according to the neural network, until the error is within a predetermined acceptable range.

1. Synthesize another RGB image and use DCT transformation. The principle of DCT transformation is as follows:

2. Select a specific threshold to filter the feature values through the pre-trained machine learning model. Traverse all thresholds in the range of [0.01, 19.51] according to the length of 0.5 steps, respectively input the length, width and threshold of the image to be compressed into the trained machine learning model, which can output the image quality prediction value, which can be dynamically selected according to demand Compression ratio. image quality assessment

3. Improve the compression rate as much as possible within the acceptable error range of the data.

It can be seen from the above description that the spectral data compression method based on image DCT transform provided by the embodiment of the present application can improve the compression rate by adopting the RGB three-channel aliasing method, and does not cause too much additional data recovery error loss. This can effectively improve the compressed storage capability of monitoring spectrum data.

In an embodiment of the spectral data compression method based on image DCT transform of the present application, referring to FIG. 2 , the following content may also be specifically included:

Step S201: Represent the received spectrum data as a two-dimensional array and normalize the data to the range of 0-255 and round it to an integer, wherein the horizontal axis is frequency, the vertical axis is time, and the number of rows is the number of columns. 1/3 rounded up;

Step S202 : if the number of columns is an integer multiple of 3, directly divide into three equal parts; if the number of columns is not divisible by three, then fill in zero columns on the right side until the number of columns is an integer multiple of 3.

In an embodiment of the spectral data compression method based on image DCT transform of the present application, referring to FIG. 3 , the following content may also be specifically included:

Step S301: equally divided into three groups of frequency parts, add a marked row in the first row, the original data column is 255, and the added all-zero column is 0;

Step S302: Divide the expanded array into thirds by column, map the three matrices after the trisection to the magnitudes of R, G, and B respectively according to their numerical values, and form an RGB picture, wherein the left matrix Corresponding to the first dimension R channel, the left second matrix corresponds to the second dimension G channel, and the rightmost matrix corresponds to the third dimension B channel.

In an embodiment of the spectral data compression method based on image DCT transform of the present application, referring to FIG. 4 , the following content may also be specifically included:

Step S401: select a specific threshold to screen the feature value through a pre-trained machine learning model;

Step S402: Traverse all thresholds in the range of [0.01, 19.51] with a length of 0.5 steps, input the length, width and threshold of the image to be compressed into the trained machine learning model, output the image quality prediction value and dynamically according to the demand Choose a compression ratio until the error is within the set acceptable range.

In order to effectively improve the ability to compress and store monitoring spectral data, the present application provides an embodiment of an image DCT-based spectral data compression apparatus for implementing all or part of the image DCT-based spectral data compression method 5, the spectral data compression device based on image DCT transformation specifically includes the following contents:

The RGB mapping module 10 is used to equally divide the spectrum data received at the same moment into three groups of frequency parts, and map to three channels of R, G, and B respectively according to the signal strength, and make up an RGB picture;

The data compression module 20 is configured to perform discrete cosine transform on the RGB picture, and the feature matrix after the discrete cosine transform predicts the error under different compression rates according to the neural network until the error is within a predetermined acceptable range.

It can be seen from the above description that the spectral data compression device based on image DCT transform provided by the embodiment of the present application can improve the compression rate by adopting the RGB three-channel aliasing method, and does not cause too much additional data recovery error loss. This can effectively improve the compressed storage capability of monitoring spectrum data.

In order to further illustrate this solution, the present application also provides a specific application example of applying the above-mentioned spectral data compression device based on image DCT transformation to realize the spectral data compression method based on image DCT transformation, see FIG. 6 to FIG. content:

Specific steps are as follows:

4. Represent the received spectrum data as a two-dimensional array, the horizontal axis is frequency, the vertical axis is time, and the data is normalized to the range of 0-255, and rounded; the number of rows is 1 of the number of columns /3 is rounded up.

5. If the number of columns is an integer multiple of 3, it can be directly divided into three equal parts; if the number of columns is not divisible by three, fill in zero columns on the right side until the number of columns is an integer multiple of 3.

