CN119210975A - A portable X-ray machine communication method, system, medium and program product - Google Patents

Abstract

A portable X-ray machine communication method, system, medium and program product, involving the field of information signal transmission, the method includes: determining the original X-ray image data and the 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; 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 the 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 the transmission feedback data of the receiving end, and adjusting the transmission parameters according to the transmission feedback data. The implementation of this method can resist electromagnetic interference and reduce the situation of image quality degradation or transmission failure.

Description

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

Technical Field

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

With the continuous development of medical technology, portable X-ray machines are becoming more and more important in medical diagnostics. The portable X-ray machine not only can be used in a hospital, but also can be used for diagnosis in complex environments such as the field, disaster sites and the like.

In the related art, a digital image acquisition technology is adopted in a portable X-ray machine, the acquired high-definition image is stored in equipment, and the image is transmitted to a remote server or an image system of a hospital in real time through Wi-Fi or a mobile network so as to carry out remote diagnosis.

However, in an environment with strong electromagnetic interference, data transmission of the high-definition X-ray image may be affected, resulting in degradation of image quality or transmission failure, and poor remote diagnosis effect.

Disclosure of Invention

The application provides a portable X-ray machine communication method, a portable X-ray machine communication system, a portable X-ray machine communication medium and a portable X-ray machine communication program product, which are used for resisting electromagnetic interference and reducing the condition of image quality degradation or transmission failure.

The application provides a portable X-ray machine communication method which is applied to a communication system and comprises the steps of receiving original X-ray image data acquired by a portable X-ray machine and electromagnetic characteristic data of the current environment, determining a subcarrier allocation scheme and transmitting frequency according to the electromagnetic characteristic data, setting an OFDM modulator according to the subcarrier allocation scheme, setting a radio frequency unit according to the transmitting frequency, setting an interference frequency band different from the electromagnetic characteristic data, detecting signal intensity in real time, combining a preset image quality threshold value, adjusting the data compression ratio and error correction level of the original X-ray image data, 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 of the receiving end, and adjusting transmission parameters according to the transmission feedback data.

In the embodiment, the communication system ensures the image quality by detecting the signal intensity in real time and adjusting the compression ratio and the error correction level, adopts the OFDM modulation and the radio frequency unit for transmission, improves the transmission efficiency and the anti-interference capability, and ensures the communication stability and the image transmission quality of the portable X-ray machine under the complex electromagnetic environment.

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

In the embodiment, the communication system improves the data safety while ensuring the transmission efficiency by introducing the lightweight encryption and the integrity check based on the chaotic sequence, wherein the chaotic sequence is used as a secret key to enhance the encryption strength, the lightweight encryption reduces the calculation complexity, and the integrity check ensures the data integrity.

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 further includes establishing parallel connection with a plurality of communication base stations based on a multiple access technology and network parameters, generating a multi-link connection table, determining antenna weight vectors according to channel state information of the plurality of communication base stations based on a beam forming algorithm, and performing data transmission by using the multi-link connection table and the antenna weight vectors as transmission reference data.

In the above embodiment, the communication system uses the multiple access technology and the beamforming algorithm to realize the parallel connection and the space diversity of multiple base stations, improve the network capacity and the reliability, optimize the signal transmission direction by the beamforming, and enhance the communication robustness and the transmission efficiency of the system in the complex network environment.

In combination with some embodiments of the first aspect, in some embodiments, parallel connection with a plurality of communication base stations is established based on a multiple access technology and network parameters to generate a multi-link connection table, which specifically includes scanning the communication base stations within a preset distance range, acquiring signal strength, bandwidth data and delay data of each communication base station, inputting the signal strength, the bandwidth data and the delay data into a preset quality evaluation model to obtain a quality score of each communication base station, sorting the communication base stations from large to small according to the quality score, connecting the first N target communication base stations in a sequence in parallel, determining IDs, current link states and resource allocation proportions of the target communication base stations, generating the multi-link connection table, and periodically updating the multi-link connection table.

In the above embodiment, the communication system realizes automatic selection and resource allocation of the optimal base station by scanning the base station, evaluating quality, sorting connection and dynamically updating the connection table, and the parallel connection improves the transmission rate, and the dynamic update ensures real-time optimality of connection, and improves the network access capability and transmission efficiency of the system.

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 further includes encoding the lost data packet into a redundant packet when detecting that the data packet is lost in network transmission, determining a retransmission time window according to the packet loss rate and the network delay, and retransmitting the redundant packet within the retransmission time window.

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

In combination with some embodiments of the first aspect, in some embodiments, after the step of receiving the transmission feedback data of the receiving end and adjusting the transmission parameters according to the transmission feedback data, the method further includes receiving an image reset instruction returned 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, and receiving an equipment control instruction returned by the receiving end, and transmitting the equipment control instruction to the portable X-ray machine so as to adjust the shooting parameters of the portable X-ray machine and perform re-shooting.

In the above embodiment, the communication system improves the acquisition quality of the X-ray image by receiving the image resetting instruction, transmitting the re-shooting instruction and adjusting the shooting parameters, provides the functions of on-line diagnosis and remote control, and improves the diagnosis efficiency.

In combination with some embodiments of the first aspect, in some embodiments, after receiving a device manipulation instruction returned by the receiving end, transmitting the device manipulation instruction to the portable X-ray machine to adjust shooting parameters of the portable X-ray machine and perform a step of re-shooting, the method further includes, after receiving a cloud assistance instruction sent by the receiving end, acquiring a work record including patient information, X-ray image data and a diagnosis record, encrypting the work record, uploading the encrypted work record to a cloud collaboration platform, acquiring diagnosis suggestions sent by a plurality of diagnosis terminals on the cloud collaboration platform, and sending the diagnosis suggestions to the receiving end.

