Intelligent X-ray machine and wireless data transmission method thereof

Abstract

The application provides a smart X-ray machine and a wireless data transmission method thereof, and belongs to the technical field of medical image digital transmission and man-machine interaction. The method comprises the following steps: performing multi-modal signal separation processing on original DICOM image data and touch screen interaction instructions transmitted by a smart X-ray machine acquisition end, encoding and compressing the separated data, and standardizing the format of the data to generate multi-modal standardized data; performing business scene classification and priority marking processing on the multi-modal standardized data to generate a multi-dimensional business feature set; and through a Wi-Fi and Bluetooth double-channel collaborative transmission mechanism, large-volume DICOM image data is disassembled according to business priority and sent in parallel on different channels, which greatly reduces the congestion probability of a single channel in a parallel sending mode, and reduces the invalid time length of a doctor end waiting for image review.

Description

Technical Field

[0001] This invention proposes a smart X-ray machine and its wireless data transmission method, belonging to the field of medical image digital transmission and human-computer interaction technology. Background Technology

[0002] As a core device in clinical imaging diagnosis, the efficiency of digital X-ray imaging systems in transmitting image data to hospital information systems, image archiving, and communication systems directly impacts the operation of the diagnostic and treatment process. Currently, such systems generally use a single wired Ethernet connection or a conventional wireless LAN to transfer full DICOM image files during data transmission. Due to the large data volume of high-definition lossless DICOM image files, a single transmission channel is prone to network congestion when multiple devices upload concurrently, causing transmission delays. Doctors must wait a considerable amount of time to obtain images, resulting in unnecessary waiting. Simultaneously, the control logic of traditional X-ray machines is relatively rigid, lacking local data preprocessing capabilities. All computational tasks rely on backend servers, further burdening the transmission link. In terms of human-computer interaction, existing equipment mostly uses general touchscreen solutions, failing to adapt to the actual clinical environment where operators often wear gloves and their hands may be obstructed, leading to sluggish operation response and limiting diagnostic and treatment efficiency. The aforementioned problems indicate that a single transmission mode and low-intelligence terminals are no longer sufficient to meet the requirements of data transmission timeliness and interactive convenience in current smart healthcare scenarios. There is an urgent need for a wireless data transmission method that can dynamically schedule communication resources according to business scenarios and has local intelligent processing capabilities. Summary of the Invention

[0003] This invention provides a smart X-ray machine and its wireless data transmission method to solve the problems mentioned in the background section above: This invention proposes a wireless data transmission method for a smart X-ray machine, the method comprising: S1. Perform multimodal signal separation processing on the raw DICOM image data and touch screen interaction commands transmitted from the intelligent X-ray machine acquisition terminal, encode and compress the separated data and standardize the format to generate multimodal standardized data; perform business scenario classification and priority labeling processing on the multimodal standardized data to generate a multi-dimensional business feature set; S2. Perform transmission path adaptation analysis on the multi-dimensional service feature set, use the wireless communication protocol selection algorithm to match the optimal transmission channel, map the service features to the preset dual-channel transmission strategy system, and generate transmission strategy label data; perform link quality assessment to extend the transmission strategy label data to generate extended transmission strategy data. S3. Perform instruction structure transformation on the extended transmission strategy data, converting multimodal service requirements into transmission scheduling instructions that can be executed in parallel via Wi-Fi and Bluetooth dual channels, generating dual-channel scheduling instruction data; perform medical information system interface matching analysis on the dual-channel scheduling instruction data, generating system interface matching data; S4. Perform dynamic bandwidth allocation processing on the system interface matching data, and differentiate the bandwidth ratio of Wi-Fi channel and Bluetooth channel based on image transmission category, timeliness level identifier and interactive operation type to generate dual-channel bandwidth allocation data; perform transmission timing priority arrangement on the dual-channel bandwidth allocation data to generate dual-channel parallel transmission sequence data; S5. Perform local intelligent task distribution processing on the dual-channel parallel transmission sequence data to generate local task distribution data; start the dual-channel collaborative transmission and local processing of the local task distribution data in parallel to generate the smart X-ray machine wireless data transmission ready signal.

[0004] This invention proposes a smart X-ray machine, which includes: One or more processors; Memory, used to store one or more programs; Wherein, when the one or more programs are executed by the one or more processors, the one or more processors are made to implement the method described in any one of the above.

[0005] The beneficial effects of this invention are as follows: By employing a dual-channel collaborative transmission mechanism using Wi-Fi and Bluetooth, large-volume DICOM image data is broken down into different channels and transmitted in parallel according to service priority. This significantly reduces the congestion probability of a single channel in concurrent scenarios and reduces the invalid time spent by doctors waiting for image retrieval. Simultaneously, the dynamic allocation of bandwidth across the dual channels ensures that time-sensitive images and routine interactive commands do not interfere with each other. This guarantees both rapid uploading of images after emergency imaging and smooth response of touchscreen operation, significantly improving the overall operational efficiency of the diagnostic and treatment process. The introduction of the Android operating system enables the device to have local image preprocessing capabilities. A large amount of compression and format conversion is completed on the terminal side, avoiding the excessive link load caused by transmitting all data back to the backend server, and also reducing the occupation of the hospital's intranet bandwidth. The touchscreen interaction has been adapted for actual clinical operation scenarios such as wearing gloves and hand occlusion, enhancing operational fault tolerance and reducing repeated executions due to accidental touches or invalid operations, thus preventing the slowdown of the diagnostic and treatment process due to poor interactive experience. The seamless integration of HIS and PACS systems makes the flow of diagnostic information between multiple departments smoother, reduces manual transfer steps, and improves the timeliness and completeness of image data sharing. Attached Figure Description

