CN116436974B - Data transmission method and system - Google Patents
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Abstract
The invention provides a data transmission method and a data transmission system, which establish a binding relation between characters and RGB color values, wherein different characters correspond to different RGB color values; the binding relation comprises RGB color values which are bound as initial identifiers; encoding the data file to be transmitted into a picture file according to the binding relation; acquiring the picture file through a high-definition camera, and performing deviation correction processing on the picture file; and (3) data decoding, namely reversely decoding the picture file subjected to deviation correction processing according to the binding relation in the step (S1) to obtain the data file. The invention solves the problem that data can not be acquired due to limited data transmission and forwarding, enlarges the data transmission quantity and ensures the data transmission safety.
Description
Technical Field
The invention belongs to the technical field of information, and particularly relates to a data transmission method and system.
Background
In the process of developing software services, data to a third party platform is often used, and the third party platform is sometimes limited in terms of security and cannot directly transfer the data out by using a network interface or other storage media. For example, the grid network and the internet are physically isolated, and internet devices are not allowed to connect for security, so it is difficult to collect and analyze plant operation data via the network or other storage medium. In order to address the situation, the method adopted in the prior art can require a third party platform to convert data into a picture form of the two-dimension code, and the two-dimension code data is read by adopting a mode of camera identification or application development and two-dimension code reading.
However, the above method has the following problems:
1) Common two-dimensional code information is easy to leak, the coding mode is fixed, and carried information can be analyzed;
2) The two-dimensional code is virus-free, but can carry website links of viruses, and risks are caused by careless clicking and downloading;
3) The data in the two-dimensional code can be loaded with limited data volume, and the data conversion is difficult to realize under the condition of large data volume.
Disclosure of Invention
The invention provides a data transmission method and a data transmission system, which solve the problem that data cannot be acquired due to limited data transmission and forwarding, and simultaneously enlarge the transmission data quantity and ensure the data transmission safety.
In order to achieve the above purpose, the technical scheme of the invention is realized as follows:
a data transmission method, comprising:
s1, establishing a binding relation between characters and RGB color values, wherein different characters correspond to different RGB color values; the binding relation comprises RGB color values which are bound as initial identifiers;
s2, data encoding, namely encoding the data file to be transmitted into a picture file according to the binding relation in the step S1;
s3, shooting by a high-definition camera to obtain the picture file, and performing correction processing on RGB color values of each pixel point on the picture file through a machine learning network;
s4, data decoding, namely reversely decoding the picture file subjected to deviation correction processing according to the binding relation in the step S1 to obtain the data file.
Further, the method of encoding into a picture file in step S2 includes:
s201, reading a data file to obtain a character data stream, and setting the resolution of the picture file according to the length of the character data stream; let the length of the character data stream be N, then the resolution of the picture file is: floor (∈n+1) ×floor (∈n);
s202, mapping different characters of the character data stream into RGB color values of different pixel points of the picture file; and obtaining a picture file.
Further, in step S3, the training method of the machine learning network includes:
s301, randomly sampling, namely photographing unit pictures with different colors at different angles or with different brightness, and randomly extracting different pictures with the same color; taking the extracted sample picture and the corresponding character according to the binding relation as input data for training by a machine learning network;
s302, sequencing corresponding RGB color values of each character according to the occurrence frequency; taking a shadow interval of 99% of normal distribution as an RGB color value interval of the character, wherein the RGB color values are in normal distribution and correspond to the same character;
s303, acquiring all characters related to the binding relation and corresponding RGB color value intervals thereof as model data of a machine learning network.
Further, in step S3, the training method of the machine learning network further includes a machine dog correction method, including:
s311, a machine dog correction module is established, wherein the machine dog correction module comprises a simulation generation data program and shooting hardware;
s312, randomly generating a data file at regular time through simulating a data generation program, and generating a corresponding picture file according to the binding relation;
s313, shooting pictures with different angles or brightness at intervals in the picture file generated in the S312 through shooting hardware;
s314, performing supervised learning of the machine learning network by using the data file, the generated picture file and the shot picture file.
