Portable instrument X-ray machine communication method and system, medium and program product

Abstract

The invention discloses a portable X-ray machine communication method and system, a medium and a program product, and relates to the field of information signal transmission, and the method comprises the steps: determining original X-ray image data and electromagnetic characteristic data of a current environment; determining a subcarrier allocation scheme and a transmitting frequency according to the electromagnetic characteristic data, setting an OFDM modulator according to the subcarrier allocation scheme, and setting a radio frequency unit according to the transmitting frequency; detecting the signal intensity in real time and adjusting the data compression ratio and the error correction level of the original X-ray image data in combination with a preset image quality threshold value; compressing and coding the original X-ray image data based on the data compression ratio and the error correction level to obtain coded data; sending the coded data to a receiving end through an OFDM modulator and a radio frequency unit; and receiving transmission feedback data of the receiving end, and adjusting the transmission parameters according to the transmission feedback data. By implementing the method, electromagnetic interference can be resisted, and the situation of image quality reduction or transmission failure can be reduced.

Description

Technical Field

[0001] The present application relates to the field of information signal transmission, and in particular to a portable X-ray machine communication method, system, medium and program product. Background Art

[0002] With the continuous development of medical technology, portable X-ray machines are becoming more and more important in medical diagnosis. Portable X-ray machines can be used not only in hospitals, but also in complex environments such as the field and disaster sites for diagnosis.

[0003] In related technologies, portable X-ray machines use digital image acquisition technology to store the acquired high-definition images in the device, and transmit the images in real time to a remote server or hospital's imaging system via Wi-Fi or mobile network for remote diagnosis.

[0004] However, in an environment with strong electromagnetic interference, the data transmission of high-definition X-ray images will be affected, resulting in reduced image quality or transmission failure, and poor remote diagnosis results. Summary of the invention

[0005] The present application provides a portable X-ray machine communication method, system, medium and program product for resisting electromagnetic interference and reducing the situation of image quality degradation or transmission failure.

[0006] In the first aspect, the present application provides a portable X-ray machine communication method, which is applied to a communication system, and the method includes: receiving original X-ray image data collected by the portable X-ray machine and electromagnetic characteristic data of the current environment; determining the subcarrier allocation scheme and the transmission frequency according to the electromagnetic characteristic data, and setting the OFDM modulator according to the subcarrier allocation scheme, and setting the radio frequency unit according to the transmission frequency; the transmission frequency is different from the interference frequency band in the electromagnetic characteristic data; real-time detection of signal strength and adjustment of the data compression ratio and error correction level of the original X-ray image data in combination with a preset image quality threshold; compressing and encoding the original X-ray image data based on the data compression ratio and error correction level to obtain encoded data; sending the encoded data to the receiving end through the OFDM modulator and the radio frequency unit; receiving transmission feedback data from the receiving end, and adjusting the transmission parameters according to the transmission feedback data.

[0007] In the above embodiment, the communication system ensures image quality by real-time detection of signal strength and adjustment of compression ratio and error correction level, adopts OFDM modulation and radio frequency unit transmission, improves transmission efficiency and anti-interference capability, and ensures communication stability and image transmission quality of the portable X-ray machine in a complex electromagnetic environment.

[0008] In combination with some embodiments of the first aspect, in some embodiments, before the step of sending the encoded data to the receiving end through the OFDM modulator and the radio frequency unit, the method also includes: generating a chaotic sequence as an encryption key based on the network security level set by the user; performing lightweight encryption processing on the encoded data based on the chaotic sequence to obtain encrypted data; calculating the integrity check value of the encrypted data, and appending the integrity check value to the encrypted data.

[0009] In the above embodiment, the communication system improves data security while ensuring transmission efficiency by introducing lightweight encryption and integrity verification based on chaotic sequences; the chaotic sequence as a key enhances the encryption strength, the lightweight encryption reduces the computational complexity, and the integrity verification ensures data integrity.

[0010] In combination with some embodiments of the first aspect, in some embodiments, before the step of receiving the original X-ray image data collected by the portable X-ray machine and the electromagnetic characteristic data of the current environment, the method also includes: based on multiple access technology and network parameters, establishing parallel connections with multiple communication base stations, and generating a multi-link connection table; based on a beamforming algorithm, determining an antenna weight vector according to channel state information of multiple communication base stations; and performing data transmission using the multi-link connection table and the antenna weight vector as transmission reference data.

[0011] In the above embodiment, the communication system uses multiple access technology and beamforming algorithm to achieve parallel connection of multiple base stations and spatial diversity, thereby improving network capacity and reliability. Beamforming optimizes the signal transmission direction and enhances the communication robustness and transmission efficiency of the system in a complex network environment.

[0012] In combination with some embodiments of the first aspect, in some embodiments, based on multiple access technology and network parameters, parallel connections with multiple communication base stations are established to generate a multi-link connection table, specifically including: scanning communication base stations within a preset distance range to obtain the signal strength, bandwidth data and delay data of each communication base station; inputting the signal strength, bandwidth data and delay data into a preset quality assessment model to obtain a quality score for each communication base station; sorting the communication base stations from large to small according to the quality score, and connecting the first N target communication base stations in the sequence in parallel; determining the ID, current link status and resource allocation ratio of the target communication base station, generating a multi-link connection table, and periodically updating the multi-link connection table.

[0013] In the above embodiment, the communication system realizes automatic selection of the optimal base station and resource allocation by scanning base stations, evaluating quality, sorting connections and dynamically updating the connection table. Parallel connections improve the transmission rate, and dynamic updates ensure the real-time optimality of the connection, thereby improving the system's network access capability and transmission efficiency.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of sending the encoded data to the receiving end through the OFDM modulator and the radio frequency unit, the method also includes: when it is detected that the data packet is lost during network transmission, encoding the lost data packet as a redundant packet; determining a retransmission time window based on the packet loss rate and network delay; and retransmitting the redundant packet within the retransmission time window.