[0006] Figure 1 This is a diagram illustrating the steps of the method described in this invention; Figure 2 This is a detailed flowchart of step S1 in this invention. Detailed Implementation

[0007] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0008] One embodiment of the present invention, such as Figure 1 As shown, a wireless data transmission method for a smart X-ray machine includes: S1. Perform multimodal signal separation processing on the raw DICOM image data and touch screen interaction commands transmitted from the acquisition end of the smart X-ray machine. Encode, compress, and standardize the format of the separated data to generate multimodal standardized data. Perform business scenario classification and priority labeling processing on the multimodal standardized data. Extract the image transmission category, timeliness level identifier, and interaction operation type from the data to generate a multi-dimensional business feature set. S2. Perform transmission path adaptation analysis on the multi-dimensional service feature set, use the wireless communication protocol selection algorithm to match the optimal transmission channel, map the service features to the preset dual-channel transmission strategy system, and generate transmission strategy label data; perform link quality assessment to expand the transmission strategy label data, supplement the alternative transmission path information in the current network environment, and generate extended transmission strategy data. S3. Perform instruction structure transformation on the extended transmission strategy data, converting multimodal service requirements into transmission scheduling instructions that can be executed in parallel via Wi-Fi and Bluetooth dual channels, generating dual-channel scheduling instruction data; perform medical information system interface matching analysis on the dual-channel scheduling instruction data, selecting interface model types compatible with the scheduling instructions from the preset HIS and PACS system interface library, generating system interface matching data. S4. Perform dynamic bandwidth allocation processing on the system interface matching data, and differentiate the bandwidth ratio of Wi-Fi channel and Bluetooth channel based on image transmission category, timeliness level identifier and interactive operation type to generate dual-channel bandwidth allocation data; perform transmission timing priority arrangement on the dual-channel bandwidth allocation data to generate dual-channel parallel transmission sequence data; S5. Perform local intelligent task distribution processing on the dual-channel parallel transmission sequence data, allocate independent computing tasks and execution threads to the image preprocessing module and touch screen interaction response module built into the Android operating system, and generate local task distribution data; start the dual-channel collaborative transmission and local processing of the local task distribution data in parallel, and generate the wireless data transmission ready signal of the smart X-ray machine.

[0009] The working principle and effects of the above technical solution are as follows: This solution, through dual-channel collaborative transmission and dynamic bandwidth allocation, can adapt to the data transmission needs of different types and time-sensitive business data of smart X-ray machines, effectively improving the transmission adaptability and overall transmission efficiency of medical image data and touch screen interaction commands. By standardizing and prioritizing data processing, the orderly transmission of different business data is improved, reducing timing chaos problems that occur during multimodal data synchronous transmission. Through intelligent task distribution and thread independent scheduling mode, the collaborative capability of local data processing and wireless transmission of the device is enhanced, reducing the probability of system lag and task conflicts. At the same time, the dual-channel transmission architecture can ensure the complete and stable transmission of high-definition DICOM image data, while also meeting the rapid response requirements of touch screen interaction commands, avoiding high load congestion and delayed emergency command response in single-channel transmission mode, thus improving the overall stability and reliability of wireless data transmission in smart X-ray machines.

[0010] One embodiment of the present invention, such as Figure 2 As shown, S1 includes: S11. A signal layered sampling unit is deployed to synchronously capture the raw DICOM image stream output by the X-ray machine detector and the level interaction commands reported in real time by the machine's touch chip. A time-domain filtering algorithm is used to remove power supply noise interference signals and spatial radio frequency spurious signals mixed in the data stream, and the image data stream and the interaction command data stream are split to generate two independent types of raw data. A hierarchical compression coding algorithm is used to perform layered compression operations on the two types of raw data after splitting, and the byte arrangement rules and data encapsulation format are unified in accordance with the general storage specifications for medical images to generate multimodal standardized data. S12. Retrieve the pre-stored radiology diagnosis and treatment business hierarchical ledger data, traverse the equipment acquisition parameter fields attached to the multimodal standardized data, match the corresponding radiography examination items and clinical application scenarios based on the field content, complete the scenario classification of the full data, synchronously read the equipment trigger mark fields embedded in the data, complete the transmission priority assignment and labeling of different data units based on the mark fields, and generate a classification dataset with hierarchical labels. S13. Analyze the data segments within the classification dataset with hierarchical labels frame by frame. Extract three types of parameters from the image data segment: pixel depth, number of image slices, and scanned part attributes to summarize the image transmission category. Extract the command trigger time and command response time limit parameters from the interactive command data segment to generate a timeliness level identifier. Decompose the button trigger source and operation execution target parameters from the command code field to classify the interactive operation type and generate basic feature materials bound to the three parameters. S14. Perform redundant field removal operation on the basic feature material, remove redundant fields such as device body number and acquisition clock that are repeatedly stored in the material, retain the effective feature parameters that can distinguish business attributes, and collect the selected and retained effective feature parameters in a structured manner according to the fixed field arrangement rules to generate temporary collected feature data. S15. Perform feature discretization conversion on the temporary aggregated feature data, convert the text-formatted business description parameters into numerical feature codes that the system can recognize, and summarize and integrate them after all encoding is completed to generate a multi-dimensional business feature set.

