Radiation monitoring data compression and real-time transmission method and system

Abstract

The invention discloses a radiation monitoring data compression and real-time transmission method and system, and relates to the technical field of data compression and transmission, and the method comprises the steps: collecting original radiation monitoring data, and carrying out the data preprocessing; carrying out lossless compression coding on the radiation monitoring data characteristics; the compressed data are divided into small segments, and the data are transmitted in a segmented mode; and after receiving the data, the receiving end decompresses the data according to the compression reverse operation. According to the invention, the transmission efficiency and reliability of radiation data under the condition of low bandwidth can be obviously improved. The compression algorithm can realize a data compression rate of at least 50% or more, and meanwhile, the integrity and precision of the data are kept. In addition, a real-time transmission mechanism ensures that data can be effectively transmitted under an extreme network condition, and the delay and packet loss rate of data transmission are remarkably reduced. The volume of radiation monitoring data can be significantly reduced without sacrificing the quality and integrity of the data.

Description

Technical Field

[0001] The present invention relates to the technical field of data compression and transmission, and in particular to a method and system for compressing and real-time transmitting radiation monitoring data. Background Art

[0002] With the development of environmental monitoring technology, radiation monitoring has become an important means to ensure environmental safety and public health. Radiation monitoring usually requires the deployment of a large number of sensors in different locations to collect real-time radiation level data in the environment. These data are usually high-dimensional and large in volume, and their real-time and effective processing and transmission are crucial to the response speed and accuracy of environmental monitoring systems.

[0003] Traditional data transmission methods often face bandwidth limitations and data transmission delays when dealing with such large amounts of data. Especially in remote areas or areas with imperfect infrastructure, low-bandwidth network environments greatly limit the ability to transmit data in real time. In addition, uncompressed data also increases the risk of data loss during transmission, especially when the network environment is unstable.

[0004] Therefore, developing an efficient data compression algorithm can not only reduce the bandwidth required for data transmission, but also ensure the integrity and real-time nature of the data, which is of great significance for improving the overall performance and reliability of the radiation monitoring system. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is: how to solve the problems of bandwidth limitation and data transmission delay often faced by traditional data transmission methods when processing such a large amount of data.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a method for compressing and transmitting radiation monitoring data in real time, comprising: collecting original radiation monitoring data and performing data preprocessing; performing lossless compression encoding on radiation monitoring data features; dividing the compressed data into small segments and transmitting the data in segments; after receiving the data, the receiving end decompresses the data according to the inverse operation of compression.

[0008] As a preferred solution of the radiation monitoring data compression and real-time transmission method described in the present invention, the original radiation monitoring data includes the type of radiation source, the expected radiation range and environmental influencing factors considered when selecting the location for sensor deployment; using a UNIX timestamp to record the collection time of the data point, recording the radiation intensity as a floating point number, the unit is such as millisievert, and collecting environmental parameter data; the data preprocessing includes using a median filter to denoise the data.

[0009] As a preferred solution of the radiation monitoring data compression and real-time transmission method described in the present invention, wherein: the radiation monitoring data characteristics include statistical characteristics of the calculated data, including peak value, mean value and standard deviation; the lossless compression includes calculating difference data for the radiation monitoring data characteristics, using Huffman coding for the difference data, and constructing an optimal prefix code tree; the Huffman coding includes counting the frequency of occurrence of each difference, and constructing a Huffman tree according to the frequency, each leaf node in the tree represents a symbol, the path length represents the codeword length of the symbol, and the path from the root node of the tree to each leaf node determines the encoding of each symbol.

[0010] As a preferred solution of the radiation monitoring data compression and real-time transmission method described in the present invention, the codeword length is expressed as:

[0011] C(w)=-log2P(w)

[0012] Where C(w) is the codeword length of symbol w, and P(w) is the probability of symbol w appearing.

[0013] As a preferred solution of the radiation monitoring data compression and real-time transmission method described in the present invention, the lossless compression also includes dictionary compression, the dictionary contains all possible single-character strings, and when a new string combination that is not in the dictionary is found in the data stream, it is added to the dictionary, the data is traversed, the longest string in the current dictionary is used to match the data stream, and the string is replaced with the corresponding dictionary index.

[0014] As a preferred solution of the radiation monitoring data compression and real-time transmission method described in the present invention, the segmented transmission includes segmenting the compressed data according to the actual network bandwidth, and small segments reduce the need for retransmission due to network fluctuations.