In the embodiment, the communication system effectively introduces remote expert resources by encrypting and uploading the work record and acquiring the multiparty diagnosis suggestion, thereby improving the accuracy and efficiency of diagnosis.

In a second aspect, embodiments of the present application provide a communication system comprising one or more processors and memory coupled to the one or more processors, the memory for storing computer program code comprising computer instructions that the one or more processors invoke the computer instructions to cause the communication system to perform a method as described in the first aspect and any of the possible implementations of the first aspect.

In a third aspect, embodiments of the present application provide a computer program product comprising instructions which, when run on a communication system, cause the communication system to perform a method as described in the first aspect and any possible implementation of the first aspect.

In a fourth aspect, embodiments of the present application provide a computer readable storage medium comprising instructions which, when run on a communication system, cause the communication system to perform a method as described in the first aspect and any possible implementation of the first aspect.

It will be appreciated that the communications system provided in the second aspect described above, the computer program product provided in the third aspect and the computer storage medium provided in the fourth aspect are each adapted to perform the methods provided by embodiments of the present application. Therefore, the advantages achieved by the method can be referred to as the advantages of the corresponding method, and will not be described herein.

One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:

1. Due to the adoption of dynamic parameter adjustment and OFDM modulation technology, the portable X-ray machine communication system can dynamically adjust transmission parameters according to the electromagnetic characteristics of the environment, can effectively interfere with the complex electromagnetic environment, and optimizes the transmission efficiency while ensuring the image quality.

2. Due to the adoption of the base station quality evaluation and dynamic connection mechanism based on multiple indexes, the portable X-ray machine communication system can automatically select the optimal base station and realize reasonable resource distribution, and the connection strategy can be timely adjusted by periodically updating the connection table so as to always maintain the optimal network state, thereby realizing intelligent network access and resource utilization of the portable X-ray equipment.

3. Because the automatic re-shooting mechanism based on remote evaluation is adopted, the portable X-ray machine communication system realizes the quality control of X-ray image acquisition, automatically triggers the re-shooting process when the quality does not reach the standard, adjusts shooting parameters according to a remote instruction, and realizes the high-efficiency and high-quality X-ray image acquisition.

Drawings

FIG. 1 is a schematic flow chart of a communication method of a portable X-ray machine in an embodiment of the application;

FIG. 2 is a schematic flow chart of another method of communication of a portable X-ray machine according to an embodiment of the application;

Fig. 3 is a schematic diagram of a physical device of a communication system according to an embodiment of the present application.

Detailed Description

The terminology used in the following embodiments of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates to the contrary. It should also be understood that the term "and/or" as used in this disclosure is intended to encompass any or all possible combinations of one or more of the listed items.

The terms "first," "second," and the like, are used below for descriptive purposes only and are not to be construed as implying or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature, and in the description of embodiments of the application, unless otherwise indicated, the meaning of "a plurality" is two or more.

The method provided in this embodiment is described in the following. Fig. 1 is a schematic flow chart of a communication method of a portable X-ray machine according to an embodiment of the application.

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

The portable X-ray machine is a movable X-ray imaging device and is used for medical diagnosis in various scenes. Raw X-ray image data refers to raw X-ray imaging results, which are high resolution gray scale images. Electromagnetic characteristic data refers to information describing the current environmental electromagnetic field conditions, including electromagnetic field strength, frequency distribution, interference sources, and the like.

The communication system performs this step after the portable X-ray machine has completed image acquisition. Specifically, the communication system first establishes a connection with the portable X-ray machine and then receives the original image data transmitted from the X-ray machine. Meanwhile, the communication system collects electromagnetic characteristic data of the surrounding environment through a built-in or external electromagnetic sensor.

It should be noted that, the communication system refers to each module assembly for uploading and downloading data, the communication system may be a module built in the portable X-ray machine, or the communication system may be external equipment, and the communication system is connected with the portable X-ray machine by a wire or wirelessly to execute the communication task of the portable X-ray machine.

In addition, in some embodiments, for original X-ray image data with large storage space occupation, the communication system may also establish an intelligent buffer system based on deep learning, and predict possible data characteristics and sizes of the original X-ray image data by analyzing historical data transmission modes. The system can allocate proper storage space in advance and conduct hierarchical storage according to the importance of the data. For the data type used by high frequency, the system reserves a quick access area in the memory, and for the data which is not commonly used, the quick access area is stored in the secondary cache, so that the delay of data receiving is reduced, and the response speed of the system is improved. Meanwhile, the system can realize dynamic storage space adjustment, automatically expand the storage capacity when the data volume is suddenly increased, and release redundant space when the data volume is idle.

S102, determining a subcarrier allocation scheme and a transmitting frequency according to the electromagnetic characteristic data, and setting an OFDM modulator with the subcarrier allocation scheme to set a radio frequency unit with the transmitting frequency.

The subcarrier allocation scheme refers to a power, bit and code allocation strategy for a plurality of orthogonal subcarriers in an OFDM system. The transmitting frequency is the central frequency of signal transmission and is different from the interference frequency band in the electromagnetic characteristic data. An OFDM modulator is a device that performs digital modulation using orthogonal frequency division multiplexing techniques. The radio frequency unit is a hardware module responsible for converting a baseband signal into a radio frequency signal and transmitting the radio frequency signal.