[0011] The working principle and effects of the above technical solution are as follows: By performing interference stripping and classification processing on the mixed data streams collected by the device, image data and touch command data can be processed separately, eliminating signal anomalies caused by the mixed transmission of the two types of data. Layered compression and format unification improve the regularity of multimodal medical data, allowing various data types to adapt to subsequent transmission and parsing operations. By classifying and prioritizing data according to scenarios, the transmission needs of different diagnosis and treatment scenarios can be met, improving the adaptability of data scheduling. The overall removal of redundant fields reduces the consumption of system resources by invalid data, lowering the operational load of data transmission and processing. The feature discretization and conversion processing method not only completes the standardized transformation of various business features to adapt to the system's automated recognition calculations, but also ensures the comprehensiveness of business feature extraction, reducing the omission of key business features or recognition errors, effectively improving the overall quality and operational efficiency of front-end data preprocessing.

[0012] In one embodiment of the present invention, S11 includes: S111. Continuously monitor the stability and integrity of the bitstream of the two types of independent raw data, check for broken frames, out-of-order data and invalid padding data in the datastream, correct and complete abnormal data segments, and generate corrected raw data in good condition. S112. For raw image data and raw interactive command data, match the corresponding compression strategies respectively, use hierarchical compression coding algorithm to perform multi-level compression operations in sequence, control the loss of image information and the distortion of command data during the compression process, and generate preliminary compressed data. S113. Read the format constraints in the general storage specification for medical images, adjust the byte order, data segmentation method and header encapsulation structure of the preliminary compressed data according to the constraints, complete the unified processing of single-type data format, and generate format-normalized data. S114. Integrate the formatted image data and interactive command data into the same architecture, unify the encapsulation protocol and field definition of the two types of data, eliminate the format differences between heterogeneous data, and generate an integrated intermediate dataset. S115. Perform overall compliance verification on the integrated intermediate dataset, verify the integrity of data encapsulation and the degree of format matching, complete the final normalization process, and generate multimodal standardized data.

[0013] The working principle and effects of the above technical solution are as follows: By performing stability monitoring and anomaly correction on the raw data, it can maximally repair frame breaks and out-of-order issues in the data stream, reducing the negative impact of raw data defects on subsequent transmission operations. The differentiated hierarchical compression processing method can precisely control the compression accuracy of different types of data, preserving the complete details of medical images while reducing the data volume of interactive commands, avoiding image distortion or inefficient compression caused by a uniform compression mode. Gradually unifying the data format and encapsulation structure at each level can eliminate format differences in heterogeneous data, improving the overall compatibility of multimodal data. Finally, the overall compliance verification process can identify data encapsulation defects and format deviations, reducing the probability of errors in subsequent data transmission and parsing, effectively improving the accuracy and overall regularity of multimodal data preprocessing for smart X-ray machines.

[0014] In one embodiment of the present invention, S112 includes: Collect and correct the bitrate parameters, data volume parameters, and data content attribute parameters of the original data, distinguish the transmission characteristics differences between image data and interactive command data, and generate attribute judgment data for the two types of data. Based on the attributes of the two types of data, the data matching differential compression adaptation rules are determined, a high-fidelity layered compression mechanism is assigned to the image data, and a high-speed lightweight compression mechanism is assigned to the interactive command data to generate classified compression adaptation data. By relying on a hierarchical compression coding algorithm, compression operations are performed on the two types of data layer by layer, reducing redundant bits of data level by level, simultaneously controlling the retention of image pixel information and the integrity of command data characters, suppressing data distortion problems during the compression process, and generating hierarchical compression intermediate data; Perform compression accuracy verification calculations on intermediate data of hierarchical compression, screen for local data loss and bit loss problems generated during hierarchical compression, correct and repair missing data segments, and generate compression verification completed data; Summarize all the verified compressed data, organize the compression hierarchy and data output format of the two types of data, complete the overall compression and integration process, and generate preliminary compressed data with uniform specifications. The working principle and effects of the above technical solution are as follows: This method identifies the differences in transmission attributes of different data to achieve targeted matching of compression methods, overcoming the drawbacks of the traditional uniform compression mode and adapting to the different processing needs of image data and interactive command data. The layered and progressive compression processing mode can gradually clean up redundant bits in the data, effectively reducing the overall data volume and improving the overall data transmission rate of subsequent data. Throughout the process, the integrity of image pixel information and command characters is controlled, which can significantly reduce data distortion caused by compression operations and ensure the accuracy of medical imaging and the effectiveness of interactive commands. The accuracy verification and defect repair operations after compression can promptly compensate for bit loss and local data defects that occur during layered compression, reducing the retention of compression defects. The final data regularization and integration processing can ensure the high-fidelity effect of image data and achieve efficient compression of interactive data, effectively improving the quality and stability of the entire machine's multimodal data compression processing.

[0015] In one embodiment of the present invention, S2 includes: S21. Retrieve the channel signal-to-noise ratio data and signal attenuation rate data collected in real time by the wireless gateway in the local area network. Perform cross-comparison calculations between each parameter of the multi-dimensional service feature set and the channel measured data. Relying on the protocol adaptation algorithm, traverse the threshold values ​​of various parameters of the Wi-Fi protocol cluster and Bluetooth protocol cluster, match the main transmission channel that matches each feature parameter, and generate the initial channel selection matching data. S22. Extract the channel attribution parameters and service characteristic parameters contained in the initial channel selection matching data, substitute them one by one into the parameter mapping calculation formula built into the dual-channel transmission strategy system, bind different types of images and interactive service parameters to the corresponding channel scheduling rules entries in the system, complete the full parameter mapping operation, and generate transmission strategy label data. S23. Start the link detection subroutine to continuously collect the instantaneous packet loss rate, channel occupancy rate and round-trip delay of all available wireless channels around the device. Calculate the real-time comprehensive link score of each idle channel based on the link loss calculation model and generate detailed measured data of the entire channel quality. S24. Traverse the main transmission channel number corresponding to each tag in the transmission strategy tag data, select idle channels with scores in the preset qualified threshold range from the full channel quality measured detailed data as backup links, and append the channel number and link score parameters of the backup links to the corresponding tag auxiliary fields one by one to generate tag supplementary intermediate data. S25. Verify the data integrity of the newly added link fields in the intermediate data of the tag supplementation, complete the tag entries with missing channel parameters, remove invalid alternative link records with link scores lower than the security threshold, and generate extended transmission strategy data after completing the field regularization.