[0015] In the above embodiment, the communication system effectively reduces the packet loss rate and improves the transmission reliability by encoding redundant packets, determining the retransmission window and selectively retransmitting.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after the step of receiving transmission feedback data from the receiving end and adjusting the transmission parameters according to the transmission feedback data, the method also includes: receiving an image reset instruction sent back by the receiving end, sending a re-shooting instruction to the portable X-ray machine, so that the portable X-ray machine enters a re-shooting standby state; receiving a device control instruction sent back by the receiving end, transmitting the device control instruction to the portable X-ray machine to adjust the shooting parameters of the portable X-ray machine and reshoot.

[0017] In the above embodiment, the communication system improves the acquisition quality of X-ray images by receiving image reset instructions, sending reshoot instructions and adjusting shooting parameters, provides online diagnosis and remote control functions, and improves diagnosis efficiency.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after receiving the device control instructions returned by the receiving end, transmitting the device control instructions to the portable X-ray machine to adjust the shooting parameters of the portable X-ray machine and reshoot, the method also includes: after receiving the cloud assistance instructions sent by the receiving end, obtaining a work record including patient information, X-ray image data and diagnosis records; encrypting the work record, and uploading the encrypted work record to the cloud collaboration platform; obtaining diagnosis suggestions sent by multiple diagnostic terminals on the cloud collaboration platform, and sending the diagnosis suggestions to the receiving end.

[0019] In the above embodiment, the communication system effectively introduces remote expert resources by encrypting and uploading work records and obtaining diagnostic suggestions from multiple parties, thereby improving the accuracy and efficiency of diagnosis.

[0020] In a second aspect, an embodiment of the present application provides a communication system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the communication system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions, and when the above-mentioned computer program product runs on a communication system, the above-mentioned communication system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a communication system, enables the communication system to execute the method described in the first aspect and any possible implementation manner of the first aspect.

[0023] It is understandable that the communication system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Due to the use of dynamic parameter adjustment and OFDM modulation technology, the portable X-ray machine communication system can dynamically adjust the transmission parameters according to the electromagnetic characteristics of the environment, effectively cope with the interference of complex electromagnetic environment, and optimize the transmission efficiency while ensuring image quality.

[0025] 2. Due to the use of base station quality evaluation and dynamic connection mechanism based on multiple indicators, the portable X-ray machine communication system can automatically select the optimal base station and realize reasonable allocation of resources. By periodically updating the connection table, it can adjust the connection strategy in time to always maintain the optimal network status, realizing the intelligent network access and resource utilization of portable X-ray equipment.

[0026] 3. Due to the adoption of an automatic reshoot mechanism based on remote evaluation, the portable X-ray machine communication system achieves quality control of X-ray image acquisition. When the quality does not meet the standard, the reshoot process is automatically triggered, and the shooting parameters are adjusted according to remote instructions, achieving efficient and high-quality X-ray image acquisition. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of a portable X-ray machine communication method in an embodiment of the present application; Figure 2 is another flow chart of the portable X-ray machine communication method in the embodiment of the present application; Figure 3 It is a schematic diagram of the structure of a physical device of the communication system in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more of the listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0030] The following is a description of the process of the method provided by this implementation. Figure 1 , which is a flow chart of the portable X-ray machine communication method in the embodiment of the present application.

[0031] S101, receiving original X-ray image data collected by a portable X-ray machine and electromagnetic characteristic data of the current environment.

[0032] Among them, portable X-ray machines are mobile X-ray imaging devices used for medical diagnosis in various scenarios. Raw X-ray image data refers to unprocessed X-ray imaging results, which are high-resolution grayscale images. Electromagnetic characteristic data refers to information describing the current environmental electromagnetic field conditions, including electromagnetic field strength, frequency distribution, interference sources, etc.

[0033] The communication system performs this step after the portable X-ray machine completes image acquisition. Specifically, the communication system first establishes a connection with the portable X-ray machine, and then receives the original image data from the X-ray machine. At the same time, the communication system collects electromagnetic characteristic data of the surrounding environment through a built-in or external electromagnetic sensor.

[0034] It should be noted that the communication system refers to the various module components for uploading and downloading data. The communication system can be a built-in module on the portable X-ray machine, or it can be an external device that is connected to the portable X-ray machine through a wired or wireless connection to perform the communication tasks of the portable X-ray machine.

[0035] In addition, in some embodiments, for raw X-ray image data that takes up a large amount of storage space, the communication system can also establish an intelligent cache system based on deep learning, and predict the possible data characteristics and size of the raw X-ray image data by analyzing historical data transmission patterns. The system can allocate appropriate storage space in advance and perform hierarchical storage according to the importance of the data. For frequently used data types, the system will reserve a fast access area in the memory, and store infrequently used data in the secondary cache, thereby reducing the delay in data reception and improving the system response speed. At the same time, the system can also implement dynamic storage space adjustment, automatically expand the storage capacity when the amount of data increases suddenly, and release excess space when idle.

[0036] S102: Determine a subcarrier allocation scheme and a transmission frequency according to the electromagnetic characteristic data, and set an OFDM modulator according to the subcarrier allocation scheme and set a radio frequency unit according to the transmission frequency.

[0037] Among them, the subcarrier allocation scheme refers to the power, bit and code allocation strategy for multiple orthogonal subcarriers in the OFDM system. The transmission frequency is the center frequency of the signal transmission, which is different from the interference frequency band in the electromagnetic characteristic data. The OFDM modulator is a device that uses orthogonal frequency division multiplexing technology for digital modulation. The RF unit is a hardware module responsible for converting the baseband signal into a RF signal and transmitting it.