The communication system performs this step after acquiring the electromagnetic property data in preparation for subsequent data transmission. Specifically, the communication system first analyzes the electromagnetic characteristic data to identify possible sources of interference and channel characteristics. Then, the system selects a frequency band avoiding the main interference as the transmitting frequency according to the analysis result. Meanwhile, the system designs an optimized subcarrier allocation scheme to maximize the spectrum efficiency and the anti-interference capability. Next, the system sets parameters of the subcarrier allocation scheme to the OFDM modulator, including the number of subcarriers, power allocation, modulation scheme, etc. Finally, the system sets the selected transmitting frequency to the radio frequency unit, and adjusts the working frequency band and transmitting power.

In some embodiments, parameter optimization and device configuration may be achieved in a number of ways, including, optionally, the communication system may optimize subcarrier allocation using genetic algorithms, taking into account factors such as channel capacity and bit error rate, then dynamically configuring the OFDM modulator via Software Defined Radio (SDR) techniques, and finally setting the transmit frequency of the radio frequency unit using adaptive frequency hopping techniques. Optionally, the communication system may employ a deep reinforcement learning algorithm to automatically generate an optimal subcarrier allocation scheme according to the historical transmission data and the current environment, then adjust parameters of the OFDM modulator in real time by a Digital Signal Processor (DSP), and finally precisely control the transmitting direction and frequency of the radio frequency unit by using a phased array antenna technology. It will be appreciated that parameter optimization and device configuration may also be implemented in other ways, such as dynamic adjustment of transmission parameters using fuzzy logic control algorithms, without limitation.

S103, detecting the signal intensity in real time and combining a preset image quality threshold value to adjust the data compression ratio and the error correction level of the original X-ray image data.

The signal strength is the power of the received wireless signal. The image quality threshold refers to the lowest acceptable image quality criterion. The data compression ratio refers to the ratio of the compressed data size to the original data size. The error correction level is redundancy of the forward error correction coding.

The communication system continues to perform this step after starting the data transmission to adapt to the dynamically changing transmission environment. Specifically, the communication system firstly obtains real-time signal strength data through feedback or local measurement of the receiving end. Then, the system compares the current signal strength with a preset image quality threshold to evaluate the current transmission condition. According to the evaluation result, the system dynamically adjusts the data compression ratio, increases the compression ratio to reduce the amount of transmitted data when the signal is weak, and decreases the compression ratio to improve the image quality when the signal is strong. Meanwhile, the system correspondingly adjusts the error correction level, increases redundancy under the severe signal environment to improve reliability, and reduces redundancy under the good signal condition to improve transmission efficiency.

In some embodiments, the adaptive adjustment may be achieved in a variety of ways, alternatively, the communication system may dynamically adjust the compression ratio according to the trend of signal strength change using a Proportional Integral Derivative (PID) control algorithm, then achieve a variable compression ratio using an adaptive arithmetic coding technique, and finally achieve flexible error correction level adjustment using a low density parity check code (LDPC). Optionally, the communication system can adopt a fuzzy neural network, comprehensively consider the signal strength, the channel condition and the image content complexity, intelligently decide the optimal compression ratio and error correction level, then use a scalable image compression algorithm based on wavelet transformation, and finally adopt Turbo codes to provide multi-level error correction capability. It will be appreciated that other means of achieving adaptive adjustment may be employed, such as dynamic optimization of transmission parameters using a reinforcement learning algorithm, as not limited herein.

S104, compressing and encoding the original X-ray image data based on the data compression ratio and the error correction level to obtain encoded data.

Data compression refers to a process of reducing data storage space or transmission bandwidth. Coding refers to a method of converting information into another form for improving transmission efficiency or increasing security. The encoded data refers to data subjected to compression and encoding processing.

The communication system performs this step after determining the compression ratio and error correction level, making the final preparation for data transmission. Specifically, the communication system first performs compression processing on the original X-ray image data according to the determined compression ratio. The compression process may include denoising, downsampling, quantization, etc. to reduce the amount of data while maintaining critical diagnostic information. The system then encodes the compressed data. The coding process comprises two parts of source coding and channel coding, wherein the source coding further compresses data to remove redundancy, and the channel coding adds redundancy information according to the determined error correction level to enhance the anti-interference capability. And finally, the system packages the encoded data, and adds necessary header information and check information to form final encoded data.

In some embodiments, the communication system may establish a multi-level error protection and recovery mechanism in order to secure data. The mechanism employs a hierarchical protection strategy based on possible transmission errors during the encoding phase. For important data, the communication system uses stronger error correcting code and more redundant information, and for secondary data, a lightweight protection mechanism is adopted. The communication system can also realize an intelligent data slicing technology, large-scale image data is divided into a plurality of independent data packets, each data packet contains enough information for local reconstruction, and even part of data packets are lost, the whole image cannot be used.

S105, the coded data is sent to a receiving end through an OFDM modulator and a radio frequency unit.

The receiving end refers to target equipment or a system at the other side of the data transmission.

The communication system performs this step after the data encoding is completed, and starts the actual data transmission process. Specifically, the communication system first inputs encoded data into the OFDM modulator. The modulator divides the data stream into a plurality of parallel sub-data streams, one for each sub-carrier. Then, the modulator modulates each subcarrier independently, and a modulation scheme such as QPSK or 16QAM may be adopted. Next, the modulator performs an inverse fourier transform (IFFT) to convert the frequency domain signal into a time domain signal. The system adds a cyclic prefix at this point to reduce inter-symbol interference. Finally, the time domain signal is sent to the radio frequency unit, and after being processed by digital-to-analog conversion, up-conversion and the like, the time domain signal is emitted in the form of electromagnetic wave and is transmitted to the receiving end.