[0016] The working principle and effects of the above technical solution are as follows: By comparing the operating parameters of the wireless channel with the service characteristic parameters in real time, the main channel suitable for the current transmission requirements can be selected, improving the matching degree between the wireless channel and medical data services and reducing transmission stuttering problems caused by improper channel adaptation. By incorporating various service parameters into the dual-channel transmission strategy system to complete parameter mapping and binding, different data services have their own exclusive scheduling rules, improving the standardization of overall transmission scheduling. By detecting the status of surrounding wireless channels across the entire domain and completing link quality scoring, the network environment can be fully understood, avoiding the use of idle or inferior channels. By matching compliant backup links to the main transmission channel and completing data regularization and verification, the stable transmission of regular image data and interactive commands can be guaranteed, and the backup link can be quickly switched when the main link status fluctuates, reducing the risk of transmission interruption caused by network fluctuations and effectively enhancing the environmental adaptability and operational stability of wireless data transmission in smart X-ray machines.

[0017] In one embodiment of the present invention, step S21 includes: The system continuously collects real-time operating parameters of the wireless network environment in which the device is located, and collects the signal-to-noise ratio, signal attenuation rate, and channel throughput rate of each wireless channel, and summarizes them to form a real-time channel environment measured dataset. Deconstruct various business parameters within the multi-dimensional business feature set, extract image transmission volume parameters, business timeliness parameters, and interactive operation transmission requirement parameters, and organize them into a dataset of business parameters to be matched; The dataset of service parameters to be matched is cross-compared item by item with the real-time channel environment measured dataset to screen the compatibility between different channel parameters and service transmission requirements, and generate channel service compatibility comparison data. The protocol adaptation algorithm is invoked to traverse the threshold values ​​of various operating parameters of the Wi-Fi protocol cluster and the Bluetooth protocol cluster. Combined with the channel service adaptation ratio data, the transmission protocol and channel carrier that are adapted to the current service characteristics are selected, and channel adaptation candidate data are generated. The candidate channel adaptation data is screened for compliance and integrated and classified. Channel options that do not meet the adaptation standards are eliminated, and the optimal combination of main transmission channels is retained to generate the initial channel selection matching data.

[0018] The working principle and effects of the above technical solution are as follows: By comprehensively collecting real-time operating parameters of the wireless network, the actual operating status of the current channel can be fully grasped, avoiding channel selection deviations caused by incomplete network information. Breaking down and organizing various service transmission parameters allows for precise differentiation of transmission differences between image data and interactive command data, ensuring channel matching aligns with real business needs. Item-by-item comparison of the two datasets improves the fit between the channel and business requirements, reducing low transmission rates caused by channel resource mismatch. By iterating through protocol parameter thresholds to select suitable transmission carriers, the operating characteristics of dual-channel transmission are aligned, improving the accuracy of channel selection. Compliance screening and integration screening of candidate channels eliminates poorly matched channel resources while retaining the optimal primary transmission channel, effectively improving the device's wireless data transmission adaptability, reducing data transmission latency caused by network adaptation anomalies, and ensuring stable transmission of various medical business data.

[0019] In one embodiment of the present invention, S3 includes: S31. Decompose the primary and backup channel parameters, service priority parameters and transmission timeliness parameters stored in the extended transmission strategy data, and separate them into two independent groups of demand parameters: image transmission demand parameters and touch screen interaction command transmission demand parameters, and generate the split sub-demand dataset. S32. Call the dual-channel instruction compilation unit, substitute each item of the requirement dataset into the instruction compilation rules, compile the corresponding transmission instruction code according to the Wi-Fi channel instruction format specification and the Bluetooth channel instruction format specification, bind the compiled single instruction code to its channel identifier, and summarize all compilation products to generate dual-channel scheduling instruction raw data. S33. Traverse the raw data of dual-channel scheduling instructions. The data interaction format code embedded in each instruction is used to retrieve the interface message encapsulation rules and data interaction protocol parameters of each type of interface built into the interface library of HIS system and PACS system. The matching and comparison calculation between instruction code and interface parameter is completed one by one to generate an initial screening and matching list of interfaces. S34. Remove interface entries from the initial screening and matching list that cannot be adapted to the single packet transmission volume of the instruction or whose handshake timing cannot be aligned with the instruction execution rhythm. Retain the docking model number corresponding to the interface that meets the parameter adaptation standard and generate intermediate interface screening data. S35. Group and classify the intermediate data of the interface screening according to the business type of the scheduling instruction. Sort and organize the multiple compliant docking models corresponding to the same business by serial number. After all grouping operations are completed, generate system interface matching data.