6. Add a marked row in the first row, the original data column is 255, and the added all zero column is 0.

7. Divide the expanded array into thirds by column:

8. Map the three matrices to the magnitudes of R, G, and B according to their numerical values:

此时数据为0-255之间的数目，可直接取整并对应到RGB通道的色彩强度。9. The left matrix corresponds to the first-dimensional R channel, the left second matrix corresponds to the second-dimensional G channel, and the rightmost matrix corresponds to the third-dimensional B channel: calculated by data normalization

At this time, the data is a number between 0-255, which can be directly rounded and corresponding to the color intensity of the RGB channel.

10. Synthesize another RGB image and use DCT transformation. The principle of DCT transformation is as follows:

11. Select a specific threshold to filter the feature values through the pre-trained machine learning model. Traverse all thresholds in the range of [0.01, 19.51] according to the length of 0.5 steps, respectively input the length, width and threshold of the image to be compressed into the trained machine learning model, which can output the image quality prediction value, which can be dynamically selected according to demand Compression ratio. image quality assessment

12. Improve the compression rate as much as possible within the acceptable error range of the data.

Decompression process: After the compressed data is restored by JPEG, the color data of the RGB three channels is separated, and spliced into a single-channel two-dimensional array in sequence, and restored into spectrum data.

From the perspective of hardware, in order to effectively improve the compression and storage capacity of monitoring spectrum data, the present application provides an embodiment of an electronic device for implementing all or part of the spectrum data compression method based on image DCT transform , the electronic equipment specifically includes the following contents:

a processor, a memory, a Communications Interface and a bus; wherein the processor, the memory, and the communication interface complete communication with each other through the bus; the communication interface is used to implement image-based Information transmission between the spectral data compression device of DCT transformation and related equipment such as core service systems, user terminals, and related databases; the logic controller may be a desktop computer, a tablet computer, a mobile terminal, etc., and this embodiment is not limited thereto. In this embodiment, the logic controller may be implemented with reference to the embodiment of the spectral data compression method based on image DCT transformation and the embodiment of the spectral data compression device based on image DCT transformation in the embodiments, and the contents of which are incorporated in Therefore, the repetition will not be repeated here.

It can be understood that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, and the like. Wherein, the smart wearable device may include smart glasses, smart watches, smart bracelets, and the like.

In practical applications, part of the spectral data compression method based on image DCT transformation may be performed on the side of the electronic device as described above, or all operations may be completed in the client device. Specifically, the selection can be made according to the processing capability of the client device and the limitations of the user's usage scenario. This application does not limit this. If all operations are performed in the client device, the client device may also include a processor.

The above-mentioned client device may have a communication module (ie, a communication unit), which may be communicatively connected with a remote server to realize data transmission with the server. The server may include a server on the task scheduling center side, and other implementation scenarios may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, a server cluster composed of multiple servers, or a server structure of a distributed device.

FIG. 9 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in FIG. 9 , the electronic device 9600 may include a central processing unit 9100 and a memory 9140 ; the memory 9140 is coupled to the central processing unit 9100 . Notably, this FIG. 9 is exemplary; other types of structures may be used in addition to or in place of this structure to implement telecommunication functions or other functions.

In one embodiment, the function of the spectral data compression method based on image DCT transform may be integrated into the central processing unit 9100 . Wherein, the central processing unit 9100 can be configured to perform the following controls:

Step S101: Divide the spectrum data received at the same time into three groups of frequency parts equally, and map them to the R, G, and B channels respectively according to the signal strength and form an RGB picture;

Step S102 : Perform discrete cosine transform on the RGB picture, and predict the error under different compression rates on the feature matrix after the discrete cosine transform according to the neural network, until the error is within a predetermined acceptable range.

It can be seen from the above description that the electronic device provided by the embodiment of the present application improves the compression rate by adopting the RGB three-channel aliasing method, and does not cause excessive data recovery error loss, thereby effectively improving the monitoring of spectrum data. compression storage capacity.

In another embodiment, the spectral data compression device based on image DCT transformation can be configured separately from the central processing unit 9100, for example, the spectral data compression device based on image DCT transformation can be configured as a chip connected to the central processing unit 9100, through the central processing unit 9100. The control of the processor realizes the function of the spectral data compression method based on the image DCT transform.

