Seafood radioactivity anomaly monitoring method, device and equipment and storage medium

Abstract

The invention relates to the technical field of radioactivity monitoring, in particular to a seafood radioactivity anomaly monitoring method, device and equipment and a storage medium. Configuring a preset nuclide identification model according to the environmental parameters, the historical radioactivity counting data and a preset three-dimensional residual network; obtaining all channels according to a preset frequency band; generating an identification data packet set and a sub-packet transmission protocol based on a preset data compression algorithm, a preset message transmission protocol and the real-time radioactivity counting data; when the updated nuclide identification model receives the transmission compressed packet, the transmission compressed packet is analyzed; generating an abnormal monitoring event according to the seafood radioactive activity value; multi-dimensional parameters and historical data are fused to optimize a nuclide recognition model, a channel set is constructed to guarantee data transmission, seafood radioactivity intelligent monitoring and early warning are automatically achieved in the whole process, the method is suitable for various scenes, and reliable technical support is provided.

Description

Technical Field

[0001] This invention relates to the field of radioactive monitoring technology, specifically to a method, apparatus, equipment, and storage medium for monitoring radioactive anomalies in seafood. Background Technology

[0002] Existing technologies for seafood radioactivity monitoring suffer from several significant shortcomings. Regarding energy consumption, traditional transmission modules are not optimized for specific environments. In cold chain environments (e.g., below -20°C) and during long-term operation at sea, energy consumption remains high, failing to meet the requirements for extended low-power operation. This undoubtedly increases the power supply burden on the equipment, especially in scenarios without fixed power sources, such as ships and ports, where frequent power changes or charging are extremely inconvenient. In terms of communication stability, existing technologies lack an adaptive communication architecture, making it difficult to effectively cope with complex and changing environments. In cold chain environments, signals are easily interfered with; in ocean environments, communication distances are limited, making stable long-distance communication beyond 10km difficult, and data transmission often experiences interruptions and packet loss, severely impacting the complete transmission of seafood radioactivity spectrum data. Furthermore, existing equipment lacks an intelligent wake-up mechanism, continuously consuming power regardless of whether a detection event occurs, failing to meet the requirements for long-term reliable operation of seafood radioactivity monitoring systems in harsh environments. Summary of the Invention

[0003] To address the shortcomings of the existing technology, this invention proposes a method for monitoring radioactive anomalies in seafood.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for monitoring radioactive anomalies in seafood includes: acquiring environmental parameters and historical radioactivity count data; configuring a preset nuclide identification model based on the environmental parameters, historical radioactivity count data, and a preset three-dimensional residual network to obtain an updated nuclide identification model; acquiring all channels according to a preset frequency band to obtain a channel set; acquiring real-time radioactivity count data; generating a set of identified data packets and a packet transmission protocol based on a preset data compression algorithm, a preset message transmission protocol, and real-time radioactivity count data; generating a transmission compressed packet according to the packet transmission protocol, the channel set, and the identified data packet set, and performing transmission processing on the transmission compressed packet; when the updated nuclide identification model receives the transmission compressed packet, analyzing the transmission compressed packet to obtain a first analysis result; generating a seafood radioactivity activity value based on the first analysis result; and generating an anomaly monitoring event based on the seafood radioactivity activity value.

[0005] Further, the step of configuring the preset nuclide identification model based on environmental parameters, historical radioactivity count data, and a preset three-dimensional residual network to obtain an updated nuclide identification model includes: constructing an environmental impact factor matrix based on environmental parameters; performing principal component analysis on the environmental impact factor matrix to obtain a second analysis result; performing dimensionality reduction processing on the environmental impact factor matrix based on the second analysis result and a preset transformation matrix to obtain a dimensionality-reduced matrix; preprocessing historical radioactivity count data according to a preset median filtering algorithm to obtain preprocessed data; performing polynomial fitting correction on the preprocessed data according to a preset detector calibration curve to obtain corrected radioactivity count data; extracting features from the corrected radioactivity count data based on the three-dimensional residual network to obtain energy spectrum features; and configuring the nuclide identification model based on the dimensionality-reduced matrix and energy spectrum features to obtain an updated nuclide identification model.

[0006] Furthermore, when the updated nuclide identification model receives the transmitted compressed packet, it analyzes the transmitted compressed packet to obtain a first analysis result, including: decompressing the transmitted compressed packet to obtain decompressed radioactive count data; extracting features from the decompressed radioactive count data to obtain the detector energy resolution and radioactive nuclide characteristics; and analyzing the detector energy resolution and radioactive nuclide characteristics according to the updated nuclide identification model to obtain the first analysis result.

[0007] Further, the feature extraction of the decompressed radioactive count data to obtain the detector energy resolution and radionuclide characteristics includes: smoothing the decompressed radioactive count data according to a preset smoothing filtering method to obtain smoothed radioactive count data; performing second-order differentiation on the smoothed radioactive count data to obtain second-order differentiated data; performing peak search on the second-order differentiated data according to a preset maximum search algorithm to obtain peak points; generating energy positions and peak counts based on peak points and decompressed radioactive count data; generating a first energy value and a second energy value based on energy positions and peak counts; calculating the difference between the first energy value and the second energy value to obtain a half-width difference (WHM); and extracting features from the smoothed radioactive count data based on the WHM, energy positions, and peak counts to obtain the detector energy resolution and radionuclide characteristics.