[0020] The working principle and effects of the above technical solution are as follows: By splitting various transmission parameters into independent business requirement data groups, the transmission standards of different data can be distinguished, avoiding the problem of instruction compilation chaos caused by the mixing of various transmission requirements. Dedicated scheduling instructions are generated through standardized channel instruction compilation methods, which can adapt to dual-channel parallel operation mechanisms and improve the device's scheduling and execution capabilities for multimodal data. By comparing the operation parameters of scheduling instructions with those of system interfaces line by line, the compatible system docking models can be accurately screened, reducing data transmission failures caused by interface incompatibility. By eliminating interface resources that do not meet compatibility standards, data anomalies caused by excessive message transmission and misaligned interaction timing can be avoided, improving the stability of system docking. Compliant interfaces are organized and sorted according to business type, which can not only adapt to the differentiated docking requirements of image data and interaction instructions, but also standardize the system docking process, effectively improving the success rate of cross-system transmission of medical data and the overall docking efficiency.

[0021] In one embodiment of the present invention, step S4 includes: S41. Extract the associated business parameters attached to the system interface matching data, and separate them into three categories of control parameters: image transmission category parameters, timeliness level identifier parameters, and interactive operation type parameters. Calculate the bandwidth weight coefficients corresponding to the three categories of parameters based on the bandwidth weight conversion formula, and generate a bandwidth weight coefficient dataset. S42. Using the coefficients of each item in the bandwidth weight coefficient dataset as the allocation benchmark, the total available wireless bandwidth resources of the whole machine are split, and the bandwidth quota for Wi-Fi channel to carry image services and Bluetooth channel to carry interactive command services are allocated according to the coefficient ratio. Additional floating bandwidth resources are reserved for urgent and time-sensitive services, and preliminary bandwidth allocation data is generated. S43. Verify the bandwidth values ​​of each channel in the initial bandwidth division data, correct abnormal allocation values ​​where the single-channel bandwidth exceeds the upper limit of the hardware transceiver module, and synchronously adjust the floating bandwidth occupancy trigger condition parameters. After correction, generate dual-channel bandwidth allocation data. S44. Extract the service timeliness parameters and priority labeling parameters bound to the dual-channel bandwidth allocation data, and use the time sequence sorting algorithm to sort all the service units to be transmitted in order of priority. High timeliness level services are arranged at the front of the time sequence, and regular services are arranged in segments according to service type to generate time sequence layout draft data. S45. Assign the service units with different order in the time sequence layout draft data to the corresponding transmission channels with allocated bandwidth, mark the channel occupancy start node and release node of each service unit, organize the full time sequence layout information, and generate dual-channel parallel transmission sequence data.

[0022] The working principle and effects of the above technical solution are as follows: By decomposing various service parameters and calculating corresponding weight coefficients, bandwidth allocation can be tailored to the operational needs of different services, reducing transmission bottlenecks caused by uneven resource allocation. Dividing channel bandwidth according to weights and setting floating resources can balance the transmission demands of both routine and urgent services, enhancing the flexibility of bandwidth scheduling. Performing numerical verification and parameter correction on the allocation results can avoid situations where bandwidth values ​​exceed hardware capacity, reducing the probability of failures caused by hardware module overload. Combining timeliness and priority in service timing arrangement allows urgent content to be transmitted first, improving overall service response speed. Matching the sorted service units to corresponding channels and recording resource usage periods ensures that both channels operate in an orderly parallel manner, preventing conflicts caused by multiple services simultaneously vying for resources, and continuously guaranteeing the smooth flow of image data and interactive commands in complex transmission scenarios.

[0023] In one embodiment of the present invention, S45 includes: Decompose all business unit information in the time sequence layout draft data, distinguish between image transmission units and interactive command transmission units, calculate the data volume and duration of each unit, and generate detailed business unit data. Retrieve the available bandwidth range of each channel from the dual-channel bandwidth allocation data, and attach the corresponding units to the corresponding transmission channels in order of service unit priority to generate intermediate channel attachment data. Combine the transmission duration of the business unit to calculate the resource occupation period, mark the start node of occupation and the resource release node of each business unit in the corresponding channel, and generate node marking data; Integrate channel mounting information and node marking information, unify the time-series recording format of the entire data, remove overlapping and disordered node content in the time-series arrangement, and generate time-series regularized data. All completed and standardized timing and channel association information is collected, integrated to form a complete transmission scheduling record, and generated dual-channel parallel transmission sequence data.

[0024] The working principle and effects of the above technical solution are as follows: By analyzing the specific data volume and runtime of various transmission service units, the transmission resource consumption of different services can be accurately grasped, providing a reliable basis for channel matching. Connecting service units to suitable transmission channels according to a predetermined order ensures the rational use of bandwidth resources, reducing resource idleness or over-occupancy. Accurately marking the resource occupation and release nodes of each service standardizes the resource scheduling rhythm of the wireless channel, avoiding resource waste caused by long-term channel occupation. Unifying the timing record format and cleaning up overlapping and disordered nodes can prevent timing disorder problems during parallel transmission of multiple services, improving the orderliness of transmission scheduling. Completely integrating various scheduling information to form a standardized transmission sequence ensures both stable transmission of image data and rapid response to interactive commands, effectively improving the overall smoothness and scheduling accuracy of dual-channel parallel transmission.