Further, the specific decoding method in step S4 includes:
s401, positioning the starting position of the data file: sequentially analyzing RGB color values corresponding to each pixel of the picture according to the sequence from left to right and from top to bottom; if the resolved RGB color value is not matched with any value in the binding relation; continuing to analyze the color value to the back, and when finding the matched RGB color value of the first pixel, judging whether the value is the RGB color value bound by the initial mark; if not, the picture needs to be rotated for re-analysis; repeating the above process until the first matched RGB color value is the RGB color value bound by the starting identifier;
s402, data transcoding and output: after the initial mark is positioned, continuing to analyze the color values to the rear according to the sequence, and transcoding all the matched pixel RGB color values according to the binding relation to obtain a data file.
Another aspect of the present invention also provides a data transmission system, including:
binding module: establishing a binding relation between characters and RGB color values, wherein different characters correspond to different RGB color values; the binding relation comprises RGB color values which are bound as initial identifiers;
and a coding module: data encoding, namely encoding a data file to be transmitted into a picture file according to the binding relation in the binding module;
and a deviation rectifying module: shooting by a high-definition camera to obtain the picture file, and performing correction processing on RGB color values of each pixel point on the picture file through a machine learning network;
and a decoding module: and (3) data decoding, namely reversely decoding the picture file subjected to deviation correction processing according to the binding relation in the binding module to obtain the data file.
Further, the encoding module includes:
resolution setting unit: reading a data file to obtain a character data stream, and setting the resolution of the picture file according to the length of the character data stream; let the length of the character data stream be N, then the resolution of the picture file is: floor (∈n+1) ×floor (∈n);
mapping unit: mapping different characters of the character data stream into RGB color values of different pixel points of the picture file; and obtaining a picture file.
Further, the machine learning network in the deviation rectifying module is provided with a training sub-module, which comprises:
sampling unit: randomly sampling, photographing unit pictures with different colors at different angles or with different brightness, and randomly extracting different pictures with the same color; taking the extracted sample picture and the corresponding character according to the binding relation as input data for training by a machine learning network;
interval unit: sequencing the corresponding RGB color values of each character according to the frequency of the RGB color values; taking a shadow interval of 99% of normal distribution as an RGB color value interval of the character, wherein the RGB color values are in normal distribution and correspond to the same character;
data unit: and acquiring all the characters related to the binding relation and the corresponding RGB color value intervals thereof as model data of the machine learning network.
Furthermore, the machine learning network in the deviation correcting module further comprises a machine dog correcting module, wherein the machine dog correcting module comprises a simulation generation data program and shooting hardware; randomly generating a data file at regular time through a simulation generation data program, and generating a corresponding picture file according to the binding relation; shooting the generated picture files at intervals at different angles or brightness by shooting hardware; and performing supervised learning of the machine learning network by using the data file, the generated picture file and the shot picture file.
Further, the decoding module includes:
the parsing unit is used for locating the starting position of the data file: sequentially analyzing RGB color values corresponding to each pixel of the picture according to the sequence from left to right and from top to bottom; if the resolved RGB color value is not matched with any value in the binding relation; continuing to analyze the color value to the back, and when finding the matched RGB color value of the first pixel, judging whether the value is the RGB color value bound by the initial mark; if not, the picture needs to be rotated for re-analysis; repeating the above process until the first matched RGB color value is the RGB color value bound by the starting identifier;
the transcoding output unit is used for transcoding and outputting data: after the initial mark is positioned, continuing to analyze the color values to the rear according to the sequence, and transcoding all the matched pixel RGB color values according to the binding relation to obtain a data file.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the method, the data file is encoded into the picture file and obtained through the high-definition camera, and then the picture file is decoded, so that the problem that data acquisition, data transmission and forwarding limitation cannot be realized due to network physical isolation of a third-party platform is solved;
2. the invention codes the data file into the picture file, solves the safety problem generated after interception in the data transmission process;
3. the invention solves the problem of collecting the data volume of the carrier, and the two-dimension code transmits 800k bytes at most, while the invention can realize the transmission of 8M data at most, and the data volume of transmission is enlarged by about 10 times.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of the present invention;
fig. 2 is a schematic diagram of the encoding result according to an embodiment of the present invention.
Detailed Description
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
For the purpose of making the objects and features of the present invention more comprehensible, embodiments accompanied with figures are described in detail below.