In some embodiments, data modulation and transmission may be achieved in a variety of ways, optionally, the communication system may dynamically select a modulation mode and a coding rate of each subcarrier according to channel conditions using an Adaptive Modulation and Coding (AMC) technique, then optimize a peak-to-average ratio of the OFDM signal using a precoding technique, and finally achieve beamforming by a smart antenna technique, thereby improving directionality and energy efficiency of signal transmission. Optionally, the communication system may employ MIMO-OFDM technology, and utilize a multiple-input multiple-output antenna system to improve spectrum efficiency, then use an interleaving technology to reduce the impact of burst errors, and finally use a power control algorithm to dynamically adjust the transmission power, thereby reducing energy consumption and interference while ensuring communication quality. It will be appreciated that data modulation and transmission may also be implemented in other ways, such as intelligent selection of optimal transmission parameters and frequency bands using cognitive radio technology, without limitation.

S106, receiving the transmission feedback data of the receiving end, and adjusting transmission parameters according to the transmission feedback data.

Wherein, the transmission feedback data refers to the information about the communication quality and the receiving condition returned by the receiving end. The transmission parameters include modulation scheme, coding rate, transmit power, subcarrier allocation, etc. of the adjustable communication system configuration.

The communication system continuously performs this step during the data transmission 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. The data may include Channel Quality Indication (CQI), signal-to-noise ratio (SNR), bit Error Rate (BER), etc. The system then analyzes and processes the feedback data to evaluate the current transmission performance. Based on the evaluation result, the system decides whether or not the transmission parameters need to be adjusted and how to adjust. Adjustment involves changing the modulation scheme, adjusting the coding rate, reassigning subcarriers, modifying the transmit power, etc. Finally, the system applies the new parameter setting to the corresponding hardware module to complete the dynamic optimization of the transmission parameters.

In some embodiments, the communication system also has an advanced feedback analysis mechanism based on machine learning. The communication system can process conventional channel state information and analyze more complex performance indexes such as end-to-end delay, throughput fluctuation, signal quality trend and the like. The communication system analyzes the feedback data by adopting a deep learning model, and identifies potential performance problems and optimization opportunities. Through historical data analysis, the system can build a network performance model, predict possible problems and adjust parameters in advance. The communication system also has abnormality detection capability, and can rapidly identify and respond to abnormal feedback data to prevent wrong parameter adjustment. Meanwhile, the system realizes a reliability evaluation mechanism of feedback data, and ensures adjustment based on accurate feedback.

The method provided in this embodiment will be described in more detail. Fig. 2 is a schematic flow chart of a communication method of a portable X-ray machine according to an embodiment of the application.

S201, based on the multiple access technology and network parameters, parallel connection with a plurality of communication base stations is established, and a multi-link connection table is generated.

Among them, the multiple access technique refers to a technique that allows a plurality of users to simultaneously use the same communication channel, such as CDMA, OFDMA, and the like. Network parameters refer to various metrics describing network status and performance, such as signal strength, delay, bandwidth, etc. The communication base station is a fixed station device for wireless communication. Parallel connection refers to communication links established simultaneously with multiple base stations. The multilink connection table is a data structure that records all active connections and their associated information.

The communication system performs this step when initializing or reconfiguring the network connection in preparation for subsequent data transmission. Specifically, the communication system first scans surrounding available communication base stations to acquire basic information such as signal strength, frequency band, etc. of each base station. Then, the system tries to establish connection with multiple base stations simultaneously according to preset multiple access protocols (e.g. LTE, 5G NR). In this process, the system may consider network parameters such as signal quality, load conditions, and the like, and select the most suitable base station for connection. After a connection is successfully established, the system assigns a unique identifier to each connection and records the relevant network parameters. And finally, the system organizes the information of all active connections into a multi-link connection table, which contains the information of base station ID, connection state, signal quality, bandwidth and the like.

In some embodiments, multi-base station parallel connection and connection table generation may be achieved in a variety of ways, optionally, the system may dynamically configure the wireless interface to support multiple access protocols using Software Defined Radio (SDR) techniques, then employ load balancing algorithms to assign connection priorities based on the load conditions of each base station, and finally use distributed database techniques to update and synchronize the multi-link connection tables in real time. Optionally, the system can predict network conditions by using an artificial intelligence algorithm, intelligently select the optimal connection combination, then apply a network slicing technology to allocate independent network resources for different types of services, and finally use a blockchain technology to ensure the security and traceability of a connection table. It will be appreciated that the multi-base station parallel connection and connection table generation may also be implemented in other ways, such as using edge computing techniques to optimize connection management and resource allocation, without limitation.

In some embodiments, the communication system scans communication base stations within a preset distance range to obtain signal strength, bandwidth data and delay data of each communication base station, inputs the signal strength, the bandwidth data and the delay data into a preset quality evaluation model to obtain a quality score of each communication base station, orders the communication base stations from large to small according to the quality scores, connects the first N target communication base stations in a sequence in parallel, determines the IDs, the current link states and the resource allocation proportion of the target communication base stations, generates a multi-link connection table, and periodically updates the multi-link connection table.