[0025] In one embodiment of the present invention, step S5 includes: S51. Decompose the dual-channel parallel transmission sequence data, separate the image preprocessing auxiliary task carried by the Wi-Fi link and the interaction instruction local parsing task carried by the Bluetooth link, and generate two types of local task details data to be implemented. S52. Read the real-time idle thread resource statistics of the Android system kernel, allocate a dedicated computing thread for the image preprocessing module from the idle thread pool according to the task computing resource consumption, and simultaneously allocate an independent scheduling thread for the touch screen interaction response module, and generate thread resource allocation details. S53. Bind the detailed data of the two types of local tasks to be landed to the thread number corresponding to the thread resource allocation details, mark the thread occupation period and resource release triggering conditions of a single task, complete the binding and collection of tasks and threads, and generate the original data of task binding. S54. Remove abnormal binding records in the original task binding data that show overlapping thread resource usage, reallocate idle threads to complete the secondary binding of abnormal tasks, organize all compliant binding entries in the group, and generate local task distribution data. S55 reads the thread-enabled node and dual-channel data transmission start node of the local task distribution data record, synchronously triggers the start command to drive the two modules to carry out local computing operations based on the allocated thread, and the dual channels synchronously start wireless data transmission and reception actions according to the predetermined sequence. After the entire link resources enter the standby switching state, the output level indicator is generated to generate the wireless data transmission ready signal of the smart X-ray machine.

[0026] The working principle and effects of the above technical solution are as follows: By splitting the transmission sequence to divide different types of local tasks, the module's work content can be clearly distinguished, preventing different computational tasks from running in a mixed manner. Allocating independent threads based on task consumption allows system resources to be allocated on demand, reducing runtime lag caused by mixed thread usage. Labeling tasks with their execution cycles and resource release rules standardizes thread usage rhythm and reduces unnecessary resource occupation. Cleaning up abnormal records of overlapping thread usage and reallocating resources avoids mutual interference between tasks, improving the stability of local computation. Synchronizing the start of local computation and wireless transmission makes the entire process more seamless, ensuring the steady progress of image preprocessing while guaranteeing timely completion of interactive command parsing and data transmission and reception, reducing end-to-end startup latency, allowing the device to quickly enter normal working state, and improving overall operating efficiency.

[0027] In one embodiment of the present invention, S53 includes: Read the computational load, execution time, and resource consumption parameters of each task in the detailed data of two types of local tasks to be deployed, distinguish the resource consumption characteristics of image preprocessing tasks and interactive parsing tasks, and generate task resource attribute data. Retrieve the hardware scheduling permissions, operating frequency bands, and resource quota information of each independent thread from the thread resource allocation details, match different task resource attributes and thread carrying capacity, and generate corresponding task thread adaptation data; According to the one-to-one matching rule, all local tasks are attached and bound to the independent threads with corresponding numbers one by one, completing the association and pairing of all task units and thread resources, and generating the initial binding dataset; Based on the overall execution flow of each task, the start time and continuous running period of a single thread are determined, the triggering conditions for automatically releasing thread resources after the task finishes running are set, and thread management parameter data is generated. All binding entries and control parameters are uniformly collected and arranged, integrating task association information, thread number information and resource scheduling parameters to complete the full data consolidation and generate original task binding data.

[0028] The working principle and effects of the above technical solution are as follows: By reading and analyzing the computational load and resource consumption of each task, the operational requirements of different tasks can be accurately grasped, providing a precise basis for thread matching. Adapting thread hardware permissions, operating frequency bands, and other information allows for a high degree of compatibility between tasks and threads, reducing resource waste or insufficient capacity. Binding and pairing tasks and threads according to a one-to-one correspondence rule standardizes the task allocation process and avoids operational conflicts caused by chaotic task mounting. Marking the thread's occupation time and period and setting release conditions allows for the orderly flow of thread resources, reducing resource stress caused by long-term occupation. Integrating all binding information and control parameters ensures the stable operation of image preprocessing and interactive parsing tasks, improves the utilization efficiency of thread resources, avoids ineffective scheduling, and makes local task execution more organized and reliable.

[0029] According to one embodiment of the present invention, a smart X-ray machine includes: One or more processors; Memory, used to store one or more programs; Wherein, when the one or more programs are executed by the one or more processors, the one or more processors are made to implement the method described in any one of the above.

[0030] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A wireless data transmission method for a smart X-ray machine, characterized in that, The method includes: S1. Perform multimodal signal separation processing on the raw DICOM image data and touch screen interaction commands transmitted from the intelligent X-ray machine acquisition terminal, encode and compress the separated data and standardize the format to generate multimodal standardized data; perform business scenario classification and priority labeling processing on the multimodal standardized data to generate a multi-dimensional business feature set; S2. Perform transmission path adaptation analysis on the multi-dimensional service feature set, use the wireless communication protocol selection algorithm to match the optimal transmission channel, map the service features to the preset dual-channel transmission strategy system, and generate transmission strategy label data; perform link quality assessment to extend the transmission strategy label data to generate extended transmission strategy data. S3. Perform instruction structure transformation on the extended transmission strategy data, converting multimodal service requirements into transmission scheduling instructions that can be executed in parallel via Wi-Fi and Bluetooth dual channels, generating dual-channel scheduling instruction data; perform medical information system interface matching analysis on the dual-channel scheduling instruction data, generating system interface matching data; S4. Perform dynamic bandwidth allocation processing on the system interface matching data, and differentiate the bandwidth ratio of Wi-Fi channel and Bluetooth channel based on image transmission category, timeliness level identifier and interactive operation type to generate dual-channel bandwidth allocation data; perform transmission timing priority arrangement on the dual-channel bandwidth allocation data to generate dual-channel parallel transmission sequence data; S5. Perform local intelligent task distribution processing on the dual-channel parallel transmission sequence data to generate local task distribution data; start the dual-channel collaborative transmission and local processing of the local task distribution data in parallel to generate the smart X-ray machine wireless data transmission ready signal.