As shown in FIG. 9 , the electronic device 9600 may further include: a communication module 9110 , an input unit 9120 , an audio processor 9130 , a display 9160 , and a power supply 9170 . It is worth noting that the electronic device 9600 does not necessarily include all the components shown in FIG. 9 ; in addition, the electronic device 9600 may also include components not shown in FIG. 9 , and reference may be made to the prior art.

As shown in FIG. 9 , a central processing unit 9100 , sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, and the central processing unit 9100 receives input and controls various aspects of the electronic device 9600 component operation.

The memory 9140, for example, may be one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure can be stored, and a program executing the related information can also be stored. And the central processing unit 9100 can execute the program stored in the memory 9140 to realize information storage or processing.

The input unit 9120 provides input to the central processing unit 9100 . The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600 . The display 9160 is used for displaying display objects such as images and characters. The display can be, for example, but not limited to, an LCD display.

The memory 9140 may be solid state memory such as read only memory (ROM), random access memory (RAM), SIM card, and the like. There may also be memories that retain information even when powered off, selectively erased and provided with more data, examples of which are sometimes referred to as EPROMs or the like. Memory 9140 may also be some other type of device. Memory 9140 includes buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage part 9142 for storing application programs and function programs or for performing operations of the electronic device 9600 through the central processing unit 9100 .

The memory 9140 may also include data storage 9143 for storing data such as contacts, digital data, pictures, sounds and/or any other data used by the electronic device. The driver storage section 9144 of the memory 9140 may include various drivers of the electronic device for communication functions and/or for executing other functions of the electronic device (eg, a messaging application, a contact book application, etc.).

The communication module 9110 is the transmitter/receiver 9110 that transmits and receives signals via the antenna 9111 . A communication module (transmitter/receiver) 9110 is coupled to the central processing unit 9100 to provide input signals and receive output signals, as may be the case with conventional mobile communication terminals.

Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module, and/or a wireless local area network module. The communication module (transmitter/receiver) 9110 is also coupled to the speaker 9131 and the microphone 9132 via the audio processor 9130 to provide audio output via the speaker 9131 and to receive audio input from the microphone 9132 for general telecommunication functions. Audio processor 9130 may include any suitable buffers, decoders, amplifiers, and the like. In addition, the audio processor 9130 is also coupled to the central processing unit 9100, thereby enabling recording on the local unit through the microphone 9132, and enabling playback of the sound stored on the local unit through the speaker 9131.

The embodiments of the present application also provide a computer-readable storage medium capable of implementing all steps in the image DCT-transform-based spectral data compression method in the above-mentioned embodiments, where the execution body is a server or a client, and the computer-readable storage medium is stored on the computer-readable storage medium. A computer program is stored, and when the computer program is executed by the processor, it realizes all the steps of the image DCT transform-based spectral data compression method in the above-mentioned embodiment where the execution body is a server or a client. For example, the processor executes the computer program. When implementing the following steps:

Step S101: Divide the spectrum data received at the same time into three groups of frequency parts equally, and map them to the R, G, and B channels respectively according to the signal strength and form an RGB picture;

Step S102 : Perform discrete cosine transform on the RGB picture, and predict the error under different compression rates on the feature matrix after the discrete cosine transform according to the neural network, until the error is within a predetermined acceptable range.

It can be seen from the above description that the computer-readable storage medium provided by the embodiment of the present application improves the compression rate by adopting the RGB three-channel aliasing method, and does not cause excessive data recovery error loss, thereby effectively improving the data recovery. Monitor the compressed storage capacity of spectrum data.

The embodiments of the present application also provide a computer program product capable of implementing all the steps in the method for compressing spectral data based on image DCT transform in which the execution body is the server or the client in the foregoing embodiments. When the computer program/instruction is executed by the processor The steps of implementing the method for compressing spectral data based on image DCT transformation, for example, the computer program/instruction implements the following steps:

Step S101: Divide the spectrum data received at the same time into three groups of frequency parts equally, and map them to the R, G, and B channels respectively according to the signal strength and form an RGB picture;

Step S102 : Perform discrete cosine transform on the RGB picture, and predict the error under different compression rates on the feature matrix after the discrete cosine transform according to the neural network, until the error is within a predetermined acceptable range.