In this embodiment, a group of floating point numbers are coded and converted into a specific picture, then the picture is collected by a high-definition camera, and then the original floating point data is read out by using reverse decoding, which specifically includes the steps as shown in fig. 1:
1. RGB colors are tied to digital symbols. The following 14 colors were chosen to represent different symbols and values, with the addition of white, RGB color values: (255, 255, 255) as background color, indicating no data. The following are provided:
yellow, RGB color values: (255, 0) indicating that the start of data reading is a start flag;
green, RGB color values: (0, 255, 0) representing: the number 0;
cyan, RGB color values: (0, 255, 255), representing: number 1;
magenta, RGB color values: (255, 0, 255) representing: number 2;
red, RGB color values (255, 0), representing: number 3;
blue, RGB color values: (0, 255) representing: number 4;
dark blue, RGB color values: (0, 139) representing: number 5;
dark cyan, RGB color values: (0, 139, 139) representing: number 6;
dark green, RGB color values: (0, 100, 0) representing: number 7;
purple, RGB color values: (128, 0, 128) representing: number 8;
dark red, RGB color values: (139,0,0) representing: number 9;
olive, RGB color values: (128, 0), representing: mathematical symbol "-" minus;
dark grey, RGB color values: (169, 169, 169) representing: mathematical symbols "," decimal points;
black, RGB color values: (0, 0) representing: in the set of numbers, a separator between every two numbers, such as: "," comma;
2. and (5) data encoding. The file data to be transmitted is encoded according to the digital symbols and the color relations. The encoding process is as follows:
1) Setting the resolution of the picture file according to the length of the character number of the data file; in this embodiment, when the text data read is "115.423411,41.060816,117.514625,41.060816", and the total length of the data characters is 41, the size of the resolution of the picture may be set to floor (v 41+1) ×floor (v 41), that is, a 7×6 rectangular picture.
2) Reading a data file to obtain a character data stream, and mapping the character data stream into RGB color values of different pixel points of the 7 multiplied by 6 rectangular picture according to different characters; the read text data is 115.423411,41.060816,117.514625,41.060816, each digit is converted into a specific RGB color value of a corresponding pixel, and the corresponding RGB color is displayed by the pixel; rectangular pictures with 7×6 conversion order are pixel point order from left to right and top to bottom. The converted picture file is encoded, as shown in fig. 2 (since the color cannot be displayed in fig. 2, the RGB color of the pixel at the position is illustrated by the chinese characters in the grid).
3. And (5) data acquisition. Under the condition that the data cannot be accessed and only the external acquisition equipment can be used, a camera is used for shooting the picture file; because the encoded data is mapped in units of pixels, the photographing quality of the camera is required to be not less than that of the converted picture pixels, and a high-definition camera can be used.
4. Performing correction processing on RGB color values of each pixel point on a picture file obtained by shooting by a high-definition camera through a machine learning network;
the machine learning network is obtained by iterative training of sample data and supervised learning using a machine dog correction module. The specific training process comprises the following steps:
randomly sampling, photographing sample pictures with different colors at different angles or with different brightness, and randomly extracting different pictures obtained by photographing the same color; taking the extracted picture and the corresponding character according to the binding relation as input data for training by a machine learning network;
sequencing the corresponding RGB color values of each character according to the frequency of the RGB color values; taking a shadow interval of 99% of normal distribution as an RGB color value interval of the character, wherein the RGB color values are in normal distribution and correspond to the same character;
and acquiring all the characters related to the binding relation and the corresponding RGB color value intervals thereof as model data of the machine learning network.
The trained machine learning network can also perform supervised learning through a machine dog correction module, wherein the machine dog correction module comprises a simulation generation data program and shooting hardware; randomly generating a data file at regular time through a simulation generation data program, and generating a corresponding picture file according to the binding relation; shooting the generated picture files at intervals at different angles or brightness by shooting hardware; and performing supervised learning of a machine learning network by using the data file, the generated picture file and the shot picture file, and repeating training until the detection accuracy reaches 99.9999%.
5. And (5) decoding data. Data decoding refers to the reverse process of encoding data. The decoding process is as follows:
1) Positioning the starting position of the data: and sequentially analyzing the RGB color values corresponding to each pixel of the picture according to the sequence from left to right and from top to bottom. If the resolved RGB color values do not match any of the 14 color values within the binding. Continuing to parse back, when the matching first pixel RGB value is found, it is determined whether the value is yellow (255, 0), i.e., the start identification of the data. If not, the rotated picture is required to be re-parsed. The above process is repeated until the first matching RGB color value is (255, 0) yellow.