The preset distance range refers to a base station searching radius predefined by the system. The signal strength refers to the power level of the wireless signal. Bandwidth data refers to the range of frequencies available for data transmission. Delay data is the time required for signal transmission. The quality assessment model is an algorithm or function for comprehensively evaluating the performance of the base station. The quality score is a quantitative assessment of the overall performance of the base station. The target communication base station refers to a base station selected for connection. The multilink connection table is a data structure that records all active connections and their associated information.

The system performs this process when a network connection is initialized or network conditions need to be re-evaluated to ensure an optimal network connection. Specifically, the system first starts a wireless scanning module, and searches 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 brief data interactions. The system then inputs the data into a pre-trained quality assessment model that employs a weighted summation or machine learning algorithm. The model outputs a composite quality score for each base station. The system sorts the base stations in descending order according to the scores, and selects the first N base stations as target base stations. The system then establishes parallel connections with these target base stations, recording their unique identifiers, the current connection status and the proportion of resources allocated to the connection. And finally, the system arranges the information into a multi-link connection table, and sets a timer to update the data in the table periodically so as to adapt to the dynamically-changed network environment.

The quality evaluation model is an integrated model for evaluating the performance of the communication base station. In the model training phase, the system collects a large amount of historical data, including indicators of signal strength, bandwidth, delay, user satisfaction, etc., which form a training set. During training, the model learns the relationship between these metrics and the overall performance of the base station, with the goal of minimizing the difference between the predictive score and the actual user experience. The model itself may be a multi-layer neural network or complex regression model capable of processing multidimensional inputs and outputting a composite score. In the usage phase, the model receives as inputs real-time signal strength, bandwidth and delay data, outputting a quality score between 0 and 100. This score is used for base station selection and resource allocation decisions.

In some embodiments, the selection and connection of the optimal base station may be achieved in a variety of ways, optionally, the system may use Software Defined Radio (SDR) techniques to flexibly configure the wireless interface to support a variety of communication protocols, then employ artificial intelligence algorithms to predict future performance of each base station, improve the look-ahead of the selection, and finally employ load balancing techniques to dynamically adjust the resource allocation ratio of each connection. Optionally, the system can share base station information with other surrounding devices by utilizing a cooperative sensing technology to expand the search range, then select the base station by using a multi-criterion decision method and considering a plurality of factors such as signal quality, energy consumption, cost and the like, and finally realize a virtual Network Function (NFV) to flexibly manage and optimize network resources. It will be appreciated that the selection and connection of the optimal base station may also be achieved in other ways, such as using blockchain techniques to ensure the security and traceability of the connection information, without limitation.

S202, determining antenna weight vectors according to channel state information of a plurality of communication base stations based on a beam forming algorithm.

Among them, the beamforming algorithm refers to a technique of enhancing or suppressing a signal in a specific direction by adjusting the phase and amplitude of an antenna array. Channel State Information (CSI) is data describing characteristics of a wireless channel, including attenuation, phase shift, and the like. The antenna weight vector is a complex vector for controlling the excitation of each element of the antenna array, and determines the direction and shape of the beam.

The communication system performs this step after establishing the multi-base station connection to optimize spatial signal transmission. Specifically, the communication system first collects channel state information from a plurality of connected base stations, which may include channel matrices, signal-to-noise ratios, and the like. 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 antenna weight vectors that contain amplitude and phase information for each antenna element for subsequent signal transmission and reception.

In some embodiments, CSI-based beamforming may be implemented in a variety of ways, optionally, the system may dynamically adjust antenna weights according to real-time CSI using an adaptive beamforming algorithm, then employ multi-user MIMO techniques to simultaneously form independent beams for multiple base stations, and finally apply interference alignment techniques to minimize mutual interference between base stations. Optionally, the system can utilize a deep learning method to train a neural network model to predict the optimal antenna weight, then use distributed cooperative beam forming to coordinate a plurality of devices to form a large-scale virtual antenna array, and finally use a cognitive radio technology to intelligently select the optimal frequency band and power for beam forming.

S203, taking the multilink connection table and the antenna weight vector as transmission reference data, and performing data transmission.

Wherein, the transmission reference data refers to configuration information for guiding a data transmission process.

The communication system performs this step after completing the multilink connection and beamforming configuration, starting the actual data transmission process. Specifically, the communication system firstly selects an optimal transmission path according to the multi-link connection table, and can adopt load balancing or intelligent routing strategies. The system then distributes the data to be transmitted over the selected plurality of links according to a predetermined policy. For each link, the system configures an antenna array using a corresponding antenna weight vector to form a directional beam. The system then encodes and modulates the data, and different coding schemes and modulation levels may be employed depending on the characteristics of the different links. Finally, the system transmits signals through the configured antenna array to realize multilink parallel data transmission.

In some embodiments, the data transmission based on the multi-link and the beam forming can be achieved in various ways, wherein the system can adjust the data distribution proportion of each link according to the real-time network condition by using a dynamic link aggregation technology, then select the optimal coding and modulation scheme for each link by adopting an Adaptive Coding Modulation (ACM) technology, and finally transmit data to a receiving end simultaneously by using a plurality of base stations by adopting a coordinated multi-point transmission (CoMP) technology. Optionally, the system can utilize network coding technology to improve reliability and efficiency of multi-link transmission, then pre-process signals at the transmitting end to counteract known channel interference by using pre-coding technology, and finally apply full duplex communication technology to transmit and receive data simultaneously at the same time and frequency band. It will be appreciated that the multilink and beamforming based data transmission may also be implemented in other ways, such as dynamically optimizing transmission strategies using artificial intelligence techniques, without limitation.