[0008] Furthermore, the step of generating an identifier data packet set and a packet transmission protocol based on a preset data compression algorithm, a preset message transmission protocol, and real-time radioactivity counting data includes: compressing real-time radioactivity counting data based on the data compression algorithm to obtain compressed data; packetizing the compressed data according to the message transmission protocol to obtain a data packet set; generating an identifier data packet set according to a preset checksum and the data packet set; and generating a packet transmission protocol according to a preset backoff algorithm and the identifier data packet set.

[0009] Furthermore, the step of generating a transmission compressed packet based on the packet transmission protocol, channel set, and identifier data packet set, and performing transmission processing on the transmission compressed packet, includes: filtering the channel set according to a preset channel quality threshold to obtain an available channel set; scoring each available channel in the available channel set according to a preset comprehensive channel quality scoring formula to obtain a score set; obtaining the highest score from the score set, and obtaining the corresponding available channel based on the highest score to obtain a communication channel; configuring the communication channel according to a preset wireless transmission mode to obtain a wireless transmission mode communication channel; generating a transmission compressed packet based on the packet transmission protocol, wireless transmission mode communication channel, wake-up event, and identifier data packet set, and performing transmission processing on the transmission compressed packet.

[0010] Furthermore, the step of generating abnormal monitoring events based on seafood radioactivity values ​​includes: obtaining the historical mean and standard deviation of radioactivity values; performing anomaly analysis on seafood radioactivity values ​​based on the historical mean and standard deviation of radioactivity values ​​to obtain a third analysis result; generating an anomaly comprehensive score based on the third analysis result; and generating an abnormal monitoring event based on the anomaly comprehensive score and a preset anomaly score threshold.

[0011] Furthermore, a seafood radioactivity anomaly monitoring device includes: a first data acquisition module for acquiring environmental parameters and historical radioactivity count data; a model configuration module for configuring a preset nuclide identification model based on environmental parameters, historical radioactivity count data, and a preset three-dimensional residual network to obtain an updated nuclide identification model; a channel acquisition module for acquiring all channels according to a preset frequency band to obtain a channel set; a second data acquisition module for acquiring real-time radioactivity count data; a first data generation module for generating a set of identifier data packets and a packet transmission protocol based on a preset data compression algorithm, a preset message transmission protocol, and real-time radioactivity count data; an instruction generation module for generating a transmission compressed packet according to the packet transmission protocol, the channel set, and the identifier data packet set, and performing transmission processing on the transmission compressed packet; an analysis module for analyzing the transmission compressed packet after the updated nuclide identification model receives it to obtain a first analysis result; a second data generation module for generating a seafood radioactivity activity value based on the first analysis result; and an event generation module for generating anomaly monitoring events based on the seafood radioactivity activity value.

[0012] Furthermore, a seafood radioactivity anomaly monitoring device includes: a memory and at least one processor, wherein the memory stores instructions; at least one processor invokes the instructions in the memory to cause the seafood radioactivity anomaly monitoring device to perform the various steps of a seafood radioactivity anomaly monitoring method as described in any one of the above descriptions.

[0013] Furthermore, a computer-readable storage medium stores instructions that, when executed by a processor, implement the steps of a seafood radioactivity anomaly monitoring method as described in any one of the above descriptions.

[0014] The beneficial effects of the seafood radioactivity anomaly monitoring method of the present invention are as follows: In terms of data acquisition and model configuration, the system integrates multi-dimensional environmental parameters and historical radioactivity counting data. By optimizing the nuclide identification model using a three-dimensional residual network, it can capture the characteristics of radioactive nuclides under different environments, significantly improving the model's generalization ability and detection accuracy, and reducing false positives and false negatives. Regarding data transmission, a channel set is constructed, and a packet-splitting mechanism is built using data compression algorithms and message transmission protocols to reduce transmission energy consumption and ensure complete and stable data transmission in complex network environments. In terms of overall functionality, the entire process from data acquisition to the generation of abnormal monitoring events is automated, enabling intelligent monitoring and early warning of seafood radioactivity, facilitating timely responses from relevant personnel. Furthermore, this solution fully considers environmental diversity, enabling the detection system to operate stably in special scenarios such as cold chain and ocean shipping, as well as in the detection of products from different sources, improving the system's adaptability and versatility, and providing reliable and efficient technical support for food safety monitoring and environmental radiation control. Attached Figure Description

[0015] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a first flowchart of a method for monitoring radioactive anomalies in seafood provided by an embodiment of the present invention; Figure 2 This is a second flowchart of a method for monitoring radioactive anomalies in seafood provided by an embodiment of the present invention; Figure 3 This is a third flowchart of a method for monitoring radioactive anomalies in seafood provided by an embodiment of the present invention; Figure 4 This is a fourth flowchart of a method for monitoring radioactive anomalies in seafood provided by an embodiment of the present invention; Figure 5 The fifth flowchart of a method for monitoring radioactive anomalies in seafood provided by an embodiment of the present invention; Figure 6 The sixth flowchart of a method for monitoring radioactive anomalies in seafood provided by an embodiment of the present invention; Figure 7 The seventh flowchart of a method for monitoring radioactive anomalies in seafood provided by an embodiment of the present invention; Figure 8 This is a schematic diagram of the structure of a seafood radioactivity anomaly monitoring device provided in an embodiment of the present invention; Figure 9 This is a schematic diagram of a seafood radioactivity anomaly monitoring device provided in an embodiment of the present invention. Detailed Implementation