2. The wireless data transmission method for a smart X-ray machine according to claim 1, characterized in that, S1 includes: S11. Synchronously capture the raw DICOM image stream output by the X-ray machine detector and the level interaction command reported in real time by the machine body touch chip, split the image data stream and the interaction command data stream to generate two independent raw data; use a hierarchical compression coding algorithm to perform hierarchical compression operation on the two types of raw data after splitting to generate multimodal standardized data. S12. Retrieve the pre-stored hierarchical ledger data of radiology diagnosis and treatment business, traverse the equipment acquisition parameter fields attached to the multimodal standardized data, match the corresponding radiography examination items and clinical application scenarios based on the field content, synchronously read the equipment trigger mark fields embedded in the data, complete the transmission priority assignment and labeling of different data units based on the mark fields, and generate a classification dataset with hierarchical labels. S13. Analyze the data segments within the classification dataset with hierarchical labels frame by frame to generate basic feature materials bound to three parameters; S14. Perform redundant field removal operations on the basic feature materials, and collect the selected and retained effective feature parameters in a structured manner according to the fixed field arrangement rules to generate temporary collected feature data. S15. Perform feature discretization conversion on the temporary aggregated feature data, convert the text-formatted business description parameters into numerical feature codes that the system can recognize, and summarize and integrate them after all encoding is completed to generate a multi-dimensional business feature set.

3. The wireless data transmission method for a smart X-ray machine according to claim 2, characterized in that, S11 includes: S111. Continuously monitor the stability and integrity of the bitstream of the two types of independent raw data, check for broken frames, out-of-order data and invalid padding data in the datastream, correct and complete abnormal data segments, and generate corrected raw data in good condition. S112. For raw image data and raw interactive command data, match the corresponding compression strategies respectively, use hierarchical compression coding algorithm to perform multi-level compression operations in sequence, control the loss of image information and the distortion of command data during the compression process, and generate preliminary compressed data. S113. Read the format constraints in the general storage specification for medical images, adjust the byte order, data segmentation method and header encapsulation structure of the preliminary compressed data according to the constraints, complete the unified processing of single-type data format, and generate format-normalized data. S114. Integrate the formatted image data and interactive command data into the same architecture, unify the encapsulation protocol and field definition of the two types of data, eliminate the format differences between heterogeneous data, and generate an integrated intermediate dataset. S115. Perform overall compliance verification on the integrated intermediate dataset, verify the integrity of data encapsulation and the degree of format matching, complete the final normalization process, and generate multimodal standardized data.

4. The wireless data transmission method for a smart X-ray machine according to claim 3, characterized in that, S112 includes: Collect and correct the bitrate parameters, data volume parameters, and data content attribute parameters of the original data, distinguish the transmission characteristics differences between image data and interactive command data, and generate attribute judgment data for the two types of data. Based on the attributes of the two types of data, the data matching differential compression adaptation rules are determined, a high-fidelity layered compression mechanism is assigned to the image data, and a high-speed lightweight compression mechanism is assigned to the interactive command data to generate classified compression adaptation data. By relying on a hierarchical compression coding algorithm, compression operations are performed on the two types of data layer by layer, reducing redundant bits of data level by level, simultaneously controlling the retention of image pixel information and the integrity of command data characters, suppressing data distortion problems during the compression process, and generating hierarchical compression intermediate data; Perform compression accuracy verification calculations on intermediate data of hierarchical compression, screen for local data loss and bit loss problems generated during hierarchical compression, correct and repair missing data segments, and generate compression verification completed data; Summarize all the verified compressed data, standardize the compression hierarchy and data output format of the two types of data, complete the overall compression and integration process, and generate preliminary compressed data with uniform specifications.

5. The wireless data transmission method for a smart X-ray machine according to claim 1, characterized in that, S2 includes: S21. Retrieve the channel signal-to-noise ratio data and signal attenuation rate data collected in real time by the wireless gateway in the local area network. Perform cross-comparison calculations between each parameter of the multi-dimensional service feature set and the channel measured data. Relying on the protocol adaptation algorithm, traverse the threshold values ​​of various parameters of the Wi-Fi protocol cluster and Bluetooth protocol cluster, match the main transmission channel that matches each feature parameter, and generate the initial channel selection matching data. S22. Extract the channel attribution parameters and service characteristic parameters contained in the initial channel selection matching data, substitute them one by one into the parameter mapping calculation formula built into the dual-channel transmission strategy system, bind different types of images and interactive service parameters to the corresponding channel scheduling rules entries in the system, complete the full parameter mapping operation, and generate transmission strategy label data. S23. Start the link detection subroutine to continuously collect the instantaneous packet loss rate, channel occupancy rate and round-trip delay of all available wireless channels around the device. Calculate the real-time comprehensive link score of each idle channel based on the link loss calculation model and generate detailed measured data of the entire channel quality. S24. Traverse the main transmission channel number corresponding to each tag in the transmission strategy tag data, select idle channels with scores in the preset qualified threshold range from the full channel quality measured detailed data as backup links, and append the channel number and link score parameters of the backup links to the corresponding tag auxiliary fields one by one to generate tag supplementary intermediate data. S25. Verify the data integrity of the newly added link field inside the supplementary intermediate data of the tag, complete the tag entries with missing channel parameters, and generate extended transmission strategy data after completing the field regularization.