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

Referring to step S101, the communication system determines raw X-ray image data and electromagnetic characteristic data.

S205, determining a subcarrier allocation scheme and a transmitting frequency according to the electromagnetic characteristic data, and setting an OFDM modulator with the subcarrier allocation scheme to set a radio frequency unit with the transmitting frequency.

Referring to step S102, the communication system sets an OFDM modulator with a subcarrier allocation scheme to set a radio frequency unit with a transmission frequency.

S206, detecting the signal intensity in real time and combining a preset image quality threshold value to adjust the data compression ratio and the error correction level of the original X-ray image data.

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

S207, compressing and encoding the original X-ray image data based on the data compression ratio and the error correction level to obtain encoded data.

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

S208, the coded data is sent to a receiving end through an OFDM modulator and a radio frequency unit.

Referring to step S105, the communication system transmits the encoded data to the receiving end.

In some embodiments, the communication system may generate a chaotic sequence as an encryption key based on a network security level set by a user, perform lightweight encryption processing on encoded data based on the chaotic sequence to obtain encrypted data, calculate an integrity check value of the encrypted data, and append the integrity check value to the encrypted data.

Where the network security level refers to a user-defined level of data protection intensity. A chaotic sequence is a seemingly random but virtually deterministic array with good unpredictability. The encryption key is a password used to encrypt and decrypt data. Lightweight encryption refers to an encryption algorithm with lower computational complexity. The encrypted data is data after being subjected to encryption processing. The integrity check value is a digital digest used to verify whether the data has been tampered with.

The system performs this process in preparation for sending 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 encryption. The system then uses a predetermined chaotic system (e.g., logistic map or Lorenz system) to generate a chaotic sequence, the length of which and the initial conditions are related to the security level. This chaotic sequence is used as an encryption key. Next, the system selects a suitable lightweight encryption algorithm (e.g., AES-128 or ChaCha 20) and encrypts the encoded data using the chaotic sequence as a key. After encryption, the system calculates a hash value (e.g., SHA-256) of the encrypted data, which is used as an 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.

In some embodiments, secure encryption of data may be achieved in a variety of ways, optionally, the system may use Quantum Key Distribution (QKD) techniques to generate true random encryption keys, then employ homomorphic encryption algorithms to allow data processing in the encrypted state, and finally use blockchain techniques to record the encryption operation log, ensuring auditability of the encryption process. Optionally, the system can utilize a Hardware Security Module (HSM) to generate and store the encryption key to improve the security of key management, then use a post quantum cryptography algorithm to resist potential quantum computing attacks, and finally implement multiple encryption strategies to adopt different encryption schemes for different types of data. It will be appreciated that secure encryption of data may also be implemented in other ways, such as using attribute-based encryption (ABE) techniques, to enable finer granularity of access control, without limitation.

In some embodiments, the communication system encodes the lost data packet into a redundant packet when detecting that the data packet is lost in network transmission, determines a retransmission time window according to the packet loss rate and the network delay, and retransmits the redundant packet in the retransmission time window.

The packet loss refers to a phenomenon that a data packet is lost in a network transmission process. The redundant packet is a data packet additionally generated in order to recover lost data. The packet loss rate is the proportion of the lost data packets to the total transmitted packet number. Network delay refers to the time required for data to be transmitted to be received. The retransmission time window is a period of time in which data retransmission is allowed.

The system performs the process when detecting packet loss during the data transmission process, so as to ensure complete transmission of the data. Specifically, the system first detects the packet loss condition by monitoring the acknowledgement information or sequence number. If the packet loss is found, the system starts a packet loss recovery mechanism. The system encodes the lost data packets using a predetermined encoding algorithm (e.g., reed-Solomon code or Raptor code) to generate redundant packets. Meanwhile, the system analyzes the network condition and calculates the current packet loss rate and network delay. Based on the information, the system determines a proper retransmission time window by using an adaptive algorithm, so that not only can enough time be ensured for retransmission, but also the influence of overlong waiting on the transmission efficiency can be avoided. And in the determined time window, the system inserts the redundant packet into the normal data stream for transmission until acknowledgement information is received or the time window is over.

In some embodiments, the packet loss processing and retransmission can be realized in various ways, wherein the system can use a network coding technology to create a redundant relation between data packets to improve the recovery efficiency, then adopts a machine learning algorithm to predict the network condition, dynamically adjusts the retransmission strategy, and finally implements priority queue management to ensure the priority retransmission of important data packets. Optionally, the system can utilize a multipath transmission protocol (such as MPTCP) to transmit data through multiple network paths simultaneously to reduce the packet loss effect, then use adaptive redundancy control to adjust the number of redundant packets according to the network condition, and finally realize a cooperative retransmission mechanism to assist data recovery by using the nearest device. It will be appreciated that the processing and retransmission of packet loss may be implemented in other manners, such as, but not limited to, using edge computing techniques to perform real-time packet loss detection and recovery at the network edge node.

It should be noted that, the network prediction model may be used to predict the network condition, and predict the network performance change in advance. The training phase of the network prediction model uses historical network data, including traffic patterns, user behaviors, environmental factors, and the like, and corresponding network performance indicators. The model training aims at minimizing the prediction error and improving the prediction accuracy of the network condition change. The model may adopt a structure such as a cyclic neural network (RNN) or a long and short time memory network (LSTM) and the like, and can capture the characteristics of time series data. In use, the model inputs network state data and environmental parameters, and outputs predictions of network performance, such as possible congestion levels, delay variations, etc., over a period of time in the future. These predictions are used to adjust the transmission strategy in advance to optimize the network resource allocation.