[0016] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0018] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 One embodiment of the method for monitoring radioactive anomalies in seafood according to the present invention includes: 101. Obtain environmental parameters and historical radioactivity count data; In this embodiment, environmental parameters include meteorological parameters such as temperature, humidity, and air pressure, electromagnetic environment parameters, and geographical parameters such as geographical location and altitude. Meteorological parameters can be measured using capacitive humidity sensors, resistive humidity sensors, thermocouple thermometers, infrared thermometers, etc. Geographical parameters can be obtained from external positioning systems, and electromagnetic environment parameters can be measured using professional equipment such as electromagnetic radiation detectors and spectrum analyzers. 102. Configure the preset nuclide identification model based on environmental parameters, historical radioactivity count data and preset three-dimensional residual network to obtain an updated nuclide identification model; In this embodiment, the three-dimensional residual network, by introducing a residual structure, can effectively alleviate the gradient vanishing and degradation problems caused by the increase in network depth, and better extract the features in the radiometric spectrum data; combined with environmental parameters and historical radiometric counting data, the nuclide identification model can learn the characteristic patterns of radionuclides under different environments, improve the model's generalization ability and accuracy, and thus obtain a more reliable updated nuclide identification model. 103. Obtain all channels according to the preset frequency band to obtain the channel set; In this embodiment, all channels are obtained according to a preset frequency band to form a channel set, providing multiple options for data transmission; all channels can be obtained through Sub-GHz frequency bands (433MHz, 868MHz, 915MHz, etc., suitable for long-distance, low-power scenarios); 104. Obtain real-time radioactivity counting data; 105. Based on a preset data compression algorithm, a preset message transmission protocol, and a real-time radioactivity counting data generation identification data packet set and packet transmission protocol; In this embodiment, the message transmission protocol can be MQTT or CoAP. MQTT and CoAP are lightweight message transmission protocols that are suitable for scenarios with unstable networks and limited bandwidth. They can ensure the stability of data transmission. Each data packet contains information such as data content and data packet sequence number. If a data packet fails to be verified, the receiving end sends a retransmission request to the sending end, and the sending end retransmits the data packet according to the data packet sequence number. 106. Generate transmission compressed packets based on the packet transmission protocol, channel set, and identifier data packet set, and perform transmission processing on the transmission compressed packets; In this embodiment, the optimal transmission path and method are selected by comprehensively considering the data packetization method, available channels and data packet identification information, and a transmission compression packet is generated to ensure that the data can be transmitted accurately and efficiently. 107. When the updated nuclide identification model receives the transmitted compressed packet, it analyzes the transmitted compressed packet to obtain the first analysis result; 108. Generate the radioactivity value of seafood based on the results of the first analysis; 109. Generate abnormal monitoring events based on the radioactivity values ​​of seafood; In this embodiment, abnormal monitoring events are generated based on the radioactivity value of seafood, so as to achieve comprehensive and accurate detection of seafood radioactivity and timely early warning of abnormal situations; In this embodiment, in terms of data acquisition and model configuration, multi-dimensional environmental parameters are integrated, historical radioactivity counting data is combined, and a three-dimensional residual network is used to optimize the nuclide identification model. This can capture the characteristics of radioactive nuclides under different environments, significantly improving the model's generalization ability and detection accuracy, and reducing false positives and false negatives. In terms of data transmission, a channel set is constructed, and a packet-splitting mechanism is built using data compression algorithms and message transmission protocols to reduce transmission energy consumption and ensure complete and stable data transmission in complex network environments. In terms of overall functionality, the entire process from data acquisition to the generation of abnormal monitoring events is automated, realizing intelligent monitoring and early warning of seafood radioactivity, which facilitates timely response by relevant personnel. In addition, this solution fully considers environmental diversity, enabling the detection system to operate stably in special scenarios such as cold chain and ocean shipping, as well as in the detection of products from different sources, improving the system's adaptability and versatility, and providing reliable and efficient technical support for food safety monitoring and environmental radiation control.