2) And (5) data transcoding and outputting. After the initial mark is positioned, continuing to analyze the color in the back direction according to the sequence, and transcoding all the matched colors according to the binding relation between RGB color values and the digital symbols. Such as: RGB color values: (0, 255, 255), to a number 1; RGB color values: (255, 0, 255) to a number 2; and the like, transcoding to obtain a data file.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (10)
1. A data transmission method, comprising:
s1, establishing a binding relation between characters and RGB color values, wherein different characters correspond to different RGB color values; the binding relation comprises RGB color values which are bound as initial identifiers;
s2, data encoding, namely encoding the data file to be transmitted into a picture file according to the binding relation in the step S1;
s3, shooting by a high-definition camera to obtain the picture file, and performing correction processing on RGB color values of each pixel point on the picture file through a machine learning network;
s4, data decoding, namely reversely decoding the picture file subjected to deviation correction processing according to the binding relation in the step S1 to obtain the data file.
2. The data transmission method according to claim 1, comprising: the method for encoding into a picture file in step S2 includes:
s201, reading a data file to obtain a character data stream, and setting the resolution of the picture file according to the length of the character data stream; let the length of the character data stream be N, then the resolution of the picture file is: floor (∈n+1) ×floor (∈n);
s202, mapping different characters of the character data stream into RGB color values of different pixel points of the picture file; and obtaining a picture file.
3. The data transmission method according to claim 1, wherein in step S3, the training method of the machine learning network includes:
s301, randomly sampling, namely photographing unit pictures with different colors at different angles or with different brightness, and randomly extracting different pictures with the same color; taking the extracted sample picture and the corresponding character according to the binding relation as input data for training by a machine learning network;
s302, sequencing corresponding RGB color values of each character according to the occurrence frequency; taking a shadow interval of 99% of normal distribution as an RGB color value interval of the character, wherein the RGB color values are in normal distribution and correspond to the same character;
s303, acquiring all characters related to the binding relation and corresponding RGB color value intervals thereof as model data of a machine learning network.
4. The data transmission method according to claim 3, wherein in step S3, the training method of the machine learning network further includes a machine dog correction method, comprising:
s311, a machine dog correction module is established, wherein the machine dog correction module comprises a simulation generation data program and shooting hardware;
s312, randomly generating a data file at regular time through simulating a data generation program, and generating a corresponding picture file according to the binding relation;
s313, shooting pictures with different angles or brightness at intervals in the picture file generated in the S312 through shooting hardware;
s314, performing supervised learning of the machine learning network by using the data file, the generated picture file and the shot picture file.
5. The data transmission method according to claim 1, wherein the decoding method in step S4 includes:
s401, positioning the starting position of the data file: sequentially analyzing RGB color values corresponding to each pixel of the picture according to the sequence from left to right and from top to bottom; if the resolved RGB color value is not matched with any value in the binding relation; continuing to analyze the color value to the back, and when finding the matched RGB color value of the first pixel, judging whether the value is the RGB color value bound by the initial mark; if not, the picture needs to be rotated for re-analysis; repeating the above process until the first matched RGB color value is the RGB color value bound by the starting identifier;
s402, data transcoding and output: after the initial mark is positioned, continuing to analyze the color values to the rear according to the sequence, and transcoding all the matched pixel RGB color values according to the binding relation to obtain a data file.
6. A data transmission system, comprising:
binding module: establishing a binding relation between characters and RGB color values, wherein different characters correspond to different RGB color values; the binding relation comprises RGB color values which are bound as initial identifiers;
and a coding module: data encoding, namely encoding a data file to be transmitted into a picture file according to the binding relation in the binding module;
and a deviation rectifying module: shooting by a high-definition camera to obtain the picture file, and performing correction processing on RGB color values of each pixel point on the picture file through a machine learning network;
and a decoding module: and (3) data decoding, namely reversely decoding the picture file subjected to deviation correction processing according to the binding relation in the binding module to obtain the data file.