S209, receiving the transmission feedback data of the receiving end, and adjusting the transmission parameters according to the transmission feedback data.

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

S210, receiving an image resetting instruction returned by the receiving end, and sending a re-shooting instruction to the portable X-ray machine so that the portable X-ray machine enters a re-shooting standby state.

The image reset instruction refers to a control command sent by the receiving end and requesting to re-acquire the X-ray image. The re-shooting instruction refers to an instruction signal which is sent to the X-ray machine by the communication system and triggers re-acquisition. The re-shooting standby state is a working state in which the X-ray apparatus is ready for re-shooting.

Specifically, the communication system first receives an image reset instruction from the receiving end through a preset control channel, wherein the instruction comprises a duplicate shooting reason code and a priority identifier. And the system performs validity verification and priority judgment on the received instruction, and confirms a valid reset request. Then, the system converts the image resetting instruction into a format of a retake instruction which can be recognized by the X-ray machine according to a preset instruction mapping rule. The system then sends a re-shooting instruction to the portable X-ray machine via a secure device control channel. Finally, the system waits and confirms that the X-ray machine successfully enters a re-shooting standby state, and in this state, the X-ray machine resets relevant parameters and waits for a shooting instruction of the next step.

In some embodiments, the remote reset control flow can be realized in various modes, namely, the system can receive a reset instruction by using a real-time communication protocol based on WebSocket, so that the real-time property of instruction transmission is ensured, then a multi-level buffer mechanism is adopted to temporarily store the current equipment state and parameter configuration, so that the quick initialization is facilitated, finally, intelligent state monitoring is realized, and whether the equipment correctly enters a standby state or not is automatically detected. Optionally, the system can record all reset operations by using a blockchain technology to ensure operation traceability, then analyze the re-shooting reason by using an artificial intelligent algorithm, automatically generate an optimization suggestion, and finally display equipment state change in real time by using a digital twin technology. It will be appreciated that the remote reset control flow may also be implemented in other ways, such as using edge computing techniques for instruction preprocessing and status monitoring locally, without limitation.

S211, receiving a device control instruction returned by the receiving end, and transmitting the device control instruction to the portable X-ray machine so as to adjust shooting parameters of the portable X-ray machine and re-shoot.

The device control instruction refers to a command set for controlling and adjusting the working state of the X-ray machine. The shooting parameters include various settings affecting image acquisition such as X-ray dose, exposure time, focal length, etc. Re-imaging refers to re-performing the acquisition process of the X-ray image.

The communication system performs this step after the portable X-ray machine enters a re-shooting standby state to optimize the effect of re-shooting. Specifically, the communication system first establishes a dedicated control channel for receiving the device manipulation instruction from the receiving end. The system then waits for and receives these instructions, which include specific parameter adjustment suggestions, such as increasing or decreasing exposure time, adjusting X-ray intensity, etc. After receiving the instruction, the system analyzes and verifies the instruction to ensure the legitimacy and safety of the instruction. Then, the system recodes the verified control instruction and transmits the recoded control instruction to the portable X-ray machine through the safety 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 the re-shooting.

In some embodiments, the communication system also has a scene recognition mechanism. The communication system can automatically identify current shooting scene characteristics, including patient body type, examination location, environmental conditions and the like. The communication system adopts a computer vision technology to analyze scene information in real time and select the most suitable shooting strategy. Meanwhile, the communication system is based on dynamic exposure control, and exposure parameters can be adjusted in real time according to scene changes. The communication system also has a positioning function, and can automatically adjust the position and angle of equipment based on the image shot by the X-ray machine, thereby ensuring the optimal shooting effect.

In some embodiments, the remote control and re-shooting process may be implemented in a variety of ways, including, optionally, the system may use a real-time flow control protocol (RTCP) to receive and transmit device control instructions, ensuring real-time performance of the instructions, then applying an intelligent parameter optimization algorithm to automatically adjust suggested parameters according to previous shooting results, and finally providing an immersive control experience for a remote operator through a Virtual Reality (VR) technique. Optionally, the system can record all control instructions and parameter adjustment by adopting a blockchain technology to ensure the traceability of operation, then provide parameter adjustment advice for an operator by using an artificial intelligent auxiliary system, and finally visually display the potential influence of the parameter adjustment on the image quality by adopting an Augmented Reality (AR) technology. It will be appreciated that the remote manipulation and re-shooting process may also be implemented in other ways, such as using network slicing techniques of a 5G network, to provide independent high priority transmission channels for important control commands, without limitation.

In some embodiments, the communication system may acquire a work record including patient information, X-ray image data and a diagnosis record after receiving a cloud assistance instruction sent by the receiving end, encrypt the work record, upload the encrypted work record to the cloud collaboration platform, acquire diagnosis suggestions sent by a plurality of diagnosis terminals on the cloud collaboration platform, and send the diagnosis suggestions to the receiving end.

The cloud assistance instruction is a command for triggering 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. The X-ray image data is an indexed X-ray imaging result. Diagnostic records are interpretation and diagnostic conclusions of the doctor of the X-ray images. The diagnosis terminal refers to a medical device or system that participates in remote diagnosis. Diagnostic advice is expert opinion of a case.