[0019] Please see Figure 2 A second embodiment of a method for monitoring radioactive anomalies in seafood according to the present invention includes: 201. Construct an environmental impact factor matrix based on environmental parameters; In this embodiment, the matrix elements in the environmental impact factor matrix reflect the weight of each environmental parameter on radioactivity detection. For example, high temperature environments may accelerate the volatilization of radioactive materials, and changes in humidity may affect the detector's response efficiency; 202. Principal component analysis was performed on the environmental impact factor matrix to obtain the second analysis results; In this embodiment, Principal Component Analysis (PCA) is a commonly used data dimensionality reduction method. PCA transforms the original high-dimensional environmental parameter data into a set of independent low-dimensional principal components by finding the main direction of change in the data. These principal components can retain as much information as possible from the original data while removing redundancy and correlation. 203. Based on the second analysis results and the preset transformation matrix, the environmental impact factor matrix is ​​reduced in dimension to obtain the dimension-reduced matrix; In this embodiment, the transformation matrix is ​​determined in advance based on the characteristics of environmental parameters and the application requirements for detecting radioactive anomalies in seafood. Its purpose is to map the high-dimensional data after principal component analysis to a lower-dimensional space. The second analysis result includes the eigenvectors and eigenvalues ​​obtained by principal component analysis. According to the operation rules of linear algebra, the environmental impact factor matrix is ​​multiplied by the preset transformation matrix, and each data point in the original environmental impact factor matrix is ​​transformed by the eigenvectors and eigenvalues, projecting it to a lower-dimensional space to obtain a dimension-reduced matrix. 204. Preprocess the historical radioactivity counting data according to the preset median filtering algorithm to obtain preprocessed data; In this embodiment, a median filter with a window size of 3-5 is applied to the historical radioactivity count data, and each data point is replaced with the median of the neighborhood. This can effectively suppress salt-and-pepper noise such as device pulses and preserve the true fluctuation characteristics of the historical radioactivity count data. 205. Perform polynomial fitting correction on the preprocessed data according to the preset detector calibration curve to obtain the corrected radioactivity count data; In this embodiment, the detector calibration curve is the detector's factory calibration curve (such as the count rate-energy response curve). The preprocessed data is fitted with a quadratic or cubic polynomial to correct the detector's nonlinear response (such as gain drift in the low-energy region and saturation effect in the high-energy region) and improve data accuracy. The expression for the detector calibration curve is as follows: In the formula, To correct the radioactivity count data, The coefficients are polynomials, determined by fitting measured calibration data (such as standard source data with known true radioactivity) using the least squares method. The degree is a polynomial, usually chosen based on the nonlinearity of the detector. This is the fitting error term, which reflects the residual between the curve and the preprocessed data. For preprocessing data; 206. Feature extraction is performed on the corrected radioactive count data using a three-dimensional residual network to obtain energy spectrum features; In this embodiment, the three-dimensional residual network is a deep neural network that has been pre-introduced with residual structures, which can more effectively extract deep features of the data. For radioactive counting data, the three-dimensional residual network can automatically learn and extract the energy spectrum features in the corrected radioactive counting data, such as the position, height, and width of the characteristic peaks, as well as the overall shape and distribution pattern of the energy spectrum. These energy spectrum features are important basis for identifying different radionuclides. 207. Configure the nuclide identification model based on the dimensionality reduction matrix and energy spectrum characteristics to obtain an updated nuclide identification model; In this embodiment, by using these two types of features as input to the model, the model learns the correlation between environmental factors and the energy spectrum characteristics of radionuclides, thereby more accurately identifying radionuclides based on different environmental conditions and energy spectrum characteristics, resulting in an updated nuclide identification model. This multi-feature fusion approach enables the model to comprehensively consider the impact of environmental factors on radioactivity detection, improving the model's recognition ability and adaptability. In this embodiment, at the data processing level, principal component analysis and a preset transformation matrix are used to reduce the dimensionality of the environmental impact factor matrix, remove redundant information, highlight key environmental impact factors, reduce computational complexity, and enhance the model's adaptability to complex environments. Median filtering effectively suppresses noise in historical radioactive count data, and combined with polynomial fitting correction of the detector calibration curve, improves data accuracy and lays a reliable foundation for subsequent analysis. In terms of feature extraction and model construction, a three-dimensional residual network is used to deeply mine the energy spectrum characteristics of radioactive count data, capture nuclide characteristics, and then the dimensionality-reduced environmental features and energy spectrum characteristics are fused to configure a nuclide identification model. This enables the model to learn the correlation between environmental and nuclide characteristics, and can more accurately identify radionuclides under different environmental conditions. The overall solution realizes a complete process improvement from data preprocessing to model optimization, effectively solving problems such as large environmental interference, high data error, and weak model generalization ability in traditional detection, providing efficient and reliable technical support for seafood radioactive safety detection.

[0020] Please see Figure 3 A third embodiment of a method for monitoring radioactive anomalies in seafood according to the present invention includes: 301. Decompress the transmitted compressed package to obtain decompressed radioactivity count data; In this embodiment, the transmission compressed package contains information related to data compression and packetization, such as compression algorithm type and packetization rules. By performing decompression, the set of identified data packets is restored to decompressed radioactivity count data, so that the data is restored to its original state that can be further analyzed, providing a basis for subsequent feature extraction and analysis. 302. Feature extraction is performed on the decompression radioactive count data to obtain the detector energy resolution and radionuclide characteristics; In this embodiment, the detector's energy resolution reflects its ability to distinguish between different energy rays and is an important indicator of detector performance. Radionuclide characteristics include key information such as the type of nuclide, its radioactivity, and decay mode; these characteristics are the core basis for identifying radionuclides. Specific algorithms and techniques, such as spectrum analysis and peak detection, are used to extract these key features from the radionuclide counting data. For example, spectrum analysis algorithms are used to analyze the spectral distribution of the counting data to determine the detector's energy resolution; peak detection algorithms are used to identify characteristic peaks in the energy spectrum, thereby inferring the type and activity of the radionuclide. 303. The detector's energy resolution and radionuclide characteristics are analyzed based on the updated nuclide identification model to obtain the first analysis result; In this embodiment, the updated nuclide identification model is a model optimized and configured through the preceding series of steps. It integrates environmental features and energy spectrum features, and can comprehensively consider the impact of environmental factors on radioactivity detection. The model takes the detector energy resolution and radionuclide characteristics as inputs, and through the algorithm and parameters inside the model, it performs in-depth analysis and processing of these features to determine the specific situation of the radionuclide, such as determining the exact type of the radionuclide, assessing whether its radioactivity exceeds the standard, and finally outputting the first analysis result. In this embodiment, the data packet is decompressed to fully restore the original state of the data, ensuring the accuracy of the analysis basis. By extracting features from the decompressed radioactive counting data, the detector energy resolution and radionuclide characteristics are obtained, deeply mining the core information of the data and laying a solid foundation for nuclide identification. Based on the updated nuclide identification model that integrates environmental and energy spectrum features, these features are analyzed and processed in depth to determine the specific situation of the radionuclide, thereby obtaining the radioactivity status of seafood, ensuring the reliability of the detection results, providing strong technical support for food safety supervision, and effectively safeguarding public health and safety.