[0038] The communication system performs this step after acquiring the electromagnetic characteristic data to prepare for subsequent data transmission. Specifically, the communication system first analyzes the electromagnetic characteristic data to identify possible interference sources and channel characteristics. Then, based on the analysis results, the system selects a frequency band that avoids major interference as the transmission frequency. At the same time, the system designs an optimized subcarrier allocation scheme to maximize spectrum efficiency and anti-interference capabilities. Next, the system sets the parameters of the subcarrier allocation scheme to the OFDM modulator, including the number of subcarriers, power allocation, modulation method, etc. Finally, the system sets the selected transmission frequency to the RF unit and adjusts its operating frequency band and transmission power.

[0039] In some embodiments, parameter optimization and device configuration can be achieved in a variety of ways: Optionally, the communication system can use a genetic algorithm to optimize subcarrier allocation, taking into account factors such as channel capacity and bit error rate; then dynamically configure the OFDM modulator through software defined radio (SDR) technology; finally, use adaptive frequency hopping technology to set the transmission frequency of the radio frequency unit. Optionally, the communication system can use a deep reinforcement learning algorithm to automatically generate the optimal subcarrier allocation scheme based on historical transmission data and the current environment; then adjust the OFDM modulator parameters in real time through a digital signal processor (DSP); finally, use phased array antenna technology to accurately control the transmission direction and frequency of the radio frequency unit. It is understandable that other methods can also be used to achieve parameter optimization and device configuration, such as using a fuzzy logic control algorithm to dynamically adjust the transmission parameters, which is not limited here.

[0040] S103, detecting the signal strength in real time and adjusting the data compression ratio and error correction level of the original X-ray image data in combination with a preset image quality threshold.

[0041] Among them, signal strength is the power of the received wireless signal. Image quality threshold refers to the minimum acceptable image quality standard. Data compression ratio refers to the ratio of the compressed data size to the original data size. Error correction level refers to the redundancy of forward error correction coding.

[0042] The communication system continues to perform this step after starting data transmission to adapt to the dynamically changing transmission environment. Specifically, the communication system first obtains real-time signal strength data through feedback from the receiving end or local measurement. Then, the system compares the current signal strength with the preset image quality threshold to evaluate the current transmission conditions. Based on the evaluation results, the system dynamically adjusts the data compression ratio, increasing the compression ratio when the signal is weak to reduce the amount of transmitted data, and reducing the compression ratio when the signal is strong to improve image quality. At the same time, the system also adjusts the error correction level accordingly, increasing redundancy to improve reliability in poor signal environments, and reducing redundancy to improve transmission efficiency under good signal conditions.

[0043] In some embodiments, adaptive adjustment can be achieved in a variety of ways: Optionally, the communication system can use a proportional integral differential (PID) control algorithm to dynamically adjust the compression ratio according to the trend of signal strength changes; then use adaptive arithmetic coding technology to achieve a variable compression ratio; finally use low-density parity check code (LDPC) to achieve flexible error correction level adjustment. Optionally, the communication system can use a fuzzy neural network to comprehensively consider signal strength, channel conditions and image content complexity, and intelligently decide the best compression ratio and error correction level; then use a scalable image compression algorithm based on wavelet transform; finally, use Turbo code to provide multi-level error correction capabilities. It is understandable that other methods can also be used to achieve adaptive adjustment, such as using a reinforcement learning algorithm to dynamically optimize transmission parameters, which is not limited here.

[0044] S104. Compress and encode the original X-ray image data based on the data compression ratio and the error correction level to obtain encoded data.

[0045] Data compression refers to the process of reducing data storage space or transmission bandwidth. Encoding refers to the method of converting information into another form to improve transmission efficiency or increase security. Encoded data refers to data that has been compressed and encoded.

[0046] The communication system performs this step after determining the compression ratio and error correction level to make final preparations for data transmission. Specifically, the communication system first compresses the original X-ray image data according to the determined compression ratio. The compression process can include operations such as denoising, downsampling, and quantization, with the goal of reducing the amount of data while maintaining key diagnostic information. The system then encodes the compressed data. The encoding process includes two parts: source coding and channel coding: source coding further compresses the data and removes redundancy; channel coding adds redundant information according to the determined error correction level to enhance anti-interference capabilities. Finally, the system packages the encoded data, adds necessary header information and checksum information, and forms the final encoded data.

[0047] In some embodiments, the communication system can establish a multi-level error protection and recovery mechanism to ensure data security. The mechanism adopts a layered protection strategy based on possible transmission errors in the encoding stage. For important data, the communication system uses stronger error correction codes and more redundant information; for secondary data, a lightweight protection mechanism is used. The communication system can also implement intelligent data slicing technology to divide large image data into multiple independent data packets, each of which contains enough information for local reconstruction, so that even if some data packets are lost, the overall image will not be unusable.

[0048] S105: Send the encoded data to the receiving end through the OFDM modulator and the radio frequency unit.

[0049] The receiving end refers to the target device or system on the other side of the data transmission.

[0050] The communication system performs this step after completing data encoding and starts the actual data transmission process. Specifically, the communication system first inputs the encoded data into the OFDM modulator. The modulator divides the data stream into multiple parallel sub-data streams, each corresponding to a subcarrier. Then, the modulator modulates each subcarrier independently, and modulation methods such as QPSK and 16QAM can be used. Next, the modulator performs an inverse Fourier transform (IFFT) to convert the frequency domain signal into a time domain signal. The system will add a cyclic prefix at this time to reduce inter-symbol interference. Finally, the time domain signal is sent to the RF unit, and after processing such as digital-to-analog conversion and up-conversion, it is emitted in the form of electromagnetic waves and transmitted to the receiving end.