[0015] As a preferred solution of the radiation monitoring data compression and real-time transmission method described in the present invention, the decompression includes the receiving end using the same dictionary or coding table as the sending end for decompression, and restoring the original data string by looking up the dictionary index.

[0016] Another object of the present invention is to provide a system for radiation monitoring data compression and real-time transmission method, which can solve the problem of radiation monitoring data compression and real-time transmission by constructing a radiation monitoring data compression and real-time transmission system.

[0017] To solve the above technical problems, the present invention provides the following technical solutions: a radiation monitoring data compression and real-time transmission system, comprising a data acquisition module, a compression module, a segmented transmission module and a decompression module; the data acquisition module is used to collect original radiation monitoring data and perform data preprocessing; the compression module is used to perform lossless compression encoding on radiation monitoring data features; the segmented transmission module is used to divide the compressed data into small segments and transmit the data in segments; the decompression module is used to decompress the data according to the inverse operation of compression after the receiving end receives the data.

[0018] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned radiation monitoring data compression and real-time transmission method when executing the computer program.

[0019] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the radiation monitoring data compression and real-time transmission method as described above.

[0020] The beneficial effects of the present invention are as follows: the radiation monitoring data compression and real-time transmission method provided by the present invention can significantly improve the transmission efficiency and reliability of radiation data under low bandwidth conditions. The compression algorithm can achieve a data compression rate of at least 50% while maintaining the integrity and accuracy of the data. In addition, the real-time transmission mechanism ensures that the data can be effectively transmitted even under extreme network conditions, significantly reducing the delay and packet loss rate of data transmission. The volume of radiation monitoring data can be significantly reduced without sacrificing the quality and integrity of the data. Specifically, the compression strategy can be dynamically adjusted according to the actual working conditions of the radiation data, so as to achieve an optimal balance between data integrity and compression efficiency. It is particularly suitable for radiation protection monitoring scenarios, such as nuclear power plants, radiotherapy centers, etc., to ensure efficient data transmission and storage even under low bandwidth conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0022] Figure 1 A flowchart of a radiation monitoring data compression and real-time transmission method provided for the first embodiment of the present invention.

[0023] Figure 2 A Huffman coding code diagram of a radiation monitoring data compression and real-time transmission method provided by the first embodiment of the present invention.

[0024] Figure 3 A dictionary compression code diagram of a radiation monitoring data compression and real-time transmission method provided by the first embodiment of the present invention.

[0025] Figure 4 A structural diagram of a radiation monitoring data compression and real-time transmission system provided for the second embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0027] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0028] Example 1, reference Figure 1 , which is the first embodiment of the present invention, provides a method for compressing and transmitting radiation monitoring data in real time, including: collecting original radiation monitoring data and performing data preprocessing; performing lossless compression encoding on radiation monitoring data features; dividing the compressed data into small segments and transmitting the data in segments; after the receiving end receives the data, decompressing it according to the inverse operation of compression.

[0029] S1. Collect original radiation monitoring data and perform data preprocessing.

[0030] Collect raw radiation monitoring data from various monitoring sites. Clean and standardize the collected data, remove noise, and extract key data features.

[0031] S1.1. Data acquisition.

[0032] When selecting the location for sensor deployment, consider the type of radiation source, expected radiation range, and environmental factors to ensure full coverage of key areas.

[0033] Sensor Type: Choose a sensor that can accurately measure different types of radiation (such as alpha, beta, and gamma rays).

[0034] Deployment strategy: Deploy systematically according to a preset pattern, such as deploying in an equidistant circle or grid around a nuclear power plant.

[0035] Data collection frequency:

[0036] Collection frequency setting: Set the collection frequency according to the purpose of monitoring and environmental conditions. For example, under normal conditions, the collection frequency is once a minute, and if abnormal radiation levels are detected, it is increased to once a second.

[0037] Adaptive adjustment: The sensor can automatically adjust the acquisition frequency according to changes in actual radiation intensity, thereby providing more data support at critical moments.

[0038] Data format and content

[0039] Data types and structures:

[0040] Timestamp: Use UNIX timestamp (integer) to record the exact time when the data point was collected.

[0041] Radiation readings: Record radiation intensity as a floating point number in units such as millisieverts (mSv).

[0042] Environmental parameters: including temperature (floating point number, unit: Celsius), humidity (floating point number, percentage), etc., and may also include data such as wind speed and wind direction as needed.

[0043] Example format:

[0044] {

[0045] "timestamp": 1625247600,

[0046] "radiation": 3.6,

[0047] "temperature": 22.5,

[0048] "humidity": 45

[0049] }

[0050] Format description: This format is JSON, which is suitable for structured storage and processing of data, and facilitates subsequent data compression and transmission.