[0021] Please see Figure 4 The fourth embodiment of a method for monitoring radioactive anomalies in seafood according to the present invention includes: 401. Smooth the decompressed radioactive count data according to the preset smoothing filtering method to obtain smoothed radioactive count data; In this embodiment, the decompressed radioactive count data is smoothed by a smoothing filter method, making the radioactive count data smoother and more stable, reducing the interference of noise on subsequent analysis, improving the reliability and accuracy of the data, and providing a high-quality data foundation for analysis. For example, common smoothing filter methods include mean filtering, Gaussian filtering, etc., and an appropriate filtering method can be selected according to the characteristics of the data. 402. Take the second derivative of the smoothed radioactivity count data to obtain the second derivative data; In this embodiment, in the radioactive energy spectrum data of the smoothed radioactive count data, the second derivative of the energy spectrum curve is closely related to the changes in the inflection point and peak value of the curve. By taking the second derivative, the changing trend of the energy spectrum curve can be highlighted, and the shape characteristics of the curve can be transformed into a numerical form that is easier to analyze, which is convenient for finding the peak position in the energy spectrum. 403. Perform peak search on the second-order derivative data according to the preset maximum search algorithm to obtain the peak points; In this embodiment, the local maxima are found by traversing the second-order derivative data through a maximum search algorithm. These maxima correspond to the peak positions in the original energy spectrum curve, which are the peak points. By accurately finding the peak points, the characteristic energy positions of radionuclides in the energy spectrum can be located. 404. Generate energy location and peak count based on peak point and decompression radioactivity count data; In this embodiment, the energy position reflects the characteristic energy of the radionuclide and is an important basis for identifying the type of radionuclide; the peak count is related to the activity of the radionuclide, and the larger the value, the higher the activity of the radionuclide. 405. Generate a first energy value and a second energy value based on the energy location and peak count; 406. Calculate the difference between the first energy value and the second energy value to obtain the half-width difference; In this embodiment, the smaller the half-width difference, the stronger the detector's ability to distinguish different energy rays, i.e., the higher the energy resolution. 407. Based on the half-width difference, energy position, and peak count, feature extraction is performed on the smoothed radioactivity count data to obtain the detector energy resolution and radionuclide characteristics; In this embodiment, a smoothing filter method is used to preprocess the decompressed radioactive counting data, effectively suppressing noise interference and making the data smoother and more stable, laying a reliable foundation for subsequent analysis and ensuring that the true characteristics of the original data are preserved. Secondly, the smoothed data is differentiated by second order, and a maximum search algorithm is used to locate the peak points of the energy spectrum, thereby determining the characteristic energy positions and peak counts of radionuclides, providing crucial evidence for nuclide identification and activity assessment. Finally, the detector energy resolution is quantified by calculating the half-width difference (WHM), further evaluating the performance of the detection system. This scheme comprehensively extracts multi-dimensional information such as detector energy resolution and radionuclide characteristics, fully characterizing the features of radioactive detection data. It not only improves nuclide identification but also enhances the ability to evaluate detector performance, providing a scientific and efficient technical means for seafood radioactive safety monitoring, effectively safeguarding food safety and public health.

[0022] Please see Figure 5 The fifth embodiment of a method for monitoring radioactive anomalies in seafood according to the present invention includes: 501. Compress real-time radioactivity counting data based on a data compression algorithm to obtain compressed data; In this embodiment, real-time radioactivity counting data can be compressed using lossless compression (such as Huffman coding, Lempel-Ziv-Welch algorithm) data compression algorithms. By compression, redundant information in the data, such as continuously repeating count values ​​and predictable background radiation data patterns, is removed, thereby converting real-time radioactivity counting data into compressed data that occupies less space and improving data transmission efficiency. 502. Divide the compressed data into packets according to the message transmission protocol to obtain a data packet set; 503. Generate an identifier data packet set based on the preset checksum and data packet set; In this embodiment, the check code is a preset CRC16 type check code (cyclic redundancy check code), and the CRC16 check code is appended to the data packet to generate an identifier data packet set. The CRC16 check code can help the receiving end detect whether errors have occurred in the data during transmission, such as data loss or data tampering. If the check code calculated by the receiving end is inconsistent with the received check code, it can be determined that the data packet has an error, and corresponding processing measures can be taken. 504. Generate a packet transmission protocol based on the preset backoff algorithm and the set of identified data packets; In this embodiment, the timing of sending each data packet and the retransmission strategy are determined by the backoff algorithm. Combined with the relevant information of the data packet set, a complete packet transmission protocol is formed to ensure that data can be transmitted in an orderly and efficient manner in complex network environments. In this embodiment, a lossless compression algorithm is used in the data compression stage to reduce data size while ensuring data integrity, effectively reducing transmission time and bandwidth consumption. This is particularly suitable for scenarios with limited network resources, such as cold chain and ocean shipping, significantly improving transmission efficiency. A combination of packet segmentation and verification mechanisms uses checksums to attach error detection tags to data packets, enabling the receiving end to quickly identify transmission errors such as data loss or tampering and trigger retransmissions in a timely manner, enhancing the accuracy and reliability of data transmission. A backoff algorithm is used to intelligently adjust the timing of data packet transmission and retransmission strategies for complex network environments, avoiding conflicts and congestion caused by simultaneous transmission from multiple devices, optimizing network resource allocation, and ensuring orderly data transmission. The overall solution addresses compression and load reduction, error verification, and transmission scheduling from multiple dimensions, not only reducing transmission energy consumption and traffic costs but also ensuring efficient and stable data transmission in harsh environments, laying a solid foundation for reliable data transmission for real-time monitoring of seafood radioactivity.