7. The data transmission system of claim 6, wherein the encoding module comprises:
resolution setting unit: reading a data file to obtain a character data stream, and setting the resolution of the picture file according to the length of the character data stream; let the length of the character data stream be N, then the resolution of the picture file is: floor (∈n+1) ×floor (∈n);
mapping unit: mapping different characters of the character data stream into RGB color values of different pixel points of the picture file; and obtaining a picture file.
8. The data transmission system of claim 6, wherein the machine learning network in the deskew module is provided with a training sub-module comprising:
sampling unit: randomly sampling, photographing unit pictures with different colors at different angles or with different brightness, and randomly extracting different pictures with the same color; taking the extracted sample picture and the corresponding character according to the binding relation as input data for training by a machine learning network;
interval unit: sequencing the corresponding RGB color values of each character according to the frequency of the RGB color values; taking a shadow interval of 99% of normal distribution as an RGB color value interval of the character, wherein the RGB color values are in normal distribution and correspond to the same character;
data unit: and acquiring all the characters related to the binding relation and the corresponding RGB color value intervals thereof as model data of the machine learning network.
9. The data transmission system of claim 8, wherein the machine learning network in the deskew module further comprises a machine dog correction module comprising a simulation generation data program and shooting hardware; randomly generating a data file at regular time through a simulation generation data program, and generating a corresponding picture file according to the binding relation; shooting the generated picture files at intervals at different angles or brightness by shooting hardware; and performing supervised learning of the machine learning network by using the data file, the generated picture file and the shot picture file.
10. The data transmission system of claim 6, wherein the decoding module comprises:
the parsing unit is used for locating the starting position of the data file: sequentially analyzing RGB color values corresponding to each pixel of the picture according to the sequence from left to right and from top to bottom; if the resolved RGB color value is not matched with any value in the binding relation; continuing to analyze the color value to the back, and when finding the matched RGB color value of the first pixel, judging whether the value is the RGB color value bound by the initial mark; if not, the picture needs to be rotated for re-analysis; repeating the above process until the first matched RGB color value is the RGB color value bound by the starting identifier;
the transcoding output unit is used for transcoding and outputting data: after the initial mark is positioned, continuing to analyze the color values to the rear according to the sequence, and transcoding all the matched pixel RGB color values according to the binding relation to obtain a data file.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016119360A1 (en) * | 2015-01-28 | 2016-08-04 | 中兴通讯股份有限公司 | Data sharing method, data sharing device and terminal |
| CN108596180A (en) * | 2018-04-09 | 2018-09-28 | 深圳市腾讯网络信息技术有限公司 | Parameter identification, the training method of parameter identification model and device in image |
| CN110363117A (en) * | 2019-06-28 | 2019-10-22 | 深圳数位传媒科技有限公司 | A kind of method and device that encrypted random coded character file is parsed |
| CN114357174A (en) * | 2022-03-18 | 2022-04-15 | 北京创新乐知网络技术有限公司 | Code classification system and method based on OCR and machine learning |
| CN116030895A (en) * | 2022-12-13 | 2023-04-28 | 中国科学院深圳先进技术研究院 | DNA information storage method based on natural and unnatural base |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10540257B2 (en) * | 2017-03-16 | 2020-01-21 | Fujitsu Limited | Information processing apparatus and computer-implemented method for evaluating source code |
-
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- 2023-06-15 CN CN202310708043.4A patent/CN116436974B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016119360A1 (en) * | 2015-01-28 | 2016-08-04 | 中兴通讯股份有限公司 | Data sharing method, data sharing device and terminal |
| CN108596180A (en) * | 2018-04-09 | 2018-09-28 | 深圳市腾讯网络信息技术有限公司 | Parameter identification, the training method of parameter identification model and device in image |
| CN110363117A (en) * | 2019-06-28 | 2019-10-22 | 深圳数位传媒科技有限公司 | A kind of method and device that encrypted random coded character file is parsed |
| CN114357174A (en) * | 2022-03-18 | 2022-04-15 | 北京创新乐知网络技术有限公司 | Code classification system and method based on OCR and machine learning |
| CN116030895A (en) * | 2022-12-13 | 2023-04-28 | 中国科学院深圳先进技术研究院 | DNA information storage method based on natural and unnatural base |
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