It can be seen from the above description that the computer program product provided by the embodiment of the present application improves the compression rate by adopting the RGB three-channel aliasing method, and does not cause excessive data recovery error loss, thereby effectively improving the monitoring frequency spectrum. Compression storage capacity of data.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (apparatus), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block in the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce Means for implementing the functions specified in a flow or flow of a flowchart and/or a block or blocks of a block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions The apparatus implements the functions specified in the flow or flow of the flowcharts and/or the block or blocks of the block diagrams.

These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the flow or blocks of the flowcharts and/or the block or blocks of the block diagrams.

In the present invention, the principles and implementations of the present invention are described by using specific embodiments, and the descriptions of the above embodiments are only used to help understand the method and the core idea of the present invention; The idea of the invention will have changes in the specific implementation and application scope. To sum up, the content of this specification should not be construed as a limitation to the present invention.

Claims (8)

1. a spectral data compression method based on image DCT transformation, is characterized in that, described method comprises: Divide the spectrum data received at the same time into three groups of frequency parts, and map them to the R, G, and B channels according to the signal strength, and form an RGB picture; The discrete cosine transform is performed on the RGB picture, and the feature matrix after the discrete cosine transform predicts the error under different compression rates according to the neural network, until the error is within the set acceptable range. 2. The spectral data compression method based on image DCT transformation according to claim 1, wherein the received spectral data at the same moment is equally divided into three groups of frequency parts, comprising: The received spectrum data is represented as a two-dimensional array, and the data is normalized to the range of 0-255 and rounded up. The horizontal axis is frequency, the vertical axis is time, and the number of rows is 1/3 of the number of columns. Rounded up; If the number of columns is an integer multiple of 3, it is directly divided into three equal parts. If the number of columns is not divisible by three, zero columns are filled on the right side until the number of columns is an integer multiple of 3. 3. the spectral data compression method based on image DCT transformation according to claim 1, is characterized in that, the described spectral data received at the same moment is equally divided into three groups of frequency parts, and is mapped to R, G and B three channels are combined into a RGB picture, including: Divide the frequency parts into three groups equally and add a marked row in the first row, the original data column is 255, and the added all zero column is 0; Divide the expanded array into three equal columns, map the three matrixes after three equal divisions to the magnitudes of R, G, and B according to their numerical values, and form an RGB picture, in which the left matrix corresponds to the first matrix. The one-dimensional R channel, the left second matrix corresponds to the second-dimensional G channel, and the rightmost matrix corresponds to the third-dimensional B channel. 4. The spectral data compression method based on image DCT transform according to claim 1, is characterized in that, the described feature matrix after described discrete cosine transform predicts the error under different compression ratios according to neural network, until the error is in the setting. within acceptable limits, including: Select a specific threshold to filter feature values through a pre-trained machine learning model; Traverse all thresholds in the range of [0.01, 19.51] according to the length of 0.5 steps, input the length, width and threshold of the image to be compressed into the trained machine learning model, output the image quality prediction value and dynamically select the compression rate according to the demand , until the error is within the set acceptable range. 5. A spectral data compression device based on image DCT transformation, characterized in that, comprising: The RGB mapping module is used to divide the spectrum data received at the same time into three groups of frequency parts, and map them to the R, G, and B channels according to the signal strength, and form an RGB picture; The data compression module is used to perform discrete cosine transform on the RGB picture, and the feature matrix after the discrete cosine transform predicts the error under different compression rates according to the neural network, until the error is within the set acceptable range. 6. An electronic device, comprising a memory, a processor and a computer program stored on the memory and running on the processor, wherein the processor implements any one of claims 1 to 4 when the processor executes the program The steps of the spectral data compression method based on image DCT transform. 7. A computer-readable storage medium on which a computer program is stored, wherein the computer program realizes the spectral data compression method based on image DCT transform according to any one of claims 1 to 4 when the computer program is executed by the processor A step of. 8. A computer program product comprising a computer program/instruction, characterized in that, when the computer program/instruction is executed by the processor, a method for realizing the spectral data compression method based on the image DCT transform of any one of claims 1 to 4 is realized. step.

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