[0023] Please see Figure 6 The sixth embodiment of a method for monitoring radioactive anomalies in seafood according to the present invention includes: 601. Filter the channel set according to the preset channel quality threshold to obtain the available channel set; In this embodiment, in a real communication environment, channels are easily affected by electromagnetic interference, obstruction and other factors, resulting in inconsistent quality. By setting a channel quality threshold, channels below the standard are eliminated, leaving channels that meet basic communication requirements, forming a set of usable channels, which provides a basis for subsequent channel selection. 602. Score each available channel in the available channel set according to the preset comprehensive channel quality scoring formula to obtain a score set; In this embodiment, the expression for the comprehensive channel quality scoring formula is as follows: In the formula, This is the signal-to-noise ratio weighting coefficient. The signal-to-noise ratio (SNR) of the channels in the available channel set. This is the bit error rate weighting coefficient. This is the broadband weighting coefficient. The signal strength of the channels in the available channel set. The bit error rate of a channel in the available channel set. For scoring, the formula considers factors such as signal-to-noise ratio (SNR), bit error rate (BER), and signal strength (RSSI), and assigns corresponding weight coefficients to each factor. The higher the SNR, the less the signal is affected by noise; the lower the BER, the higher the accuracy of data transmission; and the stronger the signal strength, the higher the reliability of communication. By quantifying each factor and calculating the weights, a score for each channel is obtained, forming a score set, which facilitates a direct comparison of channel quality. 603. Obtain the highest score from the score set, and obtain the corresponding available channel based on the highest score to obtain the communication channel; In this embodiment, this approach ensures that the selected channel has the best quality among the available channels, effectively reducing the error rate and latency of data transmission and guaranteeing data transmission quality. 604. Configure the communication channel according to the preset wireless transmission mode to obtain the wireless transmission mode communication channel; In this embodiment, the wireless transmission modes include LoRa mode, NB-IoT mode, etc. Different wireless transmission modes have different characteristics. LoRa mode is suitable for long-distance, low-power transmission, while NB-IoT mode is suitable for massive device access. By configuration, the channel and transmission mode are adapted to each other, giving full play to the advantages of both and improving data transmission performance. 605. Generate a wake-up event based on the wireless transmission mode communication channel; 606. Generate a transmission compressed packet based on the packet transmission protocol, wireless transmission mode communication channel, wake-up event, and identification data packet set, and perform transmission processing on the transmission compressed packet; In this embodiment, the wake-up event is used to control the device to switch from sleep state to working state for data transmission. Associating the wake-up event with the channel and transmission mode can ensure that the device uses high-quality channel resources to transmit data at the appropriate time, reducing unnecessary energy consumption. In this embodiment, the available channel screening process eliminates low-quality channels, laying the foundation for high-quality transmission; channel scoring quantifies various influencing factors, selects the optimal communication channel, and reduces transmission error rate and latency; adapting the wireless transmission mode allows the advantages of the channel and mode to complement each other, improving transmission performance; generating wake-up events controls the timing of device operation, reducing energy consumption; and finally, the generated transmission compressed packet integrates key elements to ensure orderly data transmission. The overall solution effectively copes with complex communication environments, ensuring reliable and efficient data transmission, and is especially suitable for scenarios with high requirements for transmission quality and power consumption.

[0024] Please see Figure 7 The seventh embodiment of a method for monitoring radioactive anomalies in seafood according to the present invention includes: 701. Obtain the mean and standard deviation of historical radioactivity values; In this embodiment, the historical mean radioactivity value reflects the average level of seafood radioactivity measured over a period of time, while the standard deviation measures the dispersion of these measurements relative to the mean, providing basic reference data for subsequent anomaly analysis. 702. Anomaly analysis was performed on the radioactivity values ​​of seafood based on the historical mean and standard deviation of radioactivity values ​​to obtain the third analysis results; In this embodiment, statistical methods are generally used. For example, it is determined whether the current seafood radioactivity value falls within a range centered on the mean and within a certain multiple of the standard deviation (such as the common three-standard-deviation principle, which states that if the real-time measured seafood radioactivity value exceeds the mean ± three times the standard deviation, it is considered abnormal). If the current seafood radioactivity value exceeds this reasonable range, it is determined to be an abnormal situation, thus obtaining the third analysis result, clarifying whether the seafood radioactivity activity is abnormal and the degree and direction of the abnormality (higher or lower than the normal range). 703. Generate an anomaly comprehensive score based on the results of the third analysis; 704. Generate anomaly monitoring events based on the anomaly comprehensive score and the preset anomaly score threshold; In this embodiment, if the overall anomaly score exceeds the anomaly score threshold, it indicates that the abnormality in the radioactivity of seafood has reached a level that requires attention and handling. At this time, the generated anomaly monitoring event can trigger the corresponding early warning mechanism to notify relevant personnel or systems to conduct further investigation and handling of the anomaly, such as conducting more stringent testing on the relevant seafood products and tracing their source. If the overall anomaly score does not exceed the anomaly score threshold, the generated anomaly monitoring event only has the function of providing feedback on the anomaly monitoring situation and does not need to trigger an early warning mechanism. In this embodiment, by obtaining historical mean and standard deviation, anomaly analysis is constructed to determine abnormalities in seafood radioactivity, clarifying the degree and direction of the abnormality. Anomaly comprehensive score quantifies the abnormal situation, and anomaly monitoring events are generated by combining the preset anomaly score threshold. When the anomaly comprehensive score exceeds the anomaly score threshold, an early warning is triggered to promote strict testing and source tracing, and timely risk prevention and control. When the threshold is not exceeded, feedback is provided to accumulate information. This solution can detect radioactive anomalies in a timely manner, ensure the safety of seafood, protect public health, assist in the precise management of radioactive contamination in the supply chain, and improve the efficiency and scientific nature of supervision.