[0051] In some embodiments, data modulation and transmission can be achieved in a variety of ways: Optionally, the communication system can use adaptive modulation and coding (AMC) technology to dynamically select the modulation mode and coding rate of each subcarrier according to the channel conditions; then use precoding technology to optimize the peak-to-average ratio of OFDM signals; finally, use smart antenna technology to achieve beamforming to improve the directionality and energy efficiency of signal transmission. Optionally, the communication system can use MIMO-OFDM technology to improve spectral efficiency using a multiple-input multiple-output antenna system; then use interleaving technology to reduce the impact of burst errors; finally, apply a power control algorithm to dynamically adjust the transmit power to reduce energy consumption and interference while ensuring communication quality. It is understandable that other methods can also be used to achieve data modulation and transmission, such as using cognitive radio technology to intelligently select the best transmission parameters and frequency bands, which are not limited here.

[0052] S106: Receive transmission feedback data from the receiving end, and adjust transmission parameters according to the transmission feedback data.

[0053] The transmission feedback data refers to the information about the communication quality and reception status transmitted back by the receiving end. The transmission parameters include the modulation mode, coding rate, transmission power, subcarrier allocation and other adjustable communication system configurations.

[0054] The communication system continuously performs this step during the data transmission process to achieve closed-loop control and dynamic optimization. Specifically, the communication system first receives feedback data from the receiving end through a preset feedback channel. This data can include information such as channel quality indication (CQI), signal-to-noise ratio (SNR), bit error rate (BER), etc. Then, the system analyzes and processes these feedback data to evaluate the current transmission performance. Based on the evaluation results, the system decides whether the transmission parameters need to be adjusted and how to adjust them. The adjustment involves changing the modulation mode, adjusting the coding rate, reallocating subcarriers, modifying the transmission power, etc. Finally, the system applies the new parameter settings to the corresponding hardware modules to complete the dynamic optimization of the transmission parameters.

[0055] In some embodiments, the communication system also has an advanced feedback analysis mechanism based on machine learning. The communication system can not only process conventional channel state information, but also analyze more complex performance indicators, such as end-to-end delay, throughput fluctuations, signal quality trends, etc. The communication system uses a deep learning model to analyze these feedback data to identify potential performance problems and optimization opportunities. Through historical data analysis, the system can establish a network performance model, predict possible problems and adjust parameters in advance. The communication system also has anomaly detection capabilities, which can quickly identify and respond to abnormal feedback data to prevent incorrect parameter adjustments. At the same time, the system implements a reliability assessment mechanism for feedback data to ensure that adjustments are made based on accurate feedback.

[0056] The following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the portable X-ray machine communication method in the embodiment of the present application.

[0057] S201. Based on multiple access technology and network parameters, establish parallel connections with multiple communication base stations and generate a multi-link connection table.

[0058] Among them, multiple access technology refers to the technology that allows multiple users to use the same communication channel at the same time, such as CDMA, OFDMA, etc. Network parameters refer to various indicators that describe the network status and performance, such as signal strength, delay, bandwidth, etc. Communication base stations are fixed-site equipment used for wireless communication. Parallel connection refers to communication links established with multiple base stations at the same time. The multi-link connection table is a data structure that records all active connections and their related information.

[0059] The communication system performs this step when initializing or reconfiguring the network connection to prepare for subsequent data transmission. Specifically, the communication system first scans the available communication base stations in the surrounding area to obtain basic information of each base station such as signal strength, frequency band, etc. Then, the system tries to establish connections with multiple base stations at the same time according to the preset multiple access protocol (such as LTE, 5G NR). In this process, the system will consider network parameters such as signal quality, load conditions, etc., and select the most suitable base station for connection. After the connection is successfully established, the system assigns a unique identifier to each connection and records the relevant network parameters. Finally, the system organizes the information of all active connections into a multi-link connection table, which contains information such as base station ID, connection status, signal quality, bandwidth, etc.

[0060] In some embodiments, multiple base station parallel connection and connection table generation can be achieved in a variety of ways: Optionally, the system can use software defined radio (SDR) technology to dynamically configure the wireless interface to support multiple access protocols; then use a load balancing algorithm to assign connection priorities according to the load conditions of each base station; finally, use distributed database technology to update and synchronize multi-link connection tables in real time. Optionally, the system can use artificial intelligence algorithms to predict network conditions and intelligently select the best connection combination; then apply network slicing technology to allocate independent network resources for different types of services; finally, use blockchain technology to ensure the security and traceability of the connection table. It is understandable that other methods can also be used to achieve multiple base station parallel connection and connection table generation, such as using edge computing technology to optimize connection management and resource allocation, which is not limited here.

[0061] In some embodiments, the communication system scans communication base stations within a preset distance range to obtain the signal strength, bandwidth data, and delay data of each communication base station; inputs the signal strength, bandwidth data, and delay data into a preset quality assessment model to obtain a quality score for each communication base station; sorts the communication base stations from large to small according to the quality score, and connects the first N target communication base stations in the sequence in parallel; determines the ID, current link status, and resource allocation ratio of the target communication base station, generates a multi-link connection table, and periodically updates the multi-link connection table.

[0062] The preset distance range refers to the base station search radius predefined by the system. Signal strength refers to the power of the wireless signal. Bandwidth data refers to the frequency range that can be used for data transmission. Delay data refers to the time required for signal transmission. The quality assessment model is an algorithm or function used to comprehensively evaluate the performance of the base station. The quality score is a quantitative evaluation of the overall performance of the base station. The target communication base station refers to the base station selected for connection. The multi-link connection table is a data structure that records all active connections and their related information.

[0063] The system performs this process when initializing a network connection or when the network status needs to be re-evaluated to ensure the best network connection. Specifically, the system first starts the wireless scanning module to search for available communication base stations within a preset range. For each detected base station, the system measures its signal strength and obtains bandwidth and delay information through a short data interaction. The system then inputs this data into a pre-trained quality assessment model that uses a weighted sum or machine learning algorithm. The model outputs a comprehensive quality score for each base station. The system sorts the base stations in descending order according to the score and selects the top N as target base stations. Next, the system establishes parallel connections with these target base stations, recording their unique identifiers, current connection status, and the proportion of resources allocated to the connection. Finally, the system organizes this information into a multi-link connection table and sets a timer to regularly update the data in the table to adapt to the dynamically changing network environment.