The system performs this process upon receiving a request for consultation by multiple specialists to provide a more comprehensive diagnostic opinion. Specifically, the system first receives a cloud assistance instruction sent by the receiving end, and the cloud assistance instruction is automatically triggered by a local doctor or the system. The system then retrieves the relevant work records from the local database, including the patient's basic information, the current taken X-ray image, and the preliminary diagnostic record. Next, the system encrypts the entire job record using Advanced Encryption Standard (AES), ensuring patient privacy and data security. The system uploads the encrypted data packet to a preset cloud cooperation platform, wherein the platform is a special medical image sharing system. After the uploading is completed, the system notifies a plurality of diagnosis terminals on the platform, and the terminals are distributed in different hospitals or clinics. The system waits for and gathers diagnostic advice sent by these diagnostic terminals, which may include detailed image annotations, diagnostic reports, and the like. Finally, the system integrates and transmits all collected diagnostic advice back to the original receiving end for reference by the local doctor.

In some embodiments, cloud collaborative diagnosis can be achieved in various ways, wherein the system can record all diagnosis operations by using a blockchain technology, ensure transparency and traceability of a diagnosis process, train an AI auxiliary diagnosis model by using data of a plurality of hospitals on the premise of protecting privacy by using a federal learning technology, and finally implement a real-time collaborative tool to support instant communication and image labeling among experts. Optionally, the system can utilize a 5G network technology to realize transmission and real-time consultation of large-scale medical images, then use a natural language processing technology to automatically integrate and summarize diagnosis opinions of multiple experts, and finally realize Virtual Reality (VR) remote consultation to provide immersive collaborative diagnosis experience. It can be appreciated that cloud collaborative diagnosis may be implemented in other manners, such as locally preprocessing image data using an edge computing technique, so as to reduce the cloud transmission burden, which is not limited herein.

In the embodiment of the application, due to the adoption of the technologies of multi-base-station parallel connection, real-time parameter self-adaptive adjustment, intelligent anti-interference technology, lightweight encryption, cloud cooperation and the like, the communication system can realize stable, high-quality and safe X-ray image transmission in a complex network environment, effectively solves the problems of easy interference, unstable transmission, poor image quality, low data security and the like in the traditional method, further realizes the improvement of remote medical diagnosis efficiency and accuracy, and expands the application range of the portable X-ray machine.

The following describes a communication system in the embodiment of the present application from the perspective of hardware processing, please refer to fig. 3, which is a schematic diagram of a physical device structure of the communication system in the embodiment of the present application.

It should be noted that the configuration of the communication system shown in fig. 3 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present invention.

As shown in fig. 3, the communication system includes a central processing unit (Central Processing Unit, CPU) 301 that can perform various appropriate actions and processes, such as performing the methods described in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 302 or a program loaded from a storage section 308 into a random access Memory (Random Access Memory, RAM) 303. In the RAM 303, various programs and data required for the system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other through a bus 304. An Input/Output (I/O) interface 305 is also connected to bus 304.

Connected to the I/O interface 305 are an input section 306 including an audio input device, a push button switch, and the like, an output section 307 including a Liquid crystal display (Liquid CRYSTAL DISPLAY, LCD) and an audio output device, an indicator lamp, and the like, a storage section 308 including a hard disk, and the like, and a communication section 309 including a Network interface card such as a LAN (local Area Network) card, a modem, and the like. The communication section 309 performs communication processing via a network such as the internet. The 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, or the like is installed on the drive 310 as needed, so that a computer program read therefrom is installed into the storage section 308 as needed.

In particular, according to embodiments of the present invention, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 309, and/or installed from the removable medium 311. When the computer program is executed by a Central Processing Unit (CPU) 301, various functions defined in the present invention are performed.

Specific examples of a computer-readable storage medium include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-Only Memory (ROM), an erasable programmable read-Only Memory (Erasable Programmable Read Only Memory, EPROM), a flash Memory, an optical fiber, a portable compact disc read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. Where each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures.

Specifically, the communication system of the present embodiment includes a processor and a memory, where the memory stores a computer program, and when the computer program is executed by the processor, the portable X-ray machine communication method provided in the foregoing embodiment is implemented.

As another aspect, the present invention also provides a computer-readable storage medium that may be included in the communication system described in the above embodiment or may exist alone without being assembled into the communication system. The storage medium carries one or more computer programs which, when executed by a processor of the communication system, cause the communication system to implement the portable X-ray machine communication method provided in the above embodiments.

While the application has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that the foregoing embodiments may be modified or equivalents may be substituted for some of the features thereof, and that the modifications or substitutions do not depart from the spirit of the embodiments.

As used in the above embodiments, the term "when..is interpreted as meaning" if..or "after..or" in response to determining..or "in response to detecting..is" depending on the context. Similarly, the phrase "when determining..or" if (a stated condition or event) is detected "may be interpreted to mean" if determined.+ -. "or" in response to determining.+ -. "or" when (a stated condition or event) is detected "or" in response to (a stated condition or event) "depending on the context.

Those of ordinary skill in the art will appreciate that implementing all or part of the above-described method embodiments may be accomplished by a computer program to instruct related hardware, the program may be stored in a computer readable storage medium, and the program may include the above-described method embodiments when executed. The storage medium includes a ROM or a random access memory RAM, a magnetic disk or an optical disk, and other various media capable of storing program codes.

Claims (10)

1. A portable X-ray machine communication method, characterized in that it is applied to a communication system, and 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 comprises: 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 comprises: 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 comprises: 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 comprises: 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 of the receiving end and adjusting the transmission parameters according to the transmission feedback data, the method further comprises: 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 comprises: 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, wherein 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 run on a communication system, the communication system is caused to execute the method according to any one of claims 1 to 7.