6. The wireless data transmission method for a smart X-ray machine according to claim 5, characterized in that, S21 includes: The system continuously collects real-time operating parameters of the wireless network environment in which the device is located, and collects the signal-to-noise ratio, signal attenuation rate, and channel throughput rate of each wireless channel, and summarizes them to form a real-time channel environment measured dataset. Deconstruct various business parameters within the multi-dimensional business feature set, extract image transmission volume parameters, business timeliness parameters, and interactive operation transmission requirement parameters, and organize them into a dataset of business parameters to be matched; The dataset of service parameters to be matched is cross-compared item by item with the real-time channel environment measured dataset to screen the compatibility between different channel parameters and service transmission requirements, and generate channel service compatibility comparison data. The protocol adaptation algorithm is invoked to traverse the threshold values ​​of various operating parameters of the Wi-Fi protocol cluster and the Bluetooth protocol cluster. Combined with the channel service adaptation ratio data, the transmission protocol and channel carrier that are adapted to the current service characteristics are selected, and channel adaptation candidate data are generated. The candidate channel adaptation data is screened for compliance and integrated and classified. Channel options that do not meet the adaptation standards are eliminated, and the best-fitting main transmission channel combination is retained to generate the initial channel selection matching data.

7. The wireless data transmission method for a smart X-ray machine according to claim 1, characterized in that, The S3 includes: S31. Decompose the primary and backup channel parameters, service priority parameters and transmission timeliness parameters stored in the extended transmission strategy data, and separate them into two independent groups of demand parameters: image transmission demand parameters and touch screen interaction command transmission demand parameters, and generate the split sub-demand dataset. S32. Call the dual-channel instruction compilation unit, substitute each item of the requirement dataset into the instruction compilation rules, compile the corresponding transmission instruction code according to the Wi-Fi channel instruction format specification and the Bluetooth channel instruction format specification, bind the compiled single instruction code to its channel identifier, and summarize all compilation products to generate dual-channel scheduling instruction raw data. S33. Traverse the raw data of dual-channel scheduling instructions. The data interaction format code embedded in each instruction is used to retrieve the interface message encapsulation rules and data interaction protocol parameters of each type of interface built into the interface library of HIS system and PACS system. The matching and comparison calculation between instruction code and interface parameter is completed one by one to generate an initial screening and matching list of interfaces. S34. Remove interface entries from the initial screening and matching list that cannot be adapted to the single packet transmission volume of the instruction or whose handshake timing cannot be aligned with the instruction execution rhythm. Retain the docking model number corresponding to the interface that meets the parameter adaptation standard and generate intermediate interface screening data. S35. Group and classify the intermediate data of the interface screening according to the business type of the scheduling instruction. Sort and organize the multiple compliant docking models corresponding to the same business by serial number. After all grouping operations are completed, generate system interface matching data.

8. The wireless data transmission method for a smart X-ray machine according to claim 1, characterized in that, The S4 includes: S41. Extract the associated business parameters attached to the system interface matching data, and separate them into three categories of control parameters: image transmission category parameters, timeliness level identifier parameters, and interactive operation type parameters. Calculate the bandwidth weight coefficients corresponding to the three categories of parameters based on the bandwidth weight conversion formula, and generate a bandwidth weight coefficient dataset. S42. Using the coefficients of each item in the bandwidth weight coefficient dataset as the allocation benchmark, the total available wireless bandwidth resources of the whole machine are split, and the bandwidth quota for Wi-Fi channel to carry image services and Bluetooth channel to carry interactive command services are allocated according to the coefficient ratio. Additional floating bandwidth resources are reserved for urgent and time-sensitive services, and preliminary bandwidth allocation data is generated. S43. Verify the bandwidth values ​​of each channel in the initial bandwidth division data, correct abnormal allocation values ​​where the single-channel bandwidth exceeds the upper limit of the hardware transceiver module, and synchronously adjust the floating bandwidth occupancy trigger condition parameters. After correction, generate dual-channel bandwidth allocation data. S44. Extract the service timeliness parameters and priority labeling parameters bound to the dual-channel bandwidth allocation data, and use the time sequence sorting algorithm to sort all the service units to be transmitted in order of priority. High timeliness level services are arranged at the front of the time sequence, and regular services are arranged in segments according to service type to generate time sequence layout draft data. S45. Assign the service units with different order in the time sequence layout draft data to the corresponding transmission channels with allocated bandwidth, mark the channel occupancy start node and release node of each service unit, organize the full time sequence layout information, and generate dual-channel parallel transmission sequence data.

9. The wireless data transmission method for a smart X-ray machine according to claim 1, characterized in that, The S5 includes: S51. Decompose the dual-channel parallel transmission sequence data, separate the image preprocessing auxiliary task carried by the Wi-Fi link and the interaction instruction local parsing task carried by the Bluetooth link, and generate two types of local task details data to be implemented. S52. Read the real-time idle thread resource statistics of the Android system kernel, allocate a dedicated computing thread for the image preprocessing module from the idle thread pool according to the task computing resource consumption, and simultaneously allocate an independent scheduling thread for the touch screen interaction response module, and generate thread resource allocation details. S53. Bind the detailed data of the two types of local tasks to be landed to the thread number corresponding to the thread resource allocation details, mark the thread occupation period and resource release triggering conditions of a single task, complete the binding and collection of tasks and threads, and generate the original data of task binding. S54. Remove abnormal binding records in the original task binding data that show overlapping thread resource usage, reallocate idle threads to complete the secondary binding of abnormal tasks, organize all compliant binding entries in the group, and generate local task distribution data. S55 reads the thread-enabled node and dual-channel data transmission start node of the local task distribution data record, synchronously triggers the start command to drive the two modules to carry out local computing operations based on the allocated thread, and the dual channels synchronously start wireless data transmission and reception actions according to the predetermined sequence. After the entire link resources enter the standby switching state, the output level indicator is generated to generate the wireless data transmission ready signal of the smart X-ray machine.

10. A smart X-ray machine, characterized in that, The intelligent X-ray machine includes: One or more processors; Memory, used to store one or more programs; Wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1 to 9.

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