[0064] It should be noted that the quality assessment model is a comprehensive model used to evaluate the performance of communication base stations. During the model training phase, the system collects a large amount of historical data, including indicators such as signal strength, bandwidth, latency, and user satisfaction, which constitute the training set. During the training process, the model learns the relationship between these indicators and the overall performance of the base station, with the goal of minimizing the difference between the predicted score and the actual user experience. The model itself may be a multi-layer neural network or a complex regression model that can process multi-dimensional inputs and output a comprehensive score. During the use phase, the model receives real-time signal strength, bandwidth, and latency data as input and outputs a quality score between 0 and 100. This score is used for base station selection and resource allocation decisions.

[0065] In some embodiments, the selection and connection of the optimal base station can be achieved in a variety of ways: Optionally, the system can use software defined radio (SDR) technology to flexibly configure the wireless interface to support multiple communication protocols; then use artificial intelligence algorithms to predict the future performance of each base station to improve the forward-looking nature of the selection; finally, apply load balancing technology to dynamically adjust the resource allocation ratio of each connection. Optionally, the system can use collaborative sensing technology to share base station information with other surrounding devices to expand the search range; then use a multi-criteria decision method to select a base station considering multiple factors such as signal quality, energy consumption, and cost; finally, implement virtualized network functions (NFV) to flexibly manage and optimize network resources. It is understandable that other methods can also be used to achieve the selection and connection of the optimal base station, such as using blockchain technology to ensure the security and traceability of connection information, which is not limited here.

[0066] S202. Based on a beamforming algorithm, determine an antenna weight vector according to channel state information of multiple communication base stations.

[0067] Among them, the beamforming algorithm refers to a technology that enhances or suppresses the signal in a specific direction by adjusting the phase and amplitude of the antenna array. Channel state information (CSI) is data that describes the characteristics of the wireless channel, including attenuation, phase shift, etc. The antenna weight vector is a complex vector used to control the excitation of each element of the antenna array, which determines the direction and shape of the beam.

[0068] The communication system performs this step after establishing a multi-base station connection to optimize spatial signal transmission. Specifically, the communication system first collects channel state information from multiple connected base stations, which may include channel matrix, signal-to-noise ratio, etc. The system then inputs the collected CSI into a preset beamforming algorithm. The algorithm is based on criteria such as maximum ratio combining (MRC), zero forcing (ZF), or minimum mean square error (MMSE). The algorithm processes the CSI and calculates antenna weights that maximize signal quality or minimize interference. Finally, the system generates an antenna weight vector, which contains the amplitude and phase information of each antenna element for subsequent signal transmission and reception.

[0069] In some embodiments, CSI-based beamforming can be implemented in a variety of ways: Optionally, the system can use an adaptive beamforming algorithm to dynamically adjust antenna weights based on real-time CSI; then use multi-user MIMO technology to form independent beams for multiple base stations at the same time; finally, apply interference alignment technology to minimize mutual interference between base stations. Optionally, the system can use deep learning methods to train a neural network model to predict the optimal antenna weights; then use distributed collaborative beamforming to coordinate multiple devices to form a large-scale virtual antenna array; finally, apply cognitive radio technology to intelligently select the best frequency band and power for beamforming.

[0070] S203: Perform data transmission using the multi-link connection table and the antenna weight vector as transmission reference data.

[0071] The transmission reference data refers to configuration information used to guide the data transmission process.

[0072] The communication system performs this step after completing the multi-link connection and beamforming configuration to start the actual data transmission process. Specifically, the communication system first selects the optimal transmission path based on the multi-link connection table, and can adopt load balancing or intelligent routing strategies. Then, the system distributes the data to be transmitted to the selected multiple links according to the predetermined strategy. For each link, the system applies the corresponding antenna weight vector to configure the antenna array to form a directional beam. Next, the system encodes and modulates the data, and different coding schemes and modulation levels can be used according to the characteristics of different links. Finally, the system transmits signals through the configured antenna array to achieve multi-link parallel data transmission.

[0073] In some embodiments, data transmission based on multi-link and beamforming can be achieved in a variety of ways: Optionally, the system can use dynamic link aggregation technology to adjust the data allocation ratio of each link according to the real-time network status; then use adaptive coding and modulation (ACM) technology to select the best coding and modulation scheme for each link; finally, apply coordinated multi-point transmission (CoMP) technology to use multiple base stations to send data to the receiving end at the same time. Optionally, the system can use network coding technology to improve the reliability and efficiency of multi-link transmission; then use precoding technology to pre-process the signal at the transmitting end to offset known channel interference; finally, apply full-duplex communication technology to send and receive data at the same time and on the same frequency band. It can be understood that other methods can also be used to achieve data transmission based on multi-link and beamforming, such as using artificial intelligence technology to dynamically optimize the transmission strategy, which is not limited here.

[0074] S204, receiving the original X-ray image data collected by the portable X-ray machine and the electromagnetic characteristic data of the current environment.

[0075] Referring to step S101 , the communication system determines original X-ray image data and electromagnetic characteristic data.

[0076] S205. Determine a subcarrier allocation scheme and a transmission frequency according to the electromagnetic characteristic data, and set an OFDM modulator according to the subcarrier allocation scheme and set a radio frequency unit according to the transmission frequency.

[0077] Referring to step S102, the communication system configures the OFDM modulator according to the subcarrier allocation scheme and configures the radio frequency unit according to the transmission frequency.

[0078] S206. Detect the signal strength in real time and adjust the data compression ratio and error correction level of the original X-ray image data in combination with a preset image quality threshold.

[0079] Referring to step S103 , the communication system adjusts the data compression ratio and error correction level of the original X-ray image data.