[0051] S1.2. Data preprocessing.

[0052] Denoising: Use a median filter to denoise the sensor data. The median filter effectively removes occasional extreme values ​​or noise points by replacing each data point with the median of its neighborhood.

[0053] For example, the consecutive radiation readings [3.5, 3.7, 10.2, 3.6, 3.5] become [3.5, 3.7, 3.6, 3.6, 3.5] after being processed using a median filter, where the outlier 10.2 is replaced.

[0054] S2. Perform lossless compression encoding on radiation monitoring data features.

[0055] Feature extraction and data reduction:

[0056] Time series analysis: Since radiation data are usually time series data, time series analysis methods, differencing and data smoothing can be used to initially reduce the redundancy in the data.

[0057] Threshold Analysis: Set a threshold to ignore small changes and only record data points when the radiation intensity changes above a certain threshold.

[0058] Data encoding strategy:

[0059] Variable-length encoding: Encode the size of data changes. Larger changes use longer codes, and smaller changes use shorter codes. This can further reduce the space occupied by data with common small fluctuations.

[0060] Pattern matching: Radiation data may show periodic or predictable patterns under certain circumstances (such as sunny or rainy days). By pre-defining these patterns and matching them in the data, only data that deviates from the known patterns is stored.

[0061] Context-Aware Compression:

[0062] Environmental parameter fusion: Combining data on environmental parameters such as temperature and humidity that affect radiation readings, using a multivariate compression algorithm, radiation data can be predicted and encoded more accurately.

[0063] Conditional Predictive Coding: Use a conditional predictive model to predict future data points based on current and past data states, storing only the difference between the prediction error and the actual data.

[0064] Application of lossless compression technology:

[0065] Entropy coding: Apply entropy coding techniques such as Huffman coding to compress the preprocessed data to further reduce useless information.

[0066] Dictionary compression: For periodically repeated data patterns, a dictionary is built and the dictionary index is used to replace the original data, effectively reducing the data volume.

[0067] Through the above steps, the compression algorithm can significantly reduce the volume of radiation monitoring data without sacrificing data quality and integrity. Specifically, the algorithm can dynamically adjust the compression strategy according to the actual working conditions of the radiation data, so as to achieve the best balance between data integrity and compression efficiency. This method is particularly suitable for radiation protection monitoring scenarios, such as nuclear power plants, radiotherapy centers, etc., to ensure efficient data transmission and storage even under low bandwidth conditions.

[0068] S2.1. Feature extraction.

[0069] Calculate statistical features of the data, such as peak, mean, standard deviation, etc. Reduce the amount of data by retaining statistical features instead of raw data points. For example, radiation data for each minute can be simplified to the highest, lowest, and average values ​​within that minute.

[0070] S2.2, Huffman coding.

[0071] Difference encoding: First, calculate the difference between consecutive data points. For example, the difference sequence of the original data [3.5, 3.6, 3.2, 3.8] is [0.1, -0.4, 0.6]. Differences are usually small and easier to encode efficiently.

[0072] Huffman coding: Huffman coding is used on the difference data to construct the optimal prefix code tree. Small differences are assigned shorter codewords, and large differences are assigned longer codewords. For example, a difference of 0.1 may be encoded as 01, while -0.4 is encoded as 110.

[0073] Count the frequency of each difference. Build a Huffman tree based on the frequency. Generate the corresponding code based on the tree.

[0074] Specific code examples are as follows Figure 2 shown.

[0075] The codeword length is expressed as:

[0076] C(w)=-log2P(w)

[0077] Where C(w) is the codeword length of symbol w, and P(w) is the probability of symbol w appearing.

[0078] S2.3, encoding process.

[0079] The frequency of occurrence of each symbol in the statistics. Use these frequencies to build a Huffman tree, where each leaf node in the tree represents a symbol and the path length represents the codeword length of the symbol. The path from the root node of the tree to each leaf node determines the encoding of each symbol.

[0080] Dictionary compression:

[0081] Dictionary initialization: Initially, the dictionary contains all possible single-character strings.

[0082] Dictionary update: When a new string combination that is not in the dictionary is found in the data stream, it is added to the dictionary.

[0083] Encoding process: traverse the data, match the data stream with the longest string in the current dictionary, and replace these strings with the corresponding dictionary index.

[0084] In this way, radiation monitoring data can be effectively compressed and the amount of data transmitted can be reduced while maintaining the integrity and accessibility of the data. Figure 3 shown.