[0025] The above describes a method for monitoring radioactive anomalies in seafood according to an embodiment of the present invention. The following describes a device for monitoring radioactive anomalies in seafood according to an embodiment of the present invention. Please refer to [link / reference]. Figure 8 One embodiment of the seafood radioactivity anomaly monitoring device of the present invention includes: First data acquisition module 1 is used to acquire environmental parameters and historical radioactivity count data; Model configuration module 2 is used to configure the preset nuclide identification model according to environmental parameters, historical radioactivity count data and preset three-dimensional residual network to obtain an updated nuclide identification model; Channel acquisition module 3 is used to acquire all channels according to a preset frequency band to obtain a channel set; The second data acquisition module 4 is used to acquire real-time radioactivity counting data; The first data generation module 5 is used to generate a set of identifier data packets and a packet transmission protocol based on a preset data compression algorithm, a preset message transmission protocol and real-time radioactivity counting data; The instruction generation module 6 is used to generate transmission compressed packets based on the packet transmission protocol, channel set, and identification data packet set, and to perform transmission processing on the transmission compressed packets; Analysis module 7 is used to analyze the transmitted compressed package after the updated nuclide identification model receives the transmitted compressed package to obtain the first analysis result; The second data generation module 8 is used to generate seafood radioactivity values ​​based on the first analysis results. Event generation module 9 is used to generate abnormal monitoring events based on the radioactivity value of seafood; In this embodiment, in terms of data acquisition and model configuration, multi-dimensional environmental parameters are integrated, historical radioactivity counting data is combined, and a three-dimensional residual network is used to optimize the nuclide identification model. This can capture the characteristics of radioactive nuclides under different environments, significantly improving the model's generalization ability and detection accuracy, and reducing false positives and false negatives. In terms of data transmission, a channel set is constructed, and a packet-splitting mechanism is built using data compression algorithms and message transmission protocols to reduce transmission energy consumption and ensure complete and stable data transmission in complex network environments. In terms of overall functionality, the entire process from data acquisition to the generation of abnormal monitoring events is automated, realizing intelligent monitoring and early warning of seafood radioactivity, which facilitates timely response by relevant personnel. In addition, this solution fully considers environmental diversity, enabling the detection system to operate stably in special scenarios such as cold chain and ocean shipping, as well as in the detection of products from different sources, improving the system's adaptability and versatility, and providing reliable and efficient technical support for food safety monitoring and environmental radiation control.

[0026] Figure 9 This is a schematic diagram of the structure of a seafood radioactivity anomaly monitoring device 900 provided in an embodiment of the present invention. This seafood radioactivity anomaly monitoring device 900 can vary considerably due to different configurations or performance. It may include one or more central processing units (CPUs) 913 (e.g., one or more processors) and a memory 920, and one or more media 930 (e.g., one or more mass storage devices) storing application programs 933 or data 932. The memory 920 and media 930 can be temporary or persistent storage. The program stored in the media 930 may include one or more modules (not shown in the diagram), each module may include a series of instruction operations on the seafood radioactivity anomaly monitoring device 900. Furthermore, the processor 913 may be configured to communicate with the media 930 and execute a series of instruction operations in the media 930 on the seafood radioactivity anomaly monitoring device 900 to implement the steps of the seafood radioactivity anomaly monitoring method provided in the above-described method embodiments.

[0027] A seafood radioactivity anomaly monitoring device 900 may further include one or more power supplies 940, one or more wired or wireless network interfaces 950, one or more input / output interfaces 960, and / or one or more operating systems 931, such as Windows Server, MacOSX, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 9 The illustrated structure of a seafood radioactive anomaly monitoring device does not constitute a limitation on a seafood radioactive anomaly monitoring device 900. It may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0028] A computer-readable medium storing instructions that, when executed by a processor, implement the steps of a seafood radioactivity anomaly monitoring method as described above.

[0029] The present invention and its embodiments have been described above. This description is not restrictive. The accompanying drawings are only one embodiment of the present invention. The actual content is not limited thereto. In short, if a person skilled in the art is inspired by this description and designs a similar structure and embodiment without departing from the spirit of the present invention, such design should fall within the protection scope of the present invention.

Claims

1. A method for monitoring radioactive anomalies in seafood, characterized in that, include: Acquire environmental parameters and historical radioactivity count data; The preset nuclide identification model is configured based on environmental parameters, historical radioactivity count data, and a preset three-dimensional residual network to obtain an updated nuclide identification model. All channels are obtained according to the preset frequency band to obtain the channel set; Obtain real-time radioactivity counting data; Based on a preset data compression algorithm, a preset message transmission protocol, and a real-time radioactivity counting data generation identification data packet set and packet transmission protocol; Based on the packet transmission protocol, channel set, and identifier data packet set, a transmission compressed packet is generated, and the transmission compressed packet is processed for transmission. Once the updated nuclide identification model receives the transmitted compressed packet, it analyzes the transmitted compressed packet to obtain the first analysis result; The radioactivity values ​​of seafood were generated based on the results of the first analysis; Anomaly monitoring events are generated based on the radioactivity values ​​of seafood.