[0080] S207. Compress and encode the original X-ray image data based on the data compression ratio and the error correction level to obtain encoded data.

[0081] Referring to step S104, the communication system generates encoded data.

[0082] S208: Send the encoded data to the receiving end through the OFDM modulator and the radio frequency unit.

[0083] Referring to step S105 , the communication system sends the encoded data to the receiving end.

[0084] In some embodiments, the communication system generates a chaotic sequence as an encryption key based on the network security level set by the user; performs lightweight encryption processing on the encoded data based on the chaotic sequence to obtain encrypted data; calculates the integrity check value of the encrypted data, and appends the integrity check value to the encrypted data.

[0085] Among them, the network security level refers to the user-defined data protection strength level. A chaotic sequence is a seemingly random but actually deterministic sequence with good unpredictability. An encryption key is a password used to encrypt and decrypt data. Lightweight encryption refers to an encryption algorithm with low computational complexity. Encrypted data is data that has been encrypted. An integrity check value is a digital summary used to verify whether the data has been tampered with.

[0086] The system performs this process when preparing to send sensitive data to protect the security of the data. Specifically, the system first reads the network security level set by the user, which determines the strength and complexity of the encryption. Then, the system uses a predetermined chaotic system (such as Logistic mapping or Lorenz system) to generate a chaotic sequence, and the length and initial conditions of the sequence are related to the security level. This chaotic sequence is used as an encryption key. Next, the system selects a suitable lightweight encryption algorithm (such as AES-128 or ChaCha20) and uses the chaotic sequence as the key to encrypt the encoded data. After encryption, the system calculates the hash value of the encrypted data (such as SHA-256), and this hash value is used as the integrity check value. Finally, the system appends the integrity check value to the end of the encrypted data to form the final secure data packet.

[0087] In some embodiments, secure encryption of data can be achieved in a variety of ways: Optionally, the system can use quantum key distribution (QKD) technology to generate true random encryption keys; then use homomorphic encryption algorithms to allow data processing in an encrypted state; finally, use blockchain technology to record encryption operation logs to ensure the auditability of the encryption process. Optionally, the system can use a hardware security module (HSM) to generate and store encryption keys to improve the security of key management; then use post-quantum cryptographic algorithms to resist potential quantum computing attacks; finally, implement multiple encryption strategies and use different encryption schemes for different types of data. It is understandable that other methods can also be used to achieve secure encryption of data, such as using attribute-based encryption (ABE) technology to achieve more fine-grained access control, which is not limited here.

[0088] In some embodiments, when a communication system detects that a data packet is lost during network transmission, it encodes the lost data packet as a redundant packet; determines a retransmission time window based on a packet loss rate and network delay; and retransmits the redundant packet within the retransmission time window.

[0089] Packet loss refers to the phenomenon that data packets are lost during network transmission. Redundant packets are additional data packets generated to recover lost data. Packet loss rate is the ratio of lost data packets to the total number of sent packets. Network latency refers to the time required for data to be sent and received. The retransmission time window is the time period during which data retransmission is allowed.

[0090] The system executes this process when it detects packet loss during data transmission to ensure complete data transmission. Specifically, the system first detects packet loss by monitoring confirmation information or sequence numbers. If packet loss is detected, the system will start the packet loss recovery mechanism. The system uses a predetermined coding algorithm (such as Reed-Solomon code or Raptor code) to encode the lost data packets and generate redundant packets. At the same time, the system analyzes the network status and calculates the current packet loss rate and network delay. Based on this information, the system uses an adaptive algorithm to determine a suitable retransmission time window, which can ensure sufficient time for retransmission and avoid long waiting that affects transmission efficiency. Within the determined time window, the system inserts redundant packets into the normal data stream for transmission until confirmation information is received or the time window ends.

[0091] In some embodiments, packet loss processing and retransmission can be achieved in a variety of ways: Optionally, the system can use network coding technology to create redundant relationships between data packets to improve recovery efficiency; then use machine learning algorithms to predict network conditions and dynamically adjust retransmission strategies; finally, implement priority queue management to ensure that important data packets are retransmitted first. Optionally, the system can use multipath transmission protocols (such as MPTCP) to transmit data simultaneously through multiple network paths to reduce the impact of packet loss; then use adaptive redundancy control to adjust the number of redundant packets according to network conditions; finally, implement a collaborative retransmission mechanism to use the nearest device to assist in data recovery. It is understandable that other methods can also be used to achieve packet loss processing and retransmission, such as using edge computing technology to perform real-time packet loss detection and recovery at network edge nodes, which is not limited here.

[0092] It should be noted that the network prediction model can be used to predict network conditions and predict changes in network performance in advance. The training phase of the network prediction model uses historical network data, including traffic patterns, user behavior, environmental factors, etc., as well as corresponding network performance indicators. The goal of model training is to minimize prediction errors and improve the accuracy of predictions of changes in network conditions. The model may use structures such as recurrent neural networks (RNNs) or long short-term memory networks (LSTMs) to capture the characteristics of time series data. When in use, the model inputs network status data and environmental parameters, and outputs network performance predictions for a period of time in the future, such as possible congestion levels, delay changes, etc. These prediction results are used to adjust transmission strategies in advance and optimize network resource allocation.

[0093] S209: Receive transmission feedback data from the receiving end, and adjust transmission parameters according to the transmission feedback data.

[0094] Referring to step S106 , the communication system adjusts the transmission parameters.

[0095] S210, receiving the image reset instruction sent back by the receiving end, and sending a reshooting instruction to the portable X-ray machine, so that the portable X-ray machine enters a reshooting standby state.

[0096] The image reset instruction refers to a control command sent by the receiving end to request re-capture of the X-ray image. The reshoot instruction refers to a command signal sent by the communication system to the X-ray machine to trigger re-capture. The reshoot standby state is a working state in which the X-ray machine is ready to reshoot.