[0085] S3. Divide the compressed data into small segments and transmit the data in segments.

[0086] The compressed data is transmitted in segments according to the actual network bandwidth. Smaller segments can reduce the need for retransmission due to network fluctuations and improve transmission efficiency.

[0087] S4. After receiving the data, the receiving end decompresses it according to the reverse operation of compression.

[0088] The receiving end uses the same dictionary or code table as the sending end for decompression.

[0089] Restore the original data string by looking up the dictionary index.

[0090] Example 2, reference Figure 4 , which is the second embodiment of the present invention, is different from the previous embodiment in that it provides a radiation monitoring data compression and real-time transmission system, including: a data acquisition module 100, a compression module 200, a segmented transmission module 300 and a decompression module 400.

[0091] The data acquisition module 100 is used to collect raw radiation monitoring data and perform data preprocessing.

[0092] The compression module 200 is used to perform lossless compression encoding on the radiation monitoring data features.

[0093] The segment transmission module 300 is used to divide the compressed data into small segments and transmit the data in segments.

[0094] The decompression module 400 is used to decompress the data after the receiving end receives the data according to the reverse operation of compression.

[0095] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0096] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0097] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0098] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0099] Embodiment 3 is the third embodiment of the present invention. It is different from the first two embodiments in that it is used to verify the technical effects adopted in the present invention so as to verify the real effects of the method.

[0100] To ensure that the entire data compression and transmission process is described in detail, the following is an example scenario, including a complete description of data examples, algorithm process, segmented transmission, and decompression. At a nuclear power plant radiation monitoring point, we have the following radiation intensity data within one minute (unit: mSv):

[0101] [0.12, 0.13, 0.13, 0.14, 0.13, 0.14, 0.15, 0.16, 0.16, 0.15]

[0102] ```

[0103] Data preprocessing and compression:

[0104] S1. Data preprocessing:

[0105] Denoising: A simple median filter (with a window size of 3) was applied to remove possible noise.

[0106] Result: [0.13, 0.13, 0.13, 0.13, 0.14, 0.14, 0.16, 0.16, 0.16].

[0107] S2. Difference calculation:

[0108] Original data: [0.13, 0.13, 0.13, 0.13, 0.14, 0.14, 0.16, 0.16, 0.16].

[0109] Difference: [0, 0, 0.01, 0, 0.02, 0, 0].

[0110] S3, Huffman coding:

[0111] Frequency statistics: `{0:6, 0.01:1, 0.02:1}`.

[0112] Huffman tree construction: Generate Huffman codes based on frequency.

[0113] Encoded result: `{0:'0', 0.01:'10', 0.02:'11'}`.

[0114] S4. Encoded data:

[0115] Encoded data: ['0', '0', '0', '0', '10', '0', '11', '0', '0'].

[0116] Binary string: `'00001001100'`.

[0117] S5, segmented transmission:

[0118] Data segmentation: Data is divided into smaller segments according to network bandwidth limitations. Assume that each transmission segment can contain 5 binary bits.

[0119] Segmentation result: ['00001', '00110', '0'].

[0120] S6. Data reception and decompression.

[0121] Receive segmented data: The data segments are received in sequence: ['00001', '00110', '0'].

[0122] Assemble data segments: Assemble the received segments and recover the binary string: `'00001001100'`.

[0123] decoding:

[0124] Use the same Huffman encoding table to reverse decode the binary string.

[0125] Decoding process:

[0126] Read `'0'` -> `0` (repeat 6 times).

[0127] Read `'10'` -> `0.01`.

[0128] Read `'0'` -> `0`.

[0129] Read `'11'` -> `0.02`.

[0130] Read `'0'` -> `0` (repeat 2 times).

[0131] Decoding result: [0, 0, 0.01, 0, 0.02, 0, 0].

[0132] Data reconstruction:

[0133] According to the decoding result and the starting value of the original data (0.13mSv), reconstruct the original data:

[0134] Reconstruction process:

[0135] 0.13, 0.13, 0.13, 0.13+0.01, 0.14, 0.14+0.02, 0.16, 0.16, 0.16`.

[0136] Reconstruction results:

[0137] [0.13, 0.13, 0.13, 0.13, 0.14, 0.14, 0.16, 0.16, 0.16].

[0138] The radiation data under stable working conditions are processed. In this case, the data changes less and is more suitable for compression using Huffman coding, because more frequent small changes can be effectively encoded by short codewords, thus achieving high compression efficiency.