2. The method for monitoring radioactive anomalies in seafood as described in claim 1, characterized in that, The process of configuring a preset nuclide identification model based on environmental parameters, historical radioactivity count data, and a preset three-dimensional residual network to obtain an updated nuclide identification model includes: An environmental impact factor matrix was constructed based on environmental parameters; Principal component analysis was performed on the environmental impact factor matrix to obtain the second analysis results; The environmental impact factor matrix is ​​dimensionality reduced based on the second analysis results and the preset transformation matrix to obtain the dimensionality-reduced matrix. Historical radioactivity counting data are preprocessed according to a preset median filtering algorithm to obtain preprocessed data; The preprocessed data is corrected by polynomial fitting based on the preset detector calibration curve to obtain corrected radioactivity count data; Feature extraction of the corrected radioactive count data is performed using a three-dimensional residual network to obtain energy spectrum characteristics; The nuclide identification model is configured based on the dimensionality reduction matrix and energy spectrum characteristics to obtain an updated nuclide identification model.

3. The method for monitoring radioactive anomalies in seafood as described in claim 1, characterized in that, When the updated nuclide identification model receives the transmitted compressed packet, it analyzes the transmitted compressed packet to obtain a first analysis result, including: The transmitted compressed package is decompressed to obtain decompressed radioactivity count data; Feature extraction is performed on the decompression radioactivity count data to obtain the detector energy resolution and radionuclide characteristics; The detector's energy resolution and radionuclide characteristics were analyzed based on the updated nuclide identification model to obtain the first analysis results.

4. The method for monitoring radioactive anomalies in seafood as described in claim 3, characterized in that, The process of extracting features from the decompression radioactive count data to obtain the detector energy resolution and radionuclide characteristics includes: The decompressed radioactive count data is smoothed according to a preset smoothing filtering method to obtain smoothed radioactive count data; The smoothed radioactivity count data is differentiated by second order to obtain the second-order derivative data; Peak points are obtained by performing peak search on the second-order derivative data according to the preset maximum search algorithm; Energy location and peak count are generated based on peak point and decompression radioactivity count data; A first energy value and a second energy value are generated based on the energy location and peak count; The difference between the first energy value and the second energy value is calculated to obtain the half-width difference. Feature extraction is performed on smoothed radioactivity count data based on the half-width-to-full-thickness difference, energy position, and peak count to obtain the detector energy resolution and radionuclide characteristics.

5. The method for monitoring radioactive anomalies in seafood as described in claim 1, characterized in that, The method based on a preset data compression algorithm, a preset message transmission protocol, and a real-time radioactivity counting data generation identification data packet set and packet transmission protocol includes: Real-time radioactivity counting data is compressed using a data compression algorithm to obtain compressed data; The compressed data is divided into packets according to the message transmission protocol to obtain a set of data packets; A set of identifier data packets is generated based on a preset checksum and data packet set; A packet transmission protocol is generated based on a preset backoff algorithm and a set of identified data packets.

6. The method for monitoring radioactive anomalies in seafood as described in claim 1, characterized in that, The step of generating a transmission compressed packet based on the packet transmission protocol, channel set, and identifier data packet set, and then performing transmission processing on the transmission compressed packet, includes: The channel set is filtered according to a preset channel quality threshold to obtain a set of available channels; Each available channel in the available channel set is scored according to a preset comprehensive channel quality scoring formula to obtain a score set; The highest score is obtained from the score set, and the corresponding available channel is obtained based on the highest score to obtain the communication channel; Configure the communication channel according to the preset wireless transmission mode to obtain the wireless transmission mode communication channel; A wake-up event is generated based on the wireless transmission mode and communication channel. A transmission compressed packet is generated based on the packet transmission protocol, wireless transmission mode communication channel, wake-up event, and identification data packet set, and the transmission compressed packet is then processed for transmission.

7. The method for monitoring radioactive anomalies in seafood as described in claim 1, characterized in that, The generation of abnormal monitoring events based on the radioactivity value of seafood includes: Obtain the historical mean and standard deviation of radioactivity values; Anomaly analysis of seafood radioactivity values ​​was performed based on the historical mean and standard deviation of radioactivity values ​​to obtain the third analysis results; An anomaly comprehensive score is generated based on the results of the third analysis. Anomaly monitoring events are generated based on the anomaly comprehensive score and the preset anomaly score threshold.

8. A device for monitoring radioactive anomalies in seafood, characterized in that, include: The first data acquisition module is used to acquire environmental parameters and historical radioactivity count data; The model configuration module is used to configure the preset nuclide identification model based on environmental parameters, historical radioactivity count data and preset three-dimensional residual network to obtain an updated nuclide identification model. The channel acquisition module is used to acquire all channels according to the preset frequency band to obtain a channel set; The second data acquisition module is used to acquire real-time radioactivity counting data; The first data generation module is used to generate a set of identifier data packets and a packet transmission protocol based on a preset data compression algorithm, a preset message transmission protocol and real-time radioactivity counting data. The instruction generation module is used to generate transmission compressed packets based on the packet transmission protocol, channel set, and identifier data packet set, and to perform transmission processing on the transmission compressed packets; The analysis module is used to analyze the transmitted compressed package after the updated nuclide identification model receives the package to obtain the first analysis result. The second data generation module is used to generate seafood radioactivity values ​​based on the first analysis results. The event generation module is used to generate abnormal monitoring events based on the radioactivity values ​​of seafood.

9. A seafood radioactivity anomaly monitoring device, characterized in that, include: A memory and at least one processor, wherein the memory stores instructions; At least one of the processors invokes the instructions in the memory to cause the seafood radioactivity anomaly monitoring device to perform the steps of the seafood radioactivity anomaly monitoring method as claimed in any one of claims 1-7.

10. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement the various steps of the seafood radioactivity anomaly monitoring method as described in any one of claims 1-7.

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