[0097] Specifically, the communication system first receives an image reset instruction from the receiving end through a preset control channel. The instruction includes a reshoot reason code and a priority identifier. The system verifies the legitimacy and priority of the received instruction and confirms it as a valid reset request. Then, the system converts the image reset instruction into a reshoot instruction format recognizable by the X-ray machine according to the preset instruction mapping rules. Next, the system sends the reshoot instruction to the portable X-ray machine through a secure device control channel. Finally, the system waits and confirms that the X-ray machine has successfully entered the reshoot standby state. In this state, the X-ray machine will reset the relevant parameters and wait for the next shooting instruction.

[0098] In some embodiments, the remote reset control process can be implemented in a variety of ways: Optionally, the system can use a real-time communication protocol based on WebSocket to receive reset instructions to ensure the real-time transmission of instructions; then use a multi-level cache mechanism to temporarily store the current device status and parameter configuration for quick initialization; finally, implement intelligent status monitoring to automatically detect whether the device has correctly entered the standby state. Optionally, the system can use blockchain technology to record all reset operations to ensure that the operations are traceable; then use artificial intelligence algorithms to analyze the reasons for reshoots and automatically generate optimization suggestions; finally, use digital twin technology to display device status changes in real time. It is understandable that other methods can also be used to implement the remote reset control process, such as using edge computing technology to perform local instruction preprocessing and status monitoring, which is not limited here.

[0099] S211, receiving the device control command sent back by the receiving end, and transmitting the device control command to the portable X-ray machine to adjust the shooting parameters of the portable X-ray machine and reshoot.

[0100] Among them, the equipment control instructions refer to the command set used to control and adjust the working status of the X-ray machine. Shooting parameters include various settings that affect image acquisition, such as X-ray dose, exposure time, and focal length. Reshooting refers to the process of re-collecting X-ray images.

[0101] The communication system performs this step after the portable X-ray machine enters the reshoot standby state to optimize the reshoot effect. Specifically, the communication system first establishes a dedicated control channel to receive device control instructions from the receiving end. Then, the system waits for and receives these instructions, which contain specific parameter adjustment suggestions, such as increasing or decreasing exposure time, adjusting X-ray intensity, etc. After receiving the instructions, the system parses and verifies the instructions to ensure their legality and security. Then, the system re-encodes the verified control instructions and transmits them to the portable X-ray machine through a secure channel. Finally, the system monitors the response of the X-ray machine, confirms whether the parameter adjustment is successful, and waits for the start of reshooting.

[0102] In some embodiments, the communication system also has a scene recognition mechanism. The communication system can automatically identify the current shooting scene features, including patient size, examination site, environmental conditions, etc. The communication system uses computer vision technology to analyze scene information in real time and select the most suitable shooting strategy. At the same time, the communication system is based on dynamic exposure control and can adjust exposure parameters in real time according to scene changes. The communication system also has a positioning function, which can automatically adjust the position and angle of the device based on the image taken by the X-ray machine to ensure the best shooting effect.

[0103] In some embodiments, the remote control and reshoot process can be implemented in a variety of ways: Optionally, the system can use the Real-time Stream Control Protocol (RTCP) to receive and transmit device control instructions to ensure the real-time nature of the instructions; then apply an intelligent parameter optimization algorithm to automatically adjust the recommended parameters based on previous shooting results; finally, use virtual reality (VR) technology to provide an immersive control experience for remote operators. Optionally, the system can use blockchain technology to record all control instructions and parameter adjustments to ensure the traceability of operations; then use an artificial intelligence-assisted system to provide parameter adjustment suggestions to the operator; finally, use augmented reality (AR) technology to intuitively display the potential impact of parameter adjustments on image quality. It is understandable that other methods can also be used to implement the remote control and reshoot process, such as using the network slicing technology of the 5G network to provide an independent high-priority transmission channel for important control instructions, which is not limited here.

[0104] In some embodiments, after receiving the cloud assistance instruction sent by the receiving end, the communication system will obtain the work record including patient information, X-ray image data and diagnosis record; encrypt the work record and upload the encrypted work record to the cloud collaboration platform; obtain the diagnosis suggestions sent by multiple diagnostic terminals on the cloud collaboration platform, and send the diagnosis suggestions to the receiving end.

[0105] Among them, the cloud assistance instruction is the command that triggers the remote diagnosis service. The work record contains comprehensive information related to the diagnosis. Patient information refers to data such as personal identity and medical history. X-ray image data refers to the digitized X-ray imaging results. The diagnosis record is the doctor's interpretation and diagnosis conclusion of the X-ray image. The diagnosis terminal refers to the medical equipment or system involved in the remote diagnosis. The diagnosis suggestion is the expert's professional opinion on the case.

[0106] The system executes this process when it receives a request for multi-expert consultation to provide a more comprehensive diagnosis. Specifically, the system first receives the cloud-assisted instruction sent by the receiving end, which is automatically triggered by the local doctor or the system. Then, the system retrieves the relevant work records from the local database, including the patient's basic information, the X-ray images taken this time, and the preliminary diagnosis record. Then, the system encrypts the entire work record using the Advanced Encryption Standard (AES) to ensure patient privacy and data security. The system uploads the encrypted data packet to the predetermined cloud collaboration platform, which is a dedicated medical image sharing system. After the upload is completed, the system notifies multiple diagnostic terminals on the platform, which are distributed in different hospitals or clinics. The system waits for and collects the diagnostic suggestions sent by these diagnostic terminals, which can include detailed image annotations, diagnostic reports, etc. Finally, the system integrates all the collected diagnostic suggestions and sends them back to the original receiving end for reference by local doctors.