[0139] Data sequence: [[0.13, 0.13, 0.13, 0.13, 0.14, 0.14, 0.16, 0.16, 0.16].

[0140] Difference and encoding: Most of the differences are 0 and are efficiently compressed using Huffman coding.

[0141] Compression effect: Since data with small changes and frequently occurring values ​​(difference 0) use the shortest code, the compression efficiency is high.

[0142] Embodiment 4 is the fourth embodiment of the present invention, which verifies and illustrates the technical effects adopted in the present invention to verify the real effects of the present method.

[0143] Consider a relatively extreme condition in environmental monitoring, a radiation spike event, and describe in detail the data compression and transmission process under this condition. In this case, the data may show significant changes in a short period of time, which is crucial for real-time monitoring and rapid response.

[0144] At monitoring points around the nuclear power plant, the following radiation intensity data (in mSv) were recorded within one minute, showing a sudden increase in radiation:

[0145] ```

[0146] [0.15, 0.15, 0.16, 0.18, 0.25, 0.40, 0.50, 0.55, 0.55, 0.55]

[0147] ```

[0148] S1. Data preprocessing and compression.

[0149] Data preprocessing:

[0150] Denoising: Use a median filter to remove potential noise points, especially when the data fluctuates greatly.

[0151] Filtering results: [0.15, 0.16, 0.18, 0.25, 0.40, 0.50, 0.55, 0.55, 0.55, 0.55].

[0152] Difference calculation:

[0153] Original data: [0.15, 0.16, 0.18, 0.25, 0.40, 0.50, 0.55, 0.55, 0.55, 0.55].

[0154] Difference: [0.01, 0.02, 0.07, 0.15, 0.10, 0.05, 0.00, 0.00, 0.00].

[0155] Huffman Coding:

[0156] Frequency statistics: `{0.01:1, 0.02:1, 0.07:1, 0.15:1, 0.10:1, 0.05:1, 0.00:3}`.

[0157] Huffman tree construction: Generate Huffman codes based on frequency.

[0158] Encoded result: `{0.00: '0', 0.01: '110', 0.02: '111', 0.07: '1010', 0.15: '1011', 0.10: '1001', 0.05: '1000'}`.

[0159] Encoded data:

[0160] Encoded data: ['110', '111', '1010', '1011', '1001', '1000', '0', '0', '0'].

[0161] Binary string: `'11011110101011110011000010000'`.

[0162] S2, segmented transmission.

[0163] Data segmentation: Based on the network bandwidth limitation, the data is divided into smaller segments, assuming that each transmission segment can contain 10 binary bits.

[0164] Segmentation result: ['1101111010', '1011110011', '00010000'].

[0165] S3. Data reception and decompression.

[0166] Receive segmented data: Receive data segments in sequence: ['1101111010', '1011110011', '00010000'].

[0167] Assemble data segments: Assemble the received segments to recover the binary string:

[0168] `'11011110101011110011000010000'`.

[0169] decoding:

[0170] Use the same Huffman encoding table to reverse decode the binary string.

[0171] Decoding process

[0172] - `'110'` -> `0.01`.

[0173] - `'111'` -> `0.02`.

[0174] - `'1010'` -> `0.07`.

[0175] - `'1011'` -> `0.15`.

[0176] - `'1001'` -> `0.10`.

[0177] - `'1000'` -> `0.05`.

[0178] - `'0'` -> `0.00` (repeat 3 times).

[0179] Decoding result: [0.01, 0.02, 0.07, 0.15, 0.10, 0.05, 0.00, 0.00, 0.00].

[0180] S4. Data reconstruction:

[0181] According to the decoding result and the starting value of the original data (0.15mSv), reconstruct the original data:

[0182] Reconstruction process:

[0183] -`0.15, 0.15+0.01, 0.16+0.02, 0.18+0.07, 0.25+0.15, 0.40+0.10, 0.50+0.05, 0.55, 0.55, 0.55`

[0184] Reconstruction results: -[0.15, 0.16, 0.18, 0.25, 0.40, 0.50, 0.55, 0.55, 0.55, 0.55].

[0185] Consider a situation where there is a sudden increase in radiation levels. In this case, the data compression needs to be able to handle large changes in value. Huffman coding is also suitable because even large differences can be effectively encoded using longer codewords.

[0186] Data sequence: [0.15, 0.16, 0.18, 0.25, 0.40, 0.50, 0.55, 0.55, 0.55, 0.55].

[0187] Difference and encoding: Several larger differences appear, and Huffman coding is used to accommodate changes of different sizes.

[0188] Compression effect: Although the difference becomes larger, through appropriate Huffman tree construction, a good compression ratio can still be maintained to meet the needs of rapid transmission of sudden high radiation values.

[0189] The patent of this invention provides a compression and real-time transmission algorithm for radiation monitoring data. The algorithm can efficiently compress data and reduce transmission bandwidth requirements while maintaining data integrity and real-time performance by preprocessing data, calculating differences, and performing dictionary compression when necessary.

[0190] The present invention has high compression efficiency: through effective coding strategies, such as Huffman coding, the codeword length is optimized according to the statistical distribution of the data to achieve a high compression ratio. Real-time guarantee: the segmented transmission strategy and fast encoding and decoding process ensure that key data can be transmitted in real time even in low-bandwidth or unstable network environments. Wide adaptability: It is suitable for a variety of monitoring environments from stable to highly dynamic changes, and can quickly adapt to sudden changes to provide support for emergency response.

[0191] The present invention is particularly suitable for use in the fields of environmental monitoring, nuclear facility management, radiation safety monitoring, etc., where the real-time and accuracy of data are crucial. Through the present invention, the performance of the radiation monitoring system can be effectively improved, ensuring the rapid and accurate transmission of data at critical moments, thereby providing solid technical support for environmental safety and public health.

[0192] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A radiation monitoring data compression and real-time transmission method, characterized in that: include, Collect raw radiation monitoring data and perform data preprocessing; Lossless compression encoding of radiation monitoring data features; Divide the compressed data into small segments and transmit the data in segments; After receiving the data, the receiving end decompresses it according to the reverse operation of compression.

2. A radiation monitoring data compression and real-time transmission method as claimed in claim 1, characterized in that: The original radiation monitoring data includes the type of radiation source, expected radiation range and environmental influencing factors considered when selecting the location for sensor deployment; Use UNIX timestamps to record the acquisition time of data points, record radiation intensity as floating point numbers, such as millisieverts, and collect environmental parameter data; The data preprocessing includes using a median filter to remove noise from the data.

3. A radiation monitoring data compression and real-time transmission method as claimed in claim 2, characterized in that: The radiation monitoring data characteristics include statistical characteristics of the calculated data, including peak value, mean value and standard deviation; The lossless compression includes calculating difference data for radiation monitoring data features, applying Huffman coding to the difference data, and constructing an optimal prefix code tree; The Huffman coding includes counting the frequency of occurrence of each difference value, and constructing a Huffman tree according to the frequency. Each leaf node in the tree represents a symbol, and the path length represents the codeword length of the symbol. The path from the root node of the tree to each leaf node determines the encoding of each symbol.

4. A radiation monitoring data compression and real-time transmission method as claimed in claim 3, characterized in that: The codeword length is expressed as, C(w)=-log2P(w) Where C(w) is the codeword length of symbol w, and P(w) is the probability of symbol w appearing.

5. A radiation monitoring data compression and real-time transmission method as claimed in claim 4, characterized in that: The lossless compression also includes dictionary compression, where the dictionary contains all possible single-character strings. When a new string combination that is not in the dictionary is found in the data stream, it is added to the dictionary, the data is traversed, the data stream is matched with the longest string in the current dictionary, and the string is replaced with the corresponding dictionary index.

6. A radiation monitoring data compression and real-time transmission method as claimed in claim 5, characterized in that: The segmented transmission includes transmitting the compressed data in segments according to the actual network bandwidth, and small segments reduce the retransmission demand caused by network fluctuations.

7. A radiation monitoring data compression and real-time transmission method as claimed in claim 6, characterized in that: The decompression includes the receiving end using the same dictionary or encoding table as the sending end to perform decompression, and restoring the original data string by looking up the dictionary index.

8. A system using a radiation monitoring data compression and real-time transmission method as claimed in any one of claims 1 to 7, characterized in that: It comprises a data acquisition module (100), a compression module (200), a segment transmission module (300) and a decompression module (400); The data acquisition module (100) is used to collect raw radiation monitoring data and perform data preprocessing; The compression module (200) is used to perform lossless compression encoding on radiation monitoring data features; The segment transmission module (300) is used to divide the compressed data into small segments and transmit the data in segments; The decompression module (400) is used to decompress data after the receiving end receives the data according to the inverse operation of compression.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a radiation monitoring data compression and real-time transmission method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a radiation monitoring data compression and real-time transmission method described in any one of claims 1 to 7 are implemented.

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