[0107] In some embodiments, cloud-based collaborative diagnosis can be achieved in a variety of ways: Optionally, the system can use blockchain technology to record all diagnostic operations to ensure the transparency and traceability of the diagnostic process; then use federated learning technology to train AI-assisted diagnostic models using data from multiple hospitals while protecting privacy; finally, implement real-time collaboration tools to support instant communication and image annotation between experts. Optionally, the system can use 5G network technology to achieve large-scale transmission and real-time consultation of medical images; then use natural language processing technology to automatically integrate and summarize the diagnostic opinions of multiple experts; finally, implement virtual reality (VR) remote consultation to provide an immersive collaborative diagnosis experience. It is understandable that other methods can also be used to achieve cloud-based collaborative diagnosis, such as using edge computing technology to pre-process image data locally to reduce the burden of cloud transmission, which is not limited here.

[0108] In the embodiments of the present application, due to the use of technologies such as multi-base station parallel connection, real-time parameter adaptive adjustment, intelligent anti-interference technology, lightweight encryption and cloud collaboration, the communication system can achieve stable, high-quality and secure X-ray image transmission in a complex network environment, effectively solving the problems of susceptibility to interference, unstable transmission, poor image quality and low data security in traditional methods, thereby achieving improved efficiency and accuracy of remote medical diagnosis and expanding the application scope of portable X-ray machines.

[0109] The communication system in the embodiment of the present invention is described below from the perspective of hardware processing. Figure 3 , is a schematic diagram of a physical device structure of a communication system in an embodiment of the present application.

[0110] It should be noted that Figure 3 The structure of the communication system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0111] like Figure 3 As shown, the communication system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0112] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (local area network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0113] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0114] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0115] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0116] Specifically, the communication system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the portable X-ray machine communication method provided in the above embodiment is implemented.

[0117] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the communication system described in the above embodiment; or may exist independently without being assembled into the communication system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the communication system, the communication system implements the portable X-ray machine communication method provided in the above embodiment.

[0118] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0119] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0120] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A portable X-ray machine communication method, characterized in that: Applied to a communication system, the method comprises: Receive the original X-ray image data collected by the portable X-ray machine and the electromagnetic characteristic data of the current environment; Determine a subcarrier allocation scheme and a transmission frequency according to the electromagnetic characteristic data, set an OFDM modulator according to the subcarrier allocation scheme, and set a radio frequency unit according to the transmission frequency; the transmission frequency is different from an interference frequency band in the electromagnetic characteristic data; Real-time detection of signal strength and adjustment of the data compression ratio and error correction level of the original X-ray image data in combination with a preset image quality threshold; Compressing and encoding the original X-ray image data based on the data compression ratio and the error correction level to obtain encoded data; Sending the encoded data to a receiving end through the OFDM modulator and the radio frequency unit; receiving transmission feedback data from the receiving end, and adjusting transmission parameters according to the transmission feedback data.

2. The method according to claim 1, characterized in that Before the step of sending the encoded data to the receiving end through the OFDM modulator and the radio frequency unit, the method further includes: Generate a chaotic sequence as an encryption key based on the network security level set by the user; Performing lightweight encryption processing on the coded data based on the chaotic sequence to obtain encrypted data; An integrity check value of the encrypted data is calculated, and the integrity check value is appended to the encrypted data.

3. The method according to claim 1, characterized in that Before the step of receiving the original X-ray image data collected by the portable X-ray machine and the electromagnetic characteristic data of the current environment, the method further includes: Based on multiple access technology and network parameters, establish parallel connections with multiple communication base stations and generate a multi-link connection table; Based on a beamforming algorithm, determining an antenna weight vector according to channel state information of the plurality of communication base stations; Data transmission is performed using the multi-link connection table and the antenna weight vector as transmission reference data.

4. The method according to claim 3, characterized in that The establishing of parallel connections with multiple communication base stations based on multiple access technologies and network parameters and generating a multi-link connection table specifically includes: Scanning communication base stations within a preset distance range to obtain signal strength, bandwidth data, and delay data of each of the communication base stations; Inputting the signal strength, the bandwidth data and the delay data into a preset quality assessment model to obtain a quality score for each of the communication base stations; Sorting the communication base stations from large to small according to the quality scores, and connecting the first N target communication base stations in the sequence in parallel; The ID, current link status and resource allocation ratio of the target communication base station are determined, a multi-link connection table is generated, and the multi-link connection table is periodically updated.

5. The method according to claim 1, characterized in that After the step of sending the encoded data to the receiving end through the OFDM modulator and the radio frequency unit, the method further includes: When it is detected that a data packet is lost during network transmission, the lost data packet is encoded as a redundant packet; Determine the retransmission time window based on the packet loss rate and network delay; The redundant packet is retransmitted within the retransmission time window.

6. The method according to claim 1, characterized in that After the step of receiving the transmission feedback data from the receiving end and adjusting the transmission parameters according to the transmission feedback data, the method further includes: receiving an image reset instruction sent back by the receiving end, and sending a reshooting instruction to the portable X-ray machine, so that the portable X-ray machine enters a reshooting standby state; Receive the device control instruction returned by the receiving end, and transmit the device control instruction to the portable X-ray machine to adjust the shooting parameters of the portable X-ray machine and reshoot.

7. The method according to claim 6, characterized in that After the step of receiving the device control instruction returned by the receiving end and transmitting the device control instruction to the portable X-ray machine to adjust the shooting parameters of the portable X-ray machine and reshoot, the method further includes: After receiving the cloud assistance instruction sent by the receiving end, obtaining a work record including patient information, X-ray image data and diagnosis record; Encrypting the work record and uploading the encrypted work record to the cloud collaboration platform; The diagnostic suggestions sent by the plurality of diagnostic terminals on the cloud collaboration platform are obtained, and the diagnostic suggestions are sent to the receiving end.

8. A communication system, characterized in that: The communication system comprises: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, and the one or more processors call the computer instructions so that the communication system executes the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a communication system, the communication system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is executed on a communication system, the communication system is caused to execute the method according to any one of claims 1 to 7.

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